EQIPD - User contributions [en]

3.5.3 Minimum reporting guidelines

2023-06-15T12:35:52Z

ChristophEmmerich:

== A. Background & Definitions ==
The purpose of the different Minimum Information (MI) guidelines is to ensure the data generated using the respective method or technique can be easily verified, analyzed and interpreted by the wider scientific community.
Ultimately, MI standards should ensure that information generated by using certain methods should be structured and organised in a way that facilitates data sharing, the verification of results and the re-use of data.

== B. Guidance & Expectations ==
The concept of ‘Minimum Information’ standards was first introduced in 2001 by developing and publishing the MIAME (Minimum Information’ about a microarray experiment) guidelines. Here the authors provided detailed guidance on what information, specifications and meta data about an microarray experiment is crucial and important to be reported - so that any data sets generated can be used to their full potential and readers obtain a comprehensive understanding of the published experiment.

== C. Resources ==
An overview of developed MI guidelines is provided by the FAIRsharing.org website (https://fairsharing.org/) with the possibility to search for specific techniques or methods of interest.

* [https://doi.org/10.25504/FAIRsharing.8vv5fc Minimum Information About a Proteomics Experiment (MIAPE)]
* [https://doi.org/10.25504/FAIRsharing.28yec8 Minimum Information About a Bioinformatics investigation (MIABi)]
* [https://doi.org/10.25504/FAIRsharing.my19zk Minimum Information About a RNAi Experiment (MIARE)]
* [https://doi.org/10.25504/FAIRsharing.kcnjj2 Minimum Information about Flow Cytometry (MIFlowCyt)]
* [https://doi.org/10.25504/FAIRsharing.gt5caz Minimum Information about a Protein Affinity Reagent (MIAPAR)]
* [https://doi.org/10.25504/FAIRsharing.c2e4ab Minimum Information about Tissue Imaging (MITI)]
* [https://doi.org/10.25504/FAIRsharing.vh2ye1 Minimum Information About Cell Migration Experiment (MIACME)]
* [https://doi.org/10.25504/FAIRsharing.5pp7gn Minimum Information about a Neuroscience Investigation (MINI)]
* [https://fairsharing.org/4183 Minimum Information Standard for Engineered Organism Experiments (MIEO)]
* [https://doi.org/10.25504/FAIRsharing.IjUe3j Minimum Information about Peptide Array Experiment (MIAPepAE)]
* [https://doi.org/10.25504/FAIRsharing.ht22t4 Minimum Information Specification For In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE)]
* [https://doi.org/10.25504/FAIRsharing.wz1w7t Minimum Information About a Spinal Cord Injury experiment (MIASCI)]
* [https://doi.org/10.25504/FAIRsharing.jw7rq3 Minimum Information for Reporting Next Generation Sequencing Genotyping (MIRING)]
* [https://doi.org/10.25504/FAIRsharing.sbfp9e Minimum Information Required for A Glycomics Experiment - Glycan Microarray Analysis (MIRAGE Glycan Microarray Analysis)]
* [https://doi.org/10.25504/FAIRsharing.mxz4jy Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE)]
* [https://doi.org/10.25504/FAIRsharing.n7src9 Minimum Information Required for A Glycomics Experiment - Mass Spectrometric Analysis (MIRAGE MS)]
* [https://doi.org/10.25504/FAIRsharing.jbg4kp Minimum Information Required for a Drug Metabolism Enzymes and Transporters Experiment (MIDE)]
* [https://doi.org/10.25504/FAIRsharing.9mfexc Minimum Information About a Genotyping Experiment (MIGen)]
* [https://doi.org/10.25504/FAIRsharing.zj6y9h Minimum Information About a Simulation Experiment (MIASE)]
* [https://doi.org/10.25504/FAIRsharing.dt7hn8 Minimum Information About a Bioactive Entity (MIABE)]
* [https://doi.org/10.25504/FAIRsharing.32b10v Minimum Information About a Microarray Experiment (MIAME)]
* [https://doi.org/10.25504/FAIRsharing.bbb81t Minimum Information about a Stem Cell Experiment (MISCE)]
* [https://doi.org/10.25504/FAIRsharing.8z3xzh Minimum Information about a Molecular Interaction Experiment (MIMIx)]
* [https://doi.org/10.25504/FAIRsharing.wYScsE Minimum Information about Animal Toxicology Experiments In Vivo (MIATE/invivo)]
* [https://doi.org/10.25504/FAIRsharing.x7nef0 Minimum Information Model for Dielectric Measurements of Biological Tissues (MINDER)]
* [https://doi.org/10.25504/FAIRsharing.7d0yv9 Minimal Information About a Cellular Assay (MIACA)]
* [https://doi.org/10.25504/FAIRsharing.ca48xs Minimal Information About a Phylogenetic Analysis (MIAPA)]
* [https://doi.org/10.25504/FAIRsharing.a55z32 Minimal Information about a high throughput SEQuencing Experiment (MINSEQE)]

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Next item: [[4.1.1 Risk assessment]]

3.5.3 Minimum reporting guidelines

2023-06-15T12:35:24Z

ChristophEmmerich:

3.5.3 Minimum reporting guidelines

2023-06-15T12:34:46Z

ChristophEmmerich:

Risk Management Case Studies

2023-04-11T08:42:01Z

ChristophEmmerich:

Case studies for the Risk assessmnet in the context of the environmental changes will be displayed here.

Risk assessment from a PI moving into a new lab: [https://paasp.sharepoint.com/:w:/s/EQIPD/Ea_IIwVKPppHshTvROgU7n4BMsWalHdU54IAai0ROFLIYw?e=L6YpPh 4.1.1 Risk Management CaseStudy.docx]

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back to [[4.1.1 Risk assessment]]

1.4.3.5 Expectations from public funders

2023-02-02T14:13:47Z

ChristophEmmerich:

=Background and Expectations=

EQIPD has developed a tool to support:
* funders willing to communicate their expectations about data management and rigor in study design, conduct, analysis, and reporting
* scientists applying for funding and willing to follow best practices in research rigor

For funders:
* it is expected that the tool is made freely available (e.g. as a link) on the website informing applicants about the scope of the tool
* the tool provides generic expectations formulated by the EQIPD working group and can be further extended by guidance and other information specific to a funding body
* it is expected that different funding bodies will decide as to whether:
** the use of this tool is mandatory or not
** whether a report generated by the tool should be made part of the application for funding
** whether any additional information or evidence should be provided to support answers collected by the tool

For scientists applying for funding:
* the primary use of the tool is to help scientists identify potential gaps in the current practices
* even if the use of the tool is not mandated by the funder, applicants may nevertheless want to add the report generated by the tool to the submission package in order to emphasize the adherence to the research practice expectations formulated by EQIPD

The tool creates a "snapshot" of the environment in which research is conducted and provides scientists/organizations with the opportunity to demonstrate to funders in a structured (comparable) way that they are aware of critical quality measures/requirements and that they have implemented (most of) these in their environment.

It ensures that funders and applicants can align on relevant quality expectations (based on the EQIPD framework) and that all parties involved speak the “same language”.

This tool is NOT supposed to guide scientists how to design and conduct specific experiments.

=Resources=

* [https://paasp.sharepoint.com/:x:/s/EQIPD/ESCLBokAH8ZIuDcre3q5pp0BlN3IzHfYVr8clB9_UbT13w?e=48hlRK Excel-based tool] (The tool can be downloaded from the Online-Excel via the tab “File” in the menu, click on “Save as” and click on "Download a copy")
* [https://public-funding-tool.paasp.net/survey Research Quality Transparency Tool] (Online version)

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Next item: [[1.5.1 Quality policy]]

1.4.3.5 Expectations from public funders

2023-02-02T14:13:05Z

ChristophEmmerich:

=Background and Expectations=

EQIPD has developed a tool to support:
* funders willing to communicate their expectations about data management and rigor in study design, conduct, analysis, and reporting
* scientists applying for funding and willing to follow best practices in research rigor

For funders:
* it is expected that the tool is made freely available (e.g. as a link) on the website informing applicants about the scope of the tool
* the tool provides generic expectations formulated by the EQIPD working group and can be further extended by guidance and other information specific to a funding body
* it is expected that different funding bodies will decide as to whether:
** the use of this tool is mandatory or not
** whether a report generated by the tool should be made part of the application for funding
** whether any additional information or evidence should be provided to support answers collected by the tool

For scientists applying for funding:
* the primary use of the tool is to help scientists identify potential gaps in the current practices
* even if the use of the tool is not mandated by the funder, applicants may nevertheless want to add the report generated by the tool to the submission package in order to emphasize the adherence to the research practice expectations formulated by EQIPD

The tool creates a "snapshot" of the environment in which research is conducted and provides scientists/organizations with the opportunity to demonstrate to funders in a structured (comparable) way that they are aware of critical quality measures/requirements and that they have implemented (most of) these in their environment.

It ensures that funders and applicants can align on relevant quality expectations (based on the EQIPD framework) and that all parties involved speak the “same language”.

This tool is NOT supposed to guide scientists how to design and conduct specific experiments.

=Resources=

* [https://paasp.sharepoint.com/:x:/s/EQIPD/ESCLBokAH8ZIuDcre3q5pp0BlN3IzHfYVr8clB9_UbT13w?e=48hlRK Excel-based tool] (The tool can be downloaded from the Online-Excel via the tab “File” in the menu, click on “Save as” and click on "Download a copy")
* [https://public-funding-tool.paasp.net/survey Research Quality Transparency Tool] Online version

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Next item: [[1.5.1 Quality policy]]

1.4.3.5 Expectations from public funders

2023-02-02T14:11:41Z

ChristophEmmerich:

=Background and Expectations=

EQIPD has developed a tool to support:
* funders willing to communicate their expectations about data management and rigor in study design, conduct, analysis, and reporting
* scientists applying for funding and willing to follow best practices in research rigor

For funders:
* it is expected that the tool is made freely available (e.g. as a link) on the website informing applicants about the scope of the tool
* the tool provides generic expectations formulated by the EQIPD working group and can be further extended by guidance and other information specific to a funding body
* it is expected that different funding bodies will decide as to whether:
** the use of this tool is mandatory or not
** whether a report generated by the tool should be made part of the application for funding
** whether any additional information or evidence should be provided to support answers collected by the tool

For scientists applying for funding:
* the primary use of the tool is to help scientists identify potential gaps in the current practices
* even if the use of the tool is not mandated by the funder, applicants may nevertheless want to add the report generated by the tool to the submission package in order to emphasize the adherence to the research practice expectations formulated by EQIPD

The tool creates a "snapshot" of the environment in which research is conducted and provides scientists/organizations with the opportunity to demonstrate to funders in a structured (comparable) way that they are aware of critical quality measures/requirements and that they have implemented (most of) these in their environment.

It ensures that funders and applicants can align on relevant quality expectations (based on the EQIPD framework) and that all parties involved speak the “same language”.

This tool is NOT supposed to guide scientists how to design and conduct specific experiments.

=Resources=

* [https://paasp.sharepoint.com/:x:/s/EQIPD/ESCLBokAH8ZIuDcre3q5pp0BlN3IzHfYVr8clB9_UbT13w?e=48hlRK Excel-based tool] (The tool can be downloaded from the Online-Excel via the tab “File” in the menu, click on “Save as” and click on "Download a copy")
* [https://public-funding-tool.paasp.net/survey Online version]

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back to [[Toolbox]]

Next item: [[1.5.1 Quality policy]]

1.4.3.5 Expectations from public funders

2023-02-02T14:09:46Z

ChristophEmmerich:

=Background and Expectations=

EQIPD has developed a tool to support:
* funders willing to communicate their expectations about data management and rigor in study design, conduct, analysis, and reporting
* scientists applying for funding and willing to follow best practices in research rigor

For funders:
* it is expected that the tool is made freely available (e.g. as a link) on the website informing applicants about the scope of the tool
* the tool provides generic expectations formulated by the EQIPD working group and can be further extended by guidance and other information specific to a funding body
* it is expected that different funding bodies will decide as to whether:
** the use of this tool is mandatory or not
** whether a report generated by the tool should be made part of the application for funding
** whether any additional information or evidence should be provided to support answers collected by the tool

For scientists applying for funding:
* the primary use of the tool is to help scientists identify potential gaps in the current practices
* even if the use of the tool is not mandated by the funder, applicants may nevertheless want to add the report generated by the tool to the submission package in order to emphasize the adherence to the research practice expectations formulated by EQIPD

The tool creates a "snapshot" of the environment in which research is conducted and provides scientists/organizations with the opportunity to demonstrate to funders in a structured (comparable) way that they are aware of critical quality measures/requirements and that they have implemented (most of) these in their environment.

It ensures that funders and applicants can align on relevant quality expectations (based on the EQIPD framework) and that all parties involved speak the “same language”.

This tool is NOT supposed to guide scientists how to design and conduct specific experiments.

=Resources=

* [https://paasp.sharepoint.com/:x:/s/EQIPD/ESCLBokAH8ZIuDcre3q5pp0BlN3IzHfYVr8clB9_UbT13w?e=48hlRK Excel-based tool] (The tool can be downloaded from the Online-Excel via the tab “File” in the menu, click on “Save as” and click on "Download a copy")
* [https://public-funding-tool.paasp.net/survey]

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Next item: [[1.5.1 Quality policy]]

4.1.1 Risk assessment

2021-10-18T13:15:49Z

ChristophEmmerich:

== A. Background & Definitions ==
This item refers to one of the [[Core Requirements]] (Core Requirement 15 - "Risk assessment must be performed to identify factors affecting the generation, processing and reporting of research data") and is, therefore, considered as essential.

Risk is the combination of the probability of occurrence of harm, and the severity of that harm. In the context of EQIPD Quality System, harm is defined as research data not being fit for purpose, research data being biased and/or inadequate data integrity.

Risk assessment is the process of identifying all risks to and from an activity or situation and assessing the potential impact on the research enviroment.

For any research unit using EQIPD Quality System, there are three main areas of risk assessment:
* alterations from strongly recommended practices (i.e. situations when EQIPD language included „should“ and the research unit provided a declaration on why it does not or cannot apply to them)
* key / support processes that are inherently associated with risks endangering the validity of results (e.g. risk of unblinding; emergency access to blinding codes; see also [[Plausibility check]])
* changes in the environment of the research unit (new hires / key people leaving; facility changes, etc.)

== B. Guidance & Expectations ==
EQIPD expects risk assessment to be conducted at three levels:
{| class="wikitable"
! scope="col"|
! scope="col"|'''By whom'''
! scope="col"|'''When / how often'''
! scope="col"|'''To be documented?'''
! scope="col"|'''Use of EQIPD tools'''
|-
! scope="col"|'''Alterations from strongly recommended practices'''
|Process Owner
|At least as part of [[4.1.2 Self assessment]]
(Process Owner may decide to conduct assessments more frequently)
|Yes, as part of a [[4.1.2 Self assessment]] protocol
|If EQIPD Planning Tool is used, alterations from the recommended practices can be marked or highlighted in the corresponding Risk Assessment column in the Action Plan.
|-
! scope="col"|'''Inherent risks of key / support processes'''
|Team member with the primary responsibility over design and execution of a particular study
|For each planned / conducted study
|Yes, as part of a [[2.1.1 Study protocol]] that should have a dedicated section or box for risk assessment summary
|Certain Toolbox items are marked with a warning sign and a "please do not forget" text in order to suggest processes that may require attention
|-
! scope="col"|'''Changes in the environment'''
|Process Owner (alone or with the team)
|Ad hoc
|Optional (to be decided by the Process Owner - see below for a template, can be easily done with Planning Tool current version[https://paasp.sharepoint.com/:u:/s/EQIPD/Eaq198yQK_xAvr24inTL1PoBDL6N7qanY62awbTLTHNN-Q?e=5DyhXi])
|Corresponding Toolbox items may be consulted for any specific guidance or references to useful resources; If EQIPD Planning Tool is used, new solutions and changes to existing solutions can be recorded
|}

Optionally, risk assessment can be conducted more often by the process owner or a dedicated person on a broader level than what is suggested above. When doing so, the following questions can be used for guidance:
* What can go wrong and have a negative impact on the research data or unit?
* How likely is it to happen?
* What are the potential consequences?
* How tolerable is the identified risk?

Outcome of such risk assessment can be documented (e.g. using the template provided below) and and store in Dossier folder 4.1.1. A case study can be found [[Risk Management Case Studies]]

'''DO NOT FORGET'''
* To justify acceptance of risk based on both probability and severity of harm

== C. Resources ==
SYRCLE Risk of Bias [https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-43]

The following techniques provide a more detailed framework and can be used if resources allow it:
* SWOT analysis [https://en.wikipedia.org/wiki/SWOT_analysis]
* FMEA [https://en.wikipedia.org/wiki/Failure_mode_and_effects_analysis]

ICH Q9 Quality Management
* FDA [https://www.fda.gov/media/71543/download]
* EMA [https://www.ema.europa.eu/en/ich-q9-quality-risk-management]

Template for optional risk assessment - [https://paasp.sharepoint.com/:w:/s/EQIPD/EfSJLIZdbOFOlKneuiRrf5MBeTug7VsZoWHsETvI3pY_mg?e=GXBsdk 4.1.1 Risk Management.docx]

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back to [[Toolbox]]

Next item: [[4.1.2 Self assessment]]

3.5.2 Protocols for methods and assays

2021-04-29T13:14:02Z

ChristophEmmerich:

== A. Background & Definitions ==
This item is related to one of the Core Requirements (Core Requirement 12 - "Protocols for experimental methods must be available") and is, therefore, considered as essential.

Protocol for experimental methods – A written description of materials and experimental method(s) that includes sufficient details that would enable the reliable and consistent use of the method(s) by different people and at different times.

== B. Guidance & Expectations ==
Protocols for experimental methods should be dated and should include author(s) to make sure that the completed and reported studies can be readily traced to the correct protocols. EQIPD has no expectations regarding the format of such protocols and does not expect protocols to follow any formal development, review or validation procedure (as in GLP).

Importantly, Protocols for experimental methods
* must be available at all times,
* must be referenced or duplicated in the experimental record
* must contain all relevant information about materials and reagents (e.g. it is advisable to use RRIDs), equipment and procedures to enable the reliable and consistent use of the method,
* must not be overwritten or deleted when amended or when a new version was created and referenced,

Further information to be included in Protocols for experimental methods may involve:
* Training/competence of personnel required to perform the method (see also Toolbox item [[3.2.4 Training on specific methods, tasks and activities| 3.2.4]])
* Definition of potential safety issues derived from the study (e.g., biosafety, chemicals, radioactivity…)
* Protocols of experimental methods should clearly state whether calibration is needed and, if yes, the procedure should be described (when/how often). If no calibration is required, it should be explicitly stated in the protocol (see also Core Requirement 14 - "Research equipment and tools must be suitable for intended use and ensure data integrity" and Toolbox item [[3.3.2 Processes to enable computerized and non-computerized systems being suitable for intended use| 3.3.2]])

For animal research:
* Justification that there are no alternative methods
* Definition of humane endpoints
* Further items from the [[ARRIVE 2.0]] recommended set

The completed protocols can be saved in the Dossier at 3.5.2.

'''RISK ASSESSMENT'''
* Is there a process in place to make sure that the most recent protocol is being used or followed?

'''PLEASE DO NOT FORGET'''
* To support versioning of the protocols (including dates and authors) to make sure that the completed and reported studies can be readily traced to the correct protocols

== C. Resources ==
Templates for experimental methods descriptions (protocols):
* [https://paasp.sharepoint.com/:w:/s/EQIPD/EfUO3B7RFxdHgxQ8JY5hhFoBEDUiPGK4C8n6BBHEprwroA?e=cxNQxR 3.5.2 Protocol for experimental method.docx]

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Next item: [[4.1.1 Risk assessment]]

Documentation in EQIPD

2021-03-24T13:10:52Z

ChristophEmmerich:

EQIPD Quality System is lean and does not require excessive documentation.

The following table describes the documentation required to address the EQIPD [[Core Requirements]].

In some cases, these are stand-alone documents. In others, expected information is typically available in other documents - mandated by the institutional regulations or being part of another process.

{| class="wikitable"
|rowspan="2"|'''Categories'''
|rowspan="2"|'''No'''
|rowspan="2"|'''Core Requirement'''
|rowspan="2"|'''Toolbox reference'''
|colspan="2"|'''Implementation of the Core Requirements'''
|-
|'''Stand-alone document'''
|'''EQIPD expectations'''
|-
|rowspan="2"|'''Research team'''
|1
|Process owner must be identified for the Quality System
|[[1.5.2.3 Process owner|1.5.2.3]]
|No
|Process Owner should act and be recognized as such by the research unit members.
|-
|2
|Communication process must be in place
|[[1.2 Scope|1.2]]
|Yes
|A concise overview of the organisational structure and the communication lines can be described using the [https://paasp.sharepoint.com/:w:/s/EQIPD/ERyfFP_pBytDiEfqCutDAJQBFbaGQEx3G1pyOmDl50o_LQ?e=wlkvHJ Communication plan template] provided by EQIPD or in a similar document.
|-
|rowspan="3"|'''Quality culture'''
|3
|The research unit must have defined quality objectives
|[[1.1 Mission|1.1]]
|Yes
|The [https://paasp.sharepoint.com/:w:/s/EQIPD/EVUTrgQRuNpKtpHkobdCOq4BhSTw1p3akXGKvI_MRgxYag?e=dJoZ5T Mission statement template] of EQIPD or a similar document can be used to describe quality goals of a research unit. These quality objectives should be known to and shared by all members of a research unit.
|-
|4
|All activities must comply with relevant legislation and policies
|[[1.4.2 Adherence to legal and regulatory considerations|1.4.2]]
|No
|Research unit complies with all applicable national and international legislation and policies and there are no compliance issues.
|-
|5
|The research unit must have a procedure to act upon concerns of potential misconduct
|[[4.2.3 Responsible conduct of research|4.2.3]]
|No
|It is expected that a research unit or its parent organization has a research integrity policy, office and/or officer (or ombudsman) and all research unit members have access to this information.
|-
|rowspan="4"|'''Data integrity'''
|6
|Generation, handling and changes to data records must be documented
|[[2.3.1 Generation, recording, handling and archiving of raw data|2.3.1]]
|Yes
|Can be described using the [https://paasp.sharepoint.com/:w:/s/EQIPD/EQTANrsKHTVIqtO8LT-ZJIEBEUJVZI6siRLkHfliUD4gdA?e=XlOwMh Documentation plan template] provided by EQIPD or in a similar document.
|-
|7
|Data storage must be secured at least for as long as required by legal, contractual or other obligations or business needs
|[[3.1.3 Data security|3.1.3]]
|Yes
|Can be described using the [https://paasp.sharepoint.com/:w:/s/EQIPD/EQTANrsKHTVIqtO8LT-ZJIEBEUJVZI6siRLkHfliUD4gdA?e=XlOwMh Documentation plan template] provided by EQIPD or in a similar document.
|-
|8
|Reported research outcomes must be traceable to experimental data
|[[3.1.2.1 Traceability of data and any person having impact on data|3.1.2.1]]
|Yes
|Can be described using the [https://paasp.sharepoint.com/:w:/s/EQIPD/EQTANrsKHTVIqtO8LT-ZJIEBEUJVZI6siRLkHfliUD4gdA?e=XlOwMh Documentation plan template] provided by EQIPD or in a similar document.
|-
|9
|Reported data must disclose all repetitions of a study, an experiment, or a test regardless of the outcome
|[[2.4 Reporting|2.4]]
|No
|it is expected that the Process Owner conducts spot checks on reported studies to make sure that all repetitions are reported.
|-
|rowspan="5"|'''Research processes'''
|10
|Investigator must declare in advance whether a study is intended to inform a formal knowledge claim
|[[2.1.4 Purpose of research|2.1.4]]
|No
|This requirement is most optimally met by providing corresponding information in the study protocol, see [[2.1.1 Study protocol]].
|-
|11
|All personnel involved in research must have adequate training and competence to perform assigned tasks
|[[3.2.1 General guidance on training|3.2.1]]
|No
|For legally required / mandatory training, training records are typically available. For other training, Process Owner decides on whether and what form of documentation should be maintained. EQIPD expects that all research units members are trained on what is considered to be raw data and how to record and handle data. Further, EQIPD expects a training program for new members of the research unit.
|-
|12
|Protocols for experimental methods must be available
|[[3.5.2 Protocols for methods and assays|3.5.2]]
|No
|Methods can be described in either standalone protocols or be part of study protocol. ([https://paasp.sharepoint.com/:w:/s/EQIPD/EfUO3B7RFxdHgxQ8JY5hhFoBEDUiPGK4C8n6BBHEprwroA?e=8IezWV template])
|-
|13
|Adequate handling and storage of samples and materials must be ensured
|[[3.3.3 Management of research materials and reagents|3.3.3]]
|No
|Although there is no requirement to have a standalone document describing the overall process of handling and storage, it is nevertheless in many circumstances to be expected that certain aspects of handling and storage are supported by relevant documentation (e.g. electronic or paper-based system for keeping a control over research chemicals and reagents).
|-
|14
|Research equipment and tools must be suitable for intended use and ensure data integrity
|[[3.3.2 Processes to enable computerized and non-computerized systems being suitable for intended use|3.3.2]]
|No
|It is expected that protocols of experimental methods clearly state whether maintenance or calibration is needed and, if yes, describe the procedure.
|-
|rowspan="3"|'''Continuous performance'''
|15
|Risk assessment must be performed to identify factors affecting the generation, processing and reporting of research data
|[[4.1.1 Risk assessment|4.1.1]]
|No
|Study protocols are expected to have a dedicated section summarizing measures against risks of bias, see [[2.1.1 Study protocol]]. Deviations from practices recommended by EQIPD as well as the risk assessment at the level of the research unit can be handled using the [https://paasp.sharepoint.com/:x:/s/EQIPD/ETo9OwIvZpNHtepp6IvxylQBjtjhk2AmRnypLCIOrGwMvA?e=n1zy3X Risk assessment template].
|-
|16
|Critical incidents and errors during study conduct must be analyzed and appropriately managed
|[[4.2.2 Error and incident management|4.2.2]]
|No
|Critical incidents and errors can be recorded in laboratory notebooks or using [https://paasp.sharepoint.com/:w:/s/EQIPD/EatOAFgLbctEvxRZTuSCdU4Bv8J1I_BitfKl-JJiieOTLA?e=z99RR1 Error reporting template] or using another electronic or paper-based system.
|-
|17
|An approach must be in place to monitor the performance of the EQIPD Quality System, and address identified issues
|[[4.1.2 Self assessment|4.1.2]]
|No
|Evidence for such approach is provided indirectly by the information described in the [https://paasp.sharepoint.com/:x:/s/EQIPD/EWbE3AdV5jhHglumN_MlrugBQX_KsZQDpJVNYbBJk6svTQ?e=qkW68H Self assessment template] that is completed at regular intervals (as a minimum, annually).
|-
|'''Sustainability'''
|18
|Resources for sustaining the EQIPD Quality System must be available
|[[1.5.5 Sustainability|1.5.5]]
|No
|No documentation needed but EQIPD does not accept lack of resources as an argument for not following the best research practices.
|}

Research units working with animals must fill out the [https://paasp.sharepoint.com/:w:/s/EQIPD/ET18tqgUVfJCsYNYxexlgA8BnwRLEn0k8JcFmeQlTiQMoA?e=zoXuUf Animal care and use form] unless they are AAALAC certified. Additional information in Toolbox section [[3.4.1 Animal characteristics, care and use]].

Additional information about expectations can be found in [https://paasp.sharepoint.com/:b:/s/EQIPD/EWXf1VnZ8ytNr2jl97dGnV0BDJCfEFnO-TH_R71BegJCiQ?e=xcdG5f this] pdf document.

Back to the [[EQIPD Quality System]] or [[Core Requirements]].

1.4.3.3 Academia-academia: Research as service

2021-03-24T13:07:13Z

ChristophEmmerich:

=Background=
Recommendations outlined in this document has been developed by a task force of members and stakeholders of the EQIPD consortium, the largest private-public partnership completely dedicated to improving data quality in preclinical research.
These recommendations are intended to improve the robustness, reliability, traceability and integrity of the data obtained from the research activities supported by academic core facilities. They aim to:
improve communication between core facilities and the users of the services and infrastructure provided by the core facilities,
minimize bias and errors in the collection, reporting or representation of such information, and
create reliable scientific and supporting evidence in resulting publications, presentations, reports, patents and other types of research output.
The experimental record and its thorough description is the ultimate source of information and documentation regarding the experiment. Therefore, the contents of the experimental record must be accurate and thorough enough to be fully traceable to permit the reproduction of the work conducted. The experimental record is the official data record for each experiment and the most important primary source of data. It is expected that the practices outlined in this document will be applied to experimental planning, record-keeping procedures and reporting, to the fullest extent possible.
Recognizing the diversity of environments and settings in which core facilities operate, the current recommendations can be used in two modes - “Training service” and “EQIPD service”.
It is expected that core facilities and their users discuss both types of services, any ambiguities or conflicts regarding the recommended practices, and ensure alignment and understanding prior to the start of the experiments.

===Training Service===
# Core Facility provides information about research practices recommended by EQIPD (items listed below) to the users.
# It is up to the Core Facility to decide how this information is shared with the users (e.g., made part of a training program, shared as a written summary in paper or electronic form).
# Unless requested by the users or otherwise enabled by the locally applicable rules and regulations, Core Facility does not assume any further role in supporting or monitoring the implementation of recommended practices.

===EQIPD Service===
# Core Facility implements those aspects of EQIPD recommendations that do not depend on the users and that enable support of EQIPD-compliant research.
# Core facility provides the users with the information about research practices recommended by EQIPD and offers to support in conducting EQIPD-compliant research.
# If the user accepts the offer (and, as necessary, provides required resources), Core Facility:
## together with the user (""PI""), identifies the best solution to implement specific recommendations (items below). Shared responsibility over implementation means ""joint decision, knowledge and transparency"" and may still require certain recommendations fully implemented at the Core Facility while others - fully implemented on the user's side.
## assumes responsibility over spot checks (requires acceptance by the PI if certain recommendations are implemented on the user's side)
## confirms to the user that the study was conducted as ""EQIPD compliant"" or not (e.g. to be stated in the report or in a publication)"
# If the user does not accept the offer, no changes in the routine practice and the studies remain to be not compliant with EQIPD recommendations.

=Proposed statements=
==Training==
* Users must be trained by CF members in order to be eligble to use CF
* Users should seek support from CF to design experiments in due time and with with optimal rigor.
** Users should seek contact with the core facility ahead of time to ensure proper preparation/consulting/animal license approval and ultimate data quality.
** Core facilities should play an educational role in proper planning of projects.
** CF member together with the PI and the user should perform risk assessment
** the users should be instructed to record any erros and report them to CFH; the CFH should debrief it; the debrief record should be circulated to all other users in order to minimize the chances for recurrance
** Users must take responsibility for the appropiate use of reagents, research subjects and equipment they bring into the CF
** It is up to CF to define which form training should take (including frequency and documentation)

==Experimental Record==
* [[2.1.2 Unique study ID|Unique study]] identifiers must be used and defined by the future owner of the raw data for each experiment. Unless this is the CF, the owner of the [[2.3.1 Generation, recording, handling and archiving of raw data|raw data]] should communicate the unique study ID to the CF.
** Best practices should be communicated by the CF during training or by providing a reference to EQIPD Toolbox (e.g. date_User inititals_experiment).
** CF and PI may generate separate study IDs but there must be a way established to communicate, to connect separate IDs and to enable traceability.
** For EQIPD, it should be clear who is in charge of generating and storing the Unique Study Identifiers.
** For complex studies that include more than one experiment, several experiments are typically part of one experimental record under one study ID.
** Unless known to CF, PI must communicate ethical approval to CF
** File naming: in order that each data is unique and retrievable we suggest that you adhere to the following file naming convention: [date YYMMDD]-[the first letter of your first name together with your last name]-[free text]. If your surname is long you may use only the first 6 letters of it. e.g. 201108-cpitzer-catwalk with mice zQ98
* Each experimental record should include, directly or by reference, the names of all scientists involved, objectives, ethical approval/number, procedures, methods, materials, equipment, dates, and any other details considered necessary for reproducibility and reconstruction.
** CF can elaborate a form (excel sheet for instance) listing all the items that are required in an experimental record, and ask users to fill in this form. This could help the user to keep tracking of his/her own detailed experimental record and is likely to increase the traceability of his/her data.
* All raw and any processed data must be retrievable and traceable, directly or by reference. No raw data should be erased.
* An experimental record must describe any significant changes and deviations from the original study protocol.
** User must report/document if study execution is not in accordance with study protocol and deviations such as changes to study hypothesis, design or analysis must be documented (including the rationale for the changes).
** Examples of significant changes: Changes in the doses, experimental conditions and groups, sample size, methods of analysis, etc.
** Any change or deviation from a protocol approved by animal welfare authority would be significant and requires documentation in the experimental record.
** If uncertain about what constitutes a significant change or deviation, the user should consult with the core facility.
* An experimental record must provide an explanation and justification for exclusion of any data points from analysis.
** Critical incidents (any unexpected or unplanned events) and errors must be recorded and made part of the experimental record.
preparation/consulting/animal license approval and ultimately data quality.

==Rigor in Study Design==
* For every study, there should be a study protocol prepared prior to the study being conducted.
** Please see a definition of the study protocol above.
** Study protocols involving more than one experiment should include a dedicated section explaining the sequence and relationships between different experimental operations or procedures.
* The [[2.1.1 Study protocol|study protocol]] '''must''' include:
** Title
** Study hypothesis
** Ethical approval number and the name of approving body (for research involving animals)
** Statement / information about controls (with choice justification if necessary)
** Description of [[2.1.6 Sample size and power analysis|sample size calculation]]
** [[2.1.9 Inclusion and exclusion criteria|Inclusion / exclusion criteria]]
** Description of animal resources, reagents and materials (as applicable)
** Study design overview for complex studies
** Detailed description of [[3.5.2 Protocols for methods and assays|experimental procedure(s)]] (or references to standalone descriptions if available)
* The protocol '''should''' include:
** Statement whether study is [[2.1.4 Purpose of research|exploratory or knowledge-claiming]]
** Statement about choice of experimental methods
** Detailed description of measures against risk of bias ([[2.1.8 Randomisation|randomization]], [[2.1.7 Blinding|blinding]]) (or references to standalone descriptions if available)
** Description of [[2.3.2 Primary analysis and evaluation of raw data|raw data analysis]]
** use versioning (or have a section for amendments)
* It is advisable to:
** Include [[2.1.3 Appraisal of literature and systematic reviews|references to relevant literature]]
** Conduct [[4.1.1 Risk assessment|risk assessment]]
** [[2.1.11 Preregistration|Preregister the study protocol]]
* Apply [[2.1.8 Randomisation|randomization]] and [[2.1.7 Blinding|blinding]]. If not done or if not maintained throughout the experiment (from subject/sample allocation to analysis), include the reasons in the study protocol, the study report and any publication.
** The CF will advise the User how to [[2.1.8 Randomisation|randomize]] / [[2.1.7 Blinding|blind]].
** The user should implement.
** The CF will verify impementation.
* Justify sample size (e.g. using [[2.1.6 Sample size and power analysis|power analysis]]) and include the justification in the [[2.1.1 Study protocol|study protocol]], the study report and any publication.
** User should perform [[2.1.6 Sample size and power analysis|samples size calculation]] and follow advice from CF staff.
* Ensure [[2.1.9 Inclusion and exclusion criteria|inclusion / exclusion criteria]] and/or acceptance criteria are stated in the [[2.1.1 Study protocol|study protocol]], the study report and any publication.

==Analysis of Experimental Data==
* Experimental / data analysis record and [[2.4.1 Non-public reporting|study report]] (e.g. [[2.4.2 Publication|publication]]) should include sufficient detail to reconstruct any analysis performed and record all process steps and calculations used.
** It is very important to define responsbility
** CF should be in the position/have the possibility to check this or run occasional audits
* Data analysis plan should be described in the [[2.1.1 Study protocol|study protocol]], carried out as described, and reported. Additional analyses are always possible, but should be identified as such.
* Minimize the risks of bias and increase internal validity by including criteria for outlier exclusion, acceptable ranges for standards, reference compound and quality controls upfront in the study protocol.
* If the data analysis is done by the users themselves, they should subsequently share this data with the core facility even if they will not be published to allow for quality control of the data analysis and of the procedures in the core facility.

==Data Storage and Traceability==
* Experimental records should be kept in an [[3.1.2 Procedures for how and when to record data|audit-trailed, version-controlled, safe storage environment]] such as an appropriate bound-paper laboratory notebook with permanent ink or an electronic laboratory notebook (ELN).
* Raw (primary) data must be stored in an un-editable read-only form as soon as it is generated and must be backed-up
** [[3.1.3 Data security|the raw data should be backed-up]]
** [[3.3.2 Processes to enable computerized and non-computerized systems being suitable for intended use|reliability of IT resources should be ensured]]
** the responsibility for saving and archiving the raw data must be clarified
* Processed (secondary) data must be clearly labeled as such and should contain a reference to raw data

==Review and Reporting==
* Reported research outcomes should be [[2.3.1 Generation, recording, handling and archiving of raw data|complete, accurate and findable]]
** The [[3.1.2.1 Traceability of data and any person having impact on data|location of the data must be identifiable]] for all data records, e.g. reference with the permanent identifier.
** Reporting must include for each analysis the exact number of biological units for each condition
* Experimental records should be reviewed by a CF for completeness and accuracy and it is advised to document this review
** Users should give CF staff the possibility to review data for analysis and reporting
* Report should always provide summaries of all related data, processes, and conclusions, and include justification for excluding any relevant experimental records or individual data points from the summary analyses.
* Any external presentation/publication whether oral or in writing should give credit to the CF where the work was performed.
** Identify all contributing researchers and reference unique identifiers for the experimental records
** Encourage the use of an unique identifier for reseachers (ORCID)
** Unique IDs for facilities
* Any external presentation/publication whether oral or in writing should include a statement of conflict of interest.

=Resources=

EQIPD NEED for Core Facilities
* The NEED can be downloaded [https://paasp.sharepoint.com/:x:/s/EQIPD/EUxUx_BKVYpCrkQYDYSTPCEB09ETCcXrMs4iRfV70ydtUw?e=hEe91t here]
* For further explanation on NEEDS visit [[4.3.2.1_Using_the_Planning_Tool]]

The website [https://q-cofa.paasp.net Quality in Core Facilities] provides general support on establishing a quality framework.

----------------
back to [[Toolbox]]

Next item: [[1.4.3.4 Academia-academia: Research as collaboration]]

Documentation in EQIPD

2021-03-24T13:06:00Z

ChristophEmmerich:

EQIPD Quality System is lean and does not require excessive documentation.

The following table describes the documentation required to address the EQIPD [[Core Requirements]].

In some cases, these are stand-alone documents. In others, expected information is typically available in other documents - mandated by the institutional regulations or being part of another process.

{| class="wikitable"
|rowspan="2"|'''Categories'''
|rowspan="2"|'''No'''
|rowspan="2"|'''Core Requirement'''
|rowspan="2"|'''Toolbox reference'''
|colspan="2"|'''Implementation of the Core Requirements'''
|-
|'''Stand-alone document'''
|'''EQIPD expectations'''
|-
|rowspan="2"|'''Research team'''
|1
|Process owner must be identified for the Quality System
|[[1.5.2.3 Process owner|1.5.2.3]]
|No
|Process Owner should act and be recognized as such by the research unit members.
|-
|2
|Communication process must be in place
|[[1.2 Scope|1.2]]
|Yes
|A concise overview of the organisational structure and the communication lines can be described using the [https://paasp.sharepoint.com/:w:/s/EQIPD/ERyfFP_pBytDiEfqCutDAJQBFbaGQEx3G1pyOmDl50o_LQ?e=wlkvHJ Communication plan template] provided by EQIPD or in a similar document.
|-
|rowspan="3"|'''Quality culture'''
|3
|The research unit must have defined quality objectives
|[[1.1 Mission|1.1]]
|Yes
|The [https://paasp.sharepoint.com/:w:/s/EQIPD/EVUTrgQRuNpKtpHkobdCOq4BhSTw1p3akXGKvI_MRgxYag?e=dJoZ5T Mission statement template] of EQIPD or a similar document can be used to describe quality goals of a research unit. These quality objectives should be known to and shared by all members of a research unit.
|-
|4
|All activities must comply with relevant legislation and policies
|[[1.4.2 Adherence to legal and regulatory considerations|1.4.2]]
|No
|Research unit complies with all applicable national and international legislation and policies and there are no compliance issues.
|-
|5
|The research unit must have a procedure to act upon concerns of potential misconduct
|[[4.2.3 Responsible conduct of research|4.2.3]]
|No
|It is expected that a research unit or its parent organization has a research integrity policy, office and/or officer (or ombudsman) and all research unit members have access to this information.
|-
|rowspan="4"|'''Data integrity'''
|6
|Generation, handling and changes to data records must be documented
|[[2.3.1 Generation, recording, handling and archiving of raw data|2.3.1]]
|Yes
|Can be described using the [https://paasp.sharepoint.com/:w:/s/EQIPD/EQTANrsKHTVIqtO8LT-ZJIEBEUJVZI6siRLkHfliUD4gdA?e=XlOwMh Documentation plan template] provided by EQIPD or in a similar document.
|-
|7
|Data storage must be secured at least for as long as required by legal, contractual or other obligations or business needs
|[[3.1.3 Data security|3.1.3]]
|Yes
|Can be described using the [https://paasp.sharepoint.com/:w:/s/EQIPD/EQTANrsKHTVIqtO8LT-ZJIEBEUJVZI6siRLkHfliUD4gdA?e=XlOwMh Documentation plan template] provided by EQIPD or in a similar document.
|-
|8
|Reported research outcomes must be traceable to experimental data
|[[3.1.2.1 Traceability of data and any person having impact on data|3.1.2.1]]
|Yes
|Can be described using the [https://paasp.sharepoint.com/:w:/s/EQIPD/EQTANrsKHTVIqtO8LT-ZJIEBEUJVZI6siRLkHfliUD4gdA?e=XlOwMh Documentation plan template] provided by EQIPD or in a similar document.
|-
|9
|Reported data must disclose all repetitions of a study, an experiment, or a test regardless of the outcome
|[[2.4 Reporting|2.4]]
|No
|it is expected that the Process Owner conducts spot checks on reported studies to make sure that all repetitions are reported.
|-
|rowspan="5"|'''Research processes'''
|10
|Investigator must declare in advance whether a study is intended to inform a formal knowledge claim
|[[2.1.4 Purpose of research|2.1.4]]
|No
|This requirement is most optimally met by providing corresponding information in the study protocol, see [[2.1.1 Study protocol]].
|-
|11
|All personnel involved in research must have adequate training and competence to perform assigned tasks
|[[3.2.1 General guidance on training|3.2.1]]
|No
|For legally required / mandatory training, training records are typically available. For other training, Process Owner decides on whether and what form of documentation should be maintained. EQIPD expects that all research units members are trained on what is considered to be raw data and how to record and handle data. Further, EQIPD expects a training program for new members of the research unit.
|-
|12
|Protocols for experimental methods must be available
|[[3.5.2 Protocols for methods and assays|3.5.2]]
|No
|Methods can be described in either standalone protocols or be part of study protocol. ([https://paasp.sharepoint.com/:w:/s/EQIPD/EfUO3B7RFxdHgxQ8JY5hhFoBEDUiPGK4C8n6BBHEprwroA?e=8IezWV template])
|-
|13
|Adequate handling and storage of samples and materials must be ensured
|[[3.3.3 Management of research materials and reagents|3.3.3]]
|No
|Although there is no requirement to have a standalone document describing the overall process of handling and storage, it is nevertheless in many circumstances to be expected that certain aspects of handling and storage are supported by relevant documentation (e.g. electronic or paper-based system for keeping a control over research chemicals and reagents).
|-
|14
|Research equipment and tools must be suitable for intended use and ensure data integrity
|[[3.3.2 Processes to enable computerized and non-computerized systems being suitable for intended use|3.3.2]]
|No
|It is expected that protocols of experimental methods clearly state whether maintenance or calibration is needed and, if yes, describe the procedure.
|-
|rowspan="3"|'''Continuous performance'''
|15
|Risk assessment must be performed to identify factors affecting the generation, processing and reporting of research data
|[[4.1.1 Risk assessment|4.1.1]]
|No
|Study (experimental) plans are expected to have a dedicated section summarizing measures against risks of bias, see [[2.1.1 Study protocol]]. Deviations from practices recommended by EQIPD as well as the risk assessment at the level of the research unit can be handled using the [https://paasp.sharepoint.com/:x:/s/EQIPD/ETo9OwIvZpNHtepp6IvxylQBjtjhk2AmRnypLCIOrGwMvA?e=n1zy3X Risk assessment template].
|-
|16
|Critical incidents and errors during study conduct must be analyzed and appropriately managed
|[[4.2.2 Error and incident management|4.2.2]]
|No
|Critical incidents and errors can be recorded in laboratory notebooks or using [https://paasp.sharepoint.com/:w:/s/EQIPD/EatOAFgLbctEvxRZTuSCdU4Bv8J1I_BitfKl-JJiieOTLA?e=z99RR1 Error reporting template] or using another electronic or paper-based system.
|-
|17
|An approach must be in place to monitor the performance of the EQIPD Quality System, and address identified issues
|[[4.1.2 Self assessment|4.1.2]]
|No
|Evidence for such approach is provided indirectly by the information described in the [https://paasp.sharepoint.com/:x:/s/EQIPD/EWbE3AdV5jhHglumN_MlrugBQX_KsZQDpJVNYbBJk6svTQ?e=qkW68H Self assessment template] that is completed at regular intervals (as a minimum, annually).
|-
|'''Sustainability'''
|18
|Resources for sustaining the EQIPD Quality System must be available
|[[1.5.5 Sustainability|1.5.5]]
|No
|No documentation needed but EQIPD does not accept lack of resources as an argument for not following the best research practices.
|}

Research units working with animals must fill out the [https://paasp.sharepoint.com/:w:/s/EQIPD/ET18tqgUVfJCsYNYxexlgA8BnwRLEn0k8JcFmeQlTiQMoA?e=zoXuUf Animal care and use form] unless they are AAALAC certified. Additional information in Toolbox section [[3.4.1 Animal characteristics, care and use]].

Additional information about expectations can be found in [https://paasp.sharepoint.com/:b:/s/EQIPD/EWXf1VnZ8ytNr2jl97dGnV0BDJCfEFnO-TH_R71BegJCiQ?e=xcdG5f this] pdf document.

Back to the [[EQIPD Quality System]] or [[Core Requirements]].

4.1.1 Risk assessment

2021-03-24T13:04:40Z

ChristophEmmerich:

== A. Background & Definitions ==
This item refers to one of the [[Core Requirements]] (Core Requirement 15 - "Risk assessment must be performed to identify factors affecting the generation, processing and reporting of reserach data") and is, therefore, considered as essential.

Risk is the combination of the probability of occurrence of harm, and the severity of that harm. In the context of EQIPD Quality System, harm is defined as research data not being fit for purpose, research data being biased and/or inadequate data integrity.

Risk assessment is the process of identifying all risks to and from an activity or situation and assessing the potential impact on the research enviroment.

For any research unit using EQIPD Quality System, there are three main areas of risk assessment:
* alterations from strongly recommended practices (i.e. situations when EQIPD language included „should“ and the research unit provided a declaration on why it does not or cannot apply to them)
* key / support processes that are inherently associated with risks endangering the validity of results (e.g. risk of unblinding; emergency access to blinding codes; see also [[Plausibility check]])
* changes in the environment of the research unit (new hires / key people leaving; facility changes, etc.)

== B. Guidance & Expectations ==
EQIPD expects risk assessment to be conducted at three levels:
{| class="wikitable"
! scope="col"|
! scope="col"|'''By whom'''
! scope="col"|'''When / how often'''
! scope="col"|'''To be documented?'''
! scope="col"|'''Use of EQIPD tools'''
|-
! scope="col"|'''Alterations from strongly recommended practices'''
|Process Owner
|At least as part of [[4.1.2 Self assessment]]
(Process Owner may decide to conduct assessments more frequently)
|Yes, as part of a [[4.1.2 Self assessment]] protocol
|If EQIPD Planning Tool is used, alterations from the recommended practices can be marked or highlighted in the corresponding Risk Assessment column in the Action Plan.
|-
! scope="col"|'''Inherent risks of key / support processes'''
|Team member with the primary responsibility over design and execution of a particular study
|For each planned / conducted study
|Yes, as part of a [[2.1.1 Study protocol]] that should have a dedicated section or box for risk assessment summary
|Certain Toolbox items are marked with a warning sign and a "please do not forget" text in order to suggest processes that may require attention
|-
! scope="col"|'''Changes in the environment'''
|Process Owner (alone or with the team)
|Ad hoc
|Optional (to be decided by the Process Owner - see below for a template, can be easily done with Planning Tool current version[https://paasp.sharepoint.com/:u:/s/EQIPD/Eaq198yQK_xAvr24inTL1PoBDL6N7qanY62awbTLTHNN-Q?e=5DyhXi])
|Corresponding Toolbox items may be consulted for any specific guidance or references to useful resources; If EQIPD Planning Tool is used, new solutions and changes to existing solutions can be recorded
|}

Optionally, risk assessment can be conducted more often by the process owner or a dedicated personon on a broader level than what is suggested above. When doing so, the following questions can be used for guidance:
* What can go wrong and have a negative impact on the research data or unit?
* How likely is it to happen?
* What are the potential consequences?
* How tolerable is the identified risk?

Outcome of such risk assessment can be documented (e.g. using the template provided below) and and store in Dossier folder 4.1.1. A case study can be found [[Risk Management Case Studies]]

'''DO NOT FORGET'''
* To justify acceptance of risk based on both probability and severity of harm

== C. Resources ==
SYRCLE Risk of Bias [https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-43]

The following techniques provide a more detailed framework and can be used if resources allow it:
* SWOT analysis [https://en.wikipedia.org/wiki/SWOT_analysis]
* FMEA [https://en.wikipedia.org/wiki/Failure_mode_and_effects_analysis]

ICH Q9 Quality Management
* FDA [https://www.fda.gov/media/71543/download]
* EMA [https://www.ema.europa.eu/en/ich-q9-quality-risk-management]

Template for optional risk assessment - [https://paasp.sharepoint.com/:w:/s/EQIPD/EfSJLIZdbOFOlKneuiRrf5MBeTug7VsZoWHsETvI3pY_mg?e=GXBsdk 4.1.1 Risk Management.docx]

----

back to [[Toolbox]]

Next item: [[4.1.2 Self assessment]]

2.3.1 Generation, recording, handling and archiving of raw data

2021-03-24T13:04:20Z

ChristophEmmerich:

== A. Background & Definitions ==
This item refers to one of the [[Core Requirements]] (Core Requirement 6 - "Generation, handling and changes to data records must be documented") and is, therefore, considered as essential.

Raw data means all original records and documentation, which are the result of the observations and activities in a study.

Raw data may include:
* photographs, videotapes, blots, chromatograms, computer readable media, dictated observations, recorded data from automated instruments, or any other medium capable of providing secure storage of information for a time period required by law or other applicable regulations;
* data directly entered into a computer through an automatic instrument interface, which are the results of primary observations and activities in a study;
* copies of original laboratory records and documentation that are complete and of good quality.

As raw data may also be recognized the processed result of original observations when these latter cannot be stored for technical reasons, e.g.:
* a research tool conducts pre-processing of original observations (example: movements of a rat in an open field are recorded by means of the photobeam breaks; research software may present the raw data as a movement track or a calculated distance traveled rather than a sequence of photobeam breaks);
* a research tool records data in a specific format that may or may not be readable at a later time point (e.g. if the license to use this research tool expires) and therefore pre-processing supports long-term accessibility of the original observations;
* a research tool generates exceptionally large volumes of data that are technically difficult to store without pre-processing to reduce the storage volume (e.g. imaging data).
Experimental Record: A research diary entry for an experiment giving access to or information about location of raw data and pertinent details of an experiment such that a peer could repeat the experiment.

== B. Guidance & Expectations ==
Implementation and maintenance of processes related to raw data is one of the main responsibilities of the [[1.5.2.3 Process owner]] or other scientist(s) to whom Process Owner delegates this task.

Processes related to raw data as well as the associated roles and responsibilities should be described in the study protocol (or protocols for specific research methods). If not such formal description is available or possible, Process Owner should ensure that the desired practices are in place and are verifiable.

Generation and recording of raw data:
* All equipment and computerized systems used for data generation must be fit-for-purpose (please check item [[3.3.2 Processes to enable computerized and non-computerized systems being suitable for intended use| 3.3.2]]).
* Every research unit has to define what is regarded as raw data for the experiments conducted in that particular research unit.
* All records should bear a [[2.1.2 Unique study ID]] and must be dated and signed / initialed by the person making the entry; this can be done electronically or on paper.
* Data should be recorded at the time of generation (meaning that any delay should be justifiable by the study protocol or associated working procedures) ([[3.1.2 Procedures for how and when to record data| 3.1.2]])

Handling of raw data:
* The processing of raw data records must be transparent (for details please check the items [[3.1.2.1 Traceability of data and any person having impact on data| 3.1.2.1]] and [[3.1.2.2 Process for witnessing of records| 3.1.2.2]]) and understandable by a third person.
* Any changes to the data records must be documented, reason for a change must be explained, dated, signed and saved; for details see the item [[3.1.2 Procedures for how and when to record data| 3.1.2]].

Storing of raw data:
* The storage of raw data must ensure readability and protection from loss, modification, destruction and unauthorized access (link to [[3.1.3 Data security| 3.1.3]]).
* Raw data should be stored in a read-only mode according to legal, contractual or other obligations.
* If raw data cannot be saved in an electronic or paper notebook (e.g. because of the volume or format), experimental record must contain a reference to the location where raw data is stored.

Common Data Elements (CDEs):
In preclinical research, the use of CDEs receives increasing attention and is encouraged as it can facilitate data sharing across research projects and provides opportunities for comparison and combination of data sets from multiple studies. CDEs are standardized key terms or concepts, established to be used in experimental studies, so that research findings can be generalized with respect to different research institutions, diverse populations, different regions, and interventions.

More detailed information can be found here:
* [https://clinfowiki.org/wiki/index.php/Common_Data_Element_(CDE) https://clinfowiki.org/wiki/index.php/Common_Data_Element_(CDE)]
* [https://onlinelibrary.wiley.com/doi/10.1002/epi4.12236 https://onlinelibrary.wiley.com/doi/10.1002/epi4.12236]

'''PLEASE DO NOT FORGET'''
* To check whether storage of raw data will not endanger traceability (i.e. whether raw data can be traced back from the reports and publications)
* To make sure that the duration of storage and accessibility of raw data is not determined by presence of a specific employee or student
* To consider adding this subject to a training program for new employees or refresher training
* To update the Documentation Plan when any changes are made to the way raw data are handled

== C. Resources ==
* DFG Guideline on the Handling of Research Data [https://www.dfg.de/download/pdf/foerderung/antragstellung/forschungsdaten/guidelines_research_data.pdf]
* The EQIPD template "Documentation Plan" is located in the Dossier folder 3.1 and also here - 3.1 Documentation Plan.docx

----------------

back to [[Toolbox]]

Next item: [[2.3.1.1 Converting non-electronic information into an electronic form]]

2.1.5 Pre-specification

2021-03-24T13:03:18Z

ChristophEmmerich:

== A. Background & Definitions ==

Pre-specification is a protective measure against so called "rationalization" („making excuses“), a defense mechanism in which controversial or questionable behaviors or feelings are justified and explained in a seemingly rational or logical manner to avoid the true explanation, and are made consciously tolerable - or even admirable and superior - by plausible means (e.g. "I didn't get the job that I applied for, but I really didn't want it in the first place").

An example of a situation when rationalization mechanisms can get engaged:
* You are running an experiment and there is a sudden noise, vibration or some other factor
* You get angry but cannot do anything other than recording in your lab journal that such event occurred at this particular time point
* When analyzing the data, you notice that one of the subjects / data points behaves strange and it is exactly when that disturbing interference occurred
* If including or excluding this subject or data point makes the results appear differently, what do you do?

Pre-specification aims to protect scientists from giving a wrong answer to the question above and, thereby, supports generation of unbiased results.

Pre-specification is mechanistically similar to precommitment or self-binding when we make the decision before being in the tempting situation (e.g. take a limited amount of money with you to curtail spending; have only healthy foods at home to avoid the temptation to go astray).

Thus, pre-specification is about a strategy that we may use to restrict the number of choices available to us at a future time.

== B. Guidance & Expectations ==

EQIPD advises to pre-specify key study and analysis details irrespective of the purpose of research.

For knowledge-claiming research ([[2.1.4 Purpose of research]]), pre-specification is mandatory and should cover all key study and analysis details as well as the key decisions, e.g.:
* study hypothesis
* data analysis plan (including statistical methods to be applied)
* inclusion and exclusion criteria (including criteria for "outliers")
* primary outcome measure
* if applicable, interpretation of the study results dependent on performance of the control groups or treatments (e.g. is the study declared "failed study" if a positive control fails)
* if applicable, decisions to be made if the primary outcome is met or not met

It is recommended that pre-specification is documented by the same scientist who prepares and documents the [[2.1.1 Study protocol]].

Pre-specification requires no special training and can be completed as a free-text summarizing the study and analysis details that are to be pre-specified.

Information about pre-specified study design and analysis details should be saved in a manner that:
* identifies the author and time of creation
* is protected against deletion and unauthorized modification
* is retrievable and readable

The most optimal way of performing pre-specification is by storing pre-specified information in a laboratory notebook (e.g. electronic) as part of a [[2.1.1 Study protocol]].

Pre-specification may also be conducted by means of [[2.1.11 Preregistration]] of the study plans.

Any amendments to pre-specified study design and analysis details should be properly documented (dated, signed, etc.).

'''PLEASE DO NOT FORGET'''

To check whether the means chosen for pre-specification ensure safe and secure storage of pre-specified information
To consider adding this subject to an awarenss training program for new employees or refresher training

== C. Resources ==
Precommitment (Wikipedia article) [https://en.wikipedia.org/wiki/Precommitment]

-------------------

back to [[Toolbox]]

Next item: [[2.1.6 Sample size and power analysis]]

ARRIVE Essential - Outcome measures

2021-03-24T13:02:34Z

ChristophEmmerich:

'''DISCLAIMER:''' Information on this and related pages is based on or copied directly from the ARRIVE guidelines 2019 (please see the original guidelines for more information, references and examples that are not included on these pages):
* [https://www.biorxiv.org/content/10.1101/703181v1/ updated guidelines for reporting animal research]
* [https://www.biorxiv.org/content/10.1101/703355v1 explanation and elaboration]

== ARRIVE Essential 10 - Item 6 - Outcome measures ==
'''6a. Clearly define all outcome measures assessed''' (e.g. cell death, molecular markers, or behavioural changes).

An outcome measure (also known as a dependent variable or a response variable) is any variable recorded during a study (e.g. volume of damaged tissue, number of dead cells, specific molecular marker) to assess the effects of a treatment or experimental intervention. Outcome measures may be important for characterising a sample (e.g. baseline data) or for describing complex responses (e.g. ‘haemodynamic’ outcome measures including heart rate, blood pressure, central venous pressure, and cardiac output).

Explicitly describe what was measured, especially when measures can be operationalised in different ways. For example, activity could be recorded as time spent moving or distance travelled. Where possible, the recording of outcome measures should be made in an unbiased manner (e.g. blinded to the treatment allocation of each experimental group; see item [[2.1.7 Blinding]]). Specify how the outcome measure(s) assessed are relevant to the objectives of the study.

'''6b. For hypothesis-testing studies''' or [[2.1.4 Purpose of research| research conducted in knowledge-claiming mode]], specify the primary outcome measure, i.e. the outcome measure that was used to determine the sample size.

In a hypothesis-testing experiment [[2.1.4 Purpose of research| research conducted in knowledge-claiming mode]], the primary outcome measure answers the main biological question. It is the outcome of greatest importance, identified in the planning stages of the experiment and used as the basis for the sample size calculation. For exploratory studies, it is not necessary to identify a single primary outcome and often multiple outcomes are assessed.

In a hypothesis-testing study [research conducted in confirmatory mode] powered to detect an effect on the primary outcome measure, data on secondary outcomes are used to evaluate additional effects of the intervention but subsequent statistical analysis of secondary outcome measures may be underpowered, making results and interpretation less reliable. Studies that claim to test a hypothesis but do not specify a pre-defined primary outcome measure, or those that change the primary outcome measure after data were collected (also known as primary outcome switching) are liable to selectively report only statistically significant results, favouring more positive findings.

Registering a protocol in advance protects the researcher against concerns about selective outcome reporting (also known as data dredging or p-hacking) and provides evidence that the primary outcome reported in the manuscript accurately reflects what was planned (see [[2.1.11 Preregistration]]).

If the study was designed to test a hypothesis and more than one outcome was assessed, explicitly identify the primary outcome measure and state if it was defined as such prior to data collection.

----------------
go to [[ARRIVE 2.0]]

go to [[2.1.1 Study protocol]]

2.1.4 Purpose of research

2021-03-24T12:56:14Z

ChristophEmmerich:

== A. Background & Definitions ==

Several commonly acknowledged risks can bias the research results ([https://doi.org/10.1186/1471-2288-14-43 Hooijmans et al. 2014]).

There are modes of research that can tolerate a certain level of uncertainty, and while not leading to a formal knowledge claim, such work is an essential part of the research process. It may be used to generate hypotheses, to provide evidence to give the investigator greater confidence that an emerging hypothesis is valid, or to “screen” compounds for potential effects prior to more formal testing.

There are also modes of research where researchers want to minimize the risks of failing due to inadequate control of the risks of bias.

For every study, EQIPD recommends scientists to apply protection against risks of bias and to be transparent about the protective measures applied.

EQIPD requires that the maximal rigor possible is applied (and exceptions explained / documented in the study protocol) to research that is conducted with the prior intention of informing a knowledge claim ([[Glossary]]). This will usually (but not always) involve some form of null hypothesis statistical testing or formal Bayesian analysis. Hypotheses are articulated in advance of data collection, with pre-specified criteria defining the primary outcome measure and the statistical test to be used. Depending on the purpose for which the knowledge claim will be used, different research strategies are appropriate. A single well conducted preclinical study may be considered sufficient to convince others that the phenomena are real enough to justify their attention, but may not be sufficient to justify major research investment such as a clinical study.

Examples of research requiring the maximal rigor possible include:
* Experimental studies to scrutinize preclinical findings through replication of results alongside investigations into boundary conditions and robustness through conduct of additional (control) conditions and multicenter studies ([https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001863 Kimmelman et al. 2014])
* Research aimed to generate evidence that enables decisions such as critical studies that, dependent on the outcome, will trigger a chain of activities and events associated with significant resource and time costs (e.g. a decision to initiate a new drug development project or to initiate GLP safety assessment of a new drug candidate)
* Studies for which any outcome would be considered diagnostic evidence about a claim from prior research ([https://doi.org/10.1371/journal.pbio.3000691 Nosek and Errington 2020])
* Labor-, resource- and/or time-intensive studies that cannot be easily repeated

== B. Guidance & Expectations ==
One of the [[Core Requirements]] of the EQIPD Quality System is that investigators must assert in advance whether a study will be conducted to inform a formal knowledge claim. This statement should be recorded in the [[2.1.1 Study protocol]] (see below for a template).

If uncertain about your study being knowledge-claiming or not, please check [[FAQ]] page or contact the EQIPD Expert Team for an advice.

Efforts to minimize the risks of bias ([https://doi.org/10.1186/1471-2288-14-43 Hooijmans et al. 2014]) should be applied to all studies that aim to inform a knowledge claim (table inspired by [https://doi.org/10.1161/STROKEAHA.116.013244 Dirnagl 2016]):

{| class="wikitable"
|
|'''All research'''
|'''Research informing a formal knowledge claim (i.e. research requiring maximal rigor)*'''
|-
|'''[[2.1.1 Study protocol|Study protocol]]'''
|should be defined and documented before starting the experiments
|must be defined and documented before starting the experiments
|-
|'''Study hypothesis'''
|advised to define
|must be pre-specified
|-
|'''[[2.1.7 Blinding|Blinding]]'''
|advised to implement
|should be implemented, exceptions must be justified and documented
|-
|'''[[2.1.8 Randomisation|Randomisation]]'''
|advised to implement
|should be implemented, exceptions must be justified and documented
|-
|'''[[2.1.6 Sample size and power analysis|Sample size and power analysis]]'''
|advised to define and document before starting the experiments
|must be defined and documented before starting the experiments (e.g. included in the study protocol)
|-
|'''[[2.3.3 Statistical analysis|Data analysis]]'''
|advised to define and document before starting the experiments
|must be defined and documented before starting the experiments (e.g. as a formal statistical analysis plan and/or included in the study protocol)
|-
|'''[[2.1.9 Inclusion and exclusion criteria|Inclusion and exclusion criteria]]'''
|advised to define and document before starting the experiments
|must be defined and documented before starting the experiments (e.g. included in the study protocol)
|-
|'''Deviations from study protocol'''
|advised to document
|must be documented
|-
|'''[[2.1.11 Preregistration|Preregistration]]'''
|no
|should be implemented
|}

* Please refer to [[Glossary]] for explanation on the EQIPD use of the verbs "must" and "should"

'''PLEASE DO NOT FORGET'''

To consider adding this subject to a training program for new employees or refresher training (if appropriate)

== C. Resources ==
Template to create a study protocol based on the above guidance - [https://paasp.sharepoint.com/:w:/s/EQIPD/Ecmg1s58CM1Nssq_Wv3-Ei8BfcCMqxgQcRzesGy9a5gdRg?e=Nc8dHL 2.1.1 Study protocol.docx], [https://paasp.sharepoint.com/:w:/s/EQIPD/ETydIHKmJ1ZEt6EC87dtn9AB9RP3u29lxF0Omb-Ugesh1Q?e=Frbycd 2.1.1 Study protocol with macro.docm]

Literature:
* Hooijmans CR, Rovers MM, de Vries RBM, Leenaars M, Ritskes-Hoitinga M, Langendam MW (2014) SYRCLE’s risk of bias tool for animal studies. BMC Medical Research Methodology 14:43 [https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-43]
* Kimmelman J, Mogil JS, Dirnagl U (2014) Distinguishing between exploratory and confirmatory preclinical research will improve translation. PLoS Biol 12(5):e1001863 [https://doi.org/10.1371/journal.pbio.1001863]
* Nosek BA, Errington TM (2020) What is replication? PLoS Biol 18(3): e3000691 [https://doi.org/10.1371/journal.pbio.3000691]
* Dirnagl U (2016) Thomas Willis Lecture: Is Translational Stroke Research Broken, and if So, How Can We Fix It? Stroke 47(8):2148-53 [https://doi.org/10.1161/STROKEAHA.116.013244]

----------------
back to [[Toolbox]]

Next item: [[2.1.5 Pre-specification]]

2.1.1 Study protocol

2021-03-24T12:55:27Z

ChristophEmmerich:

== A. Background & Definitions ==

Within the EQIPD Quality System and the Toolbox, the term "study protocol" refers to a document that is used to describe and summarize information related to a specific study (experiment).

A single study protocol may contain references to one or more research methods and assays. For protocols for specific research protocols and assays, please refer to item [[3.5.2 Protocols for methods and assays]].

== B. Guidance & Expectations ==

It is strongly recommended that, for every study (experiment), there is a study protocol prepared prior to the study being conducted.

Each study protocol should identify author(s), date when it was created and a unique ID of the study that it describes.

EQIPD has developed a template (please see below) that outlines the suggested structure and content of a study protocol.

This template is provided as a general guidance and serves only as an example:
# Title of the study
# Study hypothesis
# [[2.1.4 Purpose of research]]
## Indicate whether the research is undertaken with the intention to inform a formal knowledge claim
# Choice of experimental model(s) or method(s)
## Describe how and why specific models and/or methods were chosen (e.g. based on [[2.1.3 Appraisal of literature and systematic reviews]]
## If animal subjects are involved, justify why alternative are not suitable; as well as the selection of species, strain, age, and sex (if applicable)
# Choice of controls
## Describe the controls (negative, positive, shams), why and how these were chosen (e.g. based on [[2.1.3 Appraisal of literature and systematic reviews]]
## If a positive control is included, indicate explicitly how the study outcome will be interpreted if positive control fails.
# Measures against risks of bias
## [[2.1.8 Randomisation]] (if selected, a randomisation protocol must be available)
## [[2.1.7 Blinding]] (if selected, a blinding protocol must be available)
## If randomization and/or blinding are not applied, please describe the reasons as well as any other measures to control the risks of bias that will be applied.
# Sample size
## Describe methods used to estimate the sample size (such as power analysis).
## For definitions and guidance see section [[2.1.6 Sample size and power analysis]]
## Specify the primary outcome measure that was used to determine the sample size (see [[ARRIVE Essential - Outcome measures]])
## The sample size is the number of experimental units per group (for definition of experimental unit – see [[ARRIVE Essential - Study design]])
## Specify the exact number of experimental units allocated to each group, and the total number in each experiment.
## For definitions and guidance on sample size estimation and power analysis, please follow the link.
# Inclusion and exclusion criteria
## Indicate any [[2.1.9 Inclusion and exclusion criteria]] to be applied
# Animal resources, reagents and materials
## Include a detailed description of reagents and materials and/or provide references to separate document(s) with the relevant information (e.g. use the description in [[3.3.3 Management of research materials and reagents]] to add information to your Dossier)
##If animals are used, provide sufficient details expected for reporting ([[ARRIVE 2.0]])
# Study design overview
## For complex study designs, include a visual representation that more easily interpreted than a text description (e.g. a timeline diagram, table or flow chart – e.g. using an Experimental Design Assistant).
##Include a detailed description of methods and experimental procedures and/or provide references to separate document(s) with the relevant information (e.g. if you chose to use section 3.5.2 of the Dossier for storing [[3.5.2 Protocols for methods and assays]]).
# Experimental procedures
## Include a detailed description of methods and experimental procedures and/or provide references to separate document(s) with the relevant information (e.g. if you chose to use section 3.5.2 of the Dossier for storing [[3.5.2 Protocols for methods and assays]])
## If experimental methods are not described in separate documents, follow [[ARRIVE 2.0]] Recommended Set suggestions (items 14-17 here as well as guidance provided here)
## If more than one method or procedure is used, describe sequence or experimental workflow
## For each experimental group, including controls, describe the procedures in enough detail to allow others to replicate them, including (more guidance - [[ARRIVE Essential - Experimental procedures]])
### What was done, how it was done and what was used
### When and how often
### Where (including detail of any acclimation periods)
### Why (provide rationale for procedures)
# Data analysis
## Describe the processing of raw data (for definition of raw data – see section [[2.3.1 Generation, recording, handling and archiving of raw data]])
## Describe statistical method(s) to be applied for each analysis ([[2.3.3 Statistical analysis]])
## Describe any methods used to assess whether the data met the assumptions of the statistical approach ([[ARRIVE Essential - Statistical methods]])
# Amendments
## Describe what was changed in the original study protocol, why, when and by whom
## When amending the study protocol, please make sure not to over-write the original version
## Amendments may be saved as documents separate from the original study protocol
# References
## If necessary, include references
# Preregistration
## EQIPD strongly recommends to pre-register study protocol (more information – [[2.1.11 Preregistration]])
## If pre-registered, please indicate the platform used, registration link and other relevant reference information

'''RISK ASSESSMENT'''
* If study protocol references to other protocols or dociuments (e.g. protocol for a specific experimental method), is the reference made to the current (relevant) version)?

