Omkar2118/datasets huggingface dataset:
KanHope
Full name: Kannada Hope speech dataset

KanHope is a code mixed hope speech dataset for equality, diversity, and inclusion in Kannada, an under-resourced Dravidian language. The dataset consists of 6,176 user-generated comments in code mixed Kannada crawled from YouTube and manually labelled as bearing hope speech or not-hope speech.

Introducing paper: Hope Speech detection in under-resourced Kannada language
BEOID
Full name: Bristol Egocentric Object Interactions Dataset

The BEOID dataset includes object interactions ranging from preparing a coffee to operating a weight lifting machine and opening a door. The dataset is recorded at six locations: kitchen, workspace, laser printer, corridor with a locked door, cardiac gym, and weight-lifting machine. For the first four locations, sequences from five different operators were recorded (two sequences per operator), and from three operators for the last two locations (three sequences per operator). The wearable gaze tracker hardware (ASL Mobile Eye XG) was used to record the dataset. Synchronized wide-lens video data with calibrated 2D gaze fixations are available. Moreover, we release 3D information using a pre-built cloud point map and PTAM tracking. Three-dimensional information of the image and the gaze fixations are included.
VR traffic traces

The dataset contains traffic traces collected from 3 different VR applications.
Researchers can use this dataset to replicate the behavior of real VR traffic directly in their studies, e.g., their simulations.
Further information can be found in the repository.

Introducing paper: An Open Framework for Analyzing and Modeling XR Network Traffic
The Boston Housing Dataset

This dataset contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It was obtained from the StatLib archive (http://lib.stat.cmu.edu/datasets/boston), and has been used extensively throughout the literature to benchmark algorithms. However, these comparisons were primarily done outside of Delve and are thus somewhat suspect. The dataset is small in size with only 506 cases.
FakeAVCeleb

FakeAVCeleb is a novel Audio-Video Deepfake dataset that not only contains deepfake videos but respective synthesized cloned audios as well. 

Image source: [https://arxiv.org/pdf/2108.05080v1.pdf](https://arxiv.org/pdf/2108.05080v1.pdf)

Introducing paper: FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset
FoodLogoDet-1500

FoodLogoDet-1500 is a new large-scale publicly available food logo dataset, which has 1,500 categories, about 100,000 images and about 150,000 manually annotated food logo objects.

Introducing paper: FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network
CirCor DigiScope

CirCor DigiScope is currently the largest pediatric heart sound dataset. A total of 5282 recordings have been collected from the four main auscultation locations of 1568 patients, in the process 215780 heart sounds have been manually annotated. Each cardiac murmur has been manually annotated by an expert annotator according to its timing, shape, pitch, grading and quality.

Source: [https://arxiv.org/pdf/2108.00813v1.pdf](https://arxiv.org/pdf/2108.00813v1.pdf)

Introducing paper: The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification
Two-probe macaque monkey auditory LFP

Dataset accompanying paper Klein, N., Siegle, J.H., Teichert, T., Kass, R.E. (2021) "Cross-population coupling of neural activity based on Gaussian process current source densities". 

Auditory local field potential (LFP) recordings and evoked multi-unit activity (MUA) from two 24-electrode linear probes (V-Probes from Plexon) inserted in primary auditory cortex of a macaque monkey. The probes were arranged parallel to the iso-frequency bands in primary auditory cortex (A1), and had similar tonal response fields with preferred frequencies close to 1000 Hz. The first probe (which we call the lateral probe) was located centrally in A1, while the second probe (which we call the medial probe) was located more medially and closer to the boundary of A1 with the medio-lateral belt. The medial probe had lower response threshold, shorter MUA latencies, and overall stronger current sinks and sources than the lateral probe. The spacing between electrodes on each probe was 100 microns so that the probe spanned 2,300 microns. The treatment of the animals was in accordance with the guidelines set by the U.S. Department of Health and Human Services (NIH) for the care and use of laboratory animals, and all methods were approved by the Institutional Animal Care and Use Committee at the University of Pittsburgh.

Introducing paper: Cross-population coupling of neural activity based on Gaussian process current source densities
Neuropixels single-mouse LFP data

Single-mouse Neuropixels recordings (spikes and LFPs) in NWB format. Dataset used in the paper "Cross-population coupling of neural activity based on Gaussian process current source densities" by Klein, N., Siegle, J.H., Teichert, T., and Kass, R.E. (preprint: https://arxiv.org/abs/2104.10070). 

The data is part of the Allen Brain Observatory Neuropixels dataset (©2019 Allen Institute for Brain Science, available from https://portal.brain-map.org/explore/circuits/visual-coding-neuropixels). Six Neuropixels probes were simultaneously inserted through visual cortex, hippocampus, thalamus, and midbrain. On each probe, LFP data was recorded from up to 374 electrode locations in a checkerboard layout spanning two spatial dimensions: four columns spaced 16 microns apart, with 20 micron spacing between rows. LFP data was acquired at 2500 Hz after applying a 1000 Hz low-pass filter. Boundaries between regions were manually identified based on decreases in unit density as well as physiological signatures (such as elevated theta-band activity in the hippocampus). LFP electrodes without region labels were not included in the analysis. Spike trains and downsampled LFP data for this mouse (subject ID: 730760270; session ID: 755434585) can be accessed via the AllenSDK or via the DANDI Archive https://dandiarchive.org/dandiset/000021/draft. The original LFP data used for this analysis is available as part of the Allen Brain Observatory AWS Public Data Set https://registry.opendata.aws/allen-brain-observatory/.

