Omkar2118/datasets huggingface dataset:
DAGW
Full name: Danish Gigaword

It’s hard to develop good tools for processing Danish with computers when no large and wide-coverage dataset of Danish text is readily available. To address this, the Danish Gigaword Project (DAGW) maintains a corpus for Danish with over a billion words. The general goals are to create a dataset that is:

* representative;
* accessible;
* a suitable common starting point for Danish NLP models.

Introducing paper: The Danish Gigaword Corpus
NASA Crew Exploration Vehicle (CEV) Software Event Log

Extensible Event Stream (XES) software event log obtained through instrumenting the NASA CEV class using the tool available at {https://svn.win.tue.nl/repos/prom/XPort/}. This event log contains method-call level events describing a single run of an exhaustive unit test suite for the Crew Exploration Vehicle (CEV) example available and documented at {http://babelfish.arc.nasa.gov/trac/jpf/wiki/projects/jpf-statechart} (trac) {http://babelfish.arc.nasa.gov/hg/jpf/jpf-statechart} (mercurial repository). Note that the life-cycle information in this log corresponds to method call (start) and return (complete), and captures a method-call hierarchy. We attached a slightly preprocessed variant of this event log, where the execution of each unit test method is represented as a separate trace.
CamGes
Full name: Cambridge Hand Gesture Dataset

The size of the data set is about 1GB.
The data set consists of 900 image sequences of 9 gesture classes, which are defined by 3 primitive hand shapes and 3 primitive motions. Therefore, the target task for this data set is to classify different shapes as well as different motions at a time.
AWARE
Full name: AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation

The peer-reviewed paper of AWARE dataset is published in ASEW 2021, and can be accessed through: http://doi.org/10.1109/ASEW52652.2021.00049. Kindly cite this paper when using AWARE dataset.

Aspect-Based Sentiment Analysis (ABSA) aims to identify the opinion (sentiment) with respect to a specific aspect. Since there is a lack of smartphone apps reviews dataset that is annotated to support the ABSA task, we present AWARE: ABSA Warehouse of Apps REviews.

AWARE contains apps reviews from three different domains (Productivity, Social Networking, and Games), as each domain has its distinct functionalities and audience. Each sentence is annotated with three labels, as follows: 

Aspect Term: a term that exists in the sentence and describes an aspect of the app that is expressed by the sentiment. A term value of “N/A” means that the term is not explicitly mentioned in the sentence.
Aspect Category: one of the pre-defined set of domain-specific categories that represent an aspect of the app (e.g., security, usability, etc.).
Sentiment: positive or negative.
Note: games domain does not contain aspect terms.

We provide a comprehensive dataset of 11323 sentences from the three domains, where each sentence is additionally annotated with a Boolean value indicating whether the sentence expresses a positive/negative opinion. In addition, we provide three separate datasets, one for each domain, containing only sentences that express opinions. The file named “AWARE_metadata.csv” contains a description of the dataset’s columns.

How AWARE can be used?

We designed AWARE such that it can be used to serve various tasks. The tasks can be, but are not limited to:

Sentiment Analysis.
Aspect Term Extraction.
Aspect Category Classification.
Aspect Sentiment Analysis.
Explicit/Implicit Aspect Term Classification.
Opinion/Not-Opinion Classification.
Furthermore, researchers can experiment with and investigate the effects of different domains on users' feedback.

Source: [AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation](https://doi.org/10.1109/ASEW52652.2021.00049)

Introducing paper: AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation