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

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.

Changing climate in Hungary and trends in the annual number of heat stress days.

Global climate change can have serious direct effects on animal health and production through heat stress. In Hungary, the number of heat stress days per year (YNHD), i.e., days when the temperature humidity index (THI) is above a specific comfort threshold, has increased in recent years based on observed meteorological data. Between 1973 and 2008, the countrywide average increase in YNHD was 4.1% per year. Climate scenarios based on regional climate models (RCM) were used to predict possible changes in YNHD for the near future (2021-2050) relative to the reference period (1961-1990). This comparison shows that, in Hungary, the 30-year mean of YNHD is expected to increase by between 1 and 27 days, depending on the RCM used. Half of the scenarios investigated in this study predicted that, in large parts of Hungary, YNHD will increase by at least 1 week. However, the increase observed in the past, and that predicted for the near future, is spatially heterogeneous, and areas that currently have large cattle populations are expected to be affected more severely than other regions.