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Example Microscopy Metadata JSON files produced using Micro-Meta App to document example microscopy experiments performed at individual core facilities
<p>Example <strong>Microscopy Metadata </strong>(Microscope.JSON and Settings.JSON)<strong> files </strong>produced using<strong> <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">Micro-Meta App</a> </strong>to document the <strong>Hardware Specifications</strong> of example Microscopes and the <strong>Image Acquisition Settings</strong> utilized to acquire example images as listed in the table below.</p> <blockquote> <p>For each facility, the dataset contains two JSON files:</p> <ol> <li><strong>Microscope.JSON file</strong> (e.g., 01_marcello_uliverpool_cci_zeiss_axioobserz1_lsm710.json)</li> <li><strong>Settings.JSON file</strong> (indicated with the name of the image and with the _AS suffix)</li> </ol> </blockquote> <p><strong>Micro-Meta App was</strong> developed as part of a <strong>global community initiative</strong> including the <a href="http://www.4dnucleome.org/"><strong>4D Nucleome (4DN)</strong> </a>Imaging Working Group, <strong>BioImaging North America (BINA)</strong> <a href="https://www.bioimagingna.org/qc-dm-wg">Quality Control and Data Management Working Group</a>, and <strong>QUAlity and REProducibility for Instrument and Images in Light Microscopy</strong> (<a href="https://quarep.org/"><strong>QUAREP-LiMi</strong></a>), to extend the <strong>Open Microscopy Environment (OME)</strong> <a href="https://www.openmicroscopy.org/Schemas/Documentation/Generated/OME-2016-06/ome.html">data model</a>.</p> <blockquote> <p>The works of this <strong>global community effort</strong> resulted in multiple publications featured on a recent <strong>Nature Methods FOCUS ISSUE </strong>dedicated to <a href="https://www.nature.com/collections/djiciihhjh">Reporting and reproducibility in microscopy</a>.</p> </blockquote> <blockquote> <p><strong>Learn More!</strong> For a thorough description of <strong>Micro-Meta App</strong> consult our recent <a href="https://doi.org/10.1038/s41592-021-01315-z">Nature Methods</a> and <a href="https://doi.org/10.1101/2021.05.31.446382">BioRxiv.org</a> publications!</p> </blockquote> <p> </p> <table> <tbody> <tr> <td><strong>Nr.</strong></td> <td><strong>Manufacturer</strong></td> <td><strong>Model</strong></td> <td><strong>Tier</strong></td> <td><strong>Εxperiment Type</strong></td> <td><strong>Facility Name</strong></td> <td><strong>Department and Institution</strong></td> <td><strong>URL</strong></td> <td><strong>References</strong></td> </tr> <tr> <td>1</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1 (with LSM 710 scan head)</strong></td> <td>1</td> <td>3D visualization of superhydrophobic polymer-nanoparticles</td> <td>Centre for Cell Imaging (CCI)</td> <td>University of Liverpool</td> <td>https://cci.liv.ac.uk/equipment_710.html</td> <td>Upton et al., 2020</td> </tr> <tr> <td>2</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer (Axiovert 200M)</strong></td> <td>2</td> <td>Μeasurement of illumination stability on Chinese Hamster Ovary cells expressing Paxillin-EGFP</td> <td>Advanced BioImaging Facility (ABIF).</td> <td>McGill University</td> <td>https://www.mcgill.ca/abif/equipment/axiovert-1</td> <td>Kiepas et al., 2020</td> </tr> <tr> <td>3</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1 (with Spinning Disk)</strong></td> <td>2</td> <td>Immunofluorescence imaging of cryosection of Mouse kidney</td> <td>Imagerie Cellulaire; Quality Control managed by Miacellavie (https://miacellavie.com/)</td> <td>Centre de recherche du Centre Hospitalier Université de Montréal (CR CHUM), University of Montreal</td> <td>https://www.chumontreal.qc.ca/crchum/plateformes-et-services (the web site is for all core facilities, not specifically for the core facility hosting this microscope)</td> <td>Pilliod et al., 2020</td> </tr> <tr> <td>4</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Imager Z2 (with Apotome)</strong></td> <td>2</td> <td>Immunofluorescence imaging of mitotic division in Hela cells using </td> <td>Bioimaging Unit</td> <td>Newcastle University</td> <td>https://www.ncl.ac.uk/bioimaging/</td> <td>Watson et al., 2020</td> </tr> <tr> <td>5</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1</strong></td> <td>2</td> <td>Fluorescence microscopy of human skin fibroblasts from Glycogen Storage Disease patients.</td> <td>Life Imaging Center (LIC)</td> <td>Centre for Integrative Signalling Analysis (CISA), University of Freiburg</td> <td>https://miap.eu/equipments/sd-i-abl/</td> <td>Hannibal et al., 2020</td> </tr> <tr> <td>6</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DMI6000B</strong></td> <td>2</td> <td>3D immunofluorescence imaging rhinovirus infected macrophages </td> <td>IMAG'IC Confocal Microscopy Facility</td> <td>Institut Cochin, CNRS, INSERM, Université de Paris</td> <td>https://www.institutcochin.fr/core_facilities/confocal-microscopy/cochin-imaging-photonic-microscopy/organigram_team/10054/view</td> <td>Jubrail et al., 2020</td> </tr> <tr> <td>7</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DM5500B</strong></td> <td>2</td> <td>Immunofluorescence analysis of the colocalization of PML bodies with DNA double-strand breaks</td> <td>Bioimaging Unit</td> <td>Edwardson Building on the Campus for Ageing and Vitality, Newcastle University</td> <td>https://www.ncl.ac.uk/bioimaging/equipment/leica-dm5500/#overview</td> <td>da Silva et al., 2019; Nelson et al., 2012<br> </td> </tr> <tr> <td>8</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DMI8-CS (with TCS SP8 STED 3X)</strong></td> <td>2</td> <td>Live-cell imaging of N. benthamiana leaves cells-derived protoplasts</td> <td>Center for Advanced Imaging (CAi)</td> <td>School of Mathematics/Natural Sciences, Heinrich-Heine-Universität Düsseldorf</td> <td>https://www.cai.hhu.de/en/equipment/super-resolution-microscopy/leica-tcs-sp8-sted-3x</td> <td>Singer et al., 2017; Hänsch et al., 2020</td> </tr> <tr> <td>9</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti</strong></td> <td>2</td> <td>Immunofluorescence analysis of the cytoskeleton structure in COS cells</td> <td>Advanced Imaging Center (AIC)</td> <td>Janelia Research Campus, Howard Hughes Medical Institute</td> <td>https://www.janelia.org/support-team/light-microscopy/equipment</td> <td>Abdelfattah et al., 2019; Qian et al., 2019; Grimm et al., 2020</td> </tr> <tr> <td>10</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti-E (HCA)</strong></td> <td>2</td> <td>Τime-lapse analysis of the bursting behavior of amine-functionalized vesicular assemblies</td> <td>Light Microscopy Facility (IALS-LIF)</td> <td>Institute for Applied Life Sciences, University of Massachusetts at Amherst</td> <td>https://www.umass.edu/ials/light-microscopy</td> <td>Fernandez et al., 2020</td> </tr> <tr> <td>11</td> <td><strong>Nikon Instruments/Coleman laboratory (customized)</strong></td> <td><strong>TIRF HILO Epifluorescence light Microscope (THEM)/ Eclipse Ti</strong></td> <td>2</td> <td>Single-particle tracking of Halo-tagged PCNA in Lox cells</td> <td>Coleman laboratory</td> <td>Anatomy and Structural Biology Department, The Albert Einstein College of Medicine</td> <td>https://einsteinmed.org/faculty/12252/robert-coleman/</td> <td>Drosopoulos et al., 2020</td> </tr> <tr> <td>12</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti (with Andor Dragon Fly Spinning Disk)</strong></td> <td>2</td> <td>Investigation of the 3D structure of cerebral organoids</td> <td>Montpellier Resources Imagerie</td> <td>Centre de Recherche de Biologie cellulaire de Montpellier (MRI-CRBM), CNRS, Univerity of Montpellier</td> <td>https://www.mri.cnrs.fr/en/optical-imaging/our-facilities/mri-crbm.html</td> <td>Ayala-Nunez et al., 2019</td> </tr> <tr> <td>13</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti2</strong></td> <td>2</td> <td>Ιmmunofluorescence imaging of cryosections of mouse hearth myocardium </td> <td>Neuroscience Center Microscopy Core</td> <td>Neuroscience Center, University of North Carolina</td> <td>https://www.med.unc.edu/neuroscience/core-facilities/neuro-microscopy/</td> <td>Aghajanian et al., 2021</td> </tr> <tr> <td>14</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti2</strong></td> <td>2</td> <td>Live-cell imaging of bacterial cells expressing GFP-PopZ</td> <td>Microscopy Resources on the North Quad (MicRoN)</td> <td>Harvard Medical School </td> <td>https://micron.hms.harvard.edu/</td> <td>Lim and Bernhardt 2019; Lim et al., 2019</td> </tr> <tr> <td>15</td> <td><strong>Olympus/Biomedical Imaging Group (customized)</strong></td> <td><strong>TIRF Epifluorescence Structured light Microscope (TESM)/IX71</strong></td> <td>3</td> <td>3D distribution of HIV-1 in the nucleus of human cells</td> <td>Biomedical Imaging Group</td> <td>Program in Molecular Medicine, University of Massachusetts Medical School</td> <td>https://trello.com/b/BQ8zCcQC/tirf-epi-fluorescence-structured-light-microscope</td> <td>Navaroli et al., 2012</td> </tr> <tr> <td>16</td> <td><strong>Olympus/Computer Vision Laboratory (customized)</strong></td> <td><strong>3D BrightField Scanner/IX71</strong></td> <td>3</td> <td>Transmitted light brightfield visualization of swimming spermatocytes</td> <td>Laboratorio Nacional de Microscopia Avanzada (LNMA) and Computer Vision Laboratory of the Institute of Biotechnology</td> <td>Universidad Nacional Autonoma de Mexico (UNAM)</td> <td>https://lnma.unam.mx/wp/</td> <td>Pimentel et al., 2012; Silva-Villalobos et al., 2014</td> </tr> </tbody> </table> <p><strong>Getting started</strong></p> <p>Use these videos to get started with using Micro-Meta App after installation into OMERO and downloading the example data files:</p> <ol> <li><a href="https://vimeo.com/562022222">Video 1</a></li> <li><a href="https://vimeo.com/562022281">Video 2</a></li> </ol> <p><strong>More information</strong></p> <blockquote> <p>For full information on how to use Micro-Meta App please utilize the following resources:</p> <ol> <li>Micro-Meta App <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">website</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/index.html">Full documentation</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/intro/installation.html">Installation</a> instructions</li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/tutorials/index.html#step-by-step-instructions">Step-by-Step Instructions</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/tutorials/VideoTutorials.html#micro-meta-app-video-tutorials">Tutorial Videos</a></li> </ol> </blockquote> <p><strong>Background</strong></p> <p>If you want to learn more about the importance of <strong>metadata and quality contro</strong>l to ensure full <strong>reproducibility, quality and scientific value</strong> in light microscopy, please take a look at our recent publications describing the development of community-driven light <strong>4DN-BINA-OME Microscopy Metadata</strong> specifications <a href="https://doi.org/10.1038/s41592-021-01327-9">Nature Methods</a> and <a href="https://doi.org/10.1101/2021.04.25.441198">BioRxiv.org</a> and our <a href="https://arxiv.org/abs/1910.11370">overview manuscript</a> entitled <strong>A perspective on Microscopy Metadata: data provenance and quality control</strong>.</p> <p> </p> <p> </p>
Example Microscopy Metadata JSON files produced using Micro-Meta App to document the acquisition of example images using a custom-built TIRF Epifluorescence Structured Illumination Microscope
<p><strong>Example Microscopy Metadata JSON files produced using the <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">Micro-Meta App</a> documenting an example raw-image file acquired using the custom-built TIRF Epifluorescence Structured Illumination Microscope.</strong></p> <p>For this use case, which is presented in Figure 5 of <a href="http://doi: https://doi.org/10.1101/2021.05.31.446382">Rigano et al., 2021</a>, Micro-Meta App was utilized to document:</p> <p>1) The <strong>Hardware Specifications</strong> of the custom build TIRF Epifluorescence Structured light Microscope (TESM; <a href="https://www.pnas.org/content/109/8/E471.long">Navaroli et al., 2010</a>) developed, built on the basis of the based on Olympus IX71 microscope stand, and owned by the Biomedical Imaging Group (http://big.umassmed.edu/) at the Program in Molecular Medicine of the University of Massachusetts Medical School. Because TESM was custom-built the most appropriate documentation level is <strong>Tier 3</strong> (<em>Manufacturing/Technical Development/Full Documentation</em>) as specified by the <a href="https://doi.org/10.5281/zenodo.4710731">4DN-BINA-OME</a> Microscopy Metadata model (<a href="https://doi.org/10.1101/2021.04.25.441198">Hammer et al., 2021</a>).</p> <p>The TESM Hardware Specifications are stored in: <strong>Rigano et al._Figure 5_UseCase_Biomedical Imaging Group_TESM.JSON</strong></p> <p>2) The <strong>Image Acquisition Settings</strong> that were applied to the TESM microscope for the acquisition of an example image (FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif) obtained by Nicholas Vecchietti and Caterina Strambio-De-Castillia. For this image, TZM-bl human cells were infected with HIV-1 retroviral three-part vector (FSWT+PAX2+pMD2.G). Six hours post-infection cells were fixed for 10 min with 1% formaldehyde in PBS, and permeabilized. Cells were stained with mouse anti-p24 primary antibody followed by DyLight488-anti-Mouse secondary antibody, to detect HIV-1 viral Capsid. In addition, cells were counterstained using rabbit anti-Lamin B1 primary antibody followed by DyLight649-anti-Rabbit secondary antibody, to visualize the nuclear envelope and with DAPI to visualize the nuclear chromosomal DNA.</p> <p>The Image Acquisition Settings used to acquire the FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif image are stored in: <strong>Rigano et al._Figure 5_UseCase_AS_fswt-6hvirus-10minfix-stk_4-epi.tif.JSON</strong></p> <p><em><strong>Instructional video tutorials on how to use these example data files:</strong></em><br> Use these videos to get started with using Micro-Meta App after downloading the example data files available here.</p> <ul> <li><a href="https://vimeo.com/562022222">Part 1/2</a></li> <li><a href="https://vimeo.com/562022281">Part 2/2</a></li> </ul>
fauci-email: a json digest of Anthony Fauci's released emails
