ESIP 2021 Winter Meeting Materials for the session 'Carbon Management, Food, Agriculture, Human well-being: Using informatics to connect the climate action dots'
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'.
A visual summary of the session is presented as a concept map below. Nodes in the concept map that feature an icon at the bottom of the node indicate clickable links. A full-sized version of the concept map is accessible here.
- To see a more complex representation of the ideas that will be explored during the session, see the next section on an approach to generate a concept graph (as opposed to a concept map) using the Neo4j graph database.
Experimental concept graph visualization
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 (and, sometimes, refresh this page). 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.
