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• A presentation given by Iain Hrynaszkiewicz from BioMed Central - an overview of BioMed Central's open data projects in publishing. Presented at the Alliance for Permanent Access conference, 7th November 2012

• Includes some good information about the role of publishers in open data / data sharing.

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http://www.slideshare.net/iainh_z/biomed-centrals-open-data-initiatives

August 28, 2014

NIH Tells Genomic Researchers: ‘You Must Share Data'

By Paul Basken

Scientists who use government money to conduct genomic research will now be required to quickly share the data they gather under a policy announced on Wednesday by the National Institutes of Health.

The data-sharing policy, which will take effect with grants awarded in January, will give agency-financed researchers six months to load any genomic data they collect—from human or nonhuman subjects—into a government-established database or a recognized alternative.

http://chronicle.com/article/NIH-Tells-Genomic-Researchers-/148509/

OPEN DATA: Data Notes: Dissecting the most boring buzzword ever

This is a great post by Amye Kenall on BioMed Central and worth reading...

"By far one of the biggest concerns around Open Data is not whether we have the technology to enable researchers to make their data open but whether the cultural incentives are in place to make researchers freely share their data. Several publishers have recently started publishing ‘data journals’ or ‘data notes’. Is this latest publishing buzzword the answer to incentivising Open Data? ...

Check it out: http://blogs.biomedcentral.com/bmcblog/author/amyekenall/

Thanks Anna Shadbolt (Uni Melb) for sharing this on ands-partners

Maximising the value of research data: developing incentives and changing cultures

The value of sharing research data is widely recognised by the research community and funders are setting in place stronger policy requirements for researchers to share data. But the costs to researchers in sharing their data can be considerable and the incentives are sometimes few and far between. A recent report from the cross-disciplinary Expert Advisory Group on Data Access (EAGDA) highlights the need for a shift in cultures to provide greater support for researchers in sharing data and greater recognition for those who do it well. Dave Carr and Natalie Banner, from the Wellcome Trust, highlight some of the key findings and recommendations emerging from this work.  

http://blogs.lse.ac.uk/impactofsocialsciences/2014/07/01/maximising-value-research-data-wellcome-trust/

Infographic: Understanding Metadata

If you've ever struggled with the concept of metadata this quick reference guide is your new go to! This graphic provides a simple and high-level overview of how you can use metadata to improve your research data.

http://www.ersa.edu.au/enewsjune2014/understanding-metadata
Download a copy of the Understanding Metadata infographic (JPG 355KB)

http://online.wsj.com/articles/ad-tech-entrepreneurs-build-cancer-database-1403134613

Google invests $130M in cancer data sharing startup.

1. Data Sharing Platform Allows Cancer Centers to Share Data
Most cancer centers base treatments on clinical trial research, but this research draws from data on only about four percent of patients, since most patients do not participate in clinical trials. Flatiron Health set out to get centers better access to data on the remaining 96 percent of patients. The New York-based company developed a cloud-based storage and analytics platform that enables centers to contribute their data to a central database in exchange for access to the other centers’ data. The database now contains about 550,000 cases, and organizers hope it will provide the sample sizes necessary for new research.

Joint Declaration of Data Citation Principles - FINAL

(from :https://www.force11.org/datacitation)

Principles created by the Data Citation Synthesis Group 

Preamble

Sound, reproducible scholarship rests upon a foundation of robust, accessible data.  For this to be so in practice as well as theory, data must be accorded due importance in the practice of scholarship and in the enduring scholarly record.  In other words, data should be considered legitimate, citable products of research.  Data citation, like the citation of other evidence and sources, is good research practice and is part of the scholarly ecosystem supporting data reuse.

In support of this assertion, and to encourage good practice, we offer a set of guiding principles for data within scholarly literature, another dataset, or any other research object.  

These principles are the synthesis of work by a number of groups.  As we move into the next phase, we welcome your participation and endorsement of these principles.
 

Principles

The Data Citation Principles cover purpose, function and attributes of citations.  These principles recognize the dual necessity of creating citation practices that are both human understandable and machine-actionable.  

These citation principles are not comprehensive recommendations for data stewardship.  And, as practices vary across communities and technologies will evolve over time, we do not include recommendations for specific implementations, but encourage communities to develop practices and tools that embody these principles.

The principles are grouped so as to facilitate understanding, rather than according to any perceived criteria of importance.

1. Importance

   Data should be considered legitimate, citable products of research. Data citations should be accorded the same importance in the scholarly record as citations of other research objects, such as publications[1].

2. Credit and Attribution

   Data citations should facilitate giving scholarly credit and normative and legal attribution to all contributors to the data, recognizing that a single style or mechanism of attribution may not be applicable to all data[2].

3. Evidence

   In scholarly literature, whenever and wherever a claim relies upon data, the corresponding data should be cited[3].

4. Unique Identification

   A data citation should include a persistent method for identification that is machine actionable, globally unique, and widely used by a community[4].

5. Access

   Data citations should facilitate access to the data themselves and to such associated metadata, documentation, code, and other materials, as are necessary for both humans and machines to make informed use of the referenced data[5].

6. Persistence

   Unique identifiers, and metadata describing the data, and its disposition, should persist --  even beyond the lifespan of the data they describe[6].

7. Specificity and Verifiability 

   Data citations should facilitate identification of, access to, and verfication of the specific data that support a claim.  Citations or citation metadata should include information about provenance and fixity sufficient to facilitate verfiying that the specific timeslice, version and/or granular portion of data retrieved subsequently is the same as was originally cited[7].

8. Interoperability and flexibility

   Data citation methods should be sufficiently flexible to accommodate the variant practices among communities, but should not differ so much that they compromise interoperability of data citation practices across communities[8].
   For further information glossary, examples and references

Journal Article: Nine simple ways to make it easier to (re)use your data

Nine simple ways to make it easier to (re)use your data

Ethan P. White, Elita Baldridge, Zachary T. Brym, Kenneth J. Locey, Daniel J. McGlinn, and Sarah R. Supp

https://peerj.com/preprints/7v1.pdf

Abstract
17 Sharing data is increasingly considered to be an important part of the scientific process. Making your
18 data publicly available allows original results to be reproduced and new analyses to be conducted.
19 While sharing your data is the first step in allowing reuse, it is also important that the data be easy
20 understand and use. We describe nine simple ways to make it easy to reuse the data that you share
21 and also make it easier to work with it yourself. Our recommendations focus on making your data
22 understandable, easy to analyze, and readily available to the wider community of scientists.