EQIPD - User contributions [en]

3.5.2 Protocols for methods and assays

2025-06-10T08:37:56Z

Rene.Bernard: /* C. Resources */

== 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]

'''ADD PRO-MAP'''

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

Next item: [[3.5.3 Minimum reporting guidelines]]

3.4.2.3 Biological and technical replicates

2025-06-10T08:35:38Z

Rene.Bernard: /* C. Resources */

== A. Background & Definitions ==
As biological experiments can be complicated, technical replicate measurements are often taken to monitor the performance of the experiment. However, such technical replicates are '''not independent''' tests of the hypothesis, and so they cannot provide evidence of the reproducibility of the main results. Pseudoreplication in its various forms is a common error in the design and analysis of biological research.

* The term “pseudoreplication” can be defined as "the use of inferential statistics to test for treatment effects with data from experiments where either treatments are not replicated (though samples may be) or replicates are not statistically independent" (3).
* Biological replicates: parallel measurements of biologically distinct samples that capture random biological variation, which may itself be a subject of study or a source of noise. Biological replicates address if and how widely the results of an experiment can be generalized. For example, repeating a particular assay with independently generated samples, or samples derived from various cell types, tissue types, or organisms, to see if similar results can be observed.
* Technical replicates: repeated measurements of the same sample that represent independent measures of the random noise associated with protocols or equipment. Technical replicates address the reproducibility of the assay or technique, but not the reproducibility of the effect or event being studied.

== B. Guidance & Expectations ==
* Typically, biological variability is substantially greater than technical variability, so committing resources to sampling biologically relevant variables is usually beneficial (unless measures of technical variability are themselves of interest)
* Good experimental design practice includes planning for replication:
** First, identify the questions the experiment aims to answer
** Next, determine the proportion of variability induced by each step to distribute the capacity for replication of the experiment across steps
** Be aware of the potential for pseudoreplication and aim to design statistically independent replicates
* To differentiate between technical and biological replicates, a useful question to ask is whether or not the follow-up test should give, in expectation, exactly the '''same quantitative result''' as the original study. Technical replication tests do not introduce independency into the experimental system and can mainly be applied to measure errors in sample handling as the new findings should be quantitatively identical to the old results. In contrast, true biological replicates are based on independent raw materials (animals, cells, etc.) and therefore do not need to give the same exact results as obtained before.

Further recommendations that are supported by a simulation, which can be found [https://paasp.net/biological-vs-technical-replicates-now-from-a-data-analysis-perspective/ HERE], are:
* There is no reason to use the antiquated method of repeated measures (RM) ANOVA; in contrast to RM ANOVA, mixed effects modeling makes no sphericity assumption and handles missing data well.
* There is no reason to use nested ANOVA in this context: nesting is applicable in situations when one or another constraint does not allow crossing every level of one factor with every level of another factor. In such situations with a nested layout, fewer than all levels of one factor occur within each level of the other factor.
* The expanded descriptive summary can be highly instructive (and is yours to use freely).
* And last but not least, whatever method is used for the analysis, one should be maximally transparent about how the data were collected, what were the experimental units, what were the replicates, and what analyses were used to examine the data.

'''PLEASE DO NOT FORGET'''
* For biologically distinct conditions, averaging technical replicates can limit the impact of measurement error, but taking additional biological replicates is often preferable for improving the efficiency of statistical testing.
* Technical replicates are an important internal quality control indicating how the experiment was performed - but can not be used to infer conclusions
* Avoiding pseudoreplication (defined by Hurlbert as "the use of inferential statistics to test for treatment effects with data from experiments where either treatments are not replicated (though samples may be) or replicates are not statistically independent"): the independence of observations or samples is (in most cases) an essential requirement on which statistical methods rely. Analyzing pseudoreplicated samples ultimately results in erroneous confidence intervals, that are too small, and inaccurate p-values due to underestimating the underlying experimental variability and the degrees of freedom (number of independent observations).

== C. Resources ==
* Vaux, DL et al., Replicates and repeats-what is the difference and is it significant? EMBO Rep. 2012 Apr; 13(4): 291–296. [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3321166/]
* Blainey P et al., Points of Significance: Replication. Nature Methods 2014, 11(9): 879-880. [https://www.nature.com/articles/nmeth.3091]
* Hurlbert SH, Ecol Monogr. 1984, 54: 187-211 [https://esajournals.onlinelibrary.wiley.com/doi/abs/10.2307/1942661]

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back to [[Toolbox]]

Next item: [[3.5.1 Animal and non-animal method and assay qualification]]

'''ADD EXAMPLES for nesting'''

3.3.3 Management of research materials and reagents

2025-06-10T07:50:21Z

Rene.Bernard:

== A. Background & Definitions ==

This item refers to one of the [[Core Requirements]] (Core Requirement 13 - "Adequate handling and storage of samples and materials must be ensured") and is, therefore, considered as essential.

Research materials and reagents, both chemical and biological, including solutions, reagents, samples and other experimental materials should be fit for purpose. All of these items that enable reconstruction of the study should be properly documented and securely stored. This also applies to samples that can be categorized as the output of an experiment (for example, collected tissue that is mounted on histology slides) - in addition to samples, that enter an experiment as input and are the subject of testing.

Corresponding documentation should be sufficient to ensure that work could be repeated in the future using equivalent materials and reagents.

== B. Guidance & Expectations ==
* Research materials and reagents should be labelled (clearly, accurately, uniquely and durably) to indicate identity, concentration, expiry date and storage conditions as appropriate
* The storage and handling of research materials and reagents must be appropriate to their nature. If the storage conditions are critical, they must be monitored and recorded.

'''Extra care has to be taken:'''
* For key research materials and reagents (e.g. a chemical compound that is the subject of a study):
** the preparation date and stability data should be available.
** records for the different stages of the reagent's lifecycle should be readily tracked and maintained (including transport, storage, analysis or use and disposal).
* Key research materials and reagents should be traceable to the technician, scientist or company that produced them.

'''PLEASE DO NOT FORGET'''
* Provide training on this subject for new employees and refresher training (if appropriate)
* Disposal should be consistent with defined regulation or guidance.
* Often a clear justification when to dispose materials cannot be made for self-prepared reagents. Use a routine that is scientifically sound, that can be tested and can be applied consistently, such as
** defined period (e.g., good for 7 days)
** visual inspection in regular intervals (e.g, look for suspended particles)
** defined opening of a container (e.g., dispose after the tenth opening)

'''RISK ASSESSMENT'''
* Lack of responsibility and ownership for shared research materials and reagents bears a special risk and needs additional attention.

== C. Resources ==

tba

'''ADD MATERIAL AND REAGENT Database in ELNs ; RRIDs to be added to approproriate materials. Maybe a definition of "research material"'''

----
back to [[Toolbox]]

Next item: [[3.4.1 Animal characteristics, care and use]]

3.2.4 Training on specific methods, tasks and activities

2025-06-10T07:45:40Z

Rene.Bernard:

'''THIS NEEDS work -''' '''- How to make good reproducible methods. PRO-MAP'''
'''
Training on Tasks amd activity --> use active prokject management (examples /cost)'''

EQIPD does not provide any guidance or training on individual methods, as these are are likely to be subject to lab-specific or study-specific variations.

However, in the spirit of keeping all research quality-related information in one place, EQIPD advises that any training material on specific methods and related information is stored in the corresponding “Training on specific methods, tasks and activities” section of the Dossier.

----------------
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Next item: [[3.3.1 Fit-for-purpose working environment]]

3.2.3 Implementation of the EQIPD Quality System

2025-06-10T07:42:32Z

Rene.Bernard: /* Assessment by the EQIPD team */

== Get familiar with the key terms defined by EQIPD ==
{{#ev:youtube|https://youtu.be/bK6emsX5G1Y|400|right}}
=== What means quality? ===
EQIPD defines research quality as the extent to which research data are fit for intended use. Fitness, in this context, is defined by the stakeholders, who can be scientists themselves, but also patients, funders, sponsors, publishers and collaboration partners (e.g., peers in a multi-site research project).

=== Research rigor ===
Research rigor refers to measures against systematic error(s) in the estimated effect of an intervention, caused by inadequacies in the design, conduct, or analysis of an experiment.

=== Raw data ===
Raw data (please see [[2.3.1 Generation, recording, handling and archiving of raw data]]) means all original records and documentation, which are the result of the observations and activities in a study, such as:
* 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.

=== Knowledge-claiming research ===
Knowledge-claiming research (please see [[2.1.4 Purpose of research]]): 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.
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

=== Must vs. should ===
When reviewing materials provided by EQIPD, please note the use of "must" vs "should".
* "Must" indicates actions that EQIPD considers as imperative and mandatory or as a requirement.
* The system acknowledged that in some cases, the research environment, a specific research project or a research organization do not allow or make it less relevant to adhere to the requirements formulated below.
* In such cases, instead of using the word “must”, the expectations are communicated as “should” or “strongly recommended”. This means that failure to comply with these expectations will not be automatically regarded as a “red flag” but the research organization may need to present a good rationale for not following this strong recommendation.

For more definitions, please see [[Glossary]].

== Take a closer look at the expectations of EQIPD ==
{{#ev:youtube|https://youtu.be/D_Wl6kcs9vU|400|right}}
Please start with [https://paasp.sharepoint.com/:p:/s/EQIPD/ER4cDNHUMBVGqbtCd6vkFzEBkZM-rJvp5so_-i_N-Zarqg?e=MCebax viewing the presentation] prepared by the EQIPD team that explains why a Quality System is a good solution for research rigor needs, what it is about and how to move forward.

Next, you may want to go through the [https://paasp.sharepoint.com/:b:/s/EQIPD/EWvqq6oej6lEuGj9twOLtYQBK7o77XtOEHrA1UjC5r4PSA?e=4bvKzb self-assessment overview]] in order to check how many [[Core Requirements]] do you already meet and where potential challenges can be encountered.

Once this review is complete, you will be prepared to answer a question - Are most core requirements met?

If the answer is “yes”, we suggest that you complete the self-assessment using the provided template, send it to the EQIPD team for consultation [mailto:info@eqipd.online info@EQIPD.online] or simply complete the remaining core requirements using information provided by EQIPD online ([[4.1.2 Self assessment]]).

If the answer is "no", we suggest that you use the EQIPD tools and follow the suggested implementation path, guided by information provided by EQIPD team.

== Implementation guide ==
The following table provides an overview on how to approach the implementation of the quality system. These are suggestions from the EQIPD team to guide you when you start at zero. However, any other approach that might work better for you and leading to the implementation of the 18 [[Core Requirements]] is perfect as well.

{|class="wikitable"
|
|'''Preparation'''
|'''Phase 1'''
|'''Phase 2'''
|'''Phase 3'''
|-
|'''Recommended Action'''
|[[3.2.3 Implementation of the EQIPD Quality System#Get familiar with the key terms defined by EQIPD|Get familiar with EQIPD terminology]]
[[3.2.3 Implementation of the EQIPD Quality System#Take a closer look at the expectations of EQIPD|Take a closer look at EQIPD expectations]]
|
# [[1.5.2.3 Process owner|Define Process Owner]]
# [[1.1 Mission|Set-up Mission Statement]]
# [[1.2 Scope|Set-up Communication Plan]]
# Set-up Documentation (three resources: [[2.3.1 Generation, recording, handling and archiving of raw data|1]] [[3.1.3 Data security|2]] [[3.1.2.1 Traceability of data and any person having impact on data|3]])
# [[3.4.1 Animal characteristics, care and use|Set-up ACU document if applicable]]
# [[2.1.4 Purpose of research|Get familiar with knowledge-claiming research ]]
|[[NEED|Identify additional NEEDS]]
Develop solutions to address CR and NEEDs
|Finalize implementation
[[4.1.2 Self assessment|Check yourself again with Self-assessment]]
|-
|'''Optional Action'''
|[https://paasp.sharepoint.com/:u:/s/EQIPD/ERWfS3Yuw6NJodMY_iz8A5MBxESAFCjPzWMu-DFd3xUrfw?e=4O5622 Download Planning Tool and Dossier]
[[4.3.2.1 Using the Planning Tool|Introduction to the Planning Tool]]
|
# [[4.3.2.1 Using the Planning Tool#2. Setting up the Dashboard with the Wizard|Open Planning Tool and activate Makros to start Wizard]]
# [[4.3.2.1 Using the Planning Tool#2. Setting up the Dashboard with the Wizard|Guided implementation of items above with Wizard]]
|[[4.3.2.1 Using the Planning Tool#3. Getting familiar with the Dashboard|Track your progress on the Dashboard]]
|[[Implementation_Strategy#Assessment by the EQIPD team| Apply for EQIPD certification]]
|}

For more information, please contact the EQIPD team at [mailto:info@go-eqipd.org/ info@go-eqipd.org].