<p>We provide a processed JSON version of the 3234 page PDF document of Anthony Fauci's emails that were released in 2021 to provide a better understanding of the United States government response to the COVID-19 pandemic. The main JSON file contains a collection of 1289 email threads with 2761 emails among the threads, which includes 101 duplicate emails. For each email, we provide information about the sender, recipients, CC-list, subject, email body text, and email time stamp (when available). We also provide a number of derived datasets stored in individual JSON files: 5 different types of derived email networks, 1 email hypergraph, 1 temporal graph, and 3 tensors. Details for the data conversion process, the construction of the derived datasets, and subsequent analyses can all be found in an online technical report at <a href="https://arxiv.org/abs/2108.01239">https://arxiv.org/abs/2108.01239</a>. Updated code for processing and analyzing the data can be found at <a href="https://github.com/nveldt/fauci-email">https://github.com/nveldt/fauci-email</a>.</p>
json AST of a C file
<p>This piece of code is randomly chosen in Software Heritage popular piece of code. It is selected<br> by the size of its AST.</p> <p>To generate the AST the command is:</p> <p><br> `clang -Xclang -ast-dump=json -fsyntax-only parse_date.c > ast.json`</p>
Trait records sample: Carnivora traits (JSON tarball)
This file is part of an API design process and is placed here for internal review. It contains trait records for Carnivora, and neighboring files.<p></p>This is a gzipped tar file containing a single .json file
Wikidata Taxon Items in JSON Lines Format hash://sha256/13ffa9679bae381aa5914d810638fb5a0c75d71f5f7d47f38b3c00d750c88b9c hash://md5/bdcc99bfedfd34abdfdd3802182f225c
<p>Wikidata contains information about taxonomic names, and these taxonomic names are key to integrating biodiversity datasets across different platforms, datasets and institutions. </p> <h2>Content</h2> <table> <tbody> <tr> <td><strong>filename/alias</strong></td> <td><strong>content ids</strong></td> </tr> <tr> <td>wikidata-taxon.json.bz2</td> <td> <p><a href="https://linker.bio/hash://sha256/a7592b72c9013d67d655b6ea5d1f4f67f2057dc5b5ee52578a07f58fea835580">hash://sha256/</a><a href="https://zenodo.org/api/records/13920038/draft/files/701a1382e304a6b1bb38fe828d82f7b8b562c77f918f33097966e38bacf0b2e7/content" target="_blank" rel="noopener noreferrer">701a1382e304a6b1bb38fe828d82f7b8b562c77f918f33097966e38bacf0b2e7</a></p> <p><a href="https://linker.bio/hash://md5/d5bad3553470506f3bde383566a5dea3">hash://md5/d5bad3553470506f3bde383566a5dea3</a></p> </td> </tr> <tr> <td>wikidata-taxa.sh</td> <td> <p><a href="https://linker.bio/hash://sha256/6f4fac44054d54ec3006d091ba702f872b3f4d013628add98fcca08a3b768962">hash://sha256/6f4fac44054d54ec3006d091ba702f872b3f4d013628add98fcca08a3b768962</a></p> <p><a href="https://linker.bio/hash://md5/1d80083d498d61c8b63fbd46d51f7c5c">hash://md5/1d80083d498d61c8b63fbd46d51f7c5c</a></p> </td> </tr> <tr> <td>Q140.json (example)</td> <td> <p><a href="https://linker.bio/hash://md5/44ab0031091fb96caa063e3fe41a85f2">hash://md5/44ab0031091fb96caa063e3fe41a85f2</a></p> </td> </tr> </tbody> </table> <h2>Provenance</h2> <h3>for humans</h3> <p>This dataset contains a subset of Wikidata items referencing the taxonomic name concept https://www.wikidata.org/wiki/Q16521 and is expressed in JSON Lines format. </p> <p>An example of such item is https://wikidata.org/wiki/Q140, an item that describes the taxonomic name associated with <em>Panthera leo</em>, commonly known as Lion (English), León (Spanish), or 狮子 (Chinese). You can find a "pretty" printed example of Q140 in the file "Q140.json" included in this publication. The first 10 lines of "Q140.json" are shown below: </p> <pre><code>{ "type": "item", "id": "Q140", "labels": { "fr": { "language": "fr", "value": "lion" }, "it": { "language": "it", ...</code></pre> <p> </p> <p>The reason for creating a wikidata subset is because all of wikidata (~85G) didn't fit in Zenodo. </p> <h3>for machines</h3> <p>This dataset was generated using the script below with content id <a href="https://linker.bio/hash://sha256/6f4fac44054d54ec3006d091ba702f872b3f4d013628add98fcca08a3b768962">hash://sha256/6f4fac44054d54ec3006d091ba702f872b3f4d013628add98fcca08a3b768962</a> or <a href="https://linker.bio/hash://md5/1d80083d498d61c8b63fbd46d51f7c5c">hash://md5/1d80083d498d61c8b63fbd46d51f7c5c</a></p> <pre><code> 1 #!/bin/bash 2 # 3 # streams Wikidata taxon items (or items containing https://www.wikidata.org/wiki/Q16521) 4 # from latest data dump in line json (one json object per line) 5 # 6 curl --silent "https://dumps.wikimedia.org/wikidatawiki/entities/latest-all.json.bz2"\ 7 | bunzip2\ 8 | grep -E "Q16521[^0-9]"\ 9 | sed 's/,$//g'\ 10 | bzip2 </code></pre> <p>The script first downloads a recent copy of all wikidata entities in bzip2 compressed format (line 6), decompresses them (line 7), selects only lines containing "Q16521" (line 8), removes any trailing commas (line 9), and recompresses the output. With this, the output contains wikidata items/entities as described earlier.</p> <p>Preston, a biodiversity data tracker, was used to (a) track the script, as well as (b) recording a script execution and (c) tracking the outcome by running :</p> <pre><code>#!