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Bayesian analysis of the equation of state of quantum chromodynamics from a holographic model
<p>Prior and posterior samples obtained from a Bayesian analysis of the equation of state of quantum chromodynamics (QCD) within a holographic Einstein-Maxwell-Dilaton model, constrained by state-of-the art lattice QCD results at a vanishing net density of baryons.</p> <p>Samples contain metadata, model parameters, and model predictions for the location of the QCD critical point.</p> <p>Supplement to <a title="Bayesian location of the QCD critical point from a holographic perspective" href="https://arxiv.org/abs/2309.00579">arXiv:2309.00579</a>.</p>
Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Chemiehochhaus (FRCHEM) from 2021-01-01 to 2021-12-31 [L2]
<p>Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level.</p> <ul> <li>Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2021.</li> <li>Average, minimum and maximum air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> <p>For more details read `FRCHEM_2021_AirTemperature_MetaData.txt`.</p> <p>Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.</p>
Helical dinuclear 3d metal complexes with bis(bidentate) [S,N] ligands: synthesis, structural and computational studies
<h1>Raw data for the publication entitled:</h1> <h2>Helical dinuclear 3d metal complexes with bis(bidentate)<br>[S,N] ligands: synthesis, structural and computational<br>studies</h2> <p><em>Dalton Transactions</em>, <strong>2024</strong>, DOI: 10.1039/D4DT02395A</p> <p>Authors:<br>Jamie Allen, Jörg Saßmannshausen, Kuldip Singh, Alexander F. R. Kilpatrick*</p> <p>These folders contain the raw data which were used to prepare the above publication.</p> <h1>Information regarding the raw files of the DFT calculations.</h1> <p>The zip-files in this section containing the raw-data of the DFT calculations leading to the Zn, Co and Fe calculated structures. As filenames are notoriously bad in handling special characters, the names of the folder appear different from what is being used in the final publication. We try to provide as much information as possible to facilitate the usage of these results.</p> <p>Thus:</p> <table> <tbody> <tr> <th>Abbreviation publication</th> <th>Abbreviation folder</th> <th>Abbreviation filename</th> </tr> </tbody> <tbody> <tr> <td>[Zn(<strong>3</strong>)<sub>2</sub>]</td> <td>Zn3-2</td> <td>SNdipp2Zn</td> </tr> <tr> <td>[Co(<strong>3</strong>) <sub>2</sub>]</td> <td>Co3-2</td> <td>SNdipp2Co</td> </tr> <tr> <td>[Fe(<strong>3</strong>) <sub>2</sub>]</td> <td>Fe3-2</td> <td>SNdipp2Fe</td> </tr> <tr> <td>[Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Zn2-2</td> <td>zn2</td> </tr> <tr> <td>[Co<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Co2-2</td> <td>co2</td> </tr> <tr> <td>[Fe<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Fe2-2</td> <td>fe2</td> </tr> </tbody> </table> <p>Some test calculations were performed as well utilizing Gaussian-09. They can be found in a folders with the suffix <em>-G09</em> or <em>-g09</em>.</p> <p>The closed shell compound [Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>] was investigated further. In order to look into the influence of the used Grimme dispersion correction, we re-calculated the final result without that correction. These files are in the Zn2-2-pbe0 folder. Furthermore, we used [Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>] and removed one of the Zn atoms and replaced the dangling bonds with H. We then fully optimized that structure. The results are in the Zn2-2-cut folder.</p> <h1> </h1> <h1>Information regarding the raw characterisation data</h1> <p>The raw characterisation data files for all nuclear magnetic resonance (NMR) spectroscopy, infrared (IR) spectroscopy, cyclic voltammetry (CV), single crystal X-ray diffraction (XRD) and solution magnetometry studies are enclosed in separate .zip files.</p>
Lithic raw data for Sheppard's (1987) thesis on the Capsian
