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306 results for “data archive”
Ayres 2019: Quantitative Guidelines for Establishing and Operating Soil Archives (repackaging of occurrences published by the NEON Biorepository Data Portal)
Ayres, E. 2019. Quantitative Guidelines for Establishing and Operating Soil Archives. Soil Science Society of America Journal, 83(4): 973-981. https://doi.org/10.2136/sssaj2019.02.0050
Plant aboveground biomass data: BAC: Biodiversity and Climate (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/124/5, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cdr/386/8. The abstract below was extracted from the Level 0 data package and is included for context: Climate changes forecast for our region by GCM???s and shifts in biodiversity and composition each have the potential to alter ecosystem functioning; their interactive effects are unknown. The "BAC" experiment is designed to determine the direct and interactive effects of plant species numbers, plant community composition, temperature, and precipitation on 11 productivity, C and N dynamics, stability, and plant, microbe, and insect species abundances in CDR grassland ecosystems.
SGS-LTER Standard Production Data: 1983-2008 Annual Aboveground Net Primary Production on the Central Plains Experimental Range, Nunn, Colorado, USA 1983-2008, ARS Study Number 6 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/325/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/700/1. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The objective of the long-term ANPP study is to monitor long-term net above ground primary production of the shortgrass steppe community by species. There are 6 sites: ridgetop (ridge), midslope (mid), swale, ESA (replicate 1 not 2), Section 25 (SEC 25), and owl-creek (OC). Each site is located in a different landscape position or soil type on the shortgrass steppe and may be grazed or not. Ridgetop, midslope and swale are grazed and are sampled along a catena. Section 25 is grazed and is located in an upload grassland. ESA is an ungrazed upland grassland an is the control from the Ecosystem Stress Area experiment. Owl Creek is ungrazed and is located in the lowland along the owl creek drainage. There are 3 transects with 5 plots in each transect. Plots in the grazed
Catalog of NCBI sequence read archive (SRA) data for salamanders at the Hubbard Brook Experimental Forest 2012-2021
This project was designed to describe fine-scale population genetic differentiation of the stream salamander Gryinophilus porphyriticus among five study streams in the Hubbard Brook Experimental Forest. The data are paired with intensive capture-recapture data to assess direct fitness effects of individual genetic diversity, including effects of individual multilocus heterozygosity on stage-specific survival probabilities. This dataset publishes a manifest of the genomic sequence reads submitted to the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). These samples are published at NCBI under the BioProject ID 1090913 (https://www.ncbi.nlm.nih.gov/bioproject/1090913). The tables here include sample metadata and the NCBI URLs to each sample. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Long-term fish abundance data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/346/6, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/356/3. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in the relative abundance of fish populations representing nine species in Wisconsin lakes. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Data archive for the peer-reviewed journal article "Variability in the mass absorption cross-section of black carbon (BC) aerosols is driven by BC internal mixing state at a central European background site (Melpitz, Germany) in winter""
<p>Data archive for figures accompanying the peer-reviewed journal article "Variability in the mass absorption cross-section of black carbon (BC) aerosols is driven by BC internal mixing state at a central European background site (Melpitz, Germany) in winter". In 2020 this article was accepted for publication in the journal <em>Atmospheric Chemistry and Physics</em>. Data are uploaded in the form of Igor Pro experiment files (.pxp).</p>
