
As part of a pilot project on data and software peer review at the TU Delft Library, we developed this template for peer reviewing datasets. In creating the template, we referred to existing data peer review guidelines at journals that publish data papers, as well as existing practices in research teams. The template distinguishes between ‘technical’ and ‘scientific’ checks. Technical checks are concerned with the completeness and FAIRness of a dataset, for instance whether there is a README file, an adequate description, metadata, license, and so on. The additional scientific checks look at the quality of data and methodology of data collection, and whether supporting experiments are adequately described, research questions addressed. This would require some accompanying documentation to review, such as a short data paper or journal article. Such a review would be best suited, as with other data peer review practices, at a journal where datasets and accompanying data papers (or articles) are published. However, the template is free to use and adapt as best suited to context.
data peer review
data peer review
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
