fiddle: a tool to combat publication bias by getting research out of the file drawer and into the scientific community

Authors : René Bernard, Tracey L. Weissgerber, Evgeny Bobrov, Stacey J. Winham, Ulrich Dirnag, Nico Riedel

Statistically significant findings are more likely to be published than non-significant or null findings, leaving scientists and healthcare personnel to make decisions based on distorted scientific evidence.

Continuously expanding ´file drawers’ of unpublished data from well-designed experiments waste resources creates problems for researchers, the scientific community and the public. There is limited awareness of the negative impact that publication bias and selective reporting have on the scientific literature.

Alternative publication formats have recently been introduced that make it easier to publish research that is difficult to publish in traditional peer reviewed journals. These include micropublications, data repositories, data journals, preprints, publishing platforms, and journals focusing on null or neutral results. While these alternative formats have the potential to reduce publication bias, many scientists are unaware that these formats exist and don’t know how to use them.

Our open source file drawer data liberation effort (fiddle) tool (RRID:SCR_017327 available at: http://s-quest.bihealth.org/fiddle/) is a match-making Shiny app designed to help biomedical researchers to identify the most appropriate publication format for their data. Users can search for a publication format that meets their needs, compare and contrast different publication formats, and find links to publishing platforms.

This tool will assist scientists in getting otherwise inaccessible, hidden data out of the file drawer into the scientific community and literature. We briefly highlight essential details that should be included to ensure reporting quality, which will allow others to use and benefit from research published in these new formats.

URL : fiddle: a tool to combat publication bias by getting research out of the file drawer and into the scientific community

DOI : https://doi.org/10.1042/CS20201125

ODDPub – a Text-Mining Algorithm to Detect Data Sharing in Biomedical Publications

Authors: Nico Riedel, Miriam Kip, Evgeny Bobro

Open research data are increasingly recognized as a quality indicator and an important resource to increase transparency, robustness and collaboration in science. However, no standardized way of reporting Open Data in publications exists, making it difficult to find shared datasets and assess the prevalence of Open Data in an automated fashion.

We developed ODDPub (Open Data Detection in Publications), a text-mining algorithm that screens biomedical publications and detects cases of Open Data. Using English-language original research publications from a single biomedical research institution (n = 8689) and randomly selected from PubMed (n = 1500) we iteratively developed a set of derived keyword categories.

ODDPub can detect data sharing through field-specific repositories, general-purpose repositories or the supplement. Additionally, it can detect shared analysis code (Open Code).

To validate ODDPub, we manually screened 792 publications randomly selected from PubMed. On this validation dataset, our algorithm detected Open Data publications with a sensitivity of 0.73 and specificity of 0.97.

Open Data was detected for 11.5% (n = 91) of publications. Open Code was detected for 1.4% (n = 11) of publications with a sensitivity of 0.73 and specificity of 1.00. We compared our results to the linked datasets found in the databases PubMed and Web of Science.

Our algorithm can automatically screen large numbers of publications for Open Data. It can thus be used to assess Open Data sharing rates on the level of subject areas, journals, or institutions. It can also identify individual Open Data publications in a larger publication corpus. ODDPub is published as an R package on GitHub.

URL : ODDPub – a Text-Mining Algorithm to Detect Data Sharing in Biomedical Publications

DOI : http://doi.org/10.5334/dsj-2020-042