# Data Management Plan for the Metabolic Profiling and Metabolomic Platform (P2M2)

Olivier Filangi, Nathalie Lacrampe, Solenne Berardocco, Hélène Sotin, Nathalie Marnet, Hugues Guichard, Sophie A.B. Charton

► To cite this version:

Olivier Filangi, Nathalie Lacrampe, Solenne Berardocco, Hélène Sotin, Nathalie Marnet, et al. Data Management Plan for the Metabolic Profiling and Metabolomic Platform (P2M2). 2.2, Inrae; ifpc. 2025. hal-05325416

HAL Id: hal-05325416

https://hal.inrae.fr/hal-05325416v1

Submitted on 22 Oct 2025

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# DATA MANAGEMENT PLAN

## METABOLIC PROFILING AND METABOLOMIC PLATFORM (P2M2)

### TABLE OF CONTENTS

* Data Management Plan - Metabolic Profiling and Metabolomic Platform P2M2

* Table of Contents

* Version History

* Plan information

* Project Information

* Functional Organizational Chart

* Research Outputs

* General Data Presentation

* Thematic Scope of the Data

* Customer File

* Data Acquisition Method

* General Data Protection Regulation (GDPR) Compliance

* Sample for metabolomics analysis

* Data Acquisition Method

* Data Origin

* Data Type

* Data Formats

* Method/Application applied to a group of samples

* Data Acquisition Method

* Data Origin

* Data Type

* Data Formats

* Raw Data (Chromatogram)

* Data Acquisition Method

* Data Type

* Data Formats

* Analysis report for the client

* Data Acquisition Method

* Data Origin

* Data Type

* Data Formats

* Software development

* Data Acquisition Method

* Data Origin
Data Type

Data Formats

Intellectual Property Rights

rights to the software

rights to the acquisition data

rights to the developed methods

Sensitivity of Data

Data Sharing

Data Organization and Documentation

File Management: Version Control, Naming, and Organization

Data Storage and Security

Evolution of AgroDataRing Archiving for P2M2 Platform Acquisition Data from 01/01/2023 to 01/01/2025

Data Archiving and Preservation

Scientific Computing Core Facility

Plan de gestion de données créé à l'aide de DMP OPIDOR, basé sur le modèle "INRAE - Modèle Structure" fourni par INRAE - Institut national de recherche pour l'agriculture, l'alimentation et l'environnement.
VERSION HISTORY

<table><thead><tr><td>Date</td><td>Version</td><td>Author</td></tr></thead><tbody><tr><td>04/05/2023</td><td>1.0</td><td>Olivier Filangi</td></tr><tr><td>03/07/2025</td><td>2.0</td><td>Olivier Filangi</td></tr><tr><td>22/07/2025</td><td>2.1</td><td>Olivier Filangi</td></tr><tr><td>02/09/2025</td><td>2.2</td><td>Nathalie Lacrampe</td></tr></tbody></table>
PLAN INFORMATION

<table><tr><td>Objective/scope of the plan</td><td>This document aims to ensure the quality, traceability, and reusability of the data collected and generated by the Metabolic Profiling and Metabolomic Platform (P2M2). The specific objectives of this DMP include:<br/>1. Data collection and storage: defines the data collection and storage modalities for metabolomics data, including file formats, metadata to collect, data backup and protection strategies.<br/>2. Data management: specifies the procedures for managing metabolomics data, including documentation of experimental protocols, unique identifiers for each sample and analysis, and data access and sharing rights management.<br/>3. Data analysis and interpretation: describes the methods for analyzing and interpreting metabolomics data, including the software used, processing algorithms, data normalization, and quality.<br/>4. Data archiving and sharing: defines the modalities for archiving and sharing metabolomics data, including existing data repositories or national or international archives, data sharing policies, and data usage licenses.<br/>5. Training and awareness: provides for training and awareness-raising activities for metabolomics platform users on good practices for managing metabolomics data, as well as on policies and regulations related to data use and sharing.</td></tr><tr><td>Fields of Research (according to the OECD classification)</td><td>Agricultural biotechnology, Agriculture, forestry, and fisheries, Biological sciences (Natural sciences), Chemical engineering, Chemical sciences, Computer and information sciences</td></tr><tr><td>Language</td><td>eng</td></tr><tr><td>Date of creation</td><td>2023-05-03</td></tr><tr><td>Date of last modification</td><td>22 juil. 2025</td></tr><tr><td>Identifier</td><td>https://doi.org/10.57745/JBPH2Q</td></tr><tr><td>Identifier type</td><td>DOI</td></tr><tr><td>License</td><td>Etalab Open License 2.0</td></tr></table>
PROJECT INFORMATION

