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BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of Ni-based alloy Inconel IN718
<p>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of Ni-based alloy Inconel IN718 between room temperature and 800 °C in an additively manufactured variant (laser powder bed fusion, PBF‑LB/M) and from a conventional process route (hot rolled bar). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</p>
BAM Reference Data: Creep of Single-Crystal Ni-Based Superalloy CMSX-6
<p>This publication provides comprehensive metadata and test results of constant force creep tests according to <br>DIN EN ISO 204:2019-4 on the single crystal Ni-based superalloy CMSX-6 at T = 980 °C and initial stresses<br>between 140 MPa and 230 MPa. The tests were carried out in an accredited test laboratory using calibrated <br>measuring equipment. The data were audited and are BAM reference data.</p>
GTSM-ERA5-E dataset - Data underlying the paper "Global dataset of storm surges and extreme sea levels for 1950-2024 based on the ERA5 climate reanalysis"
<p>Extreme sea levels, generated by storm surges and high tides, have the potential to cause coastal flooding and erosion. Global datasets are instrumental for mapping of extreme sea levels and associated societal risks. Harnessing the backward extension of the ERA5 reanalysis, we present a dataset containing the statistics of water levels based on a global hydrodynamic model (GTSMv3.0) covering the period 1950-2024. This is an extension of a previously published dataset for 1979-2018 <a href="https://www.frontiersin.org/articles/10.3389/fmars.2020.00263/full" target="_blank" rel="noopener">(Muis et al. 2020)</a>. The timeseries (10-min, hourly mean and daily maxima) are available via the Climate Data Store of ECMWF at DOI: 10.24381/cds.a6d42d60. Using this extended ERA5 dataset, we calculate percentiles and estimate extreme water levels for various return periods globally. The percentiles dataset includes the 1, 5, 10, 25, 50, 75, 90, 95 and 99th percentiles. The extreme water levels include return values for 1, 2, 5, 10, 25, 50, 75 and 100 years, and they are estimated using POT-GPD method applied with a threshold of 99th percentile of the timeseries and using a 72-hour window for declustering peak events, and MLE method for fitting the GPD parameters. The parameters (shape, scale and location) are also supplied with this dataset.</p> <p>Validation of the underlying timeseries and the statistical values shows that there is a good agreement between observed and modelled sea levels, with the level of agreement being very similar to that of the previously published dataset. The extended 75-year dataset allows for a more robust estimation of extremes, often resulting in smaller uncertainties than its 40-year precursor. The present dataset can be used in global assessments of flood risk, climate variability and climate changes.</p> <p>Global modelling of water levels and extreme value analysis are associated with a number of uncertainties and limitations, that are particularly important to consider when conducting local assessments. Please refer to the Usage Notes in the corresponding manuscript (Aleksandrova et al. 2025, paper currently under review) for an overview of limitations.</p>
Data set from: Can laboratory-based XAFS compete with XRD and Mössbauer spectroscopy as a tool for quantitative species analysis?
