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3,197 results for “Atlases”
A Nu Supersymmetric Anomaly-free Atlas: anomaly-free, flavour-dependent U(1) charge assignments for the Minimally Supersymmetric Standard Model plus three Standard Model-singlet superfields
<p>We present lists of anomaly-free charge assignments up to a maximum magnitude charge Qmax=10 for the chiral fermionic content of the MSSM plus 3 right-handed neutrinos. </p> <p>Due to the large number of solutions, we compress the list into the file MSSMnuRcharges_Qmax10.gz. Please note that the unzipped file is approximately 130GB in size. We additionally include a smaller file, MSSMnuRcharges_Qmax4, containing the subset of anomaly-free charge assignments up to a maximum magnitude charge Qmax=4.</p> <p>The files searchU1MSSM.cpp and searchU1MSSM.h contain C++ files (in the 2014 standard) to produce the solutions. runsearch.sh is a bash script that compiles the programs and then runs it for a sample set of inputs.</p> <p>We provide Mathematica notebooks Analytic_solution_generator.nb and Analytic_Checks.nb which respectively provide the parametrisation of the analytic solution and checks thereof.</p> <p>The files beginning 'filter' contain example programs that read in each line in the solution list, apply a filter and print only the solutions satisfying the conditions of that filter. runfilter.sh is a bash script that compiles the filters and then runs a single filter as an example.</p> <p>These data and programs are based on this paper: https://arxiv.org/abs/2107.07926.</p>
CLDF dataset derived from Carling's "Diachronic Atlas of Comparative Linguistics" from 2017
<p>Cite the source of the dataset as:</p> <blockquote> <p>Carling, Gerd (ed.) 2017. Diachronic Atlas of Comparative Linguistics Online. Lund: Lund University. (DOI/URL: https://diacl.ht.lu.se/). Accessed on: 2019-02-07.</p> </blockquote>
A Spatio-temporal Atlas of the Developing Fetal Brain with Spina Bifida Aperta
<p>This version contains two zipped folders.</p> <ol> <li><a href="https://zenodo.org/api/files/c84cc018-9d2d-4adb-9558-9ab96649c922/spina_bifida_atlas.zip">spina_bifida_atlas.zip</a> contains a copy of our spina bifida aperta fetal brain atlas.<br> This folder is available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication), as indicated in the LICENSE file in this folder.<br> This is the same version of the atlas as the one available on synapse (<a href="https://www.synapse.org/#!Synapse:syn25887675/wiki/611424">https://www.synapse.org/#!Synapse:syn25887675/wiki/611424</a>, DOI: 10.7303/syn25887675).</li> <li><a href="https://zenodo.org/api/files/c84cc018-9d2d-4adb-9558-9ab96649c922/LucasFidon/spina-bifida-MRI-atlas-0.1.0.zip?versionId=31751dfa-7b17-4c84-89d5-2769d648eed8">LucasFidon/spina-bifida-MRI-atlas-0.1.0.zip</a> is a copy of the code that was used to compute the fetal brain atlas for spina bifida aperta in this repository.<br> This folder is available under BSD-3-Clause license, archived from GitHub, as indicated in the LICENSE file in this folder.</li> </ol> <p><strong>How to cite:</strong><br> If you find the data in this folder useful for your research please cite:</p> <p>L. Fidon, E. Viola, N. Mufti, A. L. David, A. Melbourne, P. Demaerel, S. Ourselin, T. Vercauteren, J. Deprest, M. Aertsen. A Spatio-temporal Atlas of the Developing Fetal Brain with Spina Bifida Aperta, 2021.</p>
Ancillary files for "Reinterpreting the ATLAS bounds on heavy neutral leptons in a realistic neutrino oscillation model [arXiv: 2107.12980]"
<p><em>(Description copied from Appendix A "Ancillary files" of the companion paper)</em></p> <p>In order to simplify the interpretation of experimental results within realistic HNL models, we are including a number of data files along with the present publication. They can be used to generate the relevant signal samples, or to implement the extrapolation method presented in section 3.2.</p> <p><strong>Card files for the Monte-Carlo event generation</strong></p> <p>The /attachments/card_files folder contains the MadGraph card files (ending in .dat) and scripts (ending in .txt) for generating the signal samples used in this analysis, as well as for computing the total HNL width. Due to the OSSF veto, only processes with no opposite-charge same-flavor lepton pairs have been included. Additional relevant processes can easily be added by modifying the <em>generate</em> and <em>add process</em> lines in the *.txt files. All samples (except the ones used to compute the HNL width, which are generated at parton level) are generated at leading order, include up to two hard jets, and are showered and hadronized using Pythia 8. This is essential for obtaining a realistic W spectrum. The shower parameters could probably benefit from further tuning, and further improvements in the W spectrum accuracy are expected at NLO (using a suitable model). To allow computing the signal efficiencies, all cuts have been disabled in the run card (with the exception of the maximum <span class="math-tex">\(|\eta_{\mathrm{jet}}|\)</span> which needs to be set to 5 for correct matching).</p> <p><strong>Signal cross sections</strong></p> <p>The cross sections for the various processes considered in this analysis, as well as the total HNL width (both computed using MadGraph as described in section 3.2), are provided as JSON files in the /attachments/cross_sections folder.</p> <p>The file total_hnl_width.json contains the total HNL width <span class="math-tex">\(\hat{\Gamma}_{\alpha}(M_N)\)</span> (expressed in GeV), computed for the 5 mass points used in this analysis, and under the assumption of unit mixing with a single flavor <span class="math-tex">\(\alpha\)</span>, for each flavor. The total HNL width can then be computed for any combinations of mixing angles using eq. (3.2). The file is organized as two nested dictionaries, with the first key denoting the HNL mass <span class="math-tex">\(M_N\)</span>, and the second one the flavor <span class="math-tex">\(\alpha\)</span> for which the total width <span class="math-tex">\(\hat{\Gamma}_{\alpha}(M_N)\)</span> has been computed for a unit mixing angle <span class="math-tex">\(|\Theta_{\alpha}|^2 = 1\)</span> (with <em>Wtot_e</em> for <span class="math-tex">\(\alpha=e\)</span>, <em>Wtot_mu</em> for <span class="math-tex">\(\mu\)</span> and <em>Wtot_tau</em> for <span class="math-tex">\(\tau\)</span>).</p> <p>The file cross_sections.json contains the reference cross sections <span class="math-tex">\(\sigma_P^{\mathrm{ref}}\)</span> (in pb) for all the processes <em>P</em> considered in this analysis, expressed for <span class="math-tex">\(|\Theta|_{\mathrm{ref}}^2 = 1\)</span> and <span class="math-tex">\(\Gamma_{\mathrm{ref}} = 10^{-5}\,\mathrm{GeV}\)</span>. The file is organized as two nested dictionaries, with the first key denoting the HNL mass <span class="math-tex">\(M_N \)</span> and the second the process <em>P</em>. The correspondence between the key and the physical process can be found in table 7.</p> <p><strong>Signal efficiencies</strong></p> <p>The efficiencies resulting from the event selection described in section 3.1, as well as their parametrization according to eq. (3.6) (as discussed in section 3.3) can respectively be found in the files efficiencies.json and fitted_efficiencies.json in the /attachments/efficiencies folder.</p> <p>The file efficiencies.json is organized as follows. The data is located in a triply nested dictionary under the data key: the first level corresponds to the HNL mass hypothesis <span class="math-tex">\(M_N\)</span>, the second to the process key (cf. table 7) and the third to the <span class="math-tex">\(M(l_{\mathrm{sublead}},l')\)</span> bin for which the efficiency is computed. The values of the bottom-most dictionary are lists containing the efficiencies for a number of HNL lifetimes, as listed in meters in levels/lifetime.</p> <p>Finally, the file fitted_efficiencies.json is also organized as a triply nested dictionary, with the first level corresponding to the HNL mass <span class="math-tex">\(M_N\)</span>, the second to the process key, and where the third level denotes the fit parameter from eq. (3.6). tau0 is for <span class="math-tex">\(\tau_0\)</span>, epsilon0_total for <span class="math-tex">\(\epsilon_0\)</span> (the unbinned prompt efficiency), and epsilon0_binned is a list containing the prompt efficiencies <span class="math-tex">\(\epsilon_{0,b}\)</span> for the five <span class="math-tex">\(M(l_{\mathrm{sublead}},l')\)</span> bins <em>b</em> (in the same order as in efficiencies.json). The layout described here (or a similar one) can be used by experiments to report their signal efficiencies in a way that allows theorists to compute the expected signal for arbitrary choices of mixing angles.</p>
openSAHE: Open Source Statistical Anatomical Atlas of the Human head for Electrophysiology Applications (precomputed atlases)
<p>Computed anatomical atlases of the human head at 100Hz, 1kHz 10kHz 100kHz and 1MHz. Electrical properties: resistivity, conductivity and relative permittivity in SI units. This dataset is part of the article 'Anatomical atlas of the upper part of the human head for electroencephalography and bioimpedance applications' by Moura, F, Beraldo R, Ferreira, L and Siltanen S, Physiological Measurement, Volume 42, Number 10, 2021. If you use any of these files, please add a reference to this <a href="https://iopscience.iop.org/article/10.1088/1361-6579/ac3218">article</a>.</p> <p>Source code available at https://github.com/fsmMLK/openSAHE</p>
Quail (Coturnix japonica) brain MRI template and whole-brain atlas
<p>A population average MRI brain template computed from 20 male Japanese Quails and a manually segmented atlas containing 194 regions. </p> <p>In this Version 2:</p> <ul> <li>the nomenclature in the file <em>siwiaszczyk_LUT-ITK-SNAP_v2.txt</em> was updated</li> <li>one slice of one region was completed in the file <em>siwiaszczyk_atlas_v2.nii.gz.</em></li> </ul>
ESM Atlas v0 representative random sample of predicted protein structures
<p>A representative random sample of the ESM Atlas v0 dataset introduced in "Evolutionary-scale prediction of atomic level protein structure with a language model.".<br> All predictions can be accessed in the ESM Metagenomic Atlas (<a href="https://esmatlas.com/">https://esmatlas.com</a>) open science resource, released on 2022-11-01.<br> Sample size: 997,405.</p>
ESM Atlas v0 random sample of high confidence predicted protein structures
<p>A random sample out of the 225M high confidence predictions in the ESM Atlas v0 dataset introduced in "Evolutionary-scale prediction of atomic level protein structure with a language model.".<br> All predictions can be accessed in the ESM Metagenomic Atlas (<a href="https://esmatlas.com/">https://esmatlas.com</a>) open science resource, released on 2022-11-01.<br> High confidence is defined as mean pLDDT > 0.7 and pTM > 0.7 and corresponds to ∼36% of the total 617M proteins folded.<br> This is the random sample used for analysis in the paper as well as visualization on the <a href="http://esmatlas.com/">esmatlas.com</a> Explore page.<br> Sample size: 999,520 based on 999,996 unique randomly sampled IDs and 0.05% missing data in the processing pipeline.</p>
Reconstruction of prokaryotic genomes from ten termite gut metagenomes using two distinct workflows: SnakeMAGs and ATLAS.
