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We are pleased to announce that Biology Letters is the latest journal to integrate submission of manuscripts with data to Dryad.  In this process, the journal and repository communicate behind the scenes in order to streamline data submission for authors and ensure that the article contains a permanent link to the data.

It is particularly apt because Biology Letters is published by the Royal Society, which invented the idea of sharing knowledge through a scientific journal back in 1665.  Scientific communication has come a long way from those early letters among gentleman natural philosophers to the current conception of Science as an Open Enterprise conducted in the public interest.  Reflecting these changes in science and technology, the Royal Society recently strengthened its policy on the availability of research data:

To allow others to verify and build on the work published in Royal Society journals it is a condition of publication that authors make available the data and research materials supporting the results in the article.

Datasets should be deposited in an appropriate, recognized repository and the associated accession number, link or DOI to the datasets must be included in the methods section of the article. Reference(s) to datasets should also be included in the reference list of the article with DOIs (where available). Where no discipline-specific data repository exists authors should deposit their datasets in a general repository such as Dryad.

There are already a healthy number of articles in Biology Letters with associated data in Dryad, including one of last year’s hit data packages, Monsters are people too.  The first to be published via integrated submission is:

Article:

Jevanandam N, Goh AGR, Corlett RT (2013) Climate warming and the potential extinction of fig wasps, the obligate pollinators of figs. Biology Letters 9(3): 20130041. doi:10.1098/rsbl.2013.0041

Data:

Goh AGR, Corlett RT, Jevanandam N (2013) Data from: Climate warming and the potential extinction of fig wasps, the obligate pollinators of figs. Dryad Digital Repository. doi:10.5061/dryad.hj7h2

PubMed and GenBank, from the National Center for Biotechnology Information (NCBI), are hugely popular resources for searching and retrieving article abstracts and nucleotide sequence data, respectively.  PubMed indexes the vast majority of the biomedical literature, and deposition of nucleotide sequences in GenBank or one of the other INSDC databases is a near universal requirement for publication in a scientific journal.

Thanks to NCBI’s “LinkOut” feature, it is now easy to find associated data in Dryad from either PubMed or GenBank. For example, this Dryad data package is linked from:ncbi._linkout_tjv2

  • the article’s abstract in PubMed. “LinkOut” is at the bottom of the page;  expand “+” to see the links to Dryad and other resources.
  • nucleotide data associated with the same publication in GenBank. “LinkOut” is in the right hand navigation bar

LinkOut allows the data from an article to be distributed among repositories without compromising its discoverability.

At Dryad, we intend to expand on this feature in a couple of ways. First, we plan to make Dryad content searchable via the PubMed and GenBank identifiers, which because of their wide use will provide a convenient gateway for other biomedical databases to link out to Dryad.  Second, we will be using open web standards to expose relationships between content in Dryad and other repositories, not just NCBI.  For example, keen eyes may have noted the relationship of the Dryad data package in the example above to two records in TreeBASE.

To learn more about how Dryad implements NCBI’s LinkOut feature, please see our wiki.

OSTP homepageOn Friday, the Obama administration made a long-awaited announcement regarding public access to the results of federally funded research in the United States.

There has been considerable attention given to the implications for research publications (a concise analysis here).  Less discussed so far — but just as far reaching — the new policy also has quite a lot to say about research data, a topic on which the White House solicited, and received, an earful of input just over a year ago.

What does the directive actually require?  All federal government agencies with at least $100M in R&D expenditures must develop, in the next six month, policies for digital data arising from non-classified research that address a host of objectives, including:

  • to “maximize access, by the general public and without charge, to digitally formatted scientific data created with federal funds” while recognizing that there are cases in which preservation and access may not be desirable or feasible.
  • to promote greater use of data management plans for both intramural and extramural grants and contracts, including review of such plans and mechanisms for ensuring compliance
  • to allow inclusion of appropriate costs for data management and access in grants
  • to promote the deposit of data in publicly accessible databases
  • to address issues of attribution to scientific data sets
  • to support training in data management and stewardship
  • to “outline options for developing and sustaining repositories for scientific data in digital formats, taking into account the efforts of public and private sector entities”

Interestingly, the directive is silent on the issue of embargo periods for research data, neither explicitly allowing or disallowing them.

In the words of White House Science Advisor John Holdren

…the memorandum requires that agencies start to address the need to improve upon the management and sharing of scientific data produced with Federal funding. Strengthening these policies will promote entrepreneurship and jobs growth in addition to driving scientific progress. Access to pre-existing data sets can accelerate growth by allowing companies to focus resources and efforts on understanding and fully exploiting discoveries instead of repeating basic, pre-competitive work already documented elsewhere.

