The fellowship, first initiated in 2017, is inspired by the ability of graph databases to strengthen reporting and help journalists understand large datasets. This technology was essential in uncovering ICIJ’s prize-winning investigations, including the Panama Papers, Pandora Papers, and the FinCEN Files. The 2022 fellowship brings an independent, dedicated data scientist to work with the ICIJ to help make sense of complex data and promote greater transparency.
The ICIJ has begun their search for the 2022 Connected Data Fellow. The role, description, and application can be found here. Please let your friends know about it!
Cheers,
Yolande Poirier
P.S.: We are still offering a special GraphConnect rate for our community. Join us and tell your friends to register with the code: Community50.
Gary is an IT veteran who has worn many hats over the years. He discovered Neo4j graph database while evaluating options for a Java application with an angular front-end, and came to realize the power and simplicity of Neo4j and the Cypher query language. Gary generously gives very helpful advice to anyone in the community. Thank you, Gary – our members greatly appreciate your helping hand!
Ruchika Kharwar uses graph technology to study patient data. She created a knowledge graph with synthetic data and explores it with Neo4j Bloom using Cypher queries.
Sebastian discusses how full-text indexes and other search mechanisms work. He explores interesting ways of using index queries and integrates the results into a Java application.
Sixing Huang uses an NLP pipeline to extract information from public research articles on the Carbohydrate-Active Enzymes web portal (CAZy), then adds it to the CAZy data to form a new knowledge graph on AuraDB.
Mehul Gupta describes what graph databases are and how to set one up. In his second article, he demonstrates how different path-finding algorithms in GDS can be used to solve a data science problem – analyzing an airline network – with varying results.
Laura Di Egidio connects to Neo4j with Galileo.XAI to simulate the impact of the breakdown of an energy distribution network node and calculate the allocation percentages of the network links. She applies Graph Data Science algorithms to analyze the graph globally to find individual nodes or sets of interesting nodes to focus on.
Yolande Poirier is passionate about technology and developer communities. Her goal is to empower developers and data scientists everywhere to successfully grow their projects. At Neo4j, she runs the advocacy programs, including the Ninja program. Feel free to reach out to her on LinkedIn.
