A small classic dataset from Fisher, 1936. One of the earliest known datasets used for evaluating classification methods.
Predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset.
Images of 13,611 grains of 7 different registered dry beans were taken with a high-resolution camera. A total of 16 features; 12 dimensions and 4 shape forms, were obtained from the grains.
This dataset comprises 9105 individual critically ill patients across 5 United States medical centers, accessioned throughout 1989-1991 and 1992-1994. Each row concerns hospitalized patient records who met the inclusion and exclusion criteria for nine disease categories: acute respiratory failure, chronic obstructive pulmonary disease, congestive heart failure, liver disease, coma, colon cancer, lung cancer, multiple organ system failure with malignancy, and multiple organ system failure with sepsis. The goal is to determine these patients' 2- and 6-month survival rates based on several physiologic, demographics, and disease severity information. It is an important problem because it addresses the growing national concern over patients' loss of control near the end of life. It enables earlier decisions and planning to reduce the frequency of a mechanical, painful, and prolonged dying process.
Utilize 17 clinical features for predicting survival state of patients with liver cirrhosis. The survival states include 0 = D (death), 1 = C (censored), 2 = CL (censored due to liver transplantation).
This first release of MOVER includes adult patients who underwent surgery at the University of California Irvine Medical Center from 2015 to 2022. Data for patients who underwent surgery were captured from two different sources: High-fidelity physiological waveforms from all of the operating rooms were captured in real time and matched with Electronic Medical Record Data. MOVER includes data from 58,799 unique patients and 83,468 surgeries. The dataset is freely available to all researchers who sign a data usage agreement.
We present a dataset of 21459 shell commands from 275 participants who attended cybersecurity training and solved assignments in the Linux terminal. Each acquired data record contains a command with its arguments and metadata, such as a timestamp, working directory, and host identification in the emulated training infrastructure. The commands were captured in Bash, ZSH, and Metasploit shells. The data are stored as JSON records collected using an open-source logging toolset and two open-source interactive learning environments. Researchers and developers may freely use the dataset or deploy the learning environments with the logging toolset to generate their own data in the same format.
The dataset consists of protein fragments of length nine, called 9mers, derived from 3,733 proteins selected by cullpdb . All proteins have 1) resolution less than 1.6 angstrom, 2) R-factor less than 0.25, 3) sequence identity below 20%. In addition, all proteins with identity above 20% to CASP13 targets are removed. All torsion angle-pairs are in the allowed region of the Ramachandran plot (fragments containing outliers were detected by the Ramalyze function of the crystallography software PHENIX  and removed). The dataset has ~158,000 entries randomly split into train, test, and validation sets with a 60/20/20 split.
Data set for estimating the precise number of occupants in a room using multiple non-intrusive environmental sensors like temperature, light, sound, CO2 and PIR.