Omkar2118/datasets huggingface dataset:
NNE

**NNE** is a dataset for Nested Named Entity Recognition in English Newswire

Introducing paper: NNE: A Dataset for Nested Named Entity Recognition in English Newswire
CityNet

**CityNet** is a multi-modal urban dataset containing data from 7 cities, each of which coming from 3 data sources, which can be used for urban computing and smart city research. The dataset consists of 3 types of raw data (city layout, taxi, meteorology) collected from 7 cities.

Introducing paper: CityNet: A Comprehensive Multi-Modal Urban Dataset for Advanced Research in Urban Computing
pd4ml
Full name: Physics Data for Machine Learning

**pd4ml** is a collection of datasets from fundamental physics research -- including particle physics, astroparticle physics, and hadron- and nuclear physics -- for supervised machine learning studies. These datasets, containing hadronic top quarks, cosmic-ray induced air showers, phase transitions in hadronic matter, and generator-level histories, are made public to simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics.

It currently consists on 5 datasets:

- Top Tagging Landscape (Classification) 
    - Train/val/test: 1.2M/400k/400k 
    - Structure: Four vectors 
    - Dimension: 200 particles, 4 features/particle
- Smart Backgrounds (Classification)   
    - Train/val/test: 157k/39k/84k
    - Structure: Decay Graph
    - Dimension: 100 particles, 9 features/particle
- Spinodal or Not (Classification)   
    - Train/val/test: 16.3k/4k/8.7k
    - Structure: 2D Histogram
    - Dimension: 20x20 histogram of pion spectra
- EoS (Classification)  
    - Train/val/test: 121k/25k/54k
    - Structure: 2D Histogram
    - Dimension: 24x24 histogram of pion spectra
- Air Showers (Regression)   
    - Train/val/test: 56k/30k/14k
    - Structure: 81 1D Traces
    - Dimension: 81 stations, 80 signal bins + timing

Introducing paper: Shared Data and Algorithms for Deep Learning in Fundamental Physics
Toloka WaterMeters

This datase, contains 1244 images of hot and cold water meters as well as their readings and coordinates of the displays showing those readings. Each image contains exactly one water meter. The archive also includes the pictures of the results of segmentation with the masks and collages. Toloka was used for photo capturing, segmentation, and recognizing the readings.
RuADReCT
Full name: The Russian Adverse Drug Reaction Corpus  of Tweets

Created as part of the Social Media Mining for Health Applications (#SMM4H '20) shared tasks, this dataset consists of 9515 tweets describing health issues. Each tweet is labeled for whether it contains information about an adverse side effect that occurred when taking a drug. The dataset was a joint effort with the UPenn HLP Center and the Chemoinformatics and Molecular Modeling Research Laboratory at Kazan Federal University.
LRWC
Full name: Lexical Relations from the Wisdom of the Crowd

This dataset contains the opinions of Russian native speakers about the relationship between a generic term (hypernym) and a specific instance of it (hyponym). Assembled by Dmitry Ustalov in 2017. A set of 300 most frequent nouns was extracted from the Russian National Corpus. Then each method or resource (including RuThes and RuWordNet) produced at most five hypernyms, if possible. This resulted in 10,600 unique non-empty subsumption pairs, which were annotated by seven different performers whose mother tongue is Russian and were at least 20 years old as of February 1, 2017. As a result, 4,576 out of 10,600 pairs were annotated as positive while the remaining 6,024 were annotated as negative. Interestingly, the performers were more confident in the negative answers than in the positive ones.
Human-Annotated Sense-Disambiguated Word Contexts for Russian

This dataset contains human-annotated sense identifiers for 2562 contexts of 20 words used in the RUSSE'2018 shared task on Word Sense Induction and Disambiguation for the Russian language. Assembled by Dmitry Ustalov in 2017. In particular, 80 pre-annotated contexts were used for training the human annotators, and 2562 contexts were annotated by humans such that each context was annotated by 9 different annotators. After the annotation, every context was additionally inspected (“curated”) by the organizers of the shared task.
ScanBank

ScanBank is a benchmark dataset for figure extraction from scanned electronic theses and dissertations containing 10 thousand scanned page images, manually labeled by humans as to the presence of the 3.3 thousand figures or tables found therein.

Introducing paper: ScanBank: A Benchmark Dataset for Figure Extraction from Scanned Electronic Theses and Dissertations
Delaunay triangulation

Delaunay triangulation dataset for 5, 10, 15, 20 points.

Both random and sorted datasets are included.

If you have any trouble to use this dataset, contact hunnino10@gmail.com

Introducing paper: Learning Geometric Combinatorial Optimization Problems using Self-attention and Domain Knowledge
ObMan-Ego

The ObMan-Ego is a large-scale synthetic hand dataset with egocentric scenes in which the simulated hands are provided by ObMan. The dataset is used for a hand segmentation task and its sim-to-real adaptation benchmark. Training, validation, and testing sets contain 150, 000, 6, 500, and 6, 500 images, respectively.

Introducing paper: Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation
CPTC-2018

Intrusion alert dataset captured through the Collegiate Penetration Testing Competition (CPTC) 2018. Contains alerts from 6 student teams. For details, see "A Cybersecurity Dataset Derived from the National Collegiate Penetration Testing Competition" by Nathan Munaiah et al.
SURREALvols

Added information about the subject's body height and volumes of 14 individual body parts.

Introducing paper: VolNet: Estimating Human Body Part Volumes from a Single RGB Image
Geography of Open Source Software

This dataset reports counts of active GitHub contributors (activity: 2019/2020) geolocated in early 2021. Counts are aggregated at the country level and at various regional scales. Besides countries, we report data on the EU NUTS2 level, for Brazilian, Russian, Chinese, Japanese, Indian, and US-American subnational geographies. We used a pipeline approach, attempting to infer location first from GitHub profile of a developer, then from linked Twitter accounts, then from email suffixes (country level only). Our data reports the count of developers identified by each stage of the pipeline, in case for instance one prefers to only use the GitHub account information.

Introducing paper: The Geography of Open Source Software: Evidence from GitHub
PDE solutions

In this folder, you will find solutions of the following partial differential equations:
- Burgers
- Kortweg-de-Vries
-Newell-Whitehead
- Kuramoto-Sivashinsky

You will find more info about how these were generated in the supplementary material of the paper: https://arxiv.org/abs/2106.11936
PDEs
Full name: Some PDE solutions

In this dataset, you will find solutions of the following partial differential equations: - Burgers - Kortweg-de-Vries -Newell-Whitehead - Kuramoto-Sivashinsky
You will find more info about how these were generated in the supplementary material of the paper: https://arxiv.org/abs/2106.11936

Introducing paper: Model discovery in the sparse sampling regime