dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
REFreSD | Consists of English-French sentence-pairs annotated with semantic divergence classes and token-level rationales. | Provide a detailed description of the following dataset: REFreSD |
REFUGE Challenge | REFUGE Challenge provides a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. | Provide a detailed description of the following dataset: REFUGE Challenge |
ReINTEL | 10,000 news collected from a social network in Vietnam. | Provide a detailed description of the following dataset: ReINTEL |
Relational Strategies in Customer Service (RSiCS) Dataset | Corpus for improving the quality and relational abilities of Intelligent Virtual Agents (IVAs). | Provide a detailed description of the following dataset: Relational Strategies in Customer Service (RSiCS) Dataset |
Relative Size | The Relative Size dataset contains 486 object pairs between 41 physical objects. Size comparisons are not available for all pairs of objects (e.g. bird and watermelon) because for some pairs humans cannot determine which object is bigger.
Dataset contains only object pairs that people have consistently agreed which one is bigger. Objects appear in 24 pairs on average, window with 13 pairs has the least, and eye with 35 pairs has the most number of comparisons. | Provide a detailed description of the following dataset: Relative Size |
RELLIS-3D | **RELLIS-3D** is a multi-modal dataset for off-road robotics. It was collected in an off-road environment containing annotations for 13,556 LiDAR scans and 6,235 images. The data was collected on the Rellis Campus of Texas A&M University and presents challenges to existing algorithms related to class imbalance and environmental topography. The dataset also provides full-stack sensor data in ROS bag format, including RGB camera images, LiDAR point clouds, a pair of stereo images, high-precision GPS measurement, and IMU data.
Source: [https://github.com/unmannedlab/RELLIS-3D](https://github.com/unmannedlab/RELLIS-3D)
Image Source: [https://github.com/unmannedlab/RELLIS-3D](https://github.com/unmannedlab/RELLIS-3D) | Provide a detailed description of the following dataset: RELLIS-3D |
RELX | **RELX** is a benchmark dataset for cross-lingual relation classification in English, French, German, Spanish and Turkish.
Source: [https://github.com/boun-tabi/RELX](https://github.com/boun-tabi/RELX) | Provide a detailed description of the following dataset: RELX |
Rendered Handpose Dataset | Rendered Handpose Dataset contains 41258 training and 2728 testing samples. Each sample provides:
- RGB image (320x320 pixels)
- Depth map (320x320 pixels)
- Segmentation masks (320x320 pixels) for the classes: background, person, three classes for each finger and one for each palm
- 21 Keypoints for each hand with their uv coordinates in the image frame, xyz coordinates in the world frame and a visibility indicator
- Intrinsic Camera Matrix K | Provide a detailed description of the following dataset: Rendered Handpose Dataset |
Rendered WB dataset | A dataset of over 65,000 pairs of incorrectly white-balanced images and their corresponding correctly white-balanced images. | Provide a detailed description of the following dataset: Rendered WB dataset |
Rent3D | A dataset which contains over 200 apartments. | Provide a detailed description of the following dataset: Rent3D |
Replica | The Replica Dataset is a dataset of high quality reconstructions of a variety of indoor spaces. Each reconstruction has clean dense geometry, high resolution and high dynamic range textures, glass and mirror surface information, planar segmentation as well as semantic class and instance segmentation. | Provide a detailed description of the following dataset: Replica |
RESIDE | A new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. | Provide a detailed description of the following dataset: RESIDE |
Retail50K | A dataset to encourage the community to adapt oriented bounding box (OBB) detectors for more complex environments. | Provide a detailed description of the following dataset: Retail50K |
ReviewQA | ReviewQA is a question-answering dataset based on hotel reviews. The questions of this dataset are linked to a set of relational understanding competencies that a model is expected to master. Indeed, each question comes with an associated type that characterizes the required competency. | Provide a detailed description of the following dataset: ReviewQA |
RF signal | This dataset is used for **RF signal** recognition, used to recognize different RF devices based on the signals they transmitted.
Source: [https://arxiv.org/pdf/1908.09931.pdf](https://arxiv.org/pdf/1908.09931.pdf) | Provide a detailed description of the following dataset: RF signal |
RFW | To validate the racial bias of four commercial APIs and four state-of-the-art (SOTA) algorithms. | Provide a detailed description of the following dataset: RFW |
DIML/CVl RGB-D Dataset | This dataset contains synchronized RGB-D frames from both Kinect v2 and Zed stereo camera. For the outdoor scene, the authors first generate disparity maps using an accurate stereo matching method and convert them using calibration parameters. A per-pixel confidence map of disparity is also provided. The scenes are captured at various places, e.g., offices, rooms, dormitory, exhibition center, street, road etc., from Yonsei University and Ewha University. | Provide a detailed description of the following dataset: DIML/CVl RGB-D Dataset |
RGB-DAVIS Dataset | Used to show systematic performance improvement in applications such as high frame-rate video synthesis, feature/corner detection and tracking, as well as high dynamic range image reconstruction. | Provide a detailed description of the following dataset: RGB-DAVIS Dataset |
RGB-D Object dataset | The dataset contains 300 objects organized into 51 categories and has been made publicly available to the research community so as to enable rapid progress based on this promising technology. | Provide a detailed description of the following dataset: RGB-D Object dataset |
RICE | **RICE** is a remote sensing image dataset for cloud removal. The proposed dataset consists of two parts: RICE1 contains 500 pairs of images, each pair has images with cloud and cloudless size of 512*512; RICE2 contains 450 sets of images, each set contains three 512*512 size images, respectively, the reference picture without clouds, the picture of the cloud and the mask of its cloud.
Source: [https://github.com/BUPTLdy/RICE_DATASET](https://github.com/BUPTLdy/RICE_DATASET) | Provide a detailed description of the following dataset: RICE |
Rijksmuseum Challenge 2014 | Dataset used for the challenge to apply computer vision techniques on art objects (paintings, sculptures, drawings etc) from the Rijksmuseum (in Amsterdam, the Netherlands). | Provide a detailed description of the following dataset: Rijksmuseum Challenge 2014 |
RiSAWOZ | **RiSAWOZ** is a large-scale multi-domain Chinese Wizard-of-Oz dataset with Rich Semantic Annotations. RiSAWOZ contains 11.2K human-to-human (H2H) multi-turn semantically annotated dialogues, with more than 150K utterances spanning over 12 domains, which is larger than all previous annotated H2H conversational datasets. Both single- and multi-domain dialogues are constructed, accounting for 65% and 35%, respectively. Each dialogue is labelled with comprehensive dialogue annotations, including dialogue goal in the form of natural language description, domain, dialogue states and acts at both the user and system side. In addition to traditional dialogue annotations, it also includes linguistic annotations on discourse phenomena, e.g., ellipsis and coreference, in dialogues, which are useful for dialogue coreference and ellipsis resolution tasks.
Source: [https://github.com/terryqj0107/RiSAWOZ](https://github.com/terryqj0107/RiSAWOZ) | Provide a detailed description of the following dataset: RiSAWOZ |
RISE | **RISE** is a large-scale video dataset for Recognizing Industrial Smoke Emissions. A citizen science approach was adopted to collaborate with local community members to annotate whether a video clip has smoke emissions. The dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons.
Source: [https://arxiv.org/abs/2005.06111](https://arxiv.org/abs/2005.06111)
Image Source: [https://github.com/CMU-CREATE-Lab/deep-smoke-machine](https://github.com/CMU-CREATE-Lab/deep-smoke-machine) | Provide a detailed description of the following dataset: RISE |
RIT-18 | The RIT-18 dataset was built for the semantic segmentation of remote sensing imagery. It was collected with the Tetracam Micro-MCA6 multispectral imaging sensor flown on-board a DJI-1000 octocopter.
The features this dataset include 1) very-high resolution multispectral imagery from a drone, 2) six-spectral VNIR bands, and 3) 18 object classes (plus background) with a severely unbalanced class distribution. | Provide a detailed description of the following dataset: RIT-18 |
RLLab Framework | A benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. | Provide a detailed description of the following dataset: RLLab Framework |
RMFD | **Real-World Masked Face Dataset** (**RMFD**) is a large dataset for masked face detection. | Provide a detailed description of the following dataset: RMFD |
Road Scene Graph | A special scene-graph for intelligent vehicles. Different to classical data representation, this graph provides not only object proposals but also their pair-wise relationships. By organizing them in a topological graph, these data are explainable, fully-connected, and could be easily processed by GCNs (Graph Convolutional Networks). | Provide a detailed description of the following dataset: Road Scene Graph |
RoadText-1K | A dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. The dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. | Provide a detailed description of the following dataset: RoadText-1K |
RoadTracer | **RoadTracer** is a dataset for extraction of road networks from aerial images. It consists of a large
corpus of high-resolution satellite imagery and ground truth
road network graphs covering the urban core of forty cities
across six countries. For each city, the dataset covers a region of approximately 24 sq km around the city center. The satellite imagery is obtained from Google at 60 cm/pixel resolution, and the road network from OSM.
The dataset is split into a training set with 25 cities and a
test set with 15 other cities. | Provide a detailed description of the following dataset: RoadTracer |
RoboCupSimData | A large dataset from games of some of the top teams (from 2016 and 2017) in RoboCup Soccer Simulation League (2D), where teams of 11 robots (agents) compete against each other. | Provide a detailed description of the following dataset: RoboCupSimData |
RoboNet | An open database for sharing robotic experience, which provides an initial pool of 15 million video frames, from 7 different robot platforms, and study how it can be used to learn generalizable models for vision-based robotic manipulation. | Provide a detailed description of the following dataset: RoboNet |
Robot@Home dataset | The Robot-at-Home dataset (Robot@Home) is a collection of raw and processed data from five domestic settings compiled by a mobile robot equipped with 4 RGB-D cameras and a 2D laser scanner. Its main purpose is to serve as a testbed for semantic mapping algorithms through the categorization of objects and/or rooms.
Paper: [Robot@Home, a robotic dataset for semantic mapping of home environments](https://journals.sagepub.com/doi/full/10.1177/0278364917695640) | Provide a detailed description of the following dataset: Robot@Home dataset |
Robotic Instruments | Provides 8x 225-frame robotic surgical videos, captured at 2 Hz, where a trained team at Intuitive Surgical has manually labelled the different parts and types. The users are invited to test their algorithms on 8x 75-frame videos and 2x 300-frame videos which act as a test set. | Provide a detailed description of the following dataset: Robotic Instruments |
RobustPointSet | A dataset for robustness analysis of point cloud classification models (independent of data augmentation) to input transformations. | Provide a detailed description of the following dataset: RobustPointSet |
Roll4Real | Consists of real objects rolling on complex terrains (pool table, elliptical bowl, and random height-field). | Provide a detailed description of the following dataset: Roll4Real |
Roman Urdu Data Set | Tagged for Sentiment (Positive, Negative, Neutral). | Provide a detailed description of the following dataset: Roman Urdu Data Set |
RONEC | Romanian Named Entity Corpus is a named entity corpus for the Romanian language. The corpus contains over 26000 entities in ~5000 annotated sentences, belonging to 16 distinct classes. The sentences have been extracted from a copy-right free newspaper, covering several styles. This corpus represents the first initiative in the Romanian language space specifically targeted for named entity recognition. | Provide a detailed description of the following dataset: RONEC |
ROSTD | A dataset of 4K out-of-domain (OOD) examples for the publicly available dataset from (Schuster et al. 2019). In contrast to existing settings which synthesize OOD examples by holding out a subset of classes, the examples were authored by annotators with apriori instructions to be out-of-domain with respect to the sentences in an existing dataset. | Provide a detailed description of the following dataset: ROSTD |
Rotowire-Modified | The RotoWire-Modified dataset is a cleaned extension of the RotoWire dataset, with writer information about each document. It contains 2705 samples for training, 532 for validation and 497 for testing.
Source: [https://github.com/aistairc/rotowire-modified](https://github.com/aistairc/rotowire-modified) | Provide a detailed description of the following dataset: Rotowire-Modified |
RP2K | A new large-scale retail product dataset for fine-grained image classification. Unlike previous datasets focusing on relatively few products, more than 500,000 images of retail products on shelves were collected, belonging to 2000 different products. The dataset aims to advance the research in retail object recognition, which has massive applications such as automatic shelf auditing and image-based product information retrieval. | Provide a detailed description of the following dataset: RP2K |
RPC | RPC is a large-scale retail product checkout dataset and collects 200 retail SKUs. The collected SKUs can be divided into 17 meta categories, i.e., puffed food, dried fruit, dried food, instant drink, instant noodles, dessert, drink, alcohol, milk, canned food, chocolate, gum, candy, seasoner, personal hygiene, tissue, stationery. | Provide a detailed description of the following dataset: RPC |
RSDD-Time | RSDD-Time is a dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Additionally, the dataset includes exact temporal spans that relate to the date of diagnosis. | Provide a detailed description of the following dataset: RSDD-Time |
RSICD | The **Remote Sensing Image Captioning Dataset** (**RSICD**) is a dataset for remote sensing image captioning task. It contains more than ten thousands remote sensing images which are collected from Google Earth, Baidu Map, MapABC and Tianditu. The images are fixed to 224X224 pixels with various resolutions. The total number of remote sensing images is 10921, with five sentences descriptions per image.
Source: [https://github.com/201528014227051/RSICD_optimal](https://github.com/201528014227051/RSICD_optimal)
Image Source: [https://github.com/201528014227051/RSICD_optimal](https://github.com/201528014227051/RSICD_optimal) | Provide a detailed description of the following dataset: RSICD |
RTN | A corpus of real-world spoken personal narratives comprising 10,296 narrative clauses from 594 video transcripts. | Provide a detailed description of the following dataset: RTN |
RuBQ | The first Russian knowledge base question answering (KBQA) dataset. The high-quality dataset consists of 1,500 Russian questions of varying complexity, their English machine translations, SPARQL queries to Wikidata, reference answers, as well as a Wikidata sample of triples containing entities with Russian labels. The dataset creation started with a large collection of question-answer pairs from online quizzes. The data underwent automatic filtering, crowd-assisted entity linking, automatic generation of SPARQL queries, and their subsequent in-house verification. | Provide a detailed description of the following dataset: RuBQ |
Runway | A runway dataset, designing features suitable for capturing outfit appearance, collecting human judgments of outfit similarity, and learning similarity functions on the features to mimic those judgments. | Provide a detailed description of the following dataset: Runway |
Ruralscapes | A dataset with high resolution (4K) images and manually-annotated dense labels every 50 frames. | Provide a detailed description of the following dataset: Ruralscapes |
RuSentRel | **RuSentRel** is a corpus of analytical articles translated into Russian texts in the domain of international politics obtained from foreign authoritative sources. The collected articles contain both the author's opinion on the subject matter of the article and a large number of references mentioned between the participants of the described situations. In total, 73 large analytical texts were labeled with about 2000 relations. | Provide a detailed description of the following dataset: RuSentRel |
RUSLAN | RUSLAN is a Russian spoken language corpus for text-to-speech task. RUSLAN contains 22,200 audio samples with text annotations – more than 31 hours of high-quality speech of one person – being one of the largest annotated Russian corpus in terms of speech duration for a single speaker. | Provide a detailed description of the following dataset: RUSLAN |
RuStance | Includes Russian tweets and news comments from multiple sources, covering multiple stories, as well as text classification approaches to stance detection as benchmarks over this data in this language. | Provide a detailed description of the following dataset: RuStance |
RWF-2000 | A database with 2,000 videos captured by surveillance cameras in real-world scenes. | Provide a detailed description of the following dataset: RWF-2000 |
RxR | Room-Across-Room (RxR) is a multilingual dataset for Vision-and-Language Navigation (VLN) for Matterport3D environments. In contrast to related datasets such as Room-to-Room (R2R), RxR is 10x larger, multilingual (English, Hindi and Telugu), with longer and more variable paths, and it includes and fine-grained visual groundings that relate each word to pixels/surfaces in the environment. | Provide a detailed description of the following dataset: RxR |
S2TLD | **S2TLD** is a traffic light dataset, which contains 5,786 images of approximately 1,080 * 1,920 pixels and 720 * 1,280 pixels. It also contains 5 categories (include red, yellow, green, off and wait on) of 1,4130 instances. The scenes cover a decent variety of road scenes and typical:
* Busy street scenes inner-city,
* Dense stop-and-go traffic
* Strong changes in illumination/exposure
* Flickering/Fluctuating traffic lights
* Multiple visible traffic lights
* Image parts that can be confused with traffic lights (e.g. large round tail lights)
Source: [https://github.com/Thinklab-SJTU/S2TLD](https://github.com/Thinklab-SJTU/S2TLD)
Image Source: [https://github.com/Thinklab-SJTU/S2TLD](https://github.com/Thinklab-SJTU/S2TLD) | Provide a detailed description of the following dataset: S2TLD |
S3O4D | The data consists of 100,000 renderings each of the Bunny and Dragon objects from the Stanford 3D Scanning Repository. More objects may be added in the future, but only the Bunny and Dragon are used in the paper. Each object is rendered with a uniformly sampled illumination from a point on the 2-sphere, and a uniformly sampled 3D rotation. The true latent states are provided as NumPy arrays along with the images. The lighting is given as a 3-vector with unit norm, while the rotation is provided both as a quaternion and a 3x3 orthogonal matrix. | Provide a detailed description of the following dataset: S3O4D |
SailX | A dataset for grounded language learning that consists of navigational instructions and actions in a maze-like environment. | Provide a detailed description of the following dataset: SailX |
Salient Closed Boundary Tracking | This dataset contains nine video sequences captured by a webcam for salient closed boundary tracking evaluation. Each sequence is about 30 sec (30 fps) and the frame size is 640×480 (width×height). There are 9598 frames in total. In each sequence, different motion styles such as translation, rotation and viewpoint changing are all performed.
Source: [https://github.com/NathanUA/SalientClosedBoundaryTrackingDataset](https://github.com/NathanUA/SalientClosedBoundaryTrackingDataset)
Image Source: [https://github.com/NathanUA/SalientClosedBoundaryTrackingDataset](https://github.com/NathanUA/SalientClosedBoundaryTrackingDataset) | Provide a detailed description of the following dataset: Salient Closed Boundary Tracking |
Salient-KITTI | **Salient-KITTI** is a saliency map prediction dataset based on KITTI.
Source: [https://arxiv.org/pdf/2012.11863.pdf](https://arxiv.org/pdf/2012.11863.pdf)
Image Source: [https://github.com/Saixiaoma/SBA-SLAM](https://github.com/Saixiaoma/SBA-SLAM) | Provide a detailed description of the following dataset: Salient-KITTI |
Salient Object Subitizing Dataset | A salient object subitizing image dataset of about 14K everyday images which are annotated using an online crowdsourcing marketplace. | Provide a detailed description of the following dataset: Salient Object Subitizing Dataset |
SALSA | A novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis. | Provide a detailed description of the following dataset: SALSA |
SAMM Long Videos | The **SAMM Long Videos** dataset consists of 147 long videos with 343 macro-expressions and 159 micro-expressions. The dataset is FACS-coded with detailed Action Units. | Provide a detailed description of the following dataset: SAMM Long Videos |
SAMSum Corpus | A new dataset with abstractive dialogue summaries. | Provide a detailed description of the following dataset: SAMSum Corpus |
San Francisco Landmark Dataset | The San Francisco Landmark Dataset contains a database of 1.7 million images of buildings in San Francisco with ground truth labels, geotags, and calibration data, as well as a difficult query set of 803 cell phone images taken with a variety of different camera phones. The data is originally acquired by vehicle-mounted cameras with wide-angle lenses capturing spherical panoramic images. For all visible buildings in each panorama, a set of overlapping perspective images is generated.
Paper: [D. Chen, G. Baatz, K. Koeser, S. Tsai, R. Vedantham, T. Pylvanainen, K. Roimela, X. Chen, J. Bach, M. Pollefeys, B. Girod, and R. Grzeszczuk, "City-scale landmark identification on mobile devices", IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2011.](https://ieeexplore.ieee.org/document/5995610) | Provide a detailed description of the following dataset: San Francisco Landmark Dataset |
SARA | A dataset for statutory reasoning in tax law entailment and question answering. | Provide a detailed description of the following dataset: SARA |
SARC | This dataset was designed for contextual investigations, with related works making considerable usage of said context. The dataset was constructed by scraping Reddit comments; with sarcastic entries being self-annotated by authors through the use of the \s token, which indicates sarcastic intent on the website. Posts on Reddit are often in response to another comment; SARC incorporates this information through the addition of the parent comment and further child comments surrounding a post. | Provide a detailed description of the following dataset: SARC |
SART | **SART** is a collection of three datasets for Similarity, Analogies and Relatedness for the Tatar language.
The three subsets are:
* Similarity dataset - 202 pairs of words along with averaged human scores of similarity degree between the words (in 0-to-10 scale). For example, "йорт, бина, 7.69".
* Relatedness dataset - 252 pairs of words along with averaged human scores of relatedness degree between the words. For example, "урам, балалар, 5.38".
* Analogies dataset - set of analytical questions of the form A:B::C:D, meaning A to B as C to D, and D is to be predicted. For example, "Әнкара Төркия Париж Франция". Contains 34 categories, and in total 30 144 questions.
Source: [https://github.com/tat-nlp/SART](https://github.com/tat-nlp/SART) | Provide a detailed description of the following dataset: SART |
SatStereo | Provides a set of stereo-rectified images and the associated groundtruthed disparities for 10 AOIs (Area of Interest) drawn from two sources: 8 AOIs from IARPA's MVS Challenge dataset and 2 AOIs from the CORE3D-Public dataset. | Provide a detailed description of the following dataset: SatStereo |
SAVOIAS | A visual complexity dataset that compromises of more than 1,400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism. The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics. | Provide a detailed description of the following dataset: SAVOIAS |
SberQuAD | A large scale analogue of Stanford SQuAD in the Russian language - is a valuable resource that has not been properly presented to the scientific community. | Provide a detailed description of the following dataset: SberQuAD |
SBIC | To support large-scale modelling and evaluation with 150k structured annotations of social media posts, covering over 34k implications about a thousand demographic groups. | Provide a detailed description of the following dataset: SBIC |
SBU Captions Dataset | A collection that allows researchers to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. | Provide a detailed description of the following dataset: SBU Captions Dataset |
SBWCE | This resource consists of an unannotated corpus of the Spanish language of nearly 1.5 billion words, compiled from different corpora and resources from the web; and a set of word vectors (or embeddings), created from this corpus using the word2vec algorithm, provided by the gensim package. These embeddings were evaluated by translating to Spanish word2vec’s word relation test set. | Provide a detailed description of the following dataset: SBWCE |
SCAN | SCAN is a dataset for grounded navigation which consists of a set of simple compositional navigation commands paired with the corresponding action sequences. | Provide a detailed description of the following dataset: SCAN |
Scan-CAD Object Similarity Dataset | A dataset of ranked scan-CAD similarity annotations, enabling new, fine-grained evaluation of CAD model retrieval to cluttered, noisy, partial scans. | Provide a detailed description of the following dataset: Scan-CAD Object Similarity Dataset |
ScanRefer Dataset | Contains 51,583 descriptions of 11,046 objects from 800 ScanNet scenes. ScanRefer is the first large-scale effort to perform object localization via natural language expression directly in 3D. | Provide a detailed description of the following dataset: ScanRefer Dataset |
scb-mt-en-th-2020 | scb-mt-en-th-2020 is an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources, namely news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data and government documents. | Provide a detailed description of the following dataset: scb-mt-en-th-2020 |
SCDB | Includes annotations for 10 distinguishable concepts. | Provide a detailed description of the following dataset: SCDB |
ScienceIE | The shared task ScienceIE at SemEval 2017 deals with automatic extraction of keyphrases from Computer Science, Material Sciences and Physics publications, as well as extracting types of keyphrases and relations between keyphrases.
PROCESS, TASK and MATERIAL form the fundamental objects in scientific works. Scientific research and practice is founded upon gaining, maintaining and understanding the body of existing scientific work in specific areas related to such fundamental objects. | Provide a detailed description of the following dataset: ScienceIE |
ScisummNet | Large-scale manually-annotated corpus for 1,000 scientific papers (on computational linguistics) for automatic summarization. Summaries for each paper are constructed from the papers that cite that paper and from that paper's abstract.
Source: [ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks](https://arxiv.org/pdf/1909.01716v3.pdf) | Provide a detailed description of the following dataset: ScisummNet |
SciTLDR | A new multi-target dataset of 5.4K TLDRs over 3.2K papers. SciTLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden. | Provide a detailed description of the following dataset: SciTLDR |
SciTSR | **SciTSR** is a large-scale table structure recognition dataset, which contains 15,000 tables in PDF format and their corresponding structure labels obtained from LaTeX source files.
Source: [https://github.com/Academic-Hammer/SciTSR](https://github.com/Academic-Hammer/SciTSR) | Provide a detailed description of the following dataset: SciTSR |
Scruples | Dataset with 625,000 ethical judgments over 32,000 real-life anecdotes. Each anecdote recounts a complex ethical situation, often posing moral dilemmas, paired with a distribution of judgments contributed by the community members. | Provide a detailed description of the following dataset: Scruples |
SCUT-HEAD | Includes 4405 images with 111251 heads annotated. | Provide a detailed description of the following dataset: SCUT-HEAD |
Search4Code | **Search4Code** is a large-scale web query based dataset of code search queries for C# and Java. The Search4Code data is mined from Microsoft Bing's anonymized search query logs using weak supervision technique.
Source: [https://github.com/microsoft/Search4Code](https://github.com/microsoft/Search4Code) | Provide a detailed description of the following dataset: Search4Code |
SeasonDepth | Aa new cross-season scaleless monocular depth prediction dataset from CMU Visual Localization dataset through structure from motion. | Provide a detailed description of the following dataset: SeasonDepth |
SegTHOR | SegTHOR (Segmentation of THoracic Organs at Risk) is a dataset dedicated to the segmentation of organs at risk (OARs) in the thorax, i.e. the organs surrounding the tumour that must be preserved from irradiations during radiotherapy. In this dataset, the OARs are the heart, the trachea, the aorta and the esophagus, which have varying spatial and appearance characteristics. The dataset includes 60 3D CT scans, divided into a training set of 40 and a test set of 20 patients, where the OARs have been contoured manually by an experienced radiotherapist. | Provide a detailed description of the following dataset: SegTHOR |
SelQA | SelQA is a dataset that consists of questions generated through crowdsourcing and sentence length answers that are drawn from the ten most prevalent topics in the English Wikipedia. | Provide a detailed description of the following dataset: SelQA |
SemanticPOSS | The SemanticPOSS dataset for 3D semantic segmentation contains 2988 various and complicated LiDAR scans with large quantity of dynamic instances. The data is collected in Peking University and uses the same data format as SemanticKITTI. | Provide a detailed description of the following dataset: SemanticPOSS |
SemArt | SemArt is a multi-modal dataset for semantic art understanding. SemArt is a collection of fine-art painting images in which each image is associated to a number of attributes and a textual artistic comment, such as those that appear in art catalogues or museum collections. It contains 21,384 samples that provides artistic comments along with fine-art paintings and their attributes for studying semantic art understanding. | Provide a detailed description of the following dataset: SemArt |
SEMCAT | Contains more than 6500 words semantically grouped under 110 categories. | Provide a detailed description of the following dataset: SEMCAT |
SemClinBr | Background: The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field. Methods: In this study, a semantically annotated corpus was developed using clinical text from multiple medical specialties, document types, and institutions. In addition, we present, (1) a survey listing common aspects, differences, and lessons learned from previous research, (2) a fine-grained annotation schema that can be replicated to guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations. Results: This study resulted in SemClinBr, a corpus that has 1000 clinical notes, labeled with 65,117 entities and 11,263 relations. In addition, both negation cues and medical abbreviation dictionaries were generated from the annotations. The average annotator agreement score varied from 0.71 (applying strict match) to 0.92 (considering a relaxed match) while accepting partial overlaps and hierarchically related semantic types. The extrinsic evaluation, when applying the corpus to two downstream NLP tasks, demonstrated the reliability and usefulness of annotations, with the systems achieving results that were consistent with the agreement scores. Conclusion: The SemClinBr corpus and other resources produced in this work can support clinical NLP studies, providing a common development and evaluation resource for the research community, boosting the utilization of EHRs in both clinical practice and biomedical research. To the best of our knowledge, SemClinBr is the first available Portuguese clinical corpus. Keywords: Natural language processing, Semantic annotation, Clinical narratives, Corpora, Gold standard | Provide a detailed description of the following dataset: SemClinBr |
SEN12MS | A dataset consisting of 180,662 triplets of dual-pol synthetic aperture radar (SAR) image patches, multi-spectral Sentinel-2 image patches, and MODIS land cover maps. | Provide a detailed description of the following dataset: SEN12MS |
SEND | SEND (Stanford Emotional Narratives Dataset) is a set of rich, multimodal videos of self-paced, unscripted emotional narratives, annotated for emotional valence over time. The complex narratives and naturalistic expressions in this dataset provide a challenging test for contemporary time-series emotion recognition models. | Provide a detailed description of the following dataset: SEND |
SensatUrban | The SensatUrbat dataset is an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is five times the number of labeled points than the existing largest point cloud dataset. The dataset consists of large areas from two UK cities, covering about 6 km^2 of the city landscape. In the dataset, each 3D point is labeled as one of 13 semantic classes, such as ground, vegetation, car, etc.. | Provide a detailed description of the following dataset: SensatUrban |
SentiCap | The **SentiCap dataset** contains several thousand images with captions with positive and negative sentiments. These sentimental captions are constructed by the authors by re-writing factual descriptions. In total there are 2000+ sentimental captions. | Provide a detailed description of the following dataset: SentiCap |
Sentiment140 | Sentiment140 is a dataset that allows you to discover the sentiment of a brand, product, or topic on Twitter. | Provide a detailed description of the following dataset: Sentiment140 |
Sentimental LIAR | The **Sentimental LIAR** dataset is a modified and further extended version of the LIAR extension introduced by Kirilin et al. In this dataset, the multi-class labeling of LIAR is converted to a binary annotation by changing half-true, false, barely-true and pants-fire labels to False, and the remaining labels to True. Furthermore, the speaker names are converted to numerical IDs in order to avoid bias with regards to the textual representation of names. The binary-label dataset is then extended by adding sentiments derived using the Google NLP API. Sentiment analysis determines the overall attitude of the text (i.e., whether it is positive or negative), and is quantified by a numerical score. If the sentiment score is positive, then the sample is tagged as Positive for the sentiment attribute, otherwise Negative is assigned.
A further extension is introduced by adding emotion scores extracted using the IBM NLP API for each claim, which determine the detected level of 6 emotional states namely anger, sadness, disgust, fear and joy. The score for each emotion is between the range of 0 and 1.
Source: [https://github.com/UNHSAILLab/SentimentalLIAR](https://github.com/UNHSAILLab/SentimentalLIAR) | Provide a detailed description of the following dataset: Sentimental LIAR |
Serial Speakers | An annotated dataset of 161 episodes from three popular American TV serials: Breaking Bad, Game of Thrones and House of Cards. Serial Speakers is suitable both for investigating multimedia retrieval in realistic use case scenarios, and for addressing lower level speech related tasks in especially challenging conditions. | Provide a detailed description of the following dataset: Serial Speakers |
SEWA DB | A database of more than 2000 minutes of audio-visual data of 398 people coming from six cultures, 50% female, and uniformly spanning the age range of 18 to 65 years old. Subjects were recorded in two different contexts: while watching adverts and while discussing adverts in a video chat. The database includes rich annotations of the recordings in terms of facial landmarks, facial action units (FAU), various vocalisations, mirroring, and continuously valued valence, arousal, liking, agreement, and prototypic examples of (dis)liking. This database aims to be an extremely valuable resource for researchers in affective computing and automatic human sensing and is expected to push forward the research in human behaviour analysis, including cultural studies. | Provide a detailed description of the following dataset: SEWA DB |
SFU-Store-Nav | A dataset collected in a set of experiments that involves human participants and a robot. | Provide a detailed description of the following dataset: SFU-Store-Nav |
SGD | The Schema-Guided Dialogue (SGD) dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, ranging from banks and events to media, calendar, travel, and weather. For most of these domains, the dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios. The wide range of available annotations can be used for intent prediction, slot filling, dialogue state tracking, policy imitation learning, language generation, user simulation learning, among other tasks in large-scale virtual assistants. Besides these, the dataset has unseen domains and services in the evaluation set to quantify the performance in zero-shot or few shot settings. | Provide a detailed description of the following dataset: SGD |
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