Alexandre SMIRNOV

**The ultraconserved intersubunit ribosome assembly factor**
**(InsuRAF)**

[ANR-19-CE11-0013](https://www.cdc.fr/en/annuaire-de-researche/annuaire-de-researche-de-la-cie/annuaire-de-researche-de-la-cie-19-2011-2013/19-2011-2013-anr-19-ce11-0013)

ANR - JCJC 2019

**Data Management Plan**

Version 3

December 26, 2023

<figure><img src="image_1.png" /></figure>
# 0. What changed with respect to the Version 2 of this DMP

* This DMP version is explicitly licensed under the CC BY 4.0 Attribution International license, enabling its free and widest re-use by the community.

* Table 1 is updated to reflect the actual types and volumes of data collected, produced or re-used in this project.

* Table 2 is updated to include a new column of the CER file: "Status".

* Table 3 is expanded to include new kinds of methods and their descriptors.

* Table 4, Plasmids now contains an additional series of columns "Sanger file 1, 2 etc" to directly link plasmid entries to their Sanger sequencing data.

* Table 4, Bacterial strains now has a more detailed Description section.

* Table 5 slightly modifies the structure of the lab protocol by proposing to directly reference specific experiment IDs that describe cases of successful or unsuccessful implementation of the protocol.

* Since certain datasets have been produced in several geographical locations, Table 6 now includes geographical coverage in the "Coverage" section of the standard metadata file.

* Section 2 now implements the principle of separation between primary data and analysis outputs.

* Section 2a now includes a description of EnzymeML metadata supporting enzymatic assay datasets.

* The list of persons authorised to curate and modify research resources and documentation (section 2a) has been updated.

* Section 2b incorporates further quality control measures related to equipment and certain field-specific data types.

* Image Data Resource (IDR) and Bioimage Archive (BIA) are identified as trusted repositories for microscopy data published in the frame of the InsuRAF project. REMBI is identified as an appropriate metadata format for these data. Zenodo and Research Data Gouv are identified as trusted generalist repositories for other kinds of data. The corresponding sections have been updated accordingly. By contrast, no important structural data could be generated in this project, wwPDB policies are reduced only to cover what concerns the re-use of deposited datasets.

* Section 3b now includes security measures concerning lab books.
* Section 4b is updated in part concerning the authorship and intellectual property rights on publications resulting from the InsuRAF project and database-related legislation.

* Section 5b is amended in the part concerning the embargo period of open-access publications to implement the Plan S Rights Retention Strategy.

* Links are updated throughout the document.

* Minor editorial fixes are performed.
# 1. Data description & collection or re-use of existing data

## 1a. What data (for example the kind, formats, & volumes), will be collected or produced?

Outlined below are the data types, formats and volumes collected or expected to be collected, produced or re-used, as drafted in the original proposal and further updated to keep up with the progress of the research (Table 1). In the majority of cases, the type of the instrument employed to collect data dictates the proprietary software and the format in which the data are first registered and undergo primary analysis. Similarly, databases and on-line analysis tools impose built-in search engines and retrieval options. In addition to these specific formats, storage, downstream manipulations, analysis, aggregation, and visualisation of the data will be performed in open or standard formats (TIF for most images, JPEG or PNG for gels and structure snapshots, AI and PDF for assembled images, DOCX for text documents, TXT for sequences and alignments, XLSX for spreadsheets, PDB for structures), wherever possible, as recommended in *Turning FAIR into reality* and *Guide Pratique de la publication en ligne et de la réutilisation des données publiques* ("Open data") elaborated by CNIL and CADA.

<table><caption>Table I. Data types, methodology, software, primary formats & estimated volumes</caption><thead><tr><td>Data type</td><td>Methodology, instrument</td><td>Software, database or repository</td><td>Primary formats</td><td>Estimated volume</td></tr></thead><tbody><tr><td>Deposited protein sequence, conservation, phylogenetic distribution, domain architecture (text, image, code)</td><td>Data retrieval, analysis & visualisation</td><td>InterPro, UniProt, PFAM, NCBI BLAST, NCBI Assembly, Python</td><td>FAS, TXT, JPEG, PNG, CSV</td><td>1 GB</td></tr><tr><td>Deposited protein structure (interactive structure)</td><td>Data retrieval, analysis & visualisation</td><td>RCSB PDB, PyMol, JMol, NGL Viewer</td><td>PDB, mmCIF</td><td>1 GB</td></tr><tr><td>Deposited gene or genome sequence (text)</td><td>Data retrieval & analysis</td><td>RefSeq, NCBI Nucleotide, NCBI Genome</td><td>FAS, TXT</td><td>1 MB</td></tr><tr><td>Deposited MS data (text, image, spreadsheet)</td><td>Data retrieval, analysis & visualisation</td><td>neXtProt, PeptideAtlas, ProteomeXchange Consortium, Scaffold</td><td>mzML, TXT, TSV, XLSX, JPEG, PDF</td><td>10 MB</td></tr></tbody></table>
<table><tr><td>Data from publications (gene, RNA & protein properties, interactomics, gene expression, experimental outcomes etc) (all types)</td><td>Data retrieval, analysis, & referencing</td><td>PubMed, BiblioVie, HAL, univOAK, Zotero</td><td>PDF, DOCX, XLSX, CSV, MP4, ZIP, Zotero RDF</td><td>2 GB</td></tr><tr><td>Gene properties (promoter, TSS, ORF, UTRs, SD or Kozak sequence, CRISPR sites) (text, image)</td><td>Data retrieval & analysis, prediction</td><td>RefSeq, ExPASy, CHOPCHOP, ApE, BE-Hive, CRISPOR</td><td>DOCX, TXT, JPEG, GB, CSV, XLS</td><td>1 MB</td></tr><tr><td>Predicted RNA properties (secondary structure, stability) (text, image)</td><td>Prediction</td><td>RNAfold, RNAcofold, mfold</td><td>DOCX, TXT, JPEG, PNG, PDF</td><td>10 MB</td></tr><tr><td>Predicted protein properties (MW, pI, localisation, processing & modification sites, interaction sites) (text, spreadsheet)</td><td>Prediction</td><td>ExPASy, MitoFates, TargetP, Mitoprot, RaptorX</td><td>DOCX, TXT, XLSX, JPEG</td><td>10 MB</td></tr><tr><td>Predicted protein structure (interactive structure)</td><td>Prediction</td><td>RaptorX, PyMol</td><td>PDB</td><td>100 MB</td></tr><tr><td>Sequence alignment</td><td>Analysis</td><td>NCBI BLAST</td><td>FAS, TXT</td><td>10 MB</td></tr><tr><td>Multiple sequence alignment</td><td>Analysis & visualisation</td><td>COBALT, WebLogo</td><td>FAS, TXT, DOCX, PDF, Newick, JPEG</td><td>200 MB</td></tr><tr><td>Nucleic acid expression, purification, modification or interaction pattern (image, spreadsheet)</td><td>Gel electrophoresis, ethidium bromide staining (G-Box), northern blotting (radioautography, Typhoon), immunoprecipitation, cleavage assay, EMSA</td><td>GeneTools, ImageQuant TL</td><td>SGD, GEL, TIFF, XLSX</td><td>20 GB</td></tr></table>
<table><tr><td>Protein expression, purification or modification pattern (image, spreadsheet)</td><td>Gel electrophoresis, Coomassie or silver staining (Epson perfection V700 Photo), western blotting (chemiluminescence, ChemiDoc Touch), immunoprecipitation, affinity chromatography, modification assay (radioautography, Typhoon)</td><td>Epson Scan, ImageLab</td><td>JPEG, TIFF, SCN, GEL, XLSX</td><td>20 GB</td></tr><tr><td>DNA sequence (text, image)</td><td>Sanger sequencing</td><td>Chroma</td><td>SEQ, AB1, FAS</td><td>400 MB</td></tr><tr><td>Protein concentration (text, spreadsheet)</td><td>Bradford assay (Photometer), UV absorbance (Nanodrop)</td><td>Microsoft Excel, ND-1000</td><td>XLSX, NJD</td><td>10 MB</td></tr><tr><td>Nucleic acid concentration (text, spreadsheet)</td><td>UV absorbance (Nanodrop)</td><td>ND-1000</td><td>NJD</td><td>4 MB</td></tr><tr><td>SAXS data & structure model (image, text, interactive structure)</td><td>SAXS (SWING, SOLEIL), modelling</td><td>FOXTROT, ATSAS, DADIMODO, SASREF</td><td>NXS, DAT, PDB</td><td>33 GB</td></tr><tr><td>Sedimentation properties (image, spreadsheet)</td><td>Velocity sedimentation, UV absorbance (Nanodrop), gel electrophoresis, ethidium bromide staining (Typhoon), northern & western blotting (Typhoon, ChemiDoc Touch), S20,w calculation</td><td>ND-1000, ImageQuant TL, ImageLab, Microsoft Excel</td><td>NJD, GEL, SCN, XLSX</td><td>5 GB</td></tr><tr><td>Protein MS data (identification, quantification, modifications) (image, spreadsheet)</td><td>Immunoprecipitation, crosslinking, affinity chromatography, velocity sedimentation, LC-MS/MS (TripleTOF 5600, NanoLC-Ultra-2D-Plus, Q-Exactive Plus, EASY-nanoLC-1000), ETD (Orbitrap Elite)</td><td>Mascot, Proline, Scaffold, Microsoft Excel</td><td>RAW, MGF, MZID, mzML, MSF, TIFF, PDF, XLSX</td><td>50 GB</td></tr></table>
<table><tr><td>Protein & RNA localisation (image, spreadsheet)</td><td>Immunofluorescence, smFISH, confocal microscopy (LSM700, LSM780)</td><td>ImageJ, MosaicSuit, Microsoft Excel</td><td>ZVI, TIFF, XLSX</td><td>7 GB</td></tr><tr><td>Protein-protein FRET (image, spreadsheet)</td><td>FLIM-FRET</td><td>FLIM Nikon TE2000, Microsoft Excel</td><td>FLI, BMP, XLSX</td><td>3 GB</td></tr><tr><td>Cell & mitochondrial morphology & physiology (image, spreadsheet)</td><td>Cell staining, immunofluorescence, confocal microscopy</td><td>ImageJ, Microsoft Excel</td><td>ZVI, TIFF, XLSX</td><td>5 GB</td></tr><tr><td>Oxygen consumption data (image, spreadsheet)</td><td>Respirometry (Seahorse XFe96 Analyser)</td><td>Wave Desktop, Controller 2.6, Microsoft Excel</td><td>ASYR, ASYT, XLSX</td><td>70 MB</td></tr><tr><td>Statistical data (spreadsheet, image)</td><td>Statistical tests, plotters</td><td>Microsoft Excel, Microsoft Power BI, GraphPad, Physics: Tools for science, Statistics Kingdom</td><td>XLSX, PBIX, PDF, PNG</td><td>20 MB</td></tr></table>

