Implementing FAIR data principles in the IPCC seventh assessment cycle: Lessons learned and future prospects
Bibliographic record
Abstract
Every five to seven years, the Intergovernmental Panel on Climate Change (IPCC) convenes the climate science community to assess the latest knowledge on climate change relevant to policy-makers. This generally takes the form of Assessment Reports (AR) covering the scientific basis of climate change, its impacts and future risks, and options for adaptation and mitigation. With each cycle, these reports have grown in scope, length, number of referenced papers, and underpinning datasets. During the sixth assessment cycle, a large-scale collective effort went into archiving digital products assessed and generated through the IPCC process. The main objectives driving this initiative are making IPCC’s work more transparent, improving the reproducibility and reusability of the assessment outcomes, better utilization of the services of the IPCC Data Distribution Centre (DDC), and, more generally, compliance with best practices in open science. This paper expands on the motivations for the curation and preservation of digital objects in the IPCC. It gives an overview of how FAIR (Findable, Accessible, Interoperable, Reusable) and open data principles have been implemented in practice and explores some of the successes and setbacks of the AR6 experience. It concludes with recommendations for consolidation and expansion of the approach for AR7. These include a tighter integration of digital curation activities in the IPCC timeline and workflows, better support of IPCC authors and contributors through early training and use of suitable software, improved standardization and harmonization of data and software handling across Working Groups (WGs), and close collaboration with key external data providers and research organizations.
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 | 0.299 | 0.232 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.038 | 0.042 |
| Open science | 0.015 | 0.025 |
| Research integrity | 0.022 | 0.032 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".