Electron density and altitude of the main ionospheric peak of Mars as observed by Mars Express instruments. Archived data for the paper "Seasonal and geographical variability of the Martian ionosphere from Mars Express observations", submitted to JGR-Planets
<p>This repository contains archived data for the manuscript "Seasonal and geographical variability of the Martian ionosphere from Mars Express observations", published in Journal of Geophysical Research-Planets. Details about the methods to generate the data can be found in the paper.</p> <p>5 data files plus 2 readme text files are included.</p> <p>The file MEx_ionpeak.dat (described in the readme file README_ionpeak.txt) contains the peak electron densities and peak altitudes resulting from 34539 observations. Each record includes 14 columns. The content of each column is:</p> <p>Column 1: Instrument providing the observation (MARSIS or MaRS)<br> Column 2: Mars Year at which the observation was obtained (from MY27 to MY33)<br> Column 3: Solar Longitude (Ls) of the observation (unit: degrees)<br> Column 4: Latitude of the observation (unit: degrees)<br> Column 5: Longitude of the observation (unit: degrees)<br> Column 6: Solar Zenith Angle (SZA) of the observation (unit: degrees)<br> Column 7: F10.7 solar proxy index at 1 Astronomic Unit (unit: solar flux units)<br> Column 8: Peak electron density measured by the instrument (unit: cm-3)<br> Column 9: Peak electron density at the subsolar point, i.e., corrected for the SZA variation (unit: cm-3)<br> Column 10: Peak electron density at the subsolar point and at F10.7 (1AU)=100, i.e., corrected for the SZA and the solar radiation output variations (unit: cm-3)<br> Column 11: Peak electron density at the subsolar point, at F10.7 (1AU)=100 and corrected for the seasonal variation (unit: cm-3)<br> Column 12: Peak altitude measured by the instrument (unit: km)<br> Column 13: Peak altitude at the subsolar point, i.e. corrected for the SZA variation (unit: km)<br> Column 14: Peak altitude at the subsolar point and corrected for the seasonal variation (unit: km)</p> <p> </p> <p>The files eprofiles_MaRS.dat, eprofiles_MARSIS_prof1.dat, eprofiles_MARSIS_prof2.dat and eprofiles_MARSIS_prof3.dat contain 4 electron density profiles. They are described in the file README_eprofiles.txt. Each file includes 2 columns, the first one being the altitude (unit: km) and the second one the electron density (unit: cm-3).</p> <p> </p> <p>Contact: Francisco Gonzalez-Galindo, ggalindo@iaa.es<br> </p>
Data archive for the journal article: "Comparison of co–located rBC and EC mass concentration measurements during field campaigns at several European sites"
<p>Data archive accompanying the peer-reviewed journal article "Comparison of co–located rBC and EC mass concentration measurements during field campaigns at several European sites". In January 2021 this article was accepted for publication in the journal <em>Atmospheric Measurement </em><em>Techniques</em>. Data are uploaded in the form of Igor 8.0 graphics source files (.pxp) and data exported to Excel spreadsheet (.xlsx).</p>
Checkbot API raw results from Libraries, Archives and Museums websites for evaluating a data-driven Search Engine Optimization methodology
<p>Results from Checkbot API to measure and collect 341 websites compatibility on multiple SEO variables (34 variables). Checkbot API indexes the website's code to find features capable of impacting SEO performance. Each website has been tested with the maximum number of links allowed to be crawled equally to 10.000 per test. In this way, we retrieved data about the overall websites performance including their sub-pages, and not only the main domain names. A scale from 0 (lowest rate) to 100 (highest rate) was adopted for each examined variable. This constitutes a useful managerial indicator of dealing with the quantification of websites performance while avoiding complex measurement systems that are difficult to be adopted by administrators. Websites tested were also categorized by the CMS type used. More information about the variables and the meaning of the results can be found at https://www.checkbot.io/ </p>
