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11 results for “JSON format”
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>
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>
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>
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>
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>
Dataset for interface calculations as BSON mongodump and JSON formats for the publication: "High-throughput generation of potential energy surfaces for solid interfaces"
<p>This dataset that contains the results presented in the journal article entitled "High-throughput generation of potential energy surfaces for solid interfaces" that was published in Volume 207 of the Elsevier journal Computational Materials Science.</p> <p>The dataset consists of a dump of a MongoDB database with a single collection that contains data on 6 solid interfaces including the generalized stacking fault energies, corrugation, interface distances and adhesion sites for the film and the substrate at minimum and maximum adhesion energy configurations, and images of the full potential energy surface (PES).</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 for them.</p>
User Generated Content (EOL v2): user activity collections (json format)
<p></p>https://eol-jira.bibalex.org/browse/DATA-1780 Data as of Oct 28, 2018 For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
Five star movie ratings form the MovieLens 25M dataset, grouped by user id, in JSON format
<p>From the MovieLens 25M dataset, I have extracted the five star ratings and grouped them by user ID. The original source files can be found here: </p> <p>https://grouplens.org/datasets/movielens/</p> <p> </p>
Collection of Object-Centric Event Logs (OCEL 1.0 format; JSON-OCEL specification)
<p>We provide some realistic examples of logs in the OCEL standard. The logs have originally been published at https://www.ocel-standard.org/ </p>
Primary metric measurements of USA tree rings in one dataset in 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 USA of 2298 rwl-files into Json format of data on tree growth in one file. 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 file ReadMe.pdf</p> <p> </p> <p><em>References</em></p> <p><em>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</em></p> <p><em>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</em></p> <p><em>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)</em></p> <p><em>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)</em></p> <p><em>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</em></p> <p><em>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</em></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.