Geodisy : An open-source spatial data discovery platform
Bibliographic record
Abstract
Presentation for Dataverse Community Meeting 2020. With the increasing demand for geographic components in research, there is an opportunity for Dataverse and other research data repositories to provide alternatives to text-based searching. Enter Geodisy: an open-source spatial discovery platform for Canadian open research data that is designed to work with Dataverse. Initially funded by CANARIE, Geodisy’s online interface provides a map-based search that is available alongside Canada’s Federated Research Data Repository (FRDR). Users search for data based on its location and have the ability to preview datasets as overlays on a digital map and access comprehensive metadata. Data is currently sourced from Scholars Portal Dataverse, which houses open research data from forty Canadian institutions. Geodisy delivers increased discoverability and access to Scholars Portal data, while also driving traffic back to the original Dataverse records. The platform and the software behind it use both pre-existing and custom-made components, all of which are free and open-source. The code and pipeline setup instructions are freely available via our public Github repository, and other institutions are both welcome and encouraged to adopt their own instances of the software. Geodisy’s software can be used with any Dataverse repository. This work benefits all research disciplines that use geospatial location, from anthropology to zoology to climate science. The project’s next goal is to expand the interoperability of the software to include additional data sources, such as open governmental research datasets. In this session, we will share software architecture designs, Dataverse metadata mapping processes, and a demonstration of the platform. Recording available at https://youtu.be/Ufc7oXA6ZJg
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.106 | 0.080 |
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".