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1,298 results for “Archive”
Electronic Supplement / Data Archive for "Comparison of a Neutral Density Model With the SET HASDM Density Database"
<p>These files provide supplemental data to accompany the paper "Comparison of a Neutral Density Model With the SET HASDM Density Database,” submitted to <em>Space Weather, </em>with manuscript number 2021SW002888. Details are provided in the file DataArchiveDocumentation.pdf.</p>
Cassini SAR Raw and Ancillary Data Archive
<p>This data archive contains raw Cassini downlinked telemetry and ancillary temperature data for use in reprocessing Cassini SAR data using the Cassini SAR processor.</p> <p>The Cassini SAR processor is now available at<br> https://github.com/nasa-jpl/Cassini_RADAR_Software.<br> <br> To remove artifacts in SAR imagery due to thermal noise and compression error the Cassini SAR processor needs to extract compression coefficients from the raw downlinked data stored in this archive. The data is not human readable and only of use for users of the SAR processing software. In addition to the raw data, ancillary temperature data is also provided. Without the temperature data the radiometric calibration of the SAR images produced by the SAR processor is slightly degraded.<br> <br> The data is arranged by observation. Each observation has a directory whose name is the observation identifier, specified by one or two letters to indicate the name of the target body and a number to indicate which flyby. For example, t108 indicates Titan flyby number 108. The observation directory contains two subdirectories: “raw” which contains the raw data file; and “anc” which contains the three ancillary temperature files E-2503.gph, E-2505.gph, and E-2507.gph.<br> <br> The work reported here was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. <br> Copyright 2021 All Rights Reserved</p> <p> </p>
Archival Datasets for SuperNova Artificial Inference by Lstm neural networks (SNAIL)
<p>The spectral-observation dataset (enclosed in the file archival_spec_observations.tar.gz) is comprised of 3091 observed spectra from 361 SNe Ia, largely contributed from CfA (Blondin et al. 2012), BSNIP (Silverman et al. 2012), CSP (Folatelli et al. 2013) and Supernova Polarimetry Program (Wang & Wheeler 2008; Cikota et al. 2019a; Yang et al. 2020).</p> <p>The spectral-template dataset (enclosed in the file archival_spec_templates.tar.gz) includes 361 spectral templates, each of them (covering -15 to +33d with wavelength from 3800 to 7200 A) was generated from the available spectroscopic observations of an individual SN via a LSTM neural network model.</p> <p>The auxiliary photometry dataset (enclosed in the file archival_phot_observations.tar.gz) provides the B & V light curves of these SNe (in total, 196 available SNe Ia), that were used to calibrate the synthetic B-V color of the observed spectra.</p> <p>In additional, the two master catalogs give the detailed information about the 361 SNe and their spectroscopic observations, respectively. </p> <p>These datasets are associated to the paper "Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks" (Hu et al. 2022, ApJ, accepted).</p>
Data Archive: 2021 Development of a Virtual Diagnostic for the Advanced Particle Accelerator Modeling Code WarpX
<p><strong>A current promising field of research, laser-driven ion acceleration has the potential to reduce the size, cost, and energy consumption of particle accelerators by orders of magnitude.</strong></p> <p> </p> <p><strong>To better refine the instrumentation, we have developed a virtual diagnostic to measure electromagnetic radiation such as radiation produced from scattered and transmitted laser beams which has been implemented into WarpX, an advanced Particle-in-Cell code that simulates laser-driven particle acceleration. This “FieldProbe” diagnostic provides field measurements and is parallelized using the Message Passing Interface (MPI) and can thus run on High Performance Computing systems such as the NERSC Cori cluster.</strong></p>
Data archive for 'Opportunities to curb hydrological alterations via dam re-operation in the Mekong'
<p>This repository contains the data used in the paper '<a href="https://www.nature.com/articles/s41893-022-00971-z">Opportunities to curb hydrological alterations via dam re-operation in the Mekong</a>'.</p> <p>We first use VIC-Res to simulate daily river discharge and available hydropower generation of the Mekong basin from 1996 to 2016 under 32 scenarios (NAT (natural flow conditions), BAU (business as usual), MAX_MB (dams kept at full storage in Mekong), MAX_LMB (dams kept at full storage in Lower Mekong), and 28 OPT (optimized re-operation strategies) scenarios). The 'VIC-Res' folder contains the daily discharge at Stung Treng and hydropower production in Cambodia, Laos, and Thailand. The hydropower outputs are then used in PowNet, a unit commitment/economic dispatch model for the Cambodian, Laotian, and Thai power systems. 'PowNet' folder contains the relevant input and output files for the three scenarios that are elaborated on in the paper (BAU, MAX_LMB, and OPT).</p> <p>For more information on the PowNet models, refer to the following GitHub repositories: <a href="https://github.com/kamal0013/PowNet">PowNet-Cambodia</a>, <a href="https://github.com/kamal0013/PowNet-Laos">PowNet-Laos</a>, <a href="https://github.com/kamal0013/PowNet-Thailand">PowNet-Thailand</a>.</p>