<p><strong>Abstract:</strong> This work investigated the capability of quantitative laboratory X-ray Absorption Fine Structure Spectroscopy (lab-XAFS) via Linear Combination Fitting (LCF) of reference spectra in comparison with quantitative X-ray diffraction (XRD) and Mössbauer spectroscopy. While lab-XAFS already show good results when performing LCF with significant different spectra of the species to be identified, the method is challenging when the reference spectra and possibly species in the sample are very similar as it is the case for α-Fe<sub>2</sub>O<sub>3</sub>, γ- Fe<sub>2</sub>O<sub>3</sub> and Fe<sub>3</sub>O<sub>4</sub>. For this investigation an iron oxide mineral with origin from Mexico (here named Mexican Magnetite) with different iron oxide phases was used and measured using all three methods.</p> <p> </p> <p>This data set contains the raw data of the work “<em>Can laboratory-based XAFS compete with XRD and Mössbauer spectroscopy as a tool for quantitative species analysis? Critical evaluation using the example of a natural iron ore</em>” of XAFS, XRD and Mössbauer measurements. This includes XAFS, Mössbauer and XRD spectra of the reference materials α-Fe<sub>2</sub>O<sub>3</sub>, Fe<sub>3</sub>O<sub>4</sub> and the sample Mexican magnetite, the XAFS spectra of the reference material γ- Fe<sub>2</sub>O<sub>3</sub> and the XAFS, XRD and Mössbauer spectra of three different α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures.</p> <p> </p> <p><u>Sample information/sample list</u></p> <p><strong>sample/references:</strong> The sample and the corresponding short cut name used in the data files is listed. Furthermore the method the sample was measured with is also listed.</p> <table> <tbody> <tr> <td> <p><strong>Short cut name</strong></p> </td> <td> <p><strong> Sample/reference</strong></p> </td> <td> <p><strong>Measured with</strong></p> </td> </tr> <tr> <td> <p>MexicanMagnetite</p> </td> <td> <p> Iron oxide mineral with origin in Mexico</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe2O3</p> </td> <td> <p>Fe2O3-alpha / Hematite</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe3O4</p> </td> <td> <p>Fe3O4 / Magnetite</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe</p> </td> <td> <p>Iron powder</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>Fe2O3-alpha</p> </td> <td> <p>Fe2O3-alpha / Hematite</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>Fe2O3-gamma</p> </td> <td> <p>Fe2O3-gamma / Maghemite</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>30-70</p> </td> <td> <p>Mixture of 30 % Fe2O3-alpha/ 70 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>50-50</p> </td> <td> <p>Mixture of 50 % Fe2O3-alpha/ 50 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>70-30</p> </td> <td> <p>Mixture of 70 % Fe2O3-alpha/ 30 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Mixtures ratios:</strong> The prepared Fe2O3-Fe3O4 model mixtures with the weight-in ratios and the actual achieved mass percentage ratio between the two iron species, taken impurities of the used materials into account, are listed below. The short cut name is the name used in the data files (see table above).</p> <table> <tbody> <tr> <td> <p><strong>Short cut name</strong></p> </td> <td> <p><strong>Actual achieved weigh-in ratios</strong></p> <p><strong>m(Fe2O3)/m(Fe3O4)*</strong></p> </td> <td> <p><strong>Actual achieved mass percentage ratios ωrel(Fe2O3) / ωrel(Fe3O4)</strong></p> </td> </tr> <tr> <td> <p>30-70</p> </td> <td> <p>0.31380 g / 0.7059 g</p> </td> <td> <p>31.8 / 68.2</p> </td> </tr> <tr> <td> <p>50-50</p> </td> <td> <p>0.5140 g / 0.5174 g</p> </td> <td> <p>50.6 / 49.4</p> </td> </tr> <tr> <td> <p>70-30</p> </td> <td> <p>0.7037 g / 0.3041 g</p> </td> <td> <p>70.5 / 29.5</p> </td> </tr> </tbody> </table> <p>*the given masses here, ar the masses of the materials of the mixtures before sampel prepration. For the sample prepration the mass applied on the tape or mixed with wax is about 5-10 mg.</p> <p><u>Spectrometer Specifications</u></p> <p><strong>XAFS:</strong> The experimental setup for the laboratory XAFS measurement is based on the Highly Annealed Pyrolytic Graphite (HAPG) von Hámos spectrometer with the use of a cylindrically shaped crystal.