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951 results for “Data release”
Organic and inorganic data for soil cores from Brazil and Florida Bay seagrasses to support Howard et al 2018, CO2 released by carbonate sediment production in some coastal areas may offset the benefits of seagrass “Blue Carbon” storage, Limnology and Oceanography, DOI: 10.1002/lno.10621
Using piston corers, soils from Florida Bay and Brazilian seagrass meadows were collected to complete organic and inorganic carbon inventories for the top 1 m of soil. Instrumental analyses and loss on ignition at 500C were used to measure C content of downcore slices.
DSLWP public data release v1.0
<p>This is a public data release of data captured by the DSLWP amateur radio payload. DSLWP was a Chinese satellite which was launched 21 May 2018, orbited the Moon, and impacted it on 31 July 2019.</p> <ul> <li><code>raw_frame.csv</code>: Raw frames containing telemetry and image data (transmitted using GMSK)</li> <li><code>images/*.ssdv</code>: Collected ssdv frames</li> <li><code>images/*.jpg</code>: Decoded ssdv images</li> <li><code>dslwp-image-database.tsv</code>: Collected metadata for images</li> <li><code>jt4g.csv</code>: JT4G messages</li> </ul> <p>This data is licensed under the CC-BY 4.0 license: Please attribute "DSLWP amateur radio team". When redistributing the raw data, please keep the file <code>CONTRIBUTORS.txt</code></p> <p>This data has been released from https://github.com/tammojan/dslwp-data</p>
COHERENT Collaboration data release from the first detection of coherent elastic neutrino-nucleus scattering on argon
<p>Release of COHERENT collaboration data from the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on argon. This data release corresponds with the results of "Analysis A" published in arXiv:2003.10630[nucl-ex]. The data release enables further studies of CEvNS.</p> <p>Use of the data release is presented in the accompanying pdf document within this submission. Example code is included within the release as part of this submission. The materials here are also available at http://coherent.ornl.gov/data/, which preserves the directory structure used within the accompanying document. Note the use of the example code in this release expects the directory structure written within the accompanying pdf document.</p>
First release of Illinois Workplace Wellness Study data
<p>This is the first release of public use data for the Illinois Workplace Wellness Study. The release includes online survey data as well as administrative data on health insurance claims, employment outcomes, program participation, and health behaviors. These data are also available on GitHub at <a href="https://github.com/reifjulian/illinois-wellness-data">https://github.com/reifjulian/illinois-wellness-data</a>.</p>
PADDDtracker Data Release Version 2.1
<p><strong>ReadMe File for PADDDtracker.org Data Release Version 2.1</strong></p> <p><strong>Prepared by Conservation International, May 2021</strong></p> <p>Thank you for downloading the PADDDtracker.org Data Release Version 2.1. This dataset includes data on Protected Area Downgrading, Downsizing, and Degazettement (PADDD). Most data have been validated by peer-review, with the limited exception of newly added data from the United States and Brazil; see below for further details and links to publications.</p> <p><strong>Definitions for Protected Area Downgrading, Downsizing, and Degazettement (PADDD):</strong></p> <ul> <li><em>Downgrading</em>: A decrease in legal restrictions on the number, magnitude, or extent of human activities within a protected area by a relevant authority</li> <li><em>Downsizing</em>: A decrease in the size of a protected area as a result of excision of land or sea area through a legal boundary change</li> <li><em>Degazettement</em>: A loss of legal status of a protected area under government administration (national, state, provincial or local)</li> </ul> <p> This data release contains data from the following peer-reviewed studies:</p> <ul> <li>M.B. Mascia, S. Pailler, Protected area downgrading, downsizing, and degazettement (PADDD) and its conservation implications. <em>Conservation Letters</em>. <strong>4</strong>, 9–20 (2011). DOI: 10.1111/j.1755-263X.2010.00147.x .</li> <li>M.B. Mascia <em>et al.</em>, Protected area downgrading, downsizing, and degazettement (PADDD) in Africa, Asia, and Latin America and the Caribbean, 1900–2010. <em>Biological Conservation</em>. <strong>169</strong>, 355–361 (2014). DOI: 10.1016/j.biocon.2013.11.021 .</li> <li>J.L. Forrest <em>et al.</em>, Tropical deforestation and carbon emissions from protected area downgrading, downsizing, and degazettement (PADDD). <em>Conservation Letters</em>. <strong>8</strong>, 153–161 (2015). DOI: 10.1111/conl.12144 .</li> <li>S.M. Pack <em>et al.</em>, Protected area downgrading, downsizing, and degazettement (PADDD) in the Amazon. <em>Biological Conservation</em>. <strong>197</strong>, 32–39 (2016). DOI: 10.1016/j.biocon.2016.02.004.