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1,773 results for “Supplementary data,”
Supplementary data for Atomistic Mechanism of the Nucleation of Methylammonium Lead Iodide Perovskite from Solution
<p>Supplementary data for "Atomistic Mechanism of the Nucleation of Methylammonium Lead Iodide Perovskite from Solution"</p>
Supplementary GIS data - Potential and implications of automated pre-processing of LiDAR-based digital elevation models for large-scale archaeological landscape analysis
<p>A supplementary dataset related to the paper discussing preparation of a digital elevation model derived from DMR 5G (LiDAR-based DEM of the Czech Republic) cleaned of modern artificial features. It includes data used as a clipping mask and data produced during the testing phase.</p> <p>Contents:</p> <ul> <li>..\clipping_buffers.gdb\ - Clipping buffers based on ZABAGED dataset used for masking the original data stored as ESRI geodatabase.</li> <li>..\drainages\ - Drainages with Strahler order higher than four (potential watercourses) for the original and filtered DEMs. <ul> <li>drainages_filtered - Drainges identified in the filtered DEM stored as GeoTIFF.</li> <li>drainages_original - Drainges identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\LSC\ - Locations with significant land surface curvature for the original and filtered DEMs. <ul> <li>LSC_filtered - Significant LSC identified in the filtered DEM stored as GeoTIFF. </li> <li>LSC_original - Significant LSC identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\visibility\ - Viewsheds computed over the original and filtered DEMs. <ul> <li>Libice\ - Sample viewsheds computed for the early medieval hillfort of Libice. <ul> <li>Libice_visibility_filtered - Viewshed based on the filtered DEM stored as GeoTIFF. </li> <li>Libice_visibility_original - Viewshed based on the original DEM stored as GeoTIFF. </li> <li>observer_points - Observer points used for calculating the viewsheds.</li> </ul> </li> <li>regular_grid\ - Cumulative viewsheds calculated for regularly spaced points in a 10 x 10 km grid with a visibility radius of 5 km and an observer height of 2 m; a total of 574 viewsheds. <ul> <li>visibility_filtered - Cumulative viewshed for the filtered DEM stored as GeoTIFF.</li> <li>visibility_original - Cumulative viewshed for the original DEM stored as GeoTIFF. </li> <li>visibility_test_buffers - Buffers used for the viewshed calculations stored as ESRI shapefile.</li> <li>visibility_test_observers - Observer points used for the viewshed calculations stored as ESRI shapefile.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Preprint version of the related paper:</p> <p>Novák, David and Pružinec, Filip, Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis. Available at SSRN: <a href="https://ssrn.com/abstract=4063514">https://ssrn.com/abstract=4063514</a></p>
FSS7B - Inhibiting human aversive memory by transcranial theta-burst stimulation to primary sensory cortex: Supplementary fMRI data
<p>Functional magnetic resonance imaging (fMRI) data supplementing a publication on inhibiting somatosensory fear memory in humans with transcranial magnetic stimulation (TMS). Contains 1) individual regions-of-interest (ROIs) masks in the bilateral primary somatosensory cortex (S1) to target with TMS, 2) S1 masks for left and right hemisphere used in restricting the ROIs to a priori expected area, 3) sum and probability maps of the ROIs over participants, and 4) summary group level fMRI NIFTI images including beta images and T-maps. Individual SPMs/beta images can be requested from the authors for academic research purposes (k.ojala@uke.de). Details on the methods are found in the Supplement of the publication (see linked DOI). </p>
Global patterns of current and future road infrastructure - Supplementary spatial data
<p><strong>Global patterns of current and future road infrastructure - Supplementary spatial data</strong></p> <p><strong>Authors:</strong> Johan Meijer, Mark Huijbregts, Kees Schotten, Aafke Schipper</p> <p><strong>Research paper summary: </strong>Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted <em>R</em>2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.</p> <p><strong>Contents:</strong> The GRIP dataset consists of global and regional vector datasets in ESRI filegeodatabase and shapefile format, and global raster datasets of road density at a 5 arcminutes resolution (~8x8km). The GRIP dataset is mainly aimed at providing a roads dataset that is easily usable for scientific global environmental and biodiversity modelling projects. The dataset is not suitable for navigation. GRIP4 is based on many different sources (including OpenStreetMap) and to the best of our ability we have verified their public availability, as a criteria in our research. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under a <a href="https://creativecommons.org/publicdomain/zero/1.0/deed.en">Creative Commons License (CC-0)</a> and is free to use. The GRIP database and future global road infrastructure scenario projections following the Shared Socioeconomic Pathways (SSPs) are described in the <a href="https://www.globio.info/global-patterns-of-current-and-future-road-infrastructure">paper by Meijer et al (2018)</a>. Due to shapefile file size limitations the global file is only available in ESRI filegeodatabase format.</p> <p>Regional coding of the other vector datasets in shapefile and ESRI fgdb format:</p> <ul> <li>Region 1: North America</li> <li>Region 2: Central and South America</li> <li>Region 3: Africa</li> <li>Region 4: Europe</li> <li>Region 5: Middle East and Central Asia</li> <li>Region 6: South and East Asia</li> <li>Region 7: Oceania</li> </ul> <p>Road density raster data:</p> <ul> <li>Total density, all types combined</li> <li>Type 1 density (highways)</li> <li>Type 2 density (primary roads)</li> <li>Type 3 density (secondary roads)</li> <li>Type 4 density (tertiary roads)</li> <li>Type 5 density (local roads)</li> </ul> <p><strong>Keyword:</strong> global, data, roads, infrastructure, network, global roads inventory project (GRIP), SSP scenarios</p>
Supplementary Data & Software for "Balancing the marine sulfur cycle", Johnson & Adkins (submitted)
<p>These files are the supplementary data and software for the manuscript "Balancing the marine sulfur cycle" by Johnson & Adkins (submitted).</p> <p>The MATLAB workspace file "DeepSeaSO4_ClusterAnalysisData.mat" contains organized data structures with DSDP, ODP, and IODP site metadata, porosity data, pore water sulfate concentration data, methane concentration data, total organic carbon wt% data, and calcium carbonate wt% data + their corresponding depths. These data served as the basis for the cluster analysis performed by the included MATLAB script "SO4_conc_cluster_analysis.m" in the study.</p> <p>The "DatasetS1.xlsx" spreadsheet file contains all compiled pyrite sulfur isotopic data, pyrite abundance data, and total sulfur abundance data included within the literature compilation discussed in the manuscript. Pyrite sulfur isotopic data references are listed and are cited in the main text. DOI URLs for the total sulfur abundance data from the PANGAEA repository are also included in the spreadsheet. These data have additionally been saved into the MATLAB workspace files "Pyrite_d34S_Comp.mat" and "Pyrite Abundance_Comp.mat" for import into MATLAB by other scripts.</p> <p>The MATLAB workspace file "POROSITY.mat" includes vectors of latitude (in degrees), longitude (in degrees), water depth (in mbsl), sediment depth (in mbsf), porosity (in vol%), and corresponding PANGAEA DOI URLs along with the data tables from which this information was extracted. This file is called by "PorosityPlotter.m" to extract initial porosity values, compile coring methods, and make plots of initial porosity as a function of water depth for each coring method.</p> <p>The MATLAB script "hypsometry.m" loads the global topographic data included in "topography.mat", calculates associated surface areas across different ocean depth intervals, and determines average porosity and sedimentary pyrite parameters across those intervals in tandem with "S_Iso_Mass.m". "S_Iso_Mass.m" uses the averages to calculate the steady-state sulfur isotopic composition of seawater.</p> <p>The script "phi_waterdepth_analysis.m" loads the cluster analysis data and uses the corresponding porosity data to calculate linear regressions between initial porosity and water depth for use by other scripts; the workspace files "phi_waterdepth_fit.mat" and "phi_waterdepth_trimmed_fit.mat" are saved versions of the regressions used in the manuscript and called by "hypsometry.m" for calculating averages. </p> <p>The additional included files "PyriteSAbundanceCompilationSources.pdf" and "PorosityCompilationSources.pdf" files include full citations to the data included in the pyrite S abundance and porosity compilations (respectively).</p>
Supplementary Data: Mapping of local lattice parameter ratios by projective Kikuchi pattern matching
<p>This is the experimental dataset which was analyzed in:</p> <p>"Mapping of local lattice parameter ratios by projective Kikuchi pattern matching"<br> Aimo Winkelmann, Gert Nolze, Grzegorz Cios, and Tomasz Tokarski<br> Phys. Rev. Materials <strong>2</strong> (2018) 123803<br> https://doi.org/10.1103/PhysRevMaterials.2.123803</p> <p>We describe a lattice-based crystallographic approximation for the analysis of distorted crystal structures via electron backscatter diffraction (EBSD) in the scanning electron microscope. EBSD patterns are closely linked to local lattice parameter ratios via Kikuchi bands that indicate geometrical lattice plane projections. Based on the transformation properties of points and lines in the real projective plane, we can obtain continuous estimations of the local lattice distortion based on projectively transformed Kikuchi diffraction simulations for a reference structure. By quantitative image matching to a projective transformation model of the lattice distortion in the full solid angle of possible scattering directions, we enforce a crystallographically consistent approximation in the fitting procedure of distorted simulations to the experimentally observed diffraction patterns. As an application example, we map the locally varying tetragonality in martensite grains of steel.</p>
Supplementary data from 'Predicting the distribution of Australian frogs and their overlap with Batrachochytrium dendrobatidis under climate change'
<p><strong><span>Aim: </span></strong><span>Amphibians, with over 40% of assessed species listed as threatened, are disproportionately at risk in the global extinction crisis. Among the many factors implicated in the current and ongoing loss of amphibian biodiversity are climate change and the disease chytridiomycosis, caused by the fungus <em>Batrachochytrium dendrobatidis </em>(<em>Bd</em>). These two threats are of particular concern in Australia, where <em>Bd </em>has been implicated in the declines of at least 43 frog species, and climate change is emerging as an additional threat. Here, we explore how climate change is likely to affect the distributions of Australian frog species and <em>Bd </em>to the year 2100, as well as how the spatial and climatic niche overlap between <a>chytridiomycosis-declined </a></span><span>frogs and <em>Bd</em> could shift.</span></p> <p><strong><span>Location: </span></strong><span>Australia</span></p> <p><strong><span>Methods: </span></strong><span>We used species distribution modelling to infer the current and future distribution of 141 Australian frog species and <em>Bd</em>, under two emissions scenarios. We used metrics of niche similarity, including Schoener's D and the Niche Margin Index, to quantify predicted alterations to spatial interactions between <em>Bd</em> and frog species.</span></p> <p><strong><span>Results: </span></strong><span>Climate change is likely to have a variable impact on frog distributions in Australia, with some 23 and 47 species, primarily in southern Australia, predicted to lose at least 30% of their current distributions under low and high emissions scenarios, respectively. In contrast, 69 and 68 species, respectively, have potential to increase their distributions, primarily in northern Australia. While the distribution of <em>Bd </em>is predicted to decrease, the proportional spatial and niche overlap between <em>Bd </em>and susceptible frog species is predicted to remain little changed, and in some cases, to increase.</span></p> <p><strong><span>Main conclusions: </span></strong><span>Although effects will be variable across the continent, climate change is likely to be a threatening factor to a number of Australian frog species. Additionally, chytridiomycosis is likely to remain a significant threat to many frog species, as any reductions to the pathogen's distribution largely coincide with geographic range contractions of chytridiomycosis-susceptible species.</span></p>
