Replication Data for: Mapping the landscape of geospatial data citations
Bibliographic record
Abstract
This data supports the paper entitled "Mapping the landscape of geospatial data citations". The dataset covers geospatial data-intensive research papers published between 2015-2018 retrieved using Scopus. The article's citations were assessed for data citation occurances, and coded using a data citation classification. Data were enhanced and linked to subject coverage and journal policy status information using Excel & SPSS. For more information about how the data were created and coded please review the 'Methodology' section of the paper. More information is provided below, including supplemental documentation and related publications. Abstract (paper) ABSTRACT Data citations, similar to article and other research citations, are important references to research data that underlie published research results. In support of open science directives, these citations must adhere to specific conventions in terms of consistency of both placement within an article, and the actual availability or access to research data. To better understand the level to which geospatial research data are currently cited, we undertook a study to analyse the rate of data citation within a set of data-intensive geospatial research articles. After analysing 1717 scholarly articles published between 2015 and 2018, we found that very few, or 78 (5%), meaningfully cited primary or secondary geospatial data sources in the cited references section of the article. Even fewer researchers, only 25 or 1.5%, were found to have cited data using a DOI. Given the relatively low data citation rate, a focus on contributing factors including barriers to citing geospatial data is needed. And while open sharing requirements for geospatial data may change over time, driving data citation as a result, understanding benchmarks for data citation for monitoring purposes is useful.
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 | 0.026 | 0.306 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.185 | 0.088 |
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".