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427 results for “OWL”
Barn owl diet and prey fluctuations in the Jura mountains, eastern France
<p>Based on pellet collection, the diet of the Barn Owl (<em>Tyto alba</em>) was studied over a 8-year period in the Jura mountains, eastern France, during two population surges of its main prey (common vole, <em>Microtus arvalis </em>and montane water vole, <em>Arvicola amphibius</em>); Small mammals were sampled by trapping and index methods. Results have been published in the Canadian Journal of Zoology (<a href="http://doi.org/10.1139/z10-011">Bernard et al. 2010</a>). Dominique Michelat collected Barn Owl pellets and identified prey items, Pierre Delattre, Jean-Pierre Quéré Jean-Pierre Damange and Patrick Giraudoux sampled small mammals. Patrick Giraudoux managed the data.</p> <p><a href="https://zenodo.org/record/6945677/files/Small_mammals_trapping.txt?download=1">Small_mammals_trapping.txt </a>is the file of the raw trapping results for small mammals (instant abundance index i<sub>t</sub> in the article)</p> <p><a href="https://zenodo.org/record/6945677/files/diet_smm.txt?download=1">diet_smm.txt</a> is a file with:</p> <ul> <li>the small mammal density computed by season (d<sub>t</sub> in the article, rough estimate of densities in number of individuals per ha for <em>Apodemus spp.</em>, <em>Myodes glareolus</em>, <em>Microtus arvalis</em>, or weighted interpolated i<sub>t</sub> for the other species)</li> <li>the ratios of each category of prey items on the total number of items collected in the church tower of three sites, <a href="https://www.openstreetmap.org/#map=16/46.9536/6.1189">Levier</a>, <a href="https://www.openstreetmap.org/search?whereami=1&query=46.9321%2C6.1666#map=16/46.9321/6.1666">Chapelle d'Huin</a> and <a href="https://www.openstreetmap.org/search?whereami=1&query=46.9382%2C6.1976#map=16/46.9382/6.1976">Le Souillot</a>.</li> </ul> <p>For details see the material and methods of the article.</p> <p><strong>FILE DESCRIPTION</strong></p> <p><a href="https://zenodo.org/record/6945677/files/diet_smm.txt?download=1">diet_smm.txt</a></p> <ul> <li>date, year and season: year on two digits, then P, E, A, H respectively for<em> Printemps</em> (Spring), <em>Été</em> (Summer), <em>Automne</em> (Autumn), <em>Hiver</em> (Winter)</li> <li>at_t, abundance index of <em>Arvicola amphibius</em> (ex A.<em> terrestris</em>)</li> <li>ap_t, rough density estimate of <em>Apodemus sp.</em></li> <li>cg_t, rough density estimate of <em>Myodes glareolus</em></li> <li>ma_t, rough density estimate of <em>Microtus arvalis</em></li> <li>sa_t, relative abundance of <em>Sorex spp.</em></li> <li>n_l, number of prey items at Levier</li> <li>ma_p_l, ratio of <em>M. arvalis prey</em> items at Levier</li> <li>at_p_l, ratio of <em>A. amphibius</em> prey items at Levier</li> <li>apcg_p_l, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Levier</li> <li>sa_p_l, ratio of <em>Sorex spp.</em> prey items at Levier</li> <li>au_p_l, ratio of other prey items at Levier</li> <li>n_ch, number of prey items at Chapelle d'Huin</li> <li>map_ch, ratio of <em>M. arvalis prey</em> items at Chapelle d'Huin</li> <li>at_p_ch, ratio of <em>A. amphibius</em> prey items at Chapelle d'Huin</li> <li>apcg_p_ch, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Chapelle d'Huin</li> <li>sa_p_ch, ratio of <em>Sorex spp. </em>prey items at Chapelle d'Huin</li> <li>au_p_ch, ratio of other prey items at Chapelle d'Huin</li> <li>n_ls, number of prey items at Le Souillot</li> <li>ma_p_ls, ratio of <em>M. arvalis prey</em> items at Le Souillot</li> <li>at_p_ls, ratio of <em>A. amphibius</em> prey items at Le souillot</li> <li>apcg_p_ls, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Le Souillot</li> <li>sa_p_ls, ratio of <em>Sorex spp.</em> prey items at Le Souillot</li> <li>au_p_ls, ratio of other prey items at Le Souillot</li> </ul> <p><a href="https://zenodo.org/record/6945677/files/Small_mammals_trapping.txt?download=1">Small_mammals_trapping.txt </a></p> <ul> <li>date, trapping period: digit 1-2, year; digit 3-4, month. Example: 8704 = April 1987.</li> <li>n_traplines_f, number of traplines in forest</li> <li>ap_f, average number of <em>Apodemus spp</em>. captured in forest</li> <li>cg_f, average number of <em>Myodes glareolus</em> captured in forest</li> <li>sa_f, average number of <em>Sorex spp</em>. captured in forest</li> <li>n_traplines_hfb, number of traplines in hedges and forest borders</li> <li>ap_hfb, average number of <em>Apodemus spp</em>. captured in hedges and forest borders</li> <li>cg_hfb, average number of <em>Myodes glareolus</em> captured in hedges and forest borders</li> <li>sa_hfb, average number of <em>Sorex spp.