The Way to FAIR: from data collection to citation
Collaborative session notes: https://docs.google.com/document/d/1TcuSENTx6YBFOnmAsApZ3944CwBOYwEZx9yc...
The RDA FAIR Data Maturity Model WG’s goal is to develop and maintain an RDA recommendation on a common set of core assessment criteria for FAIRness, plus a self-assessment model suitable for a wide range of disciplines and applications. The RDA Persistent Identification of Instruments WG is exploring a community-driven solution for globally unique identification of measuring instruments operated in the sciences. This session will unite the goals of these working groups by discussing the integration and assessment of FAIR principles at an early stage of research (data collection) while enhancing and standardizing the metadata associated with instrumentation during data measurement and collection.
We propose to feature speakers from the FAIR Data Maturity Model and Persistent Identification of Instruments WGs to frame the discussion, plus 3 invited speakers with expertise in instrument development and integration of metadata collection to instrumentation across a range of scientific disciplines to speak to challenges and opportunities in this area. The plenary will begin with short presentations (5 min) from each speaker, followed by a moderated panel discussion. We propose to focus on the challenges facing data collection ranging from the bench scale (chemical and biological sciences) to complex global monitoring networks (often employed in earth and environmental sciences disciplines ranging from oceanography to atmospheric science).
- 0-5 minutes: Introduction to the session. The motivation of exploring current work and interest for FAIR instrument data
- 5-15 min: Keith Russell, introduces the work of the FAIR Data Maturity Model WG.
- 15-25 min: Rolf Krahl, introduces the work of the Persistent Identification of Instruments WG
Instrument Use Cases
25-35 min: Pedro Luiz Pizzigatti Corrêa, University of São Paulo, Amazon Use Case
35-45 min: Charles Edward Catlett, Argonne National Laboratory, University of Chicago, Urban Environmental Sensor Use Case
45-55 min: Debora Pignatari Drucker, Embrapa Digital Agriculture, Airborne LIDAR Use Case
- 55-65 min: Stuart Chalk, University of North Florida, Chemical Analysis Use Case
- 65-85 min: Discussion (30 min - Moderated + Audience Questions)
- 85-90 min: Summary - goal-setting (outcomes)
Making datasets FAIR requires all research stakeholders. In this session we explore the challenges and opportunities within the instrument manufacturing community and how RDA can support this community with the goal of incorporating FAIR principles into data collection at an earlier stage. This will enable researchers to more easily compile accessible, meaningful metadata to accompany their datasets, making the data from a broad type of instruments more FAIR.
FAIR Data Maturity Model Working Group
The FAIR Data Maturity Model Working Group worked in 2019 and the first half of 2020 to develop an RDA Recommendation on assessment of FAIRness in research data. The RDA Recommendation FAIR Data Maturity Model: specification and guidelines was published on 8 June 2020 (https://doi.org/10.15497/RDA00050). Since then, the Working Group has entered maintenance mode and is currently developing a work plan for the next years.
Persistent Identification of Instruments Working Group
The Persistent Identification of Instruments Working Group began in 2017 and seeks to explore a community-driven solution for globally unique identification of measuring instruments operated in the sciences. The WG has collected use cases for persistent identification of instruments, aligns the collected metadata, and develops a metadata schema. The WG also produced a webinar in 2022 on the Persistent Identification of Instruments.
FAIR Data Maturity Group Working Group
The RDA FAIR Data Maturity Group was established at the end of 2018 with the objective to bring together stakeholders from different scientific and research disciplines, the industry and public sector, who are active and/or interested in the FAIR data principles and in particular in assessment criteria and methodologies for evaluating their real-life uptake and implementation level. The Working Group completed an RDA Recommendation, a common set of core assessment criteria for FAIRness and a generic and expandable self-assessment model for measuring the maturity level of a dataset. The group has over 250 members representing a wide range of disciplines and regions around the world.
Persistent Identification of Instruments Working Group
The Persistent Identification of Instruments Working Group seeks to explore a community-driven solution for globally unique identification of measuring instruments operated in the sciences. Our objectives are to recommend a metadata profile to describe instruments that harmonises existing identification standards and complements existing metadata schemas; to explore methodology/technology to register and resolve the new PID; and to operationalise the solution by engaging existing PID infrastructure providers, instrument developers and manufacturers, as well as instrument database providers.
Group page: https://www.rd-alliance.org/groups/fair-data-maturity-model-wg
Some adoption stories of the FAIR Data Maturity Model: https://www.rd-alliance.org/group/fair-data-maturity-model-wg/wiki/adoption-stories-fdmm
The FAIR Data Maturity Model: https://zenodo.org/record/3909563#.YhhMw-hBy71
Use cases from the Persistent Identification of Instruments WG: https://www.rd-alliance.org/groups/persistent-identification-instruments-wg
Persistent Identification of Instruments webinar: https://www.rd-alliance.org/PID-instruments-May2022_webinar
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