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Measurement database and line parameter database for air- and H2O-broadened CO2 in the 4750-5175 cm-1 region including continuum
<p>Air-broadened CO2 measurements in the temperature range 200-294 K and a total pressure range 100-1000 mbar and analysis were carried out within the ESA-funded project ISOGG (Improved Spectroscopy for satellite measurements Of Greenhouse Gases). Additionally, a H2O-broadened measurement at 294 K was recorded and analyzed.</p> <p>The measurement database is in “CO2_air-_and_H2O-broadened_measurements_4750-5175cm-1.zip” and the line parameter database in “CO2_air-_and_H2O-broadening_parameters_4750-5175cm-1_V4.zip”. The CO2/air continuum is given in “CO2_2mu0_RT_foreigncont_polcoeffs_from_scaled_4mu3_N2.txt”. The readme file is “Measurement database and line parameter database for air- and H2O-broadened CO2 in the 4750-5175 cm-1 region including continuum - Readme.docx”.</p> <p>Definitions and units of line parameters are in accordance with the HITRAN database and can be found in the readme file in <a href="https://zenodo.org/records/1009126">ESA SEOM-IAS – Spectroscopic parameters database 2.3 µm region (zenodo.org)</a>. Depletions calculated with the polynomials in temperature have the unit atm<sup>-1</sup>.</p> <p>Systematic uncertainties which were common for all lines were omitted in the line parameter database and are listed here: Air-broadening parameters 0.03%, speed-dependence of air-broadening 1.5%, air Dicke narrowing 10-20%, air line mixing 0.0001 atm<sup>-1</sup>, air pressure-induced line shift 3x10<sup>-5</sup> cm<sup>-1</sup>atm<sup>-1</sup>, temperature exponent of air-broadening parameters 0.0005, temperature dependence of air line mixing 0.1 K<sup>-1</sup>, temperature dependence of air pressure-induced line shift 5x10<sup>-7</sup> cm<sup>-1</sup>atm<sup>-1</sup>K<sup>-1</sup>. In case the speed-dependence was calculated from polynomials of gamma<sub>2</sub>/gamma<sub>0</sub> vs gamma<sub>0</sub> the systematic uncertainty is estimated to be 10%.</p>
Measurement database and line parameter database for air- and H2O-broadened CO2 in the 5970-6575 cm-1 region including continuum
<p>Air-broadened CO2 measurements in the temperature range 210-294 K and a total pressure range 100-1000 mbar and analysis were carried out within the ESA-funded project ISOGG (Improved Spectroscopy for satellite measurements Of Greenhouse Gases). Additionally, a H2O-broadened measurement at 294 K was recorded and analyzed.</p> <p>The measurement database is in “CO2_air-_and_H2O-broadened_measurements_5970-6575cm-1.zip” and the line parameter database in “CO2_air-_and_H2O-broadening_parameters_5970-6575cm-1_V2.zip”. The CO2/air continuum is given in “CO2_1mu6_RT_foreigncont_polcoeffs_from_scaled_4mu3_N2.txt”. The readme file is “Measurement database and line parameter database for air- and H2O-broadened CO2 in the 5970-6575 cm-1 region including continuum - Readme_V2.docx”.</p> <p>Definitions and units of line parameters are in accordance with the HITRAN database and can be found in the readme file in <a href="https://zenodo.org/records/1009126">ESA SEOM-IAS – Spectroscopic parameters database 2.3 µm region (zenodo.org)</a>. Depletions calculated with the polynomials in temperature have the unit atm<sup>-1</sup>.</p> <p>Systematic uncertainties which were common for all lines were omitted in the line parameter database and are listed here: Air-broadening parameters 0.07%, speed-dependence of air-broadening 1.5%, air line mixing 0.0002 atm<sup>-1</sup>, air pressure-induced line shift 6x10<sup>-5</sup> cm<sup>-1</sup>atm<sup>-1</sup>, temperature exponent of air-broadening parameters 0.003, temperature dependence of air line mixing 0.03 K<sup>-1</sup>, temperature dependence of air pressure-induced line shift 2x10<sup>-7</sup> cm<sup>-1</sup>atm<sup>-1</sup>K<sup>-1</sup>. In case the speed-dependence was calculated from polynomials of gamma<sub>2</sub>/gamma<sub>0</sub> vs gamma<sub>0</sub> the systematic uncertainty is estimated to be 10%.</p>