</em> captured in hedges and forest borders</li> <li>n_traplines_g, number of traplines in grassland</li> <li>ma_g, average number of Microtus arvalis captured in grassland</li> <li>sa_g, average number of <em>Sorex spp.</em> captured in grassland</li> </ul> <p><a href="https://zenodo.org/record/6945677/files/SmallMammalSamplingArea.kml?download=1">SmallMammalSamplingArea.kml</a> kml file locating the small mammal sampling area<br> </p>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><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 an archive for the builds on Zenodo. While the original GCP resources contained all associated files, 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 associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWL (v2.0.0 - May 2020)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-StandardRelations-OWL</i></p><p><strong>Build Date: </strong>May 10, 2020</p><p> </p><h3><strong>Important Build Information</strong></h3><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 an archive for the builds on Zenodo. While the original GCP resources contained all associated files, 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 associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-10%2C-2020">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWL (v2.0.0 - May 2020)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-InverseRelations-OWL</i></p><p><strong>Build Date: </strong>May 10, 2020</p><p> </p><h3><strong>Important Build Information</strong></h3><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 an archive for the builds on Zenodo. While the original GCP resources contained all associated files, 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 associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-10%2C-2020">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><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 an archive for the builds on Zenodo. While the original GCP resources contained all associated files, 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 associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWL (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><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 an archive for the builds on Zenodo. While the original GCP resources contained all associated files, 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 associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v2.0.0 - May 2020)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-InverseRelations-OWL</i></p><p><strong>Build Date: </strong>May 10, 2020</p><p> </p><h3><strong>Important Build Information</strong></h3><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 an archive for the builds on Zenodo. While the original GCP resources contained all associated files, 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 associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-10%2C-2020">here</a>.</li></ul>
Supporting Data for "Exploring ChatGPT-4 for Transforming Taxonomic Data into OWL: Lessons Learned and Implications for Ontology Development"
<p>Data from the trials with ChatGPT to generate OWL files for taxonomic data from the GBIF Backbone Taxonomy.</p> <p>Updates of version 2: additional prompts from the experiments with Gemini and DeepSeek.</p>
CE-MS data for Autonomous CE Mass-Spectra Examination (ACME) for the Ocean Worlds Life Surveyor (OWLS)
<p>These folders contain the original data used to develop the ACME software [1].</p> <p>The Golden and Silver dataset come from simulations. They underrepresent the complexity in the CE-MS observations but provide additional data with known peak locations and peak properties. For more information see [1]</p> <p>The Dev-, Train-, and Test-set contain CE-MS [2] observations of Mix25 (a standard set of 25 organic compounds relevant to astrobiology) and labels for peak locations from subject matter experts. </p> <p>The ACME software is available at: <br> https://github.com/JPLMLIA/OWLS-Autonomy </p> <p> </p> <p>When using the data please cite this dataset [3] and the two papers below. </p> <p>For further questions please reach out to:<br> Steffen Mauceri, Steffen.Mauceri@jpl.nasa.gov</p> <p> </p> <p>References:<br> [1] Mauceri, S., Lee, J., Wronkiewicz, M., et.al. (2022). Autonomous CE Mass-Spectra Examination (ACME) for the Ocean Worlds Life Surveyor (OWLS). (submitted) Earth and Space Science</p> <p>[2] Mora et al., F.(2021). Detection of biosignatures by capillary electrophoresis and mass spectrometry in the presence of salts relevant to missions to ocean worlds (submitted). Astrobiology.</p> <p>[3] 10.5281/zenodo.5849873</p> <p><br> © 2022. California Institute of Technology. Government sponsorship acknowledged</p>
OWL Reasoner Evaluation Results
<p>Correctness, performance and energy impact evaluation results produced with <a href="http://swot.sisinflab.poliba.it/evowluator">evOWLuator</a> for the following OWL reasoners:</p><ul><li><a href="https://bitbucket.org/dtsarkov/factplusplus">Fact++</a> (version 1.6.5);</li><li><a href="http://www.hermit-reasoner.com">HermiT</a> (version 1.3.8);</li><li><a href="http://jfact.sourceforge.net">JFact</a> (version 1.2.1);</li><li><a href="https://www.derivo.de/en/products/konclude/">Konclude</a> (version 0.6.2-544);</li><li><a href="http://swot.sisinflab.poliba.it/minime">Mini-ME</a> (version 2.0);</li><li><a href="http://swot.sisinflab.poliba.it/minime-swift">Mini-ME Swift</a> (version 1.0);</li><li><a href="https://github.com/stardog-union/pellet">Pellet</a> (version 2.3.1);</li><li><a href="http://trowl.org">TrOWL</a> (version 1.5).</li></ul><p>Ontologies mentioned in the csv files are part of the <a href="https://zenodo.org/record/10791">ORE 2014 Reasoner Competition</a> and <a href="https://bioportal.bioontology.org">BioPortal</a> datasets.</p><p><strong>Publication:</strong> <a href="http://sisinflab.poliba.it/Publications/2021/SBRGL21">A multiplatform energy-aware OWL reasoner benchmarking framework</a></p>
Supplemental dataset for Northern Spotted Owl (<i>Strix occidentalis caurina</i>) genome assembly version 1.0
<p><strong>StrOccCau_1.0_nuc.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the file that we deposited at DDBJ/ENA/GenBank as a Whole Genome Shotgun (WGS) project under accession NIFN00000000. It is is the file that you will most likely want to download if you would like to perform an alignment to this genome assembly. This file is the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without any contigs and scaffolds less than 1,000 nt and also without the contigs and scaffolds that we identified either as the mitochondrial genome sequence or as contaminant sequences.</p> <p><strong>StrOccCau_1.0_nuc_masked.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the repeat-masked (hard-masked) assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without any contigs and scaffolds less than 1,000 nt and also without the contigs and scaffolds that we identified either as the mitochondrial genome sequence or as contaminant sequences.</p> <p><strong>StrOccCau_1.0_mito.fa</strong> : This FASTA format file is the mitochondrial-genome-derived scaffold from the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012).</p> <p><strong>StrOccCau_1.0.gff.bz2</strong> : This gff format file compressed this file with bzip2 contains the gene annotations of StrOccCau_1.0_nuc.fa.</p> <p><strong>StrOccCau_1.0_transcripts.fa.bz2</strong> : This FASTA format file compressed this file with bzip2 contains the sequences of the gene transcript sequences of the genes annotated in StrOccCau_1.0.gff.</p> <p><strong>StrOccCau_1.0_proteins.fa.bz2</strong> : This FASTA format file compressed this file with bzip2 contains the protein sequences of the genes annotated in StrOccCau_1.0.gff.</p> <p><strong>StrOccCau_1.0_RM_homology_includes_LowComplexity.out.bz2</strong> : This file provides the repeat annotations produced by the homology-based masking of StrOccCau_1.0_nuc.fa that included masking of low complexity regions and simple repeats. We compressed this file with bzip2.</p> <p><strong>StrOccCau_1.0_RM_DeNovo_includes_LowComplexity.out</strong> : This file provides the repeat annotations produced by the de novo masking (which followed after first performing homology-based masking) of StrOccCau_1.0_nuc.fa that included masking of low complexity regions and simple repeats.</p> <p><strong>StrOccCau_1.0_RM_homology_no_LowComplexity.out.bz2</strong> : This file provides the repeat annotations produced by the homology-based masking of StrOccCau_1.0_nuc.fa that did not include masking of low complexity regions and simple repeats. We compressed this file with bzip2.</p> <p><strong>StrOccCau_1.0_RM_DeNovo_no_LowComplexity.out</strong> : This file provides the repeat annotations produced by the de novo masking (which followed after first performing homology-based masking) of StrOccCau_1.0_nuc.fa that did not include masking of low complexity regions and simple repeats.</p> <p><strong>StrOccCau_1.0_alignments_of_light_associated_genes.txt</strong> : This file provides alignments of light-associated gene orthologs as well as assemblies of transcriptome sequences in NEXUS format.</p> <p><strong>StrOccCau_1.0_nuc_masked_SpottedBarredOwl_variant_file.vcf.bz2</strong> : This is a raw, unfiltered variant call format file compressed with bzip2 that was generated after aligning both spotted owl and barred owl short read data aligned to StrOccCau_1.0_nuc_masked.fa.</p> <p><strong>StrOccCau_0.1.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012).</p> <p><strong>StrOccCau_0.1_masked.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the repeat-masked assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012).