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Dataset for Assessing Multi-Dimensional Impacts of Achieving Sustainability Goals by Projecting the Sustainable Agriculture Matrix into the Future
<p>This data repository feeds into the meta-repository setup for post-processing of GCAM-SAM outputs. GitHub link of meta-repository is: <a href="https://github.com/JGCRI/Kyle-etal_2022_EF">https://github.com/JGCRI/Kyle-etal_2022_EF</a> <br> <br> Folders: <br> <strong>model/</strong> is the static version of the model used to simulate 8 scenarios. See the <a href="https://github.com/pkyle/gcam-core/tree/gpk/paper/sam">GitHub GCAM-SAM repository</a> to follow active development of this model. <br> <strong>inputs/</strong> folder contains input datasets and scripts used to prepare files while postprocessing. This is to be used with <a href="https://github.com/JGCRI/Kyle-etal_2022_EF">GitHub post-processing meta-repository</a>. <br> <strong>outdata/</strong> contains <a href="http://github.com/pkyle/gcam-core/tree/gpk/paper/sam">GCAM-SAM</a> output and <a href="http://github.com/JGCRI/Kyle-etal_2022_EF">post-processed</a> output files used to plot figures. <br> <br> Key files: <br> <em><strong>SAM-matrix.dat</strong></em> is the consolidated GCAM-SAM output. Use <em>proj_load.R</em> in the <a href="https://github.com/JGCRI/Kyle-etal_2022_EF">metarepo</a> to read the file. <br> <em><strong>region_vals.csv</strong></em> has all 8 indicators in all 8 scenarios for years 2020 till 2100 on a 10 year time step. <br> <br> Short introduction to the study:</p> <p>In this paper sustainable agriculture matrix (SAM) is estimated to 2100 using Global Change Analysis Model (GCAM). We model combinatorial variations of yield intensification, dietary shift, and greenhouse gas mitigation scenarios. Findings include scenarios having significant tradeoffs across multiple environmental, economic, and social dimensions. Assessment of these multi-dimensional tradeoffs in a consistent framework improves the quality of information for decision-making.<br> <br> Should you have any questions, feel free to reach out Page Kyle at <a href="mailto:pkyle@pnnl.gov">pkyle@pnnl.gov</a>. </p>
Three-dimensional dataset of hydrating cement paste (CEM I Ladce, 273 m^2/kg Blaine, w/c=0.50) in TIFF format
<p>A detailed description of this dataset can be found in <a href="https://doi.org/10.1016/j.dib.2023.108903">https://doi.org/10.1016/j.dib.2023.108903</a><a href="https://doi.org/10.1016/j.dib.2023.108903">.</a></p> <p>This dataset contains a collection of digitized three-dimensional hardened cement paste microstructures obtained from X-ray micro-computed tomography, screened after approx. 1, 2, 3, 4, 7, 14, and 28 days of elapsed hydration at 20˚C in saturated conditions. Each paste specimen had a cylindrical shape (with a diameter of ~1 mm) and was screened at a designated time (as specified in the file name, e.g. “t23hrs”=23 hours of elapsed hydration) and finally saved as an uncompressed and unprocessed *.tif greyscale image data file in 16-bit image depth (as unsigned integers) using a little-endian byte sequence.</p> <p>The dataset contains two sets of images:</p> <p>- “full-sized” digital images stored in a three-dimensional voxel-based matrix with a fixed size of 1100x1100x1100 voxels, denoted as “CEM_I_Ladce_*” in the file name; each file size amounts to ~2.5 GB and contains the whole screened specimen with a variable voxel size in the range 1.0913 − 1.1174 µm depending on the particular specimen (as specified in the file name, e.g. “1d1174um”=1.1174 µm/voxel)</p> <p>- smaller image subvolumes, denoted as Region Of Interest (ROI), extracted from the interior of the full-sized specimen from an arbitrary location, and denoted as “filteredROI_*” in the file name; this cropped ROI has a cubic shape and stores a three-dimensional voxel-based matrix with a fixed size of 500x500x500 µm<sup>3</sup> constituted by a variable voxel count (given the fluctuating voxel size for each specimen, see above). Both the exact voxel count (i.e. three-dimensional matrix dimensions) and voxel size are further specified in each file name. A sequence of imaging filters was sequentially applied to this ROI to further enhance the contrast among the different microstructural phases, see <a href="https://doi.org/10.1016/j.cemconcomp.2022.104798">10.1016/j.cemconcomp.2022.104798</a> for details.</p> <p>Note that the same dataset stored in *raw format is available from <a href="https://doi.org/10.5281/zenodo.7193819">https://doi.org/10.5281/zenodo.7193819</a></p>
Three-dimensional dataset of hydrating cement paste (CEM I Ladce, 273 m^2/kg Blaine, w/c=0.35) in TIFF format
