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 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...

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...

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...

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...

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...

The year 2014 solution files are based on model calibration effort based on inputs and data from Year 2014 as described in Khangaonkar et al. 2018). It includes water surface elevation, currents, temperature and salinity at an hourly interval. These netcdf files with 24 hourly records include...

Interactive data plots for proteomics and phosphoproteomics data from tumor (n=83) and normal tissue from high-grade serous ovarian cancer.

Both the hourly and daily data are provided in the product. The hourly data are grouped by day in distinct NetCDF files, which are named as “EPIC_SW_PAR_Hourly_yyyymmdd.nc” where “yyyy”, “mm”, and “dd” denote year, month, and day (UTC time). The daily data are grouped by month in distinct NetCDF...

Both the hourly and daily data are provided in the product. The hourly data are grouped by day in distinct NetCDF files, which are named as “EPIC_SW_PAR_Hourly_yyyymmdd.nc” where “yyyy”, “mm”, and “dd” denote year, month, and day (UTC time). The daily data are grouped by month in distinct NetCDF...

Both the hourly and daily data are provided in the product. The hourly data are grouped by day in distinct NetCDF files, which are named as “EPIC_SW_PAR_Hourly_yyyymmdd.nc” where “yyyy”, “mm”, and “dd” denote year, month, and day (UTC time). The daily data are grouped by month in distinct NetCDF...

Both the hourly and daily data are provided in the product. The hourly data are grouped by day in distinct NetCDF files, which are named as “EPIC_SW_PAR_Hourly_yyyymmdd.nc” where “yyyy”, “mm”, and “dd” denote year, month, and day (UTC time). The daily data are grouped by month in distinct NetCDF...

Both the hourly and daily data are provided in the product. The hourly data are grouped by day in distinct NetCDF files, which are named as “EPIC_SW_PAR_Hourly_yyyymmdd.nc” where “yyyy”, “mm”, and “dd” denote year, month, and day (UTC time). The daily data are grouped by month in distinct NetCDF...

Both the hourly and daily data are provided in the product. The hourly data are grouped by day in distinct NetCDF files, which are named as “EPIC_SW_PAR_Hourly_yyyymmdd.nc” where “yyyy”, “mm”, and “dd” denote year, month, and day (UTC time). The daily data are grouped by month in distinct NetCDF...

Both the hourly and daily data are provided in the product. The hourly data are grouped by day in distinct NetCDF files, which are named as “EPIC_SW_PAR_Hourly_yyyymmdd.nc” where “yyyy”, “mm”, and “dd” denote year, month, and day (UTC time). The daily data are grouped by month in distinct NetCDF...