Omkar2118/datasets huggingface dataset:
Detecting Security Patches Via Behavioral Data
Full name: Nitzan Farhi

The code that created this dataset can be seen in [https://github.com/nitzanfarhi/SecurityPatchDetection](https://github.com/nitzanfarhi/SecurityPatchDetection) and can be reproduced by running:
```console
 python data_collection\create_dataset.py --all -o data_collection\data
```
Notice that this dataset doesn't include the commits' generated data as it is very big. This can be generated by running only :
```console
 python data_collection\create_dataset.py  --commits -data_collection\data
```

A repository name is symbolised by <COMPANY_NAME>_<REPOSITORY_NAME>

### License
This dataset is publicly available for researchers. If you are using our dataset,   
you should cite our related research paper which outlines the details of the dataset and its underlying principles:
 
@article{farhi2023detecting,
  title={Detecting Security Patches via Behavioral Data in Code Repositories},
  author={Farhi, Nitzan and Koenigstein, Noam and Shavitt, Yuval},
  journal={arXiv preprint arXiv:2302.02112},
  year={2023}
}
As well as mentioning [gharchive.org](gharchive.org), if you use their data as well.

Introducing paper: Detecting Security Patches via Behavioral Data in Code Repositories
DemogPairs

Although deep face recognition has achieved impressive results in recent years, there is increasing controversy regarding racial and gender bias of the models, questioning their trustworthiness and deployment into sensitive scenarios. DemogPairs is a validation set with 10.8K facial images and 58.3M identity verification pairs, distributed in demographically-balanced folds of Asian, Black and White females and males. We also propose a benchmark of experiments using DemogPairs over state-of-the-art deep face recognition models in order to analyze their cross-demographic behavior and potential demographic biases (see figure below).

Introducing paper: DemogPairs: Quantifying the Impact of Demographic Imbalance in Deep Face Recognition
NEnv
Full name: NEnv - Neural Environment Maps

Dataset of 30 4K HDR environment maps and trained models.

Introducing paper: NEnv: Neural Environment Maps for Global Illumination
Neuochemical and USV measurements of the effects of morphine withdrawal in rats

Data was acquired to investigate the effects of morphine withdrawal in rats, both in the brain neurochemistry and ultrasonic vocalisation.

To this end, a following experiment was performed. 38 adult male Sprague–Dawley rats were used in the experiment, divided into four groups: Saline-Control (10), Morphine-Control (9), Saline-Withdrawal (10) and Morphine-Withdrawal (9). Morphine in dose 10.0 mg/kg (s.c.) was administered repeatedly to the morphine groups over a 14 day period; saline solution was administered repeatedly (1.0 ml/kg) to other groups in the same manner. Administration was performed in testing cages in a group of four animals. The testing room was situated in a remote part of the laboratory, and significantly different than home cage room, both in the lighting conditions and in the arrangement of spatial cues, which were constant throughout the whole experiment. All animals were kept for 30 min in the testing box after each saline or morphine injection.

On day 14, Morph-Control group and control Saline-Control group were exposed to the context of drug administration, and the ultrasonic vocalizations were recorded for 20 min. Withdrawal groups, 30 min after last injection, were left undisturbed (in their home cages), whilst they were subjected to 2-week withdrawal period. On day 28, the rats were re-exposed to the context of morphine/saline administration and ultrasonic vocalization was recorded. Each rat was immediately decapitated after USVs recording session, and its brain tissues were frozen. The USVs were recorded individually for each rat, in a dark room with dim red light.

USV recordings were processed using a proprietary RatRec Pro software; individual calls were extracted and counted. All identified episodes were of the "50 kHz appetitive" class, no "aversive 22 kHz" alarm calls were recorded.

For each rat, tissue samples of 6 brain structures were extracted: medial prefrontal cortex (mPFC), nucleus accumbens (NAcc), striatum (Cpu), hippocampus (Hipp), amygdala and ventral tegmental area (VTA).

In each sample, levels of 15 compounds were measured: norepinephrine (NA), 3-methoxy-4-hydroxyphenylglycol (MHPG), 3,4-dihydroxyphenylacetic acid (DOPAC), dopamine (DA), 5-hydroxyindoleacetic acid (5-HIAA), homovanillic acid (HVA), serotonin (5-HT), 3-methoxytyramine (3-MT), taurine, glutamine (Gln), glycine (Gly), aspartic acid (Asp), glutamic acid (Glu), alanine (Ala) and gamma-aminobutyric acid (GABA).
MeViS
Full name: Motion expressions Video Segmentation

MeViS is a large-scale dataset for motion expressions guided video segmentation, which focuses on segmenting objects in video content based on a sentence describing the motion of the objects. The dataset contains numerous motion expressions to indicate target objects in complex environments.

Introducing paper: MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions