The plate tectonics of research data publication

In biology, the fields of macromolecular structural biology and sequence bioinformatics have, since the 1970s, had established international databases for the deposition of data, and journal policies mandating such deposition prior to acceptance for publication of manuscripts describing the data.  Similar good practices have developed more recently in other disciplines, notably astronomy.  But these are the exceptions, and over the majority of scientific fields data publication remains a minority activity.  For the most part, this is because the technical barriers to publication of research datasets remain so high, and the academic rewards so low, that such publication is undertaken only by the few who regard it as a moral imperative.  However, new policies are combining with new technological capabilities to bring significant change to this publication landscape.

An analogy can perhaps be made with geological plate tectonics.  The new policies of funders and journal publishers towards the open publication of research datasets arising from publicly funded research can be likened to a tectonic plate slowly moving forward with inexorable force, that is colliding with the massive stationary continental plate of established scientific practice, in which data are traditionally regarded as belonging to the research group that generated them, in which data sharing occurs only between trusted colleagues on the basis of personal request, and in which the only publications that are truly valued are those of journal articles.

This traditional position is reinforced by the metrics employed in the assessment of research quality, which, while giving lip service to the value of data publication, regard articles in high impact factor journals as of paramount importance.  And there is good reason why the scholarly research article is so highly regarded, since it is a rhetorical construct in which the authors attempt by the selective presentation of evidence to convince the readers that particular hypotheses are proven.  As such, it can both demonstrate the competence and achievements of the authors, and be evaluated against other such publications by peer review.  In contrast, the publication of a research dataset is primarily a presentation of facts, with no rhetorical content, that can only be validated on the basis of internal self-consistency, information about instrument calibrations, resolution and error estimates, and the possession of adequate descriptive metadata in appropriate formats – much more pedestrian stuff.

However, to return to our analogy, these tectonic plates are colliding, with the traditional plate of data retention facing ultimate subduction beneath the advancing plate of open data publication.  At present there is friction between them, and along much of the sheer zone there seems to be little movement, leading to a build-up of pressure.  Small projects that result in some local movement towards better data management can be likened to minor earthquakes having only local impact.  But these are the precursors to an inevitable major re-positioning of the plates to relieve the mounting pressure for change.  This will result in a major general realignment of attitudes along the whole plate boundary, resulting in a tsunami of open data publication.  We are thus on the cusp between the traditional status quo and a new dramatically reshaped scientific publication landscape, in which open data publication will take its proper place as underpinning the publication of ideas and evidences supporting hypotheses.

Technological projects such as the JISC ADMIRAL Project and its successor the JISC UMF DataFlow Project serve to facilitate this transition by ‘lubricating’ the plate boundary and enabling movement.  In particular, the two-tiered federated data management infrastructure they provide, with local services to meet the private data management needs of individual research groups (DataStage filestore instances) being linked to institutional repositories (DataBank repository instances) by automated procedures for the easy archiving and publication of selected datasets, makes the whole process easier, as illustrated by the following figures taken from the original ADMIRAL Project grant application.

Figure 1: The conventional research data lifecycle  

Four phases mark the activities undertaken in traversing the conventional data lifecycle: formulation, experimentation, interpretation and publication.       The publication outputs from one cycle provide the input to the next.  However, only selected research data are conventionally published.  The original research datasets are frequently abandoned on local hard drives or CD-ROMs, and neither datasets nor papers are submitted to institutional repositories.

Figure 2: The ADMIRAL enhanced research data lifecycle

Raw research data are first organized and annotated in a local research data filestore.  From there they can be shared and used to support publications, and can be automatically archived to institutional repositories, from which they can optionally be published as Linked Open Data on the Semantic Web for public dissemination and reuse.  This figure differs from the DCC Data Cycle model by emphasizing the importance of the local research data filestore.

Figure 3: The effort involved in submitting data to an institutional repository  

As investment is made in the local organization and annotation of research datasets, the effort involved in data submission to an institutional repository reduces to the point where it becomes feasible on a routine basis.

IBRG projects to facilitate data publication and data citation

In the previous post, I outlined reasons why researchers don’t publish data, presented as evidence to the Royal Society’s Policy Study “Science as a Public Enterprise” Call for Evidence.  Here, I summarize activities by members of my Image Bioinformatics Research Group (IBRG) at Oxford University to facilitate data publication and data citation, and thus to help catalyze a cultural shift to a situation in which data publication is as natural a part of research life as is undertaking experiments.