</p> <p><strong>StrOccCau_0.2.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without any contigs and scaffolds less than 1,000 nt.</p> <p><strong>StrOccCau_0.2_masked.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the repeat-masked assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without any contigs and scaffolds less than 1,000 nt.</p> <p><strong>StrOccCau_GapCloser_output_NoContamNoMito.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without the contigs and scaffolds that we later identified either as the mitochondrial genome sequence or as contaminant sequences.</p> <p><strong>Citations</strong> - if you utilize these data, please include these citations:</p> <p>Hanna ZR., Henderson JB., Wall JD., Emerling CA., Fuchs J., Runckel C., Mindell DP., Bowie RCK., DeRisi JL., Dumbacher JP. 2017a. Supplemental dataset for Northern Spotted Owl (<em>Strix occidentalis caurina</em>) genome assembly version 1.0. <em>Zenodo</em>. DOI: 10.5281/zenodo.822859.</p> <p>Hanna ZR., Henderson JB., Wall JD., Emerling CA., Fuchs J., Runckel C., Mindell DP., Bowie RCK., DeRisi JL., Dumbacher JP. 2017b. Northern Spotted Owl (Strix occidentalis caurina) Genome: Divergence with the Barred Owl (<em>Strix varia</em>) and Characterization of Light-Associated Genes. <em>Genome Biology and Evolution</em> 9:2522–2545. DOI: 10.1093/gbe/evx158.</p>
The Sonora Substellar Atmosphere Models. IV. Elf Owl: Atmospheric Mixing and Chemical Disequilibrium with Varying Metallicity and C/O Ratios (T- type Models)
<ul> <li><strong>Overview of V2: "The Sonora Substellar Atmosphere Models. V: A Correction to the Disequilibrium Abundance of CO2 for Sonora Elf Owl"</strong></li> </ul> <p> Version 2 of the Sonora Elf Owl Models updates the CO2 and PH3 abundances and spectra. As described in the Wogan et al. (2024) research note (URL OF NOTE GOES HERE), Version 1 of the models did not apply the CO2 quench approximation properly resulting in predicted CO2 abundances that were too small by several orders of magintude in some cases. Version 2 fixes this mistake, updating CO2 abundances and the emission spectra to reflect the new CO2 abundances. Version 2 also removes all spectra contributions of PH3 because Version 1 consistently contained too much PH3 absorption when compared to JWST data (Veiler et al. 2024, <a href="http://doi.org/10.3847/1538-4357/ad6759" target="_blank" rel="noopener noreferrer">http://doi.org/10.3847/1538-4357/ad6759</a>).</p> <p> </p> <ul> <li><strong>Overview of V1</strong></li> </ul> <p> The Sonora Elf Owl Models is a successor to the <a href="../records/5063476#:~:text=This%20particular%20set%20of%20model,g%20are%200.25%20or%200.5.">Sonora Bobcat</a> and <a href="../records/4450269">Sonora Cholla</a> models. The Sonora Elf Owl model grid includes cloud-free radiative-convective equilibrium model atmospheres with vertical mixing induced disequilibrium chemistry with sub-solar to super-solar atmospheric metallicities and Carbon-to-Oxygen ratio. The atmospheric models have been computed using the open-source radiative-convective equilibrium model <a href="https://natashabatalha.github.io/picaso/">PICASO</a>. The parameters included within this grid are effective temperature (<strong><em>Teff</em></strong>), gravity (<strong><em>log(g)</em></strong>), vertical eddy diffusion coefficient (<strong><em>log(Kzz)</em></strong>), atmospheric metallicity (<strong><em>[M/H]</em></strong>), and Carbon-to-Oxygen ratio (<strong><em>C/O</em></strong>).</p> <p>The ranges and increments of these parameters are described in the published paper.<br><br></p> <ul> <li><strong>Three grids available on three links</strong></li> </ul> <p><strong> The model grid has been presented using three Zenodo repositories. This repository has all the models between 575 to 1200 K (applicable for T- type objects). The models for Teff between 275 to 550 K (applicable for Y- type objects) are available in the Zenodo DOI :- <a href="../records/10381250">https://zenodo.org/records/10381250</a>. The models for Teff between 1300 to 2400 K (applicable for L- type objects) are available in the Zenodo DOI :- <a href="../records/10385987">https://zenodo.org/records/10385987</a>.</strong></p> <p> </p> <ul> <li><strong> File types and how to use them</strong></li> </ul> <p> The models have been presented in the Xarray format so that all the atmospheric properties including the T(P) profile, atmospheric chemistry, and thermal emission spectra can be accessed within the same files. A python based Jupyter notebook named "Reading and plotting Elf Owl Models.ipynb" has been also supplied which demonstrates how to open and use these files.