데이터셋 탐색

COLLAB is a scientific collaboration dataset. A graph corresponds to a researcher’s ego network, i.e., the researcher and its collaborators are nodes and an edge indicates collaboration between two researchers. A researcher’s ego network has three possible labels, i.e., High Energy Physics, Condensed Matter Physics, and Astro Physics, which are the fields that the researcher belongs to. The dataset has 5,000 graphs and each graph has label 0, 1, or 2. Source: 1 Introduction

Comprises 4 different subsets - Flat, House, Priory and Lab - each containing a number of different sequences that can be successfully relocalised against each other. Source: CSD

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 perception algorithms. In this work, we release DOLPHINS: Dataset for cOllaborative Perception enabled Harmonious and INterconnected Self-driving, as a new simulated large-scale various-scenario multi-view multi-modality autonomous driving dataset, which provides a ground-breaking benchmark platform for interconnected autonomous driving. DOLPHINS outperforms current datasets in six dimensions: temporally-aligned images and point clouds from both vehicles and Road Side Units (RSUs) enabling both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) based collaborative perception; 6 typical scenarios with dynamic weather conditions make the most various interconnected autonomous driving dataset; meticulously selected viewpoints providing full coverage of the key areas and every object; 42376 frames and 292549 objects, as well as the corresponding 3D annotations, geo-positions, and calibrations, compose the largest dataset for collaborative perception; Full-HD images and 64-line LiDARs construct high-resolution data with sufficient details; well-organized APIs and open-source codes ensure the extensibility of DOLPHINS. We also construct a benchmark of 2D detection, 3D detection, and multi-view collaborative perception tasks on DOLPHINS. The experiment results show that the raw-level fusion scheme through V2X communication can help to improve the precision as well as to reduce the necessity of expensive LiDAR equipment on vehicles when RSUs exist, which may accelerate the popularity of interconnected self-driving vehicles.

The eICU Collaborative Research Database is a large multi-center critical care database made available by Philips Healthcare in partnership with the MIT Laboratory for Computational Physiology. The eICU Collaborative Research Database holds data associated with over 200,000 patient stays, providing a large sample size for research studies.

Arxiv GR-QC (General Relativity and Quantum Cosmology) collaboration network is from the e-print arXiv and covers scientific collaborations between authors papers submitted to General Relativity and Quantum Cosmology category. If an author i co-authored a paper with author j, the graph contains a undirected edge from i to j. If the paper is co-authored by k authors this generates a completely connected (sub)graph on k nodes. Source: https://snap.stanford.edu/data/ca-GrQc.html

The CENTER-TBI database contains prospectively collected data of more than 4,500 patients with TBI in Europe. The Registry and Acute Care data has been collected during a 3 years’ period (2015-2017) in 65 centers in Europe. For all patients, outcome data has been collected up to 2 years after injury. The CENTER-TBI investigators welcome all forms of collaboration and data sharing. Interested scientists may obtain access to the CENTER-TBI clinical, imaging, high resolution ICU and biomarker data for the purposes of scientific investigation, teaching or planning clinical research studies. Obtaining access to and using CENTER-TBI data requires adherence to the CENTER-TBI Data Use Agreement and harmonized procedures for the data access requests as outlined in the documents listed below. The application process includes submission of an online application form. The application must include the investigator’s institutional affiliation and the proposed uses of the CENTER-TBI data. CENTER-TBI data may not be used for commercial products or redistributed in any way.

Source: Linking Datasets on Organizations Using Half-a-Billion Open-Collaborated Records (Description (Markdown and LATEX enabled)) ### High-Level Explanation of the Dataset - Scale and Composition: This repository provides millions of positive and negative name match examples (e.g., “PosMatches_mat.csv,” “NegMatches_mat.csv,” etc.) derived from LinkedIn data, alongside bipartite and Markov network representations of organizational relationships. - LinkedIn-Based Training Corpus: By leveraging the open-collaborated records from LinkedIn, the dataset offers a vast repository of string pairs—covering trillions of potential organization name matches—designed to refine and improve the accuracy of fuzzy matching techniques. ### Motivations and Summary of Content - Overcoming String Matching Challenges: Standard fuzzy matching struggles with highly variable and context-dependent organization names. This dataset was created to provide large-scale, diverse examples for training advanced machine learning models to address these limitations. - Network Representation: In addition to direct name-pair examples, the dataset includes bipartite and Markov network files that capture relationships between organizations, recognizing that organizational matching often depends on structural ties (e.g., shared employees or affiliated branches). - Examples Folder: The Example* directories illustrate how the data can be integrated into typical merging tasks: each folder includes x and y variables (e.g., by_x and by_y) and a merged z dataset, demonstrating how linkage can be evaluated and improved in applied research settings. ### Potential Use Cases 1. Entity Resolution / Record Linkage: Researchers and data scientists can use the millions of positive and negative organizational name matches to train more robust entity resolution algorithms across various domains (e.g., nonprofit, corporate, government datasets). 2. Network Analysis: The bipartite and Markov network representations allow for investigations into the structural connectivity of organizations, uncovering patterns in how organizational names (and, by extension, entities) relate to one another. 3. Benchmarking and Methodological Development: Method developers can employ this corpus to benchmark and stress-test new string matching approaches, harnessing the dataset’s size and diversity to refine algorithmic performance. 4. Domain-Specific Applications: Researchers studying lobbying firms, nonprofits, or other organizational types can integrate this dataset to improve the reliability of merges between multiple datasets that lack standardized IDs. For further details or questions, please contact the authors at the email provided in the repository.

We introduce a RGB+S dataset named “Industrial Human Action Recognition Dataset” (InHARD) from a real-world setting for industrial human action recognition with over 2 million frames, collected from 16 distinct subjects. This dataset contains 13 different industrial action classes and over 4800 action samples. The introduction of this dataset should allow us the study and development of various learning techniques for the task of human actions analysis inside industrial environments involving human robot collaborations. More details on the dataset at Zenodo: https://zenodo.org/records/4003541 Github: https://github.com/vhavard/InHARD Publication: https://doi.org/10.1109/ICHMS49158.2020.9209531