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266 results for “experimental models”
Researchers and Their Experimental Models: A Pilot Survey in the Context of the European Union Health and Life Science Research
<p>A significant debate is ongoing on the effectiveness of animal experimentation, due to the increasing reports of failure in the translation of results from preclinical animal experiments to human patients. Scientific, ethical, social and economic considerations linked to the use of animals raise concerns in a variety of societal contributors (regulators, policy makers, non-governmental organisations, industry, etc.). The aim of this study was to record researchers’ voices about their vision on this science evolution, to reconstruct as truthful as possible an image of the reality of health and life science research, by using a key instrument in the hands of the researcher: the experimental models. Hence, we surveyed European-based health and life sciences researchers, to reconstruct and decipher the varying orientations and opinions of this community over these large transformations. In the interest of advancing the public debate and more accurately guide the policy of research, it is important that policy makers, society, scientists and all stakeholders (1) mature as comprehensive as possible an understanding of the researchers’ perspectives on the selection and establishment of the experimental models, and (2) that researchers publicly share the research community opinions regarding the external factors influencing their professional work. Our results highlighted a general homogeneity of answers from the 117 respondents. However, some discrepancies on specific key issues and topics were registered in the subgroups. These recorded divergent views might prove useful to policy makers and regulators to calibrate their agenda and shape the future of the European health and life science research. Overall, the results of this pilot study highlight the need of a continuous, open and broad discussion between researchers and science policy stakeholders.</p>
Data of "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework"
<p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework",<br> journal = "Engineering Fracture Mechanics",<br> year = "2022",<br> volume = "275",<br> pages = "108844 ",<br> doi = "https://doi.org/10.1016/j.engfracmech.2022.108844",<br> author = "Antoine Hilhorst, Julien Leclerc, Thomas Pardoen, Pascal J. Jacques, Ludovic Noels, Van-Dung Nguyen"</p> <p>New version following review.</p> <p> </p> <p> </p>
Dataset: Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation
<p>This repository contains the files associated with the following article:</p> <p>Jesús Sandoval-Martínez, Ernesto I. Badano, Francisco A. Guerra-Coss, Jorge A. Flores Cano, Joel Flores, Sandra Milena Gelviz-Gelvez, Felipe Barragán-Torres, “Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation”, submitted to <em>Journal of Environmental Management</em>.</p> <p><strong>Supplementary material 01 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. A file correspond to <em>Vachellia pennatula</em> and the another file correspond to <em>Prosopis laevigata</em>. In both files, the first spreadsheet shows the occurrence data (latitude and longitude) used to calibrate the distribution model (SDM) of the corresponding species, the current values of the 19 bioclimatic variables associated with these coordinates and the Spearman correlation coefficients used to select the variables included in the SDM (selected variables are indicated in green). The second spreadsheet shows the current habitat occupancy probabilities of the target species estimated with the SDM at the geographic coordinates of occurrence points, while the table on the side shows the fraction of true presences dropping at the following probability categories: (1) habitat occupancy probabilities below 0.1 = unsuitable spatial units for the species, (2) habitat occupancy probabilities between 0.1 and 0.4 = barely suitable spatial units for the species, (3) habitat occupancy probabilities between 0.4 and 0.7 = moderately suitable spatial units for the species, and (4) habitat occupancy probabilities above 0.7 = highly suitable spatial units for the species. The third spreadsheet shows the one-thousand random geographic coordinates and the corresponding current and future habitat occupancy probabilities of each species. Future habitat occupancy probabilities are provided for three time periods (2041-2060, 2061-2080 and 2081-2100) at four radiative forcing levels each (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>).