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Extracts from Boyd et al., 2022, LIWC Manual
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Extracted experimental data for research paper titled "Micro-thermomechanical Modeling of Rocks with Temperature-dependent Friction and Damage Laws"
<p>This repository contains the experimental data on stress-strain curves extracted from the following original publications for constitutive model validation in our manuscript.</p> <p>[1] Jinping marble: Zhong, Y. Y. (2017). Research on mechanical properties of marble and the effects on rock burst under thermal-mechanical coupling (in Chinese) (Master’s thesis, Chengdu University of Technology). doi: 10.26986/d.cnki.gcdlc.2017.000109.</p> <p>[2] Beibei sandstone: Long, L. J. (2021). Study on mechanical and seepage properties of sandstone under the coupling of temperature-seepage-stress (in Chinese) (Doctoral dissertation, Chongqing University). doi: 10.27670/d.cnki.gcqdu.2021.001009.</p> <p>[3] Gongjue granite: Zhou, H. Y., Liu, Z. B., Shen, W. Q., Feng, T., & Zhang, G. Z. (2022). Mechanical property and thermal degradation mechanism of granite in thermal-mechanical coupled triaxial compression. International Journal of Rock Mechanics and Mining Sciences, 160, 105270. doi: 10.1016/j.ijrmms.2022.105270.</p>
Forest Fire Dataset for Peninsular Malaysia (2001-2023) Extracted from Multiple-Source Remote Sensing Data using Google Earth Engine
<ul> <li>Dataset: Forest Fire data</li> <li>Time Period: 2001 to 2023</li> <li>Location: Peninsular Malaysia</li> <li>Historical Fire Source: MCD64A1 and FIRMS Hotspots</li> <li>Fire Factors Extracted: Global Remote Sensing Data from GEE</li> </ul> <p>The framework extraction process can be reffered from the following publication:</p> <ul> <li>Framework to Create Inventory Dataset for Disaster Behavior Analysis Using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis</li> <li>Journal: <em>Forests</em> <strong>2024</strong>, <em>15</em>(6), 923;</li> <li><a href="https://doi.org/10.3390/f15060923">https://doi.org/10.3390/f15060923</a></li> <li>The variables name such as AET (actual evapotranspiration) can be found from the article.</li> </ul> <p>Access the framework code from: </p> <ul> <li><a href="https://github.com/chewyeejian/GEE_FrameworkForestFireDataset">https://github.com/chewyeejian/GEE_FrameworkForestFireDataset</a></li> </ul> <p>The time sequence in the variable indicate whether it's a monthly data / yearly accumulated data / seasonal data, example:</p> <ul> <li>200101_aet (Year 2001, Month 01, value for aet (actual evapotranspiration)</li> <li>2001_aet_DJF (Average of December, January, February)</li> <li>2001_aet_MAM (Seasonal Average of March, April, May)</li> <li>2001_aet_JJA (Seasonal Average of June, July, August)</li> <li>2001_aet_SON (Seasonal Average of September, October, November)</li> <li>2001_aet_annual (Annual average of 2001)</li> </ul>
Assessing the Impact of Supply-Side Policies on Oil Extraction, Health, and Employment in California
<p>California's ambitious goal to slash GHG emissions by 90% by 2045 marks a significant shift towards sustainability. Supply-side policies, such as Senate Bill 1137, which bans new oil and gas wells within 3,200 feet of sensitive areas, signal a commitment to environmental and public health protection. To gauge SB 1137's impact accurately, the existing model must be adapted to incorporate this setback distance. This capstone project aims to bridge this gap by updating the model and creating accessible educational materials for Californians. Objectives include updating the model, predicting well locations and oil extraction using machine learning, and developing a public online app with R Shiny. The MEDS capstone group will investigate the effects of the 3,200-foot setback distance on emissions, employment, and health, contributing to the evidence supporting SB 1137.</p>
Fig. 1 in Antimicrobial activity of the crude peptide extracts from Blackfin sea catfish Arius jella Day, 1877
Fig. 1 — Photograph of experimental species Arius jella
Fig 1 in Antimicrobial activity of pandanus leaves extract to against Aeromonas hydrophila which attacked catfish
Fig 1: Inhibition test of Aeromonas hydrophila bacteria
FLIP-KG: Enriching Automated Knowledge Graph Extraction with Tacit Knowledge. The Case of Lyrical Implicatures within Poems
<p>FLIP-KG: Enriching Automated Knowledge Graph Extraction with Tacit Knowledge. The Case of Lyrical Implicatures within Poems. Dataset for the task force "Vulcan" from ISWS 2024 led by Aldo Gangemi and Andrea Poltronieri</p>
Fig. 1 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 1: Map of the study area.
