Workflows for Publishing Data; Scientific Data's experience as an early adopter
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This document outlines workflows for publishing data at Scientific Data, including: - Requiring a data PID for manuscript submission, enforcing data citation policies, and using structured repositories to capture subject-specific metadata. - A data curation editor curates discovery-level metadata regardless of repository and machine readable metadata aids discovery. - Data descriptors are designed to encourage full data documentation and linked analyses are captured to provide additional context.
Workflows for Publishing Data; Scientific Data's experience as an early adopter
- 1. Workflows for Publishing Data Varsha Khodiyar, PhD Data Curation Editor, Scientific Data Nature Publishing Group varsha.khodiyar@nature.com @varsha_khodiyar @scientificdata Scientific Data's experience as an early adopter RDA P7, 1st to 3rd March 2016
- 2. Mandatory and recommended key components of data publishing – WG results Austin et al. in review. Report preprint doi:10.5281/zenodo.34542 Implemented by Scientific Data Under wider consideration by Springer Nature
- 3. Implementation of required elements • Data PID required to complete manuscript submission • Data Citation policy enforced by editorial process • Use of structured repositories which capture subject-specific metadata • Curation of discovery level metadata (regardless of repository) by dedicated Data Curation Editor • Machine readable metadata aids discovery
- 4. Additional elements - Context 4 • Data Descriptor designed to encourage full documentation of data generation • Articles analysing described data are captured in machine readable metadata (ISA format) • Linked as associated publication to Data Descriptor online • Analysis articles published in Nature Publishing Group journals link back to Data Descriptor • Software availability statement required for previously unpublished software and code
- 5. Additional elements - Quality 5 • Provision of manuscript (and metadata templates) to help authors provide reuse level metadata • Dependant on repositories for curation by domain experts • Editorial Board selected based on expertise in data generation/reuse in their field • Ensure that peer reviewers can access data easily and confidentially • Encourage peer reviewers to view and comment on the actual data as part of their assessment • Editorial office regularly asked for advice on data deposition and repository selection
- 6. • Data Descriptors aid visibility of data by considering them as first class publications • Data Descriptors discoverable via common publication indices such as PubMed • Discovery level machine readable metadata (in ISA format) generated for every Data Descriptor • Currently trialling use of metadata for data discovery (ISAexplorer) • Open to suggestions for other uses of Scientific Data’s machine readable metadata Additional elements – Visibility / Accessibility 6
Editor's Notes
- #3: Published product in Scientific Data’s case is the data paper, which we call the Data Descriptor
- #6: Peer-reviewers are not expected to check every data file or "curate" the data. This is a task we feel is best performed by expert repositories, and with support from our in-house data curation support. Rejections after review remain rare, but on at least a few occasions peer-reviewers have identified issues within the actual data files that ultimately led to rejection (e.g. evidence of data contamination or other serious quality issues). We believe that making the data easily available to peer reviewers can actually save them time in these cases, because they do not need to "play detective" -- expects can often make an assessment more rapidly and more accurately when presented with the real data.
- #7: Data Descriptors are discoverable on nature.com, visited by millions, and via common publication indices (PubMed, MEDLINE, Google Scholar -- Scopus and Thomson Reuters to come soon). This also makes them amenable to tracking by traditional metrics, like citation. Scientific Data also delivers progressively FAIR metadata (Findable, Accessible, Interoperable and Reusable)