# Managing and Opening Research Data in Life and Environmental Sciences

Eva Legras, Gaëlle Jaouen, Romuald Lorthioir, David Gasparotto

► To cite this version:

Eva Legras, Gaëlle Jaouen, Romuald Lorthioir, David Gasparotto. Managing and Opening Research Data in Life and Environmental Sciences. Printemps de la Donnée 2024, INRAE; Université Haute-Alsace; Université de Strasbourg; INSA; PNDB; AgroParisTech; Université de Lille; Sorbonne Université; Data Terra, Jun 2024, Paris, France. **hal-04682240**

HAL Id: hal-04682240

https://hal.inrae.fr/hal-04682240v1

Submitted on 30 Aug 2024

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publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
June 19, 2024

Training

# Managing and Opening Research Data in Life and Environmental Sciences

Eva Legras - Open Science and Research Data

Gaëlle Jaouen - Research Data

Romuald Lorthioir - Director of Digital Services

David Gasparotto - Director of Documentation and Cultural Heritage

AgroParisTech
Open Science, a new way of looking at research

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

Open science covers all the principles and actions facilitating:

- open and free dissemination of scientific research outputs

- transparency of research processes

- towards all parts of society

AgroParisTech open science policy, 2020.
# Open Science, a principle supported by increasingly firm policies

## › Article L112-1

Modifié par LOI n°2013-660 du 22 juillet 2013 - art. 16

La recherche publique a pour objectifs :

a) Le développement et le progrès de la recherche dans tous les domaines de la connaissance ;

b) La valorisation des résultats de la recherche au service de la société, qui s'appuie sur l'innovation et le transfert de technologie ;

c) Le partage et la diffusion des connaissances scientifiques en donnant priorité aux formats libres d'accès ;

c bis) Le développement d'une capacité d'expertise et d'appui aux associations et fondations, reconnues d'utilité publique, et aux politiques publiques menées pour répondre aux défis sociétaux, aux besoins sociaux, économiques et du développement durable ;

d) La formation à la recherche et par la recherche ;

e) L'organisation de l'accès libre aux données scientifiques.
Open Science, a principle supported by increasingly firm policies

<figure><img src="image_3.png" /></figure>

National policies

Public funders policies

Institutional policies
# Research Data (RD) and research openness

## RD challenges:

* Acceleration of the production and handling of RDs (technological boom)

* Definition of a legal and political framework for accessibility and reusability of RD

* Willingness to control the conditions of circulation and use of RDs (secure, reliable, traceable...)

## Willingness of research organisations (including AgroParisTech) to :

* Formalise common rules for both management and opening

* Support agents to develop good practices and train them to know their rights and duties

* Support research results validation

* Preserve the data heritage and the upcoming RDs (costly, unique/unreproduceable)

## Launch of several intra-agency RD policies : AgroParisTech (January 1st 2021), INRAE, IRD, CNRS, CIRAD...
# Research Data definition for AgroParisTech

Research data include _all factual records_ (digital, textual, visual or audio information...) collected, observed or created in the course of a research activity.

These data are _necessary for research construction_ and the establishment and validation of research results.

When the data have not yet been processed or contextualized, they are called raw data.

Between raw data and final results, _successive transformations of the data_ can serve as a basis for further research and can therefore also be considered as research data.
## When do we care?

* Project management

* Management of a lab, a team, a research infrastructure, a research unit...

* Management of the scientific heritage of an institution

## Research Data Life Cycle

<figure><img src="image_4.png" /></figure>

From University of Sidney
# Why anticipation is fundamental?

Have you ever:

- Lost a data file due to a breakdown?

- Inadvertently deleted data?

- Recovered data but didn't know what it meant?

- Reopened some of your old files and not remembered the details of the contents?

- Or remembered them perfectly but couldn't open and read them?

To prevent that, let's go step by step!
# Key principles and logigram (example of a research project)

## Project Setup

<figure><img src="image_5.png" /></figure>
# Legal framework of protection and openness of RD

*Legal and regulatory issues, an important point to anticipate!*

**Define RD ownership:** who is responsible for what on the project data?

⇒ RD nature? Producers? Partners? Agreement?

**Identify the obligations to be met from the data collection stage to the opening of results:**

Who can access and reuse the project data? When?

⇒ Secrecy, confidentiality

⇒ Communicability, openness (to anticipate)

Under what specific conditions data may be collected/used?

⇒ Ethical issues

⇒ Security issues

⇒ **Regulatory issues**
# Legal framework of protection and openness of RD

## Set of laws and regulations (French/European) to foster open science:

* Protect *data producers' rights* (paternity, traceability) and research subjects' rights (confidentiality, sensitivity, anonymity)

* Define *users' rights*

* Stimulate *collaborations*

*Opening up final digital public data is now a requirement, not an option!*

* Public research data = administrative documents

* Opening decision is up to the institution, not the researcher/engineer

## Some specific situations (which must be justified), especially: sensitive data, industrial secrecy, intellectual property

*« As open as possible, as closed as necessary »*

Beware: Anticipation and agreement between partners are fundamental, watch carefully the terms of contracts and conventions

Practice Sheet: Legal framework for research data
# Legal framework of protection and opening of RD

## CONFIDENTIAL DATA

* Communication is forbidden

* No third-party access except special circumstances

**Security:**

State, public and institutional

**Secrecy:**

* Professional secrecy: medical, banking and tax, judicial confidentiality

* Industrial and trade secret: methods, economic and financial information, commercial or industrial strategies

* Data resulting from a service provider contract with a private stakeholder

* Third party's knowledge provided as part of a collaboration or service + Dossier Technique Secret [secret technical file]

## DATA WHICH CAN BE COMMUNICATED UNDER CERTAIN CONDITIONS

* Confidential by default

* Third-party access is possible, can outline specific conditions

**Security:**

National scientific and technical potential (protected units and zones à régime restrictif [restricted areas])

**Secrecy:**

Statistical secrecy

**Intellectual property:**

Texts, sounds and images (exceptions: content subject to free licenses, TDM for research purposes)

**Personal data:**

Health data etc. (under certain conditions, see GDPR)

**Third-party data:**

Data resulting from collaborations with a private third-party during a research project

## MANDATORY DATA REPORTING

* Data reporting

* Access for individuals or groups of varying sizes depending on the kind of data

**CADA law:**

Right of citizens to access data produced by administrations, subject to legal provisions to the contrary.

**GDPR:**

Personal data upon request of the person concerned by the collected data.

