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5,738 results for “Standardization”
UCSB SONGS Mitigation Monitoring: Reef Performance Standard - Fish Standing Stock
These data describe annual estimates of fish standing stock (in tons) supported by the artificial reef, Wheeler North Reef, in Orange County, CA (33.40210N, 117.62420W). Data collection began in 2009 to evaluate the ability of Wheeler North Reef to compensate for losses of kelp forest habitat and associated biota caused by the operation of the San Onofre Nuclear Generating Station (SONGS).
UCSB SONGS Mitigation Monitoring: Reef Performance Standard - Fish Abundance and Richness
These data describe annual estimates of the density and species richness (evaluated as species density) of young-of-year (less than 1 year old) and resident (greater than 1 year old) reef fish from replicate transects at three subtidal reefs. Data collection began in 2009 at an artificial reef (Wheeler North Reef in Orange County, CA) and two natural reference reefs (San Mateo Kelp in Orange County, CA and Barn Kelp in San Diego County, CA) to evaluate the ability of Wheeler North Reef to compensate for losses of kelp forest habitat and associated biota caused by the operation of San Onofre Nuclear Generating Station (SONGS).
Capture data for sharks caught in standardized drumline fishing in Shark Bay, Western Australia, with accompanying abiotic data, from February 2008 to July 2014.
This dataset provides data on large sharks captured during standardized drumline fishing and is used to monitor shark catch rates, sex ratios and size distributions in Shark Bay.
Capture data for sharks caught in standardized drumline fishing in Shark Bay, Western Australia, with accompanying abiotic data, from January 2012 to April 2014.
This file provides data on standardized drumline fishing effort (gear deployment data) targeting large elasmobranchs in Shark Bay, Western Australia between 2012 and 2014
Monthly precipitation data from a network of standard gauges at the Jornada Experimental Range (Jornada Basin LTER) in southern New Mexico, January 1916 - ongoing
This ongoing dataset contains monthly precipitation measurements from a network of standard can rain gauges at the Jornada Experimental Range in Dona Ana County, New Mexico, USA. Precipitation physically collects within gauges during the month and is manually measured with a graduated cylinder at the end of each month. This network is maintained by USDA Agricultural Research Service personnel. This dataset includes 39 different locations but only 29 of them are current. Other precipitation data exist for this area, including event-based tipping bucket data with timestamps, but do not go as far back in time as this dataset.
Data relating to Clyne et al. Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational study
<p>Data relating to the study reported in the paper "Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational study".</p>
CodiEsp corpus: gold standard Spanish clinical cases coded in ICD10 (CIE10) - eHealth CLEF2020
<p><strong>Introduction</strong></p> <p>These are the train, development and test sets of the CodiEsp corpus. Train, development and test have gold standard annotations. In addition, the unannotated background set is also distributed. All documents are released in the context of the CodiEsp track for CLEF ehealth 2020 (<a href="http://temu.bsc.es/codiesp/">http://temu.bsc.es/codiesp/</a>).</p> <p>The CodiEsp corpus contains manually coded clinical cases. All documents are in Spanish language and CIE10 is the coding terminology (it is the Spanish version of ICD10-CM and ICD10-PCS). The CodiEsp corpus has been randomly sampled into three subsets: the train, the development, and the test set. The train set contains 500 clinical cases, and the development and test set 250 clinical cases each. CodiEsp participants must submit predictions for the test and background set, but they will only be evaluated on the test set.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020</p> <pre><code>@inproceedings{miranda2020overview, title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2020} }</code></pre> <p> </p> <p><strong>Annotation quality</strong></p> <p>Inter-annotator agreement: 88.6% for diagnosis coding, 88.9% for procedure coding and 80.5% for the textual reference annotation. For more information, see the <a href="http://ceur-ws.org/Vol-2696/paper_263.pdf">paper</a>.</p> <p><br> <strong>Zip structure</strong><br> Four folders: train, dev, test and background. Each one of them contains the files for the train, development, test and background corpora, respectively.</p> <ul> <li><strong>train, dev and test</strong> folders have: <ul> <li>3 tab-separated files with the annotation information relevant for each of the 3 sub-tracks of CodiEsp. </li> <li>A subfolder named <em>text_files</em> with the plain text files of the clinical cases.