<p><strong><em>SnakeMAGs</em></strong> (Nachida Tadrent, Franck Dedeine, Vincent Hervé (Submitted). <em>SnakeMAGs</em>: a simple, efficient, flexible and scalable workflow to reconstruct prokaryotic genomes from metagenomes<em>.</em> <a href="https://doi.org/10.5281/zenodo.7303463">https://doi.org/10.5281/zenodo.7303463</a>; https://github.com/Nachida08/SnakeMAGs) is a workflow for building MAGs (Metagenome Assembled Genomes) from raw Illumina metagenomic reads. During the test phase of the development of this tool, a comparative analysis with another workflow called ATLAS v2.9.1 (<em>Kieser </em>et al, 2020) was performed. To compare these two workflows, we analyzed ten publicly available termite gut metagenomes (accession numbers: SRR10402454; SRR14739927; SRR8296321; SRR8296327; SRR8296329; SRR8296337; SRR8296343; DRR097505; SRR7466794; SRR7466795) from five different studies : Waidele et al, 2019; Tokuda et al, 2018; Romero Victorica et al, 2020; Moreira et al, 2021; and Calusinska et al, 2020.</p> <p>In this repository, we provide the configuration files that were used to launch each of the workflows (SnakeMAGs_config.yaml and ATLAS_config.yaml), as well as the obtained results, <em>i.e. </em>the MAGs reconstructed from each metagenome and their taxonomic classification.</p>
Open Soil Atlas (OSA) - Raw data from Phase I (Mar-Aug 2021, Berlin)
<p>The Open Soil Atlas citizen science project collected 77 data observations, cathegorized in a set of 10 inficators, which offer information about the quality and fertility of the soil. Area of investigation: Berlin. Data collection period: March-August 2021.</p>
Single-cell atlases of two lophotrochozoan larvae highlight their complex evolutionary histories
<p>This archive contains all the code and data to reproduce the results of the associated manuscript: Piovani <em>et al</em>, "Single-cell atlases of two lophotrochozoan larvae highlight their complex evolutionary histories". We provide filtered scRNA-seq matrices, protein fasta files for each specie used to run SAMap and GenERA as well as the R-code used to generate the datasets and the jupyter notebook to generate SAMap results. In addition we provide the final Seurat objects and all analysis results which can be consulted without re-running the code.</p>
The First Transcriptomic Atlas of the Adult Lacrimal Gland Reveals Epithelial Complexity and Identifies Novel Progenitor Cells in Mice
<p>This project contains the R objects and code to reproduce the analyses and figures presented in the research article:</p> <p>'The First Transcriptomic Atlas of the Adult Lacrimal Gland Reveals Epithelial Complexity and Identifies Novel Progenitor Cells in Mice.' <em>Cells</em> <strong>2023</strong>, <em>12</em>, 1435. https://doi.org/10.3390/cells12101435</p> <p>Raw data (FASTQ files and CellRanger output files used for the preprocessing of individual datasets) can be found on Gene Expression Omnibus database (www.ncbi.nlm.nih.gov/geo/) under accession # GSE232146.</p>
Numerical Atlas Database
<p>Database containing numerical astrophysical measurements extracted from arXiv publications, as described in Crossland et al. 2021. File is an SQLite3 database containing extracted data and article abstract texts.</p>
High-Resolution Heterogeneous Digital PET [18F]FDG Brain Phantom based on the BigBrain Atlas
<p>We present the design of a digital phantom that tries to overcome the problems of the current PET digital brain phantoms, particularly for the simulation of simultaneous PET-MRI data sets. We propose a new brain digital brain phantom based on the BigBrain atlas, a free, publicly available tool that provides considerable neuroanatomical insight into the human brain with an ultrahigh-resolution 3D model of a human brain at nearly cellular resolution of 20 micrometers. We used the histology maps, the classified tissue maps and the MRI image of the BigBrain atlas, as well as the Hammersmith atlas and a PET [18F]FDG template as inputs to create an instance of this ultra high-resolution heterogeneous PET-MRI phantom.</p> <p>Full details of this phantom in Medical Physics: "Technical Note: Ultra high‐resolution radiotracer‐specific digital pet brain phantoms based on the BigBrain atlas", <a href="https://doi.org/10.1002/mp.14218">10.1002/mp.14218.</a></p> <p>You can find codes examples for reading the data at https://github.com/mabelzunce/PETBrainPhantoms </p> <p>Please cite this paper if you use this phantom in your work:</p> <p>Belzunce, M.A. and Reader, A.J. (2020), Technical Note: Ultra high‐resolution radiotracer‐specific digital pet brain phantoms based on the BigBrain atlas. Med. Phys., 47: 3356-3362. doi:<a href="https://doi.org/10.1002/mp.14218">10.1002/mp.14218</a></p>
