271 machine learning datasets
271 dataset results
This dataset provides wireless measurements from two industrial testbeds: iV2V (industrial Vehicle-to-Vehicle) and iV2I+ (industrial Vehicular-to-Infrastructure plus sensor).
Hand-disambiguation of a sample of U.S. patents inventor mentions from PatentsView.org.
This repository contains a dataset and machine learning algorithms to detect poisoned water from clean water via using equivalent Smartphone embedded Wi-Fi CSI data.
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 (
AI-based digital twins are at the leading edge of theIndustry 4.0 revolution, which are technologically empowered bythe Internet of Things and real-time data analysis. Information collected from industrial assets is produced in a continuous fashion, yielding data streams that must be processed under stringent timing constraints. Such data streams are usually subject to non-stationary phenomena, causing that the data distribution of the streams may change, and thus the knowledge captured by models used for data analysis may become obsolete (leading to the so-called concept drift effect). The early detection of thechange (drift) is crucial for updating the model’s knowledge, which is challenging especially in scenarios where the ground truth associated to the stream data is not readily available. Among many other techniques, the estimation of the model’s confidence has been timidly suggested in a few studies as a criterion for detecting drifts in unsupervised settings. The goal of this m
"The Chicago Face Database was developed at the University of Chicago by Debbie S. Ma, Joshua Correll, and Bernd Wittenbrink. The CFD is intended for use in scientific research. It provides high-resolution, standardized photographs of male and female faces of varying ethnicity between the ages of 17-65. Extensive norming data are available for each individual model. These data include both physical attributes (e.g., face size) as well as subjective ratings by independent judges (e.g., attractiveness).
WDC Block is a benchmark for comparing the performance of blocking methods that are used as part of entity resolution pipelines.
A detailed description of this dataset can be found in the Zenodo repository: https://zenodo.org/record/8119042#.ZK-jJC9BxhE
A detailed description of this dataset can be found in the Zenodo repository: https://zenodo.org/record/7845311#.ZK-jty9BxhE
A detailed description of this dataset can be found in the Zenodo repository: https://zenodo.org/record/7845361#.ZK-k7y9BxhE
Problem Statement
The OTTO session dataset is a large-scale dataset intended for multi-objective recommendation research. We collected the data from anonymized behavior logs of the OTTO webshop and the app. The mission of this dataset is to serve as a benchmark for session-based recommendations and foster research in the multi-objective and session-based recommender systems area. We also launched a Kaggle competition with the goal to predict clicks, cart additions, and orders based on previous events in a user session.
The dataset is generated from the study of computational reproducibility of Jupyter notebooks from biomedical publications. Our focus lies in evaluating the extent of reproducibility of Jupyter notebooks derived from GitHub repositories linked to publications present in the biomedical literature repository, PubMed Central. We analyzed the reproducibility of Jupyter notebooks from GitHub repositories associated with publications indexed in the biomedical literature repository PubMed Central. The dataset includes the metadata information of the journals, publications, the Github repositories mentioned in the publications and the notebooks present in the Github repositories.
This repository contains the dataset for the study of the computational reproducibility of Jupyter notebooks from biomedical publications. We analyzed the reproducibility of Jupyter notebooks from GitHub repositories associated with publications indexed in the biomedical literature repository PubMed Central. The dataset includes the metadata information of the journals, publications, the Github repositories mentioned in the publications and the notebooks present in the Github repositories.
We create a new dataset from GitTables, a data lake of 1.7M tables extracted from CSV files on GitHub. The benchmark comprises 1,746 tables including union-able table subsets under topics selected from Schema.org: scholarly article, job posting, and music playlist. We end up with these three topics since we can find a fair number of union-able tables of them from diverse sources in the corpus (we can easily find union-able tables from a single source but they are less interesting for table union search as simple syntactic methods can identify all of them because of the same schema and consistent value representations).
CSV file with a list of all examined OWL reasoners. For each item, information on usability and maintenance status, project pages, source code repositories and related documentation was gathered.
The datasets are resulting from OPFLearn.jl, a Julia package for creating AC OPF datasets. The package was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to typical dataset creation methods. The OPFLearn dataset creation method uses a relaxed AC OPF formulation to reduce the volume of the unclassified input space throughout the dataset creation process. The dataset contains load profiles and their respective optimal primal and dual solutions. Load samples are processed using AC OPF formulations from PowerModels.jl. More information on the dataset creation method can be found in our publication, "OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets" and in the package website: https://github.com/NREL/OPFLearn.jl.
Overview of the scoping review paper corpus, sorted by their diferent intent types, categories, and subcategories. Note: Papers (77) may include multiple unique intents (172) and can therefore appear in multiple categories and subcategories.
This dataset is a multi-labelled SMILES odor dataset with 138 odor descriptors. This dataset was created for replicating the paper: A principal odor map unifies diverse tasks in olfactory perception.
The file contains an annotated list of papers that are included in the literature survey.