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# Daily life in the Open Biologist's second job, as a Data Curator

Livia C.T. Scorza, Tomasz Zieliński, Irina Kalita, Alessia Lepore, Meriem El Karoui, Andrew J Millar

► To cite this version:

Livia C.T. Scorza, Tomasz Zieliński, Irina Kalita, Alessia Lepore, Meriem El Karoui, et al.. Daily life in the Open Biologist's second job, as a Data Curator. Wellcome Open Research, 2024, 9, pp.523. 10.12688/wellcomeopenres.22899.1. hal-05252424

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RESEARCH ARTICLE

REVISED

# Daily life in the Open Biologist's second job, as a Data

## Curator

[version 2; peer review: 3 approved, 1 approved with reservations]

Livia C.T. Scorza<fnref n="1" />, Tomasz Zieliński<fnref n="1" />, Irina Kalita<fnref n="1" />⁻<fnref n="3" />, Alessia Lepore<fnref n="1" />,<fnref n="2" />,<fnref n="4" />, Meriem El Karoui<fnref n="1" />,<fnref n="2" />,<fnref n="5" />, Andrew J. Millar<fnref n="1" />

**V2** First published: 12 Sep 2024, **9:523**
https://doi.org/10.12688/wellcomeopenres.22899.1

Latest published: 05 Dec 2024, **9:523**
https://doi.org/10.12688/wellcomeopenres.22899.2

## Abstract

### Background

Data reusability is the driving force of the research data life cycle. However, implementing strategies to generate reusable data from the data creation to the sharing stages is still a significant challenge. Even when datasets supporting a study are publicly shared, the outputs are often incomplete and/or not reusable. The FAIR (Findable, Accessible, Interoperable, Reusable) principles were published as a general guidance to promote data reusability in research, but the practical implementation of FAIR principles in research groups is still falling behind. In biology, the lack of standard practices for a large diversity of data types, data storage and preservation issues, and the lack of familiarity among researchers are some of the main impeding factors to achieve FAIR data. Past literature describes biological curation from the perspective of data resources that aggregate data, often from publications.

### Methods

Our team works alongside data-generating, experimental researchers so our perspective aligns with publication authors rather than aggregators. We detail the processes for organizing datasets for publication, showcasing practical examples from data curation to data sharing. We also recommend strategies, tools and web resources to

## Open Peer Review

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1. Karim Fouad, University of Alberta, Edmonton, Canada

2. Yojana Gadiya<fnref n="1" />, Fraunhofer Institute for Translational Medicine and Pharmacology, Hamburg, Germany

3. Sharif Islam<fnref n="1" />, Naturalis Biodiversity Center, Leiden, The Netherlands

4. Jian Qin<fnref n="1" />, Syracuse University, Syracuse, USA

Any reports and responses or comments on the article can be found at the end of the article.

<footnote n="1">¹Centre for Engineering Biology and School of Biological Sciences, University of Edinburgh, Edinburgh, Scotland, EH9 3BF, UK</footnote>

<footnote n="2">²Institute of Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, Scotland, EH9 3JD, UK</footnote>

<footnote n="3">³Center for Synthetic Microbiology (SYNMIKRO), Max Planck Institute for Terrestrial Microbiology, Marburg, Germany</footnote>

<footnote n="4">⁴Laboratory for Optics and Biosciences, École Polytechnique, Institut Polytechnique de Paris, Palaiseau, Île-de-France, France</footnote>

<footnote n="5">⁵Laboratoire de Biologie et Pharmacologie Appliquée (LBPA), - ENS Paris-Saclay CNRS UMR 8113, Paris, Gif-sur-Yvette, France</footnote>
maximize data reusability, while maintaining research productivity.

## Conclusion

We propose a simple approach to address research data management challenges for experimentalists, designed to promote FAIR data sharing. This strategy not only simplifies data management, but also enhances data visibility, recognition and impact, ultimately benefiting the entire scientific community.

## Plain language summary

Researchers should openly share data associated with their publications unless there is a valid reason not to. Additionally, datasets have to be described with enough detail to ensure that they are reproducible and reusable by others. Since most research institutions offer limited professional support in this area, the responsibility for data sharing largely falls to researchers themselves. However, many research groups still struggle to follow data reusability principles in practice. In this work, we describe our data curation (data organization and management) efforts working directly with the researchers who create the data.

We show the steps we took to organize, standardize, and share several datasets in biological sciences, pointing out the main challenges we faced. Finally, we suggest simple and practical data management actions, as well as tools that experimentalists can integrate into their daily work, to make sharing data easier and more effective.

## Keywords

Open science, FAIR, data sharing, data curation, repositories, reproducibility, accessibility, biological data, datasets, metadata.

**Corresponding author:** Andrew J. Millar (andrew.millar@ed.ac.uk)

**Author roles:** **Scorza LCT:** Data Curation, Investigation, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing; **Zieliński T:** Conceptualization, Methodology, Project Administration, Writing – Review & Editing; **Kalita I:** Resources, Writing – Review & Editing; **Lepore A:** Resources, Writing – Review & Editing; **El Karoui M:** Funding Acquisition, Project Administration, Resources, Writing – Review & Editing; **Millar AJ:** Conceptualization, Project Administration, Supervision, Writing – Review & Editing

**Competing interests:** No competing interests were disclosed.

**Grant information:** This work was supported by Wellcome [205008; an enrichment award to LS supplementing the Wellcome Trust Investigator award to MEL]. TZ was funded by the Medical Research Council (MRC) grant mr/x009726/1. AL was founded by a Marie Skłodowska-Curie personal fellowship (grant no. 101063725-bartas).

**Copyright:** © 2024 Scorza LCT et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**How to cite this article:** Scorza LCT, Zieliński T, Kalita I et al. Daily life in the Open Biologist's second job, as a Data Curator [version 2; peer review: 3 approved, 1 approved with reservations] Wellcome Open Research 2024, 9:523 https://doi.org/10.12688/wellcomeopenres.22899.2

**First published:** 12 Sep 2024, 9:523 https://doi.org/10.12688/wellcomeopenres.22899.1
**REVISED** Amendments from Version 1

In response to reviewer's feedback, we included several new paragraphs and references, summarized in the bullet points below:

- We commented that even when journals require a "data availability statement" they do not enforce deposition of data in repositories.

- We improved the definition of metadata.

- We included more recommendations on describing, sharing and citing research software by adding two more references suggested by reviewers. (https://www.software.ac.uk/publication/how-cite-and-describe-software and https://doi.org/10.1038/s41597-022-01710-x).

- We included a paragraph in recommendations about integrating different digital objects to avoid fragmentation of the datasets.

- We included a paragraph mentioning that although our data curation work was related to data associated to publications, the recommendations that we provide about good data management practices and sharing FAIR data can be applied to any type of data, including to sharing negative data.

- We added a paragraph about the importance of securing funding for data management and curation services for larger complex projects as well as other data related resources provided by their institution such as safe data storage, archiving, computational services, etc.

- We added a sentence to highlight that although depositing data in a repository will help to comply with all elements of FAIR, we still need to have properly described datasets.

- We commented at the end about the perspectives of the researchers (data owners) we worked with, noting that our positive interaction with them reflected a growing recognition of the benefits of sharing datasets, and cultural change towards openness and FAIR data.

Any further responses from the reviewers can be found at the end of the article

**Disclaimer**

The views expressed in this article are those of the authors. Publication in Wellcome Open Research does not imply endorsement by Wellcome.

**Introduction**

Scientific progress relies not only on the production of good quality data that ultimately leads to the publication of peer reviewed articles, but also on the re-analysis of the underlying datasets, which requires their public release. Sharing datasets is the bedrock of research data transparency, reproducibility and reusability. While it discourages fraudulent data manipulation and can reduce research duplication and hence costs, it also brings other benefits for users and data owners, such as fostering collaborations, increasing impact and generating new types of research output¹,². Data sharing is not only good research practice, but it has become a requirement for many datasets funded by most funding bodies (e.g., UKRI, Wellcome, Royal Society, EMBO, NIH). Additionally, many peer-reviewed journals now have data sharing policies that require the inclusion of a "data availability

statement" where authors must add the links to repository records that hold the datasets supporting the results presented (although often there is no strict requirement on the content added in this section where data deposition is enforced, and researchers can add sentences such as "data will be provided on request").

Data openness, however, does not automatically guarantee data reusability. To address reproducibility and reusability issues, the FAIR (Findable, Accessible, Interoperable, Reusable) principles were published as a general guidance to share research data³. According to these principles, (meta)data must be findable, i.e., datasets are assigned with persistent identifiers such as a digital object identifier (DOI), indexed in a searchable resource and described with rich metadata (metadata can be defined as the records that describe and provide information about the data). Users must be able to access and download the meta(data) using standardised communications protocols such as http(s). Datasets should be also easily interpreted by computers without specialized algorithms or vendor specific software, and metadata written using standard, subject-specific vocabularies and linked to other metadata resources when possible (interoperable). Reusability is achieved when the other principles are implemented, and when meta(data) contains all relevant details for data reproducibility and a data usage license is provided to clarify the conditions for reuse.