Supplementary data and files to Figure 9 of Eva Rosenstock, Linear Pottery and Harris (2022)
<p>Supplements to the printed version of Figure 9 in: Eva Rosenstock, Linear Pottery and Harris: A Case Study in the Spatio-temporal Logic of Archaeological Sites. In: Elke Kaiser - Michael Meyer - Silviane Scharl - Stefan Suhrbier (Hrsg.), Wissensschichten. Festschrift für Wolfram Schier zu seinem 65. Geburtstag. Studia Honoraria (Rahden/Westf. 2022) 179-198.</p> <p>HMC matrix based on Figure 8 but also considering ceramic CA information for further layer attributions, based on the data published in Claus.-Joachim Kind, Ulm-Eggingen. Die Ausgrabungen 1982 bis 1985 in der bandkeramischen Siedlung und der mittelalterlichen Wüstung (Stuttgart 1989).</p> <p>Full collapsed and uncollapsed versions in CSV, Graph Markup Language (compatible with e.g. yEd freeware <a href="https://www.yworks.com/products/yed">https://www.yworks.com/products/yed</a>) and HMCX (compatible with Harris Matrix Composer <a href="https://harrismatrixcomposer.com/#/">https://harrismatrixcomposer.com/#/</a>), and jpeg.</p>
Input data and Supplementary Results for "Turnover in life-strategies recapitulates marine microbial succession colonizing model particles"
<p><strong>README</strong></p> <p>This page contains processed input data used for downstream analysis and some Supplementary Results for the paper:</p> <p>Pascual-García, A., Schwartzman, J., Enke, T.N., Iffland-Stettner, A., Cordero, O.X., Bonhoeffer, S., Turnover in life-strategies recapitulates marine microbial succession colonizing model particles (2022).</p> <p> </p> <p><strong>Input data</strong></p> <p> </p> <ul> <li> <p>File <em>“count_table.ESV.biom”</em>: Table containing the abundance of each Exact Sequence Variant (ESV) in the different samples (biom format).</p> </li> <li> <p>File <em>“count-table_</em><em>metagenomes</em><em>_KEGGs.L3.spf”</em>. Table containing the abundances of genes found in the shotgun metagenomics experiments annotated in KEGG and then aggregated into classes according to the finest classification in KEGG's hierarchy (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“count-table_</em><em>PICRUST2</em><em>_KEGGs.L3.spf</em>”. Table containing the abundances of genes predicted with PICRUSt v2. These genes were annotated in KEGG and aggregated into classes according to the finest hierarchy in KEGG (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“count-table_Isolates_KEGGs.L3.spf</em>”. Table containing the abundances of genes found in the isolates genomes that were annotated in KEGG and aggregated into classes according to the finest classification in KEGG's hierarchy (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“samples_metadata.tsv”</em>. Metadata table describing the samples.</p> </li> <li> <p>File <em>“isolates_metadata.tsv”.</em> Metadata table describing the isolates, it includes shallow phylogenetic levels and a categorical identifier describing the environmental preference estimated for the ESV having a 100% sequence identity with a ZINB-GLM.</p> </li> <li> <p>File <em>“sequences.ESV.</em><em>fasta</em><em>”.</em> File containing the Exact Sequence Variants fasta.</p> </li> </ul> <p><strong>Supplementary Materials</strong></p> <p> </p> <ul> <li> <p>File <em>“qiime2_visualizations.zip”</em>. A file containing visualizations compatible with the qiime2 viewer (simply drag and drop the file in <a href="https://view.qiime2.org/">https://view.qiime2.org/</a>) for each sample or combination of samples, labelled as `$substrate.$medium.$replicate`, where `$medium = {Beads, Seawater}` and `$replicate = {A,B,C}`. If the label is not present for one field, it means that all samples are aggregated for that field e.g.:</p> <ul> <li> <p>“<em>count_table.ESV.Chitosan.Beads.A.bar-plots.</em><em>qzv”</em> Contains the replicate experiment A for communities on the synthetic beads in chitosan.