Open Research Data Support for FBM UNIL-CHUV Researchers

The DSBU offers tailored support for researchers depositing data into generalist and specialized repositories:

Generalist Repositories

• Options: Zenodo (FBM/CHUV community) or Dryad
• Support Provided:
  • Guidance on FAIR data sharing.
  • Assistance with metadata standards, readme files, and file formats for data sharing.
  • README files for sharing data on generalist external repositories (e.g., Zenodo or Dryad) are prepared automatically during publication processes using our tool DataSquid https://wp.unil.ch/dsbu/tools_readme/.
  • Metadata curation on the FBM/CHUV Zenodo Community
  • Advice on data reuse, copyright, and licensing.
  • Improve visibility of your Datasets
• Additional Services: Regular training sessions on data management and sharing. Check the DSBU calendar for upcoming events link

Specialized 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)
• Support Provided:
  • Assistance in selecting the most appropriate domain-specific repository.
  • Help in describing datasets accurately and ensuring metadata compliance with FAIR principles.
  • Guidance throughout the deposition process.
  • Advice on data reuse, copyright, and licensing.

FAIR Data Sharing for CHUV sensitive Data

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 CHUV IT Department (DSI-CHUV).

Key features of this catalog include:

Key features of this catalog (link) 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.

DSBU Support for HORUS CHUV Catalog

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

Please note that anonymization services are not provided by our unit; researchers should consult the Data Science Center (BDSC) for assistance with anonymization.