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1,298 results for “Archiving”
Video: Crashkurs Digitale Langzeitarchivierung - Das Referenzmodell Open Archival Information System (OAIS)
<p>Dieser Crashkurs stellt das Referenzmodell Open Archival Information System (OAIS) vor, das die verschiedenen Tätigkeitsbereiche eines Langzeitarchivs beschreibt. Als internationaler Standard (ISO 14721) bietet das OAIS-Modell eine Kommunikationsgrundlage zum Thema der Digitalen Langzeitarchivierung. So sind zentrale OAIS-Begriffe wie Preservation Planning, Archival Information Package und Designated Community in der Langzeitarchivierungs-Community etabliert.</p> <p>In dieser Einführung werden die Aufgaben eines Digitalen Langzeitarchivs anhand der verschiedenen OAIS-Funktionseinheiten beschrieben. Ebenso bietet der Crashkurs einen Überblick über die verschiedenen Verarbeitungsstadien von Informationspaketen (SIP, AIP, DIP) in einem Langzeitarchiv.</p> <p>Dieser Crashkurs wurde als Lehrvideo für das Teil-Modul "Digitale Langzeitarchivierung" des <a href="https://www.th-koeln.de/weiterbildung/zertifikatskurs-data-librarian_63393.php">Zertifikatskurses "Data Librarian"</a> erstellt. Der Zertifikatskurs wurde 2019/2020 vom Zentrum für Bibliotheks- und Informationswissenschaftliche Weiterbildung der Technischen Hochschule Köln unter wissenschaftlicher Leitung von Prof. Dr. Konrad Förstner ausgerichtet.</p>
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>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL winter wheat simulations
<p>This data set contains output data from simulations with the model LPJmL for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL spring wheat simulations
<p>This data set contains output data from simulations with the model LPJmL for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</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>
SAA2017 TAGS Tweet Archive
<p>An open archive of Tweets from SAA2017, the Society for American Archaeology's 82nd Annual Meeting, Vancouver, BC, Canada.</p>
Comprehensive Archive of Substellar and Planetary Accretion Rates
<p>Comprehensive Archive of Substellar and Planetary Accretion Rates (CASPAR) is a compilation of 1000+ published and uniformly derived accretion rates for brown dwarfs, planets, and stars. It includes photometry, astrometry, physical stellar parameters, and accretion properties. The archive is composed of two parts: (1) the Literature Database, containing the original published data, and (2) CASPAR, a uniformly rederived version with physical stellar and accretion parameters updated for consistently with: Gaia distances, <a href="https://ui.adsabs.harvard.edu/abs/2015A%26A...577A..42B/abstract">Baraffe+2015</a> evolutionary models, and <a href="https://ui.adsabs.harvard.edu/abs/2017A%26A...600A..20A/abstract">Alcalà+2017</a> scaling relations. </p> <p>The living catalogue is found as a Google spreadsheet below. Please check the spreadsheet for updates and additions. </p> <p><a href="https://bit.ly/CASPARdb">https://bit.ly/CASPARdb</a></p> <p>The first public release is posted here as .csv files and published in <a href="https://ui.adsabs.harvard.edu/abs/2023AJ....166..262B/abstract">Betti+2023</a>, and are accretion rates published prior to early 2022. When using data from the Literature Database, please cite the individual papers from which the data come from (ADS links are given in the original reference column in the google spreadsheet). Using CASPAR, please cite <a href="https://ui.adsabs.harvard.edu/abs/2023AJ....166..262B/abstract">Betti+2023 </a>and for research benefiting from this database, please cite this Zenodo post. </p> <p>Contributions to CASPAR can be sent via Google form at: <a href="https://forms.gle/wZRg9NCg42WPX8Cd9">https://forms.gle/wZRg9NCg42WPX8Cd9</a></p> <p> </p> <p>-----</p> <p>Description of this Zenodo inspired by UltracoolSheet: <a href="https://doi.org/10.5281/zenodo.4169085">https://doi.org/10.5281/zenodo.4169085</a></p>
COVID information commons archive
<p>The COVID Information Commons (CIC) is an open website portal and community to facilitate knowledge-sharing and collaboration across various COVID research efforts, funded by the <a href="https://beta.nsf.gov/funding/initiatives/convergence-accelerator" target="_blank" rel="noopener">NSF Convergence Accelerator</a> and the <a href="https://beta.nsf.gov/tip/latest" target="_blank" rel="noopener">NSF Technology, Innovation and Partnerships Directorate</a>. The CIC serves as an open resource for researchers, students, and decision-makers from academia, government, not-for-profits and industry to identify collaboration opportunities, to leverage each other's research findings, and to accelerate the most promising research to mitigate the broad societal impacts of the COVID-19 pandemic.