In an era of large-scale biomedical research, generating and sharing datasets in an open manner is an important, but non-trivial task. However, researchers are subject to the ‘publish or perish’ culture, where career progression and tenure is highly dependent on publishing papers in peer reviewed journals with high impact factors. Standard journals often have limited space available for each paper, thus much of the scientific literature has little data associated with each article.
In addition, the publication of a dataset is rarely considered as having as high an impact compared with a data analysis paper. There are also numerous technical obstacles in making datasets truly accessible. These issues combine to create a scientific culture where sharing and publishing data ends up low on a researchers’ list of priorities. However, open data can be beneficial to scientific progress in several ways; for example enabling data to be verified1 or the testing of novel hypotheses that were unforeseen at the time of data generation2.
F1000Research is working with funders and institutions to begin addressing some of these challenges. We have implemented several initiatives to provide methods and tools to capture the production of scientific data, and to establish this as an important output of research activity in itself.
References
Simonsohn U, 2013. Just Posting It works, leads to new retraction in Psychology. Data Colada [blog] 17th September [Accessed: 20 Jan 2014]
Chappell, P. R. and Lorrey, A. M., 2013. Identifying New Zealand, Southeast Australia, and Southwest Pacific historical weather data sources using Ian Nicholson\u27s Log of Logs. Geoscience Data Journal (http://dx.doi.org/10.1002/gdj3.1) [Early View (Online Version of Record published before inclusion in an issue)
