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8,924 results for “formation”
Example data set from Diamond Light Source VMXi beamline (Eiger 4M data, NeXus format)
<p>Data set recorded from Thermolysin crystal record <em>in situ</em> with Eiger 4M detector, to demonstrate file format used for this instrument at Diamond Light Source. Processing results using xia2 / DIALS:</p> <p> </p> <pre>For AUTOMATIC/DEFAULT/SAD Overall Low High High resolution limit 1.97 5.35 1.97 Low resolution limit 46.86 46.87 2.01 Completeness 59.8 73.2 6.0 Multiplicity 5.8 8.0 1.1 I/sigma 13.6 24.4 1.8 Rmerge(I) 0.072 0.050 0.308 Rmerge(I+/-) 0.067 0.048 0.000 Rmeas(I) 0.078 0.054 0.436 Rmeas(I+/-) 0.076 0.054 0.000 Rpim(I) 0.028 0.018 0.308 Rpim(I+/-) 0.035 0.023 0.000 CC half 0.997 0.998 0.450 Wilson B factor 13.401 Anomalous completeness 51.3 78.8 0.8 Anomalous multiplicity 3.3 4.8 1.0 Anomalous correlation 0.039 -0.006 0.000 Anomalous slope 0.987 dF/F 0.103 dI/s(dI) 1.086 Total observations 85069 8229 82 Total unique 14747 1031 73 Assuming spacegroup: P 6 2 2 Other likely alternatives are: P 61 2 2 P 65 2 2 P 62 2 2 P 64 2 2 P 63 2 2 Unit cell (with estimated std devs): 93.7184(3) 93.7184(3) 130.864(2) 90.0 90.0 120.0 </pre> <p> </p>
Science ready spectra of star clusters and their best-fitting models described in the research paper "Using Star Clusters as Tracers of Star Formation and Chemical Evolution: the Chemical Enrichment History of the Large Magellanic Cloud" by Chilingarian & Asa'd
<p>Science ready spectra of star clusters in the Large Magellanic Cloud and their best-fitting templates (alpha-enhanced MILES based simple stellar population models) obtained using the NBursts full spectrum fitting code. Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For each cluster, 5 spectra are provided, which correspond to [alpha/Fe] values from 0.0 to 0.4 dex with a step of 0.1 dex. The only exception is NGC2249, for which only 3 models are provided. The alpha-enhancement value of a model grid used in the fitting procedure is given in the FITS keyword MGFEGRID.</p>
Group Contribution Models for Standard molar enthalpy of formation of gas phase at 298.15K
<p>A data set of several models for Heat of formation based on additive schemes (group contribution models).</p> <p>Model building is performed by means of Ambit-GCM software (<a href="http://ambit.sourceforge.net/">http://ambit.sourceforge.net/</a>, <a href="https://doi.org/10.5281/zenodo.1470793">https://doi.org/10.5281/zenodo.1470793</a>).</p> <p>QSPR models were built with a small training data set mainly for demonstration of Ambit-GCM software usage.</p>
BIDS formatted EEG meditation experiment data
<p>This meditation experiment contains 24 subjects. Subjects were meditating and were interrupted about every 2 minutes to indicate their level of concentration and mind wandering. The scientific article (see Reference file) contains all methodological details.</p> <p>- Arnaud Delorme (October 17, 2018)</p>
A multiple model high-resolution head-related impulse response database for aided and unaided ears (HDF5 format)
<p>The Multiple-Model High Resolution HRTF database is a collection of HRTFs measured using four different Head-and-Torso Simulators at high spatial resolution (2 degree azimuth and elevation). The data here is stored in HDF5 files, the SOFA files are published in a separate dataset <a href="https://zenodo.org/record/2582553">doi:10.5281/zenodo.2582553</a>.</p>
Datasets for the paper "Group formation on a small-world: experiment and modelling"
<p>Datasets required for reproducing all the results presented in the article "Group formation on a small-world: experiment and modelling" to be published in the Journal of the Royal Society Interface.</p> <p>Take a look into the included README for a description about the data format.</p>
