Digital Extended Specimens: Enabling an Extensible Network of Biodiversity Data Records as Integrated Digital Objects on the Internet. (3rd August 2022)

• Record Type: Journal Article
• Authors: Hardisty, Alex R; Ellwood, Elizabeth R; Nelson, Gil; Zimkus, Breda; Buschbom, Jutta; Addink, Wouter; Rabeler, Richard K; Bates, John; Bentley, Andrew; Fortes, José A B; Hansen, Sara; Macklin, James A; Mast, Austin R; Miller, Joseph T; Monfils, Anna K; Paul, Deborah L; Wallis, Elycia; Webster, Michael
• Is Part Of: BioScience. Volume 72:Number 10(2022)
• Page Start: 978
• Page End: 987
• Publication Date: 2022-08-03
• Subjects: digital specimen -- extended specimen -- Digital Extended Specimen -- natural history -- biodiversity collections; Life sciences -- Periodicals; Life sciences -- Research -- Periodicals; Life sciences -- United States -- Periodicals; Life sciences -- Government policy -- United States -- Periodicals; Biology -- Periodicals; Biotechnology industries -- Periodicals; 570
• DOI: 10.1093/biosci/biac060
• Languages: English
• ISSNs: 0006-3568

Abstract

Abstract: The early twenty-first century has witnessed massive expansions in availability and accessibility of digital data in virtually all domains of the biodiversity sciences. Led by an array of asynchronous digitization activities spanning ecological, environmental, climatological, and biological collections data, these initiatives have resulted in a plethora of mostly disconnected and siloed data, leaving to researchers the tedious and time-consuming manual task of finding and connecting them in usable ways, integrating them into coherent data sets, and making them interoperable. The focus to date has been on elevating analog and physical records to digital replicas in local databases prior to elevating them to ever-growing aggregations of essentially disconnected discipline-specific information. In the present article, we propose a new interconnected network of digital objects on the Internet—the Digital Extended Specimen (DES) network—that transcends existing aggregator technology, augments the DES with third-party data through machine algorithms, and provides a platform for more efficient research and robust interdisciplinary discovery.