# Data Management Plan

Giulia Manina

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Giulia Manina. Data Management Plan. 2024. hal-05435877

HAL Id: hal-05435877

https://hal.science/hal-05435877v1

Submitted on 30 Dec 2025

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# Data Management Plan V02 – TREATABLE (ANR-21-CE15-0045)

<figure><img src="image_2.png" /></figure>

TREATABLE project focuses on tuberculosis that remains a major global-health threat, further exacerbated by the COVID-19 pandemic. Because phenotypic variation supports the tubercular pathogen in enduring drug therapy, we propose to further validate the molecular bases of this phenomenon, and to identify drugs to tackle them, in order to make the anti-tubercular therapy more effective and rapid. To achieve this goal, we established an interdisciplinary consortium, where both academia and the private sector will work in synergy, namely, the Institut Pasteur in Paris (IPP), the CERMN University of Caen (UC), and the Microfluidics Innovation Center in Paris (ELV). Together we will implement a state-of-the-art research program, at the intersection of single-cell microbiology, molecular biology, computer-aided drug design and drug discovery, advanced microfluidic culture-systems associated with live-cell microscopy. By increasing our fundamental knowledge of the single-cell biology of tuberculosis, we will generate new weapons for healthcare intervention, and contribute to a breakthrough in tuberculosis therapeutics.

The present Data Management Plan describes the data management life cycle for all datasets that will be collected, processed, or generated in this project. It is a document describing what data will be generated, how they will be processed following what methodology and standards, where they will be collected and whether and how this data will be shared and/or made open, and how datasets will be curated and preserved in the short and long term.

<table><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>1.</td><td>Information on the Data Management Plan</td><td></td></tr><tr><td>1.1.</td><td>Author(s) of the DMP</td><td>Giulia, MANINA (giulia.manina@pasteur.fr)</td></tr><tr><td>1.2.</td><td>Date of the first version of the DMP</td><td>V01, 2022/07/01</td></tr><tr><td>1.3.</td><td>Current version of the DMP and date</td><td>V02, 2024/06/28</td></tr><tr><td>1.4.</td><td>Location of storage of the DMP</td><td>Initial and intermediate versions will be stored on the Institut Pasteur data lake Owey. The final version will be published on the partners' websites.</td></tr><tr><td>2.</td><td>Information on the project</td><td></td></tr><tr><td>2.1.</td><td>Name of the project funder and funding programme</td><td>The project is funded by the Agence Nationale de la Recherche under the ANR-21-CE15-PRCE funding program.</td></tr><tr><td>2.2.</td><td>Acronym of the project</td><td>TREATABLE</td></tr><tr><td>2.3.</td><td>Title of the project</td><td>Target phenotypic variation to enhance tuberculosis therapy</td></tr><tr><td>2.4.</td><td>Project ID</td><td>ANR-21-CE15-0045</td></tr><tr><td>2.5.</td><td>Coordinator of the project</td><td>Giulia, MANINA (giulia.manina@pasteur.fr)</td></tr><tr><td>2.6.</td><td>Organization and unit of the coordinator</td><td>Institut Pasteur, Microbial Individuality and Infection</td></tr><tr><td>2.7.</td><td>Start date of the project</td><td>01/10/2021</td></tr><tr><td>2.8.</td><td>End date of the project</td><td>31/12/2025</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>3.</td><td>Overview of the data</td><td></td></tr><tr><td>3.1.</td><td>What is the purpose of the data collection/generation?</td><td>We will generate five different categories of data derived from: 1) microfabrication and fluid management; 2) medicinal chemistry, compound screening and synthesis; 3) single-cell imaging; 4) high-throughput sequencing; 5) drug efficacy studies.</td></tr><tr><td>3.2.</td><td>How many dataset(s) will you generate during this project?</td><td>≥ 50 datasets</td></tr><tr><td>3.3.</td><td>What is the nature and format of generated/collected data?</td><td>Generated data will include:<ul><li>Microfluidic system and manifold designs (CAD; GDS)</li><li>Drug design files, SAR and docking (CML; MOLFILE)</li><li>Flow cytometry (CSV; FCS)</li><li>Snapshot and time-lapse microscopy (dv; TIFF; PNG; AVI; MKV)</li><li>Text files and metadata (DOCX; TXT; PDF; XLS)</li><li>Scripts for data sorting, processing and analysis (ijm; R; py)</li><li>Spreadsheets and analysis files (CSV; XLS; PZF)</li><li>Gene and protein sequences and NGS datasets (FASTQ; FASTA; TXT; CSV; tar.gz; html)</li></ul></td></tr><tr><td>3.4.</td><td>Give the expected volume of generated data for this project</td><td>Max 5 TB<br>The largest datasets generated from microscopy activities will determine the need for more storage space, which will be progressively evaluated and upgraded according to consortium's needs.</td></tr><tr><td>3.5.</td><td>Will you also reuse existing data? If yes, specify their origin.</td><td>Most of the data will be produced within the TREATABLE project, even though a small fraction of the early data used here will come from the former ANR-17-CE11-0007-01 project PersisTB.</td></tr><tr><td>4.</td><td>Resources needed for data management</td><td></td></tr><tr><td>4.1.</td><td>What hardware resources do you need to manage your data?</td><td>External hard-drives and internal server space will be used by each partner to ensure proper back-up of data in at least two independent locations.</td></tr><tr><td>4.2.</td><td>Who is in charge of data management during the research project?</td><td>Each WP leader will be responsible for the collection, processing, analysis of data, generation of metadata, safe storage and sharing of data, relative to the scope of each WP.<br>The project coordinator will also be responsible for ensuring that essential project datasets will be regularly stored in the data lake Owey, in association with appropriate metadata.