Data supporting publication: Revealing Mode Formation in Quasi-Bound States in the Continuum Metasurfaces via Near-Field Optical Microscopy
<p>This repository includes the data corresponding to the figures shown in the journal article entitledRevealing Mode Formation in Quasi-Bound States in the Continuum Metasurfaces via Near-Field Optical Microscopy, published in Advanced Materials on 02.08.2024</p>
Stellar Mass Black Hole Formation and Multimessenger Signals from Three-dimensional Rotating Core-collapse Supernova Simulations
<p>Gravitational waveforms from <a href="https://ui.adsabs.harvard.edu/abs/2021ApJ...914..140P/abstract">Pan et al. (2021) .</a></p> <p>They are the 40 solar mass model from Woosley & Heger 2007 with different<br>initial rotational speeds:</p> <p>Model NR: Omega_0 = 0.0 rad/sec<br>Model SR: Omega_0 = 0.5 rad/sec<br>Model FR: Omega_0 = 1.0 rad/sec</p> <p>/* File content */</p> <p>They are 5 files for each simulation.</p> <p>Files "data_s40_[model]_d3_[Cross/Plus][Equator/Pole].d" are GW strains for different<br>mode of polarization [h_plus or h_cross] and viewing angles [equator or pole].</p> <p>1st column is time [s] in postbounce. <br>2nd column s the GW strain, assuming d=10 kpc.<br> <br>Files "data_s40_[model]_d3_Idotdot.d" are the second time derivative of the<br>quadruple moments.</p> <p>1st: postbounce time [s] <br>2nd: Idd_xx [cgs]<br>3rd: Idd_xy = Idd_yx [cgs]<br>4th: Idd_yy = Idd_yy [cgs]<br>5th: Idd_zx = Idd_zx [cgs]<br>6th: Idd_zy = Idd_zy [cgs]<br>7th: Idd_zz = Idd_zz [cgs]</p> <p> </p>
IODP Expedition 398 Formation temperature
Formation temperature was measured by the advanced piston corer temperature tool (APCT-3), Sediment Temperature Tool (SET), or sediment temperature pressure tool (SETP) and reduced to in situ temperature and (for SETP) pressure estimates. These data are compiled per instrument run and assigned to a core (the core cut with the APCT-3 cutting shoe or the core following the SET deployment).
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>
IODP Expedition 359 Formation temperature
Formation temperature was measured by the advanced piston corer temperature tool (APCT-3), Sediment Temperature Tool (SET), or sediment temperature pressure tool (SETP) and reduced to in situ temperature and (for SETP) pressure estimates. These data are compiled per instrument run and assigned to a core (the core cut with the APCT-3 cutting shoe or the core following the SET deployment).
Facile synthesis of CuxS electrocatalysts for CO2 conversion into formate and study of relations between Cu and S with the selectivity
<p>Datasets for figures provided in the manuscript main text.</p>
Eremohaplomydas, Haplomydas, and Lachnocorynus species descriptions in SDD format
<p>Natural-language species descriptions of Eremohaplomydas, Haplomydas, and Lachnocorynus species in SDD format exported from Lucid Builder 4.0.</p>
Anasillomos matrix-based key in SDD-format
<p>Matrix-based, multi-entry key to species of Anasillomos developed with Lucid Builder v4 in XML Structure of Descriptive Data (SDD) format.</p>
SERPENT MT data set, in MARE2DEM format
<p>This file contains the marine MT data from the SERPENT experiment offshore Nicaragua. The equipment used were the Scripps EM Lab's EM broadband receivers. The data is in MARE2DEM format. It includes the transfer functions (apparent resistivity and phase) in units of ohm-m and degrees, respectively, along with their errors. Data IDs 103 and 104 refer to the TE mode apparent resistivity and phase, respectively, while the Data IDs 105 and 106 refer to the TM mode apparent resistivity and phase, respectively.</p>
Wintertime subarctic new particle formation from Kola Peninsula sulphur emissions
