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Revision as of 07:58, January 25, 2021
Overview
How does the Sustainable Development Goals (SDG) relate to President Biden's goals for managing the US's contribution to the global carbon budget? What is the role of the farming sector and crop management best practices? How can your work as a research scientist inform farm management? These are the issues that we shall be exploring at the ESIP Winter 2021 Meeting session titled 'Carbon Management, Food, Agriculture, Human well-being: Using informatics to connect the climate action dots'.
During the session, attendees will be introduced to a high-level concept map that provides a landscape overview of the concepts that will be addressed in the session. That concept map will be incrementally evolved as each of our invited speakers present their materials. At the end of the presentations, attendees will be assigned small breakout groups to continue evolving the concept map.
You are encouraged to familiarize yourself with the "seed" concept map before the session.
Seed concept map
The "seed" concept map is shown below, and is static.
To see the map evolve as it is updated through the presentations, " click here and the 'live' concept map will be opened in a new window.
Experimental concept graph
Below, we show an experimental visualization that uses selected documents to show the connections between entities like the SDGs, journal publications, a private sector white paper, and US government reports. The visualization is part of the 'Carbon Management, Food, Agriculture, Human well-being: Using informatics to connect the climate action dots' session at the ESIP Winter 2021 Meeting.
You may need to click on the image below to initiate the rendering of a concept graph. The graph is 'live': experiment with zooming, panning, and clicking on nodes and relationships (relationships are the lines connecting nodes). Look for the "Open in a new window" icon in the menu bar, right next to the word "WORKBOOK" below. Clicking on that icon opens up a new browser tab to better visualize the concept graph.
The data for the visualization is maintained in a "labeled property graph" database called Neo4j. A database query, written in the Neo4j query language "Cypher", was issued against the database from within a Python Jupyter notebook. The query results were formatted and then sent to the Graphistry web service for visualization.