<p>A detailed description of this dataset can be found in <a href="https://doi.org/10.1016/j.dib.2023.108903">https://doi.org/10.1016/j.dib.2023.108903</a><a href="https://doi.org/10.1016/j.dib.2023.108903">.</a></p> <p>This dataset contains a collection of digitized three-dimensional hardened cement paste microstructures obtained from X-ray micro-computed tomography, screened after approx. 1, 2, 3, 4, 7, 14, and 28 days of elapsed hydration at 20˚C in saturated conditions. Each paste specimen had a cylindrical shape (with a diameter of ~1 mm) and was screened at a designated time (as specified in the file name, e.g. “t23hrs”=23 hours of elapsed hydration) and finally saved as an uncompressed and unprocessed *.tif greyscale image data file in 16-bit image depth (as unsigned integers) using a little-endian byte sequence.</p> <p>The dataset contains two sets of images:</p> <p>- “full-sized” digital images stored in a three-dimensional voxel-based matrix with a fixed size of 1100x1100x1100 voxels, denoted as “CEM_I_Ladce_*” in the file name; each file size amounts to ~2.5 GB and contains the whole screened specimen with a variable voxel size in the range 1.0913 − 1.1174 µm depending on the particular specimen (as specified in the file name, e.g. “1d1174um”=1.1174 µm/voxel)</p> <p>- smaller image subvolumes, denoted as Region Of Interest (ROI), extracted from the interior of the full-sized specimen from an arbitrary location, and denoted as “filteredROI_*” in the file name; this cropped ROI has a cubic shape and stores a three-dimensional voxel-based matrix with a fixed size of 500x500x500 µm<sup>3</sup> constituted by a variable voxel count (given the fluctuating voxel size for each specimen, see above). Both the exact voxel count (i.e. three-dimensional matrix dimensions) and voxel size are further specified in each file name. A sequence of imaging filters was sequentially applied to this ROI to further enhance the contrast among the different microstructural phases, see <a href="https://doi.org/10.1016/j.cemconcomp.2022.104798">10.1016/j.cemconcomp.2022.104798</a> for details.</p> <p>Note that the same dataset stored in *raw format is available from <a href="https://doi.org/10.5281/zenodo.7193808">https://doi.org/10.5281/zenodo.7193808</a></p>
Three-dimensional dataset of hydrating cement paste (CEM I Ladce, 391 m^2/kg Blaine, w/c=0.50) in TIFF format
<p>A detailed description of this dataset can be found in <a href="https://doi.org/10.1016/j.dib.2023.108903">https://doi.org/10.1016/j.dib.2023.108903</a><a href="https://doi.org/10.1016/j.dib.2023.108903">.</a></p> <p>This dataset contains a collection of digitized three-dimensional hardened cement paste microstructures obtained from X-ray micro-computed tomography, screened after approx. 1, 2, 3, 4, 7, 14, and 28 days of elapsed hydration at 20˚C in saturated conditions. Each paste specimen had a cylindrical shape (with a diameter of ~1 mm) and was screened at a designated time (as specified in the file name, e.g. “t23hrs”=23 hours of elapsed hydration) and finally saved as an uncompressed and unprocessed *.tif greyscale image data file in 16-bit image depth (as unsigned integers) using a little-endian byte sequence.</p> <p>The dataset contains two sets of images:</p> <p>- “full-sized” digital images stored in a three-dimensional voxel-based matrix with a fixed size of 1100x1100x1100 voxels, denoted as “CEM_I_Ladce_*” in the file name; each file size amounts to ~2.5 GB and contains the whole screened specimen with a variable voxel size in the range 1.0913 − 1.1174 µm depending on the particular specimen (as specified in the file name, e.g. “1d1174um”=1.1174 µm/voxel)</p> <p>- smaller image subvolumes, denoted as Region Of Interest (ROI), extracted from the interior of the full-sized specimen from an arbitrary location, and denoted as “filteredROI_*” in the file name; this cropped ROI has a cubic shape and stores a three-dimensional voxel-based matrix with a fixed size of 500x500x500 µm<sup>3</sup> constituted by a variable voxel count (given the fluctuating voxel size for each specimen, see above). Both the exact voxel count (i.e. three-dimensional matrix dimensions) and voxel size are further specified in each file name. A sequence of imaging filters was sequentially applied to this ROI to further enhance the contrast among the different microstructural phases, see <a href="https://doi.org/10.1016/j.cemconcomp.2022.104798">10.1016/j.cemconcomp.2022.104798</a> for details.</p> <p>Note that the same dataset stored in *raw format is available from <a href="https://doi.org/10.5281/zenodo.7193798">https://doi.org/10.5281/zenodo.7193798</a></p>
