Omkar2118/datasets huggingface dataset:
PETRAW
Full name: PEg TRAnsfer Workflow recognition by different modalities

PETRAW data set was composed of 150 sequences of peg transfer training sessions. The objective of the peg transfer session is to transfer 6 blocks from the left to the right and back. Each block must be extracted from a peg with one hand, transferred to the other hand, and inserted in a peg at the other side of the board.
All cases were acquired by a non-medical expert on the LTSI Laboratory from the University of Rennes. The data set was divided into a training data set composed of 90 cases and a test data set composed of 60 cases. A case was composed of kinematic data, a video, semantic segmentation of each frame, and workflow annotation.

Introducing paper: PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition?
ProteinKG25

ProteinKG25 is a large-scale KG dataset with aligned descriptions and protein sequences respectively to GO terms and proteins entities. ProteinKG25 contains 4,990,097 triplets (4,879,951 Protein-GO triplets and 110,146 GO-GO triplets), 612,483 entities (565,254 proteins and 47,229 GO terms) and 31 relations.

Introducing paper: OntoProtein: Protein Pretraining With Gene Ontology Embedding
SHERLOCK

**SHERLOCK** is a corpus of 363K commonsense inferences grounded in 103K images. Annotators highlight localized clues (color bubbles) and draw plausible abductive inferences about them (speech bubbles). It can be used for testing machine capacity for abductive reasoning beyond literal image contents.

Introducing paper: The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning
Deep Soccer Captioning

**Deep Soccer Captioning** is a dataset consists of 22k caption-clip pairs and three visual features (images, optical flow, inpainting) for 500 hours of SoccerNet videos.

Introducing paper: Deep soccer captioning with transformer: dataset, semantics-related losses, and multi-level evaluation
ASC (TIL, 19 tasks)
Full name: Task Incremental Aspect Sentiment Classification

A set of 19 ASC datasets (reviews of 19 products) producing a sequence of 19 tasks. Each dataset represents a task. The datasets are from 4 sources: (1) HL5Domains (Hu and Liu, 2004) with reviews of 5 products; (2) Liu3Domains (Liu et al., 2015) with reviews of 3 products; (3) Ding9Domains (Ding et al., 2008) with reviews of 9 products; and (4) SemEval14 with reviews of 2 products - SemEval 2014 Task 4 for laptop and restaurant. For (1), (2) and (3), we split about 10% of the original data as the validate data, another about 10% of the original data as the testing data. For (4), We use 150 examples from the training set for validation. To be consistent with existing research(Tang et al., 2016), examples belonging to the conflicting polarity (both positive and negative sentiments are expressed about an aspect term) are not used. Statistics and details of the 19 datasets are given on Page https://github.com/ZixuanKe/PyContinual.

Introducing paper: Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks
Dynamic OLAT Dataset
Full name: ShanghaiTech MARS Dynamic OLAT Dataset

To provide ground truth supervision for video consistency modeling, we build up a high-quality dynamic OLAT dataset.
Our capture system consists of a light stage setup with 114 LED light sources and Phantom Flex4K-GS camera (global shutter, stationary 4K ultra-high-speed camera at 1000 fps), resulting in dynamic OLAT imageset recording at 25 fps using the overlapping method.
Our dynamic OLAT dataset provides sufficient semantic, temporal and lighting consistency supervision to train our neural video portrait relighting scheme, which can generalize to in-the-wild scenarios.

Introducing paper: Neural Video Portrait Relighting in Real-time via Consistency Modeling
USR-TopicalChat

This dataset was collected with the goal of assessing dialog evaluation metrics. In the paper, USR: An Unsupervised and Reference Free Evaluation Metric for Dialog (Mehri and Eskenazi, 2020), the authors collect this data to measure the quality of several existing word-overlap and embedding-based metrics, as well as their newly proposed USR metric.

Introducing paper: USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation
USR-PersonaChat

This dataset was collected with the goal of assessing dialog evaluation metrics. In the paper, USR: An Unsupervised and Reference Free Evaluation Metric for Dialog (Mehri and Eskenazi, 2020), the authors collect this data to measure the quality of several existing word-overlap and embedding-based metrics, as well as their newly proposed USR metric.

Introducing paper: USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation
SemEval-2020 Task-8

A multimodal dataset for sentiment analysis on internet memes.

Introducing paper: SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!
HelloWorld
Full name: HelloWorld

HelloWorld is a dataset of kinesthetic demonstrations collected using a Franka Emika Panda robot. During the data collection, the robot was made to write the lower case letters $h, e, l, o, w, r, d$ one at a time on a horizontal surface and the $x$ and $y$ coordinates of the end-effector were recorded. Multiple demonstrations were collected for each letter. These demonstrations can be used for kinesthetic teaching. See further details [here](https://github.com/sayantanauddy/clfd).

Introducing paper: Continual Learning from Demonstration of Robotics Skills
wildFireClimateChangeTweets

Here I provided the datasets I used for this analysis. It includes the tweets I streamed using the Tweepy package on Python during the peach of the wildfire season in late summer/early fall of 2020.

