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. 2020 Sep 22:8:e9954.
doi: 10.7717/peerj.9954. eCollection 2020.

The reuse of public datasets in the life sciences: potential risks and rewards

Katharina Sielemann #  1   2 , Alenka Hafner #  1   3 , Boas Pucker  1   4
Affiliations

The reuse of public datasets in the life sciences: potential risks and rewards

Katharina Sielemann et al. PeerJ. .

Abstract

The 'big data' revolution has enabled novel types of analyses in the life sciences, facilitated by public sharing and reuse of datasets. Here, we review the prodigious potential of reusing publicly available datasets and the associated challenges, limitations and risks. Possible solutions to issues and research integrity considerations are also discussed. Due to the prominence, abundance and wide distribution of sequencing data, we focus on the reuse of publicly available sequence datasets. We define 'successful reuse' as the use of previously published data to enable novel scientific findings. By using selected examples of successful reuse from different disciplines, we illustrate the enormous potential of the practice, while acknowledging the respective limitations and risks. A checklist to determine the reuse value and potential of a particular dataset is also provided. The open discussion of data reuse and the establishment of this practice as a norm has the potential to benefit all stakeholders in the life sciences.

Keywords: Bioinformatics; Computational biology; Data science; Databases; Genomics; Open science; Reuse; Sequencing data.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. The evolution of data sharing behaviour.
(1) Technical progress makes global sharing of large datasets possible, (2) increased accessibility to required technology makes it widely available, (3) obligations and benefits for researchers establish sharing behaviour, (4) the size of datasets increases and makes them attractive, (5) reuse develops over time—which results in a positive feedback loop and a habit to share all data.
Figure 2
Figure 2. Types of reusable data classified into primary and derived/secondary data.
Specific examples for each data type are provided in parentheses. The data classification is based on: Wooley & Lin, 2005. (Sources of the pictures: ‘Protein Data Bank in Europe—Logo’, https://www.ebi.ac.uk/pdbe/about/logo; Pucker, Holtgräwe & Weisshaar, 2017; Schilbert et al., 2018; Mitchell et al., 2019; Frey & Pucker, 2020).
Figure 3
Figure 3. The increasing size of selected databases over time.
The number of bases/sequence entries in GenBank, the Sequence Read Archive (SRA) and UniProtKB/TrEMBL are shown, respectively. Note the logarithmic scale of the y-axes. The drop of sequence entries in UniProtKB/TrEMBL (in 2015) can be explained by the removal of duplicates.
Figure 4
Figure 4. Advantages and limitations of data reuse.
Figure 5
Figure 5. Summary of outstanding questions and challenges.
See this image and copyright information in PMC

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Grants and funding

Support for the Article Processing Charge is provided by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University. K.S. is funded by Bielefeld University. A.H. received the 2018 Richard Hardy Award (St. Catharine’s College, University of Cambridge) which partly supported an internship at Bielefeld University, leading to this collaboration. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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