Mapping the relationships between Manhattan Project scientists using network science

The Manhattan Project was a top secret program that resulted in the development of the first atomic bombs during World War II. This secret and controversial research enterprise involved many gifted and renowned scientists, including physicist J. Robert Oppenheimer.

Milán Janosov, founder of Geospatial Data Consulting and Chief Data Scientist at Baoba, recently set out to map the relationships between Manhattan Project scientists using methods rooted in network science. Network or data science is a field of research that explores the complex connections between people in a group or between individual parts of networked systems. The work is published on the arXiv preprint server.

“I have been working with social media for some time and mapping unusual datasets to uncover hidden connections,” Janosov said. “During this trip I also mapped hidden networks of scientists, including for example the network of Nobel laureates in another project published earlier this year. So I was already used to mapping the networks of scientists. After watching the highly anticipated Oppenheimer film, I decided to also unravel the collaboration and social connections behind the Manhattan Project, which is one of the largest and most impactful scientific collaborations in history of humanity.

The release of the popular film Oppenheimer in July of this year awakened significant public interest in the Manhattan Project and the substantial research efforts that led to the development of the atomic bomb. This inspired Janosov, a trained network scientist with a background in physics, to explore this topic in his research.

“A practical and traditionally accepted way of creating networks of scientists is through shared publications,” Janosov explained. “However, even today, some of the Manhattan Project science is classified, so this direction would have misrepresented the situation. So I decided to move away from this approach from classified and private data to the platform of “Most public information available: Wikipedia.”

To map the relationships between the various scientists involved in the Manhattan Project, Janosov first collected the Wikipedia page of each Nobel laureate and compiled these pages into a dataset. Subsequently, he used language processing techniques to analyze the texts included in these pages.

“This approach allowed me to quantify how often each winner’s page references others,” Janosov said. “That was all I needed to build their network, in which each scientist was a linked node based on Wikipedia mentions and references. For example, Oppenheimer’s Wiki page mentions Enrico Fermi more than 10 times , which leads to a strong bond between the two physicists.”

The map created by Janosov depicts the most renowned scientists involved in the Manhattan Project as dots and the connections between these scientists as lines that connect the dots. These dots and lines create a complex web of relationships, highlighting the research circles that closely collaborated at the time.

“It’s exciting to see how the community structure of the network describes the different departments and historically known cliques that worked in the projects, like the Theoretical Division with Feynman or the World War II refugees around Borh,” Janosov said . “However, my favorite is that of the Hungarian immigrants who introduce themselves under the nickname Martians: Teller, Wigner, Szilard and Neuman, who played a founding role in the dawn of the atom. turns out that, also in this network, using the right coloring, their strong connectivity is also clearly visible.

The colorful map of the Manhattan Project created by Janosov is one of the most recent examples of the value of network science in creating representations of human connections and visual maps of complex systems with many interacting components. Future studies in this rapidly evolving area of ​​research could shed new light on a wide range of topics rooted in both the sciences and humanities.

“Today I mainly focus on issues related to urban planning, geospatial data science and sustainability,” Janosov added. “I am currently exploring a crucial question in this area, where network science can also be appropriately applied.”

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