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The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition
Author(s) -
Daniel Arend,
Patrick König,
Astrid Junker,
Uwe Scholz,
Matthias Lange
Publication year - 2020
Publication title -
gigascience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giaa107
Subject(s) - computer science , data science , data management , big data , elixir (programming language) , data discovery , data integration , data warehouse , data quality , world wide web , database , service (business) , metadata , data mining , business , marketing , programming language
The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. Although the ELIXIR Core Data Resources and other established infrastructures provide comprehensive and long-term stable services and platforms for FAIR data management, a large quantity of research data is still hidden or at risk of getting lost. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, e.g., time series of images or high-resolution hyper-spectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institional boundaries.

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