Logo of Historical Archive Aspra Spitia
<p>Logo of the digital community Aspra Spitia.<br> Historical archive from collections of the residents of Aspra Spitia, Antikyra and the people of the hellenic aluminium industry.</p> <p> </p> <p>Λογότυπο της ψηφιακής κοινότητας Άσπρα Σπίτια.<br> Ιστορικό αρχείο από συλλογές των κατοίκων των Άσπρων Σπιτιών, της Αντίκυρας και των ανθρώπων της ελληνικής βιομηχανίας αλουμινίου.</p>
Inventaires anciens d'archives ecclésiastiques (Paris, Saint-Germain-des-Prés, Saint-Denis)
<p>Voir: <a href="https://www.siv.archives-nationales.culture.gouv.fr/siv/rechercheconsultation/consultation/ir/consultationIR.action?formCaller=GENERALISTE&irId=FRAN_IR_058159">https://www.siv.archives-nationales.culture.gouv.fr/siv/rechercheconsultation/consultation/ir/consultationIR.action?formCaller=GENERALISTE&irId=FRAN_IR_058159</a></p>
Datasample - Social Media Analytics and Metrics of Facebook Performance of Libraries, Archives and Museums
<p>The current dataset describes Facebook pages performance for 220 Libraries, Archives and Museums from all over the world. The performance is measured through 9 different social media metrics. That is, number of posts, link-posts, picture-posts, video-posts, total reactions, comments and shares, number of reactions, comments per post and reactions per post. The data harvesting process has been conducted through the use of FanPageKarma API. The gathered metrics and their values depict the performance for each Facebook page in a time-period of 30 days.</p>
Data from: Integrating tracking and resight data enables unbiased inferences about migratory connectivity and winter range survival from archival tags
<p>Archival geolocators have transformed the study of small, migratory organisms but analysis of data from these devices requires bias correction because tags are only recovered from individuals that survive and are re-captured at their tagging location. Data and code provided in this repository can be used to replicate the simulation and Painted Bunting case study results presented by Rushing et al. (2021) showing that integrating geolocator recovery data and mark–resight data enables unbiased estimates of both migratory connectivity between breeding and nonbreeding populations and region-specific survival probabilities for wintering locations.</p>
Archive of the analysis results of the microtremor data obtained from a seismic array with a radius of 0.58 m distributed to the participants of the blind prediction experiments for the ESG6 symposium
<p>This is a supplemental material of the paper "Array-size dependency of the upper limit wavelength normalized by array radius for the standard spatial autocorrelation method" by Ikuo Cho, published in Earth, Planets and Space. It consists of the analysis results of the microtremor data observed using a seismic array with a radius of 0.58 m, which were distributed to the participants of the blind prediction experiments in ESG6. It involves all analysis results and script files to draw Figure 1 of the paper. See the "Availability of data and materials" section of the paper to download the original observed data and analysis code. See the main text of the paper for the details of the analysis.</p>
Copper River, Alaska, Chinook Salmon Inriver Abundance Estimate 2018-2021 DATA ARCHIVE
<p>Long-term monitoring of returning adult Chinook salmon (<em>Oncorhynchus tshawytscha</em>) abundance on the Copper River, AK, has been conducted using fishwheels and two-sample mark-recapture methods since 2003. This data archive is from from the 2018-2021 field seasons. The annual objective was to estimate the inriver abundance of Copper River Chinook salmon such that the estimate was within 25% of the true abundance 95% of the time. This data represents annual catch, bycatch, tagging site data, recapture site data, session data, QC check tables, CPUE, mark-recapture matrix, mark-recapture stratification tables, effort, and daily catch matrix. </p> <p>See annual report for methodology, analyses and results @ http://akssf.org/default.aspx?id=3477 or contact the Alaska Sustainable Salmon Fund or U.S. Fish and Wildlife Service Office of Subsistence Management Fisheries Resource Monitoring Program or Native Village of Eyak DENR. </p> <p> </p>
A 5000 km2 ASTER alteration map of the Oman–UAE ophiolite crust: Data archive and remote sensing toolkit
<p>This archive contains data and maps accompanying the journal article <em>"Multispectral discrimination of spectrally similar hydrothermal minerals in mafic crust: A 5000 km<sup>2</sup> ASTER alteration map of the Oman–UAE ophiolite</em>".</p> <p>The archive includes the full resolution, multi-format alteraton maps of hydrothermal alteration of the entire Oman–UAE ophiolite crust generated by ASTER remote sensing. Additional files necessary to reproduce or build on this work are also provided, constituting a remote sensing toolkit for the Oman–UAE ophiolite. A complete list of contents is provided within. Please contact TMB in case of compatibility issues.</p>
TreeSatAI Benchmark Archive for Deep Learning in Forest Applications