As the scientific community acknowledged the importance of open and reusable data, several tools to facilitate data sharing and reproducibility have been developed. There is a large landscape of online repositories for sharing data, as well as tools for producing consistent metadata either using specific software or following minimum information standards established for different disciplines⁴⁻¹². However, there is still a gap between the reality of sharing practices and what is mandated by Open and FAIR principles. Among the perceived factors stopping researchers from sharing data are the time and effort to prepare data in quality suitable for sharing, as datasets need to be formatted and sufficiently described to achieve FAIR standards, as well as the lack of support and practical knowledge in effective research data curation and organization¹³⁻¹⁷. Even when researchers claimed that they shared their data, it has been observed that datasets were frequently not reusable because essential metadata information was missing, or because datasets were incomplete or no longer available¹⁸⁻²⁰. Although various resources now point researchers towards open science tools and practices, the literature lack practical guidance for preparing FAIR data from the perspective of experimental researchers, and primarily addresses the curation of biological data from the viewpoints of database managers and data aggregators²¹⁻²³. One of the missions of our group, the Biological Research Data Management (BioRDM) team, is to make data sharing a simpler task for experimental researchers in biological sciences by integrating data sharing among the routine tasks across the entire research data life cycle (Figure 1). As the driving force of the data life cycle
<figure><img src="image_12.png" /><figcaption>**Figure 1.** The stages of the research data life cycle are depicted in the blue circle, with each stage explained on the right.</figcaption></figure>

is data reuse, researchers must aim to have FAIR data ready at the sharing stage. However, datasets frequently require some degree of data curation beyond researchers' normal practice to make them FAIR. Our team works collaboratively with research groups generating data, where we help them to comply with FAIR principles by curating datasets for public sharing. In this article we show the rationale behind our data curation process, explaining how we selected, organized and shared datasets, and which tools and data management tasks were applied. We highlight the main data problems and difficulties we encountered and how we resolved them, as well as the positive elements found. Finally, we consider how to implement data management practices throughout the research data life cycle to facilitate collaboration and sharing. We show that sharing does not need to be an all or nothing approach and researchers can choose the steps and practices that most align with their data type and experimental context. It is relevant to note that we are only dealing with open data and not handling datasets with sharing restrictions.

## Data curation

Throughout the text we often showcase three datasets that were shared with the support of the BioRDM team. The datasets were shared on Zenodo, a generalist data repository funded by CERN and OpenAire (https://zenodo.org/), within the BioRDM community on that resource (https://zenodo.org/communities/biordm/):

- "SARS-CoV-2 RNA levels in Scotland's wastewater"<sup>24,25</sup>, herein presented as Scorza2022;

- "An Hfq-dependent post-transcriptional mechanism fine tunes RecB expression in *Escherichia coli*"<sup>26,27</sup>, herein presented as Kalita2023;

- "In vivo single-molecule imaging of RecB reveals efficient repair of DNA damage in *Escherichia coli*"<sup>28,29</sup> herein presented as Lepore2024.

All the datasets were released as data supporting publications. Scorza2022 was a collaborative project involving different institutions developing methods for detection of SARS-CoV-2 in wastewater, as well as data analysis strategies, as part of COVID-19 monitoring programme in Scotland<sup>25</sup>. Our work was to select the most appropriate data and other useful outputs for sharing. Because of the collaborative nature of this project, the dataset presented data and protocol integration challenges, which will be exemplified here. Kalita2023 and Lepore2024 reflect data curation alongside a research group, while preparing manuscripts to publish. Their work focuses on understanding DNA repair mechanisms in the model bacterial organism *Escherichia coli* by using data from single molecule microscopy in different strains and environmental conditions. The curation for these datasets had less data integration challenges, but still required several data organizational and annotation steps.

The main steps of our data curation methodology are described below and summarized in Figure 2.

## Selecting data and metadata records for sharing

The main investigators and data producers should collaboratively determine which data outputs from each project hold potential interest for the scientific community, thus warranting sharing. For research data accompanying a publication, all data used to produce figures and supporting conclusions should normally be shared. This can include raw data (e.g., images used to extract numerical data, raw RT-qPCR data, chromatograms, raw sequencing data, etc.) as well as processed data (e.g., normalized data, image segmentation files). The dataset must also include any original or modified methodologies to produce and analyse the data, such as protocols, hardware designs and code.

Selecting exactly which data files and information to include when sharing a dataset will depend on the type of data. For some data types data in biological sciences, there
<figure><img src="image_13.png" /><figcaption>Figure 2. The data curation steps performed by our team while preparing (FAIR) data for sharing.</figcaption></figure>

are “minimum information” guidelines available, known as “minimum information standards (MIS)”. Prior to selecting the (meta)data files, we check if MIS are available for each data type. We also search for suitable data specific repositories, as the file formats and metadata required by repositories can be used as guidance when selecting and organising (meta)data. Trustworthy repositories and MIS can be found in FAIRsharing (https://fairsharing.org/) and re3data (https://www.re3data.org/). More specifically, the Minimum Information for Biological and Biomedical Investigations (MIBBI) contains a collection of the most known standards in biology (https://fairsharing.org/3518). Funders and journals also have their own lists of recommended repositories.

**Raw and analysed data.** The main challenge for selecting data was finding the correct data files related to the manuscript figures. It was essential that we could be in contact with the authors to clarify the location and names of raw and analysed data files supporting the results being presented. To help us retrieve the correct files, we prepared a simple spreadsheet that authors filled adding data location and links to dataset description such as links to electronic laboratory notebooks entries (Figure 3 and “template_for_source_data_information.xlsx” on Zenodo³⁰). The idea is that this spreadsheet is also used when preparing figures for manuscripts or theses, as it will help to keep track of original raw data, facilitating data preservation and sharing stages.

In the Scorza2022 dataset we dealt with SARS-CoV-2 viral genetic material quantified by RT-qPCR in wastewater samples across Scotland. The samples were usually from the inflow to wastewater treatment plants. The values of viral gene copies per litre were reported for all replicates

in all locations measured, together with the mean values. Furthermore, the population-normalised values, reported as million gene copies per person per day, were shared for all replicates. Parameters used to produce normalised values were also included, such as incoming flow and ammonia levels, as well as other accompanying variables, such the dates, geographical locations of sample collection sites and population size. Additionally, all methods to produce the data was shared (see below in “Protocols”) aiming at reusability by other institutions interested in wastewater-based epidemiology monitoring.

For the Kalita2023 and Lepore2024 datasets, data from labelled mRNAs and proteins in *E. coli* cells were presented in graphs, which were derived from the analysis of fluorescence microscopy images. In this case, our approach was to select all the raw data and the methodology used to obtain the values presented. That included raw image and video data, the scripts used for processing images and obtaining the numerical values, the derived numerical values, and processed images (masks of segmented cells). The raw images/videos and segmentation masks were shared with the aim of transparency and validation so the data can be verifiable, and also for training purposes, in case one wishes to re-run the image analysis scripts and have results to compare with. The raw images can also be reused as a control dataset for those testing different algorithms for detection of fluorescent spots in cell crowded environments, as well as testing algorithms for automated cell segmentation. For the derived numerical values used in the quantitative analysis, all numbers for all replicates were included, and not only the averages. Kalita2023 also presented gene expression analysis from quantitative real-time PCR (RT-qPCR), and the same rule of sharing values for all replicates and
<table><caption>**Figure 3.** A template for a spreadsheet to be used while preparing figures for a publication with example entries. Researchers should log their manuscript figure numbers and all the background information about the data used to produce each figure, such as: experiment title, links for metadata information (e.g., laboratory notebook entries), the locations of the datasets in their network drive, folders and file names containing the data, observations and contributors. Such a table can help anyone to quickly trace back to the raw and analysed datasets used in a publication, making it easier to select files for sharing.</caption><thead><tr><th>Manuscript figure number</th><th>Experiment title</th><th>Method</th><th>Links to lab notebook and/or protocols</th><th>Number of replicates / samples</th><th>Analyzed / result files - Location</th><th>Analyzed / result files - folder / file names or IDs</th><th>Data processing method</th><th>Raw datasets - Location</th><th>Raw datasets - folder / file names or IDs</th><th>Observations</th><th>Data contributors</th></tr></thead><tbody><tr><td colspan="12">(or other data source, e.g., thesis figure,)</td></tr><tr><td></td><td>methodology used to produce raw data</td><td></td><td>(or page number and date if using paper lab notebook)</td><td></td><td>(e.g., path to network/shared drive)</td><td></td><td>add how raw data was processed to produce analysed data</td><td>e.g., path to network/share drive, database ID (e.g., Omero project number)</td><td></td><td>add details or anything unusual about the dataset</td><td>who contributed to the dataset</td></tr><tr><td>1C</td><td>stx1 mRNA in wild type cells</td><td>sm-FISH</td><td>https://www.labnot ebook.com/YMoss/ Ecoli-stxFISH</td><td>3</td><td>Y:\Lab_Data\ivyMox\stx1-WT-tracked- ss\Microscopy\FISH\stx1-WT-tracked- ss\Microscopy\FISH\stx1-WT-tracked- S02- 20230508; stx1-WT-tracked- S03- 20230730.csv;</td><td>stx1-WT-tracked- S01- 20230421.csv;</td><td>single particle tracking using Baccman (matlab)w_data</td><td>Y:\Lab_Data\Microscopy\FISH\R data\stx1-WT-S01- S02-20230508; stx1-WT-S03- 20230730</td><td>For 20180421/WT only images 1-75 Ivy Moss, Cedar were used; B strain Reed was used</td><td></td><td></td></tr><tr><td>2</td><td>Expression of stx1 gene under low temperature in E. coli O157:H7</td><td>RT-qPCR</td><td>CedarReed Notebook 1, Page 72, 2023-09-18</td><td>4</td><td>Y:\Lab_Data\CedarReed\RT-qPCR\stx1-WT\low_temp\Analyze d</td><td>stx1-temp15C- O157H7-ddCt- 2023-09-19.csv</td><td>delta delta Ct</td><td>Y:\Lab_Data\RT- O157H7-S1_S2- qPCR\stx1-WT\low_temp\Raw_d\stx1-temp15C- O157H7-S3_S4- 2023-09-18.xlsx</td><td>2 technical replicates were done for each sample</td><td>Cedar Reed</td><td></td></tr></tbody></table>

not only the averages shown in the graphs was applied. We also followed the MIS for RT-qPCR experiments, MIQE, as closely as possible³¹, sharing raw Ct values, normalized data and fold changes, as well as all the experimental details (metadata and protocols).