Raw data used for COI delineation of the Eupolybothrus species: Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
<p>Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar</p>
Data archive for 7-GridPix 'Septemboard' detector taken at CAST (2017/18)
<h1>Data archive for 7-GridPix 'Septemboard' CAST detector</h1> <p>This data archive contains datasets related to the 7-GridPix<br>'Septemboard' detector used at the CERN Axion Solar Telescope (CAST)<br>experiment in 2017/18.</p> <p>This archive assumes familiarity with the operation of the Septemboard<br>detector at CAST and the PhD thesis it was used in. Once the thesis is<br>published, I will update the Zenodo meta data to include a link to the<br>thesis. For the time being see</p> <p><a href="https://phd.vindaar.de" target="_blank" rel="noopener">https://phd.vindaar.de</a></p> <p>The archive is split into three different files. For each file an<br>explanation follows below.</p> <p>- <code>raw_data_gridpix_CAST_2017_18.tar</code> :: A single TAR ball of the entire raw<br> data recorded at CAST (and related).<br>- <code>reco_data_gridpix_CAST_2017_18.tar</code> :: A single TAR ball of the<br> entire reconstructed data computed from the raw data.<br>- <code>miscResourcesArchive.tar.gz</code> :: A single gzipped TAR ball of a large<br> number of miscellaneous files. </p> <p>As the latter two archives contain a large number of files, a<br><code>*_list_of_files.txt</code> file is provided, which contains a <code>tree</code> view<br>of the entire TAR ball.</p> <h2><code>raw_data_gridpix_CAST_2017_18.tar</code> - Raw data archive</h2> <p>This file contains all raw data recorded with the aforementioned<br>detector. Raw data means it is the data produced by the <a href="https://github.com/Vindaar/TOS" target="_blank" rel="noopener">Timepix Operating Software (TOS).</a></p> <p>The archive is a single TAR ball, which contains multiple<br>directories. They are split by the date in which they were taken and<br>their purpose.</p> <p>The directory structure is as follows:<br><code>├── 2017</code><br><code>│ ├── CalibrationRuns</code><br><code>│ ├── DataRuns</code><br><code>│ ├── XrayFingerRuns</code><br><code>│ └── development</code><br><code>├── 2018</code><br><code>│ ├── CalibrationRuns</code><br><code>│ ├── DataRuns</code><br><code>│ ├── FADC_100ns_50ns_comparisons</code><br><code>│ │ ├── 100ns</code><br><code>│ │ └── 50ns</code><br><code>│ └── XrayFingerRuns</code><br><code>├── 2018_2</code><br><code>│ ├── BadRuns</code><br><code>│ ├── CalibrationRuns</code><br><code>│ └── DataRuns</code><br><code>└── CDL_2019</code></p> <p><code>- 2017 :: Contains 'Run-2' data taken in 2017.</code><br><code> - CalibrationRuns: 55Fe runs from CAST</code><br><code> - DataRuns: Background runs from CAST (contains solar tracking data)</code><br><code> - XrayFingerRuns: Single X-ray finger run from before data taking,</code><br><code> not directly useful.</code><br><code> - development: Contains runs from development in 2017, in particular</code><br><code> the two runs showing excessive sparking from before the water</code><br><code> cooling was installed.</code><br><code>- 2018 :: Contains 'Run-2' data taken in 2018 (up to Apr 2018).</code><br><code> - CalibrationRuns: 55Fe runs from CAST</code><br><code> - DataRuns: Background runs from CAST (contains solar tracking data)</code><br><code> - XrayFingerRuns: Single X-ray finger run, taken after Run-2 data taking.</code><br><code> Useful.</code><br><code> - FADC: Contains laboratory runs with the detector mounted</code><br><code> pointing towards the zenith. Multiple runs with an FADC</code><br><code> integration time of 50ns and multiple with 100ns.</code><br><code>- 2018_2 :: Contains all 'Run-3' data taken in 2018.</code><br><code> - CalibrationRuns: 55Fe runs from CAST</code><br><code> - DataRuns: Background runs from CAST (contains solar tracking data)</code><br><code> - BadRuns: A single run to be ignored. Faulty.</code><br><code>- CDL_2019 :: Data taken in the CAST detector lab (CDL) behind an</code><br><code> X-ray tube.