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3,916 results for “reconstruction”
Avizo reconstruction of Bone-cracking Borophagus coprolite lacm:vp:158706
<p>Borophagine canids have long been hypothesized to be North American ecological ‘avatars’ of living hyenas in Africa and Asia, but direct fossil evidence of hyena-like bone consumption is hitherto unknown. We report rare coprolites (fossilized feces) of Borophagus parvus from the late Miocene of California and, for the first time, describe unambiguous evidence that these predatory canids ingested large amounts of bone. Surface morphology, micro-CT analyses, and contextual information reveal (1) droppings in concentrations signifying scent-marking behavior, similar to latrines used by living social carnivorans; (2) routine consumption of skeletons; (3) undissolved bones inside coprolites indicating gastrointestinal similarity to modern striped and brown hyenas; (4) B. parvus body weight of ~24 kg, reaching sizes of obligatory large-prey hunters; and (5) prey size ranging ~35–100 kg. This combination of traits suggests that bone-crushing Borophagus potentially hunted in collaborative social groups and occupied a niche no longer present in North American ecosystems.</p>
Avizo reconstruction of Bone-cracking Borophagus coprolite lacm:vp:158712
<p>Borophagine canids have long been hypothesized to be North American ecological ‘avatars’ of living hyenas in Africa and Asia, but direct fossil evidence of hyena-like bone consumption is hitherto unknown. We report rare coprolites (fossilized feces) of Borophagus parvus from the late Miocene of California and, for the first time, describe unambiguous evidence that these predatory canids ingested large amounts of bone. Surface morphology, micro-CT analyses, and contextual information reveal (1) droppings in concentrations signifying scent-marking behavior, similar to latrines used by living social carnivorans; (2) routine consumption of skeletons; (3) undissolved bones inside coprolites indicating gastrointestinal similarity to modern striped and brown hyenas; (4) B. parvus body weight of ~24 kg, reaching sizes of obligatory large-prey hunters; and (5) prey size ranging ~35–100 kg. This combination of traits suggests that bone-crushing Borophagus potentially hunted in collaborative social groups and occupied a niche no longer present in North American ecosystems.</p>
Avizo reconstruction of Bone-cracking Borophagus coprolite lacm:vp:158711
<p>Borophagine canids have long been hypothesized to be North American ecological ‘avatars’ of living hyenas in Africa and Asia, but direct fossil evidence of hyena-like bone consumption is hitherto unknown. We report rare coprolites (fossilized feces) of Borophagus parvus from the late Miocene of California and, for the first time, describe unambiguous evidence that these predatory canids ingested large amounts of bone. Surface morphology, micro-CT analyses, and contextual information reveal (1) droppings in concentrations signifying scent-marking behavior, similar to latrines used by living social carnivorans; (2) routine consumption of skeletons; (3) undissolved bones inside coprolites indicating gastrointestinal similarity to modern striped and brown hyenas; (4) B. parvus body weight of ~24 kg, reaching sizes of obligatory large-prey hunters; and (5) prey size ranging ~35–100 kg. This combination of traits suggests that bone-crushing Borophagus potentially hunted in collaborative social groups and occupied a niche no longer present in North American ecosystems.</p>
Avizo reconstruction of Bone-cracking Borophagus coprolite lacm:vp:158716
<p>Borophagine canids have long been hypothesized to be North American ecological ‘avatars’ of living hyenas in Africa and Asia, but direct fossil evidence of hyena-like bone consumption is hitherto unknown. We report rare coprolites (fossilized feces) of Borophagus parvus from the late Miocene of California and, for the first time, describe unambiguous evidence that these predatory canids ingested large amounts of bone. Surface morphology, micro-CT analyses, and contextual information reveal (1) droppings in concentrations signifying scent-marking behavior, similar to latrines used by living social carnivorans; (2) routine consumption of skeletons; (3) undissolved bones inside coprolites indicating gastrointestinal similarity to modern striped and brown hyenas; (4) B. parvus body weight of ~24 kg, reaching sizes of obligatory large-prey hunters; and (5) prey size ranging ~35–100 kg. This combination of traits suggests that bone-crushing Borophagus potentially hunted in collaborative social groups and occupied a niche no longer present in North American ecosystems.</p>
