9  Documentation, protocols and reporting

Documentation is metadata, that is, information about our data objects, mostly aimed at human readers and usually consisting of text and images. We recommend producing useful documentation from early on and through out the entire project. It should preferably be written in a way that can be understood by other collaborators from the start, to prevent additional efforts translating internal documentation into more publishable documents. We also recommend using discipline-specific guidelines when available, to reduce efforts and to facilitate reuse by others in the community.

9.1 Readme files

README files are text files usually written in text (.txt) or Markdown (.md) formats. They are intended for humans rather than for machine-readability. It is good practice to have at least a README file at the root directory (the topmost folder) of a project folder or code repository. They are usually concise and can be placed at multiple levels. For example, at the project-level providing information about the the project authors, institution, goals, etc. At data-level, they can provide information about the data sets and help users navigate the folder or perform actions. When accessing a code repository in Gitlab or Github, the user is presented with the content of a README file that provides the main instructions and information about the repository.

An example of the content in a README file based on https://rdmkit.elixir-europe.org/metadata_management:

PROJECT TITLE 
- Project Unique ID 
- Funding Grant Nr and period 
- Description: <provide a short description of the study/project>
- Principle Investigator:
- Data contact person 
- Link to Data management plan

ORGANIZATION <in large projects, this can also describe subfolders>
- Folder structure 
- File naming conventions (with examples) 
- File formats

9.2 Documenting data collection

Data collection is the basis for research findings. Procedures and quality measures need to be implemented and documented to have reliable data.

Key considerations

As described in https://rdmkit.elixir-europe.org/collecting :

  • Capture the provenance e.g. of samples, researchers and instruments.

  • Ensure data quality, since data can either be generated by yourself, or by another infrastructure or facility with this specialisation.

  • Check options for reusing data instead of generating new data.

  • Define the experimental design including a collection plan (e.g. repetitions, controls, randomisation) in advance.

  • Calibrate the instruments.

  • Check data protection and security issues if you work with sensitive or confidential data.

  • Define how to store the data e.g. format and volume.

  • Find suitable repository to store the data.

  • Identify suitable metadata standards.

Tools for data management and documentation during data collection

It is necessary to document what happens in the lab in an electronic format, with version control and tracked changes. If handwritten notes cannot be avoided, they should be digitized in an interoperable format as soon as possible. Suitable tools are:

  • Electronic Lab Notebooks (ELNs)
  • Electronic Data Capture (EDC) systems
  • Laboratory Information Management Systems (LIMS)

Some of these tools can be complex, expensive and have a broader scope, integrating workflows for analysis and file storage. Research groups need to find a balance between their needs and available resources (financial or in user expertise) to choose one.

Researchers should also consider:

  • Long-term sustainability of the tools (e.g., is the tool likely to be maintained in the long run?)
  • Dependencies (e.g., do researchers need a lot of technical support from external organizations? Are they accessible?)
  • Format lock-in (e.g., the tool should allow exporting data into a format that can be imported into other platforms without losing information)
  • Scalability (e.g., can it be adapted to new lab collaborators?)
  • Portability (e.g., how easy it will be to switch or transfer the data to another tool if the current runs out of maintenance?)
  • Version control (e.g., can I track changes and revert to previous versions?)

Electronic lab notebooks (ELNs)

There is a very complete section with information and resources that will help selecting a ELN suitable to your needs The Turing Way - Electronic Lab Notebooks. We highlight the following resources:

  • Article detailing considerations for choosing ELN Higgins (2003).

  • Comparison matrix of ELNs Harvard Longwood Medical Area Research Data Management Working Group (2021) (spreadsheet version here ).

  • ELN finder online tool.

Consider that also tools not specifically designed as ELNs can have similar functionalities.

Standard Operating Procedures (SOPs) and lab protocols

SOPs and lab protocols are essential to ensure that data collection has been implemented according to good practices, in a reliable manner. They can also be used to introduce new researchers in a project, facilitating collaboration. They are also key to reproducing and replicating experiments. For these reasons they constitute an important asset to share and publish. SOPs usually contain text and images. A recommended format is the standardized PDF (PDF/A-1 ISO 19005-1) which can be open with many software applications.

SOPs and protocols are often living documents that undergo relatively frequent changes and updates, thus requiring to keep track of the SOP versions.

They are necessary to comply with Good Laboratory Practices.