</li> <li>A subfolder named <em>text_files_en</em> with the plain text files machine-translated to English. Due to the translation process, the text files are sentence-splitted.</li> </ul> </li> <li>The <strong>background</strong> folder has only <em>text_files</em> and <em>text_files_en</em> subfolders with the plain text files.</li> </ul> <p><br> <strong>Format</strong><br> The CodiEsp corpus is distributed in plain text in UTF8 encoding, where each clinical case is stored as a single file whose name is the clinical case identifier. Annotations are released in a tab-separated file. Since the CodiEsp track has 3 sub-tracks, every set of documents (train and test) has 3 tab-separated files associated with it. </p> <p>For the sub-tracks CodiEsp-D and CodiEsp-P, the file has the following fields:</p> <pre>articleID ICD10-code </pre> <p>Tab-separated files for the sub-track CodiEsp-X contain extra fields that provide the text-reference and its position:</p> <pre>articleID label ICD10-code text-reference reference-position</pre> <p><br> <strong>Corpus summary statistics</strong><br> The final collection of 1000 clinical cases that make up the corpus had a total of 16504 sentences, with an average of 16.5 sentences per clinical case. It contains a total of 396,988 words, with an average of 396.2 words per clinical case.</p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/codiesp/">Web</a></strong></li> <li><strong><a href="http://ceur-ws.org/Vol-2696/paper_263.pdf">Citation</a>: </strong>Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020</li> <li><strong><a href="https://doi.org/10.5281/zenodo.3859869">Silver Standard corpus</a></strong></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3730566">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhA0crlSVCYMPqMUWd4mXc4x"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/codiesp/index.php/participants-systems/"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>For more information, visit the track webpage: http://temu.bsc.es/codiesp/ or email us at encargo-pln-life@bsc.es</p> <p> </p> <p>Copyright (c) 2019 Secretaría de Estado para el Avance Digital</p>
Fast MLE and Supervised Classification for the Beta-Liouville Multinomial -- Gold Standard Data
<p>Gold standard datasets used in the publication Fast Maximum Likelihood Estimation and Supervised Classification for the Beta-Liouville Multinomial. Datasets were prepared by Cardoso-Cachopo (2007).</p>
Spectral library of laser-induced fluorescence (LiF) properties from Smithsonian rare-earth element (REE) orthophosphate standards
<p>The spectral library presents a data set of laser-induced fluorescence (LiF) spectra from rare-earth element (REE) orthophosphates provided and distributed as reference material for microbeam analysis by the Smithsonian National Museum of Natural History (sample IDs: 16484 - NMNH 168499; Jarosewich and Boatner, 1991; Donovan et al., 2002 and 2003). The data set delivers high-resolution LiF spectra excited at three standard laser wavelengths (325 nm, 442 nm, 532 nm) recorded in the UV-visible to near-infrared spectral range (340 - 1080nm). Presented LiF spectra represent data from efficient signal excitation conditions and contain the diagnostic emission lines of individual REE including detailed information on splitting into sub-levels. The LiF spectral library data provides a reference for various applications in spectroscopy-based material composition analysis with the scope of REE identification. LiF as a tool can complement the merging technique of reflectance spectroscopy, because LiF is a particularly well suited method for REE detection and can be used to cross-validate results (e.g. Lorenz et al. 2019) The LiF library allows for transparent and reproducible result analysis in scientific studies and promotes further developments of efficient automated algorithms for REE identification and characterisation. This addresses especially the need for innovative, non-invasive techniques of raw material exploration (securing REE supply) and material stream characterisation (e.g. in e-waste recycling) or for manifold applications in other fields of geosciences (e.g. geology) and physics.</p> <p>references:</p> <p>Donovan, J., Hanchar, J., Picolli, P., Schrier, M., Boatner, L., Jarosewich, E., 2002. Contamination in the rare-earth element orthophosphate reference sam- ples. J. Res. National Institute of Standards and Technology 106, 693–701. doi:10.6028/jres.107.056. </p> <p>Donovan, J., Hanchar, J., Piccoli, P., Schrier, M., Boatner, L., Jarosewich, E., 2003. A reexamination of the rare-earth element orthophosphate reference samples for electron microprobe analysis. Canadian Mineralogist 41, 221– 232. doi:10.2113/gscanmin.41.1.221. </p> <p>Jarosewich, E., Boatner, L., 1991. Rare-earth element reference samples for electron microprobe analysis. Geostandards Newsletter 15, 397–399. doi:10. 