## 1b. How will new data be collected or produced &/or how will existing data be re-used?

The methodologies, databases and software employed to collect, produce or re-use the data are specified in Table 1. The implemented techniques rely on a maintained system of in-house protocols, research resources, instruments and software either directly available in the host laboratory or reachable via collaborations and TGIR (Synchrotron SOLEIL). Data re-use concerns both publicly available deposited and/or published data and previously collected in-house data and samples. In the collection, production, and re-use of data, we follow general archiving principles described in La vie d'une donnée au regard des réglementations "CRPA", "RGPD" et "Patrimoine".

The provenance of re-used data is documented with the help of the associated persistent identifiers (e.g. UniProt and InterPro entries, PDB ID, PXD, RefSeq ID, article reference/PMID, HAL ID, DOI). Some of the repositories used in this project (e.g. ProteomeXchange, wwPDB) have established versioning pipelines with rich provenance metadata enabling reconstruction of detailed lifelines of the deposited data. In the case of data from continuously updated databases (e.g., phylogenetic distributions, BLAST, PDB searches), the filters and the date of the search are additionally specified. For predictions, parameters, filters, and on-line software versioning information are provided. The provenance of collected and produced data is documented in the form of metadata, lab book entries, and the Central Experiment Registry (CER, see section 2).
## 2. Documentation & data quality

### 2a. What metadata & documentation (for example the methodology of data collection & way of organising data) will accompany the data?

The overall organisation of research data follows RDA-endorsed recommendations (10 things for curating reproducible and FAIR research, version 5, May 16, 2022). The research data organisation is embodied in the form of the Central Experiment Registry (CER) and the associated digital research infrastructure. CER is organised in folders assigned to each Creator. Each such folder contains subfolders corresponding to individual experiments and named "NNE#####", where NN are the Creator's initials and##### is the number of the experiment (continuous numbering). Each CER folder also includes a Microsoft Excel spreadsheet (or CER sensu stricto) with a complete list of experiments for which the Creator is responsible. Each row of this spreadsheet corresponds to one experiment. The columns contain standardised information about experiments (Table 2). These data are matched with those of the corresponding lab books and metadata files, which obligatorily use the same identifiers to ensure complete cross-referencing.

<table><caption>**Table 2. Central Experiment Registry (CER) columns**</caption><tr><td>Experiment ID</td><td>Experiment identifier in the format NNE#####</td></tr><tr><td>Participants</td><td>Initials of the person(s) directly contributing to the experiment</td></tr><tr><td>Affiliations</td><td>Affiliations of the person(s)</td></tr><tr><td>Started</td><td>Date of the beginning of the experiment</td></tr><tr><td>Completed</td><td>Date of the completion of the experiment</td></tr><tr><td>Description</td><td>Very short description/title of the experiment</td></tr><tr><td>Associated files</td><td>Complete list of all files generated in the experiment</td></tr><tr><td>Associated files 2, 3 etc</td><td>Additional columns (if the capacity of the previous cell happens to be insufficient to visualise all file names)</td></tr><tr><td>Status</td><td>The current status of the dataset. Has three possible values: "Active", "Archived", "Suppressed".</td></tr></table>

File names follow pre-established conventions and are built as follows:

NNE#####_yyyymmdd_MMMMMMMM.extension

where NNE##### is the number of the experiment, yyyyymmdd is the date (in the ISO 8601 format) when the file was generated, MMMMMMMM is a method-specific descriptor, as listed in Table 3. This name may be followed by a letter suffix ("a", "b", "c" etc) if it concerns a modified version of the initial file, or by a disambiguation number suffix ("1", "2", "3" etc) if the same type of data was acquired on the same day and needs to be distinguished from an already existing but unrelated file. Exceptions from this file naming system exist: files produced by some external services and collaborators retain their original names for the reasons of traceability and communication. In this case, a normal metadata file is nevertheless created to capture their provenance and experimental details. The data obtained at
synchrotron SOLEIL are subject to a specific [SOLEIL Data management policy (DSI-DU-P-0056; October 2, 2018)](https://www.sofis.fr/data-management-policy) covering standardised metadata formats and vocabulary, specific data formats, and the software required for their manipulation provided by SOLEIL, and attribution of permanent unique identifiers to experiments and datasets (see section 5d).

<table><caption>**Table 3.** Some method-specific descriptors used in file naming</caption><thead><tr><th>Method</th><th>Descriptors</th><th>Interpretation</th></tr></thead><tbody><tr><td rowspan="5">Gel electrophoresis</td><td>AGE_BEt</td><td>Agarose gel stained with BEt</td></tr><tr><td>PAGE_Coomassie</td><td>Polyacrylamide gel stained with Coomassie</td></tr><tr><td>PAGE_Stain-free</td><td>Stain-free polyacrylamide gel</td></tr><tr><td>PAGE_Ag</td><td>Polyacrylamide gel stained with silver</td></tr><tr><td>PAGE_Ag</td><td>Polyacrylamide gel visualised with PhosphorImager</td></tr><tr><td rowspan="4">Blotting</td><td>NB_SAO####</td><td>Northern blot with oligonucleotide SAO#### as probe</td></tr><tr><td>WB_White</td><td>Western blot white light image</td></tr><tr><td>WB_Abref_##s</td><td>Western blot with primary antibody Abref exposed for ## s; Abref = unique antibody identifier (reference number)</td></tr><tr><td>WB_Radio</td><td>Western blot visualised with PhosphorImager</td></tr><tr><td>UV-VIS spectrum</td><td>Spectra</td><td>UV-VIS spectra report</td></tr><tr><td>Sanger sequencing</td><td>Seq_pSAPnnnn_PPPP</td><td>Sanger sequencing of the plasmid pSAP#### with the primer PPPP</td></tr><tr><td>PCR</td><td>PCR_SAO#### SAO####</td><td>PCR with primers SAO#### and SAO####</td></tr><tr><td>Quantification</td><td>Quant</td><td>Spreadsheet with quantification data</td></tr><tr><td>Structure</td><td>Structure</td><td>Secondary or tertiary structure of a molecule</td></tr><tr><td>Sequence</td><td>Sequence</td><td>Annotated sequence of a gene, a transcript or a protein</td></tr><tr><td>Analysis</td><td>Analysis</td><td>A free-form qualitative analysis including images, schemes, and other kinds of data that cannot be captured under the "Quant" category</td></tr></tbody></table>
In line with recommendations formulated in 10 things for curating reproducible and FAIR research (version 5, May 16, 2022), we implement the principle of separation of primary and processed datasets. Primary research data are stored in CER folders as read-only original files. By contrast, the corresponding output files – that build on the primary or re-used data, but are not themselves primary data – are created and stored as separate digital objects (files) in dedicated "analysis" subfolders (versioned as "Analysis01", "Analysis02" etc) within each experiment folder. These "analysis" files are diverse in format (text, spreadsheet, annotated image, PyMol session etc). They cross-reference the original data and research resources they are based on using the in-house CER taxonomy (for internally produced data) or database-specific persistent identifiers (for publicly deposited data – both internally and externally produced). The "analysis" subfolders also capture, in as much detail as possible, all modifications or filtering/censoring applied to data, analysis pipelines, software versions and settings – either directly within their body (documentation) or by means of associated README files.