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>
Supplementary Materials for "Depolarization of MgH Solar Lines by Collisions with Hydrogen Atoms"
<p>The files "MgHH_Potential_Singlet" and "MgHH_Potential_Triplet" respectively hold results of ab initio calculation of the potential energy surfaces (PESs), V(R,θ) in units of cm^-1, for the singlet (1A') and triplet (3A') states of the MgH-H system. Here R represents the distance (in atomic units) from the center of mass of MgH molecule to the H atom, and θ is the rotation angle (in degrees) of the H atom around the MgH. All the PESs are obtained using the MOLPRO package (e.g. Werner et al. 2010).</p> <p><br> The files "MgHH_Sigma_0toL_Singlet.csv" and "MgHH_Sigma_0toL_Triplet.csv" respectively contain results of the infinite-order sudden (IOS) approximation calculation of cross sections, σ(0->L) in units of Angstrom^2, as functions of energy in units of cm^-1 for the 1A' and 3A' states of the MgH-H system. The IOS cross sections are calculated using MOLSCAT code (Hutson & Green 1994). The depolarization and transfer of polarization cross sections can be calculated from the IOS cross sections via Eqs. (1) & (2) of Qutub et al. 2020.</p>
A database solutions for the type two assembly line balancing problems
<p> Assembly Line Balancing Problems have a significant impact on performance of manufacturing systems, specially for the cases of mass production. These problems are widely cited and treated in the literature. </p> <p>One from the most important variants of those problems is the “Task Restrictions Assembly Line Balancing Problem” of type 2. For this problem, a set of tasks need to be affected to a predefined number of stations m from the way that minimises the cycle time and respects a set of constraints related to precedence and compatibility between tasks (Triki et al., 2016).</p> <p>For this variant we suggest an innovative speed and effective approach based on the hybridisation of two powerful tools: the ant colony optimisation and the genetic algorithm. The effectiveness of this approach is evaluated through a set of instances collected from the literature (Thomas, 1990; Triki et al., 2016) .</p> <p>This document presents the best generated solutions for those problems. </p> <p> </p> <p> </p>
Emission line models for the lowest mass core-collapse supernovae - I. Case study of a 9 M⊙ one-dimensional neutrino-driven explosion
<p>Model spectra of the 9 Msun iron-core model, and the pure H toy model, 200-600d. Distance 10 Mpc assumed.</p>
Brilliantia kiribatiensis, a new genus and species of Cladophorales (Chlorophyta) from the remote coral reefs of the Southern Line Islands, Pacific Ocean
<p>Data associated with the study "<em>Brilliantia kiribatiensis</em>, a new genus and species of Cladophorales (Chlorophyta) from the remote coral reefs of the Southern Line Islands, Pacific Ocean".</p> <p><strong>ITS.fasta, ITS_bmge.fasta, LSU.fasta, LSU_bmge.fasta, SSU.fasta, SSU_bmge.fasta: </strong>SSU rDNA, LSU rDNA and rDNA ITS sequences used in phylogenetic analyses. Sequences of Brilliantia kiribatiensis were added to updated phylogenetic datasets used previously (Leliaert et al. 2007a, Leliaert et al. 2009b), aligned in MAFFT v7.215 (Katoh and Standley 2013), and stripped of hypervariable sites in BMGE v1.1 (Criscuolo and Gribaldo 2010) by using the -h 0.4 -g 0.35 parameters. Alignments were visually checked and concatenated in Seaview v4.4.2.</p> <p><strong>concatenated_SSU_ITS_LSU.fasta</strong>: concatenated alignment with following partitions: SSU: 1-1797, ITS1+5.8S+ITS2: 1798-3335, LSU: 3336-3926.</p> <p><strong>Table_S1_sequence_sources.xls: </strong>GenBank accessions, sample isolate codes and sites of collection for sequences included in the concatenated phylogenetic data set.</p> <p><strong>Table S2. </strong>Percent cover of different algal groups in 1 m2 photoquadrats. Algal groups are identified to genus level for fleshy macroalgae or functional group for turf algae, branched red algae, crustose coralline algae, and cyanobacteria.</p> <p><strong>Table SX.</strong> Morphological measurements of <em>Brilliantia kiribatiensis</em>.</p>
