ArticleBMC bioinformatics2011
The Biodiversity Informatics Potential Index.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards mainstreaming of biodiversity data publishing: recommendations of the GBIF Data Publishing Framework Task Group.BMC bioinformatics · 2011Guideline
- Occurrence-based diversity estimation reveals macroecological and conservation knowledge gaps for global woody plants.Science advances · 2023Article
- A new model for efficient, need-driven progress in generating primary biodiversity information resources.Applications in plant sciences · 2020Article
- Global priorities for an effective information basis of biodiversity distributions.Nature communications · 2015Article
- A framework for publishing primary biodiversity data.BMC bioinformatics · 2011Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBiodiversity informatics is a relatively new discipline extending computer science in the context of biodiversity data, and its development to date has not been uniform throughout the world. Digitizing effort and capacity building are costly, and ways should be found to prioritize them rationally. The proposed 'Biodiversity Informatics Potential (BIP) Index' seeks to fulfill such a prioritization role. We propose that the potential for biodiversity informatics be assessed through three concepts: (a) the intrinsic biodiversity potential (the biological richness or ecological diversity) of a country; (b) the capacity of the country to generate biodiversity data records; and (c) the availability of technical infrastructure in a country for managing and publishing such records.
methodsBroadly, the techniques used to construct the BIP Index were rank correlation, multiple regression analysis, principal components analysis and optimization by linear programming. We built the BIP Index by finding a parsimonious set of country-level human, economic and environmental variables that best predicted the availability of primary biodiversity data accessible through the Global Biodiversity Information Facility (GBIF) network, and constructing an optimized model with these variables. The model was then applied to all countries for which sufficient data existed, to obtain a score for each country. Countries were ranked according to that score.
resultsMany of the current GBIF participants ranked highly in the BIP Index, although some of them seemed not to have realized their biodiversity informatics potential. The BIP Index attributed low ranking to most nonparticipant countries; however, a few of them scored highly, suggesting that these would be high-return new participants if encouraged to contribute towards the GBIF mission of free and open access to biodiversity data.
conclusionsThe BIP Index could potentially help in (a) identifying countries most likely to contribute to filling gaps in digitized biodiversity data; (b) assisting countries potentially in need (for example mega-diverse) to mobilize resources and collect data that could be used in decision-making; and (c) allowing identification of which biodiversity informatics-resourced countries could afford to assist countries lacking in biodiversity informatics capacity, and which data-rich countries should benefit most from such help.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.