| Title | Curation and analysis of global sedimentary geochemical data to inform Earth history |
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| Author | Mehra, A; Keller, C B; Zhang, T; Tosca, N J; McLennan, S M; Sperling, E; Farrell, U; Brocks, J; Canfield, D; Cole, D; Crockford, P; Cui, H; Dahl, T W; Dewing, K ; Emmings, J; Gaines, R R; Gibson, T; Gilleaudeau, G J; Guilbaud, R; Hodgkiss, M;
Jarrett, A; Kabanov, P; Kunzmann, M; Li, C; Loydell, D K; Lu, X; Miller, A; Mills, N T; Mouro, L D; O'Connell, B; Peters, S E; Poulton, S; Ritzer, S; Smith, E; Wilby, P; Woltz, T; Strauss, J V |
| Source | GSA Today vol. 31, issue 5, 2021 p. 4-9, https://doi.org/10.1130/GSATG484A.1  Open Access |
| Image |  |
| Year | 2021 |
| Alt Series | Natural Resources Canada, Contribution Series 20210152 |
| Publisher | Geological Society of America |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Area | Earth |
| Lat/Long WENS | -180.0000 180.0000 90.0000 -90.0000 |
| Subjects | Science and Technology; sedimentology; paleoenvironment; Databases |
| Illustrations | photographs; diagrams; graphs |
| Program | GEM-GeoNorth: Geo-mapping for Energy and Minerals |
| Released | 2021 05 01 |
| Abstract | 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 non-trivial 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 Al2O3 and U contents, two representative geochemical components of interest in sedi-mentary 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. |
| Summary | (Plain Language Summary, not published)
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 non-trivial 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 is required of how to digest, process, and analyze large data compilations. Here, we present a generalizable workflow
developed using the Sedimentary Geochemistry and Paleoenvironments Project database. We demonstrate the effects of filtering and weighted resampling using Al2O3 and U, 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 continental-crust and surface environments. |
| GEOSCAN ID | 328574 |
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