</td></tr><tr><td>4.3.</td><td>What training or support do you think is necessary to help you manage your data?</td><td>No specific training will be needed to curate and manage the data generated in this project, as each WP leader is used to handle given datasets within its team. However, if the need arises to share/access particularly complex datasets that requires specific knowledge or understanding of data structure and accessibility, dedicated meetings will be organized ad hoc, in addition to the monthly follow-up meetings.</td></tr><tr><td>4.4.</td><td>What budget do you have for managing your data? How do you intend to cover these costs?</td><td>The Institut Pasteur has a dedicated budget to provide a shared storage space in the data lake Owey and maintenance of the data of the consortium. In addition, each partner will use its own budget to ensure internal data storage and safe back up.</td></tr><tr><td>5.</td><td>Legal and ethical aspects</td><td></td></tr><tr><td>5.1.</td><td>Does your project include personal data?</td><td>No.</td></tr></tbody></table>
<table><tr><td>5.2.</td><td>Does your project include other data subject to a contractual, regulatory or legal obligation? If so, what type?</td><td>A Consortium Agreement has been signed to regulate the sharing and management of data among the partners, including confidential data, and to establish the rules related to valorization strategies and Intellectual Property Rights if a "product" is issued. Patent applications will be considered during the project, and associated data will not be disseminated before the patent application is filed.</td></tr><tr><td>6.</td><td>Data management during the project</td><td></td></tr><tr><td>6.1.</td><td>What is the storage location of your data during the project?</td><td>Relevant datasets are stored on the shared data lake Owey that is provided by the Institut Pasteur. In addition, all data are also stored in personal computers/laptops, external hard-drives, and internal servers.</td></tr><tr><td>6.2.</td><td>Do you use a file classification scheme to manage your data files? Briefly indicate how it is organized.</td><td>Yes. We use a hierarchical folder scheme from each experiment type, spanning from raw data to processed, up to final data folders. Raw datasets are never manipulated and are saved in a separate folder than further processed, analyzed and compiled data. Collection method acronyms and partner acronyms will be used, together with the hierarchical classification (raw, processed N; analyzed; final), and the date (yyyy/mm/dd).</td></tr><tr><td>6.3.</td><td>What naming conventions do you use for your data? What rules do you use for clear versioning?</td><td>We will follow appropriate naming conventions according to the FAIRSharing life science standards. We will name experiment folders and/or files with the same logic and separating different arguments (suitably acronymized) by underscores, e.g., Date_ExperimentID_PartnerID_Experimenter_Strain_Compound_Dev ice_Condition_Replica. The use of electronic notebooks can simplify the access to and quality control of experimental datasets, analyses and output files. All datasets will be associated with README files where the information needed to understand the experiment, track the samples, interpret the data, and reproduce the methodology will be collected. Template files for collections of strains, drugs, etc. and for some datasets are provided on Owey in order to homogenize accessibility. Different types of experiments are defined by abbreviations that can be found in a list, which is stored on Owey in the README_METADATA file.</td></tr><tr><td>6.4.</td><td>What measures are in place to ensure the quality of the data?</td><td>To ensure data quality, different measures will be implemented:<ul><li>Carry out independent replica of the same experiment.</li><li>Standardization of experimental conditions and data collection.</li><li>Regular review of data with the WP leader.</li><li>Periodically data quality and integrity checking.</li></ul></td></tr><tr><td>7.</td><td>Data selection and long-term preservation</td><td></td></tr><tr><td>7.1.</td><td>Are your data subject to preservation regulations? If yes, which ones?</td><td>NA</td></tr><tr><td>7.2.</td><td>Which datasets are of long-term value and should be preserved? What are the datasets to destroy?</td><td>We plan to archive drug, sequencing, and microscopy data within internal servers and/or external repositories accessible to the scientific community. This will serve both for future project development and internal benchmarking as well as for projects developed independently. Temporary data or data that did not contribute to the project conclusions will be eliminated after the results are published in peer-reviewed journals.</td></tr><tr><td>7.3.</td><td>On which platform or in which repository will the datasets be archived? Is this platform certified for long-term preservation and management?</td><td>The ZENODO repository will be preferred to ensure sustainable archiving of the final research data. Other repositories, such as PDB, PubChem, GEO, Dryad will also be used for more specific datasets.</td></tr></table>