<p><strong>Nitrate-ion (NO3-) chemical ionization (CI) </strong><strong>APi-TOF </strong><strong>masspectrometer and ion APi-TOF mass spectrometer data reported in Atmospheric Chemistry and Physics: </strong>Sipilä, M., Sarnela, N., Neitola, K., Laitinen, T., Kemppainen, D., Beck, L., Duplissy, E.-M., Kuittinen, S., Lehmusjärvi, T., Lampilahti, J., Kerminen, V.-M., Lehtipalo, K., Aalto, P. P., Keronen, P., Siivola, E., Rantala, P. A., Worsnop, D. R., Kulmala, M., Jokinen, T., and Petäjä, T.: Wintertime sub-arctic new particle formation from Kola Peninsula sulphur emissions, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2020-1202, in review, 2021.</p> <p><a href="https://zenodo.org/api/files/0375de20-1ec1-4185-82f6-ec6eda9466a5/H2SO4_vs_time_FIG3-FIG4.dat">H2SO4_vs_time_FIG3-FIG4.dat</a> </p> <p>[Matlab time, measured sulfuric acid concentration] depicted in FIGs 3c and 4c of the above publication.</p> <p><a href="https://zenodo.org/api/files/0375de20-1ec1-4185-82f6-ec6eda9466a5/SA_MSA_IA_vs_time_FIG7.dat">SA_MSA_IA_vs_time_FIG7.dat</a></p> <p>[Matlab time, measured sulfuric acid concentration, measured methane sulphonic acid concentration, measured iodic acid concentration] depicted in FIG7e of the above publication</p> <p><a href="https://zenodo.org/api/files/0375de20-1ec1-4185-82f6-ec6eda9466a5/ion_signals_vs_time_FIG7.dat">ion_signals_vs_time_FIG7.dat</a></p> <p>[Matlab time, ion signal of NO3-, ion signal of HSO4-, ion signal of H2SO4.HSO4-, ion signal of (H2SO4)2.HSO4-, ion signal of NH3.(H2SO4)3.HSO4-, ion signal of NH3.(H2SO4)4.HSO4-] depicted in FIG7f of the above publication</p> <p> </p> <p><a href="https://zenodo.org/api/files/0375de20-1ec1-4185-82f6-ec6eda9466a5/mass_defect_FIG9.dat">mass_defect_FIG9.dat</a></p> <p>[mass (Da), mass defect (Da), signal intensity (AU)] depicted in FIG9 of the above publication</p> <p>Rest of the data reported in the publication can be obtained from https://smear.avaa.csc.fi/</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Raw data from Qin et al. (2018) "Modeling the kinetics of hydrogen formation by zerovalent iron: Effects of sulfidation on micro- and nano-scale particles"
<p>Raw hydrogen concentration vs. time data from Qin, H., X. Guan, J. Z. Bandstra, R. L. Johnson, and P. G. Tratnyek (2018) “Modeling the kinetics of hydrogen formation by zerovalent iron: Effects of sulfidation on micro- and nano-scale particles” Environ. Sci. Technol. 52(23): 13887-13896. [10.1021/acs.est.8b04436]</p> <p>This manuscript reports a large set of new concentration vs. time data for dihydrogen (H2) produced by corrosion of granular zerovalent iron (i.e., the hydrogen evolution reaction, HER) in aqueous media relevant to groundwater remediation. Four alternative kinetic models are evaluated by fitting the data using global non-linear regression. Details are given in the main text and supporting information of the (open access) manuscript. </p> <p>The data provided here are in two formats: (i) a .csv file that contains only data and labels, and (ii) a .pxp file that includes the data and graphs (without fits) in the same layout as figures in the original manuscript. The .pxp file was prepared with Igor Pro 8.02 (https://www.wavemetrics.com).</p>
N3C-Formatted OMOP2OBO Mappings
<p><strong>OMOP2OBO Mappings - N3C OMOP to OBO Working group</strong></p> <p>This repository stores OMOP2OBO mappings which have been processed for use within the National COVID Cohort Collaborative (N3C) Enclave. The version of the mappings stored in this repository have been specifically formatted for use within the N3C Enclave.</p> <p><strong>N3C OMOP to OBO Working Group: </strong>https://covid.cd2h.org/ontology</p> <p> </p> <p><em><strong>Accessing the N3C-Formatted Mappings </strong></em></p> <p>You can access the three OMOP2OBO HPO mapping files in the Enclave from the Knowledge store using the following link: <a href="https://unite.nih.gov/workspace/compass/view/ri.compass.main.folder.1719efcf-9a87-484f-9a67-be6a29598567">https://unite.nih.gov/workspace/compass/view/ri.compass.main.folder.1719efcf-9a87-484f-9a67-be6a29598567</a>.