<table><tr><td>Project Title</td><td>Data Management Plan - Metabolic Profiling and Metabolomic Platform P2M2</td></tr><tr><td>Acronym</td><td>P2M2</td></tr><tr><td>Summary</td><td>The P2M2 (Metabolic Profiling and Metabolomic Platform) is a state-of-the-art analytical facility dedicated to the comprehensive and quantitative analysis of small molecules in complex biological systems, with a focus on plants and agronomic traits of interest. The platform employs cutting-edge mass spectrometry-based metabolomics technologies and bioinformatics tools to provide a wide range of metabolomics services to academic and industrial users studying plants.<br>The P2M2 platform offers high-throughput metabolomic profiling services for various plant tissues, including leaves, roots, seeds, and fruits. The goal is to identify and quantify metabolites that play crucial roles in plant growth, development, and stress response. The platform is equipped with a range of mass spectrometers and chromatography systems, enabling the analysis of a broad spectrum of plant metabolites.<br>Additionally, the platform integrates bioinformatics software development to better structure its data and aid in interpreting acquisition data. This enhances the management and processing of the data generated, ensuring high-quality and reliable results for its users.<br>The platform operates under ISO 9001 certification. A dedicated process is in place to manage the IT hardware infrastructure and the information system, thus ensuring the quality and reliability of the services provided.</td></tr><tr><td>Start Date</td><td>2022-01-03</td></tr><tr><td>Partners</td><td><ul><li>Institut Français des Productions Cidricoles (https://ifpc.eu)</li><li>Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (https://inrae.fr)</li><li>Université Rennes (https://www.univ-rennes.fr/)</li></ul></td></tr><tr><td>Attached Department at INRAE</td><td><ul><li>TRANSFORM (https://www.inrae.fr/en/divisions/transform): Science for Food, Bioproducts and Waste Engineering</li><li>BAP (https://www.inrae.fr/en/divisions/bap): Plant Biology and Breeding</li></ul></td></tr><tr><td>Web site</td><td>https://p2m2.hub.inrae.fr/</td></tr><tr><td>Sponsor/Funding Agency</td><td>INRAE, ANR, University of Rennes, Région Bretagne</td></tr></table>
FUNCTIONAL ORGANIZATIONAL CHART

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# Functional Organizational Chart

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## RESEARCH OUTPUTS

The primary research outputs of the P2M2 platform include raw data sets such as chromatograms, comprehensive client analysis reports that interpret these data, physical samples used for metabolomics analysis, detailed methodologies applied to groups of samples, and proprietary software developed to enhance data processing and analysis.

1. Customer File

2. Sample for Metabolomics Analysis

3. Method/Application Applied to a Group of Samples

4. RAW Data

5. Client Analysis Report

6. Software Development

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**Customer File**

The customer file stores basic contact and affiliation details, accessible only to authorized P2M2 staff.

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**Sample for Metabolomics Analysis**

Samples for metabolomics analysis are experimentally derived and provided by the applicant.

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**RAW Data**

Raw data are instrument-generated files in various proprietary formats, serving as the initial input for P2M2's analysis pipeline.

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**Method/Application Applied to a Group of Samples**

A method involves using GC, LC, and MS techniques to analyze samples and generate data files.

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**Client Analysis Report**

Acquired data is statistically processed and compared to analysis standards to produce targeted and reliable analysis reports.