<div> <div> <div> <p>This is a collection of six lithic raw data spreadsheets created in the course of Peter Sheppard’s doctoral project on the prehistoric tradition of Northwest Africa known as the Capsian. Sheppard’s project investigated technological and stylistic variation in Capsian lithics. The spreadsheets provided here contain about 507,000 observations collected on some 20,100 artefacts (blanks, cores, burins, backed bladelets, and geometric microliths) from 16 sites. We recovered these data uncorrupted, byte-for-byte from a 40-year-old tape. In the provided PDF document, we restate with additional clarifications the data definitions from Sheppard (1987). We also detail the steps we took to transform the files found on the tape into usable data spreadsheets.</p> </div> </div> </div>
Leaf spectroscopy and active fluorescence datasets for early drought and nitrogen stress diagnosis in tomato
<p>The dataset contains different plant physiological parameters collected during a 14-day stress and recovery experiment on tomato (<em>Solanum lycopersicum</em> L. cv Moneymaker) plants, undergoing a nitrogen deficiency, drought or control treatment. </p> <p>A full description of the experiment, together with the scientific results, is published by Pescador-Dionisio et al. (2024), and can be found through: <a href="https://doi.org/10.1111/nph.20253">https://doi.org/10.1111/nph.20253.</a></p> <p>The goal of the dataset collection was to obtain a non-invasive proximal sensing dataset at leaf level (reflectance, transmittance, upward and downward fluorescence), in parallel to gas exchange and active fluorescence measurements. The leaf spectroscopy dataset was further processed by a pigment spectral unmixing algorithm according to Van Wittenberghe et al. (2024), to calculate fluorescence quantum efficiency (<em><strong>FQE</strong></em>) and effective absorbance (<strong><em>A_eff</em></strong>) changes associated to the activation of regulated heat dissipation (<strong><em>A_eff_535_Xan</em></strong>). The latter absorption feature is linked to the xanthophyll ('<strong>Xan</strong>') absorption in the 500-600 nm range, which is modelled by the sum of three Gaussians. For a full description of this feature, see Van Wittenberghe et al. (2021).</p> <p>Gas exchange and active fluorescence measurements were carried out with a LI-6400 portable photosysthesis system (LI-COR Biosciences, Lincoln, USA) equipped with a 6400-40 leaf chamber fluorometer. Steady-state measurements were done at 300 and 1000 μmol m−2 s−1 ('<strong><em>PAR300</em></strong>' and '<em><strong>PAR1000</strong></em>'), i.e. growing light conditions and light saturating conditions. Light response curves were taken on different days. Common fluorescence parameters (e.g., <em><strong>Fv/Fm, Fo, Fm, NPQ, YNO, YNPQ</strong></em>) are provided together with 'sustained' and reversible' NPQ parameters calculated according Porcar-Castell (2011).</p> <p>Leaf spectroscopy and active steady-state fluorescence measurements were performed on the same measuring days ('<em><strong>d0</strong></em>', '<em><strong>d2</strong></em>', '<em><strong>d4</strong></em>', '<em><strong>d7</strong></em>', '<em><strong>d14</strong></em>') and on the same leaf, both at 300 and 1000 μmol m−2 s−1 ('<em><strong>PAR300</strong></em>' and '<em><strong>PAR1000</strong></em>'), taking into account an adaptation time. We used a LED light source and several filters, placed in front of a FluoWat leaf clip, which was connected to two high-performance VIS-NIR spectroradiometers (QEPRO, Ocean Insight Inc., Orlando, Florida, USA). The spectroscopy measurements are presented in the Matlab structures for each measuring day, e.g. "<strong><em>2023_d0_Leaf_Spec_Tomato_Stress.mat</em></strong>".</p> <p>The outputs of the pigment spectral fitting code are presented by Matlab structures, e.g. "<strong><em>2023_d0_Leaf_Fitting_Tomato_Stress.mat</em></strong>", which contains the effective absorbance fitting (<strong><em>A_eff</em></strong>) of each pigment (<strong>Chl a, Chl b, Carotene-b, Anthocyanins, and Xanthophylls</strong>) for the wavelength range [500-780] nm, the absorbed photosynthetically active radiation by Chlorophyll a ('<em><strong>APAR_Chla</strong></em>') for the wavelength range [400-800] nm, and the fluorescence quantum efficiency, calculated as the ratio of the emitted fluorescence photons and the flux of photons absorbed by Chlorophyll a. </p> <p>Additional metadata from HPLC photosynthetic pigment analyses, xanthophyll-related enzyme expression, biomass and total content of elemental nitrogen are provided.</p> <p>Please follow the README files for more detailed information.</p> <p> </p>
