Creating, Managing and Evaluating Data Legacies: Updates on the Global Earth Mineral Inventory
Authors
Prabhu, Anirudh
Morrison, SM
Eleish, Ahmed
Zhong, Hao
Huang, Fang
Golden, JJ
Perry, S.
Hummer, DR
Ralph, J.
Runyon, SE.
ORCID
No Thumbnail Available
Other Contributors
Issue Date
2019-12-09
Keywords
Degree
Terms of Use
Full Citation
Prabhu A, Morrison SM, Eleish A, Zhong H, Huang F, Golden JJ, Perry S, Hummer DR, Ralph J, Runyon SE, Fontaine KS. Creating, Managing and Evaluating Data Legacies: Updates on the Global Earth Mineral Inventory. InAGU Fall Meeting 2019 Dec 9. AGU.
Abstract
Geologists and geology enthusiasts have collected mineral samples and compiled data about these samples for centuries. Datasets like those found at ‘RRUFF.info’ and ‘Mindat.org’ have documented nearly all known mineral localities in the world. One of the Deep Time Data Infrastructure (http://dtdi.carnegiescience.edu/) project’s main goals has been to create and merge datasets from various scientific fields, and use statistical methods and visualization tools that inspire and test hypotheses applicable to modeling Earth's past environments. In order to achieve this goal, we have compiled various geological properties of minerals, linked them to the above mentioned mineral locality datasets and integrated them into a data resource called GEMI (Global Earth Mineral Inventory). GEMI can be considered a knowledge graph or more practically as a network with every node representing some feature of the datasets. Exploring a combination of the nodes gives the researcher faceted views of the data required for the task at hand.This inventory is accessible to researchers through the Deep Carbon Observatory Data Portal (https://deepcarbon.net/data-portal). This contribution describes our team’s efforts in compiling the Global Earth Mineral Inventory, data and citation policies for usage and sharing, and completion and impact metrics for the Data Legacy. We consider such data resources to be ‘Data Legacies’ and as valuable as scientific papers. The evaluation metrics presented here can be applied to all data resources.
Description
Department
Publisher
AGU