DISSCo - Distributed System of Scientific Collections
The Distributed System of Scientific Collections is a new world-class Research Infrastructure (RI) for Natural Science Collections. The DiSSCo RI aims to create a new business model for one European collection that digitally unifies all European natural science assets under common access, curation, policies and practices that ensure that all the data is easily Findable, Accessible, Interoperable and Reusable (FAIR principles).
DiSSCo represents the largest ever formal agreement between natural history museums, botanic gardens and collection-holding universities in the world.
DiSSCo is all about services. It will create a unique access point for integrated data analysis and interpretation through a wide array of digital services provided by its community.
Web page: https://www.dissco.eu/
Funding details: Related items
Teams: Specimen Data Refinery
Organizations: The University of Manchester
Teams: Specimen Data Refinery
Organizations: The Natural History Museum London
https://orcid.org/0000-0002-7341-1842
Teams: IBISBA Workflows, GalaxyProject SARS-CoV-2, BioBB Building Blocks, Common Workflow Language (CWL) community, BioExcel Best Practice Guides, Specimen Data Refinery, FAIR Computational Workflows, Vertebrate Genomes Pipelines in Galaxy, TRE-FX, EuroScienceGateway, Biodiversity Genomics Europe (general), BY-COVID Baseline Use Case: SARS-CoV-2 Vaccine(s) effectiveness in preventing SARS-CoV-2 infection, BY-COVID (general), BioDT additional pipelines, BioDT Use Case 4.1.1.1 Biodiversity dynamics, BioDT Use Case 4.1.2.2 DNA detected biodiversity, poorly known habitats, BioDT Use Case 4.1.2.1 Crop wild relatives and genetic resources for food security, BioDT Use Case 4.1.3.1 Invasive species, BioDT Use Case 4.1.3.2 Endangered species, BioDT Use Case 4.1.4.1 Disease outbreaks, BioDT Use Case 4.1.4.2 Pollinators, BioDT Use Case 4.1.1.2 Ecosystem services
Organizations: The University of Manchester
https://orcid.org/0000-0001-9842-9718
Teams: Specimen Data Refinery, Air Quality Prediction
Organizations: The University of Manchester
The SDR is concerned with digitisation pipelines for digital access to natural history collections
The SDR integrate machine learning, Artificial Intelligence, and human approaches to extract, enhance, and annotate data from digital images and records at scale. Many collections-holding institutions still need to digitise the bulk of their collections. Digitisation takes time and resources. One of the major challenges in digitising massive collections is finding ways of ensuring high-quality ...
Space: DISSCo - Distributed System of Scientific Collections
Public web page: https://www.synthesys.info/
Start date: 1st Feb 2019
End date: 31st Dec 2023
An example input file for the Specimen Data Refinery workflow
Creators: None
Submitter: Oliver Woolland
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Abstract (Expand)
Authors: 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
Date Published: 24th Mar 2022
Publication Type: Journal
Abstract (Expand)
Authors: Alex Hardisty, Paul Brack, Carole Goble, Laurence Livermore, Ben Scott, Quentin Groom, Stuart Owen, Stian Soiland-Reyes
Date Published: 7th Mar 2022
Publication Type: Journal
Abstract (Expand)
Authors: Stephanie Walton, Laurence Livermore, Olaf Bánki, Robert W. N. Cubey, Robyn Drinkwater, Markus Englund, Carole Goble, Quentin Groom, Christopher Kermorvant, Isabel Rey, Celia M Santos, Ben Scott, Alan Williams, Zhengzhe Wu
Date Published: 14th Aug 2020
Publication Type: Journal
Example workflow which allows the use of Mothra
Accepts (e.g.) these input files, bundled as a collection.
An example workflow for the Specimen Data Refinery tool, allowing an individual tool to be used
Type: Galaxy
Creators: Laurence Livermore, Oliver Woolland, Oliver Woolland
Submitter: Oliver Woolland
An example workflow for the Specimen Data Refinery tool, allowing an individual tool to be used
An example workflow to allow users to run the Specimen Data Refinery tools on data provided in an input CSV file.
