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.
12,393
datasets available to search
ShareScore release 0.9.0
Dataset results
12,393 results for “materiality”
GrainLearning: A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials
GrainLearning is a Bayesian uncertainty quantification and propagation toolbox for computer simulations of granular materials. The software is primarily used to infer and quantify parameter uncertainties in computational models of granular materials from observation data, also known as inverse analyses or data assimilation. Implemented in Python, GrainLearning can be loaded into a Python environment to process the simulation and observation data, or alternatively, as an independent tool where simulation runs are done separately, e.g., via a shell script.
Supplementary materials for "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone"
<p>This is a ReadMe for the supplementary material for "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone" written by Rina Noguchi and Wataru Nakagawa.</p> <p>----------------------<br>[ReadMe.txt]<br>ReadMe text file.</p> <p>[FigS1.png]<br>This figure is a supplementary figure which appeared as "Figure S1" in the main text.<br>Caption: Figure S1. Examples of conduits (dashed green lines) and loser conduits (solid magenta lines) were observed in the experiments with original and contrast-enhanced images.</p> <p>[FigS2.png]<br>This figure is a supplementary figure which appeared as "Figure S2" in the main text.<br>Caption: Figure S2. Relationships between the thickness of poured heated syrup and (A) mass losses caused by baking soda decomposition, (B) number of conduits, (C) total conduit area, (D) average conduit area, (E) number of failed conduits, and (F) sum number of conduits and failed conduits. Each plot and error bar represents the average and standard deviation in three repeated experiments, respectively. The red plots and error bars show the 350 g of heated syrup case, which performed ten repeated experiments to verify the reproducibility. Note that horizontal error bars are derived from the difficulty of strict heated syrup-pouring control.</p> <p>[Experimental_datasheet.xlsx]<br>This EXCEL file includes two sheets: a mass loss change log and a summary of experimental results.</p> <p>[movie/SSS_X_x15.mp4]<br>These MP4 files are fast-forward movies (x15) for each experiment. SSS = the amount of poured hearty syrup (g), and X = round in each condition.<br>----------------------</p> <p>For more details, please refer to a research paper "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone".</p> <p>If you have any questions, please send an e-mail to:<br>r-noguchi@env.sc.niigata-u.ac.jp<br>or<br>flugel555@gmail.com<br>.<br>(R. Noguchi)</p>
The price of safety: Order picking in warehouses with in-house traffic regulations (Supplementary material)
<p>In what follows, you will find the code and results of the paper:</p> <p>"The price of safety: Order picking in warehouses with in-house traffic regulations" published in IISE Transactions.</p> <p> </p> <p>List of files:</p> <p>- Zip file: "Order Picking Problem with in-house traffic regulations" containing C# Code used to generate solutions for all safety policies</p> <p>- Result.csv containing all generated results</p> <p>- createPlots.py containing code to generate figures and tables from the paper</p> <p> </p> <p>The C# code is object-oriented and contains a Main function in the Program.cs file that converts the Example.OPP file with the InstanceReaders to an OPPInstance and uses the Solve function from either the DynamicProgrammic.cs or RuralPostman.cs file to solve the OPPInstance with all the TrafficRegulations as described in the paper.</p> <p> </p> <p>The Example.OPP defines the Depot location (0: decentral, 1: central), AisleLength, i.e. the number of pick positions within each aisle, and other dimensions of the warehouse. Finally, the items are defined by their picking aisle, shelf, position in the shelf, and region.