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**Software Development**

Software for metabolomics is developed in R, Scala, Python, andTypeScript to support P2M2's research needs, and is distributed via institutional and public repositories.
# GENERAL DATA PRESENTATION

## THEMATIC SCOPE OF THE DATA

The data generated and analyzed by the P2M2 platform encompasses a broad range of thematic areas, reflecting the diverse applications of metabolomics in scientific research and industrial development. The key thematic scopes include:

* Plant Health and Pathology

* Plant Breeding and Plant Products

* Omics

* Food and Food Processing

* Chemistry and Chemical Engineering

# CUSTOMER FILE

The customer file contains the following information: first name, last name, email address, and affiliated laboratory. This data is accessible exclusively to authorized P2M2 personnel.

## DATA ACQUISITION METHOD

The MAMA - MetaboHUB Analyses MManager information system is used as the primary tool for managing customer files and processing requests. Data acquisition is performed as follows:

* Customer data (name, surname, email address, affiliated laboratory) is collected through secure online forms integrated within the MAMA system portal during service requests or project submissions.

* Users authenticate themselves to access the system, and submission of information is managed via encrypted communication channels.

* The platform manages the entire workflow from request input to data analysis and results delivery, ensuring traceability at every step.

* Collected data is only accessible to authorized staff members involved in project handling and data processing, with access rights managed by the platform's user profiles

## GENERAL DATA PROTECTION REGULATION (GDPR) COMPLIANCE

The management of customer data within the MAMA platform adheres strictly to GDPR and personal data protection requirements:

* Data Controller: INRAE, UMR1332 BFP - MetaboHUB acts as the data controller for all customer information processed through the system.

* Purpose of Processing: Personal data is collected and processed solely for the management of service requests, communication with users, project monitoring, and compliance with legal obligations.

* Minimal Data Collection: Only the minimum necessary data (identification, contact, affiliation) is collected, with mandatory/optional fields clearly indicated at data entry.

* Storage and Retention: Data is securely stored for the duration of service use and archived for legally mandated periods before permanent deletion

* User Rights: In accordance with GDPR, users have the right to access, rectify, erase, restrict, object to the processing, and request portability of their data. Procedures for exercising these rights are communicated on the MetaboHUB/ MAMA website
## DATA ACQUISITION METHOD

**The data acquisition method** involves samples that are provided by the applicant.

### DATA ORIGIN

The origin of the samples is experimentation.

### DATA TYPE

**The data type** for this acquisition method is a physical object, specifically different tissues and/or structures such as leaves, stems, roots, flowers, fruits, or seeds.

### DATA FORMATS

For this process, there are no specific data formats required.

## METHOD/APPLICATION APPLIED TO A GROUP OF SAMPLES

### DATA ACQUISITION METHOD

**The acquisition methods** involve setting up Gas Chromatography (GC), Liquid Chromatography (LC), and Mass Spectrometry (MS) analyses.

### DATA ORIGIN

An analytical method is manually performed based on what one intends to detect using Mass Spectrometry (MS). This approach allows for customizing the machine settings to ultimately retrieve traces of relevant chemical elements for a specific study and research topic.

### DATA TYPE

These **files** are created using the constructor software and can be saved in the constructor's format for backup purposes. Additionally, these files can also be manually constructed in Excel formats to be integrated into the constructor software.

### DATA FORMATS

**The data format** for this acquisition method is proprietary.

## RAW DATA (CHROMATOGRAM)

### DATA ACQUISITION METHOD

Raw data refers to the output generated by analytical instruments and is typically encoded in a manufacturer-specific format. At P2M2, a range of formats are utilized for the acquisition of data generated by its mass spectrometry-based metabolomics technologies. These formats include Xcalibur, GCMS Solution, MassLynx, Open Lab, Empower, Chromeleon, and ChemStation. These acquisition data files contain critical information about the composition of a
sample and are initially saved on the local disks of the control PCs. They represent the first step in the analysis pipeline of the P2M2 platform and are used to generate more advanced data processing and analysis.

## DATA TYPE

Metabolomics acquisition data refers to the **RAW Data** generated by mass spectrometry-based technologies that capture signals representing the small molecules present in a given biological sample. The format of this acquisition data is typically proprietary, encoded in binary format by the instrument manufacturer.

