Omkar2118/datasets huggingface dataset:
Kinect-WSJ

Kinect-WSJ is a multichannel, multispeaker, reverberated, noisy dataset which extends the [WSJ0-2mix](/dataset/wsj0-2mix-1) singlechannel, non-reverberated, noiseless dataset to the strong reverberation and noise conditions and the Kinect-like microphone array geometry used in [CHiME-5](/dataset/chime-5).

Introducing paper: Analyzing the impact of speaker localization errors on speech separation for automatic speech recognition
BiasBios
Full name: Bias in Bios

The purpose of this dataset was to study gender bias in occupations. Online biographies, written in English, were collected to find the names, pronouns, and occupations. Twenty-eight most frequent occupations were identified based on their appearances.  The resulting dataset consists of 397,340 biographies spanning twenty-eight different occupations. Of these occupations, the professor is the most frequent, with 118,400 biographies, while the rapper is the least frequent, with 1,406 biographies. 
Important information about the biographies:
1. The longest biography is 194 tokens, while the shortest is eighteen; the median biography length is seventy-two tokens.
2. It should be noted that the demographics of online biographies’ subjects differ from those of the overall workforce and that this dataset does not contain all biographies on the Internet.

Introducing paper: Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
BG-20k
Full name: Background Dataset - 20k

BG-20k contains 20,000 high-resolution background images excluded salient objects, which can be used to help generate high quality synthetic data.

Introducing paper: Bridging Composite and Real: Towards End-to-end Deep Image Matting
SPoC
Full name: Pseudocode-to-Code

Pseudocode-to-Code (SPoC) is a program synthesis dataset, containing 18,356 programs with human-authored pseudocode and test cases.

Image source: [https://sumith1896.github.io/spoc/](https://sumith1896.github.io/spoc/)

Introducing paper: SPoC: Search-based Pseudocode to Code
OntoNotes 5.0

**OntoNotes 5.0** is a large corpus comprising various genres of text (news, conversational telephone speech, weblogs, usenet newsgroups, broadcast, talk shows) in three languages (English, Chinese, and Arabic) with structural information (syntax and predicate argument structure) and shallow semantics (word sense linked to an ontology and coreference).

OntoNotes Release 5.0 contains the content of earlier releases - and adds source data from and/or additional annotations for, newswire, broadcast news, broadcast conversation, telephone conversation and web data in English and Chinese and newswire data in Arabic.

Source: [https://catalog.ldc.upenn.edu/LDC2013T19](https://catalog.ldc.upenn.edu/LDC2013T19)

Introducing paper: Towards Robust Linguistic Analysis using OntoNotes
MECCANO

The MECCANO dataset is the first dataset of egocentric videos to study human-object interactions in industrial-like settings.
The MECCANO dataset has been acquired in an industrial-like scenario in which subjects built a toy model of a motorbike. We considered 20 object classes which include the 16 classes categorizing the 49 components, the two tools (screwdriver and wrench), the instructions booklet and a partial_model class.

Additional details related to the MECCANO:

20 different subjects in 2 countries (IT, U.K.)
Video Acquisition: 1920x1080 at 12.00 fps
11 training videos and 9 validation/test videos
8857 video segments temporally annotated indicating the verbs which describe the actions performed
64349 active objects annotated with bounding boxes
12 verb classes, 20 objects classes and 61 action classes

Introducing paper: The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain
ecoset

**Ecoset**, an ecologically motivated image dataset, is a large-scale image dataset designed for human visual neuroscience, which consists of over 1.5 million images from 565 basic-level categories. Category selection was based on English nouns that most frequently occur in spoken language (estimated on a set of 51 million words obtained from American television and film subtitles) and concreteness ratings from human observers. Ecoset consists of basic-level categories (including human categories man, woman, and child) that describe physical things in the world (rather than abstract concepts) that are important to humans.

Introducing paper: An ecologically motivated image dataset for deep learning yields better models of human vision
AW-OIE
Full name: All Words OpenIE

**All Words Open IE** (**AW-OIE**) is an open information extraction dataset derived from [Question-Answer Meaning Representation (QAMR)](/dataset/qamr) dataset.

Introducing paper: Supervised Open Information Extraction
MSU Deinterlacer Benchmark

This is a dataset for video deinterlacing problem. The dataset contains 28 video sequences. Each sequence's length is 60 frames. Resolution of all video sequences is 1920x1080. TFF interlacing was used to get interlaced data from GT.
StrategyQA

**StrategyQA** is a question answering benchmark where the required reasoning steps are implicit in the question, and should be inferred using a strategy.
It includes 2,780 examples, each consisting of a strategy question, its decomposition, and evidence paragraphs.
Questions in StrategyQA are short, topic-diverse, and cover a wide range of strategies.

Introducing paper: Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
ICB
Full name: Image Compression Benchmark

A carefully chosen set of high-resolution high-precision natural images suited for compression algorithm evaluation.

The images historically used for compression research (lena, barbra, pepper etc...) have outlived their useful life and its about time they become a part of history only. They are too small, come from data sources too old and are available in only 8-bit precision.

These high-resolution high-precision images have been carefully selected to aid in image compression research and algorithm evaluation. These are photographic images chosen to come from a wide variety of sources and each one picked to stress different aspects of algorithms. Images are available in 8-bit, 16-bit and 16-bit linear variations, RGB and gray.

These Images are available without any prohibitive copyright restrictions.

These images are (c) there respective owners. You are granted full redistribution and publication rights on these images provided:

1. The origin of the pictures must not be misrepresented; you must not claim that you took the original pictures. If you use, publish or redistribute them, an acknowledgment would be appreciated but is not required.
2. Altered versions must be plainly marked as such, and must not be misinterpreted as being the originals.
3. No payment is required for distribution of this material, it must be available freely under the conditions stated here. That is, it is prohibited to sell the material.
4. This notice may not be removed or altered from any distribution.

*For grayscale evaluation, use the Grayscale 8 bit dataset, for color evaluation, use the Color 8 bit dataset.*

```
@online{icb,
  author = {Rawzor},
  title  = {Image Compression Benchmark},
  url    = {http://imagecompression.info/}
}
```
LIVE (Public-Domain Subjective Image Quality Database)

The **LIVE Public-Domain Subjective Image Quality Database** is a resource developed by the Laboratory for Image and Video Engineering at the University of Texas at Austin. It contains a set of images and videos whose quality has been ranked by human subjects. This database is used in Quality Assessment (QA) research, which aims to make quality predictions that align with the subjective opinions of human observers.

The database was created through an extensive experiment conducted in collaboration with the Department of Psychology at the University of Texas at Austin. The experiment involved obtaining scores from human subjects for many images distorted with different distortion types. The QA algorithm may be trained on part of this data set, and tested on the rest.

The database is available to the research community free of charge. If you use these images in your research, the creators kindly ask that you reference their website and their papers. There are two releases of the database. Release 2 includes more distortion types and more subjects than Release 1. The distortions include JPEG-compressed images, JPEG2000-compressed images, Gaussian blur, and white noise.
Classic5

Five classic grayscale images commonly used for image quality assessment tasks.

Introducing paper: Pointwise shape-adaptive DCT for high-quality deblocking of compressed color images