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624 results for “Gold”
Specific Heat of Holmium in Gold and Silver at Low Temperatures - Data
<p>Data from measurements on the specific heat of a variety of Au:Ho and Ag:Ho alloys. This data is associated with the manuscript:</p> <p>Herbst, M., Reifenberger, A., Velte, C. <em>et al.</em> Specific Heat of Holmium in Gold and Silver at Low Temperatures. <em>J Low Temp Phys</em> <strong>202, </strong>106–120 (2021). https://doi.org/10.1007/s10909-020-02531-1</p> <p>For information on the motivation, measurement techniques, equipment, and data processing, please refer to this manuscript.</p>
Gold-Caps_LMD-Matched_General
<p>This dataset contains captions for the <a href="https://colinraffel.com/projects/lmd/">Lakh MIDI Dataset-matched</a> music dataset (~30,000 tracks with accompanying MIDI files).</p><p>These captions were generated by the <strong>gpt-4-1106-preview</strong> chat endpoint prompted to describe each track based on the track title and artist. The captions have not been filtered or post-processed in any way.</p><p><strong>Prompt used:</strong><br>"Give a general description of the track <title> by <artist_name> in one sentence. Don't mention the title or artist."</p>
Conformations and cryo-force spectroscopy of spray-deposited single-strand DNA on gold: Lifting atomic coordinates
<p>Here we provide the atomic coordinates and the topology file concerning the lifting process of a single stranded DNA molecule previously adsorbed on gold. In order to visualize it you will need a visualization software. Using VMD, you would only need to do in a terminal:</p> <p>vmd -e visualize.vmd </p> <p>and that is it. If you find this useful, please cite the corresponding paper:<br> Nature Communications 10, 685 (2019) [DOI: https://doi.org/10.1038/s41467-019-08531-4 ]</p>
Annual Article Processing Charges (APCs) and number of gold and hybrid open access articles in Web of Science indexed journals published by Elsevier, Sage, Springer-Nature, Taylor & Francis and Wiley 2015-2018
<p><strong>Dataset of annual Article Processing Charges (APCs) for 6,252 journals from 2015 to 2018. </strong>The dataset contains annual APCs for journals indexed in the Web of Science (WoS) and published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley). It also includes an estimate of the total APCs paid by the academic community based on the number of gold and hybrid articles published between 2015 and 2018. The dataset was created using publication data from WoS, OA status from Unpaywall and annual APC prices from open datasets (<a href="https://doi.org/10.5281/ZENODO.3841568">Matthias, 2020</a>; <a href="https://doi.org/10.5683/SP2/84PNSG">Morrison, 2021</a>) and historical fees retrieved via the Internet Archive Wayback Machine. </p> <p>Detailed methods and findings are reported in the following journal article</p> <p>Butler, L.-A., Matthias, L., Simard, M.-A., Mongeon, P., & Haustein, S. (2023). The Oligopoly's Shift to Open Access. How the Big Five Academic Publishers Profit from Article Processing Charges. <em>Quantitative Science Studies</em>. Preprint: <a href="https://doi.org/10.5281/zenodo.8322555">https://doi.org/10.5281/zenodo.8322555</a></p> <p><strong>Description of included files (v1):</strong></p> <p><em>APCs.csv: </em>contains the annual APCs for gold and hybrid OA journals indexed in Web of Science published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley) between 2015 and 2018 including the total estimate of APCs paid per journal per year. It contains APC data for 18,846 journal-year-OA status combinations.</p> <p><em>countries.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs paid per country per journal per year.</p> <p><em>oecd.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs per discipline per journal per year.</p> <p><em>ReadMe.csv</em>: contains a description of the variables used in <em>APCs.csv</em>, <em>countries.csv</em> and <em>oecd.csv</em>.</p> <p> </p>
Energy recovery by an unbiased gas phase photofuel cell with a nickel foam supported WO3 photoanode decorated with plasmonic gold clusters
<p>Dataset for the article titled "Energy recovery by an unbiased gas phase photofuel cell with a nickel foam supported WO3 photoanode decorated with plasmonic gold clusters".</p> <p>This research was conducted at Antwerp Engineering, Photoelectrochemistry and Sensing (A-PECS) group, University of Antwerp, Belgium.</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>
RRID Gold Set of annotations for software tools in the biomedical literature