ATLAS Backward Trajectory Dataset for the Palau Atmospheric Observatory Balloon-borne ozonesonde record 2016-2019
<p>The ATLAS Backward Trajectory Dataset for the Palau Atmospheric Observatory (PAO) Balloon-borne ozonesonde record V1.0 provides backward trajectory data in NetCDF format calculated by the transport module of the Lagrangian Chemistry and Transport Model ATLAS (Wohltmann and Rex, 2009; Wohltmann et al., 2010) for coinciding Electrochemical Concentration Cell (ECC) ozonesonde measurements from the PAO located in Koror, Palau (7.3420° N, 134.4722° E), in the Tropical West Pacific (TWP) from 2016- October 2019 (Müller 2020, Müller et al. 2023). 30-days backward trajectories with a time step of 10 minutes were initialized at the time of an ozonesonde measurement at the PAO for every tenth ozonesonde reading within a profile and for a total number of 138 soundings (= days) and between 0 and 20 km altitude.<br> The model was driven by 3D wind fields, temperatures and diabatic heating rates from the ECMWF ERA5 reanalysis dataset (1.125° x 1.125°) with a 3 hour temporal resolution (compare Hoffmann et al., 2019). The model uses a hybrid vertical coordinate (zeta) which gradually transforms from "pressure" at the surface to "potential temperature" in the stratosphere (see Wohltmann and Rex, 2009). The corresponding vertical velocities change from vertical winds in pressure coordinates to diabatic heating rates.</p> <p> </p> <p>The Palau ECC record is currently being continued, will be available in the SHADOZ database in SHADOZ standard format in the near future, and can for now be found here: https://zenodo.org/record/6920648.</p> <p><strong>Please email katrin.mueller@awi.de and let us know what your intended purpose for the use of the data is. You will then receive updates if an improved version becomes available.</strong></p>
Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts. Soil and airborne mineral fraction datasets.
<p>These datasets correspond to soil and airbone mass mineral fractions as described and generated for "Modeling dust mineralogical composition: sensitivity to soil mineralogy" by Gonçalves Ageitos, M., Obiso, V., Miller, R.L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, M.M., Green, R.O., Thompson, D.R. and Pérez García-Pando, C., ACP, 2023. </p> <p>There are 4 netCDF files that include the soil mass mineralogical fractions (0-1) in the clay (0-2 <span class="math-tex">\(\mu\)</span>m in diameter) and silt (2-63 <span class="math-tex">\(\mu\)</span>m in diameter) size classes as derived from the works of Claquin et al., (1999), and updated by Nickovic et al. (2012): <strong>C1999-SMA</strong>, and Journet et al. (2014): <strong>J2014-SMA</strong>. The data is mapped in a regular global grid with a horizontal resolution of 0.083º. Additional information on the FAO soil units, and soil texture data from HWSDv1.2 is provided in the J2014-SMA files. </p> <p>File details: </p> <ul> <li>C1999-SMA_CLAY_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>C1999-SMA_SILT_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_CLAY_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_SILT_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> </ul> <p>There are 2 additional files that report the multiannual (2006-2010 period) monthly mean of the <strong>aerosol mass mineral fractions</strong> as obtained from the <strong>MONARCH model</strong> simulations described in Gonçalves Ageitos et al. (2023). The mass fractions are provided in each of the 8 size bins used in the model (ranging from 0.2 to 20 <span class="math-tex">\(\mu\)</span>m in diameter), and normalized so as to sum 1 (i.e., the sum of all minerals in all bins equals 1). Note that in order to reduce the size of these files, the variables have been compressed to short format and include an offset and scale factor as attributes. </p> <p>File details: </p> <ul> <li>20062010_monarch_minfrac_C1999.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH C1999 experiment. </li> <li>20062010_monarch_minfrac_J2014.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH J2014 experiment. </li> </ul> <p> </p> <p><em>Legend for the minerals:</em></p> <p>quar: quartz, feld: feldspars, calc: calcite, gyps: gypsum, illi: illite, mont: montmorillonite/smectite, kaol: kaolinite, verm:vermiculite, chlo: chlorite, mica: mica, hema: hematite, goet: goethite, irox:iron oxides (hematite and goethite). </p> <p>References:</p> <p>Claquin, T., Schulz, M., and Balkanski, Y. J.: Modeling the mineralogy of atmospheric dust sources, Journal of Geophysical Research<br> Atmospheres, https://doi.org/10.1029/1999JD900416, 1999.