1111/j.1751-908X.1991.tb00115.x. </p> <p>Lorenz, S., Beyer, J., Fuchs, M., Seidel, P., Turner, D., Heitmann, J., Gloaguen, R., 2019. The Potential of Reflectance and Laser Induced Luminescence Spectroscopy for Near-Field Rare Earth Element Detection in Mineral Ex- ploration. Remote Sensing 11, 21. doi:10.3390/rs11010021.</p>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWLNETS (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWL (v2.0.0 - May 2020)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-StandardRelations-OWL</i></p><p><strong>Build Date: </strong>May 10, 2020</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-10%2C-2020">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWLNETS (v2.0.0 - May 2020)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-StandardRelations-OWLNETS</i></p><p><strong>Build Date: </strong>May 10, 2020</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-10%2C-2020">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v2.0.0 - May 2020)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>May 10, 2020</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-10%2C-2020">here</a>.</li></ul>
Country Compendium of the Global Register of Introduced and Invasive Species: Standardization to Records in World Flora Online or the World Checklist of Vascular Plants
<p>The <strong>Country Compendium of the Global Register of Introduced and Invasive Species (GRIIS)</strong> is a collation of data across 196 individual country checklists of alien species, along with a designation of those species associated with evidence of impact at a country level. This compendium is available via <a href="https://zenodo.org/records/6348164">Zenodo</a> and was described by Pagad et al. <a href="https://www.nature.com/articles/s41597-022-01514-z">2022</a>:</p><ul><li>Shyama Pagad, Stewart Bisset, & Melodie A. McGeoch. (2022). Country Compendium of the Global Register of Introduced and Invasive Species. Dataset. (V1_0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6348164">https://doi.org/10.5281/zenodo.6348164</a></li><li>Pagad, S., Bisset, S., Genovesi, P. <i>et al.</i> Country Compendium of the Global Register of Introduced and Invasive Species. <i>Sci Data</i> <strong>9</strong>, 391 (2022). <a href="https://doi.org/10.1038/s41597-022-01514-z">https://doi.org/10.1038/s41597-022-01514-z</a></li></ul><p> </p><p>Here I provide direct and fuzzy matches for species listed for the Plantae Kingdom in GRIIS with accepted plant names in <strong>World Flora Online</strong> (<a href="https://www.worldfloraonline.org/downloadData">version 2023.03</a>; Borsch et al. <a href="https://doi.org/10.1002/tax.12373">2020</a>) or the <strong>World Checklist of Vascular Plants</strong> (<a href="https://doi.org/10.34885/nswv-8994">version 10</a>; Govaerts et al. <a href="https://www.nature.com/articles/s41597-021-00997-6">2021</a>). Matching was done in <i>R</i> through the <a href="https://cran.r-project.org/package=WorldFlora">WorldFlora</a> package (Kindt <a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">2020</a>). The taxonomic standardization process was similar to the one completed <a href="https://www.worldagroforestry.org/output/agroforestry-species-switchboard-30">during the preparation of the third major release</a> of the <a href="https://apps.worldagroforestry.org/products/switchboard">Agroforestry Species Switchboard</a> and when preparing the <strong>GlobalUsefulNativeTrees database</strong> (GlobUNT; <a href="https://worldagroforestry.org/output/globalusefulnativetrees">https://worldagroforestry.org/output/globalusefulnativetrees</a>) .</p><p>Where a matching species was found in GlobUNT, the species name in the GlobUNT database has been shown. GlobUNT has been described in the following publication: Kindt et al. (<a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) <strong>GlobalUsefulNativeTrees, a database of 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in restoration</strong>. <i>Sci Rep</i> <strong>13</strong>, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a>.</p><p>The developments of this dataset and GlobUNT were supported by the Darwin Initiative to project DAREX001 of <a href="https://www.darwininitiative.org.uk/project/DAREX001/"><i>Developing a Global Biodiversity Standard certification for tree-planting and restoration</i></a> and by Norway's International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the <a href="https://www.worldagroforestry.org/project/provision-adequate-tree-seed-portfolio-ethiopia"><i>Provision of Adequate Tree Seed Portfolio</i></a> project in Ethiopia. </p>
ICOPS Workshop Series - Standard Vocabularies and Ontologies
<p><strong>This is the fifth workshop in the International Committee on Open Phytolith Science (ICOPS) workshop series on Open Research Skills. </strong></p><p>In this workshop we had multiple speakers:</p><ul><li>Introduction - Henriette Harmse - slides in the main presentation</li><li>Case study and demo of image database - Frances Wong - slides attached as pdf.</li><li>Phytolith standarised nomenclature ICPN 2.0 - Luc Vrydaghs [*presentation not included*]</li><li>Phytolith ontology - Celine Kerfant and Zach Dunseth - slides in the main presentation</li></ul><p>Youtube video of the workshop:</p><p><a href="https://youtu.be/qZaWkJlVXvE">https://youtu.be/qZaWkJlVXvE</a></p>