<p><strong>Context and Aim</strong></p> <p>Deep learning in Earth Observation requires large image archives with highly reliable labels for model training and testing. However, a preferable quality standard for forest applications in Europe has not yet been determined. The <em>TreeSatAI </em>consortium investigated numerous sources for annotated datasets as an alternative to manually labeled training datasets.</p> <p>We found the federal forest inventory of Lower Saxony, Germany represents an unseen treasure of annotated samples for training data generation. The respective 20-cm Color-infrared (CIR) imagery, which is used for forestry management through visual interpretation, constitutes an excellent baseline for deep learning tasks such as image segmentation and classification.</p> <p> </p> <p><strong>Description</strong></p> <p>The data archive is highly suitable for benchmarking as it represents the <em>real-world</em> data situation of many German forest management services. One the one hand, it has a high number of samples which are supported by the high-resolution aerial imagery. On the other hand, this data archive presents challenges, including class label imbalances between the different forest stand types.</p> <p>The <em>TreeSatAI Benchmark Archive </em>contains:</p> <ul> <li> <p>50,381 image triplets (aerial, Sentinel-1, Sentinel-2)</p> </li> <li> <p>synchronized time steps and locations</p> </li> <li> <p>all original spectral bands/polarizations from the sensors</p> </li> <li> <p>20 species classes (single labels)</p> </li> <li> <p>12 age classes (single labels)</p> </li> <li> <p>15 genus classes (multi labels)</p> </li> <li> <p>60 m and 200 m patches</p> </li> <li> <p>fixed split for train (90%) and test (10%) data</p> </li> <li> <p>additional single labels such as English species name, genus, forest stand type, foliage type, land cover</p> </li> </ul> <p>The geoTIFF and GeoJSON files are readable in any GIS software, such as QGIS. For further information, we refer to the PDF document in the archive and publications in the reference section.</p> <p> </p> <p><strong>Version history</strong></p> <p>v1.0.2 - Minor bug fix multi label JSON file</p> <p>v1.0.1 - Minor bug fixes in multi label JSON file and description file</p> <p>v1.0.0 - First release</p> <p> </p> <p><strong>Citation</strong></p> <p>Ahlswede, S., Schulz, C., Gava, C., Helber, P., Bischke, B., Förster, M., Arias, F., Hees, J., Demir, B., and Kleinschmit, B.: <em>TreeSatAI Benchmark Archive</em>: a multi-sensor, multi-label dataset for tree species classification in remote sensing, Earth Syst. Sci. Data, 15, 681–695, <a href="https://doi.org/10.5194/essd-15-681-2023">https://doi.org/10.5194/essd-15-681-2023</a>, 2023.</p> <p> </p> <p><strong>GitHub</strong></p> <p>Full code examples and pre-trained models from the dataset article (Ahlswede et al. 2022) using the <em>TreeSatAI Benchmark Archive</em> are published on the GitLab and GitHub repositories of the Remote Sensing Image Analysis (RSiM) Group (<a href="https://git.tu-berlin.de/rsim/treesat_benchmark">https://git.tu-berlin.de/rsim/treesat_benchmark</a>) and the Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) (<a href="https://github.com/DFKI/treesatai_benchmark">https://github.com/DFKI/treesatai_benchmark</a>). Code examples for the sampling strategy can be made available by Christian Schulz via email request.</p> <p> </p> <p><strong>Folder structure</strong></p> <p>We refer to the proposed folder structure in the PDF file.</p> <ul> <li> <p>Folder “aerial” contains the aerial imagery patches derived from summertime orthophotos of the years 2011 to 2020. Patches are available in 60 x 60 m (304 x 304 pixels). Band order is near-infrared, red, green, and blue. Spatial resolution is 20 cm.</p> </li> <li> <p>Folder “s1” contains the Sentinel-1 imagery patches derived from summertime mosaics of the years 2015 to 2020. Patches are available in 60 x 60 m (6 x 6 pixels) and 200 x 200 m (20 x 20 pixels). Band order is VV, VH, and VV/VH ratio. Spatial resolution is 10 m.</p> </li> <li> <p>Folder “s2” contains the Sentinel-2 imagery patches derived from summertime mosaics of the years 2015 to 2020. Patches are available in 60 x 60 m (6 x 6 pixels) and 200 x 200 m (20 x 20 pixels). Band order is B02, B03, B04, B08, B05, B06, B07, B8A, B11, B12, B01, and B09. Spatial resolution is 10 m.</p> </li> <li> <p>The folder “labels” contains a JSON string which was used for multi-labeling of the training patches. Code example of an image sample with respective proportions of 94% for Abies and 6% for Larix is: "Abies_alba_3_834_WEFL_NLF.tif": [["Abies", 0.93771], ["Larix", 0.06229]]</p> </li> <li> <p>The two files “test_filesnames.lst” and “train_filenames.lst” define the filenames used for train (90%) and test (10%) split. We refer to this fixed split for better reproducibility and comparability.</p> </li> <li> <p>The folder “geojson” contains geoJSON files with all the samples chosen for the derivation of training patch generation (point, 60 m bounding box, 200 m bounding box).