/bin/bash # # run the script with id hash://sha256/13ff... # preston bash\ --remote https://linker.bio\ -c "hash://sha256/13ffa9679bae381aa5914d810638fb5a0c75d71f5f7d47f38b3c00d750c88b9c" </code></pre> <p>The recording of this process is identified with hash://sha256/13ffa9679bae381aa5914d810638fb5a0c75d71f5f7d47f38b3c00d750c88b9c and hash://md5/bdcc99bfedfd34abdfdd3802182f225c , and can be reconstructed using </p> <pre><code>preston ls\ --remote https://linker.bio/,https://zenodo.org/records/13920038/files\ --anchor hash://sha256/13ffa9679bae381aa5914d810638fb5a0c75d71f5f7d47f38b3c00d750c88b9c</code></pre> <p>Which is expected to produce:</p> <pre><code><https://preston.guoda.bio> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#SoftwareAgent> <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><https://preston.guoda.bio> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#Agent> <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><https://preston.guoda.bio> <http://purl.org/dc/terms/description> "Preston is a software program that finds, archives and provides access to biodiversity datasets."@en <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#Activity> <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> <http://purl.org/dc/terms/description> "Executes script and captures stdout"@en <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> <http://www.w3.org/ns/prov#startedAtTime> "2024-10-10T16:37:36.659Z"^^<http://www.w3.org/2001/XMLSchema#dateTime> <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> <http://www.w3.org/ns/prov#wasStartedBy> <https://preston.guoda.bio> <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><https://doi.org/10.5281/zenodo.1410543> <http://www.w3.org/ns/prov#usedBy> <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><https://doi.org/10.5281/zenodo.1410543> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://purl.org/dc/dcmitype/Software> <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><https://doi.org/10.5281/zenodo.1410543> <http://purl.org/dc/terms/bibliographicCitation> "Jorrit Poelen, Icaro Alzuru, & Michael Elliott. 2018-2024. Preston: a biodiversity dataset tracker (Version 0.9.9-SNAPSHOT) [Software]. Zenodo. https://doi.org/10.5281/zenodo.1410543"@en <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#Entity> <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/dc/terms/description> "A biodiversity dataset graph archive."@en <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><hash://sha256/e76276c283090381fc4b3efe28fc61c28f5bf03db0f3743f7178b999ebccada2> <http://www.w3.org/ns/prov#usedBy> <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><hash://sha256/6f4fac44054d54ec3006d091ba702f872b3f4d013628add98fcca08a3b768962> <http://purl.org/dc/elements/1.1/format> "text/x-shellscript" .<br><urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> <http://www.w3.org/ns/prov#used> <hash://sha256/6f4fac44054d54ec3006d091ba702f872b3f4d013628add98fcca08a3b768962> .<br><urn:uuid:7ecf1c84-0438-4224-8909-0804028cf3f6> <http://www.w3.org/ns/prov#wasGeneratedBy> <urn:uuid:fdc316b0-457d-4d22-85a1-d2ce65c2e440> .<br><hash://sha256/701a1382e304a6b1bb38fe828d82f7b8b562c77f918f33097966e38bacf0b2e7> <http://www.w3.org/ns/prov#wasGeneratedBy> <urn:uuid:bb57ae4b-1ba1-4188-8e95-3f3f6cdcab6b> <urn:uuid:bb57ae4b-1ba1-4188-8e95-3f3f6cdcab6b> .<br><hash://sha256/701a1382e304a6b1bb38fe828d82f7b8b562c77f918f33097966e38bacf0b2e7> <http://www.w3.org/ns/prov#qualifiedGeneration> <urn:uuid:bb57ae4b-1ba1-4188-8e95-3f3f6cdcab6b> <urn:uuid:bb57ae4b-1ba1-4188-8e95-3f3f6cdcab6b> .<br><urn:uuid:bb57ae4b-1ba1-4188-8e95-3f3f6cdcab6b> <http://www.w3.org/ns/prov#generatedAtTime> "2024-10-11T02:08:03.739Z"^^<http://www.w3.org/2001/XMLSchema#dateTime> <urn:uuid:bb57ae4b-1ba1-4188-8e95-3f3f6cdcab6b> .<br><urn:uuid:bb57ae4b-1ba1-4188-8e95-3f3f6cdcab6b> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#Generation> <urn:uuid:bb57ae4b-1ba1-4188-8e95-3f3f6cdcab6b> .<br><urn:uuid:bb57ae4b-1ba1-4188-8e95-3f3f6cdcab6b> <http://www.w3.org/ns/prov#used> <urn:uuid:7ecf1c84-0438-4224-8909-0804028cf3f6> <urn:uuid:bb57ae4b-1ba1-4188-8e95-3f3f6cdcab6b> .<br><urn:uuid:7ecf1c84-0438-4224-8909-0804028cf3f6> <http://purl.org/pav/hasVersion> <hash://sha256/701a1382e304a6b1bb38fe828d82f7b8b562c77f918f33097966e38bacf0b2e7> <urn:uuid:bb57ae4b-1ba1-4188-8e95-3f3f6cdcab6b> .<br><https://preston.guoda.bio> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#SoftwareAgent> <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> . <https://preston.guoda.bio> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#Agent> <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> . <https://preston.guoda.bio> <http://purl.org/dc/terms/description> "Preston is a software program that finds, archives and provides access to biodiversity datasets."