ArticleNational science review2021
The Deep-Time Digital Earth program: data-driven discovery in geosciences.
Article in National science review, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Staking out the frontiers: The limits of the metazoan shallow marine fossil record.Science advances · 2026Article
- AI-4-SPS: An Artificial Intelligence-Driven Semantic Database for Shale Pore Structure Characterization.ACS omega · 2026Article
- Mineral biosignature identification from Raman spectroscopy using machine learning.PNAS nexus · 2026Article
- A global mantle xenolith bulk-rock and paired-mineral dataset.Scientific data · 2026Article
- The billion-dollar case for sustaining palaeontology's digital databases.Nature ecology & evolution · 2026Article
- Coastal Zone Information Model: A comprehensive architecture for coastal digital twin by integrating data, models, and knowledge.Fundamental research · 2026Review
- R-CKGAT: a recommendation algorithm based on scientific fitness knowledge graph.Scientific reports · 2025Article
- A database for igneous rocks of the Newfoundland Appalachians.Scientific data · 2025Article
- Artificial intelligence for geoscience: Progress, challenges, and perspectives.Innovation (Cambridge (Mass.)) · 2024Review
- Mass-Balance-Consistent Geological Stock Accounting: A New Approach toward Sustainable Management of Mineral Resources.Environmental science & technology · 2024Review
- Reconstructing Earth's atmospheric oxygenation history using machine learning.Nature communications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current barriers hindering data-driven discoveries in deep-time Earth (DE) include: substantial volumes of DE data are not digitized; many DE databases do not adhere to FAIR (findable, accessible, interoperable and reusable) principles; we lack a systematic knowledge graph for DE; existing DE databases are geographically heterogeneous; a significant fraction of DE data is not in open-access formats; tailored tools are needed. These challenges motivate the Deep-Time Digital Earth (DDE) program initiated by the International Union of Geological Sciences and developed in cooperation with national geological surveys, professional associations, academic institutions and scientists around the world. DDE's mission is to build on previous research to develop a systematic DE knowledge graph, a FAIR data infrastructure that links existing databases and makes dark data visible, and tailored tools for DE data, which are universally accessible. DDE aims to harmonize DE data, share global geoscience knowledge and facilitate data-driven discovery in the understanding of Earth's evolution.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.