</p> </li> </ul> <p>CAUTION: As we could not upload the aerial patches as a single zip file on Zenodo, you need to download the 20 single species files (aerial_60m_…zip) separately. Then, unzip them into a folder named “aerial” with a subfolder named “60m”. This structure is recommended for better reproducibility and comparability to the experimental results of Ahlswede et al. (2022), </p> <p> </p> <p><strong>Join the archive</strong></p> <p>Model training, benchmarking, algorithm development… many applications are possible! Feel free to add samples from other regions in Europe or even worldwide. Additional remote sensing data from Lidar, UAVs or aerial imagery from different time steps are very welcome. This helps the research community in development of better deep learning and machine learning models for forest applications. You might have questions or want to share code/results/publications using that archive? Feel free to contact the authors.</p> <p> </p> <p><strong>Project description</strong></p> <p>This work was part of the project <em>TreeSatAI </em>(Artificial Intelligence with Satellite data and Multi-Source Geodata for Monitoring of Trees at Infrastructures, Nature Conservation Sites and Forests). Its overall aim is the development of AI methods for the monitoring of forests and woody features on a local, regional and global scale. Based on freely available geodata from different sources (e.g., remote sensing, administration maps, and social media), prototypes will be developed for the deep learning-based extraction and classification of tree- and tree stand features. These prototypes deal with real cases from the monitoring of managed forests, nature conservation and infrastructures. The development of the resulting services by three enterprises (liveEO, Vision Impulse and LUP Potsdam) will be supported by three research institutes (German Research Center for Artificial Intelligence, TUB Remote Sensing Image Analysis Group, TUB Geoinformation in Environmental Planning Lab).</p> <p> </p> <p><strong>Project publications</strong></p> <p>Ahlswede, S., Schulz, C., Gava, C., Helber, P., Bischke, B., Förster, M., Arias, F., Hees, J., Demir, B., and Kleinschmit, B.: <em>TreeSatAI Benchmark Archive</em>: a multi-sensor, multi-label dataset for tree species classification in remote sensing, <em>Earth System Science Data</em>, 15, 681–695, <a href="https://doi.org/10.5194/essd-15-681-2023">https://doi.org/10.5194/essd-15-681-2023</a>, 2023.</p> <p>Schulz, C., Förster, M., Vulova, S. V., Rocha, A. D., and Kleinschmit, B.: Spectral-temporal traits in Sentinel-1 C-band SAR and Sentinel-2 multispectral remote sensing time series for 61 tree species in Central Europe. <em>Remote Sensing of Environment</em>, <em>307</em>, 114162, <a href="https://doi.org/10.1016/j.rse.2024.114162">https://doi.org/10.1016/j.rse.2024.114162</a>, 2024.</p> <p> </p> <p><strong>Conference contributions</strong></p> <p>Ahlswede, S. Madam, N.T., Schulz, C., Kleinschmit, B., and Demіr, B.: <em>Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods</em>, IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, <a href="https://doi.org/10.48550/arXiv.2201.07495">https://doi.org/10.48550/arXiv.2201.07495</a>, 2022.</p> <p>Schulz, C., Förster, M., Vulova, S., Gränzig, T., and Kleinschmit, B.: <em>Exploring the temporal fingerprints of mid-European forest types from Sentinel-1 RVI and Sentinel-2 NDVI time series</em>, IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, <a href="https://doi.org/10.1109/IGARSS46834.2022.9884173">https://doi.org/10.1109/IGARSS46834.2022.9884173</a>, 2022.</p> <p>Schulz, C., Förster, M., Vulova, S., and Kleinschmit, B.: <em>The temporal fingerprints of common European forest types from SAR and optical remote sensing data</em>, AGU Fall Meeting, New Orleans, USA, 2021.</p> <p>Kleinschmit, B., Förster, M., Schulz, C., Arias, F., Demir, B., Ahlswede, S., Aksoy, A.K., Ha Minh, T., Hees, J., Gava, C., Helber, P., Bischke, B., Habelitz, P., Frick, A., Klinke, R., Gey, S., Seidel, D., Przywarra, S., Zondag, R., and Odermatt B.: <em>Artificial Intelligence with Satellite data and Multi-Source Geodata for Monitoring of Trees and Forests</em>, Living Planet Symposium, Bonn, Germany, 2022.</p> <p>Schulz, C., Förster, M., Vulova, S., Gränzig, T., and Kleinschmit, B.: <em>Exploring the temporal fingerprints of sixteen mid-European forest types from Sentinel-1 and Sentinel-2 time series</em>, ForestSAT, Berlin, Germany, 2022.</p>
Archive dataset for sugarcane simulations with the JULES model
<p>This dataset contains supporting information to simulate sugarcane growth and development with the JULES model. It provides a collection of csv files with crop responses to CO2, Temperature and Soil moisture conditions (CTW-response), comparison against field and regional observations (performance), and climate change projections (projections). The parameters' values and model configuration are provided in sub-folders "sim_db" and "jules_run". Model runs were carried out with <a href="https://github.com/Murilodsv/wpy-jules">wpy-jules</a>, whereas the scientific documentation is described in Vianna et al. (2022). </p> <p>This work was supported by the Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil). We acknowledge the use of the Monsoon HPC system, maintained through a strategic partnership between the Met Office and the Natural Environment Research Council.</p> <p><strong>Summary</strong>:</p> <p>- CTW-response: CSV files initiated with "C", "T" and "W"<br> - performance: CSV files with suffix "perf" as well as "yield_var"<br> - projections: CSV files with suffix "future"<br> - input: Folder "sim_db"<br> - configuration: Folder "jules_run"</p> <p><strong>References</strong></p> <p>Vianna et al. (2022). Improving the representation of sugarcane crop in the JULES model for climate impact assessment. Global Change Biology Bioenergy (<a href="https://doi.org/10.1111/gcbb.12989">https://doi.org/10.1111/gcbb.12989</a>).</p>