== C. Resources ==
Template to create a study protocol based on the above guidance - [https://paasp.sharepoint.com/:w:/s/EQIPD/Ecmg1s58CM1Nssq_Wv3-Ei8BfcCMqxgQcRzesGy9a5gdRg?e=Nc8dHL 2.1.1 Study protocol.docx], [https://paasp.sharepoint.com/:w:/s/EQIPD/ETydIHKmJ1ZEt6EC87dtn9AB9RP3u29lxF0Omb-Ugesh1Q?e=dAnKJg 2.1.1 Study protocol with macro.docm]

Experimental design tools:
* MANILA (Matched Animal Analysis): link to the original article ([https://www.nature.com/articles/srep30723]) and the tool ([https://biomedportal.utu.fi/utu-apps/Rvivo/])
* NC3Rs’ Experimental Design Assistant [https://www.nc3rs.org.uk/experimental-design-assistant-eda]

----------------
back to [[Toolbox]]

Next item: [[2.1.2 Unique study ID]]

Core Requirements

2021-03-24T12:54:21Z

ChristophEmmerich:

{| class="wikitable"
|'''Categories'''
|'''No'''
|'''Core Requirement'''
|'''Toolbox reference'''
|'''EQIPD provided templates<sup>*</sup>'''
|-
|rowspan="2"|'''Research team'''
|1
|Process owner must be identified for the Quality System
|[[1.5.2.3 Process owner]]
| -
|-
|2
|Communication process must be in place
|[[1.2 Scope]]
|[https://paasp.sharepoint.com/:w:/s/EQIPD/ERyfFP_pBytDiEfqCutDAJQBFbaGQEx3G1pyOmDl50o_LQ?e=wlkvHJ Communication plan]
|-
|rowspan="3"|'''Quality culture'''
|3
|The research unit must have defined quality objectives
|[[1.1 Mission]]
|[https://paasp.sharepoint.com/:w:/s/EQIPD/EVUTrgQRuNpKtpHkobdCOq4BhSTw1p3akXGKvI_MRgxYag?e=dJoZ5T Mission statement]
|-
|4
|All activities must comply with relevant legislation and policies
|[[1.4.2 Adherence to legal and regulatory considerations]]
| -
|-
|5
|The research unit must have a procedure to act upon concerns of potential misconduct
|[[4.2.3 Responsible conduct of research]]
| -
|-
|rowspan="4"|'''Data integrity'''
|6
|Generation, handling and changes to data records must be documented
|[[2.3.1 Generation, recording, handling and archiving of raw data]]
|rowspan="3"|[https://paasp.sharepoint.com/:w:/s/EQIPD/EQTANrsKHTVIqtO8LT-ZJIEBEUJVZI6siRLkHfliUD4gdA?e=GJusMa Documentation plan]
|-
|7
|Data storage must be secured at least for as long as required by legal, contractual or other obligations or business needs
|[[3.1.3 Data security]]
|-
|8
|Reported research outcomes must be traceable to experimental data
|[[3.1.2.1 Traceability of data and any person having impact on data]]
|-
|9
|Reported data must disclose all repetitions of a study, an experiment, or a test regardless of the outcome
|[[2.4 Reporting]]
| -
|-
|rowspan="5"|'''Research processes'''
|10
|Investigator must declare in advance whether a study is intended to inform a formal knowledge claim
|[[2.1.4 Purpose of research]]
|[https://paasp.sharepoint.com/:w:/s/EQIPD/Ecmg1s58CM1Nssq_Wv3-Ei8BfcCMqxgQcRzesGy9a5gdRg?e=PbqvhE Study protocol]
|-
|11
|All personnel involved in research must have adequate training and competence to perform assigned tasks
|[[3.2.1 General guidance on training]]
| -
|-
|12
|Protocols for experimental methods must be available
|[[3.5.2 Protocols for methods and assays]]
|[https://paasp.sharepoint.com/:w:/s/EQIPD/EfUO3B7RFxdHgxQ8JY5hhFoBEDUiPGK4C8n6BBHEprwroA?e=8IezWV Protocols for experimental methods]
|-
|13
|Adequate handling and storage of samples and materials must be ensured
|[[3.3.3 Management of research materials and reagents]]
| -
|-
|14
|Research equipment and tools must be suitable for intended use and ensure data integrity
|[[3.3.2 Processes to enable computerized and non-computerized systems being suitable for intended use]]
| -
|-
|rowspan="3"|'''Continuous performance'''
|15
|Risk assessment must be performed to identify factors affecting the generation, processing and reporting of research data
|[[4.1.1 Risk assessment]]
|[https://paasp.sharepoint.com/:w:/s/EQIPD/Ecmg1s58CM1Nssq_Wv3-Ei8BfcCMqxgQcRzesGy9a5gdRg?e=PbqvhE Study protocol] and [https://paasp.sharepoint.com/:x:/s/EQIPD/ETo9OwIvZpNHtepp6IvxylQBjtjhk2AmRnypLCIOrGwMvA?e=n1zy3X Risk assessment template]
|-
|16
|Critical incidents and errors during study conduct must be analyzed and appropriately managed
|[[4.2.2 Error and incident management]]
|[https://paasp.sharepoint.com/:w:/s/EQIPD/EatOAFgLbctEvxRZTuSCdU4Bv8J1I_BitfKl-JJiieOTLA?e=z99RR1 Error reporting template]
|-
|17
|An approach must be in place to monitor the performance of the EQIPD Quality System, and address identified issues
|[[4.1.2 Self assessment]]
|[https://paasp.sharepoint.com/:x:/s/EQIPD/EWbE3AdV5jhHglumN_MlrugBQX_KsZQDpJVNYbBJk6svTQ?e=qkW68H Self assessment]
|-
|'''Sustainability'''
|18
|Resources for sustaining the EQIPD Quality System must be available
|[[1.5.5 Sustainability]]
| -
|}
<sup>*</sup>The provided templates are suggestions which may be used. However, it is not mandatory to use the templates developed by EQIPD. It is up to the research unit to modify them, develop new or use existing documentation.

For information on required documentation within EQIPD please visit: [[Documentation in EQIPD]]

Back to the [[EQIPD Quality System]].

2.1.4 Purpose of research

2021-03-24T12:51:41Z

ChristophEmmerich:

== A. Background & Definitions ==

Several commonly acknowledged risks can bias the research results ([https://doi.org/10.1186/1471-2288-14-43 Hooijmans et al. 2014]).

There are modes of research that can tolerate a certain level of uncertainty, and while not leading to a formal knowledge claim, such work is an essential part of the research process. It may be used to generate hypotheses, to provide evidence to give the investigator greater confidence that an emerging hypothesis is valid, or to “screen” compounds for potential effects prior to more formal testing.

There are also modes of research where researchers want to minimize the risks of failing due to inadequate control of the risks of bias.

For every study, EQIPD recommends scientists to apply protection against risks of bias and to be transparent about the protective measures applied.

EQIPD requires that the maximal rigor possible is applied (and exceptions explained / documented in the study protocol) to research that is conducted with the prior intention of informing a knowledge claim ([[Glossary]]). This will usually (but not always) involve some form of null hypothesis statistical testing or formal Bayesian analysis. Hypotheses are articulated in advance of data collection, with pre-specified criteria defining the primary outcome measure and the statistical test to be used. Depending on the purpose for which the knowledge claim will be used, different research strategies are appropriate. A single well conducted preclinical study may be considered sufficient to convince others that the phenomena are real enough to justify their attention, but may not be sufficient to justify major research investment such as a clinical study.

Examples of research requiring the maximal rigor possible include:
* Experimental studies to scrutinize preclinical findings through replication of results alongside investigations into boundary conditions and robustness through conduct of additional (control) conditions and multicenter studies ([https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001863 Kimmelman et al. 2014])
* Research aimed to generate evidence that enables decisions such as critical studies that, dependent on the outcome, will trigger a chain of activities and events associated with significant resource and time costs (e.g. a decision to initiate a new drug development project or to initiate GLP safety assessment of a new drug candidate)
* Studies for which any outcome would be considered diagnostic evidence about a claim from prior research ([https://doi.org/10.1371/journal.pbio.3000691 Nosek and Errington 2020])
* Labor-, resource- and/or time-intensive studies that cannot be easily repeated

== B. Guidance & Expectations ==
One of the [[Core Requirements]] of the EQIPD Quality System is that investigators must assert in advance whether a study will be conducted to inform a formal knowledge claim. This statement should be recorded in the [[2.1.1 Study protocol]] (see below for a template).

If uncertain about your study being knowledge-claiming or not, please check [[FAQ]] page or contact the EQIPD Expert Team for an advice.

Efforts to minimize the risks of bias ([https://doi.org/10.1186/1471-2288-14-43 Hooijmans et al. 2014]) should be applied to all studies that aim to inform a knowledge claim (table inspired by [https://doi.org/10.1161/STROKEAHA.116.013244 Dirnagl 2016]):

{| class="wikitable"
|
|'''All research'''
|'''Research informing a formal knowledge claim (i.e. research requiring maximal rigor)*'''
|-
|'''[[2.1.1 Study (experimental) plan|Study (experimental) plan]]'''
|should be defined and documented before starting the experiments
|must be defined and documented before starting the experiments
|-
|'''Study hypothesis'''
|advised to define
|must be pre-specified
|-
|'''[[2.1.7 Blinding|Blinding]]'''
|advised to implement
|should be implemented, exceptions must be justified and documented
|-
|'''[[2.1.8 Randomisation|Randomisation]]'''
|advised to implement
|should be implemented, exceptions must be justified and documented
|-
|'''[[2.1.6 Sample size and power analysis|Sample size and power analysis]]'''
|advised to define and document before starting the experiments
|must be defined and documented before starting the experiments (e.g. included in the study protocol)
|-
|'''[[2.3.3 Statistical analysis|Data analysis]]'''
|advised to define and document before starting the experiments
|must be defined and documented before starting the experiments (e.g. as a formal statistical analysis plan and/or included in the study protocol)
|-
|'''[[2.1.9 Inclusion and exclusion criteria|Inclusion and exclusion criteria]]'''
|advised to define and document before starting the experiments
|must be defined and documented before starting the experiments (e.g. included in the study protocol)
|-
|'''Deviations from study protocol'''
|advised to document
|must be documented
|-
|'''[[2.1.11 Preregistration|Preregistration]]'''
|no
|should be implemented
|}

* Please refer to [[Glossary]] for explanation on the EQIPD use of the verbs "must" and "should"

'''PLEASE DO NOT FORGET'''

To consider adding this subject to a training program for new employees or refresher training (if appropriate)

== C. Resources ==
Template to create a study protocol based on the above guidance - [https://paasp.sharepoint.com/:w:/s/EQIPD/Ecmg1s58CM1Nssq_Wv3-Ei8BfcCMqxgQcRzesGy9a5gdRg?e=Nc8dHL 2.1.1 Study plan.docx], [https://paasp.sharepoint.com/:w:/s/EQIPD/ETydIHKmJ1ZEt6EC87dtn9AB9RP3u29lxF0Omb-Ugesh1Q?e=Frbycd 2.1.1 Study plan with macro.docm]

Literature:
* Hooijmans CR, Rovers MM, de Vries RBM, Leenaars M, Ritskes-Hoitinga M, Langendam MW (2014) SYRCLE’s risk of bias tool for animal studies. BMC Medical Research Methodology 14:43 [https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-43]
* Kimmelman J, Mogil JS, Dirnagl U (2014) Distinguishing between exploratory and confirmatory preclinical research will improve translation. PLoS Biol 12(5):e1001863 [https://doi.org/10.1371/journal.pbio.1001863]
* Nosek BA, Errington TM (2020) What is replication? PLoS Biol 18(3): e3000691 [https://doi.org/10.1371/journal.pbio.3000691]
* Dirnagl U (2016) Thomas Willis Lecture: Is Translational Stroke Research Broken, and if So, How Can We Fix It? Stroke 47(8):2148-53 [https://doi.org/10.1161/STROKEAHA.116.013244]

----------------
back to [[Toolbox]]

Next item: [[2.1.5 Pre-specification]]

1.4.3.3 Academia-academia: Research as service

2021-03-24T12:49:27Z

ChristophEmmerich:

=Background=
Recommendations outlined in this document has been developed by a task force of members and stakeholders of the EQIPD consortium, the largest private-public partnership completely dedicated to improving data quality in preclinical research.
These recommendations are intended to improve the robustness, reliability, traceability and integrity of the data obtained from the research activities supported by academic core facilities. They aim to:
improve communication between core facilities and the users of the services and infrastructure provided by the core facilities,
minimize bias and errors in the collection, reporting or representation of such information, and
create reliable scientific and supporting evidence in resulting publications, presentations, reports, patents and other types of research output.
The experimental record and its thorough description is the ultimate source of information and documentation regarding the experiment. Therefore, the contents of the experimental record must be accurate and thorough enough to be fully traceable to permit the reproduction of the work conducted. The experimental record is the official data record for each experiment and the most important primary source of data. It is expected that the practices outlined in this document will be applied to experimental planning, record-keeping procedures and reporting, to the fullest extent possible.
Recognizing the diversity of environments and settings in which core facilities operate, the current recommendations can be used in two modes - “Training service” and “EQIPD service”.
It is expected that core facilities and their users discuss both types of services, any ambiguities or conflicts regarding the recommended practices, and ensure alignment and understanding prior to the start of the experiments.

===Training Service===
# Core Facility provides information about research practices recommended by EQIPD (items listed below) to the users.
# It is up to the Core Facility to decide how this information is shared with the users (e.g., made part of a training program, shared as a written summary in paper or electronic form).
# Unless requested by the users or otherwise enabled by the locally applicable rules and regulations, Core Facility does not assume any further role in supporting or monitoring the implementation of recommended practices.

===EQIPD Service===
# Core Facility implements those aspects of EQIPD recommendations that do not depend on the users and that enable support of EQIPD-compliant research.
# Core facility provides the users with the information about research practices recommended by EQIPD and offers to support in conducting EQIPD-compliant research.
# If the user accepts the offer (and, as necessary, provides required resources), Core Facility:
## together with the user (""PI""), identifies the best solution to implement specific recommendations (items below). Shared responsibility over implementation means ""joint decision, knowledge and transparency"" and may still require certain recommendations fully implemented at the Core Facility while others - fully implemented on the user's side.
## assumes responsibility over spot checks (requires acceptance by the PI if certain recommendations are implemented on the user's side)
## confirms to the user that the study was conducted as ""EQIPD compliant"" or not (e.g. to be stated in the report or in a publication)"
# If the user does not accept the offer, no changes in the routine practice and the studies remain to be not compliant with EQIPD recommendations.

=Proposed statements=
==Training==
* Users must be trained by CF members in order to be eligble to use CF
* Users should seek support from CF to design experiments in due time and with with optimal rigor.
** Users should seek contact with the core facility ahead of time to ensure proper preparation/consulting/animal license approval and ultimate data quality.
** Core facilities should play an educational role in proper planning of projects.
** CF member together with the PI and the user should perform risk assessment
** the users should be instructed to record any erros and report them to CFH; the CFH should debrief it; the debrief record should be circulated to all other users in order to minimize the chances for recurrance
** Users must take responsibility for the appropiate use of reagents, research subjects and equipment they bring into the CF
** It is up to CF to define which form training should take (including frequency and documentation)

==Experimental Record==
* [[2.1.2 Unique study ID|Unique study]] identifiers must be used and defined by the future owner of the raw data for each experiment. Unless this is the CF, the owner of the [[2.3.1 Generation, recording, handling and archiving of raw data|raw data]] should communicate the unique study ID to the CF.
** Best practices should be communicated by the CF during training or by providing a reference to EQIPD Toolbox (e.g. date_User inititals_experiment).
** CF and PI may generate separate study IDs but there must be a way established to communicate, to connect separate IDs and to enable traceability.
** For EQIPD, it should be clear who is in charge of generating and storing the Unique Study Identifiers.
** For complex studies that include more than one experiment, several experiments are typically part of one experimental record under one study ID.
** Unless known to CF, PI must communicate ethical approval to CF
** File naming: in order that each data is unique and retrievable we suggest that you adhere to the following file naming convention: [date YYMMDD]-[the first letter of your first name together with your last name]-[free text]. If your surname is long you may use only the first 6 letters of it. e.g. 201108-cpitzer-catwalk with mice zQ98
* Each experimental record should include, directly or by reference, the names of all scientists involved, objectives, ethical approval/number, procedures, methods, materials, equipment, dates, and any other details considered necessary for reproducibility and reconstruction.
** CF can elaborate a form (excel sheet for instance) listing all the items that are required in an experimental record, and ask users to fill in this form. This could help the user to keep tracking of his/her own detailed experimental record and is likely to increase the traceability of his/her data.
* All raw and any processed data must be retrievable and traceable, directly or by reference. No raw data should be erased.
* An experimental record must describe any significant changes and deviations from the original study protocol.
** User must report/document if study execution is not in accordance with study protocol and deviations such as changes to study hypothesis, design or analysis must be documented (including the rationale for the changes).
** Examples of significant changes: Changes in the doses, experimental conditions and groups, sample size, methods of analysis, etc.
** Any change or deviation from a protocol approved by animal welfare authority would be significant and requires documentation in the experimental record.
** If uncertain about what constitutes a significant change or deviation, the user should consult with the core facility.
* An experimental record must provide an explanation and justification for exclusion of any data points from analysis.
** Critical incidents (any unexpected or unplanned events) and errors must be recorded and made part of the experimental record.
preparation/consulting/animal license approval and ultimately data quality.

==Rigor in Study Design==
* For every study, there should be a study protocol prepared prior to the study being conducted.
** Please see a definition of the study protocol above.
** Study protocols involving more than one experiment should include a dedicated section explaining the sequence and relationships between different experimental operations or procedures.
* The [[2.1.1 Study (experimental) plan|study protocol]] '''must''' include:
** Title
** Study hypothesis
** Ethical approval number and the name of approving body (for research involving animals)
** Statement / information about controls (with choice justification if necessary)
** Description of [[2.1.6 Sample size and power analysis|sample size calculation]]
** [[2.1.9 Inclusion and exclusion criteria|Inclusion / exclusion criteria]]
** Description of animal resources, reagents and materials (as applicable)
** Study design overview for complex studies
** Detailed description of [[3.5.2 Protocols for methods and assays|experimental procedure(s)]] (or references to standalone descriptions if available)
* The protocol '''should''' include:
** Statement whether study is [[2.1.4 Purpose of research|exploratory or knowledge-claiming]]
** Statement about choice of experimental methods
** Detailed description of measures against risk of bias ([[2.1.8 Randomisation|randomization]], [[2.1.7 Blinding|blinding]]) (or references to standalone descriptions if available)
** Description of [[2.3.2 Primary analysis and evaluation of raw data|raw data analysis]]
** use versioning (or have a section for amendments)
* It is advisable to:
** Include [[2.1.3 Appraisal of literature and systematic reviews|references to relevant literature]]
** Conduct [[4.1.1 Risk assessment|risk assessment]]
** [[2.1.11 Preregistration|Preregister the study protocol]]
* Apply [[2.1.8 Randomisation|randomization]] and [[2.1.7 Blinding|blinding]]. If not done or if not maintained throughout the experiment (from subject/sample allocation to analysis), include the reasons in the study protocol, the study report and any publication.
** The CF will advise the User how to [[2.1.8 Randomisation|randomize]] / [[2.1.7 Blinding|blind]].
** The user should implement.
** The CF will verify impementation.
* Justify sample size (e.g. using [[2.1.6 Sample size and power analysis|power analysis]]) and include the justification in the [[2.1.1 Study (experimental) plan|study protocol]], the study report and any publication.
** User should perform [[2.1.6 Sample size and power analysis|samples size calculation]] and follow advice from CF staff.
* Ensure [[2.1.9 Inclusion and exclusion criteria|inclusion / exclusion criteria]] and/or acceptance criteria are stated in the [[2.1.1 Study (experimental) plan|study protocol]], the study report and any publication.

==Analysis of Experimental Data==
* Experimental / data analysis record and [[2.4.1 Non-public reporting|study report]] (e.g. [[2.4.2 Publication|publication]]) should include sufficient detail to reconstruct any analysis performed and record all process steps and calculations used.
** It is very important to define responsbility
** CF should be in the position/have the possibility to check this or run occasional audits
* Data analysis plan should be described in the [[2.1.1 Study (experimental) plan|study protocol]], carried out as described, and reported. Additional analyses are always possible, but should be identified as such.
* Minimize the risks of bias and increase internal validity by including criteria for outlier exclusion, acceptable ranges for standards, reference compound and quality controls upfront in the study protocol.
* If the data analysis is done by the users themselves, they should subsequently share this data with the core facility even if they will not be published to allow for quality control of the data analysis and of the procedures in the core facility.

==Data Storage and Traceability==
* Experimental records should be kept in an [[3.1.2 Procedures for how and when to record data|audit-trailed, version-controlled, safe storage environment]] such as an appropriate bound-paper laboratory notebook with permanent ink or an electronic laboratory notebook (ELN).
* Raw (primary) data must be stored in an un-editable read-only form as soon as it is generated and must be backed-up
** [[3.1.3 Data security|the raw data should be backed-up]]
** [[3.3.2 Processes to enable computerized and non-computerized systems being suitable for intended use|reliability of IT resources should be ensured]]
** the responsibility for saving and archiving the raw data must be clarified
* Processed (secondary) data must be clearly labeled as such and should contain a reference to raw data

==Review and Reporting==
* Reported research outcomes should be [[2.3.1 Generation, recording, handling and archiving of raw data|complete, accurate and findable]]
** The [[3.1.2.1 Traceability of data and any person having impact on data|location of the data must be identifiable]] for all data records, e.g. reference with the permanent identifier.
** Reporting must include for each analysis the exact number of biological units for each condition
* Experimental records should be reviewed by a CF for completeness and accuracy and it is advised to document this review
** Users should give CF staff the possibility to review data for analysis and reporting
* Report should always provide summaries of all related data, processes, and conclusions, and include justification for excluding any relevant experimental records or individual data points from the summary analyses.
* Any external presentation/publication whether oral or in writing should give credit to the CF where the work was performed.
** Identify all contributing researchers and reference unique identifiers for the experimental records
** Encourage the use of an unique identifier for reseachers (ORCID)
** Unique IDs for facilities
* Any external presentation/publication whether oral or in writing should include a statement of conflict of interest.

=Resources=

EQIPD NEED for Core Facilities
* The NEED can be downloaded [https://paasp.sharepoint.com/:x:/s/EQIPD/EUxUx_BKVYpCrkQYDYSTPCEB09ETCcXrMs4iRfV70ydtUw?e=hEe91t here]
* For further explanation on NEEDS visit [[4.3.2.1_Using_the_Planning_Tool]]

The website [https://q-cofa.paasp.net Quality in Core Facilities] provides general support on establishing a quality framework.

----------------
back to [[Toolbox]]

Next item: [[1.4.3.4 Academia-academia: Research as collaboration]]

EQIPD for Core Facilities

2021-03-24T12:48:26Z

ChristophEmmerich:

=Background=
Recommendations outlined in this document has been developed by a task force of members and stakeholders of the [https://quality-preclinical-data.eu/ EQIPD consortium], the largest private-public partnership completely dedicated to improving data quality in preclinical research.