</p> <p> </p> <p>The dynamic program (DP) described in the paper is implemented in DynamicProgrammic.cs. A HashSet of DPNode represents each layer of the DP. A DPNode basically consists of components, nodeDegrees, and a value. Depending on the TrafficRegulation the nodeDegrees are either NodeDegreeClassic, i.e. Null, Uneven, or Even, or NodeDegreeInAndOutDifference, i.e. the difference of the in- and out-degree. To construct the solution at the end, the inEdge is also saved for each DPNode and the additional member depotIsConnected ensures that the depot is visited. The DPNodes in the next layer of the DP are created by the functions MakeNextLayerVertical and MakeNextLayerHorizontal by determining all possibleTransitions per node in the current layer and combining them into a newNode. Products are stored with their position on the shelves in the item list within a PickingAisle. All vertical possibleTransitions are determined in a preprocessing step depending on the TrafficRegulations and are saved within the respective PickingAisle. All horizontal possibleTransitions are determined during the DP with specific functions depending on the TrafficRegulation in HorizontalTransition.cs. When the layers are created, the best feasible DPNode per layer is saved and the best one, i.e. the one with the lowest value, is returned at the end.</p> <p> </p> <p>The paper describes that certain safety policies cannot be solved with the DP. These OPPInstances are solved as a RuralPostman problem (RPP) by generating a Graph that adopts the rectangular structure of the warehouse. Within the Graph, requiredEdges are determined that correspond to PickingAisles containing items. The resulting RPP can be transformed into a traveling salesman problem (TSP) as described by applying an arc-oriented Dijkstra or, in certain cases, to a generalized TSP (GTSP) where one of the two directed edges must be visited. If necessary, the GTSP is transformed to an asymmetric TSP in GTSPInstance and then solved with TSPSolver using LKH-3.exe (Helsgaun 2017, http://webhotel4.ruc.dk/~keld/research/LKH-3/). To use LKH-3.exe, the TSP instance is saved in a TSPLIB format and a parameter file (.par) for LKH and a solution file (.sol) are created in the bin folder. These files are named according to the name specified in the instance.Solve function, where one can also choose to save or delete these files afterward.</p> <p> </p> <p>For more information on LKH-3 see: Keld Helsgaun: An Extension of the Lin-Kernighan-Helsgaun TSP Solver for Constrained Traveling Salesman and Vehicle Routing Problems (Technical Report, Roskilde University, 2017)</p> <p> </p> <p>Evaluation.py</p> <p>A Python script that generates figures 8, 9, and 10 and tables 6, 7 and 8 (in csv-format) of the paper by processing data from Results.csv.</p> <p>It requires Results.csv to be in the same directory as the code.</p> <p>It also requires the following Python packages:</p> <p>- matplotlib</p> <p>- pandas</p> <p>- seaborn</p>
US Material Handling Equipment Market 2024 to 2033
<p><strong>Reports Description</strong></p> <p>According to current market research conducted by the CMI Team, the global <a href="https://www.custommarketinsights.com/report/us-material-handling-equipment-market/" target="_blank" rel="noopener"><strong>US Material Handling Equipment Market</strong></a> is expected to record a CAGR of <strong>7.89%</strong> from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD <strong>42.37 Billion</strong>. By 2033, the valuation is anticipated to reach USD <strong>83.92 Billion</strong><strong>.</strong></p> <p>Dynamic and evolving quickly, driven by technical improvement, the U.S. Material Handling Equipment market has many changes. This contains an assortment of devices composed of forklifts, conveyors, automated storage and retrieval systems, and robots to improve handling processes concerning materials in industries. The main trends shaping this space include automation and robotization, energy efficiency with a sustainability focus on solutions, and an increased level of e-commerce and distribution channels.</p> <p>DOWNLOAD FREE SAMPLE Now at <a href="https://www.custommarketinsights.com/request-for-free-sample/?reportid=59218" target="_blank" rel="noopener">https://www.custommarketinsights.com/request-for-free-sample/?reportid=59218</a></p>
New Andrews-Curtis trivializations for Miller-Schupp groups presentations. Supplementary materials.