</p> <p>The CIC was developed as a collaborative proposal led by the <a href="http://nebigdatahub.org/" target="_blank" rel="noopener">Northeast Big Data Innovation Hub</a>, hosted by Columbia University, in collaboration with the <a href="https://midwestbigdatahub.org/" target="_blank" rel="noopener">Midwest Big Data Innovation Hub</a>, <a href="https://southbigdatahub.org/" target="_blank" rel="noopener">South Big Data Innovation Hub</a>, and <a href="https://www.westbigdatahub.org/" target="_blank" rel="noopener">West Big Data Innovation Hub</a>. It was funded by the NSF Convergence Accelerator (<a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028999&HistoricalAwards=false" target="_blank" rel="noopener">NSF #2028999</a>) in May 2020 and launched in July 2020. The initial focus of the CIC website was on the 723 NSF-funded COVID Rapid Response Research (RAPID) projects funded in 2020. The CIC-E: COVID Information Commons Extension for Pandemic Recovery project was proposed and funded in 2021 (<a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2139391&HistoricalAwards=false" target="_blank" rel="noopener">NSF #2139391</a>) by the <a href="https://covidinfocommons.datascience.columbia.edu/content/project-team">CIC project team</a> with the goal to increase researcher collaboration across NSF and NIH awardees and with global collaborators, as we continue to combat the novel coronavirus, and glean learnings for future uses of innovations developed for COVID response and recovery, including potential insights which can be leveraged for future pandemics.</p> <p>The CIC extension launched on June 30, 2022 increasing the corpus of awards from just NSF to include NIH-funded COVID related awards, both present and past, through all funding vehicles, in pertinent areas of COVID research, response and recovery. The CIC-extension provides more opportunity for multi-agency and multidisciplinary research collaboration as all the Principal Investigators (PIs) for awards in the CIC are invited to present their research and collaborate on CIC Research Lighting Talk Webinars and Collaboration Sessions.</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>
PheKnowLator Human Disease Knowledge Graph Benchmarks Archive
<h2><strong>PKT Human Disease KG Benchmark Builds</strong></h2> <p>The PheKnowLator (PKT) Human Disease KG (PKT-KG) was built to model mechanisms of human disease, which includes the Central Dogma and represents multiple biological scales of organization including molecular, cellular, tissue, and organ. The knowledge representation was designed in collaboration with a PhD-level molecular biologist (<a href="https://user-images.githubusercontent.com/8030363/195469903-86598760-40b7-4126-857c-3d6368305a86.png">Figure</a>). </p> <p>The <strong>PKT Human Disease KG</strong> was constructed using 12 OBO Foundry ontologies, 31 Linked Open Data sets, and results from two large-scale experiments (<a href="https://doi.org/10.48550/arXiv.2307.05727">Supplementary Material</a>). The 12 OBO Foundry ontologies were selected to represent chemicals and vaccines (i.e., ChEBI and Vaccine Ontology), cells and cell lines (i.e., Cell Ontology, Cell Line Ontology), gene/gene product attributes (i.e., Gene Ontology), phenotypes and diseases (i.e., Human Phenotype Ontology, Mondo Disease Ontology), proteins, including complexes and isoforms (i.e., Protein Ontology), pathways (i.e., Pathway Ontology), types and attributes of biological sequences (i.e., Sequence Ontology), and anatomical entities (Uberon ontology). The RO is used to provide relationships between the core OBO Foundry ontologies and database entities.</p> <p>The <strong>PKT Human Disease KG</strong> contained 18 node types and 33 edge types. Note that the number of nodes and edge types reflects those that are explicitly added to the core set of OBO Foundry ontologies and does not take into account the node and edge types provided by the ontologies. These nodes and edge types were used to construct 12 different PKT Human Disease benchmark KGs by altering the Knowledge Model (i.e., class- vs. instance-based), Relation Strategy (i.e., standard vs. inverse relations), and Semantic Abstraction (i.e., OWL-NETS (yes/no) with and without Knowledge Model harmonization [OWL-NETS Only vs. OWL-NETS + Harmonization]) parameters. Benchmarks within the PheKnowLator ecosystem are different versions of a KG that can be built under alternative knowledge models, relation strategies, and with or without semantic abstraction. They provide users with the ability to evaluate different modeling decisions (based on the prior mentioned parameters) and to examine the impact of these decisions on different downstream tasks.</p> <p>The Figures and Tables explaining attributes in the builds can be found <a href="https://github.com/callahantiff/PheKnowLator/wiki/Archived-Builds">here</a>.</p> <p> </p> <h3><strong>Build Data Access</strong></h3> <h4><strong>Important Build Information</strong></h4> <p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed this Zenodo-based archive for the builds. While the original GCP resources contained all of the resources needed to generate the builds, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the logs associated with each build.</p> <p>🗂 For additional information on the KG file types please see the following <a href="https://github.com/callahantiff/PheKnowLator/wiki/KG-Construction#table-knowledge-graph-build-output">Wiki page</a>, which is also available as a download from this repository (PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx). </p> <h4><strong>v1.0.0</strong></h4> <ul> <li>KGs: <a href="../doi/10.5281/zenodo.7030200">https://zenodo.org/doi/10.5281/zenodo.7030200</a></li> <li>Embeddings: <a href="../doi/10.5281/zenodo.7030188">https://zenodo.org/doi/10.5281/zenodo.7030188</a></li> </ul> <h4><strong>All Other Build Versions</strong></h4> <p><strong>Class-based Builds</strong></p> <p><em>Standard Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029957">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180239">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180539">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180774">MAY2021</a>;<a href="../doi/10.5281/zenodo.8180825"> JUN2021</a>; <a href="../doi/10.5281/zenodo.8180972">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183987">AUG2021</a>;<a href="../doi/10.5281/zenodo.8184090"> SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184131">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184205">NOV2021</a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029953">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180255">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180545">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180772">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180827">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180974">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183989">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184088">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184133">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184208">NOV2021</a></li> </ul> </li> </ul> <p><em>Inverse Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029893">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180269">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180550">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180766">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180829">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180976">JUL2021</a>;<a href="../doi/10.5281/zenodo.8183991"> AUG2021</a>; <a href="../doi/10.5281/zenodo.8184086">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184135">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184210">NOV2021</a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029921">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180279">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180555">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180768">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180833">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180982">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183993">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184084">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184137">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184212">NOV2021</a></li> </ul> </li> </ul> <p><strong>Instance-based Builds</strong></p> <p><em>Standard Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029941">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180333">JAN2021</a>;<a href="../doi/10.5281/zenodo.8180558"> FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180764">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180835">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180984">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183995">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184082">SEP2021 </a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184139">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184216">NOV2021 </a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029939">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180335">JAN2021</a>;<a href="../doi/10.5281/zenodo.8180564"> FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180762">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180837">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180986">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183997">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184080">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184141">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184218">NOV2021</a></li> </ul> </li> </ul> <p><em>Inverse Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029945">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180338">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180588">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180758">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180878">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180992">JUL2021</a>; <a href="../doi/10.5281/zenodo.8184001">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184078">SEP2021 </a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184143">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184220">NOV2021</a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029919">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180340">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180584">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180756">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180823">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180996">JUL2021</a>; <a href="../doi/10.5281/zenodo.8184003">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184076">SEP2021 </a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184145">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184222">NOV2021</a></li> </ul> </li> </ul>
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>
National Archives of India. Plan. Fort of Dhar, 1860.