**Protocols.** Original and modified protocols were identified for sharing together with datasets. Scorza2022 involved multiple institutions and obtaining the correct, up to date protocols was more challenging, as the methodologies were scattered in different locations, such as institution's websites, reports or internal laboratory files. Discrepancies between different versions of the protocols were also an issue to be resolved. Communication with the authors was key to confirm and update protocols that had been modified since their first version. We transcribed the most up to date protocols on a protocol sharing platform, protocols.io³²⁻³⁴. (https://www. protocols.io/) Protocols.io allows users to share the protocol with collaborators, so the protocols could be checked and modified where necessary by the different members of the project before publishing. If updates are needed after publishing the protocol, the updated version can be published whilst keeping the previous versions and the same DOI.

**Code (analysis scripts and software).** Any code developed to produce and/or analyse a dataset, should be shared with an open-source license. In our data curation work the issues we found when sharing code were mostly related to code that had been shared without a licence. For the groups we worked with we advised on suitable open licenses to add. When reusing code from others that did not have a clear license, we first clarified with the code creators which license they would like to add to their code so we could cite and re-share their software package appropriately.

Ideally code use should not be dependent on commercial software. However, we are aware that certain commercial programs such as MATLAB (https://mathworks.com) are widely used in biological sciences workflows, such as image analysis or bioinformatics, and some MATLAB files can be used in open-source software such as Octave³⁵ (https://octave.org/). When following the best possible open-source practices, MATLAB code can still be shared publicly. Analysis scripts from Kalita2023 and Lepore2024 were shared in MATLAB format on Zenodo together with the full dataset, and the dataset was linked to their corresponding GitLab pages. Additional to the code, documentation containing scripts usage and instructions was provided in specific README or analysis workflow text files. Controlled test datasets and parameters were also included to facilitate testing, validation and reuse.

**Metadata.** Metadata is the description of the data, and it is particularly important for data reproducibility. Good metadata must contain all information about the dataset content, such as authors, context of research, information about samples, protocols, data formats, file organization, etc. In order to prepare fully descriptive metadata to share with data files, we identified information searching in different sources from the data creators, such as records in electronic laboratory notebooks (ELNs), information written in spreadsheets together with data files, metadata files generated automatically by software (e.g., microscopy image parameters saved during image acquisition), and descriptions in reports and in the manuscripts prepared for publication. Particularly, we found that having access to ELNs facilitated the accessibility and reusability of metadata, as experimental details could be easily copied and edited for clarification in our metadata files. Again, communication with data creators was key to clarify ambiguities and cases where samples were poorly described.
From these sources, we were able to create descriptive meta-data in the form of README files to accompany the datasets. We explain how we wrote README files in "Writing Metadata: README files" section.

## General dataset and metadata organization

After identifying and selecting the datasets to share, we had a good understanding of the distinct data types being presented, as well as the number of files for each data type and their categories (e.g., raw sequencing or imaging data, analysed data, data tables, etc). This allowed us to prepare an organizational structure for the files in folders, as well as organize metadata files. These steps will be described below.

**Creating a folder structure to organize files.** The aim of organizing data in a sensible folder structure is to improve data findability by interested users. The folder structure used to organize the data will depend on the type of research, the number of files and parameters, or even on the stage of the research project. Some scientific fields already have established standards for organizing data, such as the "Brain Imaging Data Structure"³⁶ or computing science projects³⁷. Following the same standard when organizing similar data types optimizes data reuse, as users will intuitively search and interact with the data when they already know the structure, and the same analysis pipelines can be used for different datasets. Research groups can also create their own structure based on their data user audience and how they expect others to reuse the data. For example, is it more likely that they will be interested in the raw datasets or in the analysis pipeline? Is it expected that they will reanalyse your data or just reuse your code for their own data?

When sharing data presented in a publication, a folder structure that follows the order of the figures presented in the paper can be a good option, as it will help readers to quickly find the related data files. For Kalita2023, we followed this structure first dividing the dataset in main data types (Growth and viability data, Fluorescence microscopy, Source code and RT-qPCR) and then organizing the data types by figure numbers of the publication (e.g., folders "Fig1-RecB_mRNAs_abundance; "Fig2-RecB_protein_abundance"). Each figure folder had its files split into raw data and quantification results. However, for Lepore2024, we chose to organize data "by genotype" instead of "by figure", because Lepore2024 used the same image datasets to produce different figures in the manuscript. A text document showing the relationship between datasets and figures was included to help readers to quickly find the correct data. Image analysis scripts were included in a separate folder.

Scorza2022 was a "data descriptor" type of publication and its figures were mainly for illustrational purposes, so the dataset was divided in data types. In this case most files were numerical data in CSV format, but the dataset also contained

processing scripts. The files were then split in a "data" folder and a source code folder named "src". The "data" folder contained the CSV files representing the values for quantification of the N1 gene of SARS-CoV-2. "src" contained R files with scripts used for data transformation, as well as scripts used to produce the interactive map and heatmap figures shown in the publication.

**Establishing naming conventions for files and folders.** In addition to having a thoughtful folder structure, folders and files need to have meaningful names so users can have a grasp of the content without opening all folders and files. Therefore, naming conventions should be created with experimental factors encoded in them.

The original datasets we curated did not have a naming convention established during the data creation and analysis stages by the authors. For example, folders and files from different replicates or samples of the same type of experiment (e.g., same treatment and organism genotype) were often named differently, sometimes using the strain name, or the genotype name or even the treatment name. This made it much more difficult to relate samples with each other and to tell the content of the folders without the help of the data creators.

When organizing such datasets for sharing, we created naming conventions that followed relevant experimental factors and sample numbers. For example, for experiments using different genotypes and techniques in Kalita2023, we created naming conventions that had "genotype_technique_sample-number" where examples of the resulting names were: "wild_type_FISH_S1", which means cells from the wild type strain visualized using fluorescence in situ hybridization (FISH), sample 1; or "delta_hfq_pQE80L_HaloTag_S3", which means cells from the delta *hfq* mutant carrying the pQE80L plasmid visualized using HaloTag labelling, sample 3. Note also that names do not contain spaces or special characters (non-alphabetical or numerical), as this can cause errors when loading files for programmatic analysis.

Once a name convention is established, it is important to use it consistently, to ensure standardization across the dataset. Even if a researcher decides to change the file names along the project, it will be much easier to change them in an automated way (e.g., using command line or a file rename app) if the same naming convention had been consistently applied, and if similar files, e.g., files from replicate samples, are organized and saved together. Taking the previous example, if files had been named "with_cipro" and "without_cipro" and one wished to change them to "on_cipro" and "off_cipro" it would be a matter of selecting the folder where names should be changed and replace all files containing "with" in the name with "on" and all files containing "without" with "off". However, if the researcher collecting data did not name and organized files consistently, the changes must be done by
hand, which can take much longer. When possible, we used a free file-renaming software "Bulk rename utility" to rename files and folders (https://www.bulkrenameutility.co.uk/).

The folder structure and naming conventions that we used were then documented in the main README files accompanying the dataset.

**Writing metadata: README files.** An effective and simple way of recording and sharing metadata, especially if sharing in a generalist data repository such as Zenodo, is to add README files together with the data files³⁸. The README files allow a detailed description of the dataset without being restricted to metadata fields in the repositories. Additionally, README files are useful for keeping metadata information saved together with the files when storing and archiving data.

Examples of information to be added to README files are project title, authors (ORCID numbers), research project details (e.g., funding codes, experimental context), sample information (species, genotype, strains, etc.), experimental design, methods and parameters used, potential data reuse, dataset file/folder overview and more. When dealing with several types of data in the same dataset, such as in Kalita2023, specific README files might be added for each data type (e.g., README-FM for fluorescence microscopy data, README-SRC for source code), apart from the general README that describes the whole dataset structure (see READMEs in 17).

Furthermore, when applicable, controlled vocabularies should be used. Controlled vocabularies are formal, accessible, and widely recognized terminology for describing data and provide a list of terms to be used in a particular field. For example, MeSH (Medical Subject Headings; https://www.nlm.nih.gov/mesh/meshhome.html), a controlled vocabulary used to index biomedical information, has a list of entry terms for *Escherichia coli* and related categories. “MeSH on demand” is a tool to that automatically detects MeSH terms in a text (e.g., in an abstract) which can help to identify subject keywords. The use of controlled vocabularies to describe the data either in README files or in metadata fields in repositories will help to reduce duplications and ambiguities amongst similar datasets and will facilitate data findability and interoperability.

The process of writing README files was not done in a single step (see Figure 2). We created the READMEs and started filling them as soon as we started organizing the datasets, and gradually updated and expanded these files throughout the data curation stages. For example, information related to data organization and data cleaning were added in later stages. Researchers could therefore initiate these files at the time of data generation.

The README files were created in text format (.txt) and added to archived data on Zenodo. On GitHub, READMEs were saved in markdown format (.md).

We provide a template for READMEs with sections we believe are relevant for biological sciences datasets in our data curation materials deposited on Zenodo³⁰. An example of a real README file can be seen in the Lepore2024 dataset²⁸.

## Data cleaning and transformation

After selecting the appropriate data and metadata, we verified if table files are “tidy”, i.e., well-structured consistently, and easy to manipulate and analyse. When organizing data tables for sharing, we followed practices from materials developed by our own “FAIR in (Bio)Practice” workshop³⁹ (https://carpentries-incubator.github.io/fair-bio-practice/), and “Tidy data”⁴⁰.

We found that even when care has been taken when data were recorded by researchers, some mistakes or bad practices would still occur. Some of the common problems that we encountered were data tables without headers, header names that contained spaces or non-alphabetical characters, use of problematic null values, or data with misspelled names. All of these were spotted and changed before sharing.

There are different tools that can be used for data cleaning, such as *OpenRefine* (https://openrefine.org/), or R (https://www.r-project.org/) and Python packages specifically designed for data cleaning tasks such as pandas and Numpy (https://pandas.pydata.org/; https:// numpy.org/). We mostly used R and Python, and in some cases data cleaning was done manually. During the process of data cleaning for Scorza2022, we prepared showcase notebooks for data cleaning with R, as R markdown files⁴¹. The aim of these notebooks is to provide practical training to other researchers and data curators using real data. We give examples of data cleaning and how to process the files to identify errors and fix them. For example, we show how to deal with several issues found in data tables, such as problematic header names, misspelled values, converting data to International Organization for Standardization (ISO) standards, etc.

Below we will go through some of the data cleaning steps that we have performed when sorting data tables for sharing:

**Adding / fixing header names.** Data in tables must contain self-explanatory header names in every column that contains data. The name convention for headers can follow the one described above for folder and file names, although headers should be shorter. Practical examples of fixing/ adding header names can be found in our data cleaning notebook “data-wrangling-01.Rmd”. A python script for adding headers to a set of CSV files is also provided in our data curation materials deposited on Zenodo³⁰.

**Removing inconsistencies.** In certain occasions when a data creator recorded new data into a spreadsheet, they either misspelled or chose a slightly different name for the same sample type or variable. This often happens when a naming convention hasn’t been established. Inconsistencies might cause problems when analysing data, especially when sorting
data by sample name, as some values from misspelled / different names will be missed. For example, when sorting datasets for Scorza2022, we were dealing with 122 different wastewater treatment centres (named "sites" in the dataset), but the original data (i.e., before the data cleaning process) had 167 unique site names, indicating that the dataset probably had some site name duplications. To help spot these duplications, we sorted the sites alphabetically and found that they were mostly misspelled names. Another option for spotting these was to count the reported measurements for SARS-CoV-2 N1 gene for each site and find those that had less than 5 measurements. As each site was monitored more than few times, the ones found with less than 5 measurements would probably be the misspelled names (see "data-wrangling-02.Rmd")

**Separating different types of values into their own individual columns.** Sometimes authors included more than one piece of information in the same cell, which can limit data analysis. For example, when dealing with RT-qPCR data, sample information, gene analysed, and replicate number were originally all grouped in the same cell. A simple way to separate these into different columns was using "Convert Text to Columns" wizard in Excel.

**Dealing with null values.** When presenting quantitative data, it is important to use clearly defined and consistent null indicators. Good choices for null values depending on software applications can be found here<sup>42</sup>. Blank cells to indicate null values is a compatible option with most applications (R, Python, SQL) and NA is also a good option and is compatible with R. To change null values that had already been assigned, we used either a script to treat a set of files at the same time, or simply the substitution function in Excel to change one or two files. We also used a python script to delete rows containing null values, which can be found in our data curation materials on Zenodo<sup>30</sup>.

**Convert data to standard formats.** Certain types of information such as dates, time and geographical locations are often presented in different formats. For example, date elements [year(Y), month (M) and day (D)], are frequently shown as either "DD/MM/YYYY", "DD-MM-YY", "MM-DD-YYYY", "DD month YYYY" and other variations. Therefore, we recommend converting all dates to ISO standard (YYYY-MM-DD or YYYYMMDD; ISO 8601:2019). The date elements can also be split into separate columns, especially when analysing data by date element. Similarly, time values and geolocations should also be converted (ISO 8601:2019 and ISO 6709:2022, respectively). We have an example of converting dates and geolocations to ISO in our notebooks "data-wrangling-02.Rmd" and "data-wrangling-05.Rmd" respectively.

**Organize Excel tables for compatibility with text formats.** Although older Excel file formats (XLS) were originally proprietary, its current specification (XLSX) is open and can be used by third-party tools, as such it is considered an open

and interoperable format. However, Excel data tables should maintain their integrity when exported to text formats (CSV, TSV, TXT). Avoid creating multiple tables in the same spreadsheet, using multiple tabs, or using formatting to convey information in Excel. These practices often cause information loss and disordering of data in their proper columns and rows when exported to text format. To prevent any compatibility issues we chose to export all Excel files to CSV.

**Data transformation - converting files to open interoperable formats.** Open file formats are non-proprietary, meaning they do not require commercial software to be accessed, and should be used whenever possible (e.g., CSV, TIFF, MP4, XML, or FASTA). Usually, proprietary software has functionalities to export data to open formats. If not available, data producers should seek other ways to convert files whenever possible, but exceptions may apply. For example, certain proprietary formats are the standard in a particular field or certain file types might be too complicated to convert to a different format and risk losing functionality (e.g., code produced using MATLAB). Therefore, we did not modify any MATLAB code, but we did export data tables that were stored in MATLAB format to CSV.

Other examples of data cleaning and organization such as aggregating data and reorganizing columns can be found in our R notebooks<sup>41</sup>. The data cleaning process such as naming headers, indicators for null values, and file formats was also recorded in the README files.

## Data and metadata sharing

**Uploading data and metadata to selected repository(ies).** Most repositories have a web-based tool for uploading files. To deposit data on Zenodo, we saved the main data type folders in separate .zip files so users can choose specific data to download. However, for Kalita2023 the image dataset was too large, and the Zenodo platform could not handle the upload of a single large file. Therefore, we split the image dataset into the next organizational level, which was the "folder by figure" to successfully upload the files. The README files were uploaded separately so users could see the content using the preview option. It is worth noting that larger files (typically > 10GB) took longer to upload (sometimes over an hour) and the upload process was prone to fail, sometimes repeatedly (at the time of this data preparation). Even when using the University's network this remained an issue, so it is important to consider upload time when planning the sharing of larger datasets.

Data repositories have both mandatory and optional metadata fields, and it is important to fill them as thoroughly as possible. Mandatory fields often include information such as title, authors, publication date and a description. However, optional fields are also very important for dataset findability. PIDs such as ORCID numbers, DOIs of related datasets and publications, and funding numbers integrate the dataset with other sources and research field categories and keywords
maximise discoverability based on browsers search. When uploading our datasets, we filled these fields as thoroughly as possible, and linked information about code from either GitHub or GitLab.

Administrators often offer an embargo period if, for example, data submitters would like to make the data available only after a manuscript is accepted. Finally, adding a license is either mandatory or automatically assigned by the repository. Licenses will be discussed below.

**Adding an open license.** The license will state what data users can do with the data and how or if it can be redistributed and repurposed for something else. To comply with open science and FAIR principles, an open license must be used, as it will attribute no or few restrictions on data reuse. We used CC-BY 4.0<sup>43</sup> for the data files we shared (apart from code). CC-BY 4.0 works well for scientific data as it states that data may be used freely, but attribution must be given to the original dataset. CC0<sup>44</sup> is a less restrictive option and does doesn't legally require users of the data to cite the source, though attribution can still be requested as norm of good research practice. For code, the Open Source Initiative (OSI) has a list of approved licenses that comply with open source definitions. Among these, the MIT licence<sup>45</sup> is our recommended option for its simplicity, as anyone can deal with the software without restrictions. Zenodo now allows users to add more than one license to same dataset, so we could maintain files that had different license requirements together in the same upload.

**Writing a dataset publication.** Datasets with high reusability potential can also be shared as a specific type of publication. For example, Scorza2022 was published as a “Data Descrip- tor” publication in *Scientific Data*<sup>25</sup>. These publications describe datasets and focus on their reusability (methods for data collection and validation). Having a dataset published in a peer-reviewed journal can be an additional, valuable output derived from a properly curated dataset.

**Integrating data resources: Creating a project website.**
A more complex and longer project might benefit from having their own webpage where datasets and metadata are inte- grated in a user friendly and interactive way, and users can navi- gate the various parts of the project within the same resource. Summary paragraphs for each section and links to external resources (repositories, protocols, publications) help with finding relevant content quickly. Additionally, researchers can add infor- mation about ongoing experiments and methodologies being developed, and not be restricted to published data, attract- ing potential new collaborations. A webpage can also be more accessible for users that are not formal scientists or that are from different research backgrounds (e.g., citizen science, collaborators in administrative areas of the project). The main issue with a project's webpage is its durability, as web hosting involves costs and, in too many cases, the pages become

inaccessible soon after their project ends. A good, free alternative is GitHub Pages (https://pages.github.com/). They can be added to any git repository and are a cost-efficient way of providing stable web resources. We used GitHub pages to create a website for the SARS-CoV-2 RNA levels in Scotland's wastewater project (https://covid-ww-scotland.github.io/) where we shared not only the published datasets and publications, but also contextual information about the project, as well as links to related published protocols, ongoing research, and links to other resources such as other wastewater-based monitoring programmes.

## Discussion and perspectives

When working with data curation projects we encountered many challenges regarding data organization, and consequently issues with finding the correct data files and metadata infor- mation. In addition to this, as we worked with data from researchers that were either away in different jobs or had moved on to work on different projects, we often had to wait for their response to clarify missing or ambiguous information in their records. These factors, added to the tasks shown in Figure 2 and described above, made the data curation process take longer than anticipated. For example, for one project, creating folders and re-organizing files (excluding the process of deciding on a folder structure) took longer than 12 hours in total, and just the process of reviewing README files for completeness prior to upload took approximately 8 hours. Despite the challenges, we could still communicate with data creators and get the clarifications we needed, and the data files were all archived in safe locations that we had access to. Therefore, we were able to complete our data curation work and share data openly and as closely to FAIR principles as possible.

