iSpot research paper published

iSpot research paper published - Global : [upload-images-zookeys_200.jpg] A new paper describing iSpot's crowd-sourcing approach to species identification has been pu

We're pleased to announce that a new paper in the journal ZooKeys has been published, describing the thinking behind iSpot's approach to species identifications, outlining how iSpot makes use of its 'reputation system' to help highlight reliable identifications, and providing an overview of some of the activity on the site to date.

Thanks to everyone who has ever added an observation, identification or comment to iSpot - you have all contributed to the results described in the paper!

The summary is copied below, and click here for the full paper:

Silvertown J, Harvey M, Greenwood R, Dodd M, Rosewell J, Rebelo T, Ansine J, McConway K (2015) Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys 480: 125-146. doi: 10.3897/zookeys.480.8803


Accurate species identification is fundamental to biodiversity science, but the natural history skills required for this are neglected in formal education at all levels. In this paper we describe how the web application and its sister site (collectively, “iSpot”) are helping to solve this problem by combining learning technology with crowdsourcing to connect beginners with experts. Over 94% of observations submitted to iSpot receive a determination. External checking of a sample of 3,287 iSpot records verified > 92% of them. To mid 2014, iSpot crowdsourced the identification of 30,000 taxa (>80% at species level) in > 390,000 observations with a global community numbering > 42,000 registered participants. More than half the observations on were named within an hour of submission. iSpot uses a unique, 9-dimensional reputation system to motivate and reward participants and to verify determinations. Taxon-specific reputation points are earned when a participant proposes an identification that achieves agreement from other participants, weighted by the agreers’ own reputation scores for the taxon. This system is able to discriminate effectively between competing determinations when two or more are proposed for the same observation. In 57% of such cases the reputation system improved the accuracy of the determination, while in the remainder it either improved precision (e.g. by adding a species name to a genus) or revealed false precision, for example where a determination to species level was not supported by the available evidence. We propose that the success of iSpot arises from the structure of its social network that efficiently connects beginners and experts, overcoming the social as well as geographic barriers that normally separate the two.

The network linking 5,000 participants who posted observations to without an identification and those providing a likely identification for those observations (see full paper for details).


02 Feb 2015
Martin Harvey