dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
VOICe | **VOICe** is a dataset for the development and evaluation of domain adaptation methods for sound event detection. VOICe consists of mixtures with three different sound events ("baby crying", "glass breaking", and "gunshot"), which are over-imposed over three different categories of acoustic scenes: vehicle, outdoors, and indoors. Moreover, the mixtures are also offered without any background noise.
VOICe consists of 1,449 different mixtures of three different sound events:
* 1,242 mixtures with background noise of three different categories of acoustic scenes ("vehicle"," outdoors", and "indoors"), mixed under 2 SNR values (-3, -9 dB), that is 207 mixtures x 3 acoustic scenes x 2 SNRs = 1,242
* 207 mixtures without any background noise. | Provide a detailed description of the following dataset: VOICe |
FieldSAFE | The FieldSAFE dataset is a multi-modal dataset for obstacle detection in agriculture. It comprises 2 hours of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016.
Ground truth information on object location and class labels for both static and moving obstacles is available as timestamped global (geographic) coordinates. | Provide a detailed description of the following dataset: FieldSAFE |
SocNav1 | **SocNav1** is a dataset for social navigation conventions. The aims of SocNav1 are two-fold: a) enabling comparison of the algorithms that robots use to assess the convenience of their presence in a particular position when navigating; b) providing a sufficient amount of data so that modern machine learning algorithms such as deep neural networks can be used. Because of the structured nature of the data, SocNav1 is particularly well-suited to be used to benchmark non-Euclidean machine learning algorithms such as Graph Neural Networks | Provide a detailed description of the following dataset: SocNav1 |
Public Git Archive | The **Public Git Archive** is a dataset of 182,014 top-bookmarked Git repositories from GitHub totalling 6 TB. The dataset provides the source code of the projects, the related metadata, and development history. | Provide a detailed description of the following dataset: Public Git Archive |
AIR-Act2Act | **AIR-Act2Act** is a human-human interaction dataset for teaching non-verbal social behaviors to robots. It is different from other datasets because elderly people have participated in as performers. The authors recruited 100 elderly people and two college students to perform 10 interactions in an indoor environment. The entire dataset has 5,000 interaction samples, each of which contains depth maps, body indexes and 3D skeletal data that are captured with three Microsoft Kinect v2 cameras. In addition, the dataset also contains the joint angles of a humanoid NAO robot which are converted from the human behavior that robots need to learn. | Provide a detailed description of the following dataset: AIR-Act2Act |
Nighttime Driving | **Nighttime Driving** is a dataset of road scenes consisting of 35,000 images ranging from daytime to twilight time and to nighttime.
Image source: [http://people.ee.ethz.ch/~daid/NightDriving/#](http://people.ee.ethz.ch/~daid/NightDriving/#) | Provide a detailed description of the following dataset: Nighttime Driving |
Dark Zurich | **Dark Zurich** is an image dataset containing a total of 8779 images captured at nighttime, twilight, and daytime, along with the respective GPS coordinates of the camera for each image. These GPS annotations are used to construct cross-time-of-day correspondences, i.e., to match each nighttime or twilight image to its daytime counterpart.
These attributes allow the usage of Dark Zurich as a dataset to build models and systems that perform:
1) domain adaptation (unsupervised, weakly supervised or semi-supervised), e.g. for semantic segmentation or object detection,
2) image translation / style transfer to different times of day,
3) robust image matching / visual localization across diverse domains, and
4) other visual perception tasks that are central for autonomous vehicles and other robotic applications. | Provide a detailed description of the following dataset: Dark Zurich |
DX7 Timbre Dataset | This is a dataset of 22.5 hours of synthesized audio using the open-source learnfm clone of the DX7 FM synthesizer, based upon 31K presets from Bobby Blue. These represent "natural'' synthesis sounds---i.e.presets devised by humans.
The authors generated 4-second samples playing midi note 69 (A440) with a note-on duration of 3 seconds. For each preset, the authors varied only the velocity, from 1--127, and perceptually normalized the level of each sound. Sounds that were completely identical were removed from the dataset. DX7 FM synthesis is good for this purpose because it doesn't have a noise oscillator. Thus, for a particular preset, there is a timbral variation as the velocity increases. 8K presets had only one unique sound. The median was 51 unique sound per preset, mean 41.9, stddev 27.4. | Provide a detailed description of the following dataset: DX7 Timbre Dataset |
CSRC | **CSRC** is a collection of data for Children Speech Recognition. The data for this challenge is divided into 3 datasets, referred to as A (Adult speech training set), C1 (Children speech training set) and C2 (Children conversation training set). All dataset combined amount to 400 hours of Mandarin speech data. | Provide a detailed description of the following dataset: CSRC |
NELA-GT-2018 | **NELA-GT-2018** is a dataset for the study of misinformation that consists of 713k articles collected between 02/2018-11/2018. These articles are collected directly from 194 news and media outlets including mainstream, hyper-partisan, and conspiracy sources. It includes ground truth ratings of the sources from 8 different assessment sites covering multiple dimensions of veracity, including reliability, bias, transparency, adherence to journalistic standards, and consumer trust. | Provide a detailed description of the following dataset: NELA-GT-2018 |
NELA-GT-2019 | NELA-GT-2019 is an updated version of the [NELA-GT-2018](nela-gt-2018) dataset. NELA-GT-2019 contains 1.12M news articles from 260 sources collected between January 1st 2019 and December 31st 2019. Just as with NELA-GT-2018, these sources come from a wide range of mainstream news sources and alternative news sources. Included with the dataset are source-level ground truth labels from 7 different assessment sites covering multiple dimensions of veracity. | Provide a detailed description of the following dataset: NELA-GT-2019 |
NELA-GT-2020 | **NELA-GT-2020** is an updated version of the [NELA-GT-2019](nela-gt-2019) dataset. NELA-GT-2020 contains nearly 1.8M news articles from 519 sources collected between January 1st, 2020 and December 31st, 2020. Just as with NELA-GT-2018 and NELA-GT-2019, these sources come from a wide range of mainstream news sources and alternative news sources. Included in the dataset are source-level ground truth labels from Media Bias/Fact Check (MBFC) covering multiple dimensions of veracity. Additionally, new in the 2020 dataset are the Tweets embedded in the collected news articles, adding an extra layer of information to the data. | Provide a detailed description of the following dataset: NELA-GT-2020 |
WhatsApp, Doc? | This is a large-scale dataset collected from WhatsApp public groups. It has been created from 178 public groups containing around 45K users and 454K messages.