Some examples of publicly available collections of SOPs:

  • University of Zurich and Swiss Federal Institute of Technology, Animal Welfare and 3Rs, SOPs

  • University of Zurich, Institute of Laboratory Animal Science, SOPs

  • University of Michigan, animal care program SOPs

  • John Hopkins University, research animal resources SOPs

9.3 Experiment planning guidelines: PREPARE

The PREPARE planning guidelines by Norecopa were written to complement reporting guidelines. The PREPARE checklist, in 37 languages, is also available in a fillable Word format so that it can be used to build a comprehensive Study Plan. PREPARE is based upon experience from managing animal facilities for several decades, supervising animal research and holding courses in Lab Animal Science.

The main sections of the checklist are the following:

A. Formulation of the study

1. Literature searches

2. Legal issues

3. Ethical issues, harm-benefit assessment and humane endpoints

4. Experimental design and statistical analysis

B. Dialogue between scientists and the animal facility

5. Objectives and timescale, funding and division of labour

6. Facility evaluation

7. Education and training

8. Health risks, waste disposal and decontamination

C. Quality control of the components of the study

9. Test substances and procedures

10. Experimental animals

11. Quarantine and health monitoring

12. Housing and husbandry

13. Experimental procedures

14. Humane killing, release, reuse or rehoming

15. Necropsy

9.4 Reporting guidelines: ARRIVE

A reporting guideline is a simple, structured tool for researchers to use while writing manuscripts. It provides a minimum list of information needed to ensure a manuscript can be understood, replicated, or used for a practical purpose or in a meta-analysis. You find more information on reporting guidelines in general at the equator network.

The ARRIVE guidelines are a reporting guideline for Animal Research: Reporting of In Vivo Experiments are a checklist of recommendations for the full and transparent reporting of research involving animals (Percie Du Sert et al. 2020) . They are oriented to maximize quality and reliability of published research, therefore contributing to the 3R principles. These guidelines describe and give examples for the the following 10 essentials points (from ARRIVE Guidelines 2.0):

  1. Study design

    1a. The groups being compared, including control groups. If no control group has been used, the rationale should be stated

    1b. The experimental unit (e.g., a single animal, litter, or cage of animals).

  2. Sample size

    2a. Specify the exact number of experimental units allocated to each group, and the total number in each experiment. Also indicate the total number of animals used.

    2b. Explain how the sample size was decided. Provide details of any a priori sample size calculation, if done.

  3. Inclusion and exclusion criteria

    3a. Describe any criteria used for including or excluding animals (or experimental units) during the experiment, and data points during the analysis. Specify if these criteria were established a priori. If no criteria were set, state this explicitly.

    3b. For each experimental group, report any animals, experimental units, or data points not included in the analysis and explain why. If there were no exclusions, state so.

    3c. For each analysis, report the exact value of n in each experimental group.

  4. Randomisation

    4a. State whether randomisation was used to allocate experimental units to control and treatment groups. If done, provide the method used to generate the randomisation sequence.

    4b. Describe the strategy used to minimise potential confounders such as the order of treatments and measurements, or animal/cage location. If confounders were not controlled, state this explicitly.

  5. Blinding

    Describe who was aware of the group allocation at the different stages of the experiment (during the allocation, the conduct of the experiment, the outcome assessment, and the data analysis).

  6. Outcome measures

    6a. Clearly define all outcome measures assessed (e.g., cell death, molecular markers, or behavioural changes).

    6b. For hypothesis-testing studies, specify the primary outcome measure, i.e., the outcome measure that was used to determine the sample size.

  7. Statistical methods

    7a. Provide details of the statistical methods used for each analysis, including software used.

    7b. Describe any methods used to assess whether the data met the assumptions of the statistical approach, and what was done if the assumptions were not met.

  8. Experimental animals

    8a. Provide species-appropriate details of the animals used, including species, strain and substrain, sex, age or developmental stage, and, if relevant, weight.

    8b. Provide further relevant information on the provenance of animals, health/immune status, genetic modification status, genotype, and any previous procedures.

  9. Experimental procedures

    9a. What was done, how it was done, and what was used.

    9b. When and how often.

    9c. Where (including detail of any acclimatisation periods).

    9d. Why (provide rationale for procedures).

  10. Results

    10a. Summary/descriptive statistics for each experimental group, with a measure of variability where applicable (e.g., mean and SD, or median and range).

    10b. If applicable, the effect size with a confidence interval.