Machine Learning as a Service for DiSSCo’s Digital Specimen Architecture

Biodiversity Information Science and Standards

article

Jonas Grieb¹  Claus Weiland¹  Alex Hardisty²  Wouter Addink³  Sharif Islam³  Sohaib Younis⁵  Marco Schmidt⁶ 

[1] Senckenberg - Leibniz Institution for Biodiversity and Earth System Research;School of Computer Science & Informatics, Cardiff University;Naturalis Biodiversity Center;Distributed System of Scientific Collections - DiSSCo;Department of Mathematics and Computer Science, Philipps-University Marburg;Palmengarten der Stadt Frankfurt

关键词: FAIR Digital Object;    Distributed System of Scientific Collections;    plant organ detection;    deep learning;    region-based convolutional neural network;    image annotation;   

DOI  :  10.3897/biss.5.75634

来源: Pensoft

【 摘 要 】

International mass digitization efforts through infrastructures like the European Distributed System of Scientific Collections (DiSSCo), the US resource for Digitization of Biodiversity Collections (iDigBio), the National Specimen Information Infrastructure (NSII) of China, and Australia’s digitization of National Research Collections (NRCA Digital) make geo- and biodiversity specimen data freely, fully and directly accessible. Complementary, overarching infrastructure initiatives like the European Open Science Cloud (EOSC) were established to enable mutual integration, interoperability and reusability of multidisciplinary data streams including biodiversity, Earth system and life sciences (De Smedt et al. 2020). Natural Science Collections (NSC) are of particular importance for such multidisciplinary and internationally linked infrastructures, since they provide hard scientific evidence by allowing direct traceability of derived data (e.g., images, sequences, measurements) to physical specimens and material samples in NSC. To open up the large amounts of trait and habitat data and to link these data to digital resources like sequence databases (e.g., ENA), taxonomic infrastructures (e.g., GBIF) or environmental repositories (e.g., PANGAEA), proper annotation of specimen data with rich (meta)data early in the digitization process is required, next to bridging technologies to facilitate the reuse of these data.

【 授权许可】

Unknown