Content Providers
Keywords: GitHub or Gitlab or Kinetic modeling or Machine Learning or Metadata harmonisation or The Carpentries
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Glittr.org
Glittr (https://glittr.org) is a web application that helps you find and compare bioinformatics training materials on GitHub and GitLab.
621 training materialsGlittr.org https://glittr.org https://tess.elixir-europe.org/content_providers/glittr-org Glittr (https://glittr.org) is a web application that helps you find and compare bioinformatics training materials on GitHub and GitLab. /system/content_providers/images/000/000/692/original/logo-with-domain.png?1698843802 -
Library Carpentry
Library Carpentry develops lessons and teaches workshops for and with people working in library- and information-related roles. Our goal is to create an on-ramp to empower this community to use software and data in their own work as well as be advocates for and train others in efficient,...
0 events (114 past events)Library Carpentry https://librarycarpentry.org/ https://tess.elixir-europe.org/content_providers/library-carpentry Library Carpentry develops lessons and teaches workshops for and with people working in library- and information-related roles. Our goal is to create an on-ramp to empower this community to use software and data in their own work as well as be advocates for and train others in efficient, effective and reproducible data and software practices. /system/content_providers/images/000/000/113/original/Screenshot_2019-06-12_at_09.33.06.png?1560327765 -
CINECA PROJECT
CINECA’s aim is to deliver a federated infrastructure for data discovery and sharing of human genetic and phenotypic data for research that is interoperable across continents. CINECA [Common Infrastructure for National Cohorts in Europe, Canada and Africa] is funded by the European Union Horizon...
17 training materials0 events (3 past events)CINECA PROJECT https://www.cineca-project.eu/ https://tess.elixir-europe.org/content_providers/cineca CINECA’s aim is to deliver a federated infrastructure for data discovery and sharing of human genetic and phenotypic data for research that is interoperable across continents. CINECA [Common Infrastructure for National Cohorts in Europe, Canada and Africa] is funded by the European Union Horizon 2020 programme and the Canadian Institutes of Health Research. /system/content_providers/images/000/000/140/original/CINECA_logo.png?1589875546 -
OpenRiskNet
The main objective of OpenRiskNet is to develop an open e-Infrastructure providing resources and services to a variety of communities requiring risk assessment, including chemicals, cosmetic ingredients, therapeutic agents and nanomaterials.
OpenRiskNet is
* a virtual research environment for...0 events (10 past events)OpenRiskNet https://openrisknet.org/ https://tess.elixir-europe.org/content_providers/openrisknet The main objective of OpenRiskNet is to develop an open e-Infrastructure providing resources and services to a variety of communities requiring risk assessment, including chemicals, cosmetic ingredients, therapeutic agents and nanomaterials. OpenRiskNet is * a virtual research environment for predictive toxicology and chemical and nanomaterial risk assessment, * harmonising access to data and facilitating interoperability of software, * easily deployable to single computers, public and in-house cloud solutions, * addressing the needs of industry and academic researchers, risk assessors, regulators and informed public. OpenRiskNet (Grant Agreement 731075) is a 3-years project funded by the European Commission within the Horizon2020 Programme /system/content_providers/images/000/000/097/original/ORN-Web_Logo3.png?1533933310 -
proteomicsML
ProteomicsML provides ready-made datasets for machine learning models accompanied by tutorials on how to work with even the most complex data types in the field of proteomics. The resource is set up to evolve together with the field, and we welcome everyone to contribute to the project by adding...
1 training materialproteomicsML https://proteomicsml.org/ https://tess.elixir-europe.org/content_providers/proteomicsml ProteomicsML provides ready-made datasets for machine learning models accompanied by tutorials on how to work with even the most complex data types in the field of proteomics. The resource is set up to evolve together with the field, and we welcome everyone to contribute to the project by adding new datasets and accompanying notebooks. ProteomicsML was set up as a joint effort of SDU, CompOmics, LUMC, PeptideAtlas, NIST, PRIDE, and MSAID. We believe that ProteomicsML is solid step forward for the field towards more open and reproducible science! /system/content_providers/images/000/000/676/original/proteomicsml-logo.png?1686658675