</p> </li> <li> <p>“<em>count_table.ESV.Chitosan.bar-plots.</em><em>qzv”</em> Contains all samples in chitosan (both seawater communities and the three replicates of communities on the beads).</p> </li> </ul> </li> <li> <p>File <em>“README.odt”</em>. This readme in libreoffice format.</p> </li> <li> <p>File <em>“</em><em>Genome_deposition_information.xlsx”. </em> NCBI identifiers for the isolates’ genomes.</p> </li> <li> <p>File "barcodes_to_samples_MGRAST.xlsx". Contains the barcodes of each sample and its metadata as it was deposited in MG-RAST. In a second tab, there is a subset of samples with a low number of reads that MG-RAST analyzed together, generating a single entry (termed "mixed").</p> </li> <li> <p>Access to raw an processed metagenomes and analysis are provided through MG-RAST following [this link](<a href="https://www.mg-rast.org/mgmain.html?mgpage=project&project=mgp85635">https://www.mg-rast.org/mgmain.html?mgpage=project&project=mgp85635</a>).</p> <ul> <li> <p>As of May 30th, 2022, there are two issues with the dataset in MG-RAST which are out of our scope to solve. We will report any update here. The first problem is related to the entry TCCTGAGC-GTAAGGAG-s_2_, which does not load in MG-RAST. These are very low samples and were discarded in most analyses. In addition, you will find in the metadata 17 metagenomes that do not belong to our project.</p> </li> </ul> </li> </ul>
Supplementary data for: McFadden et al., Revisionary systematics of Octocorallia (Cnidaria: Anthozoa) guided by phylogenomics
<p class="MsoNormal">The anthozoan sub-class Octocorallia includes over 3500 nominal species of soft corals and gorgonian sea fans, many of which serve as critical foundation species in benthic marine ecosystems in shallow waters to the deep sea. Despite their familarity and ecological importance, the diversity and taxonomy of octocorals remain poorly known. All of the orders, subordinal groups and a majority of families have been recognized to be poly- or paraphyletic, but poor resolution of the deeper nodes in mitochondrial or single-locus nuclear gene trees have hindered formal revision of the higher level taxonomy of the group. We used sequence data from target-capture of 739 ultraconserved and exon loci to reconstruct a fully resolved phylogeny for 185 octocoral taxa representing 55 of 63 currently recognized families. We use this phylogeny, supplemented with a gene tree for mitochondrial <em>mtMutS</em> for an additional 107 taxa, to guide a revision of the families and orders of Octocorallia. We: (1) elevate the anthozoan sub-classes Octocorallia and Hexacorallia to the rank of Class; (2) replace the three currently recognized orders of Octocorallia (Alcyonacea, Pennatulacea, Helioporacea) with two new orders reflecting reciprocally monophyletic major clades; and (3) revise all families with the exception of the 15 recognized families of sea pens, which we accommodate within a new superfamily. The revised classification of Octocorallia thus comprises 79 families, including 18 that are newly described and three that have been reinstated or elevated in rank. In addition, two new genera are described and another three reinstated. We leave the family assignment of 46 of 411 genera as <em>incertae sedis</em> until further molecular or morphological data can be obtained to confirm their phylogenetic affinities.</p>
Scan files, 3D reconstructions, data spreadsheet and supplementary files for Heterochrony and parallel evolution of echinoderm, hemichordate and cephalochordate internal bars
<p><span>Deuterostomes comprise three phyla with radically different body plans. Phylogenetic bracketing of the living deuterostome clades suggests the latest common ancestor of echinoderms, hemichordates and chordates was a bilaterally symmetrical worm with pharyngeal openings, with these characters lost in echinoderms. Early fossil echinoderms with pharyngeal openings have been described, but their interpretation is highly controversial. Here, we critically evaluate the evidence for pharyngeal structures (gill bars) in the extinct stylophoran echinoderms <em>Lagynocystis pyramidalis</em> and <em>Jaekelocarpus oklahomensis</em> using virtual models based on high-resolution X-ray