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Abstract Data‐driven discovery in geoscience requires an enormous amount of FAIR (findable, accessible, interoperable and reusable) data derived from a multitude of sources. Many geology resources include data based on the geologic time scale, a system of dating that relates layers of rock (strata) to times in Earth history. The terminology of this geologic time scale, including the names of the strata and time intervals, is heterogeneous across data resources, hindering effective and efficient data integration. To address that issue, we created a deep‐time knowledge base that consists of knowledge graphs correlating international and regional geologic time scales, an online service of the knowledge graphs, and an R package to access the service. The knowledge base uses temporal topology to enable comparison and reasoning between various intervals and points in the geologic time scale. This work unifies and allows the querying of age‐related geologic information across the entirety of Earth history, resulting in a platform from which researchers can address complex deep‐time questions spanning numerous types of data and fields of study. 

Abstract Ecological observations and paleontological data show that communities of organisms recur in space and time. Various observations suggest that communities largely disappear in extinction events and appear during radiations. This hypothesis, however, has not been tested on a large scale due to a lack of methods for analyzing fossil data, identifying communities, and quantifying their turnover. We demonstrate an approach for quantifying turnover of communities over the Phanerozoic Eon. Using network analysis of fossil occurrence data, we provide the first estimates of appearance and disappearance rates for marine animal paleocommunities in the 100 stages of the Phanerozoic record. Our analysis of 124,605 fossil collections (representing 25,749 living and extinct marine animal genera) shows that paleocommunity disappearance and appearance rates are generally highest in mass extinctions and recovery intervals, respectively, with rates three times greater than background levels. Although taxonomic change is, in general, a fair predictor of ecologic reorganization, the variance is high, and ecologic and taxonomic changes were episodically decoupled at times in the past. Extinction rate, therefore, is an imperfect proxy for ecologic change. The paleocommunity turnover rates suggest that efforts to assess the ecological consequences of the present-day biodiversity crisis should focus on the selectivity of extinctions and changes in the prevalence of biological interactions. 

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.