## DATA FORMATS

The P2M2 platform utilizes several proprietary data formats generated by acquisition software from various mass spectrometry-based technologies. These formats ensure that the raw data retains its high resolution and integrity during the initial stages of data collection. The primary data formats used are:

* **Xcalibur**: Used for capturing data from Thermo Scientific mass spectrometers.

* **GCMS Solution**: Employed for data acquisition from Shimadzu gas chromatography-mass spectrometry systems.

* **MassLynx**: The software associated with Waters mass spectrometry instruments.

* **Open Lab**: Utilized for Agilent gas chromatography and mass spectrometry systems.

* **Empower**: Specific to Waters HPLC (High-Performance Liquid Chromatography) systems.

* **Chromleon**: Used for Thermo Scientific chromatography systems.

* **ChemStation**: Associated with Agilent chromatography and mass spectrometry solutions.

These proprietary formats encode the raw data in binary format specific to each manufacturer, ensuring the precision and reliability needed for advanced metabolomics analysis.

## ANALYSIS REPORT FOR THE CLIENT

### DATA ACQUISITION METHOD

Acquired data is statistically processed and compared to analysis standards to produce targeted and reliable analysis reports.

### DATA ORIGIN

**Observation**: data collection through direct or indirect observation of the phenomenon being studied

**Experimentation**: data collection through controlled tests and experiments to evaluate relationships between variables

### DATA TYPE

Targeted Metabolomics Analysis Reports (Analysis report for the client) is text data that provide in-depth insights into specific metabolites using Mass Spectrometry (MS) data. These reports offer detailed information on metabolite identification and quantification.

### DATA FORMATS

The Targeted Metabolomics Analysis Reports are delivered in docx format, providing a readable text document that includes detailed information on metabolite identification and quantification.
## DATA ACQUISITION METHOD

The data acquisition method involves software sources from P2M2, which are developed using R, Scala, and TypeScript programming languages. We utilize the institutional software forge INRAE (https://forge.inrae.fr/p2m2) and the GitHub platform (https://www.github.com/p2m2) for this process.

## DATA ORIGIN

Software production arises from the platform's need for specific processing related to the activities of the P2M2 platform. It concerns the discovery of new molecules in the context of an agronomic study of the plants studied, as well as the development of information systems and business databases.

## DATA TYPE

The production consists of executable software for different environments (web for online use, computing clusters for performance). This software is available from software forges as well as repositories such as Docker Hub, Maven Central Repository, and rpm.

## DATA FORMATS

The software is developed in various programming languages: R, Scala, Python andTypeScript.

## IMPLEMENTATION AND DATABASE REFERENCES

### BrachemDB:

A database dedicated to chemical compounds and agronomically relevant biomarkers related to plant resistance and biotic and abiotic stress themes. This database is maintained manually by the IGEPP unit and serves as a shared bibliographic resource across different projects within the platform.

### Positional-carbon13-enrichment:

The 13C positional enrichments are computed by combining mean 13C enrichments of several mass fragments sharing part of the carbon backbone from the same TMS derivative. A specific workflow has been developed to calculate these positional enrichments directly from the outputs of our 13C processing method.

### AdducTrackR:

An R package designed to compute all possible peptide oligomers from the exact masses of proteinogenic amino acids, for any specified degree of oligomerization. It supports in silico modeling of peptide-polyphenol interactions, aiding the annotation of high-resolution mass spectrometry data.

## INTELLECTUAL PROPERTY RIGHTS

### RIGHTS TO THE SOFTWARE

The P2M2 platform retains institutional control over the source code of software developed by its staff. These software products are released under either the MIT license or the GNU General Public License (GPL).

* Software released under the MIT license may be freely used, modified, and redistributed, including in private, academic, or commercial contexts, with minimal restrictions other than attribution.

* Software released under the GNU GPL license is also made openly available to the scientific community. However, any redistribution, modification, or reuse of GPL-licensed software in another project—including in private or commercial contexts—must comply with the GPL's "copyleft" requirement. This means that any derivative works must also be distributed under the same GPL license and include the corresponding source code, thus ensuring ongoing openness and accessibility.
Both licensing models facilitate the development, adaptation, and sharing of these tools by the broader community, but users should ensure their intended use is compatible with the specific license applied to each software product or library.