</p> <p>FAO-UNESCO: Soil Map of the World- Volume I Legend, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Paris, http://www.fao.org/3/as360e/as360e.pdf, 1974.</p> <p>FAO-UNESCO: Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization. Digital Soil Map of the World and Derived Soil Properties, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Rome, 1995.</p> <p>FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.2), Food and Agriculture Organization, FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012.</p> <p>Journet, E., Balkanski, Y., and Harrison, S. P.: A new data set of soil mineralogy for dust-cycle modeling, Atmospheric Chemistry and<br> Physics, 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, 2014.</p> <p>Nickovic, S., Vukovic, A., Vujadinovic, M., Djurdjevic, V., and Pejanovic, G.: Technical Note: High-resolution mineralogical database of dust-productive soils for atmospheric dust modeling, Atmospheric Chemistry and Physics, 12, 845–855, https://doi.org/10.5194/acp-12-845-2012, 2012.</p> <p> </p>
ATLAS Database — Videos
<p>We need affect-based stimuli specifically conceived to investigate architectural spaces. The RESONANCES project crafted ATLAS, a dATabase of visuaL Atmospheric Stimuli. It collects a series of spatial patterns born from a systematic selection of generators of atmosphere. Generators of atmosphere are architectural features designed to afford atmospheric effects (such as lights, colors, materials, and proportions). ATLAS is an open-access tool that supports researchers interested in studying emotional reactions to architectural features by providing reliable, standardized, and reproducible stimuli. In this dataset, ATLAS stimuli are presented as videos.</p>
CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.
<p><strong>CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.</strong></p> <p>This is a shoreline atlas of California at 30m resolution, to support analysis of CoastSat/CoastSeg-derived shoreline time-series and other shoreline data, and miscellaneous analyses of coastal shoreline data. The dataset consists of a GeoJSON files containing a 30-m shoreline estimate for California, based on an analysis of 2014 Landsat imagery (Sayre et al., 2019). This shoreline vector has been attributed with the following fields that may be useful in analyses of shoreline patterns and regional variability:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT (m)</li> <li>TIDAL_RANGE (m)</li> <li>CHLOROPHYLL (mg/L)</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE (descriptive)</li> <li>EMU_PHYSICAL (descriptive)</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE (%)</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY (descriptive)</li> <li>LENGTH_GEO</li> <li>ch_label (descriptive)</li> <li>river_label (descriptive)</li> <li>sinuosity_label (descriptive)</li> <li>slope_label (descriptive)</li> <li>tidal_label (descriptive)</li> <li>turbid_label (descriptive)</li> <li>wave_label (descriptive)</li> <li>CSU_Descriptor (descriptive)</li> <li>CSU_ID</li> <li>elevation (m)</li> <li>aspect (degrees N)</li> <li>slope (degrees)</li> </ol> <p>Fields 1 to 21 inclusive originally come from raw data https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk, which is described in Sayre et al (2019)</p> <p>Fields 22 and 24 come from raw data originally in the U.S. Geological Survey Elevation Derivatives for National Applications (EDNA) database (https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-elevation-derivatives-national), accessed through Earth Explorer and processed in QGIS.</p> <p>The figure shows distributions of selected quantities. A python script to reproduce this plot is provided</p> <p>A subset of numeric-only variables and descriptive-only variables has also been prepared and made available. A CSV version of the full dataset is also provided</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></li> <li><a href="https://doi.org/10.5066/F7TD9VTQ">Elevation Derivatives for National Applications (EDNA) Seamless Three-Dimensional Hydrologic Database Digital Object Identifier (DOI) number: /10.5066/F7TD9VTQ</a></li> </ol> <p> </p>
A Complement Atlas identifies interleukin 6 dependent alternative pathway dysregulation as a key druggable feature of COVID-19.
