The BeYond-COVID project (BY-COVID) aims to make COVID-19 data accessible to scientists in laboratories but also to anyone who can use it, such as medical staff in hospitals or government officials.

Pursuing to go beyond SARS-CoV-2 data, the project will serve as the groundwork to make data from other infectious diseases open and accessible to everyone.

The BY-COVID project strives to simplify data access and reuse through four key ‘pillars’:

  1. Mobilise data: ensuring raw sequencing data from across the world can be easily submitted to core data hubs (e.g. SARS-CoV-2 Data Hubs, European Nucleotide Archive (ENA), Federated European Genome Archive (FEGA), CESSDA social science archives, and BBMRI biobank directory).
  2. Connect data: build the technical capacity to allow linking of sequence data and metadata - expanding beyond scientific and medical data to broader metadata from for example public health and economics. Support integration to the COVID-19 Data Portal.
  3. Standardise data: provide recommended data management protocols to encourage Findable, Accessible, Interoperable and Reusable (FAIR) data standards and interoperability among resources.
  4. Expose and analyse data: support exposure and analysis of FAIR data on infectious diseases such as the regular VEO reports on mutations and variation in publicly shared SARS-CoV-2 data and the open COVID-19 Galaxy analysis platform).

BY-COVID is an exciting interdisciplinary project that unites life science, medical, policy, social science and public health experts from across Europe. Led by ELIXIR, the project has 53 partners from 20 European countries. The BY-COVID project will run for three years and is part of the European Commission’s HERA Incubator plan Anticipating together the threat of COVID-19 variants.

eScience Lab involvement

Continuing work done with ELIXIR and EOSC-Life, eScience Lab is involved in BY-COVID to facilitate use of RDMKit, WorkflowHub and RO-Crate for FAIR sharing of workflows, COVID-19 data and their citations.

UNIMAN participates in Work packages and tasks:

  • WP3: COVID-19 integration platform
    • Task 3.1: Metadata standards
    • Task 3.3: COVID-19 Data Portal
    • Task 3.5: Usage indicators and credit attribution
  • WP4: Connecting the COVID-19 data platform to analysis tools and local portals
    • Task 4.1: Infectious diseases toolkit
    • Task 4.3: Analysis transparency, sharing and trusted exchange
    • Task 4.6: Training

BY-COVID concept diagram

Relevant Deliverables

Yo Yehudi, Lukas Hughes-Noehrer, Carole Goble, Caroline Jay (2022):
COVID-19: An exploration of consecutive systemic barriers to pathogen-related data sharing during a pandemic.
arXiv 2205.12098 [cs.CY] (preprint)
https://doi.org/10.48550/arXiv.2205.12098

Stian Soiland-Reyes, Leyla Jael Castro, Daniel Garijo, Marc Portier, Carole Goble, Paul Groth (2022):
Updating Linked Data practices for FAIR Digital Object principles.
1st International Conference on FAIR Digital Objects (FDO 2022) (abstract).
Research Ideas and Outcomes 8:e94501 https://doi.org/10.3897/rio.8.94501 (in production)

Stian Soiland-Reyes, Peter Sefton, Leyla Jael Castro, Frederik Coppens, Daniel Garijo, Simone Leo, Marc Portier, Paul Groth (2022):
Creating lightweight FAIR Digital Objects with RO-Crate.
1st International Conference on FAIR Digital Objects (FDO 2022) (poster)
Research Ideas and Outcomes 8:e93937 https://doi.org/10.3897/rio.8.93937 (in production)

Stian Soiland-Reyes, Peter Sefton, Mercè Crosas, Leyla Jael Castro, Frederik Coppens, José M. Fernández, Daniel Garijo, Björn Grüning, Marco La Rosa, Simone Leo, Eoghan Ó Carragáin, Marc Portier, Ana Trisovic, RO-Crate Community, Paul Groth, Carole Goble (2022):
Packaging research artefacts with RO-Crate.
Data Science 5(2)
https://doi.org/10.3233/DS-210053
[RO-Crate]

Paul Brack, Peter Crowther, Stian Soiland-Reyes, Stuart Owen, Douglas Lowe, Alan R Williams, Quentin Groom, Mathias Dillen, Frederik Coppens, Björn Grüning, Ignacio Eguinoa, Philip Ewels, Carole Goble (2022):
Ten Simple Rules for making a software tool workflow-ready.
PLOS Computational Biology 18(3):e1009823
https://doi.org/10.1371/journal.pcbi.1009823