Description and Annotation of Biomedical Data Sets

Deposition of biomedical data sets is on the rise as more scientists submit experimental data to accompany their publications. Scientists are also increasingly reusing these publicly available data sets in their own work. Despite these developments, lack of both context and metadata can create barriers to understanding and repurposing these data sets. Researchers from the Bioinformatics Core Group in the Harvard School of Public Health attempted to address this issue by assembling a team of data curators who used the open source software suite ISA tools to annotate and contextualize microarray data sets. This paper describes the workflow and software used in curating these data sets, discusses similarities and differences in the approaches of team members to the work, and suggests possible roles for librarians in similar data curation projects. Biomedical data deposition is on the rise as more scientists make their experimental data openly available (Piwowar and Chapman 2010). This phenomenon can likely be attributed in part to increasing pressure from publishers and funding agencies to encourage and even mandate data deposition to accompany publication. In a recent survey, more than 40% of peer reviewers for the journal Science indicated that they routinely access or use the data sets that accompany publications (Science 2011). Researchers use these data sets in a variety of ways, including validation and testing of statistical models, and critical evaluation of data discussed in publications. Some works rely heavily upon this body of publicly available data sets, employing data mining for much of their investigative basis. In perhaps the best known example, Mootha and colleagues (2003) successfully identified the human genetic defect that gives rise to Leigh syndrome by first mining publicly available data. Despite these developments, lack of context and metadata can still create obstacles to understanding and reuse of data sets. Certain types of biomedical data, such as sequence data, can be interpreted fairly simply; little additional context aside from the sequence itself is necessary to make use of the data. Gene expression microarray data, on the other hand, require thorough understanding of the experimental context and conditions that produced it. As a result, comprehension and reuse of microarray data *Jen Ferguson is presently the Data Services Librarian at Northeastern University, Boston, MA, USA. Correspondence to Jen Ferguson: [email protected]