</p> <p><strong>Supplementary material 02 </strong>is a compressed file that contains a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>. Each of these folders contains the summaries of the MaxEnt outputs that support the results of the corresponding SDM.</p> <p><strong>Supplementary material 03 </strong>is a compressed Keyhole Markup Language file (KMZ) that contains interactive maps that are optimized for the desktop version of Google Earth. To accelerate visualization of maps, we recommend installing this software in a computer meeting the following requirements: CPU Intel Core i5 9<sup>th</sup> generation or higher, CPU clock speed 1.8 GHz or higher, random-access memory (RAM) 8 GB or higher, and video random access memory (VRAM) 1 GB or higher. Otherwise, opening this file may take several minutes. These maps are organized in a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>, which must be expanded for accessing the following information (click on the arrow on the left of folders to expand them):</p> <ul> <li><strong>Current climate </strong>– Activating this folder (click the fox on the left of the folder) display the map of habitat occupancy probabilities of species across Mexico under the current climate.</li> <li><strong>Period 2041-2060, 2061-2080 and 2081-2100 </strong>– Expanding each of these folders (click on the arrow on the left of folders) shows four subfolders that correspond to different radiative forcing levels (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>). Activating each of these sub folders (click the fox on the left of subfolders) display the map of habitat occupancy probabilities of species across Mexico expected on the corresponding time period and radiative forcing level. These maps also show the areas classified as climatically unsuitable in the multivariate environmental similarity surface (MESS) analysis. Clicking on the names of subfolders displays a figure showing the relationship between current and future habitat occupancy probabilities of the species on the corresponding time period and radiative forcing level. In these figures, the red line is the empirical relationship between these variables and the solid blue line is the theoretical relationship with intercept = 0 and slope = 1. The statistical results that support these relationships are also shown in these figures.</li> </ul> <p><strong>Supplementary material 04 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. the file labeled as “Microclimate data” contains two spreadsheets, which correspond to the temperature and rainfall values measured in controls under the current climate and climate change simulation plots located of the field experiments. The file levelled as “Seedling emergence and survival” contains a spreadsheet for <em>Vachellia pennatula</em> and another one for <em>Prosopis laevigata</em>, which contains the data used to estimate the seedling emergence and survival rates in controls and climate change simulation plots.</p>
Experimental data for Understanding frictional behavior in fascia tissues through tribological modeling and material substitution
<p>Dataset (experimental data) for publication titled: Understanding frictional behavior in fascia tissues through tribological modeling and material substitution. The article deals with the development of a tribological model of fascia. As in the paper, the dataset is divided into the three phenomena studied: Effect of geometry, Models, Hyaluronic acid - model E. </p>
DSA-380 experimental data and a non-linear model in Matlab
<p><span>Experimental data of the DSA 380 pantograph is provided in a dataset and the Matlab files neccesary to simulate a model of the pantograph. Readme available.</span></p>
Replication data for "Learning When to Quit: An Empirical Model of Experimentation in Standards Development"
<p>This is the replication data for the article "Learning When To Quit: An Empirical Model of Experimentation in Standards Development," published in the <em>American Economic Journal: Microeconomics</em>. </p> <p>A detailed description of the data construction can be found in the Data Appendix (Section E in the Online Appendix published with the article's supplementary material).</p> <p>The file _readme-LWTQ-data.xls contains a description of the variables.</p> <p>If you use the data, cite the paper!</p> <p><strong>Ganglmair, Bernhard, Timothy Simcoe, and Emanuele Taranntino (2024): "Learning When To Quit: An Empirical Model of Experimentation in Standards Development," <em>American Economic Journal: Microeconomics</em>, forthcoming<em>.</em></strong></p>
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>
Adsorption of drugs on nanoplastics: Modeling challenges and experimental proof - datasets and structures
Open the record for dataset details and reuse information.
Thermophysical properties of hydrogen mixtures relevant for the development of the hydrogen economy: Review of available experimental data and thermodynamic models
<p>File: 1-s2.0-S096014812201271X-mmc1.docx</p> <p>This file (DOCX) contains additional figures associated with the hydrogen-containing systems.</p> <p>File: 1-s2.0-S096014812201271X-mmc2.xlsx</p> <p>This file (XLSX) contains tables with the coordinates of the VLE associated with the hydrogen-containing systems.</p> <p>File: 1-s2.0-S096014812201271X-mmc3.xlsx</p> <p>This file (XLSX) contains tables with the density data associated with the hydrogen-containing systems.</p> <p>File: 1-s2.0-S096014812201271X-mmc4.xlsx</p> <p>This file (XLSX) contains tables with the calorific data associated with the hydrogen-containing systems.</p> <p> </p> <p>File: 2022_Renewable Energy_Manuscript_repository.docx</p> <p>This is an author-created, un-copyedited version of an article accepted for publication in Renewable Energy (2022, 198, 1398-1429). The editor of the Journal is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher-authenticated, Open-Access version is available online at: https://doi.org/10.1016/j.renene.2022.08.096</p>
Towards Robust Hemolysis Modeling with Uncertainty Quantification: A Universal Approach to Address Experimental Variance
<p>This repository contains the implementation of <strong>Robust Hemolysis Modeling with Uncertainty Quantification: A Universal Approach to Address Experimental Variance</strong>.</p> <p>The provided Python script demonstrates the construction of the MCMC (Markov Chain Monte Carlo) method and illustrates how to use MCMC for generating hemolysis distributions. Please note that the actual hemolysis calculations should be performed using your preferred CFD (Computational Fluid Dynamics) software.</p> <p>If there are any questions, please contact:</p> <p>blum@ame.rwth-aachen.de </p>
Thermodynamic characterization of the (H2 + C3H8) system significant for the hydrogen economy: Experimental (p, rho, T) determination and equation of-state modelling
<p>File: 2023_IJHE_Manuscript_repository.docx</p> <p>This is an author-created, un-copyedited version of an article accepted for publication in the International Journal of Hydrogen Energy (2023, 48 (23), 8645-8667). The editor of the Journal is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher-authenticated, Open-Access version is available online at: https://doi.org/10.1016/j.ijhydene.2022.11.170<br><br>File: 2023_IJHE_Results_Repository.xlsx</p> <p>This is the MS Excel data file of the paper. </p> <p> </p> <p> </p>
Experimental raw data and numerical model
<p>Experimental raw data and numerical models for the work of Siyuan Qiang et, al., (2023), which focuses on the relationship between the saturation exponents and pore water distribution.</p> <p>Run the main.m file for numerical simulation<br>The simulation schemes could be easily changed by updating the SIG1.txt file.</p> <p>.mat file:<br>variable 'SIP':measured SIP frequency magnitude and phase (in degree) in experiments<br>variable 'Sw':measured water saturation in experiments</p>
3D models experimental cores
<p>290 3D models (.ply files) of cores made in quartzite (15 bifacial multipolar centripetal cores and 15 unifacial unipolar cores) from a sequential experiment. Each core was scanned at ten different moments of the reduction. In total, there are 30 3D models of cobbles and 260 3D models of cores. </p>
Modeling the early evolution of massive OB stars with an experimental wind routine. The first bi-stability jump and the angular momentum loss problem
<p>MESA run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017A&A...598A...4K">Keszthelyi et al. (2017)</a>. MESA version 7624.</p> <p>Publication DOI: <a href="https://doi.org/10.1051/0004-6361/201629468">10.1051/0004-6361/201629468</a></p>
Water Vapor Sorption Properties of Illinois Shales under Dynamic Water Vapor Conditions: Experimentation and Modeling
<p>Data sets for the Publication 'Water Vapor Sorption Properties of Illinois Shales under Dynamic Water Vapor Conditions: Experimentation and Modeling' in Water Resources Research.</p>
Experimental data for Dynamic cover effects in lateral bedrock channel bank abrasion: Experiment and model comparison
<p>Experimental data for bank erosion.xlsx contains the data used for the figures in the paper, and the distribution of bedrock bank erosion in the longitudinal direction in Run 1 - Run 18.</p>
Experimental dataset: "Minimal vertex model explains how the amnioserosa avoids fluidization during Drosophila dorsal closure"