</li> <li>R.E. Golden Kroner, R. Krithivasan, M.B. Mascia, Effects of protected area downsizing on habitat fragmentation in Yosemite National Park (USA), 1864 - 2014. <em>Ecology and Society</em>. <strong>21</strong> (2016). DOI: 10.5751/ES-08679-210322.</li> <li>C.N. Cook, R.S. Valkan, M.B. Mascia, M.A. McGeoch, Quantifying the extent of protected-area downgrading, downsizing, and degazettement in Australia. <em>Conservation Biology</em>. <strong>31</strong>, 1039–1052 (2017). DOI: 10.1111/cobi.12904.</li> <li>R.E. Golden Kroner <em>et al.</em>, The uncertain future of protected lands and waters. <em>Science.</em> <strong>31, </strong>364 (6443), 881-886 (2019). DOI: 10.1126/science.aau5525.</li> <li>T. Dorji, S. Linke, F. Sheldon, Half century of protected area dynamism in the country of Gross National Happiness, Bhutan. <em>Conservation Science and Practice</em>. (2019). DOI: 10.1111/csp2.46.</li> <li>A. De Vos, H. Clements, D. Biggs, G.S. Cumming, The dynamics of proclaimed privately protected areas in South Africa over 83 years. <em>Conservation Letters</em>. <strong>12 </strong>(6) (2019). DOI: 10.1111/conl.12644.</li> <li>R. Albrecht, C.N. Cook, O. Andrews, K.E. Roberts, M.F.J. Taylor, M.B. Mascia, R.E. Golden Kroner, Protected area downgrading, downsizing, and degazettement (PADDD) in marine protected areas. <em>Marine Policy</em>. <strong>129</strong> (2021). 104437, ISSN 0308-597X. DOI: 10.1016/j.marpol.2021.104437.</li> </ul> <p>Please contact paddd.team@gmail.com to request full text versions of publications if not otherwise open access.</p> <p>Please note that 353 (7% of) records in the database (new records from the United States and Brazil) have not yet been validated by peer review; see Olsson et al. 2021 for more information about these data:</p> <ul> <li>Olsson, E., Albrecht, R., & Golden Kroner, R.E. (2021). PADDDtracker Data Release Version 2.1: Technical Notes. Conservation International, Arlington, VA. DOI: 10.5281/zenodo.4749615.</li> </ul> <p><strong>Differences between Version 2.1 and previous data releases:</strong></p> <p>PADDDtracker data release Version 2.1 contains 21 new fields:</p> <ul> <li>Sys_Code</li> <li>Map_Details</li> <li>Map_Source</li> <li>Notes</li> <li>Last_Update</li> <li>AddedBy</li> <li>isLatestVe</li> <li>Data_Status</li> <li>Peer_Reviewed</li> <li>Study_Link</li> <li>Off_Type</li> <li>Off_Area</li> <li>Off_Details</li> <li>Off_Source</li> <li>Rev_Type</li> <li>Rev_Area</li> <li>Rev_Details</li> <li>Rev_Source</li> <li>Legal_Type</li> <li>Marine</li> <li>Marine_ZID</li> </ul> <p>Two fields included in the previous data release have been archived; these are both out-of-date ID fields that are no longer necessary to retain.</p> <ul> <li>PADDDIDOld</li> <li>GID_String</li> </ul> <p><strong>Version history</strong></p> <ul> <li>Version 1.0 was released in January 2014, and contains 601 PADDD events from Africa, Asia, Latin America and the Caribbean, spanning from 1900 to 2012.</li> <li>Version 1.1 was released in January 2017, and contains a total of 721 PADDD events, including updated data from version 1.0 as well as additional PADDD events in the Democratic Republic of the Congo, Peru and Brazil, spanning from 1900 to 2016.</li> <li>Version 2.0 was released in May 2019, and contains a total of 4597 PADDD events, including updated PADDD events from previous data releases (1.0 and 1.1), and new records from around the world (Africa, Asia, Australia, Europe, Latin America and the Caribbean, North America).</li> <li>Version 2.1 contains a total of 4962 PADDD events, including updated data from version 2.0 as well as additional PADDD events in Australia, Brazil, Bhutan, Palau, South Africa, and the United States. It also includes several new fields for supporting details data, including for offsets and reversals. Please consult the Field Definitions PDF and/or Olsson et al. 2021 in the data release package for information on these new field names, field name definitions, data values, and clarifications.</li> </ul> <p>In the folder <strong>PADDDtracker_DataReleaseV2_1_2021</strong>, you will find:</p> <p><strong>PADDDtracker_DataReleaseV2_1_2021.xlsx</strong>: this Excel file contains data on all known PADDD events, including numerical and categorical data. This includes data on location, dates, areas, IUCN categories, proximate causes and other descriptive attributes associated with PADDD events. The file contains the following tabs:</p> <ul> <li><strong><em>ReadMe</em></strong>: introduction to dataset.</li> <li><strong><em>FieldDefinitions</em></strong>: contains definitions for all fields (attributes) in the database.</li> <li><strong><em>PADDDEvents</em></strong>: Dataset of known enacted and proposed PADDD events, including attributes describing location, timing, proximate causes, and associated supporting information and sources. The Supporting Details and References are omitted from the shapefile, as the text exceeds the character limit.