**Data relating to emissions of substances into the environment:**
See Aarhus Convention

## MANDATORY DATA DISSEMINATION

* Automatic publication (do not wait for a request)

* Available to anyone with an internet connection

**Societal interest:**

Databases and data whose publication is of economic, social, environmental and health interest, produced in the framework of a public service activity, and in the absence of legal provisions to the contrary.

**CADA law:**

Data that has already been disclosed to a citizen in the context of a CADA request.

**Source codes:**

Codes for any programme created during a public service mission.

**Geographical data:**

See INSPIRE directive
# Legal framework of protection and opening of RD

**Confidential Data**

Highly sensitive data. Disclosure prohibited on principle.

**Security:**

State, public and institutional

**Secrecy:**

* Professional secrecy: medical, banking and tax, judicial confidentiality

* Industrial and trade secret: methods, economic and financial information, commercial or industrial strategies

* Data resulting from a service provider contract with a private stakeholder

* Third party's knowledge provided as part of a collaboration or service + Dossier Technique Secret [secret technical file]
# Legal framework of protection and opening of RD

## Conditionally accessible data

## Confidential by default

### Security:

National scientific and technical potential: protected units and ZRR (restricted areas)

### Secrecy:

Statistical secrecy

### Intellectual property:

Texts, sounds and images. Exceptions: content subject to free licenses, text and data mining (TDM) for research purposes

### Third-party data:

Data resulting from collaborations with a private third-party during a research project

### Personal data:

Health data, etc.: application of the General Data Protection Regulation (GDPR)
# Legal framework of protection and opening of RD

## Conditionally accessible data : focus on research under contract (Kévin Angers, APTI)

Before opening data/results => ask yourself the right questions to ensure that you are not failing in some of our obligations to our partners

### Q1 – In what context were the data/results obtained?

* **Own research**: Research on own funds, without contracting or using data obtained through a confidentiality agreement

-> The "classic" rules of openness apply

* **Research under contract: Provision, Collaboration, Consortium,...**

-> In this case, special rules may apply -> **Q2**

### Q2 – What are the terms of the contract?

Common points : obligation of confidentiality for the partners' own knowledge (information not in the public domain)

In the usual way :

* **Provision**: results belong to the Partner, it is not possible to publish them without express agreement

* **Collaboration/Consortium**: results are co-owned by the Parties, for the duration of the study and for a certain period of time thereafter, each Party shall submit its publication projects for validation. If the results can be protected by an intellectual property title, a delay of 18 months may be granted
# Legal framework of protection and opening of RD

## Conditionally accessible data : focus on the Nagoya Protocol on Access and Benefit-sharing

### Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from their Utilization (2010)

**Objective:** allow access to genetic resources (plants, animals, bacteria or other organisms) for research purposes while protecting the rights of indigenous communities holding specific knowledge.

### ABS mechanism : Access and Benefit-sharing

* Right to request compensation from countries holding genetic resources

* Please note: check the compatibility of these compensations with the regulations governing open data and any exceptions provided for by law (authorized confidentiality linked to intellectual property rights, etc.)

### Resources:

- INRAE, Protocole de Nagoya et Open Data, 2019

- FRB, Réglementation française sur l'accès et le partage des avantages (APA), 2019

<figure><img src="image_6.png" /></figure>
# Legal framework of protection and opening of RD

## Conditionally accessible data : focus on GDPR

### Issues:

Give EU citizens guaranteed **control** and **visibility** over their **personal data**, regardless of where it is stored and processed:

- What data is collected?

- How is it collected?

- Where is it stored?

- How are they updated?

- How can they be retrieved upon request?

### Perimeter:

**All economic and social actors** collecting personal data of European citizens: companies, associations, public bodies and their subcontractors.

### Two important concepts:

- responsibility of all those involved in the processing of personal data

- a posteriori control

<figure><img src="image_7.png" /></figure>
# Legal framework of protection and opening of RD

## Conditionally accessible data : focus on GDPR

**Personal data = any information relating to an identified or identifiable individual**

*These individuals must retain control of it!*

An individual can be identified:

* Directly: first and last name

* Indirectly: phone number, license plate, social security number, postal or e-mail address, but also voice or image

The identification can be achieved:

* from a single piece of data: name

* from the crossing of a set of data: a woman living at such and such an address, born on such and such a day and member of such and such an association...

On the other hand, the contact details of companies (the "Company A" with its postal address, the phone number of its switchboard and a generic contact e-mail "compagnie1@email.com") are not, in principle, personal data.
# Legal framework of protection and opening of RD

## Conditionally accessible data : focus on GDPR

In order to be implemented, any data processing must be based on one of the "legal bases" provided by the

GDPR = the lawfulness of the processing (article 6 GDPR)

Processing is lawful only if, and to the extent that, at least one of the following conditions is met:

a) consent

b) contract

c) legal obligation

d) vital interests of the individual

e) public interest mission

f) legitimate interests
# Legal framework of protection and opening of RD

## Conditionally accessible data : focus on GDPR

## Reminder of the 5 main principles of personal data protection rules:

* **The principle of finality**: the person in charge of a file can only record and use information on natural persons for a specific, legal and legitimate purpose;

* **The principle of proportionality and relevance**: the information recorded must be relevant and strictly necessary for the purpose of the file;

* **The principle of a limited retention period**: information about individuals cannot be kept in a file indefinitely. A precise retention period must be set, depending on the type of information recorded and the purpose of the file;

* **The principle of security and confidentiality**: the person responsible for the file must guarantee the security and confidentiality of the information it holds. In particular, he must ensure that only authorized persons have access to this information ;

* **Individuals' rights**: right to information, right to access, right to rectification, right to erasure, right to limitation of processing, right to data portability, right to opposition
# Legal framework of protection and opening of RD

## Conditionally accessible data : focus on GDPR

### Derogations for scientific research :

* **The principle of finality** : Admission of a certain degree of indeterminacy in the initial purposes of the data treatment: a data controller may change purposes as long as the new purposes remain "compatible" with the initial one.

* **The principle of proportionality and relevance** : **No derogation** from the principle of minimization.

* **The principle of a limited retention period** : Data may be retained beyond the time necessary to fulfil the research purpose (e.g. beyond the duration of a specific research project) as long as they are then retained solely for research purposes.

* **The principle of security and confidentiality** : To avoid any possibility of access by unauthorized persons, anonymization or pseudonymization procedures must be applied to guarantee the protection of personal data.

* **Individuals' rights** : The GDPR and the legislator have provided that several of these rights may be derogated from under certain conditions if these rights would make it impossible, seriously hinder or jeopardize the achievement of the purposes of the processing, and when such derogation is necessary to achieve these purposes. Assessment is made on a case-by-case basis.
# Legal framework of protection and opening of RD

## Conditionally accessible data : focus on GDPR

**ANONYMIZATION** = applying a set of techniques to personal data to make it impossible to re-identify the person, *by any means whatsoever*. **IRREVERSIBLE OPERATION**

**Usefulness**: Anonymization opens up the potential for re-using data, enabling users to exploit and share their data without infringing on the privacy of individuals. It also enables data to be stored without limitation.