The files include:
1- Public: 
39 items of CSV files for the dates (day by day) starting 09/06/2020 to 09/23/2020 
  1 file combined for all the dates (+185k of tweets with the desired keywords) 
2- Government
  Locals: 
  CA: Counties of Napa, Mendocino, Santa Clara, Sonoma, and Fresno 
  OR: City of Salem, and Counties of Lane, Clackamas, Jackson, and Multnomah 
  CO: County of Larimer and Boulder Cities of Boulder, Grand Junction, Glenwood Springs, and Cortex
  15 accounts in total 
 State-level: 
  Governors of three states, California, Colorado, and Oregon 
  Congress representatives: 5 for each state 
  18 accounts in total 
Federal: 6 senators, California, Colorado, and Oregon

 Information Provided for each tweet:
 Date/Time 
 UserID 
 UserName 
 Tweet's text 
 Number of retweets 
 Number of likes
Provide:

Introducing paper: Wildfires and Climate Change: What does social media tell us on linkages and public understanding? (Preliminary White Paper, February 11, 2022)
DSC (10 tasks)
Full name: Task Incremental Document Sentiment Classification

A set of 10 DSC datasets (reviews of 10 products) to produce sequences of tasks. The products are Sports, Toys, Tools, Video, Pet, Musical, Movies, Garden, Offices, and Kindle. 2500 positive and 2500 negative training reviews per task . The validation reviews are with 250 positive and 250 negative and the test reviews are with 250
positive and 250 negative reviews. The detailed statistic on page https://github.com/ZixuanKe/PyContinual

Introducing paper: Continual Learning with Knowledge Transfer for Sentiment Classification
Weibo-Douban
Full name: WD

This dataset is used for user identity linkage across two online social networks in Chinese. It contains two popular Chinese social platforms: Sina Weibo\footnote{https://weibo.com} and Douban\footnote{https://www.douban.com}. 

Details:
* 9,714 users and 117,218 relations in Weibo; 9,526 users and 120,245 relations in Douban; 1,397 pair of matched users.
* Approximate power-law degree distribution and high aggregation coefficient.
* Multiple text attributes available, including username, geographical location and recent (text) posts of the users.
* Construction time: April 2020.

Introducing paper: MAUIL: Multi-level Attribute Embedding for Semi-supervised User Identity Linkage
Ransomware PCAP repository

This is a repository of PCAP files obtained by executing ransomware binaries and capturing the network traffic created when encrypting a set of files shared from an SMB server. There are 94 samples from 32 different ransomware families downloaded from malware-traffic-analysis and hybrid-analysis. There is a link to an info page for each sample, offering some information about the sample and about the scenario where it ran ('More info' column in the table).
You can download 10% of the packets from each traffic trace for free. If you find it useful and you want to download the whole samples, we ask for your e-mail and institution name, in order to keep a record of hoy many people are interested in these files. This helps us to keep this repository up and include more samples (as it proves that it is interesting for the community). We do not send you any kind of spam. We will only send you a link to download the full pcap files. In order to refer to this repository please include the link in your paper, cite the repository shared in IEEE dataPort (here) and/or cite this paper in which the repository is explained in more detail

We also offer a text file containing a description of all the input/output operations that appear in the SMB traffic. We had to create our own software in order to extract this information from large pcap files.

Introducing paper: Crypto-ransomware detection using machine learning models in file-sharing network scenario with encrypted traffic
20Newsgroup (10 tasks)

This dataset has 20 classes and each class has about 1000 documents. The data split for train/validation/test is 1600/200/200. We created 10 tasks, 2
classes per task.  Since this is topic-based text classification data, the classes are very different and have little shared knowledge. As mentioned above, this application (and dataset) is mainly used to show a CL model's ability to overcome forgetting. Detailed statistics please on page https://github.com/ZixuanKe/PyContinual

Introducing paper: Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning
NMED-T
Full name: Naturalistic Music EEG Dataset - Tempo

Losorelli, Steven, Nguyen, Duc T., Dmochowski, Jacek P., and Kaneshiro, Blair

This dataset contains cortical (EEG) and behavioral data collected during natural music listening. Dense-array EEG was recorded from 20 adult participants who each heard a set of 10 full-length songs with electronically produced beats at various tempos. In a separate subsequent listen, each participant tapped to the beat of a 35-second excerpt from each song. Participants also delivered ratings of familiarity and enjoyment for each full-length song during the EEG recording. Finally, the dataset includes basic demographic information about the participants, as well as Matlab scripts to perform the illustrated analyses presented in the paper introducing the dataset (Losorelli et al., 2017). Cleaned and aggregated data are published in Matlab format; raw EEG is published in Matlab format, while raw tapping data are published in .txt format. Stimulus audio is not published, but metadata links are provided.
F-CelebA (10 tasks)
Full name: Federated-CelebA (10 tasks)

F-CelebA - This dataset is adapted from federated learning. Federated learning
is an emerging machine learning paradigm with an emphasis on data privacy. The idea is to train
through model aggregation rather than conventional data aggregation and keep local data staying
on the local device. This dataset naturally consists of similar tasks and each of the 10 tasks contains images of a celebrity labeled by whether he/she is smiling or not. More detailed please check page https://github.com/ZixuanKe/CAT

Introducing paper: Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks
ADIMA

**ADIMA** is a novel, linguistically diverse, ethically sourced, expert annotated and well-balanced multilingual profanity detection audio dataset comprising of 11,775 audio samples in 10 Indic languages spanning 65 hours and spoken by 6,446 unique users.

