Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
15,733
datasets available to search
ShareScore release 0.9.0
Dataset results
15,733 results for “article”
Supplementary Material to article "Molecular Diversity of Mycobacterium avium subsp. paratuberculosis in Four Dairy Goat Herds from Thuringia (Germany)"
<p>These data (supplementary material) belong to the publication "Molecular Diversity of <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i> in Four Dairy Goat Herds from Thuringia (Germany)". The study determined the diversity of <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i> (MAP) isolated from four goat herds affected by paratuberculosis in Thuringia (Germany), as well as the detailed distribution of MAP genotypes among the animals and their environment in one herd (herd 1). A combination of three methods was used to genotype isolates from fecal samples of infected goats, from various intestinal and other tissues of clinically affected goats, and from environmental samples. The six MAP-C genotypes identified could be assigned to five different phylogenetic subgroups. The results suggest individual infection strains within each herd. In herd 1, one predominant strain was found, and two strains occurred sporadically. The identified genotypes were not goat specific.</p>
Data set for the journal article ''Nanoscale chemical reaction exploration with a quantum magnifying glass''
<div>This data set includes the raw data of the esterification and hydrogenation discussed in the journal article alongside with the Scine Puffin Singularity container, steering protocol files, Swoose parameters, (pre-)releases of the software, and Python scripts for individual steps without the graphical user interface to reproduce the data.</div>
Multilingual news article similarity dataset
<p>This dataset contains the extended version of the authors' earlier work: <a href="../records/6507872">https://zenodo.org/records/6507872,</a> where pairs of news articles drawn from the first half of 2020 are annotated for seven aspects of similarity in the original version as well as an additional FRAME aspect:</p> <ul> <li><strong>GEO</strong>: How similar is the geographic focus (places, cities, countries, etc.) of the two articles?</li> <li><strong>ENT:</strong> How similar are the named entities (e.g., people, companies, organizations, products, named living beings), excluding previously considered locations appearing in the two articles?</li> <li><strong>TIME</strong> Are the two articles relevant to similar time periods or describing similar time periods?</li> <li><strong>NAR</strong> How similar are the narrative schemas presented in the two articles?</li> <li><strong>OVERALL</strong> Overall, are the two articles covering the same substantive news story? (excluding style, framing, and tone)</li> <li><strong>STYLE</strong> Do the articles have similar writing styles?</li> <li><strong>TONE</strong> Do the articles have similar tones?</li> <li><strong>FRAME</strong> Do the articles have similar framing and express similar opinions?</li> </ul>
Data for Table S10 of the article "Source-to-sink aeolian fluxes from arid landscape dynamics in the Lut Desert"
<p>Exhaustive list of the 227 individual denudation rates in arid areas compiled to estimate median denudation rate and sediment discharge for the internal river system of the Lut watershed.</p>
Dataset of the article "Bayesian phylogenetics illuminate shallower relationships in Trans-Himalayan languages in Tibet-Arunachal area"
<p>This repository archives the dataset of the article "Bayesian phylogenetics illuminate shallower relationships in Trans-Himalayan languages in Tibet-Arunachal area". The cognate annotation of Tshangla, Kho-Bwa, Hrusish, Mishmic, and Tani languages were done by us. The cognate decision on the other languages was annotated by Sagart et al. (2019). Please use the following information to cite our work: <br> Wu, M.-S, Bodt, T. A, Tresoldi, T. (2022). Bayesian phylogenetics illuminate shallower relationships Trans-Himalayan languages in the Tibet-Arunachal area. Linguistics of the Tibeto-Burman Area. [forthcoming]</p>
Resources to compute TF-IDF weightings on press articles and tweets
<p>These two datasets of features are used in order to compute TF-IDF weightings of documents. It is meant to be used with the <a href="https://pypi.org/project/compute-tf-idf-vectors/">compute-tf-idf-vectors</a> program written in Python and available on Pypi.org.</p> <p>- features_tweets.csv contains features (tokens, lemmas and entities) extracted from Tweets published by press agencies in french, german, spanish and english.</p> <p>- features_news.csv contains features (tokens, lemmas and entities) extracted from articles published by Deutsche Welle in the same languages.</p>
SemEval-2022 Task 8: Multilingual news article similarity
