Research Dataset Catalog and ShareScore Ratings | ScienceDex

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Index based dataset for training ML classification models

<p>This dataset contains 202122 rows of data containing 61 unique indices from different world urban areas.</p>

opencc-by-4.0May 2024View details →

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Supporting material for: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data

<p>Supplementary material for the paper called: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data. The data without "indexes" in the name are map-projected M3 cubes, they can be used in the python library <i><strong>MoonIndex </strong></i>to obtain the spectral indexes stored in the files with "indexes" in the name.</p><p>This research was done on the framework of the EXPLORE project, that has received funding from the European Union's 2020 research and innovation program under grant agreement No 101004214. </p>

opencc-by-4.0Oct 2023View details →

zenodo40/100

Wikipedia-Index-2024-03-20

<p>The English Wikipedia of March 2024 (<a href="https://dumps.wikimedia.org/enwiki/20240320/">enwiki-20240320-pages-articles-multistream.xml</a>), parsed using <a href="https://github.com/daveshap/PlainTextWikipedia">PlainTextWikipedia</a>, segmented by headline (wikipedia-segments.ndjson.gz) and indexed with ElasticSearch 8.13.2. The unzipped elasticsearch-wikipedia-index directory can be directly mounted in docker.elastic.co/elasticsearch/elasticsearch:8.13.2 at /usr/share/elasticsearch/data.</p>

opencc-by-4.0May 2024View details →

zenodo40/100

Lost Labour Index over Bangladesh (Hourly; 1980-2023)

<p>The Lost Labour Index is an estimate of the amount of time per hour (in minutes) lost to outdoor workers due heat conditions. The index is adapted from work rest guidelines based on wet bulb globe temperatures. The work-rest guidelines are described by Brimicombe et al. (2023) and Jacklitsch et al. (2016), and summarised below. </p> <p>WBGT (°C) | Approximated work/rest cycles (minutes) </p> <p>>33           |  0/60 (Rest) </p> <p>30–33        | 15/45</p> <p>28–30        | 30/30</p> <p>25–28        | 30/15</p> <p>23–25        | 45/15</p> <p><23           | 60/0 (No recommendations)</p> <p>The Lost Labour Index is extrapolated from these work rest guidelines, assuming that if it is recommended that a worker must rest for n number of minutes every hour to remain safe in given temperature conditions, that labour/time is "lost" to the worker. The exact conversion is shown below: </p> <p>WBGT (°C) | Approximated work/rest cycles (minutes) | Lost Labour (minutes)</p> <p>>33           |  0/60 (Rest)                                                 | 60</p> <p>30–33        | 15/45                                                          | 45</p> <p>28–30        | 30/30                                                          | 30</p> <p>25–28        | 30/15                                                          | 20</p> <p>23–25        | 45/15                                                          | 15</p> <p><23           | 60/0 (No recommendations)                      | 0</p> <p>The datasets used to calculate the initial wet bulb globe temperatures (from which the Lost Labour Index was derived) were ERA5 Land (t2m, d2m, u10, v10, ssrd, ssr, strd, and strr variables) (Muñoz Sabater, 2019) and ERA5 (fdir variable - resampled to ERA5 Land spatial resolution) (Hersbach et al., 2020). The wet bulb globe temperature data was calculated using the Thermofeel library (Brimicombe et al. 2022). </p> <p>The Lost Labour Index in these files is calculated for a spatial area that covers the whole of Bangladesh (20-27 °N, 87-93 °E) and a temporal period of 1980-01-01T00:00 - 2023-12-31T23:00. The data has a spatial resolution of 0.1° and an hourly temporal resolution. Individual files are provided as zipped netCDFs for the decades of 1980-1989, 1990-1999, 2000-2009, and 2010-2019. Two separate files are provided for data from 2020-2022 and 2023 respectively. </p> <div> </div> <div><strong>References</strong></div> <div> <div> <div>Brimicombe, C. <em>et al.</em> (2022) ‘Thermofeel: A python thermal comfort indices library’, <em>SoftwareX</em>, 18, p. 101005. Available at: <a href="https://doi.org/10.1016/j.softx.2022.101005">https://doi.org/10.1016/j.softx.2022.101005</a>.</div> <div> </div> </div> </div> <div> <div>Brimicombe, C. <em>et al.</em> (2023) ‘Wet Bulb Globe Temperature: Indicating Extreme Heat Risk on a Global Grid’, <em>GeoHealth</em>, 7(2), p. e2022GH000701. Available at: <a href="https://doi.org/10.1029/2022GH000701">https://doi.org/10.1029/2022GH000701</a>.</div> <div> </div> <div> <div> <div>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., Thépaut, J-N. (2017): Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service (C3S) Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.143582cf">10.24381/cds.143582cf </a></div> </div> </div> <div> </div> <div>Jacklitsch, B., Williams, J., Musolin, K., Coca, A., Kim, J.-H., & Turner, N. (2016). Criteria for a recommended standard: Occupational exposure to heat and hot environments.</div> <div> <p>Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac</p> </div> </div>

opencc-by-4.0Jun 2024View details →

zenodo40/100

Escherichia coli lineage deconvolution indexes for Themisto, mSWEEP/mGEMS, and demix_check