Avizo reconstruction of Bone-cracking Borophagus coprolite lacm:vp:158713
<p>Borophagine canids have long been hypothesized to be North American ecological ‘avatars’ of living hyenas in Africa and Asia, but direct fossil evidence of hyena-like bone consumption is hitherto unknown. We report rare coprolites (fossilized feces) of Borophagus parvus from the late Miocene of California and, for the first time, describe unambiguous evidence that these predatory canids ingested large amounts of bone. Surface morphology, micro-CT analyses, and contextual information reveal (1) droppings in concentrations signifying scent-marking behavior, similar to latrines used by living social carnivorans; (2) routine consumption of skeletons; (3) undissolved bones inside coprolites indicating gastrointestinal similarity to modern striped and brown hyenas; (4) B. parvus body weight of ~24 kg, reaching sizes of obligatory large-prey hunters; and (5) prey size ranging ~35–100 kg. This combination of traits suggests that bone-crushing Borophagus potentially hunted in collaborative social groups and occupied a niche no longer present in North American ecosystems.</p>
Avizo reconstruction of Bone-cracking Borophagus coprolite lacm:vp:158717
<p>Borophagine canids have long been hypothesized to be North American ecological ‘avatars’ of living hyenas in Africa and Asia, but direct fossil evidence of hyena-like bone consumption is hitherto unknown. We report rare coprolites (fossilized feces) of Borophagus parvus from the late Miocene of California and, for the first time, describe unambiguous evidence that these predatory canids ingested large amounts of bone. Surface morphology, micro-CT analyses, and contextual information reveal (1) droppings in concentrations signifying scent-marking behavior, similar to latrines used by living social carnivorans; (2) routine consumption of skeletons; (3) undissolved bones inside coprolites indicating gastrointestinal similarity to modern striped and brown hyenas; (4) B. parvus body weight of ~24 kg, reaching sizes of obligatory large-prey hunters; and (5) prey size ranging ~35–100 kg. This combination of traits suggests that bone-crushing Borophagus potentially hunted in collaborative social groups and occupied a niche no longer present in North American ecosystems.</p>
Avizo reconstruction of Bone-cracking Borophagus coprolite lacm:vp:158714
<p>Borophagine canids have long been hypothesized to be North American ecological ‘avatars’ of living hyenas in Africa and Asia, but direct fossil evidence of hyena-like bone consumption is hitherto unknown. We report rare coprolites (fossilized feces) of Borophagus parvus from the late Miocene of California and, for the first time, describe unambiguous evidence that these predatory canids ingested large amounts of bone. Surface morphology, micro-CT analyses, and contextual information reveal (1) droppings in concentrations signifying scent-marking behavior, similar to latrines used by living social carnivorans; (2) routine consumption of skeletons; (3) undissolved bones inside coprolites indicating gastrointestinal similarity to modern striped and brown hyenas; (4) B. parvus body weight of ~24 kg, reaching sizes of obligatory large-prey hunters; and (5) prey size ranging ~35–100 kg. This combination of traits suggests that bone-crushing Borophagus potentially hunted in collaborative social groups and occupied a niche no longer present in North American ecosystems.</p>
Data for "Traceable X-ray focal spot reconstruction by circular edge analysis: From sub-microfocus to mesofocus"
<p>Raw data used to create figures for the paper "Traceable X-ray focal spot reconstruction by circular edge analysis: From sub-microfocus to mesofocus" <a href="https://doi.org/10.1088/1361-6501/ac6225">https://doi.org/10.1088/1361-6501/ac6225</a></p>
Reconstructed SST-NINO3.4 anomalies for the years 850 to 1981
<p>This data set gives values of reconstructed SST-NINO3.4 anomalies for the years 850 to 1981.</p> <p>SST-NINO3.4 is a key ENSO (El Niño Southern Oscillation) index, defined as the spatial average of Sea Surface Temperature (SST) over the NINO3.4 spatial box (170°W-120°W, 5°S-5°N).</p> <p>The reconstructed SST-NINO3.4 anomalies are obtained from a new multiproxy reconstruction based on 45 proxy records obtained from the Past Global Changes 2k database (PAGES 2k Consortium, 2017, 2019) and a so-called Random Forest method. The reconstructed anomalies are relative to the 1870-2014 averaged SST-NINO3.4 obtained from the HadISST product (Rayner et al. 2003). Units are degrees Celsius</p>
Phase Object Reconstruction for 4D-STEM using Deep Learning, (4D-STEM Example Data)
<p><strong>Overview </strong></p> <p>This repository contains 2 example 4D-STEM datasets format from the paper <a href="https://arxiv.org/abs/2202.12611">"Phase Object Reconstruction for 4D-STEM using Deep Learning"</a>. The data was written to hdf5 for compatibility with the python programming language. When reading from these files consider possibly different storage conventions (Row major vs. column major format). Data may need to be transposed accordingly.</p> <p> </p> <p><strong>Parameters</strong></p> <p>The twisted bilayer graphene dataset is simulated. The smaller file is an experimental SrTiO<sub>3</sub> dataset.</p> <table> <thead> <tr> <th scope="row"> </th> <th scope="col">Graphene</th> <th scope="col">STO</th> </tr> </thead> <tbody> <tr> <th scope="row">E0</th> <td>200kV</td> <td>300kV</td> </tr> <tr> <th scope="row">Apeture</th> <td>25 mrad</td> <td>20 mrad</td> </tr> <tr> <th scope="row">Detector Size</th> <td>2.5 Å<sup>-1</sup></td> <td>1.6671 Å<sup>-1</sup></td> </tr> <tr> <th scope="row">Dimensions</th> <td>101x101x128x128</td> <td>60x60x64x64</td> </tr> <tr> <th scope="row">Step Size</th> <td>0.2</td> <td>0.1818</td> </tr> </tbody> </table> <p><br> </p>
Reconstruction of Mediterranean sea-level changes and contributions for 1960-2018
<p><strong>Data supporting the paper:</strong></p> <p><strong>Calafat, F. M., Frederikse, T., and Horsburgh, K. (2022). The Sources of Sea-Level Changes in the Mediterranean Sea since 1960, Journal of Geophysical Research Oceans, under review.</strong></p> <p>Please cite the paper above when using this data set.</p> <p>This new version of the data set has been published to support the paper above and includes more data than the previous version as well as several improvements and refinements. Version 2.3 has been created to include regional estimates of rates due to the inverse barometer effect.</p> <p><em>Data description:</em></p> <ul> <li><strong>Bayesian_estimates_Mediterranean_sea_level.nc:</strong> this file contains gridded estimates of relative sea-level changes and their instantaneous rates for 1960-2018 in the Mediterranean Sea, separated into the individual contributions of: <ol> <li>Sterodynamic changes (i.e., ocean dynamics and thermal expansion).</li> <li>Contemporary GRD (i.e., changes in Earth gravity, Earth rotation, and solid-earth deformation due to land-mass changes).</li> <li>GIA (i.e., glacial isostatic adjustment).</li> <li>Short-term variability (interannual to decadal). </li> <li>Inverse barometer effect.</li> </ol> </li> <li><strong>data_input.mat:</strong> this file contains all of the data needed to run the Bayesian hierarchical model. This includes the observational data from tide gauges and satellite altimetry as well as the ensemble-mean and ensemble covariance matrices for the sea-level fingerprints associated with contemporary GRD effects and GIA.</li> </ul> <p>The Bayesian estimates have been obtained using a spatiotemporal Bayesian hierarchical model (see paper). This work has been carried out within the framework of the EuroSea project funded by European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626.</p>
Global continental and ocean basin reconstructions since 200 Ma
<div>Description of Resources - Seton et al. (2012)</div> <div> </div> <div>This file provides a detailed description of all of the files that make up the data collection associated with the publication: Seton, M., Müller, R. D., Zahirovic, S., Gaina, C., Torsvik, T., Shephard, G., Talsma, A., Gurnis, M., Turner, M., Maus, S., Chandler, M. (2012). Global continental and ocean basin reconstructions since 200 Ma. Earth-Science Reviews, 113(3), 212-270. doi:<a href="https://doi.org/10.1016/j.earscirev.2012.03.002" target="_blank" rel="noopener">10.1016/j.earscirev.2012.03.002</a></div> <div> </div> <div>Note: For information on file formats and what programs to use to interact with various file formats, see "File Formats and Recommended Programs”.</div> <div> </div> <div> </div> <div>The files associated with this data collection allow for the visualisation and/or manipulation of the global plate motion model presented by Seton et al. (2012), they include:</div> <div>• <strong>Rotations </strong>- Global rotation model that contains the reconstruction poles that describe the motions of the continents and oceans.</div> <div>* Seton_etal_ESR2012_2012.1.rot (410 KB)</div> <div> </div> <div>• <strong>Plate IDs </strong>- A list of all the plate IDs used in the rotation and geometry files and their corresponding plate names.</div> <div>* Seton_etal_ESR2012_PlateIDs.pdf (102 KB) </div> <div> </div> <div>• <strong>Coastlines </strong>- Geometries of the present-day coastlines.</div> <div>* Seton_etal_ESR2012_Coastline_2012.1.gpml (17.5 MB)</div> <div>* Seton_etal_ESR2012_Coastline_2012.1_polyline.shp (3.3 MB inc. auxiliary files, datum-WGS 1984)</div> <div>* Seton_etal_ESR2012_Coastline_2012.1_polyline.txt (2.7 MB)</div> <div>* Seton_etal_ESR2012_Coastline_2012.1_polyline.kml (4.4 MB)</div> <div> </div> <div>• <strong>Continent-ocean boundaries </strong>(COBs) - Locations of the boundaries between oceanic and continental crust for plates involved in this study.</div> <div>* Seton_etal_ESR2012_COB_2012.1.gpml (410 KB)</div> <div>* Seton_etal_ESR2012_COB_2012.1.shp (152 KB inc. auxiliary files, datum-WGS 1984)</div> <div>* Seton_etal_ESR2012_COB_2012.1.txt (86 KB)</div> <div>* Seton_etal_ESR2012_COB_2012.1.kml (176 KB)</div> <div> </div> <div>• <strong>Plate polygons and boundary geometries</strong> - Topologically closed plate polygons are constructed from the intersection of ridges, transforms, subduction zones and other plate boundary geometries. These 'resolved topologies' are valid at 1 Myr intervals (0-200 Ma). The plate boundary geometries and plate polygons have been assigned plate reconstruction IDs to allow them to be reconstructed using the supplied rotation file. </div> <div>* Seton_etal_ESR2012_PP_2012.1.gpml (29.5 MB)</div> <div> </div> <div>Note:</div> <div>Paleo age grids used by Seton et al. (2012) are released in Müller et al. (2013) [Müller, R. D., Dutkiewicz, A., Seton, M., & Gaina, C. (2013). Seawater chemistry driven by supercontinent assembly, breakup, and dispersal. Geology, 41(8), 907-910. doi: <a href="https://doi.org/10.1130/G34405.1" target="_blank" rel="noopener">10.1130/g34405.1</a>]</div> <div> </div> <div>The paleo age grids associated with this model can be accessed at: <a href="https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Seton_etal_2012_ESR/" target="_blank" rel="noopener">https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Seton_etal_2012_ESR/</a></div>
A Phanerozoic gridded dataset for palaeogeographic reconstructions
<p>This repository provides access to five pre-computed reconstruction files as well as the static polygons and rotation files used to generate them. This set of palaeogeographic reconstruction files provide palaeocoordinates for three global grids at H3 resolutions 2, 3, and 4, which have an average cell spacing of ~316 km, ~119 km, and ~45 km, respectively. Grids were reconstructed at a temporal resolution of one million years throughout the entire Phanerozoic (540–0 Ma). The reconstruction files are stored as comma-separated-value (CSV) files which can be easily read by almost any spreadsheet program (e.g. Microsoft Excel and Google Sheets) or programming language (e.g. Python, Julia, and R). In addition, R Data Serialization (RDS) files—a common format for saving R objects—are also provided as lighter (and compressed) alternatives to the CSV files. The structure of the reconstruction files follows a wide-form data frame structure to ease indexing. Each file consists of three initial index columns relating to the H3 cell index (i.e. the 'H3 address'), present-day longitude of the cell centroid, and the present-day latitude of the cell centroid. The subsequent columns provide the reconstructed longitudinal and latitudinal coordinate pairs for their respective age of reconstruction in ascending order, indicated by a numerical suffix. Each row contains a unique spatial point on the Earth's continental surface reconstructed through time. NA values within the reconstruction files indicate points which are not defined in deeper time (i.e. either the static polygon does not exist at that time, or it is outside the temporal coverage as defined by the rotation file).