Data and Software Archive for "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada"
<p>This is the Zenodo archive for the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada" (Mucaki EJ, Shirley BC and Rogan PK. <em>F1000Research</em> 2021, <strong>10</strong>:1312, DOI: <a href="http://dx.doi.org/10.12688/f1000research.75891.1">10.12688/f1000research.75891.1</a>). This study aimed to produce community-level geo-spatial mapping of patterns and clusters of symptoms, and of confirmed COVID-19 cases, in near real-time in order to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. This archive will contain data and image files from this study, which were too numerous to be included in the manuscript for this study. It also provides all program files pertaining to the <em>Geostatistical Epidemiology Toolbox </em>(Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript and other software developed (cluster, outlier, streak identification and pairing)..</p> <p>We also provide a guide which provides a general description of the contents of the four sections in this archive (<em>Documentation_for_Sections_of_Zenodo_Archive.docx</em>). If you have any intent to utilize the data provided in Section 3, we greatly advise you to review this document as it describes the output of all geostatistical analyses performed in this study in detail.</p> <p><strong>Data Files:</strong></p> <p><strong>Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"</strong></p> <p>This section contains all additional tables and figures described in the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada". Additional tables S1 to S7 are presented in an Excel document. These 7 tables provide summary statistics of various geostatistical tests described in the study (“Section 1 – Tables S1-S4”) and lists all identified single and paired high-case cluster streaks (“Section 1 – Tables S5-S7”). This section also contains 11 additional figures referred to in the manuscript (“Section 1 – Figures S1-S11”) both individually and within a Word document which describes them.</p> <p><strong>Section 2. "Section_2.Localized_Hotspot_Lists.zip"</strong></p> <p>All localized hotspots (identified through kriging analysis) were catalogued for each municipality evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex). These files indicate the FSA in which the hotspot was identified, the date in which it was identified (utilizing 3-day case data at the postal code level), the amount of cases which occurred within the FSA within these 3 dates, the range of cases interpolated by kriging analysis (between 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-50, >50), and whether or not the FSA was deemed a hotspot by Gi* relative to the rest of Ontario on any of the three dates evaluated. Please see Section 4 for map images of these localized hotspots.</p> <p><strong>Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"</strong></p> <p>Section 3 – All output files from the geostatistical tests performed in this study are provided in this section. This includes the output from Ontario-wide FSA-level Gi* and Cluster and Outlier analyses, and PC-level Cluster and Outlier, Spatial Autocorrelation, and kriging analysis of 6 municipal regions. It also includes kriging analysis of 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). This section also provides data files from our analyses of stratified case data (by age, gender, and at-risk condition). All coordinates presented in these data files are given in “PCS_Lambert_Conformal_Conic” format. Case values between 1-5 were masked (appear as “NA”).</p> <p><strong>Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"</strong></p> <p>Sets of image files which map the results of our geostatistical analyses onto a map of Ontario or within the municipalities evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex) are provided. This includes: Kriging analysis (PC-level), Local Moran's I cluster and outlier analysis (FSA and PC-level), normal and space-time Gi* analysis, and all images for all analyses performed on stratified data (by age, gender and at-risk condition). Kriging contour maps are also included for 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). </p> <p><strong>Software:</strong></p> <p>This Zenodo archive also provides all program files pertaining to the <em>Geostatistical Epidemiology Toolbox </em>(Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript. This geostatistical toolbox was developed by CytoGnomix Inc., London ON, Canada and is distributed freely under the terms of the GNU General Public License v3.0. It can be easily modified to accommodate other Canadian provinces and, with some additional effort, other countries. </p> <p>This distribution of the <em>Geostatistical Epidemiology Toolbox </em>does not include postal code (PC) boundary files (which are required for some of the tools included in the toolbox). The PC boundary shapefiles used to test the toolbox were obtained from <a href="https://www.dmtispatial.com/">DMTI</a> (<a href="https://www.google.com/url?q=https://www.dmtispatial.com/canmap/&sa=D&source=hangouts&ust=1637875735980000&usg=AOvVaw2wG3iVnyGyrkTIkN5FQ4NS">https://www.dmtispatial.com/canmap/</a>) through the Scholar's Geoportal at the University of Western Ontario (<a href="http://geo2.scholarsportal.info/">http://geo2.scholarsportal.info/</a>). The distribution of these files (through sharing, sale, donation, transfer, or exchange) is strictly prohibited. However, any equivalent PC boundary shape file should suffice, provided it contains polygon boundaries representing postal code regions (see guide for more details).