<p>This dataset contains pairs of news articles drawn from the first half of 2020 and annotated for seven aspects of similarity:</p> <ul> <li><strong>GEO</strong>: How similar is the geographic focus (places, cities, countries, etc.) of the two articles?</li> <li><strong>ENT:</strong> How similar are the named entities (e.g., people, companies, organizations, products, named living beings), excluding previously considered locations appearing in the two articles?</li> <li><strong>TIME</strong> Are the two articles relevant to similar time periods or describing similar time periods?</li> <li><strong>NAR</strong> How similar are the narrative schemas presented in the two articles?</li> <li><strong>OVERALL</strong> Overall, are the two articles covering the same substantive news story? (excluding style, framing, and tone)</li> <li><strong>STYLE</strong> Do the articles have similar writing styles?</li> <li><strong>TONE</strong> Do the articles have similar tones?</li> </ul> <p>Further details are provided in</p> <blockquote> <p>Chen et al. (2022). SemEval-2022 Task 8: Multilingual news article similarity. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022). <a href="https://aclanthology.org/2022.semeval-1.155/">https://aclanthology.org/2022.semeval-1.155/</a></p> </blockquote> <p>The data in this repository includes pairs of URLs and annotations. The text of webpages is generally via the Internet Archive in this special collection: https://archive.org/details/2020-multilingual-news-article-similarity . A script to download and process the webpages is available at https://github.com/euagendas/semeval_8_2022_ia_downloader . </p>
Data and code of the article: "Early Warning Signals of the Termination of the African Humid Period(s)"
<p>Data and MATLAB Code of the article Trauth, M.H., Asrat, A., Fischer, M.L., Hopcroft, P.O., Foerster, V., Kaboth-Bahr, S., Kindermann, K., Lamb, H.F., Marwan, N., Maslin, M.A., Schaebitz, F., Valdes, P.J. (2024) Early Warning Signals of the Termination of the African Humid Period(s), Nature Communications, https://doi.org/10.1038/s41467-024-47921-1. The individual directories contain the data and the MATLAB code used to generate Fig. 1 and 2 and Supplementary Fig. 1 to 7 published with the article.</p>
Radar measurements for the article "Dynamic differential reflectivity calibration using vertical profiles in rain and snow"
<p><strong>Dataset documentation</strong></p> <p>The archives in hdf5 format provided at this link contain the datasets used in the manuscript <em>Dynamic differential reflectivity calibration using vertical profiles in rain and snow</em>, submitted to <em>Remote Sensing</em> (MDPI) by Alfonso Ferrone and Alexis Berne in 2020.</p> <p> </p> <p><strong>File content</strong></p> <p>Each file is structured as a table, with each column referring to a specific variable and each row containing a different realization (in space or time). The set of available variables is campaign dependent, and the possibilities are:</p> <ul> <li> <p><strong>idx</strong>, and integer index that starts at 1 for the first scan of the dataset and increases by 1 for every successive scan;</p> </li> <li> <p><strong>t</strong>, the timestamp of the scan, in seconds since seconds since Jan 01, 1970;</p> </li> <li> <p><strong>r</strong> or <strong>rg</strong> (depending on the file), the distance from the radar in meters;</p> </li> <li> <p><strong>az</strong>, the azimuth angle in degrees;</p> </li> <li> <p><strong>el</strong>, the elevation angle in degrees;</p> </li> <li> <p><strong>zdr</strong>, the uncalibrated differential reflectivity, in dB;</p> </li> <li> <p><strong>zh</strong>, the horizontal reflectivity, in dBZ;</p> </li> <li> <p><strong>rhovh</strong> or <strong>rho</strong>, the co-polar correlation coefficient, unitless;</p> </li> <li> <p><strong>snr_h</strong> or <strong>snr</strong>, the signal to noise ratio for the horizontal channel in dB;</p> </li> <li> <p><strong>snrv</strong>, the signal to noise ratio for the vertical channel, in dB,</p> </li> <li> <p><strong>ngates</strong>, the number of unique range gates.</p> </li> </ul> <p>For the comparison of the data collected by MXPol and DX50 during the PAYERNE campaign, two auxiliary variables were added to the archives:</p> <ul> <li> <p><strong>x</strong> the horizontal distance from the current radar, computed on a line passing through the location of two radars;</p> </li> <li> <p><strong>z</strong> the vertical distance from the current radar.</p> </li> </ul> <p> </p> <p><strong>Usage</strong></p> <p>The dataset are provided in the the Hierarchical Data Format version 5 (HDF5), an open source file format, supported by several programming language.</p> <p>They archives were created using the <em>vaex</em> library for Python 3:</p> <p>https://github.com/vaexio/vaex</p> <p>The function <em>vaex.open</em> from the same library can be used for accessing the archives and converting them to <em>vaex.DataFrame</em>.