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4,753 results for “shape”
Data for: Vitrimer transition phenomena from the perspective of thermal volume expansion and shape (in)stability
<p>The data is supplementary to the publication "Vitrimer transition phenomena from the perspective of thermal volume expansion and shape (in)stability", DOI: <a href="https://pubs.acs.org/doi/10.1021/acs.macromol.4c00207" target="_blank" rel="noopener">10.1021/acs.macromol.4c00207</a></p> <p>Key words: Vitrimer transition temperature, Thermo-mechanical analyses, Temperature-modulated optical refractometry, Thermal volume expansion, Dynamic polymer networks, Shape instabilities</p> <p>The data sets contain measured data on Thermo-mechanical analysis (TMA) and Temperature-modulated optical refractometry (TMOR) of a epoxy-based vitrimer and a reference material.</p> <p>Material details:</p> <ul> <li>Bisphenol A Diglycidyl ether (DGEBA, DER332) + Difunctional and trifunctional carbocylic acids (Pripol1040, Croda) + 1,5,7-triazabicyclo[4.4.0]dec-5-en (TBD, 10 mol-% relative to carboxylic acid functions)</li> <li>Bisphenol A Diglycidyl ether (DGEBA, DER332) + Difunctional and trifunctional carbocylic acids (Pripol1040, Croda) +pyridine</li> </ul> <p>Funding received from:</p> <ul> <li>German Research Foundation (DFG), project number: 521902629.</li> <li>Sample preparation: (Austrian) Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology and the Federal Ministry for Digital and Economic Affairs (COMET-Module project “Repairtecture”, project-no.: 904927)</li> </ul>
Spherical harmonic models of the shape of the Moon (principal axis coordinate system) [LDEM128]
<p>This archive contains five spherical harmonic models of the shape of the Moon in a principal axis coordinate system, truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 11519, which was generated from a lunar shape model sampled at 128 pixels per degree.</p> <p>The dataset used to generate these models is the file <a href="https://doi.org/10.60903/LOLA_PA">LDEM128_PA_gridline_202405.grd</a>. As described by Neumann (2024), this shape mode is based on a combination of laser altimeter data obtained by the LOLA instrument on the Lunar Reconaissance Orbiter spacecraft and the SLDEM2015 shape model that makes use of both LOLA and Kaguya terrain camera data. The netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The five files in this archive are</p> <ul> <li>Moon_LDEM128_shape_pa_11519.sh.gz</li> <li>Moon_LDEM128_shape_pa_5759.sh.gz</li> <li>Moon_LDEM128_shape_pa_2879.sh.gz</li> <li>Moon_LDEM128_shape_pa_1439.sh.gz</li> <li>Moon_LDEM128_shape_pa_719.sh.gz</li> </ul> <p>The numbers 11519, 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 128, 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p> <p>This shape model uses the same coordinate system as most lunar gravity models. The principal axis coordinate system differs from the more common mean Earth/polar axis system by about 1 km at the equator. For a mean Earth/polar axis model, use <a href="../records/10796823">Spherical harmonic models of the shape of the Moon</a>.</p>
Spherical harmonic models of the shape of asteroid (16) Psyche
<p>This archive contains spherical harmonic models of the shape of asteroid (16) Psyche.</p> <p><strong>Psyche-Shepard2017.sh</strong></p> <p>This is a degree and order 29 spherical harmonic model of the shape of Psyche that was constructed from the shape model of Shepard et al. (2017). The spherical harmonic coefficients were obtained from a least squares inversion that made use of the vertex coordinates from the file <code>psyche.v.final.mod.mod</code>. The least squares inversion was performed using the pyshtools routine <code>SHCoeffs.from_least_squares()</code> and tests show that the power spectrum is stable for maximum degrees up to, and including, 29. The coefficients are in meters and should be used with 4-pi normalized spherical harmonic functions.</p> <p><strong>Psyche-Shepard2021.sh</strong></p> <p>This is a degree and order 10 spherical harmonic model of the shape of Psyche that was constructed from the shape model of Shepard et al. (2021). The spherical harmonic coefficients were obtained from a least squares inversion that made use of the vertex coordinates from the file <code>psyche.vertex.obj</code>. The least squares inversion was performed using the pyshtools routine <code>SHCoeffs.from_least_squares()</code>, and tests show that the power spectrum decreases dramatically for maximum spherical harmonic degrees beyond 10. The coefficients are in meters and should be used with 4-pi normalized spherical harmonic functions.</p>
3D body shapes - INKREATE
<p>This dataset contains 56 3D complete human body shapes in STL format (file <strong>STL.zip</strong>).</p> <p>The table <strong>measurements.csv </strong>contains the code of the SLT files, gender and measurements. The measurements are explained in <strong>ibvtape_doc.pdf</strong></p> <p>The dataset was created for testing purposes in the project <a href="https://www.inkreate.eu/">INKREATE </a>: <em>Transfer the real 3D world to interactive creative endeavours in the apparel industry</em></p> <p>This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no. 731885</p>
Accompanying data for paper "Electrical and Thermal Conductivity of Complex-Shaped Contact Spots"
<div> </div> <div>This repository contains the numerical data of the conductivity of complex-shaped contact spots on isotropic and linear conducting half-space obtained by Boundary and Finite Element methods. These data were used to construct some figures from the manuscript "Electrical and Thermal Conductivity of Complex-Shaped Contact Spots". The data is organized in folders corresponding to different types of contact spots: annular, flower-, star- and gear-shaped, Koch's snowflake, and self-affine spots. Each folder contains the results of numerical simulations in the form of `.npz` files, which can be loaded using `numpy` library in Python. The data is used to construct figures in the manuscript and can be used to reproduce the results or to perform additional analysis.</div> <div> </div>
Voxel-level summary statistics of hippocampus shape, white matter microstructure, and cortical surface curvature in UK Biobank (n=33,324)
<p>This deposit hosts GWAS summary statistics of hippocampus shape (n=33,324), white matter microstructure (n=33,324), and cortical surface curvature (n=15,752) using UKB unrelated white subjects. The data was generated by using the highly efficient imaging genetics (<a href="https://github.com/Zhiwen-Owen-Jiang/heig">HEIG v1.1.0</a>) framework where only the triplets - summary statistics of low-dimensional representations (LDRs), the functional bases, and the variance-covariance matrix LDRs - are shared, which is sufficient to recover all voxel-variant pairs as well as to conduct voxel-level heritability and (cross-trait) genetic correlation analysis. Check the <a href="https://github.com/Zhiwen-Owen-Jiang/heig/wiki">tutorial</a> and the <a href="../records/13770930">example data</a> used in the tutorial. </p> <p>The shared data includes:</p> <p>1. Triplets for hippocampus shape measured by the radial distance from the medial model for each vertex. The original images contain 30,000 vertices while the shared data contains 49 LDRs. Left and right hemispheres were analyzed separately, each with 15,000 vertices.</p> <p>2. Triplets for 21 white matter tracts measured by fractional anisotropy. The original images contain 32,217 voxels and each tract contains 88 ~ 3503 voxels while the shared data contains 1,034 LDRs. Tracts were analyzed separately.</p> <p>3. Triplets for cortical surface curvature. The original images contain 59,412 vertices while the shared data contains 1,750 LDRs. The entire brain was analyzed as a whole.</p> <p>4. LD matrix and its inverse for 22 chromosomes including 460k genotyped SNPs. LD matrix and its inverse were estimated by using two separate datasets each containing 8.4k white unrelated subjects in UKB. Two regularization levels are provided: {85%, 80%} for heritability and genetic correlations within images and {75%, 70%} for cross-trait genetic correlations.</p> <p>5. LD matrix and its inverse for 22 chromosomes including 1.2 million imputed HapMap3 SNPs. LD matrix and its inverse were estimated by using two separate datasets each containing 42k white unrelated subjects in UKB. Two regularization levels are provided: {98%, 95%} for heritability and genetic correlations within images and {90%, 85%} for cross-trait genetic correlations.</p>
Design of an Ontology-Driven Constraint Tester (ODCT) and Application to SAREF & Smart Energy Appliances: Datasets, SHACL Shapes, Demo Video of Web Application, and Detailed Performance Reports
