Distributed databases for citizen science
Bibliographic record
Abstract
Citizen science is often heavily dependent on software tools that allow members of the general population to collect, view and submit environmental data to a common database. While several such software platforms exist, these often require expert knowledge to set up and maintain, and server and data hosting costs can become quite costly in the long term, especially if a project is successful in attracting many users and data submissions. In the context of time-limited project funding, these limitations can pose serious obstacles to the long-term sustainability of citizen science projects as well as their ownership by the community. One the other hand, distributed database systems (such as Qri and Constellation) dispense with the need for a centralised server and instead rely on the devices (smartphone or computer) of the users themselves to store and transmit community-generated data. This new approach leads to the counterintuitive result that distributed systems, contrarily to centralised ones, become more robust and offer better availability and response times as the size of the user pool grows. In addition, since data is stored by users’ own devices, distributed systems offer interesting potential for strengthening communities’ ownership over their own environmental data (data sovereignty). This presentation will discuss the potential of distributed database systems to address the current technological limitations of centralised systems for open data and citizen science-led data collection efforts and will give examples of use cases with currently available distributed database software platforms.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.030 |
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".