<p>This data is a subset of a larger human curated, machine assisted gold standard data set of RRID citations within the text of the scientific literature. The full set is accessible via Hypothes.is at https://hypothes.is/users/scibot?q=group%3A__world__ and via individual RRID records such as RRID:SCR_016250 https://scicrunch.org/resolver/SCR_016250/mentions?q=&i=rrid:scr_016250 </p><p>The data is based on authors who added RRIDs into their manuscripts. The data was then extracted by SciBot (RRID:SCR_016250), into Hypothes.is (RRID:SCR_000430) and then manually checked by a curator to determine if the author and the database agreed. The list of annotators is available in Hypothes.is user group: SciBotCurationGroup.</p><p>There are 78,140 rows and each row contains an annotation (annotation id, URI), linked to a paper (paper identifiers: PMID, DOI, PMC) and linked to the RRID (scr_id, exact, text_quote_selector). </p><p>A second spreadsheet contains a list of 8,322 software tools from the SciCrunch Registry (available here https://scicrunch.org/resources/data/source/nlx_144509-1/search ), enhanced by additions by thousands of individual authors, and curated over 10 years (Ozyurt et al., 2016). </p><p>The third spreadsheet contains a data dictionary and links to related ontologies, and tagging sets. </p><p> </p><p> </p>
Dataset: Submicron‐ and Nanoplastic Detection at Low Micro‐ to Nanogram Concentrations Using Gold Nanostar‐Based Surface‐Enhanced Raman Scattering (SERS) Substrates
<p>ABSTRACT</p> <p>The presence of submicron- (1 µm – 100 nm) and nanoplastic (< 100 nm) particles within various sample matrices, ranging from marine environments to foods and beverages, has become a topic of increasing interest in recent years. Despite this interest, very few analytical techniques remain that allow for the detection of these small plastic particles in the low concentration ranges that they are anticipated to be present at. Research focused on optimizing surface-enhanced Raman scattering (SERS) to enhance signal obtained in Raman spectroscopy has been shown to have great potential for the detection of plastic particles below conventional resolution limits. In this study, we produce SERS substrates composed of gold nanostars and assess their potential for submicron- and nanoplastic detection. The results show 33 nm polystyrene could be detected down to 1.25 µg/mL while 36 nm poly(ethylene terephthalate) was detected down to 5 µg/mL. These results confirm the promising potential of the gold nanostar-based SERS substrates for nanoplastic detection. Furthermore, combined with findings for 121 nm polypropylene and 126 nm polyethylene particles, they highlight potential differences in analytical performance that depend on the properties of the plastics being studied.</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>
Annotated Dataset for Uncertainty Mining : Gold Standard
<p> </p> <h1>Description of the dataset</h1> <p>In order to study the expression of uncertainty in scientific articles, we have put together an interdisciplinary corpus of journals in the fields of Science, Technology and Medicine (STM) and the Humanities and Social Sciences (SHS). The selection of journals in our corpus is based on the Scimago Journal and Country Rank (SJR) classification, which is based on Scopus, the largest academic database available online. We have selected journals covering various disciplines, such as medicine, biochemistry, genetics and molecular biology, computer science, social sciences, environmental sciences, psychology, arts and humanities. For each discipline, we selected the five highest-ranked journals. In addition, we have included the journals PLoS ONE and Nature, both of which are interdisciplinary and highly ranked.</p> <p>Based on the corpus of articles from different disciplines described above, we created a set of annotated sentences as follows:</p> <ul> <li>593 were pre-selected automatically, by studying the occurrences of the lists of uncertainty indices proposed by Bongelli et al. (2019), Chen et al. (2018) and Hyland (1996).</li> <li>The remaining sentences were extracted from a subset of articles, consisting of two randomly selected articles per journal. These articles were examined by two human annotators to identify sentences containing uncertainty and to annotate them.</li> <li>600 sentences not expressing scientific uncertainty were manually identified and reviewed by two annotators<br><br></li> </ul> <p>The sentences were annotated by two independent annotators following the annotation guide proposed by Ningrum and Atanassova (2024). The annotators were trained on the basis of an annotation guide and previously annotated sentences in order to guarantee the consistency of the annotations. <br>Each sentence was annotated as expressing or not expressing uncertainty (<strong>Uncertainty</strong> and <strong>No Uncertainty)</strong>.<br>Sentences expressing uncertainty were then annotated along five dimensions: Reference , Nature, Context , Timeline and Expression. <br>The annotators reached an average agreement score of 0.414 according to Cohen's Kappa test, which shows the difficulty of the task of annotating scientific uncertainty.<br>Finally, conflicting annotations were resolved by a third independent annotator.</p> <p><br>Our final corpus thus consists of a total of 1 840 sentences from 496 articles in 21 English-language journals from 8 different disciplines.<br>The columns of the table are as follows:</p> <ol> <li><strong>journal</strong>: name of the journal from where the article originates</li> <li><strong>article_title</strong>: title of the article from where the sentence is extracted</li> <li><strong>publication_year</strong>: year of publication of the article</li> <li><strong>sentence_text</strong>: text of the sentence expressing or not expressing uncertainty</li> <li><strong>uncertainty</strong>: 1 if the sentence expresses uncertainty and 0 otherwise;</li> <li><strong>ref, nature, context, timeline, expression</strong>: annotations of the type of uncertainty according to the annotation framework proposed by Ningrum and Atanassova (2023). The annotation of each dimension in this dataset are in numeric format rather than textual. The mapping betwen textual and numeric labels is presented in the Table below.</li> </ol> <table> <tbody> <tr> <td>Dimension</td> <td>1</td> <td>2</td> <td>3</td> <td>4</td> <td>5</td> </tr> <tr> <td>Reference</td> <td>Author</td> <td>Former</td> <td>Both</td> <td> </td> <td> </td> </tr> <tr> <td>Nature</td> <td>Epistemic</td> <td>Aleatory</td> <td>Both</td> <td> </td> <td> </td> </tr> <tr> <td>Context</td> <td>Background</td> <td>Methods</td> <td>Res&Disc</td> <td>Conclusion</td> <td>Others</td> </tr> <tr> <td>Timeline</td> <td>Past</td> <td>Present</td> <td>Future</td> <td> </td> <td> </td> </tr> <tr> <td>Expression</td> <td>Quantified</td> <td>Unquantified</td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table> <p><br>This gold standard has been produced as part of the <a href="https://project-inscim.github.io/">ANR InSciM (Modelling Uncertainty in Science) project.</a> </p> <h1>References</h1> <p><br>Bongelli, R., Riccioni, I., Burro, R., & Zuczkowski, A. (2019). Writers’ uncertainty in scientific and popular biomedical articles. A comparative analysis of the British Medical Journal and Discover Magazine [Publisher: Public Library of Science]. PLoS ONE, 14 (9). <a href="https://doi.org/10.1371/journal.pone.0221933">https://doi.org/10.1371/journal.pone.0221933</a></p> <p>Chen, C., Song, M., & Heo, G. E. (2018). A scalable and adaptive method for finding semantically equivalent cue words of uncertainty. Journal of Informetrics, 12 (1), 158–180. <a href="https://doi.org/10.1016/j.joi.2017.12.004">https://doi.org/10.1016/j.joi.2017.12.004</a></p> <p><br>Hyland, K. E. (1996). Talking to the academy forms of hedging in science research articles [Publisher: SAGE Publications Inc.]. Written Communication, 13 (2), 251–281. <a href="https://doi.org/10.1177/0741088396013002004">https://doi.org/10.1177/0741088396013002004</a></p> <p>Ningrum, P. K., & Atanassova, I. (2023). Scientific Uncertainty: An Annotation Framework and Corpus Study in Different Disciplines. 19th International Conference of the International Society for Scientometrics and Informetrics (ISSI 2023). <a href="https://doi.org/10.5281/zenodo.8306035">https://doi.org/10.5281/zenodo.8306035</a></p> <p>Ningrum, P. K., & Atanassova, I. (2024). Annotation of scientific uncertainty using linguistic patterns. Scientometrics. <a href="https://doi.org/10.1007/s11192-024-05009-z">https://doi.org/10.1007/s11192-024-05009-z</a></p>
Gold standard for tractograms and meshes io testing
<p>Gold standard for tractograms (<em>trk, tck, trx, dpy, vtk, fib</em>) and meshes (<em>gii, pial, vtk, ply, stl, obj</em>) IO testing for the StatefulTractogram (SFT) and StatefulSurface (SFS) in Dipy.</p>
Tilt-series 4DSTEM dataset of DNA origami with gold nanoparticles
<p>Raw TEM data for the manuscript "Three-dimensional Electron Ptychography of Organic-inorganic Hybrid Nanostructures".</p>
Daily GBP Gold Price 1919-68
<p>Daily London Gold Fixings price from 1919-68 constructed from an <em>Annual Report of the Deputy Master and Comptroller of the Royal Mint</em>, the <em>Quins Metals Handbooks and Statistics</em> and the <em>Metal Bulletin magazine.</em></p>
Data: multimodal cell tracking from systemic administration to tumour growth by combining gold nanorods and reporter genes
<p>This data set includes multispectral optoacoustic tomography images supporting an article on cell tracking (preprint: bioRxiv 199836; https://doi.org/10.1101/199836). The corresponding bioluminescence results are included too, as well as the spectra used for the multispectral processing. </p>
Data: Stability and biological response of PEGylated gold nanoparticles
<p><span>This dataset is focused on thermal stability of PEGylated Au NPs at 4 and 37 °C and after sterilization in autoclave.</span></p>
Carl Theodor Golde (g1730)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Carl Theodor Golde<br><u>musiXplora-ID</u>: g1730<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/g1730">https://musixplora.de/mxp/g1730</a><br><u>Gender</u>: m<br><u>Date of Birth</u>: 06 October 1803<br><u>Place of Birth</u>: Marienberg<br><u>Date of Death</u>: 30 December 1873<br><u>Place of Death</u>: Dresden<br><u>First Mentioned</u>: 1832<br><u>Sectors</u>: Instrumentenbau<br><u>Professions (Musical)</u>: Holzblasinstrumentenbauer<br><u>Main Place of Activity</u>: Dresden<br><br><br><u>Portfolio:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Fagott</td><td><a href="https://musixplora.de/mxp/2001465">2001465</a></td></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Klarinette</td><td><a href="https://musixplora.de/mxp/2001466">2001466</a></td></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Flöte</td><td><a href="https://musixplora.de/mxp/2001480">2001480</a></td></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Oboe</td><td><a href="https://musixplora.de/mxp/2001485">2001485</a></td></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Bassklarinette</td><td><a href="https://musixplora.de/mxp/2001642">2001642</a></td></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Englischhorn</td><td><a href="https://musixplora.de/mxp/2001656">2001656</a></td></tr></tbody></table><br><u>Institutionen:</u><br><table><tbody><tr><th>Role</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Lost And Found</td><td><a href="https://musixplora.de/mxp/3080409">3080409</a></td></tr></tbody></table><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>New Langwill Index 1993</td><td>The New Langwill Index. A Dictionary of Musical Wind-Instrument Makers and Inventors. NLI</td><td><a href="https://musixplora.de/mxp/5001112">5001112</a></td></tr><tr><td>Related</td><td>Heise 2013</td><td>Goldene Klänge im mystischen Grund. Musikinstrumente für Richard Wagner. Ausstellungskatalog</td><td><a href="https://musixplora.de/mxp/5002025">5002025</a></td></tr><tr><td>Related</td><td>Tank 1993</td><td>Verzeichnis der Blasinstrumentenmacher. nach der Abschrift des Heyer-III-Kataloges. [von Georg Kinsky, 1926]. Unveröffentlichtes Manu- und Typoskript</td><td><a href="https://musixplora.de/mxp/5033436">5033436</a></td></tr><tr><td>Related</td><td>Heyde 1993b</td><td>Maker's marks on wind instruments. translated by William Waterhouse. In: The New Langwill Index</td><td><a href="https://musixplora.de/mxp/5034219">5034219</a></td></tr></tbody></table><br><u>Ereignisse:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Hersteller</td><td></td><td>Herstellung</td><td><a href="https://musixplora.de/mxp/6009083">6009083</a></td></tr><tr><td>Hersteller</td><td></td><td>Herstellung</td><td><a href="https://musixplora.de/mxp/6009271">6009271</a></td></tr><tr><td>Hersteller</td><td></td><td>Herstellung</td><td><a href="https://musixplora.de/mxp/6013558">6013558</a></td></tr><tr><td>Hersteller</td><td></td><td>Herstellung</td><td><a href="https://musixplora.de/mxp/6021277">6021277</a></td></tr><tr><td>Hersteller</td><td></td><td>Herstellung</td><td><a href="https://musixplora.de/mxp/6021438">6021438</a></td></tr><tr><td>Hersteller</td><td></td><td>Herstellung</td><td><a href="https://musixplora.de/mxp/6026251">6026251</a></td></tr><tr><td>Hersteller</td><td></td><td>Herstellung</td><td><a href="https://musixplora.de/mxp/6026252">6026252</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Open Access levels of Dutch universities' output 2016-2017 (articles & reviews): green, gold, hybrid and bronze - May 2018
<p>Using Web of Science and Unpaywall data, we here provide an update of Open Access (OA) levels of Dutch universities, for 2016 and 2017.</p> <p>Our previous analysis (<a href="http://doi.org/10.5281/zenodo.1133759">10.5281/zenodo.1133759</a> and <a href="http://doi.org/10.7287/peerj.preprints.3520v1">10.7287/peerj.preprints.3520v1</a>) looked at OA classification as included in Web of Science (gold and green OA, based on Unpaywall data), and supplemented that with a breakdown of gold OA into pure gold, hybrid and bronze, taken from Unpaywall data (formerly OADOI) directly. Here, we improve on this by running all DOIs retrieved from WoS through Unpaywall data (using their web interface that allows batch checking of up to 10,000 DOIs at a time). Unlike WoS, Unpaywall data itself includes author-submitted versions in their green OA classification, resulting in more complete green OA levels. </p> <p>In addition, since our initial analysis of December 2017, Unpaywall data has considerably expanded its coverage of institutional repositories (IRs) (see <a href="https://unpaywall.org/sources">https://unpaywall.org/sources</a>). This now includes coverage of the IRs from all Dutch universities. </p> <p>Taken together, the current data show higher levels of green open access, including author-submitted versions, compared to our previous analysis. </p> <p>In this update, we include output (articles and reviews) from 2016 and 2017 for all 14 universities in the Netherlands. </p> <p>The following categories are distinguished (description taken from Piwowar at al., 2018, doi: <a href="https://doi.org/10.7717/peerj.4375">10.7717/peerj.4375</a>)</p> <ul> <li><strong>Pure gold</strong>: Published in an open-access journal (as defined by the DOAJ)</li> <li><strong>Hybrid</strong>: Free under an open license in a toll-access journal</li> <li><strong>Bronze</strong>: Free to read on the publisher page, but without a license</li> <li><strong>Green: </strong>Available from an institutional or disciplinary repository (including PubMedCentral)</li> </ul> <p>Data for Dutch universities were collected from Web of Science using the organization-enhanced field. Only articles and reviews were included. DOIs were extracted from the Web of Science export, run through the Unpaywall data <a href="https://unpaywall.org/products/simple-query-tool">Simple Query Tool</a>. From the resulting data from Unpaywall, OA classification was done using a simple formula in Excel (to be replaced by an R script in a future update). The Excel template used is included in this dataset, as is the OADOI API output for each Dutch university's article subset, and the lists of DOIs derived from Web of Science. The dataset also includes summarized data and three charts generated from these data, showing levels of different types of OA for 2016, 2017 and the two years compared. </p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p>
Gold standard corpus, ontologies, and Entity-Quality ontology annotations for evolutionary phenotypes
<p>This data set includes a gold-standard corpus of evolutionary phenotype descriptions (in the form of character state descriptions pulled from a variety of phylogenetic systematics studies), and their corresponding expert-curated annotations with ontology terms in the form of Entity-Quality (EQ) statements. EQ annotatons allow machine-reasoning (through the semantics encoded in the requisite ontologies from which the ontology terms are drawn), and machine-reasoning in turn enables computing metrics for quantifying the semantic similarity between different phenotype descriptions as represented by their EQ annotations.</p> <p>Also included are the ontologies, and the human expert-generated and Semantic Charaparser (i.e., machine) generated EQ annotations used to assess Semantic Charaparser performance relative to inter-curator variation and to the effect of having access to external knowledge. The ontologies include those used as input, the "augmented" ontologies created by human curators in each experiment round, and the merged ontology used to maximize Semantic Charaparser's performance.</p> <p>The production of the gold standard corpus, annotation experiments, and evaluation of the results are described in detail in the following manuscript:</p> <blockquote> <p>Dahdul et al (2018) Annotation of phenotypes using ontologies: a Gold Standard for the training and evaluation of natural language processing systems. BioRxiv https://doi.org/10.1101/322156. Submitted to Database.</p> </blockquote> <p>The analysis code for evaluating the gold standard corpus (and the input data and ontologies for that) are available separately from the following:</p> <blockquote> <p>Manda et al (2018) Code and data for analysis of evolutionary phenotype ontology annotations and gold standard corpus. Zenodo. https://doi.org/10.5281/zenodo.1218010</p> </blockquote> <p>In comparison to the previous version (v1.0.0), this record includes a file of MD5 checksums of the Gold Standard data files. The data files themselves are unchanged.</p>
Structure Sensitivity in the Electrocatalytic Reduction of CO2 with Gold Catalysts
<p>Dataset for the manuscript:</p> <p>Mezzavilla, Stefano, Sebastian Horch, Ifan E. L. Stephens, Brian Seger, and Ib Chorkendorff. “Structure Sensitivity in the Electrocatalytic Reduction of CO <sub>2</sub> with Gold Catalysts.” <em>Angewandte Chemie International Edition</em>, February 11, 2019. <a href="https://doi.org/10.1002/anie.201811422">https://doi.org/10.1002/anie.201811422</a>.</p> <p> </p> <p>The following files have been uploaded:</p> <p>1) "raw-data- figures and tables" - Excel file with all the data used in the figures and tables (both main text and SI)</p> <p>2) "Exerimental Methods" - Word file with the details of all the experimental methods used in the work</p> <p> </p>
ScienceDex guides
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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)
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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.