</p> <p><strong>Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"</strong></p> <p>The Geostatistical Epidemiology Toolbox is a set of custom Python-based geoprocessing tools which function as any built-in tool in the ArcGIS system. This toolbox implements data preprocessing, geostatistical analysis and post-processing software developed to evaluate the distribution and progression of COVID-19 cases in Canada. The purpose of developing this toolbox is to allow external users without programming knowledge to utilize the software scripts which generated our analyses and was intended to be used to evaluate Canadian datasets. While the toolbox was developed for evaluating the distribution of COVID-19, it could be utilized for other purposes. </p> <p>The toolbox was developed to evaluate statistically significant distributions of COVID-19 case data at Canadian Forward Sortation Area (FSA) and Postal Code-level in the province of Ontario utilizing geostatistical tools available through the ArcGIS system. These tools include: 1) Standard Gi* analysis (finds areas where cases are significantly spatially clustered), 2) spacetime based Gi* analysis (finds areas where cases are both spatially and temporally clustered), 3) cluster and outlier analysis (determines if high case regions are an regional outlier or part of a case cluster), 4) spatial autocorrelation (determines the cases in a region are clustered overall) and, 5) Empirical Bayesian Kriging analysis (creates contour maps which define the interpolation of COVID-19 cases in measured and unmeasured areas). Post-processing tools are included that import these all of the preceding results into the ArcGIS system and automatically generate PNG images. </p> <p>This archive also includes a guide ("UserManual_GeostatisticalEpidemiologyToolbox_CytoGnomix.pdf") which describes in detail how to set up the toolbox, how to format input case data, and how to use each tool (describing both the relevant input parameters and the structure of the resultant output files).</p> <p><strong>Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"</strong></p> <p>In the manuscript associated with this archive, Perl scripts were utilized to evaluate postal code-level Cluster and Outlier analysis to identify significantly, highly clustered postal codes over consecutive periods (i.e., high-case cluster “streaks”). The identified streaks are then paired to those in close proximity, based on the neighbors of each postal code from PC centroid data ("paired streaks"). Multinomial logistic regression models were then derived in the R programming language to measure the correlation between the number of cases reported in each paired streak, the interval of time separating each streak, and the physical distance between the two postal codes. Here, we provide the 3 Perl scripts and the R markdown file which perform these tasks:</p> <p><em>“Ontario_City_Closest_Postal_Code_Identification.pl”</em></p> <p>Using an input file with postal code coordinates (by centroid), this program identifies the nearest neighbors to all postal codes for a given municipal region (the name of this region is entered on the command line). Postal code centroids were calculated in ArcGIS using the “Calculate Geometry” function against DMTI postal code boundary files (not provided). Input from other sources could be used, however, as long as the input includes a list of coordinates with a unique label associated with a particular municipality.</p> <p>The output of this program (for the same municipal region being evaluated) is required for the following two Perl scripts:</p> <p><em>“Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl”</em></p> <p>This program uses the output of postal code-level Cluster and Outlier analysis for a municipality (these files are available in a second Zenodo archive: <a href="http://doi.org/10.5281/zenodo.5585812">doi.org/10.5281/zenodo.5585812</a>) and the output from <em>“Ontario_City_Closest_Postal_Code_Identification.pl” </em>(for the same municipal region) as input to identify high-case clustered postal codes that occur consecutively over a course of several dates (referred to as high-case cluster “streaks”). The script allows for a single day in which the PC was either not clustered or did not meet the minimum case count threshold of ≥ 6 cases within the 3-day sliding window (i.e. if clustered for 3 days, then not significant for one, then clustered for 3 more days, it will considered a 7 day streak). This script also lists any neighbors that are also identified to have streaks during these same dates.