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In a recent Industry Forum report by Thomson Reuters, several pertinent statements were made:

• The growing accumulation of data produced by academics which is not destined for publication represents an impediment to scientific progress.
• Conventional research assessment methods do not recognise or reward data sharing.
• A researcher’s overall contribution to scientific progress is greater than their peer-reviewed publication record.

At F1000Research we couldn’t agree more!

Traditionally scientific contributions have been measured purely on a researcher’s peer-reviewed publication record, with the quality of these contributions being based on the impact factors of the journals they appear in.  Whilst the generation of a large dataset is not a trivial task and is essential to modern bioscience research, this activity in itself is often not considered as contributing to an institute’s research assessment exercise.

Clearly things are changing, with most funders e.g. the NIH, NSF, MRC and the Wellcome Trust, recognising the importance of sharing data by making the provision of a data sharing plan an essential requirement of funding applications.  In the meantime, how can a researcher ensure that the time and effort spent generating a dataset gains appropriate scientific recognition for career progression?  We suggest publishing a data-only article with F1000Research.

Why an F1000Research data article?

Publishing a data article has many benefits, which include:

1. Appropriate attribution of credit to researchers involved in the non-trivial task of generating the dataset who may not always be the same people that will analyse the data and write the more traditional results/conclusions article.
2. Increased discoverability of the dataset.
3. Increased traffic for the data repository holding the data.
4. The potential for an increase in collaborative approaches to the data generating group.
5. A potential increase in ‘value’ of the data, as different groups may analyse the data with a hypothesis different to that for which the data was originally generated.

What is an F1000Research data article?

A data article is simply the raw dataset(s) together with enough protocol information to enable someone else to be able to try and replicate the data and to be able to reuse the data.  We ask you to also include your original hypothesis and any limitations of the datasets.  More detailed guidance on these articles is available here.

With regards hosting of the data, authors are required to utilise an appropriate repository for their data where there are field-specific standards (e.g. sequence data in GenBank). For other types of data, authors are encouraged to use an accredited repository or can send the data directly to us where we will organise hosting of the data.  We will be releasing more detailed guidance on what an ‘accreditated repository’ means in the next few weeks.

Furthermore, publishing a dataset (with the associated protocol information) will not prejudice subsequent publication of an analysis paper in almost all the major journals, including Nature and Science. (see the full list of journals happy to accept papers analysing previously published datasets.)

So make your unpublished datasets work for you by sending us your data articles!