SERENA EJPSOIL SK Erosion SoilErosion
<div> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>The present data was prepared according to the methodology of SERENA Soil erosion cookbook for the territory of Slovakia. The map of soil loss by water erosion (soil threat) was based on the RUSLE model.</p> <p>The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. Soil loss was used as an indicator for soil erosion (ST). </p> </div> <div> <p>To create the soil loss map we used theese data: </p> </div> <div> <p> R factor - we used data from 100 automatic rain stations on minute rainfall for about 10-year period (national dataset) </p> </div> <div> <p>K factor – we used the source proposed in the cookbook from ESDAC dataset: Soil Erodibility (K- Factor) High Resolution dataset for Europe </p> </div> <div> <p>LS factor – we used the source proposed in the cookbook from ESDAC dataset: LS-factor (Slope Length and Steepness factor) for Slovakia </p> </div> <div> <p>C factor – we used LPIS database-this has information about crops on agricultural soil. We have values of C factor for all crops. </p> </div> <div> <p>P factor – we used the source proposed in the cookbook from ESDAC dataset: P factor for Slovakia. This map has values about 0.99 for Slovakia, so P-factor does not have much effect on the resulting erosion. </p> </div> <div> <p>The delivered map was prepared in GeoTIFF format in the resolution of 500 * 500 m. </p> </div>
Kleptotrace-micro-dataset
<p>This micro-benchmark dataset was made for evaluation of the proposed pipeline in Koletsis et al. Entity Extraction from High-Level Corruption<br>Schemes via Large Language Models. BDA4FCT@IEEE Big Data 2024. Also available at https://arxiv.org/abs/2409.13704</p> <p>This dataset comprises 15 articles, totaling 441 sentences, focused on topics related to financial corruption. It includes 2 lists of individuals and organizations mentioned within these articles.</p>
National contributions to climate change due to historical emissions of carbon dioxide, methane and nitrous oxide
<p>A complete description of the dataset is given by <a href="http://doi.org/10.1038/s41597-023-02041-1">Jones et al. (2023)</a>. Key information is provided below.</p> <p><strong>Background</strong></p> <p>A dataset describing the global warming response to national emissions CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O from fossil and land use sources during 1851-2021.</p> <p>National CO<sub>2 </sub>emissions data are collated from the Global Carbon Project (Andrew and Peters, 2024; Friedlingstein et al., 2024). </p> <p>National CH<sub>4</sub> and N<sub>2</sub>O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2024).</p> <p>We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH<sub>4</sub> and N<sub>2</sub>O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021).</p> <p>Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST).</p> <p>The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O or 3-GHG total) and source (fossil emissions, land use emissions or the total).</p> <p><strong>Data records: overview</strong></p> <p>The data records include three comma separated values (.csv) files as described below.</p> <p>All files are in ‘long’ format with one value provided in the <em>Data</em> column for each combination of the categorical variables <em>Year, Country Name, Country ISO3 code, Gas, and Component</em> columns.</p> <p><em>Component</em> specifies fossil emissions, LULUCF emissions or total emissions of the gas.</p> <p><em>Gas</em> specifies CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O or the three-gas total (labelled 3-GHG).</p> <p><em>Country ISO3 codes</em> are specifically the unique ISO 3166-1 alpha-3 codes of each country.</p> <p><strong>Data records: specifics</strong></p> <p>Data are provided relative to 2 reference years (denoted <em>ref_year </em>below): 1850 and 1991. 1850 is a mutual first year of data spanning all input datasets. 1991 is relevant because the United Nations Framework Convention on Climate Change was operationalised in 1992.