</p> <p>As detector unit the pixelated X-ray hybrid-CMOS detector Dectris Eiger2 R 500k was used. The area of detection is 77.3 mm x 38.6 mm with a pixel size of 75 µm x 75 µm. The X-ray source was a water-cooled micro focus X-ray tube with molybdenum as anode material, a power of 30 Watt optimised at 15 kV and a spot size of 70 µm.</p> <p><strong>Sample preparation</strong>: α-Fe<sub>2</sub>O<sub>3</sub>, Fe<sub>3</sub>O<sub>4</sub>, the three α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures and the sample Mexican magnetite were applied on adhesive tape, sliced in 1cm x 1cm pieces characterized with XRF to determine the iron content as [<em>Q</em>] = mg/cm² and then stacked by taking the iron content of each slice into account to achieve an absorption of <em>µ*Q</em> of about 1 at the edge.</p> <p>The γ- Fe<sub>2</sub>O<sub>3</sub> and also the three α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures were prepared as Pellet. Here the sample material was mixed with Hoechst Wax C in a ratio of 1:6, mixed in a vortex shaker and then pressed with a hydraulic press with a Pellet diameter of 13 mm. The amount of the wax/sample powder material was weight before inserting in the press to the amount of <em>Q</em> to achieve a <em>µ*Q</em> of about 1 with a 13 mm Pellet.</p> <p>Shifts of the energy axis as well as a widening or compression of this axis could be present when comparing the data with other data sets of other spectrometer or synchrotron radiation facilities, since no precise energy calibration was carried out due to the reason that the samples were compared to the measured references and would have the same shift, widening or compression.</p> <p> </p> <p><strong>XRD:</strong> Two different commercial XRD set ups have been used. For the Mexican magnetite the Benchtop XRD spectrometer Bruker D2Phaser with a Cobalt X-ray source and a SSD160 detector (active length = 12 mm) was used. The measurement range was 10°- 90° 2theta with 0.014° step size and 4.8 s/step, resulting in a total measurement time of 8h. During the measurement the sample was rotated with 10 rpm. The sample was filled in PMMA-holders (Ø 2.5 mm) using the top-loading technique. The analysis was carried out using a 1-mm fixed divergence slit, a 2.5° primary and a 4° secondary soller collimator, a fixed knife edge (3 mm above the sample surface), and an Fe Kβ filter (2.5).</p> <p>For the X-ray diffraction measurements of the α-Fe2O3/Fe3O4 mixtures and the pure references a Panalytical X’Pert PRO diffractometer with a Bragg-Brentano setup was used. The diffractometer operates with a Cu anode and without a monochromator (Cu-Kalpha radiation) at 40 kV and 30 mA. The diffraction data were obtained over a measurement range of 10–120° 2theta. Samples were applied flat on a cut-off Si wafer attached to the sample holder.</p> <p><em> </em></p> <p><strong>Mössbauer:</strong> Mössbauer spectroscopy was performed at a MIMOS II type spectrometer with a <sup>57</sup>Co source (in rhodium matrix). For the analyses the <sup>57</sup>Fe-γ-line E = 14.4 keV was used and α-iron (α-Fe foil) was applied for the velocity calibration before the samples were analyzed. The samples were prepared in plastic powder sample holders and measured in transmission mode at room temperature. The measurement time varied between 12 h and 120 h depending on the sample.</p> <p> </p> <p><strong>Information on data sets</strong></p> <p>XAFS - this folder contains the XAFS spectra as intensity file with I0 (without the sample) and the It (transmission signal through the sample) for each sample. Multiple samples (It) share the same I0 and are therefore in the same data set. The Number in the filename between “XAFS“ and “data-set..” is the date of the measurement in the following format: YYYY_MM_DD. The first column in each file is the energy in unit eV. The abbreviation “WP” after each sample name in the header means “<strong>W</strong>ax <strong>P</strong>ellet” and indicates that the measurement was performed on a sample prepared as a wax pellet, the number (WP<strong>1</strong>) indicates the number of the pellet. Two pellets of each mixture were prepared to investigate the influence of the sample preparation. If the sample name is missing “WP#” the sample was prepared on adhesive tape as described above. The information on the contents of each data set as well as the measurement time (t = #h) for each It of the sample/reference can be found in data_dictionary_v2.txt.