tomography scans of three-dimensionally preserved fossil specimens. Multivariate analyses of the size, spacing and arrangement of the internal bars in these fossils indicate they are substantially more similar to gill bars in modern enteropneust hemichordates and cephalochordates than to other internal bar-like structures in fossil blastozoan echinoderms. The close similarity between the internal bars of the stylophorans <em>L. pyramidalis</em> and <em>J. oklahomensis</em> and the gill bars of extant chordates and hemichordates is strong evidence for their homology. Differences between these internal bars and bar-like elements of the respiratory systems in blastozoans suggest these structures might have arisen through parallel evolution across deuterostomes, perhaps underpinned by a common developmental genetic mechanism.</span></p>
Supplementary Data: OpenCOVID model output underlaying Figures 1 and 2 of "Modelling the impact of Omicron and emerging variants on SARS-CoV-2 transmission and public health burden"
<p>Supplementary data files <strong>Figure_1.xlsx</strong> and <strong>Figure_2.xlsx</strong> contain the model simulation outcomes for Figures 1 and 2 of <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock <em>et al</em></a> "<strong>Modelling the impact of Omicron and emerging variants on SARS-CoV-2 transmission and public health burden</strong>" (2022)</p> <ul> <li><strong>Figure 1</strong>: Peak daily hospital occupancy (number of beds per 100,000 population over the six-month simulation period) for three variant properties; infectivity (relative to Delta), immune evading capacity (%), and severity (relative to Delta)<br> </li> <li><strong>Figure 2</strong>: Percentage of COVID-19 infections and deaths averted by third-dose vaccines for adults and vaccinating 5-11-year-olds with doses one and two.<br> </li> <li>Open access source-codes of the associated plotting functions are published <a href="http://zenodo.org/record/6532404#.Yqw7cezMKdb">here</a> on Zenodo.<br> </li> <li>Open access source-codes for the OpenCOVID model of all analyses as presented in <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock <em>et al.</em> (2022)</a> are publicly available at <a href="https://github.com/SwissTPH/OpenCOVID/tree/manuscript_december_2021/src">https://github.com/SwissTPH/OpenCOVID/tree/manuscript_december_2021/src</a>.<br> </li> <li>Detailed model descriptions and model equations of individual-based transmission model <strong>OpenCOVID</strong> are described in <a href="https://pubmed.ncbi.nlm.nih.gov/34923396/">Shattock <em>et al</em>. (2022)</a> and <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock <em>et al.</em> (2022).</a></li> </ul>
Spanish melon landraces: revealing useful diversity by genomic, morphologic, and metabolomic analysis. Supplementary data.
<p>Original data linked to the publication Spanish melon landraces: revealing useful diversity by genomic, morphologic, and metabolomic analysis. It includes, Supp. Table 1: Genomic data; Supp. Table 2: Characterization data; Supp. Table 3: sugar and acids data; Supp Table Supp. Table 4: Voaltile organic compounds data; Supp Table 5: Germplasm details; Supp. table 6: Cromatographic parameters</p>
In-network data collection and data processing - Supplementary materials for deliverable D5.1 - EU-H2020 FET project 'Watchplant'
<p>Supplementary material for D5.1 - Watchplant. Contains collected dataset from plant experiments with blue and red light stimuli, classification results based on statistical methods, and plots of the recorded electropotentials.</p>
Supplementary data to: Climate change threatens terrestrial water storage over the Tibetan Plateau
<p>This data archive includes the boundary of the Tibetan Plateau (TP) and river basins, and projected changes in terrestrial water storage by the mid-21<sup>st</sup> century (up to 2060). The boundary of the TP and river basins is in the shapefile (.shp) format, and all other processed data are in the geotiff (.tif) format. Please see Readme for more data information. For calculation details please see the publication.</p>
Mehrabi et al. 2022. Research priorities for global food security under extreme events. Supplementary data and code.