These recommendations are intended to improve the robustness, reliability, traceability and integrity of the data obtained from the research activities supported by academic core facilities. They aim to:
* improve communication between core facilities and their users of the services and infrastructure provided by the core facilities,
* minimize bias and errors in the collection, reporting or representation of such information, and
* create reliable scientific and supporting evidence in resulting publications, presentations, reports, patents and other types of research output.

The experimental record and its thorough description is the ultimate source of information and documentation regarding the experiment. Therefore, the contents of the experimental record must be accurate and thorough enough to be fully traceable to permit the reproduction of the work conducted. The experimental record is the official data record for each experiment and the most important primary source of data. It is expected that the practices outlined in this document will be applied to experimental planning, record-keeping procedures and reporting, to the fullest extent possible.

Recognizing the diversity of environments and settings in which core facilities operate, the current recommendations can be used in two modes - “Training service” and “EQIPD service”.

It is expected that core facilities and their users discuss both types of services, any ambiguities or conflicts regarding the recommended practices, and ensure alignment and understanding prior to the start of the experiments.

===Training Service===
# Core Facility provides information about research practices recommended by EQIPD (items listed below) to the users.
# It is up to the Core Facility to decide how this information is shared with the users (e.g., made part of a training program, shared as a written summary in paper or electronic form).
# Unless requested by the users or otherwise enabled by the locally applicable rules and regulations, Core Facility does not assume any further role in supporting or monitoring the implementation of recommended practices.

===EQIPD Service===
# Core Facility implements those aspects of EQIPD recommendations that do not depend on the users and that enable support of EQIPD-compliant research.
# Core facility provides the users with the information about research practices recommended by EQIPD and offers to support in conducting EQIPD-compliant research.
# If the user accepts the offer (and, as necessary, provides required resources), Core Facility:
## together with the user (and supervisor/PI if necessary), identifies the best solution to implement specific recommendations. Shared responsibility over implementation means ""joint decision, knowledge and transparency"" and may still require certain recommendations fully implemented at the Core Facility while others - fully implemented on the user's side.
## assumes responsibility over spot checks (requires acceptance by the user if certain recommendations are implemented on the user's side).
## confirms to the user that the study was conducted as ""EQIPD compliant"" or not (e.g. to be stated in the report or in a publication)"
# If the user does not accept the offer, no changes in the routine practice and the studies remain to be not compliant with EQIPD recommendations.

=Proposed statements=
==Training==
* Users must be trained by CF members in order to be eligble to use CF
* Users should seek support from CF to design experiments in due time and with with optimal rigor.
** Users should seek contact with the core facility ahead of time to ensure proper
** Core facilities should play an educational role in proper planning of projects.
** CF member together with the PI and the user should perform risk assessment
** the users should be instructed to record any erros and report them to CFH; the CFH should debrief it; the debrief record should be circulated to all other users in order to minimize the chances for recurrance
** Users must take responsibility for the appropiate use of reagents, research subjects and equipment they bring into the CF
** It is up to CF to define which form training should take (including frequency and documentation)

==Experimental Record==
* Unique study identifiers must be used and defined by the future owner of the raw data for each experiment. Unless this is the CF, the owner of the raw data should communicate the unique study ID to the CF.
** Best practices should be communicated by the CF during training or by providing a reference to EQIPD Toolbox (e.g. date_User inititals_experiment).
** CF and PI may generate separate study IDs but there must be a way established to communicate, to connect separate IDs and to enable traceability.
** For EQIPD, it should be clear who is in charge of generating and storing the Unique Study Identifiers.
** For complex studies that include more than one experiment, several experiments are typically part of one experimental record under one study ID.
** Unless known to CF, PI must communicate ethical approval to CF
** File naming: in order that each data is unique and retrievable we suggest that you adhere to the following file naming convention: [date YYMMDD]-[the first letter of your first name together with your last name]-[free text]. If your surname is long you may use only the first 6 letters of it. e.g. 201108-cpitzer-catwalk with mice zQ98
* Each experimental record should include, directly or by reference, the names of all scientists involved, objectives, ethical approval/number, procedures, methods, materials, equipment, dates, and any other details considered necessary for reproducibility and reconstruction.
** CF can elaborate a form (excel sheet for instance) listing all the items that are required in an experimental record, and ask users to fill in this form. This could help the user to keep tracking of his/her own detailed experimental record and is likely to increase the traceability of his/her data.
* All raw and any processed data must be retrievable and traceable, directly or by reference. No raw data should be erased.
* An experimental record must describe any significant changes and deviations from the original study protocol.
** User must report/document if study execution is not in accordance with study protocol and deviations such as changes to study hypothesis, design or analysis must be documented (including the rationale for the changes).
** Examples of significant changes: Changes in the doses, experimental conditions and groups, sample size, methods of analysis, etc.
** Any change or deviation from a protocol approved by animal welfare authority would be significant and requires documentation in the experimental record.
** If uncertain about what constitutes a significant change or deviation, the user should consult with the core facility.
* An experimental record must provide an explanation and justification for exclusion of any data points from analysis.
** Critical incidents (any unexpected or unplanned events) and errors must be recorded and made part of the experimental record.

==Rigor in Study Design==
* For every study, there should be a study protocol prepared prior to the study being conducted.
** Please see a definition of the study protocol above.
** Study protocols involving more than one experiment should include a dedicated section explaining the sequence and relationships between different experimental operations or procedures.
* The study protocol '''must''' include:
** Title
** Study hypothesis
** Ethical approval number and the name of approving body (for research involving animals)
** Statement / information about controls (with choice justification if necessary)
** Description of sample size calculation
** Inclusion / exclusion criteria
** Description of animal resources, reagents and materials (as applicable)
** Study design overview for complex studies
** Detailed description of experimental procedure(s) (or references to standalone descriptions if available)
* The protocol '''should''' include:
** Statement whether study is exploratory or knowledge-claiming
** Statement about choice of experimental methods
** Detailed description of measures against risk of bias (randomization, blinding) (or references to standalone descriptions if available)
** Description of raw data analysis
** use versioning (or have a section for amendments)
* It is '''advisable''' to:
** Include references to relevant literature
** Conduct risk assessment
** Preregister the study protocol

==Analysis of Experimental Data==
* Experimental / data analysis record and study report (e.g. publication) should include sufficient detail to reconstruct any analysis performed and record all process steps and calculations used.
** It is very important to define responsbility
** CF should be in the position/have the possibility to check this or run occasional audits

==Data Storage and Traceability==
* Experimental records should be kept in an audit-trailed, version-controlled, safe storage environment such as an appropriate bound-paper laboratory notebook with permanent ink or an electronic laboratory notebook (ELN).
* Raw (primary) data must be stored in an un-editable read-only form as soon as it is generated and must be backed-up
** the raw data should be backed-up
** reliability of IT resources should be ensured
** the responsibility for saving and archiving the raw data must be clarified
* Processed (secondary) data must be clearly labeled as such and should contain a reference to raw data

==Review and Reporting==
* Reported research outcomes should be complete, accurate and findable
** the location of the data must be identifiable for all data records, e.g. reference with the permanent identifier.
** Reporting must include for each analysis the exact number of biological units for each condition
* Experimental records should be reviewed by a member of the CF for completeness and accuracy and it is advised to document this review
** Users should give CF staff the possibility to review data for analysis and reporting
* Report should always provide summaries of all related data, processes, and conclusions, and include justification for excluding any relevant experimental records or individual data points from the summary analyses.
* Any external presentation/publication whether oral or in writing should give credit to the CF where the work was performed.
** Identify all contributing researchers and reference unique identifiers for the experimental records
** encourage the use of an unique identifier for reseachers ([https://orcid.org ORCID])
** Unique IDs for facilities
* Any external presentation/publication whether oral or in writing should include a statement of conflict of interest.

2.4 Reporting

2021-03-24T12:47:13Z

ChristophEmmerich:

== A. Background & Definitions ==

This Toolbox item refers to one of the [[Core Requirements]] (Core Requirement 9 - "Reported data must disclose all repetitions of a study, an experiment, or a test regardless of the outcome").

There is no formal definition what "repetition" is. One has to acknowledge that there are often methodological differences between two or more consecutive repetitions of a study that may be too significant to be considered as repetitions.

EQIPD offers the following basic guidance on how to define repetitions:

Repetitions are studies, experiments or tests that:
* answer the same research question (even if this involves certain variations in the study conditions, experimental variables and outcomes)
* are run consecutively
* are technically valid (i.e. no documented failure of hardware, software, research tool or reagent)
** failure of positive or negative control may usually not be recognized as a technical failure justifying the exclusion of a repetition from being disclosed
** failure of a positive or negative control may justify declaring the test results not valid, but
** failure of a positive or negative control does not justify failing to disclose the repetition

This core requirement applies to both [[2.4.1 Non-public reporting]] and scientific [[2.4.2 Publication]]s.

== B. Guidance & Expectations ==
In all cases, it is up to scientists to decide whether a study (experiment, test) was repeated or not, and, if yes, decide whether reports should disclose data for all repetitions or simply acknowledge the repetitions.

EQIPD expects that a Process Owner conducts regular spot checks on non-public reports and manuscripts prepared for journal submission to make sure that all repetitions are disclosed.

It is up to a Process Owner to decide which method is used to make sure that she/he can identify all repetitions of a study, e.g.:
* use a common [[2.1.2 Unique study ID]] for all repetitions
* add to a repetition study protocol reference(s) to previous studies
* maintain a central (for a research unit) list of studies planned and completed

== C. Resources ==
to be added

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back to [[Toolbox]]

Next item: [[2.4.1 Non-public reporting]]

2.2.1 Use of SOPs for standard experiments

2021-03-24T12:46:53Z

ChristophEmmerich:

== A. Background & Definitions ==
Standard operating procedures (SOPs) is a set of written instructions that define a standardized way to perform routine activities. SOPs contribute to maintain the quality and integrity of generated data.

== B. Guidance & Expectations ==
* SOPs must be clear and concise, so that all persons involved in the study understand how to perform their activities
* SOPs should be dated, signed/approved by an authorized scientist and saved in a secure place (e.g. non-modifiable by non-authorized persons). When amending an SOP, previous version of the SOP must be retained
* SOPs should be distributed to everyone involved in the study
* Everyone involved in the study should confirm reading of the SOP. Proof of understanding by everyone involved in a study (e.g. validation of training on achievement of routine activities described in the SOPs) should be documented before the realization of the study
* Quality professionals should have access to SOPs to verify that procedures are performed as intended
* Deviations to SOP should be described and reported as well as their impact on the data and conclusions of the study

'''PLEASE DO NOT FORGET'''
* SOPs must be clear and adapted to the activities, poorly written SOPs are a source of errors. Regular review of the SOPs improves their quality.
* If no SOPs are available, the study protocol should describe extensively instructions needed to perform the study, including routine activities.

== C. Resources ==
* McElroy J (2018) Writing Bulletproof SOPs: Best Practices For Life Sciences Companies. [https://www.outsourcedpharma.com/doc/writing-bulletproof-sops-best-practices-for-life-sciences-companies-0001?vm_tId=2074360&user=4a4511ca-f38f-497a-bf6f-42310c5093dd&utm_source=et_6214173&utm_medium=email&utm_campaign=OUTPH_07-18-2018&utm_term=4a4511ca-f38f-497a-bf6f-42310c5093dd&utm_content=Writing+Bulletproof+SOPs%253a+Best+Practices+For+Life+Sciences+Companies]
* Template and instructions of standard operating procedure: HANDBOOK: QUALITY PRACTICES IN BASIC BIOMEDICAL RESEARCH, Appendix 3 [https://www.who.int/tdr/publications/documents/quality_practices.pdf?ua=1]

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Next item: [[2.2.2 Use of template for (manual) data recording]]

2.1.9 Inclusion and exclusion criteria

2021-03-24T12:46:36Z

ChristophEmmerich:

== A. Background & Definitions ==

Inclusion criteria are criteria that qualify subjects or specific observations for inclusion in the study or in the study analysis.

Exclusion criteria are criteria that disqualify subjects or specific observations or data points from inclusion in the study or in the study analysis.

Inclusion / exclusion criteria may apply not only to individual subjects or observations but may also be pre-specified as "test acceptance criteria” or "pass / fail criteria on experimental level". For example, one may include a positive control in the study and would therefore need to pre-specify whether the entire study will be considered as “failed” if a positive control fails. Another example is given by pre-specification of performance of vehicle controls (e.g. against historical values).

== B. Guidance & Expectations ==
Inclusion and exclusion criteria should be pre-specified when the experiment is being designed (i.e. should be established prospectively) and it should be explicitly documented (e.g. listed and saved in the study protocol).

The best way to incorporate pre-specification into your experiment is by storing pre-specified information such as inclusion / exclusion criteria in a laboratory notebook (e.g. electronic) as part of a study protocol. Pre-specification may also be conducted by means of [[2.1.11 Preregistration]] of the study protocol.

If exclusion criteria were not pre-specified, certain values may still be excluded, but only if the decision is taken by a person blinded to the treatment conditions or other study design aspects that could bias the decision (i.e. ideally, the decison is made during the primary data review and verification before the study is unblinded).

Existence of any inclusion and exclusion criteria must be explicitly stated in the study reports (including scientific publications).

Inclusion criteria (examples):
* Species, strain, sex, weight and age of animals included in the study
* Transgene copy number
* Conditions/ performance criteria that must be fulfilled (e.g., after amphetamine administration, rats showing ≥5 full turns/min in the direction ipsilateral to the unilateral nigrostriatal lesion are selected for further experiments; Dekundy et al. 2006)
* Area of the calibration curve where the measured values should be in order for the study to be declared valid

Exclusion criteria (examples):
* Statistical outliers
** Pre-specify methods to be used to identify outliers and decision-making
* Exclusion of subjects, observations or data points if the study or data collection was or could be affected by unforeseen environmental or technical circumstances:
** Loud noise or vibration (e.g. due to ongoing construction in the facility)
** Malfunctioning of the research equipment
** Technical errors during the study conduct (e.g. failed injection procedure)
* Exclusion of subjects, observations or data points due to animals' health (e.g. exclusion of SOD1 mice that died from non-ALS causes; Scott et al., 2008)

'''PLEASE DO NOT FORGET'''
* To check whether there are any pass/fail criteria on experimental level that may be applicable
* To consider adding this subject to a training program for new employees or refresher training

== C. Resources ==
* Online Grubb’s test for outlier detection - [https://www.graphpad.com/quickcalcs/Grubbs1.cfm]

Guidelines on reporting of inclusion and exclusion criteria (in vivo research):
* [[ARRIVE 2.0]]

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Next item: [[2.1.10 Plausibility check]]

2.1.6 Sample size and power analysis

2021-03-24T12:46:10Z

ChristophEmmerich:

UNDER CONSTRUCTION

== A. Background & Definitions ==

Statistical power is defined as the probability of detecting a statistically significant effect of a pre-specified size. Formally, power is equal to 1 minus the Type II error rate (beta or ß). The Type II error rate is the probability of obtaining a non-significant result when the null hypothesis is false — in other words, failing to find a difference or relationship when one exists.

Balancing sample size, effect size and power is critical to good study design. When the power is low, only large effects can be detected, and negative results cannot be reliably interpreted. The consequences of low power are particularly dire in the search for high-impact results, when the researcher may be willing to pursue low-likelihood hypotheses for a groundbreaking discovery (see Fig. 1 in [https://www.nature.com/articles/nmeth.2738 Krzywinski & Altman 2013]). Ensuring that sample sizes are large enough to detect the effects of interest is an essential part of study design.

Studies with inadequate power are a waste of research resources and arguably unethical when subjects are exposed to potentially harmful or inferior experimental conditions.

Statistical power analysis exploits the relationships among the four variables involved in statistical inference: sample size (N), significance criterion (α), effect size (ES), and statistical power.

== B. Guidance & Expectations ==

General advice - DO:

* Whenever possible, seek professional biostatistician support to estimate sample size.
* Use power prospectively for planning future studies.
* Put science before statistics. It is easy to get caught up in statistical significance and such; but studies should be designed to meet scientific goals, and you need to keep those in sight at all times (in planning and analysis). The appropriate inputs to power/sample-size calculations are effect sizes that are deemed scientifically important, based on careful considerations of the underlying scientific (not statistical) goals of the study. Statistical considerations are used to identify a plan that is effective in meeting scientific goals – not the other way around.
* Do pilot studies. Investigators tend to try to answer all the world’s questions with one study. However, you usually cannot do a definitive study in one step. It is far better to work incrementally. A pilot study helps you establish procedures, understand and protect against things that can go wrong, and obtain variance estimates needed in determining sample size. A pilot study with 20-30 degrees of freedom for error is generally adequate for obtaining reasonably reliable sample-size estimates.
* Generate sample size estimates for a range of power and effect size values to explore the gains and losses in power or detectable effect size due to increasing or decreasing n. This is why the term ‘sample size estimation’ is often preferred over ‘sample size calculation’. Although the arrival at a number for the required sample size is invariably based on (often complex) formulae, the term ‘calculation’ implies an unwarranted degree of precision. The purpose of sample size estimation is not to give an exact number but rather to subject the study design to scrutiny, including an assessment of the validity and reliability of data collection ([https://www.sciencedirect.com/science/article/pii/S1466853X05000714 Batterham & Atkinson 2005]).
* Remember to consider attrition rate (i.e. possibility that some subjects or samples are lost during the conduct of the study or follow-up for technical and other data analysis-unrelated reasons)

General advice - DO NOT:

* Avoid using the definition of “small,” “medium,” or “large” effect size based on Cohen's d of .20, .50, or .80, respectively. Cohen's assessments are based on an extensive survey of statistics reported in the literature in the social sciences and may not apply to other fields of science. Further, this method uses a standardized effect size as the goal. Think about it: for a “medium” effect size, you’ll choose the same n regardless of the accuracy or reliability of your instrument, or the narrowness or diversity of your subjects. Clearly, important considerations are being ignored here. “Medium” is definitely not the message!

* Retrospective power calculations should be avoided, because they add no new information to an analysis (i.e. avoid using observed power to interpret the results of the statistical test). You’ve got the data, did the analysis, and did not achieve “significance.” So you compute power retrospectively to see if the test was powerful enough or not. This is an empty question. Of course it wasn’t powerful enough – that’s why the result isn’t significant. Power calculations are useful for design, not analysis.

Guidance on sample size estimation:

--- to be added / revised (please do not edit - placeholder) ---

.. getting a solid grip on the existing literature in one's topic, drilling down to what effects were identified and obtaining the corresponding ES values either directly from the publication or from appropriate calculations based on the printed documentation.

.. being sure that the estimate you obtain is the one that fits the study design correctly; one cannot necessarily generalize across disparate research designs.

.. and citing the algorithm or software used to generate the estimates. A power calculation result given without this detail can be viewed with suspicion.

--- to be added / revised (please do not edit - placeholder) ---

What to do if you have no choice about sample size:

Limited budget, limited supply of research materials, or a difficult-to-overcome guidance from a collaborator, a funder or a senior colleagues may leave no choice but to consider running a study with a certain potentially small sample size. What can be done in such situations?

* consider study designs involving correlated data (e.g. repeated measures, crossover or matched-pairs designs) that are usually associated with greater statistical power than those involving separate samples allocated to different treatment groups ([[https://www.sciencedirect.com/science/article/pii/S1466853X05000714 see section 2.1 here]).
* consider intervening variables or pre-intervention measurements for stratification; if not possible, one can still improve statistical power by entering these variables as covariates in the analysis (this approach has its limitations and therefore should be consulted with the statisticians)
* make sure that the most suited randomization schedule is used to control for random influences
* explore and engage all other means to minimize variation (including using properly maintained and calibrated research instruments, adequate and well controlled environmental conditions, making sure that experiments are performed by competent and adequately trained scientists)
* if a study has low power because of the given sample size, reflect this limitation in the study protocol and indicate to all stakeholders that the study cannot be run as knowledge-claiming (decision-enabling, confirmatory).
* evaluate power not only for the given sample size for also for the values around and discuss the impact of the sample size on power with the stakeholders - in some cases, it may help to lift or revise the original sample size restrictions. These discussions make sense and are justifiable only if they take place prior to the conduct of the study (i.e. not post hoc).

== C. Resources ==

Tools to sample size estimation:

* [http://www.gpower.hhu.de/ G*Power]
* [https://wise1.cgu.edu/power/index.asp WISE power tutorial]
* [http://davidmlane.com/hyperstat/power.html JAVA applets for power and sample size]
* [https://www.psychometrica.de/effect_size.html Computation of sample sizes @Psychometrica]
* [http://powerandsamplesize.com/Calculators/ Overview of sample size and power calculators]

Educational instruments and resources:

* Mayo clinical online simulator - Size matters [https://rtools.mayo.edu/size_matters/]
* Scientists talking to biostatisticians [https://www.youtube.com/watch?v=PbODigCZqL8&feature=youtu.be]

Useful literature (for non-statisticians):

* [https://stat.uiowa.edu/sites/stat.uiowa.edu/files/techrep/tr303.pdf Practical advice on sample size estimation by Russell Lenth]
* [https://www.sciencedirect.com/science/article/pii/S1466853X05000714 A primer on murky world of sample size estimation by Alan Batterham & Greg Atkinson]

Useful literature:

* [https://pdfs.semanticscholar.org/1325/24bdfe70504fcd67016b17305ccddb4bcd14.pdf Power in various ANOVA designs by Joel Levin]

* [https://www.ncbi.nlm.nih.gov/books/NBK43321/ https://www.ncbi.nlm.nih.gov/books/NBK43321/]
* [http://davidmlane.com/hyperstat/power.html http://davidmlane.com/hyperstat/power.html]
* [http://powerandsamplesize.com/Calculators/Test-1-Mean/1-Sample-Equality http://powerandsamplesize.com/Calculators]

Guidelines on reporting of sample size (in vivo research):
* [[ARRIVE 2.0]]

----------------
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Next item: [[2.1.7 Blinding]]

2.1.4 Purpose of research

2021-03-24T12:45:28Z

ChristophEmmerich:

== A. Background & Definitions ==

Several commonly acknowledged risks can bias the research results ([https://doi.org/10.1186/1471-2288-14-43 Hooijmans et al. 2014]).

There are modes of research that can tolerate a certain level of uncertainty, and while not leading to a formal knowledge claim, such work is an essential part of the research process. It may be used to generate hypotheses, to provide evidence to give the investigator greater confidence that an emerging hypothesis is valid, or to “screen” compounds for potential effects prior to more formal testing.

There are also modes of research where researchers want to minimize the risks of failing due to inadequate control of the risks of bias.

For every study, EQIPD recommends scientists to apply protection against risks of bias and to be transparent about the protective measures applied.

EQIPD requires that the maximal rigor possible is applied (and exceptions explained / documented in the study protocol) to research that is conducted with the prior intention of informing a knowledge claim ([[Glossary]]). This will usually (but not always) involve some form of null hypothesis statistical testing or formal Bayesian analysis. Hypotheses are articulated in advance of data collection, with pre-specified criteria defining the primary outcome measure and the statistical test to be used. Depending on the purpose for which the knowledge claim will be used, different research strategies are appropriate. A single well conducted preclinical study may be considered sufficient to convince others that the phenomena are real enough to justify their attention, but may not be sufficient to justify major research investment such as a clinical study.

