Journal:Earth science data analytics: Definitions, techniques and skills
| Full article title | Earth science data analytics: Definitions, techniques and skills |
|---|---|
| Journal | Data Science Journal |
| Author(s) | Kempler, Steve; Mathews, Tiffany |
| Author affiliation(s) | NASA Goddard Space Flight Center, NASA Langley Research Center |
| Primary contact | Email: gulliver2100 at verizon dot net |
| Year published | 2017 |
| Volume and issue | 16 |
| Page(s) | 6 |
| DOI | 10.5334/dsj-2017-006 |
| ISSN | 1683-1470 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | http://datascience.codata.org/articles/10.5334/dsj-2017-006/ |
| Download | http://datascience.codata.org/articles/10.5334/dsj-2017-006/galley/632/download/ (PDF) |
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Abstract
The continuous evolution of data management systems affords great opportunities for the enhancement of knowledge and advancement of science research. To capitalize on these opportunities, it is essential to understand and develop methods that enable data relationships to be examined and information to be manipulated. Earth science data analytics (ESDA) comprises the techniques and skills needed to holistically extract information and knowledge from all sources of available, often heterogeneous, data sets. This paper reports on the ground-breaking efforts of the Earth Science Information Partners' (ESIP) ESDA cluster in defining ESDA and identifying ESDA methodologies. As a result of the void of earth science data analytics in the literature, the ESIP ESDA definition and goals serve as an initial framework for a common understanding of techniques and skills that are available, as well as those still needed to support ESDA. Through the acquisition of earth science research use cases and categorization of ESDA result oriented research goals, ESDA techniques/skills have been assembled. The resulting ESDA techniques/skills provide the community with a definition for ESDA that is useful in articulating data management and research needs, as well as a working list of techniques and skills relevant to the different types of ESDA.
Keywords: data science, analytics, techniques, skills, information, knowledge
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Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. The original article had citations listed alphabetically; they are listed in the order they appear here due to the way the wiki works.