</p> <p><em>EMISSIONS_ANNUAL_{ref_year-20}-2023.csv:</em> <em>Data </em>includes annual emissions of CO<sub>2</sub> (Pg CO<sub>2</sub> year<sup>-1</sup>), CH<sub>4</sub> (Tg CH<sub>4</sub> year<sup>-1</sup>) and N<sub>2</sub>O (Tg N<sub>2</sub>O year<sup>-1</sup>) during the period <em>ref_year-20 </em>to 2023. The <em>Data</em> column provides values for every combination of the categorical variables. Data are provided from <em>ref_year-20</em> because these data are required to calculate GWP* for CH<sub>4</sub>.</p> <p><em>EMISSIONS_CUMULATIVE_CO2e100_{ref_year+1}-2023.csv: Data </em>includes the cumulative CO<sub>2</sub> equivalent emissions in units Pg CO<sub>2</sub>-e<sub>100</sub> during the period <em>ref_year+1</em> to 2023 (i.e. since the reference year). The <em>Data</em> column provides values for every combination of the categorical variables. </p> <p><em>GMST_response_{ref_year+1}-2023.csv:</em> <em>Data</em> includes the change in global mean surface temperature (GMST) due to emissions of the three gases in units °C during the period <em>ref_year+1</em> to 2023 (i.e. since the reference year). The <em>Data</em> column provides values for every combination of the categorical variables. </p> <p><strong>Accompanying Code</strong></p> <p>Code is available at: <a href="https://github.com/jonesmattw/National_Warming_Contributions">https://github.com/jonesmattw/National_Warming_Contributions</a> .</p> <p>The code requires Input.zip to run (see README at the GitHub link).</p> <p><strong>Further info: Country Groupings</strong></p> <p>We also provide estimates of the contributions of various country groupings as defined by the UNFCCC:</p> <ul> <li>Annex I countries (number of countries, n = 42)</li> <li>Annex II countries (n = 23)</li> <li>economies in transition (EITs; n = 15)</li> <li>the least developed countries (LDCs; n = 47)</li> <li>the like-minded developing countries (LMDC; n = 24).</li> </ul> <p>And other country groupings:</p> <ul> <li>the organisation for economic co-operation and development (OECD; n = 38)</li> <li>the European Union (EU27 post-Brexit)</li> <li>the Brazil, South Africa, India and China (BASIC) group.</li> </ul> <p>See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group.</p>
InSAR stack of Fernandina volcano in Galápagos, Ecuador from Sentinel-1 descending track 128 processed with ISCE2/topsStack
<p>A stack of unwrapped interferograms on Fernandina volcano, Galápagos, Ecuador</p> <p>Sensor: Sentinel-1descending track 128</p> <p>Processor: ISCE/topsStack</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p><strong>Version 1.x (~750 MB)</strong><br> Time: 2014.12.13 - 2018.06.19 (98 acquisitions, 288 interferograms)</p> <p><strong>Version 0.1 (~280 MB; for fast testing of code development)</strong><br> Time: 2014.12.13 - 2016.05..24 (36 acquisitions, 102 interferograms)</p>
Improving Artificial Teachers by Considering How People Learn and Forget: Dataset
<p>This dataset contains the results of the experiment described in <a href="https://dl.acm.org/doi/10.1145/3397481.3450696">Nioche et al. (2021)</a>. </p> <p>This dataset contains 4 data files:</p> <ul> <li><em>data.csv</em>: the main data file.</li> <li><em>stimuli.csv:</em> the description/listing of the stimuli.</li> <li><em>demographic_info.csv</em>: the demographic information about the users.</li> <li><em>data_incl_preliminary_exp.csv</em>: an additional data file that includes the user of the preliminary experiments</li> </ul> <p>The main data file contains the logs of 53 different users using a self-teaching application for one week. The goal of the users was to learn the English meaning of Japanese kanji. Each user completed between 1370 trials and 1608 trials. Each user saw between 85 and 204 characters. </p> <p>Two additional files are also joint to the data files:</p> <ul> <li><em>info.ipynb</em>: A Jupyter notebook that provides information about each data file, a few descriptive plots, and an example of data manipulation.</li> <li><em>info.pdf: </em>A pdf rendering of the notebook.</li> </ul> <p>If you use this dataset, please refer to it by citing <a href="https://dl.acm.org/doi/10.1145/3397481.3450696">Nioche et al. (2021)</a>.</p>
Sample data for "Machine learning for large-scale forecasting"
