The challenge of sharing data well, how publishers can help
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The document discusses the challenges of data sharing faced by researchers, including costs, time constraints, and confusion regarding repositories and copyright. It outlines the structure of data descriptors used in the Scientific Data journal to facilitate data sharing, emphasizing the importance of metadata curation and peer review for data reusability. Additionally, it provides resources for researchers on data storage, licensing, and navigating recommended repositories.
The challenge of sharing data well, how publishers can help
- 1. The challenge of sharing data well and how publishers can help Varsha Khodiyar, PhD DREAM Challenges and Epidemium@RECOMB workshop, Paris 19.04.2018
- 2. 1 What are the challenges to sharing data? 12% 16% 20% 23% 28% Costs of sharing data Lack of time to deposit data Not knowing which repository to use Unsure about copyright and licensing Organising data in a presentable and useful wayTotal respondents: 7719 Stuart, D. et al. Practical challenges for researchers in data sharing. Springer Nature whitepaper (2018) https://doi. org/10.6084/m9.figshare.5975011
- 3. 2 Scientific Data, a Nature Research journal nature.com/scientificdata
- 4. 3 The Data Descriptor Sections • Title • Abstract • Background & Summary • Methods • Data Records • Technical Validation • Usage Notes • Figures & Tables • References • Data Citations • Detailed descriptions of the methods and technical analyses supporting the quality of the measurements. • Does not contain tests of new scientific hypotheses • Data must be archived in a repository at submission • Peer reviewers are asked to comment on the reusability of the work as presented
- 5. 4 Data peer review nature.com/sdata/policies/for-referees Experimental Rigor and Technical Data Quality Were data produced in a sound manner? Technical quality of data – appropriate statistical analyses? Experimental rigor - appropriate depth, coverage? Completeness of the Description Sufficient detail to allow others to reproduce these steps? Sufficient detail to allow others to reuse this data? Consistent with relevant minimum reporting standards? Integrity of the Data Files and Repository Record Do data files appear complete and match manuscript descriptions? Are data archived to the most appropriate repository?
- 6. 5 We capture information about the dataset being described in each Data Descriptor. During the metadata curation process • Manuscript re-read • Data archive checked • Minor issues with the data and/or manuscript often identified • Metadata captured in ISA-Tab format Metadata curation and final data checking
- 7. 6 ISA-Tab metadata files scientificdata.isa-explorer.org Structured Summary table appears after abstract github.com/ScientificDataLabs/ISA-tab
- 8. 7 Data Descriptors help researchers share their data in a reusable way 7 “The Data Descriptor made it easier to use the data, for me it was critical that everything was there…all the technical details like voxel size.” Professor Daniele Marinazzo
- 9. 8 Helping researchers know where to share their data nature.com/sdata/data-policies/repositories Browse our recommended data repositories online. • We currently list more than 100 repositories, across biological, medical, physical and social sciences • When requested we provide guidance to authors on the best place to store their data
- 10. 9 Helping researchers understand data licensing springernature.com/gp/authors/research-data-policy
- 11. 10 Reuse of Challenge data published at Scientific Data
- 12. 11 Other Challenge dataset publications at Scientific Data
- 13. 12 How can we help Challenge participants? Visit nature.com/scientificdata Email scientificdata@nature.com Tweet @ScientificData
Editor's Notes
- #3: Main findings: Researchers do share and use one another’s data but lack places to put it. They would value a high quality data publication
- #6: We do not expect reviewers to open every file or reuse the data in it’s entirety.
- #7: Often identify minor issues with the data, files, or archive as part of review process, e.g. Typos in data archive and/or manuscript tables File names differences between archive and manuscript Typos in accession IDs in manuscript Final data citation checking (e.g. changes to data DOI) Essentially facilitates an additional final check that the data and manuscript are as accurate as possible prior to final publication, so that data is maximally reusable. The curation process adds another layer of checks to the manuscript, ensuring that the published article and data archive are as accurate as possible, maximising data reusability.
- #9: Daniele knew about the dataset prior to Chris’ paper being published, as Chris had shared this in Torrent Exchange. However he did not access the data from this. He saw on Twitter when the SciData paper was published and then read the paper. Daniele said “I would never have collected this data myself, as it’s not my primary field of work”. He said the Data Descriptor made it easier to use the data “for me it was critical that everything was there [in the Data Descriptor]…all the technical details like voxel size.”