This dataset allows researchers to ask questions like (i) Are WhatsApp groups a broadcast, multicast or unicast medium? (ii) How interactive are users, and how do these interactions emerge over time? (iii) What geographical span do WhatsApp groups have, and how does geographical placement impact interaction dynamics? (iv) What role does multimedia content play in WhatsApp groups, and how do users form interaction around multimedia content? (v) What is the potential of WhatsApp data in answering further social
science questions, particularly in relation to bias and representability? | Provide a detailed description of the following dataset: WhatsApp, Doc? |
THÖR | **THÖR** is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for position, head orientation, gaze direction, social grouping, obstacles map and goal coordinates. THOR also contains sensor data collected by a 3D lidar and involves a mobile robot navigating the space. | Provide a detailed description of the following dataset: THÖR |
EGAD | The Evolved Grasping Analysis Dataset (EGAD) comprises over 2000 generated objects aimed at training and evaluating robotic visual grasp detection algorithms. The objects in EGAD are geometrically diverse, filling a space ranging from simple to complex shapes and from easy to difficult to grasp, compared to other datasets for robotic grasping, which may be limited in size or contain only a small number of object classes. | Provide a detailed description of the following dataset: EGAD |
Weibo-COV | Weibo-COV is a large-scale COVID-19 social media dataset from Weibo, covering more than 30 million posts from 1 November 2019 to 30 April 2020. Moreover, the field information of the dataset is very rich, including basic posts information, interactive information, location information and retweet network. | Provide a detailed description of the following dataset: Weibo-COV |
COUGHVID | The COUGHVID dataset provides over 20,000 crowdsourced cough recordings representing a wide range of subject ages, genders, geographic locations, and COVID-19 statuses. First, the dataset was filtered using an open-sourced cough detection algorithm. Second, experienced pulmonologists labeled more than 2,000 recordings to diagnose medical abnormalities present in the coughs, thereby contributing one of the largest expert-labeled cough datasets in existence that can be used for a plethora of cough audio classification tasks. | Provide a detailed description of the following dataset: COUGHVID |
JVS | JVS is a Japanese multi-speaker voice corpus which contains voice data of 100 speakers in three styles (normal, whisper, and falsetto). The corpus contains 30 hours of voice data including 22 hours of parallel normal voices. | Provide a detailed description of the following dataset: JVS |
FacebookVideoLive18 | **FacebookVideosLive18** dataset includes 1,000,000 Facebook live videos with their metadata (title, source, length, creation time, description, etc.), broadcasters locations and viewers locations. We are using a set of synchronised scripts that allow to have a global view of the real time streaming system every 3 minutes. We believe that our dataset is the first that tracks the locations and behaviors of live viewers. We expect FacebookVideosLive18 to support various trending research areas such as cloud computing, multimedia data allocation, multi-cloud allocation, edge computing, edge caching and transcoding, data analytics, etc. | Provide a detailed description of the following dataset: FacebookVideoLive18 |
Pushshift Telegram | The Pushshift Telegram dataset is made up of over 27.8K channels and 317M messages from 2.2M unique users. The Pushshift Telegram dataset can help researchers from a variety of disciplines interested in studying online social movements, protests, political extremism, and disinformation. | Provide a detailed description of the following dataset: Pushshift Telegram |
FTR-18 | **FTR-18** is a multilingual rumour dataset on football transfer news. Transfer rumours are continuously published by sports media. They can both harm the image of player or a club or increase the player's market value. The proposed dataset includes transfer articles written in English, Spanish and Portuguese. It also comprises Twitter reactions related to the transfer rumours. FTR-18 is suited for rumour classification tasks and allows the research on the linguistic patterns used in sports journalism. | Provide a detailed description of the following dataset: FTR-18 |
RWCP-SSD-Onomatopoeia | RWCP-SSD-Onomatopoeia is a dataset consisting of 155,568 onomatopoeic words paired with audio samples for environmental sound synthesis. | Provide a detailed description of the following dataset: RWCP-SSD-Onomatopoeia |
HuGaDB | **HuGaDB** is human gait data collection for analysis and activity recognition consisting of continues recordings of combined activities, such as walking, running, taking stairs up and down, sitting down, and so on; and the data recorded are segmented and annotated. Data were collected from a body sensor network consisting of six wearable inertial sensors (accelerometer and gyroscope) located on the right and left thighs, shins, and feet. Additionally, two electromyography sensors were used on the quadriceps (front thigh) to measure muscle activity. This database can be used not only for activity recognition but also for studying how activities are performed and how the parts of the legs move relative to each other. Therefore, the data can be used (a) to perform health-care-related studies, such as in walking rehabilitation or Parkinson's disease recognition, (b) in virtual reality and gaming for simulating humanoid motion, or (c) for humanoid robotics to model humanoid walking. | Provide a detailed description of the following dataset: HuGaDB |
GitHub Repository Deduplication | This is a dataset of 10.6 million GitHub projects that are copies of others, and link each record with the project's ultimate parent. The ultimate parents were derived from a ranking along six metrics. The related projects were calculated as the connected components of an 18.2 million node and 12 million edge denoised graph created by directing edges to ultimate parents. The graph was created by filtering out more than 30 hand-picked and 2.3 million pattern-matched clumping projects. Projects that introduced unwanted clumping were identified by repeatedly visualizing shortest path distances between unrelated important projects. | Provide a detailed description of the following dataset: GitHub Repository Deduplication |
PersianQA | # PersianQA: a dataset for Persian Question Answering
Persian Question Answering (PersianQA) Dataset is a reading comprehension
dataset on [Persian Wikipedia](https://fa.wikipedia.org/). The crowd-sourced
the dataset consists of more than 9,000 entries. Each entry can be either an
_impossible-to-answer_ or a question with one or more answers spanning in the
passage (the _context_) from which the questioner proposed the question.
Much like the SQuAD2.0 dataset, the impossible or _unanswerable_ questions can be
utilized to create a system which "knows that it doesn't know the answer".
Moreover, the dataset has 900 test data available. On top of that, the very
first models trained on the dataset, Transformers, are available online.
All the crowd workers of the dataset are native Persian speakers. Also, it worth
mentioning that the contexts are collected from all categories of the Wiki
(Historical, Religious, Geography, Science, etc). | Provide a detailed description of the following dataset: PersianQA |
CRIC Cervix | he Center for Recognition and Inspection of Cells (CRIC) platform enables the creation of CRIC Cervix collection, currently with 400 images (1,376 x 1,020 pixels) curated from conventional Pap smears, with manual classification of 11,534 cells. | Provide a detailed description of the following dataset: CRIC Cervix |
VISION | The dataset contains more than 35000 images and 600 videos captured using 35 different portable devices of 11 major brands. In addition to the original acquisitions, images were shared through Facebook and WhatsApp whereas videos were shared through YouTube and WhatsApp platforms.
The dataset was introduced in the following paper: Shullani, Dasara, et al. "VISION: a video and image dataset for source identification." EURASIP Journal on Information Security 2017.1 (2017): 1-16, https://doi.org/10.1186/s13635-017-0067-2. | Provide a detailed description of the following dataset: VISION |
EVA | The dataset contains 7000 videos: native, altered and exchanged through social platforms. The altered contents include manipulations with FFmpeg, AVIdemux, Kdenlive and Adobe Premiere. The social platforms used to exchange the native and altered videos are Facebook, Tiktok, Youtube and Weibo.
A detailed description of the dataset is available in the journal paper by Yang, Pengpeng, et al. "Efficient Video Integrity Analysis Through Container Characterization." IEEE Journal of Selected Topics in Signal Processing 14.5 (2020): 947-954, 10.1109/JSTSP.2020.3008088. | Provide a detailed description of the following dataset: EVA |
Light-Field Material | This is a 4D light-field dataset of materials. The dataset contains 12 material categories, each with 100 images taken with a Lytro Illum, from which we extract about 30,000 patches in total. | Provide a detailed description of the following dataset: Light-Field Material |
CASCONet | **CASCONet** is a a collection of data about the CAS Conference (CASCON) for the past 25 years including information about papers, technology showcase demos, workshops, and keynote presentations. | Provide a detailed description of the following dataset: CASCONet |
VeReMi | The Vehicular Reference Misbehavior (VeReMi) dataset, is a dataset for the evaluation of misbehavior detection mechanisms for VANETs (vehicular networks). This dataset consists of message logs of on-board units, including a labelled ground truth, generated from a simulation environment. The dataset includes malicious messages intended to trigger incorrect application behavior, which is what misbehavior detection mechanisms aim to prevent. The initial dataset contains a number of simple attacks: the idea of this dataset release is not just to provide a baseline for the comparison of detection mechanisms, but also to serve as a starting point for more complex attacks. | Provide a detailed description of the following dataset: VeReMi |
EDNA-Covid | **EDNA-Covid** is a multilingual, large-scale dataset of coronavirus-related tweets collected since January 25, 2020. EDNA-Covid includes, at time of this publication, over 600M tweets from around the world in over 10 languages. | Provide a detailed description of the following dataset: EDNA-Covid |
YT-UGC | **YT-UGC** is a large scale UGC (User Generated Content) dataset (1,500 20 sec video clips) sampled from millions of YouTube videos. The dataset covers popular categories like Gaming, Sports, and new features like High Dynamic Range (HDR). This dataset can be used to study video compression and quality assessment. | Provide a detailed description of the following dataset: YT-UGC |
putEMG | putEMG and putEMG-Force datasets are databases of surface electromyographic activity recorded from forearm. Datasets allows for development of algorithms for gesture recognition and grasp force recognition. Experiment was conducted on 44 participants, with two repetitions separated by, minimum of one week. The dataset includes 7 active gestures (like hand flexion, extension, etc.) + idle and a set of trials with isometric contractions. sEMG was recorded using a 24-electrode matrix. | Provide a detailed description of the following dataset: putEMG |
DeepMIMO | **DeepMIMO** is a generic dataset for mmWave/massive MIMO channels. The DeepMIMO dataset generation framework has two important features. First, the DeepMIMO channels are constructed based on accurate ray-tracing data obtained from Remcom Wireless InSite. The DeepMIMO channels, therefore, capture the dependence on the environment geometry/materials and transmitter/receiver locations, which is essential for several machine learning applications. Second, the DeepMIMO dataset is generic/parameterized as the researcher can adjust a set of system and channel parameters to tailor the generated DeepMIMO dataset for the target machine learning application. The DeepMIMO dataset can then be completely defined by the (i) the adopted ray-tracing scenario and (ii) the set of parameters, which enables the accurate definition and reproduction of the dataset. | Provide a detailed description of the following dataset: DeepMIMO |
US-Accidents | This is a countrywide traffic accident dataset, which covers 49 states of the United States. The data is continuously being collected from February 2016, using several data providers, including two APIs which provide streaming traffic event data. These APIs broadcast traffic events captured by a variety of entities, such as the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road-networks. Currently, there are about 4.2 million accident records in this dataset. | Provide a detailed description of the following dataset: US-Accidents |
COOLL | Controlled On/Off Loads Library (**COOLL**) is a dataset of high-sampled electrical current and voltage measurements representing individual appliances consumption. The measurements were taken in June 2016 in the PRISME laboratory of the University of Orléans, France. The appliances are mainly controllable appliances (i.e. we can precisely control their turn-on/off time instants). 42 appliances of 12 types were measured at a 100 kHz sampling frequency. | Provide a detailed description of the following dataset: COOLL |
Pushshift Reddit | Pushshift makes available all the submissions and comments posted on Reddit between June 2005 and April 2019. The dataset consists of 651,778,198 submissions and 5,601,331,385 comments posted on 2,888,885 subreddits. | Provide a detailed description of the following dataset: Pushshift Reddit |
HoaxItaly | **HoaxItaly** consists of over 1 million tweets shared during 2019 and containing links to thousands of news articles published on two classes of Italian outlets: (1) disinformation websites, i.e. outlets which have been repeatedly flagged by journalists and fact-checkers for producing low-credibility content such as false news, hoaxes, click-bait, misleading and hyper-partisan stories; (2) fact-checking websites which notably debunk and verify online news and claims. The dataset includes title and body for approximately 37k news articles. | Provide a detailed description of the following dataset: HoaxItaly |
GED | **GED** is a dataset on the economic activity of mainland China, which measures the volume of establishments at a 0.01 latitude by 0.01 longitude scale. Specifically, the dataset captures the geographically based opening and closing of approximately 25.5 million firms that registered in mainland China over the period 2005-2015. The characteristics of fine granularity and long-term observability give the GED a high application value. | Provide a detailed description of the following dataset: GED |
Interactive Gibson Environment | Interactive Gibson is a comprehensive benchmark for training and evaluating Interactive Navigation: robot navigation strategies where physical interaction with objects is allowed and even encouraged to accomplish a task. The benchmark has two main components:
* The **Interactive Gibson Environment**, which simulates high fidelity visuals of indoor scenes, and high fidelity physical dynamics of the robot and common objects found in these scenes.
* set of Interactive Navigation metrics which allows one to study the interplay between navigation and physical simulation. | Provide a detailed description of the following dataset: Interactive Gibson Environment |
TAU Spatial Sound Events 2019 | TAU Spatial Sound Events 2019 consists of 2 datasets: Ambisonic (FOA) and Microphone Array (MIC), of identical sound scenes with the only difference in the format of the audio. The FOA dataset provides four-channel First-Order Ambisonic recordings while the MIC dataset provides four-channel directional microphone recordings from a tetrahedral array configuration. Both formats are extracted from the same microphone array.
Both the datasets, consists of a development and evaluation set. The development set consists of 400 one-minute long recordings sampled at 48000 Hz, divided into four cross-validation splits of 100 recordings each. The evaluation set consists of 100 one-minute long recordings. These recordings were synthesized using spatial room impulse response (IRs) collected from five indoor environments, at 504 unique combinations of azimuth-elevation-distance. | Provide a detailed description of the following dataset: TAU Spatial Sound Events 2019 |
BugHunter | The **BugHunter** dataset is an automatically constructed and freely available bug dataset containing code elements (files, classes, methods) with a wide set of code metrics and bug information. | Provide a detailed description of the following dataset: BugHunter |
Natural Hazards Twitter Dataset | **Natural Hazards** is a natural disaster dataset with sentiment labels, which contains nearly 50,00 Twitter data about different natural disasters in the United States (e.g., a tornado in 2011, a hurricane named Sandy in 2012, a series of floods in 2013, a hurricane named Matthew in 2016, a blizzard in 2016, a hurricane named Harvey in 2017, a hurricane named Michael in 2018, a series of wildfires in 2018, and a hurricane named Dorian in 2019). | Provide a detailed description of the following dataset: Natural Hazards Twitter Dataset |
20-MAD | 20-MAD, a dataset linking the commit and issue data of Mozilla and Apache projects. It includes over 20 years of information about 765 projects, 3.4M commits, 2.3M issues, and 17.3M issue comments, and its compressed size is over 6 GB. The data contains all the typical information about source code commits (e.g., lines added and removed, message and commit time) and issues (status, severity, votes, and summary). The issue comments have been pre-processed for natural language processing and sentiment analysis. This includes emoticons and valence and arousal scores. | Provide a detailed description of the following dataset: 20-MAD |
Dataset of Video Game Development Problems | This is a grounded dataset describing software-engineering problems in video-game development extracted from postmortems. The dataset was created using an iterative method through which the authors manually coded more than 200 postmortems spanning 20 years (1998 to 2018) and extracted 1,035 problems related to software engineering while maintaining traceability links to the postmortems. The problems were grouped in 20 different types. This dataset is useful to understand the problems faced by developers during video-game development, providing researchers and practitioners a starting point to study video-game development in the context of software engineering. | Provide a detailed description of the following dataset: Dataset of Video Game Development Problems |
iBugMask | iBugMask is an in-the-wild face parsing dataset that contains 1,000 challenging face images and manually annotated labels for 11 semantic classes: background, facial skin, left/right brow, left/right eye, nose, upper/lower lip, inner mouth, and hair. The images are curated from challenging in-the-wild face alignment datasets, including 300W and Menpo. Compared with the existing face parsing datasets, iBugMask contains in-the-wild scenarios such as “party” and “conference”, which include more challenging appearance variations or multiple faces. There is a larger number of profile faces. More expressions other than ”neutral” and ”smile” are also included (e.g. ”surprise” and ”scream”). The dataset can be downloaded on [here](https://github.com/hhj1897/face_parsing#ibugmask-dataset). | Provide a detailed description of the following dataset: iBugMask |
A Dataset of State-Censored Tweets | This is a dataset of 583,437 tweets by 155,715 users that were censored between 2012-2020 July. It also contains 4,301 accounts that were censored in their entirety. Additionally, another set of tweets is related, consisting of 22,083,759 supplemental tweets made up of all tweets by users with at least one censored tweet as well as instances of other users retweeting the censored user. | Provide a detailed description of the following dataset: A Dataset of State-Censored Tweets |
Enterprise-Driven Open Source Software | This is a dataset of open source software developed mainly by enterprises rather than volunteers. This can be used to address known generalizability concerns, and, also, to perform research on open source business software development. Based on the premise that an enterprise's employees are likely to contribute to a project developed by their organization using the email account provided by it, we mine domain names associated with enterprises from open data sources as well as through white- and blacklisting, and use them through three heuristics to identify 17,264 enterprise GitHub projects. We provide these as a dataset detailing their provenance and properties. A manual evaluation of a dataset sample shows an identification accuracy of 89%. | Provide a detailed description of the following dataset: Enterprise-Driven Open Source Software |
Test Scene Dataset for Physically Based Rendering | This is a comprehensive test database of scenes that treat different light setups in conjunction with diverse materials. It delivers a comprehensive foundation for evaluating existing and newly developed rendering techniques.
The source files are provided in the Blender file format for easy editing and additional exports to the Mitsuba XML format are included. | Provide a detailed description of the following dataset: Test Scene Dataset for Physically Based Rendering |
QA-SRL Bank 2.0 | **QA-SRL Bank 2.0** is a large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations. The corpus consists of over 250,000 question-answer pairs for over 64,000 sentences across 3 domains and was gathered with a new crowd-sourcing scheme that was shown to have high precision and good recall at modest cost. | Provide a detailed description of the following dataset: QA-SRL Bank 2.0 |
HomebrewedDB | **HomebrewedDB** is a dataset for 6D pose estimation mainly targeting training from 3D models (both textured and textureless), scalability, occlusions, and changes in light conditions and object appearance. The dataset features 33 objects (17 toy, 8 household and 8 industry-relevant objects) over 13 scenes of various difficulty. It also consists of a set of benchmarks to test various desired detector properties, particularly focusing on scalability with respect to the number of objects and resistance to changing light conditions, occlusions and clutter. | Provide a detailed description of the following dataset: HomebrewedDB |
DIHARD II | The DIHARD II development and evaluation sets draw from a diverse set of sources exhibiting wide variation in recording equipment, recording environment, ambient noise, number of speakers, and speaker demographics. The development set includes reference diarization and speech segmentation and may be used for any purpose including system development or training. | Provide a detailed description of the following dataset: DIHARD II |
SPHERE | The dataset for the SPHERE challenge consists on a multimodal activity recognition dataset consisting of accelerometer, RGB-D and environmental data. Accelerometer is samplled at 20 Hz and given in its raw format. Raw video is not given in order to preserve anonymity of the participants. Instead, extracted features that relate to the centre of mass and bounding box of the identified persons are provided. Environmental data consists of Passive Infra-Red (PIR) sensors, and these is given in raw format.
Twenty (posture/ambulation) activities labels are annotated in the dataset. | Provide a detailed description of the following dataset: SPHERE |
ANIMAL | 10 classes with 50, 000 training and 5, 000 testing images. Please note that, in ANIMAL10N, noisy labels were injected naturally by human mistakes, where its noise rate was estimated at 8%. | Provide a detailed description of the following dataset: ANIMAL |
Ghera | **Ghera** is a repository of Android app vulnerabilities. | Provide a detailed description of the following dataset: Ghera |
ManySStuBs4J | The ManySStuBs4J corpus is a collection of simple fixes to Java bugs, designed for evaluating program repair techniques. We collect all bug-fixing changes using the SZZ heuristic, and then filter these to obtain a data set of small bug fix changes.
These are single statement fixes, classified where possible into one of 16 syntactic templates which we call SStuBs.
The dataset contains simple statement bugs mined from open-source Java projects hosted in GitHub.
There are two variants of the dataset. One mined from the 100 Java Maven Projects and one mined from the top 1000 Java Projects.
The dataest contains 153,652 single statement bugfix changes mined from 1,000 popular open-source Java projects, annotated by whether they match any of a set of 16 bug templates, inspired by state-of-the-art program repair techniques. | Provide a detailed description of the following dataset: ManySStuBs4J |
DACT | DACT contains two subsets of annotated car trajectories data. The dataset contains 50 trajectories which cover about 13 hours of driving data. In DACT, we manually specified significant driving patterns by using an interactive framework. A significant driving pattern can be anything like a turn, speed-up, slow-down, etc. The annotation process consists of a crowd-sourcing task followed by comprehensive aggregation phases. The aggregation is done by two different strategies: Strict and Easy. For the first one, we used some strict constraints to aggregate crowd-sourcing results, while we used flexible constraints to generate the second subset of DACT. | Provide a detailed description of the following dataset: DACT |
OMG-Emotion | The One-Minute Gradual-Emotional Behavior dataset (**OMG-Emotion**) dataset is composed of Youtube videos which are around a minute in length and are annotated taking into consideration a continuous emotional behavior. The videos were selected using a crawler technique that uses specific keywords based on long-term emotional behaviors such as "monologues", "auditions", "dialogues" and "emotional scenes".