Considering the challenges we encountered, we propose the following solutions to help to alleviate the potential burden of preparing data for sharing (most of which are also highlighted on Figure 4):

*Include README files during the data creation, processing and analysis stages:* descriptive README files should be added to data folders by researchers when creating data to help with comprehension of the datasets by themselves and future users. This will accelerate the creation of metadata for sharing.

*When preparing a manuscript, list the locations and names of data files:* identifying and finding the correct data files that should be shared can be a demanding task. Listing the loca- tion and names of data files while preparing the publication figures is a simple way to help one to quickly trace back to the raw and analysed datasets used. We suggest either a simple spreadsheet like the one on Figure 3 (also in data curation materials on Zenodo<sup>30</sup> “template_for_source_data_information.xlsx”) or a text file listing the figure numbers, a title for the data content, the location of the data
<figure><img src="image_14.png" /><figcaption>**Figure 4.** On the left are general recommendations for FAIR projects, with suggestions to assist with the actions taken to achieve FAIR data sharing. The actions are represented around the stages where they typically occur in the research data life cycle, but they can also happen repeatedly or in different stages (e.g., datasets can also be uploaded to a repository soon after data creation). Such actions were taken during the data curation process for data sharing (coloured circles next to each action relate to colours of data curation steps shown in Figure 2). If researchers incorporate these actions throughout their projects, getting data ready to share can become much quicker and more manageable.</figcaption></figure>

files (e.g., a path in a network drive) and the file names. Naming the main contributors helps with later queries, and potentially also with the attribution of specific data deposits.

*Create naming conventions and a sensible folder structure at the beginning of the project:* The process of re-structuring the datasets, creating naming conventions and renaming files and folders took a significant amount of time during the data curation process. Ideally, naming conventions and folder structure should be established in the initial stages of the project and documented in a data management plan as well as in the README files accompanying the data, and updated as changes are made.

*Use electronic laboratory notebooks (ELNs):* Because information from ELNs can be easily searched and reused, the use of electronic laboratory notebooks (ELNs) for record keeping was extremely helpful for us when sourcing metadata, especially when more experimental details were needed. Additionally, ELNs have a date stamp, which helped linking information from ELNs to data files that had been saved with date information, speeding up the process of gathering metadata.

*Prepare tidy tables:* Data cleaning is essential for data preservation and as it improves data quality, interoperability and reusability. Steps for data cleaning during data curation could have been minimised or even totally avoided if researchers collecting and recording data were aware of good practices

for spreadsheet use and had used them during data creation, processing and analysis stages.

*Add license to code and use version control systems:* As stated previously, one of the main issues we encountered was the lack of a clear license in shared code/software. For code to be open it should comply with the open-source definition (see https://opensource.org/osd/), where the source-code is included and shared using an open-source license. Another problem when reusing code from others, was broken links when trying to download a software package. Therefore, we advise researchers to follow good practices when describing and citing software³⁷ (https://www.software.ac.uk/publication/how-cite-and-describe-software). Additionally, researchers must use a version control system such as Git and share their code either on GitHub (https://github.com/) or GitLab (https://gitlab.com/). If possible, we recommend using Git from the beginning of the project, as it will allow collaborative work and the version control will help code developers to keep track of the changes without losing information or breaking the code. Features such as sharing ownership of repositories only with collaborators and restricting visibility might help researchers feel more confident before sharing all the code publicly. Further guidance on implementing FAIR principles specifically for research software can be found in the published work from The FAIR for Research Software Working Group⁴⁶

*Incorporate good data management practices during the research data life cycle:* If datasets are left to be organized
only at the end of the projects, complying with FAIR principles can be daunting. In cases where research groups do not have the resources to assign a data curator for a project, and the data creators are not careful when recording meta(data), sharing datasets can even become impossible, or if shared, the data are unlikely to be reusable. Figure 5 summarizes some of the important data management decisions to make before starting a FAIR project. Researchers should also be familiar with best practices in data management and data file organization, for example by attending specific training. Tips for implementing new research practices after such training were recently published¹⁷. We also prepared a list where we suggest online resources for self-paced reading or training in Table 1. Good data management practices are not only important for the sharing stage of the dataset, but also for the continuation of the projects. A common factor in academic research environments is that researchers, especially those at post-graduate and postdoctoral levels, often have short stays in laboratories, leaving research projects to be continued by new group members. Good data management will help new group members to access, find and understand previously recorded data and all the underlying experimental details. Having all-hands “data curation days” for (re)organizing, fixing and cleaning data can be a good option to keep datasets organized during a project.

**Use collaborative digital resources:** Research projects that involve several collaborators must agree on an organizational structure that is meaningful for all members to work effectively, avoiding time wasting associated with file retrieval and preventing file duplications and data loss. To assist with (meta)data co-creation in a developing project, we recommend the use of cloud storage services (OneDrive, GoogleDrive, Dropbox)

when working on documents such as data management plans, project reports, and manuscripts (Figure 4). There is also a selection of electronic laboratory notebooks that can be used jointly by different members of a project, such as Benchling (paid, but has free version https://www.benchling.com/), OSF Wiki (free; https://osf.io/pfhqc/wiki/home/) or JupyterLab for users who perform data analysis using programming in Python, R and others (free; https://jupyter.org/).

**Share protocols:** Using open online protocol platforms can help to minimize confusion with different versions of protocols and lack of detailed information. Also, platforms such as protocols.io issue a DOI for published protocols, which adds extra research outputs to a researcher's portfolio. In some cases, depending on its originality and impact, protocols can also be shared as separate peer-reviewed publications (e.g., in Nature Protocols, Lab Protocols -PLOS, Methods and Protocols -MDPI, or JoVE Journal).

**Integrate resources and digital objects:** related digital objects (datasets, protocols, software, etc. that were uploaded separately in repositories) can be integrated in a dedicated project website as discussed previously. However, it is also important to interlink all related data outputs by adding the corresponding URLs and/or DOIs to these objects in the appropriate metadata fields and/or README files, linking the data that is stored across different platforms. This laborious, manual process will hopefully be alleviated by better resource integration in future.

In Supplementary Figure 1 (see in our data curation materials on Zenodo³⁰) we show how sharing a dataset in a digital data repository such as Zenodo will help to comply with all

<table><caption>**Table 1. List of suggested training materials for Open and FAIR data.**</caption><thead><tr><td>Course / Resource name</td><td>Link to resource</td><td>Source (citation)</td></tr></thead><tbody><tr><td>FAIR in (biological) Practice</td><td>https://carpentrles-incubator.github.io/fair-bio-practice/</td><td>39</td></tr><tr><td>The Turing Way guide for reproducible research</td><td>https://book.the-turing-way.org/reproducible-research/reproducible-research/</td><td>48</td></tr><tr><td>Good enough practices in scientific computing</td><td>https://carpentrles-lab.github.io/good-enough-practices/</td><td>37,49</td></tr><tr><td>Escape from spreadsheet hell</td><td>https://instr.iastate.libguides.com/spreadsheets/home</td><td>50</td></tr><tr><td>Library Carpentry: Tidy data for Librarians</td><td>https://librarycarpentry.org/lc-spreadsheets/</td><td>51</td></tr><tr><td>Version control with Git</td><td>https://swcarpentry.github.io/git-novice/</td><td>52</td></tr><tr><td>Data cleaning with OpenRefine for Ecologists</td><td>https://datacarpentry.org/OpenRefine-ecology-lesson/</td><td>53</td></tr><tr><td>ELIXIR guidelines and best practices</td><td>https://elixir-europe.org/what-we-offer/guidelines<br>https://faircookbook.elixir-europe.org/<br>https://rdmkit.elixir-europe.org/</td><td>ELIXIR Europe (https://elixir-europe.org/)</td></tr><tr><td>Bringing data to life: Data management for the biomolecular sciences</td><td>https://www.ebi.ac.uk/training/online/courses/bringing-data-to-life-data-management/</td><td>54</td></tr></tbody></table>
elements of FAIR data (so long as data is properly described and organized following FAIR principles, as many repositories do not enforce extensive metadata or interoperable formats).

While the data curation described in this paper focuses on published datasets, the underlying principles can (and should) be extended to encompass all associated data, including so-called “negative data”. Although some journals are now dedicated to or open to publishing negative data, researchers can still share these datasets in repositories like Zenodo, adhering to FAIR principles, and publicise the shared data by means other than a journal publication.

Our recommendations aim to help experimental researchers to incorporate FAIR principles to their data in their daily work. However, it is important to highlight that many research projects present a complexity that will require professional data management advice and data curation services. These must be identified and costed when securing funding during grant application. Researchers should also explore the data management services available from their institution and cost for those when necessary (e.g., project and data management platforms, data sharing and archiving, computational services, etc.).

Sharing open and FAIR data is not only beneficial for the scientific community and public in general due to the availability of reusable datasets, but it is also a benefit for the data owners who are making their data available. For example, evidence shows that linking shared datasets to papers increases citations⁵⁵,⁵⁶. Additionally, sharing datasets as separate outputs provides the opportunity to vary authorship and give proper credit to different contributors. For instance, the first author of a deposited dataset might be its lead producer, who is not the first author of the overall publication.

Finally, following the adoption of the San Francisco Declaration on Research Assessment (DORA; https://sfdora.org/) by most research institutions and funders, the way that researchers' contributions are being evaluated is moving beyond traditional journal indicators and considering wider contributions, often using Narrative CVs. As an example, a narrative CV template proposed by UKRI (The Résumé for Research and Innovation R4RI) suggests including a researcher's contributions “to the development of new tools, methodologies”, “to the wider research and innovation community”, and “to broader research or innovation-users and audiences, and towards wider societal benefit”. The research outputs shared as datasets in repositories, online protocols, open collaborative code on GitHub, project webpages, and dataset publications will all count positively in this new evaluation system and will boost a researchers' impact helping them to stand out from the crowd.

