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
Introduction
The continuous evolution of data management systems affords great opportunities to the enhancement of knowledge and advancement of earth science research. With the growing need and desire to leverage information from various sources to better understand our environment, it becomes evident — through community experience and foresight — that this can be maximized by accepting new ways to cross-examine this information. As excerpts in The Fourth Paradigm explain[1]:
We have to do better at producing tools to support the whole research cycle—from data capture and data curation to data analysis and data visualization. (pg. xvii)
Clearly, data-intensive science … must move beyond data warehouses and closed systems, striving instead to allow access to data to those outside the main project teams, allow for greater integration of sources, and provide interfaces to those who are expert scientists but not experts in data administration and computation. (pg. 147)
We are already seeing some attempts to infer knowledge based on the world’s information. (pg. 167)
To capitalize on these opportunities, it is essential to develop a data analytics framework in which we scope the scientific, technical, and methodological components that contribute to advancing science research. Through this framework, we can categorize discussions among individuals of like component interests, instead of attempting to draw specific direction from a set of starting points that may greatly vary.
Data analytics is the process of examining large amounts of data of a variety of types to reveal hidden patterns, unknown correlations, and other useful information, key to facilitating Earth science research opportunities. Thus, the research presented here is motivated by the need to determine and categorize available data analytics techniques and skills and to identify the gaps for where they are still needed.
Today (and well into the foreseeable future) there is a rapid growth in the amount of earth science data and value-added heterogeneous information that many earth science researchers have not yet holistically leveraged. It is important to realize that this rate of data growth is new and challenging. It is new in that information technology is just beginning to provide the tools for advancing the analysis of heterogeneous datasets in a "big" way to provide opportunities to discover non-obvious scientific relationships, previously invisible to the science eye. The challenge is it takes individuals, or teams of individuals, with just the right combination of skills to understand the data and develop the methods to glean knowledge from data and information.
The ability to apply information technology, tools, and services necessary to facilitate the advancement of earth science research is becoming more obvious and necessary at a rate that is accelerating. That is, if data manipulation (subsetting, data transformation, format conversion, etc.) extract information from data, then data analytics techniques and skills glean knowledge from information.[2]
The objectives of the Earth Sciences Information Partners (ESIP)<ref name="ESDAWiki">"Earth Science Data Analytics". Federation of Earth Science Information Partners. 14 December 2016. http://wiki.esipfed.org/index.php/Earth_Science_Data_Analytics. >/ref> Federation Earth Science Data Analytics (ESDA) cluster is to understand, define, and facilitate the implementation of earth science data analytics. As a result of cluster efforts, an ESIP-adopted definition of ESDA has been generated, along with 10 ESDA goals, to set the framework for a common understanding for advancing earth science research. In addition, the ESIP ESDA cluster performed an exhaustive search identifying ESDA types, techniques, and skills used in performing data analytics.
In this paper, we present the ESIP-derived definition of ESDA and differentiate it from other publicized definitions of data analytics. We then describe different types of ESDA and their driving goals. This is followed by an exhaustive survey of current techniques and skills for performing ESDA, made available for the benefit of data scientists exploring new earth science data analytics methodologies, and their potential use.
Literature review
References
- ↑ Hey, T.; Tansley, S.; Tolle, K. (2009). The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research. pp. 284. ISBN 9780982544204.
- ↑ Kempler, S. (12 March 2014). "How Do We Facilitate the Use of Large Amounts of Heterogeneous Data". Information Science and Technology Colloquium, Goddard Institutional Repository. NASA Goddard Space Flight Center. https://gsfcir.gsfc.nasa.gov/colloquia/5064/how-do-we-facilitate-the-use-of-large-amounts-of-heterogeneous-data-or-is-gleaning-knowledge-from-information-in-reach.
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.