@en <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> . <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#Activity> <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> . <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> <http://purl.org/dc/terms/description> "Executes script and captures stdout"@en <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> . <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> <http://www.w3.org/ns/prov#startedAtTime> "2024-06-22T10:40:12.016Z"^^<http://www.w3.org/2001/XMLSchema#dateTime> <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> . <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> <http://www.w3.org/ns/prov#wasStartedBy> <https://preston.guoda.bio> <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> . <https://doi.org/10.5281/zenodo.1410543> <http://www.w3.org/ns/prov#usedBy> <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> . <https://doi.org/10.5281/zenodo.1410543> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://purl.org/dc/dcmitype/Software> <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> . <https://doi.org/10.5281/zenodo.1410543> <http://purl.org/dc/terms/bibliographicCitation> "Jorrit Poelen, Icaro Alzuru, & Michael Elliott. 2021. Preston: a biodiversity dataset tracker (Version 0.8.4) [Software]. Zenodo. https://doi.org/10.5281/zenodo.1410543"@en <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> . <urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#Entity> <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> . <urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/dc/terms/description> "A biodiversity dataset graph archive."@en <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> . <hash://sha256/6f4fac44054d54ec3006d091ba702f872b3f4d013628add98fcca08a3b768962> <http://purl.org/dc/elements/1.1/format> "text/x-shellscript" . <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> <http://www.w3.org/ns/prov#used> <hash://sha256/6f4fac44054d54ec3006d091ba702f872b3f4d013628add98fcca08a3b768962> . <urn:uuid:6fa51a99-a137-4387-90b1-23589d7b60ae> <http://www.w3.org/ns/prov#wasGeneratedBy> <urn:uuid:096ba92f-9d5c-4cb1-9a3d-7a95c5228758> . <hash://sha256/a7592b72c9013d67d655b6ea5d1f4f67f2057dc5b5ee52578a07f58fea835580> <http://www.w3.org/ns/prov#wasGeneratedBy> <urn:uuid:e0d76a06-5241-4dfe-8429-46164190ab0e> <urn:uuid:e0d76a06-5241-4dfe-8429-46164190ab0e> . <hash://sha256/a7592b72c9013d67d655b6ea5d1f4f67f2057dc5b5ee52578a07f58fea835580> <http://www.w3.org/ns/prov#qualifiedGeneration> <urn:uuid:e0d76a06-5241-4dfe-8429-46164190ab0e> <urn:uuid:e0d76a06-5241-4dfe-8429-46164190ab0e> . <urn:uuid:e0d76a06-5241-4dfe-8429-46164190ab0e> <http://www.w3.org/ns/prov#generatedAtTime> "2024-06-22T19:01:55.863Z"^^<http://www.w3.org/2001/XMLSchema#dateTime> <urn:uuid:e0d76a06-5241-4dfe-8429-46164190ab0e> . <urn:uuid:e0d76a06-5241-4dfe-8429-46164190ab0e> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#Generation> <urn:uuid:e0d76a06-5241-4dfe-8429-46164190ab0e> . <urn:uuid:e0d76a06-5241-4dfe-8429-46164190ab0e> <http://www.w3.org/ns/prov#used> <urn:uuid:6fa51a99-a137-4387-90b1-23589d7b60ae> <urn:uuid:e0d76a06-5241-4dfe-8429-46164190ab0e> . <urn:uuid:6fa51a99-a137-4387-90b1-23589d7b60ae> <http://purl.org/pav/hasVersion> <hash://sha256/a7592b72c9013d67d655b6ea5d1f4f67f2057dc5b5ee52578a07f58fea835580> <urn:uuid:e0d76a06-5241-4dfe-8429-46164190ab0e> . </code></pre>
OpenAIRE ScholeXplorer Service: Scholix JSON Dump
<p>This dataset contains the GZ-compressed dump of the Scholix links (<a href="https://doi.org/10.5281/zenodo.6351557">schema Version 4</a>) exposed by the OpenAIRE ScholeXplorer service. It consists of 417+Mi bi-directional links (i.e. 975+Mi directed links) between literature-dataset and dataset-dataset involving 24+ Mi literature objects and 37+ Mi datasets (showing an increase of around 160Mi links wrt the previous release). Links are collected from publishers (CrossRef, EventData), data centers (DataCite and data centers), institutional/thematic repositories (OpenAIRE), life-science databases (EMBL-EBI), and inferred by OpenAIRE via text-mining around 14Mi publication's PDFs. The dataset is structured in 30 compressed files, each of at most ~10 Gb, for a total of ~328GB.</p> <p><strong>Note that the dataset matches a new version of the schema (</strong><a href="https://doi.org/10.5281/zenodo.6351557">schema Version 4</a>). Changes are minor, backward compatible, and regard optional fields and extensions of vocabularies. The <strong>readme.doc</strong> file includes a description of the schema changes and statistics about the dataset.</p>
wikidata jsons (in pre-processed format)
<p>This is a collection of pre-processed wikidata jsons which were used in the creation of CSQA dataset (Ref: <a href="https://arxiv.org/abs/1801.10314">https://arxiv.org/abs/1801.10314</a>).</p> <p>Please refer to <a href="https://amritasaha1812.github.io/CSQA/download/">https://amritasaha1812.github.io/CSQA/download/</a> for more details.</p>
Conversion of measurements of tree ring gains: Canada, Africa, Mexico, South America from RWL- files to JSON format.