CER and the file naming system are rooted in the research resource taxonomy which includes unique identifiers for protocols, cell lines, bacterial strains, plasmids, DNA oligonucleotides, antibodies, and synthetic nucleic acids. Whereas the latter two categories almost invariably use manufacturer-provided references (unless the resource in question was specifically created for exclusive in-house use), the remaining in-house resources follow the conventional naming and numbering system: cell lines – SAL####, bacterial strains – SAB#####, plasmids – pSAP#####, DNA oligonucleotides – SAO#####, which corresponds to their physical organisation in storage cryo-boxes. Each kind of resource is catalogued in a dedicated spreadsheet which ensures cross-referencing to other resource types (Table 4).

<table><caption>Table 4. Key research resource catalogues & their columns</caption><thead><tr><th colspan="2">DNA oligonucleotides</th></tr></thead><tbody><tr><td>Oligo ID</td><td>Oligonucleotide identifier SAO#####</td></tr><tr><td>Created</td><td>Date of order</td></tr><tr><td>Sequence</td><td>Oligonucleotide sequence in the 5'-to-3' direction</td></tr><tr><td>Description</td><td>Intended uses of the oligonucleotide, its peculiarities (modification, restriction site, amplified or detected gene & organism etc)</td></tr><tr><td>Notes</td><td>Any useful information or observations</td></tr></tbody></table>
## Plasmids

<table><tr><td>Plasmid</td><td>Plasmid ID pSAP####</td></tr><tr><td>Created</td><td>Date of confirmation by Sanger sequencing</td></tr><tr><td>Host strain</td><td>Bacterial stock strain for storage & re-isolation; SAB####</td></tr><tr><td>Resistance</td><td>Resistance markers</td></tr><tr><td>Backbone</td><td>Vector identity</td></tr><tr><td>Insert</td><td>Gene name with eventual mutation in the format (genotype=prototype) or added tags</td></tr><tr><td>Species of the insert</td><td>Species specification for the inserted sequence</td></tr><tr><td>Cloning sites</td><td>Sites used for cloning the insert</td></tr><tr><td>Concentration, ng/µl</td><td>Concentration of the current stock</td></tr><tr><td>Notes</td><td>Intended uses, provenance, construction notes & any useful observations</td></tr><tr><td>Oligos used for cloning</td><td>Complete list of oligonucleotides (SAO####) used to create the plasmid</td></tr><tr><td>Sanger file 1, 2 etc</td><td>A series of columns containing links to Sanger sequencing data of the corresponding plasmid</td></tr></table>

## Bacterial strains

<table><tr><td>Strain</td><td>Strain ID SAB####</td></tr><tr><td>Date created</td><td>Date of the original DMSO/glycerol stock</td></tr><tr><td>Species & strain</td><td>Bacterial species & strain</td></tr><tr><td>Genotype</td><td>Genetic background with eventual mutations or modifications</td></tr><tr><td>Plasmids</td><td>Hosted plasmids pSAP#### (with an ordinary name if empty vector)</td></tr><tr><td>Resistance</td><td>All known resistances of the strain</td></tr><tr><td>Description</td><td>Intended use of the strain: "Primary stock" (genetically unmodified, usually lacking plasmids), "Storage strain" (for plasmid storage & isolation), "Expression strain" (for protein overexpression), "Constructed strain" (genetically modified in a specific way)</td></tr><tr><td>Notes</td><td>Any useful information & observations (e.g. growth peculiarities, unusual phenotypes & sensitivities), eventually provenance & alternative ID (if received from third parties)</td></tr></table>

Protocols (NNM####) are named by the Creator's initials (NN) and numbered (####). Their versions are labelled with letter suffixes ("a", "b", "c" etc). Protocols have an invariant structure, as outlined in Table 5. They necessarily cross-reference all related research resources.
<table><caption>Table 5. Protocol structure</caption><thead><tr><td>Entry</td><td>Content</td></tr></thead><tbody><tr><td>Title</td><td>Name of the protocol</td></tr><tr><td>Date</td><td>Date of the last edit (excluding major modification, which normally requires versioning)</td></tr><tr><td>Associated protocols</td><td>Any other protocols related to the present one</td></tr><tr><td>Solutions & reagents</td><td>Complete list & compositions of buffers & chemicals required for the experiment</td></tr><tr><td>Kits</td><td>Any kits required for the experiment (with links to manuals)</td></tr><tr><td>Instruments & specific consumables</td><td>Any instruments or consumables required for the experiment</td></tr><tr><td>Biological materials</td><td>Organism, tissue, specific strain, cell lysate, or any other material directly used in the protocol</td></tr><tr><td>Time estimate</td><td>Minimal (with no facultative breaks used) and maximal (with all facultative breaks included) duration of the protocol</td></tr><tr><td>Breaks</td><td>Steps at which experimental manipulations may of have to be interrupted for more than 12 h</td></tr><tr><td>Successful implementation</td><td>Examples of strains, proteins, or other biological entities for which the protocol has already been successfully used in house, with references to specific experiments</td></tr><tr><td>Unsuccessful implementation</td><td>Examples of strains, proteins, or other biological entities for which the protocol failed in house (with brief notes & proposed alternatives), with references to specific experiments</td></tr><tr><td>Protocol</td><td>Step-by-step experimental guide</td></tr></tbody></table>

In addition to built-in metadata associated with instrument-specific primary files (including such information as date, instrument, and acquisition settings), a dedicated TXT metadata file accompanies each dataset. The metadata file name is built following these conventions:

NNE#####_DublinCore_metadata.txt

where NNE##### is the number of the experiment in CER. An experiment may have several associated datasets and the corresponding number of metadata files which, in this case, assume a suffix "_1", "_2", "_3" etc. When the experiment is finished and all associated data have been collected, it is archived, and the metadata file name is extended with "_archived". In this state, it is not subject to modification any more (read-only). (At this stage, however, it is still considered as 'intermediate data' not necessarily deemed for long-storage preservation - see section 5a.)

A standard metadata file is organised in accordance with the Dublin Core format and includes 15 generic entries (Table 6), eventually followed by the concluding tag #archived#.
<table><caption>**Table 6. Metadata file organisation (for research data)**</caption><thead><tr><td>Entry</td><td>Description</td></tr></thead><tbody><tr><td>1. Title</td><td>Short title of the experiment</td></tr><tr><td>2. Creator</td><td>Person (name, ORCID) &/or institution who created the metadata file, usually the main experimenter</td></tr><tr><td>3. Subject</td><td>Type of the experiment (method)</td></tr><tr><td>4. Description</td><td>Concise description of the experiment, including (i) relevant IDs of in-house research resources (cell lines, strains, plasmids, oligonucleotides, antibodies, protocols with eventual deviations), (ii) all generated files in the dataset & their descriptions (type of data, order & amount of samples, technical observations), (iii) any additional textual information which does not make part of a separate file (e.g., OD measurements, concentrations, qualitative remarks)</td></tr><tr><td>5. Publisher</td><td>Université de Strasbourg</td></tr><tr><td>6. Contributor</td><td>Person(s) (names, ORCIDs) &/or institution(s) who directly contributed to the experiment</td></tr><tr><td>7. Date</td><td>Date of the last modification</td></tr><tr><td>8. Type</td><td>Nature of the data (e.g. gel scan, 3D model, UV absorbance spectrum)</td></tr><tr><td>9. Format</td><td>Name of the folder & eventually subfolder(s) in which the data are classified</td></tr><tr><td>10. Identifier</td><td>ID of the experiment & a complete list of the files & subfolders in the dataset</td></tr><tr><td>11. Source</td><td>Primary or secondary (in the case of data re-use or re-analysis)</td></tr><tr><td>12. Language</td><td>English</td></tr><tr><td>13. Relation</td><td>IDs of all other experiments &/or data or metadata to which the present dataset is related in terms of deliverables, continuation, replication, modification, re-use, or re-analysis</td></tr><tr><td>14. Coverage</td><td>Time period between the beginning of the dataset & its completion; geographical coverage (city, state)</td></tr><tr><td>15. Rights</td><td>IPR Unistra</td></tr></tbody></table>

As an alternative metadata format, an MS PowerPoint file (PPT), containing the experimental image with detailed annotation and relevant information on the experimental design, is used by some collaborators. This file is named in the same way as the original file it refers to (see below), followed by “_annoté”.
An additional metadata file is generated for enzymatic assays. It uses the EnzymeML macros template (Lauterbach & al., "EnzymeML: seamless data flow and modelling of enzymatic data", Nat Methods 2023) based on Systems Biology Markup Language (SBML). The metadata file name is built following these conventions:

NNE#####_EnzymeML_metadata.xlsx

where `NNE#####` is the number of the experiment in CER. Once the template is filled out, the file is saved as a regular `..xlsx` file, as recommended by the developers. The EnzymeML metadata and the associated infrastructure are designed to support the FAIR principles. The metadata are formatted in a way that directly enables downstream kinetic modelling in `COPASI` and data deposition in the EnzymeML Dataverse, `SABIO-RK`, or `STRENDA DB` repositories.

