The meaning of data publication
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The meaning of data “publication”. Stéphane Goldstein Head of Programmes, RIN Research Data Management Workshop University of Oxford 13 June 2008. Sharing can be good…. …even research data. Photo: ryancr on Flickr. The premise. Research outputs take many forms… increasingly so

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The meaning of data “publication”

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The meaning of data “publication”

Stéphane Goldstein

Head of Programmes, RIN

Research Data Management Workshop

University of Oxford

13 June 2008


Sharing can be good…

…even research data

Photo: ryancr on Flickr


The premise

  • Research outputs take many forms… increasingly so

  • Research data are one of them, and represent and valuable long-term resource

  • As such, there are two reasons for making them as publicly available as possible:

    • to make them part of the scholarly record, for validating and testing

    • to allow them to be re-used and aggregated by others in new research


The questions

But it is not always clear how researchers are actually behaving with regard to the data that they create:

  • Are they making their data available to others?

  • If so, in what form?

  • If not, why not?

  • What issues do they encounter when taking decisions about their data?


Publish or “publish”?

  • The RIN report investigates these issues across a range of disciplines

  • It examines the ways that data are made available and disseminated…

  • …in other words, how it is “published”…

  • …which is not the same as publishing conclusions from data analysis in journals


The report is articulated around four themes

  • Creating and caring for data

  • Motivations and constrains for “publishing” data

  • Discovery, access and usability of datasets

  • Quality assurance


Creating and caring for data (1)

  • Researchers create many different kinds and categories of data

  • Data are generated for a multiplicity of purposes

  • Because of this, there are important distinctions to be made in:

    • deciding which data should be preserved and shared

    • how adding value to data can become less ad hoc

    • how the variable quality of metadata can be improved

Policy-makers need to take full account of the different kinds of data researchers produce, the different values they have and the different needs of researchers and other users


Creating and caring for data (2)

  • Long-term viability of data is a critical issue

  • Storage of research data is often ill-thought or haphazard

  • There are centralised data centres, but they don’t cover all areas, and their survival can never be guaranteed

  • Local data storage is also possible, but is hindered by shortage of local expertise

There is a need for co-operation between researchers, funders and institutions to ensure that sustainable arrangements are in place to preserve valuable data and make them accessible


Motivations and constraints for “publishing” data

  • Motivations include:

    • altruism

    • encouragement from peers

    • opportunities for collaborations

  • But there are also constraints, such as:

    • lack of explicit career rewards

    • wish, in the first instance, to retain exclusive use of the data

    • legal and/or ethical issues

    • Lack of time, resources, expertise, support services…

Research funders and institutions should actively promote data dissemination and re-use, with measures to incentivise, support or reward good practice among researchers


Discovery, access and usability of datasets

  • Some publishers already provide persistent links from source data to journal articles…

  • …but few researchers have the expertise, resources or inclination to make their own data available and usable by others

  • Other obstacles to locating or gaining access to datasets include:

    • inadequate metadata

    • refusal to release the data

    • Need for licences and/or payment of fees

There is scope for publishers to promote ease of access/use of datasets, and a need to clarify confusion regarding access for text-mining tools. The take-up of Web 2.0 applications should be monitored and its implications considered


Quality assurance

  • No consistent approach to assessing the scholarly content or technical usability of datasets

  • Data centres often apply rigorous quality control procedures, many researchers lack the skills to meet these standards without substantial help from specialists

Need for further work on acceptable approaches to the formal assessment of data across the disciplinary spectrum


The aim is to influence…

  • Report published earlier this week

  • Dissemination effort over the coming months

  • Should be read in conjunction with RIN’s framework of principles on stewardship of digital research data, published in February 2008


Ultimately, it’s all about benefits…

The findings of the report are intended to provide benefits:

  • Research benefits

    • the scholarly record

    • re-use and aggregation

  • Economic benefits


Report available at:www.rin.ac.uk/data-publicationData stewardship principles:www.rin.ac.uk/data-principles

[email protected]

www.rin.ac.uk


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