FAIR principles for climate services information systems
Bibliographic record
Abstract
Ongoing climate change is increasingly impacting ecosystems and living conditions. To understand climate change effects on all scales ranging from regional to global and to develop appropriate response strategies, reliable, easily accessible climate location information is crucial. The United Nations framework of climate change policy emphasizes the role of open data as an essential component to enable efficient implementation of appropriate climate change strategies. Data offered at the various portals and climate services needs to be Findable, Accessible, Interoperable, and Reusable (FAIR). This is particularly important when several communities need to work together in order to develop the most effective response strategies. These communities not only involve climate scientists and meteorologists, but also climate impact analysts, hydrologists, agronomists, urban planners, ecologists, and many more. Screening the web for available data, it becomes apparent that there is no shortage of portal solutions built upon climate data archives. Portal solutions have turned out in the past of often being targeted towards a specific, and sometimes rather small, number of users from within a single community. Cross-community integration and thus enhanced reusability and interoperability was not in focus. Due to recent ongoing international domain crossing efforts, FAIR principles are increasingly respected also for the portal architectures of the information systems itself. For example, the Open Geospatial Consortium (OGC) develops standards and best practices that enable FAIR principles across communities. FAIR principles across communities require a set of essential ingredients to work effectively. These ingredients include metadata models that allow discovery (Findable), interfaces to access the data (Accessible), data models that are well documented (Interoperable) and can be efficiently consumed by others (Reusable). Because data volumes are continuously growing and therefore require new approaches for efficient data processing, OGC has extended the ‘Reusable’ component in FAIR. ‘Reusable’ now includes mechanisms for executing applications close to the physical location of the data. What was previously a data provisioning system now needs to be extended to support processing capacities up to the level where user-defined applications can be deployed and executed. In a sense, for data to be FAIR, it needs to be accompanied by equally FAIR services. This presentation is showing current realisations of leading climate services information systems that implement the extended FAIR principle. The presentation will sort out roles and capabilities of standardized web APIs that can be assembled in line with data and processing environments for interoperable climate data across communities in the most efficient way. Once paired with OGC’s new “Applications-to-the-Data” architecture and strong metadata models, the web APIs enable effective integration of climate data with data from other disciplines within state-of-the art cloud environments that feature not only reusability of data, but also of applications, data processes, and scientific workflows.
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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.050 | 0.022 |
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".