<table><tr><td>7.4.</td><td>Specify the formats chosen for archiving.</td><td>We will use more frequently the following data formats: CAD; GDS; CML; MOLFILE; FASTQ; FASTA; TXT; CSV; html; FCS; TIFF; PNG; AVI; DOCX; TXT; PDF; XLS; R; PZF.</td></tr><tr><td>7.5.</td><td>How long will the data be preserved?</td><td>Datasets will be retained for the maximum duration allowed by the repository. For Zenodo, this corresponds to the lifetime of the host laboratory CERN, which currently has an experimental programme defined for the next 20 years at least. The data will be also kept internally until the space allocated is available, ideally up to 10 years.</td></tr><tr><td>7.6.</td><td>What is the expected volume of archived data?</td><td>About 10 TB</td></tr><tr><td>7.7.</td><td>If a long-term preservation is needed, how do you intend to cover these costs?</td><td>The costs of long-term preservation will be covered by the Institut Pasteur.</td></tr><tr><td><b>8.</b></td><td><b>Dataset #1</b></td><td><b>Microfabrication and flow control</b></td></tr><tr><td><b>8.1.</b></td><td><b>Data description</b></td><td></td></tr><tr><td>8.1.1.</td><td>ID and name of the dataset</td><td>MD; MF; MA; FC; MC</td></tr><tr><td>8.1.2.</td><td>Who is the provider or producer of the data?</td><td>This dataset is generated by ELV</td></tr><tr><td>8.1.3.</td><td>What are the nature and format of the data in this dataset?</td><td>Microfluidic system and manifold designs (CAD; GDS)<br>Images (TIFF; PNG)<br>Tabulated data in .csv, .xlxs formats<br>Metadata in .txt and .docx formats.</td></tr><tr><td>8.1.4.</td><td>Describe in more detail the data in this dataset</td><td>This dataset includes design of an upgraded microfluidic platform connected to flow controller and valves to handle multiple inlet fluids; improve dosing function; facilitate safe seeding. It also includes the design and manufacturing of a PMMA chip.</td></tr><tr><td>8.1.5.</td><td>Describe the method of data collection and/or generation</td><td>These tasks are mainly manual construction and assembly preceded by a drawing phase and followed by a testing phase. The types of data generated here will therefore be visuals and measurements of fluid rates and velocities. In addition, fluorescence microscopy data will be generated as part of the PMMA chip fabrication.</td></tr><tr><td>8.1.6.</td><td>Describe your dataset with keywords</td><td>Microfabrication; fluidics; flow rates.</td></tr><tr><td>8.1.7.</td><td>Indicate the URL or the persistent identifier to access your dataset</td><td>https://app.owey.io/dashboard/TREATABLE_ANR-21-CE15-0045</td></tr><tr><td>8.1.8.</td><td>What is the expected volume of data in this dataset?</td><td>50 GB</td></tr><tr><td><b>8.2.</b></td><td><b>Making data openly accessible</b></td><td></td></tr><tr><td>8.2.1.</td><td>Will this dataset be freely accessible?</td><td>Data will be freely available upon publication.</td></tr><tr><td>8.2.2.</td><td>Which repository did you choose to store the data of your dataset and make them accessible?</td><td>Microfabrication and associated datasets will be most likely deposited at ZENODO and GitHub repositories upon publication.</td></tr><tr><td>8.2.3.</td><td>Will this dataset be the subject of a patent application? If yes, this dataset has to be kept confidential.</td><td>If applicable, data from this dataset will be kept confidential before filing of a patent application.</td></tr><tr><td>8.2.4.</td><td>If this dataset has to be kept closed for other reasons, explain why.</td><td>NA</td></tr><tr><td>8.2.5.</td><td>Specify how access to this dataset will be provided in case of restriction</td><td>NA</td></tr></table>
<table><tr><td>8.2.6.</td><td>What software is necessary to read or access the data? Do you provide the documentation or the open source code of the software?</td><td>Microsoft office tools.</td></tr><tr><td>8.3.</td><td>Making data findable</td><td></td></tr><tr><td>8.3.1.</td><td>Is this dataset identified by a persistent and unique identifier such as DOI (Digital Object Identifiers)? If not, describe how data and this dataset are identified.</td><td>Datasets will be identified by naming convention that follows ELV's and BHL's industrial standards.</td></tr><tr><td>8.3.2.</td><td>Which metadata standards do you use? If you don't use metadata standards, outline what type(s) of metadata will be created and how.</td><td>A README.txt file will be compiled for each data folder to describe the dataset, experimental conditions, analysis carried out and the main conclusions. This file will also help to clarify the organization of the associated dataset.</td></tr><tr><td>8.3.3.</td><td>Is this dataset described by keywords in order to make it easily findable?</td><td>Yes, simple keywords will be used to easily identify the dataset type.</td></tr><tr><td>8.3.4.</td><td>Do you provide a supplementary documentation in order to describe more precisely your data?</td><td>All the necessary information to understand the datasets will be provided via the associated README.txt file.</td></tr><tr><td>8.4.</td><td>Making data interoperable</td><td></td></tr><tr><td>8.4.1.</td><td>Are the data of this dataset technically interoperable?</td><td>Yes, the aim is to produce datasets that are compatible with technologies typically present in other microfabrication labs and can therefore be integrated and used without further instructions, thanks to the associated metadata.</td></tr><tr><td>8.4.2.</td><td>If not, what methodologies will you apply to make your data interoperable?</td><td>NA</td></tr><tr><td>8.4.3.</td><td>Specify whether you will be using standard vocabulary for all data types of your dataset, to allow inter-disciplinary interoperability. If not, will you provide mapping to more commonly used ontologies?</td><td>We will use vocabulary typical of industrial standards for microfabrication and flow control, fully compatible with research standards.</td></tr><tr><td>8.5.</td><td>Increase data reuse</td><td></td></tr><tr><td>8.5.1.</td><td>At the end of the project, can the data of this dataset be reused by third parties? If reuse is restricted, explain why.</td><td>Once published, this dataset may be reused by the scientific community, with the restriction that the data may not be used for commercial purposes (a CC-BY-NC license will be associated with the dataset). However, if the datasets will be subject of a patent application, they cannot be reused without a license agreement.</td></tr><tr><td>8.5.2.</td><td>What license will be assigned to your dataset to permit the widest reuse possible?