**Example:** in a file containing the date of birth of individuals, replace this information with the year of birth only, or a time interval (for example: individuals aged between 25 and 30).

**PSEUDONYMIZATION** = processing of personal data so that it can no longer be attributed to an identified individual *without further information*. **REVERSIBLE OPERATION** (possibility of recovering a person's identity if additional information is available)

**Example:** replacing directly identifying data (surname, first names, etc.) with indirectly identifying data (alias, sequential number, etc.): a CV database in which only a person's surname and first names have been replaced by a number that corresponds uniquely to that person enables that person to be identified. Such a database would be considered pseudonymized rather than anonymized.
# Legal framework of protection and opening of RD

## Conditionally accessible data : focus on GDPR

### Derogations for scientific research :

* **The special case of "sensitive" data**

Racial or ethnic origin, political opinions, religious or philosophical beliefs, trade-union membership, genetic or biometric data for the purpose of uniquely identifying a natural person, sex life or sexual orientation, health data

⇒ Treatment prohibited on principle

⇒ Scientific research case : Notion of "[proportionality] to the objective pursued, respecting the essence of the right to data protection and providing for appropriate and specific measures to safeguard the fundamental rights and interests of the data subject"

* **The health data**

"All data relating to a data subject's health status that reveals information about the data subject's past, present, or future physical or mental health status."

⇒ Also included in the list of "special" data whose processing is in principle prohibited

⇒ The text stresses the interest of being able to process health data in the context of research: "By combining information from registers, researchers can gain new knowledge of great interest with regard to widespread medical problems such as cardiovascular disease, cancer and depression."
# Legal framework of protection and opening of RD

## Conditionally accessible data : focus on GDPR

* Identify data that falls under the scope of the GDPR

* Contact the person responsible for implementing the RGPD in organizations = Data Protection Officer (DPO) (or Délégué à la Protection des Données (DPD)) and the department responsible for IT services to:

* Ensure that the collection and processing are compliant

* Follow the procedure indicated by the DPO to declare the relevant data and processing

* Implement appropriate security guarantees for storage and access

* Designate a data correspondent in the project who will ensure compliance with the actions indicated

The RGPD is also a virtuous process of self-improvement in the management of its data. Take advantage of it!

AgroParisTech : [dpo@agroparistech.fr](mailto:dpo@agroparistech.fr)

For the Join Research Units : a DPO is designated among those of the parent institutes
# Legal framework of protection and opening of RD

## Mandatory data reporting

Data to be communicated to the person requesting it.

## CADA law

Right of citizens to access data produced by administrations, subject to legal provisions to the contrary.

Points of contact in the administrations : person responsible for access to administrative documents and issues relating to the re-use of public information = PRADA.

## Data relating to emissions of substances into the environment: Aarhus Convention

Data relating to emissions of substances into the environment must be communicated to anyone who requests it, except in very specific cases (threat to national security, impact on legal proceedings, communication infringing intellectual property rights). (guide d'analyse juridique, p. 12)
# Legal framework of protection and opening of RD

## Mandatory data dissemination

Automatic publication without waiting for a request. Available to anyone with an internet connection

## Societal interest :

Data bases and data whose publication is of economic, social, environmental and health interest, produced in the framework of a public service activity, and in the absence of legal provisions to the contrary.

## CADA law:

Data that has already been disclosed to a citizen in the context of a CADA request.

## Source codes:

Codes for any program created during a public service mission.

## Geographical data:

See INSPIRE directive : administrations must automatically publish online the geographic data they produce in electronic format: data needed to find your way in the territory; additional general data (e.g. altimetry, geology), thematic data (buildings, public utilities, etc.) (guide d'analyse juridique, p. 11)
# Key principles and logigram (example of a research project)

## Project Setup

<figure><img src="image_8.png" /></figure>

Data Management Plan

Duty? Rights? Legal, reglementary and ethical frameworks
# Data Management Plan

A synthetic, collaborative and iterative reminder document, listing all aspects of the project's RD management: production, documentation, organization, storage, sharing, protection, opening, archiving, human and financial resources

## Goals :

* Raise the points to be discussed for the good progress of the project

* Avoid unpleasant surprises and oversights

* Plan the necessary means and resources, anticipate the costs

* Reach agreement within the project, set the rules

<figure><img src="image_9.png" /></figure>

Many templates (ANR, Horizon Europe, institutional, AgroParisTech templates for Projects and Entities) available on DMP Opidor

## Precautionary measures :

* List the types of data and their projected volume to anticipate their particularities and the precautions to be taken (legal framework, infrastructures...)

* Choose one or more DMP manager(s) in the project

Practice Sheet: Data Management Plan
# Data Management Plan

AgroParisTech framework for a research project:

1) Information on the data management plan

2) Information on the project

3) Information about the data: origin; data quality and documentation

4) Legal, contractual and ethical measures

5) Management and infrastructure: storage; backups; sharing and collaborative access to files

6) Data opening

7) Archiving

An example of a first version of DMP for the [fictitious Forestcription project](#)
# Data Management Plan and funders

* For more and more funding agencies: Mandatory data management plan.

* Europe, ANR: First version at 6 months after the beginning of the project, updates during the project.

* Expected deliverable, financial consequences in case of non completion.

<figure><img src="image_10.png" /></figure>

Source : [Inrae, Rédiger un PGD, 2020](https://www.icpsr.umich.edu/icpsrweb/content/datamanagement/dmp/table.html)

Usage of Elements Across a Sample of Organizations
https://www.icpsr.umich.edu/icpsrweb/content/datamanagement/dmp/table.html
# Key principles and logigram (example of a research project)

<figure><img src="image_11.png" /></figure>
<figure><img src="image_12.png" /></figure>

See you at the **practical training**
for more details!

# Good Practices of Data Management

## Set of rules et reflexes to optimize:

- **Management** of files and of data in the files (documentation, naming, organization, versionning, homogeneity)

- **Collaboration** (open formats, collaborative tools)

- Limitation of confusion risks, mistakes, data loss (storage security, backup policy)

**Save time and... avoid disaster!!**

## Precautions to take:

- Remain realistic and take into account the available means (human, technical, financial)

- All members of the project follow the **same rules** so that **everyone understand each other**!

- **Document** as much as possible to be transparent and accessible to new project members or new colleagues
# Key principles and logigram (example of a research project)

<figure><img src="image_13.png" /></figure>
# Metadata

All the elements of description of the RD = <u>Identity card of the dataset:</u>

- context of acquisition (why, how, author(s)...)

- unit of measurement

- dates of collection, revision

- file format

- Etc.

To ensure <u>understanding and informed reusability of RDs, retain RD genesis information, facilitate collaborations</u>

**Precautions to take:**

Choose an appropriate <u>metadata standard</u> (Dublin Core, Darwin core, EML, DataCite...) according to the project and to the scientific community, have <u>the richest metadata as possible</u>

**Resources** : RDAlliance ; DCC ; FairSharing

Thesauri : Loterre ; INRAe Open vocabularies
Metadata

Example of Metadata from an ecological dataset, based on Dublin Core template:

```xml
▼<metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/"
           xmlns="http://dublincore.org/documents/dci-terms/">
    <dcterms:tittle>Paracou forest permanent plots details</dcterms:title>
    <dcterms:identifier>https://doi.org/10.18167/DVN1/8G8AHY</dcterms:identifier>
    <dcterms:creator>Jaouen, Gaëlle</dcterms:creator>
    <dcterms:publisher>CIRAD Dataverse</dcterms:publisher>
    <dcterms:issued>2022-10-20</dcterms:issued>
    <dcterms:modified>2022-10-20T17:38:20Z</dcterms:modified>
    <dcterms describe>Paracou Research Station</a>. Plots size, location and type. <a href="http://www.ecofog.gf/spip.php?article364">Guyafor
    network</a></dcterms describe>
    <dcterms:subject>Earth and Environmental Sciences</dcterms:subject>
    <dcterms:subject>Forest Ecology</dcterms:subject>
    <dcterms:subject>Forest Management</dcterms:subject>
    <dcterms:subject>Tropical Rain Forest</dcterms:subject>
    <dcterms:subject>Permanent Plots</dcterms:subject>
    <dcterms:subject>French Guiana</dcterms:subject>
    <dcterms:language>English</dcterms:language>
    <dcterms:isReferencedBy>Gourlet-Fleury Sylvie, Guehl Jean-Marc, Laroussinie Olivier. 2004. Ecology and management of a neotropical rainforest : lessons drawn from Paracou, a long-term
    experimental research site in French Guiana. Paris : Elsevier, 336 p., ISBN 2-84299-455-8, http://www.gip-ecofor.org/ecology-and-management-of-neotropical-rainforest-elsevier-ouvrage-
    telechargeable</dcterms:isReferencedBy>
    <dcterms:contributor>Jaouen, Gaëlle</dcterms:contributor>
    <dcterms:contributor>Derroire, Géraldine</dcterms:contributor>
    <dcterms:-dateSubmitted>2021-08-30</dcterms:-dateSubmitted>
    <dcterms:temporal>1984</dcterms:temporal>
    <dcterms:temporal>1984</dcterms:temporal>
    <dcterms:relation><a href="https://doi.org/10.18167/DVN1/B8FHHA">Guyafor Data Dictionary</a></dcterms:relation>
    <dcterms:type>Experimental data</dcterms:type>
    <dcterms:type>Observational data</dcterms:type>
    <dcterms:type>Temporal series</dcterms:type>
    <dcterms:spatial>French Guiana</dcterms:spatial>
    <dcterms:license>NONE</dcterms:license>
    <dcterms:rights><a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">img alt="Licence Creative Commons" style="border-width:0"
    src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /></ce(tte) œuvre est mise à disposition selon les termes de la <a rel="license"
    href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Licence Creative Commons Attribution - Pas d'Utilisation Commerciale - Partage dans les Mêmes Conditions 4.0 International</a>
    </dcterms:rights>
</metadata>
```
# Metadata

## Examples of thesauri for indexing datasets:

<figure><img src="image_14.png" /></figure>

Food and Agriculture Organization
of the United Nations

AGROVOC

About

Feedback

Help

| Interface language: English →

### AGROVOC Multilingual Thesaurus

Alphabetical

Hierarchy

A A B C D E F G H I J K L M

N O P Q R S S T U V W X Y Z

0-9

A horizons

Aaptosyax gruppus

Aaron's rod → Verbascum

ABA

abaca → Triplochiton scleroxylon

Abalistes stellaris

abalone culture

abalone fisheries → gastropod fisheries

abalone viral ganglioneuritis

abalonecs

abamectin

abandoned land

abattoir by-products

abattoirs →

Abottina rivularis

Content language

English →

<figure><img src="image_15.png" /></figure>

Search

### Vocabulary information

TITLE

AGROVOC Multilingual Thesaurus

LAST MODIFIED

Tuesday, March 5, 2024 08:24:42

TYPE

http://www.w3.org/2004/02/skos/core#ConceptScheme

SKOSXL-PREFLABEL

http://aims.fao

VOID:INDATASET

http://aims.fao

URI

http://aims.fao

### Resource counts by type

Type

Count

Concept

41263

<figure><img src="image_16.png" /></figure>

LOTERRE

### Agriculture et systèmes d'élevage

Accueil / Explorer / Naviguer / Vocabulaires / Agriculture et systèmes d'élevage

Liste

Hiérarchie

Groupes

A B C D E F G H I J L M O P R

S T U

A → agriculture biologique

activité agricole

agriculture

agriculture biologique

agriculture durable

agriculture résonnée

agropastalisme → pastoralisme

allotement

apprentissage animal

apprentissage cognitif → apprentissage animal

assolement

assurance qualité

### Description du vocabulaire

Titre

Agriculture et systèmes d'élevage

Sujet(s)

Science des productions animales

Sciences et techniques de l'agriculture

Description

Ce vocabulaire français-anglais définit 89 concepts d'agronomie.

Chaque concept est clairement défini et accompagné d'équivalents

anglais dans leur contexte.

Créateur(s)

Institut national de la recherche agronomique-INRA

Licence

http://www.etabl.gouv.fr/licence-ouverte-open-licence

Langue(s)

http://lexvo.org/id/iso639-3/eng

http://lexvo.org/id/iso639-3/fra

http://lexvo.org/id/iso639-3/spa

Version

Version 1

Date de dernière modification

jeudi 3 avril 2008 00:00:00

Nom d'attribution

Institut national de recherche pour l'agriculture, l'alimentation et l'environnement

http://www.inrae.fr

URL d'attribution

http://www.inrae.fr

Licence

http://creativecommons.org/licenses/by/4.0/

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## Vocabulaires Ouverts @ INRAE

### Thésaurus INRAE

Accueil / Vocabulaires / Thésaurus INRAE

A-Z

Hiérarchie

Groupes

Nouveautés

A A B C D E F G H I J K L

M N O P Q R S T U V W X Y Z

0-9

A.C. → analyse canonique

AAI → virus adeno-associé

abalsomment de nappe → rabattement de nappe

abandon agricole → dépise agricole

abandon d'exploitation → cessation d'activité

abattu

abattue d'abre

abattue d'abattu

Langue des données

français →

Chercher

### Description du vocabulaire

TITRE

DESCRIPTION

Thésaurus ouvert et partagé couvrant les domaines de recherche d'INRAE. Il sert de référentiel au sein de l'Institut pour indexer et annoter des documents, pages web, descriptions d'activités, jeux de données, à des fins de recherche ou d'analyse de l'information.

Contribuez en proposant des modifications, ajouts ou corrections de thésaurus en cliquant sur 'Nous contacter'

LANGUE

VERSION

DATE DE PUBLICATION

DATE DE DERNIÈRE
MODIFICATION

2.0

http://id.loc.gov/vocabulary/iso639-1/en

http://id.loc.gov/vocabulary/iso639-1/fr
# Key principles and logigram (example of a research project)

<figure><img src="image_18.png" /></figure>
# Data storage strategy

A few definitions:

**Storage of digital data:** refers to all methods and technologies for storing digital data

**Living data:** frequently modified. Most of the time, production data (project, experiment, collaboration)

**Dead data:** almost never updated (archive data)

**Raw data:** not yet processed or interpreted (ex: data produced by instruments).

**Always work on copies of raw data!**

*Storage is good but not enough.* It's necessary to:

**Map** the stored data, be able to **find and access** them easily and rapidly and be able to **read** them:

⇒ a necessary sequence otherwise the storage is useless!
# Data storage strategy

Access to digital data is ALWAYS done through a software compatible with the format of the data to be read :

- From a simple command (as edit, more,...)

- or software (Excel, R, QGis, OMERO....)

- up to LIMS (integrated digital platform for lab management)

The main storage devices for digital data:

- Local : USB key, computers, lab devices

- Local and secured : direct access and secured storage, harddisk connected to computer

- Internal network storage as NAS (Novell, Samba, Windows server...)

- Institutional clouds (Seafile, Teams, Nextcloud, Owncloud...)

- Data repositories

- Public clouds (Azure, Google, AWS...)

The cost of acquisition and operation, the access modalities, the ecological impact, the security issues, ... are very different from one storage solution to another.
# Research data lifecycle

**CREATING DATA:** designing research, DMPs, planning consent, locate existing data, data collection and management, capturing and creating metadata

CREATING
DATA

**RE-USING DATA:**
follow-up research, new research, undertake research reviews, scrutinising findings, teaching & learning

RE-USING
DATA

PROCESSING
DATA

**PROCESSING DATA:**
entering, transcribing, checking, validating and cleaning data, anonymising data, describing data, manage and store data

**ACCESS TO DATA:**
distributing data, sharing data, controlling access, establishing copyright, promoting data

GIVING
ACCESS TO
DATA

ANALYSING
DATA

**ANALYSING DATA:**
interpreting, & deriving data, producing outputs, authoring publications, preparing for sharing

PRESERVING
DATA

**PRESERVING DATA:** data storage, back-up & archiving, migrating to best format & medium, creating metadata and documentation

From the [UK Data Archive](https://www.ukdataarchive.org/)

Data storage strategy is to be considered and anticipated for each step of the research data lifecycle
# Data storage strategy

Storage strategy starts with the « magic question » to ask at each step of the data lifecycle in the research projet

=> The project needs

**What volume(s) of data?**

**What kind of data?**

**Which modalities of access?**

**What storage time?**

**Which degree of sensitivity?**

**What economic and ecological models?**

**What regulatory and contractual constraints?**

Answers are used to select candidate storage types: existing or to be planned.

Identifying this strategy does not mean that an answer can be provided in a simple and quick way

=> **Anticipation is the best way to guarantee the feasibility of the strategy**
# Data storage strategy

## What volume(s) of data?

- < 1 To: many available solutions

- Between 1To and 10 To: to be studied in advance

- > 10 To: to be studied very early on, according to the means defined by the storage strategy

## What kind of data?

- Flat files

- Databases

<table><thead><tr><th>A</th><th>B</th><th>C</th><th>D</th><th>E</th><th>F</th></tr></thead><tbody><tr><td>1</td><td>ANNÉE</td><td>Président</td><td>Vice-président</td><td>Secrétaire</td><td>Trésorier</td></tr><tr><td>2</td><td>1988</td><td>Michel Sardou</td><td>Bruno Pelletier</td><td>Daniel Lavoie</td><td>Johnny Hallyday</td></tr><tr><td>3</td><td>1989</td><td>Michel Sardou</td><td>Daniel Balavoine</td><td>Bruno Pelletier</td><td>Johnny Hallyday</td></tr><tr><td>4</td><td>1990</td><td>Michel Sardou</td><td>Daniel Balavoine</td><td>Bruno Pelletier</td><td>Gilles Vigneault</td></tr><tr><td>5</td><td>1991</td><td>Michel Sardou</td><td>Joe Dassin</td><td>Bruno Pelletier</td><td>Gilles Vigneault</td></tr><tr><td>6</td><td>1992</td><td>Michel Sardou</td><td>Joe Dassin</td><td>Bruno Pelletier</td><td>Gilles Vigneault</td></tr><tr><td>7</td><td>1993</td><td>Luc Flamondon</td><td>Michel Sardou</td><td>Bruno Pelletier</td><td>Gilles Vigneault</td></tr><tr><td>8</td><td>1994</td><td>Luc Flamondon</td><td>Michel Sardou</td><td>Bruno Pelletier</td><td>Gilles Vigneault</td></tr><tr><td>9</td><td>1995</td><td>Joe Dassin</td><td>Isabelle Boulay</td><td>Bruno Pelletier</td><td>Gilles Vigneault</td></tr><tr><td>10</td><td>1996</td><td>Joe Dassin</td><td>Diane Dufresne</td><td>Roch Voisine</td><td>Gilles Vigneault</td></tr><tr><td>11</td><td>1997</td><td>Bruno Pelletier</td><td>Diane Dufresne</td><td>Patrick Bruel</td><td>Gilles Vigneault</td></tr><tr><td>12</td><td>1998</td><td>Bruno Pelletier</td><td>Diane Dufresne</td><td>Patrick Bruel</td><td>Gilles Vigneault</td></tr></tbody></table>

<figure><img src="image_19.png" /></figure>

- Non-structured data: typically those of social networks
# Data storage strategy

**Which modalities of access?** how to access the data and for what purposes?

**Accesses:**

- Single or multi-user?