== Assessment by the EQIPD team ==
Once all core requirements are considered to be met, please approach the EQIPD team [mailto:info@go-eqipd.org info@go-eqipd.org] that can do an assessment and, in case of a positive evaluation, will certify the successful implementation of the Quality System.

----------------
back to [[EQIPD Quality System]]

back to [[Toolbox]]

Item: [[3.2.4 Training on specific methods, tasks and activities]]

3.2.3 Implementation of the EQIPD Quality System

2025-06-10T07:42:09Z

Rene.Bernard: /* Implementation guide */

== Get familiar with the key terms defined by EQIPD ==
{{#ev:youtube|https://youtu.be/bK6emsX5G1Y|400|right}}
=== What means quality? ===
EQIPD defines research quality as the extent to which research data are fit for intended use. Fitness, in this context, is defined by the stakeholders, who can be scientists themselves, but also patients, funders, sponsors, publishers and collaboration partners (e.g., peers in a multi-site research project).

=== Research rigor ===
Research rigor refers to measures against systematic error(s) in the estimated effect of an intervention, caused by inadequacies in the design, conduct, or analysis of an experiment.

=== Raw data ===
Raw data (please see [[2.3.1 Generation, recording, handling and archiving of raw data]]) means all original records and documentation, which are the result of the observations and activities in a study, such as:
* 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.

=== Knowledge-claiming research ===
Knowledge-claiming research (please see [[2.1.4 Purpose of research]]): 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.
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

=== Must vs. should ===
When reviewing materials provided by EQIPD, please note the use of "must" vs "should".
* "Must" indicates actions that EQIPD considers as imperative and mandatory or as a requirement.
* The system acknowledged that in some cases, the research environment, a specific research project or a research organization do not allow or make it less relevant to adhere to the requirements formulated below.
* In such cases, instead of using the word “must”, the expectations are communicated as “should” or “strongly recommended”. This means that failure to comply with these expectations will not be automatically regarded as a “red flag” but the research organization may need to present a good rationale for not following this strong recommendation.

For more definitions, please see [[Glossary]].

== Take a closer look at the expectations of EQIPD ==
{{#ev:youtube|https://youtu.be/D_Wl6kcs9vU|400|right}}
Please start with [https://paasp.sharepoint.com/:p:/s/EQIPD/ER4cDNHUMBVGqbtCd6vkFzEBkZM-rJvp5so_-i_N-Zarqg?e=MCebax viewing the presentation] prepared by the EQIPD team that explains why a Quality System is a good solution for research rigor needs, what it is about and how to move forward.

Next, you may want to go through the [https://paasp.sharepoint.com/:b:/s/EQIPD/EWvqq6oej6lEuGj9twOLtYQBK7o77XtOEHrA1UjC5r4PSA?e=4bvKzb self-assessment overview]] in order to check how many [[Core Requirements]] do you already meet and where potential challenges can be encountered.

Once this review is complete, you will be prepared to answer a question - Are most core requirements met?

If the answer is “yes”, we suggest that you complete the self-assessment using the provided template, send it to the EQIPD team for consultation [mailto:info@eqipd.online info@EQIPD.online] or simply complete the remaining core requirements using information provided by EQIPD online ([[4.1.2 Self assessment]]).

If the answer is "no", we suggest that you use the EQIPD tools and follow the suggested implementation path, guided by information provided by EQIPD team.

== Implementation guide ==
The following table provides an overview on how to approach the implementation of the quality system. These are suggestions from the EQIPD team to guide you when you start at zero. However, any other approach that might work better for you and leading to the implementation of the 18 [[Core Requirements]] is perfect as well.

{|class="wikitable"
|
|'''Preparation'''
|'''Phase 1'''
|'''Phase 2'''
|'''Phase 3'''
|-
|'''Recommended Action'''
|[[3.2.3 Implementation of the EQIPD Quality System#Get familiar with the key terms defined by EQIPD|Get familiar with EQIPD terminology]]
[[3.2.3 Implementation of the EQIPD Quality System#Take a closer look at the expectations of EQIPD|Take a closer look at EQIPD expectations]]
|
# [[1.5.2.3 Process owner|Define Process Owner]]
# [[1.1 Mission|Set-up Mission Statement]]
# [[1.2 Scope|Set-up Communication Plan]]
# Set-up Documentation (three resources: [[2.3.1 Generation, recording, handling and archiving of raw data|1]] [[3.1.3 Data security|2]] [[3.1.2.1 Traceability of data and any person having impact on data|3]])
# [[3.4.1 Animal characteristics, care and use|Set-up ACU document if applicable]]
# [[2.1.4 Purpose of research|Get familiar with knowledge-claiming research ]]
|[[NEED|Identify additional NEEDS]]
Develop solutions to address CR and NEEDs
|Finalize implementation
[[4.1.2 Self assessment|Check yourself again with Self-assessment]]
|-
|'''Optional Action'''
|[https://paasp.sharepoint.com/:u:/s/EQIPD/ERWfS3Yuw6NJodMY_iz8A5MBxESAFCjPzWMu-DFd3xUrfw?e=4O5622 Download Planning Tool and Dossier]
[[4.3.2.1 Using the Planning Tool|Introduction to the Planning Tool]]
|
# [[4.3.2.1 Using the Planning Tool#2. Setting up the Dashboard with the Wizard|Open Planning Tool and activate Makros to start Wizard]]
# [[4.3.2.1 Using the Planning Tool#2. Setting up the Dashboard with the Wizard|Guided implementation of items above with Wizard]]
|[[4.3.2.1 Using the Planning Tool#3. Getting familiar with the Dashboard|Track your progress on the Dashboard]]
|[[Implementation_Strategy#Assessment by the EQIPD team| Apply for EQIPD certification]]
|}

For more information, please contact the EQIPD team at [mailto:info@go-eqipd.org/ info@go-eqipd.org].

== Assessment by the EQIPD team ==
Once all core requirements are considered to be met, please approach the EQIPD team [mailto:info@eqipd.online info@EQIPD.online] that can do an assessment and, in case of a positive evaluation, will certify the successful implementation of the Quality System.

----------------
back to [[EQIPD Quality System]]

back to [[Toolbox]]

Item: [[3.2.4 Training on specific methods, tasks and activities]]

3.2.2 Good Research Practice training

2025-06-10T07:39:42Z

Rene.Bernard:

'''== Definition ==
What do we understand as Good Research practice'''

'''== Resources == All NEED a bit of description'''
The EQIPD consortium presents training material to support research rigor
* EQIPD e-learning platform [https://quality-preclinical-data.eu/learning-environment/eqipd-e-learning-modules/]

A community lead initiative presents resources for workshops and training material to increase reproducibility
* Original article on eLife [https://elifesciences.org/articles/64719?utm_source=twitter&utm_medium=social]
* Reproducibility for Everyone web page [https://www.repro4everyone.org]

----------------
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Next item: [[Implementation Strategy|3.2.3 Implementation of the EQIPD Quality System]]

3.2.2 Good Research Practice training

2025-06-10T07:39:25Z

Rene.Bernard:

'''== Definition ==
What do we understand as Good Research practice'''

== Resources == All NEED a bit of description
The EQIPD consortium presents training material to support research rigor
* EQIPD e-learning platform [https://quality-preclinical-data.eu/learning-environment/eqipd-e-learning-modules/]

A community lead initiative presents resources for workshops and training material to increase reproducibility
* Original article on eLife [https://elifesciences.org/articles/64719?utm_source=twitter&utm_medium=social]
* Reproducibility for Everyone web page [https://www.repro4everyone.org]

----------------
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Next item: [[Implementation Strategy|3.2.3 Implementation of the EQIPD Quality System]]

3.2.2 Good Research Practice training

2025-06-10T07:38:53Z

Rene.Bernard: /* Resources */

'''== Definition ==
What do we understand as Good Research practice'''
== Definition ==

What do we understand as GRP?

== Resources == All NEED a bit of description
The EQIPD consortium presents training material to support research rigor
* EQIPD e-learning platform [https://quality-preclinical-data.eu/learning-environment/eqipd-e-learning-modules/]

A community lead initiative presents resources for workshops and training material to increase reproducibility
* Original article on eLife [https://elifesciences.org/articles/64719?utm_source=twitter&utm_medium=social]
* Reproducibility for Everyone web page [https://www.repro4everyone.org]

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

Next item: [[Implementation Strategy|3.2.3 Implementation of the EQIPD Quality System]]

3.2.2 Good Research Practice training

2025-06-10T07:37:17Z

Rene.Bernard:

'''== Definition ==
What do we understand as Good Research practice'''
== Resources ==
The EQIPD consortium presents training material to support research rigor
* EQIPD e-learning platform [https://quality-preclinical-data.eu/learning-environment/eqipd-e-learning-modules/]

A community lead initiative presents resources for workshops and training material to increase reproducibility
* Original article on eLife [https://elifesciences.org/articles/64719?utm_source=twitter&utm_medium=social]
* Reproducibility for Everyone web page [https://www.repro4everyone.org]

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

Next item: [[Implementation Strategy|3.2.3 Implementation of the EQIPD Quality System]]

3.2.1 General guidance on training

2025-05-16T15:39:37Z

Rene.Bernard: /* C. Resources */

== A. Background & Definitions ==

This item refers to one of the [[Core Requirements]] (Core Requirement 11 - "All personnel involved in research must have adequate training and competence to perform assigned tasks") and is, therefore, considered as essential.

== B. Guidance & Expectations ==

It is generally expected that it is the task of Process Owner to define the training needs of the research unit:
* All mandatory/legally required trainings need to be documented securely maintained for all members of research unit, e.g.
** Training related to animal care and use
** Training to securely work in S1/s2 laboratories
** Training to work with human samples
* Documentation should include details like the training content, date, trainee and trainer signatures, and a summary of the material covered. Records should be kept secure, accessible, and organized. This can include using online systems as well as paper records.

* For all other trainings: Unless prioritized and set by the parent organization, the Process Owner need to prioritize and set content, enforcement and documentation requirements based on RU's quality criteria, e.g.
** Training related to detection, recording, reporting and avoidance of Conflict of Interest
** Training for new employees joining the research unit
** Training related to equipment used for the first time; materials and reagents that require special handling, storage, or disposal
* Training must be provided to all employees on how to record data and what is considered sufficient data recording

'''PLEASE DO NOT FORGET'''
* To consider the need to develop and provide refresher training
* Where it matters, provide specifics of the training, such as details about the training method, course content, and any specific procedures or techniques covered.
* Consider to include a verification of Skills: A form of confirmation that the trainee has demonstrated the necessary skills and knowledge to perform the procedures.

