Macrostrat: A Platform for Geological Data Integration and Deep‐Time Earth Crust Research

Peters, Shanan E.; Husson, Jon M.; Czaplewski, John

doi:10.1029/2018gc007467

Cited by 64 publications

(54 citation statements)

References 63 publications

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“…Second, the SGP database has incorporated sedimentary geochemical data from the United States Geological Survey (USGS) National Geochemical Database (NGDB), comprising samples from projects completed between the 1960s and 1990s. These samples, which (Peters et al, 2018). Finally, the SGP database includes data from the USGS Global Geochemical Database for Critical Metals in Black Shales project (CMIBS; Granitto et al, 2017), culled to remove ore-deposit related samples.…”

Section: Case Study: the Sedimentary Geochemistry And Paleoenvironments Projectmentioning

confidence: 99%

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Curation and Analysis of Global Sedimentary Geochemical Data to Inform Earth History

Mehra¹,

Keller²,

Zhang³

et al. 2021

GSAT

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Large datasets increasingly provide critical insights into crustal and surface processes on Earth. These data come in the form of published and contributed observations, which often include associated metadata. Even in the best-case scenario of a carefully curated dataset, it may be nontrivial to extract meaningful analyses from such compilations, and choices made with respect to filtering, resampling, and averaging can affect the resulting trends and any interpretation(s) thereof. As a result, a thorough understanding of how to digest, process, and analyze large data compilations is required. Here, we present a generalizable workflow developed using the Sedimentary Geochemistry and Paleoenvironments Project database. We demonstrate the effects of filtering and weighted resampling on Al 2 O 3 and U contents, two representative geochemical components of interest in sedimentary geochemistry (one major and one trace element, respectively). Through our analyses, we highlight several methodological challenges in a "bigger data" approach to Earth science. We suggest that, with slight modifications to our workflow, researchers can confidently use large collections of observations to gain new insights into processes that have shaped Earth's crustal and surface environments. 1 Supplemental Material: table of valid lithologies; map depicting sample locations; crossplot illustrating analytical uncertainty; flowchart of the proposed workflow; histograms showing the effects of progressive filtering, the distribution of spatial and age scales, and proximity and probability values; and results of sensitivity tests.

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Section: Case Study: the Sedimentary Geochemistry And Paleoenvironments Projectmentioning

confidence: 99%

“…CC-BY-NC. *Akshay.K.Mehra@dartmouth.edu databases such as The Paleobiology Database (PBDB; Peters and McClennen, 2016), Macrostrat (Peters et al, 2018), EarthChem (Walker et al, 2005), Georoc (Sarbas, 2008), and the Sedimentary Geochemistry and Paleoenvironments Project (SGP, this study).…”

Section: Introductionmentioning

confidence: 99%

Curation and Analysis of Global Sedimentary Geochemical Data to Inform Earth History

Mehra¹,

Keller²,

Zhang³

et al. 2021

GSAT

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“…For example, Macrostrat is a platform that links the GeoDeepDive digital library with a machinereading system for rapidly aggregating geological data relevant to the spatial and temporal distribution of rocks as well as the data extracted from them. The efficiency of machine reading and extracting information from publications clearly convinces researchers that the cutting-edge technology of these approaches can not only significantly increase the big 'V's of data but also, notably, release them from spending time and effort on data handling (Peters et al 2018). In the remaining sections of this paper, we will elaborate on the opportunities and challenges facilitating data-driven Earth science transformation.…”

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confidence: 99%

A new international initiative for facilitating data-driven Earth science transformation

Cheng

Oberhänsli

Zhao

2020

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Data-driven techniques including machine-learning (ML) algorithms with big data are re-activating and re-empowering research in traditional disciplines for solving new problems. For geoscientists, however, what matters is what we do with the data rather than the amount of it. While recent monitoring data will help risk and resource assessment, the long-earth record is fundamental for understanding processes. Thus, how big data technologies can facilitate geoscience research is a fundamental question for most organizations and geoscientists. A quick answer is that big data technology may fundamentally change the direction of geoscience research. In view of the challenges faced by governments and professional organizations in contributing to the transformation of Earth science in the big data era, the International Union of Geological Sciences has established a new initiative: the IUGS-recognized Big Science Program. This paper elaborates on the main opportunities and benefits of utilizing data-driven approaches in geosciences and the challenges in facilitating data-driven earth science transformation. The main benefits may include transformation from human learning alone to integration of human learning and AI, including ML, as well as from known questions seeking answers to formulating as-yet unknown questions with unknown answers. The key challenges may be associated with intelligent acquisition of massive, heterogeneous data and automated comprehensive data discovery for complex Earth problem solving.

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“…After the relation extractions of genera or binomials and geologic ages, we performed several steps of post-processing. We used Macrostrat [35], supplemented in a few cases by online lookups, to obtain the minimum and maximum ages of each named time interval. As genus names are commonly abbreviated (e.g.…”

Section: Methodsmentioning

confidence: 99%

Text-mined fossil biodiversity dynamics using machine learning

2019

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Documented occurrences of fossil taxa are the empirical foundation for understanding large-scale biodiversity changes and evolutionary dynamics in deep time. The fossil record contains vast amounts of understudied taxa. Yet the compilation of huge volumes of data remains a labour-intensive impediment to a more complete understanding of Earth's biodiversity history. Even so, many occurrence records of species and genera in these taxa can be uncovered in the palaeontological literature. Here, we extract observations of fossils and their inferred ages from unstructured text in books and scientific articles using machine-learning approaches. We use Bryozoa, a group of marine invertebrates with a rich fossil record, as a case study. Building on recent advances in computational linguistics, we develop a pipeline to recognize taxonomic names and geologic time intervals in published literature and use supervised learning to machine-read whether the species in question occurred in a given age interval. Intermediate machine error rates appear comparable to human error rates in a simple trial, and resulting genus richness curves capture the main features of published fossil diversity studies of bryozoans. We believe our automated pipeline, that greatly reduced the time required to compile our dataset, can help others compile similar data for other taxa.

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Macrostrat: A Platform for Geological Data Integration and Deep‐Time Earth Crust Research

Cited by 64 publications

References 63 publications

Curation and Analysis of Global Sedimentary Geochemical Data to Inform Earth History

Curation and Analysis of Global Sedimentary Geochemical Data to Inform Earth History

A new international initiative for facilitating data-driven Earth science transformation

Text-mined fossil biodiversity dynamics using machine learning

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