</p> <ul> <li> <strong>Spectra</strong></li> </ul> <p> The emission spectra for each atmospheric model has been computed between 0.6 to 15 microns. The reported flux is in the units of erg/s/cm<sup>2</sup>/cm. Note that these fluxes need to be multiplied with R<sup>2</sup>/D<sup>2</sup> before comparing them with the typically observed flux of brown dwarfs/exoplanets. R is the radius of the object, and D is the distance here.</p> <div> </div> <div> <ul> <li><strong>Note on CH4</strong></li> </ul> </div> <p> As stated in <a href="https://ui.adsabs.harvard.edu/abs/2023ApJ...942...71M/abstract">Mukherjee et al. 2023 </a>our CH4 opacity is derived using the <a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab7a1a">Hargreaves et al. 2020</a> HITEMP line list and computed using the HAPI code (<a href="https://www.sciencedirect.com/science/article/abs/pii/S0022407315302466">Kochanov et al. 2016</a>). HAPI automatically pre-weights the isotopologues according to earth abundances that are listed on the HITRAN website (<a href="https://hitran.org/lbl/2?6=on" target="_blank" rel="noopener noreferrer">see here for CH4</a>). Therefore, users should note that there will be minor features of CH3D included in the models. Given the general absence of deuterated molecules in brown dwarfs (Teff>~300) we will include a second posting of models which includes the Elf Owl grid with <strong>only</strong> the major CH4 isotopologue (12C-H4).</p> <div> <ul> <li><strong>Note on PH3</strong></li> </ul> </div> <p> PH3 abundance is treated separately from the general disequilibrium scheme. This is because of the current non-detection of PH3 in many brown dwarf atmospheres (see citations in paper). The current PH3 treatment uses the chemical equilibrium treatment described in Visscher et al. However, after publishing this grid and using the model for analysis of high precision JWST data, we noticed that even the simple chemical equilibrium treatment which reduces the abundance, introduces a noticeable PH3 feature. Therefore in our v2 of this model grid we will further diminish the abundance.</p>
The Sonora Substellar Atmosphere Models. IV. Elf Owl: Atmospheric Mixing and Chemical Disequilibrium with Varying Metallicity and C/O Ratios (L- type Models)
<ul> <li><strong>Overview of V2: "The Sonora Substellar Atmosphere Models. V: A Correction to the Disequilibrium Abundance of CO2 for Sonora Elf Owl"</strong></li> </ul> <p> Version 2 of the Sonora Elf Owl Models updates the CO2 and PH3 abundances and spectra. As described in the Wogan et al. (2024) research note (URL OF NOTE GOES HERE), Version 1 of the models did not apply the CO2 quench approximation properly resulting in predicted CO2 abundances that were too small by several orders of magintude in some cases. Version 2 fixes this mistake, updating CO2 abundances and the emission spectra to reflect the new CO2 abundances. Version 2 also removes all spectra contributions of PH3 because Version 1 consistently contained too much PH3 absorption when compared to JWST data (Veiler et al. 2024, <a href="http://doi.org/10.3847/1538-4357/ad6759" target="_blank" rel="noopener noreferrer">http://doi.org/10.3847/1538-4357/ad6759</a>).</p> <p> </p> <ul> <li><strong>Overview of V1</strong></li> </ul> <p> The Sonora Elf Owl Models is a successor to the <a href="../records/5063476#:~:text=This%20particular%20set%20of%20model,g%20are%200.25%20or%200.5.">Sonora Bobcat</a> and <a href="../records/4450269">Sonora Cholla</a> models. The Sonora Elf Owl model grid includes cloud-free radiative-convective equilibrium model atmospheres with vertical mixing induced disequilibrium chemistry with sub-solar to super-solar atmospheric metallicities and Carbon-to-Oxygen ratio. The atmospheric models have been computed using the open-source radiative-convective equilibrium model <a href="https://natashabatalha.github.io/picaso/">PICASO</a>. The parameters included within this grid are effective temperature (<strong><em>Teff</em></strong>), gravity (<strong><em>log(g)</em></strong>), vertical eddy diffusion coefficient (<strong><em>log(Kzz)</em></strong>), atmospheric metallicity (<strong><em>[M/H]</em></strong>), and Carbon-to-Oxygen ratio (<strong><em>C/O</em></strong>).</p> <p>The ranges and increments of these parameters are described in the published paper.<br><br></p> <ul> <li><strong>Three grids available on three links</strong></li> </ul> <p><strong> The model grid has been presented using three Zenodo repositories. This repository has all the models between 1300 to 2400 K (applicable for L- type objects). The models for Teff between 275 to 550 K (applicable for Y- type objects) are available in the Zenodo DOI :- <a href="../records/10381250">https://zenodo.org/records/10381250</a>. The models for Teff between 575 to 1200 K (applicable for T- type objects) are available in the Zenodo DOI :- <a href="../records/10385821">https://zenodo.org/records/10385821</a>.