</li> <li><strong><em>PADDDReversals</em></strong>: information about location, dates, etc. on PADDD events with full or partial reversals for which spatial data are available. The Supporting Details and References are omitted from the shapefile, as the text exceeds the character limit.</li> <li><strong><em>PADDDOffsets</em></strong>: information about location, dates, etc. on PADDD events with known spatial or regulatory offsets for which spatial data are available. The Supporting Details and References are omitted from the shapefile, as the text exceeds the character limit.</li> <li><strong><em>PADDDMPAZones</em></strong>: information about location, dates, zone name(s), and other descriptive information on PADDD events in marine protected areas (MPAs) in cases where MPAs were split into two or more use zones (see FieldDefinitions tab for zone domain values). The Supporting Details and References are omitted from the shapefile, as the text exceeds the character limit.</li> </ul> <p><strong>Primary GIS Datasets:</strong></p> <ul> <li><strong>PADDDtracker_DataReleaseV2_1_2021_Pts.shp: </strong>This shapefile contains point data for all validated PADDD events corresponding with the accompanying Excel sheet.</li> </ul> <p>Please note that for an event for which the exact location is unknown, it is represented by a point placed either at the PA centroid or within the PA extent if a multipart polygon (for downgrades or downsizes), or on the capital city of the country. If using PADDD events data for spatially explicit analyses for which locations of event areas are necessary, please use the field “Location_K” as a filter to remove events with unknown locations.</p> <ul> <li><strong>PADDDtracker_DataReleaseV2_1_2021_Poly.shp: </strong>This shapefile contains validated polygon data for PADDD events corresponding with the accompanying Excel sheet.</li> </ul> <p><strong>Supplemental GIS Datasets:</strong></p> <ul> <li><strong>PADDDtracker_DataReleaseV2_1_2021_Pts_Reversals.shp: </strong>Point shapefile of reversals to PADDD events.</li> <li><strong>PADDDtracker_DataReleaseV2_1_2021_Poly_Reversals.shp: </strong>Polygon shapefile of reversals to PADDD events.</li> <li><strong>PADDDtracker_DataReleaseV2_1_2021_Poly_Offsets.shp: </strong>Polygon shapefile of offsets to PADDD events.</li> <li><strong>PADDDtracker_DataReleaseV2_1_2021_Poly_MPAZones.shp: </strong>Polygon shapefile of zoning changes that constitute PADDD events.</li> </ul> <p><strong>Additional Resources </strong></p> <ul> <li><strong>PADDDtracker Technical Guide V2, 2020: </strong>contains guidance for researchers to collect PADDD data. </li> <li><strong>PADDDtracker Data Release 2.1 Technical Note, 2021: </strong>provides more details on PADDD Reversals, PADDD Offsets, Marine Zoning, and data that have not yet been peer-reviewed. </li> <li><strong>PADDD Annotated Bibliography V1.1, 2021: </strong>contains citations and overviews of peer-reviewed papers that have contributed research or data for PADDD. </li> </ul> <p>If you have any questions or comments about the dataset that are not answered in this ReadMe, PADDDtracker.org, or the Technical Guide,or would like to request earlier versions of the data, please contact <a href="mailto:paddd.team@gmail.com">paddd.team@gmail.com</a>.</p> <p>Please refer to Albrecht et al. 2021, De Vos et al. 2019, Dorji et al. 2019, Golden Kroner et al. 2019, Mascia et al. 2014, Forrest et al. 2015, and Pack et al. 2016, as well as the technical guide to PADDDtracker.org, for more detailed methods and notes. PADDD data from Malaysia, as published in Forrest et al. 2015, are available through request for purposes of replicating the original analyses.</p> <p><strong>How to Attribute:</strong></p> <p>Please use the following attribution when using or referencing this data:</p> <p>Conservation International and World Wildlife Fund. 2021. PADDDtracker.org Data Release Version 2.1 (May 2021). Arlington, VA: Conservation International. Washington, DC: World Wildlife Fund. padddtracker.org. 10.5281/zenodo.4974336</p> <p><strong>Terms of Use:</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License: <a href="https://creativecommons.org/licenses/by-nc-nd/4.0/">https://creativecommons.org/licenses/by-nc-nd/4.0/</a></p>
Data release for "OrchID: a Generalized Framework for Taxonomic Classification of Images Using Evolved Artificial Neural Networks"