In a similar manner, additional metadata are generated for other datasets deposited in dedicated repositories (section 5), following their established conventions and requirements (e.g. ProteomeXchange Consortium data submission guidelines, version 3.0.1; October 13, 2019; or Image Data Resource submission guidelines, version 1.0; March 2017; Biolmage Archive submission - Lab Guide). In particular, `REMBI metadata` will be created to accompany published microscopy datasets selected for deposition to Biolmage Archive. The possibility of introducing other data type-specific FAIR-compatible metadata formats, approved by the corresponding communities, continues to be studied.

All the research resources are shared between the participants of the project via the protected Seafile cloud space hosted by the University of Strasbourg. They are retrievable by all participants of the project. They are curated and modified by authorised participants (Alexandre SMIRNOV, Christelle GRUFFAZ, Anna SMIRNOVA, Zhen LIAO, Théo MARKEZIC). The maintenance of the research infrastructure is ensured by the Lab Manager (Christelle GRUFFAZ) and supervised by the Principal Investigator (Alexandre SMIRNOV).

CNRS has recently begun to deploy, on voluntary bases and at the scale of entire research units, digital lab books (CNRS Research data plan, November 2020). This tool would permit linking together CERs, data, metadata, protocols, and research resource IDs, and their sharing between the participants of the project would become even more straightforward. Although, for the time being, UMR7156-GMGM made choice not to enrol, we foresee that future introduction of digital lab books may require reshaping of the metadata system to avoid redundancy between them (since the digital lab book is expected to contain most of the information currently stored within metadata files, even though in a less formal way). However, the essential external function of metadata to make data FAIR (Turning FAIR into reality. Final report and action plan from the European Commission Expert Group on FAIR data, November 2018) and the necessity to be able to share intermediate data selectively, without disclosing other, unrelated and/or sensitive intermediate data impose the metadata as the primary entities which necessarily have to be complete and self-sufficient. In this perspective, the lab book retains the roles of:

(i) the connecting node between various data, metadata, protocols, and research resources that organises them in experiments and ensures cross-referencing;
(ii) the contextualisation tool that explains the place of data and processes in the research;

(iii) the space for data post-analysis (e.g., plotting, display of adjusted images to visualise specific features, association of non-data objects and links etc) as well as for free-style interpretation and hypotheses.

Finally, the present Data Management Plan (DMP) can be considered as another piece of metadata describing the entire organisation of the data, metadata, protocols, and research resources produced and/or enacted in the frame of the InsuRAF project. It follows the outline and requirements elaborated in the Science Europe **Practical guide to the International alignment of research data management** (January 2021), further detailed in **Additional guidance to the Science Europe DMP assessment rubric**, and is subject to publication on Zenodo under the **CC BY Attribution 4.0 International license**.

## 2b. What data quality control measures will be used?

Three cases should be distinguished: collected, re-used, and produced data.

Since all the methods employed to collect data in this project have been extensively characterised and widely used by the scientific community, their quality can be evaluated by compliance with field standards. Those include technical, design, reproducibility, documentation, and digital measures and criteria (Table 7). In addition to these general criteria, further quality checks (implemented in specific metadata and data presentation modes) are applied to select types of data with community-endorsed "minimal information" requirements (see **Minimum Information for Biological and Biomedical Investigations**) and "best-practice" guidelines, such as microscopy (**Community-developed checklists for publishing images and image analyses**), qPCR (**MIQE**), molecular interaction (**MIMIx**) or enzymatic activity (**STRENDA**) data.

The main task of data quality control falls first to the experimenter, who ensures the compliance of the protocol and the experimental outcome with the established standards, documents and interprets the experiment (in the lab book and CER), and then to the principal investigator (Alexandre SMIRNOV), who reviews the protocol, data and metadata and re-evaluates their compliance with the community standards and the validity of the interpretation. The Lab Manager (Christelle GRUFFAZ) ensures the identity, digital and physical maintenance of the associated research resources (reagents, protocols, cell lines, strains, plasmids, oligonucleotides, synthetic RNAs, antibodies) and equipment (pipettes, instruments). The experimenter ensures the identity and maintenance of collected data and metadata.
<table><caption>**Table 7. Data quality measures & criteria**</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>Technical</td><td>Completeness (all samples are present, all parameters & settings are known & kept constant); functionality of the instrument & reagents (calibration, absence of obvious artefacts, maintenance); authentication &/or certification of internal & external research materials (cell lines, strains, plasmids, antibodies etc); compliance with a registered protocol; compliance of the protocol with established standards; sample integrity (absence of degradation, aggregation, or another undesirable modification); signal-to-noise ratio; dynamic range; saturation; number of events (e.g., in MS); CI of measurement; FDR; resolution; coverage; compliance of the behaviour of standards & controls with previously reported observations ("typical picture")</td></tr><tr><td>Design</td><td>Presence of negative & positive controls; standards; complementation; optimal design of time or concentration series (within the dynamic range); complete combinatorial design; uniformity of measurement (e.g., all samples on the same gel/membrane or in the same run/session); statistical power & sample size estimation; correspondence between the experimental design & the statistical analysis approach (independence, pairedness, distribution, homoscedasticity, & other assumptions; continuous, count, or categorical data)</td></tr><tr><td>Reproducibility</td><td>Technical & biological replication; single blinding (where possible); cross-experimenter replication (where possible); cross-batch replication; statistical convergence (absence of overdispersion)</td></tr><tr><td>Documentation</td><td>The experiment is completely described in the lab book, data follow the conventional vocabulary, are accompanied by properly registered & complete metadata & referenced in CER</td></tr><tr><td>Digital</td><td>Data can be opened & modified with original software; data can be extracted & converted into a widely accepted format without losing information, quality, or interoperability; unambiguous association between data, metadata & the corresponding research resources (protocol, cell lines, strains, plasmids, oligonucleotides, antibodies, external datasets); fidelity of digitalised images; absence of non-documented or irreversible modifications to original files</td></tr></tbody></table>

Re-used external data are normally expected to follow the same standards. However, data-specific checks need to be performed and eventual deviations from the norm flagged in order to establish whether the external data in question require censoring. Inclusion guidelines for such data are provided in Table 8. Filtering parameters and the decision to censor select data are systematically justified and included in the metadata. For all types of external data, permanent unique identifiers and duly documented provenance are required for inclusion.
<table><caption>**Table 8. Inclusion guidelines for external data**</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>Protein sequence, conservation, phylogenetic distribution</td><td>The sequence is complete; experimental design, as reported in the original publication, makes unlikely data contamination; independent sequencing of other isolates of the same strain, other strains of the same species, or other species of the same genus strongly support the sequencing variant; specifics of the genetic code are correctly captured (verified with ExpasyTranslate); NCBI BLAST confirms the identity of the entry & finds highly similar sequences in sister clades; COBALT confirms their clustering against selected outer groups; hallmarks of the protein family are clearly recognisable; the choice of the canonical splice isoform is based on well-described homologues from model species; significantly deviating sequences (COBALT) are inspected individually for misannotation</td></tr><tr><td>Protein structure</td><td>Quality of the model is evaluated based on the experimental details in the original publication & with the help of NGL Viewer (built in RCSB PDB) for the quality of fit & chain geometry</td></tr><tr><td>Gene sequence</td><td>The sequence is complete & is translated in silico into a protein that satisfies the above criteria</td></tr><tr><td>Genome sequence</td><td>The genome must be assembled to at least the chromosome level</td></tr><tr><td>MS data</td><td>Accompanied by proper metadata reporting on provenance, experimental protocol, & original publication</td></tr><tr><td>Data from publications</td><td>High technical standard, as commonly required in the field; independent corroboration (wherever applicable)</td></tr></tbody></table>

