19,997 machine learning datasets
19,997 dataset results
Dubbing Test Set consists of two subsets extracted from the En→De test set of COVOST-2, a large-scale multilingual speech translation corpus based on Common Voice. Specifically, the first subset is created by randomly sampling 91 sentences (test91), while the second is randomly sampled 101 sentences from the longest 10% of the De part of the test set (test101).
This dataset was acquired in a retrospective study from a cohort of pediatric patients admitted with abdominal pain to Children’s Hospital St. Hedwig in Regensburg, Germany. Multiple abdominal B-mode ultrasound images were acquired for most patients, with the number of views varying from 1 to 15. The images depict various regions of interest, such as the abdomen’s right lower quadrant, appendix, intestines, lymph nodes and reproductive organs. Alongside multiple US images for each subject, the dataset includes information encompassing laboratory tests, physical examination results, clinical scores, such as Alvarado and pediatric appendicitis scores, and expert-produced ultrasonographic findings. Lastly, the subjects were labeled w.r.t. three target variables: diagnosis (appendicitis vs. no appendicitis), management (surgical vs. conservative) and severity (complicated vs. uncomplicated or no appendicitis). The study was approved by the Ethics Committee of the University of Regensburg (
A dataset of 18,731 proteins with their PDB code, index of the first residue in their respective DSSP file, their residue sequence and 9-category secondary structure sequence (including polyproline helices).
OpenD5 is a a meta-dataset which aggregates 675 open-ended problems ranging across business, social sciences, humanities, machine learning, and health, and uses a set of unified evaluation metrics: validity, relevance, novelty, and significance. It is designed for the new task, D5, that automatically discovers differences between two large corpora in a goal-driven way.
MuscleMap136 is a dataset for video-based Activated Muscle Group Estimation (AMGE) aiming at identifying currently activated muscular regions of humans performing a specific activity. Video-based AMGE is an important yet overlooked problem. To this intent, the MuscleMap136 dataset features 15K video clips with 136 different activities and 20 labeled muscle groups.
For creating, optimizing, and evaluating our statistical model, we used the Public Unified Bug Dataset for Java. It contains the data entries of 5 different public bug datasets (PROMISE, Eclipse Bug Dataset, Bug Prediction Dataset, Bugcatchers Bug Dataset, and GitHub Bug Dataset) in a unified manner. The dataset contains 47,618 Java Classes altogether, from which 8,780 contain at least one bug, while 38,838 are bug-free. The total number of bugs recorded in the dataset is 17,365, which means that each bugged Java Class contains 1.98 bugs in average (with standard deviation of 2.39). Unfortunately, the PLS-DA implementation in PLS_Toolbox was too slow due to the tremendous amount of administrative calculations it performs. Therefore, we have developed and used a much faster PLS-DA script independently from PLS_Toolbox. According to the literature, there is no obvious way to choose the fastest and most accurate algorithm. Thus, we had to find the right balance between speed and accuracy,
The Winograd schema challenge composes tasks with syntactic ambiguity, which can be resolved with logic and reasoning.
The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) is a large-scale collaborative research project at the University of Cambridge, launched in October 2010, with substantial initial funding from the Biotechnology and Biological Sciences Research Council (BBSRC), followed by support from the Medical Research Council (MRC) Cognition & Brain Sciences Unit (CBU) and the European Union Horizon 2020 LifeBrain project. The Cam-CAN project uses epidemiological, cognitive, and neuroimaging data to understand how individuals can best retain cognitive abilities into old age.
TRR360D is based on the ICDAR2019MTD modern table detection dataset, it refers to the annotation format of the DOTA dataset. The training set contains 600 rotated images and 977 annotated instances, and the test set contains 240 rotated images and 499 annotated instances.
IoT-23 is a dataset of network traffic from Internet of Things (IoT) devices. It has 20 malware captures executed in IoT devices, and 3 captures for benign IoT devices traffic. It was first published in January 2020, with captures ranging from 2018 to 2019. These IoT network traffic was captured in the Stratosphere Laboratory, AIC group, FEL, CTU University, Czech Republic. Its goal is to offer a large dataset of real and labeled IoT malware infections and IoT benign traffic for researchers to develop machine learning algorithms. This dataset and its research was funded by Avast Software. The malware was allow to connect to the Internet.
ChatGPT Software Testing Study Dataset contains questions from a well-known software testing book by Ammann and Offutt. It uses all the textbook questions in Chapters 1 to 5 that have solutions available on the book’s official website. These solutions are made publicly available to help students learn. Questions that are not in the student solution are omitted because publishing our results might expose answers that the authors of the book do not intend to make public.
VTQA is a dataset containing open-ended questions about image-text pairs. This dataset requires the model to align multimedia representations of the same entity to implement multi-hop reasoning between image and text and finally use natural language to answer the question. The aim of this dataset is to develop and benchmark models that are capable of multimedia entity alignment, multi-step reasoning and open-ended answer generation. VTQA dataset consists of 10,238 image-text pairs and 27,317 questions. The images are real images from MSCOCO dataset, containing a variety of entities. The annotators are required to first annotate relevant text according to the image, and then ask questions based on the image-text pair, and finally answer the question open-ended.
We construct a largest publicly Cervical Cell Edge Detection Dataset (CCEDD) based on our Local Label Point Correction (LLPC). Our dataset is ten times larger than the previous datasets, which greatly facilitates the development of overlapping cell edge detection.
In this dataset UR5 robot used 6 tools: metal-scissor, metal-whisk, plastic-knife, plastic-spoon, wooden-chopstick, and wooden-fork to perform 6 behaviors: look, stirring-slow, stirring-fast, stirring-twist, whisk, and poke. The robot explored 15 objects: cane-sugar, chia-seed, chickpea, detergent, empty, glass-bead, kidney-bean, metal-nut-bolt, plastic-bead, salt, split-green-pea, styrofoam-bead, water, wheat, and wooden-button kept cylindrical containers. The robot performed 10 trials on each object using a tool, resulting in 5,400 interactions (6 tools x 6 behaviors x 15 objects x 10 trials). The robot records multiple sensory data (audio, RGB images, depth images, haptic, and touch images) while interacting with the objects.
AutoFR Dataset is broken down by each site that we crawl within a zip file. It contains multiple different experiments that we conducted in our paper. The overall dataset contains 1042 sites that we crawled where we detected ads within the Top-5K.
https://github.com/uci-plrg/iotcheck-data
A.2.1 AN OPEN, LARGE-SCALE DATASET FOR ZERO-SHOT DRUG DISCOVERY DERIVED FROM PUBCHEM We constructed a large public dataset extracted from PubChem (Kim et al., 2019; Preuer et al., 2018), an open chemistry database, and the largest collection of readily available chemical data. We take assays ranging from 2004 to 2018-05. It initially comprises 224,290,250 records of molecule-bioassay activity, corresponding to 2,120,854 unique molecules and 21,003 unique bioassays. We find that some molecule-bioassay pairs have multiple activity records, which may not all agree. We reduce every molecule-bioassay pair to exactly one activity measurement by applying majority voting. Molecule-bioassay pairs with ties are discarded. This step yields our final bioactivity dataset, which features 223,219,241 records of molecule-bioassay activity, corresponding to 2,120,811 unique molecules and 21,002 unique bioassays ranging from AID 1 to AID 1259411. Molecules range up to CID 132472079. The dataset has 3 di
This data set contains annotated text versions of 1635 two-page abstracts published at the Lunar and Planetary Science Conference from 1998 to 2020 of relevance to four Mars missions. The annotations were generated using named entity recognition and relation extraction provided by the MTE processing pipeline (available at https://github.com/wkiri/MTE), followed by manual review. Annotated entities include Element, Mineral, Property, and Target. Annotated relations include Contains(Target, Element | Mineral) and HasProperty(Target, Property). The extracted information (without full texts) is also available as a database (stored in .csv files) at https://pds-geosciences.wustl.edu/missions/mte/mte.htm .
The 1DSfM Landmarks is a collection of community-based image reconstruction by Kyle Wilson and is comprised of 14 datasets with comparison to bundler ground truth. Notredame is provided separately. Datasets (tar.gz, 642 MB) Alamo images (tar, 2.0 GB) Ellis Island images (tar, 1.6 GB) Madrid Metropolis images (tar, 0.7 GB) Montreal Notre Dame images (tar, 1.6 GB) NYC_Library images (tar, 1.6 GB) Piazza del Popolo images (tar, 1.5 GB) Piccadilly images (tar, 3.7 GB) Roman Forum images (tar, 1.5 GB) Tower of London images (tar, 1.1 GB) Trafalgar images (tar, 8.5 GB) Union Square images (tar, 3.6 GB) Vienna Cathedral images (tar, 3.3 GB) Yorkminster images (tar, 2.2 GB) Gendarmenmarkt images (tar, 1.0 GB) References Robust Global Translations with 1DSfM Kyle Wilson and Noah Snavely, ECCV 2014
The data set contains point cloud data captured in an indoor environment with precise localization and ground truth mapping information. Two ”stop-and-go” data sequences of a robot with mounted Ouster OS1-128 lidar are provided. This data-capturing strategy allows recording lidar scans that do not suffer from an error caused by sensor movement. Individual scans from static robot positions are recorded. Additionally, point clouds recorded with the Leica BLK360 scanner are provided as mapping ground-truth data.