zbMATHOpenRec: A Gold Standard Dataset for Recommending Scientific Documents with Mathematical Content
<p> </p> <p>Here we include the first gold standard dataset for recommending scientific documents with mathematical content. </p> <p><strong>Contents: </strong></p> <p>As of Feb-2023, there are 421 recommendation pairs with 80 seed documents.</p> <ol> <li>All recommendation pairs are available: recommendationPairs.csv</li> <li>Each document's contents, such as title, abstract/review/summary, authors, MSC codes, Full-text link, references, etc. are available in: documentContents.csv</li> </ol> <p><strong>Dataset construction process</strong>:</p> <p>This is the first gold standard content-based RS dataset, consisting of 421 scientific research entry recommendation pairs with mathematical content. The purpose is to enable math in scientific documents for document recommendations, meaning if two documents have similar math content, one could be recommended to the other. </p> <p>To create this dataset, we analyzed 4.5 million research entires from zbMATH Open (https://zbmath.org/) and performed the following steps to obtain the final dataset:</p> <ol> <li>We selected 80 seeds that capture the most word and math tokens in zbMATH Open using statistical measures.</li> <li>Three experts, one with several years of experience reviewing research entries in mathematics, curated the recommendations for 80 seeds.</li> </ol> <p>Using this dataset, researchers can accelerate the development and testing of recommendation approaches for scientific literature with mathematical content, improving recommendations for the STEM fields where mathematical content is currently being ignored</p> <p>## License </p> <p>Legal restrictions and copyright: The zbMATH Open data is subject to the Terms and Conditions for the zbMATH Open API Service of FIZ Karlsruhe – Leibniz-Institut für Informationsinfrastruktur GmbH. Content generated by zbMATH Open, such as reviews, classifications, software, or author disambiguation data, are distributed under CC-BY-SA 4.0. This defines the license for the whole dataset, which also contains non-copyrighted bibliographic metadata and reference data derived from I4OSC (CC0).</p>
Trees of India Version 1: Standardization to Records in World Flora Online and the World Checklist of Vascular Plants, with matches in GlobalTreeSearch and GlobalUsefulNativeTrees
<p>The <strong>Trees of India (ToI, Version-I)</strong> includes data on 3708 tree species distributed across 35 states/union territories of India. The database is based on systematic review of 313 literature sources published from 1872-2022.This compendium is available via <a href="https://figshare.com/articles/dataset/ToI_Ver_-I_Trees_of_India_Version-I/23226281">Figshare</a> and was described by Mugal et al. <a href="https://link.springer.com/article/10.1007/s10531-023-02659-y">2023</a>:</p> <ul> <li>Khuroo, Anzar Ahmad; Mugal, Muzamil Ahmad; Wani, Sajad Ahmad (2023). ToI, Ver.-I : Trees of India, Version-I. figshare. Dataset. <a href="https://doi.org/10.6084/m9.figshare.23226281.v1">https://doi.org/10.6084/m9.figshare.23226281.v1</a></li> <li>Mugal, M.A., Wani, S.A., Dar, F.A. <em>et al.</em> Bridging global knowledge gaps in biodiversity databases: a comprehensive data synthesis on tree diversity of India. <em>Biodivers Conserv</em> <strong>32</strong>, 3089–3107 (2023). <a href="https://doi.org/10.1007/s10531-023-02659-y">https://doi.org/10.1007/s10531-023-02659-y</a></li> </ul> <p> </p> <p>Here I provide direct and fuzzy matches for taxa listed with accepted plant names in <strong>World Flora Online</strong> (<a href="https://www.worldfloraonline.org/downloadData">version 2023.03</a>; Borsch et al. <a href="https://doi.org/10.1002/tax.12373">2020</a>) and the <strong>World Checklist of Vascular Plants</strong> (WCVP <a href="https://doi.org/10.34885/nswv-8994">version 10</a>; Govaerts et al. <a href="https://www.nature.com/articles/s41597-021-00997-6">2021</a>). Matching was done in <em>R</em> through the <a href="https://cran.r-project.org/package=WorldFlora">WorldFlora</a> package (Kindt <a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">2020</a>). The taxonomic standardization process was similar to the one completed <a href="https://www.worldagroforestry.org/output/agroforestry-species-switchboard-30">during the preparation of the third major release</a> of the <a href="https://apps.worldagroforestry.org/products/switchboard">Agroforestry Species Switchboard</a> and when preparing the <strong>GlobalUsefulNativeTrees database</strong> (GlobUNT; <a href="https://worldagroforestry.org/output/globalusefulnativetrees">https://worldagroforestry.org/output/globalusefulnativetrees</a>).