<p>Improvements in COVID-19 treatments, especially for the critically ill, require deeper understanding of the mechanisms driving disease pathology. The complement system is a crucial component of innate host defense, but can also contribute to tissue injury. Although all complement pathways have been implicated in COVID-19 pathogenesis, the upstream drivers and downstream effects on tissue injury remain poorly defined. We demonstrate that complement activation is primarily mediated by the alternative pathway, and we provide a comprehensive atlas of the complement alterations around the time of respiratory deterioration. Proteomic and single-cell sequencing mapping across cell types and tissues reveals a division of labor between lung epithelial, stromal, and myeloid cells in complement production, in addition to liver-derived factors. We identify IL-6 and STAT1/3 signaling as an upstream driver of complement responses, linking complement dysregulation to approved COVID-19 therapies. Furthermore, an exploratory proteomic study indicates that inhibition of complement C5 decreases epithelial damage and markers of disease severity. Collectively, these results support complement dysregulation as a key druggable feature of COVID-19.</p>
Data and figures for "Atlas of Science Collaboration, 1971–2020"
<p><strong>Abstract</strong></p><p>The evolving landscape of interinstitutional collaborative research across 15 natural science disciplines is explored using the open data sourced from OpenAlex. This extensive exploration spans the years from 1971 to 2020, facilitating a thorough investigation of leading scientific output producers and their collaborative relationships based on coauthorships. The findings are visually presented on world maps and other diagrams, offering a clear and insightful portrayal of notable variations in both national and international collaboration patterns across various fields and time periods. These visual representations serve as valuable resources for science policymakers, diplomats and institutional researchers, providing them with a comprehensive overview of global collaboration and aiding their intuitive grasp of the evolving nature of these partnerships over time.</p><p> </p><p><strong>Intended Readership</strong></p><ul><li>The booklet, entitled<i> '</i><a href="https://arxiv.org/abs/2308.16810"><i>Atlas of Science Collaboration</i></a><i>'</i>, aims to offer a broad overview of international and interinstitutional research collaboration, shedding light on its present status and evolution on a global scale. While it might not delve into intricate scholarly or academic data analysis, it remains a valuable resource for those seeking a general understanding of the collaborative relationships that have been established between research institutions in the world of science.</li><li>The intended readership including science and technology (S&T) policymakers and diplomats, government research and development (R&D) agencies, international organisations, S&T think tanks, as well as institutional research divisions of universities or R&D institutions.</li></ul><p> </p><p><strong>Data Source</strong></p><ul><li>The<i> </i><a href="https://arxiv.org/abs/2308.16810"><i>Atlas of Science Collaboration</i></a><i> </i>is based on data retrieved from <a href="https://docs.openalex.org/">OpenAlex</a>, a free and open (the CC0 license) catalogue of the world's scholarly papers, researchers, journals and institutions. Launched in January 2022, OpenAlex replaced <a href="https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/">Microsoft Academic Graph (MAG)</a>, which retired at the beginning of 2022.</li><li>OpenAlex collects information on scientific publications, including journal articles, non-journal articles, preprints, conference papers, books and datasets—hereafter collectively referred to as 'works'—from various platforms such as <a href="https://www.crossref.org/">Crossref</a>, <a href="https://orcid.org/">ORCID</a>, <a href="https://ror.org/">ROR</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/">PubMed</a>, preprint servers like <a href="https://arxiv.org/">arXiv</a>, and institutional or disciplinary repositories like <a href="https://zenodo.org/">Zenodo</a>. For comparison with other scholarly data sources such as <a href="https://www.scopus.com/">Scopus</a>, <a href="https://clarivate.com/products/scientific-and-academic-research/research-discovery-and-workflow-solutions/webofscience-platform/">Web of Science</a> and <a href="https://www.dimensions.ai/">Dimensions</a>, please refer to <a href="https://openalex.org/about#comparison">OpenAlex's website</a>.</li><li>OpenAlex offers extensive coverage of meta-information across a diverse spectrum of works, encompassing not only journal publications but also non-journal works, non-English works and contributions from the Global South. This attribute proves beneficial by providing a more precise augmentation of the extent of R&D activities, along with their associated scholarly outputs. This is especially crucial in fields where journals are not the predominant channel for disseminating research outcomes. Furthermore, OpenAlex effectively captures outputs in the preprint format, which might persist for varying durations, spanning from months to years or even indefinitely, without necessarily transitioning into journal publications.</li><li>The present edition (August 2023) of the<i> </i><a href="https://arxiv.org/abs/2308.16810"><i>Atlas of Science Collaboration</i></a><i> </i>was compiled using data obtained via the <a href="https://docs.openalex.org/how-to-use-the-api/api-overview">OpenAlex API</a> during the period from the 12th to the 15th of August 2023. It is essential to note that OpenAlex is an ongoing project, continuously updating its data and improving its system. Consequently, the visualisations in this booklet may not provide the most comprehensive view or accurate data. Expect more accurate results when acquiring data in the future as OpenAlex undergoes further upgrades. Revised editions of the<i> Atlas of Science Collaboration </i>may be made available on <a href="https://zenodo.org/">Zenodo</a> or other open platforms beyond this release.