Data set for ODI cricket matches from 1987 to 2023 (extracted from ESPN Cricinfo) and code (R) used for a statistical study
<p>Here I present the data and code that has been used to study the statistical evolution of ODI cricket. The preprint for this research is available at: </p> <div> <div> <div> <table> <tbody> <tr> <td><a href="https://doi.org/10.48550/arXiv.2406.11652">https://doi.org/10.48550/arXiv.2406.11652</a> <div><span>Focus to learn more</span></div> </td> </tr> </tbody> </table> </div> </div> </div> <div> </div>
Figure 1 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 1. Study design.
Relationship between Channel Island Melospiza melodia (Song Sparrow) bill size and vegetation, seed extraction time, bite force, and climate (2014-2016)
<p>Inferring the environmental selection pressures responsible for phenotypic variation is a challenge in adaptation studies as traits often have multiple functions and are shaped by complex selection regimes. We provide indirect evidence that morphology of the multifunctional avian bill is primarily shaped by climate and thermoregulatory ability in <em>Melospiza melodia</em> (Song Sparrows) on the California Channel Islands. Our research builds on a study in Song Sparrow museum specimens that demonstrated a positive correlation between bill surface area and maximum temperature, suggesting a greater demand for dry heat dissipation in hotter, xeric environments. We sampled contemporary sparrow populations across three climatically distinct islands to test the hypotheses that bill morphology is influenced by habitat differences with functional consequences for foraging efficiency and is related to maximum temperature and, consequently, important for thermoregulation. Measurements of >500 live individuals indicated a significant, positive relationship between maximum temperature and bill surface area when correcting for body size. In contrast, maximum bite force, seed extraction time, and vegetation on breeding territories (a proxy for food resources) were not significantly associated with bill dimensions. While we cannot exclude the influence of foraging ability and diet on bill morphology, our results are consistent with the hypothesis that variation in Song Sparrows' need for thermoregulatory capacity across the northern Channel Islands selects for divergence in bill surface area.</p>
Experimental Dataset for Deep learning absolute phase extraction from a single pattern.
<p>Experimental dataset of interferograms obtained by a SLM and respective Zernike coefficientes of the corresponding absolute modulating phase</p>
Fig. 6 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 6: Scatterplot matrices for Large (A) and Linear (B) FM functions.
Fig. 8 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 8: High values of the FP c index as estimated by Local Moran's I test.
Fig. 5 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 5: Spatial representation of coastal vessels activity indexes (Ac).
Fig. 4 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 4: Spatial representation of the Coastal fishery suitability index (Sc).
Fig. 3 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 3: Spatial representation of the criteria ranking taken into account in MCDA.
Development of innovative antioxidant food packaging systems based on natural extracts from food industry waste and Moringa oleifera leaves
<p>This is the dataset of "Development of innovative antioxidant food packaging systems based on natural extracts from food industry waste and Moringa oleifera leaves"</p> <p> </p> <p><span>This research is funded by the project 21GRD07 PlasticTrace. This project has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States.</span></p> <p><span>Funder name: European Partnership on Metrology<br>• Funder ID: 10.13039/100019599<br>• Grant number: 21GRD07 PlasticTrace</span></p>
VasTexture: Vast repository of textures and PBR Materials extracted from images using unsupervised approach
<h2><strong>VasTexture: Vast repository of textures and SVBRDF/PBR Materials extracted from images using an unsupervised approach.</strong></h2> <p> </p> <p>This dataset contains hundreds of thousands of textures and PBR/SV-BRDF materials extracted from real-world natural images.</p> <p> </p> <p>The repository is composed of RGB images of textures given as RGB images (each image is one uniform texture) and folders of PBR/SVBRDF materials given as a set of property maps (base color, roughness, metallic, etc).</p> <p>Note that this contain subset of repository more could be found in the <a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">main project page</a>.</p> <p>Visualisation of sampled PBRs and Textures can be seen in: <a href="../records/11391127/files/PBR_examples.jpg?download=1" target="_blank" rel="noopener">PBR_examples.jpg</a> and <a href="../records/11391127/files/Textures_Examples.jpg?download=1" target="_blank" rel="noopener">Textures_Examples.jpg</a></p> <p><a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">Link to the main project page</a></p> <p><a href="https://www.arxiv.org/pdf/2403.03309" target="_blank" rel="noopener">Link to paper</a></p> <p> </p> <h2>File structure</h2> <p>Texture images are given in the <strong><a href="../records/11391127/files/Extracted_textures_1.zip?download=1" target="_blank" rel="noopener">Extracted_textures_</a>*.zip</strong> files.