Mobile Applications to Conservation Applications: Incentivizing FAIR Principles of Data Management by Providing Users with Flexible Mobile Data Collection Tools
Bibliographic record
Abstract
Robust, accessible data are critical for the conservation of biodiversity. Biodiversity monitoring data is collected by numerous professional and volunteer projects all over the world, but finding and accessing these disparate datasets for use in research and conservation is problematic. Integrating individual datasets into a metadatabase that abides the FAIR principles by being Findable, Accessible, Interoperable, and Reusable may be the best way to maximize the usefulness of these abundant data, but barriers such as cost and a lack of access to necessary tools may prohibit this. Birds Canada seeks to resolve this long-standing issue with the NatureCounts mobile app. The app’s extremely flexible and customizable data architecture accommodates a wide variety of data collection methodologies. Users define protocols tailored to their monitoring project, then the app dynamically renders user-friendly and intuitive data entry forms specific to each of the project’s protocols. In this way, it can provide mobile data entry support to almost any standardized avian survey with just a few hours of setup. The app includes many innovative tools, such as a first-of-its-kind point-count feature that uses satellite imagery to improve and digitize one of the most important methodologies for biodiversity monitoring. Data collected through the app flow into the FAIR-abiding NatureCounts database, which—at 360 million records—is the largest biodiversity database in Canada. Data in NatureCounts can be made available via browser-based tools, an R package, and several data products. NatureCounts supports over 9000 data requests annually from the conservation community. Data from NatureCounts have been used for species assessments, land use planning, impact assessment, climate change mitigation, and over 4200 scientific publications to date. Flexible data access permissions support the security of sensitive records and Indigenous data sovereignty. Where data permissions allow, datasets collected through the app can also be integrated into the Global Biodiversity Information Facility (GBIF). By improving accuracy in several key areas and removing the tedious step of data transcription, the app offers immense time and cost savings and reduces errors. Its user-friendly interface (Fig. 1) walks observers through protocols, ensuring consistency and increasing accessibility for staff and volunteers of varying skill levels. These benefits incentivise programs to adopt the platform. In doing so, their data are integrated into a FAIR repository at the outset, eliminating the need for re-formatting and integration, steps which are often overlooked or unfunded. By providing software tools across the data pipeline, from collection through application, the NatureCounts platform seeks to support projects in collecting robust data and meeting the FAIR principles of data management. This support allows projects of all sizes and capacities to make a positive contribution to the data landscape and to achieve the targets of the The Kunming-Montreal Global Biodiversity Framework (Convention on Biological Diversity 2022).
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.025 |
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".