The Open Science (OS) movement is rapidly gaining traction among policy-makers, research funders, scientific journals and individual scientists. Despite these tendencies, the pace of implementing OS throughout the scientific process and across the scientific community remains slow. Thus, a better understanding of the conditions that affect OS engagement, and in particular, of how practitioners learn, use, conduct and share research openly can guide those seeking to implement OS more broadly. We ...

Is there something wrong with the scientific method?

Careful scrutiny of one hundred studies in journals that mandate data archival reveals that over half do not comply with their journal’s data policy.

When it comes to public access, the tree of life has holes. A new study shows about 70 percent of published genetic sequence comparisons are not publicly accessible, leaving researchers worldwide unable to get to critical data they may need to tackle a host a problems ranging from climate change to disease control.

Presentation for Ecological Society of America 2013 Meeting in Minneapolis, MN on 6 August 2013. Results published in Ecosphere doi: 10.1890/ES12-00139.1

Author(s): Hampton, Stephanie; Strasser, Carly; Tewksbury, Joshua; Gram, Wendy; Budden, Amber; Batcheller, Archer; Duke, Clifford; Porter, John | Abstract: The need for sound ecological science has escalated alongside the rise of the information age and “big data” across all sectors of society. Big data generally refer to massive volumes of data not readily handled by the usual data tools and practices and present unprecedented opportunities for advancing science and inform- ing resource management through data-intensive approaches. The era of big data need not be propelled only by “big science” – the term used to describe large-scale efforts that have had mixed success in the individual-driven culture of ecology. Collectively, ecologists already have big data to bolster the scientific effort – a large volume of distributed, high-value information – but many simply fail to contribute. We encourage ecologists to join the larger scientific community in global initiatives to address major scientific and societal problems by bringing their distributed data to the table and harnessing its collective power. The scientists who contribute such information will be at the forefront of socially relevant science – but will they be ecologists?

Objectives: (1) to identify common errors in data organization and metadata completeness that would preclude a “reader” from being able to interpret and re-use the data for a new purpose; and (2) to develop a set of best practices derived from these common errors that would guide researchers in creating more usable data products that could be readily shared, interpreted, and used. Methods: We used directed qualitative content analysis to assess and categorize data and metadata errors identified by peer reviewers of data papers published in the Ecological Society of America’s (ESA) Ecological Archives. Descriptive statistics provided the relative frequency of the errors identified during the peer review process. Results: There were seven overarching error categories: Collection & Organization, Assure, Description, Preserve, Discover, Integrate, and Analyze/Visualize. These categories represent errors researchers regularly make at each stage of the Data Life Cycle. Collection & Organization and Description errors were some of the most common errors, both of which occurred in over 90% of the papers.  Conclusions: Publishing data for sharing and reuse is error prone, and each stage of the Data Life Cycle presents opportunities for mistakes. The most common errors occurred when the researcher did not provide adequate metadata to enable others to interpret and potentially re-use the data. Fortunately, there are ways to minimize these mistakes through carefully recording all details about study context, data collection, QA/ QC, and analytical procedures from the beginning of a research project and then including this descriptive information in the metadata.

Morpho allows ecologists to create metadata, (i.e. describe their data in a standardized format), and create a catalog of data & metadata upon which to query, edit and view data collections. In addition, It also provides the means to access network servers, in order to query, view and retrieve all relevant, public ecological data!

A comprehensive framework to encourage and facilitate the sharing of biodiversity data has been published in a peer-reviewed journal.