It contains 567 emotion videos with an average length of 1 minute, collected from a variety of Youtube channels. The videos were separated into clips based on utterances, and each utterance was annotated by at least five independent subjects using the Amazon Mechanical Turk tool. | Provide a detailed description of the following dataset: OMG-Emotion |
JVS-MuSiC | **JVS-MuSiC** is a Japanese multispeaker singing-voice corpus called "JVS-MuSiC" with the aim to analyze and synthesize a variety of voices. The corpus consists of 100 singers' recordings of the same song, Katatsumuri, which is a Japanese children's song. It also includes another song that is different for each singer. | Provide a detailed description of the following dataset: JVS-MuSiC |
SUMMIT | **SUMMIT** is a high-fidelity simulator that facilitates the development and testing of crowd-driving algorithms. By leveraging the open-source OpenStreetMap map database and a heterogeneous multi-agent motion prediction model developed in our earlier work, SUMMIT simulates dense, unregulated urban traffic for heterogeneous agents at any worldwide locations that OpenStreetMap supports. SUMMIT is built as an extension of [CARLA](carla) and inherits from it the physical and visual realism for autonomous driving simulation. SUMMIT supports a wide range of applications, including perception, vehicle control, planning, and end-to-end learning. | Provide a detailed description of the following dataset: SUMMIT |
KinGaitWild | To study kinship verification from gait, we collected the dataset KinGaitWild consisting of several videos from youtube. Most of the videos were taken under uncontrolled conditions in terms of background, camera motion, luminance and viewpoints. The KinGaitWild dataset contains 105 videos of celebrities and their relatives. The average time duration of each video is around 10 seconds. The database includes 60 pairs of Father-Son (FS) relationships. These pairs are equally split into 5 groups. We focus in this study on the Father-Son relationships.
The database collection was done as follows. First, we used the YouTube Data API to search for videos showing celebrities walking in the wild. To avoid biases, we selected
the pairs of celebrities so that the videos are not originated from the same source nor environment. For each video, we labeled the position of each specified person by a bounding box (bbox). These bboxes are used to estimate the human pose for silhouette-based approaches and to crop the region of interest for both the video-based and deep-learning-based approaches. | Provide a detailed description of the following dataset: KinGaitWild |
FINO-Net | **FINO-Net** is a multimodal (RGB, depth and audio) dataset, containing 229 real-world manipulation data of 5 different manipulation types recorded with a Baxter robot. | Provide a detailed description of the following dataset: FINO-Net |
DSSN | **DSSN** is a spatiotemporal dataset of 0.7 million data points of continuous location data logged at an interval of every 2 minutes by mobile phones of 46 subjects. The total number of data points reported in this dataset are 6,59,268. The total number of subjects using the application to record data are 74, however with cleaning based on quality checks. The number was reduced to 46. The data recorded varies in accuracy with an average accuracy of 36.0 meters. | Provide a detailed description of the following dataset: DSSN |
CoronaVis | **CoronaVis** is a dataset of tweets related to coronavirus. | Provide a detailed description of the following dataset: CoronaVis |
DroidBugs | **DroidBugs** is a benchmark for Automated Program Repair (APR) of Android applications. | Provide a detailed description of the following dataset: DroidBugs |
Multi-Codec DASH | This is a multi-codec DASH dataset comprising AVC, HEVC, VP9, and AV1 in order to enable interoperability testing and streaming experiments for the efficient usage of these codecs under various conditions. | Provide a detailed description of the following dataset: Multi-Codec DASH |
UCR Time Series Classification Archive | The UCR Time Series Archive - introduced in 2002,
has become an important resource in the time series data mining
community, with at least one thousand published papers making
use of at least one data set from the archive. The original
incarnation of the archive had sixteen data sets but since that
time, it has gone through periodic expansions. The last expansion
took place in the summer of 2015 when the archive grew from
45 to 85 data sets. This paper introduces and will focus on the
new data expansion from 85 to 128 data sets. Beyond expanding
this valuable resource, this paper offers pragmatic advice to
anyone who may wish to evaluate a new algorithm on the archive.
Finally, this paper makes a novel and yet actionable claim: of the
hundreds of papers that show an improvement over the standard
baseline (1-nearest neighbor classification), a large fraction may
be misattributing the reasons for their improvement. Moreover,
they may have been able to achieve the same improvement with
a much simpler modification, requiring just a single line of code. | Provide a detailed description of the following dataset: UCR Time Series Classification Archive |
Near-Collision | **Near-Collision** is a large-scale dataset of 13,658 egocentric
video snippets of humans navigating in indoor hallways. In
order to obtain ground truth annotations of human pose, the
videos are provided with the corresponding 3D point cloud
from LIDAR. | Provide a detailed description of the following dataset: Near-Collision |
Apiza Corpus | The Apiza Corpus is a WoZ-like (Wizard of Oz) set of dialogues between 30 programmers and a simulated virtual assistant. This corpus can be used to study or train a virtual assistant for software engineering. | Provide a detailed description of the following dataset: Apiza Corpus |
YoutubeGraph-Dyn | YoutubeGraph-Dyn is an evolving graph dataset generated from YouTube real-world interactions. It can be used to study temporal evolution on graphs. YoutubeGraph-Dyn provides intra-day time granularity (with 416 snapshots taken every 6 hours for a period of 104 days), multi-modal relationships that capture different aspects of the data, multiple attributes including timestamped, non-timestamped, word embeddings, and integers. | Provide a detailed description of the following dataset: YoutubeGraph-Dyn |
HoMG | HoMG is a holoscopic 3D micro-gesture dataset captured with a holoscopic 3D camera. HoMG database recorded the image sequence of 3 conventional gestures from 40 participants under different settings and conditions. For the purpose of H3D micro-gesture recognition, HoMG has a video subset of 960 videos and a still image subset with 30,635 images. | Provide a detailed description of the following dataset: HoMG |
Wildtrack | Wildtrack is a large-scale and high-resolution dataset. It has been captured with seven static cameras in a public open area, and unscripted dense groups of pedestrians standing and walking. Together with the camera frames, we provide an accurate joint (extrinsic and intrinsic) calibration, as well as 7 series of 400 annotated
frames for detection at a rate of 2 frames per second. This results in over 40 000 bounding boxes delimiting every person present in the area of interest, for a total of more than
300 individuals. | Provide a detailed description of the following dataset: Wildtrack |
pixraw10P | face image datasets | Provide a detailed description of the following dataset: pixraw10P |
warpPIE10P | face dataset | Provide a detailed description of the following dataset: warpPIE10P |
iris | The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician, eugenicist, and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species. Two of the three species were collected in the Gaspé Peninsula "all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus". | Provide a detailed description of the following dataset: iris |
UPFD-POL | The PolitiFact variant of the [UPFD](https://paperswithcode.com/dataset/upfd) dataset for benchmarking.
Please refer to the [UPFD](https://paperswithcode.com/dataset/upfd) dataset for more details of the data. | Provide a detailed description of the following dataset: UPFD-POL |
UPFD-GOS | The Gossipcop variant of the [UPFD](https://paperswithcode.com/dataset/upfd) dataset for benchmarking.
Please refer to the [UPFD](https://paperswithcode.com/dataset/upfd) dataset for more details of the data. | Provide a detailed description of the following dataset: UPFD-GOS |
australian | Data Set Information:
This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data.
This dataset is interesting because there is a good mix of attributes -- continuous, nominal with small numbers of values, and nominal with larger numbers of values. There are also a few missing values. | Provide a detailed description of the following dataset: australian |
Dataset of Rendered Chess Game State Images | This dataset contains 4,888 synthetic images of chess game states that occurred in games played by Magnus Carlsen. The images were rendered in Blender at different angles and lighting conditions. | Provide a detailed description of the following dataset: Dataset of Rendered Chess Game State Images |
POT-210 | Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-theart algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change, rotation, perspective distortion, motion blur, occlusion, out-of-view, and unconstrained. The ground truth is carefully annotated semi-manually to ensure the quality. Moreover, eleven state-of-the-art algorithms are evaluated on the benchmark using two evaluation metrics, with detailed analysis provided for the evaluation results. We expect the proposed benchmark to benefit future studies on planar object tracking. | Provide a detailed description of the following dataset: POT-210 |
Risk-Aware Planning Dataset | **Risk-Aware Planning** is a dataset that contains the overhead images and their semantic segmentation captured by a drone from the CityEnviron environment in AirSim simulator. | Provide a detailed description of the following dataset: Risk-Aware Planning Dataset |
Fire Drill Anti-Pattern Dataset | **Fire Drill Anti-Pattern Dataset** is a collection of nine real-world software projects for detection of the fire drill anti-pattern with ground truth, issue-tracking data, source code density, models and code. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. It includes data, ground truth, code, and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. Therefore, this package includes the following:
Fire Drill in issue-tracking data:
* __Ground truth__ for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus.
* Coefficients for indicators for the first method, per project.
* Detailed issue-tracing data for each project: what occurred and when.
* Time logs for each project.
Fire Drill in source-code data:
* Three technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits.
* Source code density data (metrics) for each commit in each of the nine projects as a separate dataset.
* Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R). | Provide a detailed description of the following dataset: Fire Drill Anti-Pattern Dataset |
Mobility Flow | This is a multiscale dynamic human mobility flow dataset across the United States, with data starting from January 1st, 2019. By analyzing millions of anonymous mobile phone users’ visit trajectories to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state.
Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behavior changes under the unprecedented public health crisis. | Provide a detailed description of the following dataset: Mobility Flow |
York Urban Line Segment Database | The York Urban Line Segment Database is a compilation of 102 images (45 indoor, 57 outdoor) of urban environments consisting mostly of scenes from the campus of York University and downtown Toronto, Canada. The images are 640 x 480 in size and have been taken with a calibrated Panasonic Lumix DMC-LC80 digital camera.
Each image in the database has been hand-labelled to identify the set of line segments satisfying the “Manhattan assumption” (Coughlan & Yuille 2003), i.e., the set of line segments that conform to the 3D orthogonal frame of the urban environment.
These hand-labelled data have been used to identify the three Manhattan vanishing points in each image and from these to identify the Euler angles relating the camera frame to the Manhattan frame of the scene.
The database provides the original images, camera calibration parameters, ground truth line segments, and estimated Manhattan frame relative to the camera for each image. | Provide a detailed description of the following dataset: York Urban Line Segment Database |
MuSe-CaR | The **MuSe-CAR** database is a large, multimodal (video, audio, and text) dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity.
The estimated age range of the professional, semi-professional (influncers), and casual reviewers is between the middle of 20s until the late 50s. Most are native English speakers from the UK or the US, while a small minority are non-native, yet fluent English speakers. | Provide a detailed description of the following dataset: MuSe-CaR |
OpenWPM Crawls | **OpenWPM Crawls** is a dataset of 103 online, mostly mainstream news websites. With the help of two experts, alongside data from the Media Ownership Monitor of the Reporters without Borders, we label these websites according to their partisanship (Left, Right, or Centre). We study and compare user tracking on these sites with different metrics: numbers of cookies, cookie synchronizations, device fingerprinting, and invisible pixel-based tracking. We find that Left and Centre websites serve more cookies than Right-leaning websites. However, through cookie synchronization, more user IDs are synchronized in Left websites than Right or Centre. Canvas fingerprinting is used similarly by Left and Right, and less by Centre. Invisible pixel-based tracking is 50% more intense in Centre-leaning websites than Right, and 25% more than Left. Desktop versions of news websites deliver more cookies than their mobile counterparts. A handful of third-parties are tracking users in most websites in this study. | Provide a detailed description of the following dataset: OpenWPM Crawls |
MRPB 1.0 | **MRPB 1.0** is a mobile robot local planning benchmark. The benchmark facilitates both motion planning researchers who want to compare the performance of a new local planner relative to many other state-of-the-art approaches as well as end users in the mobile robotics industry who want to select a local planner that performs best on some problems of interest. | Provide a detailed description of the following dataset: MRPB 1.0 |
Algonauts 2021 | The Algonauts dataset provides human brain responses to a set of 1,102 3-s long video clips of everyday events. The brain responses are measured with functional magnetic resonance imaging (fMRI). fMRI is a widely used brain imaging technique with high spatial resolution that measures blood flow changes associated with neural responses.