## RIGHTS TO THE ACQUISITION DATA

Ownership of acquisition data and information resulting from the processing of samples belongs to the applicant.

## External Service Provision

In the case of external service provision, P2M2 does not claim ownership of the generated data. The P2M2 platform is solely responsible for secure storage of the data and does not exploit the data commercially or otherwise. Clients are informed and must agree that their data may be processed using open-source software and, where necessary, on foreign-based platforms.

## Collaborative Projects

In collaborative projects where P2M2 acts as a partner, the generated data are co-owned by the involved parties. Data ownership and usage are governed by established consortium or project agreements specific to the funding or contractual arrangements underpinning the study.

## RIGHTS TO THE DEVELOPED METHODS

All analytical methods and protocols developed by the P2M2 platform remain the intellectual property of the platform itself.

## SENSITIVITY OF DATA

Chromatogram raw data, processed data, and analysis reports generated for clients, as well as physical samples, are not published or made publicly accessible by the platform. Access to these resources is strictly restricted to platform personnel and the applicant. Data is available exclusively through the platform's internal network or, upon the client's request, via a secure file transfer system. These measures ensure that all sensitive information remains confidential and is only shared with authorized individuals.

Analytical methods developed by the platform may, however, be published in scientific literature, provided they do not disclose any client-specific confidential information.

The source codes of the platform are published on the GitHub repository (https://github.com/p2m2) and on the institutional INRAE forge (https://www.forge.inrae.fr/p2m2). The source codes and releases of the software produced, whose code is not sensitive, are primarily published on the GitHub platform. The software and database developments, such as the dereplication database for the study of plant matrices, are hosted in private repositories on the institutional forge.

## DATA SHARING

There is no formal requirement to share the data. However, any software source code developed within the framework of P2M2 activities is made available on GitHub and the institutional Gitlab under the p2m2 organization. This ensures that the developed tools and resources are accessible to the community, while respecting the privacy and confidentiality of the data used in the projects. The software sources are released under the MIT license to facilitate reuse by both the academic and industrial communities.

Samples, processed data are not reused or made available for reuse.

Specific acquisition software is required to read the acquisition data and method files. The main software tools used for this purpose include Xcalibur, GCMS Solution, MassLynx, Open Lab, Empower, Chromeleon, and ChemStation. For analysis reports, the Microsoft Office Suite is used.

Data will be shared through publication on the Data INRAE portal and on GitHub, with open access for everyone. The data will be distributed under an Open License (https://www.etalab.gouv.fr/licence-ouverte-open-licence), which is compatible with the CC-BY license.
DATA ORGANIZATION AND DOCUMENTATION

Metabolomics data acquisition and processing follow a standardized and secure workflow designed to ensure data integrity, accessibility, and long-term preservation. Initially, raw data are generated using liquid chromatography (LC) or gas chromatography (GC) coupled with mass spectrometry (MS) techniques. Proprietary acquisition software such as Xcalibur, GCMS Solution, MassLynx, Open Lab, Empower, Chromeleon, and ChemStation are used to produce these raw data files from the acquisition methods.

To minimize the risk of data loss and ensure robust security, a three-replication infrastructure is implemented for chromatogram data. This infrastructure relies on centralized backups, with data stored both in a secure Data Center and on the AgroDataring platform[2]. Data transfer and archiving from acquisition PCs are fully automated, reinforcing security without disrupting laboratory workflows or affecting the connection between laboratory instruments and acquisition computers. Acquisition PCs are kept on a secure network with no Internet access, and any relocation of these PCs requires intervention from IT services to reconfigure network access, maintaining a strict security perimeter. Remote access for service providers is possible via PEPS accounts, allowing efficient collaboration while upholding strict security controls. Users benefit from permanent, secure access to scientific data from their workstations, with all sensitive files managed to prevent unauthorized access or accidental loss.

<figure><img src="image_12.png" /><figcaption>Network and storage architecture for securing acquisition data at P2M2</figcaption></figure>

Once the raw data are secured, the manufacturer's software is used to preprocess the data, removing noise and enhancing quality. For targeted analyses, further workflow steps (such as those defined in Workflow4Metabolomics[1]) are applied according to protocols established with the client. Basic statistical analyses are performed using spreadsheet software, and analysis reports are generated with office tools such as Microsoft Office.

Software source code developed for these processes is created using open-source editors and maintained under version control on GitHub and the INRAE forge platform. The development workflow is organized into different branches, with production versions clearly identified by tags, and the entire software lifecycle is managed through these collaborative platforms.

<footnote>[1] Franck Giacomoni, Gildas Le Corguillé, Misharl Monsoor, Marion Landi, Pierre Pericard, et al., Workflow4Metabolomics: A collaborative research infrastructure for computational metabolomics. Bioinformatics, 2015, 31 (9), pp.1493-1495. (10.1093/bioinformatics/btu813). (hal-01123263)</footnote>

<footnote>[2] Pierre Adenot, Stéphane Bansard, David Benaben, Veronique Brunaud, Christophe Caron, et al., AgroDataRing: A shared and mutualized infrastructure for long-term data storage. Cahier des Techniques de l'INRA, 2018, pp.1-7. (hal-02097192)</footnote>
## FILE MANAGEMENT: VERSION CONTROL, NAMING, AND ORGANIZATION

Data files are organized by project but are not subject to version control. In contrast, software source code is also organized into projects but is managed using version control systems, allowing for tracking of changes and maintenance of different development stages. This approach ensures clear organization for both data and software, while providing robust change management for code development.

## Data Quality Control Process

For software sources, a continuous integration process is used, incorporating the definition and execution of unit tests to automatically verify code quality and functionality at each stage of development. This comprehensive approach ensures both data and software meet rigorous quality requirements throughout their lifecycle.

## DATA STORAGE AND SECURITY

At present, the organization's information systems have not undergone a formal risk analysis or approval process. Digital data—including raw files, reports, and analytical methods—are stored and accessed by users via Nextcloud/Genouest-AgroDataRing and the P2M2 server, which is located in the Île-de-France data center. This infrastructure provides secure, centralized storage and facilitates easy access, while minimizing the risk of data loss.

Software source code is managed using collaborative platforms such as GitHub and the INRAE forge, enabling effective version control and secure, distributed development.

Data transfers are secured using the SSH protocol, which encrypts all exchanged information and authenticates users to prevent unauthorized access.

Currently, 6.5 TB of data are stored on AgroDataRing, 2.6 TB locally, and 3.9 TB on datacenter network storage. These data are primarily acquisition data, and the total volume continues to grow regularly due to extensive use of high-throughput acquisition devices on the platform. AgroDataRing storage is scheduled for renewal in 2026. Local storage has a total capacity of 14 TB, while datacenter network storage can be expanded as needed through requests to the INRAE IT department. This setup ensures our infrastructure can adapt to the increasing demands for data storage and provides reliability, accessibility, and long-term preservation of both data and software assets.

<figure><img src="image_13.png" /><figcaption>EVOLUTION OF AGRODATA RING ARCHIVING FOR P2M2 PLATFORM ACQUISITION DATA FROM 01/01/2023 TO 01/01/2025</figcaption></figure>
## DATA ARCHIVING AND PRESERVATION

A dedicated data archiving policy is necessary for acquisition of data. In contrast, analysis reports and method files are less critical and do not require specific archiving procedures.

For targeted analyses, data are retained for two years. Non-targeted analyses are kept for the duration of the project, following Genouest's policy. Archived data remain available for as long as the AgroDataRing project is active.

## SCIENTIFIC COMPUTING CORE FACILITY

We benefit from the Biogenouest regional mesocenter, which provides both storage capacity and significant computing power through a cluster equipped with GPU hardware resources. This mesocenter also offers an OpenStack cloud, facilitating the deployment of tools developed as web applications. In parallel, we rely on the IFB infrastructure, which provides similar services.

In 2024, we requested additional computing hours and locally deployed a conversational agent based on LLMs via the university project Ragarenn.

Since the end of 2024, we have submitted an eDARI request (AUTOANNOT project) to access the national GENCI infrastructure, aiming to obtain substantial GPU resources on Jean Zay V100 at IDRIS (20,000 GPU hours allocated), which are essential for constructing the FORUM Plants knowledge graph.