Examples of research requiring the maximal rigor possible include:
* Experimental studies to scrutinize preclinical findings through replication of results alongside investigations into boundary conditions and robustness through conduct of additional (control) conditions and multicenter studies ([https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001863 Kimmelman et al. 2014])
* Research aimed to generate evidence that enables decisions such as critical studies that, dependent on the outcome, will trigger a chain of activities and events associated with significant resource and time costs (e.g. a decision to initiate a new drug development project or to initiate GLP safety assessment of a new drug candidate)
* Studies for which any outcome would be considered diagnostic evidence about a claim from prior research ([https://doi.org/10.1371/journal.pbio.3000691 Nosek and Errington 2020])
* Labor-, resource- and/or time-intensive studies that cannot be easily repeated

== B. Guidance & Expectations ==
One of the [[Core Requirements]] of the EQIPD Quality System is that investigators must assert in advance whether a study will be conducted to inform a formal knowledge claim. This statement should be recorded in the [[2.1.1 Study (experimental) plan]] (see below for a template).

If uncertain about your study being knowledge-claiming or not, please check [[FAQ]] page or contact the EQIPD Expert Team for an advice.

Efforts to minimize the risks of bias ([https://doi.org/10.1186/1471-2288-14-43 Hooijmans et al. 2014]) should be applied to all studies that aim to inform a knowledge claim (table inspired by [https://doi.org/10.1161/STROKEAHA.116.013244 Dirnagl 2016]):

{| class="wikitable"
|
|'''All research'''
|'''Research informing a formal knowledge claim (i.e. research requiring maximal rigor)*'''
|-
|'''[[2.1.1 Study (experimental) plan|Study (experimental) plan]]'''
|should be defined and documented before starting the experiments
|must be defined and documented before starting the experiments
|-
|'''Study hypothesis'''
|advised to define
|must be pre-specified
|-
|'''[[2.1.7 Blinding|Blinding]]'''
|advised to implement
|should be implemented, exceptions must be justified and documented
|-
|'''[[2.1.8 Randomisation|Randomisation]]'''
|advised to implement
|should be implemented, exceptions must be justified and documented
|-
|'''[[2.1.6 Sample size and power analysis|Sample size and power analysis]]'''
|advised to define and document before starting the experiments
|must be defined and documented before starting the experiments (e.g. included in the study protocol)
|-
|'''[[2.3.3 Statistical analysis|Data analysis]]'''
|advised to define and document before starting the experiments
|must be defined and documented before starting the experiments (e.g. as a formal statistical analysis plan and/or included in the study protocol)
|-
|'''[[2.1.9 Inclusion and exclusion criteria|Inclusion and exclusion criteria]]'''
|advised to define and document before starting the experiments
|must be defined and documented before starting the experiments (e.g. included in the study protocol)
|-
|'''Deviations from study protocol'''
|advised to document
|must be documented
|-
|'''[[2.1.11 Preregistration|Preregistration]]'''
|no
|should be implemented
|}

* Please refer to [[Glossary]] for explanation on the EQIPD use of the verbs "must" and "should"

'''PLEASE DO NOT FORGET'''

To consider adding this subject to a training program for new employees or refresher training (if appropriate)

== C. Resources ==
Template to create a study protocol based on the above guidance - [https://paasp.sharepoint.com/:w:/s/EQIPD/Ecmg1s58CM1Nssq_Wv3-Ei8BfcCMqxgQcRzesGy9a5gdRg?e=Nc8dHL 2.1.1 Study plan.docx], [https://paasp.sharepoint.com/:w:/s/EQIPD/ETydIHKmJ1ZEt6EC87dtn9AB9RP3u29lxF0Omb-Ugesh1Q?e=Frbycd 2.1.1 Study plan with macro.docm]

Literature:
* Hooijmans CR, Rovers MM, de Vries RBM, Leenaars M, Ritskes-Hoitinga M, Langendam MW (2014) SYRCLE’s risk of bias tool for animal studies. BMC Medical Research Methodology 14:43 [https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-43]
* Kimmelman J, Mogil JS, Dirnagl U (2014) Distinguishing between exploratory and confirmatory preclinical research will improve translation. PLoS Biol 12(5):e1001863 [https://doi.org/10.1371/journal.pbio.1001863]
* Nosek BA, Errington TM (2020) What is replication? PLoS Biol 18(3): e3000691 [https://doi.org/10.1371/journal.pbio.3000691]
* Dirnagl U (2016) Thomas Willis Lecture: Is Translational Stroke Research Broken, and if So, How Can We Fix It? Stroke 47(8):2148-53 [https://doi.org/10.1161/STROKEAHA.116.013244]

----------------
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Next item: [[2.1.5 Pre-specification]]

2.1.4 Purpose of research

2021-03-24T12:44:07Z

ChristophEmmerich:

== A. Background & Definitions ==

Several commonly acknowledged risks can bias the research results ([https://doi.org/10.1186/1471-2288-14-43 Hooijmans et al. 2014]).

There are modes of research that can tolerate a certain level of uncertainty, and while not leading to a formal knowledge claim, such work is an essential part of the research process. It may be used to generate hypotheses, to provide evidence to give the investigator greater confidence that an emerging hypothesis is valid, or to “screen” compounds for potential effects prior to more formal testing.

There are also modes of research where researchers want to minimize the risks of failing due to inadequate control of the risks of bias.

For every study, EQIPD recommends scientists to apply protection against risks of bias and to be transparent about the protective measures applied.

EQIPD requires that the maximal rigor possible is applied (and exceptions explained / documented in the study protocol) to research that is conducted with the prior intention of informing a knowledge claim ([[Glossary]]). This will usually (but not always) involve some form of null hypothesis statistical testing or formal Bayesian analysis. Hypotheses are articulated in advance of data collection, with pre-specified criteria defining the primary outcome measure and the statistical test to be used. Depending on the purpose for which the knowledge claim will be used, different research strategies are appropriate. A single well conducted preclinical study may be considered sufficient to convince others that the phenomena are real enough to justify their attention, but may not be sufficient to justify major research investment such as a clinical study.

Examples of research requiring the maximal rigor possible include:
* Experimental studies to scrutinize preclinical findings through replication of results alongside investigations into boundary conditions and robustness through conduct of additional (control) conditions and multicenter studies ([https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001863 Kimmelman et al. 2014])
* Research aimed to generate evidence that enables decisions such as critical studies that, dependent on the outcome, will trigger a chain of activities and events associated with significant resource and time costs (e.g. a decision to initiate a new drug development project or to initiate GLP safety assessment of a new drug candidate)
* Studies for which any outcome would be considered diagnostic evidence about a claim from prior research ([https://doi.org/10.1371/journal.pbio.3000691 Nosek and Errington 2020])
* Labor-, resource- and/or time-intensive studies that cannot be easily repeated

== B. Guidance & Expectations ==
One of the [[Core Requirements]] of the EQIPD Quality System is that investigators must assert in advance whether a study will be conducted to inform a formal knowledge claim. This statement should be recorded in the [[2.1.1 Study (experimental) plan]] (see below for a template).

If uncertain about your study being knowledge-claiming or not, please check [[FAQ]] page or contact the EQIPD Expert Team for an advice.

Efforts to minimize the risks of bias ([https://doi.org/10.1186/1471-2288-14-43 Hooijmans et al. 2014]) should be applied to all studies that aim to inform a knowledge claim (table inspired by [https://doi.org/10.1161/STROKEAHA.116.013244 Dirnagl 2016]):

{| class="wikitable"
|
|'''All research'''
|'''Research informing a formal knowledge claim (i.e. research requiring maximal rigor)*'''
|-
|'''[[2.1.1 Study (experimental) plan|Study (experimental) plan]]'''
|should be defined and documented before starting the experiments
|must be defined and documented before starting the experiments
|-
|'''Study hypothesis'''
|advised to define
|must be pre-specified
|-
|'''[[2.1.7 Blinding|Blinding]]'''
|advised to implement
|should be implemented, exceptions must be justified and documented
|-
|'''[[2.1.8 Randomisation|Randomisation]]'''
|advised to implement
|should be implemented, exceptions must be justified and documented
|-
|'''[[2.1.6 Sample size and power analysis|Sample size and power analysis]]'''
|advised to define and document before starting the experiments
|must be defined and documented before starting the experiments (e.g. included in the study plan)
|-
|'''[[2.3.3 Statistical analysis|Data analysis]]'''
|advised to define and document before starting the experiments
|must be defined and documented before starting the experiments (e.g. as a formal statistical analysis plan and/or included in the study plan)
|-
|'''[[2.1.9 Inclusion and exclusion criteria|Inclusion and exclusion criteria]]'''
|advised to define and document before starting the experiments
|must be defined and documented before starting the experiments (e.g. included in the study plan)
|-
|'''Deviations from study (experimental) plan'''
|advised to document
|must be documented
|-
|'''[[2.1.11 Preregistration|Preregistration]]'''
|no
|should be implemented
|}

* Please refer to [[Glossary]] for explanation on the EQIPD use of the verbs "must" and "should"

'''PLEASE DO NOT FORGET'''

To consider adding this subject to a training program for new employees or refresher training (if appropriate)

== C. Resources ==
Template to create a study (experimental) plan based on the above guidance - [https://paasp.sharepoint.com/:w:/s/EQIPD/Ecmg1s58CM1Nssq_Wv3-Ei8BfcCMqxgQcRzesGy9a5gdRg?e=Nc8dHL 2.1.1 Study plan.docx], [https://paasp.sharepoint.com/:w:/s/EQIPD/ETydIHKmJ1ZEt6EC87dtn9AB9RP3u29lxF0Omb-Ugesh1Q?e=Frbycd 2.1.1 Study plan with macro.docm]

Literature:
* Hooijmans CR, Rovers MM, de Vries RBM, Leenaars M, Ritskes-Hoitinga M, Langendam MW (2014) SYRCLE’s risk of bias tool for animal studies. BMC Medical Research Methodology 14:43 [https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-43]
* Kimmelman J, Mogil JS, Dirnagl U (2014) Distinguishing between exploratory and confirmatory preclinical research will improve translation. PLoS Biol 12(5):e1001863 [https://doi.org/10.1371/journal.pbio.1001863]
* Nosek BA, Errington TM (2020) What is replication? PLoS Biol 18(3): e3000691 [https://doi.org/10.1371/journal.pbio.3000691]
* Dirnagl U (2016) Thomas Willis Lecture: Is Translational Stroke Research Broken, and if So, How Can We Fix It? Stroke 47(8):2148-53 [https://doi.org/10.1161/STROKEAHA.116.013244]

----------------
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Next item: [[2.1.5 Pre-specification]]

2.1.3 Appraisal of literature and systematic reviews

2021-03-24T12:43:13Z

ChristophEmmerich:

== A. Background & Definitions ==
Prior to initiating any study or experiment, it is highly recommended to conduct a thorough analysis of the literature. In order to make sure that there is no previously reported (animal or human) evidence that would trigger a revision in the study hypothesis, design or analysis.

For example; it may help to:
* justifiy the selection of experimental model(s) (including selection of experimental subjects, if applicable)
* justifiy the choice of controls (positive, negative, sham, etc.)
* justify the need of your study (presence of a gap of knowledge, and no unnecessary duplication of studies)
* justify the details regarding the intervention
* justify in case of a preclinical study that the outcomes selected are outcomes related to patient important outcomes
* estimate anticipated effect size(s) and support sample size calculations (see Section [[2.1.6 Sample size and power analysis]])

The conduct of a systematic review is a valuable tool to justify the design of your animal experiment.

A systematic review (SR) is a literature review that aims to identify, select, appraise and synthesize all relevant studies to answer a specific research question ([https://www.ncbi.nlm.nih.gov/pubmed/?term=The+Usefulness+of+Systematic+Reviews+of+Animal+Experiments+for+the+Design+of+Preclinical+and+Clinical+Studies de Vries et al. 2014]).

SR follows a series of standard steps that are determined a priori and described in a protocol ([https://www.ncbi.nlm.nih.gov/pubmed/?term=The+Usefulness+of+Systematic+Reviews+of+Animal+Experiments+for+the+Design+of+Preclinical+and+Clinical+Studies de Vries et al. 2014]).

The conduct of a full SR before starting a new animal experiment may not always be feasible, however, a comprehensive thorough analyses of the literature is feasible and strongly recommended.

== B. Guidance & Expectations ==
A thorough analyses of the literature may or may not be in the form of a systematic review but it is recommended to develop and document the key steps of the review.

A systematic review of animal studies is comprised of seven main steps:
# Phrase the preclinical research question ([https://www.ncbi.nlm.nih.gov/pubmed/?term=29324741 Hooijmans et al. 2018]; [https://www.ncbi.nlm.nih.gov/pubmed/?term=22037056 Leenaars et al. 2012])
# Search for all evidence related to this question ([https://www.ncbi.nlm.nih.gov/pubmed/?term=22037056 Leenaars et al. 2012])
# Select the relevant animal/preclinical studies ([https://www.ncbi.nlm.nih.gov/pubmed/?term=22037056 Leenaars et al. 2012])
# Extract study data (characteristics and outcome data)
# Assess the study quality ([https://www.ncbi.nlm.nih.gov/pubmed/?term=15060322 Macleod et al. 2004]; [https://www.ncbi.nlm.nih.gov/pubmed/?term=24667063 Hooijmans et al. 2014])
# Synthesize the data (and if possible, perform a meta-analysis) ([https://www.ncbi.nlm.nih.gov/pubmed/?term=24099992 Vesterinen et al. 2014]; [https://www.ncbi.nlm.nih.gov/pubmed/?term=25541544 Hooijmans et al. 2014])
# Interpret your results. ([https://www.ncbi.nlm.nih.gov/pubmed/?term=29324741 Hooijmans et al. 2018])

If a systematic review is performed, it is recommended to follow the guidance for its conduct, reporting, and critical appraisal.

For a thorough analysis of the literature, it is recommended at least to:
* phrase a preclinical research question
* conduct a comprehensive search
* qualitatively synthesize the results

Together with a study protocol, a thorough appraisal of literature can be disclosed prior to the study in a form of a pre-registered report (see Section [[2.1.11 Preregistration]]).

'''PLEASE DO NOT FORGET'''

To consider adding this subject to a training program for new employees or refresher training (if appropriate)
To check whether there are feedback channels installed so that your colleagues can identify, record and report errors and critical incidents related to this subject (if appropriate)

== C. Resources ==
* de Vries RB, Wever KE, Avey MT et al (2014) The usefulness of systematic reviews of animal experiments for the design of preclinical and clinical studies. ILAR J 55:427-437. PubMed[https://www.ncbi.nlm.nih.gov/pubmed/?term=The+Usefulness+of+Systematic+Reviews+of+Animal+Experiments+for+the+Design+of+Preclinical+and+Clinical+Studies]
* Sena ES, Currie GL, McCann SK. et al (2014) Systematic reviews and meta-analysis of preclinical studies: why perform them and how to appraise them critically. J Cereb Blood Flow Metab 34:737-742. PubMed[https://www.ncbi.nlm.nih.gov/pubmed/?term=Systematic+reviews+and+meta-analysis+of+preclinical+studies:+why+perform+them+and+how+to+appraise+them+critically]
* Leenaars M, Hooijmans CR, van Veggel N et al (2012) A step-by-step guide to systematically identify all relevant animal studies. Laboratory Animals 46:24–31. PubMed[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+step-by-step+guide+to+systematicallyidentify+all+relevant+animal+studies]
* Vesterinen HM, Sena ES, Egan KJ et al (2014) Meta-analysis of data from animal studies: A practical guide. J Neurosci Methods 221:92-102. PubMed [https://www.ncbi.nlm.nih.gov/pubmed/24099992]

----

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Next item: [[2.1.4 Purpose of research]]

2.1.2 Unique study ID

2021-03-24T12:42:14Z

ChristophEmmerich:

== A. Background & Definitions ==
Unique study ID is a tag assigned to a study that is essential to ensure data traceability (see section [[3.1.2.1 Traceability of data and any person having impact on data]] for further guidance and explanations).

The Unique study ID is one of the most recognized approaches to ensure traceability as it provides the possibility to identify the source of reported data.

== B. Guidance & Expectations ==
Each study should be assigned a unique ID.
* Electronic lab notebooks (ELNs) may provide the unique study ID automatically
* In case of paper-based lab notebooks or other custom-built data management solutions, the unique study ID must be set up by the researchers themselves

Unique study ID should be created before initiating the study (as an example when creating the study protocol).

It is recommended to agree on one format to be used throughout the research unit.

Possible ID structure may include the date, experiment or project acronym and researcher's initials, e.g. date_experiment acronym_researcher initials.

For animal research, unique study ID may also be built based on the approval number of animal welfare protocol or authorization received from a responsible institutional, national or other body.

Format for date designations can be numeric (e.g. YYMMDD => 190101, better for sorting) or alphanumeric notations (e.g. DDMMMYYY => 01JAN2019, better for distinguishing day, month and year).

Once created, the study ID should be added to study-related documents (e.g. study protocol, records of raw data, reports, etc...) that are necessary for traceability of reported data back to raw data.

'''PLEASE DO NOT FORGET'''

If study IDs are not used, please provide a description of the means to ensure adequate data traceability (see section [[3.1.2.1 Traceability of data and any person having impact on data]] for further guidance on data traceability).

== C. Resources ==
to ba added

----------------
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Next item: [[2.1.3 Appraisal of literature and systematic reviews]]

1.5.2.4 Principal investigators and study directors

2021-03-23T19:47:43Z

ChristophEmmerich:

== A. Background & Definitions ==
to be added

== B. Guidance & Expectations ==
* Enforces a climate of good scientific practice, rigor and reproducibility, and continued excellence in preclinical data.
* Establishes effective communication with research team and quality professionals (if present); enables training for research team so that approriate expertise is guaranteed to conduct with the appropriate quality needs.
* Develops the [[2.1.1 Study (experimental) plan]] and ensures all procedures specified in the study protocol including amendments are followed and all data generated during a study are fully traceable and documented. Makes study protocol and amendments available to research team and quality professionals.
* Ensure that all raw data generated are fully documented and recorded; Signs and dates the final report to indicate acceptance of responsibility for the validity of the data; Ensures that, after completion of the study, the study protocol, the final report, raw data and supporting material are archived.

== C. Resources ==
to be added

----------------
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Next item: [[1.5.2.5 Research team]]

1.5.2.5 Research team

2021-03-23T19:47:13Z

ChristophEmmerich:

== A. Background & Definitions ==
to be added

== B. Guidance & Expectations ==
* must be knowledgeable to apply the needed principles of Good Research Practice to the study; responsible for execution of the study protocol and appropriate documentation;
* effective communication to study director and quality professionals prior and during the study; especially study protocol deviations; communication of relevant health or medical conditions that may affect the study to appropriate person
* responsibility for prompt recording of raw data after their generation in compliance with Good Research Practice and study protocol. Documentation of study protocol deviations; seek training, as needed, to perform assigned study responsibilities.

== C. Resources ==
to be added

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Next item: [[1.5.2.6 Supporting team]]

1.5.2.7 Quality professionals

2021-03-23T19:46:50Z

ChristophEmmerich:

== A. Background & Definitions ==
to be added

== B. Guidance & Expectations ==
* Verifies in real time that the requirements for quality of the study related activities have been fulfilled according to study protocol
* Ensures research team's compliance with the study protocol and the complete and acccurate documentation of the study
* Checks data integrity and sample tracebility and reports results to study director; verification of study protocol adherence; follow up on study protocol deviations

== C. Resources ==
to be added

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back to [[Toolbox]]

Next item: [[1.5.3 Management of resources]]

2.1.1 Study protocol

2021-03-23T19:45:56Z

ChristophEmmerich:

== A. Background & Definitions ==

Within the EQIPD Quality System and the Toolbox, the term "study protocol" refers to a document that is used to describe and summarize information related to a specific study (experiment).

A single study protocol may contain references to one or more research methods and assays. For protocols for specific research protocols and assays, please refer to item [[3.5.2 Protocols for methods and assays]].

== B. Guidance & Expectations ==

It is strongly recommended that, for every study (experiment), there is a study protocol prepared prior to the study being conducted.

Each study protocol should identify author(s), date when it was created and a unique ID of the study that it describes.

EQIPD has developed a template (please see below) that outlines the suggested structure and content of a study protocol.

This template is provided as a general guidance and serves only as an example:
# Title of the study
# Study hypothesis
# [[2.1.4 Purpose of research]]
## Indicate whether the research is undertaken with the intention to inform a formal knowledge claim
# Choice of experimental model(s) or method(s)
## Describe how and why specific models and/or methods were chosen (e.g. based on [[2.1.3 Appraisal of literature and systematic reviews]]
## If animal subjects are involved, justify why alternative are not suitable; as well as the selection of species, strain, age, and sex (if applicable)
# Choice of controls
## Describe the controls (negative, positive, shams), why and how these were chosen (e.g. based on [[2.1.3 Appraisal of literature and systematic reviews]]
## If a positive control is included, indicate explicitly how the study outcome will be interpreted if positive control fails.
# Measures against risks of bias
## [[2.1.8 Randomisation]] (if selected, a randomisation protocol must be available)
## [[2.1.7 Blinding]] (if selected, a blinding protocol must be available)
## If randomization and/or blinding are not applied, please describe the reasons as well as any other measures to control the risks of bias that will be applied.
# Sample size
## Describe methods used to estimate the sample size (such as power analysis).
## For definitions and guidance see section [[2.1.6 Sample size and power analysis]]
## Specify the primary outcome measure that was used to determine the sample size (see [[ARRIVE Essential - Outcome measures]])
## The sample size is the number of experimental units per group (for definition of experimental unit – see [[ARRIVE Essential - Study design]])
## Specify the exact number of experimental units allocated to each group, and the total number in each experiment.
## For definitions and guidance on sample size estimation and power analysis, please follow the link.
# Inclusion and exclusion criteria
## Indicate any [[2.1.9 Inclusion and exclusion criteria]] to be applied
# Animal resources, reagents and materials
## Include a detailed description of reagents and materials and/or provide references to separate document(s) with the relevant information (e.g. use the description in [[3.3.3 Management of research materials and reagents]] to add information to your Dossier)
##If animals are used, provide sufficient details expected for reporting ([[ARRIVE 2.0]])
# Study design overview
## For complex study designs, include a visual representation that more easily interpreted than a text description (e.g. a timeline diagram, table or flow chart – e.g. using an Experimental Design Assistant).
##Include a detailed description of methods and experimental procedures and/or provide references to separate document(s) with the relevant information (e.g. if you chose to use section 3.5.2 of the Dossier for storing [[3.5.2 Protocols for methods and assays]]).
# Experimental procedures
## Include a detailed description of methods and experimental procedures and/or provide references to separate document(s) with the relevant information (e.g. if you chose to use section 3.5.2 of the Dossier for storing [[3.5.2 Protocols for methods and assays]])
## If experimental methods are not described in separate documents, follow [[ARRIVE 2.0]] Recommended Set suggestions (items 14-17 here as well as guidance provided here)
## If more than one method or procedure is used, describe sequence or experimental workflow
## For each experimental group, including controls, describe the procedures in enough detail to allow others to replicate them, including (more guidance - [[ARRIVE Essential - Experimental procedures]])
### What was done, how it was done and what was used
### When and how often
### Where (including detail of any acclimation periods)
### Why (provide rationale for procedures)
# Data analysis
## Describe the processing of raw data (for definition of raw data – see section [[2.3.1 Generation, recording, handling and archiving of raw data]])
## Describe statistical method(s) to be applied for each analysis ([[2.3.3 Statistical analysis]])
## Describe any methods used to assess whether the data met the assumptions of the statistical approach ([[ARRIVE Essential - Statistical methods]])
# Amendments
## Describe what was changed in the original study protocol, why, when and by whom
## When amending the study protocol, please make sure not to over-write the original version
## Amendments may be saved as documents separate from the original study protocol
# References
## If necessary, include references
# Preregistration
## EQIPD strongly recommends to pre-register study protocol (more information – [[2.1.11 Preregistration]])
## If pre-registered, please indicate the platform used, registration link and other relevant reference information

'''RISK ASSESSMENT'''
* If study protocol references to other protocols or dociuments (e.g. protocol for a specific experimental method), is the reference made to the current (relevant) version)?

