This is a demo of an analysis of funding ecology. The jupyter notebooks in this repository allow to scrape grants from websites and then to represent and analyze the grant ecology as a network. An explicit description of the project and the motivation behind it can be found in the file "Coordination in complex stakeholder ecology v2.pdf".
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Coordination in complex stakeholder ecology v2.pdf
Grant_scraping.ipynb
README.md
allgrants.ipynb

README.md

grantecology

This is a demo of an analysis of funding ecology. The jupyter notebooks in this repository allow to scrape grants from websites and then to represent and analyze the grant ecology as a network.

An explicit description of the project and the motivation behind it can be found in the file "Coordination in complex stakeholder ecology v2.pdf".

This note summarizes an approach where graph theory and big data tools are employed for representation and analysis of a network of grants. To that end grants, grantees and funders are represented as nodes in a network or graph where edges indicate financial flows or similarities of activities.
With the help of such network representation and analysis concepts like the centrality of a network can be measured to identify partitions or segments of the network. Big data tools and natural language processing are used to identify similar grants automatically.

The data used in this paper are descriptions of environmental, conservation and climate change grants that were scraped from the websites of the Hewlett Foundation, the Packard Foundation, the Oak Foundation, and the Children’s Investment Fund Foundation in July 2018. The web scraping resulted in a data set of 3113 grants to 948 with a total funding of $2,625 billion.