</p> <p>The mapping set includes three files, but you only need to merge the following two files with existing data in the Enclave in order to be able to create the concept sets:</p> <ul> <li><em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv</em></li> <li><em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv</em></li> </ul> <p> </p> <p>The first file <em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv</em>, contains columns for the OMOP concept ids and codes as well as specifies information like whether or not the OMOP concept’s descendants should be included when deriving the concept sets (defaults to FALSE). The other file <em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv</em>, contains details on the mapping’s label (i.e., the HPO curie and label in the concept_set_id field) and its provenance/evidence (the specific column to access for this information is called intention).</p> <p> </p> <p><em><strong>Creating Concept Sets</strong></em></p> <p>Merge these files together on the column named <em>codeset_id</em> and then join them with existing Enclave tables like concept and <em>condition_occurrence</em> to populate the actual concept sets. The name of the concept set can be obtained from the <em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv</em> file and is stored as a string in the column called <em>concept_set_id</em>. Although not ideal (but is the best way to approach this currently given what fields are available in the Enclave), to get the HPO CURIE and label will require applying a regex to this column.</p> <p>An example mapping is shown below (highlighting some of the most useful columns):</p> <pre><code>codeset_id: 900000000 concept_set_id: [OMOP2OBO] hp_0002031-abnormal_esophagus_morphology concept: 23868 code: 69771008 codeSystem: SNOMED includeDescendants: False intention: Mixed - This mapping was created using the OMOP2OBO mapping algorithm (https://github.com/callahantiff/OMOP2OBO). The Mapping Category and Evidence supporting the mappings are provided below, by OMOP concept: 23868 ******* Mapping Category: Automatic Exact - Concept ------------------------------------------------ Mapping Provenance ------------------ OBO_DbXref-OMOP_ANCESTOR_SOURCE_CODE:snomed_69771008 | OBO_DbXref-OMOP_CONCEPT_SOURCE_CODE:snomed_69771008 | CONCEPT_SIMILARITY:HP_0002031_0.713</code></pre> <p> </p> <p><strong>Release Notes - v2.0.0</strong></p> <p><em>Preparation</em></p> <p>In order to import data into the Enclave, the following items are needed:</p> <ul> <li>Obtain API Token, which will be included in the authorization header (stored as GitHub Secret)</li> <li>Obtain username hash from the Enclave</li> <li><a href="https://zenodo.org/record/7250177">OMOP2OBO Mappings (v1.5.0)</a></li> </ul> <p><em>Data</em></p> <ul> <li>Concept Set Container (<em>concept_set_container</em>): <em>CreateNewConceptSet</em></li> <li>Concept Set Version (<em>code_sets</em>): C<em>reateNewDraftOMOPConceptSetVersion</em></li> <li>Concept Set Expression Items (<em>concept_set_version_item</em>): <em>addCodeAsVersionExpression</em></li> </ul> <p><em>Script</em></p> <ul> <li><a href="https://github.com/callahantiff/OMOP2OBO/blob/master/applications/N3C/n3c_mapping_conversion.py">n3c_mapping_conversion.py</a></li> </ul> <p><em>Generated Output</em></p> <p>Need to have the <em>codeset_id</em> filled from self-generation (ideally, from a conserved range) prior to beginning any of the API steps. The current list of assigned identifiers is stored in the file named <em>omop2obo_enclave_codeset_id_dict_v2.0.0.json</em>. Note that in order to accommodate the 1:Many mappings the codeset ids were re-generated and rather than being ampped to HPO concepts, they are mapped to SNOMED-CT concepts. This creates a cleaner mapping and will easily scale to future mapping builds.