<p>This folder contains supplementary materials for the papers<br>"New Andrews-Curtis trivializations for Miller-Schupp groups presentations" and</p> <p>"Automated Theorem Proving Reveals a Lengthy Andrews-Curtis<br>Trivialization for a Miller-Schupp Trivial Group Presentation" (tentative, as of january 2025) </p>
Data for: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling
<h2>Description</h2> <p>DATA REPOSITORY FOR</p> <p>Title: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and <br> multi-scale material modeling<br>By: Eva Jägle, Jithender J. Timothy, Daniel Jansen, Alisa Machner<br>Accepted by: Cement and Concrete Research</p> <p>This dataset presents the data of the paper 'Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling' submitted to and accepted by Cement and Concrete Research. The dataset follows the structure of the paper such that the calculations described therein can be reproduced.</p> <p>Data is available on three types of cement: Two ordinary Portland cements of different grinding fineness (CEM I 42.5 R und CEM I 52.5 R) and one limestone-containing blended cement (CEM II/A-LL 42.5 R). The data refer to the first 24 hours of hydration and temperature conditions of 20°C (for CEM I 42.5 R, CEM I 52.5 R, CEM II/A-LL 42.5 R) and 35°C (for CEM I 52.5 R). All data were retrieved for cement pastes with a water-to-cement ratio of 0.45.</p> <p>The dataset contains raw and processed data from quantitative X-ray diffraction, 5PL cement dissolution fitting, thermodynamic simulation with GEMS, multi-scale material modeling, ultrasonic testing and Vicat penetration tests. The data is mainly available in .xlsx files together with short descriptions in ReadMe.txt files.</p>
Dataset on Physics-Based Indicators for Optimizing Phase Change Material Effectiveness in Building Design
<p>This research dataset includes the results as well as the EnrgyPlus models developed to investigate and validate newly proposed indicators to quantify the effectiveness of phase change materials in buildings.</p>
Dataset - SciVisContest - Materials Discovery Challenge - version 2025
<p>This the updated version of the dataset that is made available for the SciVisContest Materials Discovery Challenge.</p> <p>The data provided for this challenge was generated for the specific use case of developing a new Al-based alloy suitable for additive manufacturing by blending different available aluminum metal scrap such as automotive Al-Si piston alloys and other alloys from different sectors. Different alloy designs were initially generated based on mixing ratios between available scrap alloys. The CALPHAD method was used to perform equilibrium and non-equilibrium calculations to predict relevant thermo-physical and mechanical variables such as the content of volatile elements like Mg and Zn, phase formation and their fractions, solidification intervals, thermo-physical parameters, yield strength and hot crack sensitivity for each alloy composition.</p>
Supplementary material for Targeted gene knock-in reduces variation between transformants in the mushroom-forming fungus Schizophyllum commune
<p>Supplementary data for "Targeted gene knock-in reduces variation between transformants in the mushroom-forming fungus <em>Schizophyllum commune</em>"</p> <p>Dataset consists of fluorescent images of <em>S. commune</em> strains with an ectopic or targeted integration of <em>dTomato </em>under the control of the <em>tubulin </em>promoter and <em>hom2 </em>terminator and the obtained fluorescent intensity of each strain. For thesholding the mean intensity of all pixels above 14 (range 0, 255) was calculated.</p> <p>Files are names according to strain (E1-E12 for ectopic integrations and TI1-TI6 for targeted integrations and WT for wildtype) and replicate.</p>
Dataset of optimal cement-based panels enhanced with microencapsulated phase change material for EnergyPlus simulations
<p>This dataset includes:<br> - A series of EnergyPlus models of the BESTEST - Case 900 - from ANSI/ASHRAE Standard 140-2011 for the original (Baseline_Case900) and three enhanced designs (Opt-1, Opt-2, Opt-5) by using a cement-based panel containing microencapsulated phase change material.<br> - All the optimal solutions (parameters and corresponding performance) in XLSX format, which were obtained for two multiobjective optimization studies of the thermophysical properties (ParetoFront_CaseA and ParetoFront_CaseB).<br> - The typical meteorological year (TMY) of Sofia city employed to obtain the results, which is freely provided by Climate.One.Building.Org repository (https://climate.onebuilding.org/)</p>
Complementary material of the WorkStream 1 of project 4SECURail
<p>Deliverables, models, artifacts developed within WP2 of the Shift2Rail project 4SECURAil.</p>
Emissions from building materials - concentration of micropollutants and heavy metals in stormwater runoff of two new development areas in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (27) and heavy metals (7) for stormwater runoff from different sampling points at two test sites (A and B) in Berlin, Germany. Both sites are new development areas of similar size that were both constructed in 2017 (1 – 1.5 years prior to the start of the monitoring campaign). Composite samples of individual rain events were taken at three sampling points of each test site: façade runoff, roof runoff and corresponding stormwater runoff from the catchment area. Samples were taken as part of the research project BaSaR (<a href="http://www.kompetenz-wasser.de/en/forschung/projekte/basar/">www.kompetenz-wasser.de/en/forschung/projekte/basar/</a>) of Kompetenzzentrum Wasser Berlin, Ostschweizer Fachhochschule and Berliner Wasserbetriebe. More information including sampling and analytical methods are detailed in the corresponding journal paper "Emissions from building materials – a thread for the environment?", submitted to the MDPI-journal <em>Water</em>.