<h2>Description</h2> <p>This repository presents the resources used for validating the compliance of <strong>smart energy appliances</strong> against the <strong>Smart Appliances REFerence (SAREF)</strong> ontology and its extension <strong>SAREF4ENER</strong>, as part of the <strong>Ontology-Driven Constraint Tester (ODCT)</strong> project. The ODCT tool is specifically designed to ensure <strong>semantic interoperability</strong> and adherence to standardized ontological frameworks, which are crucial for integrating smart devices into modern energy management systems.</p> <h2>ODCT Overview</h2> <p>The <strong>Ontology-Driven Constraint Tester (ODCT)</strong> is a robust framework created to validate datasets against ontologies defined by <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, both established under ETSI SmartM2M. This tool has been applied to the <strong>Flexible Start use case</strong> from the <strong>Joint Research Centre’s (JRC) Code of Conduct for Energy Smart Appliances</strong>. The ODCT tool ensures that smart devices like energy-efficient washing machines, thermostats, and connected lighting operate in compliance with established ontologies, thereby enhancing their <strong>interoperability</strong> within energy management systems and smart grids.</p> <h2>Repository Contents</h2> <p>This repository contains essential resources used in the ODCT compliance testing process:</p> <ul> <li> <p><strong>Compliant Dataset</strong>: This dataset represents a fully compliant scenario where no errors are present in the smart energy appliances’ profiles, demonstrating the ODCT’s accuracy under ideal conditions.</p> </li> <li> <p><strong>Modified Datasets</strong>: These datasets introduce various types of errors to showcase ODCT’s ability to handle diverse compliance scenarios:</p> <ol> <li><strong>Modified Dataset 1</strong>: Introduces type mismatches and spelling errors in key attributes.</li> <li><strong>Modified Dataset 2</strong>: Contains extraneous properties and missing required properties, including details about energy consumption and efficiency class.</li> <li><strong>Modified Dataset 3</strong>: Includes both extraneous and missing properties, and additional priority levels for energy profiles.</li> </ol> </li> <li> <p><strong>SHACL Shapes</strong>: The SHACL shapes used in the compliance testing for both SAREF and SAREF4ENER ontologies are included in this repository to allow reproducibility of the validation process.</p> </li> </ul> <ul> <li> <p><strong>Error Detection Results and Performance Reports</strong>: After conducting compliance tests using ODCT we got the Results and Performance Reports, the repository includes comprehensive reports detailing the results. These reports highlight the types of errors detected and provide a performance analysis of the tool under various scenarios.</p> </li> <li> <p><strong>Demonstration Video</strong>: A video is provided to guide users through the <strong>ODCT web application</strong>, showcasing how the tool detects errors and generates detailed compliance reports based on smart energy appliance datasets.</p> </li> </ul> <h2>Background</h2> <p>The integration of smart energy appliances into modern power grids is key to improving <strong>energy management</strong> and supporting <strong>sustainability goals</strong> like the <strong>European Green Deal</strong>. However, ensuring that these devices communicate effectively and conform to <strong>standardized protocols</strong> is a challenge. The <strong>ODCT</strong> tool addresses this challenge by providing a rigorous, ontology-based validation framework that is both <strong>protocol-agnostic</strong> and <strong>technology-flexible</strong>.</p> <p>This work is grounded in the broader context of <strong>global warming</strong> and the need for <strong>energy efficiency</strong> and <strong>demand-side flexibility</strong> in energy systems. By ensuring compliance with <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, ODCT supports the EU’s ambitions for <strong>carbon neutrality</strong> by 2050, contributing to a connected, efficient, and sustainable energy ecosystem.</p> <h2>Methodology</h2> <p>ODCT uses a structured methodology that involves:</p> <ol> <li><strong>Generating relevant datasets</strong> for validation.</li> <li><strong>Defining SHACL shape constraints</strong> based on ontologies.</li> <li><strong>Developing a user-friendly web application</strong> to facilitate compliance testing.</li> <li><strong>Performing compliance tests</strong> that validate datasets against SHACL shapes, ensuring interoperability and adherence to energy management standards.</li> </ol> <h2>Why It Matters</h2> <p>Researchers and developers working on smart energy appliances will benefit from ODCT by:</p> <ul> <li>Ensuring their devices meet standardized ontological requirements for <strong>interoperability</strong>.</li> <li>Reducing <strong>compliance issues</strong> in the development phase, leading to smoother integration into energy management systems.</li> <li>Supporting the <strong>sustainability efforts</strong> by enhancing device communication in <strong>smart grids</strong>.</li> </ul> <p>This repository showcases the potential of ODCT in fostering <strong>data accuracy</strong>, <strong>semantic interoperability</strong>, and <strong>compliance</strong> with essential energy standards. It offers comprehensive resources for furthering research and development in the field of smart energy appliances and energy management.</p>