</p> <p>The following five Global Plate Models are provided (abbreviation, temporal coverage, reference) within the GPMs folder:</p> <ul> <li>WR13, 0–550 Ma, (Wright et al., 2013)</li> <li>MA16, 0–410 Ma, (Matthews et al., 2016)</li> <li>TC16, 0–540 Ma, (Torsvik and Cocks, 2016)</li> <li>SC16, 0–1100 Ma, (Scotese, 2016)</li> <li>ME21, 0–1000 Ma, (Merdith et al., 2021)</li> </ul> <p>In addition, the H3 grids for resolutions 2, 3, and 4 are provided within the grids folder. Finally, we also provide two scripts (python and R) within the code folder which can be used to generate reconstructed coordinates for user data from the reconstruction files.</p> <p>For access to the code used to generate these files:</p> <p><a href="https://github.com/LewisAJones/PhanGrids">https://github.com/LewisAJones/PhanGrids</a></p> <p>For more information, please refer to the article describing the data:</p> <p>Jones, L.A. and Domeier, M.M. 2024. A Phanerozoic gridded dataset for palaeogeographic reconstructions. (2024).</p> <p>For any additional queries, contact: </p> <p>Lewis A. Jones (lewisa.jones@outlook.com) or Mathew M. Domeier (mathewd@uio.no)</p> <p>If you use these files, please cite: </p> <p>Jones, L.A. and Domeier, M.M. 2024. A Phanerozoic gridded dataset for palaeogeographic reconstructions. DOI: <a href="../doi/10.5281/zenodo.10069221">10.5281/zenodo.10069221</a></p> <p><strong>References </strong></p> <ol> <li>Matthews, K. J., Maloney, K. T., Zahirovic, S., Williams, S. E., Seton, M., & Müller, R. D. (2016). Global plate boundary evolution and kinematics since the late Paleozoic. <em>Global and Planetary Change</em>, 146, 226–250. <a href="https://doi.org/10.1016/j.gloplacha.2016.10.002">https://doi.org/10.1016/j.gloplacha.2016.10.002</a>.</li> <li>Merdith, A. S., Williams, S. E., Collins, A. S., Tetley, M. G., Mulder, J. A., Blades, M. L., Young, A., Armistead, S. E., Cannon, J., Zahirovic, S., & Müller, R. D. (2021). Extending full-plate tectonic models into deep time: Linking the Neoproterozoic and the Phanerozoic. <em>Earth-Science Reviews</em>, 214, 103477. <a href="https://doi.org/10.1016/j.earscirev.2020.103477">https://doi.org/10.1016/j.earscirev.2020.103477</a>.</li> <li>Scotese, C. R. (2016). Tutorial: PALEOMAP paleoAtlas for GPlates and the paleoData plotter program: PALEOMAP Project, Technical Report.</li> <li>Torsvik, T. H., & Cocks, L. R. M. (2017). Earth history and palaeogeography. <em>Cambridge University Press</em>. <a href="https://doi.org/10.1017/9781316225523">https://doi.org/10.1017/9781316225523</a>.</li> <li>Wright, N., Zahirovic, S., Müller, R. D., & Seton, M. (2013). Towards community-driven paleogeographic reconstructions: Integrating open-access paleogeographic and paleobiology data with plate tectonics. <em>Biogeosciences</em>, 10, 1529–1541. <a href="https://doi.org/10.5194/bg-10-1529-2013">https://doi.org/10.5194/bg-10-1529-2013</a>.</li> </ol>
Statistical analysis and dataset for: Three-dimensional body reconstruction enables quantification of liquid consumption in small invertebrates