Introducing paper: ADIMA: Abuse Detection In Multilingual Audio
VizWiz-VQA-Grounding

The **VizWiz-VQA-Grounding** dataset is a dataset that visually grounds answers to visual questions asked by people with visual impairments.

Training Set:

- 6,494 examples

Validation Set:

- 1,131 examples

Test Set:

- 2,373 examples

Introducing paper: Grounding Answers for Visual Questions Asked by Visually Impaired People
Wukong

**Wukong** is a large-scale Chinese cross-modal dataset for benchmarking different multi-modal pre-training methods to facilitate the Vision-Language Pre-training (VLP). This dataset contains 100 million Chinese image-text pairs from the web. This base query list is taken from and is filtered according to the frequency of Chinese words and phrases.

Introducing paper: Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark
MeLa BitChute

**MeLa BitChute** is a near-complete dataset of over 3M videos from 61K channels over 2.5 years (June 2019 to December 2021) from the social video hosting platform BitChute, a commonly used alternative to YouTube. Additionally, the dataset includes a variety of video-level metadata, including comments, channel descriptions, and views for each video.

The dataset contains data from 3,036,190 videos, 61,229 channels, and 11,434,571 comments between June 28th, 2019 and December 31st, 2021. This dataset provides timestamped activities and estimates on views for the majority of channels and videos on the platform, allowing researchers to align BitChute videos with behavior on other platforms. Therefore, this dataset can facilitate both studies of BitChute in isolation and studies of BitChute’s role in the larger ecosystem.

Introducing paper: The MeLa BitChute Dataset
UAV_udc

https://github.com/zzr-idam/Under-Display-Camera-UAV

Introducing paper: Zero-Reference Image Restoration for Under-Display Camera of UAV
LAW
Full name: The Laboratory for Web Algorithmics

The Laboratory for Web Algorithmics (LAW) was established in 2002 at the Dipartimento di Scienze dell'Informazione (now merged in the Computer Science Department) of the Università degli studi di Milano.

The LAW is part of the NADINE FET EU project.

Research at LAW concerns all algorithmic aspects of the study of the web and of social networks.
Taillard Instances
Full name: Taillard Instances for Job Shop Scheduling

Taillar's permutation flow shop, the job shop, and the open shop scheduling problems instances:
We restrict ourselves to basic problems: the processing times are fixed, there are neither set-up times nor due dates nor release dates, etc. Then, the objective is the minimization of the makespan.
FICS PCB Image Collection (FPIC)

Optical images of printed circuit boards as well as detailed annotations of any text, logos, and surface-mount devices (SMDs). There are several hundred samples spanning a wide variety of manufacturing locations, sizes, node technology, applications, and more.

- pcb_image: Optical images of each PCB surface and rear, tagged with a unique identifier. 
- color_checker: Pallette to account for environmental illumination factors as well as a scale reference for the photo resolution. Each pcb image indicates which color checker it is associated with.
- ocr_annotation: Optical Character Recognition annotations. This includes polygon boundaries around all relevant text on a PCB image. Whether the piece of text is on the board or a device, whether it is a logo or not, orientation, and more are noted within the columns of the csv.
- smd_annotation: Surface-mount Device (SMD) annotations. This includes polygon boundaries around all relevant SMD devices such as resistors, capacitors, inductors, transistors, diodes, LEDs, and more. Along with each component, its associated silkscreen designator ('L', 'R', 'C', 'U', etc.) is recorded.
- vtp_annotation: Vias, traces, and pins (VTP) annotations. These are regions of connectivity between SMDs on a PCB. Few annotations currently exist, this is considered in 'beta' mode currently. 
- metadata: Holds two files corresponding to information about image files. 

    * pcb.csv holds information about the physical PCB samples such as their color, online item description, and any notes.
    * color_checker.csv indicates the pixels per millimeter (ppmm) of any image associated with that color checker, whether an X-Rite ColorChecker Passport or Nano was used, what camera performed the acquisition, and any relevant notes. 
 

Each annotation file is designed to be compatible with the S3A application (https://gitlab.com/ficsresearch/s3a or https://pypi.org/project/s3a/), a Python tool for visualizing polygon annotations on an image.

Introducing paper: FPIC: A Novel Semantic Dataset for Optical PCB Assurance