- One site, several sites? From the same institute, from several institutes, undefined...?

- Manual or automatic access (through a software)?

**Purposes:**

- Collaboration : access to several according to differentiated and defined rights (reading, modification, creation...)

- Treatment: local computations, intensive distributed calculations (**pay attention to data access performance, speed and latency in particular**)
# Data storage strategy

## What storage time? What regulatory and contractual constraints?

Often linked => GDPR, proof material, experiment reproducibility...

Storage time depends on the purpose of the storage and is not necessarily identical to the duration of the research project.

The duration of storage must take into account issues of data format and accessibility.

## Which degree of sensitivity?

Sensitivity (= impacts of loss, alteration, disclosure of stored data) is assessed (at a minimum) according to two axes:

**Safety** = the risks of accidental data loss

**Security** = the risks of attacks on data in order to steal, alter or divert them or to disturb the good running of the project.

European countries are increasingly the target of cyberattacks, including in the fields of research.
# Data storage strategy

## What economic and ecological models?

**Digital data = environmental cost:** electricity consumption, infrastructure manufacturing/transportation, etc.

## Levers for reflection: storage

- What to keep? For how long? What infrastructures? Don't "keep everything just in case": strike a balance between the files needed to validate results and the continuation of work!

- Pool services as much as possible

⇒ Examples : [AgroDataRing INRAE](https://www.inrae.fr/), [UPSaclay mesocenter](https://www.mesocenter.inrae.fr/), [mutualized lorrain DataCenter](https://www.datacenter.lorraine.gouv.fr/)

**Other ideas:** limit the number of attachments sent, favor open formats, limit the physical distance between storage and processing, etc.

## To go further:

Produire, traiter, analyser, stocker, diffuser, réutiliser des données : et la sobriété numérique dans tout ça ? , Journée d'études ULorraine, 14 mars 2024

Impacts environnementaux de la gestion et de l'ouverture des données, UParis-Saclay, 2024
# Data storage strategy

## AgroParisTech's own solutions:

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

**Seafile private cloud:** allows storing and sharing libraries, folders and files on centralized and backed up equipment. 100 Go per user of AgroParisTech's IT system. Dedicated storage spaces for each department, service, research unit, projects (on demand and after feasibility study).

A research project may contribute to Seafile storage space purchase => win-win relationship

**Storage/archiving solutions** are possible through the purchase of local disks (at the charge of the units but can be supported by the local IT service): secured disks connected to a computer with an economical and ecological moderated cost.

**No simple and available solutions to access structured data as databases or software.** Needs must be studied case by case and, thus, really anticipated.
# Data storage strategy

To sum up, some good practices of storage for the data of a research project:

Before the project :

- Identify the data life-cycle in the project

- Ask the magic question for each step of the cycle and each type of data

- Identify the storage solutions proposed by each of the project's partners

- Discuss between partners and establish the financial plan for the storage (free use, required investment, renting, partnership...)

During the project:

- Evaluate the adequacy between the allocated storage means and the needs

- Maintain control over storage, data mapping (location, access methods) and data reading (control of formats)
# Key principles and logigram (example of a research project)

<figure><img src="image_21.png" /></figure>
# FAIR Principles

## What is it?

Another reminder! 4 sets of precautions to take
to make RD Findable, Accessible, Interoperable and Reusable

<figure><img src="image_22.png" /></figure>

## What for?

Standardize (free and open formats, thesaurus/ontology, units...), document (metadata), trace (permanent identifier...)

<figure><img src="image_23.png" /></figure>

FAIR principles ≠ mandatory openness : FAIR data can remain confidential

## Big advantages?

Facilitates preservation, traceability, publication, collaborations for the opening and even before!

## Precautions to take?

Respect standards
# Key principles and logigram (example of a research project)

<figure><img src="image_24.png" /></figure>
# Data Repositories

Turnkey tool to publish your data = online database + FAIR principles

<figure><img src="image_25.png" /></figure>

Publish ≠ Share

**Publish = Open RDs to a targeted or larger community in a sustainable, efficient and legally defined manner**

Everything is **guided and framed**: metadata grids, data formatting, attribution of permanent identifiers (DOI, PURL...), search and discovery tools...

* **Choosing a data repository:** Thematic/Generalist? Governance? Sustainability? Certification? (ex CoreTrustSeal)

* Ensure your right to open your data

* Link your data to your scientific papers (thanks to permanent identifiers)

** Repositories Directories : re3data, FairSharing, OAD, OpenDOAR**

** Repositories examples: thematic : GBIF (biodiversity), European Nucleotide Archive (genetics), Nakala (humanities) ; generalist: Recherche Data Gouv ; institutional : Cirad DataVerse, IRD DataSuds...**
Ouvrir
la science !

Sélectionner
un entrepôt
thématique
de confiance
pour le dépôt
de données :
méthodologie
et analyse de
l'offre
existante

Collège Données de la recherche

Mars 2024

COSO, 2024

# Data Repositories

## Method for identifying trusted thematic data repositories established by the Committee for Open Science (March 2024):

### 7 exclusion criteria:

- No moderation of deposits

- Lack of permanent identifiers

- No guarantee of infrastructure continuity

- Practice of transfer of rights

- Excessive pricing policy

- Data localization outside the EU for certain types of data (notably personal data)

- Repository restricted by institutional affiliation

### 7 description criteria:

- Disciplinary field

- Accepted data

- Permanent identifier

- Perenniality of data

- Type of moderation

- Possibility of adding an embargo

- Volume limit

1st list based on these criteria:
49 identified repositories
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Dataverse

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Search

User Guide

Support

Gaëlle Jaouen

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ECOFOG

UMR EcoFoG

(AgroParisTech, CIRAD, CNRS, INRAE, Univ Antilles, Univ Guyane)

Ecologie des Forêts de Guyane

CIRAD Dataverse >

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Success! – You have successfully updated the theme for this dataverse

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Contact

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Share

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Edit

The Joint Research Unit Ecology of Guianan Forests (EcoFoG) gathers AgroParisTech, INRAE, Cirad, CNRS, the University of the French West Indies and the University of French Guiana. The purpose of the unit is to integrate approaches in ecology and material sciences to understand the relations between diversity and ecosystem functioning, considering the effects of logging, anthropic pressure and global change and to impulse innovation to valorize forest ressources, taking into account its large diversity and the particular constraints of moist tropical environment.

Search this dataverse...

