rOpenSci - open tools for open science
Abstract
Solving many of the basic and applied challenges in ecology and evolution requires access to large amounts of data, often spanning long spatial and temporal scales. The long-established model where researchers collect and analyze their own data will soon be replaced by one where disparate datasets are brought to bear on both basic and applied problems. As science becomes more data-driven, it faces a whole new set of challenges. Researchers will not only have to maintain expertise in their domains but also learn new skills to curate, retrieve, and analyze these newly available data. In order to fully realize the potential of data-driven science and allow researchers to draw insights from these vast data stores, we need to address challenges associated with all aspects of the research life cycle. To foster and support a new generation of data-driven science, my colleagues and I founded a project called rOpenSci (http://ropensci.org). The project is an integrated effort to build tools and training using Ecology and Evolution as a model community. In this talk I will outline several of the barriers that need to be overcome including better incentive mechanisms for data, training gaps, and lowering technical barriers.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2013
- Bibcode:
- 2013AGUFMED43E..04R
- Keywords:
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- 1916 INFORMATICS Data and information discovery