</p> <p><em>“Local_Morans_Analysis.Clustered_Streak_Pairing_Program.pl”</em></p> <p>This program uses the output from “<em>Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl</em>” to pair streaks that were identified in two closely situated postal codes spatially (requires output from <em>“Ontario_City_Closest_Postal_Code_Identification.pl”)</em>. The output of this script provides the postal codes of the streaks which are paired, describe the interval of each streak (and whether they occur concurrently), the number of cases which occurred during these streaks, and how these streaks are separated (both distance [in meters] and temporally [in days]).</p> <p>"<em>Streak_Analysis_using_Multinomial_Logistic_Regression_Models.Rmd</em>"</p> <p>This R Markdown file contains the code which derived multinomial logistic regression models to describe the relation between the number of COVID-19 case counts, physical distance (in meters), and the time interval between paired streaks (in days). The script then performs a Wald two-tailed z-test to identify which factors are significantly correlated (relative to total case counts between streaks [i.e., the response variable]). The p-values computed from the Wald test are then reported. This script requires the 'multinom' function of the 'nnet' package in R.</p> <p>Two data files in which these models were derived (a list of all consecutive Toronto paired streaks for COVID-19 wave 2 and wave 3) are also included. </p>
References extracted from an archived version of the hyperfiction novel "The Unknown"
<p>This repository contains reference data extracted from an archived version of the hyperfiction novel "The Unknown". [1] Archiving was done with `wget`, data was written to a WARC file. We can't provide the WARC file here due to copyright reasons, but we're happy to provide the WARC file upon request, e.g. if you want to reproduce our findings starting from the original WARC.</p> <p>All reference data has been extracted using warc2graph, a Python package available via the Python Package Index and on Github: <a href="https://github.com/dla-marbach/warc2graph/">https://github.com/dla-marbach/warc2graph/</a> . This software is under development, extraction for this study was done with <a href="https://pypi.org/project/warc2graph/0.1.1/">version 0.1.1</a>.</p> <p>We use the term reference data as warc2graph extracts more information than what is defined as a link in the HTML 5 specification. For more info about the data model see <a href="https://elmcip.net/node/16380">(Schlesinger, Blessing, Hein, Ulrich 2021)</a>. [2]</p> <p>We provide the following files, which are all derivatives of the original WARC file. Gephi files contain layout decisions and reduced data according to our research question and as presented in our presentation of this study at <a href="https://dh2022.adho.org/">DH 2022</a>.</p> <ul> <li><strong>extracted-references.complete.the-unknown.hypertext-novel.gexf</strong>: full reference data extracted with warc2graph in GEXF format.</li> <li><strong>extracted-references.internal-only.the-unknown.hypertext-novel.gephi</strong>: internal links only (= no links pointing to sites other than unknownhypertext.com), Gephi format.</li> <li><strong>extracted-references.internal-only.no-navigation.the-unknown.hypertext.novel.gephi</strong>: internal links only, minus nodes that mirror the navigation menu on the bottom of very many pages, Gephi format.</li> <li><strong>external_links</strong>: list of outgoing links, plain text format</li> </ul> <p> </p> <p>[1] Gillespie, William, Scott Rettberg, and Dirk Stratton. ‘The Unknown’, 2002 1998. <a href="https://unknownhypertext.com/">https://unknownhypertext.com/</a>.</p> <p>[2] Schlesinger, Claus-Michael, Mona Ulrich, André Blessing, and Pascal Hein. ‘Networks of Net Literature - Modelling, Extracting and Visualizing Link-Based Networks in the DLA Corpus of Net Literature’. Bergen: ELMCIP, 2021. https://elmcip.net/node/16380.</p>
Data archive for the peer-reviewed journal article "Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data"
<p>Data archive accompanying the peer-reviewed journal article "Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data". This article was accepted for publication in the journal <em>Atmospheric Measurement Techniques</em> in 2022. The original contributions presented in the study are included in the article and its supplementary information. The GRASP-OPEN model was used to perform forward calculations: this model is publicly available on the official GRASP website (https://www.grasp-open.com/; last access: 14 September, 2022). The specific GRASP-OPEN model outputs that were used for the study are contained in this data archive. </p>
Hash data files for "Hashes are not suitable to verify fixity of the public archived web"
<p>This work investigates the fixity of a set of archived webpages, or mementos. We conducted a study on 16,627 mementos from 17 public web archives. We replayed and downloaded the mementos 39 times using a headless browser over a period of 442 days and generated a hash for each memento after each download, resulting in 39 hashes per memento. The hashes were generated by creating Merkle trees to represent hashes at each level of the memento. A hash was generated for each resource used to construct the full webpage and then the hashes were combined to generate an overall hash for the composite memento.</p> <p>There are 39 data files, one for each download. </p> <p>The mementos downloaded come from the dataset at <a href="https://github.com/oduwsdl/mementos-fixity/">https://github.com/oduwsdl/mementos-fixity/</a></p>
