2
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A strategy to digitise natural history collections with limited resources

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          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

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

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

            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.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Digitization of herbaria enables novel research

               Pamela Soltis (2017)
                Bookmark

                Author and article information

                Contributors
                Journal
                Biodivers Data J
                Biodivers Data J
                1
                urn:lsid:arphahub.com:pub:F9B2E808-C883-5F47-B276-6D62129E4FF4
                urn:lsid:zoobank.org:pub:245B00E9-BFE5-4B4F-B76E-15C30BA74C02
                Biodiversity Data Journal
                Pensoft Publishers
                1314-2836
                1314-2828
                2020
                23 October 2020
                : 8
                Affiliations
                [1 ] University of Coimbra, Centre for Functional Ecology - Science for People & the Planet, Department of Life Sciences, Coimbra, Portugal University of Coimbra, Centre for Functional Ecology - Science for People & the Planet, Department of Life Sciences Coimbra Portugal
                [2 ] University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering Coimbra Portugal
                Author notes
                Corresponding author: Joaquim Santos ( joaquimsantos@ 123456gmail.com ).

                Academic editor: Vincent Smith

                Article
                55959 14165
                10.3897/BDJ.8.e55959
                7599201
                Joaquim Santos, Paulo Rupino da Cunha, Fátima Sales

                This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 3, Tables: 1, References: 19
                Categories
                Methods

                Comments

                Comment on this article