Sharing

FAIR Data Sharing & Open Research Data Support for FBM UNIL-CHUV Researchers

1. Criteria for Repository Selection

Prioritize Domain-Specific or Data Type-Specific Repositories

Before considering a generalist repository like Zenodo, first check if a data type-specific or domain-specific repository is available. These specialized platforms often provide better visibility and functionality tailored to your field.

For example:

  • If you work with large imaging datasets, consider repositories such as IDR (Image Data Resource) or BioImage Archive (BIA).
  • If your research is in neuroscience, a specialized repository in that domain may offer greater visibility and discoverability than a generalist platform.

Your repository must meet the following criteria:

  • Free of Charge, Regardless of Dataset Size → freely accessible repository with no cost for submission or access.
  • Tailored Infrastructure for the interested data type → Optimized to host raw and processed data type, with tools for visualizing, downloading, or querying complex datasets.
  • Integrated with resources to enhance data accessibility and utility.
  • Reliable Long-Term Preservation → the repository Follows robust policies including infrastructure continuity, regular backups, and lifecycle data managementTo support this decision, we provide FAIRshare Explorer, a tool listing recommended repositories by data type and research discipline. Please consult it before choosing where to deposit your data.
  • FAIR-Compliant by Design → insures that sample metadata are Findable, Accessible, Interoperable, and Reusable, following community metadata standards and supporting compliance with institutional and funder Open Science mandates (e.g., SNSF, Horizon Europe).
  • Flexible Licensing → Ideally supports open licensing options such as Creative Commons CC BY 4.0, enabling broad data reuse while ensuring proper attribution.
  • European/National Hosting → Ensures compliance with data protection and Open Science standards and policies. https://www.ebi.ac.uk/long-term-data-preservation/

To support this decision, we provide FAIRshare Explorer, a tool listing recommended repositories by data type and research discipline. Please consult it before choosing where to deposit your data.

Mandatory Local Archiving

All data supporting a publication (raw, processed, and analytical) must also be archived on long-term storage provided by DCSR-UNIL or DSI-CHUV (link).

2. Use FAIRShare Explorer ©DSBU Tool to find adapted repository and file formats/metadata Standards

Your Guide to find FAIR, Secure, and Sustainable Data Repositories

What you’ll find in the Explorer:

  • Complete list of repositories with key informative details for efficient submission: data types and formats, submission process, metadata standards, sharing licenses, and more 
  • Repository Usage Recommendations from the FBM Dean’s Office and DSBU
  • Indication on the type of support provided by the DSBU
  • Filters Tailored for UNIL–CHUV Researcher
  • A “Best Fit Repository Finder” to quickly identify:
    • Recommended repositories (green)
    • Repositories to avoid (red), based on the criteria above

Link to FAIRShare Explorer ©DSBU  https://dsbu.unil.ch/datasquid-fbm/repositories

→ Detailed explanations about the tool : link

3. Publishing Your Dataset to Data Type-Specific or Discipline-Specific Repositories

Scope: Designed for specific or disciplinary data types, such as:

  • Genomic data (ENA: nucleotide sequence data, EGA: human data that requires controlled access).
  • RNA sequencing.
  • Electron microscopy (EMPIAR: raw image data).
  • FACS data (FlowRepository: database of flow cytometry experiments)
  • Light microscopy (BioImage Archive: bioimaging data).
  • Open research infrastructure that gathers data, tools and computing facilities for brain-related research (EBRAINS)

Use Recommended Standards for File Formats and Metadata Schemas

To ensure interoperability, discoverability, and long-term reuse, always follow the file formats and metadata schemas endorsed by your chosen repository. These community-driven standards are tailored to each data type and archive—and you can discover them quickly with the FAIRShare Explorer @DSBU tool:

  • Accepted File Formats → e.g., OME-TIFF, NIfTI, FASTQ, etc.
  • Metadata Standards → e.g., REMBI, MIAME, MIAPE, CDISC, etc.

Use FAIRShare Explorer to:

  1. Identify the exact formats and schemas required by each repository.
  2. Read the repository descriptions—including data types supported, submission workflows, licensing rules, and access conditions.
  3. Access step-by-step deposit instructions and examples for every repository.

Launch FAIRShare Explorer ©DSBU https://dsbu.unil.ch/datasquid-fbm/repositories

4. FBM UNIL–CHUV Zenodo Community if no suitable specialized repository exists

If no suitable specialized repository exists, the FBM Dean’s Office strongly recommends depositing your dataset in the following generalist repository:

Zenodo – FBM UNIL–CHUV Community : https://zenodo.org/communities/fbm-unil-chuv

Depositing in this community allows you to:

  • Receive metadata curation and guidance from the Data Stewardship Biomed Unit (DSBU)
  • Increase your dataset’s visibility within and beyond FBM
  • Ensure traceability of data produced by the faculty
  • Link your dataset to your publication, making it easier to cite and access

We recommend submitting your dataset early, ideally during the manuscript review process, so that it can be referenced and properly cited in your article.

Zenodo FBM/CHUV Community Submission Process – Key Information

  • You can only submit drafts (unpublished records) to a Zenodo community.
  • Both you and the community curators (DSBU) can edit metadata and files during the review phase.
  • Your record can be automatically published once approved by the community curators.

Submit your record for review in 3 simple steps:

See the Zenodo guide https://help.zenodo.org/docs/share/submit-for-review/

  1. Find the community: Search for “FBM UNIL–CHUV” on Zenodo and go to the community page.
  2. Submit for review: Upload your dataset and request inclusion.
  3. Manage your submission: Monitor review status and edit if needed.

