dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
NCI | Contains a wide range of texts in Irish, including fiction, news reports, informative texts and official documents.
Kilgarriff, A., Rundell, M., and Uí Dhonnchadha, E. (2006). Efficient corpus development for lexicography: building the New Corpus for Ireland. Language Resources and Evaluation, 40:127–152. https://li... | Provide a detailed description of the following dataset: NCI |
Irish Wikipedia | Text from Irish Wikipedia, an online encyclopedia. | Provide a detailed description of the following dataset: Irish Wikipedia |
Hearthstone | This dataset contains card descriptions of the card game Hearthstone and the code that implements them. These are obtained from the open-source implementation Hearthbreaker (https://github.com/danielyule/hearthbreaker). | Provide a detailed description of the following dataset: Hearthstone |
Unsplash_1k | Inpainting networks are typically benchmarked on samples from Places2 dataset. However, this dataset does not have high resolution images for evaluation purposes. Instead, we will use images from the Unsplash-Lite Dataset, which contains 25k high resolution nature-themed photos. We randomly sampled 1000 images from the... | Provide a detailed description of the following dataset: Unsplash_1k |
Snips-SmartLights | The SmartLights benchmark from Snipstests the capability of controlling lights in different rooms.
It consists of 1660 requests which are split into five partitions for a 5-fold evaluation.
A sample command could be: “please change the [bedroom] lights to [red]” or “i’d like the [living room] lights to be at [twelve... | Provide a detailed description of the following dataset: Snips-SmartLights |
Snips-SmartSpeaker | The SmartSpeaker benchmark tests the performance of reacting to music player commands in English as well as in French.
It has the difficulty of containing many artist or music tracks with uncommon names in the commands, like
“play music by [a boogie wit da hoodie]” or “I’d like to listen to [Kinokoteikoku]”. | Provide a detailed description of the following dataset: Snips-SmartSpeaker |
SVRT | The Synthetic Visual Reasoning Test (SVRT) is a series of 23 classification problems involving images of randomly generated shapes.
https://www.pnas.org/doi/10.1073/pnas.1109168108
Comparing machines and humans on a visual categorization test | Provide a detailed description of the following dataset: SVRT |
AOM-CTC | This is the Current Video sequence set from the AOM-CTC. | Provide a detailed description of the following dataset: AOM-CTC |
AnoShift | AnoShift is a large-scale anomaly detection benchmark, which focuses on splitting the test data based on its temporal distance to the training set, introducing three testing splits: IID, NEAR, and FAR. This testing scenario proves to capture the in-time performance degradation of anomaly detection methods for classical... | Provide a detailed description of the following dataset: AnoShift |
DaNewsroom | The first large-scale non-English language dataset specifically curated for automatic summarisation. The document-summary pairs are news articles and manually written summaries in the Danish language. | Provide a detailed description of the following dataset: DaNewsroom |
Mechanical MNIST – Distribution Shift | The Mechanical MNIST – Distribution Shift dataset contains the results of finite element simulation of heterogeneous material subject to large deformation due to equibiaxial extension at a fixed boundary displacement of d = 7.0. The result provided in this dataset is the change in strain energy after this equibiaxial e... | Provide a detailed description of the following dataset: Mechanical MNIST – Distribution Shift |
GPI corpus | The GPI Corpus is a collection of 1,043 privacy laws, regulations, and guidelines ("GPIs") covering 182 jurisdictions around the world. These documents are provided in two file formats (i.e., PDF showing the original formatting on the source website and TXT containing just the text of the GPI) and, in some cases, in mu... | Provide a detailed description of the following dataset: GPI corpus |
TFH_Annotated_Dataset | ## **Dataset Introduction**
TFH_Annotated_Dataset is an annotated patent dataset pertaining to *thin film head* technology in hard-disk. To the best of our knowledge, this is the second labeled patent dataset public available in technology management domain that annotates both entities and the semantic relations betwe... | Provide a detailed description of the following dataset: TFH_Annotated_Dataset |
Astock | (1) provide financial news for each specific stock. (2) provide various stock technical factors and fundamental factors for each stock. | Provide a detailed description of the following dataset: Astock |