<p>Data and script for reproducing the final results shown in Mehrabi et al., Research priorities for global food security under extreme events, One Earth (2022), https://doi.org/10.1016/j.oneear.2022.06.008.</p> <p>Simply download and read the Mehrabi2022_EEGFS.pdf or the Mehrabi2022_EEGFS.Rmd file from which it was created.</p>
Supplementary data and files to Figure 8 of Eva Rosenstock, Linear Pottery and Harris (2022)
<p>Supplements to the printed version of Figure 8 in: Eva Rosenstock, Bandkeramik and Harris: a case study in the spatio-temporal logic of archaeological sites. In: Elke Kaiser - Michael Meyer - Silviane Scharl - Stefan Suhrbier (Hrsg.), Wissensschichten. Festschrift für Wolfram Schier zu seinem 65. Geburtstag. Studia Honoraria (Rahden/Westf. 2022) 179-198.</p> <p>HMC matrix based on an alternative model for Ulm-Eggingen along stratigraphic observations and spatial relationships between postholes, fences, buildings, and corresponding longitudinal pits, based on the data published in C.-J. Kind, Ulm-Eggingen. Die Ausgrabungen 1982 bis 1985 in der bandkeramischen Siedlung und der mittelalterlichen Wüstung (Stuttgart 1989).</p> <p>Full collapsed and uncollapsed versions in CSV, Graph Markup Language (compatible with e.g. yEd freeware <a href="https://www.yworks.com/products/yed">https://www.yworks.com/products/yed</a>) and HMCX (compatible with Harris Matrix Composer <a href="https://harrismatrixcomposer.com/#/">https://harrismatrixcomposer.com/#/</a>), and jpeg.</p>
Supplementary data and files to Figure 7 of Eva Rosenstock, Linear Pottery and Harris (2022)
<p>Supplements to the printed version of Figure 7 in: Eva Rosenstock, Bandkeramik and Harris: a case study in the spatio-temporal logic of archaeological sites. In: Elke Kaiser - Michael Meyer - Silviane Scharl - Stefan Suhrbier (Hrsg.), Wissensschichten. Festschrift für Wolfram Schier zu seinem 65. Geburtstag. Studia Honoraria (Rahden/Westf. 2022) 179-198.</p> <p>HMC matrix rendering of the spatio-temporal model for Ulm-Eggingen according to the yard model analysis by C.-J. Kind, Ulm-Eggingen. Die Ausgrabungen 1982 bis 1985 in der bandkeramischen Siedlung und der mittelalterlichen Wüstung (Stuttgart 1989).</p> <p>Full collapsed and uncollapsed versions in CSV, Graph Markup Language (compatible with e.g. yEd freeware <a href="https://www.yworks.com/products/yed">https://www.yworks.com/products/yed</a>) and HMCX (compatible with Harris Matrix Composer <a href="https://harrismatrixcomposer.com/#/">https://harrismatrixcomposer.com/#/</a>), and jpeg.</p>
Supplementary data for "How adequately are elevated moist layers represented in reanalysis and satellite observations?"
<p>The NetCDF files are the collocation datasets over Manus Island between GRUAN radiosondes, ERA5, the CLIMCAPS Aqua Level 2 retrieval dataset and the IASI L2 Climate Data Record (CDR). The collocation criteria are 30 minutes and 50 km. Additional filter criteria for the individual datasets and processing steps are described in the manuscript.</p> <p>The datasets are created using the collocation toolkit included in the python package "typhon". Variables in each dataset are split into two groups that represent the two collocated datasets. Each group contains a selection of the original dataset's variables, which are used in the manuscript such as H2O VMR, temperature, cloud fraction, etc. The variables are organized along the dimension "collocation" and along dataset and variable specific additional dimensions. Further documentation about the collocation toolkit and the structure of the resulting datasets can be found at https://github.com/atmtools/typhon.</p> <p> </p> <p> </p>
Supplementary data: Medicago transcriptomics DRMN analysis
<p>Summary of DRMN per-gene module assignments, module motif enrichments, inferred network edge weights, and MTG-LASSO predictions, Supplementary data tables 1-4 of this submission, respectively. </p>
ScienceDex guides
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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.