</code></p> <h2><code>reco_data_gridpix_CAST_2017_18.tar</code> - Reconstructed data archive</h2> <p>This data archive contains all reconstructed data of the dataset taken<br>with the 'Septemboard' detector at CAST in 2017/18. The<br>reconstruction of the data is done via the tools part of <a href="https://github.com/Vindaar/TimepixAnalysis" target="_blank" rel="noopener">TimepixAnalysis.</a></p> <p>It is a single TAR ball, which contains multiple directories. They are<br>split by the type of data mainly. The main data files are those named<br><code>Calibration/DataRuns_2017/8_Raw/Reco.h5</code> as well as the similarly<br>named <code>CDL</code> files. The naming follows that of the raw data archive. See below the directory structure for more details.</p> <p>The directory structure is as follows:<br><br><code>├── CDL_2019</code><br><code>│ ├── CDL_2019_Raw.h5</code><br><code>│ ├── CDL_2019_Reco.h5</code><br><code>│ └── calibration-cdl-2018.h5</code><br><code>├── CalibrationRuns2017_Raw.h5</code><br><code>├── CalibrationRuns2017_Reco.h5</code><br><code>├── CalibrationRuns2018_Raw.h5</code><br><code>├── CalibrationRuns2018_Reco.h5</code><br><code>├── DataRuns2017_Raw.h5</code><br><code>├── DataRuns2017_Reco.h5</code><br><code>├── DataRuns2018_Raw.h5</code><br><code>├── DataRuns2018_Reco.h5</code><br><code>├── FakeData</code><br><code>│ ├── fakeData_500k_0_to_3keV_decrease.h5</code><br><code>│ └── fakeData_500k_uniform_energy_0_10_keV.h5</code><br><code>├── lhoodOutput</code><br><code>│ ├── lhood_lnL_17_11_23_septem_fixed</code><br><code>│ │ ├── lhood_c18_R2_crAll_sEff_0.7_lnL.h5</code><br><code>│ │ ├── lhood_c18_R2_crAll_sEff_0.7_lnL.log</code><br><code>│ │ ├── .... similar other files</code><br><code>│ └── lhood_mlp_17_11_23_adam_tanh30_sigmoid_mse_82k</code><br><code>│ ├── lhood_c18_R2_crAll_sEff_0.85_mlp_mlp_tanh_sigmoid_MSE_Adam_30_2checkpoint_epoch_82000_loss_0.0249_acc_0.9662.h5</code><br><code>│ ├── lhood_c18_R2_crAll_sEff_0.85_mlp_mlp_tanh_sigmoid_MSE_Adam_30_2checkpoint_epoch_82000_loss_0.0249_acc_0.9662.log</code><br><code>│ ├── .... similar other files</code><br><code>├── limitOutput</code><br><code>│ ├── lhood_limits_21_11_23</code><br><code>│ │ ├── lhood_c18_R2_crAll_sEff_0.85_scinti_fadc_line_mlp_mlp_tanh_sigmoid_MSE_Adam_30_2checkpoint_epoch_82000_loss_0.0249_acc_0.9662_vQ_0.99</code><br><code>│ │ │ ├── mc_limit_lkMCMC_skInterpBackground_nmc_15000_uncertainty_ukUncertain_σs_0.0281_σb_0.0028_posUncertain_puUncertain_σp_0.0500.csv</code><br><code>│ │ │ ├── .... similar other files</code><br><code>│ │ ├── lhood_c18_R2_crAll_sEff_0.85_scinti_fadc_septem_line_mlp_mlp_tanh_sigmoid_MSE_Adam_30_2checkpoint_epoch_82000_loss_0.0249_acc_0.9662_vQ_0.99</code><br><code>│ │ │ ├── mc_limit_lkMCMC_skInterpBackground_nmc_2500_uncertainty_ukUncertain_σs_0.0281_σb_0.0028_posUncertain_puUncertain_σp_0.0500.csv</code><br><code>│ │ │ ├── .... similar other files</code><br><code>│ │ ├── lhood_c18_R2_crAll_sEff_0.85_scinti_fadc_septem_mlp_mlp_tanh_sigmoid_MSE_Adam_30_2checkpoint_epoch_82000_loss_0.0249_acc_0.9662_vQ_0.99</code><br><code>│ │ │ ├── .... more files</code><br><code>│ │ ├── Similar directories</code><br><code>│ ├── lhood_limits_axion_photon_11_01_24</code><br><code>│ │ │ ├── .... more files</code><br><code>│ └── lhood_limits_chameleon_12_01_24</code><br><code>│ │ │ ├── .... more files</code><br><code>28 directories, 249 files</code></p> <p><code>- Root of the archive ::</code><br><code> - ~CalibrationRuns2017/18_Raw~: Raw data files of the 55Fe calibration</code><br><code> runs taken during the CAST data taking.</code><br><code> - ~CalibrationRuns2017/18_Reco~: Fully reconstructed data of the</code><br><code> same.</code><br><code> - ~Data*~: Same schema for the actual CAST data, containing both</code><br><code> background and solar tracking data.</code><br><code>- ~FakeData~ :: A directory of two HDF5 files containing synthetic X-ray</code><br><code> data used for the training of MLPs as classifiers.</code><br><code>- ~lhoodOutput~ :: A directory containing a large number of files</code><br><code> containing the output files of the ~likelihood~ program part of</code><br><code> ~TimepixAnalysis~. That is, files containing clusters passing cuts</code><br><code> of different setups of classifiers and vetoes. These are the files</code><br><code> needed as inputs for background rate and limit calculations.