</strong></p> <p> </p> <ul> <li><strong> File types and how to use them</strong></li> </ul> <p> The models have been presented in the Xarray format so that all the atmospheric properties including the T(P) profile, atmospheric chemistry, and thermal emission spectra can be accessed within the same files. A python based Jupyter notebook named "Reading and plotting Elf Owl Models.ipynb" has been also supplied which demonstrates how to open and use these files.</p> <ul> <li> <strong>Spectra</strong></li> </ul> <p> The emission spectra for each atmospheric model has been computed between 0.6 to 15 microns. The reported flux is in the units of erg/s/cm<sup>2</sup>/cm. Note that these fluxes need to be multiplied with R<sup>2</sup>/D<sup>2</sup> before comparing them with the typically observed flux of brown dwarfs/exoplanets. R is the radius of the object, and D is the distance here.</p> <div> </div> <div> <ul> <li><strong>Note on CH4</strong></li> </ul> </div> <p> As stated in <a href="https://ui.adsabs.harvard.edu/abs/2023ApJ...942...71M/abstract">Mukherjee et al. 2023 </a>our CH4 opacity is derived using the <a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab7a1a">Hargreaves et al. 2020 </a>HITEMP line list and computed using the HAPI code (<a href="https://www.sciencedirect.com/science/article/abs/pii/S0022407315302466">Kochanov et al. 2016</a>). HAPI automatically pre-weights the isotopologues according to earth abundances that are listed on the HITRAN website (<a href="https://hitran.org/lbl/2?6=on" target="_blank" rel="noopener noreferrer">see here for CH4</a>). Therefore, users should note that there will be minor features of CH3D included in the models. Given the general absence of deuterated molecules in brown dwarfs (Teff>~300) we will include a second posting of models which includes the Elf Owl grid with <strong>only</strong> the major CH4 isotopologue (12C-H4).</p> <div> <ul> <li><strong>Note on PH3</strong></li> </ul> </div> <p> In v1, PH3 abundance was treated separately from the general disequilibrium scheme. This is because of the current non-detection of PH3 in many brown dwarf atmospheres (see citations in paper). The current PH3 treatment uses the chemical equilibrium treatment described in Visscher et al. However, after publishing this grid and using the model for analysis of high precision JWST data, we noticed that even the simple chemical equilibrium treatment which reduces the abundance, introduces a noticeable PH3 feature. In v2 we completely remove the contribution of PH3. </p>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWL (v2.1.0 - May 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>May 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><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 an archive for the builds on Zenodo. While the original GCP resources contained all associated files, 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 associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWL (v2.1.0 - May 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>May 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><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 an archive for the builds on Zenodo. While the original GCP resources contained all associated files, 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 associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWL (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><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 an archive for the builds on Zenodo. While the original GCP resources contained all associated files, 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 associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWL (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><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 an archive for the builds on Zenodo. While the original GCP resources contained all associated files, 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 associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWL (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><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 an archive for the builds on Zenodo. While the original GCP resources contained all associated files, 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 associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><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 an archive for the builds on Zenodo. While the original GCP resources contained all associated files, 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 associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><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 an archive for the builds on Zenodo. While the original GCP resources contained all associated files, 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 associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.