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.
831
datasets available to search
ShareScore release 0.9.0
Dataset results
831 results for “automaticity”
Automatic Method of Flow Direction Reasoning for Bridging Tributaries
<p>Please see the detailed description of the data in the Read me.txt file. </p>
AH-CID: A Tool to Automatically Detect Human-Centric Issues in App Reviews
<p>The keyword list used in the paper to pre-filter the app reviews.</p>
Manuscript data for "PASV: Automatic protein partitioning and validation using conserved residues"
<p>Manuscript data for "PASV: Automatic protein partitioning and validation using conserved residues"</p>
Data for Automatic recognition of element classes and boundaries in the birdsong with variable sequences
<p>Data for Automatic recognition of element classes and boundaries in the birdsong with variable sequences</p>
Data for manuscript "Automatic detection of orientation entropy within scenes"
<p>Raw Neuropscan .cnt data for 15 participants with conditions and stimulus order described in the logbook for each participant.</p>
An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy
<p>Data related to the article:</p> <p>Front. Oncol.</p> <p>Sec. Radiation Oncology</p> <p>Volume 13 - 2023 | doi: 10.3389/fonc.2023.1285725</p> <p> </p> <p>An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy</p> <p>Ebbe Laugaard Lorenzen 1,2*, Bahar Celik 1, Nis Sarup1, Lars Dysager3, Rasmus Lübeck Christiansen1, Anders Smedegaard Bertelsen1, Uffe Bernchou1,2, Søren Nielsen Agergaard1, Maximilian Lukas Konrad1, Carsten Brink1,2*, Faisal Mahmood1,2, Tine Schytte2,3, Christina Junker Nyborg3</p> <p>1 Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark </p> <p>2 Department of Clinical Research, University of Southern Denmark, J.B. Winsløws Vej 19 3., 5000 Odense C, Denmark</p> <p>3 Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark</p> <p>* Correspondence: </p> <p>Ebbe Laugaard Lorenzen</p> <p>ebbe.lorenzen@rsyd.dk</p> <p>Carsten Brink </p> <p>carsten.brink@rsyd.dk</p> <p> </p> <p>NOTE: Version 2 of this repository contains the nnU-Net v2 model, while version 1 contains the original, nnU-Net v1 model</p> <p> </p>
Automatic Knife - By: caarloos_sj
Automatic Knife - By: caarloos_sj Source: Objaverse 1.0 / Sketchfab
Diapol 9839 Automat automatic slides projector
A slide projector is an optical device for displaying enlarged images on a screen. The design of projectors in the Diapol series was the joint work of Witold Woźniakowski, Jan Figlewicz and Zbyszek Kramarz. The external design of the projector was prepared by Jan Krzysztof Meisner. The Diapol 9839 Automat projector is designed for projecting black-and-white or colour positive slides in the 24x38 mm format, framed in 5x5 cm frames. The projector's optical arrangement uses the Krytar 2.8 lens with a focal point of 85 mm from 0.5 to 10 m. The device is equipped with a frame for the automatic feeding of slides. Thanks to the use of a strong halogen lamp, the projector can be used in home conditions, as well as in educational facilities. Manufacturer: Polskie Zakłady Optyczne, Warsaw, 1981 Inv. No. MIM118/VI-28 Model prepared on the basis of photogrammetric measurements Licence: CC BY-NC-SA (CC Attribution-NonCommercial-ShareAlike) Source: Objaverse 1.0 / Sketchfab
Supplementary Material for "Detecting Automatic Software Plagiarism via Token Sequence Normalization"
<p>This repository contains additional material supporting the paper titled "Detecting Automatic Software Plagiarism via Token Sequence Normalization", presented at ICSE 2024 (research track).</p> <div> <div> <div> <p>The paper presents a defense mechanism against automated plagiarism generators utilizing program dependence graphs and demonstrates its effectiveness in countering insertion-based and reordering-based obfuscation attacks.</p> </div> </div> </div> <p>The defense mechanism was also integrated into the software plagiarism detector <a title="JPlag Repository on GitHub" href="https://github.com/jplag/JPlag">JPlag</a>, thus providing a widely accessible solution.</p> <p><strong>Contents Overview:</strong></p> <ul> <li> <p><strong>Datasets:</strong> Two datasets from the <a title="PROGpedia Repository" href="../record/7449056">PROGpedia</a> collection and two internal datasets. For the latter, only the metadata is available due to the sensitive nature of the data.