</code><br><code>- ~limitOutput~ :: A directory of output results from limit</code><br><code> calculations.</code></p> <p>See the file <code>reco_data_gridpix_CAST_2017_18_list_of_files.txt </code>for a<br>list of all files contained.</p> <h2><code>miscResourcesArchive.tar.gz</code> - Miscellaneous files</h2> <p>This data archive contains a large amount of miscellaneous data<br>related to the 2017/18 data taking campaign of the 7-GridPix<br>'Septemboard' detector at CAST.</p> <p>In particular, to understand the context of the files stored in this<br>TAR ball, it is mandatory to read the extended version of the PhD<br>thesis as well as the additional notes (<code>StatusAndProgress</code> as well as<br>the <code>journal</code> linked under the URL linked at the top). Note that the<br>vast majority of these files is likely not of significant interest,<br>unless someone wishes to understand certain studies that were done. If<br>however, someone reads one of these files and wishes to look into any<br>of the referenced data files, I prefer to make them available.</p> <p>One particular set of interesting data is contained in the<br><code>MLP_snapshots</code> directory. It contains all snapshots of every MLP I<br>ever trained during the work on my thesis. This includes the best<br>performing MLP I eventually used for the results in my thesis.</p> <p>The other two directories contained are <code>phdResources</code> and<br><code>orgResources</code>. They are named such as they represent a <code>resources</code><br>directory part of my <code>phd</code> git repository and my <code>org</code> git repository<br>(the latter is a repository for miscellaneous notes and things). </p> <p>See the file <code>miscDataArchive_list_of_files.txt</code> for a<br>list of all files contained.</p>
Data archive for: Resting cells of Skeletonema marinoi assimilate organic compounds and respire by dissimilatory nitrate reduction to ammonium in dark, anoxic conditions
<p>Data archive for: “Resting cells of <em>Skeletonema marinoi</em> assimilate organic compounds and respire by dissimilatory nitrate reduction to ammonium in dark, anoxic conditions” <a href="https://doi.org/10.1111/1462-2920.16625">https://doi.org/10.1111/1462-2920.16625</a></p> <p> </p> <p>Dataset of single cell assimilation of organic/inorganic C/N by resting cells of the marine diatom <em>Skeletonema marinoi</em> captured using secondary ion mass spectrometry (SIMS) and stable isotopic tracers. The dataset also contains POC/PON changes over time during dormancy, DNRA (<sup>15</sup>N-NH<sub>4</sub><sup>+</sup> production), denitrification (<sup>15</sup>N-N<sub>2</sub> production) and a germination assay to determine survival rate, most probable number analysis (MPN). </p> <p>Two strains (GF04 and R05) were incubated in dark and anoxic conditions in two different incubation experiments.</p> <p>Incubation 1: Diatoms treated with antibiotics before entering dormancy compared to a control not treated with antibiotics then given <sup>15</sup>N-NO<sub>3</sub><sup>-</sup> in dark anoxic conditions.</p> <p>Incubation 2: Diatoms treated with antibiotics given, <sup>15</sup>N & <sup>13</sup>C urea, <sup>15</sup>N & <sup>13</sup>C urea + <sup>14</sup>N-NO<sub>3</sub><sup>-</sup>, <sup>13</sup>C-acetate, <sup>13</sup>C-acetate + <sup>15</sup>N-NO<sub>3</sub><sup>-</sup>, or <sup>15</sup>N-NO<sub>3</sub><sup>-</sup>.</p> <p>See the main manuscript for a extensive experimental setup.</p> <p> </p> <p><strong>Each file is uploaded as both a .CSV and .XLSX, so that you can choose which you prefer.</strong></p> <p><strong>DNRA_and_denitrification.csv/xlsx:</strong> DRNA and denitrification depending on volume (Incubation 1)</p> <p><strong>DNRA_per_cell.csv/xlsx:</strong> DNRA per cell (Incubation 1 & 2)</p> <p><strong>MPN_data.csv/xlsx:</strong> Most probable number analysis (Incubation 1 & 2)</p> <p><strong>POC_PON.csv/xlsx:</strong> POC and PON per cell and volume (Incubation 1 & 2)</p> <p><strong>SIMS_data.csv/xlsx:</strong> SIMS data (Incubation 1 & 2)</p> <p> </p> <p> </p>
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>
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>
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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.