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.06.14.599002).</p> <p><em><strong>Abstract</strong></em></p> <p>Quantifying feeding patterns provides valuable insights into animal behaviour. However, small invertebrates often consume incredibly small amounts of food. This renders traditional methods, such as weighing individuals before and after food acquisition, either inaccurate or prohibitively expensive. Here, we present a non-invasive method to quantify food consumption of small invertebrates whose body expands during feeding. Using the markerless pose estimation software DeepLabCut, we three-dimensionally track the body of Argentine ants, <em>Linepithema humile</em>. Using these extracted markers, we developed an algorithm which computationally reconstructs the ant’s body, directly measuring volumetric change over time. Moreover, we provide measures of accuracy and quantify the ant’s feeding response to a range of sucrose concentrations, as well as a gradient of caffeine-laced sucrose solutions. Small invertebrates are often prolific invasive species and disease vectors, causing significant ecological and economical damage. Understanding their feeding behaviour could be an important step towards effective control strategies.</p> <p> </p> <ul> <li><strong>VolEst_C1_volume_calculation_multiprocessing.py</strong>: Takes as input H5 3D DeepLabCut files, calculates the gaster volume at every frame using seven different methods and outputs these as CSV files.</li> <li><strong>VolEst_C2_interactive_GUI.py</strong>: Given a folder with Volume CSV files, interactively plots the volume over time, 3D coordinates tracked by DeepLabCut and the frame of interest for both cameras.</li> <li><strong>VolEst_C3_linear_regression.py</strong>: Applies a linear regression to each feeding event tracked and provides measures of interest such as crop load and consumption rate.</li> <li><strong>VolEst_C4_statistical_analysis</strong>: Complete statistical analysis and code for the manuscript.</li> <li><strong>VolEst_D1_sucrose_density.csv</strong>: Data obtained to quantify the density of sucrose solutions of varying molarity.</li> <li><strong>VolEst_D2_accuracy_weight_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D3_accuracy_weight.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D4_accuracy_nanoliter_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D5_accuracy_nanoliter.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D6_sucrose_caffeine_consumption_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_D7_sucrose_caffeine_consumption.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_Camera_A-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera A.</li> <li><strong>VolEst_Camera_B-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera B.</li> <li><strong>VolEst_base.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> <li><strong>VolEst_platform.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> </ul>
Data-driven reconstructions of the Indian palaeomonsoon (1500–1995 CE)
<p>These datasets are fully described in the paper <strong>A novel explainable deep learning framework for reconstructing South Asian palaeomonsoons</strong>. <br><br><strong>Paper abstract:</strong><br>We present novel explainable deep learning techniques for reconstructing South Asian palaeomonsoon rainfall over the last 500 years, leveraging long instrumental precipitation records and palaeoenvironmental datasets from South and East Asia to build two types of model: dense neural networks (`timeline models') and convolutional neural networks (CNNs). The timeline models are trained individually on seven regional rainfall datasets and while they capture decadal-scale variability and significant droughts, they underestimate interannual variability. The CNNs, designed to account for spatial relationships in both predictor and target, demonstrate higher skill in reconstructing rainfall patterns and produce robust spatiotemporal reconstructions. The 19th and 20th centuries were characterised by marked inter-annual variability in the monsoon, but earlier periods were characterised by more decadal- to centennial-scale oscillations. Multidecadal droughts occurred in the mid-seventeenth and nineteenth centuries, while much of the eighteenth century (particularly the early part of the century) was characterised by above-average monsoon precipitation. Extreme droughts tend to be concentrated in south and west India and often coincide with recorded famines. Our findings offer insights into the historical variability of the Indian summer monsoon and highlight the potential of deep learning techniques in palaeoclimate reconstruction.<br><br><strong>Dataset description:</strong><br><em>regional-famines.csv:</em> Derived from https://en.wikipedia.org/wiki/Timeline_of_major_famines_in_India_prior_to_1765 and https://en.wikipedia.org/wiki/Timeline_of_major_famines_in_India_during_British_rule. Columns are the seven homogeneous rainfall regions of India, rows are years. Variable is 1 is a significant famine occurred somewhere in that region in that year, else 0.