A strategy to digitise natural history collections with limited resources

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

The present work is a contribution towards accelerating the digitisation process of natural history collections, usually a slow process. A two-stage process was developed at the herbarium of the University of Coimbra: (i) a new workflow was established to automatically create records in the herbarium master database with minimum information, while capturing digital images; (ii) these records are then used to populate a web-based crowdsourcing platform where citizens are involved in the transcription of specimen labels from the digital images. This approach simplifies and accelerates databasing, reduces specimen manipulation and promotes the involvement of citizens in the scientific goals of the herbarium. The novel features of this process are: (i) the validation method of the crowdsourcing contribution that ensures quality control, enabling the data to integrate the master database directly and (ii) the field-by-field integration in the master database enables immediate corrections to any record in the catalogue.

Related collections

Most cited references 19

Record: found
Abstract: not found
Book: not found

International Code of Nomenclature for algae, fungi, and plants

Nicholas Turland, John H. Wiersema, Fred R. Barrie … (2018)

0 comments Cited 313 times – based on 0 reviews

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

A generalized approach for producing, quantifying, and validating citizen science data from wildlife images

Alexandra Swanson, Margaret Kosmala, Chris Lintott … (2016)

Abstract Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org, more than 28,000 volunteers classified 1.51 million images taken in a large‐scale camera‐trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics—level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported “nothing here” for an image that was ultimately classified as containing an animal (fraction blank)—to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert‐verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post‐hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large‐scale monitoring of African wildlife.