== C. Resources ==

* NRC Guide for the care and use of laboratory animals 8th edition (2011)- page 15:Personnel Management [https://grants.nih.gov/grants/olaw/guide-for-the-care-and-use-of-laboratory-animals.pdf]

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

Nxt item: [[3.2.2 Good Research Practice training]]

3.2.1 General guidance on training

2025-05-16T15:25:45Z

Rene.Bernard: /* B. Guidance & Expectations */

== A. Background & Definitions ==

This item refers to one of the [[Core Requirements]] (Core Requirement 11 - "All personnel involved in research must have adequate training and competence to perform assigned tasks") and is, therefore, considered as essential.

== B. Guidance & Expectations ==

It is generally expected that it is the task of Process Owner to define the training needs of the research unit:
* All mandatory/legally required trainings need to be documented securely maintained for all members of research unit, e.g.
** Training related to animal care and use
** Training to securely work in S1/s2 laboratories
** Training to work with human samples
* Documentation should include details like the training content, date, trainee and trainer signatures, and a summary of the material covered. Records should be kept secure, accessible, and organized. This can include using online systems as well as paper records.

* For all other trainings: Unless prioritized and set by the parent organization, the Process Owner need to prioritize and set content, enforcement and documentation requirements based on RU's quality criteria, e.g.
** Training related to detection, recording, reporting and avoidance of Conflict of Interest
** Training for new employees joining the research unit
** Training related to equipment used for the first time; materials and reagents that require special handling, storage, or disposal
* Training must be provided to all employees on how to record data and what is considered sufficient data recording

'''PLEASE DO NOT FORGET'''
* To consider the need to develop and provide refresher training
* Where it matters, provide specifics of the training, such as details about the training method, course content, and any specific procedures or techniques covered.
* Consider to include a verification of Skills: A form of confirmation that the trainee has demonstrated the necessary skills and knowledge to perform the procedures.

== C. Resources ==
to be added

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

Nxt item: [[3.2.2 Good Research Practice training]]

3.2.1 General guidance on training

2025-05-16T15:18:41Z

Rene.Bernard: /* B. Guidance & Expectations */

3.1.3 Data security

2025-05-16T14:52:10Z

Rene.Bernard: /* C. Resources */

== A. Background & Definitions ==

This item refers to one of the [[Core Requirements]] (Core Requirement 7 - "Data storage must be secured at least for as long as required by legal, contractual or other obligations or business needs") and is, therefore, considered as essential.

Data Security: The degree to which data sets are protected from
# the risk of accidental or malicious alteration or destruction, and/or
# from unauthorized access or disclosure.

The user is responsible to follow security processes aiming to protect the integrity of research data.

== B. Guidance & Expectations ==
* Check whether there are guidelines to data security at your parent organisation. IT department is a good starting point for industry, for academia check the good scientific practice guidelines of your institution. Make sure to implement the recommendaed security measures of your parent organisation.
* Data should be stored safely for as long as required by legal, contractual or other obligations or business needs. Make sure to implement appropriate calendar reminders that includes key personnel.
* Paper-based documentation and data sets:
** If bound-paper laboratory notebooks are used to complete the experimental record, entries should be made in permanent ink. All experimental records should be kept in a safe environment (fireproof, locked cabinet) during the course of the activity and thereafter archived and systemically filed for long-term storage. Make sure user restrictions apply to storage areas.
* Electronic documentation and data sets:
** Electronic data should be safely stored right after data creation and data store locations and long term archiving locations should be specified. Data should be stored on systems, which should be access restricted, regularly backed up and transferred to an archive server, which is managed to ensure long-term storage and data retrieval upon request.
* Data Access management process:
** A list of people, who may need to access archived data sets and experimental records, should be created and updated when needed.
** For paper bound archived data, a data access log book is a possibility to record data access.

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 for the Toolbox items [[2.3.1 Generation, recording, handling and archiving of raw data]] and [[3.1.2.1 Traceability of data and any person having impact on data]].

== C. Resources ==
* EQIPD Documentation Plan template - [https://paasp.sharepoint.com/:w:/s/EQIPD/EQTANrsKHTVIqtO8LT-ZJIEBEUJVZI6siRLkHfliUD4gdA?e=PYbUos 3.1 Documentation Plan.docx]

* Organising, storing and securely handling research data [https://www.ukdataservice.ac.uk/media/604451/2016-06-15_storing_data.pdf]

* Storing data during Research [https://www.uu.nl/en/research/research-data-management/guides/storing-and-preserving-data]

* DfG Good Scientific Practice guidelines [https://www.dfg.de/en/basics-topics/basics-and-principles-of-funding/good-scientific-practice]

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

back to [[Toolbox]]

Next item: [[3.2.1 General guidance on training]]

3.1.3 Data security

2025-05-16T14:49:10Z

Rene.Bernard: /* B. Guidance & Expectations */

== A. Background & Definitions ==

This item refers to one of the [[Core Requirements]] (Core Requirement 7 - "Data storage must be secured at least for as long as required by legal, contractual or other obligations or business needs") and is, therefore, considered as essential.

Data Security: The degree to which data sets are protected from
# the risk of accidental or malicious alteration or destruction, and/or
# from unauthorized access or disclosure.

The user is responsible to follow security processes aiming to protect the integrity of research data.

== B. Guidance & Expectations ==
* Check whether there are guidelines to data security at your parent organisation. IT department is a good starting point for industry, for academia check the good scientific practice guidelines of your institution. Make sure to implement the recommendaed security measures of your parent organisation.
* Data should be stored safely for as long as required by legal, contractual or other obligations or business needs. Make sure to implement appropriate calendar reminders that includes key personnel.
* Paper-based documentation and data sets:
** If bound-paper laboratory notebooks are used to complete the experimental record, entries should be made in permanent ink. All experimental records should be kept in a safe environment (fireproof, locked cabinet) during the course of the activity and thereafter archived and systemically filed for long-term storage. Make sure user restrictions apply to storage areas.
* Electronic documentation and data sets:
** Electronic data should be safely stored right after data creation and data store locations and long term archiving locations should be specified. Data should be stored on systems, which should be access restricted, regularly backed up and transferred to an archive server, which is managed to ensure long-term storage and data retrieval upon request.
* Data Access management process:
** A list of people, who may need to access archived data sets and experimental records, should be created and updated when needed.
** For paper bound archived data, a data access log book is a possibility to record data access.

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 for the Toolbox items [[2.3.1 Generation, recording, handling and archiving of raw data]] and [[3.1.2.1 Traceability of data and any person having impact on data]].

== C. Resources ==
EQIPD Documentation Plan template - [https://paasp.sharepoint.com/:w:/s/EQIPD/EQTANrsKHTVIqtO8LT-ZJIEBEUJVZI6siRLkHfliUD4gdA?e=PYbUos 3.1 Documentation Plan.docx]

Organising, storing and securely handling research data [https://www.ukdataservice.ac.uk/media/604451/2016-06-15_storing_data.pdf]

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

back to [[Toolbox]]

Next item: [[3.2.1 General guidance on training]]

3.1.3 Data security

2025-05-16T14:47:27Z

Rene.Bernard: /* B. Guidance & Expectations */

== A. Background & Definitions ==

This item refers to one of the [[Core Requirements]] (Core Requirement 7 - "Data storage must be secured at least for as long as required by legal, contractual or other obligations or business needs") and is, therefore, considered as essential.

Data Security: The degree to which data sets are protected from
# the risk of accidental or malicious alteration or destruction, and/or
# from unauthorized access or disclosure.

The user is responsible to follow security processes aiming to protect the integrity of research data.

== B. Guidance & Expectations ==
* Check whether there are guidelines to data security at your parent organisation. IT department is a good starting point for industry, for academia check the GWP guidelines of your instiution. Make sure to implement the recommendaed security measures of your parent organisation.
* Data should be stored safely for as long as required by legal, contractual or other obligations or business needs. Make sure to implement appropriate calendar reminders that includes key personnel.
* Paper-based documentation and data sets:
** If bound-paper laboratory notebooks are used to complete the experimental record, entries should be made in permanent ink. All experimental records should be kept in a safe environment (fireproof, locked cabinet) during the course of the activity and thereafter archived and systemically filed for long-term storage. Make sure user restrictions apply to storage areas.
* Electronic documentation and data sets:
** Electronic data should be safely stored right after data creation and data store locations and long term archiving locations should be specified. Data should be stored on systems, which should be access restricted, regularly backed up and transferred to an archive server, which is managed to ensure long-term storage and data retrieval upon request.
* Data Access management process:
** A list of people, who may need to access archived data sets and experimental records, should be created and updated when needed.
** For paper bound archived data, a data access log book is a possibility to record data access.

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 for the Toolbox items [[2.3.1 Generation, recording, handling and archiving of raw data]] and [[3.1.2.1 Traceability of data and any person having impact on data]].

== C. Resources ==
EQIPD Documentation Plan template - [https://paasp.sharepoint.com/:w:/s/EQIPD/EQTANrsKHTVIqtO8LT-ZJIEBEUJVZI6siRLkHfliUD4gdA?e=PYbUos 3.1 Documentation Plan.docx]

Organising, storing and securely handling research data [https://www.ukdataservice.ac.uk/media/604451/2016-06-15_storing_data.pdf]

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

back to [[Toolbox]]

Next item: [[3.2.1 General guidance on training]]

3.1.2.2 Process for witnessing of records

2025-05-16T14:12:25Z

Rene.Bernard: /* C. Resources */

==A. Background & Definitions==
Witness: An individual at the research unit (and/or is under confidentiality) who did not participate in the design and execution of the experiment (and someone who won't be a co-inventor of any invention resulting from the experiment) but is knowledgeable of the subject matter and understands the experiment; preferably such an individual also observed all or part of the performance of the experiment.

Witnessing of experiments is important to safeguard patents and intellectual property.

==B. Guidance & Expectations==

The author (i.e. the scientists who performed the experiments) should review and ensure completeness of the activity description and the data set.
The author should sign the record and archive the data at the completion of the activity.
A witness should review the documentation and sign the record.
Expectation: Each witness must read and understand what is written in the experimental record, and therefore should have an appropriate technical or scientific background and experience to understand the experimental design, resulting data and conclusions.
Witnessing is expected to occur in a timely manner (e.g. within 14 days or 1 month of experimental conduct).

Extra care has to be taken:

If any changes are made after the witness signs the experimental record, the witness must acknowledge these changes and document this by initialing and dating where the changes were made.

'''PLEASE DO NOT FORGET'''

Whether using paper or electronic notebooks, it is critical that each page of the notebook is signed, dated and witnessed.
For paper-based notebooks, all blank space should be filled (either by writing till the end of each page or by crossing-out unused space).
Witnessing does not replace a thorough quality control (QC) review of the data (to ensure reported results are accurate and can be reconstructed from the raw data).