We believe our data curation work provided new perspectives to the data creators we worked with, helping them recognize the tangible benefits of sharing research data. They were not only receptive to our data curation and sharing approach but also valued our taking on the time-intensive preparation of

FAIR data, particularly when undertaken alongside manuscript preparation. Formal co-authorship did not follow in publications but this positive, personal engagement highlights one bottom-up approach to change research culture towards openness and FAIR data sharing.

## Ethics and consent

Ethics approval and consent were not required

## Data availability

Zenodo: Data curation materials in “Daily life in the Open Biologist’s second job, as a Data Curator”. https://zenodo.org/doi/10.5281/zenodo.12734112⁵⁷.

The project contains the following data:

- The python scripts we used for data cleaning

- The template for source data information

- The README template

- Supplementary Figure 1

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

## Software availability

The scripts used for data curation are shared in our notebooks on GitHub (BioRDM data wrangling with R: Show-case notebooks for data cleaning with R, https://github.com/BioRDM/data-wrangling-with-r) and on Zenodo (Data curation materials in “Daily life in the Open Biologist’s second job, as a Data Curator”, https://zenodo.org/doi/10.5281/zenodo.12734112).

## Authors' contributions

LCTS curated the datasets and wrote the manuscript with input and editing from TZ and AJM. TZ and AJM supervised the data curation process. IK and AL produced the datasets Kalita2023 and Lepore2024 supervised by MEK. IK, AL and MEK provided input for the data curation of their datasets. All authors have read and provided input for the manuscript.

## Acknowledgements

We thank all current and past members of the BioRDM team for helping to build and organize materials, especially for the “FAIR in (biological) practice” training (https://carpentries-incubator.github.io/fair-bio-practice), a resource that we used extensively. We also thank the Centre of Expertise for Waters (CREW), the Scottish Environment Protection Agency (SEPA) and the Rural & Environment Science & Analytical Services of the Scottish Government (RESAS) for previously funding and providing data for the Scorza2022 dataset. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.
## References

1. Allen C, Mehler DMA: Open science challenges, benefits and tips in early career and beyond. *PLoS Biol*. 2019; **17**(5): e3000246.

PubMed Abstract | Publisher Full Text | Free Full Text

2. Bertram MG, Sundin J, Roche DG, et al.: Open science. *Curr Biol*. 2023; **33**(15): R792-R797.

PubMed Abstract | Publisher Full Text

3. Wilkinson MD, Dumontier M, Aalbersber IJ, et al.: The FAIR guiding principles for scientific data management and stewardship. *Sci Data*. 2016; 3: 160018.

PubMed Abstract | Publisher Full Text | Free Full Text

4. Musen MA, O'Connor MJ, Schultes E, et al.: Modeling community standards for metadata as templates makes data FAIR. *Sci Data*. 2022; 9(1): 696.

PubMed Abstract | Publisher Full Text | Free Full Text

5. Diederich K, Schmitt K, Schwedhelm P, et al.: A guide to open science practices for animal research. *PLoS Biol*. 2022; 20(9): e3001810.

PubMed Abstract | Publisher Full Text | Free Full Text

6. Taylor CF, Field D, Sansone SA, et al.: Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project. *Nat Biotechnol*. 2008; 26(8): 889-896.

PubMed Abstract | Publisher Full Text | Free Full Text

7. Wilson SL, Way GP, Bittremieux W, et al.: Sharing biological data: why, when, and how. *FEBS Lett*. 2021; **595**(7): 847-863.

PubMed Abstract | Publisher Full Text | Free Full Text

8. McEntyre J, Sarkans U, Brazma A: The BioStudies database. *Mol Syst Biol*. 2015; **11**(12): 847.

PubMed Abstract | Publisher Full Text | Free Full Text

9. Teytelman L, Stolliartchouk A, Kindler L, et al.: Protocols.io: virtual communities for protocol development and discussion. *PLoS Biol*. 2016; 14(8): e1002538.

PubMed Abstract | Publisher Full Text | Free Full Text

10. Oughtred R, Rust J, Chang C, et al.: The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions. *Protein Sci*. 2021; 30(1): 187-200.

PubMed Abstract | Publisher Full Text | Free Full Text

11. Williams E, Moore J, Li SW, et al.: Image Data Resource: a bioimage data integration and publication platform. *Nat Methods*. 2017; **14**(8): 775-781.

PubMed Abstract | Publisher Full Text | Free Full Text

12. McQuilton P, Gonzalez-Beltran A, Rocca-Serra P, et al.: BioSharing: curated and crowd-sourced metadata standards, databases and data policies in the life sciences. *Database (Oxford)*. 2016; 2016: baw075.

PubMed Abstract | Publisher Full Text | Free Full Text

13. Kattge D, Diaz S, Wirth C: Of carrots and sticks. *Nat Geosci*. 2014; **7**(11): 778-779.

Publisher Full Text

14. Gomes DGE, Pottier P, Crystal-Ornelas R, et al.: Why don't we share data and code? Perceived barriers and benefits to public archiving practices. *Proc Biol Sci*. 2022; **289**(1987): 20221113.

PubMed Abstract | Publisher Full Text | Free Full Text

15. Kim Y: Data sharing by biologists: a comparative study of genome sequence data and lab experiment data. *Libr Inf Sci Res*. 2022; **44**(1): 101139.

Publisher Full Text

16. Tenopir C, Rice NM, Allard S, et al.: Data sharing, management, use and reuse: practices and perceptions of scientists worldwide. *PLoS One*. 2020; **15**(3): e0229003.

PubMed Abstract | Publisher Full Text | Free Full Text

17. Zečević K, Houghton C, Noone C, et al.: Exploring factors that influence the practice of open science by early career health researchers: a mixed methods study [version 2; peer review: 2 approved]. *HRB Open Res*. 2021; 3: 56.

PubMed Abstract | Publisher Full Text | Free Full Text

18. Hamilton DG, Page MJ, Finch S, et al.: How often do cancer researchers make their data and code available and what factors are associated with sharing? *BMC Med*. 2022; 20(1): 438.

PubMed Abstract | Publisher Full Text | Free Full Text

19. Carbon S, Champieux R, McMurry JA, et al.: An analysis and metric of reusable data licensing practices for biomedical resources. *PLoS One*. 2019; **14**(3): e0213090.

PubMed Abstract | Publisher Full Text | Free Full Text

20. Deeb H, Wong HY, Usman T, et al.: A decade of progress: insights of open data practices in biosciences at the University of Edinburgh. *bioRxiv*. 2024; 2024.02.18.580901.

Publisher Full Text

21. Odell SG, Lazo GR, Woodhouse MR, et al.: The art of curation at a biological database: principles and application. *Curr Plant Biol*. 2017; **11**-12: 2-11.

Publisher Full Text

22. Bishop BW, Hank C: Data curation profiling of biocollections. *Proc Assoc Inf Sci Technol*. 2016; **53**(1): 1-9.

Publisher Full Text

23. Tang YA, Pichler K, Füllgrabe A, et al.: Ten quick tips for biocuration. *PLoS Comput Biol*. 2019; **15**(5): e1006906.

PubMed Abstract | Publisher Full Text | Free Full Text

24. Scorza LCT, Baby S, Zielinski T: SARS-CoV-2 RNA levels in Scotland's wastewater. *Zenodo*.

Publisher Full Text

25. Scorza LCT, Cameron GJ, Murray-Williams R, et al.: SARS-CoV-2 RNA levels in Scotland's wastewater. *Sci Data*. 2022; 9: 713.

PubMed Abstract | Publisher Full Text | Free Full Text

26. Kalita I, Iosub IA, McLaren L, et al.: An Hfq-dependent post-transcriptional mechanism fine tunes RecB expression in *Escherichia coli*. *Zenodo*. 2024. Publisher Full Text

27. Kalita I, Iosub IA, McLaren L, et al.: An Hfq-dependent post-transcriptional mechanism fine tunes RecB expression in *Escherichia coli*. *bioRxiv*. 2021. Publisher Full Text

28. Lepore A, Thédié, D, McLaren L, et al.: In vivo single-molecule imaging of RecB reveals efficient repair of DNA damage in *Escherichia coli*. *Zenodo*. 2021. Publisher Full Text

29. Lepore A, Thédié, D, McLaren L, et al.: In vivo single-molecule imaging of RecB reveals efficient repair of DNA damage in *Escherichia coli*. *bioRxiv*. 2023. Publisher Full Text

30. Scorza LCT, Zielinski T, Millar AJ: Data curation materials in 'Daily life in the Open Biologist's second job, as a Data Curator'. *Zenodo*. 2024. Publisher Full Text

31. Bustin SA, Benes V, Garson JA, et al.: The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. *Clin Chem*. 2009; **55**(4): 611-622.

PubMed Abstract | Publisher Full Text

32. Findlay D, Bolland J, Cerghizan B, et al.: RNA extraction from wastewater for detection of SARS-CoV-2. *protocols.io*. 2022. Publisher Full Text

33. Findlay D, Bolland J, Cerghizan B, et al.: RT-qPCR for detection of SARS-CoV-2 in wastewater V.1. *protocols.io*. 2022. Publisher Full Text

34. Roberts A, Fang Z, Mayer CD, et al.: Data normalisation of RT-qPCR data for detection of SARS-CoV-2 in wastewater V.1. *protocols.io*. 2022. Publisher Full Text

35. Eaton JW, Bateman D, Hauberg S, et al.: GNU Octave Version 9.1.0 manual: a high-level interactive language for numerical computations. 2024. Reference Source

36. Gorgolewski KJ, Auer T, Calhoun VD, et al.: The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. *Sci Data*. 2016; 3: 160044.