== C. Resources ==
Template to create a study protocol based on the above guidance - [https://paasp.sharepoint.com/:w:/s/EQIPD/Ecmg1s58CM1Nssq_Wv3-Ei8BfcCMqxgQcRzesGy9a5gdRg?e=Nc8dHL 2.1.1 Study plan.docx], [https://paasp.sharepoint.com/:w:/s/EQIPD/ETydIHKmJ1ZEt6EC87dtn9AB9RP3u29lxF0Omb-Ugesh1Q?e=dAnKJg 2.1.1 Study plan with macro.docm]

Experimental design tools:
* MANILA (Matched Animal Analysis): link to the original article ([https://www.nature.com/articles/srep30723]) and the tool ([https://biomedportal.utu.fi/utu-apps/Rvivo/])
* NC3Rs’ Experimental Design Assistant [https://www.nc3rs.org.uk/experimental-design-assistant-eda]

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Next item: [[2.1.2 Unique study ID]]

2.1.7 Blinding

2021-03-23T19:43:56Z

ChristophEmmerich:

== A. Background & Definitions ==

'''Blinding''' refers to the masking of the treatment, physiological or pathological condition, genotype or any prior intervention for the person(s) performing the experiment, collecting data and assessing outcome. Blinding aims to make sure that each person involved has no information on the experimental units (animals, subjects or samples) in each experimental group that could systematically influence his/her performance. The intended result is that (as far as possible) all experimental units in the experiment are dealt with equally.

In the discussion below, experimental groups refers to '''all''' groups within an experiment, for example: control, sham, treated with drug A, treated with drug B, etc.

Group allocation describes which experimental unit (animal, subject or sample) has been allocated to which experimental group.

The group allocation, actions and outcome assessments are ‘'''blinded'''’. People are ‘'''blind'''’ to particular information.

Blinding requires at least 2 people, one blinded person (unaware of experimental condition) and a non-blinded person (knows the experimental condition and the blinding code). The non-blinded person is the keeper of the blinding code, which needs to be concealed until all processes under blinding are concluded.

The most effective blinding covers every step in an experiment - from allocation to treatment conditions, application of treatment to data collection and analysis - this is often referred to as '''full blinding'''.

Blinding should '''not''' be seen as "all or none". There are several situations in which partial blinding may be applied (i.e., blinding of the most risk-prone step(s) in the experimental process). For example, partial blinding can be considered when:
* a research unit with no prior experience with blinding is introducing a blinding procedure and, for organizational or other reasons, follows a step-wise implementation
* a research unit has significantly constrained human resources and does not intend to conduct knowledge-claiming research

In all cases, however, full and transparent reporting of how blinding was applied is expected.

== B. Guidance & Expectations ==

EQIPD expects that the method(s) used to implement blinding are described in as much detail as possible:
* either as a dedicated protocol (please see below for a template that may serve as an example of how to build such a protocol),
* or as a separate section of a study protocol.

Depending on the breadth of research methods in use, a given research unit may have one or more blinding protocols that can support blinding for specific types of experiments.

When preparing a blinding protocol, the main objective is to have a description that is understandable for the actual users - i.e., bench scientists (especially those that are new to the unit). Therefore, it should be written in a simple language with as many examples (specific to the research) as appropriate.

A blinding protocol may describe the following:

* Training and competence
** is there any training needed?
** are there any additional supporting tools or materials available?

* Feasibility assessment (to avoid applying blinding when it makes no sense or would actually do harm)
** how high is the risk of unintentional unblinding?
** are the required resources available?
** are emergency scenarios considered?

* "Who does what?"
** сlearly define the roles for those involved in the experiment and the blinding procedure (e.g., see section 4 in the [https://paasp.sharepoint.com/:w:/s/EQIPD/EZbRvZmZGoRGtsjc1Wk_XOsBLsnNAbg-FBFVj9h199oYMA?e=vIXtkO blinding protocol]);
** the blinding protocol should make clear who is aware of the group allocation at the different stages of the experiment (during the allocation, the conduct of the experiment, the outcome assessment, and the data analysis);
** to effectively blind a study, create a sequence containing all experimental steps of the study and, for each step, indicate the name of each person involved in the conduct and analysis of the study. For each experimental step, document for each person whether they are blinded or not blinded to the condition. Such an overview systematically creates a transparent workflow of blinded and unblinded personnel and shows at which step unintended unblinding might occur. Such an overview (e.g., as a table) can be made part of the experimental documentation and reporting.
** it is generally expected and strongly recommended that any process using humans as perceptors, raters or interpreters needs to be blinded until the decision-making is concluded.

* Blinding code
** describe how the blinding code is developed and which specific steps are taken to practically apply it
** one simple blinding strategy is to assign each subject / sample a separate number or letter (or a combination thereof). This approach may create compliance issues with large numbers of subjects / samples and the need to administer treatment repeatedly over extended periods of time.
** another blinding strategy is to assign each experimental group a separate number or letter (or a combination thereof). This approach may be problematic when human processing and rating is involved in outcome assessment. The assessor may not know the condition behind the code but the knowledge of a group affiliation of a sample can influence rating.
** the decision of which strategy to follow is made by the researchers, taking into account the specifics and associated risks for each experiment.

'''PRACTICAL TIPS'''

* Generation of alphanumeric codes for blinding
** if possible, use blinding schemes with no repeating codes. This can be easily done with an alphanumeric code consisting of 4 letter/number combinations, such as T7Z4. Such codes can be generated in Excel using the following formula:
*** =CHAR(RANDBETWEEN(65;))&RANDBETWEEN(0;9)&CHAR(RANDBETWEEN(65;))&RANDBETWEEN(0;9)
*** enter this formula in a row of cells for which you need coded samples and copy the outcome to another worksheet with the command Past Special-->Paste Values.

* Allocation concealment in animal experiments
** to prevent selection bias, the investigator shall not be aware and/or have the choice of which treatment group an animal is allocated to;
** therefore, the assignment to a specific group needs to be concealed and every animal should have the same chance to be assigned to each of the groups;
** this can be achieved by separating the assignment of animal_IDs to each animal (e.g., individual ear mark or subcutaneous chip) and randomization of treatments (see randomization) into two independent processes and then merging the two.

'''RISK ASSESSMENT'''
* Under some circumstances, unintentional unblinding (e.g., due to a difference in appearance of a positive control in solution or suspension) may be a risk to be assessed and/or controlled
* Experimental treatments may produce adverse effects and attending veterinarians and animal care stuff may need to be informed in advance of the possibility of such adverse effects occurring and, if necessary, have emergency access to the blinding protocol.
* if a blinding code is added to another code such as animal_ID, measurement_ID or file name, watch out for hidden cues in such IDs, containing temporal or sequential information that could increase rater bias. Also metadata, such as creation date and time of a file containing measurements can give away experimental conditions.

'''PLEASE DO NOT FORGET'''

* Blinding is sometimes not possible especially when certain cues cannot be blinded, such as skin color or weight/size of transgenic mice or color of a solution in a well. It is important to document this and to communicate in reports where blinding could or could not be achieved.
* Unblinding of the experimental conditions should be done when all blinded processes for the entire study are concluded. Early and partial unblinding for "checking" should be avoided and, if necessary, be part of the study protocol.
* Control group(s) (e.g., positive control group) should not be excluded from the blinding procedure.
* Provide training on how to apply the blinding procedure.

== C. Resources ==

Guidelines on reporting of blinding (in vivo research):
* [[ARRIVE 2.0]]

Template to develop a written description of the method used to implement blinding:
* [https://paasp.sharepoint.com/:w:/s/EQIPD/EZbRvZmZGoRGtsjc1Wk_XOsBLsnNAbg-FBFVj9h199oYMA?e=vIXtkO blinding protocol]

Reading material:

* [https://link.springer.com/chapter/10.1007/164_2019_279 Handbook of Experimental pharmacology chapter on randomization and blinding]

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Next item: [[2.1.8 Randomisation]]

2.1.10 Plausibility check

2021-03-23T19:43:37Z

ChristophEmmerich:

== A. Background & Definitions ==

There are instances when experiments are very well designed and the research team has an intention to do the study according to the design but there are often "unforeseen" situations and factors that force changes in the study protocol.

Such "unforeseen" factors can in many cases be predicted and appropriate mitigation strategies discussed and implemented.

Analysis of such "unforeseen" factors is the essence of the plausibility check that aims to avoid negative impact on the research rigor (e.g. not being able to enrol a required number of subjects, study becomes underpowered).

== B. Guidance & Expectations ==

Development of a study protocol should be concluded by a formal or informal plausibility check in order to:

Identify obstacles for performing the experiment (e.g. sufficient amount and quality of materials and reagents; fit-for-purpose status of key hardware and software; availability of trained and properly informed colleagues to support execution of the study)
Confirm that the experiment can be performed within expected timelines

'''PLEASE DO NOT FORGET'''
* To consider adding this subject to a training program for new employees or refresher training (if appropriate)
* To check whether there are feedback channels installed so that your colleagues can identify, record and report errors and critical incidents related to this subject (if appropriate)

== C. Resources ==
to be added

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back to [[Toolbox]]

Next item: [[2.1.11 Preregistration]]

2.1.11 Preregistration

2021-03-23T19:43:13Z

ChristophEmmerich:

== A. Background & Definitions ==

Preregistration refers to a process of registration of study protocol and data analysis plan before conducting a study.

Registered Report is a maximal form of registration, in which study manuscript that includes the study hypothesis, rationale, methods, experimental protocol and a detailed analysis plan is peer reviewed (Stage 1) before study data is collected. After data collection, the final manuscript that includes results and discussion sections undergoes a conventional peer review (Stage 2) where adherence to the original (Stage 1) procedures is confirmed. Following favorable reviews and, regardless of study results, the manuscript is accepted for publication ([https://www.nature.com/articles/s41562-016-0021 Munafo et al. 2017], [https://openscience.bmj.com/pages/registered-reports-guidelines/ BMJ Open Science, Registered Reports Guidelines]).

Benefits of preregistration:

* Increases transparency
* Saves resources by avoiding unnecessary duplication of efforts
* Serves to reduce:
** The risk of publication bias
** "HARKing" (hypothesizing after the results are known)
** P-hacking (analytical decisions after the results are known)
** In case of a Registered Report, registration helps also against "CARKing" (unjustified critique of the article by reviewers after the results are known; Munafo et al. 2017).

== B. Guidance & Expectations ==
* If study is done to inform a knowledge claim ([[2.1.4 Purpose of research]]), it is strongly recommended to preregister the study protocol before data are collected.
* It is strongly recommended to register systematic review protocols.
* Training on planning and benefits of preregistration is highly recommended.

'''PLEASE DO NOT FORGET'''
* To consider adding this subject to a training program for new employees or refresher training (if appropriate)
* To check for risks of disclosing confidential or otherwise sensitive or proprietary information (e.g. in the context of existing or emerging intellectual property)

== C. Resources ==
Online registry platforms:
* Specialized platforms
** [https://www.preclinicaltrials.eu Preclinicaltrials.eu]
** [https://www.animalstudyregistry.org Animal Study Registry]
** [https://osf.io/rr OSF for Registered Reports]
** [https://www.crd.york.ac.uk/prospero/#guidancenotes_animals PROSPERO, a platform for registering a systematic review of animal studies]
* Broad scope platforms
** [https://aspredicted.org AsPredicted]
** [https://cos.io/prereg Center for Open Science]
* [https://paasp.sharepoint.com/:b:/s/EQIPD/ERA-rz_pc3xDh_uYg0Q-B3cBuPRVFFsNwgQUQZ3Oz5qiRg?e=CXBnL2 Platform comparison]
Publications:
* Nosek BA, Ebersole CR, DeHaven AC et al. (2018) The preregistration revolution. Proc Natl Acad Sci U S A. 115:2600-2606. PubMed [https://www.ncbi.nlm.nih.gov/pubmed/?term=the+preregistr%E2%80%8Bation+revolution+and+Nosek]
* De Vries RBM, Hooijmans CR, Langendam MW, et al. (2015) A protocol format for the preparation, registration and publication of systematic reviews of animal intervention studies. Evid Based Preclin Med. 2:1–9. [https://onlinelibrary.wiley.com/doi/full/10.1002/ebm2.7]
* Chambers C (2014) Registered Reports: A change in scientific publishing

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Next item: [[2.2.1 Use of SOPs for standard experiments]]

2.3.1.1 Converting non-electronic information into an electronic form

2021-03-10T09:31:05Z

ChristophEmmerich:

== A. Background & Definitions ==
Digitization is the process of converting information into a digital (i.e. computer-readable) format. It is the primary way of storing non-electronic information in a form suitable for transmission and computer processing, whether scanned from two-dimensional analog originals or captured using an image sensor-equipped device such as a digital camera.

== B. Guidance & Expectations ==
* Save electronic files in a universal file format, such as pdf, to maximize usability (i.e. readability).
* Whatever storage medium is chosen, it is important to make sure that the medium is reliable and that files can still be read at a later date (see [[3.1.2 Procedures for how and when to record data]]).
* As with hard copy paper data, it is important that electronic data are time/date stamped when the data are created/generated.
* Indexing: A record of data locations must be maintained. This can be accomplished by adding dates or other index fields where required so they can be searched for and accessed easily. This indexing process is usually not required when using an electronic lab notebook system as the storage medium since each record should have a unique study ID (see [[2.1.2 Unique study ID]]).
* If the electronic version of the data/information is stored and archived (instead of the non-electronic version), this is acceptable only if the electronic copy meets all criteria of raw data (see [[2.3.1 Generation, recording, handling and archiving of raw data]]). Most importantly, it is important to define and control the interval between data generation and creation of the electronic record)
* For each digitization procedure, it should be checked that the electronic version is complete and of sufficient quality (e.g. when scanning an image/film/photography)

'''PLEASE DO NOT FORGET'''
* Scientists should be aware that some electronic documents e.g. PDFs can be edited. Critical information (e.g. for IP reasons) must be handled with particular care as well as discussed and agreed with collaboration partner(s) (if applicable) - see [[2.2.2 Use of template for (manual) data recording]].

== C. Resources ==
tba

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Next item: [[2.3.2 Primary analysis and evaluation of raw data]]

2.3.1 Generation, recording, handling and archiving of raw data

2021-03-10T09:30:02Z

ChristophEmmerich:

== A. Background & Definitions ==
This item refers to one of the [[Core Requirements]] (Core Requirement 6 - "Generation, handling and changes to data records must be documented") and is, therefore, considered as essential.

Raw data means all original records and documentation, which are the result of the observations and activities in a study.

Raw data may include:
* photographs, videotapes, blots, chromatograms, computer readable media, dictated observations, recorded data from automated instruments, or any other medium capable of providing secure storage of information for a time period required by law or other applicable regulations;
* data directly entered into a computer through an automatic instrument interface, which are the results of primary observations and activities in a study;
* copies of original laboratory records and documentation that are complete and of good quality.

As raw data may also be recognized the processed result of original observations when these latter cannot be stored for technical reasons, e.g.:
* a research tool conducts pre-processing of original observations (example: movements of a rat in an open field are recorded by means of the photobeam breaks; research software may present the raw data as a movement track or a calculated distance traveled rather than a sequence of photobeam breaks);
* a research tool records data in a specific format that may or may not be readable at a later time point (e.g. if the license to use this research tool expires) and therefore pre-processing supports long-term accessibility of the original observations;
* a research tool generates exceptionally large volumes of data that are technically difficult to store without pre-processing to reduce the storage volume (e.g. imaging data).
Experimental Record: A research diary entry for an experiment giving access to or information about location of raw data and pertinent details of an experiment such that a peer could repeat the experiment.

== B. Guidance & Expectations ==
Implementation and maintenance of processes related to raw data is one of the main responsibilities of the [[1.5.2.3 Process owner]] or other scientist(s) to whom Process Owner delegates this task.

Processes related to raw data as well as the associated roles and responsibilities should be described in the experimental plan (or protocols for specific research methods). If not such formal description is available or possible, Process Owner should ensure that the desired practices are in place and are verifiable.

Generation and recording of raw data:
* All equipment and computerized systems used for data generation must be fit-for-purpose (please check item [[3.3.2 Processes to enable computerized and non-computerized systems being suitable for intended use| 3.3.2]]).
* Every research unit has to define what is regarded as raw data for the experiments conducted in that particular research unit.
* All records should bear a [[2.1.2 Unique study ID]] and must be dated and signed / initialed by the person making the entry; this can be done electronically or on paper.
* Data should be recorded at the time of generation (meaning that any delay should be justifiable by the experimental plan or associated working procedures) ([[3.1.2 Procedures for how and when to record data| 3.1.2]])

Handling of raw data:
* The processing of raw data records must be transparent (for details please check the items [[3.1.2.1 Traceability of data and any person having impact on data| 3.1.2.1]] and [[3.1.2.2 Process for witnessing of records| 3.1.2.2]]) and understandable by a third person.
* Any changes to the data records must be documented, reason for a change must be explained, dated, signed and saved; for details see the item [[3.1.2 Procedures for how and when to record data| 3.1.2]].

Storing of raw data:
* The storage of raw data must ensure readability and protection from loss, modification, destruction and unauthorized access (link to [[3.1.3 Data security| 3.1.3]]).
* Raw data should be stored in a read-only mode according to legal, contractual or other obligations.
* If raw data cannot be saved in an electronic or paper notebook (e.g. because of the volume or format), experimental record must contain a reference to the location where raw data is stored.

Common Data Elements (CDEs):
In preclinical research, the use of CDEs receives increasing attention and is encouraged as it can facilitate data sharing across research projects and provides opportunities for comparison and combination of data sets from multiple studies. CDEs are standardized key terms or concepts, established to be used in experimental studies, so that research findings can be generalized with respect to different research institutions, diverse populations, different regions, and interventions.

More detailed information can be found here:
* [https://clinfowiki.org/wiki/index.php/Common_Data_Element_(CDE) https://clinfowiki.org/wiki/index.php/Common_Data_Element_(CDE)]
* [https://onlinelibrary.wiley.com/doi/10.1002/epi4.12236 https://onlinelibrary.wiley.com/doi/10.1002/epi4.12236]

'''PLEASE DO NOT FORGET'''
* To check whether storage of raw data will not endanger traceability (i.e. whether raw data can be traced back from the reports and publications)
* To make sure that the duration of storage and accessibility of raw data is not determined by presence of a specific employee or student
* To consider adding this subject to a training program for new employees or refresher training
* To update the Documentation Plan when any changes are made to the way raw data are handled

== C. Resources ==
* DFG Guideline on the Handling of Research Data [https://www.dfg.de/download/pdf/foerderung/antragstellung/forschungsdaten/guidelines_research_data.pdf]
* The EQIPD template "Documentation Plan" is located in the Dossier folder 3.1 and also here - 3.1 Documentation Plan.docx

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Next item: [[2.3.1.1 Converting non-electronic information into an electronic form]]

2.3.1.1 Converting non-electronic information into an electronic form

2021-03-10T09:28:46Z

ChristophEmmerich:

== A. Background & Definitions ==
Digitization is the process of converting information into a digital (i.e. computer-readable) format. It is the primary way of storing non-electronic information in a form suitable for transmission and computer processing, whether scanned from two-dimensional analog originals or captured using an image sensor-equipped device such as a digital camera.

== B. Guidance & Expectations ==
* Save electronic files in a universal file format, such as pdf, to maximize usability (i.e. readability).
* Whatever storage medium is chosen, it is important to make sure that the medium is reliable and that files can still be read at a later date (see [[3.1.2 Procedures for how and when to record data]]).
* As with hard copy paper data, it is important that electronic data are time/date stamped when the data are created/generated.
* Indexing: A record of data locations must be maintained. This can be accomplished by adding dates or other index fields where required so they can be searched for and accessed easily. This indexing process is usually not required when using an electronic lab notebook system as the storage medium since each record should have a unique study ID (see 2.1.2 Unique study ID).
* If the electronic version of the data/information is stored and archived (instead of the non-electronic version), this is acceptable only if the electronic copy meets all criteria of raw data (see 2.3.1 Generation, recording, handling and archiving of raw data). Most importantly, it is important to define and control the interval between data generation and creation of the electronic record)
* For each digitization procedure, it should be checked that the electronic version is complete and of sufficient quality (e.g. when scanning an image/film/photography)

'''PLEASE DO NOT FORGET'''
* Scientists should be aware that some electronic documents e.g. PDFs can be edited. Critical information (e.g. for IP reasons) must be handled with particular care as well as discussed and agreed with collaboration partner(s) (if applicable) - see 2.2.2 Use of template for (manual) data recording.

== C. Resources ==
tba

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Next item: [[2.3.2 Primary analysis and evaluation of raw data]]

2.3.1.1 Converting non-electronic information into an electronic form

2021-03-10T09:26:30Z

ChristophEmmerich:

== A. Background & Definitions ==
Digitization is the process of converting information into a digital (i.e. computer-readable) format. It is the primary way of storing non-electronic information in a form suitable for transmission and computer processing, whether scanned from two-dimensional analog originals or captured using an image sensor-equipped device such as a digital camera.

== B. Guidance & Expectations ==
* Save electronic files in a universal file format, such as pdf, to maximize usability (i.e. readability).
* Whatever storage medium is chosen, it is important to make sure that the medium is reliable and that files can still be read at a later date (see 3.1.2 Procedures for how and when to record data).
* As with hard copy paper data, it is important that electronic data are time/date stamped when the data are created/generated.
* Indexing: A record of data locations must be maintained. This can be accomplished by adding dates or other index fields where required so they can be searched for and accessed easily. This indexing process is usually not required when using an electronic lab notebook system as the storage medium since each record should have a unique study ID (see 2.1.2 Unique study ID).
* If the electronic version of the data/information is stored and archived (instead of the non-electronic version), this is acceptable only if the electronic copy meets all criteria of raw data (see 2.3.1 Generation, recording, handling and archiving of raw data). Most importantly, it is important to define and control the interval between data generation and creation of the electronic record)
* For each digitization procedure, it should be checked that the electronic version is complete and of sufficient quality (e.g. when scanning an image/film/photography)

'''PLEASE DO NOT FORGET'''
* Scientists should be aware that some electronic documents e.g. PDFs can be edited. Critical information (e.g. for IP reasons) must be handled with particular care as well as discussed and agreed with collaboration partner(s) (if applicable) - see 2.2.2 Use of template for (manual) data recording.

== C. Resources ==
tba

----------------

back to [[Toolbox]]

Next item: [[2.3.2 Primary analysis and evaluation of raw data]]

2.3.1.1 Converting non-electronic information into an electronic form

2021-03-10T09:23:48Z

ChristophEmmerich: Created page with "A. Background & Definitions Digitization is the process of converting information into a digital (i.e. computer-readable) format. It is the primary way of storing non-electron..."

A. Background & Definitions
Digitization is the process of converting information into a digital (i.e. computer-readable) format. It is the primary way of storing non-electronic information in a form suitable for transmission and computer processing, whether scanned from two-dimensional analog originals or captured using an image sensor-equipped device such as a digital camera.

B. Guidance & Expectations
• Save electronic files in a universal file format, such as pdf, to maximize usability (i.e. readability).
• Whatever storage medium is chosen, it is important to make sure that the medium is reliable and that files can still be read at a later date (see 3.1.2 Procedures for how and when to record data).
• As with hard copy paper data, it is important that electronic data are time/date stamped when the data are created/generated.
• Indexing: A record of data locations must be maintained. This can be accomplished by adding dates or other index fields where required so they can be searched for and accessed easily. This indexing process is usually not required when using an electronic lab notebook system as the storage medium since each record should have a unique study ID (see 2.1.2 Unique study ID).
• If the electronic version of the data/information is stored and archived (instead of the non-electronic version), this is acceptable only if the electronic copy meets all criteria of raw data (see 2.3.1 Generation, recording, handling and archiving of raw data). Most importantly, it is important to define and control the interval between data generation and creation of the electronic record)
• For each digitization procedure, it should be checked that the electronic version is complete and of sufficient quality (e.g. when scanning an image/film/photography)

Please do not forget:
• Scientists should be aware that some electronic documents e.g. PDFs can be edited. Critical information (e.g. for IP reasons) must be handled with particular care as well as discussed and agreed with collaboration partner(s) (if applicable) - see 2.2.2 Use of template for (manual) data recording.