==C. Resources==

* Quinn G (2017) Patent Drafting: Understanding the Enablement Requirement [http://www.ipwatchdog.com/2017/10/28/patentability-drafting-enablement-requirement/id=89721/]
* Laboratory Record Keeping Procedures [https://www.uno.edu/otmc/laboratory-record-keeping-procedures#:~:text=No%20entries%20should%20be%20made,the%20institution's%20record%20retention%20policy]
* Example of an sign & witness function in an ELN [https://support.labforward.io/support/solutions/articles/19000135877-sign-and-witness]

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

Next item: [[3.1.3 Data security]]

3.1.2.1 Traceability of data and any person having impact on data

2025-05-16T14:03:12Z

Rene.Bernard: /* C. Resources */

==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 which 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]

Data Traceability Best Practices [https://www.pantomath.com/data-pipeline-automation/data-traceability]

Data Organization [https://researchdata.se/en/manage-data/organize-data]

----

back to [[Toolbox]]

[[3.1.2.2 Process for witnessing of records|Next Page]]

3.1.2.1 Traceability of data and any person having impact on data

2025-05-16T13:59:34Z

Rene.Bernard: /* C. Resources */

==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 which 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]

Data Traceability Best Practices [https://www.pantomath.com/data-pipeline-automation/data-traceability]

Data Organization [https://researchdata.se/en/manage-data/organize-data]

----

back to [[Toolbox]]

Next item: [[3.1.2.2 Process for witnessing of records]]

3.1.2.1 Traceability of data and any person having impact on data

2025-05-16T13:58:20Z

Rene.Bernard: /* C. Resources */

==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 which 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]

Data Traceability Best Practices [https://www.pantomath.com/data-pipeline-automation/data-traceability]

Data Organization [https://researchdata.se/en/manage-data/organize-data]

back to [[Toolbox]]

Next item: [[https://eqipd-toolbox.paasp.net/wiki/3.1.2.2_Process_for_witnessing_of_records]]

3.1.2.1 Traceability of data and any person having impact on data

2025-05-16T13:57:13Z

Rene.Bernard: /* C. Resources */

==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 which 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]

Data Traceability Best Practices [https://www.pantomath.com/data-pipeline-automation/data-traceability]

Data Organization [https://researchdata.se/en/manage-data/organize-data]

back to [[Toolbox]]

Next item: [[3.1.2.2_Process_for_witnessing_of_records]]

3.1.2.1 Traceability of data and any person having impact on data

2025-05-16T13:56:25Z

Rene.Bernard: /* C. Resources */

==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 which 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]

Data Traceability Best Practices [https://www.pantomath.com/data-pipeline-automation/data-traceability]

Data Organization [https://researchdata.se/en/manage-data/organize-data]

back to [[Toolbox]]

Next item: [[3.1.3 Process for witnessing of records]]

3.1.2.1 Traceability of data and any person having impact on data

2025-05-16T13:55:44Z

Rene.Bernard: /* C. Resources */

==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 which 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]

Data Traceability Best Practices [https://www.pantomath.com/data-pipeline-automation/data-traceability]

Data Organization [https://researchdata.se/en/manage-data/organize-data]

back to [[Toolbox]]

Next item: [[3.1.2.2 Process for witnessing of records]]

3.1.2.1 Traceability of data and any person having impact on data

2025-05-16T13:55:14Z

Rene.Bernard:

==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 which 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]

Data Traceability Best Practices [https://www.pantomath.com/data-pipeline-automation/data-traceability]

Data Organization [https://researchdata.se/en/manage-data/organize-data]

==
back to [[Toolbox]]

Next item: [[3.1.2.2 Process for witnessing of records]]

3.1.2.1 Traceability of data and any person having impact on data

2025-05-16T13:52:32Z

Rene.Bernard: /* C. Resources */

3.1.2.1 Traceability of data and any person having impact on data

2025-05-16T13:52:13Z

Rene.Bernard:

==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 which 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]
Data Traceability Best Practices [https://www.pantomath.com/data-pipeline-automation/data-traceability]
Data Organization [https://researchdata.se/en/manage-data/organize-data]

3.1.2 Procedures for how and when to record data

2025-05-16T13:01:00Z

Rene.Bernard: /* C. Resources */

== A. Background & Definitions ==
Electronic data: Output from an instrument and/or its software application. The information needed for review and/or analysis of electronic data may include metadata, such as machine settings, software type and version, etc.

Hard copy/paper data: Records may also be made on a non-electronic medium (e.g., paper notebook, chart recording).

Experimental Data consists of three components:
* Primary data (raw data): All original recordings that are the result of the original observations and activities in an experiment.
* Secondary data (derived data): transformed primary data and outputs of the analysis of Raw Data.
* Metadata: All additional data that describe the primary and secondardy data

The user should define how to record the data of each experiment. As far as possible, data should be recorded without delay after their generation.

== B. Guidance & Expectations ==
* The creation of an experimental record should be concurrent with the initiation of an experiment.
* Ensuring quality and integrity of the experimental records will permit researchers / peers to appropriately evaluate and, if necessary, repeat the reported experiments. Thus, it is critical to ensure that experimental records are attributable, legible, contemporaneous, original, accurate as well as complete, consistent, enduring and available (commonly referred to as ALCOAC+):
** '''A'''ttributable: 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.
** '''L'''egible: The record must be readable and recorded in/or on a permanent medium (paper or electronic).
** '''C'''ontemporaneous: The newly-generated/collected data, as well as new scientific discussions and ideas, should be recorded at the time of the observation.
** '''O'''riginal: The first recording of the data should be retained.
** '''A'''ccurate: The recorded observations must be true and accurate.
** Complete: Records should be complete to ensure their traceability, prompt and accurate retrieval, and to enable exact reconstruction or review of the work described. Therefore, each experimental record should have a unique identifier in accordance with the applicable procedure (e.g. SOPs) and all related experimental records must be cross-referenced.
** Consistent: The data should be chronologically arranged, including a date and time stamp that is in the expected sequence.
** Enduring: The material used to record the data should be in a manner which will last a sufficiently long duration of time without losing the readability.
** Available: Data should be accessible whenever needed, over the life of the data. Availability ensures the data meets it’s use, since it can be applied when the need arises.

'''Extra care has to be taken:'''
* All changes/alterations to data already inputted into an experimental record should be clearly described and explained.
* Subsequent changes to source data should not obscure the original data and should be explained. The changes must include identification of the person making the change and the date to make the change attributable/traceable.
* If changes are made in an open experiment, they may be made directly in the experimental record. If changes need to be made after an experimental record is closed, the experimental record can be reopened or another experimental record, citing the original experimental record, can be opened. It is recommended that both records are signed upon completion and witnessed.

'''PLEASE DO NOT FORGET'''
* To minimize chances of loss or damage to the experimental data, to the extent possible, they should be recorded directly into the data recording platform.
* For computer application(s) used to collect, analyze, plot, summarize, or otherwise characterize experimental data, provide the following information: Name, version and vendor of the application and where in the experiment or record-keeping the application was used (e.g., tabulating data).
* Processed original data may also be recognized as raw data when original observations 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 generate exceptionally large volumes of data that are technically difficult to store without pre-processing to reduce the storage volume (e.g. imaging data).

== C. Resources ==
* NIH Checklist of Scientific Record Keeping Best Practices [https://oir.nih.gov/system/files/media/file/2021-08/checklist-scientific_recordkeeping_best_practices-dec2014.pdf]
* Creating a [https://support.microsoft.com/en-us/office/add-or-remove-a-digital-signature-for-microsoft-365-files-70d26dc9-be10-46f1-8efa-719c8b3f1a2d digital signature in Microsoft Office applications]
* ALCOA+ Explainer Video [https://www.youtube.com/watch?v=F3ImjUkgxyQ&ab_channel=CRAMbridge]

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

back to [[Toolbox]]

Next item: [[3.1.2.1 Traceability of data and any person having impact on data]]

3.1.2 Procedures for how and when to record data

2025-05-16T12:12:39Z

Rene.Bernard: /* C. Resources */

== A. Background & Definitions ==
Electronic data: Output from an instrument and/or its software application. The information needed for review and/or analysis of electronic data may include metadata, such as machine settings, software type and version, etc.

Hard copy/paper data: Records may also be made on a non-electronic medium (e.g., paper notebook, chart recording).

Experimental Data consists of three components:
* Primary data (raw data): All original recordings that are the result of the original observations and activities in an experiment.
* Secondary data (derived data): transformed primary data and outputs of the analysis of Raw Data.
* Metadata: All additional data that describe the primary and secondardy data

The user should define how to record the data of each experiment. As far as possible, data should be recorded without delay after their generation.

== B. Guidance & Expectations ==
* The creation of an experimental record should be concurrent with the initiation of an experiment.
* Ensuring quality and integrity of the experimental records will permit researchers / peers to appropriately evaluate and, if necessary, repeat the reported experiments. Thus, it is critical to ensure that experimental records are attributable, legible, contemporaneous, original, accurate as well as complete, consistent, enduring and available (commonly referred to as ALCOAC+):
** '''A'''ttributable: 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.
** '''L'''egible: The record must be readable and recorded in/or on a permanent medium (paper or electronic).
** '''C'''ontemporaneous: The newly-generated/collected data, as well as new scientific discussions and ideas, should be recorded at the time of the observation.
** '''O'''riginal: The first recording of the data should be retained.
** '''A'''ccurate: The recorded observations must be true and accurate.
** Complete: Records should be complete to ensure their traceability, prompt and accurate retrieval, and to enable exact reconstruction or review of the work described. Therefore, each experimental record should have a unique identifier in accordance with the applicable procedure (e.g. SOPs) and all related experimental records must be cross-referenced.
** Consistent: The data should be chronologically arranged, including a date and time stamp that is in the expected sequence.
** Enduring: The material used to record the data should be in a manner which will last a sufficiently long duration of time without losing the readability.
** Available: Data should be accessible whenever needed, over the life of the data. Availability ensures the data meets it’s use, since it can be applied when the need arises.

'''Extra care has to be taken:'''
* All changes/alterations to data already inputted into an experimental record should be clearly described and explained.
* Subsequent changes to source data should not obscure the original data and should be explained. The changes must include identification of the person making the change and the date to make the change attributable/traceable.
* If changes are made in an open experiment, they may be made directly in the experimental record. If changes need to be made after an experimental record is closed, the experimental record can be reopened or another experimental record, citing the original experimental record, can be opened. It is recommended that both records are signed upon completion and witnessed.

'''PLEASE DO NOT FORGET'''
* To minimize chances of loss or damage to the experimental data, to the extent possible, they should be recorded directly into the data recording platform.
* For computer application(s) used to collect, analyze, plot, summarize, or otherwise characterize experimental data, provide the following information: Name, version and vendor of the application and where in the experiment or record-keeping the application was used (e.g., tabulating data).
* Processed original data may also be recognized as raw data when original observations 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 generate exceptionally large volumes of data that are technically difficult to store without pre-processing to reduce the storage volume (e.g. imaging data).

== C. Resources ==
* NIH Checklist of Scientific Record Keeping Best Practices [https://oir.nih.gov/system/files/media/file/2021-08/checklist-scientific_recordkeeping_best_practices-dec2014.pdf]
* Creating a [https://support.microsoft.com/en-us/office/add-or-remove-a-digital-signature-for-microsoft-365-files-70d26dc9-be10-46f1-8efa-719c8b3f1a2d digital signature in Microsoft Office applications]

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

back to [[Toolbox]]

Next item: [[3.1.2.1 Traceability of data and any person having impact on data]]

3.1.2 Procedures for how and when to record data

2025-05-16T12:09:44Z

Rene.Bernard: /* C. Resources */

== A. Background & Definitions ==
Electronic data: Output from an instrument and/or its software application. The information needed for review and/or analysis of electronic data may include metadata, such as machine settings, software type and version, etc.

Hard copy/paper data: Records may also be made on a non-electronic medium (e.g., paper notebook, chart recording).

Experimental Data consists of three components:
* Primary data (raw data): All original recordings that are the result of the original observations and activities in an experiment.
* Secondary data (derived data): transformed primary data and outputs of the analysis of Raw Data.
* Metadata: All additional data that describe the primary and secondardy data

The user should define how to record the data of each experiment. As far as possible, data should be recorded without delay after their generation.

== B. Guidance & Expectations ==
* The creation of an experimental record should be concurrent with the initiation of an experiment.
* Ensuring quality and integrity of the experimental records will permit researchers / peers to appropriately evaluate and, if necessary, repeat the reported experiments. Thus, it is critical to ensure that experimental records are attributable, legible, contemporaneous, original, accurate as well as complete, consistent, enduring and available (commonly referred to as ALCOAC+):
** '''A'''ttributable: 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.
** '''L'''egible: The record must be readable and recorded in/or on a permanent medium (paper or electronic).
** '''C'''ontemporaneous: The newly-generated/collected data, as well as new scientific discussions and ideas, should be recorded at the time of the observation.
** '''O'''riginal: The first recording of the data should be retained.
** '''A'''ccurate: The recorded observations must be true and accurate.
** Complete: Records should be complete to ensure their traceability, prompt and accurate retrieval, and to enable exact reconstruction or review of the work described. Therefore, each experimental record should have a unique identifier in accordance with the applicable procedure (e.g. SOPs) and all related experimental records must be cross-referenced.
** Consistent: The data should be chronologically arranged, including a date and time stamp that is in the expected sequence.
** Enduring: The material used to record the data should be in a manner which will last a sufficiently long duration of time without losing the readability.
** Available: Data should be accessible whenever needed, over the life of the data. Availability ensures the data meets it’s use, since it can be applied when the need arises.