<p>This dataset includes sample data for the Netherlands to run the machine learning baseline as described in the paper titled <em>Machine learning for large-scale crop yield forecasting</em>, accessible at <a href="https://doi.org/10.1016/j.agsy.2020.103016">https://doi.org/10.1016/j.agsy.2020.103016</a>. The software implementation of the machine learning baseline is available at: <a href="https://github.com/BigDataWUR/MLforCropYieldForecasting">https://github.com/BigDataWUR/MLforCropYieldForecasting</a>.</p> <p><strong>Notes:</strong></p> <p>The NUTS classification (Nomenclature of territorial units for statistics) is a hierarchical system for dividing up the economic territory of the EU and the UK (see Eurostat, 2016) for more details).</p> <p>Data</p> <p>The dataset consists of 11 CSV files. They are formatted to work as sample inputs to the machine learning baseline.</p> <ol> <li><strong>Crop Area Fractions </strong>(NUTS2, NUTS1): We aggregated the predictions of the machine learning baseline from NUTS2 to national (NUTS0) level by weighting them on the modeled crop area. Cerrani and López Lozano (2017) have described in detail the algorithm used to model crop areas for different NUTS levels. The data comes from the MARS Crop Yield Forecasting System (MCYFS) of European Commission's Joint Research Centre (JRC) (see Lecerf et al., 2019).</li> <li><strong>Centroids (NUTS2)</strong>: Data includes latitude, longitude and distance to coast of the centroids of NUTS2 regions.</li> <li><strong>Meteo Daily Data and Meteo Dekadal Data </strong>(NUTS2): The data comes from MCYFS (see EC-JRC, 2020). By default, the implementation uses daily data.</li> <li><strong>Remote Sensing Data</strong> (NUTS2, see Copernicus Global Land Service, 2020): Data includes fraction of absorbed photosynthetically active radiation (FAPAR) aggregated to NUTS2.</li> <li><strong>Soil Data</strong>: Data includes soil moisture information that can be used to calculate soil water holding capacity. The data comes from MCYFS (see Lecerf et al., 2019).</li> <li><strong>WOFOST data </strong>(NUTS2): The World Food Studies (WOFOST) crop model (van Diepen et al., 1989; Supit et al., 1994; de Wit et al. 2019) is a simulation model for the quantitative analysis of the growth and production of annual field crops. It is a mechanistic, dynamic model that explains daily crop growth on the basis of the underlying processes, such as photosynthesis, respiration and how these processes are influenced by environmental conditions. The crop simulation is fed by weather, soil and crop data. Observed meteorological data is interpolated on a regular 25 km grid using a method based on the distance, altitude and climatic region similarity between the center of grid cells and weather stations (see Van der Goot, 1998). WOFOST runs on the intersection between the 25 km meteorological grid and soil units based on the European soil map (http://esdac.jrc.ec.europa.eu/). In order to have the output data aggregated to administrative regions such as countries or provinces, simulation units are further intersected with the boundaries of these regions. The outputs at soil unit (STU) level are aggregated to grid level in an area weighted manner. Gridded simulations are aggregated to lowest NUTS level 3 considering the arable land area of each grid, derived from GLOBCOVER and CORINE Land Cover (Cerrani and Lopez Lozano, 2017). From NUTS3 to higher levels, crop area fractions for the current year, retrieved from Eurostat, are used to weight and aggregate the output (Cerrani and Lopez Lozano, 2017).</li> <li><strong>GAES data</strong>: GAES data includes agro-climatic features of regions, such as elevation and slope (from USGS-EROS, 2021), field size (from Lesiv et al., 2019), irrigated (crop) areas (from EC-JRC, 2020) and crop areas (from EC-JRC, 2020).</li> <li><strong>National yield statistics </strong>(NUTS0): These are the official Eurostat national yield statistics (Eurostat, 2020a). We used these yield statistics as reference to compare the machine learning predictions aggregated to NUTS0 and the actual MCYFS forecasts (see van der Velde and Nisini, 2019).</li> <li><strong>Regional yield statistics </strong>(NUTS2): We used NUTS2 yield statistics as labels to train and evaluate machine learning algorithms. We got NUTS2 yield statistics from The Central Bureau of Statistics (CBS) of the Netherlands (NL-CBS, 2020).