</td><td>CC-BY-NC license (https://creativecommons.org/licenses/by-nc/4.0/)</td></tr><tr><td>8.5.3.</td><td>When will the dataset be available for reuse? If applicable, specify why and for what period an embargo is needed.</td><td>Data will be available for reuse immediately after manuscript publication.</td></tr><tr><td>8.5.4.</td><td>Specify how long the dataset will remain reusable</td><td>Data will be stored during the maximum allowed by the Institut Pasteur storage allowance capacity and the repository chosen to publish the data. Data will also be stored in external hard disks at ELVESYS.</td></tr><tr><td>8.6.</td><td>Data security</td><td></td></tr><tr><td>8.6.1.</td><td>Does this dataset have to remain confidential during your project? If so, can you specify to whom it can be made accessible?</td><td>Until publication, raw data and processed dataset will be accessible only the consortium members.</td></tr></table>
<table><tr><td>8.6.2.</td><td>During the project (before storage of data in a repository), is the dataset safely stored?</td><td>During the project, data will be stored in secure internal servers. In addition, data will be also stored in personal laptops and external hard drives.</td></tr><tr><td>8.6.3.</td><td>Has the data repository chosen to store the dataset after the project implemented a security policy regarding its information system?</td><td>Data will be most likely secured in the ZENODO and GitHub repositories, unless a repository will be made available by the Institut Pasteur before the end of the project.</td></tr><tr><td>8.6.4.</td><td>What security measures are in place for data collection and exchange?</td><td>Before publication, data will be shared using the secure data lake Owey, provided to all partners by the Institut Pasteur.</td></tr><tr><td>8.</td><td>Dataset #2</td><td>In silico drug design and synthesis</td></tr><tr><td>8.1.</td><td>Data description</td><td></td></tr><tr><td>8.1.1.</td><td>ID and name of the dataset</td><td>ISD; DK; DS; DP; SAR; MST</td></tr><tr><td>8.1.2.</td><td>Who is the provider or producer of the data?</td><td>This dataset is generated by CERMN UC</td></tr><tr><td>8.1.3.</td><td>What are the nature and format of the data in this dataset?</td><td>Chemical structures and process synthesis, in silico simulations and docking analysis. The following file formats will be used: CSV; JSON; CML; GDS; SMILES; PDB; MOL; GLIDE; MOL; PNG.</td></tr><tr><td>8.1.4.</td><td>Describe in more detail the data in this dataset</td><td>This dataset will be related to the design of compounds, chemical structures, and chemical synthesis. Also docking between the compounds and target proteins will be carried out.</td></tr><tr><td>8.1.5.</td><td>Describe the method of data collection and/or generation</td><td>In silico drug design and structure activity relationship study.</td></tr><tr><td>8.1.6.</td><td>Describe your dataset with keywords</td><td>Chemical synthesis; compounds; simulation; docking; SAR.</td></tr><tr><td>8.1.7.</td><td>Indicate the URL or the persistent identifier to access your dataset</td><td>https://app.owey.io/dashboard/TREATABLE_ANR-21-CE15-0045</td></tr><tr><td>8.1.8.</td><td>What is the expected volume of data in this dataset?</td><td>100 GB</td></tr><tr><td>8.2.</td><td>Making data openly accessible</td><td></td></tr><tr><td>8.2.1.</td><td>Will this dataset be freely accessible?</td><td>Data will be freely available upon publication.</td></tr><tr><td>8.2.2.</td><td>Which repository did you chose to store the data of your dataset and make them accessible?</td><td>Chemistry datasets will be deposited at PubChem and ZENODO upon publication.</td></tr><tr><td>8.2.3.</td><td>Will this dataset be the subject of a patent application? If yes, this dataset has to be kept confidential.</td><td>If applicable, data from this dataset will be kept confidential before filing of a patent application.</td></tr><tr><td>8.2.4.</td><td>If this dataset has to be kept closed for other reasons, explain why.</td><td>NA</td></tr><tr><td>8.2.5.</td><td>Specify how access to this dataset will be provided in case of restriction</td><td>NA</td></tr><tr><td>8.2.6.</td><td>What software is necessary to read or access the data? Do you provide the documentation or the open source code of the software?</td><td>In silico drug design software but standard image files will also be provided to simplify visualization to non-experts.</td></tr><tr><td>8.3.</td><td>Making data findable</td><td></td></tr><tr><td>8.3.1.</td><td>Is this dataset identified by a persistent and unique identifier such as DOI (Digital Object Identifiers)? If not, describe how data and this dataset are identified.</td><td>Compounds will be associated with a unique identifier, such as PubChem CID, and proteins to unique PDB identifiers.</td></tr></table>
<table><tr><td>8.3.2.</td><td>Which metadata standards do you use? If you don't use metadata standards, outline what type(s) of metadata will be created and how.</td><td>A README.txt file will be compiled to describe the dataset, synthesis conditions if applicable, analysis carried out and main conclusions.</td></tr><tr><td>8.3.3.</td><td>Is this dataset described by keywords in order to make it easily findable?</td><td>Yes, this dataset will be described by maximum 5 keywords.</td></tr><tr><td>8.3.4.</td><td>Do you provide a supplementary documentation in order to describe more precisely your data?</td><td>NA</td></tr><tr><td>8.4.</td><td>Making data interoperable</td><td></td></tr><tr><td>8.4.1.</td><td>Are the data of this dataset technically interoperable?</td><td>Yes, they are standard interoperable datasets.</td></tr><tr><td>8.4.2.</td><td>If not, what methodologies will you apply to make your data interoperable?</td><td>NA</td></tr><tr><td>8.4.3.</td><td>Specify whether you will be using standard vocabulary for all data types of your dataset, to allow inter-disciplinary interoperability. If not, will you provide mapping to more commonly used ontologies?