Reproducibility of published research results is increasingly coming into question. Efforts from funding agencies and publishers are calling for more and more transparency around research data, but so far, little attention seems to have been paid to a crucial aspect of experimental reproducibility: publication of detailed methodologies. A new NIH-led effort seems a first step in correcting this oversight.
While access to research data is valuable for potential reuse and extension of research results, much of the emphasis stated for data policies has been to improve the reproducibility of the research behind it. As a recent PLOS blog post puts it:
Availability of the data underlying a published study is probably the most significant way in which journals can, now, ensure reproducibility of the published literature.
I’m not sure I agree. Being able to review the data does indeed allow one to see if a researcher’s analysis and conclusions drawn are accurate for that dataset. But it does little to validate the quality and accuracy of the dataset itself. I can look at the gene expression data derived from your cell lines and see if it really shows the activity of the gene you claim it shows, but I can’t tell if you really used the cell lines and conditions you claim you used.