WAT03 Climate legacy effects shape tallgrass prairie nitrogen cycling
Climate change is expected to shift precipitation regimes in the North American Central Plains with likely impacts on ecosystem functioning. In tallgrass prairies, water and nitrogen (N) can co-limit ecosystem processes, so changes in precipitation may have complex effects on carbon (C) and N cycling. Rates of N supply such as N mineralization and nitrification respond differently to short- and long-term patterns in water availability, and previous climate patterns may exert legacy effects on current N cycling that could alter ecosystem sensitivity to current precipitation regimes. We used a long-term precipitation manipulation at Konza Prairie (Kansas, USA) to assess how previous and current precipitation influence tallgrass prairie N cycling. Supplemental irrigation was applied across upland and lowland prairie for ~25 years to reduce water deficits; in 2017, we reversed some of these treatments and added a reduced rainfall treatment across both historic rainfall regimes, allowing us to assess how previous climate and current rainfall patterns interact to shape N cycling. In lowland prairie, previous irrigation doubled N mineralization and nitrification rates the year following cessation of irrigation. Reduced microbial C/N ratio and lower relative investment in N-acquiring enzymes in previously irrigated lowlands suggested that a wetter climate created a legacy of increased N availability for microbes. Internal plant N resorption increased under short-term irrigation but recovered to ambient levels following previous irrigation. Together, these results suggest that a history of wetter conditions prairie can create a legacy of accelerated N cycling and with consequences for both plant and microbial functioning.
Associative Prediction of Visual Shape in the Hippocampus
Open the record for dataset details and reuse information.
3D scans of two types of railway ballast including shape analysis information
<p>This data set contains 3D scanner data of two types of railway ballast “Calcite” (stems from Croatia) and “Kieselkalk”, also known as Helvetic Siliceous Limestone, (stems from Switzerland).<br> From each type of ballast 25 stones are scanned. The files are provided in .ply format.<br> For the scanned meshes several shape descriptors are provided: elongation, flatness, sphericity, convexity index.<br> Additional to the 3D scans, both simplified and rounded versions of the meshes are included.<br> For these meshes information on three different angularity indices are available.<br> The scanned ballast types are the same, as previously investigated in uniaxial compression tests and direct shear tests:<br> Suhr, Bettina, & Six, Klaus. (2018).<br> "Compression tests and direct shear test of two types of railway ballast [Data set]"<br> Zenodo. http://doi.org/10.5281/zenodo.1423742</p> <p> </p> <p>A detailed shape analysis of the results is conducted in:<br> Bettina Suhr, William A. Skipper, Roger Lewis, and Klaus Six<br> "Shape analysis of railway ballast stones: curvature-based calculation of particle angularity"<br> <em>Scientific Reports, </em><strong>2020</strong><em>, 10</em>, 6045<br> DOI: https://doi.org/10.1038/s41598-020-62827-w</p> <p>A summary of several shape descriptors can be found in:<br> B. Suhr and K. Six:<br> "Simple particle shapes for DEM simulations of railway ballast -- influence of shape descriptors on packing behaviour"<br> Granular Matter, <strong>2020</strong><em>, 22</em><br> DOI: https://doi.org/10.1007/s10035-020-1009-0</p> <p><br> This data set is organised as follows:<br> 1_ScanMeshesCleaned<br> scanned meshes:<br> K_1.ply - K_25.ply Calcite (German: Kalzit)<br> KK_1.ply - KK_25.ply Kieselkalk<br> 2_CSE1 <br> simplifications of the scanned meshes, little simplifications, used in the detailed shape analysis<br> CSE1_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 3_CSE2 <br> simplifications of the scanned meshes, more simplified, used in the detailed shape analysis<br> CSE2_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 4_CSE3 <br> simplifications of the scanned meshes, even more simplified, used in the detailed shape analysis<br> CSE3_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 5_CSE4 <br> simplifications of the scanned meshes, most simplified, used in the detailed shape analysis<br> CSE4_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 6_RoundedMeshes<br> artificially rounded versions of the scanned ballast meshes, used in the detailed shape analysis<br> RoundedMeshes_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 7_TestBodies <br> meshes of artificial test bodies, constructed for testing different angularity indices in the detailed shape analysis<br> TestBodies_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> scanMeshesInfo.csv: summary of several shape descriptors of the scanned meshes<br> README.txt </p> <p><br> Check the README.txt file for more information on the technical aspects of scanning.</p> <p> </p>
