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Persistent Identification Of Instruments
- Source :
- Data Science Journal, Data Science Journal; Vol 19 (2020); 18, Data Science Journal, Vol 19, Iss 1 (2020)
- Publication Year :
- 2020
-
Abstract
- Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin f\"ur Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements.
- Subjects :
- FOS: Computer and information sciences
010504 meteorology & atmospheric sciences
Computer science
Information Science, Digital Libraries
01 natural sciences
Working groups
Schema (psychology)
Computer Science (miscellaneous)
Use case
Persistent Identification
Instruments
Metadata
DOI
Handle
Digital Libraries (cs.DL)
14. Life underwater
Persistent Identification, Instruments, Metadata, DOI, Handle
Helmholtz
lcsh:Science (General)
Implementation
0105 earth and related environmental sciences
Oceanographic data
Persistent identifier
business.industry
Data reuse
05 social sciences
metadata
Data quality
Stakeholder
handle
Computer Science - Digital Libraries
Metadata schema
Data science
Research data
Computer Science Applications
doi
persistent identification
instruments
Data center
Computer applications
0509 other social sciences
050904 information & library sciences
business
lcsh:Q1-390
Subjects
Details
- Language :
- English
- ISSN :
- 16831470
- Database :
- OpenAIRE
- Journal :
- Data Science Journal, Data Science Journal; Vol 19 (2020); 18, Data Science Journal, Vol 19, Iss 1 (2020)
- Accession number :
- edsair.doi.dedup.....c153b486358cbbc2f370de9374c2be70