<p>The International Tree Rings Data Bank (ITRDB) is the most comprehensive tree growth database (https://www1.ncdc.noaa.gov/pub/data/paleo/treering).</p> <p>Shoudong Zhao, et al. (2019, 2018) analyzes the representativity of dendrochronological data (ITRDB) and proposes a corrected database with error indications. One of the bottlenecks of data use (ITRDB) is that the data is loaded as a collection of separate files in the Tucson positional format.</p> <p>The purpose of our data presentation is to change the Tucson data format to JSON format and combine the separate files into one.</p> <p>We convert the initial data for the <strong>Canada</strong>, <strong>Africa</strong>, <strong>Mexico</strong> and <strong>Southamerica</strong> rwl-files into Json format of data on tree growth in four files: <strong>canada.json</strong>, <strong>africa.json</strong>, <strong>mexico.json</strong> and <strong>southamerica.json</strong>. The data was converted using the R programming language and the dplR program library Bunn, A. (2008)</p> <p>The experience of developing the structure of dendroclimatic data in JSON format is described in the works of Kachaev A. (2016, 2017, 2020).</p> <p>Description of the structure of JSON data format is attached in the files ReadMe.pdf</p> <p> </p> <p>References</p> <p>Bunn, A. G. (2008). A dendrochronology program library in R (dplR). Dendrochronologia, 26, 115-124. https://doi.org/10.1016/j.dendro.2008.01.002</p> <p>Kachaev, Alexander (2020), "Compact dataset of dendrochronological data of pri-mary metric characteristics of tree rings of Asia.", Mendeley Data, V1, doi: 10.17632 / p9zhpmzgtk.1</p> <p>Kachaev A. V. (2017) Model for describing the structure of dendroclimatic data In the collection: Regional problems of remote sensing of the Earth Materials of the IV international scientific conference. Siberian Federal University, Institute of Space and Information Technologies. p. 120-122. (Russia)</p> <p>Kachaev A. V. (2016) NOSQL Approach for Development of Dendroclimatic Data Bank. In the collection: Regional problems of remote sensing of the Earth. Materials of the III International Scientific Conference. p. 89-91. (Russia)</p> <p>Shoudong Zhao, et al. (2019). The International Tree-Ring Data Bank (ITRDB) revisited: Data availability and global ecological representativity. Journal of Biogeography, 46 (2), 355-368. doi: 10.1111 / jbi.13488</p> <p>Zhao, Shoudong et al. (2018), Data from: The International Tree-Ring Data Bank (ITRDB) revisited: data availability and global ecological representativity, Dryad, Dataset, https://doi.org/10.5061/dryad.kh0qh06</p>
SModelS analyses json for HEPData
<p>Json file specifying the analyses implemented in the SModelS database, adapted for HEPData linking.</p>
JSON files containing parameters of training gene models for ab-initio prediction software
<p>These are the JSON files containing parameters of training gene models for ab-initio prediction software. These training datasets are Phytophthora specific and can be further utilized for the gene prediction and annotation of other related Phytophthora strains.</p>
APIS JSON Serialization
<p>This datatset contains a Json serialization of the enriched version of the Austrian Biographical Dictionary. The data was enriched during the Austrian Prosopographical Information System (APIS) project. It contains data on ~19.000 persons who had an impact on Austrian soil and died between 1815 and 1955.</p> <p>This version of the APIS data uses the internal Json format and contains therefore the richest data available.</p> <p>Please refer to <a href="https://www.oeaw.ac.at/acdh/team/current-team/matthias-schloegl/">Matthias Schlögl</a> for any inquiries.</p>
The Use of Keywords in Archaeornithology Literature Appendix B in JSON
<p>Appendix B – Vocabulary Matching Tool output with Getty Art & Architecture Thesaurus terms. Formatted in JSON.</p>
JSON file of the AST of a C program
<p>This piece of code is randomly chosen in Software Heritage popular piece of code. It is selected<br> by the size of its AST.</p> <p>To generate the AST the command is:</p> <p><br> `clang -Xclang -ast-dump=json -fsyntax-only parse_date.c > ast.json`<br> </p>
OPTIMADE compliant results database + Detailed input/outputs of VASP calculations as BSON mongodump and JSON formats
<p>This dataset that contains the results presented in the journal article entitled "SurfFlow: High-throughput surface energy calculations for arbitrary crystals" that was published in Volume 234 of the Elsevier journal Computational Materials Science.</p> <p>The dataset consists of a dump of a MongoDB database with two collections; an OPTIMADE compliant results collection, and a supplementary collection that contains detailed data on the VASP calculations performed. Each entry in the OPTIMADE-compliant results collection contains an id, and every VASP calculation that was involved in the calculation of surface energies of that entry can be found by filtering for the same id matching the "optimade_id" in the VASP data collection.</p> <p>The data is served in two different formats; a BSON mongodump folder that can be restored to a MongoDB instance using the mongorestore tool, and additionally as simple .json files. The contents are identical and the users are encouraged to choose the format that is convenient to them.</p> <p>Here's a simple tree-view of the uncompressed attachment:</p> <p>./surfflow-bson/surfflow:<br>2.2M optimade.bson.gz<br> 153 optimade.metadata.json.gz<br> 40M vasp_data.bson.gz<br> 154 vasp_data.metadata.json.gz</p> <p>./surfflow-json:<br> 27M optimade.json<br>346M vasp_data.json</p>
20th Century Press Archives JSON-LD dump for CdV 2018 Rhein-Main: persons and companies