Detailed point cloud data on stem size and shape of Scots pine trees
<p>This data set is comprised of three packed zip files and they include text files of 3D information from terrestrial laser scanning (TLS) and aerial imagery from unmanned aerial vehicle (UAV) from individual Scots pine trees within 27 sample plots from three test sites located in southern Finland.</p> <p>TLS data acquisition was carried out with Trimble TX5 3D laser scanner (Trible Navigation Limited, USA) for all three study sites between September and October 2018. Eight scans were placed to each sample plot and scan resolution of point distance approximately 6.3 mm at 10-m distance was used. Artificial constant sized spheres (i.e. diameter of 198 mm) were placed around sample plots and used as reference objects for registering the eight scans onto a single, aligned coordinate system. The registration was carried out with FARO Scene software (version 2018). Aerial images were obtained by using an UAV with Gryphon Dynamics quadcopter frame. Two Sony A7R II digital cameras were mounted on the UAV in +15° and -15° angles. Images were acquired in every two seconds and image locations were recorded for each image. The flights were carried out on October 2, 2018. For each study site, eight ground control points (GCPs) were placed and measured. Flying height of 140 m and a flying speed of 5 m/s was selected for all the flights, resulting in 1.6 cm ground sampling distance. Total of 639, 614 and 663 images were captured for study site 1, 2, and 3, respectively, resulting in 93% and 75% forward and side overlaps, respectively. Photogrammetric processing of aerial images was carried out following the workflow as presented in Viljanen et al. (2018). The processing produced photogrammetric point clouds for each study site with point density of 804 points/m<sup>2</sup>, 976 points/m<sup>2</sup>, and 1030 points/m<sup>2</sup> for study site 1, 2, and 3, respectively.</p> <p>The sample plots within the three test sites have been managed with different thinning treatments in either 2005 or 2006. The experimental design of the sample plots includes two levels of thinning intensity and three thinning types resulting in six different thinning treatments, namely i) moderate thinning from below, ii) moderate thinning from above, iii) moderate systematic thinning, iv) intensive thinning from below, v) intensive thinning from above, and vi) intensive systematic thinning, as well as a control plot where no thinning has been carried out since the establishment. More information about the study sites and samples plots as well as the thinning treatments can be found in Saarinen et al. (2020a).</p> <p>The data set includes stem points of individual Scot pine trees extracted from the point clouds. More about the method of extraction can be found in Saarinen et al. (2020a, 2020b) and Yrttimaa et al. (2020). The title of the zip file refers to the study sites 1, 2, and 3. The title of the text files includes the information on the test site, the plot within the test site, and the tree within the plot. The text files contain stem points extracted from the TLS point clouds. The columns “x” and “y” contain x- and y-coordinates in a local coordinate system (in meters), in column “h” is the height of each point in meters above ground, and treeID is the tree identification number. The columns are separated by space.</p> <p>Based on the study site and plot number, files from different thinning treatments can be identified by using the information in Table 1 in Saarinen et al. (2020b).