From data to knowledge - big data needs stewardship, a plant phenomics perspective
- PMID: 35535481
- DOI: 10.1111/tpj.15804
From data to knowledge - big data needs stewardship, a plant phenomics perspective
Abstract
The research data life cycle from project planning to data publishing is an integral part of current research. Until the last decade, researchers were responsible for all associated phases in addition to the actual research and were assisted only at certain points by IT or bioinformaticians. Starting with advances in sequencing, the automation of analytical methods in all life science fields, including in plant phenotyping, has led to ever-increasing amounts of ever more complex data. The tasks associated with these challenges now often exceed the expertise of and infrastructure available to scientists, leading to an increased risk of data loss over time. The IPK Gatersleben has one of the world's largest germplasm collections and two decades of experience in crop plant research data management. In this article we show how challenges in modern, data-driven research can be addressed by data stewards. Based on concrete use cases, data management processes and best practices from plant phenotyping, we describe which expertise and skills are required and how data stewards as an integral actor can enhance the quality of a necessary digital transformation in progressive research.
Keywords: FAIR data; data stewardship; plant phenomics; research data.
© 2022 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.
References
REFERENCES
-
- Andrés-Hernández, L., Halimi, R.A., Mauleon, R., Mayes, S., Baten, A. & King, G.J. (2021) Challenges for FAIR-compliant description and comparison of crop phenotype data with standardized controlled vocabularies. Database, 2021, baab028. Available from: https://doi.org/10.1093/database/baab028
-
- Araus, J.L., Kefauver, S.C., Zaman-Allah, M., Olsen, M.S. & Cairns, J.E. (2018) Translating high-throughput phenotyping into genetic gain. Trends in Plant Science, 23(5), 451-466. Available from: https://doi.org/10.1016/j.tplants.2018.02.001
-
- Arend, D., Junker, A., Scholz, U., Schüler, D., Wylie, J. & Lange, M. (2016) PGP repository: a plant phenomics and genomics data publication infrastructure. Database, 2016, baw033. Available from: https://doi.org/10.1093/database/baw033
-
- Arend, D., König, P., Junker, A., Scholz, U., & Lange, M. (2020) The on-premise data sharing infrastructure e!DAL: foster FAIR data for faster data acquisition. GigaScience, 9(10), giaa107. Available from: https://doi.org/10.1093/gigascience/giaa107
-
- Arend, D., Lange, M., Chen, J., Colmsee, C., Flemming, S., Hecht, D. et al. (2014) e!DAL - a framework to store, share and publish research data. BMC Bioinformatics, 15(1), 214. Available from: https://doi.org/10.1186/1471-2105-15-214
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources