Omkar2118/datasets huggingface dataset:
Arxiv HEP-TH citation graph

**Arxiv HEP-TH (high energy physics theory) citation graph** is from the e-print **arXiv** and covers all the citations within a dataset of 27,770 papers with 352,807 edges. If a paper i cites paper j, the graph contains a directed edge from i to j. If a paper cites, or is cited by, a paper outside the dataset, the graph does not contain any information about this.
The data covers papers in the period from January 1993 to April 2003 (124 months).

Source: [https://snap.stanford.edu/data/cit-HepTh.html](https://snap.stanford.edu/data/cit-HepTh.html)
ECE

The ECE dataset (Gui et al., 2016a) is collected from SINA city news and contains 2105 instances. Its document has only one emotion word and one or more emotion causes.

Introducing paper: Event-Driven Emotion Cause Extraction with Corpus Construction
LEVIR-CD

LEVIR-CD is a new large-scale remote sensing building Change Detection dataset. The introduced dataset would be a new benchmark for evaluating change detection (CD) algorithms, especially those based on deep learning.

LEVIR-CD consists of 637 very high-resolution (VHR, 0.5m/pixel) Google Earth (GE) image patch pairs with a size of 1024 × 1024 pixels. These bitemporal images with time span of 5 to 14 years have significant land-use changes, especially the construction growth. LEVIR-CD covers various types of buildings, such as villa residences, tall apartments, small garages and large warehouses. Here, we focus on building-related changes, including the building growth (the change from soil/grass/hardened ground or building under construction to new build-up regions) and the building decline. These bitemporal images are annotated by remote sensing image interpretation experts using binary labels (1 for change and 0 for unchanged). Each sample in our dataset is annotated by one annotator and then double-checked by another to produce high-quality annotations. The fully annotated LEVIR-CD contains a total of 31,333 individual change-building instances.

Introducing paper: A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection
EmoryNLP

EmoryNLP comprises 97 episodes, 897 scenes, and 12,606 utterances, where each utterance is annotated with one of the seven emotions borrowed from the six primary emotions in the Willcox (1982)’s feeling wheel, sad, mad, scared, powerful, peaceful, joyful, and a default emotion of neutral.

Introducing paper: Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks
4D Light Field Dataset

4D Light Field Dataset is a light field benchmark consisting of 24 carefully designed synthetic, densely sampled 4D
light fields with highly accurate disparity ground truth.
WHAMR!
Full name: WHAM! with synthetic reverberated sources

**WHAMR!** is a dataset for noisy and reverberant speech separation. It extends [WHAM!](/dataset/wham) by introducing synthetic reverberation to the
speech sources in addition to the existing noise. Room impulse responses were generated and convolved using `pyroomacoustics`. Reverberation times were chosen to approximate domestic and classroom environments (expected to be similar to the restaurants and coffee shops where the WHAM! noise was collected), and
further classified as high, medium, and low reverberation based on a
qualitative assessment of the mixture’s noise recording.

Introducing paper: WHAMR!: Noisy and Reverberant Single-Channel Speech Separation