</p> </li> <li> <p><strong>Plagiarized Submissions:</strong> Generated plagiarism instances illustrating various obfuscation methods such as insertion, reordering, and insert-after-reordering.</p> </li> <li> <p><strong>Evaluation Data:</strong> JSON files detailing calculated similarities and runtime measurements for all datasets.</p> </li> <li> <p><strong>Source Code:</strong> The implementation of our defense mechanism based on the software plagiarism detector <a title="JPlag Repository on GitHub" href="https://github.com/jplag/JPlag">JPlag</a> (v4.0.0). Note that JPlag is licensed under the GPL-3.0 license.</p> </li> <li> <p><strong>Evaluation Code:</strong> Python code for runtime measurements.</p> </li> <li> <p><strong>Interactive Plots:</strong> HTML visualizations of the paper's plots, offering dynamic insights into the research findings. particularly focusing on the detection of automatic software plagiarism through token sequence normalization.</p> </li> <li><strong>Demo:</strong> A packaged JAR of our implementation alongside an instruction on how to execute it.</li> </ul>
The Available dataset of paper - A Holistic Approach to Automatic Mixed-Precision Code Generation and Tuning for Affine Programs
<p>Here are all publicly available datasets for the paper: A Holistic Approach to Automatic Mixed-Precision Code Generation and Tuning for Affine Programs.</p>
Dataset used in "SynQuant: an automatic tool to quantify synapses from microscopy images"
<p>All data used in the SynQuant paper,</p> <ul> <li>Synthetic data with label</li> <li>Real data with annotations</li> <li>The code that is used to generate the synthetic data.</li> </ul> <p> </p>
A comparison of model-based and model-free agents in solving semi-automatically generated PPDDL problems - Plots
<p>A collection of all plots generated for deriving the conclusions seen in "<span><span>A comparison of model-based and model-free agents in solving semi-automatically generated PPDDL problems</span></span>".</p>
Limitations of Human Identification of Automatically Generated Text: Dataset
<p>This repository contains the data for the paper:</p> <p><span>Nadège Alavoine, Maximin Coavoux, Emmanuelle Esperança-Rodier, Romane Gallienne, Carlos-Emiliano González-Gallardo, Jérôme Goulian, Jose G. Moreno, Aurélie Névéol, Didier Schwab, Vincent Segonne, and Johanna Simoens. 2024. <a href="https://aclanthology.org/2024.lrec-main.919">Limitations of Human Identification of Automatically Generated Text</a>. In <em>Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)</em>, pages 10511–10516, Torino, Italia. ELRA and ICCL.</span></p>
Database for "Automatic Detection of Instream Large Wood in Videos Using Deep Learning"
<p>The database contains 21 datasets. Each dataset consists of a folder ('jpgs') with images and a folder ('txts') with detections according to the YOLO standard.</p>
Processed KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
<p>The original public dataset is published in https://zenodo.org/records/10439422, we processed the KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework.</p>
Automatically measured birch pollen grains from the KM23B and Li-BPa records
<p>The data set includes pollen grain images produced for the manuscript "First automatic size measurements for the separation of dwarf birch and tree birch pollen in MIS 6 to MIS 1 records from Northern Germany". </p> <p>The images are cut-outs of birch pollen produced to automatically determine the size (area and mean diameter) of birch pollen grains. </p> <p>The workflow includes the following three steps, each of which is conducted using trained convoulutional neural networks: </p> <ol> <li>automatic detection and classification of pollen with the Tofsi algorithm (Theuerkauf et al. 2023, https://doi.org/10.1177/09596836231211876)</li> <li>automatic distinction between pollen grain suitable (i.e. in polar view) or not suitable (some other orientation) for size measurement </li> <li>automatic outline detection and size measurement (the green area indicates the area that was identified as the area of each pollen grain) </li> </ol>
Dataset for Automatic Calibration of Microproperties for 3D Parallel Bond Model of Ultra-Deep Carbonate Rocks and the Influence of Confining Pressure on Crack Propagation Patterns
<p>The data set is composed of results of three-dimensional (3D) Discrete Element Method (DEM) modeling performed by Xiaoyun Cheng et al., with the licensed commercial <em>Particle Flow Code </em>3D version 6.0 (PFC3D 6.0) from Itasca Consulting Group, Ltd. Part of the Figures were made with Origin Pro, Version 2021. OriginLab Corporation,Northampton, MA, USA. Part of the Figures were made with ParaView, Version 5.13.1.</p>
Artifacts for paper "PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing" submitted to TOSEM
<p>The project includes the data and code used in the submitted TOSEM paper titled "PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing"</p>
AI4PROFHEALTH - Automatic Occupations Gazetteer and Occupations Co-occurrence with Clinical Concepts
<div> <div>This dataset comprises an occupations gazetteer generated with automatically extracted terminology from the Mesinesp2 corpus, a manually annotated corpus in which domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts, as well as clinical case reports. In addition, this dataset also includes the co-occurrences among occupations, and of professions with other clinical concepts that have been extracted automatically, including diseases, procedures, symptoms, species, drugs, and neoplasia morphologies. </div> <br> <div>The repository contains a .zip file for all the results obtained from Mesinesp2 and another for the clinical cases, both containing a .tsv file for the professions gazetteer, one for the professions internal co-occurrence, and one for the professions co-occurrence with other semantic classes:</div> </div> <ul> <li><strong>mesinesp2_profession_gazetteer_and_cooccurrence.zip (Mesinesp2)</strong> <ul> <li>mesinesp2_professions_gazetteer.tsv</li> <li>mesinesp2_professions_cooccurrences.tsv</li> <li>mesinesp2_professions_cooccurrences_with_other_classes.tsv</li> </ul> </li> </ul> <ul> <li><strong>clinicalcases_profession_gazetteer_and_cooccurrence.zip (clinical cases)</strong> <ul> <li>clinicalcases_professions_gazetteer.tsv</li> <li>clinicalcases_professions_cooccurrences.tsv</li> <li>clinicalcases_professions_cooccurrences_with_other_classes.tsv</li> </ul> </li> </ul> <p>The gazetteer is divided into two columns, one with the name of the extracted terms and the other one with their total count.</p> <p>The professions co-occurrences .tsv file is divided into three columns. The first two columns contain the professions that co-occur, and the third column, named "count," indicates the number of co-occurrences calculated at the document level for each pair of detected entities. The professions co-occurrences with other classes .tsv file is structured in a similar manner, where the first column contains professions, the second one the clinical concepts that they co-occur with, the third one the counts of co-occurrences, and the fourth one the class to which the clinical concept belongs.</p> <p><strong>License</strong></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>Contact</strong></p> <p>If you have any questions or suggestions, please contact us at:</p> <p>- Sergi Marsol Torrent (<sergi [dot] marsol [at] bsc [dot] es>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Additional resources and corpora</strong></p> <p>If you are interested, you might want to check out these corpora and resources:</p> <ul> <li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li> <li><a href="10.5281/zenodo.5070540" target="_blank" rel="noopener">MEDDOPROF corpus </a></li> <li><a href="https://zenodo.org/record/4720833">Annotation Guidelines</a></li> </ul> <p><strong>Acknowledgements</strong></p> <p>This resource been funded by the Spanish National Proyectos I+D+i 2020 AI4ProfHealth project PID2020-119266RA-I00 (<strong>PID2020-119266RA-I0/AEI/10.13039/501100011033).</strong></p>
Automatic exhaustive calculations of large material space by Korringa-Kohn-Rostoker coherent approximation method --- Applied to equiatomic quaternary high entropy alloys
<p>Calculated data of equiatomic quaternary solid solution phase (high-entropy alloys) on local magnetic moment, total magnetization, magnetic phase transition temperature and residual resistivity.</p> <p>The data was added on October 28.</p>
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.