</td><td>A standard chemical nomenclature will be used.</td></tr><tr><td>8.5.</td><td>Increase data reuse</td><td></td></tr><tr><td>8.5.1.</td><td>At the end of the project, can the data of this dataset be reused by third parties? If reuse is restricted, explain why.</td><td>Once published, this dataset may be reused by the scientific community, with the restriction that the data may not be used for commercial purposes (a CC-BY-NC license will be associated with the dataset). However, if the datasets will be subject of a patent application, they cannot be reused without a license agreement.</td></tr><tr><td>8.5.2.</td><td>What license will be assigned to your dataset to permit the widest reuse possible?</td><td>CC-BY-NC license (https://creativecommons.org/licenses/by-nc/4.0/)</td></tr><tr><td>8.5.3.</td><td>When will the dataset be available for reuse? If applicable, specify why and for what period an embargo is needed.</td><td>Data will be available for reuse immediately after manuscript publication.</td></tr><tr><td>8.5.4.</td><td>Specify how long the dataset will remain reusable</td><td>Data will be stored during the maximum allowed by CERMN and Institut Pasteur storage allowance capacity and the repository chosen to publish the data. Data will also be stored in external hard disks in the laboratory.</td></tr><tr><td>8.6.</td><td>Data security</td><td></td></tr><tr><td>8.6.1.</td><td>Does this dataset have to remain confidential during your project? If so, can you specify to whom it can be made accessible?</td><td>Until publication, raw data and processed dataset will be accessible only the consortium members.</td></tr><tr><td>8.6.2.</td><td>During the project (before storage of data in a repository), is the dataset safely stored?</td><td>During the project, data will be stored in secure internal servers. In addition, data will be also stored in personal laptops and external hard drives.</td></tr><tr><td>8.6.3.</td><td>Has the data repository chosen to store the dataset after the project implemented a security policy regarding its information system?</td><td>Selected repositories are PubChem, PDB and ZENODO.</td></tr><tr><td>8.6.4.</td><td>What security measures are in place for data collection and exchange?</td><td>Before publication, data will be shared using the secure data lake Owey, provided to all partners by the Institut Pasteur.</td></tr><tr><td>8.</td><td>Dataset #3</td><td>Single-cell analyses</td></tr><tr><td>8.1.</td><td>Data description</td><td></td></tr><tr><td>8.1.1.</td><td>ID and name of the dataset</td><td>SM; TLM; FACS</td></tr><tr><td>8.1.2.</td><td>Who is the provider or producer of the data?</td><td>This dataset is generated by IPP</td></tr></table>
<table><tr><td>8.1.3.</td><td>What are the nature and format of the data in this dataset?</td><td>Time-resolved images in .dv (raw) and TIFF (processed) formats. Tabulated data in .csv, .xlxs and. pzf formats (analyzed). Metadata in .txt and .docx formats.</td></tr><tr><td>8.1.4.</td><td>Describe in more detail the data in this dataset</td><td>This dataset includes phase-contrast and fluorescence image stacks of Mycobacterium tuberculosis single-cell dynamics during optimal growth conditions and drug treatment. The dataset will include image stacks and related movies of single-cell imaging, survival and mortality rates, fractions of subpopulations.</td></tr><tr><td>8.1.5.</td><td>Describe the method of data collection and/or generation</td><td>Data are generated by wide-field microscopy and analyzed by Fiji software. Excel will be used for data sorting and processing, and GraphPad Prism will be used for statistical analysis and representation.</td></tr><tr><td>8.1.6.</td><td>Describe your dataset with keywords</td><td>Single-cell imaging; quantitative biology; subpopulation dynamics; cell fate.</td></tr><tr><td>8.1.7.</td><td>Indicate the URL or the persistent identifier to access your dataset</td><td>https://app.owey.io/dashboard/TREATABLE_ANR-21-CE15-0045</td></tr><tr><td>8.1.8.</td><td>What is the expected volume of data in this dataset?</td><td>2 TB</td></tr><tr><td><b>8.2.</b></td><td><b>Making data openly accessible</b></td><td></td></tr><tr><td>8.2.1.</td><td>Will this dataset be freely accessible?</td><td>Data will be freely available upon publication.</td></tr><tr><td>8.2.2.</td><td>Which repository did you chose to store the data of your dataset and make them accessible?</td><td>TLM datasets will be deposited at ZENODO data repository upon publication.</td></tr><tr><td>8.2.3.</td><td>Will this dataset be the subject of a patent application? If yes, this dataset has to be kept confidential.</td><td>If applicable, data from this dataset will be kept confidential before filing of a patent application.</td></tr><tr><td>8.2.4.</td><td>If this dataset has to be kept closed for other reasons, explain why.</td><td>NA</td></tr><tr><td>8.2.5.</td><td>Specify how access to this dataset will be provided in case of restriction</td><td>NA</td></tr><tr><td>8.2.6.</td><td>What software is necessary to read or access the data? Do you provide the documentation or the open source code of the software?</td><td>Standard scientific software:<br/>- Microsoft office tools<br/>- ImageJ<br/>- GraphPad</td></tr><tr><td><b>8.3.</b></td><td><b>Making data findable</b></td><td></td></tr><tr><td>8.3.1.</td><td>Is this dataset identified by a persistent and unique identifier such as DOI (Digital Object Identifiers)? If not, describe how data and this dataset are identified.</td><td>Datasets will be identified by a clear naming convention, and overall compiled into a folder associated with a DOI from the data repository.</td></tr><tr><td>8.3.2.</td><td>Which metadata standards do you use? If you don't use metadata standards, outline what type(s) of metadata will be created and how.</td><td>A README.txt file will be compiled for each data folder to describe the dataset, experimental conditions, analysis carried out and the main conclusion from each experiment folder.