Data for "Shell microstructures (disturbance lines) of Arctica islandica (Bivalvia) – A potential proxy for severe oxygen depletion"
<p>All data used in the publication "Shell microstructures (disturbance lines) of <em>Arctica islandica</em> – A potential proxy for severe oxygen depletion" currently under review. This includes in situ environmental data from the Mecklenburg Bight, Baltic Sea (ODIN 2, Leibnitz Institute for Baltic Sea Research, https://odin2.io-warnemuende.de/), and biomineral unit (BMU) morphology measurements in scanning electron microscopy images of shells of <em>Arctica islandica</em>, as well as the BMU classifier used in Ilastik (Berg et al., 2019).</p> <p>Each BMU measurement represents summary statistics of the 15 % largest BMUs within one image (25, 50 and 75 % percentile of each BMU parameter). Environmental data were measured at 20 m water depth, ca. 5 m above the sediment surface.</p>
The ICDAR 2003 Informal Competition for the Recognition of On-line Words: The Unipen-ICROW-03 benchmark set - Version 0.0
<p>Proposal for an informal benchmark on word recognition. See for the related ImUnipen collection<br> of word images from on-line vectorial handwriting data: https://zenodo.org/record/1195059</p> <p>At the time (ICDAR 2003) there was not a lot of interest so the project was not pursued.</p> <p>Lambert Schomaker - February 2023</p> <p>_______________________________________________________________________________</p> <p>The ICDAR 2003 Informal Competition for the Recognition of On-line Words:<br> The Unipen-ICROW-03 benchmark set <br> Version 0.0</p> <p>Lambert Schomaker / International Unipen Foundation</p> <p>The ICROW suite of test files for the recognition of isolated on-line<br> free-style (handprint, mixed and cursive) words has been<br> composed. Different tablets, nationalities and languages<br> are involved. Only the ASCII set is used within word labels.</p> <p>The set contains:</p> <p> 13119 written words<br> 884 unique lexical word entries<br> 72 writers </p> <p>Language: Dutch, English, Italian.<br> Nationalities: Dutch, Irish, Italian, + mixed</p> <p>The benchmark test is a good estimator for <br> "walk-up" recognition performance.</p> <p>[Note: some of the writers (NIC-Pc95*.dat set) are present in the<br> UNIPEN R01/V07 distribution, but the actual words are unseen <br> outside of the Int. Unipen Foundation.]</p> <p>Please note the Copyright notice in the <br> accompanying file 'Copyright'</p> <p>Wed Jul 16 21:20:10 CEST 2003</p> <p>Lambert Schomaker</p> <p>---------------------------------------------------------------------------</p> <p>Instructions for the ICDAR 2003 informal competition for<br> the recognition of on-line words.</p> <p>1 - unpack the .tgz file<br> 2 - use the UNIPEN files as input for your recognizer.<br> 3 - report, for each writer, a file <writer-id>.res</p> <p> Example: do-my-recognizer < NIC-Hi93b-marc.dat > NIC-Hi93b-marc.res</p> <p>Format of the .res file.</p> <p>No XML for this moment: simplicity does it.</p> <p>We assume that the recognizer is able to produce a top-10 list<br> of likely words, sorted from most likely to least likely.<br> The output for each word is on a single line. The correct<br> target word is in the first column.</p> <p><targetword 1> <best word hyp.> <2nd-best word hyp.> ... <10th-best word hyp><br> <targetword 2> <best word hyp.> <2nd-best word hyp.> ... <10th-best word hyp></p> <p>Example with two words:</p> <p>summertime slumbertime slipknot summertime somatome spumante simulative semitone schoolmate sermonette semimature<br> Aberdeen Adamson Aberdeen Addison Armageddon Abyssinian Araban Albanian Alabamian Abraham Adelaide</p> <p><br> 4 - pack the *.res files in a .tgz or .zip file and send them<br> to schomaker@ai.rug.nl<br> All *.dat files need to be processed.</p> <p>LS.<br> </p> <p> </p>
