The lack of interoperability between tools presents a significant barrier to streamlining workflows throughout the research lifecycle. These gaps prevent the comprehensive collection and incorporation of research data and metadata into the research record captured during the active research phase. Furthermore, it limits the scope for passing this data and metadata on to data repositories, thus undermining FAIR data principles and reproducibility. This presentation explores DataCite's participation in the Implementing FAIR Workflow project aimed at building and implementing an exemplar FAIR and Open research workflow based on the reality of an entire research lifecycle
The FAIR Principles have two aspects: They were written specifically for research data and they also...
Research data is accumulating rapidly, and with it the challenge of irreproducible science. As a con...
Despite the fact that the implementation of the FAIR principles (Findable, Accessible, Reusable, Int...
Extended abstract to be presented at the CRIS2022 conference in Dubrovnik.-- Event programme availab...
Researchers are increasingly asked to make their research open and FAIR, but what does this mean in ...
This presentation describes the use of registries like WorkflowHub (https://workflowhub.eu/) for mak...
A major barrier to streamlining workflows throughout the research lifecycle is lack of interoperabil...
The Implementing FAIR Workflows project is a 3-year project to implement exemplar workflows in cogni...
Findable, accessible, interoperable and reusable (FAIR) data are an increasingly important aspect of...
The FAIR principles have been accepted globally as guidelines for improving data-driven science and ...
Computational workflows describe the complex multi-step methods that are used for data collection, d...
The Implementing FAIR Workflows Project aims to leverage existing persistent identifier infrastructu...
The Implementing FAIR Workflows Project aims to leverage the existing persistent identifier infrastr...
Harvard Data Commons (HDC) is a university-wide initiative at Harvard University to support the life...
As the FAIR Principles about findability, accessiblity, interoperability and reusability of research...
The FAIR Principles have two aspects: They were written specifically for research data and they also...
Research data is accumulating rapidly, and with it the challenge of irreproducible science. As a con...
Despite the fact that the implementation of the FAIR principles (Findable, Accessible, Reusable, Int...
Extended abstract to be presented at the CRIS2022 conference in Dubrovnik.-- Event programme availab...
Researchers are increasingly asked to make their research open and FAIR, but what does this mean in ...
This presentation describes the use of registries like WorkflowHub (https://workflowhub.eu/) for mak...
A major barrier to streamlining workflows throughout the research lifecycle is lack of interoperabil...
The Implementing FAIR Workflows project is a 3-year project to implement exemplar workflows in cogni...
Findable, accessible, interoperable and reusable (FAIR) data are an increasingly important aspect of...
The FAIR principles have been accepted globally as guidelines for improving data-driven science and ...
Computational workflows describe the complex multi-step methods that are used for data collection, d...
The Implementing FAIR Workflows Project aims to leverage existing persistent identifier infrastructu...
The Implementing FAIR Workflows Project aims to leverage the existing persistent identifier infrastr...
Harvard Data Commons (HDC) is a university-wide initiative at Harvard University to support the life...
As the FAIR Principles about findability, accessiblity, interoperability and reusability of research...
The FAIR Principles have two aspects: They were written specifically for research data and they also...
Research data is accumulating rapidly, and with it the challenge of irreproducible science. As a con...
Despite the fact that the implementation of the FAIR principles (Findable, Accessible, Reusable, Int...