Today, we are pleased to announce our first Advisory Panel and Editorial Board members from the social sciences. Researchers from these communities are now invited to submit Data Descriptor manuscripts describing quantitative datasets, particularly those that may be of use for integrative analyses that stretch across the traditional boundaries between the natural and social sciences.
Advisory Panel ➥
Matthew Woollard, University of Essex, UK
Editorial Board ➥
Mercè Crosas, Harvard University, USA
David Reinstein, University of Essex, UK
In parallel, we have updated our journal’s scope statement and we have created a new section in our recommended data repository list for social sciences resources. At present, we currently recommend the Harvard Dataverse Network for datasets from the social sciences, and we plan to add more repositories to this section in due course, according to our existing criteria.
One of the main aims of Scientific Data is to promote integrative, collaborative research. Rich data descriptions, like those provided in our articles, help data travel across field boundaries and be productively reused by researchers who may not be experts in the original data-generating techniques. Our peer-review and curation process also ensure that data associated with our articles are technically sound, again giving non-experts confidence when integrating data from diverse sources.
The need to increase reproducibility and reduce bias is important to all fields of science. These issues have been prominently debated in political and social sciences in 2014, and coincide with new reproducibility initiatives focused on biological research at the Nature Publishing Group and other publishers (see these two editorials at Nature for more). Some social science journals, such as Palgrave Communications, are also promoting data citation.
Indeed, the boundaries between the social and natural sciences can already be decidedly blurry in fields like public health, cognitive science, or anthropology, all fields where we have published well-received Data Descriptors (see these examples, Mylne et al. 2014, Hanke et al. 2014, & Plooij et al. 2014). By providing a venue for sound datasets from both sides of the social and natural science divide, we feel that Scientific Data can further promote data sharing and innovative integrative research in these and other fields.