'''Extra care has to be taken:'''
* All changes/alterations to data already inputted into an experimental record should be clearly described and explained.
* Subsequent changes to source data should not obscure the original data and should be explained. The changes must include identification of the person making the change and the date to make the change attributable/traceable.
* If changes are made in an open experiment, they may be made directly in the experimental record. If changes need to be made after an experimental record is closed, the experimental record can be reopened or another experimental record, citing the original experimental record, can be opened. It is recommended that both records are signed upon completion and witnessed.

'''PLEASE DO NOT FORGET'''
* To minimize chances of loss or damage to the experimental data, to the extent possible, they should be recorded directly into the data recording platform.
* For computer application(s) used to collect, analyze, plot, summarize, or otherwise characterize experimental data, provide the following information: Name, version and vendor of the application and where in the experiment or record-keeping the application was used (e.g., tabulating data).
* Processed original data may also be recognized as raw data when original observations 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 generate exceptionally large volumes of data that are technically difficult to store without pre-processing to reduce the storage volume (e.g. imaging data).

== C. Resources ==
* NIH Checklist of Scientific Record Keeping Best Practices [https://oir.nih.gov/system/files/media/file/2021-08/checklist-scientific_recordkeeping_best_practices-dec2014.pdf]
* Creating a [https://paasp.sharepoint.com/sites/EQIPD/EQIPD%20QS/digital%20signature%20in%20Microsoft%20Office%20applications.aspx digital signature in Microsoft Office applications]

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

back to [[Toolbox]]

Next item: [[3.1.2.1 Traceability of data and any person having impact on data]]

Talk:3.1.2 Procedures for how and when to record data

2025-05-16T12:08:04Z

Rene.Bernard:

Metadata are missing as thrird important category! - Added!

3.1.2 Procedures for how and when to record data

2025-05-16T12:07:47Z

Rene.Bernard:

== A. Background & Definitions ==
Electronic data: Output from an instrument and/or its software application. The information needed for review and/or analysis of electronic data may include metadata, such as machine settings, software type and version, etc.

Hard copy/paper data: Records may also be made on a non-electronic medium (e.g., paper notebook, chart recording).

Experimental Data consists of three components:
* Primary data (raw data): All original recordings that are the result of the original observations and activities in an experiment.
* Secondary data (derived data): transformed primary data and outputs of the analysis of Raw Data.
* Metadata: All additional data that describe the primary and secondardy data

The user should define how to record the data of each experiment. As far as possible, data should be recorded without delay after their generation.

== B. Guidance & Expectations ==
* The creation of an experimental record should be concurrent with the initiation of an experiment.
* Ensuring quality and integrity of the experimental records will permit researchers / peers to appropriately evaluate and, if necessary, repeat the reported experiments. Thus, it is critical to ensure that experimental records are attributable, legible, contemporaneous, original, accurate as well as complete, consistent, enduring and available (commonly referred to as ALCOAC+):
** '''A'''ttributable: 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.
** '''L'''egible: The record must be readable and recorded in/or on a permanent medium (paper or electronic).
** '''C'''ontemporaneous: The newly-generated/collected data, as well as new scientific discussions and ideas, should be recorded at the time of the observation.
** '''O'''riginal: The first recording of the data should be retained.
** '''A'''ccurate: The recorded observations must be true and accurate.
** Complete: Records should be complete to ensure their traceability, prompt and accurate retrieval, and to enable exact reconstruction or review of the work described. Therefore, each experimental record should have a unique identifier in accordance with the applicable procedure (e.g. SOPs) and all related experimental records must be cross-referenced.
** Consistent: The data should be chronologically arranged, including a date and time stamp that is in the expected sequence.
** Enduring: The material used to record the data should be in a manner which will last a sufficiently long duration of time without losing the readability.
** Available: Data should be accessible whenever needed, over the life of the data. Availability ensures the data meets it’s use, since it can be applied when the need arises.

'''Extra care has to be taken:'''
* All changes/alterations to data already inputted into an experimental record should be clearly described and explained.
* Subsequent changes to source data should not obscure the original data and should be explained. The changes must include identification of the person making the change and the date to make the change attributable/traceable.
* If changes are made in an open experiment, they may be made directly in the experimental record. If changes need to be made after an experimental record is closed, the experimental record can be reopened or another experimental record, citing the original experimental record, can be opened. It is recommended that both records are signed upon completion and witnessed.

'''PLEASE DO NOT FORGET'''
* To minimize chances of loss or damage to the experimental data, to the extent possible, they should be recorded directly into the data recording platform.
* For computer application(s) used to collect, analyze, plot, summarize, or otherwise characterize experimental data, provide the following information: Name, version and vendor of the application and where in the experiment or record-keeping the application was used (e.g., tabulating data).
* Processed original data may also be recognized as raw data when original observations 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 generate exceptionally large volumes of data that are technically difficult to store without pre-processing to reduce the storage volume (e.g. imaging data).

== C. Resources ==
* Guidelines for SCIENTIFIC RECORD KEEPING in the Intramural Research Program at the NIH [https://oir.nih.gov/sites/default/files/uploads/sourcebook/documents/ethical_conduct/guidelines-scientific_recordkeeping.pdf]
* Creating a [https://paasp.sharepoint.com/sites/EQIPD/EQIPD%20QS/digital%20signature%20in%20Microsoft%20Office%20applications.aspx digital signature in Microsoft Office applications]

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

back to [[Toolbox]]

Next item: [[3.1.2.1 Traceability of data and any person having impact on data]]

Talk:3.1.2 Procedures for how and when to record data

2025-05-16T11:59:43Z

Rene.Bernard: Created page with "Metadata are missing as thrird important category!"

Metadata are missing as thrird important category!

3.1.1 Platform to record data

2025-05-16T10:30:14Z

Rene.Bernard: /* C. Resources */

== A. Background & Definitions ==
ELN (Electronic Laboratory Notebook): primary documentation and storage place for information created in the laboratory in an electronic format (also handwritten notes, e.g. during measurement procedures, should be stored in the ELN).

LIMS: A Laboratory Information Management System (LIMS) is a software that allows to effectively manage samples and associated data. By using a LIMS, a research unit can automate workflows, integrate instruments, and manage samples and associated information.

Record: A research diary entry for an experiment recording all data and pertinent details of an experiment such that a peer could repeat the experiment.

Audit trail: An audit trail is a transparent description of the research steps taken from the start of a research project to the development and reporting of findings. These are records of a sequence of events from which a history may be reconstructed and that are kept regarding what was done in an research investigation.

Each user should define the platform used to record data and ensure their traceability and integrity.

== B. Guidance & Expectations ==
* Detailed records of an experiment must be provided (see item [[2.2.3 Documentation of the experiment and deviations]]), either via an electronic or paper-based notebook.
* Data security (see item [[3.1.3 Data security]]) and witnessing (see item [[3.1.2.2 Process for witnessing of records]]) requirements need to be implemented and in place.

* Paper-based documentation:
** A bound paper notebook must be used. The use of loose pieces of paper must be avoided.
** Entries should be made in permanent ink.

* Electronic documentation:
** An electronic system must be used that can be understood by a third party.
** It is advisable to use a system with full audit trail.
** Electronic documentation systems include ELNs or LIMS solutions. Other electronic (commercially available or custom built) data capturing systems may also be used.

'''Extra care has to be taken'''
* Traceability of experimental data should be ensured by using the data recording platform (see item [[3.1.2.1 Traceability of data and any person having impact on data]]).

'''PLEASE DO NOT FORGET'''
* To train new researchers in your lab how to use the data recording platform of choice.
* To provide the required infrastructure so that the platform of choice can be used properly (e.g. have spare lab notebooks or electronic work spaces available).

== C. Resources ==
* The ELN Finder helps you to search and select a suitable Electronic Lab Notebook for your purposes. [https://eln-finder.ulb.tu-darmstadt.de/home]
* NIH Checklist of Scientific Record Keeping Best Practices [https://oir.nih.gov/system/files/media/file/2021-08/checklist-scientific_recordkeeping_best_practices-dec2014.pdf]

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

Next item: [[3.1.2 Procedures for how and when to record data]]

3.1.1 Platform to record data

2025-05-16T10:26:25Z

Rene.Bernard: /* C. Resources */

== A. Background & Definitions ==
ELN (Electronic Laboratory Notebook): primary documentation and storage place for information created in the laboratory in an electronic format (also handwritten notes, e.g. during measurement procedures, should be stored in the ELN).

LIMS: A Laboratory Information Management System (LIMS) is a software that allows to effectively manage samples and associated data. By using a LIMS, a research unit can automate workflows, integrate instruments, and manage samples and associated information.

Record: A research diary entry for an experiment recording all data and pertinent details of an experiment such that a peer could repeat the experiment.

Audit trail: An audit trail is a transparent description of the research steps taken from the start of a research project to the development and reporting of findings. These are records of a sequence of events from which a history may be reconstructed and that are kept regarding what was done in an research investigation.

Each user should define the platform used to record data and ensure their traceability and integrity.

== B. Guidance & Expectations ==
* Detailed records of an experiment must be provided (see item [[2.2.3 Documentation of the experiment and deviations]]), either via an electronic or paper-based notebook.
* Data security (see item [[3.1.3 Data security]]) and witnessing (see item [[3.1.2.2 Process for witnessing of records]]) requirements need to be implemented and in place.

* Paper-based documentation:
** A bound paper notebook must be used. The use of loose pieces of paper must be avoided.
** Entries should be made in permanent ink.

* Electronic documentation:
** An electronic system must be used that can be understood by a third party.
** It is advisable to use a system with full audit trail.
** Electronic documentation systems include ELNs or LIMS solutions. Other electronic (commercially available or custom built) data capturing systems may also be used.

'''Extra care has to be taken'''
* Traceability of experimental data should be ensured by using the data recording platform (see item [[3.1.2.1 Traceability of data and any person having impact on data]]).

'''PLEASE DO NOT FORGET'''
* To train new researchers in your lab how to use the data recording platform of choice.
* To provide the required infrastructure so that the platform of choice can be used properly (e.g. have spare lab notebooks or electronic work spaces available).

== C. Resources ==
* The ELN Finder helps you to search and select a suitable Electronic Lab Notebook (ELN) for your purposes. [https://eln-finder.ulb.tu-darmstadt.de/home]

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

Next item: [[3.1.2 Procedures for how and when to record data]]

Talk:EQIPD Quality System

2025-05-14T14:44:22Z

Rene.Bernard: /* Pages with "to be added sections" */

On this page we can collect ideas and errors within the entire Wiki.