</p> <p><strong>Description of fields:</strong></p> <ul> <li><strong>SiteID</strong>: site identifier <ul> <li>A: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in northern part of Berlin (124 apartments)</li> <li>B: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in southeastern part of Berlin (122 appartments)</li> </ul> </li> <li><strong>SamplingPoint</strong> <ul> <li>facade runoff: runoff from plastered facade collected with gutters during individual rain events</li> <li>roof runoff: roof runoff collected from one downpipe during individual rain events</li> <li>storm sewer: stormwater runoff sampled during individual rain events in a manhole receiving runoff from the entire catchment (A or B)</li> </ul> </li> <li><strong>LocalDateTime_StartRain</strong>: start time of sampled rain event (CET / CEST)</li> <li><strong>LocalDateTime_EndRain</strong>: end time of sampled rain event (CET / CEST)</li> <li><strong>CardinalDirection</strong>: only relevant for facade runoff <ul> <li>N: runoff from facade oriented to the north</li> <li>W: runoff from facade oriented to the west</li> </ul> </li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p>One data file is provided in comma separated format:<br> "BaSaR_data.csv" contains concentrations of all samples.</p>
Fabrication of a Soft Robotic Gripper With Integrated Strain Sensing Elements Using Multi-Material Additive Manufacturing
<p>With the purpose of making soft robotic structures with embedded sensors, additive manufacturing techniques like fused deposition modeling (FDM) are popular. Thermoplastic polyurethane (TPU) filaments, with and without conductive fillers, are now commercially available. However, conventional FDM still has some limitations because of the marginal compatibility with soft materials. Material selection criteria for the available material options for FDM have not been established. In this study, an open-source soft robotic gripper design has been used to evaluate the FDM printing of TPU structures with integrated strain sensing elements in order to provide some guidelines for the material selection when an elastomer and a soft piezoresistive sensor are combined. Such soft grippers, with integrated strain sensing elements, were successfully printed using a multi-material FDM 3D printer. Characterization of the integrated piezoresistive sensor function, using dynamic tensile testing, revealed that the sensors exhibited good linearity up to 30% strain, which was sufficient for the deformation range of the selected gripper structure. Grippers produced using four different TPU materials were used to investigate the effect of the Shore hardness of the TPU on the piezoresistive sensor properties. The results indicated that the <em>in situ</em> printed strain sensing elements on the soft gripper were able to detect the deformation of the structure when the tentacles of the gripper were open or closed. The sensor signal could differentiate between the picking of small or big objects and when an obstacle prevented the tentacles from opening. Interestingly, the sensors embedded in the tentacles exhibited good reproducibility and linearity, and the sensitivity of the sensor response changed with the Shore hardness of the gripper. Correlation between TPU Shore hardness, used for the gripper body and sensitivity of the integrated <em>in situ</em> strain sensing elements, showed that material selection affects the sensor signal significantly.</p>
Multi-material 3D Printing of Thermoplastic Elastomers for Development of Soft Robotic Structures with Integrated Sensor Elements
<p>Embedded sensing can benefit soft robots with the ability to interact with their environment but producing embedded soft sensors can be challenging. Multi-material Fused Deposition Modeling (FDM) additive manufacturing allows producing complex structures, by combining more than one kind of polymeric material. For multi-material FDM, conductive thermoplastic elastomer filaments have been developed. This allows the printing of flexible functional structures, based on thermoplastic elastomer structures with conductive paths that are of great interest for stretchable electronics and soft robotic applications. In this study, stretchable piezoresistive elastomer strain sensor composites were successfully produced by using multi-material FDM. A piezoresistive thermoplastic elastomer was printed on the top of a nonconductive, flexible thermoplastic elastomer strip using FDM multi-material 3D printer. FDM elastomer filaments with different shore hardness as substrate materials for the gripper structure were used. The hardness of the elastomer affected the printability and the adhesion to the conductive elastomer material, which was used as a strain sensor material. The hardness affected the strain sensor properties too. The piezoresistive response, dynamic behavior, drift, relaxation and sensitivity of the printed multi-material strips were investigated by tensile tests. Soft robotic grippers with integrated sensing elements to detect deformation while touching the objective were selected as a case study. The soft grippers with the integrated sensors exhibited intelligent response by recognizing when they were griping a small or big object and when an obstacle was inhibiting their function.</p>
Alkali-silica reaction. A multi-disciplinary approach. Supplementary Materials: Movie file.