</li> <li><strong>Past MCYFS Yield Forecasts </strong>(NUTS0): These are actual forecasts made by MCYFS in the past (see van der Velde and Nisini, 2019). We used the official Eurostat national yield statistics (see point 7 above) as the reference to compare the machine learning predictions aggregated to NUTS0 and MCYFS forecasts.</li> </ol> <p><strong>Crop ID and name mapping</strong></p> <p>2 : grain maize</p> <p>6 : sugar beets</p> <p>7 : potatoes</p> <p>90 : soft wheat</p> <p>93 : sunflower</p> <p>95 : spring barley</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We would like to thank S. Niemeyer from the European Commission’s Joint Research Centre (JRC) for the permission to provide open access to the Netherlands data. Similarly, we would like to thank M. van der Velde, L. Nisini and I. Cerrani from JRC for sharing with us past MCYFS forecasts and Eurostat national yield statistics.</p>
SMAP L1B Brightness Temperatures Arctic
<p>This is a data set of polarised brightness temperatures (TBs) from the L-band (1.4 GHz) passive microwave sensor flying onboard the Soil Moisture Active Passive (SMAP) satellite. The data set was produced to enable a consistent combination of TBs from SMAP with those measured by the SMOS (Soil Moisture and Ocean Salinity) mission.</p> <p>It is based on the version 3 SMAP L1B brightness temperatures (Piepmeier et al., 2016), which are not corrected with respect to solar and cosmic radiation or atmospheric effects. The data are aggregated daily and gridded to a north polar EASE-grid 2.0 (Brodzik et al. 2012) with a grid size of 12.5 km. The data were produced within the framework of the EU Horizon2020 project SPICES and therefore only covers the period from the first available SMAP data to the end of the project (1 April 2015 to 31 May 2018).</p> <p>Within SPICES, SMAP and SMOS data were combined to a homogenized data set, which was then used to estimate sea ice thickness. For details see Schmitt and Kaleschke (2018) and the related data sets of SMOS TBs and SMOS/SMAP sea ice thickness.</p> <p>The files contain the following data fields:<br> <strong>Tbv</strong> - brightness temperatures at vertical polarisation<br> <strong>Tbh</strong> - brightness temperatures at horizontal polarisation<br> <strong>Tbv_std</strong> - weighted standard error of Tbv<br> <strong>Tbh_std</strong> - weighted standard error of Tbh<br> <strong>nmp</strong> - effective number of measurements used for averaging</p> <p>The grid coordinates are provided as a separate file <em>Latlon_e12.5.nc</em></p>
S71 | CECSCREEN | HBM4EU CECscreen: Screening List for Chemicals of Emerging Concern Plus Metadata and Predicted Phase 1 Metabolites
<p>This is the collection associated with list S71 CECSCREEN HBM4EU CECscreen: Screening List for Chemicals of Emerging Concern Plus Metadata and Predicted Phase 1 Metabolites<strong> </strong>on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>CECScreen is part of the HBM4EU project (coord. UBA) > WP16 "emerging chemicals" (lead INRA, JP Antignac/L Debrauwer) > Task 16.1 (lead IRAS, J Vlanderen / R Vermeulen) > Main contributor (J Meijer) > Involved Partners (M Lamoree, T Hamers, S Hutinet, A, Covaci, C Huber, M Krauss, DI Walker, EL Schymanski). Further details in Meijer et al (2021) DOI: <a href="https://doi.org/10.1016/j.envint.2021.106511">10.1016/j.envint.2021.106511</a>. Dataset DOI: <a href="https://doi.org/10.5281/zenodo.3956586">10.5281/zenodo.3956586</a>.</p> <p>Update 23/7/2020 (v0.1.1): updated MetFrag files to remove elements causing errors (Os, Pd, Ag, Be). Update 8 Nov 2022 (v0.1.2) removed new lines in several synonyms as detected at BioHackEU22.</p>
Circumpolar mid-winter thaw and refreeze based on fusion of Metop ASCAT and SMOS, 2011/2012 - 2021/2022