<p><strong>Abstract</strong></p> <p>Taxonomic expertise for the identification of species is rare and costly. On-going advances in computer vision and machine learning have led to the development of numerous semi- and fully automated species identification systems. However, these systems are rarely agnostic to specific morphology, rarely can perform taxonomic “approximation” (by which we mean partial identification at least to higher taxonomic level if not to species), and frequently rely on costly scientific imaging technologies.</p> <p>We present a generic, hierarchical identification system for automated taxonomic approximation of organisms from images. We assessed the effectiveness of this system using photographs of slipper orchids (Cypripedioideae), for which we implemented image pre-processing, segmentation, and colour and shape feature extraction algorithms to obtain digital phenotypes for 116 species. The identification system trained on these digital phenotypes uses a nested hierarchy of artificial neural networks for pattern recognition and automated classification that mirrors the Linnean taxonomy, such that user-submitted photos can be assigned a genus, section, and species classification by traversing this hierarchy.</p> <p>Performance of the identification system varied depending on photo quality, number of species included for training, and desired taxonomic level for identification. High quality photos were scarce for some taxa and were under-represented in the training set, resulting in imbalanced network training. The image features used for training were sufficient to reliably identify photos to the correct genus but less so to the correct section and species.</p> <p>The outcomes of this project include a library of feature extraction algorithms called <em>ImgPheno</em>, a collection of scripts for neural network training called <em>NBClassify</em>, a library for evolutionary optimization of artificial neural network construction called <em>AI::FANN::Evolving</em> and a planned web application called <em>OrchID</em> for identification of user-submitted images. All project outcomes are open source and freely available.</p> <p><strong>About this release</strong></p> <p>This release corresponds belongs with our response to the reviewers of PLoS One. At this stage of the review cycle the manuscript is assessed as 'minor revision'. Consequently, we don't anticipate making more releases until publication.</p>
Open-data release of aggregated Australian school-level information. Edition 2016.1
<p>The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes:<br /> (a) the names of schools<br /> (b) staffing levels, including full-time and part-time teaching and non-teaching staff<br /> (c) student enrolments, including the number of boys and girls<br /> (d) school financial information, including Commonwealth government, state government, and private funding<br /> (e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'<br /> <br /> Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.<br /> <br /> The database was constructed directly from the other included files by running the following command at a command-line prompt:<br /> <em>sqlite3 school-data-2016-1.db < school-data-2016-1.sql</em><br /> Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.<br /> <br /> Briefly, the data are organized as follows.<br /> (a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this.<br /> (b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'.<br /> (3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line).<br /> (4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'.<br /> (5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages.<br /> (6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it.<br /> (7) Geocoding of the schools is not available in this edition.<br /> (8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy.<br /> (9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.</p>
O(3P)+CO2 scattering cross sections at superthermal collision energies for planetary aeronomy: Raw data release
<p>Raw data and codes used in M. Gacesa, R. J. Lillis, and K. J. Zahnle, "O(3P)+CO2 scattering cross sections at superthermal collision energies for planetary aeronomy", MNRAS 491, 5650-5659 (2020).</p> <ul> <li>v1.1 includes <strong>differential cross section</strong> data for inelastic scattering: O(3P)+CO2(v=0,j=ji) -> O(3P)+CO2(v=0,jf) and energy transfer to the internal degrees of freedom calculated as in Gacesa & Kharchenko, Geophys. Res. Lett. 39, L10203 (2012).</li> </ul> <p>These files are distributed under GNU General Public License v3.0 and include NO liability or warranty of any kind. No support is provided by the authors. We cannot promise to answer any questions related to this dataset nor to prepare different products for you.</p> <p>Please cite this work as: Marko Gacesa, Lillis, Robert J., & Zahnle, Kevin J. (2019). O(3P)+CO_2 scattering cross sections at superthermal collision energies for planetary aeronomy: Raw data pre-release (Version v0.9-beta) [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.3256699">http://doi.org/10.5281/zenodo.3256699</a></p>
Gene family data from the PhyloGenes (release version 5.0, phylogenes.org)