Introducing paper: Cross-population coupling of neural activity based on Gaussian process current source densities
SPACE

**SPACE** is a simulator for physical Interactions and causal learning in 3D environments. The SPACE simulator is used to generate the SPACE dataset, a synthetic video dataset in a 3D environment, to systematically evaluate physics-based models on a range of physical causal reasoning tasks. Inspired by daily object interactions, the SPACE dataset comprises videos depicting three types of physical events: containment, stability and contact.

Introducing paper: SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments
HatemojiCheck

**HatemojiCheck** is a test suite for detecting emoji-based hate of 3,930 test cases covering seven functionalities of emoji-based hate and six identities.

Introducing paper: Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-based Hate
WikiScenes

The **WikiScenes** dataset consists of paired images and language descriptions capturing world landmarks and cultural sites, with associated 3D models and camera poses. WikiScenes is derived from the massive public catalog of freely-licensed crowdsourced data in the Wikimedia Commons project, which contains a large variety of images with captions and other metadata. 

The dataset contains two forms of textual descriptions for each image: (1) Captions associated with images, describing the image using free-form language, and (2) The WikiCategory hierarchy obtained according to the hierarchy of WikiCategories associated with each image (see the examples in the image below). Overall, WikiScenes contains approximately 63K images with textual descriptions.

Introducing paper: Towers of Babel: Combining Images, Language, and 3D Geometry for Learning Multimodal Vision
ACS PUMS

**ACS PUMS** stands for American Community Survey (ACS) Public Use Microdata Sample (PUMS) and has been used to construct several tabular datasets for studying fairness in machine learning:

- ACSIncome: to predict whether an individual’s income is above $50,000.

- ACSPublicCoverage: to predict whether an individual is covered by public health insurance.

- ACSMobility: to predict whether an individual had the same residential address one year ago.

- ACSEmployment: to predict whether an individual is employed.

- ACSTravelTime: predict whether an individual has a commute to work that is longer than 20 minutes.

Introducing paper: Retiring Adult: New Datasets for Fair Machine Learning
Computer Vision Values Dataset

This is a corpus of about 500 computer vision datasets, from which the authors sampled 114 dataset publications across different vision tasks and coded for themes through both structured and qualitative content analysis. This work most closely pairs with the following research question: How do dataset developers in CV and NLP research, describe and motivate the decisions that go into their creation?

Introducing paper: Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development
PADv2
Full name: Purpose-driven Affordance Dataset v2

With complex scenes and rich annotations, the **PADv2** dataset can be used as a test bed to benchmark affordance detection methods and may also facilitate downstream vision tasks, such as scene understanding, action recognition, and robot manipulation. 

It contains 30k diverse images covering 39 affordance categories as well as 103 object categories from different scenes.

Introducing paper: One-Shot Object Affordance Detection in the Wild
CDR
Full name: BioCreative V CDR Task Corpus

The BioCreative V CDR task corpus is manually annotated for chemicals, diseases and chemical-induced disease (CID) relations. It contains the titles and abstracts of 1500 PubMed articles and is split into equally sized train, validation and test sets. It is common to first tune a model on the validation set and then train on the combination of the train and validation sets before evaluating on the test set. It is also common to filter negative relations with disease entities that are hypernyms of a corresponding true relations disease entity within the same abstract (see Appendix C of [this paper](https://api.semanticscholar.org/CorpusID:247939302) for details).
Screen2Words

**Screen2Words** is a large-scale screen summarization dataset annotated by human workers. The dataset contains more than 112k language summarization across 22k unique UI screens. This dataset can be used for Mobile User Interface Summarization, which is a task where a model generates succinct language descriptions of mobile screens for conveying important contents and functionalities of the screen.

Introducing paper: Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning
Stereo Waterdrop

**Steredo Waterdrop** is a real-world dataset for research on stereo waterdrop removal. The dataset contains 837 stereo image pairs captured from 129 indoor and outdoor scenes with various waterdrops, disparities, and illumination conditions. We use the ZED 2 stereo camera for data collection.

Introducing paper: Stereo Waterdrop Removal with Row-wise Dilated Attention
KTH-TIPS2

The KTH-TIPS (Textures under varying Illumination, Pose and Scale) image database was created to extend the CUReT database in two directions, by providing variations in scale as well as pose and illumination, and by imaging other samples of a subset of its materials in different settings.

The KTH-TIPS2 databases took this a step further by imaging 4 different samples of 11 materials, each under varying pose, illumination and scale.

For more information about the databases, please view the documentation. The databases may be downloaded here.