**Splits:**
*Training:*
The training set consists of 1,000 video clips and the associated brain responses. The brain responses are provided here in two tracks corresponding to two independent tracks in the Algonauts challenge. In the first track, brain responses provided are from a set of specific regions of interest (ROIs) known to play a key role in visual perception. These ROIs start in early and mid-level visual cortex (V1, V2, V3, and V4) and extend into higher-level cortex that responds preferentially to all objects or particular categories (Body- EBA; Face - FFA, STS; Object - LOC; Scene - PPA). In the second track, brain responses provided are from selected voxels across the whole brain showing reliable responses to videos.
*Test:*
The test set consists of 102 short videos. The associated brain responses will be released at a later date.
For further details see [here](http://algonauts.csail.mit.edu/). | Provide a detailed description of the following dataset: Algonauts 2021 |
BLM-17m | **BLM-17m** is a labeled dataset for topic detection that contains 17 million tweets. These Tweets are collected from 25 May 2020 to 21 August 2020 that covers 89 days from start of the George Floy incident. The dataset was labelled by monitoring most trending news topics from global and local newspapers. | Provide a detailed description of the following dataset: BLM-17m |
Healthline | **Healthline** is a nutrition related dataset for multi-document summarization, using scientific studies. | Provide a detailed description of the following dataset: Healthline |
LoLi-Phone | **LoLi-Phone** is a large-scale low-light image and video dataset for Low-light image enhancement (LLIE). The images and videos are taken by different mobile phones' cameras under diverse illumination conditions. | Provide a detailed description of the following dataset: LoLi-Phone |
NAVVS | **NAVVS** is a volumetric dataset of naturalistic actions whose captured sound and visual appearance yield an open-access resource for immersive and interactive research within an artificial 3D audio-visual environment, such as VR/AR/XR with six degree-of-freedom (6DoF) interaction. It includes a variety of short volumetric sounding actions. It provides a valuable resource for multimodal research and testing under realistic conditions. The dataset includes ten different actions designed with both semantic and acoustic diversity. For each action, four 2-seconds takes are available to provide a total of forty audio-visual clips.
The scenes were captured at the Centre for Vision, Speech & Signal Processing (CVSSP) of the University of Surrey (UK) with the aid of multiple cameras and multiple microphones.
Along with the final clips' volumetric textured instances and the audio stereo mix, additional data is provided. This includes: the separated microphones' audio channels, raw images from the 16 UHD cameras, binary masks, camera calibration data, coarse visual hull reconstruction, and volumetric stereo refinement. | Provide a detailed description of the following dataset: NAVVS |
FSVOD-500 | **FSVOD-500** is a large-scale video dataset comprising of 500 classes with class-balanced videos in each category for few-shot learning. FSVOD-500 is the first benchmark specially designed for few-shot video object detection for evaluating the performance of a given model on novel classes. | Provide a detailed description of the following dataset: FSVOD-500 |
Tracking the Trackers | **Tracking the Trackers** is a large-scale analysis of third-party trackers on the World Wide Web. We extract third-party embeddings from more than 3.5 billion web pages of the CommonCrawl 2012 corpus, and aggregate those to a dataset containing more than 140 million third-party embeddings in over 41 million domains. | Provide a detailed description of the following dataset: Tracking the Trackers |
Dataset for Mid-Price Forecasting of Limit Order Book Data | This is a benchmark dataset for mid-price forecasting of limit order book data. It is a dataset of high-frequency limit order markets for mid-price prediction. The authors extracted normalized data representations of time series data for five stocks from the NASDAQ Nordic stock market for a time period of ten consecutive days, leading to a dataset of ~4,000,000 time series samples in total. A day-based anchored cross-validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state-of-the-art methodologies. | Provide a detailed description of the following dataset: Dataset for Mid-Price Forecasting of Limit Order Book Data |
Analytic Provenance | **Analytic provenance** is a data repository that can be used to study human analysis activity, thought processes, and software interaction with visual analysis tools during exploratory data analysis. It was collected during a series of user studies involving exploratory data analysis scenario with textual and cyber security data. Interactions logs, think-alouds, videos and all coded data in this study are available online for research purposes.
Analysis sessions are segmented in multiple sub-task steps based on user think-alouds, video and audios captured during the studies. These analytic provenance datasets can be used for research involving tools and techniques for analyzing interaction logs and analysis history. | Provide a detailed description of the following dataset: Analytic Provenance |
CSI | **CSI** is a criminal conversational dataset for speaker identification built from the CSI television show. The authors collected transcripts of 39 episodes and video/audio of 4 episodes. Each episode involves on average more than 30 speakers. Utterances last on average 3 to 4 seconds. There are around 45 to 50 distinct scenes/conversations per episode. | Provide a detailed description of the following dataset: CSI |
Coronavirus-themed Mobile Malware | This is a dataset for coronavirus-themed malware for Android devices. It is a daily growing COVID-19 themed mobile app dataset, which contains 4,322 COVID-19 themed apk samples (2,500 unique apps) and 611 potential malware samples (370 unique malicious apps) by the time of mid-November, 2020. | Provide a detailed description of the following dataset: Coronavirus-themed Mobile Malware |
Dataset of Grouped Commit Author IDs after Identity Resolution | This Dataset contains the IDs of 5,427,024 commit authors who have created commits in git version control system, and have more than 1 ID in git. It is a compressed CSV file (separated by ; ) with 14,861,538 author IDs, where the first column is the group ID, which is same as the first (randomly selected) author ID of the group, and the second column is the author ID that is part of the group. If an author was found to have 2 different IDs: I1, I2, then it is recorded in the file in 2 separate lines, with the lines being I1;I1 and I1;I2, i.e. the first column is the group identifier, which is one of the IDs in a group, and the second column contains the different author IDs in separate lines. This data set contains email addresses for various Git author's, but the '@' within the email address has been replaced with a '#'. | Provide a detailed description of the following dataset: Dataset of Grouped Commit Author IDs after Identity Resolution |
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