Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
4
datasets available to search
ShareScore release 0.9.0
Dataset results
4 results for “out-of-distribution detection”
Data for: Exploring Transition States of Protein Conformational Changes via Out-of-Distribution Detection in the Hyperspherical Latent Space
<p>This contains all the TS-DART training results as well as the raw MD simulation data reported in the preprint "Exploring Transition States of Protein Conformational Changes via Out-of-Distribution Detection in the Hyperspherical Latent Space".</p>
Out-of-distribution detection algorithms for robust insect classification dataset and models
<p>This folder contains trained models and datasets for reproducing the results in the paper on out-of-distribution detection algorithms for robust insect classification. Specifically, it contains the following folders: </p> <p> </p> <ul> <li>OODInsect (out-of-distribution data)</li> <li>MSP, MAH, and EBM trained models, each wrapped around the three classifiers of ResNet50, RegNet32, and VGG11, and different combinations of ID and OOD test data for reproducing RQ1, RQ2, and RQ3.</li> <li>ID3 (in-distribution test data)</li> </ul>
NINCO (Out-Of-Distribution detection dataset for ImageNet)
<p>The <strong>NINCO</strong> (<strong>N</strong>o <strong>I</strong>mageNet <strong>C</strong>lass <strong>O</strong>bjects) dataset is introduced in the ICML 2023 paper<strong> </strong><a href="https://arxiv.org/abs/2306.00826">In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation</a>. The images in this dataset are free from objects that belong to any of the 1000 classes of ImageNet-1K (ILSVRC2012), which makes NINCO suitable for evaluating out-of-distribution detection on ImageNet-1K .</p> <p>The NINCO main dataset consists of 64 OOD classes with a total of 5879 samples. These OOD classes were selected to have no categorical overlap with any classes of ImageNet-1K. Each sample was inspected individually by the authors to not contain ID objects.</p> <p>Besides NINCO, included are (in the same .tar.gz file) <strong>truly OOD versions of 11 popular OOD datasets</strong> with in total 2715 OOD samples.</p> <p>Further included are <strong>17 OOD unit-tests</strong>, with 400 samples each.</p> <p>Code for loading and evaluating on each of the three datasets is provided at <strong><a href="https://github.com/j-cb/NINCO">https://github.com/j-cb/NINCO</a>.</strong></p> <p> </p> <p>When using <strong>NINCO</strong>, please consider citing (besides the bibtex given below) the following data sources that were used to create NINCO:</p> <ul> <li>Hendrycks et al.: ”Scaling out-of-distribution detection for real-world settings”, ICML, 2022. </li> <li>Bossard et al.: ”Food-101 – mining discriminative components with random forests”, ECCV 2014. </li> <li>Zhou et al.: ”Places: A 10 million image database for scene recognition”, IEEE PAMI 2017. </li> <li>Huang et al.: ”Mos: Towards scaling out-of-distribution detection for large semantic space”, CVPR 2021. </li> <li>Li et al.: ”Caltech 101 (1.0)”, 2022.</li> <li>Ismail et al.: ”MYNursingHome: A fully-labelled image dataset for indoor object classification.”, Data in Brief (V. 32) 2020.</li> <li>The iNaturalist project: https://www.inaturalist.org/</li> </ul> <p>When using <strong>NINCO_popular_datasets_subsamples</strong>, additionally to the above, please consider citing:</p> <ul> <li>Cimpoi et al.: ”Describing textures in the wild”, CVPR 2014. </li> <li>Hendrycks et al.: ”Natural adversarial examples”, CVPR 2021. </li> <li>Wang et al.: ”Vim: Out-of-distribution with virtual-logit matching”, CVPR 2022. </li> <li>Bendale et al.: ”Towards Open Set Deep Networks”, CVPR 2016. </li> <li>Vaze et al.: ”Open-set Recognition: a Good Closed-set Classifier is All You Need?”, ICLR 2022. </li> <li>Wang et al.: ”Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition.” ICML, 2022. </li> <li>Galil et al.: “A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet”, ICLR 2023.<br> </li> </ul> <p>For citing our paper, we would appreciate using the following bibtex entry (this will be updated once the ICML 2023 proceedings are public):</p> <p><br> @inproceedings{<br> bitterwolf2023ninco,<br> title={In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation},<br> author={Julian Bitterwolf and Maximilian Mueller and Matthias Hein},<br> booktitle={ICML},<br> year={2023},<br> url={https://proceedings.mlr.press/v202/bitterwolf23a.html}<br> }</p>
Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels
<p>This folder contains datasets, code, and analysis scripts for reproducing the results in the paper "Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels". Specifically, it contains the following folders: </p> <ol> <li>Datasets <ol> <li>Dataset for each Vessel model in different maneuvers and conditions, i.e., waypoint, zigzag, ocean current (including IND and OOD).</li> <li>Configurations (Configurations used to train the DTM for each vessel in different maneuvers).</li> </ol> </li> <li>Results <ol> <li>Raw results for RQ1 and RQ2 for each vessel in different maneuvers, including the results from DTM-R and DTM-E.</li> <li>Results for RQ3 (statistical tests) derived from the above results. </li> </ol> </li> <li>Scripts and Code <ol> <li>Scripts used for statistical tests.</li> <li>DTC implementation (shown with one vessel as example).</li> <li>Code used for hyperparameter optimization.</li> <li>Inference class used for integration of DTM and DTC.</li> </ol> </li> </ol>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.