</p> <p>After matching species with the WCVP, information was compiled on the <strong>native distribution</strong> documented in the WCVP for level-3 units of the <a href="https://github.com/tdwg/wgsrpd">World Geographical Scheme for Recording Plant Distributions</a> that correspond to India, including India (IND), Assam (ASS), West Himalaya (WHM), East Himalaya (EHM), Laccadive Is. (LDV), Andaman Is. (AND) and Nicobar Is. (NCB). Also included after matching with the WCVP is information on the geographic area, lifeform and main biome. Similar information is available when searching for species from <a href="https://powo.science.kew.org/">Plants of the World Online</a>.</p> <p>Where a matching species was found in <strong>GlobalTreeSearch</strong> (Beech et al. <a href="https://www.tandfonline.com/doi/full/10.1080/10549811.2017.1310049">2017</a>; <a href="https://tools.bgci.org/global_tree_search.php">https://tools.bgci.org/global_tree_search.php</a>; accessed on 28th June 2023) filtered for India, the species name in GlobalTreeSearch is shown. Note that GlobalTreeSearch documents the <strong>native country distribution</strong> of tree species.</p> <p>Where a matching species was found in the <strong>GlobalUsefulNativeTrees</strong> database (GlobUNT, version 2023.11) filtered for India, the species name in the GlobUNT database is shown. GlobUNT has been described in the following publication: Kindt et al. (<a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) <strong>GlobalUsefulNativeTrees, a database of 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in restoration</strong>. <em>Sci Rep</em> <strong>13</strong>, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a>.</p> <p>See the metadata for information on versions.</p> <p> </p> <ul> <li>Borsch, T., Berendsohn, W., Dalcin, E., Delmas, M., Demissew, S., Elliott, A., Fritsch, P., Fuchs, A., Geltman, D., Güner, A., Haevermans, T., Knapp, S., le Roux, M.M., Loizeau, P.-A., Miller, C., Miller, J., Miller, J.T., Palese, R., Paton, A., Parnell, J., Pendry, C., Qin, H.-N., Sosa, V., Sosef, M., von Raab-Straube, E., Ranwashe, F., Raz, L., Salimov, R., Smets, E., Thiers, B., Thomas, W., Tulig, M., Ulate, W., Ung, V., Watson, M., Jackson, P.W. and Zamora, N. (2020), World Flora Online: Placing taxonomists at the heart of a definitive and comprehensive global resource on the world's plants. TAXON, 69: 1311-1341. <a href="https://doi.org/10.1002/tax.12373">https://doi.org/10.1002/tax.12373</a></li> <li>Govaerts, R., Nic Lughadha, E., Black, N. <em>et al.</em> The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. <em>Sci Data</em> <strong>8</strong>, 215 (2021). <a href="https://doi.org/10.1038/s41597-021-00997-6">https://doi.org/10.1038/s41597-021-00997-6</a></li> <li>E. Beech, M.Rivers, S. Oldfield & P. P. Smith (2017)GlobalTreeSearch: The first complete global database of tree species and country distributions, Journal of Sustainable Forestry, 36:5, 454-489, DOI: <a href="https://doi.org/10.1080/10549811.2017.1310049">10.1080/10549811.2017.1310049</a></li> <li>Kindt, R. 2020. WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data. <em>Applications in Plant Sciences</em> 8(9): e11388. <a href="https://doi.org/10.1002/aps3.11388">https://doi.org/10.1002/aps3.11388</a></li> </ul> <p> </p> <p>The developments of this dataset and GlobUNT were supported by the Darwin Initiative to project DAREX001 of <a href="https://www.darwininitiative.org.uk/project/DAREX001/"><em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em></a>.</p>
CaliParticles: A Benchmark Standard for Experiments in Granular Materials
<p>Granular materials are discrete particulate media that can flow like a liquid but also be rigid like a solid. This complex mechanical behavior originates in part from the particles shape. How particle shape affects mechanical behavior remains poorly understood. Understanding this micro-macro link would enable the rational design of potentially cheap, light weight or robust materials. To aid this development, we have produced a set of standard particle shapes that can be used as benchmarks for granular materials research. Here we describe the collection of benchmark shapes. Some part of the particles are modeled on superquadrics, others are custom designed. The particles used so far were made from polyoxymethylene (POM) and Thermoplastic elastomers (TPE) whose specifications are also listed. The benchmark shapes are available as molds in a plastics manufacturing company, whose contact information is also included. The company is capable of making other molds as well, giving access to more particle shapes. The same particle shapes can thus also be made in different types of (colored) plastic, and in amounts of 50.000 particles or more, larger than conveniently be produced with a 3D printer. We also provide the associated .step and .stl files in the repository in which this document is included. </p>
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