<p>The data files were generated from the PhyloGenes 5.0release (see release notes <a href="https://conf.arabidopsis.org/display/PHGSUP/About+PhyloGenes">here</a>).</p> <p>About the two zip files: </p> <p>1. phyloXML.zip</p> <p>PhyloGenes gene family trees in PhyloXML format, one file per family (e.g. <family_ID>.xml).</p> <p>The following information is provided for each node of a tree:<br>1) leaf node:<br>branch length<br>name <gene_id><br>taxonomy scientific_name<br>sequence accession <UniProt ID></p> <p>2) non-leaf node:<br>branch length<br>events <duplication or speciation></p> <p><br>2. panther_csv.zip</p> <p>Functional information of family members in CSV format, one file per family (e.g. <family_ID>.csv). </p> <p>A CSV file includes the following columns:<br>Uniprot ID<br>Gene <Gene name. If none then Gene ID><br>Gene ID<br>Gene name<br>Organism<br>Subfamily name</p> <p>The columns displayed after 'Subfamily name', if any, are GO annotations. Each column is a GO molecular function or biological process term that is annotated to at least one member of the gene family AND the annotation is supported by an experimental evidence (indicated by 'EXP') or phylogenetic inference (indicated by 'IBA'). A '0' indicates absence of either annotations.</p>
A floating 3D printed formulation for the coadministration and sustained release of antihypertensive drugs - Underlying CT data
<p>Underlying CT data of <strong>"A floating 3D printed formulation for the coadministration and sustained release of antihypertensive drugs"</strong></p> <p>Paola Zgouro1, Orestis L. Katsamenis3,4, Thomas Moschakis5, Georgios K. Eleftheriadis6, Athanasios S. Kyriakidis6, Konstantina Chachlioutaki1,2, Paraskevi Kyriaki Monou1,2, Marianna Ntorkou7, Constantinos K. Zacharis7, Nikolaos Bouropoulos8,9, Dimitrios G. Fatouros1,2, Christina Karavasili1, Christos I. Gioumouxouzis1</p> <p><em>1 Laboratory of Pharmaceutical Technology, Department of Pharmaceutical Sciences, Aristotle University of Thessaloniki, GR-54124, Thessaloniki, Greece</em><br><em>2 Center for Interdisciplinary Research and Innovation (CIRI-AUTH), 57001 Thessaloniki, Greece</em><br><em>3 μ-VIS X-Ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK</em><br><em>4 Institute for Life Sciences, University of Southampton, University Rd, Highfield, Southampton, SO17 1BJ, UK</em><br><em>5 Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, GR-541 24 Thessaloniki, Greece</em><br><em>6 Pharmacare Premium Limited, R&D Department, HHF003 Hal Far Industrial Estate, Birzebbugia BBG3000, Malta</em><br><em>7 Laboratory of Pharmaceutical Analysis, Department of Pharmacy, Aristotle University of Thessaloniki, GR-54124, Greece</em><br><em>8 Department of Materials Science, University of Patras, 26504 Rio, Patras, Greece</em><br><em>9 Foundation for Research and Technology Hellas, Institute of Chemical Engineering and High Temperature Chemical Processes, Patras, Greece</em></p> <p><strong>Microfocus Computed Tomography (μCT)</strong></p> <p>X-ray microfocus computed tomography (μCT) was employed for the characterization of the microstructure of the printed object, assessing the overall volume, porosity, local thickness and other printing defects. The imaging took place at the University of Southampton’s μ-VIS X-ray Imaging Centre (<a title="&mu;-VIS X-ray Imaging Centre at the University of Southampton" href="https://www.muvis.org" target="_blank" rel="noopener">www.muvis.org</a>) / 3D X-ray Histology facility using a customized μCT scanner optimized for 3D X-ray histology (<a title="3D X-ray Histology facility at University of Southampton" href="https://www.xrayhistology.org" target="_blank" rel="noopener">www.xrayhistology.org</a>) (<a title="A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications" href="https://doi.org/10.12688/wellcomeopenres.19666.2" target="_blank" rel="noopener">Katsamenis et al., 2023</a>) based on Nikon’s XTH225ST system (Nikon Metrology, Castle Donington, UK). The scanner was operated at 110 kVp / 90 μA (9.9 W), with the X-ray beam prefiltered using 0.04 mm of aluminum. The source-to-object and source-to-detector distances were 28.4 mm and 1136.7 mm, respectively, resulting in a magnification factor of 40x. Acquisition parameters included 2201 projections, averaging 4 frames per projection, with an exposure time of 177 ms per projection. The 2850 x 2850 dexels detector was binned 2x (virtual detector: 1425 × 1425 dexels), resulting in an isotropic voxel edge of 7.5 μm. The reconstructed data underwent visualization and analysis using Dragonfly software (Comet Technologies Canada Inc.; software available at http://www.theobjects.com/dragonfly).</p>
On the spectrum of mesons in quenched Sp(2N) gauge theories---Data release
