Omkar2118/datasets huggingface dataset:
COME15K

**COME15K** is an RGB-D saliency detection dataset which contains 15,625 image pairs with high quality polygon-/scribble-/object-/instance-/rank-level annotations.

Introducing paper: RGB-D Saliency Detection via Cascaded Mutual Information Minimization
safe-control-gym

**safe-control-gym** is an open-source benchmark suite that extends OpenAI's Gym API with (i) the ability to specify (and query) symbolic models and constraints and (ii) introduce simulated disturbances in the control inputs, measurements, and inertial properties. We provide implementations for three dynamic systems -- the cart-pole, 1D, and 2D quadrotor -- and two control tasks -- stabilization and trajectory tracking.

Introducing paper: safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics
AStitchInLanguageModels

**AStitchInLanguageModels** is a dataset for the exploration of idiomaticity in pre-trained language models.

Introducing paper: AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models
Diagnosis of COVID-19 and its clinical spectrum

This dataset contains anonymized data from patients seen at the Hospital Israelita Albert Einstein, at São Paulo, Brazil, and who had samples collected to perform the SARS-CoV-2 RT-PCR and additional laboratory tests during a visit to the hospital.

All data were anonymized following the best international practices and recommendations. All clinical data were standardized to have a mean of zero and a unit standard deviation.
WHPA
Full name: Wellhead Protection Area prediction from breakthrough curves

This dataset was created as part of the following study, which was published in the Journal of Hydrology: *A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area* [https://doi.org/10.1016/j.jhydrol.2021.126903](https://doi.org/10.1016/j.jhydrol.2021.126903).
The pre-print is available on arXiv: [https://arxiv.org/pdf/2105.05539.pdf](https://arxiv.org/pdf/2105.05539.pdf)

**Files description**
This dataset contains 4148 simulation results, i.e., 4148 pairs of predictor/target.
**bkt.npy** contains the breakthrough curves from all 6 injection wells recorded at the pumping well.
**pz.npy** contains the 2D coordinates of the backtracked particles' end points, used to delineate the WHPA.

**Introduction**
The Wellhead Protection Area (WHPA) is a zone around a pumping well where human activities are limited in order to preserve water resources, usually based on how long dangerous chemicals in the area will take to reach the pumping well (according to local regulation). The flow velocity in the subsurface around the well determines it, and it can be computed numerically using particle tracking or transport simulation, or in practice using tracer testing. A groundwater model is typically calibrated against field data before being used to calculate the WHPA. In highly populated places where land occupation is a big concern, the introduction of such zones could have a large socioeconomic impact.

**WHPA prediction**
Different tracers emerge from six data sources (injection wells) scattered across the pumping well. Their job is to inject individual tracers into the system in order to predict their transport and record their breakthrough curves (BCs) at the pumping well location. 
Numerous particles are artificially positioned around the pumping well, and their origins are traced backward in time to identify the associated WHPA.

Our predictor and target will be generated using the USGS' open-source finite-difference code Modflow. To get different sets of predictors and targets, we will run different hydrologic models with one variable parameter, namely hydraulic conductivity in metres per day. To obtain a satisfactory heterogeneity in the hydraulic conductivity fields, which will control the shape and extent of our target, the PAs, we use sequential gaussian simulation based on arbitrarily defined variograms. The pumping well is located at the 1000, 500 metres mark and is surrounded by six injection wells.

Introducing paper: A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area
IECSIL FIRE-2018 Shared Task

The dataset is taken from the First shared task on Information Extractor for Conversational Systems in Indian Languages (IECSIL) . It consists of 15,48,570 Hindi words in Devanagari script and corresponding NER labels. Each sentence end is marked by \newline" tag. Fig. 1 shows a snapshot of one sentence in the dataset. Our Dataset has nine classes, namely, Datenum, Event, Location, Name, Number, Occupation, Organization, Other, Things.

Image taken from paper:  https://www.researchgate.net/publication/349190662_Analysis_of_Contextual_and_Non-contextual_Word_Embedding_Models_for_Hindi_NER_with_Web_Application_for_Data_Collection
Spectre-v1

* Description

  A dataset of assembly functions that are vulnerable to Spectre-V1 attack.

* Motivation

Several techniques have been proposed to detect vulnerable Spectre gadgets in widely deployed commercial software. Unfortunately, detection techniques proposed so far rely on hand-written rules which fall short in covering subtle variations of known Spectre gadgets as well as demand a huge amount of time to analyze each conditional branch in software. Moreover, detection tool evaluations are based only on a handful of these gadgets, as it requires arduous effort to craft new gadgets manually.

* Potential Use Cases

 Generating assembly code.

 Evaluating new detection tools.

Introducing paper: FastSpec: Scalable Generation and Detection of Spectre Gadgets Using Neural Embeddings
MAVS
Full name: Multilingual Audio-Visual Smartphone dataset

**MAVS** is an audio-visual smartphone dataset captured in five different recent smartphones. This new dataset contains 103 subjects captured in three different sessions considering the different real-world scenarios. Three different languages are acquired in this dataset to include the problem of language dependency of the speaker recognition systems.

Introducing paper: Multilingual Audio-Visual Smartphone Dataset And Evaluation
TIAGE

TIAGE is a topic-shift aware dialog benchmark constructed utilizing human annotations on topic shifts. Based on TIAGE, three tasks can be conducted to investigate different scenarios of topic-shift modeling in dialog settings: topic-shift detection, topic-shift triggered response generation and topic-aware dialog generation.