</td></tr><tr><td>8.3.3.</td><td>Is this dataset described by keywords in order to make it easily findable?</td><td>Yes, this dataset will be described by maximum 5 keywords.</td></tr><tr><td>8.3.4.</td><td>Do you provide a supplementary documentation in order to describe more precisely your data?</td><td>If deemed necessary, a word and an excel file will be used to further detail data processing, analysis, and dataset organization.</td></tr><tr><td><b>8.4.</b></td><td><b>Making data interoperable</b></td><td></td></tr><tr><td>8.4.1.</td><td>Are the data of this dataset technically interoperable?</td><td>Yes, the microscopy images are in TIFF format, and data analysis files are in CSV format, and therefore interoperable.</td></tr><tr><td>8.4.2.</td><td>If not, what methodologies will you apply to make your data interoperable?</td><td>NA</td></tr><tr><td>8.4.3.</td><td>Specify whether you will be using standard vocabulary for all data types of your dataset, to allow inter-disciplinary interoperability. If not,</td><td>A uniform vocabulary will always be used across different experiments and files inside the dataset and across datasets. Microscopy data will be covered with this standard vocabulary to help interoperability.</td></tr></table>
<table><tr><td></td><td>will you provide mapping to more commonly used ontologies?</td><td></td></tr><tr><td>8.5.</td><td>Increase data reuse</td><td></td></tr><tr><td>8.5.1.</td><td>At the end of the project, can the data of this dataset be reused by third parties? If reuse is restricted, explain why.</td><td>Once published, this dataset may be reused by the scientific community, with the restriction that the data may not be used for commercial purposes (a CC-BY-NC license will be associated with the dataset). However, if the datasets will be subject of a patent application, they cannot be reused without a license agreement.</td></tr><tr><td>8.5.2.</td><td>What license will be assigned to your dataset to permit the widest reuse possible?</td><td>CC-BY-NC license (https://creativecommons.org/licenses/by-nc/4.0/)</td></tr><tr><td>8.5.3.</td><td>When will the dataset be available for reuse? If applicable, specify why and for what period an embargo is needed.</td><td>Data will be available for reuse immediately after manuscript publication.</td></tr><tr><td>8.5.4.</td><td>Specify how long the dataset will remain reusable</td><td>Data will be stored during the maximum allowed by the Institut Pasteur storage allowance capacity and the repository chosen to publish the data. Data will also be stored in external hard disks in the laboratory.</td></tr><tr><td>8.6.</td><td>Data security</td><td></td></tr><tr><td>8.6.1.</td><td>Does this dataset have to remain confidential during your project? If so, can you specify to whom it can be made accessible?</td><td>Until publication, raw data and processed dataset will be accessible only the consortium members.</td></tr><tr><td>8.6.2.</td><td>During the project (before storage of data in a repository), is the dataset safely stored?</td><td>During the project, data will be stored in secure internal servers. In addition, data will be also stored in personal laptops and external hard drives.</td></tr><tr><td>8.6.3.</td><td>Has the data repository chosen to store the dataset after the project implemented a security policy regarding its information system?</td><td>Data will be most likely secured in the ZENODO repository unless a repository will be made available by the Institut Pasteur before the end of the project.</td></tr><tr><td>8.6.4.</td><td>What security measures are in place for data collection and exchange?</td><td>Before publication, data will be shared using the secure data lake Owey, provided to all partners by the Institut Pasteur.</td></tr><tr><td>8.</td><td>Dataset #4</td><td>Omics</td></tr><tr><td>8.1.</td><td>Data description</td><td></td></tr><tr><td>8.1.1.</td><td>ID and name of the dataset</td><td>WGS; RNAseq</td></tr><tr><td>8.1.2.</td><td>Who is the provider or producer of the data?</td><td>These datasets will be generated by IPP</td></tr><tr><td>8.1.3.</td><td>What are the nature and format of the data in this dataset?</td><td>Single nucleotide polymorphisms and differentially expressed genes will be overall reported as .html quality reports, detailed in TXT and CSV files, and summarized in pdf and image files.</td></tr><tr><td>8.1.4.</td><td>Describe in more detail the data in this dataset</td><td>This data will include whole genome sequencing of spontaneous M. tuberculosis mutants resistant to a given compound, associated with a variant calling analysis; and transcriptional profiling of M. tuberculosis exposed for 6 hours to a high concentration of a given compound, associated with differential and functional analyses of differentially expressed genes.</td></tr><tr><td>8.1.5.</td><td>Describe the method of data collection and/or generation</td><td>Either total genomic DNA or total RNA depleted of 16S rRNA will be extracted from M. tuberculosis cultures and standard libraries will be built for Illumina sequencing. Afterward, either genome annotation or transcriptome analyses will be carried out.</td></tr><tr><td>8.1.6.</td><td>Describe your dataset with keywords</td><td>Omics; NGS; WGS; SNPs; RNAseq; DEGs.</td></tr><tr><td>8.1.7.</td><td>Indicate the URL or the persistent identifier to access your dataset</td><td>https://app.owey.io/dashboard/TREATABLE_ANR-21-CE15-0045</td></tr></table>
<table><tr><td>8.1.8.</td><td>What is the expected volume of data in this dataset?</td><td>200 GB</td></tr><tr><td>8.2.</td><td>Making data openly accessible</td><td></td></tr><tr><td>8.2.1.</td><td>Will this dataset be freely accessible?</td><td>Data will be freely available upon publication.</td></tr><tr><td>8.2.2.</td><td>Which repository did you chose to store the data of your dataset and make them accessible?</td><td>High-throughput sequencing datasets will be deposited at Gene Expression Omnibus (GEO) repository.</td></tr><tr><td>8.2.3.</td><td>Will this dataset be the subject of a patent application? If yes, this dataset has to be kept confidential.</td><td>It is unlikely that these datasets will become subject of a patent application. However, if a new cellular target is discovered, this will be be patented as a new anti-tubercular target.</td></tr><tr><td>8.2.4.</td><td>If this dataset has to be kept closed for other reasons, explain why.</td><td>NA</td></tr><tr><td>8.2.5.</td><td>Specify how access to this dataset will be provided in case of restriction</td><td>NA</td></tr><tr><td>8.2.6.</td><td>What software is necessary to read or access the data? Do you provide the documentation or the open source code of the software?</td><td>Standard software:<br/>- Microsoft office tools<br/>- R-studio</td></tr><tr><td>8.3.</td><td>Making data findable</td><td></td></tr><tr><td>8.3.1.</td><td>Is this dataset identified by a persistent and unique identifier such as DOI (Digital Object Identifiers)? If not, describe how data and this dataset are identified.</td><td>Datasets will be identified by clear naming convention and associated with GEO GPL IDs from the data repository.</td></tr><tr><td>8.3.2.</td><td>Which metadata standards do you use? If you don't use metadata standards, outline what type(s) of metadata will be created and how.</td><td>A README.txt file will be compiled for each data folder to describe the dataset, experimental conditions, analysis carried out and the main conclusions.</td></tr><tr><td>8.3.3.</td><td>Is this dataset described by keywords in order to make it easily findable?</td><td>Yes, this dataset will be described by maximum 5 keywords.</td></tr><tr><td>8.3.4.</td><td>Do you provide a supplementary documentation in order to describe more precisely your data?</td><td>All required information will be included in the .html and README.txt files.</td></tr><tr><td>8.4.</td><td>Making data interoperable</td><td></td></tr><tr><td>8.4.1.</td><td>Are the data of this dataset technically interoperable?</td><td>Yes, these datasets will be assembled into standard data formats for the high-throughput sequencing field and are therefore interoperable.</td></tr><tr><td>8.4.2.</td><td>If not, what methodologies will you apply to make your data interoperable?</td><td>NA</td></tr><tr><td>8.4.3.</td><td>Specify whether you will be using standard vocabulary for all data types of your dataset, to allow inter-disciplinary interoperability. If not, will you provide mapping to more commonly used ontologies?</td><td>We will use uniform vocabulary to simplify data accessibility and understanding.</td></tr><tr><td>8.5.</td><td>Increase data reuse</td><td></td></tr><tr><td>8.5.1.</td><td>At the end of the project, can the data of this dataset be reused by third parties? If reuse is restricted, explain why.</td><td>Once published, this dataset may be reused by the scientific community, with the restriction that the data may not be used for commercial purposes (a CC-BY-NC license will be associated with the dataset). However, if the datasets will be subject of a patent application, they cannot be reused without a license agreement.</td></tr><tr><td>8.5.2.</td><td>What license will be assigned to your dataset to permit the widest reuse possible?</td><td>CC-BY-NC license (https://creativecommons.org/licenses/by-nc/4.0/)</td></tr><tr><td>8.5.3.</td><td>When will the dataset be available for reuse? If applicable, specify why and for what period an embargo is needed.</td><td>Data will be available for reuse immediately after manuscript publication.</td></tr></table>
<table><tr><td>8.5.4.</td><td>Specify how long the dataset will remain reusable</td><td>Data will be stored during the maximum allowed by the Institut Pasteur storage allowance capacity and the repository chosen to publish the data. Data will also be stored in external hard disks in the laboratory.</td></tr><tr><td>8.6.</td><td>Data security</td><td></td></tr><tr><td>8.6.1.</td><td>Does this dataset have to remain confidential during your project? If so, can you specify to whom it can be made accessible?</td><td>Until publication, raw data and processed dataset will be accessible only the consortium members.</td></tr><tr><td>8.6.2.</td><td>During the project (before storage of data in a repository), is the dataset safely stored?</td><td>During the project, data will be stored in secure internal servers. In addition, data will also be stored in personal laptops and external hard drives.</td></tr><tr><td>8.6.3.</td><td>Has the data repository chosen to store the dataset after the project implemented a security policy regarding its information system?</td><td>Data will be deposited at GEO.</td></tr><tr><td>8.6.4.</td><td>What security measures are in place for data collection and exchange?</td><td>Before publication, data will be shared using the secure data lake Owey, provided to all partners by the Institut Pasteur.</td></tr><tr><td>8.</td><td>Dataset #5</td><td>Microbiology and Toxicity</td></tr><tr><td>8.1.</td><td>Data description</td><td></td></tr><tr><td>8.1.1.</td><td>ID and name of the dataset</td><td>CFU; MIC; IC50: MBC; FIC; SI</td></tr><tr><td>8.1.2.</td><td>Who is the provider or producer of the data?</td><td>This dataset is generated by IPP</td></tr><tr><td>8.1.3.</td><td>What are the nature and format of the data in this dataset?</td><td>These datasets will be manually compiled on Excels and Prism files from the results of biological experiments carried out with bacteria and/or eukaryotic cell lines.</td></tr><tr><td>8.1.4.</td><td>Describe in more detail the data in this dataset</td><td>These data will include toxicity evaluation of the various drugs on M. tuberculosis and cells in order to assess the efficacy and selectivity of selected compounds.</td></tr><tr><td>8.1.5.