===New pages needed===
* Sex differences in animal research

===Errors on the pages===

===Dedicated working groups===
====General====
* Checking incorrect items:
** Sara: Section 2
** Björn: Section 1
***Error
** René: Section 3
* Checking missing pages
**Everyone when screening though the website, with René as a contact
* Find mechanisms to keep it up-to-date
**Tbd

====Specific task forces formed already:====
* Quality culture and improvmenent
** Arnoud, Sandrine, Jonathan
* Antibodies and validation
** Harv, Marija
* Creating page on sex differences
** Christina, Vootele

====Orphans-Do not link to any other EQIPD pages:====
1.4.3.2 Quality in collaborative research - https://eqipd-toolbox.paasp.net/wiki/1.4.3.2_Quality_in_collaborative_research

EQIPD - https://eqipd-toolbox.paasp.net/wiki/EQIPD

EQIPD for Core Facilities - https://eqipd-toolbox.paasp.net/wiki/EQIPD_for_Core_Facilities

Examples of implementing a unique study ID - https://eqipd-toolbox.paasp.net/wiki/Examples_of_implementing_a_unique_study_ID

Experimental Record - https://eqipd-toolbox.paasp.net/wiki/Experimental_Record

Hauptseite - https://eqipd-toolbox.paasp.net/wiki/Hauptseite

Performance Standards - https://eqipd-toolbox.paasp.net/wiki/Performance_Standards

Spot checks - https://eqipd-toolbox.paasp.net/wiki/Spot_checks

Why quality matters - https://eqipd-toolbox.paasp.net/wiki/Why_quality_matters

====Glossary: might need extension====
SABV

FAIR (data)

====Pages needed ====
- Questionable Research Practices

- CoARA process, research evaluation

- protocols (reusable, reproducible, management)

- AI in research quality?

- feature Marten Kas as EQIPD use case examples

- data (format) community standards

- FAIR data, data repositories, DMP

- more reporting guidelines/recommendations, like RIVER (not just minimum standards)

====Pages with "to be added sections" ====

''(needs priorisation before adding content)''

1.3.1 Transparency

1.3.3 Promotion criteria within a research unit

1.4.1 Research ethics

1.4.2 Adherence to legal and regulatory considerations

1.4.2.7 Animal care and use

1.4.2.8 Human data protection

1.4.3.4 Academia-academia: Research as collaboration

1.5.2 Roles and responsibilities of involved personnel and organization

1.5.2.1 Organigram

1.5.2.2 Management

1.5.2.4 Principal investigators and study directors

1.5.2.5 Research team

1.5.2.6 Supporting team

1.5.2.7 Quality professionals

1.5.3 Management of resources

1.5.3.1 Personnel

1.5.3.2 Facilities

1.5.3.3 Laboratory and experimental equipment used for measurement

1.5.3.4 Electronic information system

1.5.3.5 Organization-specific know how

1.5.3.6 Documentation requirement for sample and material

1.5.3.7 Retained personnel records

1.5.4.1 Independence of internal compliance assessment

1.5.4.2 Internal assessment of the organizations' quality professionals

1.5.5 Sustainability

2.1.6 Sample size and power analysis

2.1.10 Plausibility check

2.3.2 Primary analysis and evaluation of raw data

2.4 Reporting

2.4.1 Non-public reporting

3.2.1 General guidance on training

3.5.1 Animal and non-animal method and assay qualification

4.1.3 External assessment

4.2.1 Installation of solutions, actions and mitigation strategies

4.3.1.2 Deleting items

==== training resources needed to be embedded ====
https://go-eqipd.org/about-eqipd/webinar-2023/

https://go-eqipd.org/resources/eqipd-e-learning/

https://research.columbia.edu/ReaDI-program

find more more online templates

===overall to do's===

- proof reading sections: look for errors, outdated information, broken links, plausibility?

- QUALITY (evaluation) aspects (criteria, checks, checklists, how-to) should be in focus and a priority..to generate products and data with secured high quality

Talk:EQIPD Quality System

2025-05-14T14:44:06Z

Rene.Bernard: /* Pages needed */

On this page we can collect ideas and errors within the entire Wiki.

===New pages needed===
* Sex differences in animal research

===Errors on the pages===

===Dedicated working groups===
====General====
* Checking incorrect items:
** Sara: Section 2
** Björn: Section 1
***Error
** René: Section 3
* Checking missing pages
**Everyone when screening though the website, with René as a contact
* Find mechanisms to keep it up-to-date
**Tbd

====Specific task forces formed already:====
* Quality culture and improvmenent
** Arnoud, Sandrine, Jonathan
* Antibodies and validation
** Harv, Marija
* Creating page on sex differences
** Christina, Vootele

====Orphans-Do not link to any other EQIPD pages:====
1.4.3.2 Quality in collaborative research - https://eqipd-toolbox.paasp.net/wiki/1.4.3.2_Quality_in_collaborative_research

EQIPD - https://eqipd-toolbox.paasp.net/wiki/EQIPD

EQIPD for Core Facilities - https://eqipd-toolbox.paasp.net/wiki/EQIPD_for_Core_Facilities

Examples of implementing a unique study ID - https://eqipd-toolbox.paasp.net/wiki/Examples_of_implementing_a_unique_study_ID

Experimental Record - https://eqipd-toolbox.paasp.net/wiki/Experimental_Record

Hauptseite - https://eqipd-toolbox.paasp.net/wiki/Hauptseite

Performance Standards - https://eqipd-toolbox.paasp.net/wiki/Performance_Standards

Spot checks - https://eqipd-toolbox.paasp.net/wiki/Spot_checks

Why quality matters - https://eqipd-toolbox.paasp.net/wiki/Why_quality_matters

====Glossary: might need extension====
SABV

FAIR (data)

====Pages needed ====
- Questionable Research Practices

- CoARA process, research evaluation

- protocols (reusable, reproducible, management)

- AI in research quality?

- feature Marten Kas as EQIPD use case examples

- data (format) community standards

- FAIR data, data repositories, DMP

- more reporting guidelines/recommendations, like RIVER (not just minimum standards)

====Pages with "to be added sections" ====

(needs priorisation before adding content)
1.3.1 Transparency

1.3.3 Promotion criteria within a research unit

1.4.1 Research ethics

1.4.2 Adherence to legal and regulatory considerations

1.4.2.7 Animal care and use

1.4.2.8 Human data protection

1.4.3.4 Academia-academia: Research as collaboration

1.5.2 Roles and responsibilities of involved personnel and organization

1.5.2.1 Organigram

1.5.2.2 Management

1.5.2.4 Principal investigators and study directors

1.5.2.5 Research team

1.5.2.6 Supporting team

1.5.2.7 Quality professionals

1.5.3 Management of resources

1.5.3.1 Personnel

1.5.3.2 Facilities

1.5.3.3 Laboratory and experimental equipment used for measurement

1.5.3.4 Electronic information system

1.5.3.5 Organization-specific know how

1.5.3.6 Documentation requirement for sample and material

1.5.3.7 Retained personnel records

1.5.4.1 Independence of internal compliance assessment

1.5.4.2 Internal assessment of the organizations' quality professionals

1.5.5 Sustainability

2.1.6 Sample size and power analysis

2.1.10 Plausibility check

2.3.2 Primary analysis and evaluation of raw data

2.4 Reporting

2.4.1 Non-public reporting

3.2.1 General guidance on training

3.5.1 Animal and non-animal method and assay qualification

4.1.3 External assessment

4.2.1 Installation of solutions, actions and mitigation strategies

4.3.1.2 Deleting items

==== training resources needed to be embedded ====
https://go-eqipd.org/about-eqipd/webinar-2023/

https://go-eqipd.org/resources/eqipd-e-learning/

https://research.columbia.edu/ReaDI-program

find more more online templates

===overall to do's===

- proof reading sections: look for errors, outdated information, broken links, plausibility?

- QUALITY (evaluation) aspects (criteria, checks, checklists, how-to) should be in focus and a priority..to generate products and data with secured high quality

Talk:EQIPD Quality System

2025-05-14T14:43:31Z

Rene.Bernard: /* overall to do's */

On this page we can collect ideas and errors within the entire Wiki.

===New pages needed===
* Sex differences in animal research

===Errors on the pages===

===Dedicated working groups===
====General====
* Checking incorrect items:
** Sara: Section 2
** Björn: Section 1
***Error
** René: Section 3
* Checking missing pages
**Everyone when screening though the website, with René as a contact
* Find mechanisms to keep it up-to-date
**Tbd

====Specific task forces formed already:====
* Quality culture and improvmenent
** Arnoud, Sandrine, Jonathan
* Antibodies and validation
** Harv, Marija
* Creating page on sex differences
** Christina, Vootele

====Orphans-Do not link to any other EQIPD pages:====
1.4.3.2 Quality in collaborative research - https://eqipd-toolbox.paasp.net/wiki/1.4.3.2_Quality_in_collaborative_research

EQIPD - https://eqipd-toolbox.paasp.net/wiki/EQIPD

EQIPD for Core Facilities - https://eqipd-toolbox.paasp.net/wiki/EQIPD_for_Core_Facilities

Examples of implementing a unique study ID - https://eqipd-toolbox.paasp.net/wiki/Examples_of_implementing_a_unique_study_ID

Experimental Record - https://eqipd-toolbox.paasp.net/wiki/Experimental_Record

Hauptseite - https://eqipd-toolbox.paasp.net/wiki/Hauptseite

Performance Standards - https://eqipd-toolbox.paasp.net/wiki/Performance_Standards

Spot checks - https://eqipd-toolbox.paasp.net/wiki/Spot_checks

Why quality matters - https://eqipd-toolbox.paasp.net/wiki/Why_quality_matters

====Glossary: might need extension====
SABV

FAIR (data)

====Pages needed ====
- Questionable Research Practices

- CoARA process, research evaluation

- protocols (reusable, reproducible, management)

- AI in research quality?

- feature Marten Kas as EQIPD use case examples

- data (format) community standards

- FAIR data, data repositories, DMP

- more reporting guidelines/recommendations, like RIVER (not just minimum standards)

====Pages with "to be added sections" ==== (needs priorisation before adding content)
1.3.1 Transparency

1.3.3 Promotion criteria within a research unit

1.4.1 Research ethics

1.4.2 Adherence to legal and regulatory considerations

1.4.2.7 Animal care and use

1.4.2.8 Human data protection

1.4.3.4 Academia-academia: Research as collaboration

1.5.2 Roles and responsibilities of involved personnel and organization

1.5.2.1 Organigram

1.5.2.2 Management

1.5.2.4 Principal investigators and study directors

1.5.2.5 Research team

1.5.2.6 Supporting team

1.5.2.7 Quality professionals

1.5.3 Management of resources

1.5.3.1 Personnel

1.5.3.2 Facilities

1.5.3.3 Laboratory and experimental equipment used for measurement

1.5.3.4 Electronic information system

1.5.3.5 Organization-specific know how

1.5.3.6 Documentation requirement for sample and material

1.5.3.7 Retained personnel records

1.5.4.1 Independence of internal compliance assessment

1.5.4.2 Internal assessment of the organizations' quality professionals

1.5.5 Sustainability

2.1.6 Sample size and power analysis

2.1.10 Plausibility check

2.3.2 Primary analysis and evaluation of raw data

2.4 Reporting

2.4.1 Non-public reporting

3.2.1 General guidance on training

3.5.1 Animal and non-animal method and assay qualification

4.1.3 External assessment

4.2.1 Installation of solutions, actions and mitigation strategies

4.3.1.2 Deleting items

==== training resources needed to be embedded ====
https://go-eqipd.org/about-eqipd/webinar-2023/

https://go-eqipd.org/resources/eqipd-e-learning/

https://research.columbia.edu/ReaDI-program

find more more online templates

===overall to do's===

- proof reading sections: look for errors, outdated information, broken links, plausibility?

- QUALITY (evaluation) aspects (criteria, checks, checklists, how-to) should be in focus and a priority..to generate products and data with secured high quality

Talk:EQIPD Quality System

2025-05-14T14:43:13Z

Rene.Bernard: /* training resources needed to be embedded */

On this page we can collect ideas and errors within the entire Wiki.