Development of the multi-dimensional Mobility Divide Index as a methodology to assess the accessibility level of public transport systems
<p>This paper presents the development of a multi-dimensional Mobility Divide Index (MDI) for assessing the accessibility of public transport developed using a co-design approach, directly involving end-users in the index design process. The index measures the gap that persons with disabilities feel they need to over-come to use public transport in the same way non-disabled citizens do. The MDI covers six accessibility-related dimensions: 1) safety, 2) convenience, 3) comfort, 4) affordability, 5) travel time, 6) autonomy. The method paper describes the step-by-step approach to create the MDI as a set of indicators to be rated by people with different access needs to a) provide evidence of the main criticalities to be addressed through the design and implementation of new inclusive mobility solutions, b) guide the design of new inclusive mobility solutions and measure their impacts and c) inform the transport sector encouraging positive changes in transport by providing recommendations for policy-making, new directions for service innovation, improvements and practical advice or highlighting investment priorities to pave the way for a more inclusive mobility. We present our findings in ways that can inform universal design and provide actionable information to researchers, policymakers, transport and urban planners, operators and stakeholders’ representatives to promote inclusive and equitable mobility solutions for all. Finally, we suggest follow up research and innovation, as well as recommendations for its uptake and utilisation in the pursuit of European accessibility standards and requirements for products and services in the mobility sector. </p>
Data Set "Quantum-chemical calculation of two-dimensional infrared spectra using localized-mode VSCF/VCI"
<p>This data set accompanies the publication "Quantum-chemical calculation of two-dimensional infrared spectra using localized-mode VSCF/VCI"</p> <p>It contains:</p> <p>- xyz files of all considered molecular structures.</p> <p>- Results data from the harmonic and anharmonic vibrational calculations.</p> <p>- Data and code for calculating 2D-IR spectra.</p>
Three-dimensional anisotropic phase velocity and S-wave velocity models in Northeastern Tibet
<p>This supplementary material contains three parts.</p> <p>The file named "COR_XM2_all.tar.gz" contains the ambient noise cross-correlation waveforms of the Rayleigh wave.</p> <p>The file named "phase_velocity.rar" contains the anisotropic phase velocity using double beamforming tomography (DBF) method.</p> <p>The file named "Anisotropic_Vs_model.rar" contains the anisotropic S-wave velocity models.</p>
Anticipating fluctuations of bigeye tuna in the Pacific Ocean from three-dimensional ocean biogeochemistry
<p>1. Subseasonal to decadal ocean forecasting can make significant contributions to achieving effective management of living marine resources in a changing ocean. Most applications rely on indirect proxies, however, often measured at the ocean surface and lacking a direct mechanistic link to the dynamics of marine populations.</p> <p>2. Here we take advantage of three-dimensional, dynamical reconstructions and forecasts of ocean biogeochemistry based on a global Earth System Model to hindcast and assess the capacity to anticipate fluctuations in the dynamics of bigeye tuna (<em>Thunnus obesus</em> Lowe) in the Pacific Ocean during the last six decades. We reconstructed spatial patterns in catch per unit effort (CPUE) through the combination of physiological indices capturing both habitat preferences and physiological tolerance limits in bigeye tuna.</p> <p>3. Our analyses revealed a sequence of four distinct regimes characterized by changes in the zonal distribution and average CPUE of bigeye tuna in the Pacific Ocean. Habitat models accounting for basin-wide fluctuations in the thermal structure and oxygen concentration throughout the water column captured interannual fluctuations in CPUE and regime switches that models based solely on surface information were unable to reproduce. Decade-long forecast experiments further suggested that forecasts of three-dimensional biogeochemical information might enable anticipation of fluctuations in bigeye tuna several years ahead.</p> <p>4. <em>Synthesis and applications.</em> Together, our results reveal the impact of variability of biogeochemical conditions in the ocean interior on the dynamics of bigeye tuna in the Pacific Ocean, raising concerns about the future impact of ocean warming and deoxygenation. The results also lend support to incorporating subsurface biogeochemical information into ecological forecasts to implement efficient dynamic management strategies and promote the sustainable use of marine living resources.</p>