</p> <p>The intensity is normalized to counts per 1800 seconds in a 0.25 eV (for data-set-1) and 1 eV (for data-set-2, data-set-3 and data-set-4) energy interval with the indicated central bin energy.</p> <p> </p> <p>XRD - this folder contains the raw intensity files over 2theta (ASC-file). Each sample has its own file with the first column for the 2theta in unit degree and the second column for the measured intensity.</p> <p>The Number in the file name between XRD and sample name (e. g. Fe2O3, 30-70) is the date of the measurement in the following format: YYYY_MM_DD.</p> <p> </p> <p>MOESSBAUER - this folder contains the recoil Lorentz site analysis fit data of the samples. The files consist of the observed intensity (Iobs) over the velocity (v (mm/s)), including the calcucalted intensity (Icalc) and the fits of the subspectra (Sextet Site 1, etc. ). Each sample has it owns file. While the references substances <br>Fe2O3 and Fe3O4 were measured between 2016 and 2019, the MexicanMagnetite was measured 2020. An exact measurement date can’t be determined anymore.</p> <p> </p> <p>The corresponding sample to the short cut name (e. g. Fe2O3, 30-70,..) in the files can be found above and is listed in the <em>data_dictionary.txt</em> file as well.</p> <p> </p>
Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing
<p>We produced a spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing from 2001-2024.</p> <p>GDP and building surface area are yearly data.</p> <p>PDSI is monthly data.</p>
Data from: Molecular Dating of Phylogeny of Sturgeons (Acipenseridae) Based on Total Evidence Analysis
<p>Bayesian chronograms (original and updated 08.10.2022) of cladogenesis of fossil and recent Acipenseriformes reconstructed on the basis of combined (mtDNA, morphological characters) data.</p>
DATA SET USED IN THE PHYLOGENETIC ANALYSIS ?, condition not preserved. Coding for Paraortygoides based on BMNH PAL A 6217 (holotype of P. radagasti) and SMRME 1303 (holotype of P. messelensis) in The Fossil Galliform Bird Paraortygoides from the Lower Eocene of the United Kingdom
DATA SET USED IN THE PHYLOGENETIC ANALYSIS ?, condition not preserved. Coding for Paraortygoides based on BMNH PAL A 6217 (holotype of P. radagasti) and SMRME 1303 (holotype of P. messelensis)
Data and source code for Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary
<p>We present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods.</p>
Data from: Improving the Effectiveness of the Solid-Solution-Strengthening Elements Mo, Re, Ru and W in Single-Crystalline Nickel-Based Superalloys
<p>This dataset is the basis for the journal article "Improving the Effectiveness of the Solid-Solution-Strengthening Elements Mo, Re, Ru and W in Single-Crystalline Nickel-Based Superalloys" (<a href="https://doi.org/10.3390/met11111707">doi.org/10.3390/met11111707</a>). </p> <p>Differential Scanning Calorimetry (DSC), Electron-Probe Mirco-Analysis (EPMA equipped with WDS detectors), Compression Creep and CalPhaD calculation data are included in this dataset. In the resulting article the partitioning and solid solution strengthening behavior of the elements Mo, Re, Ru, and W are investigated in three different alloy collections:</p> <p>"Reference" alloys: ERBO/1 (based on the commercial alloy CMSX-4), ERBO/13 (optimized alloy: <a href="http://doi.org/10.1088/0965-0393/23/3/035004">doi.org/10.1088/0965-0393/23/3/035004</a>) and ERBO/15 (optimized alloy: <a href="http://doi.org/10.1002/9781119075646.ch4">doi.org/10.1002/9781119075646.ch4</a>)</p> <p>"Model" alloys: ERBO/17, ERBO/18, ERBO/19 and ERBO/32 - experimental SX Nickel-Based superalloys to investigate the influence of Ti and Ta on the partitioning behavior of W</p> <p>"Experimental" alloys: EXP10, EXP11, EXP12, EXP13, EXP14, EXP15, EXP16, EXP17, EXP18 - general investigation of the behavior of Mo, Re, Ru and W, in terms of partitioning and thermophysical properties.</p> <p>_________</p> <p> </p> <p>CalPhaD data: short meta data file included in the respective subdirectory</p> <p>DSC data: Meta data as header in each file</p> <p>EPMA data: For each alloy a directory includes a general meta data file "0.cnd", an element specific meta data file "*.cnd" and the actual mapping data of each element "*.txt" (element name is listed in the corresponding specific meta data file). All element compositions are given in wt.-% in the mapping data .txt files. Exceptions: "COMPO" maps represent the total amount of detector counts; The values correspond to counts if the mapping .txt files contain non-float (e.g. integer) values (usually this means that there is very little or none of that element present and should therefore not be quantified to a wt.-% value).</p> <p>Creep: short meta data file included in the respective subdirectory</p>
Training and test data for retrievals based on HATPRO observations during MOSAiC
<p>The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of temperature (ta) and humidity (hua) profiles, integrated water vapour (prw), and liquid water path (clwvi) from brightness temperatures (tb) measured by a HATPRO (humidity and temperature profiler). A regression with quadratic terms, except for the boundary layer scan which is confined to linear terms, is performed to derive these meteorological variables. The trained retrieval is applied on the HATPRO observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on Ny-Ålesund radiosonde observations. An IDL-based radiative transfer model has been used to simulate brightness temperatures for each radiosonde. Several elevation angles (ele) are given in the file because the HATPRO radiometer performs elevation scans in between zenith scans to increase the resolution of temperature profiles in the atmospheric boundary layer.</p>
Raw data for Ultrathin wide-bandgap a-Si:H based solar cells for transparent photovoltaic applications
<p>In the following the raw data lying the foundation of the paper “Ultrathin wide-bandgap a-Si:H based solar cells for transparent photovoltaic applications” (Lopez-Garcia et al.) published in Solar Rapid Research Letters, DOI: 10.1002/solr.202100909 (2021), are described. They were obtained under the funding provided by the European Union H2020 Framework Programme under Grant Agreement no. 826002 (Tech4Win) and by the Mater-One (Refs. PID 2020-116719RB-C42 and PID 2020-116719RB-C41) and SCALED (Ref. PID 2019-109215RB-C4) projects funded by the Spanish MCIN/AEI/10.13039/5011000110033.</p> <p>UV–vis measurements were acquired with a dual-beam spectrophotometer setup (Perkin Elmer Lambda L35) in transmittance mode (light source and detector normal to sample’s surface (i.e., 0<sup>o</sup>)) and in reflectance mode (with an Integrating sphere) scanning from 300 to 800 nm.</p> <p>J–V measurements under illumination were carried out using a homemade setup consisting on a AAA solar simulator calibrated using a NREL-certified Si reference solar cell (Abet Technologies, Model 15150). Electrical measurements were carried out with a source-measure unit (Keithley 2400) in four-wire sense mode, controlled by the software Tracer (ReRa solutions) using a IEEE 488 GPIB Instrument Control Device (National Instruments GPIB-USB-HS).</p>
Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Norway
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Luxembourg
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - the United kingdom
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Finland
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Sweden
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Data for - EU's bioethanol potential from wheat straw and maize stover and the environmental footprint of residue-based bioethanol
<p>To reduce greenhouse gas (GHG) emissions, the European Union (EU) has targets for utilizing energy from renewable sources. By 2030, a minimum of 3.5% of energy in the EU’s transport sector should come from renewable biological sources, such as crop residues. This paper analyzed EU’s “advanced bioethanol” potential from wheat straw and maize stover and evaluated its environmental (land, water, and carbon) footprint. We differentiated between gross and net bioethanol output, the latter by subtracting the energy inputs in production. Results suggest that the annual amount of the sustainably harvestable wheat straw and maize stover is 81.9 Megatonnes (Mt) at field moisture weight (65.3 Mt as dry weight), yielding 470 PJ as gross (404 PJ as net) advanced bioethanol output. Calculated net advanced bioethanol can replace 2.95% of EU transport sector’s energy