Low Energy Nuclear Reactions Search Engine
After more than three decades of research, Low Energy Nuclear Reactions (LENR) still pose a challenge to comprehensive understanding. In this study, we introduce a preliminary framework of analytical tools that could assist the LENR community in accessing literature and applying machine
learning for mining insights. We first collected and structured a dataset of over 4,500 LENR publications. Following that, we designed and deployed a descriptive analytics tool to search and
draw insights by using a platform that allows data slicing based on keywords, authors, publication dates, and other metadata. Additionally, we applied unsupervised machine learning algorithms to the data to generate clusters of publications based on semantics and other features. Through interactive interfaces, we enable targeted investigation of specific reported phenomena.
After more than three decades of research, Low Energy Nuclear Reactions (LENR) still pose a challenge to comprehensive understanding. In this study, we introduce a preliminary framework of analytical tools that could assist the LENR community in accessing literature and applying machine
learning for mining insights. We first collected and structured a dataset of over 4,500 LENR publications. Following that, we designed and deployed a descriptive analytics tool to search and
draw insights by using a platform that allows data slicing based on keywords, authors, publication dates, and other metadata. Additionally, we applied unsupervised machine learning algorithms to the data to generate clusters of publications based on semantics and other features. Through interactive interfaces, we enable targeted investigation of specific reported phenomena.
After more than three decades of research, Low Energy Nuclear Reactions (LENR) still pose a challenge to comprehensive understanding. In this study, we introduce a preliminary framework of analytical tools that could assist the LENR community in accessing literature and applying machine
learning for mining insights. We first collected and structured a dataset of over 4,500 LENR publications. Following that, we designed and deployed a descriptive analytics tool to search and
draw insights by using a platform that allows data slicing based on keywords, authors, publication dates, and other metadata. Additionally, we applied unsupervised machine learning algorithms to the data to generate clusters of publications based on semantics and other features. Through interactive interfaces, we enable targeted investigation of specific reported phenomena.
Data Analytics
Search Engine
Big Data and ML Pipelines
Low-energy nuclear reaction (LENR) research represents an extensive yet disorganized data corpus distributed across thousands of papers, presentations, and reports. To facilitate robust analysis of the heterogeneous LENR literature, we need the ability to rapidly search, filter, and aggregate across thousands of documents and metadata fields. For this critical information retrieval task, we leverage Apache Solr, a highly scalable open-source enterprise search platform built on Lucene. To complement the indexed search and retrieval capabilities provided by Solr, we designed and developed interactive
visualizations to reveal insights and trends within the corpus metadata that is publicly available at:
https://lenrdashboard.com/.
This research demonstrates the potential of applying modern data science techniques to synthesize insights from the expansive and scattered literature on low-energy nuclear reactions. By collecting, organizing, and analyzing the corpus of LENR literature, we were able to illuminate key trends, themes, and relationships.