<p>This release contains all data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2312.08465" target="_blank" rel="noopener">On the spectrum of mesons in quenched Sp(2N) gauge theories</a>.</p> <p>Included are:</p> <ul> <li>The file <code>README.md</code>, containing descriptions of the data formats used for other data in this submission.</li> <li>The raw log output for: <ul> <li>The gauge field generation</li> <li>The correlation function computation</li> <li>The Wilson flow computation</li> </ul> </li> </ul> <p>in the file <code>raw_data.zip</code>.<br>These include all numbers used in the publication (aside from fit parameters) in plaintext form. The archive contains a separate <code>README.md</code> documenting the layout of these data.</p> <ul> <li>All metadata used for the fitting and subsequent analysis of these data, in the file <code>metadata.zip</code>, in files described in more detail in the README.</li> <li>All data presented in plots and tables in the paper, in CSV format, in files described in more detail in the README.</li> <li>All input files given during the gauge field generation, in <code>input_files.zip</code>. These are compatible with<a href="https://github.com/sa2c/HiRep" target="_blank" rel="noopener">the Sp(2N) extension of HiRep</a>.</li> </ul>
Tabular Data for "A Comprehensive Catalog of UVIT Observations I: Catalog Description and First Release of Source Catalog (UVIT DR1)"
<p>The first comprehensive catalog of UVIT includes the sources from the observations between 2016 and 2017. </p> <p>The catalog is formatted according to the machine-readable format used by the AAS Journals and CDS/VizieR. Specific information on the structure of MRT files can be found at:</p> <p><span> </span>AAS: <a href="https://journals.aas.org/mrt-overview/"><span>https://journals.aas.org/mrt-overview/</span></a></p> <p><span> </span>CDS: <a href="http://cds.u-strasbg.fr/doc/catstd.htx"><span>http://cds.u-strasbg.fr/doc/catstd.htx</span></a></p> <p><span> </span>These files can be read in python using the astropy package<span> </span>or with the most recent version of TOPCAT (> Version 4.8)</p>
Second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'
<p>This is the second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'.</p> <p>The paper was published in Geophysical Research Letters. We provide the data that has been smoothed by moving filter and not. The data can be loaded by the <em>raster </em>package in <em>R.</em> Note that the unit is ppmv.</p> <p>Please note that both of these files must be in the same directory to open in <em>R</em> properly<em>.</em></p> <p>Please get in touch with the authors if you have any issues, email: xliu21@smail.nju.edu.cn or wanghk@nju.edu.cn</p>
Supplementary data release for "Cosmology and modified gravitational wave propagation from binary black hole population models"
<p>We release the data products associated to the paper <a href="https://arxiv.org/abs/2112.05728">"Cosmology and modified gravitational wave propagation from binary black hole population models", </a><a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.064030"><em>Phys.Rev.D</em> 105 (2022) 6 </a>.</p> <p>The data can be used in conjunction with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> to reproduce the results of the paper. </p> <p>The data product contains the following folders:</p> <p>* injections_GWTC3: injections used to analyze the GWTC3 catalog, generated with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> . Injections are available separately for O1-O2, O3a, O3b for minimum SNR of 10, 11, 12 (folder names are self-explicative). Each folder contains a file named selected.h5 with the injections. For loading them, refer to the tutorial of the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> .</p> <p>* mock_BPL_5yr_GR : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to General Relativity (see the paper for details)</p> <p>* mock_BPL_5yr_MG : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to a modified gravity model with modified gravitational-wave propagation (see the paper for details)</p> <p>* injections_mock : injections for analyzing the mock datasets above</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Parameter Estimation Data Release
<p>This material is part of several data products associated with GWTC-2.1, an update to the second Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2100063/public">https://dcc.ligo.org/LIGO-P2100063/public</a>), the related material linked from this page, and the GWTC-2.1 data release documentation (<a href="https://www.gw-openscience.org/GWTC-2.1/">https://www.gw-openscience.org/GWTC-2.1/</a>).</p> <p><strong>Parameter estimation data release</strong></p> <p>This data release contains posterior samples (*.h5) for gravitational-wave candidates through the first part of the third observing run (O3a). We provide results for the 44 candidates that have a probability of astrophysical origin of over 0.5 from O3 as well as the 10 previously-reported binary-black-hole candidates from GWTC-1 (this excludes GW170817). There are two .h5 files per event</p> <ul> <li> <p>Cosmologically reweighted (*cosmo.h5)</p> </li> <li> <p>Not cosmologically reweighted (*nocosmo.h5)</p> </li> </ul> <p>The cosmologically reweighted posteriors are reweighted to have a luminosity-distance prior that has a uniform merger rate in the source's comoving frame. Each .h5 file contains samples for multiple runs with keys C01:RUN_NAME, where RUN_NAME is the waveform used for the run (and additional prior-choice information if necessary) or Mixed, indicating an equal mixture of samples from runs with similar physics if they exist. In cases where only one waveform was used, the Mixed dataset is simply a resampling of those results . GW190425 does not have Mixed samples. See the <a href="https://dcc.ligo.org/LIGO-P2100063/public">paper</a> appendices for further information. In addition to containing the posterior samples, the .h5 files also contain metadata about the analyses including the configuration files (which specify details such as the detector data analyzed), noise power spectral densities (potentially for a superset of the detectors used in the analysis) and calibration uncertainty envelopes.