Line-level Named Entity Recognition annotation for the George Washington and IAM datasets
<p>Line-level Named Entity annotation for the George Washington and IAM datasets. The word-level annotations from Oliver Tüselmann [3] were extended to line-level to enable experimentation with line-level coupled HTR+NER models. We also publish the line-level partition files that result from the partition proposed by [3].</p>
Influence of protein corona on cytotoxicity of metal oxide nanoparticles against human keratinocyte cell line (HaCaT)
<p>The model identified, among the factors determining the cytotoxic properties of metal oxide nanoparticles against HaCaT cell lines, a number of variables related to the processes occurring on the surface of nanoparticles in a biological medium, including the ability to form protein corona.</p> <p>The selected descriptors describe both the electronic structure of the metal oxides that are the components of the nanoparticles, i.e. the ionization potential (IP_ActivM_SM_#1, IP_ActivM_SM_#2) and the initial nanoforms, i.e. the particle size (Primary size) and the percentage content of the metal oxide which is the main component of the nanoparticle (Purity_#1) ; characterize nanoparticles in the medium, i.e. the isoelectric point (PZZP_#2), stability (Stability), potential for dissolution (Dissolution), generation of reactive oxygen species (ROS production) and protein adsorption (Protein adsorption). The listed descriptors reflect the features that are discussed in the literature as potentially related to the toxic effect of nanoparticles.</p>
Photodegradation of phenol (τOH) by TiO2-based nanophotocatalysts determined in line with the SAPNet methodology
<p>The independent variable (predictor) is the intensity of photoluminescence at 398 nm and with use of logistic regression model connects ability of photocatalytic degradation of phenol (endpoint) with this experimentally derived property. The equation goes as follow:<br> 4.32(±2.10) – 0.051(±0.03)(PL398)</p> <p>The developed model is included into the SAPNet workflow (Structure-Activity Prediction Network). In an additional step of SAPNet workflow developed model correlates the structure of a nanomaterial to the selected endpoint- photodegradation of phenol (τOH) by titanium dioxide synthesized in the presence of ionic liquids (IL). </p> <p>Each sample is described by surface area, the amount of nitrogen and carbon atoms, ionic liquid decomposition rate (ΔIL), molar ratio and the type of cations and anions, that influences photoluminescence </p>
Predictive nano-QSAR modeling of the cytotoxicity using epithelial cells obtained from Chinese hamster ovary (CHO-K1 cell line) for hybrid TiO2-based nanomaterials
<p>Results obtained from developed model indicated that the cytotoxicity of hybrid TiO2-based nanomaterials is related to additive electronegativity (χmix) of studied nanomaterials that are indirectly related to the electron generation and ROS formation. ROS production is the most common toxicity cause as discussed in the literature in the case of nanoparticles. The high efficiency of surface modified TiO2-based semiconductors can be attributed to the involvement of TiO2 band gap (Eg) excitation and absence of noble metals at the TiO2 surface. It can be expected that noble metals (i.e. Pd/Pt) may trap holes (h+), at the same time photo-generated electrons can be then transferred from the valence band to the conduction band of TiO2 and to its surface where redox processes were initiated. Thus, observed reduction of the electron–hole pair recombination influences the reactive oxygen species (ROS) formation and the photocatalytic redox process initiation.</p> <p>Since the electronegativity was positively correlated with the cytotoxicity it can be expected that some ions are released from the TiO2 surface easier than others.</p>
Field Line Resonances estimated using Machine Learning methods
