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 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...

Sara Gosline received BA in Computer Science from Columbia University and spent two years working in software before returning to graduate school full time. She received her Masters and PhD in Computer Science from McGill University with a specialty in Bioinformatics and then moved to the...

Biography Kelly is a senior data scientist in the Computational Biology group within the Biological Sciences Division at Pacific Northwest National Laboratory (PNNL). After earning a MS in Biostatistics from the University of Washington in 2012, she worked at a cancer research company for two years...

David Degnan is a biological data scientist who develops bioinformatic and statistical pipelines for multi-omics data, specifically the fields of proteomics, metabolomics, and multi-omics (phenotypic) data integration. He has experience with top-down & bottom-up proteomics analysis, 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...

The EyeSea underwater video dataset was assembled for developing algorithms for detecting fish in real world underwater video data. The data were recorded as part of environmental monitoring efforts at three different water power sites. The Ocean Renewable Power Company (ORPC) data were recorded in...

Dataset The dataset will consist of: About 1200 news stories from the Alderwood Daily News plus a few other items collected by the previous investigators A few photos A few maps of Alderwood and vicinity (in bitmap image form) A few files with other mixed materials, e.g. a spreadsheet with voter...

It is Fall of 2004 and one of your analyst colleagues has been called away from her current tasks to an emergency. The boss has given you the assignment of picking up her investigation and completing her task. She has been asked to pursue a line of investigation into some unexpected activities...

Mini Challenge 1: Wiki Editors The Paraiso movement is controversial and is having considerable social impact in a specific area of the world. We have extracted a segment of the Paraiso (the movement) Wikipedia edits page. Please note this is not the Paraiso Manifesto Wiki page which is part of the...

This year’s VAST Challenge focuses on visual analytics applications for both large scale situation analysis and cyber security. We have two mini-challenges to test your analytical skills and confound your visual analytics applications. In the first mini-challenge, (the imaginary) BankWorld's largest...

The VAST 2009 Challenge scenario concerned a fictitious, cyber security event. An employee leaked important information from an embassy to a criminal organization. Participants were asked to discover the identity ofthe employee and the structure of the criminal organization. Participants were...

The VAST 2010 Challenge consisted of three mini-challenges (MC) and one Grand Challenge (GC). Each MC had a data set, instructions and a number of questions to be answered. The GC required participants to pull together information from all three data sets and write a debrief summarizing the...