Omkar2118/datasets huggingface dataset:
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. 

Source: [Joint Embedding of 3D Scan and CAD Objects](/paper/joint-embedding-of-3d-scan-and-cad-objects)

Introducing paper: Joint Embedding of 3D Scan and CAD Objects
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.

Source: [ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language](/paper/scanrefer-3d-object-localization-in-rgb-d)

Introducing paper: ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language
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.

Source: [scb-mt-en-th-2020: A Large English-Thai Parallel Corpus](/paper/scb-mt-en-th-2020-a-large-english-thai)
Image Source: [Lowphansirikul et al](https://arxiv.org/pdf/2007.03541v1.pdf)

Introducing paper: scb-mt-en-th-2020: A Large English-Thai Parallel Corpus
SCDB
Full name: Simple Concept DataBase

Includes annotations for 10 distinguishable concepts.

Source: [Explaining AI-based Decision Support Systems using Concept Localization Maps](/paper/explaining-ai-based-decision-support-systems)

Introducing paper: Explaining AI-based Decision Support Systems using Concept Localization Maps
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. 

Source: [ScienceIE](https://scienceie.github.io/)
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. 

Source: [TLDR: Extreme Summarization of Scientific Documents](/paper/tldr-extreme-summarization-of-scientific)

Introducing paper: TLDR: Extreme Summarization of Scientific Documents
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)

Introducing paper: Complicated Table Structure Recognition
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.

Source: [Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes](/paper/scruples-a-corpus-of-community-ethical)

Introducing paper: Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes
SCUT-HEAD

Includes 4405 images with 111251 heads annotated.

Source: [Detecting Heads using Feature Refine Net and Cascaded Multi-Scale Architecture](/paper/detecting-heads-using-feature-refine-net-and)

Introducing paper: Detecting Heads using Feature Refine Net and Cascaded Multi-Scale Architecture
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)

Introducing paper: Search4Code: Code Search Intent Classification Using Weak Supervision
SeasonDepth

Aa new cross-season scaleless monocular depth prediction dataset from CMU Visual Localization dataset through structure from motion.

Source: [SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments](/paper/seasondepth-cross-season-monocular-depth)

Introducing paper: SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments
SegTHOR
Full name: Segmentation of THoracic Organs at Risk

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. 

Source: [SegTHOR: Segmentation of Thoracic Organs at Risk in CT images](/paper/segthor-segmentation-of-thoracic-organs-at)
Image Source: [Lambert et al](https://paperswithcode.com/paper/segthor-segmentation-of-thoracic-organs-at)

Introducing paper: SegTHOR: Segmentation of Thoracic Organs at Risk in CT images
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. 

Source: [SelQA: A New Benchmark for Selection-based Question Answering](/paper/selqa-a-new-benchmark-for-selection-based)

Introducing paper: SelQA: A New Benchmark for Selection-based Question Answering
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.

Source: [SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances](/paper/semanticposs-a-point-cloud-dataset-with-large)

Introducing paper: SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances
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.

Source: [How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval](/paper/how-to-read-paintings-semantic-art)

Introducing paper: How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval
SEMCAT

Contains more than 6500 words semantically grouped under 110 categories.

Source: [Semantic Structure and Interpretability of Word Embeddings](/paper/semantic-structure-and-interpretability-of)

Introducing paper: Semantic Structure and Interpretability of Word Embeddings
SemClinBr
Full name: A multi‑institutional and multi‑specialty semantically annotated corpus for Portuguese clinical NLP tasks

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

Source: [SemClinBr -- a multi institutional and multi specialty semantically annotated corpus for Portuguese clinical NLP tasks](/paper/semclinbr-a-multi-institutional-and-multi)

Introducing paper: SemClinBr -- a multi institutional and multi specialty semantically annotated corpus for Portuguese clinical NLP tasks
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.

Source: [SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion](/paper/sen12ms-a-curated-dataset-of-georeferenced)

Introducing paper: SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion
SEND
Full name: Stanford Emotional Narratives Dataset

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.

Source: [Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset](/paper/modeling-emotion-in-complex-stories-the)

Introducing paper: Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset
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..

Source: [https://github.com/QingyongHu/SensatUrban](https://github.com/QingyongHu/SensatUrban)
Image Source: [https://github.com/QingyongHu/SensatUrban](https://github.com/QingyongHu/SensatUrban)

Introducing paper: Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges
Sentiment140

Sentiment140 is a dataset that allows you to discover the sentiment of a brand, product, or topic on Twitter.

Source: [Sentiment140](http://help.sentiment140.com/home)
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)

Introducing paper: Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification
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.

Source: [Serial Speakers: a Dataset of TV Series](/paper/serial-speakers-a-dataset-of-tv-series)

Introducing paper: Serial Speakers: a Dataset of TV Series
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.

Source: [SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild](/paper/sewa-db-a-rich-database-for-audio-visual)

Introducing paper: SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild
SFU-Store-Nav

A dataset collected in a set of experiments that involves human participants and a robot.

Source: [SFU-Store-Nav: A Multimodal Dataset for Indoor Human Navigation](/paper/sfu-store-nav-a-multimodal-dataset-for-indoor)

Introducing paper: SFU-Store-Nav: A Multimodal Dataset for Indoor Human Navigation