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.
152
datasets available to search
ShareScore release 0.9.0
Dataset results
152 results for “Scratching”
COSN paper data (The Chinese Open Science Network (COSN): Building an Open Science community from scratch)
<p>This is the dataset for generating figure1 and figure 3 in the manuscript <em>The Chinese Open Science Network (COSN): Building an Open Science community from scratch </em>(Accepted by AMPPS). Preprint at: <a href="https://doi.org/10.31234/osf.io/ac9by">https://doi.org/10.31234/osf.io/ac9by</a>.</p> <p>All the data and codes are available in repo: <a href="https://github.com/OpenSci-CN/COSN_AMPPS_Paper">COSN_AMPPS_Paper</a> Accepted Version.</p>
FIGURE 5 A – L in Scratching the surface? Taxonomic revision of the subgenus Schizoptera (Odontorhagus) reveals vast undocumented biodiversity in the largest litter bug genus Schizoptera Fieber (Hemiptera: Dipsocoromorpha)
FIGURE 5 A – L. Line drawings of dorsal (above) and ventral (below) views of male genitalia, different colors correspond to different structures labeled on S. (Odonotorhagus) acuta, n. sp.
FIGURE 6 M – W in Scratching the surface? Taxonomic revision of the subgenus Schizoptera (Odontorhagus) reveals vast undocumented biodiversity in the largest litter bug genus Schizoptera Fieber (Hemiptera: Dipsocoromorpha)
FIGURE 6 M – W. Line drawings of dorsal (above) and ventral (below) views of male genitalia, different colors correspond to different structures labeled on S. (Odonotorhagus) acuta, n. sp. (Fig. 5 A).
Machine Learning based scratches on printed paper detection, in high-speed printing systems [Dataset]
<p>Printing industry rapidly is adopting digital technologies and the requirements in terms of speed and print quality are also becoming more demanding. The is a wide range of possible quality defects in printed paper. This makes it impossible to have humans inspect the printed paper for such a big amount of possible quality defects at the high-speeds the printouts are produced.</p> <p>Printing industry is not taking advantage of the Artificial Intelligence to detect defects in printed paper at speed without human intervention. It is possible to generate millions of images (captures) with printed content from a printing system every day. Most of these images will not have any defect but some other will and can be used to generate a data set to be used in a machine learning system.</p> <p>The intention of this research work is to find ways artificial intelligence can help on automatically detecting defects on printed paper in a printing system and classifying them, without human intervention. Focusing on scratches, I’ve explored what are the actual proposals and solutions, and how machine learning can help improving them by using datasets with different techniques, implementing possible solutions and comparing the obtained results.</p>
Datasets for the Results of Scratch Tests of Green Wood and Results of Scratch Tests of Timber Components (D2.1) and Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk (D2.2) of 5G-TIMBER EU Project
<p>7 June 2024: added D2.2_data_statistics.zip and D2.2_analysis_results.zip, which are the datasets for D2.2 "<span>Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</span></p> <p>D2.2 presents the Hygro-Thermo-Mechanical (HTM) models and the finite element (FE) analyses of selected wooden components that use the material properties of wood presented in deliverable D2.1 "Input database for selected wood parts and timber components: material properties, representative environmental conditions,<br>and loads" (see below). </p> <p>------</p> <p>Figures_22_23_24_25.xlsx : Results of Scratch Tests of Green Wood</p> <p>corrected_Figures_26_27_28_29_30.xlsx : Results of results of Scratch Tests of Timber Components (new version, uploaded on 26 October 2023)</p> <p>This dataset consists of 2 Excel files that correspond to the scratch test results reported in the deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</p> <p>The purpose of D2.1, to which this dataset is related, is to present the input data needed for the Hygro-Thermo-Mechanical (HTM) models and the related finite element (FE) analyses planned for a follow-up deliverable, i.e., the D2.2. (Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains And Stresses And Crack Risk). The data include the material properties for green wood and selected wooden components, as well as the plans to collect environmental conditions and loads to be considered in the analyses for prediction of the crack risk of timber components under moisture variations. In additions, new results of scratch tests of wood and wooden components, supported by computed tomography (CT) investigations, are collected to define a model for shear failure risk to be added to the HTM computational models.</p> <p>In D2.1, scratch tests carried out at VTT are described and their results are collected to provide information about the moisture effects of wood logs during cutting operations in sawmills, as well as on relevant fracture and shear properties for wooden components in sawing centres before using them to produce wooden elements of modular buildings in the production. The scratch tests are supported by CT tomography investigations and these results are also reported in the deliverable.</p> <p>D2.1 is available here: <a title="Deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" " href="../records/10577505" target="_blank" rel="noopener">https://zenodo.org/records/10577505</a> </p>
Scratch test on DLC
<p>Representative scratch test on DLC topcoat applied to prototypes in the ALCOM PoC Project – Spoke 11</p>
Linked collectors and determiners for: Scratching the surface? Taxonomic revision of the subgenus Schizoptera (Odontorhagus) reveals vast undocumented biodiversity in the largest litter bug genus Schizoptera Fieber (Hemiptera: Dipsocoromorpha).