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Advanced Search

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Add Data

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Dataverses (16)

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Datasets (70)

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Files (140)

Dataverse Category

Research Group (12)

Research Project (3)

Publication Year

2023 (28)

2022 (18)

2021 (2)

1 to 10 of 86 Results

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Sort

Paracou Disturbance Experiment - Level2 Treatment Plots

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23 avr. 2024 - Paracou

Derroire, Géraldine; Hérault, Bruno; Rossi, Vivien; Blanc, Lillian; Gourlet-Fleury, Sylvie; Schmitt, Laurent, 2022, "Paracou Disturbance Experiment - Level2 Treatment Plots", https://doi.org/10.18167/DVN1/HWTD4U, CIRAD Dataverse, V6, UNF 6: CUOD/JP83Ap6kX0x7J2P7w== [fileUNF]

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Paracou Research Station. In 1984, the Cirad team set up 12 square 9ha plots, among which 9 were subjected to logging induced disturbances in 1986-1988. The level 2 treatment plots have undergone selective felling for timber plus thinning by poison-girdling of all non-commercial...

Paracou Disturbance Experiment - Level1 Treatment Plots

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22 avr. 2024 - Paracou

Derroire, Géraldine; Hérault, Bruno; Rossi, Vivien; Blanc, Lillian; Gourlet-Fleury, Sylvie; Schmitt, Laurent, 2022, "Paracou Disturbance

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Example of a **Joint Research Unit data repository** on an institutional **DataVerse**
Focus on a national solution: Recherche Data Gouv

<figure><img src="image_48.png" /></figure>

* Open in July 2022: project based on the DataINRAE repository

* Technical solution: Dataverse

* Includes institutional spaces for several research institutes and universities

* Objectives:
  * to host datasets that do not have a recognized thematic repository

* to identify French research datasets

* Tutorials, Virtual classrooms

* Associated with an ongoing structuring of DR support: the data workshops
Data Repositories and Funders

<figure><img src="image_49.png" /></figure>

* Data openness = default principle ; justify cases of non-openness (exceptions allowed, to be specified in the DMP)

* License CC-BY or CC-O (or similar)

* Request to deposit data in a trusted repository, specifying in the metadata the tools needed for data reuse/validation (software, etc.)

* Digital or physical access required for data needed to validate conclusions presented in publications.

* Data management costs are eligible.

<figure><img src="image_50.png" /></figure>

* Openness of data = recommandation, no obligation at this stage. Application of the principle «as open as possible, as closed as necessary».

* Data management costs are eligible.

More information:

Europe

ANR
# Key principles and logigram (example of a research project)

<figure><img src="image_51.png" /></figure>
# Data Paper

**Article which describes precisely a dataset:** precise and complete genesis, reuse potential, data availability and access. BUT no scientific results, analyses or interpretations: may be published in association with a scientific paper for these aspects.

* *Informs the community* of the existence and value of the data

* *Synthesizes and facilitates data communication*

* *Completes the deposit* in a data repository and/or the publication of a scientific paper

* *Makes the time spent on data management worthwhile and acknowledged*

## Precautions to take:

* Choice of the journal (thematic, audience, peer review...)

* Choice of the data repository (compatibility)

* Respect of the funders' instructions

**Resources :** lists of specialized journals published by [INRAE](https://www.inrae.org), [Cirad](https://www.cirad.org), [GBIF](https://www.gbi.fao.org)
Data Paper

Example of a [DataPaper](#) and the linked [dataset publication (GBIF)](#):

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OPEN

DATA DESCRIPTOR

Fungi of French Guiana gathered in
a taxonomic, environmental and
molecular dataset

Received: 23 April 2019

Accepted: 3 September 2019

Published online: 16 October 2019

Gaëlle Jaouen<sup>1</sup>, Audrey Sagne<sup>2</sup>, Bart Puy<sup>3</sup>, Céline Decock<sup>4</sup>, Eliana Guiana<sup>5</sup>

Sophie Manzi<sup>5</sup>, Christopher Baraloto<sup>6</sup>, M

In Amazonia, the knowledge about Fungi re
is particularly the case in French Guiana where
coastal localities. Here, we aimed at filling the
particularly focusing on the Basidiomycota.

Fungi of French Guiana

Published by Unité Mixte de Recherche EcoFoG (Ecologie des Forêts de Guyane)

Schimann H • Roy M • Jaouen G

DATASET

PROJECT

METRICS

ACTIVITY

DOWNLOAD

5,219 OCCURRENCES

33 CITATIONS

Fungi of French Guiana: a dataset to gather taxonomic, environmental and molecular data thanks to a comprehensive, stratified and reproducible sampling scheme in 126 plots positioned representative of the main forests habitats

Publication date: April 18, 2019

Metadata last modified: April 18, 2019

Hosted by: GBIF France

Licence: CC BY 4.0

How to cite

DOI

10.15468/ymlrp

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5,219

Occurrences

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100%

With taxon match

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99.1%

With coordinates

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100%

With year
# Key principles and logigram (example of a research project)

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# Open Licenses

## What are we talking about?

Legal tool for publicly specifying the _diffusion and reuse conditions_ authorized by the rights holder of a content

## What is it for?

Choose what you authorize once and for all with your published data (reproduction, reuse, transformation, commercial use...)

## The main benefits?

_Synthesizes requirements_, free, easy to use. Protects data producers.

## What precautions should be taken?

_Check the specifics_ of the RDs, the agreements of the partners, the conditions requested by the project funders and the DataJournals or the journals in which you plan to publish your DataPapers

**Current licenses :** [Creative Commons](https://creativecommons.org/licenses/by/4.0/) (ex: [CC BY SA](https://openerti.ccmo.org/cc-by-sa)).

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**For French law :** [https://www.data.gouv.fr/fr/licences](https://www.data.gouv.fr/fr/licences)

**Practice Sheet :** Diffusion licenses
Open Licenses

Example of diffusion license on a data publication:

Fungi of French Guiana

Published by Unité Mixte de Recherche EcoFoG (Ecologie des Forêts de Guyane)

Schimann H • Roy M • Jaouen G

DATASET

PROJECT

METRICS

ACTIVITY

<figure><img src="image_59.png" /></figure>

DOWNLOAD

5,219 OCCURRENCES

Fungi of French Guiana: a dataset to gather taxonomic, environmental and molecular data thanks to a comprehensive, stratified and reproducible sampling scheme in 126 plots positioned representative of the main forests habitats

Publication date: April 18, 2019

Metadata last modified: April 18, 2019

Hosted by: GBIF France

Licence: CC BY 4.0

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How to cite

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DOI 10.15468/ymlrp

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5,219
Occurrences

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100%
With taxon match

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99.1%
With coordinates

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100%
With year

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# Key principles and logigram (example of a research project)

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# Archiving

« **Archiving** is a set of actions designed to guarantee the long-term accessibility of information (files, documents, data) that must or wishes to be preserved for legal, historical or cultural reasons. It involves rules (procedures), skills and infrastructures.» (*Wikipedia*)

## Archiving ≠ Storage:

- Data repositories: several years

- Archives: several decades

**Criteria to consider to archive:** non-reproducible data, strategic interest for the disciplinary field, technical features to ensure long-term conservation

Example of interlocutor in France: [Cines](https://www.cines.fr/)

**To go further:** [Digital Archiving of Research Data](https://www.grenoble-digital-archiving.org/en/digital-archiving-of-research-data), Université de Grenoble, 2019
# Key principles and logigram (example of a research project)

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# From data opening to the birth of a new project: Searching for data

## Why ?

Usually, researchers generate their own data, but being able to search for equivalent data already existing instead of collecting new experimental data can save you time and money.

