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BioVeL: a virtual laboratory for data analysis and modelling in biodiversity science and ecology

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Fernandez-Guerra,  Antonio
Microbial Genomics Group, Department of Molecular Ecology, Max Planck Institute for Marine Microbiology, Max Planck Society;

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Kottmann,  Renzo
Microbial Genomics Group, Department of Molecular Ecology, Max Planck Institute for Marine Microbiology, Max Planck Society;

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Yilmaz,  Pelin
Microbial Genomics Group, Department of Molecular Ecology, Max Planck Institute for Marine Microbiology, Max Planck Society;

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Fernandez_Guerra_2016_01_5.pdf
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Citation

Hardisty, A., Bacall, F., Beard, N., Balcazar-Vargas, M., Balech, B., Barcza, Z., et al. (2016). BioVeL: a virtual laboratory for data analysis and modelling in biodiversity science and ecology. BMC Ecology, 16: 49, pp. 1-16.


Cite as: http://hdl.handle.net/21.11116/0000-0001-C263-4
Abstract
Background: Making forecasts about biodiversity and giving support to policy relies increasingly on large collections of data held electronically, and on substantial computational capability and capacity to analyse, model, simulate and predict using such data. However, the physically distributed nature of data resources and of expertise in advanced analytical tools creates many challenges for the modern scientist. Across the wider biological sciences, presenting such capabilities on the Internet (as "Web services") and using scientific workflow systems to compose them for particular tasks is a practical way to carry out robust "in silico" science. However, use of this approach in biodiversity science and ecology has thus far been quite limited. Results: BioVeL is a virtual laboratory for data analysis and modelling in biodiversity science and ecology, freely accessible via the Internet. BioVeL includes functions for accessing and analysing data through curated Web services; for performing complex in silico analysis through exposure of R programs, workflows, and batch processing functions; for on- line collaboration through sharing of workflows and workflow runs; for experiment documentation through reproducibility and repeatability; and for computational support via seamless connections to supporting computing infrastructures. We developed and improved more than 60 Web services with significant potential in many different kinds of data analysis and modelling tasks. We composed reusable workflows using these Web services, also incorporating R programs. Deploying these tools into an easy-to-use and accessible 'virtual laboratory', free via the Internet, we applied the workflows in several diverse case studies. We opened the virtual laboratory for public use and through a programme of external engagement we actively encouraged scientists and third party application and tool developers to try out the services and contribute to the activity. Conclusions: Our work shows we can deliver an operational, scalable and flexible Internet-based virtual laboratory to meet new demands for data processing and analysis in biodiversity science and ecology. In particular, we have successfully integrated existing and popular tools and practices from different scientific disciplines to be used in biodiversity and ecological research.