<p>This data set contains the machine learning input matrix (composed by 1D Fourier cross-spectra) + additional information, for the Classification algorithm implemented in Foldes et al. (Automatic Detection of Field Line Resonance Frequencies in the Earth’s Plasmasphere, 2023) for the pair of station Tartu-Birzai (TAR-BRZ).</p> <p>Each file contains the following header at line 1. Columns are:</p> <p>- P(f0)-P(f211): Cross-phase value per frequency bin</p> <p>- YEAR</p> <p>- DOY (Day Of Year)</p> <p>- HOUR</p> <p>- ToD_flag: "Umbra", "Penumbra", 'Light'</p> <p>- L: McIllwain parameter</p> <p>- stat_tag: "tarbrz"</p> <p>- Kp</p> <p>- Kp_w_05d: Kp index weighted on a 12hrs time window</p> <p>- Kp_w_10d: Kp index weighted on a 24hrs time window</p> <p>- Kp_w_15d: Kp index weighted on a 36hrs time window</p> <p>- Kp_w_20d: Kp index weighted on a 2-day time window</p> <p>- Kp_w_25d: Kp index weighted on a 2.5-day time window</p> <p>- Kp_w_30d: Kp index weighted on a 3-day time window</p> <p>- Kp_m_05d: Kp index max on a 12hrs time window</p> <p>- Kp_m_10d: Kp index max on a 24hrs time window</p> <p>- Kp_m_15d: Kp index max on a 36hrs time window</p> <p>- Kp_m_20d: Kp index max on a 2-day time window</p> <p>- Kp_m_25d: Kp index max on a 2.5-day time window</p> <p>- Kp_m_30d: Kp index max on a 3-day time window</p> <p>- DST</p> <p>- DST_m_05d: DST index min on a 12hrs time window</p> <p>- DST_m_10d: DST index min on a 24hrs time window</p> <p>- DST_m_15d: DST index min on a 36hrs time window</p> <p>- DST_m_20d: DST index min on a 2-day time window</p> <p>- DST_m_25d: DST index min on a 2.5-day time window</p> <p>- DST_m_30d: DST index min on a 3-day time window</p> <p>- F107: F10.7 solar activity proxy</p> <p>- EField: Earth Electric co-rotation field</p> <p>- f(mHz): FLR frequency in mHz</p> <p>- df(mHz): Uncertainty on the validated frequency</p> <p>- class: 0 for "NoFreq", 1 for "Freq" and 2 for "PBL"</p>
Genetic diversity, population structure, and linkage disequilibrium among tropical quality protein maize (QPM) lines assessed with high-density SNP markers
<p>The study of genetic diversity (GD), population structure, and linkage disequilibrium (LD) provides a better understanding of the genetic relationships between individuals in a population which can be utilized in crop research and improvement. Genotyping-by-sequencing (GBS) was used to detect and genotype single nucleotide polymorphisms (SNPs) in a collection of 74 quality protein maize (QPM) lines and further to characterize their genetic diversity, population structure, and linkage disequilibrium. A total of 235,214 high-quality SNPs were used for different genetic analyses except for structure analysis where 11,950 SNPs were used. Analysis of molecular variance (AMOVA) based on these SNPs revealed high genetic heterozygosity among the five populations with 1% of the total genetic variation present among the subpopulations and 99% of the variation among individuals within the populations. Population structure analysis using Bayesian-based clustering revealed that the 74 lines could be clustered into four groups. However, neighbor-joining trees indicate the lines are grouped into three major clusters. Further analysis using principal component analyses (PCA) clustered the genotypes into five groups which are concordant with the groups based on pedigree information. Higher genetic diversity was detected in population 1 with a GD value of 0.484 and the lowest in population 5 (0.396) and overall, with a mean of 0.434. The LD pattern in the quality protein maize was investigated and we observed a relatively rapid LD decay of 3.53kb and 10.66kb at r<sup>2</sup> =0.2 and r<sup>2</sup>= 0.1, respectively. Our findings provide important information for future Linkage mapping studies, genome-wide association analyses, and marker-assisted selective breeding of maize as well as genomic prediction-based selection in tropical germplasm.</p>
Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper
<p>Corpora used in the publication:</p> <ul> <li>Cristina España-Bonet. 2023. <strong>Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper. </strong>In <em>Findings of the Association for Computational Linguistics: EMNLP 2023</em>, Singapore. Pages 11757–11777. Association for Computational Linguistics.</li> </ul> <p>Three corpora are included:</p> <ol> <li>Newspaper articles extracted from the OSCAR corpus in English, German, Spanish and Catalan automatically annotated for political stance (left vs right) and topic</li> <li>Newspaper-like article generations by different versions of ChatGPT for 101 topics in the 4 languages</li> <li>Newspaper-like article generations by Bard for 101 topics in the 4 languages</li> </ol> <p>See the README file and the original article for further details.</p>
Calhoun Lines small mammal trapping transect data from UC Berkeley - Sagehen Creek Field Station, CA between 1950 and 1989
Database contains detailed records from a series of Calhoun Lines established in Sagehen Creek Basin, California. Thirteen lines were established and run, at various intervals, between 1950 and 1989. The database contains vegetation analysis, small mammal trapline data, habitat photos, trapline charts and graphs, aerial photos, and sketches of individual Calhoun lines. This database was created from photocopies of original records. In some cases, data was unreadable or illegible. It cannot be guaranteed that this is a complete record of all Calhoun line data at Sagehen Creek Field Station. Dataset and metadata is available under the Mammals section on the Sagehen data resources page. As part of the Sagehen Forest Project monitoring, this dataset was resampled in summer, 2011 and will be again in summer 2019.
CBP01 Variable distance line-transect sampling of bird population numbers in different habitats on Konza Prairie (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/339/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-knz/26/11. The abstract below was extracted from the Level 0 data package and is included for context: Records of bird species based on line transect sampling, giving perpendicular distance of sighting from the transect line on 16 separate transects. Bird surveys were conducted 2-4 times per year in January, April, June, and October for a 29-year period from 1981 to 2009. Transects were designed to determine bird communities and population numbers associated with tallgrass prairie habitats with different experimental treatments (fire frequency, grazed by bison vs. ungrazed), riparian habitats on forest edge, and gallery forests dominated by oak woodland.
Coarse woody debris volume and mass from line transect inventory from reference stands and inventory plots of the Pacific Northwest, 1997 to 2005
These data can be used to provide an inventory of the volume of coarse woody debris stored in forests.
Fine woody detritus volume and mass from line transect inventory in WS06, WS07, WS08, and WS09 at Andrews Experimental Forest, 2002 to 2003
Three small watersheds (WS06, WS07, WS08, and WS09) at the Andrews Experimental Forest were inventoried for fine woody detritus (FWD) using the line transect method as described in Guidelines for measurements of woody detritus in forest ecosystems (Harmon and Sexton 1996). In each watershed plot, four transects of 4 meters in length were inventoried. Slopes of the transect were determined as the plots were not laid out slope corrected. The number of pieces of FWD were tallied along each transect for two size classes, 0.63-2.54 cm and 2.54-7.62 cm. Volume calculations were determined by use of the formula in Harmon and Sexton 1996. Mean bulk density was determined from samples collected and measured. Field measurements used to determine density included diameter and length. In the lab, the samples were weighed to determine wet weight and dried.
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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.