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Data management services and data repositories

We are developing tools and services to assist researchers in their local data management, for their own personal benefit, while facilitating automated data submission to appropriate institutional or subject-specific data repositories, in ways that fit with their normal working practices and impose as little as possible in terms of cognitive overhead – what we term sheer curation.  These include the two-stage data management services we are currently funded to develop by the University Modernization Fund through the JISC DataFlow Project, namely (a) DataStage, a private local data management file system, with automated backup, Web access, and security access control, for use by individual research groups, and (b) DataBank, a cloud-deployable data repository for use by universities, research institutes or large research consortia.  These open source services will be made available for installation by third parties on the Eduserv academic cloud and elsewhere, as required by research groups, institutions and universities both in the UK and internationally.  We seek early adopters!

Curation by addition

For automated data submissions from DataStage to DataBank, that will use the SWORDv2 repository submission protocol to standardize data package ingest, we are intentionally lowering the barriers in terms of metadata requirements for initial data submission, with the the possibility of enriching the metadata at a later date – what we call curation by addition – in order to kick-start the cultural sea change required for data deposition to become routine.  We are trying to avoid the best – the requirement for perfect and complete metadata – becoming the enemy of the good – data publication by any means.


We are, through the JISC Dryad-UK Project, working to promote the Dryad Data Repository, a domain-specific repository for biological datasets linked to peer-reviewed journal articles, by bringing additional publishers and journals on board, and enabling Dryad metadata to be published as open linked data.


We are also promoting the adoption of SWORDv2 repository communication protocol for data package wrapping, to permit automated deposit to DataBank, Dryad or other SWORD-compliant repositories, and the exchange of metadata between them.

SPAR (Semantic Publishing and Referencing) Ontologies

To enable Dryad, DataBank and similar repository metadata to be published as open linked data, we are creating appropriate data description and data citation ontologies, including FaBiO and CiTO4Data, as part of our suite of SPAR Ontologies, and are using them to provide mappings from the DataCite XML Metadata Kernel to RDF.

Data citation

We are working with DataCite to assign DOIs to Dryad and DataBank datasets, so that data publications become citable, gaining academic credit for the data depositor.

These data citations, when they exist, will fit naturally within the Open Citations Corpus, a collection of some 3.4 million bibliographic citations from within PubMed Central that we have recently established as open linked data, as part of the JISC Open Citations Project.

We have also worked to establish best practice for citing data publications from within the literature, and with one open access journal publisher to influence their Data Publishing Policies and Guidelines to Authors regarding data citation, as detailed in earlier posts on this blog.

Tools for metadata curation

The above tools and services are generic.  Specifically in the biomedical area, we are developing MIIDI, a Minimal Information standard for reporting an Infectious Disease Investigation, to specify the metadata that should for completeness accompany such an investigation, and have recently developed MIIDI Forms, a web tool that facilitates the entry of such metadata, that involves interaction with appropriate web services to enable autocompleting of bibliographic information and specification of geo-coordinates for place names, and permits automated look-up of ontology terms from the NCBI BioPortal

Open Research Reports

We are working to create Open Research Reports, open access structured digital abstracts in both human- and machine-readable form that describe datasets or journal articles that relate to infectious disease, based on MIIDI and to be published in an instant data journal format with DOIs to permit referencing and citation.

Tools for creating data management plans

We have recently started working with the Digital Curation Centre to help improve their DMPonline data management planning tool for creating the data management plans increasingly required to accompany grant applications, and useful for managing the flow of data from funded projects.  If our current funding application is successful, this work will be carried forward in the OXFORD DMPonline Project, in which, in addition to adoption, adaption, customization and integration of the tool for use by University of Oxford researchers, we will develop the following generic improvements to the tool that will be fed back to the DCC as open source enhancements for general use across UK academia and internationally:

a)     creation of DaMO, a simple data management ontology,

b)     use of DaMO to create RDF metadata for data management plans,

c)     SWORDv2-wrapping of data management plans for repository submission, and

d)     creation of DMPBank, a DataBank instance specifically tailored for archiving and publishing data management plans.

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