</p> <p>Each image in this zip file is a single texture, the textures were extracted and cropped from the <a href="https://storage.googleapis.com/openimages/web/index.html" target="_blank" rel="noopener">open images dataset</a>. </p> <p> </p> <p>PBR Materials are available in <strong><a href="../records/11391127/files/PBR_O0_1.zip?download=1" target="_blank" rel="noopener">PBR_*.zip</a></strong> files these PBRs were generated from the texture images in an unsupervised way (with no human intervention). Each subfolder in this file contains the properties map of the PBRs (roughness, metallic, etc, suitable for blender/unreal engine). Visualization of the rendered material appears in the file Material_View.jpg in each PBR folder.</p> <p>PBR materials and textures which are larger then 512x512 pixels contain '<strong>large' </strong>or<strong> 'larger' </strong>in their file name there about 40k in this repository but 100k more can be found in the <a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">main project page</a>.</p> <p>PBR materials and textures who are seamless marked as contain<strong> 'seamless'</strong> in their file name. </p> <p> </p> <p>PBR materials that were generated by mixing other PBR materials are available in files with the names<a href="../records/11391127/files/PBR_mix_O2_2.zip?download=1"> </a><strong><a href="../records/11391127/files/PBR_mix_O2_2.zip?download=1">PBR_mix*.zip</a> </strong></p> <p> </p> <p>Samples for each case can be found in files named: <strong> Sample_*.zip</strong></p> <p> </p> <p>File with the word <strong>Seamless </strong>contain tileable seamless textures this files also contain textures and PBR>512 size that were extracted from the Segment Anything Dataset.</p> <p>Since the textures were extracted fom natural images they were not natively seamless but were turned to seamless using the code at <strong><a href="https://github.com/sagieppel/convert-image-into-seamless-tileable-texture">this url</a></strong></p> <p> </p> <p> </p> <p><strong>Documented code used to extract the textures and generate the PBRs is available at: </strong></p> <p><strong><a href="../records/11391127/files/Texture_And_Material_ExtractionCode_And_Documentation.zip?download=1" target="_blank" rel="noopener">Texture_And_Material_ExtractionCode_And_Documentation.zip</a></strong></p> <h2>Details:</h2> <p>The materials and textures were extracted from real-world images using an unsupervised extraction method (code supplied). As such they are far more diverse and wide in scope compared to existing repositories, at the same time they are much more noisy and contain more outliers compared to existing repositories. This repository is probably more useful for things that demand large-scale and very diverse data, yet can use noisy and lower quality compared to professional repositories with manually made assets like ambientCG. It can be very useful for creating machine learning datasets, or large-scale procedural generation. It is less suitable for areas that demand precise clean and categorized PBR like CGI art and graphic design. For preview It is recommended to look at <a href="../records/11391127/files/PBR_examples.jpg?download=1" target="_blank" rel="noopener">PBR_examples.jpg</a> and <a href="../records/11391127/files/Textures_Examples.jpg?download=1" target="_blank" rel="noopener">Textures_Examples.jpg</a> or download the Sample files and look at the Material_View.jpg files to visualize the quality of the materials.</p> <h3>Scale:</h3> <p>Currently, there are a few hundred of thousands PBR materials and textures but the goal is to make this into over a million in the near future.</p> <h2>Data generation code:</h2> <p>The Python scripts used to extract these assets are supplied at: </p> <p><strong><a href="../records/11391127/files/Texture_And_Material_ExtractionCode_And_Documentation.zip?download=1" target="_blank" rel="noopener">Texture_And_Material_ExtractionCode_And_Documentation.zip</a></strong></p> <p>The code could be run in any folder of random images extract regions with uniform textures and turn these into PBR materials. </p> <h2>Alternative download sources:</h2> <p>Alternative download sources:</p> <p><a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">https://sites.google.com/view/infinitexture/home</a></p> <p><a href="https://e.pcloud.link/publink/show?code=kZON5TZtxLfdvKrVCzn12NADBFRNuCKHm70" target="_blank" rel="noopener">https://e.pcloud.link/publink/show?code=kZON5TZtxLfdvKrVCzn12NADBFRNuCKHm70</a></p> <p><a href="https://icedrive.net/s/jfY1xSDNkVwtYDYD4FN5wha2A8Pz" target="_blank" rel="noopener">https://icedrive.net/s/jfY1xSDNkVwtYDYD4FN5wha2A8Pz</a></p> <p> </p> <h2>Paper</h2> <p>This work was done as part of the paper "<a href="https://www.arxiv.org/pdf/2403.03309" target="_blank" rel="noopener">Learning Zero-Shot Material States Segmentation,</a></p> <p><a href="https://www.arxiv.org/pdf/2403.03309" target="_blank" rel="noopener">by Implanting Natural Image Patterns in Synthetic Data</a>".</p> <p>@article{eppel2024learning,</p> <p> title={Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data},</p> <p> author={Eppel, Sagi and Li, Jolina and Drehwald, Manuel and Aspuru-Guzik, Alan},</p> <p> journal={arXiv preprint arXiv:2403.03309},</p> <p> year={2024}</p> <p>}</p> <p> </p> <h2>License:</h2> <p>All the code and repositories are available on CC0 (free to use) licenses.</p> <p>Textures were extracted from the open images dataset which is an Apache license.</p>
Fig.1 in Protocol Optimization For Genomic Dna Extraction And Rapd-Pcr Of Alien Ponto-Caspian Amphipod Pontogammarus Robustoides
Fig.1. Localities of sampling sities in the Latvian reservoirs.
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.