C. Resources
tba

3.1.2.1 Traceability of data and any person having impact on data

2021-02-17T17:28:09Z

ChristophEmmerich:

== A. Background & Definitions ==

This item refers to one of the [[Core Requirements]] (Core Requirement 8 - "Reported research outcomes must be traceable to experimental data") and is, therefore, considered as essential.

'''Traceability''': The ability to find the source of data (primary and secondary) and any person having relevant impact on data sets that are presented in a report or other presentation.

'''Experimental Record''': An entry in an (electronic) laboratory notebook for an experiment recording all data and pertinent details of an experiment such that a peer could repeat the experiment.

The user must ensure the traceability and integrity of the data so that the reported results can be reconstructed.

Traceability is directly related to the FAIR principles that are endorsed by both [https://www.nature.com/articles/d41586-019-01720-7 academic] and [https://www.sciencedirect.com/science/article/pii/S1359644618303039?dgcid=raven_sd_via_email industry] research communities as well as by the growing number of funders.

== B. Guidance & Expectations ==

* Each experimental record should contain or cross-reference/link to:
** Names of all individuals involved in generating the content of the experimental record.
** Specific research plan, objective, or hypothesis to be addressed by the experiment.
** All protocols, standard operating procedures, test methods, statistical tools (and/or software used for data analysis) used.
** Description of all materials and equipment used, including the source and lot number of all starting materials and test compounds.
** Date each activity was performed.
** Location of records and materials: Clearly identified location(s) of data files and their content.
** Other supporting information needed for independent analysis of raw data obtained in experiment and interpretation of results.
** All raw, processed, and final/reported data generated in the experiment.
** A proper cross-reference should be added if any raw data is kept separate from the experimental record and cannot be attached to the experimental record, or any raw data is obtained by other researchers performing supporting experiments.
* Expectation: A qualified reviewer should be able to:
** link figures, graphs, conclusions, and other summary data to the raw data that was processed/analyzed.
** link the summary data to the corresponding experiment described in a lab notebook entry.
** link the lab notebook entry to the raw data (e.g., where generated by an automated instrument).
* All related experimental records and supporting research must be linked/cross-referenced in the main experiment (via the respective unique identifiers).
* The raw data obtained in an experiment may be stored in a separate archival system but should be referenced in the experimental record (see [[3.1.2 Procedures for how and when to record data]]).
* If a new analysis of data from previous experimental records needs to be performed to generate a new result or conclusion, a new experimental record should be created, which should clearly cross-reference the earlier experimental records (by their unique identifiers) and conclusions reached.
* The EQIPD template "Documentation Plan" located in folder 3.1 in the Dossier (and below in Section C) provides a central space to describe this Core Requirement. The document is also used in the Toolbox items [[3.1.4 Data security]] and [[2.3.1 Generation, recording, handling and archiving of raw data]].

'''Extra care has to be taken:'''

* The author(s), all individuals who participated and/or contributed to the experiment, including, where applicable, recorder(s) must be clearly identified, so that the data can be traced, by name and date to each individual's contribution. The above ensures that the record is attributable.
* Clear guidelines and conventions on file-naming for all data files and experimental record should be established for consistency and traceability (see Traceability of data and any person having impact on data).

For technical non-public reports (e.g. R&D reports used in regulatory submissions), it is easy and fairly common to provide direct references to a lab notebook containing the relevant information.

For scientific publications, it is not common to include such references and the following options may be considered to establish traceability between published data and internal records:
* develop a “for internal use” system (e.g. a plain Excel file accessible to all members of the research unit) where reports about completed studies (and associated manuscripts and publications) are matched with the corresponding unique study IDs
* including unique study IDs or references to the laboratory notebooks in the publications themselves (e.g. in the supplementary materials)
* include unique study IDs in the preregistered protocols

'''PLEASE DO NOT FORGET'''

* To ensure traceability, each experimental record should have a unique identifier in accordance with the applicable procedure(s), e.g., SOPs.
* Responsibility for creating experimental records and documentation of the resulting data rests with the researcher who generates the data. If multiple researchers collaborate in data generation, then it should be identified as such.

== C. Resources ==

[https://paasp.sharepoint.com/:w:/s/EQIPD/EQTANrsKHTVIqtO8LT-ZJIEBEUJVZI6siRLkHfliUD4gdA?e=GJusMa EQIPD Documentation Plan]

3.1.2.1 Traceability of data and any person having impact on data

2021-02-17T17:26:35Z

ChristophEmmerich:

3.1.2.1 Traceability of data and any person having impact on data

2021-02-17T17:25:53Z

ChristophEmmerich:

== A. Background & Definitions ==

This item refers to one of the [[Core Requirements]] (Core Requirement 8 - "Reported research outcomes must be traceable to experimental data") and is, therefore, considered as essential.

'''Traceability''': The ability to find the source of data (primary and secondary) and any person having relevant impact on data sets that are presented in a report or other presentation.

'''Experimental Record''': An entry in an (electronic) laboratory notebook for an experiment recording all data and pertinent details of an experiment such that a peer could repeat the experiment.

The user must ensure the traceability and integrity of the data so that the reported results can be reconstructed.

Traceability is directly related to the FAIR principles that are endorsed by both [[https://www.nature.com/articles/d41586-019-01720-7 academic]] and industry research communities as well as by the growing number of funders.

== B. Guidance & Expectations ==

* Each experimental record should contain or cross-reference/link to:
** Names of all individuals involved in generating the content of the experimental record.
** Specific research plan, objective, or hypothesis to be addressed by the experiment.
** All protocols, standard operating procedures, test methods, statistical tools (and/or software used for data analysis) used.
** Description of all materials and equipment used, including the source and lot number of all starting materials and test compounds.
** Date each activity was performed.
** Location of records and materials: Clearly identified location(s) of data files and their content.
** Other supporting information needed for independent analysis of raw data obtained in experiment and interpretation of results.
** All raw, processed, and final/reported data generated in the experiment.
** A proper cross-reference should be added if any raw data is kept separate from the experimental record and cannot be attached to the experimental record, or any raw data is obtained by other researchers performing supporting experiments.
* Expectation: A qualified reviewer should be able to:
** link figures, graphs, conclusions, and other summary data to the raw data that was processed/analyzed.
** link the summary data to the corresponding experiment described in a lab notebook entry.
** link the lab notebook entry to the raw data (e.g., where generated by an automated instrument).
* All related experimental records and supporting research must be linked/cross-referenced in the main experiment (via the respective unique identifiers).
* The raw data obtained in an experiment may be stored in a separate archival system but should be referenced in the experimental record (see [[3.1.2 Procedures for how and when to record data]]).
* If a new analysis of data from previous experimental records needs to be performed to generate a new result or conclusion, a new experimental record should be created, which should clearly cross-reference the earlier experimental records (by their unique identifiers) and conclusions reached.
* The EQIPD template "Documentation Plan" located in folder 3.1 in the Dossier (and below in Section C) provides a central space to describe this Core Requirement. The document is also used in the Toolbox items [[3.1.4 Data security]] and [[2.3.1 Generation, recording, handling and archiving of raw data]].

'''Extra care has to be taken:'''

* The author(s), all individuals who participated and/or contributed to the experiment, including, where applicable, recorder(s) must be clearly identified, so that the data can be traced, by name and date to each individual's contribution. The above ensures that the record is attributable.
* Clear guidelines and conventions on file-naming for all data files and experimental record should be established for consistency and traceability (see Traceability of data and any person having impact on data).

For technical non-public reports (e.g. R&D reports used in regulatory submissions), it is easy and fairly common to provide direct references to a lab notebook containing the relevant information.

For scientific publications, it is not common to include such references and the following options may be considered to establish traceability between published data and internal records:
* develop a “for internal use” system (e.g. a plain Excel file accessible to all members of the research unit) where reports about completed studies (and associated manuscripts and publications) are matched with the corresponding unique study IDs
* including unique study IDs or references to the laboratory notebooks in the publications themselves (e.g. in the supplementary materials)
* include unique study IDs in the preregistered protocols

'''PLEASE DO NOT FORGET'''

* To ensure traceability, each experimental record should have a unique identifier in accordance with the applicable procedure(s), e.g., SOPs.
* Responsibility for creating experimental records and documentation of the resulting data rests with the researcher who generates the data. If multiple researchers collaborate in data generation, then it should be identified as such.

== C. Resources ==

EQIPD Documentation Plan template

3.1.2.1 Traceability of data and any person having impact on data

2021-02-17T17:23:51Z

ChristophEmmerich:

== A. Background & Definitions ==

This item refers to one of the [[Core Requirements]] (Core Requirement 8 - "Reported research outcomes must be traceable to experimental data") and is, therefore, considered as essential.

'''Traceability''': The ability to find the source of data (primary and secondary) and any person having relevant impact on data sets that are presented in a report or other presentation.

'''Experimental Record''': An entry in an (electronic) laboratory notebook for an experiment recording all data and pertinent details of an experiment such that a peer could repeat the experiment.

The user must ensure the traceability and integrity of the data so that the reported results can be reconstructed.

Traceability is directly related to the FAIR principles that are endorsed by both academic and industry research communities as well as by the growing number of funders.

== B. Guidance & Expectations ==

* Each experimental record should contain or cross-reference/link to:
** Names of all individuals involved in generating the content of the experimental record.
** Specific research plan, objective, or hypothesis to be addressed by the experiment.
** All protocols, standard operating procedures, test methods, statistical tools (and/or software used for data analysis) used.
** Description of all materials and equipment used, including the source and lot number of all starting materials and test compounds.
** Date each activity was performed.
** Location of records and materials: Clearly identified location(s) of data files and their content.
** Other supporting information needed for independent analysis of raw data obtained in experiment and interpretation of results.
** All raw, processed, and final/reported data generated in the experiment.
** A proper cross-reference should be added if any raw data is kept separate from the experimental record and cannot be attached to the experimental record, or any raw data is obtained by other researchers performing supporting experiments.
* Expectation: A qualified reviewer should be able to:
** link figures, graphs, conclusions, and other summary data to the raw data that was processed/analyzed.
** link the summary data to the corresponding experiment described in a lab notebook entry.
** link the lab notebook entry to the raw data (e.g., where generated by an automated instrument).
* All related experimental records and supporting research must be linked/cross-referenced in the main experiment (via the respective unique identifiers).
* The raw data obtained in an experiment may be stored in a separate archival system but should be referenced in the experimental record (see [[3.1.2 Procedures for how and when to record data]]).
* If a new analysis of data from previous experimental records needs to be performed to generate a new result or conclusion, a new experimental record should be created, which should clearly cross-reference the earlier experimental records (by their unique identifiers) and conclusions reached.
* The EQIPD template "Documentation Plan" located in folder 3.1 in the Dossier (and below in Section C) provides a central space to describe this Core Requirement. The document is also used in the Toolbox items [[3.1.4 Data security]] and [[2.3.1 Generation, recording, handling and archiving of raw data]].

'''Extra care has to be taken:'''

* The author(s), all individuals who participated and/or contributed to the experiment, including, where applicable, recorder(s) must be clearly identified, so that the data can be traced, by name and date to each individual's contribution. The above ensures that the record is attributable.
* Clear guidelines and conventions on file-naming for all data files and experimental record should be established for consistency and traceability (see Traceability of data and any person having impact on data).

For technical non-public reports (e.g. R&D reports used in regulatory submissions), it is easy and fairly common to provide direct references to a lab notebook containing the relevant information.

For scientific publications, it is not common to include such references and the following options may be considered to establish traceability between published data and internal records:
* develop a “for internal use” system (e.g. a plain Excel file accessible to all members of the research unit) where reports about completed studies (and associated manuscripts and publications) are matched with the corresponding unique study IDs
* including unique study IDs or references to the laboratory notebooks in the publications themselves (e.g. in the supplementary materials)
* include unique study IDs in the preregistered protocols

'''PLEASE DO NOT FORGET'''

* To ensure traceability, each experimental record should have a unique identifier in accordance with the applicable procedure(s), e.g., SOPs.
* Responsibility for creating experimental records and documentation of the resulting data rests with the researcher who generates the data. If multiple researchers collaborate in data generation, then it should be identified as such.

== C. Resources ==

EQIPD Documentation Plan template

3.1.2.1 Traceability of data and any person having impact on data

2021-02-17T17:22:38Z

ChristophEmmerich:

== A. Background & Definitions ==

This item refers to one of the Core Requirements (Core Requirement 8 - "Reported research outcomes must be traceable to experimental data") and is, therefore, considered as essential.

'''Traceability''': The ability to find the source of data (primary and secondary) and any person having relevant impact on data sets that are presented in a report or other presentation.

'''Experimental Record''': An entry in an (electronic) laboratory notebook for an experiment recording all data and pertinent details of an experiment such that a peer could repeat the experiment.

The user must ensure the traceability and integrity of the data so that the reported results can be reconstructed.

Traceability is directly related to the FAIR principles that are endorsed by both academic and industry research communities as well as by the growing number of funders.

== B. Guidance & Expectations ==

* Each experimental record should contain or cross-reference/link to:
** Names of all individuals involved in generating the content of the experimental record.
** Specific research plan, objective, or hypothesis to be addressed by the experiment.
** All protocols, standard operating procedures, test methods, statistical tools (and/or software used for data analysis) used.
** Description of all materials and equipment used, including the source and lot number of all starting materials and test compounds.
** Date each activity was performed.
** Location of records and materials: Clearly identified location(s) of data files and their content.
** Other supporting information needed for independent analysis of raw data obtained in experiment and interpretation of results.
** All raw, processed, and final/reported data generated in the experiment.
** A proper cross-reference should be added if any raw data is kept separate from the experimental record and cannot be attached to the experimental record, or any raw data is obtained by other researchers performing supporting experiments.
* Expectation: A qualified reviewer should be able to:
** link figures, graphs, conclusions, and other summary data to the raw data that was processed/analyzed.
** link the summary data to the corresponding experiment described in a lab notebook entry.
** link the lab notebook entry to the raw data (e.g., where generated by an automated instrument).
* All related experimental records and supporting research must be linked/cross-referenced in the main experiment (via the respective unique identifiers).
* The raw data obtained in an experiment may be stored in a separate archival system but should be referenced in the experimental record (see [[3.1.2 Procedures for how and when to record data]]).
* If a new analysis of data from previous experimental records needs to be performed to generate a new result or conclusion, a new experimental record should be created, which should clearly cross-reference the earlier experimental records (by their unique identifiers) and conclusions reached.
* The EQIPD template "Documentation Plan" located in folder 3.1 in the Dossier (and below in Section C) provides a central space to describe this Core Requirement. The document is also used in the Toolbox items [[3.1.4 Data security]] and [[2.3.1 Generation, recording, handling and archiving of raw data]].

'''Extra care has to be taken:'''

* The author(s), all individuals who participated and/or contributed to the experiment, including, where applicable, recorder(s) must be clearly identified, so that the data can be traced, by name and date to each individual's contribution. The above ensures that the record is attributable.
* Clear guidelines and conventions on file-naming for all data files and experimental record should be established for consistency and traceability (see Traceability of data and any person having impact on data).

For technical non-public reports (e.g. R&D reports used in regulatory submissions), it is easy and fairly common to provide direct references to a lab notebook containing the relevant information.

For scientific publications, it is not common to include such references and the following options may be considered to establish traceability between published data and internal records:
* develop a “for internal use” system (e.g. a plain Excel file accessible to all members of the research unit) where reports about completed studies (and associated manuscripts and publications) are matched with the corresponding unique study IDs
* including unique study IDs or references to the laboratory notebooks in the publications themselves (e.g. in the supplementary materials)
* include unique study IDs in the preregistered protocols

'''PLEASE DO NOT FORGET'''

* To ensure traceability, each experimental record should have a unique identifier in accordance with the applicable procedure(s), e.g., SOPs.
* Responsibility for creating experimental records and documentation of the resulting data rests with the researcher who generates the data. If multiple researchers collaborate in data generation, then it should be identified as such.

== C. Resources ==

EQIPD Documentation Plan template

3.1.2.1 Traceability of data and any person having impact on data

2021-02-17T17:22:00Z

ChristophEmmerich:

== A. Background & Definitions ==

This item refers to one of the Core Requirements (Core Requirement 8 - "Reported research outcomes must be traceable to experimental data") and is, therefore, considered as essential.

'''Traceability''': The ability to find the source of data (primary and secondary) and any person having relevant impact on data sets that are presented in a report or other presentation.

'''Experimental Record''': An entry in an (electronic) laboratory notebook for an experiment recording all data and pertinent details of an experiment such that a peer could repeat the experiment.

The user must ensure the traceability and integrity of the data so that the reported results can be reconstructed.

Traceability is directly related to the FAIR principles that are endorsed by both academic and industry research communities as well as by the growing number of funders.

== B. Guidance & Expectations ==

* Each experimental record should contain or cross-reference/link to:
** Names of all individuals involved in generating the content of the experimental record.
** Specific research plan, objective, or hypothesis to be addressed by the experiment.
** All protocols, standard operating procedures, test methods, statistical tools (and/or software used for data analysis) used.
** Description of all materials and equipment used, including the source and lot number of all starting materials and test compounds.
** Date each activity was performed.
** Location of records and materials: Clearly identified location(s) of data files and their content.
** Other supporting information needed for independent analysis of raw data obtained in experiment and interpretation of results.
** All raw, processed, and final/reported data generated in the experiment.
** A proper cross-reference should be added if any raw data is kept separate from the experimental record and cannot be attached to the experimental record, or any raw data is obtained by other researchers performing supporting experiments.
* Expectation: A qualified reviewer should be able to:
** link figures, graphs, conclusions, and other summary data to the raw data that was processed/analyzed.
** link the summary data to the corresponding experiment described in a lab notebook entry.
** link the lab notebook entry to the raw data (e.g., where generated by an automated instrument).
* All related experimental records and supporting research must be linked/cross-referenced in the main experiment (via the respective unique identifiers).
* The raw data obtained in an experiment may be stored in a separate archival system but should be referenced in the experimental record (see [[3.1.2 Procedures for how and when to record data]]).
* If a new analysis of data from previous experimental records needs to be performed to generate a new result or conclusion, a new experimental record should be created, which should clearly cross-reference the earlier experimental records (by their unique identifiers) and conclusions reached.
* The EQIPD template "Documentation Plan" located in folder 3.1 in the Dossier (and below in Section C) provides a central space to describe this Core Requirement. The document is also used in the Toolbox items 3.1.4 Data security and 2.3.1 Generation, recording, handling and archiving of raw data.

'''Extra care has to be taken:'''

* The author(s), all individuals who participated and/or contributed to the experiment, including, where applicable, recorder(s) must be clearly identified, so that the data can be traced, by name and date to each individual's contribution. The above ensures that the record is attributable.
* Clear guidelines and conventions on file-naming for all data files and experimental record should be established for consistency and traceability (see Traceability of data and any person having impact on data).

For technical non-public reports (e.g. R&D reports used in regulatory submissions), it is easy and fairly common to provide direct references to a lab notebook containing the relevant information.

For scientific publications, it is not common to include such references and the following options may be considered to establish traceability between published data and internal records:
* develop a “for internal use” system (e.g. a plain Excel file accessible to all members of the research unit) where reports about completed studies (and associated manuscripts and publications) are matched with the corresponding unique study IDs
* including unique study IDs or references to the laboratory notebooks in the publications themselves (e.g. in the supplementary materials)
* include unique study IDs in the preregistered protocols

'''PLEASE DO NOT FORGET'''

* To ensure traceability, each experimental record should have a unique identifier in accordance with the applicable procedure(s), e.g., SOPs.
* Responsibility for creating experimental records and documentation of the resulting data rests with the researcher who generates the data. If multiple researchers collaborate in data generation, then it should be identified as such.

== C. Resources ==

EQIPD Documentation Plan template

3.1.2.1 Traceability of data and any person having impact on data

2021-02-17T17:19:59Z

ChristophEmmerich:

== A. Background & Definitions ==

This item refers to one of the Core Requirements (Core Requirement 8 - "Reported research outcomes must be traceable to experimental data") and is, therefore, considered as essential.

'''Traceability''': The ability to find the source of data (primary and secondary) and any person having relevant impact on data sets that are presented in a report or other presentation.

'''Experimental Record''': An entry in an (electronic) laboratory notebook for an experiment recording all data and pertinent details of an experiment such that a peer could repeat the experiment.

The user must ensure the traceability and integrity of the data so that the reported results can be reconstructed.

Traceability is directly related to the FAIR principles that are endorsed by both academic and industry research communities as well as by the growing number of funders.

== B. Guidance & Expectations ==

* Each experimental record should contain or cross-reference/link to:
** Names of all individuals involved in generating the content of the experimental record.
** Specific research plan, objective, or hypothesis to be addressed by the experiment.
** All protocols, standard operating procedures, test methods, statistical tools (and/or software used for data analysis) used.
** Description of all materials and equipment used, including the source and lot number of all starting materials and test compounds.
** Date each activity was performed.
** Location of records and materials: Clearly identified location(s) of data files and their content.
** Other supporting information needed for independent analysis of raw data obtained in experiment and interpretation of results.
** All raw, processed, and final/reported data generated in the experiment.
** A proper cross-reference should be added if any raw data is kept separate from the experimental record and cannot be attached to the experimental record, or any raw data is obtained by other researchers performing supporting experiments.
* Expectation: A qualified reviewer should be able to:
** link figures, graphs, conclusions, and other summary data to the raw data that was processed/analyzed.
** link the summary data to the corresponding experiment described in a lab notebook entry.
** link the lab notebook entry to the raw data (e.g., where generated by an automated instrument).
* All related experimental records and supporting research must be linked/cross-referenced in the main experiment (via the respective unique identifiers).
* The raw data obtained in an experiment may be stored in a separate archival system but should be referenced in the experimental record (see Procedures for how and when to record data).
* If a new analysis of data from previous experimental records needs to be performed to generate a new result or conclusion, a new experimental record should be created, which should clearly cross-reference the earlier experimental records (by their unique identifiers) and conclusions reached.
* The EQIPD template "Documentation Plan" located in folder 3.1 in the Dossier (and below in Section C) provides a central space to describe this Core Requirement. The document is also used in the Toolbox items 3.1.4 Data security and 2.3.1 Generation, recording, handling and archiving of raw data.

'''Extra care has to be taken:'''

* The author(s), all individuals who participated and/or contributed to the experiment, including, where applicable, recorder(s) must be clearly identified, so that the data can be traced, by name and date to each individual's contribution. The above ensures that the record is attributable.
* Clear guidelines and conventions on file-naming for all data files and experimental record should be established for consistency and traceability (see Traceability of data and any person having impact on data).

For technical non-public reports (e.g. R&D reports used in regulatory submissions), it is easy and fairly common to provide direct references to a lab notebook containing the relevant information.

For scientific publications, it is not common to include such references and the following options may be considered to establish traceability between published data and internal records:
* develop a “for internal use” system (e.g. a plain Excel file accessible to all members of the research unit) where reports about completed studies (and associated manuscripts and publications) are matched with the corresponding unique study IDs
* including unique study IDs or references to the laboratory notebooks in the publications themselves (e.g. in the supplementary materials)
* include unique study IDs in the preregistered protocols

'''PLEASE DO NOT FORGET'''

* To ensure traceability, each experimental record should have a unique identifier in accordance with the applicable procedure(s), e.g., SOPs.
* Responsibility for creating experimental records and documentation of the resulting data rests with the researcher who generates the data. If multiple researchers collaborate in data generation, then it should be identified as such.

== C. Resources ==

EQIPD Documentation Plan template