Omkar2118/datasets huggingface dataset:
Microsoft Malware Classification Challenge

The Microsoft Malware Classification Challenge was announced in 2015 along with a publication of a huge dataset of nearly 0.5 terabytes, consisting of disassembly and bytecode of more than 20K malware samples. Apart from serving in the Kaggle competition, the dataset has become a standard benchmark for research on modeling malware behaviour. To date, the dataset has been cited in more than 50 research papers. Here we provide a high-level comparison of the publications citing the dataset. The comparison simplifies finding potential research directions in this field and future performance evaluation of the dataset.

Introducing paper: Microsoft Malware Classification Challenge
AVSD
Full name: Audio-Visual Scene-Aware Dialog

The Audio Visual Scene-Aware Dialog (**AVSD**) dataset, or DSTC7 Track 3, is a audio-visual dataset for dialogue understanding. The goal with the dataset and track was to design systems to generate responses in a dialog about a video, given the dialog history and audio-visual content of the video.

Source: [The Eighth Dialog System Technology Challenge](https://arxiv.org/abs/1911.06394)
Image Source: [http://workshop.colips.org/dstc7/papers/DSTC7_Task_3_overview_paper.pdf](http://workshop.colips.org/dstc7/papers/DSTC7_Task_3_overview_paper.pdf)

Introducing paper: Audio Visual Scene-Aware Dialog (AVSD) Challenge at DSTC7
eQASC
Full name: eQASC

This dataset contains 98k 2-hop explanations for questions in the QASC dataset, with annotations indicating if they are valid (~25k) or invalid (~73k) explanations.

This repository addresses the current lack of training data for distinguish valid multihop explanations from invalid, by providing three new datasets. The main one, eQASC, contains 98k explanation annotations for the multihop question answering dataset [QASC](https://allenai.org/data/qasc), and is the first that annotates multiple candidate explanations for each answer.

The second dataset, eQASC-perturbed, is constructed by crowd-sourcing perturbations (while preserving their validity) of a subset of explanations in QASC, to test consistency and generalization of explanation prediction models. The third dataset eOBQA is constructed by adding explanation annotations to the [OBQA dataset](https://allenai.org/data/open-book-qa) to test generalization of models trained on eQASC.

Source: [Allen Institute for AI](https://allenai.org/data/eqasc)

Introducing paper: Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering
Salinas
Full name: Salinas Scene

**Salinas Scene** is a hyperspectral dataset collected by the 224-band AVIRIS sensor over Salinas Valley, California, and is characterized by high spatial resolution (3.7-meter pixels). The area covered comprises 512 lines by 217 samples. 20 water absorption bands were discarder: [108-112], [154-167], 224. This image was available only as at-sensor radiance data. It includes vegetables, bare soils, and vineyard fields. Salinas groundtruth contains 16 classes.
Math23K
Full name: Math23K for Math Word Problem Solving

Math23K is a dataset created for math word problem solving, contains 23, 162 Chinese problems crawled from the Internet. Refer to our paper for more details:
The dataset is originally introduced in the paper __Deep Neural Solver for Math Word Problems__.  The original files are originally split into train/test split, while other research efforts (https://github.com/2003pro/Graph2Tree) perform the train/dev/test split.

Introducing paper: Deep Neural Solver for Math Word Problems
SQA
Full name: SequentialQA

The SQA dataset was created to explore the task of answering sequences of inter-related questions on HTML tables. It has 6,066 sequences with 17,553 questions in total.

Source: [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253)

Introducing paper: Search-based Neural Structured Learning for Sequential Question Answering
BC4CHEMD
Full name: BioCreative IV Chemical compound and drug name recognition

Introduced by Krallinger et al. in [The CHEMDNER corpus of chemicals and drugs and its annotation principles](https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-7-S1-S2)

**BC4CHEMD** is a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators.