Data Publishing Webinar

Our recent updates via Data Forwards covered the theme of data publishing and citation. Here is another good resource for you to stay current on data publishing.

The RDAWDS Publishing Data IG organized a first webinar (1:23) covering the topic of data publishing. Six speakers representing data centers/archives, publishers, and research institutes talk about data publishing workflows, business models, and the design for an infrastructure to link data and publications.

Presentation slides are also available.

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New Video Shows How to Cite Data

By properly citing data in research publications, it not only acknowledges sources, it also makes it easier for others to discover, (re-)use, and re-purpose the data. Data citation is quickly becoming best practice across all disciplines. This new video tutorial shows data users how to retrieve citation information directly from UK Data Service resources. Although this video particularly focuses on how-to-cite datasets from the UK Data Service, it covers general principles of data citation, including what it is and why it is important.

In addition to the UK Data Service website guidance, other data centers and repositories also provide similar data citation guidance, including the Inter-university Consortium for Political and Social Research (ICPSR).

 

DataPub Blog on Data Publication

DataPub at CDL has recently published a blog post, “Fifteen Ideas about Data Validation (and Peer Review),” by John Kratz who discusses a new concept of ‘data validation’ and emerging practices of dataset peer review. Kratz mentions that “(s)ome form of validation at some stage in a data publication process is essential… However, the scientific literature’s validation mechanisms don’t translate as directly to data as its mechanism for, say, citation.”

If you’d like to find out more about data citation, DataPub’s new webpage may be of interest to you.

 

Call For Papers for 2nd “Workshop on Linking and Contextualizing Publications and Datasets”

The second “Workshop on Linking and Contextualizing Publications and Datasets” is now inviting submissions by June 30, 2014. The workshop will be held on September 12 as part of the Digital Libraries 2014 conference in London.

Details can be found at: http://lcpd2014.research-infrastructures.eu/call.html

New Article “Peer Review of Datasets: When, Why, and How”

This paper by Mayernik et al. discusses issues related to data peer review, in particular the peer review processes, needs, and challenges related to the following scenarios:

  1. Data analyzed in traditional scientific articles
  2. Data articles published in traditional scientific journals
  3. Data submitted to open access data repositories
  4. Datasets published via articles in data journals.

A full text in PDF is available from the following link:

http://journals.ametsoc.org/doi/pdf/10.1175/BAMS-D-13-00083.1

 

 

Survey Research on Data Publication Practices and Perceptions by CDL

Asking researchers what they would expect and want from a data publication

The California Digital Library has opened a survey of researcher perceptions and practices around data publication. The library community has been productively discussing these issues for some time. The CDL research team hopes to contribute by asking researchers directly what they would expect and want from a data publication.

You can find more information about the CDL survey research here and take a survey.

New Journal Option for Biodiversity Data Publishers

Collaboration aims at new journal option for biodiversity data publishers

A new collaboration aims to increase the options for researchers to gain visibility and recognition for biodiversity datasets published through the GBIF network. GBIF is collaborating with the editors of Scientific Data, the new open access, online journal from Nature Publishing Group due to launch in May 2014.

The journal introduces a new type of content called the ‘data descriptor’ (data descriptors closely resemble the data paper concept), aimed at making scientifically valuable datasets more discoverable, interpretable and reusable.

More information available at: http://www.gbif.org/page/2997