Data Mining and Knowledge Discovery for Geoscientists
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Users of GeoDeepDive do not have direct access to original documents. Instead, they have access to output generated by millions of computing hours devoted to parsing millions of published papers. Full citation data, along with URLs pointing back to original sources, are always available. The third barrier to leveraging the published literature is the time and effort that is required to develop software tools that can recognize and extract data and information that are relevant to a given problem. There is currently no single solution to this problem.
Data Mining and Knowledge Discovery for Geoscientists | Semantic Scholar
Instead, the goal of GeoDeepDive is to make the development of useful applications as easy and efficient as possible. To develop a GeoDeepDive application, users must write code that reads and analyzes the output of natural language processing NLP , OCR, or custom document parsing and annotation tools. The original application, modified by Peters et al. It then locates stratigraphic names e. The output is a list of individual mentions of stromatolites, the rock units that contain them, and a citation or link to the relevant publication. Data processing applications can be run on GeoDeepDive for all relevant documents.
Another research intern, Erika Ito, is developing an application to investigate the completeness of the Paleobiology Database relative to the published literature. This app, written in the R language , uses NLP output to locate mentions of geologic formations that are described as fossil bearing but that are not currently in the Paleobiology Database.
The end result is a machine-executed assessment of how thoroughly paleontologists have collated the scientific literature. The application also produces a priority list for future data acquisition efforts.
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GeoDeepDive infrastructure can also be used to provide links to published documents that contain specific terms and phrases of interest. For example, the Macrostrat database uses the Web-based GeoDeepDive application programming interface API to link lithostratigraphic names in geologic maps to the published literature Figure 4.
When users click on named rock units in a Web browser or tap on named map units in a mobile app , the GeoDeepDive API retrieves snippets of text around mentions of those same terms in the full text of documents, along with citation metadata and document URLs. This allows one-click discovery of publications that are potentially relevant to users who are exploring geological maps in the field. Individuals wishing to explore how GeoDeepDive can be used in their projects have several options.
The API is open and offers some useful capabilities right now, such as the ability to explore journal coverage and retrieve links to documents that contain specific terms. New dictionaries can be ingested at any time, and we are eager to involve individuals and communities who have structured vocabularies that can be indexed against the literature. Several basic documentation-type tutorials and example apps are also available on GitHub Figure 5. Development efforts to date have focused on forging agreements with publishers and improving GeoDeepDive infrastructure for downloading, storing, parsing, and annotating documents.
User applications have focused on text-based information [e. We are working to allow users to write applications that read the full text of documents, identify specific tables and figures of interest, and then extract data from them. We are still far from the pie-in-the-sky capabilities that are often imagined in the context of machine reading and learning applied to the scientific literature.
However, GeoDeepDive is establishing a solid building block for the future by focusing on building a comprehensive digital library, backed by agreements and partnerships with publishers and content owners, that is supported by a reliable high-throughput computing infrastructure. We look forward to helping geoscientists to more efficiently discover and leverage hard-earned data locked in the scientific literature.
- Data Mining and Knowledge Discovery for Geoscientists.
- Data Mining and Knowledge Discovery for Geoscientists by Guangren Shi | | Booktopia.
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Corsetti, F. Kaufman , Stratigraphic investigations of carbon isotope anomalies and Neoproterozoic ice ages in Death Valley, California, Geol.
Liu, C. Peters, S. Husson, and J. Wilcots, J. Husson, and S. Peters , Stromatolite distribution in space and time: A machine-reading assisted quantitative analysis, Geol. Programs , 47, Shanan E. Feature 1 October News 19 July Meeting Report 2 July News 5 June News 19 November Science Update 31 October News 15 November Geophysical Research Letters.
Space Weather. Global Biogeochemical Cycles. Larger Image.
Data Mining and Knowledge Discovery for Geoscientists
Description Table of Contents Goodreads reviews "In the early 21 century, data mining DM was predicted to be "one of the most revolutionary developments of the next decade," and chosen as one of 10 emerging technologies that will change the world Hand et al. In fact, in the recent 20 years, the field of DM has seen enormous success, both in terms of broad-ranging application achievements and in terms of scientific progress and understanding.
- Data Mining and Knowledge Discovery for Geoscientists - tretconcthersellven.gq.
- 1st Edition.
DM is the computerized process of extracting previously unknown and important actionable information and knowledge from database DB. This knowledge can then be used to make crucial decisions by leveraging the individual's intuition and experience to objectively generate opportunities that might otherwise go undiscovered"-- Shi introduces geological scientists to algorithms that are widely used for data mining and knowledge discovery, describes how they have been and could be applied in the geosciences, and surveys some successful applications.
The algorithms fall into the categories of probability and statistics, artificial neural networks, support vector machines, decision trees, Bayesian classification, cluster analysis, the Kriging method, and fuzzy mathematics and other soft computing methods.
Show more Show less. The algorithms fall into the categories of probability and statistics, artificial neural networks, support vector machines, decision trees, Bayesian classification, cluster analysis, the Kriging method, and fuzzy mathematics Authored by a global thought leader in data mining, this book summarizes the latest developments and practical data application techniques for geoscientists.
Preface vii 1 Introduction 1 22 1.