</p> <p>The python notebook explains how to use the posterior samples. This data release also contains .FITS skymap files, which can be read with <a href="https://lscsoft.docs.ligo.org/ligo.skymap/#">ligo.skymap</a>, and skymap statistics in *.txt files.</p> <p>The inference of the source parameters were performed with <a href="https://lscsoft.docs.ligo.org/bilby/">Bilby</a>, <a href="https://lscsoft.docs.ligo.org/parallel_bilby/">Parallel Bilby</a> and <a href="https://git.ligo.org/richard-oshaughnessy/research-projects-RIT/tree/temp-RIT-Tides">RIFT</a>. The results are formatted using <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a>.</p> <p><a href="https://zenodo.org/record/5546663#.YnAAcvPMKqC">A similar release has been made to accompany GWTC-3</a> for results from the second part of the third observing run.</p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5117702 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p>For more general background on gravitational-wave parameter estimation, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
Data Release: "No evidence that the majority of black holes in binaries have zero spin"
<p>This dataset contains the results presented in "<em>No evidence that the majority of black holes in binaries have zero spin</em>".</p> <p>In this paper, we systematically explored the effective and component spin distributions of binary black holes among the LIGO/Virgo GWTC-3 catalog. In particular, we tried to answer the following core questions, which have been the subject of active exploration and some debate in the literature:</p> <p><em>1. Is there an excess of binary black holes with vanishing spin, as predicted by some theories of angular momentum transport in stellar cores?</em></p> <p><strong>We find no evidence for an excess of vanishing spin systems.</strong> This finding is confirmed by three complementary analyses: one relying only on the Bayes factors between spinning and non-spinning priors for each BBH observation, one that seeks to model the distribution of effective aligned spins, and one modeling the distribution of component spin magnitudes and misalignment angles. Instead, we find BBH spin magnitudes to be consistent with a single, continuous distribution that remains finite at magnitude zero.</p> <p><em>2. Do there exist binaries with component spins misaligned by more than 90 degrees relative to their orbits?</em></p> <p><strong>We find a strong preference for the existence of such strongly misaligned spins.</strong> Our analysis of the BBH component spin distribution indicates that at least some component spins are misaligned from their orbits by more than 90 degrees. This result is robust under a variety of modeling choices regarding both the distribution of component spin magnitudes and tilts.</p> <p>The code used to generate this data can be found in the repository <a href="https://github.com/tcallister/gwtc3-spin-studies/">https://github.com/tcallister/gwtc3-spin-studies/</a>. This repository includes <a href="https://github.com/tcallister/gwtc3-spin-studies/tree/main/data">jupyter notebooks</a> that can be used to open, explore, and plot the files contained in this data set. Additional information about reproducing and/or using this dataset can be found in <a href="https://tcallister.github.io/gwtc3-spin-studies/build/html/index.html">our associated documentation</a>.</p> <p>Further notes:</p> <ul> <li>The files <em>sampleDict_FAR_1_in_1_yr.pickle</em> and <em>injectionDict_FAR_1_in_1.pickle</em>, used as inputs to our analyses, are created via code in the repository <a href="https://github.com/tcallister/get-lvk-data">https://github.com/tcallister/get-lvk-data</a> (see also <a href="https://zenodo.org/record/6505409">https://zenodo.org/record/6505409</a>).</li> <li>The file <em>posteriors_gaussian_spin_samples_FAR_1_in_1.json</em>, used for figure generation, was published by the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration in support of the paper "<a href="https://arxiv.org/abs/2111.03634">The population of merging compact binaries inferred using gravitational waves through GWTC-3</a>" (see <a href="https://zenodo.org/record/5655785">https://zenodo.org/record/5655785</a>).</li> </ul>