</p> <p> </p> <p><strong>Campaign-specific information</strong></p> <p>Some of the parameters associated to the variables included in the archives may change depending on the campaign. The following subsection provide a summary of these information.</p> <p> </p> <p><strong>dataframe_HYMEX_2013_from_20130907-040344_to_20131105-175944.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the HYMEX campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 44.61° N</p> </li> <li> <p>Longitude: 4.55° E</p> </li> <li> <p>Altitude: 604 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: Z-PHI method</p> </li> <li> <p>Note on reflectivity calibration: The original manufacturer calibration constant was 7.56 dBZ. The value used here derives from comparison with disdrometers during the HYMEX campaign.</p> </li> </ul> <p> </p> <p><strong>dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5</strong></p> <p>Contains PPI scans at 90° elevation performed by MXPol during the PAYERNE campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.81° N</p> </li> <li> <p>Longitude: 6.94° E</p> </li> <li> <p>Altitude: 496 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DAVOS_2014_from_20140704-090224_to_20141231-105720.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the DAVOS campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.82° N</p> </li> <li> <p>Longitude: 9.82° E</p> </li> <li> <p>Altitude: 2220 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_APRES3_from_20151207-123944_to_20160129-125856.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the APRES3 campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 66.66 S</p> </li> <li> <p>Longitude: 140.00 E</p> </li> <li> <p>Altitude: 40 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 354.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_VALAIS_2016_from_20161104-154312_to_20170306-195912.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the VALAIS campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.12 N</p> </li> <li> <p>Longitude: 7.10 E</p> </li> <li> <p>Altitude: 460 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.27°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 30 m</p> </li> <li> <p>Range to the first gate: 226.95 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5</strong></p> <p>Contains PPI scans at 90° elevation performed by DX50 during the PAYERNE campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.84° N</p> </li> <li> <p>Longitude: 6.92° E</p> </li> <li> <p>Altitude: 450 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.459 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.273°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 0.0 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.0</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DX50_RHI_201405.hdf5</strong></p> <p>Contains RHI scans from DX50 the PAYERNE campaign.</p> <p>The remaining information equal to the ones listed for <em>dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5</em>.</p> <p> </p> <p><strong>dataframe_MXPol_RHI_201405.hdf5</strong></p> <p>Contains RHI scans from MXPol the PAYERNE campaign.</p> <p>The remaining information is equal to the ones listed for <em>dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5</em>.</p>
Diffraction images used to solve the structures published in the article "Structure of human endo-α-1,2-mannosidase (MANEA), an antiviral host-glycosylation target"
<p>Raw diffraction images used for generating the structures published in the article "Structure of human endo-α-1,2-mannosidase (MANEA), an antiviral host-glycosylation target" (available <a href="https://doi.org/10.1073/pnas.2013620117">here</a>). Full single-crystal datasets, including images that were not used in the final analyses, are published. The software used for the processing of each dataset is listed in their respective PDB entries. Datasets 6ZJ1 and 6ZJ5 were cut anisotropically using STARANISO, other datasets were processed isotropically.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Diffraction images used to solve the structures published in the article "An Epoxide Intermediate in Glycosidase Catalysis"
<p>Raw diffraction images used for generating the structures published in the article "An Epoxide Intermediate in Glycosidase Catalysis" (available <a href="https://doi.org/10.1021/acscentsci.0c00111">here</a>). Full single-crystal datasets, including images that were not used in the final analyses, are published. The software used for the processing of each dataset is listed in their respective PDB entries.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Diffraction images used to solve the structures published in the article "Exploration of Strategies for Mechanism-Based Inhibitor Design for Family GH99 endo-alpha-1,2-Mannanases."
