↓ Skip to main content

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

Overview of attention for article published in BMC Ecology, October 2016
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

twitter
11 tweeters

Citations

dimensions_citation
31 Dimensions

Readers on

mendeley
125 Mendeley
citeulike
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
BioVeL: a virtual laboratory for data analysis and modelling in biodiversity science and ecology
Published in
BMC Ecology, October 2016
DOI 10.1186/s12898-016-0103-y
Pubmed ID
Authors

Alex R. Hardisty, Finn Bacall, Niall Beard, Maria-Paula Balcázar-Vargas, Bachir Balech, Zoltán Barcza, Sarah J. Bourlat, Renato De Giovanni, Yde de Jong, Francesca De Leo, Laura Dobor, Giacinto Donvito, Donal Fellows, Antonio Fernandez Guerra, Nuno Ferreira, Yuliya Fetyukova, Bruno Fosso, Jonathan Giddy, Carole Goble, Anton Güntsch, Robert Haines, Vera Hernández Ernst, Hannes Hettling, Dóra Hidy, Ferenc Horváth, Dóra Ittzés, Péter Ittzés, Andrew Jones, Renzo Kottmann, Robert Kulawik, Sonja Leidenberger, Päivi Lyytikäinen-Saarenmaa, Cherian Mathew, Norman Morrison, Aleksandra Nenadic, Abraham Nieva de la Hidalga, Matthias Obst, Gerard Oostermeijer, Elisabeth Paymal, Graziano Pesole, Salvatore Pinto, Axel Poigné, Francisco Quevedo Fernandez, Monica Santamaria, Hannu Saarenmaa, Gergely Sipos, Karl-Heinz Sylla, Marko Tähtinen, Saverio Vicario, Rutger Aldo Vos, Alan R. Williams, Pelin Yilmaz

Abstract

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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 1 <1%
Brazil 1 <1%
Finland 1 <1%
United Kingdom 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 119 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 34 27%
Student > Ph. D. Student 17 14%
Student > Master 11 9%
Other 10 8%
Professor > Associate Professor 7 6%
Other 20 16%
Unknown 26 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 20%
Environmental Science 15 12%
Computer Science 14 11%
Engineering 8 6%
Social Sciences 7 6%
Other 23 18%
Unknown 33 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 October 2017.
All research outputs
#2,325,485
of 14,056,840 outputs
Outputs from BMC Ecology
#135
of 346 outputs
Outputs of similar age
#65,987
of 290,711 outputs
Outputs of similar age from BMC Ecology
#19
of 42 outputs
Altmetric has tracked 14,056,840 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 346 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.1. This one has gotten more attention than average, scoring higher than 60% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 290,711 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.