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