The breadth of research impacted by this directive is notable.  Based on the White House’s proposed 2013 budget, the covered agencies would spend more then $60 billion on R&D.  A partial list includes:

  • The National Institutes of Health (NIH)
  • The National Science Foundation (NSF)
  • The National Aeronautics and Space Administration (NASA)
  • The Department of Energy (DOE)
  • The Department of Agriculture (USDA)
  • The National Oceanic and Atmospheric Administration (NOAA)
  • The National Institutes for Standards and Technology (NIST)
  • The Department of the Interior (which includes the Geological Survey)
  • The Environmental Protection Agency (EPA)
  • and even the Smithsonian Institution

We applaud OSTP for moving to dramatically improve the availability of research data collected in the public interest with federal funds.

You can read the full memo here: the data policies are covered in Section 4.

Photo by DAVID ILIFF. License: CC-BY-SA 3.0

Mark Your Calendar!

The 2013 Dryad Membership Meeting

St Anne’s College, Oxford, UK

24 May 2013


The Dryad Membership Meeting will cap off a series of separate but related events spotlighting trends in scholarly communication and research data.  Highlights include:

  • A data publishing symposium on May 22 – Featuring new initiatives and current issues in data publishing (open to the public, nominal registration fee may apply).
  • A Joint Dryad-ORCID Symposium on Research Attribution on May 23 - On the changing culture and technology of how credit is assigned and tracked for data, software, and other research outputs (Public).
  • Dryad Membership Meeting on May 24 - Help chart the course for the organization’s future (Dryad Members only).

More details to be announced soon.

The following guest post is from Tim Vines, Managing Editor of Molecular Ecology and Molecular Ecology Resources.  ME and MER have among the most effective data archiving policies of any Dryad partner journal, as measured by the availability of data for reuse [1].  In this post, which may be useful to other journals figuring out how to support data archiving, Tim explains how Molecular Ecology’s approach has been refined over time.

newsman

Ask almost anyone in the research community, and they’ll say that archiving the data associated with a paper at publication is really important. Making sure it actually happens is not quite so simple. One of the main obstacles is that it’s hard to decide which data from a study should be made public, and this is mainly because consistent data archiving standards have not yet been developed.

It’s impossible for anyone to write exhaustive journal policies laying out exactly what each kind of study should archive (I’ve tried), so the challenge is to identify for each paper which data should be made available.

Before I describe how we currently deal with this issue, I should give some history of data archiving at Molecular Ecology. In early 2010 we joined with the five other big evolution journals in adopting the ‘Joint Data Archiving Policy’, which mandates that “authors make all the data required to recreate the results in their paper available on a public archive”. This policy came into force in January 2011, and since all five journals brought it in at the same time it meant that no one journal suffered the effects of bringing in a (potentially) unpopular policy.

To help us see whether authors really had archived all the required datasets, we started requiring that authors include ‘Data Accessibility’ (DA) section in the final version of their manuscript. This DA section lists where each dataset is stored, and normally appears after the references.  For example:

Data Accessibility:

  • DNA sequences: Genbank accessions F234391-F234402
  • Final DNA sequence assembly uploaded as online supplemental material
  • Climate data and MaxEnt input files: Dryad doi:10.5521/dryad.12311
  • Sampling locations, morphological data and microsatellite genotypes: Dryad doi:10.5521/dryad.12311

We began back in 2011 by including a few paragraphs about our data archiving policies in positive decision letters (i.e. ‘accept, minor revisions’ and ‘accept’), which asked for a DA section to be added to the manuscript during their final revisions. I would also add a sticky note to the ScholarOne Manuscripts entry for the paper indicating which datasets I thought should be listed. Most authors added the DA, but generally only included some of the data. I then switched to putting my list into the decision letter itself, just above the policy itself. For example:

“Please don’t forget to add the Data Accessibility section- it looks like this needs a file giving sampling details, morphology and microsatellite genotypes for all adults and offspring. Please also consider providing the input files for your analyses.”

This was much more effective than expecting the authors to work out which data we wanted. However, it still meant that I was combing through the abstract and the methods trying to work out what data had been generated in that manuscript.

We use ScholarOne Manuscripts’ First Look system for handling accepted papers, and we don’t export anything to be typeset until we’re satisfied with the DA section. Being strict about this makes most authors deal with our DA requirements quickly (they don’t want their paper delayed), but a few take longer while we help authors work out what we want.