<p>Rain-on-Snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snow pack properties are changing and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event.</p> <p>Active and passive microwave data have been combined to identify events over land North of 65°N (Bartsch et al. 2023). In a first step Metop ASCAT (C-Band radar) was used to identify potential sudden snow structure change. In a second step, results have been masked for coincident observation of wet snow within +- 3 days based on SMOS (derived from Centre Aval de Traitement des Données SMOS (CATDS) level 3 product). Note that the SMOS retrievals can have data gaps due to radio frequency interferences (RFI) what leads to gaps in the event detection.</p> <p>The dataset is structured by centre points of the hexagonal grid of the used Metop ASCAT product (EUMETSAT, approximately 12.5 km nominal resolution). Attributes include point ID (GPI), latitude, longitude and</p> <ul> <li>aggregated number of events for the months November to February, 2011/12 to 2021/22, and their sum per winter (referred to as annual), or</li> <li>in case of daily results (date in file name) the magnitude of ASCAT backscatter change in dB (DSigma0; no data value is '0.0').</li> </ul> <p>The dataset extents Seawinds QuikScat (Ku-band) based results for 2000-2009 (Bartsch 2010, Freund and Bartsch 2020).</p>
Nabro volcano event catalogue from Lapins et al., 2021, JGR Solid Earth
<p>Catalogue of seismic events from Nabro volcano (Sep 2011 - Oct 2012). Data format is a csv file.</p> <p>Events were detected by U-GPD phase arrival picking model. See following paper for details on event detection and location procedure: <em>A Little Data Goes A Long Way Way: Automating Seismic Phase Arrival Picking at Nabro Volcano With Transfer Learning</em> by Lapins et al., 2021, <a href="https://doi.org/10.1029/2021JB021910">https://doi.org/10.1029/2021JB021910</a>).</p> <p>Original seismic waveforms are from the Nabro Urgency Array (Hammond et al., 2011; <a href="https://doi.org/10.7914/SN/4H_2011">https://doi.org/10.7914/SN/4H_2011</a>), which is publicly available through IRIS Data Services (<a href="http://service.iris.edu/fdsnws/dataselect/1/">http://service.iris.edu/fdsnws/dataselect/1/</a>). See Hammond et al. (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0025">2011</a>) for further details on waveform data access and availability.</p> <p>Full code to reproduce our U-GPD transfer learning model, perform model training, run the U-GPD model over continuous sections of data and use model picks to locate events in NonLinLoc (Lomax et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0044">2000</a>) are available at <a href="https://github.com/sachalapins/U-GPD">https://github.com/sachalapins/U-GPD</a>, with the release (v1.0.0) associated with this study also archived and available through Zenodo (Lapins, <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0036">2021</a>; <a href="https://doi.org/10.5281/zenodo.4558121">https://doi.org/10.5281/zenodo.4558121</a>).</p> <p> </p> <p>Dataset column key:</p> <p>time = Origin time of seismic event (UTC)</p> <p>lat = Hypocentre latitude in decimal degrees</p> <p>lon = Hypocentre longitude in decimal degrees</p> <p>depth = Hypocentre depth in km</p> <p>rms = RMS error for phase arrival picks and hypocentre (sec)</p> <p>erh = Estimate of horizontal Gaussian error (km)</p> <p>erz = Estimate of vertical Gaussian error (km)</p> <p>azgap = Azimuthal gap (maximum angle separating two adjacent seismic stations, measured from earthquake epicentre)</p> <p>cluster = HDBSCAN cluster number (see Chapter 6 of Lapins, 2021 doctoral thesis: <em>Detecting and characterising seismicity associated with volcanic and magmatic processes through deep learning and the continuous wavelet transform</em>. Persistent URL: <a href="https://hdl.handle.net/1983/ea90148c-a1b2-47ae-afad-5dd0a8b5ebbd">https://hdl.handle.net/1983/ea90148c-a1b2-47ae-afad-5dd0a8b5ebbd</a>)</p> <p>nab*_p_time = P-wave arrival time for station NAB* (UTC)</p> <p>nab*_p_prob = Maximum detection 'probability' around P-wave phase arrival from U-GPD model (between 0 and 1)</p> <p>nab*_s_time = S-wave arrival time for station NAB* (UTC)</p> <p>nab*_s_prob = Maximum detection 'probability' around S-wave phase arrival from U-GPD model (between 0 and 1)</p> <p> </p> <p>Station csv column key:</p> <p>Network = Seismic network name</p> <p>Station = Seismic station name</p> <p>Latitude = Latitude in decimal degrees</p> <p>Longitude = Longitude in decimal degrees</p> <p>Elevation_asl_km = Station elevation in km above sea level</p>
The deployment of temporary nurses and its association with permanent nurses' outcomes in Swiss psychiatric hospitals: A secondary analysis.
