Addressing discoverability, trust and data quality in peer-to-peer distributed databases for citizen science
Bibliographic record
Abstract
Peer-to-peer distributed databases show promise for lowering the barrier to entry for citizen science projects. These databases, which do not require a centralised server to store and exchange data, instead use participants’ devices (phones or computers) to store and transfer data directly between project participants. This offers concrete advantages in terms of avoiding usually very costly and time-consuming server maintenance for the research team, as well as improving data access and sovereignty for the participating communities.However, several technical challenges remain to the routine use of distributed databases in citizen science projects. In particular, indexing data and discovering peers who hold data of interest or from the same project; managing safety, trust and permissions; and ensuring data quality all without relying on a central server to perform these functions requires a rethinking of the standard paradigms of database and user account management.This presentation will give a brief overview of the Constellation software for distributed scientific databases before presenting several novel approaches (concentric recursive data search, user network-centric trust, and multiple data quality verification layers) it has adopted to respond to the above-mentioned challenges. Examples of concrete applications of Constellation for data sharing in the fields of hydrology and agronomy will be included.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".