Archive of NASA-Unified WRF model daily forecasting simulations for DOE TRACER IOP
<pre># Copyright 2022 NASA GSFC All rights reserved. # Creative commons attribution 4.0 international license NASA-Unified WRF model daily simulations for DOE TRACER IOP Document updated: 22 June 2022 Point of contact: Takamichi Iguchi (ESSIC UMD, Code612 NASA GSFC), takamichi.iguchi@nasa.gov Toshi Matsui (ESSIC UMD, Code612 NASA GSFC), toshihisa.matsui-1@nasa.gov Contents: ./READMEtracer.txt # this file ./namelist.wps.tracer_iop_31.template # namelist.wps file to configure WRF Pre-Processing System (WPS) ./namelist.input.real.tracer_iop_31.template # namelist.input file for NU-WRF model real.exe ./namelist.input.wrf.tracer_iop_31.template # namelist.input file for NU-WRF model wrf.exe ./${YYYY}${MM}${DD} # these directories contain files produced from 48-hours NU-WRF forecasting from 00UTC on ${YYYY}${MM}${DD}: pyplot_${YYYY}-${MM}-${DD}_${HH}${MN}${SC}.png # Plot for Composite radar reflectivity (dBZ) # PBL height (m) + 10-m horizontal wind (850hPa-level wind in plots before 06/02/2022), # OLR TOA (W m-2), and 5-mins-accumulated IC+CG lighting flash extent density (flash km-2) # Note that this composite dBZ is calculated from NSSL 2-moment microphysics for S-band, # not from POLARRIS radar simulator pyplot.gif # Gif annimation file combining the png plot files for 1~48 hours in the forecasting accprecip_${YYYY}-${MM}-${DD}_${HH}${MN}${SC}.png # Plot for 1, 3, 6-hours, and total accumulated surface precipitation (mm) accprecip.gif # Gif annimation file combining the png plot files for 1~48 hours in the forecasting polarris_zh_zdr_rh_vr_${YYYY}_${MM}${DD}_${HH}${MN}${SC}.png # Plot from POLARRIS radar simulator in NU-WRF for QCed Reflectivity (dBZ), # differential reflectivity (dB), cross-polar correlation (-), and # radial velocity (m s-1) at 0.5 degree elevation angle polarris_zh_zdr_rh_vr.gif # Gif annimation file combining the png plot files roughly every hour # for 1~48 hours in the forecasting polarris_zh_4sweeps_${YYYY}_${MM}${DD}_${HH}${MN}${SC}.png # Plot from POLARRIS radar simulator in NU-WRF for QCed Reflectivity (dBZ) # at 0.5, 1.8, 4.0 and 8.0 degree elevation angles polarris_zh_4sweeps.gif # Gif annimation file combining the png plot files roughly every hour # for 1~48 hours in the forecasting # following files are produced 3 days late # day1 represent the first 0-24hr forecast, day2 represents the 24-48hr forecast. CFAD_con_day?.png # Convective part of Contoured Frequency of Altitude Diagrams CFAD_str_day?.png # Stratiform part of Contoured Frequency of Altitude Diagrams QVP_con_day?.png # Convective part of QVP-like domain-mean radar profiles QVP_str_day?.png # Stratiform part of QVP-like domain-mean radar profiles RadarFrac_day?.png # Composite Radar Horizontal Fraction (0-1) by different minimum reflectivity thresholds</pre>
Data archive: Trophic structure of cold-water coral communities revealed from the analysis of tissue isotopes and fatty acid composition
<p>Data belonging to the paper: </p> <p>Dick van Oevelen, Gerard C. A. Duineveld, Marc S. S. Lavaleye, Tina Kutti and Karline Soetaert (2017) Trophic structure of cold-water coral communities revealed from the analysis of 55 tissue isotopes and fatty acid composition. Marine Biology Research, DOI: https://doi.org/10.1080/17451000.2017.1398404</p> <p>Abstract:</p> <p>The trophic structure of cold-water coral reef communities at two contrasting locations, the 800-<br> m deep Belgica Mounds (Irish margin) and 300-m deep Træna reefs (Norwegian Shelf), was<br> investigated using stable isotope (δ13C and δ15N) and fatty-acid composition analysis. A<br> broad range of specimens, with emphasis on (commercial) fish species, and organic matter<br> sources were sampled using a variety of tools. Irrespective of the environmental and<br> geographical setting, the δ15N values indicated that the food web encompasses roughly 1.5<br> to 3 trophic levels. Mobile echinoderms, i.e. sea urchins and sea stars, had highest δ15N<br> values, indicative of a high trophic position in the food web. The fraction of bacterial fatty<br> acids in reef fauna was generally low (<5%), indicating that enhanced bacterial production in<br> the water column through seafloor seepage of nutrients (‘hydraulic theory’) does not form a<br> significant energy pathway into the food web. The high fraction of algal and essential fatty<br> acids in reef fauna and fish at both locations indicates a close coupling with surface<br> productivity, but the transport mechanism depends on the hydrographic setting. At Træna,<br> Calanus copepods and euphausiids form an additional link between primary production and<br> fish, which is largely absent at Belgica Mounds. At Belgica Mounds, the reef community is<br> primarily supported by phytodetritus, as evidenced by the high contribution of algal fatty<br> acids in faunal tissue and seasonal chlorophyll a deposition and marine snow at the reef. The<br> environmental setting of cold-water coral reefs influences the structure of the associated<br> food web.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.