Evaluating the feasibility of automating dataset retrieval for biodiversity monitoring
Aim Effective management strategies for conserving biodiversity and mitigating the impacts of global change rely on access to comprehensive and up-to-date biodiversity data. However, manual search, retrieval, evaluation, and integration of this information into databases present a significant challe...
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PeerJ Inc.
2025-01-01
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| author | Alexandre Fuster-Calvo Sarah Valentin William C. Tamayo Dominique Gravel |
| author_facet | Alexandre Fuster-Calvo Sarah Valentin William C. Tamayo Dominique Gravel |
| author_sort | Alexandre Fuster-Calvo |
| collection | DOAJ |
| description | Aim Effective management strategies for conserving biodiversity and mitigating the impacts of global change rely on access to comprehensive and up-to-date biodiversity data. However, manual search, retrieval, evaluation, and integration of this information into databases present a significant challenge to keeping pace with the rapid influx of large amounts of data, hindering its utility in contemporary decision-making processes. Automating these tasks through advanced algorithms holds immense potential to revolutionize biodiversity monitoring. Innovation In this study, we investigate the potential for automating the retrieval and evaluation of biodiversity data from Dryad and Zenodo repositories. We have designed an evaluation system based on various criteria, including the type of data provided and its spatio-temporal range, and applied it to manually assess the relevance for biodiversity monitoring of datasets retrieved through an application programming interface (API). We evaluated a supervised classification to identify potentially relevant datasets and investigate the feasibility of automatically ranking the relevance. Additionally, we applied the same appraoch on a scientific literature source, using data from Semantic Scholar for reference. Our evaluation centers on the database utilized by a national biodiversity monitoring system in Quebec, Canada. Main conclusions We retrieved 89 (55%) relevant datasets for our database, showing the value of automated dataset search in repositories. Additionally, we find that scientific publication sources offer broader temporal coverage and can serve as conduits guiding researchers toward other valuable data sources. Our automated classification system showed moderate performance in detecting relevant datasets (with an F-score up to 0.68) and signs of overfitting, emphasizing the need for further refinement. A key challenge identified in our manual evaluation is the scarcity and uneven distribution of metadata in the texts, especially pertaining to spatial and temporal extents. Our evaluative framework, based on predefined criteria, can be adopted by automated algorithms for streamlined prioritization, and we make our manually evaluated data publicly available, serving as a benchmark for improving classification techniques. |
| format | Article |
| id | doaj-art-12b614ffe0714b28ace34b1737d60f63 |
| institution | Kabale University |
| issn | 2167-8359 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj-art-12b614ffe0714b28ace34b1737d60f632025-01-31T15:05:17ZengPeerJ Inc.PeerJ2167-83592025-01-0113e1885310.7717/peerj.18853Evaluating the feasibility of automating dataset retrieval for biodiversity monitoringAlexandre Fuster-Calvo0Sarah Valentin1William C. Tamayo2Dominique Gravel3Biology Department, University of Sherbrooke, Sherbrooke, Quebec, CanadaJoint Research Unit Land, Remote Sensing and Spatial Information (UMR TETIS), French Agricultural Research Centre for International Development (CIRAD), Montpellier, FranceBiology Department, University of Sherbrooke, Sherbrooke, Quebec, CanadaBiology Department, University of Sherbrooke, Sherbrooke, Quebec, CanadaAim Effective management strategies for conserving biodiversity and mitigating the impacts of global change rely on access to comprehensive and up-to-date biodiversity data. However, manual search, retrieval, evaluation, and integration of this information into databases present a significant challenge to keeping pace with the rapid influx of large amounts of data, hindering its utility in contemporary decision-making processes. Automating these tasks through advanced algorithms holds immense potential to revolutionize biodiversity monitoring. Innovation In this study, we investigate the potential for automating the retrieval and evaluation of biodiversity data from Dryad and Zenodo repositories. We have designed an evaluation system based on various criteria, including the type of data provided and its spatio-temporal range, and applied it to manually assess the relevance for biodiversity monitoring of datasets retrieved through an application programming interface (API). We evaluated a supervised classification to identify potentially relevant datasets and investigate the feasibility of automatically ranking the relevance. Additionally, we applied the same appraoch on a scientific literature source, using data from Semantic Scholar for reference. Our evaluation centers on the database utilized by a national biodiversity monitoring system in Quebec, Canada. Main conclusions We retrieved 89 (55%) relevant datasets for our database, showing the value of automated dataset search in repositories. Additionally, we find that scientific publication sources offer broader temporal coverage and can serve as conduits guiding researchers toward other valuable data sources. Our automated classification system showed moderate performance in detecting relevant datasets (with an F-score up to 0.68) and signs of overfitting, emphasizing the need for further refinement. A key challenge identified in our manual evaluation is the scarcity and uneven distribution of metadata in the texts, especially pertaining to spatial and temporal extents. Our evaluative framework, based on predefined criteria, can be adopted by automated algorithms for streamlined prioritization, and we make our manually evaluated data publicly available, serving as a benchmark for improving classification techniques.https://peerj.com/articles/18853.pdfAutomated data retrievalBiodiversity monitoringData repositoriesEcological dataMachine learning |
| spellingShingle | Alexandre Fuster-Calvo Sarah Valentin William C. Tamayo Dominique Gravel Evaluating the feasibility of automating dataset retrieval for biodiversity monitoring PeerJ Automated data retrieval Biodiversity monitoring Data repositories Ecological data Machine learning |
| title | Evaluating the feasibility of automating dataset retrieval for biodiversity monitoring |
| title_full | Evaluating the feasibility of automating dataset retrieval for biodiversity monitoring |
| title_fullStr | Evaluating the feasibility of automating dataset retrieval for biodiversity monitoring |
| title_full_unstemmed | Evaluating the feasibility of automating dataset retrieval for biodiversity monitoring |
| title_short | Evaluating the feasibility of automating dataset retrieval for biodiversity monitoring |
| title_sort | evaluating the feasibility of automating dataset retrieval for biodiversity monitoring |
| topic | Automated data retrieval Biodiversity monitoring Data repositories Ecological data Machine learning |
| url | https://peerj.com/articles/18853.pdf |
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