<p>This repository contains experimental data supporting our paper "Minimal vertex model explains how the amnioserosa avoids fluidization during Drosophila dorsal closure".</p> <p><strong>Original Publication:</strong><br>Tah, I., Haertter, D., et al. (2024). Minimal vertex model explains how the amnioserosa avoids fluidization during Drosophila dorsal closure. PNAS (in press).<br>Preprint available at: <a href="https://doi.org/10.1101/2023.12.20.572544">https://doi.org/10.1101/2023.12.20.572544</a></p> <h2>Dataset Structure</h2> <h2>amnioserosa_time_lapses/</h2> <p>This directory contains confocal microscopy time-lapse recordings of eCadherin-labeled Drosophila melanogaster embryos during dorsal closure:</p> <ul> <li><strong>Imaging parameters:</strong> <ul> <li>Frame interval: 15 seconds</li> <li>Pixel size: 0.158 µm</li> </ul> </li> <li><strong>Data files:</strong> <ul> <li><code>*_data.p</code>: Morphometric features of individual amnioserosa cells during closure</li> <li><code>*_junctions.p</code>: Morphometric features of individual cell-cell adherens junctions</li> <li><code>read_filter_data.py</code>: Python script demonstrating data reading and filtering procedures</li> </ul> </li> </ul> <h2>junction_laser_cuts/</h2> <p>This directory contains high-speed recordings of UV-laser junction ablation experiments:</p> <ul> <li><strong>Imaging parameters:</strong> <ul> <li>Frame rate: 5 Hz</li> <li>Pixel size: 0.364 µm</li> </ul> </li> <li><strong>Content:</strong> Time series of eCadherin-labeled embryos before, during, and after precise UV-laser severing of individual junctions</li> </ul> <h2>data_figures/</h2> <p>This directory contains processed data and analysis results presented in Figures 1-3 of the main manuscript. The data are organized by figure number and include all measurements and simulation results used to generate the plots shown in these figures.</p> <p> </p> <p>For detailed experimental methods and protocols, please refer to the published paper.</p>
Data from: Voice efficiency for different voice qualities combining experimentally derived sound signals and numerical modeling of the vocal tract
<p>This dataset contains Stereo-Lithographic (STL) surface models of a human vocal tract, derived Finite-Element-Models, numerical results, and scripts for analyzing these results and (re-)running the computation.</p> <p> </p> <p><strong>In the main folder, this dataset contains:</strong></p> <p>1) Python files (*fig*.py) for the creation of figures and tables (*tab*.py)</p> <p>2) Python files (*.py) for analyzing Finite-Element (FE) calculations (x_resonances.py, x_libs.py, x_fem2excel.py)</p> <p>3) Python-files (*.py) for analyzing stl-data (x_analyzeSTL.py)</p> <p>4) Python files (*.py) for deriving Infinite-Impulse-Response (IIR) filter and their impulse responses (x_IIR.py)</p> <p>5) Excel files (*.xlsx) containing Volume-velocity-transfer-functions (Vlg.xlsx), Pressure-transfer-functions at the lips (Hlg.xlsx), and the glottis (Hgg.xlsx) based on FE, the sound spectra of audio signals (sound_spectra.xlsx), the polynomials describing the IIR (IIR_polynomial.xlsx) and their impulse responses (IIR_impulse_responses.xlsx), and glottal waveforms (glottal_waveform.xlsx) and spectra (glottal_spectra.xlsx)</p> <p>6) Several figures (*.pdf)</p> <p> </p> <p><strong>In folder „x_fenics/x_Subject-1“ (and sub-folders), this data set contains:</strong></p> <p>1) Surface models of the human vocal tract for different voice qualities (glottis.stl, wall.stl, lips.stl)</p> <p>2) Sub-volumes of the vocal tract cavities (*ET.stl, *HPl.stl, *HPu.stl, *OPf.stl, *OPr.stl, *SP.stl, *.VV.stl)</p> <p>3) Derived gmsh volume meshes (*.msh) (www.gmsh.info)</p> <p>3) Derived volume models applicable to FE-Solvers (*.h5, *.xdmf)</p> <p>4) Results of the FE-calculation (*pvtf*.txt, *vvtf*.txt, *pglottis*.txt)</p> <p>5) Formant frequencies computed by inverse filtering (*.for)</p> <p> </p> <p><strong>In folder „x_fenics/x_misc“ the data set contains:</strong></p> <p>1) Python-files (*.py) for (re-)running the calculations using the FE-Method</p>
Experimental and computational approach to biomechanical human head modelling: advanced Head models for safety Enhancement And medical Development (aHEAD)
<p>Data regarding <strong>Experimental and computational approach to biomechanical human head modelling: advanced Head models for safety Enhancement And medical Development (aHEAD)</strong></p>
Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds
<p>The tables in this repository represent the data used in the figures and analyses of the paper "Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds", published in the Planetary Science Journal. The provided data was collected between 2020 and 2022.</p> <ul> <li>AllResults.xlsx: compilation of tables 4, 5, 6, and 7 on the paper.</li> <li>WarmA1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>WarmA2.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>CryoA1.xlsx: data presented in tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth Estimation [m], Power [W]".</li> <li>CryoB1.xlsx: data presented in figures 9 and 10, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB3.xlsx: data presented in figures 6, 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC3.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Truncated Coarse Depth [m], Power [W]".</li> </ul> <p> </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.