VNDS | A single-document Vietnamese summarization dataset | Provide a detailed description of the following dataset: VNDS |
Mapping Topics in 100,000 Real-Life Moral Dilemmas | This dataset accompanies the ICWSM 2022 paper "Mapping Topics in 100,000 Real-Life Moral Dilemmas". | Provide a detailed description of the following dataset: Mapping Topics in 100,000 Real-Life Moral Dilemmas |
ScribbleKITTI | ScribbleKITTI is a scribble-annotated dataset for LiDAR semantic segmentation. | Provide a detailed description of the following dataset: ScribbleKITTI |
DailyTalk | DailyTalk is a high-quality conversational speech dataset designed for Text-to-Speech. We sampled, modified, and recorded 2,541 dialogues from the open-domain dialogue dataset DailyDialog which are adequately long to represent context of each dialogue. | Provide a detailed description of the following dataset: DailyTalk |
Short Stories, Adjudicator Scores and Written Reflections | In this Adjudicator Scores_Short Stories and Written Reflections folder:
Four files from four student participants of the contest. Each file contains
1.1 the short story version that was scored by third party reviewers,
1.2 the scores given by the reviewers,
1.3 the planning of the short story,
1.4 the short ... | Provide a detailed description of the following dataset: Short Stories, Adjudicator Scores and Written Reflections |
BRIND | BRIND is a short name of BSDS-RIND is the first public benchmark that dedicated to studying simultaneously the four edge types, namely Reflectance Edge (RE), Illumination Edge (IE), Normal Edge (NE) and Depth Edge (DE) | Provide a detailed description of the following dataset: BRIND |
Pre-Contest Workshop Video Recordings | In this Pre-Contest Workshop Video Recordings folder:
Seven screen and audio recordings of seven pre-contest workshops | Provide a detailed description of the following dataset: Pre-Contest Workshop Video Recordings |
Student Reflections, Coding and Coding Scheme | In this Coding and Coding Scheme spreadsheet:
Student answers to reflection questions from pre-context workshops; coding scheme for student reflections; and coding of student reflection | Provide a detailed description of the following dataset: Student Reflections, Coding and Coding Scheme |
Pre-Contest Workshop Slidedeck | In this Pre-Contest Workshop Slidedeck.pdf:
Instructional materials delivered for the seven pre-contest workshops | Provide a detailed description of the following dataset: Pre-Contest Workshop Slidedeck |
STARSS22 | The Sony-TAu Realistic Spatial Soundscapes 2022(STARSS22) dataset consists of recordings of real scenes captured with high channel-count spherical microphone array (SMA). The recordings are conducted from two different teams at two different sites, Tampere University in Tammere, Finland, and Sony facilities in Tokyo, J... | Provide a detailed description of the following dataset: STARSS22 |
ARAUS | Choosing optimal maskers for existing soundscapes to effect a desired perceptual change via soundscape augmentation is non-trivial due to extensive varieties of maskers and a dearth of benchmark datasets with which to compare and develop soundscape augmentation models. To address this problem, we make publicly availabl... | Provide a detailed description of the following dataset: ARAUS |
Urban Soundscapes of the World | A main goal of the Urban Soundscapes of the World project is to create a reference database of examples of urban acoustic environments, consisting of high-quality immersive audiovisual recordings (360-degree video and spatial audio), in adherence to ISO 12913-2. Ultimately, this database may set the scope for immersive... | Provide a detailed description of the following dataset: Urban Soundscapes of the World |
PyMigBench | A benchmark for Python library migration. | Provide a detailed description of the following dataset: PyMigBench |
Names pairs dataset | Includes co-referent name string pairs along with their similarities. | Provide a detailed description of the following dataset: Names pairs dataset |
SKIPP'D | Large-scale integration of photovoltaics (PV) into electricity grids is challenged by the intermittent nature of solar power. Sky-image-based solar forecasting using deep learning has been recognized as a promising approach to predicting the short-term fluctuations. However, there are few publicly available standardize... | Provide a detailed description of the following dataset: SKIPP'D |
Sample EEG dataset and looking times data for NEAR (Newborn EEG Artifact Removal) | The sample EEG dataset consists of the newborn EEG data recorded for the work published as:
Buiatti M. et al. "Cortical route for facelike pattern processing in human newborns." Proceedings of the National Academy of Sciences 116.10 (2019): 4625-4630.
Please refer to the original article for the description of ho... | Provide a detailed description of the following dataset: Sample EEG dataset and looking times data for NEAR (Newborn EEG Artifact Removal) |
UMLS-43 | UMLS-43 is a variant of the UMLS knowledge graph that is robust to data leakage through inverse relations. It has been derived by removing three edge types that should be considered problematic by Dettmers' definition: 'degree_of', 'precedes', and 'derivative_of'. It is presented here* as a .tsv edgelist, such that eac... | Provide a detailed description of the following dataset: UMLS-43 |
SMAC-Exp | The StarCraft Multi-Agent Challenges+ requires agents to learn completion of multi-stage tasks and usage of environmental factors without precise reward functions. The previous challenges (SMAC) recognized as a standard benchmark of Multi-Agent Reinforcement Learning are mainly concerned with ensuring that all agents c... | Provide a detailed description of the following dataset: SMAC-Exp |
Pile of Law | Pile of Law is a ∼256GB (and growing) dataset of legal and administrative data which can be used for assessing norms on data sanitization across legal and administrative settings. | Provide a detailed description of the following dataset: Pile of Law |
FLORES-200 | FLORES-200 doubles the existing language coverage of FLORES-101. Given the nature of the new languages, which have less standardization and require more specialized professional translations, the verification process became more complex. This required modifications to the translation workflow. FLORES-200 has several la... | Provide a detailed description of the following dataset: FLORES-200 |
OmniBenchmark | Omni-Realm Benchmark (OmniBenchmark) is a diverse (21 semantic realm-wise datasets) and concise (realm-wise datasets have no concepts overlapping) benchmark for evaluating pre-trained model generalization across semantic super-concepts/realms, e.g. across mammals to aircraft.
[**ECCV2022**] | Provide a detailed description of the following dataset: OmniBenchmark |
Off_Near_parallel | smac+ offensive near scenario with 20 parallel episodic buffer | Provide a detailed description of the following dataset: Off_Near_parallel |
Off_Distant_parallel | SMAC+ offense distant scenario. | Provide a detailed description of the following dataset: Off_Distant_parallel |
SMAC+ Def_infantry_episodic | SMAC+ defensive infantry scenario with sequential episodic buffer | Provide a detailed description of the following dataset: SMAC+ Def_infantry_episodic |
Def_Infantry_parallel | smac+ defense infantry scenario with parallel episodic buffer | Provide a detailed description of the following dataset: Def_Infantry_parallel |
Def_Infantry_sequential | SMAC+ defensive infantry scenario with sequential episodic buffer | Provide a detailed description of the following dataset: Def_Infantry_sequential |
Off_Complicated_parallel | smac+ offensive complicated scenario with 20 parallel episodic buffer. | Provide a detailed description of the following dataset: Off_Complicated_parallel |
Def_Armored_parallel | smac+ defense armored scenario with parallel episodic buffer | Provide a detailed description of the following dataset: Def_Armored_parallel |
Def_Outnumbered_parallel | smac+ defense outnumbered scenario with parallel episodic buffer | Provide a detailed description of the following dataset: Def_Outnumbered_parallel |
Off_Hard_parallel | smac+ offensive hard scenario with 20 parallel episodic buffer. | Provide a detailed description of the following dataset: Off_Hard_parallel |
Off_Superhard_parallel | smac+ offensive scenario with 20 parallel episodic buffer. | Provide a detailed description of the following dataset: Off_Superhard_parallel |
Def_Armored_sequential | SMAC+ defensive armored scenario with sequential episodic buffer | Provide a detailed description of the following dataset: Def_Armored_sequential |
Def_Outnumbered_sequential | SMAC+ defensive outnumbered scenario with sequential episodic buffer | Provide a detailed description of the following dataset: Def_Outnumbered_sequential |
Off_Near_sequential | SMAC+ offensive near scenario with sequential episodic buffer | Provide a detailed description of the following dataset: Off_Near_sequential |