consumption. EU’s advanced bioethanol has a land footprint of 0.28 m<sup>2</sup> MJ<sup>−1</sup> for wheat straw and 0.18 m<sup>2</sup> MJ<sup>−1</sup> for maize stover. The average water footprint of advanced bioethanol is 173 L MJ<sup>−1</sup> for wheat straw and 113 L MJ<sup>−1</sup> for maize stover. The average carbon footprint per unit of advanced bioethanol is 19.4 and 19.6 g CO<sub>2</sub>eq MJ<sup>−1</sup> for wheat straw and maize stover, respectively. Using advanced bioethanol can lead to emission savings, but EU’s advanced bioethanol production potential is insufficient to achieve EU’s target of a minimum share of 3.5% of advanced biofuels in the transport sector by 2030, and the associated water and land footprints are not smaller than footprints of conventional bioethanol.</p>
Raw Data of "Selective laser melting of a Fe-Si-Cr-B-C-based complex-shaped amorphous soft-magnetic electric motor rotor with record dimensions"
<p>This data set includes the RAW DATA of the publication. ABSTRACT: A record large amorphous rotor bearing an intricate 3D-geometry is produced through additive manufacturing via selecting laser melting using a powder of a traditional bulk metallic glass-forming composition of the Fe-Si-Cr-B-C system. Not only does this technique overcome the technical limitations characteristic of casting processes for amorphous alloys, but the possibility to print complex 3D geometries is expected to greatly facilitate the channeling of the magnetic flux, when such component is used as a rotor in an electric machine. The as-built part is characterized in comparison to the powder material as well as as-spun ribbons using a wide range of complementary techniques, including synchrotron x-ray diffraction, calorimetry, electron microscopy as well as room temperature ferromagnetic and hardness testing. The built part has extraordinarily high values of hardness (877 HV) and remarkable high magnetic susceptibility (9.17). This latter feature leads to a better magnetic response in the presence of an external magnetic field evidenced by a faster approach to saturation. The coercivity is small (0.51 kA/M) and the magnetic saturation relatively high (1.29 T). In addition, a large anisotropic effect on the magnetization reaction in connection with the partial crystallization in the melt pool areas is investigated experimentally.</p>
Processed Data of "Selective laser melting of a Fe-Si-Cr-B-C-based complex-shaped amorphous soft-magnetic electric motor rotor with record dimensions"
<p>This data set includes the processed data of the pubblication. ABSTRACT: A record large amorphous rotor bearing an intricate 3D-geometry is produced through additive manufacturing via selecting laser melting using a powder of a traditional bulk metallic glass-forming composition of the Fe-Si-Cr-B-C system. Not only does this technique overcome the technical limitations characteristic of casting processes for amorphous alloys, but the possibility to print complex 3D geometries is expected to greatly facilitate the channeling of the magnetic flux, when such component is used as a rotor in an electric machine. The as-built part is characterized in comparison to the powder material as well as as-spun ribbons using a wide range of complementary techniques, including synchrotron x-ray diffraction, calorimetry, electron microscopy as well as room temperature ferromagnetic and hardness testing. The built part has extraordinarily high values of hardness (877 HV) and remarkable high magnetic susceptibility (9.17). This latter feature leads to a better magnetic response in the presence of an external magnetic field evidenced by a faster approach to saturation. The coercivity is small (0.51 kA/M) and the magnetic saturation relatively high (1.29 T). In addition, a large anisotropic effect on the magnetization reaction in connection with the partial crystallization in the melt pool areas is investigated experimentally.</p>
Model output data and figures' code for Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security
<p>Model output data and figures' code for "Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security" in Nature Food (DOI: 10.1038/s43016-022-00464-4)</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)
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.