</p> <p> </p> <p><strong>References</strong></p> <p>Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020a. Assessing the effects of stand dynamics on stem growth allocation of individual Scots pines. bioRxiv 2020.03.02.972521. <a href="https://doi.org/10.1101/2020.03.02.972521">https://doi.org/10.1101/2020.03.02.972521</a></p> <p>Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020b. Detailed point cloud data on stem size and shape of Scots pine trees. bioRxiv 2020.03.09.983973. <a href="https://doi.org/10.1101/2020.03.09.983973">https://doi.org/10.1101/2020.03.09.983973</a></p> <p>Viljanen, N., Honkavaara, E., Näsi, R., Hakala, T., Niemeläinen, O., Kaivosoja, J. 2018. A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone. Agriculture 8: 70. <a href="https://doi.org/10.3390/agriculture8050070">https://doi.org/10.3390/agriculture8050070</a></p> <p>Yrttimaa, T., Saarinen, N., Kankare, V., Hynynen, J., Huuskonen, S., Holopainen, M., Hyyppä, J., Vastaranta, M. 2020. Performance of terrestrial laser scanning to characterize managed Scots pine (<em>Pinus sylvestris</em> L.) stands is dependent on forest structural variation. EarthArXiv. March 5. <a href="https://doi.org/10.31223/osf.io/ybs7c">https://doi.org/10.31223/osf.io/ybs7c</a></p>
Beak Shape in Birds and Squid: Principal Components Analysis of 2D Landmarks
<p>R code to analyze observations of beak traces from specimens of birds and squid.</p> <p>Notes are in the code. Watch for updates.</p> <p>Where the csv files include data published by different authors, the doi references to the original publications are included in the R code. I took care to correctly download/process/transcribe where applicable, but please do notify me if there are errors.</p>
SHAPE-ID Literature Review dataset: bibliography on IDR/TDR
<p><strong>Background and methodology:</strong></p> <p>The dataset consists of 5040 records of publication metadata (author, abstract, title, keywords, tags etc.), produced for the purposes of the <a href="https://doi.org/10.5281/zenodo.3760417">systematic literature review</a> in the framework of the <a href="https://www.shapeid.eu/">SHAPE-ID</a> project. </p> <p>In the course of the review Project team queried Web of Science (WoS), Scopus and JSTOR databases for records on interdisciplinarity and transdisciplinarity (IDR/TDR). In the case of WoS and Scopus, <a href="https://doi.org/10.5281/zenodo.4034333">complex search strings</a> were created to reflect the main research questions of the Literature Review: different understandings of IDR/TDR and factors and indicators of success or failure of integration of IDR/TDR in research and research policy. JSTOR database offers less advanced data-analytical tools, but the project team decided to include items that have interdisciplinarity or transdisciplinarity in the title, to counterbalance the reported biases against Arts, Humanities and Social Sciences in Scopus and WoS. These three data sources were complemented with bibliographies prepared during the preliminary scoping analysis of IDR/TDR literature. The query results were compiled in reference managers Zotero and Endnote. During data processing the records were normalized and duplicates were removed. </p> <p>Based on systematic review, a sample of the literature had been selected for qualitative analysis. At the same time, the bibliographic metadata was analysed with computationally assisted quantitative methods.</p> <p><strong>Description of the file:</strong></p> <p>This is a csv file exported from the Zotero database, and formatted according to the <a href="https://www.zotero.org/support/kb/item_types_and_fields">Zotero metadata model</a>. It contains a collection of 5040 bibliographic records compiled for the purpose of the SHAPE-ID Literature Review.</p>
SHAPE-ID Literature Review dataset: journal occurrences with ASJC codes
<p><strong>Background and methodology:</strong></p> <p>The dataset consists of a list of 2202 journal titles represented in the <a href="https://doi.org/10.5281/zenodo.4034507">SHAPE-ID Literature Review bibliography</a>, prepared for the purposes of quantitative analysis.</p> <p>The list of journals is based on 3955 journal articles in the bibliography dataset that had an International Standard Serial Number (ISSN). To each journal title the project team attributed:</p> <p>- a weight factor based on how many articles from the given journal featured in bibliography dataset</p> <p>- at least one <a href="https://service.elsevier.com/app/answers/detail/a_id/15181/supporthub/scopus/">All Science Journal Classification</a> (ASJC) code, representing different scientific disciplines</p> <p>- a country of publication. </p> <p>In case of 1853 of those journal titles, the attribution was automatised (we matched the ISSNs of journal titles in our sample against the Scopus Sources list from February 2019). In case of the remaining 349 titles the attribution was accomplished manually, based on the information available in SCOPUS, Web of Science, JSTOR, Information Matrix for the Analysis of Journals (MIAR) and ISSN databases.