Data journals are publications whose primary purpose is to expose datasets. They enable the author to focus on the data itself, rather than producing an extensive analysis of the data which occurs in the traditional journal model. In many cases, the greatest value of a dataset lies in sharing it, not necessarily in providing interpretation or analysis.
Fundamentally, data journals seek to:
- promote scientific accreditation and re-use
- improve transparency of scientific method and results
- support good data management practices and
- provide an accessible, permanent and resolvable route to the dataset.
Why might I publish in one?
Publishing in a data journal may be of interest to researchers and data producers for whom data is a primary research output. In some cases, the publication cycle may be quicker than traditional journals, and where there is a requirement to deposit data in an “approved repository”, long term curation and access to the data is assured.
Publishing a data paper may be regarded as best practice in data management as it:
- includes an element of peer review of the dataset
- maximises opportunities for reuse of the dataset and
- provides academic accreditation for data scientists as well as front-line researchers.
Data papers thoroughly describe datasets, and do not usually include any interpretation or discussion (an exception may be discussion of different methods to collect the data). Some data papers are published in a distinct “Data Papers” section of a well-established journal. It is becoming more common, however, to see journals that exclusively focus on the publication of datasets. The purpose of a data journal is to provide quick access to high-quality datasets that are of broad interest to the scientific community. They are intended to facilitate reuse of the dataset, which increases its original value and impact, and speeds the pace of research by avoiding unintentional duplication of effort.
Can I still publish my research results in a traditional journal as well?
It’s worth noting that publishing data through a data journal does not necessarily prevent the publication of data analyses and research results in a traditional journal, along with a reference and links to the data journal paper. This provides readers with access to all relevant information about a piece of research and may result in citation of both the journal article and data paper.
How do I publish in a data journal?
Data preservation is a corollary of data papers, not their main purpose. Most data journals do not archive data in-house. Instead, they generally require that authors submit the dataset to a repository like CORD. Repositories archive the data, provide persistent access, and assign the dataset a unique identifier (DOI). Repositories do not always require that the dataset(s) be linked with a publication but if you’re going to the trouble of submitting a dataset to a repository, you might consider exploring the option of publishing a data paper to support it.
Can I track the usage of my data?
Formal publication and citation of data supports the recognition of research data as a first class research output. It also enables the generation of citation metrics for research data outputs. With products such as the Thomson Reuters Data Citation Index (DCI) capturing data citation metrics, the potential for formal recognition and reward mechanisms based on data publishing is enhanced.
A number of data journals also support ‘altmetrics’ that track the number of article views, number of downloads, and social media ‘likes’ and recommendations. These can be early indicators of the impact of data, before the long tail of formal citation metrics can be assessed.
As is the case with traditional journals, an increasing number of data journals have Journal Impact Factor rankings reflecting the number of times articles are cited boosting the credibility of the journal.The Journal Research Data Policy Bank (JoRD) project was established by JISC in 2012 to shed light on the policies devised by academic publishers to promote linkage between journal articles and underlying research data. The project conducted a feasibility study into the scope and shape of a sustainable service to collate and summarise research data policies of journals and publishers – not unlike the Sherpa/Romeo service which maintains a comprehensive list of journal publishers’ copyright and self-archiving policies.
Find out more
For more information, and to find lists of Open Data Journals, please visit Open Data Journals and Data Journals.
Photo by Carlos Muza on Unsplash