The US National Ecological Observatory Network and the Global Biodiversity Framework: national research infrastructure with a global reach
Bibliographic record
Abstract
The US National Science Foundation's National Ecological Observatory Network (NEON) is a continental-scale program intended to provide open data, samples, and infrastructure to understand changing ecosystems for a period of 30 years.NEON collects co-located measurements of drivers of environmental change and biological responses, using standardized methods at 81 field sites to systematically sample variability and trends to enable inferences at regional to continental scales.Alongside key atmospheric and environmental variables, NEON measures the biodiversity of many taxa, including microbes, plants, and animals, and collects samples from these organisms for long-term archiving and research use.Here we review the composition and use of NEON resources to date as a whole and specific to biodiversity as an exemplar of the potential of national research infrastructure to contribute to globally relevant outcomes.Since NEON initiated full operations in 2019, NEON has produced, on average, 1.4 M records and over 32 TB of data per year across more than 180 data products, with 85 products that include taxonomic or other organismal information relevant to biodiversity science.NEON has also collected and curated more than 503,000 samples and specimens spanning all taxonomic domains of life, with up to 100,000 more to be added annually.Various metrics of use, including web portal visitation, data download and sample use requests, and scientific publications, reveal substantial interest from the global community in NEON.More than 47,000 unique IP addresses from around the world visit NEON's web portals each month, requesting on average 1.8 TB of data, and over 200 researchers have engaged in sample use requests from the NEON Biorepository.Through its many global partnerships, particularly with the Global Biodiversity Information Facility, NEON resources have been used in more than 900 scientific publications to date, with many using biodiversity data and samples.These outcomes demonstrate that the data and samples provided by NEON, situated in a broader network of national research infrastructures, are critical to scientists, conservation practitioners, and policy makers.They enable effective approaches to meeting global targets, such as those captured in the Kunming-Montreal Global Biodiversity Framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".