PubMed Abstract | Publisher Full Text | Free Full Text

37. Wilson G, Bryan J, Cranston K, et al.: Good enough practices in scientific computing. *PLoS Comput Biol*. 2017; **13**(6): e1005510.

PubMed Abstract | Publisher Full Text | Free Full Text

38. Zielinski T, Hodge JJJ, Millar AJ: Keep it simple: using README files to advance standardization in chronobiology. *Clocks Sleep*. 2023; 5(3): 499-506.

PubMed Abstract | Publisher Full Text | Free Full Text

39. Zielinski T, Romanowski A: FAIR in (biological) practice. *GitHub*. 2022. Reference Source

40. Wickham H: *Tidy Data*. *J Stat Softw*. 2014; **59**(10): 1-23. Publisher Full Text

41. Zielinski T, Baby SV: BioRDM data wrangling with R: showcase notebooks for data cleaning with R. *GitHub*, 2024. Reference Source

42. White EP, Baldridge E, Brym ZT, et al.: Nine simple ways to make it easier to (re)use your data. *Ideas Ecol Evol*. 2013; **6**(2).

Publisher Full Text

43. CC BY 4.0 Deed | Attribution 4.0 International Creative Commons. Reference Source

44. CC0. Creative Commons. Reference Source

45. The MIT License. Open Source Initiative. 2006. Reference Source

46. Barker M, Chue Hong NP, Katz DS, et al.: Introducing the FAIR Principles for research software. *Sci Data*. 2022; 9(1): 622.

PubMed Abstract | Publisher Full Text | Free Full Text

47. Heise V, Holman C, Lo H, et al.: Ten simple rules for implementing open and reproducible research practices after attending a training course. *PLoS Comput Biol*. 2023; **19**(1): e1010750.

PubMed Abstract | Publisher Full Text | Free Full Text

48. The Turing Way Community: The Turing Way: a handbook for reproducible, ethical and collaborative research. *Zenodo*. 2022. Publisher Full Text

49. Wallace E, Zielinski T, Wilson E, et al.: carpentries-lab/good-enough-practices: v1.0 Lesson after Carpentries Lab acceptance, as submitted to JOSE. *Zenodo*. 2024.

Publisher Full Text
50. O'Donnell M, Stacy-Bates K, Wampole K: Library guides: escape from spreadsheet hell. Reference Source

51. Lake S, Dennis T, Cope J, et al.: Library carpentry: tidy data for librarians, July 2020. Zenodo. 2020. Publisher Full Text

52. Munk M, Koziar K, Leinweber K, et al.: swcarpentry/git-novice: Software Carpentry: version control with git, June 2019. Zenodo. 2019. Publisher Full Text

53. Paul D, Macdonell C: Data carpentry: data cleaning with OpenRefine ecology lesson. 2017. Reference Source

54. Teperek M, Türkyilmaz-van der Velden Y, Birney E, et al.: Bringing data to life: data management for the biomolecular sciences. EMBL-EBI, 2021. Publisher Full Text

55. Colavizza G, Hrynaszkiewicz I, Staden I, et al.: The citation advantage of linking publications to research data. PLoS One. 2020; 15(4): e0230416. PubMed Abstract | Publisher Full Text | Free Full Text

56. Piwowar HA, Vision TJ: Data reuse and the open data citation advantage. PeerJ. 2013; 1: e175. PubMed Abstract | Publisher Full Text | Free Full Text

57. Scorza LCT, Zieliński T, Millar AJ: Data curation materials in "Daily life in the Open Biologist's second job, as a Data Curator". [Data set]. Zenodo. 2024. http://www.doi.org/10.5281/zenodo.12734112
# Open Peer Review

Current Peer Review Status: ? ✅ ✅ ✅

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## Version 2

Reviewer Report 06 December 2024

https://doi.org/10.21956/wellcomeopenres.25748.r114691

© 2024 **Islam S.** This is an open access peer review report distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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**Sharif Islam**

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Naturalis Biodiversity Center, Leiden, South Holland, The Netherlands

no new comments.

**Competing Interests:** No competing interests were disclosed.

**Reviewer Expertise:** Biodiversity Informatics, Data Architecture, Data Standards, Research Data Management, FAIR implementation.

**I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.**

Reviewer Report 06 December 2024

https://doi.org/10.21956/wellcomeopenres.25748.r114692

© 2024 **Gadiya Y.** This is an open access peer review report distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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**Yojana Gadiya**

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Fraunhofer Institute for Translational Medicine and Pharmacology, Hamburg, Germany

All comments seem to be addressed.

**Competing Interests:** No competing interests were disclosed.

**Reviewer Expertise:** Chemoinformatics, FAIRification, Data science
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Version 1

Reviewer Report 15 October 2024

https://doi.org/10.21956/wellcomeopenres.25215.r99252

© 2024 Qin J. This is an open access peer review report distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

<figure><img src="image_20.png" /></figure>

Jian Qin ID

This paper provides a detailed account of curation planning, decision-making with justifications, and execution of the plan for three datasets in biological/biomedical research. The strength of this paper is in its very practical, step-by-step documentation of how curation decisions were made and executed, which offers insightful guidance and reference for data curators. The technical details involving use of cloud services, uploading large files, and creating README documents are especially valuable as these are the foundational conditions for ensuring the FAIR-ness of data.

The paper is very easy to read and follow. The narrative follows the workflows of data curation with frequent reference to the visual steps (in figures) enforced the clarity and coherence.

One thing I had hoped to see but the paper only mentioned as a sideline in this paper is the challenges and difficulties related data culture and practice. I was curious about what perceptions and reactions the scientists had for an "outsider" data curator (non-project member) to have access to and "messing around" with their data, what attitudes they had for the various "imposed" best practices (such as data formats, cleaning, naming conventions, etc.), and how the data curation team handled such challenges and hurdles. If a formal evaluation is not feasible (I understand this paper has covered a great deal) in this paper, some informal assessment and even anecdotes for scientists' feelings toward this data curation process would have provided a richer story for this data curation case analysis. After all, technical operations are usually the easier part in data curation; it is the human and culture side of the data curation that is more difficult and challenging because changes in human nature and culture take more time and effort to happen.

**Is the work clearly and accurately presented and does it cite the current literature?**

Yes

**Is the study design appropriate and is the work technically sound?**

<footnote n="1">¹ Syracuse University, Syracuse, New York, USA</footnote>

<footnote n="2">² Syracuse University, Syracuse, New York, USA</footnote>
Yes

**Are sufficient details of methods and analysis provided to allow replication by others?**

Yes

**If applicable, is the statistical analysis and its interpretation appropriate?**

Not applicable

**Are all the source data underlying the results available to ensure full reproducibility?**

Yes

**Are the conclusions drawn adequately supported by the results?**

Yes

**Competing Interests:** No competing interests were disclosed.

**Reviewer Expertise:** Metadata, ontology, data curation/research data management, scientific collaboration networks, science of science

**I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.**

Author Response 02 Dec 2024

Livia Scorza

Thank you for your feedback. The researchers we worked with were generally receptive to our data curation approach as they understood the value of shared FAIR data. Additionally, because the release of datasets was a requirement from the journal, the data creators recognized the value of having such support during manuscript preparation, which can be very time consuming. We added a new paragraph at the end of the manuscript to comment on our collaboration experience and their perception. "We believe our data curation work provided new perspectives to the data creators we worked with, helping them recognize the tangible benefits of sharing research data. They were not only receptive to our data curation and sharing approach but also valued our taking on the time-intensive preparation of FAIR data, particularly when undertaken alongside manuscript preparation. Formal co-authorship did not follow in publications but this positive, personal engagement highlights one bottom-up approach to change research culture towards openness and FAIR data sharing"

**Competing Interests:** No competing interests were disclosed.

Reviewer Report 01 October 2024

https://doi.org/10.21956/wellcomeopenres.25215.r99247
© 2024 **Islam S.** This is an open access peer review report distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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**Sharif Islam**

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The article discusses practical steps for data curation, highlighting the challenges and solutions involved in making biological data FAIR. The paper is well-organised, with a logical flow of topics. The figures are helpful, providing clear representations of processes such as describing the data life cycle steps and the template for a spreadsheet.

The examples, particularly those on establishing naming conventions for files and folders, decision logging, README guidelines, file reorganisation, and communication with researchers, are based on actual projects, adding significant practical value. The mention of the time required for some of these tasks ("... for one project, creating folders and reorganizing files (excluding the process of deciding on a folder structure) took longer than 12 hours in total") reflects the day-to-day reality of these efforts, which are often not visible and reported in the final publication.

Although software and GitHub are briefly mentioned, the paper would benefit from integrating FAIR principles for research software (https://doi.org/10.1038/s41597-022-01710-x), particularly the idea of software citation practices (https://www.software.ac.uk/publication/how-cite-and-describe-software). As FAIR principles are increasingly extended to software, algorithms, and protocols, it is important to address how these digital objects can be managed alongside datasets.

Another useful addition to the paper would be the discussion of collaboration and communication with data stewards and IT support personnel within organisations. The paper touches on large file uploads to repositories like Zenodo, which can be problematic with files larger than 10GB. Early involvement of IT personnel in such cases could prevent issues like slow uploads or failures. Many institutions lack the necessary resources, which underscores the importance of early communication between data creators and IT departments to ensure smooth submissions.

While the paper suggests attending data curation workshops, it would be helpful to emphasise the organisational challenges of securing time, funding, and management support. Many institutions still mistakenly associate FAIR and open datasets as "cost-free", which can lead to an underestimation of the resources needed for proper curation.

