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Network of biological collections in Paraná State, Brazil—Taxonline: a success story

2025/08/09 by Luciane Marinoni, Diego Santos Souza, Isaac Jorge +25 · 1 voice
Agricultural and Biological Sciences · #Botanical Research and Applications #Plant Diversity and Evolution #Plant and animal studies

paper · doi:10.1093/biolinnean/blaf083

openalex publication_date 2025/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

Abstract Scientific biological collections play a vital role in preserving biodiversity knowledge, particularly in regions experiencing rapid environmental degradation. In Brazil, these collections encompass centuries of data, offering key insights into species distributions, ecosystem change, and conservation strategies. However, despite this recognized value, they remain underprioritized by managing institutions and policymakers, with limited funding for their maintenance, thereby constraining their potential impact. To address these challenges, the adoption of collaborative networks and open-access data sharing have emerged as effective strategies to support and strengthen the contributions of collections in research and broader societal applications. Herein, we summarize the 20-year existence of the Taxonline Network of Biological Collections in Paraná State, a regional initiative that has fostered institutional cooperation and improved data accessibility for scientific biological collections. We integrate information from curators, from the Brazilian Biodiversity Information System (SiBBr), from the speciesLink project, and also from a national diagnosis of scientific collections to assess the number, geographic distribution, institutional status, infrastructure, staffing, and digitization status of collections affiliated with Taxonline. This review demonstrates that network-based collaboration can help mitigate structural challenges, enhance infrastructure and data quality, and increase both public and scientific visibility of collections, offering a sustainable model for other regions facing similar constraints.

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