DataStream's open data platform for sharing water quality data
Bibliographic record
Abstract
Significant investments are made in the collection of water quality data. Yet barriers to effective data sharing and reuse hamper the ability to leverage this information to its full potential in research and water management decisions. Because water monitoring data are collected by a wide range of organizations, through programs of varying scope and focus, and often within jurisdictional or institutional silos, it can be difficult to connect this information together in standardized and accessible formats. DataStream is an online open-access platform that was developed by The Gordon Foundation and its partners to address the challenge of water data accessibility in Canada. DataStream is free to use and allows users to query, visualize, and download water quality data aligned with widely-adopted data and metadata standards (e.g., Water Quality eXchange, ISO19115 and schema.org). The path towards DataStream evolving as a collaborative and open data platform has been guided by the FAIR and CARE data principles. To date, over 140 different groups across Canada are using DataStream to publish water monitoring results including watershed groups, Indigenous organizations, researchers and governments at all levels. We will highlight our lessons learned in developing the platform to align with FAIR data principles and the elements we believe have been key to our success including DataStream’s open data schema, clear data licensing and regional partnership model.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.032 |
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".