Abstract The UK National Geological Repository (UKNGR) is the largest collection of British geoscience samples, with 16 m ‘specimens’, including 600 km of drillcore. Samples are available for study/subsampling by commercial organizations and researchers. Data, reports and publications must be returned. Raw data are available after 2 years. The scientific method requires published results to be repeatable, necessitating the archiving of samples. Re-purposing samples for new research saves money and time and thereby reduces risk. The National Geological Repository (NGR) has cost over £200 bn to collect and the cost of a single deep cored borehole would be outside the funding of most research projects, so the operation of an NGR makes financial sense. Many of the boreholes have been extensively characterized, so new research can build on the wealth of published data. The NGR has been at the forefront of international efforts to utilize digitization and the World Wide Web to improve the impact of the collections. Geographical information system (GIS) access was provided to the onshore borehole collection in 2000, and GIS access and text searching were added to the other collections over the next 10 years. This was followed by high-resolution images of the UK Continental Shelf (UKCS) cores, petrological thin-sections, and images, stereo anaglyphs and 3D-digital models of British-type fossils.

Abstract The paper documents the process of proclaiming International Geodiversity Day as a grassroots initiative of the Earth science community for the benefit of our present civilization and for future generations. The huge support that this initiative has gained among scientists around the world has contributed to the rapid circulation of documents necessary to establish an international day at the UNESCO Forum. The paper presents the timeline of this process of proclaiming International Geodiversity Day, its implications and the course of the first celebrations in 2022.

ABSTRACT Data science is receiving increased attention in a variety of geoscience disciplines and applications. Many successful data-driven geoscience discoveries have been reported recently, and the number of geoinformatics and data science sessions at many geoscience conferences has begun to increase. Across academia, industry, and government, there is strong interest in knowing more about current progress as well as the potential of data science for geoscience. To address that need, this paper provides a review from the perspective of a data life cycle. The key steps in the data life cycle include concept, collection, preprocessing, analysis, archive, distribution, discovery, and repurpose. Those subjects are intuitive and easy to follow even for geoscientists with very limited experience with cyberinfrastructure, statistics, and machine learning. The review includes two key parts. The first addresses the fundamental concepts and theoretical foundation of data science, and the second summarizes highlights and sharable experience from existing publications centered on each step in the data life cycle. At the end, a vision about the future trends of data science applications in geoscience is provided that includes discussion of open science, smart data, and the science of team science. We hope this review will be useful to data science practitioners in the geoscience community and will lead to more discussions on the best practices and future trends of data science for the geosciences.

ABSTRACT In the recent decade, knowledge graph has been a key technique under quick development in artificial intelligence. Due to its great potential for tackling big data and solving complex scientific questions in the geosciences, it has attracted the attention of both computer scientists and geoscientists. In this paper, we review concepts and technologies relevant to the knowledge graph, the workflow of geoscience knowledge graph construction, and state-of-the-art examples from several geoscience disciplines. There are two general strategies for constructing geoscience knowledge graphs: top-down and bottom-up. The detailed technologies include geoscience domain knowledge modeling, data collection, knowledge extraction, knowledge cleaning and fusion, knowledge storage, and knowledge service and discovery. A few recent studies have shown that knowledge graph is a useful tool for improving our understanding of the evolution of the Earth and can assist in data-intensive geoscience studies. At the end of the paper, we discuss the best practices from the studies reviewed and propose research topics for future work. Both knowledge and rules in existing human-curated databases and text mining from the literature should be leveraged in constructing geoscience knowledge graphs. Moreover, development of a higher level schema for existing ontology models and a comparable training corpus should be considered.

ABSTRACT The use of artificial intelligence (AI) and machine learning (ML) methods in the geosciences can be categorized into three types, those that: (1) accelerate computationally expensive Earth system models; (2) fill the vacuum where numerical and physics-based models struggle; and (3) enable and enlighten data-driven discoveries. To achieve these tasks, many cyberinfrastructure (CI) systems are required. This chapter reviews the cutting-edge CI aiding the implementation of AI in the geosciences. Each technique presented is evaluated to assist geoscientists in determining how appropriate it is. Use cases in the subdomains of seismology, hydrology, and climatology are introduced to help readers understand the workflows. Challenges and future opportunities for CI development center on big data, provenance, interoperability, and heterogeneity due to the scale and complexity that future AI models in the geosciences will require.

ABSTRACT For science to reliably support new discoveries, its results must be reproducible. Assessing reproducibility is a challenge in many fields—including the geosciences—that rely on computational methods to support these discoveries. Reproducibility in these studies is particularly difficult; the researchers conducting studies must agree to openly share research artifacts, provide documentation of underlying hardware and software dependencies, ensure that computational procedures executed by the original researcher are portable and execute in different environments, and, finally, verify if the results produced are consistent. Often these tasks prove to be tedious and challenging for researchers. Sciunit ( https://sciunit.run ) is a system for easily containerizing, sharing, and tracking deterministic computational applications across environments. Geoscience applications in the fields of hydrology, solid Earth, and space science have actively used Sciunit to encapsulate, port, and repeat workflows across computational environments. In this chapter, we provide a comprehensive survey of geoscience applications that have used Sciunit to improve sharing and reproducibility. We classify the applications based on their reproducibility requirements and show how Sciunit accommodates relevant interfaces and architectural components to support reproducibility requirements within each application. We aim to provide these applications as a Sciunit compendium of use cases for replicability, benchmarking, and improving the conduct of reproducible science in other fields.

This unusual book, published to honor the late iconoclast and geologist extraordinaire Warren Bell Hamilton, comprises a diverse, cross-disciplinary collection of bold new ideas in Earth and planetary science. Some chapters audaciously point out all-too-obvious deficits in prevailing theories. Other ideas are embryonic and in need of testing and still others are downright outrageous. Some are doubtless right and others likely wrong. See if you can tell which is which. See if your students can tell which is which. This unique book is a rich resource for researchers at all levels looking for interesting, unusual, and off-beat ideas to investigate or set as student projects.