Natural history specimen data linked to collectors and determiners held within, "Scratching the surface? Taxonomic revision of the subgenus Schizoptera (Odontorhagus) reveals vast undocumented biodiversity in the largest litter bug genus Schizoptera Fieber (Hemiptera: Dipsocoromorpha)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6dce6de7-19d7-462d-a123-e94ad5135e89">https://bionomia.net/dataset/6dce6de7-19d7-462d-a123-e94ad5135e89</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6dce6de7-19d7-462d-a123-e94ad5135e89">https://gbif.org/dataset/6dce6de7-19d7-462d-a123-e94ad5135e89</a>. Formatted as a Frictionless Data package.
Scratching the Surface by Vhils
This piece was done by Vhils and within a Portuguese project: ARTEJO | Programa Arte Pública Fundação EDP - which is located in Atalaia, Vila Nova da Barquinha, Portugal. My entry for the final at the #3dflowcup18 representing Portugal - C.Faustino Unfortunately there is not completely finished, missing the top part of the building. Processed 282 dslr photos. Source: Objaverse 1.0 / Sketchfab
Old Scratched Can
# This is a 3D Scan of an old scratched can * Highpoly * Real World Scaling and Alignmend * VR Ready * 8K JPEG Texture - Diffuse x 2 * FBX File Format Source: Objaverse 1.0 / Sketchfab
Dados do quasi-experimento. Scratch no Desenvolvimento do Pensamento Computacional: um Quasi-Experimento com Alunos 9º ano
<p>Conjunto de dados obtidos durante o quase-experimento sobre o ensino do pensamento computacional na educação básica.</p> <p>Artigo: Scratch no Desenvolvimento do Pensamento Computacional: um Quasi-Experimento com Alunos 9º ano</p> <p>#computacionalthinking <br>#pensamentocomputacional <br>#Scratch <br>#computacaoonaEducacaoBasica</p>
Artefact for "Automated Test Generation for Scratch Programs"
<p><strong>Automated Test Generation for Scratch Programs</strong></p> <p>Replication package for our study on <a href="https://arxiv.org/pdf/2202.06274.pdf">Automated Test Generation for Scratch Programs</a>.</p> <pre><code>@article{Deiner2022AutomatedTG, title={Automated Test Generation for Scratch Programs}, author={Adina Deiner and Patric Feldmeier and Gordon Fraser and Sebastian Schweikl and Wengran Wang}, journal={ArXiv}, year={2022}, volume={abs/2202.06274} }</code></pre> <p>It contains:</p> <ul> <li>A comprehensive README,</li> <li>the source code of <a href="https://github.com/se2p/whisker/tree/emse22">Whisker</a> and our custom <a href="https://github.com/se2p/scratch-vm/tree/emse22">Scratch VM</a>,</li> <li>a docker image of Whisker for a controlled execution environment,</li> <li>the datasets (as <code>*.sb3</code> Scratch project files) used in the study,</li> <li>the Whisker configuration files we used to generate tests with, and</li> <li>all experimental data from the paper as CSV files, along with scripts to re-create the plots.</li> </ul> <p>In case of questions, <a href="mailto:Patric.Feldmeier@uni-passau.de,Sebastian.Schweikl@uni-passau.de">feel free to contact us</a>.</p>
A pillar with war scratches in Daihouonji
three pillars were visible with katana scratches and spear holes, this is the second one Source: Objaverse 1.0 / Sketchfab
Data from: Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy
<p>Mice are the most commonly used model animals for itch research and for the development of anti-itch drugs. Most laboratories manually quantify mouse scratching behavior to assess itch intensity. This process is labor-intensive and limits large-scale genetic or drug screenings. In this study, we developed a new system, Scratch-AID (Automatic Itch Detection), which could automatically identify and quantify mouse scratching behavior with high accuracy. Our system included a custom-designed videotaping box to ensure high-quality and replicable mouse behavior recording and a convolutional recurrent neural network trained with frame-labeled mouse scratching behavior videos, induced by nape injection of chloroquine. The best-trained network achieved 97.6% recall and 96.9% precision on previously unseen test videos. Remarkably, Scratch-AID could reliably identify scratching behavior in other major mouse itch models, including the acute cheek model, the histaminergic model, and the chronic itch model. Moreover, our system detected significant differences in scratching behavior between control and mice treated with an anti-itch drug. Taken together, we have established a novel deep learning-based system that could replace manual quantification for mouse scratching behavior in different itch models and for drug screening. This dataset includes all videos for the study to establish a novel deep learning-based system for automatic mouse scratching behavior quantification.</p>
Scratch Project Dataset - Comment Data
<p>The dataset contains a list of Scratch projects with comment data. It has a total of 1237 comments available from 288 different Scratch projects. </p>
Scratch Projects - Comment Data
<p>The dataset contains a list of Scratch projects with comment data. It has a total of 1237 comments available from 288different Scratch projects. </p>
Research Data for "In-vacuo scratching yields undisturbed insight into the bulk of lithium-ion battery positive electrode materials"
<p>This are the datasets supporting the figures and tables in the manuscript and supporting information of the publication "In-vacuo scratching yields undisturbed insight into the bulk of lithium-ion battery positive electrode materials".</p> <p>Available in ACS Energy Letters under <span><a href="https://doi.org/10.1021/acsenergylett.4c02106"><span>https://doi.org/10.1021/acsenergylett.4c02106</span></a></span></p>
Bark-scratching of storm-felled trees preserves biodiversity at lower economic costs compared to debarking
<p>The abundance data presented here focus on saproxylic beetles collected over two years on trees in a mountain forest ecosystem (analysed and discussed in Thorn et al. <a href="https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecm.1343#ecm1343-bib-0043">2016</a>). The design consists of 12 plots each composed of three experimentally felled trees, resulting in a total of 36 experimental felled trees. In each plot, the bark of one tree was completely removed, the bark of a second tree was only partially removed (i.e., bark-scratched), and the third tree served as a control. The design is thus composed of 12 replications of three different treatments (i.e., control, bark-scratched, and debarked). A total of 120 species of saproxylic beetles were trapped with emergence traps on felled trees (Thorn et al. <a href="https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecm.1343#ecm1343-bib-0043">2016</a>).</p>
A Multimodal Sensing Ring for Quantification of Scratch Intensity
<p>Dataset and code accompanying the paper, A Multimodal Sensing Ring for Quantification of Scratch Intensity. More information located on the paper's <a href="https://github.com/RCHI-Lab/Wearable_Scratch_Intensity/tree/main">github</a> page. For the most updated version of code and data and for a README, please visit the github page. </p>
Neual activities of 126 mouse brain areas under itch-scratching cycles
<p>The data in a Cell Report paper: "An Atlas of Itch-associated Neural Dynamics in the Mouse Brain".</p> <p>The paper will be online about on Nov 1st, 2023.</p>
Endometrial Scratch Effect on Pregnancy Rates in Patients Undergoing Egg-donation IVF
ClinicalTrials.gov study NCT03108157. IPD Sharing: NO. Countries: 1. Publications: 20.
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.