Multi-Objective Evolutioary Algorithms for Synset Dimensionality Reduction
<p>Multi-Objective Evolutioary Algorithms for Synset Dimensionality Reduction</p> <p>This work has been developed to discover the usage of Multi-Objective Evolutionary computation to reduce the dimensionality of synset-based datatsets. </p> <p>The objective of this code is to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using Multi-Objective Evolutionary Algorithms (MOEA).</p>
Dataset: A three-dimensional approach to general plant fire syndromes
<p>1. Plant fire syndromes are usually defined as combinations of fire response traits, the most common being resprouting (R) and seeding (S). Plant flammability (F), on the other hand, refers to a plant's effects on communities and ecosystems. Despite its important ecological and evolutionary implications, F has rarely been considered to define plant fire syndromes and, if so, usually separated from response syndromes.</p> <p>2. We propose a three-dimensional model that combines R, S and F, encapsulating both plant response to fire regimes and the capacity to promote them. Each axis is divided into three possible standardized categories, reflecting low, medium and high values of each variable, with a total of 27 possible combinations of R, S and F.</p> <p>3. We hypothesized that different fire histories should be reflected in the position of species within the three-dimensional space and that this should help assess the importance of fire as an evolutionary force in determining R-S-F syndromes.</p> <p>4. To illustrate our approach we compiled information on the fire syndromes of 24 dominant species of different growth forms from the Chaco seasonally-dry forest of central Argentina, and we compared them to 33 species from different Mediterranean-type climate ecosystems (MTCEs) of the world.</p> <p>5. Chaco and MTCEs species differed in the range (seven syndromes vs. thirteen syndromes, respectively) and proportion of extreme syndromes (i.e. species with extreme values of R, S and/or F) representing 29% of species in the Chaco vs. 45% in the MTCEs.</p> <p>6. Additionally, we explored the patterns of R, S and F of 4032 species from seven regions with contrasting fire histories, and found significantly higher frequencies of extreme values (predominantly high) of all three variables in MTCEs compared to the other regions, where intermediate and low values predominated, broadly supporting our general hypothesis.</p> <p>7. The proposed three-dimensional approach should help standardize comparisons of fire syndromes across taxa, growth forms and regions with different fire histories. This will contribute to the understanding of the role of fire in the evolution of plant traits and assist vegetation modelling in the face of changes in fire regimes.</p>
Dataset for the manuscript: "Three-dimensional species distribution modeling reveals the realized spatial niche for coral recruitment on contemporary Caribbean reefs"
<p>Whether the three-dimensional (3D) structure of habitats influences and partition recruitment niches of corals is unknown. We developed a new method that combined Species Distribution Modeling and Structure from Motion to characterize and map the three-dimensional recruitment niches of two ecosystem engineers on Caribbean coral reefs, scleractinian corals and octocorals. </p> <p>In this repository, we include 48 3D models of small areas of the reef (i.e., within ~ 0.25 m<sup>2</sup> quadrats) reconstructed with Structure-from-Motion, as well as the geospatial data used to characterize and map the realized recruitment niche for scleractinian corals and octocorals on Caribbean coral reefs. We conducted the study at two shallow, fringing reefs off the south shore of St. John, US Virgin Islands, named Grootpan and Europa Bays (18° 18.360’N, 64° 43.140’W, and 18° 19.016’N, 64° 43.798’W, respectively). Within each 0.25 m<sup>2</sup> quadrat, we counted and marked all recruits (octocorals ≤ 5 cm height, and scleractinians ≤ 4 cm wide).</p> <p><em>DATASET DESCRIPTIONS:</em></p> <ul> <li><strong>"Quadname_data.zip":</strong> In each of this folders we included all the data calculated within a quadrat: <ul> <li>ASCII files (.txt).</li> <li>The annotated dense point cloud (.las) for each quadrat.</li> <li>The quadrat 3D model texture (.jpg).</li> <li>The quadrat 3D polygon mesh (.ply).</li> <li>The quadrat 2.5D Digital Elevation Model (i.e., DEM; .tif).