</p> <p><em>timeline-model-regional-prcp.csv: </em>Reconstructed seasonal (Jun–Sep) monsoon precipitation anomalies for each of the seven homogeneous rainfall regions of India, as well as all-India. Period covered is 1501–1995.</p> <p><em>cnn-ensemble-prcp.nc: </em> NetCDF file containing spatial maps of reconstructed seasonal monsoon precipitation anomalies from the CNN model. Available for each year from 1501–1995, at a resolution of 0.25°x0.25°. Two variables are included, the full ten-member ensemble and its mean.</p> <p><em>cnn-ensemble-mean-individual-years.zip: </em>A ZIP archive containing maps showing the CNN ensemble mean for each year.</p> <p><em>cnn-ensemble-mean-all-India.csv: </em>The all-India average of the CNN ensemble mean for each year, giving a yearly monsoon index from 1501–1995.</p> <p> </p>
Data for: Rational Approximation of Golden Angles: Accelerated Reconstructions for Radial MRI
<p>Magnetic Resonance Imaging data used in the work "Rational Approximation of Golden Angles: Accelerated Reconstructions for Radial MRI". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p> <p> </p> <p>The *_ind.{cfl,hdr} files store the indices of the different spokes of the corresponding datasets:</p> <p> </p> <p><strong>data_res_S{1597,0987,0377,0233,0089,0055,0021}</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 3.2|2.04</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi_{16,15,13,12,10,9,7}^1</p> <p>Sampling: RAGA</p> <p><br> </p> <p><strong>data_bin_raga, data_bin_ga</strong></p> <p>Type: Radial Single-Shot Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: IR FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi_{13}^1, 2\psi^1</p> <p>Sampling: RAGA</p> <p><br> </p> <p><strong>data_bin_ga</strong></p> <p>Type: Radial Single-Shot Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: IR FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi^1</p> <p>Sampling: GA</p> <p><br><br> </p> <p><strong>data_invivo_ga</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice cardiac short-axis</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 320</p> <p>Spoke Angle: \psi^1</p> <p>Sampling: Golden-Ratio</p> <p><br> </p> <p> </p> <p><strong>data_invivo_raga</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice cardiac short-axis</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 320</p> <p>Spoke Angle: \psi_{13}^1</p> <p>Sampling: RAGA</p> <p><br> </p>
Bayesian Methods for Ancestral State Reconstruction in Morphosyntax
<p>Supplementary files to accompany journal submission.</p> <p>Files are:</p> <p> </p> <p>tree.pdf - pdf consensus tree, for illustration</p> <p>data.txt - coding file</p> <p>TREE_Set.t - nexus format sample of trees.</p> <p>sources.pdf - source materials used for languages</p>
Direct observation of electron density reconstruction at the metal-insulator transition in NaOsO3
<p>Open access data set for manuscript "Direct observation of electron density reconstruction at the metal- insulator transition in NaOsO3" published in Physical Review B, 98, 115116 (2018)</p>
Munda cognate set with proto-Munda reconstructions
<p>This data set contains a set of 127 cognates with reconstructions for proto-Munda and references to MKCD and Pinnow (1959). The set contains materials from Gorum, Sora, Remo, Gutob, Kharia, Juang, Gtaʔ, Santali, Mundari, Ho, Korwa, and Korku.</p>
MORSE image reconstruction example dataset
<p>This repository contains raw k-space datasets from 3T and 7T multi-echo spoiled gradient echo MRI scans, which along with the source code uploaded on <a href="https://github.com/fil-physics/gadgetron-matlab">GitHub</a>, can be used to demonstrate FIL Physics MORSE image reconstruction as in the following manuscript:</p> <p>"MORSE CODE: Multiple Orthogonal Reference Sensitivity Encoding Combined Over Dominant Eigencoils" by O. Josephs and B. Dymerska et al.</p> <p>If you use these data or MORSE image reconstruction, please make sure to cite the paper.</p>
ScienceDex guides
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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.