Produced data (i.e., those resulting from analysis of collected or re-used data) should be themselves based on high-quality data, as outlined above, and additionally satisfy a set of specific criteria. Those include:

a) unequivocal association with the corresponding primary data (by including raw data and referring to their unique identifiers or databases),

b) metadata with clear information about the date and the analysis protocol (including definitions, prediction parameters, information about binning, thresholding, normalisation, transformation, statistical analyses, software etc) captured as within-file documentation or in a separate README file,

c) respecting field conventions for each kind of analysis or justification of deviations from them,

d) compliance with the assumptions and requirements of adopted statistical analyses and visualisation modes,

e) sample inclusion and exclusion criteria,

f) portable and convertible format permitting facile re-run or alternative visualisation with desktop or online tools,

g) proper organisation in an "analysis" subfolder with a transparent structure that captures the processing workflow.
### 3. Storage & backup during the research process

#### 3a. How will data & metadata be stored & backed up during the research?

The data and metadata are stored on the office computers and monthly backed up on two 1-Tb hard drives which are kept in a different location. Additionally, primary data are stored on original instruments and on the servers of the services and collaborators who generated them (e.g., SOLEIL), ensuring that several copies of the most important data are preserved in geographically distinct locations. The Seafile cloud storage of the University of Strasbourg is used to store and share between collaborators the research resources of the laboratory and some data. A more reliable, permanent, and spacious solution for heavy data storage is expected to be provided by the Data Centre of the University of Strasbourg.

All primary data are read-only to avoid unintentional modification.

The storage of intermediate data and metadata is regulated by the French law acts (Circulaire du 2 novembre 2001 relative à la gestion des archives dans les services et établissements publics de l'Etat).

#### 3b. How will data security & protection of sensitive data be taken care of during the research?

The recoverability of the data is ensured by the multiplicity of stored copies resulting from monthly backups of all data. In the event of an incident, a complete copy of all the data will be reconstituted from backup on a reliable machine.

The access to the research infrastructure is open to all GMGM researchers involved in the InsuRAF project. The access of GMGM team members to the data is controlled by the Principal Investigator (Alexandre SMIRNOV) or directly by the Creators of the data. External collaborators receive relevant data and metadata upon request handled by the Principal Investigator (Alexandre SMIRNOV) via protected Seafile links. This policy is reciprocal. Non-collaborators in general do not have access to the data during the research, unless those are already deposited in open repositories and/or published (see section 5). Exceptions are handled individually by the Principal Investigator (Alexandre SMIRNOV) upon consultation with collaborators. Lab books are stored in a securely locked team's office; only in exceptional cases, the Principal Investigator (Alexandre SMIRNOV) may authorise their removal from the laboratory by Creators or other authorised participants of the InsuRAF project.

No sensitive data were treated or are expected to be treated in the frame of this project.

General guidelines for data handling, protection, and sharing are governed by a specific policy of the University of Strasbourg, as a member of the SupDPO network (La Vie d'une Donnée au regard des réglementations "CRPA", "RGPD" et "Patrimoine", v1.0, June 2019), and by the CNRS Policy of Security of Information Systems (PSSI). Data collected at the synchrotron SOLEIL are subject to the SOLEIL Data management policy (DSI-DU-P-0056, October 2, 2018), which regulates the access to, re-use, and publication of the data (see sections 4 and 5).
# 4. Legal & ethical requirements, code of conduct

## 4a. If personal data are processed, how will compliance with legislation on personal data & on security be ensured?

No personal data were treated or are expected to be treated in the frame of this project.

## 4b. How will legal issues, such as intellectual property rights & ownership, be managed? What legislation is applicable?

Ownership of the data collected and produced in this research is regulated by the Code de la Propriété Intellectuelle, Code de la Recherche and, more specifically, the Loi pour une République Numérique (LOI n° 2016-1321 du 7 octobre 2016) under the French law. These research data are the property of the University of Strasbourg (Code de la propriété intellectuelle; October 3, 1996; section R611-12), and the access to them is controlled by the Principal Investigator (Alexandre SMIRNOV).

Authorship and intellectual property rights on all published documents belong to their authors (physical persons), as stipulated by the French law (loi n° 2006-961 du 1 août 2006 relative au droit d'auteur et aux droits voisins dans la société de l'information; see also the comprehensive Guide pratique de la publication en ligne et de la réutilisation des données publiques ("Open data") elaborated by CNIL and CADA) and internationally promoted through the Rights Retention Strategy (RRS) by cOAlition S (ANR is a member of cOAlition S).

Ownership of the primary data obtained at the synchrotron SOLEIL is regulated by the SOLEIL Data management policy (DSI-DU-P-0056; October 2, 2018) and should be interpreted in the sense that all derived products (results, data interpretation, IPR), being performed by participants of the InsuRAF project, remain the property of the University of Strasbourg (Code de la propriété intellectuelle; October 3, 1996; section R611-12).

The data deposited in the ProteomeXchange Consortium remain the property of the Principal Investigator and of the PRIDE Submitter even after their public release, as specified in the Data submission guidelines for the ProteomeXchange Consortium (version 3.0.1; October 13, 2019).

IDR encourages authors to submit imaging datasets under the CC BY license (with licensing information included in the accompanying metadata) to facilitate their widest re-use (Submission Guidelines v1.0; March 2017). Authors retain copyright, while IDR receives a license to publish the data on behalf of the authors.

BiolImage Archive policies (version 1.1) make deposited data available under the CC0 license by default. This is potentially problematic since, unlike CC BY, CC0 does not protect author rights and leaves citation of the data at re-users' discretion. However, the same policies acknowledge that some deposited datasets are available under different licenses (indicated as dataset attributes). InsuRAF will strive to deposit microscopy data under the CC BY license. BIA has a removal procedure if the ownership or provenance of datasets is called into question (BiolImage Archive Policies, version 1.1; see also section 5a).
Zenodo General Policies (version 1.0) recognise the ownership for the parties prior to submission and do not imply any transfer of property rights.

Recherche Data Gouv uses the open-access etalab2.0 license by default, but permits the use alternative/additional licenses, including CC BY. etalab2.0 is designed to be compatible with CC BY.

There are no restrictions on the re-use of third-party data deposited to public databases within the perimeter and at the scale foreseen in this study (see section 1), as guaranteed by French law (article L342-3 du Code de la Propriété Intellectuelle) that implements the Directive 96/9/EC of the European Parliament and of the Council. Specifically, no third-party intellectual property or sui generis rights were or are expected to be affected. The data deposited in the wwPDB are open-access without copyright restrictions. However, the wwPDB Deposition Policies and wwPDB Biocuration Procedures (version 5.2; April 2023) reserve the authorship rights to Entry Authors, Contact Authors and Citation Authors. Furthermore, the re-use of deposited third-party data is subjected to specific rules, as defined by the corresponding repositories. The ProteomeXchange Consortium data re-use requirements are outlined in the Guidelines for handling ProteomeXchange reprocessed datasets (version 1.0.2; September 2019): the unique dataset identifier (PXD) should be cited, reprocessed data linked to the source PX dataset, reprocessed PX XML files should respect specific formatting. The SOLEIL data re-use requirements are outlined in the SOLEIL Data management policy (DSI-DU-P-0056; October 2, 2018): wherever appropriate, the Main Proposer or the Principal Investigator of the dataset should be contacted in order to inform them, propose collaboration or co-authorship; the source of the data, citing the unique persistent identifier and any publications linked to the same raw data, should be acknowledged.

Section 5b more specifically deals with the open-access publication of datasets and documents associated with the InsuRAF project and cites the corresponding legal norms and community standards.

**4c. What ethical issues & codes of conduct are there, & how will they be taken into account?**

Not applicable (no human or animal subjects, communities, their cultural and/or genetic resources, personal or other ethically sensitive data were used in the study).

**5. Data sharing & long-term preservation**

**5a. How will data for preservation be selected, & where will data be preserved long-term (for example a data repository or archive)?**

At this stage of the project, when most intermediate data have already been obtained and partially published, and the quality and value of each piece of data can be evaluated with more confidence and in a broader context, the selection of data for ultimate archiving can be initiated. To this end, the intermediate data are parsed in four groups:

1) actively used data, independently of their quality, associated with or potentially important for ongoing or future research tasks within the InsuRAF project or for other ongoing research projects (e.g. EURICA);

2) high-quality published or ready-to-publish data;
3) other high-quality data which are not expected to be used in the foreseeable future but are required to fully document the research process and, if needed, support published data or priority claims;

4) low-quality data without significant scientific value which are not expected to be used in the foreseeable future.

In addition to the quality criteria described in section 2b, curation of datasets is carried out in accordance with *10 things for Curating Reproducible and FAIR Research*. The destiny of each category of data is different.

The **Group 1 data** are not subject to archiving and continue to be managed, as described in section 3.

The **Group 2 data** are formatted and deposited to appropriate trusted data repositories, according to Science Europe recommendations (*Practical guide to the International alignment of research data management*, January 2021) and established standards in the field. Key mass spectrometry data are deposited in the ProteomeXchange Consortium (via the PRIDE repository). Microscopy data are deposited to Biolmage Archive (BIA) or Image Data Resource (IDR). Source codes are deposited on GitHub. The deposited data follow the corresponding policies and guidelines for formatting, storage, and sharing, as outlined in sections 4b, 5b, 5c, and 5d. The deposition to these selected databases is free of charge and, at the scale of the InsuRAF project, without effective size constrains. Foreseeable uses of the data include but are not limited to follow-up, replication or comparative studies, re-analysis, benchmarking, algorithm training, and database creation. Below are outlined some specific rules governing data deposition, archiving, and withdrawal in the repositories concerned by the present DMP.

SOLEIL acts as custodian for all raw data and associated metadata collected at the synchrotron. It ensures long-term storage and curation of the data and the associated metadata. These data and metadata follow the *SOLEIL Data management policy* (DSI-DU-P-0056; October 2, 2018) for formatting, storage and sharing. SOLEIL does not curate reduced or processed data and the associated metadata. SOLEIL stores but does not curate results issued from analyses performed on raw data and metadata using SOLEIL means. Foreseeable uses of the data include but are not limited to re-analysis, benchmarking, and algorithm training.

*Data submission guidelines for the ProteomeXchange Consortium* (version 3.0.1; October 13, 2019) regulate the deposition of mass spectrometry data. The ProteomeXchange Consortium similarly permits withdrawing unreleased datasets by the authors' decision. The withdrawn datasets will still be present in the list of unreleased entries, and their internal copy will be kept by ProteomeXchange Consortium but will remain inaccessible to external users. Released datasets associated with published research cannot be withdrawn (unless there are exceptional circumstances, such as major errors, retraction of the corresponding article, or violation of property rights). Preprints are not considered publications under the ProteomeXchange Consortium policies, and the repository does not encourage releasing datasets prior the acceptance of the final paper for publication. PRIDE (as part of the ProteomeXchange Consortium) in its *General guidelines for submitted datasets* (version 1.0, May 2022) aligns with these policies and engages to maintain all published datasets publicly accessible as part of scientific record. The PRIDE team
also offers the creation of additional metadata (e.g. [Sample and Data Relationship Files](https://www.example.com), `mzTab` and `mzIdentML` files) to facilitate the re-use of deposited datasets.

**Biolmage Archive (BIA)** functions as a permanent public repository of imaging data associated with peer-reviewed publications or of value beyond a single experiment. It also provides archiving services for other bioimaging databases, e.g. IDR. It supports **FAIR Sharing** and implements **REMBI** guidelines for FAIR data. **Biolmage Archive Policies** (version 1.1) provide general guidelines about deposition of datasets. BIA allows for up to 3 months of transient storage for uploaded data, after which the dataset is either formally submitted for permanent deposition or deleted. The data can be released either right away or upon article publication (and in any case no later than 2 years after submission). All deposited data are permanently accessible for scientific record. They can be amended or updated, or even removed from visibility by search (in case of an erroneous submission). BIA reserves the right to obsolete datasets due to integrity, correctness, ownership, or provenance issues (e.g. article retraction or other official decision of integrity bodies). In either case, the data in question will nevertheless remain accessible via their PID. The quality and accuracy of the deposited data are the responsibility of the authors.

**Image Data Resource (IDR)** is a more specialised repository focused on reference data from published (or only submitted) studies with particularly rich metadata to enable their re-use by third parties (Williams et al., *Nat Methods*, 2017). **IDR Submission Guidelines** (v1.0, March 2017) require the datasets to be complete and integrated with other databases and biomolecular resources via PIDs. IDR supports a wide variety of proprietary and open file formats (with the preference for the latter), including those implemented in the InsuRAF project (section 1a).

**BIA scope and submission to related resources** explains the difference in scope between Biolmage Archive and other, more specialised repositories, such as IDR, thus guiding the choice of repository for different kinds of imaging data. BIA and IDR actually overlap on this point and work together to coordinate and link submissions between the two repositories; more specialised IDR deposits are imported into BIA. BIA plans to provide a possibility of parallel submission to both the databases.

**Zenodo** permits the deposition of any kinds of research data in any format under any access conditions. Zenodo does not perform quality checks, does not guarantee usability or understandability of deposited data. This repository stores metadata in the JSON format, with the possibility of export to other widely used formats, e.g. Dublin Core, via OAI-PMH. Submitters themselves specify the desired license in the process of submission, which determines use and re-use possibilities. Metadata are licensed under the CC0 license. **Zenodo General Policies** (version 1.0) reserve the right to remove datasets and revoke their DOI in cases where the datasets in question do not fall into the scope of the repository or violate its terms of use. Under exceptional circumstances, the submitter can request withdrawal of one of his datasets, which requires full justification. If the withdrawal is granted, the dataset page is replaced by a "tombstone" page, but the original DOI and URL are retained.

Finally, the recently created general-use French national platform for research data, **Recherche Data Gouy**, permits deposition of any types of data for which no trustworthy repositories currently exist as well as cataloguing/linking to data deposited in other trusted national or international repositories. The deposited data must be complete and accompanied by structured metadata. [Recherche Data](https://www.example.com)
Gouv does not store intermediate data and does not perform scientific quality checks; however, it does provide curation (including post-deposition, long-term curation) and formal validation of completed datasets submitted to the repository. Recherche Data Gouv policies enable deposition either in open or reserved access (the latter is possible for datasets that constitute a legal exception to the open-access principle, e.g. professional or commercial secret, personal data). As in the other repositories mentioned above, data deposited to Recherche Data Gouv are findable and citable. Curators reserve the right to delete provisional submissions, modify submissions or withdraw published datasets or their versions from circulation, upon justification.

Other trusted repositories will be considered in agreement with [Science Europe guidelines](https://www.sciencedirect.com/science/europe guidelines) and journal recommendations.

Some Group 2 data (e.g., images, tabulated or graphed data) are directly published in the corresponding articles. The present DMP is deposited and updated, as described in section 6a, on Zenodo under [Creative Commons Attribution 4.0 International (CC BY)](https://creativecommons.org/licenses/by/4.0/). licence and communicated to ANR as a specific project deliverable.

As a general rule, the default license for deposited InsuRAF data is, wherever possible, [Creative Commons Attribution 4.0 International (CC BY)](https://creativecommons.org/licenses/by/4.0/).

The versioning of deposited data generally follows RDA recommendations (Klump & al. 2019). In particular, ProteomeXchange Consortium announces new versions of deposited PX datasets and enables the preservation and tracking of all versions of a dataset in ProteomeCentral (Data submission guidelines for the ProteomeXchange Consortium, version 3.0.1; October 13, 2019). Moreover, ProteomeXchange Consortium distinguishes the terms 'revision' (previously 'version') and 'reanalysis' of PX datasets. The former refers to technical updates on data or metadata without changing the original intent of the submission; new revisions render the previous ones obsolete. By contrast, 'reanalysis' is reprocessing of original raw data to achieve new results; it does not imply that other reanalyses are obsolete and does not supersede them. The ProteomeXchange Consortium has a specific policy regarding PIDs associated with reprocessed datasets (Guidelines for handling ProteomeXchange reprocessed datasets, version 1.0.2; September 2019).

Zenodo versions all deposited documents and assigns each new revision a unique DOI. For example, the two previous versions of this DMP can be [here](https://doi.org/10.5281/zenodo.5767156) and [here](https://doi.org/10.5281/zenodo.5776148).

Recherche Data Gouv its own [versioning policy](https://www.recherche-daf.org/en/research-data-research-publication-and-communication#versioning) that permits adding, removing or deleting files, adding or modifying metadata, and changing license terms. Any change results in republication of the dataset as a version under the same DOI (DOIs are only linked to the latest version of the dataset). The repository distinguishes minor (labelled 1.1, 1.2 etc) and major revisions (labelled as 2.0, 3.0 etc). The latter option is used to accommodate any changes in files or citations.

The **Group 3 data** undergo archiving. They may be later re-used by the research team or collaborators and shared with third parties (members of the research community or governing bodies), following the modalities described in section 3b, for research purposes, or to ensure the integrity of the research record, or to support
priority claims. Foreseeable uses of such data include but are not limited to
publication, follow-up, replication or comparative studies, re-analysis, benchmarking,
algorithm training. The expected volume of the collected and produced data (see
Table 1) permits their long-term preservation on hard drives or in the Data Centre in
house. Since no personal data were collected or are expected to be collected in
this research, there is no legally imposed limit on the duration of data preservation.

The archiving procedure includes three components: (i) evaluation and, if needed, fixing of the quality and completeness of the dataset (see section 2b), (ii) copying and transferring the dataset in question to the archive hard drive and (iii) creation of the corresponding records management metadata. All archived data and the associated metadata are by default read-only. The archive hard drive is preserved in a separate location. The archived copy of the dataset is accompanied by a records management metadata file in a TXT format, based on the [Dublin Core](https://www.dublincore.org/) model and named according to the following conventions:

NNE#####_RM_metadata.txt

where NNE##### is the number of the archived experiment in CER (see section 2a).
This file is organised as described in Table 9.

Another copy of the archived dataset continues to be preserved on the original
hard drive but is moved to a dedicated folder "Archive"; it is not subject to backup
any more. Its place in CER is taken by a read-only records management metadata
file identical to the one in the archive, to enable localisation of the archived data
and reconstitution of their record management.
<table><caption>Table 9. Records management metadata file organisation (for archiving)</caption><thead><tr><td>Entry</td><td>Description</td></tr></thead><tbody><tr><td>1. Title</td><td>"Records management metadata for the experiment NNE#####"</td></tr><tr><td>2. Creator</td><td>Person (name, ORCID) who created the metadata file</td></tr><tr><td>3. Subject</td><td>"Records management metadata"</td></tr><tr><td>4. Description</td><td>Concise & dated description of significant events accompanying the archiving procedure (e.g., revision of metadata; format conversion, movement or suppression of select data & the reasons for this) & the subsequent history of the archived dataset (e.g., copying, re-use in another experiment in house or by authorised external users, publication etc)</td></tr><tr><td>5. Publisher</td><td>"Université de Strasbourg"</td></tr><tr><td>6. Contributor</td><td>Person(s) (names, ORCIDs) &/or institution(s) who perform archiving and manage the archive</td></tr><tr><td>7. Date</td><td>Date of the last modification</td></tr><tr><td>8. Type</td><td>"CER experiment dataset"</td></tr><tr><td>9. Format</td><td>Name of the folder in which the archived data are classified ("NNE#####")</td></tr><tr><td>10. Identifier</td><td>ID of the experiment ("NNE#####")</td></tr><tr><td>11. Source</td><td>Primary or secondary (in the case of data re-use or re-analysis)</td></tr><tr><td>12. Language</td><td>"English"</td></tr><tr><td>13. Relation</td><td>IDs of other experiments &/or data or metadata to which the present dataset is related in terms of deliverables, continuation, replication, modification, re-use or re-analysis</td></tr><tr><td>14. Coverage</td><td>Time period between the beginning of the dataset & the last modification of its records management metadata; physical location of the archive</td></tr><tr><td>15. Rights</td><td>"IPR Unistra"</td></tr></tbody></table>

The **Group 4** data are suppressed. The suppression procedure includes three components: (i) deleting the data in question from the corresponding CER folder while preserving all the metadata of the suppressed dataset, (ii) transferring the folder with the metadata to the archive, and (iii) creation of the corresponding records management metadata. The latter follows the [Dublin Core](https://www.dublincore.org/) model, as described in Table 10.
<table><caption>Table 10. Records management metadata file organisation (for suppression)</caption><thead><tr><td>Entry</td><td>Description</td></tr></thead><tbody><tr><td>1. Title</td><td>"Records management metadata for the experiment NNE#####"</td></tr><tr><td>2. Creator</td><td>Person (name, ORCID) &/or institution who created the metadata file</td></tr><tr><td>3. Subject</td><td>"Records management metadata"</td></tr><tr><td>4. Description</td><td>Concise & dated description of the reasons & the circumstances of the dataset suppression & the subsequent history of the remaining metadata (e.g., copying, damage, or loss); person (name, ORCID) who authorised the suppression (usually, Principal Investigator, Alexandre SMIRNOV)</td></tr><tr><td>5. Publisher</td><td>Université de Strasbourg</td></tr><tr><td>6. Contributor</td><td>Person(s) (names, ORCIDs) &/or institution(s) who performed the dataset suppression</td></tr><tr><td>7. Date</td><td>Date of the last modification</td></tr><tr><td>8. Type</td><td>CER experiment dataset</td></tr><tr><td>9. Format</td><td>Name of the folder in which the archived data are classified ("NNE#####")</td></tr><tr><td>10. Identifier</td><td>ID of the experiment ("NNE#####") & a complete list of all suppressed files</td></tr><tr><td>11. Source</td><td>Primary or secondary (in the case of data re-use or re-analysis)</td></tr><tr><td>12. Language</td><td>English</td></tr><tr><td>13. Relation</td><td>IDs of other experiments &/or data or metadata to which the present dataset is related in terms of deliverables, continuation, replication, modification, re-use or re-analysis</td></tr><tr><td>14. Coverage</td><td>Time period between the beginning of the dataset & the suppression; physical location of the archive</td></tr><tr><td>15. Rights</td><td>IPR Unistra</td></tr></tbody></table>

As in the case of archived datasets, the place of the suppressed folder in CER is taken by a read-only records management metadata file identical to the one in the archive, to enable the localisation of the remaining metadata of the suppressed dataset and the reconstitution of their record management.

The sorting of intermediate data and the decision to eventually archive/suppress them will be taken on a monthly basis and will only concern the datasets (CER experiments) that have not been in use for at least one year. The decision to suppress a dataset must be systematically approved by a hierarchical authority (in this case, the Principal Investigator, Alexandre SMIRNOV).

Note a difference in the granularity of the suppression procedure between the Group 3 and the Group 4 data. Group 4 datasets are suppressed entirely, whereas individual files from Group 3 datasets may be suppressed prior archiving, following a similar suppression procedure, if they are damaged or devoid of scientific value,
provided that the suppression does not affect the completeness of the archived dataset. In either case, suppression is systematically motivated and authorised in the accompanying records management metadata.

Long-term curation of the archive is ensured by the Lab Manager (Christelle GRUFFAZ) and by the Principal Investigator (Alexandre SMIRNOV). The archived datasets are not subject to suppression; their preservation is assured by the University of Strasbourg. In the case where the activity of the latter is ceased, the archived datasets should move to an appropriate state archive, according to the French law regulations (Code du patrimoine, article L212-5).

The legal requirements for the re-use of published data, data archiving, and destruction are fixed by the French law and can be found in the Guide pratique de la publication en ligne et de la réutilisation des données publiques ("Open data") elaborated by CNIL and CADA, in La Vie d'une Donnée au regard des réglementations "CRPA", "RGPD" et "Patrimoine" (version 1.0; Juin 2019) compiled by SupDPO, and in Ouverture des données de recherche – Guide d'analyse du cadre juridique en France (version 2, December 2017). Specifically, creation, conservation, and protection of archives is regulated by the Régime général des archives – Chapter 2 ("Collecte, conservation et protection" – Articles L212-1 to L212-29) of the Code du Patrimoine and general guidelines are given in the Circulaire du 2 novembre 2001 relative à la gestion des archives dans les services et établissements publics de l'Etat. They are also regulated by international norms (see Maday & Taillefer. Les métadonnées du records management du point de vue des norms ISO. In: La Gazette des archives, n°228, 2012-4). Namely, ISO 23081-1 requires documentation of any changes introduced in the content, the context, or the structure of the record; ISO 15489-1 requires the approval of a hierarchical authority for any permanent destruction of records. Importantly, while the French law requires all data produced by public institutions to be publicly accessible (with a few exceptions that are irrelevant in the context of the InsuRAF project), this only concerns completed ("achevés") datasets, i.e. Group 1 data under this DMP are not subject to communication (see an overview of these norms in Ouverture des données de recherche – Guide d'analyse du cadre juridique en France, version 2, December 2017).