The downside of this whole approach is that it takes me quite a lot of effort to work out what should appear in the DA section, and would be impossible in a journal where an academic does not see the final version of the paper. A more robust long-term strategy has to involve the researcher community in identifying which data should be archived.

I’ll flesh out the steps below, but simply put our new approach is to ask authors to include a draft Data Accessibility section at initial submission. This draft DA section should list each dataset and say where the authors expect to archive it. As long as the DA section is there (even if it’s empty) we send the paper on to an editor. If it makes it to reviewers, we ask them to check the DA section and point out what datasets are missing.

A paper close to acceptance can thus contain a complete or nearly complete DA section. Furthermore, any deficiencies should have been pointed out in review and corrected in revision. The editorial office now has the much easier task of checking over the final DA section and making sure that all the accession numbers etc. are added before the article is exported to be typeset.

The immediate benefit is that authors are encouraged to think about data archiving while they’re still writing the paper – it’s thus much more an integral part of manuscript preparation than an afterthought. We’ve also found that a growing proportion of papers (currently about 20%) are being submitted with a completed DA section that requires no further action on our part. I expect that this proportion will be more like 80% in two years, as this seems to be how long it takes to effect changes in author or reviewer behavior.

Since the fine grain of the details may be of interest, I’ve broken down the individual steps below:

1) The authors submit their paper with a draft ‘Data Accessibility’ (DA) statement in the manuscript; this lists where the authors plan to archive each of their datasets. We’ve included a required checkbox in the submission phase that states ‘A draft Data Accessibility statement is present in the manuscript’.

2) Research papers submitted without a DA section are held in the editorial office checklist and the authors contacted to request one. In the first few months of using this system we have found that c. 40% of submissions don’t have the statement initially, but after we request it the DA is almost always emailed within 3-4 days. If we don’t hear for five working days we unsubmit the paper; this has happened to about only 5% of papers.

3) If the paper makes it out to review, the reviewers are asked to check whether all the necessary datasets are listed, and if not, request additions in the main body of their review. Specifically, our ‘additional questions’ section of the review tab in S1M now contains the question: “Does the Data Accessibility section list all the datasets needed to recreate the results in the manuscript? If ‘No’, please specify which additional data are needed in your comments to the authors.”  Reviewers can choose ‘yes’, ‘no’ or ‘I didn’t check’; the latter is important because reviewers who haven’t looked at the DA section aren’t forced to arbitrarily click ‘yes’ or ‘no’.

4) The decision letter is sent to the authors with the question from (3) included. Since we’re still in the early days of this system and less than a quarter of our reviewers understand how to evaluate the DA section, I am still checking the data myself and requesting that any missing datasets be included in the revision. This is much easier than before as there is a draft DA section to work with and sometimes some feedback from the reviewers.

5) The editorial office then makes sure that any deficiencies identified by myself or the reviewers are dealt with by the time the paper goes to be typeset; this is normally dealt with at the First Look stage.

I’d be very happy to help anyone that would like to know more about this system or its implementation – please contact me at managing.editor@molecol.com

[1] Vines TH, Andrew RL, Bock DG, Franklin MT, Gilbert KJ, Kane NC, Moore JS, Moyers BT, Renaut S, Rennison DJ, Veen T, Yeaman S. Mandated data archiving greatly improves access to research data. FASEB J. 2013 Jan 8. Epub ahead of print.  Update: Also available from arXiv.

If you have data packages in Dryad, consider adding a button like this next to each one on the publication list of your website or your electronic CV.

You can make a link between the button and the individual data package page on Dryad to enrich your publication list and make it easy to find your data.

Props to our early adopters below.  Check out their pages for some examples.

For other ways to show your support, please visit our page of publicity material on the Dryad wiki.  Let us know if you come up with creative ways to promote your data in Dryad. And additional suggestions are always welcome at help@datadryad.org.

Have at it!

Lee Dirks

We are profoundly saddened by the untimely and tragic death of our dear friend and colleague Lee Dirks, who was killed together with his wife Judy Lew in a road accident in the Peruvian Andes.

Lee had recently been elected to the Board of Directors for Dryad.  He also served on the Board of Visitors for the UNC School of Information Sciences (of which he was a proud alumnus) and was a member of the Board of the SILS Metadata Research Center.  Lee made a named for himself in recent years as Director of Education and Scholarly Communication at Microsoft.

Lee was a visionary information scientist, a warm and generous personality, and a man who loved adventure.  The number of people whose lives he touched in his own short life was staggeringly large.

Lee and his wife are survived by their two young daughters, who were at home in Seattle at the time of the accident.  Our thoughts are with them.  And we will miss Lee greatly.

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