</li> <li>Shape files with recruits local coordinates within each quadrat (.dbf, .prj, .shp, .shx).</li> </ul> </li> <li><strong>"datawide.rds": </strong>This is the file needed to run the analyses performed in Martínez-Quintana et al., 2023. This file is obtained after processing all the raw data calculated within each quadrat. All code associated with the workflow used to obtain the datawide.rds file and run the analyses performed in Martínez-Quintana et al., 2023 is available at <a href="https://github.com/AdamWilsonLab/meshSDM">github.com/AdamWilsonLab/meshSDM</a>.</li> </ul> <p><strong>IMPORTANT NOTES: </strong></p> <ul> <li>Quadrat names starting with the letters “eu” indicate the data were collected at Europa Bay, whereas those starting with the letters “ec” indicate that data were collected at Grootpan Bay (commonly named East Cabritte).</li> <li>Each ASCII file (quadname_ASCII_subsampled_X.txt) contains the slope and roughness of the quadrat calculated on the point cloud at 5, 10, 20, and 100 mm scales, and the smooth point cloud used to calculate the topographic exposure index (TEI) described in Martínez-Quintana et al., 2023. Calculations were performed and ASCII files were created with CloudCompare.</li> <li>Each dense point cloud, mesh, texture, and DEM were calculated with Agisoft Metashape.</li> <li>Agisoft Metashape allows the user to classify and annotate groups of points in the dense point cloud. However, the list of classes provided by the software corresponds to the standard list used for terrestrial LiDAR data; these classes cannot be renamed within the software. Thus, for the present study, we coded the automatic semantic classifications available in Metashape as follows: <ul> <li>Ground = Calcareous rock.</li> <li>Building = Igneous rock.</li> <li>High noise = Sand.</li> <li>Low vegetation = Adult Scleractinian corals.</li> <li>Medium vegetation = Adult Octocoral base.</li> <li>High vegetation = Sponge.</li> <li>Water = Octocoral recruit (named also ocr).</li> <li>Road Surface = Scleractinian recruit (named also scr).</li> <li>Unclassified = created points but never classified (excluded from the analyses).</li> <li>Low Point = noise (unreliable points).</li> <li>Transmission tower and Rail = Points outside the quadrat and excluded from the analysis.</li> </ul> </li> </ul>
CH2O coordinate manifolds and their analysis for the paper "A global view of reactive coordinate manifolds from nonlinear dimensionality reduction"
<p>This dataset contains the the following data created for the paper "A global view of reactive coordinate manifolds from nonlinear dimensionality reduction" by Dmitrij Rappoport.</p> <p>The data was generated using the manic open-source code that is publicly accessible at https://bitbucket.org/rappoport/manic/.</p> <p>All the data in this dataset is for the CH2O coordinate manifold</p> <p>Data listing:</p> <p>MCMC chains (using uniform sampling, average-distance weighted sampling, Jacobian-norm weighted sampling)<br> - mcmc/uniform-sampling<br> - mcmc/ave-distance-weighted<br> - mcmc/jacobian-norm-weighted</p> <p>MLE dimension estimates (from uniform sampling, average-distance weighted sampling, Jacobian-norm weighted sampling)<br> - mle/uniform-sampling<br> - mle/ave-distance-weighted<br> - mle/jacobian-norm-weighted</p> <p>Isomap embeddings for d = 2, 3, 4, 6 (from uniform sampling, average-distance weighted sampling, Jacobian-norm weighted sampling)<br> - isomap/uniform-sampling<br> - isomap/ave-distance-weighted<br> - isomap/jacobian-norm-weighted</p> <p>LLE embeddings for d = 2 (from uniform sampling)<br> - isomap/uniform-sampling</p> <p>Embedding of critical points of the CH2O PES in coordinate manifolds (from uniform sampling, average-distance weighted sampling, Jacobian-norm weighted sampling)<br> - isomap-nystrom/uniform-sampling<br> - isomap-nystrom/ave-distance-weighted<br> - isomap-nystrom/jacobian-norm-weighted</p>
Coexistence and coupling of ferroelectricity and ferromagnetism in an oxide two-dimensional electron gas
<p>The dataset contains X-ray absorption spectroscopy (XAS), electric polarization and magnetotransport measurements obtained on LaAlO3/EuTiO3/Ca:SrTiO3 heterostructures, at which interface a two-dimensional electron gas appears.</p> <p>X-ray magnetic circular dichroism (XMCD) extracted from the XAS data are also included, as well as X-ray linear dichroism (XLD) alongside atomic multiplet calculations for two sets of parameters corresponding to "up" and "down" remanent polarization states (data of Fig. 1 and 2).</p>
Theoretical prediction and shape-controlled synthesis of two-dimensional semiconductive Ni3TeO6