<p>Movie file being part of the Supplementary Materials document of the manuscript with the same title and submitted to the <a href="https://letters.rilem.net/index.php/rilem">RILEM Technical Letters</a>.</p>
Raincheck: A new diachronic series of rainfall maps for Southwest Asia over the Holocene - Supplementary Material
<p>Supplementary Online Material for the publication</p> <p>Hewett, Z., de Gruchy, M., Hill, D., and Lawrence, D. (forthcoming) Raincheck: A new diachronic series of rainfall maps for Southwest Asia over the Holocene. <em>Levant</em>.</p> <p>Included are all the necessary data files and scripts (R) needed to create the rainfall maps that are the subject of the article.</p>
Data and Material for 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'
<p>Data and Material supporting the paper 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'.</p>
ELF dielectric functions for various materials.
<p><strong>Data for the energy loss function (ELF) of selected materials compiled from various publications.</strong></p> <p>The imaginary part of the inverse of the dielectric function, <em>Im</em>{−1/ε(<em>k</em>,hω)}, denoted the energy loss function (ELF), is the leading function that describes the material response to account for energy losses of swift electrons in matter. For this reason, it is a practical advantage to express the dielectric function in the form of a parametrized ELF, instead of the dielectric function itself, as input for the calculation of inelastic scattering cross sections. Here the ELF is expressed as a sum of Drude-Lindhard type oscillators (see e.g. Yubero F, Sanz JM, Ramskov B, Tougaard S. Model for quantitative analysis of reflection-electron-energy-loss spectra: Angular dependence. Phys Rev B. 1996;53:9719-9727)</p> <p><strong>The ELF data describe the dielectric properties of materials.</strong><br> It may be applied to calculate energy loss processes of fast electrons moving in a material as well as in geometries met in REELS, XPS and AES where the effect of the surface and a static core hole are included (see e.g. QUEELS-software that can be downloaded at: doi: 10.5281/zenodo.6022426)</p> <p><strong>The data structure is:</strong><br> For each material, a reference to the data for each material is given<br> After a line with @ , the ELF data follows in lines with the following meaning</p> <p>1. Excitation parameters ac, bc to model core excitations (see Penn, J. Elec. Spectr., 9(1976)29).<br> When ac = 0.001 and bc = -2: no data was available. </p> <p>2. Energy gap (eV)</p> <p>3. Refractive Index</p> <p>in the next lines, are the parameters for the oscillators: one line for each oscillator</p> <p>4. energy position (eV), intensity (eV^2), width (eV), alpha<br> 5.<br> 6.<br> .<br> .</p> <p>Examples of data files that can be directly read by QUEELS for Au and Si are seen in the files ELF_Au.txt and ELF_Si.txt respectively.</p>
Dataset for Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy
<p>Raw and processed image data resulting from the paper "Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy", by S. Mitchell, F. Parés, D. Faust Akl, S. M. Collins, D. M. Kepaptsoglou, Q. M. Ramasse, D. Garcia-Gasulla, J. Pérez-Ramírez, and N. López (JACS, 2021). </p> <p>The corresponding code can be found under: <a href="https://github.com/HPAI-BSC/AtomDetection_ACSTEM">GitHub - HPAI-BSC/AtomDetection_ACSTEM</a></p>
Supplementary materials for the paper "Users' Privacy Concerns and Attitudes towards Usage-Based Insurance: an empirical approach"
<p>These are materials necessary to replicate the study discussed in <em>Users' Privacy Concerns and Attitudes towards Usage-Based Insurance: an empirical approach</em>, accepted for publication at VEHITS 2022.</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.