-> To answer other research questions,

-> To participate in meta-analyzes,

-> To create or validate models,

-> To evaluate or reproduce results,

-> To optimize the use of data produced in a strict ethical framework (ex: animal experiments) or having mobilized costly resources (technical and financial resources, associated environmental issues, etc.).

-> To bring credibility and arguments to your work.
# Searching for data

## 4 main sources among a lot:

### 1. Scientific literature

The bibliographic search of articles can lead to find data in articles themselves or in appendices ("supplementary materials/files" or "data papers")

Data can also be published separately. In this case, the article will mention the reference of the published dataset, usually accessible in a data repository.

### 2. Data repositories

Online database allow to deposit, store and give accesses to standardized datasets described by rich metadata.

Data repositories thus ensures **accessibility, traceability and durability**.
# Searching for data

## 3. Institutional data portals

Institutions who regularly collect data (ex: statistics) can make their data available through their own portals.
Ex : Eurostat, [French government](https://www.gouv.fr), [MétéoFrance](https://www.meteo.fr)

## 4. Data search engines

A search engine allows you to quickly find and navigate between datasets or "supplementary material" published by researchers. There are many.

<figure><img src="image_69.png" /></figure>
# Searching for data

## Metadata description to optimize data reuse

Ensure <u>understanding and informed reusability</u> of RDs, <u>preserve information</u> about the genesis of RDs, facilitate <u>collaborations</u>

- Preserve the memory of the production context

- Allow collaborative work within the framework of the project or beyond

- Facilitate technical sharing and data interoperability
# Searching for data

## Some tools

### Datacite

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DataCite

Commons

Type to search...

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Works

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People

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Organizations

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Repositories

Search works by keyword(s) or DOI.

Search people by name, keyword(s), or ORCID iD.

Search organizations by organization name, keyword(s), or ROR ID.

Search repositories by repository name or keyword(s).

Some services: APIs, works with Crossref (DOI request or search), possibility to implement your ORCID (like for Hal)
# Searching for data

## Filter

Subjects ⚙

Content Types ⚙

Countries ⚙

AID systems ⚙

API ⚙

Certificates ⚙

Data access ⚙

Data access restrictions ⚙

Database access ⚙

Database access restrictions ⚙

Database licenses ☐

Apache License 2.0 (38)

BSD (12)

CC (149)

CC0 (72)

Copyrights (139)

ODC (13)

Public Domain (23)

other (146)

Data licenses ☐

Apache License 2.0 (47)

BSD (35)

CC (984)

CC0 (203)

## Re3data

## Browsing

Browse by subject

Browse by content type

Browse by country

## Facet

## Syntax

Search...

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Search

Toogle short help

* at the end of a keyword allows wildcard searches

" quotes can be used for searching phrases

+ represents an AND search (default)

| represents an OR search

- represents a NOT operation

( and ) implies priority

~N after a word specifies the desired edit distance (fuzziness)

~N after a phrase specifies the desired slop amount
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RÉPUBLIQUE
FRANÇAISE

recherche.data.gouv.fr

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Recherche ▼

À propos

Guide d'utilisation

Support

Français ▼

S'inscrire

Se connecter

Chercher dans cette collection...

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Recherche avancée

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Collections (793)

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Jeux de données (3 805)

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Fichiers (47 108)

1 à 10 de 4 598 résultats

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LASIE (La Rochelle Université)

27 mars 2024 La Rochelle Université

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Towards convergence of data repositories in France

Based on Dataverse open-source software

Sandbox repository (for tests and training)
# Key principles and logigram (example of a research project)

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# RD policy for AgroParisTech

* Formalization of data management at projects and research units levels; DMP highly recommended

* Implementation of FAIR principles

* Formalization of storage and archiving strategies and procedures

* Opening by default of data produced by the institution, in the absence of legal barriers; particular attention paid to data used in publications (limit the "data upon request" syndrome ...)

* Systematically anticipate issues related to the openness or confidentiality of data in all research projects

* **Choice of data repository:** give preference to the thematic repositories of reference in the discipline; as a second choice, the repositories of partners within the joint research units (DataInrae, DataCirad etc.); as a last alternative, the generalist repositories (Recherche Data Gouv, etc.). Additional criteria to consider (certification, governance, etc.)
# RD policy for AgroParisTech

* Encourage the publication of **Data Papers** when relevant.

* Use of open data to feed teaching materials, practical exercises, etc. when relevant.

* Consideration of the issue of RD in **student internship agreements**.

* Request for doctoral students to produce DMPs and to open/restitute the data produced during the thesis in the absence of regulatory barriers.

* Encourage the **reuse** of open data.

* Anticipation and attention to security issues and timetable of openness in case of **data leading to economic valorization**.

AgroParisTech est partenaire de deux ateliers de la donnée :

* [DatASaclay](coord. Université Paris-Saclay)

* [ADOC](coord. Université de Lorraine)

**Means:**

* [Training](#)

* Support

⇒ AgroParisTech research data cell :
[donnees-recherche@agroparistech.fr](mailto:recherche@agroparistech.fr)

[Further information here](#)

⇒ Research Data Correspondants in
research units

[Further information here](#)

* Consideration in the monitoring indicators of the research activity

* Openness policy associated with research support tools

[Research Data for AgroParisTech](#)
Additional resources (in French for the moment...) :

Practice Sheets AgroParisTech :

RD legal framework ; GDPR legal framwork for research ; Data Management Plan (DMP) ; AgroParisTech DMP templates ; Good management practices ;

Metadata ; Storage strategy for digital data ; FAIR principles ;

Data Repository ; Data paper ; Diffusion License

ToolBox « Open Science and research projects set up »

Doranum

Inrae DataPartage

CoopIST Cirad
Any questions ?

AgroParisTech

To go further, let's' make
some **practical work**!