An expert jury has selected six highly innovative finalists from among 23 submissions received for the inaugural GBIF Ebbe Nielsen Challenge, inviting their creators to compete for €25,000 during the innovation prize’s second and final round.
The six finalists reflect a wide range of uses of open-access biodiversity data from the GBIF network. They range from browser-based insights on dataset quality to regional bird and frog soundscapes, and from a web application that provides interactive predictive modelling to social media-powered biodiversity assessments,
The winners (in alphabetical order) are:
#myGBIF, created by Tom August (United Kingdom)
ecoSpace, created by Miguel Porto (Portugal)
Three teams and three individuals located in seven countries are responsible creating the six finalists (profiled briefly below). These entries vary widely in geographic scale, analytics and media integration, and all competition entries are available on the Challenge pages on ChallengePost.
“The creativity and ambition displayed by the finalists is inspiring’, said Roderic Page, chair of the Challenge jury and the GBIF Science Committee, who introduced the Challenge at GBIF’s 2014 Science Symposium in October.
“My biggest hope for the Challenge was that the biodiversity community would respond with innovative—even unexpected—entries,” Page said. “My expectations have been exceeded, and the Jury is eager to see what the finalists can achieve between now and the final round of judging.”
The six finalists, each of whom will receive a €1,000 initial prize, now receive an invitation to further refine their submissions while competing for €25,000 in prizes during round two of the Challenge. The first- and second-place winners—who will receive €20,000 and €5,000, respectively—will be announced at the GBIF Governing Board meeting (GB22) in Madagascar in October 2015.
2015 GBIF Ebbe Nielsen Challenge Finalists
Developed by Tom August
This service leverages location information from Twitter users who use the the hashtag #myGBIF—“One hashtag, infinite possibilities”, as August suggests. Functioning as a hyper-local personalized biodiversity assessment, the call to #myGBIF retrieves species occurrence data from the user’s area, mashes them up with common names and IUCN extinction risks via the Encyclopedia of Life, and promptly tweets a resulting infographic back to the user.
Developed by Richard Pyle
Biodiversity informatics needs reliable, persistent and actionable digital identifiers, but the community has made little progress toward resolving its glut of multiple identifiers and sources for any given data object. BioGUID.org provides a common service for indexing and cross-linking a wide range of identifiers, making it easier to cross-link biodiversity datasets, and making them more powerful by harnessing identifiers.
Developed by Miguel Porto
Promising “a new way of looking at species occurrence datasets”, this dynamic web application lets users navigate the relationships within regional species groups that are displayed as an ecologically meaningful network. Its interactive interface shows species’ ecological or biogeographical affinities based on user-selected bioclimatic variables and expands at each step to reveal the network’s complexity.
GBIF dataset metrics
This team has created a ready-to-use browser extension for Google Chrome, offering quick insights about the datasets available through GBIF.org. Its chart-based visual approach provides at-a-glance assessments of the occurrences contained in a dataset, enabling a user to assess its fitness for his or her use without having to download, filter and clean the data.
This application taps the rich if unconventional stream of information available in sound recordings associated with some occurrence records. Drawing on the GBIF API, it reconstructs the natural “soundscapes” using audio files associated with bird and frog occurrences in selected regions.
Wallace (beta v0.1): Harnessing Digital Biodiversity Data for Predictive Modeling, Fueled by R
This submission combines data from the GBIF network with two recently developed tools developed in R, a high-powered programming language for statistical computing and graphics, allowing users to generate predictive distribution models. The web application enables researchers working online or offline to map, filter and remove occurrence records and build, evaluate and visualize complex predictions.