</li></ul><p> </p><p><strong>R&D Disciplines</strong></p><ul><li>In this current edition, the primary focus centres around the level-1 'concepts' listed in the following table sourced from the OpenAlex classification, as previously explored in <a href="https://doi.org/10.48550/arXiv.2211.04429">Okamura (2023)</a>. Each level-1 concept is accompanied by 'related concepts', which can offer a finer or broader delineation compared to the level-1 concept. Using this characteristic, an enhanced notion of R&D discipline is constructed by including all associated subconcepts of level 2 or higher for each of the 15 level-1 concepts. For instance, our defined discipline of 'Artificial Intelligence' includes OpenAlex's level-2 concepts of '<a href="https://explore.openalex.org/concepts/C50644808">Artificial Neural Network</a>' and '<a href="https://explore.openalex.org/concepts/C108583219">Deep Learning</a>', but not the level-0 concepts of '<a href="https://explore.openalex.org/concepts/C41008148">Computer Science</a>' or '<a href="https://explore.openalex.org/concepts/C33923547">Mathematics</a>'.</li></ul><p> </p><p> OpenAlex Concept / Identifier / Discipline Code </p><ol><li>Artificial intelligence / <a href="https://explore.openalex.org/concepts/C154945302">C154945302</a> / "ai"</li><li>Quantum mechanics / <a href="https://explore.openalex.org/concepts/C62520636">C62520636</a> / "quantum"</li><li>Biotechnology / <a href="https://explore.openalex.org/concepts/C150903083">C150903083</a> / "bio"</li><li>Nanotechnology / <a href="https://explore.openalex.org/concepts/C171250308">C171250308</a> / "nano"</li><li>Agricultural engineering / <a href="https://explore.openalex.org/concepts/C88463610">C88463610</a> / "agri"</li><li>Particle physics / <a href="https://explore.openalex.org/concepts/C109214941">C109214941</a> / "particle"</li><li>Aerospace engineering / <a href="https://explore.openalex.org/concepts/C146978453">C146978453</a> / "aerospace"</li><li>Nuclear engineering / <a href="https://explore.openalex.org/concepts/C116915560">C116915560</a> / "nuclear"</li><li>Marine engineering / <a href="https://explore.openalex.org/concepts/c199104240">C199104240</a> / "marine"</li><li>Neuroscience / <a href="https://explore.openalex.org/concepts/c169760540">C169760540</a> / "neuro"</li><li>Condensed matter physics / <a href="https://explore.openalex.org/concepts/C26873012">C26873012</a> / "condensed"</li><li>Environmental engineering / <a href="https://explore.openalex.org/concepts/C87717796">C87717796</a> / "envi"</li><li>Earth science / <a href="https://explore.openalex.org/concepts/c1965285">C1965285</a> / "earth"</li><li>Astronomy / <a href="https://explore.openalex.org/concepts/c1276947">C1276947</a> / "astro"</li><li>Pure mathematics / <a href="https://explore.openalex.org/concepts/C202444582">C202444582</a> / "math"</li></ol><p> </p><p><strong>Analysis and Visualisation</strong></p><ul><li>First, the<i> World Map of Science Collaboration</i> ('<strong>wmap_bilat</strong>' folder) divides the period from 1971 to 2020 into four intervals: 1971–1990, 1991–2000, 2001–2010 and 2011–2020. For each period and discipline, bubbles represent the top 199 research institutions in terms of work production. Additionally, for the top 50 research institutions, their locations are connected on the world map using great circle curves (the shortest route between them) to illustrate bilateral coauthorship relationships. Coauthorship relationships with fewer than five coauthored papers are not displayed. The background world map utilises the world data from the <a href="https://cran.r-project.org/package=maps">maps</a> package in R. The connection visualisation between two research institutions leverages the gcIntermediate() function from the <a href="https://cran.r-project.org/package=geosphere">geosphere</a> package in R. The sizes of the bubbles are proportional to the volume of work and can be compared across the different period panels.</li><li>Second, the<i> Top 30 Productive Institutions on the World Map </i>('<strong>wmap_topinst</strong>' folder) displays the leading 30 institutions in terms of work production on the World Map for each discipline and the three respective periods: 1991–2000, 2001–2010 and 2011–2020. The background world map employs the world data from the <a href="https://cran.r-project.org/package=maps">maps</a> package in R along with the <a href="https://cran.r-project.org/package=ggplot2">ggplot2</a> package16 in R. The sizes of the bubbles are proportional to the volume of work, standardised within each period panel, and cannot be compared across panels.</li><li>Third, the<i> Interregional Collaboration Matrix Diagram </i>('<strong>halfmat</strong>' folder) exhibits a half-matrix diagram at the country level for each discipline and the three respective periods: 1991–2000, 2001–2010 and 2011–2020. It counts the number of bilateral coauthorship relationships represented on the World Map. Each bubble's size (area) displayed in the matrix cell is proportional to the number of bilateral coauthorship relationships. This edition particularly focuses on five pivotal parties: the US, China, EU27, the UK and Japan. These parties were specifically selected due to their substantial contributions to work production across all scientific fields from 1971 to 2020. These choices also align with the nations acclaimed as the 'Big 5' science nations (the US, China, Germany, the UK and Japan) in the <a href="https://www.nature.com/articles/d41586-022-00569-7"><i>Nature Index</i></a>. Please note that the Matrix Diagram only takes into account the top 50 institutions in terms of work production for each period and discipline. Therefore, if a cell shows zero (as small dots), it does not necessarily imply the absence of coauthorship relationships for the corresponding bilateral pair.