File Formats and Metadata Recommended Standards for Zenodo 

To ensure interoperability, discoverability, and long-term reuse, it is essential to follow the file formats and metadata schemas recommended by the repository itself. These standards are typically developed in collaboration with the scientific community and tailored to the type of data being archived.

Accepted File Formats →  Recommended Standards Files format for unstructred data from our service for data sharing or archiving

Recommended Standards Files format 

Ranked in descending order of preference

Text:
• PDF/A
– PDF/X
• Plain text (.txt)
• Open Office (.odt)
• XML / HTML (with schema)
• Word XML (.docx)
• RTF
• LaTeX

Images:
• Bitmap
– TIFF (uncompressed)
– PNG
– JPEG2000
– (GIF)
• Vector
– SVG

Tabular data:
• CSV (comma, tab, semi-colon)
• Open Office (.ods)
• XML / HTML (with schema)
• Excel (.xlsx)
• .SQL

Video:
• MPEG-4 (H.264) (~ MP4)
• Motion JPEG 2000
• MPEG-1/2

Audio:
• WAV (preferably Broadcast Wave Format, LPCM)
• AIFF (LPCM)
• OGG Vorbis
• MP3 MPEG Layer III
• AAC MPEG-4

Metadata Standards → DataCite Metadata schema

Useful standard for describing general research datasets when there is no data category or discipline specific standard.

DataCite Metadata schema

The DataCite Metadata Schema for Publication and Citation of Research Data distinguishes between 3 different levels of obligation for the metadata properties:

  • Mandatory (M) properties must be provided,
  • Recommended (R) properties are optional, but strongly recommended and
  • Optional (O) properties are optional and provide richer description.

Table 1 and table 2 list the different items you should document about your dataset based on the 3 different levels of obligation. 

Table 1: DataCite Mandatory Properties

Table 2: DataCite Recommended and Optional Properties

4. Use Horus Dataset Catalog for Sensitive Non Anonymized Data at CHUV

Managing sensitive biomedical data requires strict adherence to privacy and confidentiality standards. Open access to such data is contingent upon explicit consent and the anonymization of personal information.

For sensitive datasets that cannot be anonymized, researchers should utilize the HORUS Dataset Catalog at CHUV, developed and managed by the Data Science Group – DSI

Key features of this catalog include:

  • Metadata Highlighting: Provides detailed metadata descriptions of sensitive clinical datasets generated at CHUV.
  • Controlled Access: Ensures secure access to sensitive datasets in compliance with legal restrictions.
  • High visibility and compliance to FAIR international standard: Enables interoperability between the CHUV catalog and external repository Zenodo, automatically transmitting FAIR metadata for public visibility in the FBM UNIL-CHUV Zenodo community.

Submit your Dataset

The DSBU offers end-to-end assistance with depositing sensitive data via the HORUS CHUV catalog:

Process Overview

  1. Dataset Submission & Metadata Entry: Researchers are supported by the DSBU to upload their dataset into the HORUS Dataset Catalog and fill in all necessary metadata fields describing the dataset.
  2. Metadata Review & Curation: A data steward (curator) from the DSBU carefully reviews the submission to ensure the metadata is complete, accurate, and compliant with FAIR principles.
  3. Metadata Publication to Zenodo: Once validated, the metadata is automatically published to the Zenodo FBM UNIL-CHUV Community, making the dataset discoverable on an international level, even though the data itself remains protected on CHUV internal servers.
  4. Data Access Request by contacting the PI: When other researchers read a publication referring to a dataset hosted in HORUS, or when they browse Zenodo and find the associated metadata, they gain a detailed understanding of the dataset’s content. If they are interested in using the data, they can contact the Principal Investigator (PI) through its e-mail address listed in the metadata.
  5. Access Evaluation & Agreement: The PI, possibly with a designated review committee, evaluates whether the data access request is legitimate. Criteria include:
    • The resquester comply to the Data Sharing Requests and Procedures
    • The requester works in a relevant field,
    • Their study is clearly aligned with the dataset’s subject and goals,
    • A clear scientific justification for the data reuse is provided.
  6. Collaboration & Data Transfer and Use Agreement (DTUA):
    • If the PI and committee approve the request, a collaboration agreement can be established.
    • Data can then be shared with the external team under the terms of a CHUV DTUA (The template used for this agreement is based on the one published by SPHN).
    • SPHN DTUA link

Check out our information page to get started

Please note that anonymization services are not provided by our unit; researchers should consult the Data Science Center (BDSC) for assistance with anonymization.nked in descending order of preference):

5. DSBU Support Provided

Personalized Assistance

  • Tutorials and short, hands-on training sessions
  • Guidance on preparing your dataset for FAIR data sharing.
  • Assistance with metadata standards and Open file formats (link) for data sharing.
  • Tools and templates to help produce a structured README file describing data types, acquisition methods, instrumentation, software, etc. using  DataSquid → https://wp.unil.ch/dsbu/tools_readme/.
  • Metadata curation on the FBM/CHUV Zenodo Community and Horus Dataset Catalog
  • Advice on data reuse, copyright, and licensing.
  • Improve visibility of your Datasets
  • Support with secondary data deposition on institutional infrastructure, ensuring secure long-term preservation through tape-based archiving.

Tailored Workshops and Team-Based Data Support

To foster good data practices across the faculty, the DSBU also offers:

  • Workshops on FAIR data, repository use, metadata standards, and data documentation
  • Live tutorials and demos on using DataSquid and FairShare Explorer
  • Group support sessions (by appointment) for research teams, labs, or departments

Custom training sessions can be scheduled on demand and adapted to your team’s specific data types and needs.

For any questions or to schedule support contact us : https://wp.unil.ch/dsbu/contact/

Additional Services: Regular training sessions on data management and sharing. Check the DSBU calendar for upcoming events link