Lattice studies of the Sp(4) gauge theory with two fundamental and three antisymmetric Dirac fermions—data release
<p>This dataset contains the raw data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2202.05516">Lattice studies of the Sp(4) gauge theory with two fundamental and three antisymmetric Dirac fermions</a>. </p> <p>Included are:</p> <ul> <li>The raw log output from the configuration generation, correlation function calculation, and Dirac eigenvalue computation, as well as metadata describing the ensembles used for the mass spectrum calculation, in `raw_data.zip`. These include all numbers used in the publication (aside from fit parameters) in plaintext form.</li> <li>All numbers included in the above logs, restructured into HDF5 format for convenience, in `data.h5`.</li> <li>The fit parameters used to compute the spectrum, including the thermalisation length, and the plateau start and end points, in `fit_params.zip`.</li> <li>The data underlying tables 2–6 of the publication above, in CSV format.</li> </ul> <p>More details can be found in the file README.md.</p> <p>Version history:</p> <ul> <li>v1.1: Replace out_corr_48x24x24x24b6.5mas-1.01mf-0.71 due to a mistake where the wrong version of the measurement code was used.</li> <li>v1.0: Initial release</li> </ul>
amel-github/sars-ani: Releasing new data fields in SARS-ANI dataset
<p>2022-06-20 - Release v1.1</p> <p>The original SARS-ANI dataset displayed common and scientific names of the animal host as found in the information source and/or inferred from the literature or expert knowledge.<br> Misspelled animal names and errors in taxonomy can lead to incorrect scientific conclusions and poor policy design. Moreover, harmonized host names can aid integrating other datasets (e.g. data on host biological traits, geographic distribution, or association with other pathogens).<br> Therefore, for each event, we programmatically performed taxonomic validation of the animal host name, using the R package taxize (Chamberlain et al. 2013). For more information on our validation process, see the R script <strong>sars_ani_validation.R.</strong></p> <p>Version 1.1. contains seven fields related to the identification of the animal host:</p> <ul> <li> <p>host_com_orig: Most specific designation of the animal host provided by the source(s), in English.</p> </li> <li> <p>host_sci_orig: Scientific name of the animal host as mentioned in the source(s) (scientific names are harmonized so that only the first letter of the genus is capitalized).</p> </li> <li> <p>host_com_res: Common name of the animal host, harmonized against the National Center for Biotechnology Information (<a href="https://www.ncbi.nlm.nih.gov/">NCBI</a>) taxonomic backbone.</p> </li> <li> <p>host_sci_res: Scientific name of the animal host (resolved to species or subspecies level), harmonized against the National Center for Biotechnology Information (<a href="https://www.ncbi.nlm.nih.gov/">NCBI</a>) taxonomic backbone.</p> </li> <li> <p>host_colloq: The colloquial name of the host, i.e. the name commonly used to identify the animal in non-specialist language (e.g. "tiger" for "Sumatran tiger").</p> </li> <li> <p>host_sci_spec_res: The scientific name of the host resolved to the species level.</p> </li> <li> <p>family: Animal family of the animal host.</p> </li> </ul>
Sp(2N) Yang-Mills theories on the lattice: scale setting and topology—data release
<p>This release contains all data and metadata used to prepare the publications <a href="https://arxiv.org/abs/2205.09254">Topological susceptibility in Yang-Mills theories</a> and <a href="https://arxiv.org/abs/2205.09364">Sp(2N) Yang-Mills theories on the lattice: scale setting and topology</a>.</p> <p>Included are:</p> <ul> <li>The raw log output from the Wilson flow computation, as well as metadata describing the ensembles used, in `raw_data.zip`. These include all numbers used in the publication (aside from fit parameters) in plaintext form. The archive contains a separate `README.md` describing the layout of the data.</li> <li>All numbers included in the above logs, restructured into HDF5 format for convenience, in `datapackage.h5`.</li> <li>The data presented in all tables in both papers, in CSV format, as described in more detail below.</li> </ul> <p>Further details are given in the file README.md.</p>
Gaia Data Release 3: Basis function configuration for internally calibrated BP/RP spectra
<p>This XML file contains the basis function configuration adopted for the internally calibrated BP and RP spectra published in Gaia Data Release 3. The same file is included in the GaiaXPy (https://gaia-dpci.github.io/GaiaXPy-website/index.html) python package offering some useful functions to use the spectra.</p> <p>The content of the file and its basic usage are described in detail in Appendix C in the paper "Gaia Data Release 3: Processing and validation of BP/RP low-resolution spectral data", De Angeli, F. et al. A&A (2022).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.