<h2><strong><em>Escherichia coli</em> lineage deconvolution indexes for Themisto, mSWEEP/mGEMS, and demix_check</strong></h2> <p>This dataset contains the indexes used in a study conducted in Punjab, Pakistan that investigated <em>E. coli</em> colonisation diversity in healthy carriage with the use of CLED enrichment plates. The following files are included in the download:</p> <ul> <li>Themisto v3 pseudoalignment index.</li> <li>PopPUNK database.</li> <li>Demix_check index.</li> <li>Raw assembly data.</li> </ul> <h3><strong>About</strong></h3> <h4><strong>Version history</strong></h4> <p><strong>v0.1.1 (current version)</strong></p> <ul> <li>Added reference to the study.</li> </ul> <p><strong>v0.1.0</strong></p> <ul> <li>Added brief description with a few missing parts.</li> </ul> <h4><strong>Distribution</strong></h4> <p>These files are made available under a CC-BY 4.0 license. If you use these assemblies in your study please cite the source as appropriate (study will be added in a later version).</p> <h4><strong>Citation</strong></h4> <p>Khawaja, T., Mäklin, T., Kallonen, T. et al. Deep sequencing of _Escherichia coli_ exposes colonisation diversity and impact of antibiotics in Punjab, Pakistan. Nature Communications 15, 5196 (2024). <a href="https://doi.org/10.1038/s41467-024-49591-5">https://doi.org/10.1038/s41467-024-49591-5</a></p> <h3><strong>Methods briefly</strong></h3> <h4><strong>Data</strong></h4> <p>Assembly data included originates from the following studies:</p> <ul> <li>Horesh G, et al., A comprehensive and high-quality collection of <em>Escherichia coli</em> genomes and their genes. <em>Microbial Genomics</em> 2021. doi: <a href="https://doi.org/10.1099/mgen.0.000499">10.1099/mgen.0.000499</a></li> <li>Gladstone R, et al., Emergence and dissemination of antimicrobial resistance in <em>Escherichia coli</em> causing bloodstream infections in Norway in 2002–17: a nationwide, longitudinal, microbial population genomic study. <em>The Lancet Microbe</em> 2021. doi: <a href="https://doi.org/10.1016/S2666-5247(21)00031-8">10.1016/S2666-5247(21)00031-8</a></li> <li>Shao Y, et al., Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth. <em>Nature</em> 2019. <a href="https://doi.org/10.1038/s41586-019-1560-1">10.1038/s41586-019-1560-1</a></li> <li>Snaith AE, et al. The highly diverse plasmid population found in <em>Escherichia coli</em> colonizing travellers to Laos and its role in antimicrobial resistance gene carriage. <em>Microbial Genomics</em> 2023. doi: <a href="https://doi.org/10.1099/mgen.0.001000">10.1099/mgen.0.001000</a></li> <li>Habib A, et al. Dissemination of carbapenemase-producing Enterobacterales in the community of Rawalpindi, Pakistan. <em>PLOS ONE</em> 2022. doi: <a href="https://doi.org/10.1371/journal.pone.0270707">10.1371/journal.pone.0270707</a></li> <li>Runcharoen C, et al. Whole genome sequencing of ESBL-producing <em>Escherichia coli</em> isolated from patients, farm waste and canals in Thailand. <em>Genome Medicine</em> 2017. doi: <a href="https://doi.org/10.1186/s13073-017-0471-8">10.1186/s13073-017-0471-8</a></li> <li>Musicha P, et al. Trends in antimicrobial resistance in bloodstream infection isolates at a large urban hospital in Malawi (1998–2016): a surveillance study. <em>The Lancet Infectious Diseases</em> 2017. doi: <a href="https://doi.org/10.1016/S1473-3099(17)30394-8">10.1016/S1473-3099(17)30394-8</a></li> </ul> <h4><strong>Themisto index construction</strong></h4> <p>Assemblies were indexed with <a href="https://github.com/algbio/themisto">Themisto</a> v3.0.0-rc using <em>k</em>-mer size 31 and the `--file-colors` option.</p> <h4><strong>PopPUNK clustering</strong></h4> <p>We followed the approach described in <a href="https://doi.org/10.1038/s41467-022-35178-5">Mäklin et al. 2022</a> using <a href="https://github.com/bacpop/poppunk">PopPUNK</a> v2.5.0.</p> <h4><strong>Demix check indexing</strong></h4> <p>The index was generated using the `setup_reference.sh` script from <a href="https://github.com/tmaklin/coreutils_demix_check">tmaklin/coreutils_demix_check</a>.</p> <h3><strong>Contact</strong></h3> <p>Tommi Mäklin <tommi'at'maklin.fi>.</p>

opencc-by-4.0Nov 2023View details →

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Dataset: First Trust NASDAQ Clean Edge Smart Grid Infrastructure Index Fund (GRID) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.