**5b. How & when will data be shared? Are there possible restrictions to data sharing or embargo reasons?**

Access to completed datasets and documents associated with the InsuRAF project is by default open and free, in the spirit on European, French, supDPO and CNRS recommendations and legal acts (Turning FAIR into reality; November 2018; Deuxième Plan national pour la science ouverte; July 2021; Vie d'une Donnée au regard des réglementations "CRPA", "RGPD et "Patrimoine", version 1.0; June 2019; Guide pratique de la publication en ligne et de la reutilisation des données publiques ("Open Data"), CNIL, CADA; Ouverture des données de recherche – Guide d'analyse du cadre juridique en France, version 2, December 2017; CNRS Research data plan; November 2020). This said, the data discoverability and sharing are intimately connected with the processes of publication and eventually patenting. Access to intermediate data is restricted to the participants of the project, but can be granted to collaborators or prospective collaborators by the Principal Investigator (Alexandre SMIRNOV). Unpublished data and metadata as well as lab
books and data arrays susceptible to become object of IPR can be shared with third parties upon request directly handled by the Principal Investigator (Alexandre SMIRNOV) upon consultation with collaborators and eventually SATT Conectus Alsace. Pursuing a patent constitutes a reason for which the relevant data may not be released until the patent is granted or rejected.

Prior to article publication, relevant datasets are deposited in dedicated repositories (see section 5a) and become discoverable by their unique identifiers (see section 5d) and key words through search engines. Upon publication (or, in some journals, prior to publication), they are rendered open-access (under the CC BY license, wherever possible) and follow the policies of the corresponding repositories.

The data, metadata and results obtained at the synchrotron SOLEIL are discoverable via a searchable on-line catalogue (restricted to registered users). The embargo period is defined in the [SOLEIL Data management policy](DSI-DU-P-0056; October 2, 2018) as 3 years after the end of the beamtime (default), and cannot exceed 5 years. It can be shortened upon request from the Principal Investigator (e.g., if the corresponding results are published). Past this time, all the data and metadata are automatically rendered open-access under the CC BY licence. Access to the data during the embargo period is controlled by the Principal Investigator (Alexandre SMIRNOV) and by the Main Proposer (Benoît MASQUIDA). Additionally, §4.18 of the [SOLEIL Data Management Policy]() grants SOLEIL access to all collected data and metadata for curation and sharing purposes.

In the case of the ProteomeXchange Consortium ([Data submission guidelines for the ProteomeXchange Consortium](version 3.0.1; October 13, 2019), unreleased data are by default private (password-protected); access to them is controlled by the Principal Investigator (Alexandre SMIRNOV) and the Submitter. Access to unreleased data can be granted to third-parties upon consultation with collaborators. It is systematically granted to journals for the convenience of referees. Upon the publication of the original article, the corresponding datasets are immediately released under the CC0 licence without embargo (note, however, that ProteomeXchange Consortium envisages moving to the CC BY license as default). PRIDE (as part of the ProteomeXchange Consortium) in its [General guidelines for submitted datasets](version 1.0, May 2022) aligns with these policies. Of note, regardless the choice of the CC0 license, PRIDE insists that users re-using a deposited dataset should give appropriate credit to the original authors/submitters by citing the dataset or the associated paper.

[IDR Submission Guidelines](version 1.0) permit datasets to remain private over a defined period of time (typically until article publication) but require them to be released thereafter. Once deposited data have been validated, their publication takes at least 3 weeks and DOI minting another week. There is no opportunity to generate private access tokens for referees.

[BiolImage Archive Policies](version 1.1) do not allow for embargo to be placed on deposited datasets.

Datasets deposited to [Recherche Data Gouy](https://www.recherche-data-gouy.com/) can be embargoed for up to 18 months. There is a possibility to create a private URL to enable access to unpublished datasets by referees.

[Zenodo](https:// zenodo.org/) permits to reserve access to deposited data for indefinite time.
InsuRAF will not recur to embargoing or other access limitation forms for its deposited
datasets beyond what may be imposed by their publication pipeline.

All articles and the data contained within them are either directly open-access, due
to the journal policy, or de facto open-access without delay due to the mandatory
deposition of the accepted author versions in HAL and univOAK simultaneously with
publication, under the French law (Code de la Recherche, article L. 533-4; Loi n°
2016-1321 du 7 octobre 2016 pour une République numérique, section 30) and
according to recommendations of Plan S Rights Retention Strategy (RRS) by
cOAlition S (ANR is a member of cOAlition S).

All published data which could not be deposited in trustworthy repositories and are
therefore preserved in house are made freely available by the Principal Investigator
(Alexandre SMIRNOV) upon request under the CC BY license.

5c. What methods or software tools are needed to access & use data?

Wherever possible, the data are stored in both the original and alternative widely
accepted open and portable formats. Therefore, potential users can choose to use
either (i) the original (often proprietary) software (as specified in Table 1) or (ii) more
common and sustainable alternatives working with general formats specified in
section 1a and Table 1. The latter include the Microsoft Office suite, PyMol, ImageJ,
Adobe Suite etc.

Specific data types and the software required to access and re-use the data
deposited in the ProteomeXchange Consortium are described in the Data
submission guidelines for the ProteomeXchange Consortium (version 3.0.1; October
13, 2019) and in PRIDE recommendations.

According to the [SOLEIL Data management policy](#), access to raw data, metadata and results stored by SOLEIL is possible for registered users via a searchable on-line catalog. SOLEIL engages to make available means to read, reduce and/or process raw data. Download of the data may be subject to volume restrictions.

BIA, while accepting a number of proprietary formats, encourages submission in
widely supported formats, e.g. OME-TIFF or OME-NGFF. All users can access and
download deposited data.

[Image Tools Resource (ITR)](https://www.idr.utexas.edu/data-management-software-tools) of IDR provides and maintains links to a wide variety of software tools to work with data.

Recherche Data Gouv (Displaying and exploring data, April 3, 2023) offers multiple
options to explore and download deposited data in function of their format and
access conditions. Any user can explore and download published datasets; only
authorised users can access unpublished datasets.

Zenodo General Policies (version 1.0) guarantee access to deposited data (in their
original format) to unregistered users.
## 5d. How will the application of a unique & persistent identifier (such as a Digital Object Identifier, DOI) to each dataset be ensured?

All selected trusted repositories assign persistent identifiers (PIDs) to deposited datasets with modalities following A Persistent Identifier (PID) policy for the European Open Science Cloud (October 2020). The ProteomeXchange Consortium associates all deposited datasets with its own unique and persistent identifiers (PXD and RPXD assigned by ProteomeCentral). Similarly, BIA assigns its own accession codes to each directly submitted dataset; by contrast, BIA keeps the PIDs of datasets imported from partner repositories. IDR Submission Guidelines (version 1.0, March 2017) introduced idr-codes as informal accession numbers unique to each dataset; however, they encourage using DOI (automatically minted for each dataset) as a formal way of dataset citation. SOLEIL is set to assign unique persistent identifiers to both its experiments and datasets. Recherche Data Gouv and Zenodo assign DOI to all deposited documents. See also section 5a for the special case of data versioning. ORCIDs to identify authors and contributors are added to metadata wherever possible or required by repositories.

# 6. Data management responsibilities & resources

## 6a. Who (for example role, position, & institution) will be responsible for data management (i.e. the data steward)?

Data stewardship responsibilities and their assignments are outlined in Table 11.

The Principal Investigator (Alexandre SMIRNOV) is responsible for the DMP implementation, review, and revision.

This 3rd version of the DMP is the definitive one; it is not subject to updating anymore.

<table><caption>**Table 11. Data stewardship responsibilities**</caption><thead><tr><td>Responsibility</td><td>Responsible</td></tr></thead><tbody><tr><td>Data capture</td><td>All Creators, SOLEIL, ProteomeXchange Consortium</td></tr><tr><td>Metadata production</td><td>All Creators, SOLEIL, SOLEIL Main Proposer (Benoît MASQUIDA), PRIDE Submitter</td></tr><tr><td>Maintenance of associated digital research resources</td><td>Lab Manager (Christelle GRUFFAZ)</td></tr><tr><td>Data quality</td><td>All Creators, SOLEIL, Principal Investigator (Alexandre SMIRNOV)</td></tr><tr><td>Storage & backup</td><td>All Creators, IT Manager (Bruno PARTOUCHE), SOLEIL, ProteomeXchange Consortium</td></tr><tr><td>Data archiving</td><td>Principal Investigator (Alexandre SMIRNOV)</td></tr><tr><td>Data sharing</td><td>Principal Investigator (Alexandre SMIRNOV), SOLEIL Main Proposer (Benoît MASQUIDA), SOLEIL, PRIDE Submitter, ProteomeXchange Consortium</td></tr></tbody></table>
**6b. What resources (for example financial & time) will be dedicated to data management & ensuring that data will be FAIR (Findable, Accessible, Interoperable, Re-usable)?**

The time effort from each data steward is estimated at ~1 person-month/year. It makes part of contract or statutory obligations of the stewards. Financial costs include expenses for hard drives (~600 €) and eventual access to the Data Centre (in process of negotiation). Data storage in all selected trusted repositories is free of charge.