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Title: Clean Energy Innovation Ecosystems Discovery - Measuring Innovation Through Data Analytics (MIDAS)

Abstract

As part of the Mission Innovation rollout at the Paris COP 21, 20 countries announced to double their R&D budgets over 5 years to accelerate lab-to-market clean energy innovations. These innovations must address technical urgency and navigate complex institutional, social and financial environments. To inform budget and program development, policymakers need to understand existing clean energy innovation ecosystems (IE) and their characteristics at the local, state, and regional levels within the US. Researchers at ORNL have implemented a ground-breaking application of natural language processing, link analysis, and other computational techniques to transform text and numerical data into metrics on clean energy innovation activity and geography. The 'Measuring Innovation through Data Analytics' (MIDAS) application uses data to determine where clean energy innovation ecosystems exist and the underlying scoring algorithm "ranks" the strength of the ecosystem for several clean energy technologies. The selection of the data sources is driven by the fact that a clean energy IE can be identified as an intersection of nascent clean technology efforts and financing mechanisms and related large companies located in a geographically concentrated area with an enabling environment to encourage commercialization and networking assets to facilitate communication. MIDAS is a machine-assisted methodology that provides themore » user with a replicable method to rapidly identify, quantify and characterize clean energy innovation ecosystems. This software application and the underlying datasets have the potential to address many important policy questions. The initial broad list of U.S. clean energy ecosystems, with geographic area, technology focus, and list and types of involved organizations can help describe regional tech activities and capabilities. Other potential applications include understanding the effect of private sector investments on ecosystem activity, and trends analysis.« less

Authors:
 [1];  [1];  [1]
  1. Oak Ridge National Laboratory
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1466246
Report Number(s):
MIDAS; 005795MNFRM00
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Software
Software Revision:
00
Software Package Number:
005795
Software CPU:
MNFRM
Source Code Available:
Yes
Country of Publication:
United States

Citation Formats

Chinthavali, Supriya, Dunivan Stahl, Chelsey K, and Lee, Sangkeun. Clean Energy Innovation Ecosystems Discovery - Measuring Innovation Through Data Analytics (MIDAS). Computer software. Vers. 00. USDOE. 31 Aug. 2017. Web.
Chinthavali, Supriya, Dunivan Stahl, Chelsey K, & Lee, Sangkeun. (2017, August 31). Clean Energy Innovation Ecosystems Discovery - Measuring Innovation Through Data Analytics (MIDAS) (Version 00) [Computer software].
Chinthavali, Supriya, Dunivan Stahl, Chelsey K, and Lee, Sangkeun. Clean Energy Innovation Ecosystems Discovery - Measuring Innovation Through Data Analytics (MIDAS). Computer software. Version 00. August 31, 2017.
@misc{osti_1466246,
title = {Clean Energy Innovation Ecosystems Discovery - Measuring Innovation Through Data Analytics (MIDAS), Version 00},
author = {Chinthavali, Supriya and Dunivan Stahl, Chelsey K and Lee, Sangkeun},
abstractNote = {As part of the Mission Innovation rollout at the Paris COP 21, 20 countries announced to double their R&D budgets over 5 years to accelerate lab-to-market clean energy innovations. These innovations must address technical urgency and navigate complex institutional, social and financial environments. To inform budget and program development, policymakers need to understand existing clean energy innovation ecosystems (IE) and their characteristics at the local, state, and regional levels within the US. Researchers at ORNL have implemented a ground-breaking application of natural language processing, link analysis, and other computational techniques to transform text and numerical data into metrics on clean energy innovation activity and geography. The 'Measuring Innovation through Data Analytics' (MIDAS) application uses data to determine where clean energy innovation ecosystems exist and the underlying scoring algorithm "ranks" the strength of the ecosystem for several clean energy technologies. The selection of the data sources is driven by the fact that a clean energy IE can be identified as an intersection of nascent clean technology efforts and financing mechanisms and related large companies located in a geographically concentrated area with an enabling environment to encourage commercialization and networking assets to facilitate communication. MIDAS is a machine-assisted methodology that provides the user with a replicable method to rapidly identify, quantify and characterize clean energy innovation ecosystems. This software application and the underlying datasets have the potential to address many important policy questions. The initial broad list of U.S. clean energy ecosystems, with geographic area, technology focus, and list and types of involved organizations can help describe regional tech activities and capabilities. Other potential applications include understanding the effect of private sector investments on ecosystem activity, and trends analysis.},
doi = {},
year = {2017},
month = {8},
note =
}

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