TY  - JOUR
A1  - Caliskan, Aylin
A1  - Dangwal, Seema
A1  - Dandekar, Thomas
T1  - Metadata integrity in bioinformatics: bridging the gap between data and knowledge
T2  - Computational and Structural Biotechnology Journal
N2  - In the fast-evolving landscape of biomedical research, the emergence of big data has presented researchers with extraordinary opportunities to explore biological complexities. In biomedical research, big data imply also a big responsibility. This is not only due to genomics data being sensitive information but also due to genomics data being shared and re-analysed among the scientific community. This saves valuable resources and can even help to find new insights in silico. To fully use these opportunities, detailed and correct metadata are imperative. This includes not only the availability of metadata but also their correctness. Metadata integrity serves as a fundamental determinant of research credibility, supporting the reliability and reproducibility of data-driven findings. Ensuring metadata availability, curation, and accuracy are therefore essential for bioinformatic research. Not only must metadata be readily available, but they must also be meticulously curated and ideally error-free. Motivated by an accidental discovery of a critical metadata error in patient data published in two high-impact journals, we aim to raise awareness for the need of correct, complete, and curated metadata. We describe how the metadata error was found, addressed, and present examples for metadata-related challenges in omics research, along with supporting measures, including tools for checking metadata and software to facilitate various steps from data analysis to published research.

Highlights
• Data awareness and data integrity underpins the trustworthiness of results and subsequent further analysis.
• Big data and bioinformatics enable efficient resource use by repurposing publicly available RNA-Sequencing data.
• Manual checks of data quality and integrity are insufficient due to the overwhelming volume and rapidly growing data.
• Automation and artificial intelligence provide cost-effective and efficient solutions for data integrity and quality checks.
• FAIR data management, various software solutions and analysis tools assist metadata maintenance.
KW  - meta-data
KW  - error
KW  - annotation
KW  - error-transfer
KW  - wrong labelling
KW  - patient data
KW  - control group
KW  - tools overview
Y1  - 2023
UR  - https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/34999
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-349990
SN  - 2001-0370
VL  - 21
ER  -