</p> <p> </p> <p>To be consistent with OMOP tools, specifically Atlas, we have also created Atlas-formatted json files for each mapping, which are stored in the zipped directory named <em>atlas_json_files_v2.0.0.zip</em>. Note that as mentioned above, to enable the representation of 1:Many mappings the filenames are no longer named after HPO concepts they are now named with the OMOP concept_id and label and additional fields have been added within the JSON files that includes the HPO ids, labels, mapping category, mapping logic, and mapping evidence.</p> <p> </p> <p><strong>File 1: concept_set_container</strong></p> <ul> <li><strong>Generated Data:</strong> <em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_container.csv</em></li> <li>Columns: <ul> <li>concept_set_id</li> <li>concept_set_name</li> <li>intention</li> <li>assigned_informatician</li> <li>assigned_sme</li> <li>project_id</li> <li>status</li> <li>stage</li> <li>n3c_reviewer</li> <li>alias</li> <li>archived</li> <li>created_by</li> <li>created_at</li> </ul> </li> </ul> <p> </p> <p><strong>File 2: concept_set_expression_items</strong></p> <ul> <li><strong>Generated Data: </strong><em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv</em> </li> <li>Columns: <ul> <li>codeset_id</li> <li>concept_id</li> <li>code</li> <li>codeSystem</li> <li>ontology_id</li> <li>ontology_label</li> <li>mapping_category</li> <li>mapping_logic</li> <li>mapping_evidence</li> <li>isExcluded</li> <li>includeDescendants</li> <li>includeMapped</li> <li>item_id</li> <li>annotation</li> <li>created_by</li> <li>created_at</li> </ul> </li> </ul> <p> </p> <p><strong>File 3: concept_set_version</strong></p> <ul> <li><strong>Generated Data: </strong><em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv</em> </li> <li>Columns: <ul> <li>codeset_id</li> <li>concept_set_id</li> <li>concept_set_version_title</li> <li>project</li> <li>source_application</li> <li>source_application_version</li> <li>created_at</li> <li>atlas_json</li> <li>most_recent_version</li> <li>comments</li> <li>intention</li> <li>limitations</li> <li>issues</li> <li>update_message</li> <li>status</li> <li>has_review</li> <li>reviewed_by</li> <li>created_by</li> <li>provenance</li> <li>atlas_json_resource_url</li> <li>parent_version_id</li> <li>is_draft</li> </ul> </li> </ul> <p> </p> <p><strong><em>Generated Output:</em></strong></p> <ul> <li>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_container.csv</li> <li>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv</li> <li>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv</li> <li>atlas_json_files_v2.0.0.zip</li> <li>omop2obo_enclave_codeset_id_dict_v2.0.0.json</li> </ul>
Advanced open source data formats for geometrically and physically coupled systems - examples
<p>Some model files to describe a geometrically and physically coupled PDE and a ODE system, arising from discretization in space. The files correspond to:</p> <ul> <li>a simplified two component problem with coupling and an FMU in <strong>withFMU.json</strong></li> <li>the corresponding ODE in <strong>io_withFMU_FECoupled.json</strong></li> <li>a PDE model of a complete machine in <strong>ictimt_coupledModel.zip</strong>. This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> <li>the corresponding discrete model in <strong>ictimt_feCoupled.zip </strong>This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> </ul> <p> </p> <p> </p>
Raw data: Local-scale feedbacks influencing cold-water coral growth and subsequent reef formation
<p>Spreadsheets with the raw data of ADV-measured current velocity, coral growth derived from buoyant weight measurements and stress-related protein activities and concentrations.</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.