===New pages needed===
* Sex differences in animal research

===Errors on the pages===

===Dedicated working groups===
====General====
* Checking incorrect items:
** Sara: Section 2
** Björn: Section 1
***Error
** René: Section 3
* Checking missing pages
**Everyone when screening though the website, with René as a contact
* Find mechanisms to keep it up-to-date
**Tbd

====Specific task forces formed already:====
* Quality culture and improvmenent
** Arnoud, Sandrine, Jonathan
* Antibodies and validation
** Harv, Marija
* Creating page on sex differences
** Christina, Vootele

====Orphans-Do not link to any other EQIPD pages:====
1.4.3.2 Quality in collaborative research - https://eqipd-toolbox.paasp.net/wiki/1.4.3.2_Quality_in_collaborative_research

EQIPD - https://eqipd-toolbox.paasp.net/wiki/EQIPD

EQIPD for Core Facilities - https://eqipd-toolbox.paasp.net/wiki/EQIPD_for_Core_Facilities

Examples of implementing a unique study ID - https://eqipd-toolbox.paasp.net/wiki/Examples_of_implementing_a_unique_study_ID

Experimental Record - https://eqipd-toolbox.paasp.net/wiki/Experimental_Record

Hauptseite - https://eqipd-toolbox.paasp.net/wiki/Hauptseite

Performance Standards - https://eqipd-toolbox.paasp.net/wiki/Performance_Standards

Spot checks - https://eqipd-toolbox.paasp.net/wiki/Spot_checks

Why quality matters - https://eqipd-toolbox.paasp.net/wiki/Why_quality_matters

====Glossary: might need extension====
SABV

FAIR (data)

====Pages needed ====
- Questionable Research Practices

- CoARA process, research evaluation

- protocols (reusable, reproducible, management)

- AI in research quality?

- feature Marten Kas as EQIPD use case examples

- data (format) community standards

- FAIR data, data repositories, DMP

- more reporting guidelines/recommendations, like RIVER (not just minimum standards)

====Pages with "to be added sections" ==== (needs priorisation before adding content)
1.3.1 Transparency

1.3.3 Promotion criteria within a research unit

1.4.1 Research ethics

1.4.2 Adherence to legal and regulatory considerations

1.4.2.7 Animal care and use

1.4.2.8 Human data protection

1.4.3.4 Academia-academia: Research as collaboration

1.5.2 Roles and responsibilities of involved personnel and organization

1.5.2.1 Organigram

1.5.2.2 Management

1.5.2.4 Principal investigators and study directors

1.5.2.5 Research team

1.5.2.6 Supporting team

1.5.2.7 Quality professionals

1.5.3 Management of resources

1.5.3.1 Personnel

1.5.3.2 Facilities

1.5.3.3 Laboratory and experimental equipment used for measurement

1.5.3.4 Electronic information system

1.5.3.5 Organization-specific know how

1.5.3.6 Documentation requirement for sample and material

1.5.3.7 Retained personnel records

1.5.4.1 Independence of internal compliance assessment

1.5.4.2 Internal assessment of the organizations' quality professionals

1.5.5 Sustainability

2.1.6 Sample size and power analysis

2.1.10 Plausibility check

2.3.2 Primary analysis and evaluation of raw data

2.4 Reporting

2.4.1 Non-public reporting

3.2.1 General guidance on training

3.5.1 Animal and non-animal method and assay qualification

4.1.3 External assessment

4.2.1 Installation of solutions, actions and mitigation strategies

4.3.1.2 Deleting items

==== training resources needed to be embedded ====
https://go-eqipd.org/about-eqipd/webinar-2023/

https://go-eqipd.org/resources/eqipd-e-learning/

https://research.columbia.edu/ReaDI-program

find more more online templates

===overall to do's===
- proof reading sections: look for errors, outdated information, broken links, plausibility?
- QUALITY (evaluation) aspects (criteria, checks, checklists, how-to) should be in focus and a priority..to generate products and data with secured high quality

Talk:EQIPD Quality System

2025-05-14T14:42:57Z

Rene.Bernard: /* Pages with "to be added sections" */

On this page we can collect ideas and errors within the entire Wiki.

===New pages needed===
* Sex differences in animal research

===Errors on the pages===

===Dedicated working groups===
====General====
* Checking incorrect items:
** Sara: Section 2
** Björn: Section 1
***Error
** René: Section 3
* Checking missing pages
**Everyone when screening though the website, with René as a contact
* Find mechanisms to keep it up-to-date
**Tbd

====Specific task forces formed already:====
* Quality culture and improvmenent
** Arnoud, Sandrine, Jonathan
* Antibodies and validation
** Harv, Marija
* Creating page on sex differences
** Christina, Vootele

====Orphans-Do not link to any other EQIPD pages:====
1.4.3.2 Quality in collaborative research - https://eqipd-toolbox.paasp.net/wiki/1.4.3.2_Quality_in_collaborative_research

EQIPD - https://eqipd-toolbox.paasp.net/wiki/EQIPD

EQIPD for Core Facilities - https://eqipd-toolbox.paasp.net/wiki/EQIPD_for_Core_Facilities

Examples of implementing a unique study ID - https://eqipd-toolbox.paasp.net/wiki/Examples_of_implementing_a_unique_study_ID

Experimental Record - https://eqipd-toolbox.paasp.net/wiki/Experimental_Record

Hauptseite - https://eqipd-toolbox.paasp.net/wiki/Hauptseite

Performance Standards - https://eqipd-toolbox.paasp.net/wiki/Performance_Standards

Spot checks - https://eqipd-toolbox.paasp.net/wiki/Spot_checks

Why quality matters - https://eqipd-toolbox.paasp.net/wiki/Why_quality_matters

====Glossary: might need extension====
SABV

FAIR (data)

====Pages needed ====
- Questionable Research Practices

- CoARA process, research evaluation

- protocols (reusable, reproducible, management)

- AI in research quality?

- feature Marten Kas as EQIPD use case examples

- data (format) community standards

- FAIR data, data repositories, DMP

- more reporting guidelines/recommendations, like RIVER (not just minimum standards)

====Pages with "to be added sections" ==== (needs priorisation before adding content)
1.3.1 Transparency

1.3.3 Promotion criteria within a research unit

1.4.1 Research ethics

1.4.2 Adherence to legal and regulatory considerations

1.4.2.7 Animal care and use

1.4.2.8 Human data protection

1.4.3.4 Academia-academia: Research as collaboration

1.5.2 Roles and responsibilities of involved personnel and organization

1.5.2.1 Organigram

1.5.2.2 Management

1.5.2.4 Principal investigators and study directors

1.5.2.5 Research team

1.5.2.6 Supporting team

1.5.2.7 Quality professionals

1.5.3 Management of resources

1.5.3.1 Personnel

1.5.3.2 Facilities

1.5.3.3 Laboratory and experimental equipment used for measurement

1.5.3.4 Electronic information system

1.5.3.5 Organization-specific know how

1.5.3.6 Documentation requirement for sample and material

1.5.3.7 Retained personnel records

1.5.4.1 Independence of internal compliance assessment

1.5.4.2 Internal assessment of the organizations' quality professionals

1.5.5 Sustainability

2.1.6 Sample size and power analysis

2.1.10 Plausibility check

2.3.2 Primary analysis and evaluation of raw data

2.4 Reporting

2.4.1 Non-public reporting

3.2.1 General guidance on training

3.5.1 Animal and non-animal method and assay qualification

4.1.3 External assessment

4.2.1 Installation of solutions, actions and mitigation strategies

4.3.1.2 Deleting items

==== training resources needed to be embedded ====
https://go-eqipd.org/about-eqipd/webinar-2023/
https://go-eqipd.org/resources/eqipd-e-learning/
https://research.columbia.edu/ReaDI-program
find more more online templates

===overall to do's===
- proof reading sections: look for errors, outdated information, broken links, plausibility?
- QUALITY (evaluation) aspects (criteria, checks, checklists, how-to) should be in focus and a priority..to generate products and data with secured high quality

Talk:EQIPD Quality System

2025-05-14T14:41:24Z

Rene.Bernard: /* Pages needed */

On this page we can collect ideas and errors within the entire Wiki.

===New pages needed===
* Sex differences in animal research

===Errors on the pages===

===Dedicated working groups===
====General====
* Checking incorrect items:
** Sara: Section 2
** Björn: Section 1
***Error
** René: Section 3
* Checking missing pages
**Everyone when screening though the website, with René as a contact
* Find mechanisms to keep it up-to-date
**Tbd

====Specific task forces formed already:====
* Quality culture and improvmenent
** Arnoud, Sandrine, Jonathan
* Antibodies and validation
** Harv, Marija
* Creating page on sex differences
** Christina, Vootele

====Orphans-Do not link to any other EQIPD pages:====
1.4.3.2 Quality in collaborative research - https://eqipd-toolbox.paasp.net/wiki/1.4.3.2_Quality_in_collaborative_research

EQIPD - https://eqipd-toolbox.paasp.net/wiki/EQIPD

EQIPD for Core Facilities - https://eqipd-toolbox.paasp.net/wiki/EQIPD_for_Core_Facilities

Examples of implementing a unique study ID - https://eqipd-toolbox.paasp.net/wiki/Examples_of_implementing_a_unique_study_ID

Experimental Record - https://eqipd-toolbox.paasp.net/wiki/Experimental_Record

Hauptseite - https://eqipd-toolbox.paasp.net/wiki/Hauptseite

Performance Standards - https://eqipd-toolbox.paasp.net/wiki/Performance_Standards

Spot checks - https://eqipd-toolbox.paasp.net/wiki/Spot_checks

Why quality matters - https://eqipd-toolbox.paasp.net/wiki/Why_quality_matters

====Glossary: might need extension====
SABV

FAIR (data)

====Pages needed ====
- Questionable Research Practices

- CoARA process, research evaluation

- protocols (reusable, reproducible, management)

- AI in research quality?

- feature Marten Kas as EQIPD use case examples

- data (format) community standards

- FAIR data, data repositories, DMP

- more reporting guidelines/recommendations, like RIVER (not just minimum standards)

====Pages with "to be added sections" ====
1.3.1 Transparency
1.3.3 Promotion criteria within a research unit
1.4.1 Research ethics
1.4.2 Adherence to legal and regulatory considerations
1.4.2.7 Animal care and use
1.4.2.8 Human data protection
1.4.3.4 Academia-academia: Research as collaboration
1.5.2 Roles and responsibilities of involved personnel and organization
1.5.2.1 Organigram
1.5.2.2 Management
1.5.2.4 Principal investigators and study directors
1.5.2.5 Research team
1.5.2.6 Supporting team
1.5.2.7 Quality professionals
1.5.3 Management of resources
1.5.3.1 Personnel
1.5.3.2 Facilities
1.5.3.3 Laboratory and experimental equipment used for measurement
1.5.3.4 Electronic information system
1.5.3.5 Organization-specific know how
1.5.3.6 Documentation requirement for sample and material
1.5.3.7 Retained personnel records
1.5.4.1 Independence of internal compliance assessment
1.5.4.2 Internal assessment of the organizations' quality professionals
1.5.5 Sustainability

2.1.6 Sample size and power analysis
2.1.10 Plausibility check
2.3.2 Primary analysis and evaluation of raw data
2.4 Reporting
2.4.1 Non-public reporting

3.2.1 General guidance on training
3.5.1 Animal and non-animal method and assay qualification

4.1.3 External assessment
4.2.1 Installation of solutions, actions and mitigation strategies
4.3.1.2 Deleting items

==== training resources needed to be embedded ====
https://go-eqipd.org/about-eqipd/webinar-2023/
https://go-eqipd.org/resources/eqipd-e-learning/
https://research.columbia.edu/ReaDI-program
find more more online templates

===overall to do's===
- proof reading sections: look for errors, outdated information, broken links, plausibility?
- QUALITY (evaluation) aspects (criteria, checks, checklists, how-to) should be in focus and a priority..to generate products and data with secured high quality

Talk:EQIPD Quality System

2025-05-14T14:41:05Z

Rene.Bernard: /* Glossary: might need extension */

On this page we can collect ideas and errors within the entire Wiki.