</p> <p><strong>Description of the file:</strong></p> <p>This is a csv file containing a list of 2202 journal titles represented in the SHAPE-ID Literature Review bibliography, with country of publication and ASJC codes assigned. </p> <p>The file is formatted as follows:</p> <p>Column A: ISSN of the journal</p> <p>Column B: information on how country and ASJC codes were attributed. Value “N” indicates automatic attribution based on match with Scopus list of sources. Other values indicate manual attribution. Values WOS, SCOPUS, JSTOR indicate source of information. Valu “Y” indicates that information was compiled based on multiple sources. </p> <p>Column C: numeric values correspond to the weight factor, i.e. number of time articles from each journal featured in the SHAP-ID Literature Review bibliography. </p> <p>Column D: SHAPE-ID Zotero bibliography identifier.</p> <p>Column E: Journal title</p> <p>Column F: The country of publication</p> <p>Columns G-AD: ASJC codes (numeric and word values) associated with journal entries. </p>
Contour method and neutron diffraction dataset to determine the weld fusion zone shape on residual stress in submerged arc welding
<p>This is a dataset which formed the basis for "The effect of the weld fusion zone shape on residual stress in submerged arc welding" by A. Ishigami, M. J. Roy, J. N. Walsh and P. J. Withers appearing in the Journal of Advanced Manufacturing Technology.</p> <p>Two X-grade steel specimens with different high speed, submerged arc welds with very slight differences in fusion zone shape were compared with a novel contour method application as well as with neutron diffraction. Neutron diffraction was carried out with the SALSA instrument at the Institut Laue-Langevin in Grenoble, France with the assistance of T. Pirling. Data files with 441 in the descriptor refer to 'conventional' parameters (see publication), while 241 refers to 'new'.</p> <p>Provided in this dataset are four *.dat files, which contains data is in the form of a point cloud with one point per line, whitespace delimited in microns. Data was captured with a Nanofocus CF-4 laser profilometer sensor with point spacing 30 µm apart. Data with z coordinates below or above 500 µm are considered outside of the surface detection limits.</p> <p>Also included is an Excel worksheet, which contains the calculated residual stresses as found with LAMP (https://www.ill.eu/instruments-support/computing-for-science/cs-software/all-software/lamp/). Raw data is available here:</p> <p>P. J. Withers, A. Ishigami, T. Pirling, M. Roy, J. Walsh (2014). The effect of weld bead shape on residual stress in novel low heat input welding of steel [Data set]. ILL. http://doi.ill.fr/10.5291/ILL-DATA.1-02-145</p> <p>The authors would like to thank JFE Steel Corporation for both direct and in-direct support of this research. The authors would also like to thank the Institut Max von Laue-Paul Langevin for the allocation of beamtime at SALSA and gratefully acknowledge the help of Thilo Pirling for his assistance in performing the neutron diffraction experiments. A. Ishigami would like to thank Kenji Oi for his support of this research. M. J. Roy would like to thank Ian Winstanley for his assistance in performing the contour cuts. M. J. Roy acknowledges financial support from the EPSRC (EP/L01680X/1) through the Materials for Demanding Environments Centre for Doctoral Training.</p>
Ecological filtering shapes the impacts of agricultural deforestation on biodiversity
<p>This dataset and associated code are for the manuscript titled "Ecological filtering shapes the impacts of agricultural deforestation on biodiversity", which is due to be published in the journal Nature Ecology & Evolution (accepted on September 20, 2023). The abstract of this manuscript is as follows:</p><p> </p><p>The biodiversity impacts of agricultural deforestation vary widely across regions. Previous efforts to explain this variation have focused exclusively on the landscape features and management regimes of agricultural systems, neglecting the potentially critical role of ecological filtering in shaping deforestation tolerance of extant species assemblages at large geographical scales via selection for functional traits. Here we provide a large-scale test of this role using a global database of species abundance ratios between matched agricultural and native forest sites that comprises 71 avian assemblages reported in 44 primary studies, and a companion database of ten functional traits for all 2,647 species involved. Using meta-analytic, phylogenetic, and multivariate methods, we show that beyond agricultural features, filtering by the extent of natural environmental variability and the severity of historical anthropogenic deforestation shapes the varying deforestation impacts across species assemblages. For assemblages under greater environmental variability – proxied by drier and more seasonal climates under greater disturbance regime – and longer deforestation histories, filtering has attenuated the negative impacts of current deforestation by selecting for functional traits linked to stronger deforestation tolerance. Our study provides a heretofore largely missing piece of knowledge in understanding and managing the biodiversity consequences of deforestation by agricultural deforestation.</p>
