This presentation introduces the community guidelines for sharing dataset quality information, developed by members of multiple, international groups. The guidelines follow the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). The FAIR dataset quality information guidelines are intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to achieve the following: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR guiding principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to...
Thousands of community-developed (meta)data guidelines, models, ontologies, schemas and formats have...
Thousands of community-developed (meta)data guidelines, models, ontologies, schemas and formats have...
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A dive...
This presentation introduces the community guidelines for sharing dataset quality information, devel...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Knowledge about the quality of data, metadata, and software is important to support informed decisio...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
There is a growing demand for quality criteria for research datasets. We will argue that the Data Se...
Thousands of community-developed (meta)data guidelines, models, ontologies, schemas and formats have...
Thousands of community-developed (meta)data guidelines, models, ontologies, schemas and formats have...
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A dive...
This presentation introduces the community guidelines for sharing dataset quality information, devel...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
Knowledge about the quality of data, metadata, and software is important to support informed decisio...
Open-source science builds on open and free resources that include data, metadata, software, and wor...
There is a growing demand for quality criteria for research datasets. We will argue that the Data Se...
Thousands of community-developed (meta)data guidelines, models, ontologies, schemas and formats have...
Thousands of community-developed (meta)data guidelines, models, ontologies, schemas and formats have...
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A dive...