https://www.nih.gov/about-nih/what-we-do/mission-goals NIH’s mission is to seek fundamental knowledge about the nature and behavior of living systems and the application of that knowledge to enhance health, lengthen life, and reduce illness and disability. The goals of the agency are: to foster...

This data is a model of synthetic adversarial activity surrounded by noise and was funded by DARPA. The various versions include gradually more complex networks of activities.

Fusarium sp. DS682 Proteogenomics Statistical Data Analysis of SFA dataset download: 10.25584/KSOmicsFspDS682/1766303 . GitHub Repository Source: https://github.com/lmbramer/Fusarium-sp.-DS-682-Proteogenomics MaxQuant Export Files (txt) Trelliscope Boxplots (jsonp) Fusarium Report (.Rmd, html)...

This data is a model of synthetic adversarial activity surrounded by noise and was funded by DARPA. The various versions include gradually more complex networks of activities.

The VAST Challenge 2019 presents three mini-challenges and a grand challenge for you to apply your visual analytics research and technologies to help a city grapple with the aftermath of an earthquake that damages their nuclear power plant. These challenges are open to participation by individuals...

The Environmental Determinants of Diabetes in the Young (TEDDY) study is searching for factors influencing the development of type 1 diabetes (T1D) in children. Research has shown that there are certain genes that correlate to higher risk of developing T1D, but not all children with these genes...

The Diabetes Autoimmunity Study in the Young (DAISY) seeks to find environmental factors that can trigger the development of type 1 diabetes (T1D) in children. DAISY follows children with high-risk of developing T1D based on family history or genetic markers. Genes, diets, infections, and...

Machine learning is a core technology that is rapidly advancing within type 1 diabetes (T1D) research. Our Human Islet Research Network (HIRN) grant is studying early cellular response initiating β cell stress in T1D through the generation of heterogenous low- and high-throughput molecular...

This data is a model of synthetic adversarial activity surrounded by noise and was funded by DARPA. The various versions include gradually more complex networks of activities. This data was generated by PNNL. Activities can be phone calls, transactions, or any other type of communications. Most of...

Dr. Katrina Waters is the division director for Biological Sciences at the Pacific Northwest National Laboratory. Waters has a Ph.D. in biochemistry and more than 15 years of experience in microarray and proteomics data analysis. Her research interests are focused on the integration of genomics...

This data was generated by the organization IvySys. Activities can be phone calls, transactions, or any other type of communications. Most of the files are of the type .edges, .rdf, or .csv; but all can be opened in a text editor. A good introduction to this data can be found in \Tutorial1\MAA...

This data was generated by the organization GORDIAN. Activities can be phone calls, transactions, or any other type of communications. Most of the files are of the type .csv; and can be opened in a text editor. A description of the data and how to read it can be found in the html files for the...

Various graph datasets, documentation, and tools are provided here by the SDGG-MAA project. PNNL DARPA MAA Graph Dataset V1.0 (September 2017) – Three preliminary channel background graphs with and without embedded signal patterns.

Various graph datasets, documentation, and tools are provided here by the SDGG-MAA project. PNNL DARPA MAA Graph Dataset V2.0 (October 2017) – Preliminary channel background graph with embedded signal patterns at various sizes (~7K, ~50K, and ~100K nodes). Signal patterns have also been extracted...