Phlorest phylogeny derived from Honkola et al. 2013 'Cultural and climatic changes shape the evolutionary history of the Uralic languages'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Honkola T, Vesakoski O, Korhonen K, Lehtinen J, Syrjänen K & Wahlberg N. 2013. Cultural and climatic changes shape the evolutionary history of the Uralic languages. Journal of Evolutionary Biology, 26(6):1244–1253.</p> </blockquote>
Wavefront shaping through a free-form scattering object
<p>The basic publication is:</p><p>Alfredo Rates, Ad Lagendijk, Aurele Adam, Wilbert IJzerman, and Willem Vos, "Wavefront shaping through a free-form scattering object", Opt. Express <strong>31</strong>, 43351-43361 (2023). DOI: 10.1364/OE.505974.<br> <br>We have uploaded to the Zenodo database all data enabling everyone to reuse our data, and to reproduce all the figures of our paper.</p><p>The upload contains the file "Metadata.txt" explaining the content of the upload.</p>
Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media
<p>Dataset associated to the publication</p><p>"Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media"</p><p>By</p><p>David Scheidweiler, Ankur Deep Bordoloi, Wenqiao Jiao, Vladimir Sentchilo, Monica Bollani, Audam Chhun, Philipp Engel and Pietro de Anna</p><p>Folder named "Figure_X" contains the original raw data, analysed data and source data for each plot within figure "X" on the manuscript and supplementary information.</p><p>We do not provide raw data for each replica as one flow&growth experiment consists in 50 large images for a total of about 12 GB per dataset. Thus, we provide here the original data for the Wild Type experiment and the control D-luxS mutant. The data for the replicas and other control experiment can be available upon request.</p><p>We provide Matlab scripts to read and analyze the original images.</p>
Spherical harmonic models of the shape of Mercury
<p>The data used to generate these spherical harmonic models is the global digital elevation model (DEM) of Mercury, produced by the U.S. Geological Survey (USGS). The DEM was derived from from stereo image pairs (stereo photogrammetry) captured by the Mercury Dual Imaging System (MDIS) narrow-angle camera (NAC) and multispectral wide-angle camera (WAC) on board the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft. </p> <p>The global DEM was downloaded throught the <a href="https://astrogeology.usgs.gov/search/map/Mercury/Topography/MESSENGER/Mercury_Messenger_USGS_DEM_Global_665m_v2">Astropedia catalog</a> in geoTIFF format and equirectangular projection. Using the <a href="https://rasterio.readthedocs.io/en/stable/">rasterio</a> package, the dataset was loaded in python, scaled to the local height and radius (described in the Astropedia documentation), and exported into .dat format. Then, the file was converted into a netcdf format and resampled into a gridline registration using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> as follows:<br><code>gmt xyz2grd filename.dat -Gfilename.grd -R0/360/-90/90 -I0.015625/0.015625 -ZTLd -fg -rp</code><br><code>gmt grdsample filename.grd -Gfilename_gridline.grd -T</code></p> <p>The resulting netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software with the <code>SHGrid.from_netcdf()</code> and expanded into spherical harmonics using the function <code>SHGrid.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)m. The units of the coefficients are meters.</p> <p>The four files in this archive are</p> <p>- Mercury_shape_5759.sh.gz<br>- Mercury_shape_2879.sh.gz<br>- Mercury_shape_1439.sh.gz<br>- Mercury_shape_719.sh.gz</p> <p>The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p> <p>The spherical harmonic coefficients can be loaded with pyshtools as follows:<br><code>SHCoeffs.from_file("filename.sh.gz", format='bshc')</code></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)
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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.