<p>Raw diffraction images used for generating the structures published in the article "Exploration of Strategies for Mechanism-Based Inhibitor Design for Family GH99 endo-a-1,2-Mannanases" (available <a href="https://doi.org/10.1002/chem.201800435">here</a>). Full single-crystal datasets are published. The software used for the processing of each dataset is listed in their respective PDB entries.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Scholarly journals publishing articles by family and community physicians in Brazil, up to December 2018
<p>This is the dataset of manuscript titled "In which journals do family and community physicians in Brazil publish? The <em>Trajetórias MFC</em> project". There are two spreadsheets: the dataset proper and the data dictionary. See the manuscript for background.</p> <p>All spreadsheets are in the CSV (comma-separated values) format, delimited with semicolons and encoded in UTF-8 with the byte-order mark (BOM). The spreadsheets can be opened with desktop or Web application software (LibreOffice Calc, Microsoft Excel, Google Sheets) or with statistical software such as R.</p> <p>A <a href="https://zenodo.org/record/3905255">previous version</a> of this dataset was used in a <a href="https://doi.org/10.1101/19005744">preprint</a>. This version should be cited by an upcoming article.</p> <p>See also the <a href="https://doi.org/10.1136/fmch-2020-000321">article</a>, <a href="https://doi.org/10.5281/zenodo.3376310">dataset</a> and <a href="https://doi.org/10.5281/zenodo.3381576">supplementary table</a> for an earlier milestone, about the postgraduate education of family and community physicians in Brazil.</p>
Diffraction images used to solve the structures published in the article "Contribution of Shape and Charge to the Inhibition of a Family GH99 endo-α-1,2-Mannanase"
<p>Raw diffraction images used for generating the structures published in the article "Contribution of Shape and Charge to the Inhibition of a Family GH99 endo-α-1,2-Mannanase" (available <a href="https://doi.org/10.1021/jacs.6b10075">here</a>). Full single-crystal datasets are published. The software used for the processing of each dataset is listed in their respective PDB entries.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Diffraction images used to solve the structures published in the article "A Family of Dual-Activity Glycosyltransferase-Phosphorylases Mediates Mannogen Turnover and Virulence in Leishmania Parasites"
<p>Raw diffraction images used for generating the structures published in the article A Family of Dual-Activity Glycosyltransferase-Phosphorylases Mediates Mannogen Turnover and Virulence in Leishmania Parasites" (available <a href="https://doi.org/10.1016/j.chom.2019.08.009">here</a>). The software used for the processing of each dataset is listed in their respective PDB entries.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Diffraction images used to solve the structures published in the article "From 1,4-Disaccharide to 1,3-Glycosyl Carbasugar: Synthesis of a Bespoke Inhibitor of Family GH99 Endo-α-mannosidase"
<p>Raw diffraction images used for generating the structures published in the article "From 1,4-Disaccharide to 1,3-Glycosyl Carbasugar: Synthesis of a Bespoke Inhibitor of Family GH99 Endo-α-mannosidase" (available <a href="https://doi.org/10.1021/acs.orglett.8b03260">here</a>). Full single-crystal datasets, including images that were not used in the final analyses, are published. The software used for the processing of each dataset is listed in their respective PDB entries. An additional 720 degree dataset is provided, which has been collected from the same crystal as PDB 6HMH. This dataset has not been used to solve the structure presented in the paper. It works very well as an example of sulfur SAD phasing.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Supplementary data for the article: Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges
<p>This repository provides the supplementary data to the paper titled <a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener"><em>"Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges"</em></a>, published 2024 in <em>Resources, Conservation and Recycling</em>.</p> <h4><strong>Contents</strong></h4> <p>The repository is split in 3 parts and comprises the following files (more details are provided in the <em>README.md</em>):</p> <p><strong>A_Database of reviewed studies:</strong></p> <ul> <li>contains the detailed review data, meant for readers to use as an overview file to gather studies relevant to them. It also includes an overview of all data sources that the reviewed studies used.</li> </ul> <p><strong>B_Scientific supplement to paper:</strong></p> <ul> <li>Contains all data relevant to the related publication Harpprecht et al. (2024), such as studies screened , FAIR data analysis, or analyzed impact trends.</li> </ul> <p><strong>C_Data for figures in paper:</strong></p> <ul> <li>This file contains all the data for Figures 3, 4 and 5 in tabular form, representing impact trends, scenario variables, scenario modelling approaches and data sources used.</li> </ul> <h4><strong>Summary</strong></h4> <p>These files allow to reproduce the results of our study. In this work, we systematically reviewed studies which assessed future environmental impacts of metal supply chains. Our review yielded 40 publications covering 15 metals: copper, iron, aluminium, nickel, zinc, lead, cobalt, lithium, gold, manganese, neodymium, dysprosium, praseodymium, terbium, and titanium. We evaluated their results regarding future impact trends, and their methods, i.e., modelling approaches, scenario variables, and data sources of scenario variables. We identified 15 scenario variables. The most common variables are background electricity mix, ore grade, recycling shares, demand, and energy efficiency. We identified 229 unique data sources for the reviewed scenario variables.</p> <h4><strong>Related publication</strong></h4> <p>More details on the data and its interpretation as well as the scientific context are provided in the publication itself:</p> <p><a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener">Harpprecht, C., Miranda Xicotencatl, B., van Nielen, S., van der Meide, M., Li, C. , Li, Z., Tukker, A., Steubing, B. (2024). <em>Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges.</em> Resources, Conservation and Recycling.</a></p> <h4><strong>Funding </strong></h4> <p>Carina Harpprecht received funding from the Energy Program of the German Aerospace Center in 2022. Zhijie Li received funding from the European Institute of Innovation and Technology (EIT) under the project Valomag (Project No. 14049).</p> <h4><strong>License</strong></h4> <p>CC-BY 4.0 license for DLR (German Aerospace Center)</p>
Dataset for "Reduction-responsive immobilised and protected enzymes" research article
<p>The dataset for the paper titled "Reduction-responsive immobilised and protected enzymes".<br>The dataset includes the following items:<br><br>1. Unprocessed tif files (4 items) of the scanning electron microscopy (SEM) micrographs;<br>These SEM micrographs are presented in Fig. 3a and Fig. 3b in the manuscript and also in Fig. S1a and Fig. S1b in the supporting information document.<br><br>2. "Reduction-responsive immobilised and protected enzymes" xls file (1 file) including 4 datasheets;<br>- The "<strong>Cell experiment" </strong>sheet includes the cell viability results in Fig. 5d and Fig. S5.<br>- The "<strong>Enzyme activity - B-Gal"</strong> sheet includes all the results regarding the B-Gal enzyme activity in Fig. 4, main text, and Fig. S3.<br>- The "<strong>Enzyme activity - ASNase"</strong> sheet includes all the results regarding the ASNase enzyme activity in Fig. 5b, Fig. 5c, and Fig. S4.<br>- The<strong> "B-Gal and ASNase layer growth"</strong> sheet includes all the results regarding the layer growth reactions and kinetics of B-Gal and ASNase enzymes in Fig. 3d, Fig. 5a, and Fig. S1d.<br><br>3. "SNP and layer growth analysis" xls file (1 file) including 13 datasheets;<br>These datasheets contain the raw data of the size measurements of the silica nanoparticles conducted on the SEM micrographs before and after layer growth for each sampling timepoint for both B-Gal and ASNase enzymes with glutaraldehyde (Glu) and DSP as linkers.<br>Note: These data were used to make the graphs in the "<strong>B-Gal and ASNase layer growth" </strong>sheet.</p>
Data for the article "Computational micromagnetics based on normal modes: Bridging the gap between macrospin and full spatial discretization"
<p>Data for the article "Computational micromagnetics based on normal modes: Bridging the gap between macrospin and full spatial discretization".</p> <p>Link to publisher: https://www.sciencedirect.com/science/article/abs/pii/S0304885321009197</p> <p>Link to Arxiv preprint: https://arxiv.org/abs/2105.08829</p>
Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach"
<p>Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach", published in Journal of Catalysis 2022 408:1–8, doi: <a href="https://doi.org/10.1016/j.jcat.2022.02.014">10.1016/j.jcat.2022.02.014</a></p> <p>Folder names describe the type of data content.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.