===New pages needed===
* Sex differences in animal research

===Errors on the pages===

===Dedicated working groups===
====General====
* Checking incorrect items:
** Sara: Section 2
** Björn: Section 1
***Error
** René: Section 3
* Checking missing pages
**Everyone when screening though the website, with René as a contact
* Find mechanisms to keep it up-to-date
**Tbd

====Specific task forces formed already:====
* Quality culture and improvmenent
** Arnoud, Sandrine, Jonathan
* Antibodies and validation
** Harv, Marija
* Creating page on sex differences
** Christina, Vootele

====Orphans-Do not link to any other EQIPD pages:====
1.4.3.2 Quality in collaborative research - https://eqipd-toolbox.paasp.net/wiki/1.4.3.2_Quality_in_collaborative_research

EQIPD - https://eqipd-toolbox.paasp.net/wiki/EQIPD

EQIPD for Core Facilities - https://eqipd-toolbox.paasp.net/wiki/EQIPD_for_Core_Facilities

Examples of implementing a unique study ID - https://eqipd-toolbox.paasp.net/wiki/Examples_of_implementing_a_unique_study_ID

Experimental Record - https://eqipd-toolbox.paasp.net/wiki/Experimental_Record

Hauptseite - https://eqipd-toolbox.paasp.net/wiki/Hauptseite

Performance Standards - https://eqipd-toolbox.paasp.net/wiki/Performance_Standards

Spot checks - https://eqipd-toolbox.paasp.net/wiki/Spot_checks

Why quality matters - https://eqipd-toolbox.paasp.net/wiki/Why_quality_matters

====Glossary: might need extension====
SABV

FAIR (data)

====Pages needed ====
- Questionable Research Practices
- CoARA process, research evaluation
- protocols (reusable, reproducible, management)
- AI in research quality?
- feature Marten Kas as EQIPD use case examples
- data (format) community standards
- FAIR data, data repositories, DMP
- more reporting guidelines/recommendations, like RIVER (not just minimum standards)

====Pages with "to be added sections" ====
1.3.1 Transparency
1.3.3 Promotion criteria within a research unit
1.4.1 Research ethics
1.4.2 Adherence to legal and regulatory considerations
1.4.2.7 Animal care and use
1.4.2.8 Human data protection
1.4.3.4 Academia-academia: Research as collaboration
1.5.2 Roles and responsibilities of involved personnel and organization
1.5.2.1 Organigram
1.5.2.2 Management
1.5.2.4 Principal investigators and study directors
1.5.2.5 Research team
1.5.2.6 Supporting team
1.5.2.7 Quality professionals
1.5.3 Management of resources
1.5.3.1 Personnel
1.5.3.2 Facilities
1.5.3.3 Laboratory and experimental equipment used for measurement
1.5.3.4 Electronic information system
1.5.3.5 Organization-specific know how
1.5.3.6 Documentation requirement for sample and material
1.5.3.7 Retained personnel records
1.5.4.1 Independence of internal compliance assessment
1.5.4.2 Internal assessment of the organizations' quality professionals
1.5.5 Sustainability

2.1.6 Sample size and power analysis
2.1.10 Plausibility check
2.3.2 Primary analysis and evaluation of raw data
2.4 Reporting
2.4.1 Non-public reporting

3.2.1 General guidance on training
3.5.1 Animal and non-animal method and assay qualification

4.1.3 External assessment
4.2.1 Installation of solutions, actions and mitigation strategies
4.3.1.2 Deleting items

==== training resources needed to be embedded ====
https://go-eqipd.org/about-eqipd/webinar-2023/
https://go-eqipd.org/resources/eqipd-e-learning/
https://research.columbia.edu/ReaDI-program
find more more online templates

===overall to do's===
- proof reading sections: look for errors, outdated information, broken links, plausibility?
- QUALITY (evaluation) aspects (criteria, checks, checklists, how-to) should be in focus and a priority..to generate products and data with secured high quality

Talk:EQIPD Quality System

2025-05-14T14:40:41Z

Rene.Bernard: /* Orphans-Do not link to any other EQIPD pages: */

On this page we can collect ideas and errors within the entire Wiki.

===New pages needed===
* Sex differences in animal research

===Errors on the pages===

===Dedicated working groups===
====General====
* Checking incorrect items:
** Sara: Section 2
** Björn: Section 1
***Error
** René: Section 3
* Checking missing pages
**Everyone when screening though the website, with René as a contact
* Find mechanisms to keep it up-to-date
**Tbd

====Specific task forces formed already:====
* Quality culture and improvmenent
** Arnoud, Sandrine, Jonathan
* Antibodies and validation
** Harv, Marija
* Creating page on sex differences
** Christina, Vootele

====Orphans-Do not link to any other EQIPD pages:====
1.4.3.2 Quality in collaborative research - https://eqipd-toolbox.paasp.net/wiki/1.4.3.2_Quality_in_collaborative_research

EQIPD - https://eqipd-toolbox.paasp.net/wiki/EQIPD

EQIPD for Core Facilities - https://eqipd-toolbox.paasp.net/wiki/EQIPD_for_Core_Facilities

Examples of implementing a unique study ID - https://eqipd-toolbox.paasp.net/wiki/Examples_of_implementing_a_unique_study_ID

Experimental Record - https://eqipd-toolbox.paasp.net/wiki/Experimental_Record

Hauptseite - https://eqipd-toolbox.paasp.net/wiki/Hauptseite

Performance Standards - https://eqipd-toolbox.paasp.net/wiki/Performance_Standards

Spot checks - https://eqipd-toolbox.paasp.net/wiki/Spot_checks

Why quality matters - https://eqipd-toolbox.paasp.net/wiki/Why_quality_matters

====Glossary: might need extension====
SABV
FAIR (data)

====Pages needed ====
- Questionable Research Practices
- CoARA process, research evaluation
- protocols (reusable, reproducible, management)
- AI in research quality?
- feature Marten Kas as EQIPD use case examples
- data (format) community standards
- FAIR data, data repositories, DMP
- more reporting guidelines/recommendations, like RIVER (not just minimum standards)

====Pages with "to be added sections" ====
1.3.1 Transparency
1.3.3 Promotion criteria within a research unit
1.4.1 Research ethics
1.4.2 Adherence to legal and regulatory considerations
1.4.2.7 Animal care and use
1.4.2.8 Human data protection
1.4.3.4 Academia-academia: Research as collaboration
1.5.2 Roles and responsibilities of involved personnel and organization
1.5.2.1 Organigram
1.5.2.2 Management
1.5.2.4 Principal investigators and study directors
1.5.2.5 Research team
1.5.2.6 Supporting team
1.5.2.7 Quality professionals
1.5.3 Management of resources
1.5.3.1 Personnel
1.5.3.2 Facilities
1.5.3.3 Laboratory and experimental equipment used for measurement
1.5.3.4 Electronic information system
1.5.3.5 Organization-specific know how
1.5.3.6 Documentation requirement for sample and material
1.5.3.7 Retained personnel records
1.5.4.1 Independence of internal compliance assessment
1.5.4.2 Internal assessment of the organizations' quality professionals
1.5.5 Sustainability

2.1.6 Sample size and power analysis
2.1.10 Plausibility check
2.3.2 Primary analysis and evaluation of raw data
2.4 Reporting
2.4.1 Non-public reporting

3.2.1 General guidance on training
3.5.1 Animal and non-animal method and assay qualification

4.1.3 External assessment
4.2.1 Installation of solutions, actions and mitigation strategies
4.3.1.2 Deleting items

==== training resources needed to be embedded ====
https://go-eqipd.org/about-eqipd/webinar-2023/
https://go-eqipd.org/resources/eqipd-e-learning/
https://research.columbia.edu/ReaDI-program
find more more online templates

===overall to do's===
- proof reading sections: look for errors, outdated information, broken links, plausibility?
- QUALITY (evaluation) aspects (criteria, checks, checklists, how-to) should be in focus and a priority..to generate products and data with secured high quality

Talk:EQIPD Quality System

2025-05-14T14:39:18Z

Rene.Bernard: /* Dedicated working groups */

On this page we can collect ideas and errors within the entire Wiki.

===New pages needed===
* Sex differences in animal research

===Errors on the pages===

===Dedicated working groups===
====General====
* Checking incorrect items:
** Sara: Section 2
** Björn: Section 1
***Error
** René: Section 3
* Checking missing pages
**Everyone when screening though the website, with René as a contact
* Find mechanisms to keep it up-to-date
**Tbd

====Specific task forces formed already:====
* Quality culture and improvmenent
** Arnoud, Sandrine, Jonathan
* Antibodies and validation
** Harv, Marija
* Creating page on sex differences
** Christina, Vootele

====Orphans-Do not link to any other EQIPD pages:====
1.4.3.2 Quality in collaborative research - https://eqipd-toolbox.paasp.net/wiki/1.4.3.2_Quality_in_collaborative_research
EQIPD - https://eqipd-toolbox.paasp.net/wiki/EQIPD
EQIPD for Core Facilities - https://eqipd-toolbox.paasp.net/wiki/EQIPD_for_Core_Facilities
Examples of implementing a unique study ID - Examples of implementing a unique study ID
Experimental Record - https://eqipd-toolbox.paasp.net/wiki/Experimental_Record
Hauptseite - https://eqipd-toolbox.paasp.net/wiki/Hauptseite
Performance Standards - https://eqipd-toolbox.paasp.net/wiki/Performance_Standards
Spot checks - https://eqipd-toolbox.paasp.net/wiki/Spot_checks
Why quality matters - https://eqipd-toolbox.paasp.net/wiki/Why_quality_matters

====Glossary: might need extension====
SABV
FAIR (data)

====Pages needed ====
- Questionable Research Practices
- CoARA process, research evaluation
- protocols (reusable, reproducible, management)
- AI in research quality?
- feature Marten Kas as EQIPD use case examples
- data (format) community standards
- FAIR data, data repositories, DMP
- more reporting guidelines/recommendations, like RIVER (not just minimum standards)

====Pages with "to be added sections" ====
1.3.1 Transparency
1.3.3 Promotion criteria within a research unit
1.4.1 Research ethics
1.4.2 Adherence to legal and regulatory considerations
1.4.2.7 Animal care and use
1.4.2.8 Human data protection
1.4.3.4 Academia-academia: Research as collaboration
1.5.2 Roles and responsibilities of involved personnel and organization
1.5.2.1 Organigram
1.5.2.2 Management
1.5.2.4 Principal investigators and study directors
1.5.2.5 Research team
1.5.2.6 Supporting team
1.5.2.7 Quality professionals
1.5.3 Management of resources
1.5.3.1 Personnel
1.5.3.2 Facilities
1.5.3.3 Laboratory and experimental equipment used for measurement
1.5.3.4 Electronic information system
1.5.3.5 Organization-specific know how
1.5.3.6 Documentation requirement for sample and material
1.5.3.7 Retained personnel records
1.5.4.1 Independence of internal compliance assessment
1.5.4.2 Internal assessment of the organizations' quality professionals
1.5.5 Sustainability

2.1.6 Sample size and power analysis
2.1.10 Plausibility check
2.3.2 Primary analysis and evaluation of raw data
2.4 Reporting
2.4.1 Non-public reporting

3.2.1 General guidance on training
3.5.1 Animal and non-animal method and assay qualification

4.1.3 External assessment
4.2.1 Installation of solutions, actions and mitigation strategies
4.3.1.2 Deleting items

==== training resources needed to be embedded ====
https://go-eqipd.org/about-eqipd/webinar-2023/
https://go-eqipd.org/resources/eqipd-e-learning/
https://research.columbia.edu/ReaDI-program
find more more online templates

===overall to do's===
- proof reading sections: look for errors, outdated information, broken links, plausibility?
- QUALITY (evaluation) aspects (criteria, checks, checklists, how-to) should be in focus and a priority..to generate products and data with secured high quality