Abstract
The FAIR principles for scientific data (Findable, Accessible, Interoperable, Reusable) are also relevant to other digital objects such as research software and scientific workflows that operate on scientific data. The FAIR principles can be applied to the data being handled by a scientific workflow as well as the processes, software, and other infrastructure which are necessary to specify and execute a workflow. The FAIR principles were designed as guidelines, rather than rules, that would allow for differences in standards for different communities and for different degrees of compliance. There are many practical considerations which impact the level of FAIR-ness that can actually be achieved, including policies, traditions, and technologies. Because of these considerations, obstacles are often encountered during the workflow lifecycle that trace directly to shortcomings in the implementation of the FAIR principles. Here, we detail some cases, without naming names, in which data and workflows were Findable but otherwise lacking in areas commonly needed and expected by modern FAIR methods, tools, and users. We describe how some of these problems, all of which were overcome successfully, have motivated us to push on systems and approaches for fully FAIR workflows.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 507-512 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781665461245 |
| DOIs | |
| State | Published - 2022 |
| Event | 18th IEEE International Conference on e-Science, eScience 2022 - Salt Lake City, United States Duration: Oct 10 2022 → Oct 14 2022 |
Publication series
| Name | Proceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022 |
|---|
Conference
| Conference | 18th IEEE International Conference on e-Science, eScience 2022 |
|---|---|
| Country/Territory | United States |
| City | Salt Lake City |
| Period | 10/10/22 → 10/14/22 |
Funding
This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-000R22725. Notice: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00R0 22725 with the U.S. Department of Energy. The publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non-exclusive, paid up, irrevocable, worldwide license to publish or reproduce the published form of the manuscript, or allow others to do so, for U.S. Government purposes. The DOE will provide public access to these results in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Notice: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-000R22725 with the U.S. Department of Energy. The publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non-exclusive, paid up, irrevocable, worldwide license to publish or reproduce the published form of the manuscript, or allow others to do so, for U.S. Government purposes. The DOE will provide public access to these results in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- FAIR principles
- data science
- high performance computing
- workflows