Omkar2118/datasets huggingface dataset:
DBE-KT22

**DBE-KT22** contains student exercise answering activities collected through an online practicing platform for the database systems course taught at the Australian National University within the period 2018-2021. The dataset is useful for research targeting students' knowledge tracing given historical sequences of exercise answering.

Introducing paper: DBE-KT22: A Knowledge Tracing Dataset Based on Online Student Evaluation
BCI Competition IV: ECoG to Finger Movements

#####Prediction of Finger Flexion IV Brain-Computer Interface Data Competition

The goal of this dataset is to predict the flexion of individual fingers from
signals recorded from the surface of the brain (electrocorticography (ECoG)). This data set contains
brain signals from three subjects, as well as the time courses of the flexion of each of five fingers.
The task in this competition is to use the provided flexion information in order to predict finger
flexion for a provided test set. The performance of the classifier will be evaluated by calculating the
average correlation coefficient r between actual and predicted finger flexion.


ECoG data during individual flexions of the five fingers; movements acquired with a data glove.
[48 - 64 ECoG channels (0.15-200Hz), 1000Hz sampling rate, 3 subjects]
Stanford ECoG library: ECoG to Finger Movements

Electrophysiological data from implanted electrodes in the human brain are rare, and therefore scientific access to it has remained somewhat exclusive. Here we present a freely-available curated library of implanted electrocorticographic (ECoG) data and analyses for 16 benchmark behavioral experiments, with 204 individual datasets from 34 patients made with the same amplifiers (at the same sampling rate and filter settings). In every case, electrode positions have been carefully registered to brain anatomy. A large set of fully-commented analysis scripts to interpret these data using modern techniques is embedded in the library alongside the data. All data, anatomic correlations, and analysis files (MATLAB code) are in a common, intuitive file structure at https://searchworks.stanford.edu/view/zk881ps0522. The library may be used as course material or serve as a starter package for researchers early in their career or for established groups, to modify the analyses and re-apply them in new settings.

Patients were cued with a word displayed on a bedside monitor to move individual fingers repetitively (contralateral to electrode array) during 2 s cue periods while finger position was recorded with a dataglove.
filename - fingerflex.zip

Introducing paper: A library of human electrocorticographic data and analyses
HuTu 80
Full name: HuTu 80 cell populations

The image set contains 180 high-resolution color microscopic images of human duodenum adenocarcinoma HuTu 80 cell populations obtained in an in vitro scratch assay (for the details of the experimental protocol, we refer to (Liang et al., 2007)). Briefly, cells were seeded in 12-well culture plates ($20 \times 10^3$ cells per well) and grown to form a monolayer with 85\% or more confluency. Then the cell monolayer was scraped in a straight line using a pipette tip ($200 \mu L$). The debris was removed by washing with a growth medium and the medium in wells was replaced. The scratch areas were marked to obtain the same field during the image acquisition. Images of the scratches were captured immediately following the scratch formation, as well as after 24, 48 and 72 h of cultivation.

Images were obtained with the Zeiss Axio Observer 1.0 microscope (Carl Zeiss AG, Oberkochen, Germany) with 400x magnification. All images have been manually annotated by domain experts as a part of the original experimental [study](https://link.springer.com/article/10.1134/S0026893320010173). Here we use these manual annotations as a reference (``ground truth'').  To improve the reproducibility of our analysis, we made corresponding images and their manual annotations fully available at [https://gitlab.com/digiratory/biomedimaging/bcanalyzer](https://bit.ly/3hlvli9).