</li><li>Forth, the<i> Interinstitutional Collaboration Dendrogram </i>('<strong>cdend</strong>' folder) elucidates the development and evolution of interinstitutional research collaboration clusters spanning the last five decades. This is accomplished through hierarchical clustering analysis of institutions, considering the top 50 institutions in terms of work production across the four periods: 1971–1990, 1991–2000, 2001–2010 and 2011–2020.<ul><li>The method used for hierarchical clustering analysis is the same as developed in <a href="https://doi.org/10.48550/arXiv.2211.04429">Okamura (2023)</a>. The distance between institutions X and Y is defined as the number of works with nationalities from both X and Y divided by the total number of works with nationalities from at least one of X and Y, subtracted from 1. Hierarchical clustering analysis was performed on the distance matrix using the hclust function implemented in R with the ward.D2 option (i.e. the original Ward's method) specified.</li><li>The method of dendrogram visualisation is primarily derived from an example detailed on the <a href="https://cran.r-project.org/web/packages/dendextend/vignettes/dendextend.html">dendextend website</a>. Circular dendrograms were created using the <a href="https://cran.r-project.org/package=dendextend">dendextend</a> and <a href="https://cran.r-project.org/package=circlize">circlize</a> packages in R. As one moves inward from the outer edge of the circle towards its centre, institutions or clusters of institutions that are in closer proximity to each other merge earlier.</li><li>To indicate the country where the institutions are located, the country names are included at the beginning of the terms of research institutions, using the two-letter <a href="https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2">ISO3166-1alpha-2</a> code. The accompanied circularised bar graphs represent the number of works for the institutions involved. <a href="https://ror.org/">ROR</a>s are used as the canonical identifiers of the research institutions. Readers of this booklet in PDF format can click on the ROR-based URL ('https://ror.org/...') in the diagrams to view the corresponding ROR webpage from their browser.</li></ul></li><li>Additionally, for each discipline and the respective periods of 1971–1990, 1991–2000, 2001–2010 and 2011–2020, the top 100 institutions in terms of work production are displayed in tabular format ('<strong>table</strong>' folder), showing their respective country codes and production volumes. If multiple research institutions have equal production volumes during each period, they are organised alphabetically by country codes and then by organisation names. Even if distinct rankings are shown, they lack significance and are treated as ties.</li></ul><p> </p><p><strong>Important Notes</strong></p><ul><li>It is worth reiterating that the data from OpenAlex used to compile the <a href="https://arxiv.org/abs/2308.16810"><i>Atlas of Science Collaboration</i></a>, even when incorporating bibliometric data related to past works, lacks consistent finality. As of the data acquisition for this version (August 2023), OpenAlex encompassed information regarding approximately 240 million works, with an additional influx of about 50,000 new data entries related to works being added daily. Furthermore, for a substantial portion of these works, information regarding the corresponding institution to which the authors belong remains unknown. As a result, should the same analyses as those embedded within this booklet be replicated in the future, although the qualitative extent of change remains uncertain, it is undeniable that quantitatively distinct data will be acquired. Nonetheless, for individuals seeking an understanding of the global scope and evolution of international and interinstitutional collaborative research, the potential availability of this booklet or an enhanced, continuously updated evidence base holds inherent value.</li><li>Further, it is worth reiterating that the term 'works' encompasses a wide variety of scholarly publications. The analyses conducted in the compilation of this booklet do not take into consideration whether these works are peer-reviewed articles or not, nor do they encompass considerations of their prominence, impact or quality. It is emphasised that the primary intent behind the visualisations in this booklet is to quantitatively capture the momentum of scholarly knowledge production outputs from diverse research institutions, and to identify how productive institutions collaborate internationally and interinstitutionally. Caution must be exercised, with acknowledgment that relying solely on the quantity of scholarly output produced by institutions falls short in encompassing discussions about their research potential, contributions to academia, or their relative superiority or inferiority. Further, it is recommended to consider the limitations discussed in <a href="https://doi.org/10.48550/arXiv.2211.04429">Okamura (2023)</a> when using this booklet.</li></ul><p> </p><p><strong>Miscellaneous</strong></p><ul><li>It is important to note that some research institutions may encounter difficulties in accurately assessing the actual production volume at the institutional level within each analysis period due to challenges related to name disambiguation and the influence of historical organisational changes in bibliometric databases.</li><li>For the Interinstitutional Collaboration Dendrograms and the rankings of the top 100 productive institutions, entities like universities and R&D institutions are primarily identified using the nomenclature employed in OpenAlex. However, certain portions have been presented through abbreviations or acronyms, both for illustrative purposes and to effectively accommodate limited space. For instance, 'University of' is abbreviated as 'U.', 'Institution' and 'Institute' as 'Inst', 'National Laboratory' as 'NL', and 'Science' and 'Technology' as 'Sci' and 'Tech', correspondingly, among others. Should readers possess more fitting suggestions for abbreviations specific to particular organisations, or any other ideas aimed at enhancing the content of this booklet, we would greatly appreciate their input.</li></ul><p> </p>
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