<p>The objective of this analysis was to investigate the frequency of temporary nurses’ deployment and their association with nurse staffing levels and permanent nurses’ outcomes in Swiss psychiatric hospitals. The data is based on the Match<sup>RN</sup> Psychiatry study including 79 psychiatric units and 651 nurses and provides unit level frequency of temporary nurses’ deployment and individual nurse level data on staffing levels and permanent nurses’ outcomes namely job satisfaction, burnout, and intention to leave organization or profession. The data was collected in 2019 and 2020. We provide the unit and nurse-level dataset, a codebook and the r code to replicate the analyses of the paper. You can find the paper here: <a href="https://peerj.com/articles/15300/">https://peerj.com/articles/15300/</a> </p>
ClostriTof microflex Biotyper library plugin and associated raw Maldi spectra version 2.0
<p>This dataset contains the ClostriTof microflex Biotyper library plugin, an installation guide as well as the raw spectral data for all library and validation strains used to construct the ClostriTof library plugin.</p> <p>If you use this library for your research, please cite Asare et al., Frontiers in Microbiology, 2023; <a href="https://doi.org/10.3389/fmicb.2023.1104707">https://doi.org/10.3389/fmicb.2023.1104707</a></p> <p>We would like to thank Thomas Maier for his help with assembling version 2.0 of the ClostriTOF Database.</p>
Cross-phyla protein annotation by structural prediction and alignment
<p><strong>Background:</strong> Protein annotation is a major goal in molecular biology, yet experimentally determined knowledge is typically limited to a few model organisms. In non-model species, the sequence-based prediction of gene orthology can be used to infer protein identity, however this approach loses predictive power at longer evolutionary distances. Here we propose a workflow for protein annotation using structural similarity, exploiting the fact that similar protein structures often reflect homology and are more conserved than protein sequences.</p> <p><strong>Results:</strong> We propose a workflow of openly available tools for the functional annotation of proteins via structural similarity (MorF: <strong>Mor</strong>pholog<strong>F</strong>inder) and use it to annotate the complete proteome of a sponge. Sponges are highly relevant for inferring the early history of animals, yet their proteomes remain sparsely annotated. MorF accurately predicts the functions of proteins with known homology in >90% cases, and annotates an additional 50% of the proteome beyond standard sequence-based methods. We uncover new functions for sponge cell types, including extensive FGF, TGF and Ephrin signalling in sponge epithelia, and redox metabolism and control in myopeptidocytes. Notably, we also annotate genes specific to the enigmatic sponge mesocytes, proposing they function to digest cell walls.</p> <p><strong>Conclusions:</strong> Our work demonstrates that structural similarity is a powerful approach that complements and extends sequence similarity searches to identify homologous proteins over long evolutionary distances. We anticipate this to be a powerful approach that boosts discovery in numerous -omics datasets, especially for non-model organisms.</p>
Prevalence and determinants of cardiovascular risk factors in Lesotho: a population-based survey
<p>These are pseudo-anonymised data from the ComBaCaL survey and belong to the manuscript "Prevalence and determinants of cardiovascular risk factors in Lesotho: a population-based survey" which can be found at <a href="https://doi.org/10.1093/inthealth/ihad058">https://doi.org/10.1093/inthealth/ihad058</a>. </p> <p>The data dictionary explains the critical data available in the dataset. Between November 2021 and August 2022 , 6061 participants over 18 years old were visited in their households in two districts of Lesotho. </p>
doi_________::06ada5fc94e8bb7974e9f1dc1fd6700b
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