Off_Distant_sequential | SMAC+ offensive distant scenario with sequential episodic buffer | Provide a detailed description of the following dataset: Off_Distant_sequential |
Off_Complicated_sequential | SMAC+ offensive complicated scenario with sequential episodic buffer | Provide a detailed description of the following dataset: Off_Complicated_sequential |
Off_Hard_sequential | SMAC+ offensive hard scenario with sequential episodic buffer | Provide a detailed description of the following dataset: Off_Hard_sequential |
Off_Superhard_sequential | SMAC+ offensive superhard scenario with sequential episodic buffer | Provide a detailed description of the following dataset: Off_Superhard_sequential |
MOTFront | MOTFront provides photo-realistic RGB-D images with their corresponding instance segmentation masks, class labels, 2D & 3D bounding boxes, 3D geometry, 3D poses and camera parameters. The MOTFront dataset comprises 2,381 unique indoor sequences with a total of 60,000 images and is based on the 3D-FRONT dataset. | Provide a detailed description of the following dataset: MOTFront |
Korea Composite Stock Price Index | The data contains the following attributes for Korea Stock Price Index (KOSPI) for January
2000–December 2016:
1. Date (YYYY.M(M).D(D))
2. Opening Price for the date, PX_OPEN
3. Highest Price for the date, PX_HIGH
4. Lowest Price for the date, PX_LOW
5. Closing Price for the date, PX_LAST
6. Total volume traded ... | Provide a detailed description of the following dataset: Korea Composite Stock Price Index |
FewSOL | The **Few**-**S**hot **O**bject **L**earning (FewSOL) dataset can be used for object recognition with a few images per object. It contains 336 real-world objects with 9 RGB-D images per object from different views. Object segmentation masks, object poses and object attributes are provided. In addition, synthetic images... | Provide a detailed description of the following dataset: FewSOL |
QPT | Quantum process tomography (QPT) is a method for experimentally reconstructing the quantum channel from measurement data. A QPT experiment prepares multiple input states, evolves them by the circuit, then performs multiple measurements in different measurement bases. | Provide a detailed description of the following dataset: QPT |
cTDaR | Table is a compact and efficient form for summarizing and presenting correlative information in handwritten and printed archival documents, scientific journals, reports, financial statements and so on. Table recognition is fundamental for the extraction of information from structured documents. The ICDAR 2019 cTDaR eva... | Provide a detailed description of the following dataset: cTDaR |
wifi_data | Wi-Fi dataset: the dataset may be downloaded from this link. If you use this dataset, please cite the following reference:
Anisa Allahdadi, Ricardo Morla, and Jaime S. Cardoso. "802.11 wireless simulation and anomaly detection using HMM and UBM".
CoRR, abs/1707.02933, 2017. URL http://arxiv.org/abs/1707.02933.
... | Provide a detailed description of the following dataset: wifi_data |
SC2ReSet: StarCraft II Esport Replaypack Set | Raw StarCraft II data is subject to processing under the Blizzard end user license agreement (EULA), and in special cases Blizzard AI and Machine Learning License may be applied. Please refer to the materials listed below.
The dataset contains data in MPQ format (.SC2Replay) that can be processed with multiple open-... | Provide a detailed description of the following dataset: SC2ReSet: StarCraft II Esport Replaypack Set |
SC2EGSet: StarCraft II Esport Game State Dataset | SC2EGSet: StarCraft II Esport Game State Dataset
Pre-processed data that was generated from the SC2ReSet: StarCraft II Esports Replaypack Set
Data Modeling
Our aplication programing interface (API) implementation supports downloading, unpacking, loading and data access features. Please refer to: https://github... | Provide a detailed description of the following dataset: SC2EGSet: StarCraft II Esport Game State Dataset |
TGRDB | 1. Our TGRDB dataset was collected with a 180 fisheye
RGB camera on-board of a moving tour-guide robot.
2. A first dataset in tour-guide scenario. Statistical comparisons between TGRDB and existing
datasets are refferred to https://arxiv.org/abs/2207.03726.
3. We hope this dataset will drive the progress of resear... | Provide a detailed description of the following dataset: TGRDB |
Simulacra Aesthetic Captions | **Simulacra Aesthetic Captions** is a dataset of over 238000 synthetic images generated with AI models such as CompVis latent GLIDE and Stable Diffusion from over forty thousand user submitted prompts. The images are rated on their aesthetic value from 1 to 10 by users to create caption, image, and rating triplets. In ... | Provide a detailed description of the following dataset: Simulacra Aesthetic Captions |
OBJ-MDA | The dataset contains images of 16 artworks included in the cultural site “Galleria Regionale di Palazzo Bellomo2”. The collection covers different types of artworks, as well as books, sculptures and paintings. The dataset three domains:
i) synthetic images generated from a 3D model of the cultural site and automatical... | Provide a detailed description of the following dataset: OBJ-MDA |
CareerCoach 2022 | The CareerCoach 2022 gold standard is available for download in the NIF and JSON format, and draws upon documents from a corpus of over 99,000 education courses which have been retrieved from 488 different education providers.
The corpus contains two partitions.:
* **Partition (P1)** supports the **content extrac... | Provide a detailed description of the following dataset: CareerCoach 2022 |
CERBERUS DARPA Subterranean Challenge Datasets | Dataset link: https://github.com/leggedrobotics/cerberus_darpa_subt_datasets | Provide a detailed description of the following dataset: CERBERUS DARPA Subterranean Challenge Datasets |
MSU HDR Video Reconstruction Benchmark | This is a dataset for a video inverse-tone-mapping task. The dataset contains various contents for the task of restoring HDR video: fireworks, flowers, football, night city, scenes with reflections. Videos have different brightness ranges and contain different types of lighting. The camera for shooting the dataset capt... | Provide a detailed description of the following dataset: MSU HDR Video Reconstruction Benchmark |
Multilingual Persuasion Detection | This dataset contains dialogue lines from the games Knights of the Old Republic 1 & 2 and Neverwinter Nights 1. Some of the dialogue lines are marked as persuasive (which is when the player character is attempting a Persuade skill check.)
If you use this data, please cite:
Pöyhönen, T., Hämäläinen, M., Alnajjar,... | Provide a detailed description of the following dataset: Multilingual Persuasion Detection |
Taskography | PDDL dataset of [Rearrangement](https://arxiv.org/abs/2011.01975) tasks in large-scale 3D scene graphs. | Provide a detailed description of the following dataset: Taskography |
V2XSet | A large-scale V2X perception dataset using CARLA and OpenCDA | Provide a detailed description of the following dataset: V2XSet |
Online retail dataset | This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.
https://archive.ics.uci.edu/ml/datasets/online+ret... | Provide a detailed description of the following dataset: Online retail dataset |
CAP | The Consented Activities of People (CAP) dataset is a fine grained activity dataset for visual AI research curated using the Visym Collector platform. The CAP dataset contains annotated videos of fine-grained activity classes of consented people. Videos are recorded from mobile devices around the world from a third per... | Provide a detailed description of the following dataset: CAP |
N-Caltech 101 | The Neuromorphic-Caltech101 (N-Caltech101) dataset is a spiking version of the original frame-based Caltech101 dataset. The original dataset contained both a "Faces" and "Faces Easy" class, with each consisting of different versions of the same images. The "Faces" class has been removed from N-Caltech101 to avoid confu... | Provide a detailed description of the following dataset: N-Caltech 101 |
CoCaHis | Highlights
• Publicly available dataset with 82 H&E stained images of frozen sections.
• Images are acquired on 19 patients with metastatic colon cancer in a liver.
• Originally stained and two stain-normalized sets of images included
• Pixel wise ground truths provided by seven domain experts. | Provide a detailed description of the following dataset: CoCaHis |
VALSE | We propose VALSE (Vision And Language Structured Evaluation), a novel benchmark designed for testing general-purpose pretrained vision and language (V&L) models for their visio-linguistic grounding capabilities on specific linguistic phenomena. VALSE offers a suite of six tests covering various linguistic constructs. S... | Provide a detailed description of the following dataset: VALSE |
MFR | During the COVID-19 coronavirus epidemic, almost everyone wears a facial mask, which poses a huge challenge to face recognition. Traditional face recognition systems may not effectively recognize the masked faces, but removing the mask for authentication will increase the risk of virus infection. Inspired by the COVID-... | Provide a detailed description of the following dataset: MFR |
MatriVasha: | MatriVasha the largest dataset of handwritten Bangla compound characters for research on handwritten Bangla compound character recognition. The proposed dataset contains 120 different types of compound characters that consist of 306,464 images written where 152,950 male and 153,514 female handwritten Bangla compound c... | Provide a detailed description of the following dataset: MatriVasha: |
MS-CXR | The MS-CXR dataset provides 1162 image–sentence pairs of bounding boxes and corresponding phrases, collected across eight different cardiopulmonary radiological findings, with an approximately equal number of pairs for each finding. This dataset complements the existing [MIMIC-CXR](/dataset/mimic-cxr) v.2 dataset and c... | Provide a detailed description of the following dataset: MS-CXR |
The Mafia Dataset | The Mafia Dataset was created to model the behavior of deceptive actors in the context of the Mafia game, as described in the paper “Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia”. We hope that this dataset will be of use to others studying the effects of deception on language use. | Provide a detailed description of the following dataset: The Mafia Dataset |
ANTILLES | `ANTILLES` is a part-of-speech tagging corpus based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb).
Originally, the corpus consists of 400,399 words ... | Provide a detailed description of the following dataset: ANTILLES |
UniMorph 4.0 | The Universal Morphology (UniMorph) project is a collaborative effort to improve how NLP handles complex morphology in the world’s languages. The goal of UniMorph is to annotate morphological data in a universal schema that allows an inflected word from any language to be defined by its lexical meaning, typically carri... | Provide a detailed description of the following dataset: UniMorph 4.0 |
Long Video Dataset | We randomly selected three videos from the Internet, that are longer than 1.5K frames and have their main objects continuously appearing. Each video has 20 uniformly sampled frames manually annotated for evaluation. | Provide a detailed description of the following dataset: Long Video Dataset |
Long Video Dataset (3X) | We randomly selected three videos from the Internet, that are longer than 1.5K frames and have their main objects continuously appearing. Each video has 20 uniformly sampled frames manually annotated for evaluation. Each video has been played back and forth to generate videos that are three times as long. | Provide a detailed description of the following dataset: Long Video Dataset (3X) |
WinoNB | This dataset consists of Winograd schemas that test coreference resolution systems' ability to differentiate singular vs plural they/them pronouns. It consists of 4077 templates, each with a group of people, a singular person (which can be filled with a name or a generic "someone") and a single they/them pronoun to res... | Provide a detailed description of the following dataset: WinoNB |
WorldStrat | **Nearly 10,000 km² of free high-resolution and paired multi-temporal low-resolution satellite imagery** of unique locations which ensure stratified representation of all types of land-use across the world: from agriculture to ice caps, from forests to multiple urbanization densities.
Those locations are also enri... | Provide a detailed description of the following dataset: WorldStrat |
iSarcasmEval | iSarcasmEval is the first shared task to target intended sarcasm detection: the data for this task was provided and labelled by the authors of the texts themselves. Such an approach minimises the downfalls of other methods to collect sarcasm data, which rely on distant supervision or third-party annotations. The shared... | Provide a detailed description of the following dataset: iSarcasmEval |
ArSarcasm | ArSarcasm is a new Arabic sarcasm detection dataset. The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD) and adds sarcasm and dialect labels to them. The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic. | Provide a detailed description of the following dataset: ArSarcasm |
ArSarcasm-v2 | ArSarcasm-v2 is an extension of the original ArSarcasm dataset published along with the paper From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset. ArSarcasm-v2 conisists of ArSarcasm along with portions of DAICT corpus and some new tweets. Each tweet was annotated for sarcasm, sentiment and diale... | Provide a detailed description of the following dataset: ArSarcasm-v2 |
Wind speed and power potential for Switzerland | Summary:
This dataset contains an estimation of the average yearly wind speed and of the wind power potential for Switzerland, at a spatial resolution of 250 x 250 meters and over the period from 2008 to 2017.
Wind speed data are obtained by modelling data collected at an hourly frequency on a set of up to 208 mo... | Provide a detailed description of the following dataset: Wind speed and power potential for Switzerland |
TCIA 4D-Lung | This data collection consists of images acquired during chemoradiotherapy of 20 locally-advanced, non-small cell lung cancer patients. The images include four-dimensional (4D) fan beam (4D-FBCT) and 4D cone beam CT (4D-CBCT). All patients underwent concurrent radiochemotherapy to a total dose of 64.8-70 Gy using daily ... | Provide a detailed description of the following dataset: TCIA 4D-Lung |
Figment | A dataset for fine-grained entity typing of knowledge graph entities built from Freebase.
It can be used to evaluate entity representations and also mention-level entity typing. | Provide a detailed description of the following dataset: Figment |
Article Bias Prediction | # Article-Bias-Prediction
## Dataset
The articles crawled from www.allsides.com are available in the ```./data``` folder, along with the different evaluation splits.
The dataset consists of a total of 37,554 articles. Each article is stored as a ```JSON``` object in the ```./data/jsons``` directory, and contains... | Provide a detailed description of the following dataset: Article Bias Prediction |
Crowd Activity Dataset | This dataset concentrates on the activities of the crowd for a fine-grained image classification task, named as Crowd Activity dataset, as automatically understanding crowd activity is meaningful for social security. This dataset is newly collected, where the images are mainly searched on the Internet and collected fro... | Provide a detailed description of the following dataset: Crowd Activity Dataset |
DOLPHINS | Vehicle-to-Everything (V2X) network has enabled collaborative perception in autonomous driving, which is a promising solution to the fundamental defect of stand-alone intelligence including blind zones and long-range perception. However, the lack of datasets has severely blocked the development of collaborative percept... | Provide a detailed description of the following dataset: DOLPHINS |
AiTLAS: Benchmark Arena | **AiTLAS: Benchmark Arena** is an open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). | Provide a detailed description of the following dataset: AiTLAS: Benchmark Arena |
Nocturne | Nocturne is a 2D, partially observed, driving simulator, built in C++ for speed and exported as a Python library.
It is currently designed to handle traffic scenarios from the Waymo Open Dataset, and with some work could be extended to support different driving datasets. Using the Python library nocturne, one is ab... | Provide a detailed description of the following dataset: Nocturne |
BC7 NLM-Chem | Full-text chemical identification and indexing in PubMed articles.
Identifying named entities is an important building block for many complex knowledge extraction tasks. Errors in identifying relevant biomedical entities is a key impediment to accurate article retrieval, classification, and further understanding of ... | Provide a detailed description of the following dataset: BC7 NLM-Chem |
LineCap | LineCap is a dataset of line charts scraped from scientific papers each accompanied with crowd-sourced captions describing the trends of individual lines in the figure and the figure as a whole. | Provide a detailed description of the following dataset: LineCap |
HTDM | Hypertention Disease Medication dataset. | Provide a detailed description of the following dataset: HTDM |
DEVIL | Diagnostic Evaluation of Video Inpainting on Landscapes (DEVIL) benchmark is composed of a curated video/occlusion mask dataset and a comprehensive evaluation scheme | Provide a detailed description of the following dataset: DEVIL |
PosePrior | Accurate modeling of priors over 3D human pose is fundamental to many problems in computer vision.
Most previous priors are either not general enough for the diverse nature of human poses or not restrictive enough to avoid invalid 3D poses. We propose a physically-motivated prior that only allows anthropometrically ... | Provide a detailed description of the following dataset: PosePrior |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.