Time for leadership

Minimum information guideline for spectrophotometric and fluorometric methods to assess biofilm formation in microplates

The lack of reproducibility of published studies is one of the major issues facing the scientific community, and the field of biofilm microbiology has been no exception. One effective strategy against this multifaceted problem is the use of minimum information guidelines. This strategy provides a guide for authors and reviewers on the necessary information that a manuscript should include for the experiments in a study to be clearly interpreted and independently reproduced. As a result of several discussions between international groups working in the area of biofilms, we present a guideline for the spectrophotometric and fluorometric assessment of biofilm formation in microplates. This guideline has been divided into 5 main sections, each presenting a comprehensive set of recommendations. The intention of the minimum information guideline is to improve the quality of scientific communication that will augment interlaboratory reproducibility in biofilm microplate assays.

Who shares? Who doesn't? Factors associated with openly archiving raw research data

Many initiatives encourage investigators to share their raw datasets in hopes of increasing research efficiency and quality. Despite these investments of time and money, we do not have a firm grasp of who openly shares raw research data, who doesn't, and which initiatives are correlated with high rates of data sharing. In this analysis I use bibliometric methods to identify patterns in the frequency with which investigators openly archive their raw gene expression microarray datasets after study publication. Automated methods identified 11,603 articles published between 2000 and 2009 that describe the creation of gene expression microarray data. Associated datasets in best-practice repositories were found for 25% of these articles, increasing from less than 5% in 2001 to 30%-35% in 2007-2009. Accounting for sensitivity of the automated methods, approximately 45% of recent gene expression studies made their data publicly available. First-order factor analysis on 124 diverse bibliometric attributes of the data creation articles revealed 15 factors describing authorship, funding, institution, publication, and domain environments. In multivariate regression, authors were most likely to share data if they had prior experience sharing or reusing data, if their study was published in an open access journal or a journal with a relatively strong data sharing policy, or if the study was funded by a large number of NIH grants. Authors of studies on cancer and human subjects were least likely to make their datasets available. These results suggest research data sharing levels are still low and increasing only slowly, and data is least available in areas where it could make the biggest impact. Let's learn from those with high rates of sharing to embrace the full potential of our research output.

The gel electrophoresis markup language (GelML) from the Proteomics Standards Initiative

The Human Proteome Organisation's Proteomics Standards Initiative has developed the GelML (gel electrophoresis markup language) data exchange format for representing gel electrophoresis experiments performed in proteomics investigations. The format closely follows the reporting guidelines for gel electrophoresis, which are part of the Minimum Information About a Proteomics Experiment (MIAPE) set of modules. GelML supports the capture of metadata (such as experimental protocols) and data (such as gel images) resulting from gel electrophoresis so that laboratories can be compliant with the MIAPE Gel Electrophoresis guidelines, while allowing such data sets to be exchanged or downloaded from public repositories. The format is sufficiently flexible to capture data from a broad range of experimental processes, and complements other PSI formats for MS data and the results of protein and peptide identifications to capture entire gel-based proteome workflows. GelML has resulted from the open standardisation process of PSI consisting of both public consultation and anonymous review of the specifications.

Public sharing of research datasets: a pilot study of associations

The public sharing of primary research datasets potentially benefits the research community but is not yet common practice. In this pilot study, we analyzed whether data sharing frequency was associated with funder and publisher requirements, journal impact factor, or investigator experience and impact. Across 397 recent biomedical microarray studies, we found investigators were more likely to publicly share their raw dataset when their study was published in a high-impact journal and when the first or last authors had high levels of career experience and impact. We estimate the USA's National Institutes of Health (NIH) data sharing policy applied to 19% of the studies in our cohort; being subject to the NIH data sharing plan requirement was not found to correlate with increased data sharing behavior in multivariate logistic regression analysis. Studies published in journals that required a database submission accession number as a condition of publication were more likely to share their data, but this trend was not statistically significant. These early results will inform our ongoing larger analysis, and hopefully contribute to the development of more effective data sharing initiatives.

Proteomics data collection (ProDaC): publishing and collecting proteomics data sets in public repositories using standard formats

In Proteomics, fast enhancements with regard to technology are responsible for the creation of huge data sets. Consequently, in 2006 the European Commission funded a Coordination Action named ProDaC (Proteomics Data Collection) within the 6th EU Framework Programme to foster a community-wide data collection and data sharing. The aims of ProDaC were the development of documentation and storage standards, setup of a standardized data submission pipeline and collection of data.To reach these goals, the necessary work was structured in six thematic fields (work packages): Standards for Proteomics Data Representation, Standards Implementation, Data Integration Tools, Proteomics Repository Adaptation, Data Flow Management, and Proteomics Data Exploitation. The methods building the basis of the respective fields and the achieved results are described in the following sections.

Towards a data sharing culture: recommendations for leadership from academic health centers

Rebecca Crowley and colleagues propose that academic health centers can and should lead the transition towards a culture of biomedical data sharing.