</td><td>Describe the method of data collection and/or generation</td><td>Colorimetric and/or fluorometric assays in multi-well plates and cell survival counts.</td></tr><tr><td>8.1.6.</td><td>Describe your dataset with keywords</td><td>MIC; CFU; IC50; Selectivity index.</td></tr><tr><td>8.1.7.</td><td>Indicate the URL or the persistent identifier to access your dataset</td><td>https://app.owey.io/dashboard/TREATABLE_ANR-21-CE15-0045</td></tr><tr><td>8.1.8.</td><td>What is the expected volume of data in this dataset?</td><td>100 GB</td></tr><tr><td>8.2.</td><td>Making data openly accessible</td><td></td></tr><tr><td>8.2.1.</td><td>Will this dataset be freely accessible?</td><td>Data will be freely available upon publication.</td></tr><tr><td>8.2.2.</td><td>Which repository did you choose to store the data of your dataset and make them accessible?</td><td>These data will be most likely deposited at ZENODO data repository upon publication.</td></tr><tr><td>8.2.3.</td><td>Will this dataset be the subject of a patent application? If yes, this dataset has to be kept confidential.</td><td>If applicable, data from this dataset will be kept confidential before filing of a patent application.</td></tr><tr><td>8.2.4.</td><td>If this dataset has to be kept closed for other reasons, explain why.</td><td>NA</td></tr></table>
<table><tr><td>8.2.5.</td><td>Specify how access to this dataset will be provided in case of restriction</td><td>NA</td></tr><tr><td>8.2.6.</td><td>What software is necessary to read or access the data? Do you provide the documentation or the open source code of the software?</td><td>Standard software:<br/>- Microsoft office tools<br/>- GraphPad.</td></tr><tr><td>8.3.</td><td>Making data findable</td><td></td></tr><tr><td>8.3.1.</td><td>Is this dataset identified by a persistent and unique identifier such as DOI (Digital Object Identifiers)? If not, describe how data and this dataset are identified.</td><td>Datasets will be identified by clear naming convention, indicating data producer; data type; experimental replica and date. They will be most likely associated with Dataset #2 for deposition into ZENODO.</td></tr><tr><td>8.3.2.</td><td>Which metadata standards do you use? If you don't use metadata standards, outline what type(s) of metadata will be created and how.</td><td>A README.txt file will be compiled for each data folder to describe the dataset, experimental conditions, analysis carried out and the main conclusions.</td></tr><tr><td>8.3.3.</td><td>Is this dataset described by keywords in order to make it easily findable?</td><td>Yes, this dataset will be described with a few keywords to easily identify the associated compound and cellular target.</td></tr><tr><td>8.3.4.</td><td>Do you provide a supplementary documentation in order to describe more precisely your data?</td><td>NA</td></tr><tr><td>8.4.</td><td>Making data interoperable</td><td></td></tr><tr><td>8.4.1.</td><td>Are the data of this dataset technically interoperable?</td><td>These datasets and associated metadata will be organized into standard CSV; XLS and TXT files, thus will be interoperable.</td></tr><tr><td>8.4.2.</td><td>If not, what methodologies will you apply to make your data interoperable?</td><td>NA</td></tr><tr><td>8.4.3.</td><td>Specify whether you will be using standard vocabulary for all data types of your dataset, to allow inter-disciplinary interoperability. If not, will you provide mapping to more commonly used ontologies?</td><td>Standard pharmacodynamic vocabulary and concentrations will be used to simplify interpretation of the dataset.</td></tr><tr><td>8.5.</td><td>Increase data reuse</td><td></td></tr><tr><td>8.5.1.</td><td>At the end of the project, can the data of this dataset be reused by third parties? If reuse is restricted, explain why.</td><td>Once published, this dataset may be reused by the scientific community, with the restriction that the data may not be used for commercial purposes (a CC-BY-NC license will be associated with the dataset). However, if the datasets will be subject of a patent application, they cannot be reused without a license agreement.</td></tr><tr><td>8.5.2.</td><td>What license will be assigned to your dataset to permit the widest reuse possible?</td><td>CC-BY-NC license (https://creativecommons.org/licenses/by-nc/4.0/)</td></tr><tr><td>8.5.3.</td><td>When will the dataset be available for reuse? If applicable, specify why and for what period an embargo is needed.</td><td>Data will be available for reuse immediately after manuscript publication.</td></tr><tr><td>8.5.4.</td><td>Specify how long the dataset will remain reusable</td><td>Data will be stored during the maximum allowed by the Institut Pasteur storage allowance capacity and the repository chosen to publish the data. Data will also be stored in external hard disks in the laboratory.</td></tr><tr><td>8.6.</td><td>Data security</td><td></td></tr><tr><td>8.6.1.</td><td>Does this dataset have to remain confidential during your project? If so, can you specify to whom it can be made accessible?</td><td>Until publication, raw data and processed dataset will be accessible only the consortium members.</td></tr><tr><td>8.6.2.</td><td>During the project (before storage of data in a repository), is the dataset safely stored?</td><td>During the project, data will be stored in secure internal servers. In addition, data will also be stored in personal laptops and external hard drives.</td></tr><tr><td>8.6.3.</td><td>Has the data repository chosen to store the dataset after the project implemented a security policy regarding its information system?</td><td>Data will be most likely secured in the ZENODO repository unless a repository will be made available by the Institut Pasteur before the end of the project.</td></tr></table>
8.6.4. What security measures are in place for data collection and exchange?

Before publication, data will be shared using the secure data lake
Owey, provided to all partners by the Institut Pasteur.