The paper could also highlight the importance of raising FAIR awareness at the institutional level, specifically pointing out how researchers need support to justify the time and resources required for data curation. This ties into the discussion around the shift to Narrative CVs, which value contributions beyond publications.

**Is the work clearly and accurately presented and does it cite the current literature?**

Yes

**Is the study design appropriate and is the work technically sound?**

<footnote n="1">¹ Naturalis Biodiversity Center, Leiden, South Holland, The Netherlands</footnote>

<footnote n="2">² Naturalis Biodiversity Center, Leiden, South Holland, The Netherlands</footnote>
Yes

**Are sufficient details of methods and analysis provided to allow replication by others?**

Yes

**If applicable, is the statistical analysis and its interpretation appropriate?**

Not applicable

**Are all the source data underlying the results available to ensure full reproducibility?**

Yes

**Are the conclusions drawn adequately supported by the results?**

Yes

**Competing Interests:** No competing interests were disclosed.

**Reviewer Expertise:** Biodiversity Informatics, Data Architecture, Data Standards, Research Data Management, FAIR implementation.

**I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.**

Author Response 02 Dec 2024

Livia Scorza

Thank you for your suggestions. We have included the suggested references related to sharing software, modifying slightly our recommendations on software/ code:

> *"Therefore, we advise researchers to follow good practices when describing and citing software (https://www.software.ac.uk/publication/how-cite-and-describe-software). Additionally, researchers must use a version control system such as Git and share it on GitHub (https://github.com/) or GitLab (https://gitlab.com/). If possible, we recommend using Git from the beginning of the project, as it will allow collaborative work and the version control will help code developers to keep track of the changes without losing information or breaking the code. Features such as sharing ownership of repositories only with collaborators and restricting visibility might help researchers feel more confident before sharing all the code publicly. Further guidance on implementing FAIR principles specifically for research software can be found in the published work from The FAIR for Research Software Working Group (Barker, M., Chue Hong, N.P., Katz, D.S. et al. Introducing the FAIR Principles for research software. Sci Data 9, 622 (2022). https://doi.org/10.1038/s41597-022-01710-x)"*

We also included a paragraph in the recommendations (Discussion) commenting on the need of integrating different digital objects:

> *"Integrate resources and digital objects: related digital objects (datasets, protocols, software, etc. that were uploaded separately in repositories) can be integrated in a dedicated project website as discussed previously. However, it is also important to interlink all related data outputs by adding the corresponding URLs or DOIs to these objects in the appropriate metadata fields and/or README files, linking the data that is stored across different platforms. This laborious, manual*
process will hopefully be alleviated by better resource integration in future." We also included a paragraph commenting that researchers must cost data management and curation services in grant applications, when those are required: "*Our recommendations aim to help experimental researchers to incorporate FAIR principles to their data in their daily work. However, it is important to highlight that many research projects present a complexity that will require professional data management advice and data curation services. These must be identified and costed when securing funding during grant application. Researchers should also explore the data management services available from their institution and cost for those when necessary (e.g., project and data management platforms, data sharing and archiving, computational services, etc.)*"

**Competing Interests:** No competing interests were disclosed.

Reviewer Report 30 September 2024

https://doi.org/10.21956/wellcomeopenres.25215.r99253

© 2024 Gadiya Y. This is an open access peer review report distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Yojana Gadiya

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The article sheds light on the importance of FAIR. It is known in the research community that data should be FAIR, but researchers struggle with the applicability of these principles in real-world scenarios. Additionally, the technical jargon for biologists adds to the reluctance to "do FAIR." This article is a great approach to bridging the gap between biologists and other researchers and the FAIR principles.

A few suggestions are:

1. To change reference no. 12 to https://www.nature.com/articles/s41587-019-0080-8 since sharing is superseded by fair sharing now.

2. For the ""*data availability statement*" statement in the introduction, I would add that despite this effort, there is no strict regulation of content entered here, and statements like "data will be provided on request" have also emerged recently.

3. When describing about the three use cases discussed, I would recommend adding a few lines on the different data formats that you would be dealing with as well as different software where the data was scattered in.

**Is the work clearly and accurately presented and does it cite the current literature?**

<footnote n="1">¹ Fraunhofer Institute for Translational Medicine and Pharmacology, Hamburg, Germany</footnote>

<footnote n="2">² Fraunhofer Institute for Translational Medicine and Pharmacology, Hamburg, Germany</footnote>
Yes

**Is the study design appropriate and is the work technically sound?**

Yes

**Are sufficient details of methods and analysis provided to allow replication by others?**

Yes

**If applicable, is the statistical analysis and its interpretation appropriate?**

Yes

**Are all the source data underlying the results available to ensure full reproducibility?**

Yes

**Are the conclusions drawn adequately supported by the results?**

Yes

**Competing Interests:** No competing interests were disclosed.

**Reviewer Expertise:** Chemoinformatics, FAIRification, Data science

**I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.**

Author Response 02 Dec 2024

**Livia Scorza**

Thank you for the suggestions. The Reference 12 is within a set of references that points to standards in Biology (4-12). The FAIRsharing publication suggested is referenced in number 3. We also added the following sentence in the first paragraph about data availability statements: "Additionally, many peer-reviewed journals now have data sharing policies that require the inclusion of a "data availability statement" where authors must add the links to repository records that hold the datasets supporting the results presented (although often there is no strict requirement on the content added in this section where data deposition is enforced, and researchers can add sentences such as "data will be provided on request").

**Competing Interests:** No competing interests were disclosed.

Reviewer Report 18 September 2024

https://doi.org/10.21956/wellcomeopenres.25215.r99244

© 2024 Fouad K. This is an open access peer review report distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
<figure><img src="image_25.png" /></figure>

Karim Fouad

¹ Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Alberta, T6G 2G4, Canada

² Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Alberta, T6G 2G4, Canada

This is a very timely article focusing on FAIR data sharing and related challenges. The current cultural change in publishing research findings is a torturous path, with many researchers feeling the burden of new mandates. This manuscript provides important guidance and a good overview of the challenge. There is only one major shortcoming as this manuscript seems to focus on data associated with published papers. These papers are generally positive and promote a well-recognized publication bias. This could be counteracted by publishing all research data, an important part of the NIH data management and sharing mandate (which by the way should be mentioned in this manuscript). As it reads the authors do not mention the publication of data that are frequently referred to as negative. It seems that this is a missed opportunity.

**Minor comments:**

* it would be useful if the authors discuss the use of Common Data Elements in the section regarding "header naming convention" or "controlled vocabularies". CDEs are associated with and promoted by the NIH also in preclinical research, and might become globally accepted.

* Considering that researchers struggle with data terminology, a better definition of meta data would be useful, including the introduction of the term data dictionaries

* many general repositories consider themselves as FAIR, however it is difficult to define and control for fairness, especially the reusability and thus many do underdeliver in that regard. This challenge could be discussed

**Is the work clearly and accurately presented and does it cite the current literature?**

Partly

**Is the study design appropriate and is the work technically sound?**

Yes

**Are sufficient details of methods and analysis provided to allow replication by others?**

Yes

**If applicable, is the statistical analysis and its interpretation appropriate?**

Not applicable

**Are all the source data underlying the results available to ensure full reproducibility?**

No source data required

**Are the conclusions drawn adequately supported by the results?**

Yes

**Competing Interests:** No competing interests were disclosed.
**Reviewer Expertise:** As a preclinical researcher I have started a FAIR share data repository, we provide curation and regularly publish advice on data management: e.g.: doi: 10.1016/j.expneurol.2024.114815

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Author Response 02 Dec 2024

Livia Scorza

Thank you for the suggestions and the feedback. We have added NIH in the funding bodies requirement examples: "Data sharing is not only good research practice, but it has become a requirement for many datasets funded by most funding bodies (e.g., UKRI, Wellcome, Royal Society, EMBO, NIH)."

**Unpublished results/ Negative data:** In the paper we focused on research data associated with publications because our collaboration projects were specifically on preparing data for that purpose. However, we agree we could have commented that the recommendations proposed in the discussion are applicable to all data, including negative data, as part of good research practice. Therefore, we added the following paragraph to the discussion: "While the data curation described in this paper focuses on published datasets, the underlying principles can (and should) be extended to encompass all associated data, including so-called "negative data"—data that does not support the initial hypothesis. Although some journals are now dedicated to or open to publishing negative data, researchers can still share these datasets in repositories like Zenodo, adhering to FAIR principles, and publicise the shared data by means other than a journal publication"

**CDEs and data dictionaries:** the paper highlights our experience curating data in biology and focuses on recommendations based on problems we encountered along the way. Although we recognize the value of CDEs and data dictionaries, we do not mention them as they were not part of our practical work (for example, CDEs are more often used in clinical research and environmental science, rather than the data context shown in the paper).

**Metadata:** we added a couple of sentences to improve its definition in the text: In the Introduction: "(...i.e., datasets are assigned with persistent identifiers such as a digital object identifier (DOI), indexed in a searchable resource and described with rich metadata. (metadata can be defined as the records that describe and provide information about the data). In the Methodology "Metadata. Metadata is the description of the data, and it is particularly important for data reproducibility. Good metadata must provide all information about the dataset content, such as authors, context of research, samples, protocols, data formats and file organization, etc". F

**AIR and repositories:** Along the paper we make clear that only depositing research data in repositories does not make data FAIR. Data needs to be properly described, transformed and organized to qualify as suitable for FAIR sharing. To highlight that in the discussion we added the following: "In Supplementary Figure 1 (see in our data curation materials on Zenodo) we show how sharing a dataset in a digital data repository such as Zenodo will help to comply with all elements of FAIR data (so long as data is properly described and organized following FAIR principles, as many repositories do not enforce extensive metadata or
interoperable formats)".

**Competing Interests:** No competing interests were disclosed.