<p>Folder metadata for all person and company folders of PM20, which have publicly accessible documents. Published for the "Coding da Vinci" Hackathon 2018.</p> <p>For a preview and further information, please see https://github.com/zbw/cdv2018-pressemappe20 (mostly in German)</p> <p> </p>
National Weather Service Coded Surface Bulletins, 2003- (JSON format)
<p>This dataset contains the Coded Surface Bulletin dataset reformatted as JSON files. The Coded Surface Bulletin dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003.</p> <p>Each JSON file contains one top-level object corresponding to one bulletin. The top-level object is composed of name/value pairs with the names bulletinType, createDate, validDate, Highs, Lows, ColdFronts, WarmFronts, OccludedFronts, StationaryFronts, and Troughs. The name/value pairs for bulletinType, createDate, and validDate are always present. The other name/value pairs are only present if there is corresponding data. The value for bulletinType is either "LR" or "HR", for low-resolution or high-resolution, respectively. The values for createDate and validDate are UTC timestamp strings. If the bulletinType value is "LR", the longitudes and latitudes have 1° precision. If the bulletinType value is "HR", the longitudes and latitudes have 0.1° precision.</p> <p>The value associated with the name High in the top-level object is itself an object composed of three name value pairs that describe the geographic locations and surface air pressure levels for one or more high pressure centers. The names of the object elements are lats, lons, and pressures. The values for these are all arrays. For a given object, the arrays will all have the same size. The arrays contain latitudes in degrees, longitudes in degrees, and pressures in millibars. If the arrays contain N elements apiece, the object is describing N pressure centers. The object associated with the name Low in the top-level object is structured in the same way. It describes the geographic locations and surface air pressure levels for one or more low pressure centers.</p> <p>The ColdFronts, WarmFronts, StationaryFronts, OccludedFronts, and Troughs names in the top-level object, when present, have values that are arrays. In each case, the array is composed of one or more objects. Each object represents a front or trough of the given type. Each object is composed of three name/value pairs with the names lats, lons, and strength. The value for the name strength is a string that is one of "weak", "moderate", "strong", or "unstated". The values associated with the names lats and lons are arrays. This pair of arrays represent the vertices of a polyline describing the location of a frontal boundary or trough.</p> <p>The primary source for this dataset is an internal archive maintained by personnel at the WPC and provided to the author. It is also provided at DOI 10.5281/zenodo.2642801. Some bulletins missing from the WPC archive were filled in with data acquired from the <a href="https://mesonet.agron.iastate.edu/">Iowa Environmental Mesonet</a>.</p>
UNIC JSON template for uploading the individual files of aligned corpus data
<p>A ZIP of JSON files is needed to upload aligned corpus data to UNIC, with each file structured as the template. Please reach out to unic@dipintra.it for assistance. </p>
DL-FRONT MERRA-2 vectorized weather fronts over North America, 1980-2018 (JSON format)
<p>DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of hourly JSON files containing geospatial vector polylines describing the locations of four types of weather fronts—cold front, warm front, stationary front, and occluded front, over the time span 1980-2018.</p> <p>This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) <a href="https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/">Modern-Era Retrospective analysis for Research and Applications, Version 2</a> (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.</p> <p>At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).</p> <p>Each probability map was then processed to obtain polyline skeletons of the weather front boundary regions found by DL-FRONT. These vector representations of the fronts were then written to JSON files—one file for each hour. Each JSON file contains one top-level object composed of name/value pairs with the names issuanceDate, validDate, ColdFronts, WarmFronts, OccludedFronts, and StationaryFronts. The name/value pairs for createDate and validDate are always present. The other name/value pairs are only present if there is corresponding data. The values for issuanceDate and validDate are UTC timestamp strings.</p> <p>The ColdFronts, WarmFronts, StationaryFronts, and OccludedFronts names in the top-level object, when present, have values that are arrays. In each case, the array is composed of one or more objects. Each object represents a front of the given type. Each object is composed of five name/value pairs with the names lats, lons, cols, rows, and confidence. The value for the name confidence is a number that is the average of the values of the probability map cells intersected by the front polyline. The values associated with the names lats, lons, cols, and rows are arrays. These arrays represent the vertices of a polyline describing the location of a frontal boundary in both geospatial and grid cell coordinates.</p>
Openfood fact JSON file
<pre>Data extracted from <a href="http://https://wiki.openfoodfacts.org/Open_Food_Facts_Search_API_Version_2">openfood</a> fact API. To extract it from the endpoint run </pre> <pre><code class="language-bash">curl "https://world.openfoodfacts.org/cgi/search.pl?action=process&tagtype_0=categories&tag_contains_0=contains&tag_0=cheeses&tagtype_1=labels&&json=1" > /tmp/openfood.json</code></pre> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.