<p>National Archives of India. Plan. Fort of Dhar, 1860 (registration number 1841-60-74).<strong> © National Archives of India. Not to be reproduced without written permission.</strong></p>
Figure 2 in Archival sea turtles in National Zoological Collections of Zoological Survey of India
Figure 2. Representatives of the archival Sea turtle specimens (the Green Sea Turtle, Chelonia mydas) preserved in Zoological Survey of India, Kolkata. A. Hatchling of C. mydas, Reg. No. ZSI 14544 (dorsal view, wet collection). B. Hatchling of C. mydas, Reg. No. ZSI 14543 (ventral view, wet collection). C. Eggs of C. mydas (wet collection). D. Skull of C. mydas, Reg. No. ZSI 389 (1404) (lateral view, dry collection). E. Adult individuals of C. mydas, Reg. No. ZSI 22489 (lateral view, wet collection).
Figure 1 in Archival sea turtles in National Zoological Collections of Zoological Survey of India
Figure 1. Representatives of the archival Sea turtle specimens (the Hawksbill Sea Turtle, Eretmochelys imbricata) preserved in Amphibia and Reptilia gallery of Indian museum, Kolkata and morphometric measurements. HD= Head diameter, FL= Forelimb length, HL= Hindlimb length, CCL=Curved carapace length, CPL= Curved plastron length, CCW= Curved carapace width, CPW= Curved plastron width, TL= Total length.
Pyricularia MAX effectors Web Site Archive
<p><span>Collection of validated MAX AlphaFold models</span></p>
UNEP GEMS/Water Global Freshwater Quality Archive
<p>Large-sample datasets are essential in hydrological science to support modelling studies and global assessments. The present dataset compiles all freshwater quality data that is available under open data policy (CC BY 4.0 or equivalent) at the GEMStat database for global water quality (<a href="http://www.gemstat.org">www.gemstat.org</a>). It includes over 20,000,000 measurements on 608 water quality parameters, covering 13,660 stations in 37 countries over the time period from 1906 to 2023.</p> <p>GEMStat is operated by the GEMS/Water programme of the United Nations Environment Programme (UNEP) and hosted at the International Centre for Water Resources and Global Change (ICWRGC) and the German Federal Institute of Hydrology (BfG). The data in GEMStat is provided by National Hydrological Services of UN member states.</p> <p>The data available here at Zenodo consists of a zip archive with individual csv files for different parameter groups. Three csv files provide metadata information on stations (GEMStat_station_metadata.csv), parameters (GEMStat_parameter_metadata.csv) and analytical methods (GEMStat_method_metadata.csv). A text file (README_output_format.txt) gives information on the different entriy classes in the data files.</p> <p>If you have querries regarding the dataset, please contact the GEMS/Water Data Centre via email to <a href="mailto:gwdc@bafg.de">gwdc@bafg.de</a>.</p> <p><strong>Version v2</strong></p> <p>An error was corrected that led to overestimation for all parameters in Version v1.</p> <p>To avoid further download of Version v1, access to it has been restricted while Version v2 is publicly available.</p>
LegacyVegetation: Northern Hemisphere reconstruction of past plant cover and total tree cover from pollen archives of the last 14 ka
Open the record for dataset details and reuse information.
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International Brain Laboratory public data
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OpenNeuro
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