<p>Check the reference:</p> <p>Fernández-Catalá, J.; Kistanov, A., A.; Bai, Y.; Singh, H.; Cao, W. <em>npj 2D Mater. Appl.</em> <strong>2023</strong>, 7, 1, 48</p>
Data and simulation files for: "Silicon anisotropy in a bi-dimensional optomechanical cavity"
<p>Data and simulation files for: "Silicon anisotropy in a bi-dimensional optomechanical cavity"</p>
Appendix Data for Manuscript : Application of OSL surface exposure dating with the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy
<p>Appendix Data for Manuscript 'Application of OSL surface exposure dating with the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy'.</p>
Three dimensional reconstruction of an existing age-old wooden shingle roof
<p>This dataset provides a complete and thorough three-dimensional reconstruction of an age-old shingled roof located in Rossinière, Switzerland (<a href="https://www.google.com/maps/dir//46.468247,7.059739/@46.4670828,7.012801,12z/data=!4m2!4m1!3e0">46.46837286488975, 7.059749511857417</a>), which provides reliable documentation to archive this valuable type of architecture.</p> <p>This dataset was obtained using a 3D Farco Focus scanner and the point clouds were processed using FaroScene software.</p> <p>This research was funded by the National Centres of Competence in Research (<a href="https://www.snf.ch/en/FJBJ8XGQ1tjG8J8w/funding/programmes/national-centres-of-competence-in-research-nccr">NCCRs</a>) and EPFL.</p>
Data files for "Ground state and spectral properties of the doped one-dimensional optical Hubbard-Su-Schrieffer-Heeger model"
<p>This repo contains the relevant data files for the paper D. Banerjee et al., "Ground state and spectral properties of the doped one-dimensional optical Hubbard-Su-Schrieffer-Heeger model". (2023) Preprint: arXiv:2303.10193</p> <p> </p>
Three-dimensional morphology of the biramous appendages in Isoxys from the early Cambrian of South China, and its implications for early euarthropod evolution
<p>Early euarthropod evolution involved a major transition from lobopodian-like taxa to organisms featuring a segmented, well-sclerotized trunk (arthrodization) and limbs (arthropodization). However, the precise origin of a completely arthrodized trunk and arthropodized ventral biramous appendages remain controversial, as well as the early onset of anterior-posterior limb differentiation in stem-group euarthropods. New fossil material and micro-computed tomography inform the detailed morphology of the arthropodized biramous appendages in the carapace-bearing euarthropod <em>Isoxys curvirostratus </em>from the early Cambrian Chengjiang biota. In addition to well-developed grasping frontal appendages. <em>I. curvirostratus</em> possesses two batches of morphologically and functionally distinct biramous limbs. The first batch consists of four pairs of short cephalic appendages with robust endites with a feeding function, whereas the second batch has more elongate trunk appendages for locomotion. Critically, our new material shows that the trunk of <em>I. curvirostratus</em> was not arthrodized. The results of our phylogenetic analyses recover isoxyids as some of the earliest branching sclerotized euarthropods, and strenghens the hypothesis that arthropodized biramous appendage evolved before full body arthrodization.</p>
The Impact of the Three-Dimensional Structure of a Subduction Zone on Time-dependent Crustal Deformation Measured by HR-GNSS
<p>Companion data set to the paper "<strong>The Impact of the </strong><strong>Three-Dimensional Structure of a Subduction Zone on Time-dependent Crustal Deformation Measured by HR-GNSS.</strong>" The 'GNSS_Stations' folder contains the GNSS stations used for 2011 <strong>M</strong>7.9 Ibaraki, 2011 <strong>M</strong>7.4 Iwate, 2011A <strong>M</strong>7.3 Miyagi and 2003 <strong>M</strong>8.3 Tokachi 2003 earthquakes. The 'Mesh' folder contains the fault geometry mesh files for the Japan Trench, including the GMSH files. The '3D_velocity_model' folder contains the 3D velocity model for the combined West and East domains of the 3D Japan Integrated Velocity Structure Model in a rfile format and the corresponding ifile format. The 'Ruptures' folder contains the projected rupture models for each earthquake on the Japan Trench mesh and the corresponding 100 realizations of the mean rupture models generated using FakeQuakes. If you use these data, please cite the associated publication:</p> <p>Fadugba, ­O. I., Sahakian, V. J., Diego Melgar, D., Rodgers, A. & Shimony, R. (2023). The Impact of the Three-Dimensional Structure of a Subduction Zone on Time-dependent Crustal Deformation Measured by HR-GNSS.</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.