Introducing paper: TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling
FusedChat

FusedChat is an inter-mode dialogue dataset. It contains dialogue sessions fusing task-oriented dialogues (TOD) and open-domain dialogues (ODD). Based on MultiWOZ, FusedChat appends or prepends an ODD to every existing TOD. See more details in the paper.

Introducing paper: Fusing task-oriented and open-domain dialogues in conversational agents
BUG

**BUG** is a large-scale gender bias dataset of 108K diverse real-world English sentences, sampled semiautomatically from large corpora using lexical syntactic pattern matching

Introducing paper: Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation
MuViHand

**MuViHand** is a dataset for 3D Hand Pose Estimation that consists of multi-view videos of the hand along with ground-truth 3D pose labels. The dataset includes more than 402,000 synthetic hand images available in 4,560 videos. The videos have been simultaneously captured from six different angles with complex backgrounds and random levels of dynamic lighting. The data has been captured from 10 distinct animated subjects using 12 cameras in a semi-circle topology.

Introducing paper: Multi-View Video-Based 3D Hand Pose Estimation
ItaCoLA

**ItaCoLA** is a corpus for monolingual and cross-lingual acceptability judgments which contains almost 10,000 sentences with acceptability judgments.

Introducing paper: Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus
Paint4Poem

Paint4Poem consists of 301 high-quality poem-painting pairs collected manually from an influential modern Chinese artist Feng Zikai.

Introducing paper: Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poems
MOLD
Full name: Marathi Offensive Language Dataset

**MOLD** is a Marathi dataset for offensive language identification

Introducing paper: Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi
ROF
Full name: Real World Occluded Faces

**ROF** is a dataset for occluded face recognition that contains faces with both upper face occlusion, due to sunglasses, and lower face occlusion, due to masks.

Introducing paper: On Recognizing Occluded Faces in the Wild
Mr. TYDI

**Mr. TyDi** is a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense representations. The goal of this resource is to spur research in dense retrieval techniques in non-English languages, motivated by recent observations that existing techniques for representation learning perform poorly when applied to out-of-distribution data.

Introducing paper: Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval
ChMusic

**ChMusic** is a traditional Chinese music dataset for training model and performance evaluation of musical instrument recognition. This dataset cover 11 musical instruments, consisting of Erhu, Pipa, Sanxian, Dizi, Suona, Zhuiqin, Zhongruan, Liuqin, Guzheng, Yangqin and Sheng.

Introducing paper: ChMusic: A Traditional Chinese Music Dataset for Evaluation of Instrument Recognition
TFRD
Full name: Temperature Field Reconstruction Dataset

**TFRD** is a dataset to evaluate machine learning modelling methods for theTemperature field reconstruction of heat source systems (TFR-HSS).

The password for the dataset download is "tfrd"

Introducing paper: A Machine Learning Surrogate Modeling Benchmark for Temperature Field Reconstruction of Heat-Source Systems
Fishyscapes

**Fishyscapes** is a public benchmark for uncertainty estimation in a real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates towards the detection of anomalous objects in front of the vehicle.

Introducing paper: The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
EU-ADR
Full name: EU-ADR

The **EU-ADR** corpus is a biomedical relation extraction dataset that contains 100 abstracts, with relations between drug, disorder, and targets.
CAT
Full name: Context Adjustment Training

**CAT** is a specialized dataset for co-saliency detection - one of the core tasks in the field of computer vision. This dataset is intended for both helping to assess the performance of vision algorithms and supporting research that aims to exploit large volumes of annotated data, e.g., for training deep neural networks. CAT consists of 33,500 images

Introducing paper: Free Lunch for Co-Saliency Detection: Context Adjustment
FewGLUE_64_labeled
Full name: A new version of FewGLUE with 64 training examples

### Introduction
The FewGLUE_64_labeled dataset is a new version of FewGLUE dataset. It contains a 64-sample training set, a development set (the original SuperGLUE development set), a test set, and an unlabeled set. It is constructed to facilitate the research of few-shot learning for natural language understanding tasks.

Compared with the original FewGLUE dataset, it differs in the number of labeled data examples in the training set, where the original FewGLUE has 32 training examples while FewGLUE_64_labeled has 64 labeled examples. Purposes for constructing a new version of FewGLUE dataset include:

1. To answer the questions that what is the best performance that few-shot learning can achieve and whether it is possible to further close the performance gap between few-shot learning and fully-supervised systems.

2. To explore to which degree the number of labeled training examples influences the few-shot performance.

Please refer to the [FewNLU paper](https://arxiv.org/pdf/2109.12742.pdf) as well as the [FewNLU leaderboard](fewnlu.github.io) for more details.

### Acknowledgement
Part of the FewGLUE_64_labeled dataset is based on the original 32-sample version of [FewGLUE](https://github.com/timoschick/fewglue). We collect them together in one package for the convenience of usage. We appreciate all the contributors who made their dataset public, which greatly advanced few-shot learning as well as the [FewNLU project](https://github.com/THUDM/FewNLU).

Introducing paper: FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding
DSSE-200

The DSSE-200 is a complex document layout dataset including various dataset styles. The dataset contains 200 images from pictures, PPT, brochure documents, old newspapers and scanned documents.

Introducing paper: Learning to Extract Semantic Structure From Documents Using Multimodal Fully Convolutional Neural Networks