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30,334 results for “Responsiveness”
Cellular and Humoral Immune Responses after Immunisation with Low Virulent African Swine Fever Virus in the Large White Inbred Babraham Line and Outbred Domestic Pigs
<p>Raw data for manuscript. Contains temperature, clinical scores, qPCR, blood cell numbers and immune responses over time for two groups of pigs immunised with low virulent African swine fever virus and challenged with highly virulent virus. Data for each panel or figure is displayed on a separate worksheet in the file. The readme worksheet contains a brief description of each figure. The majority of data is displayed in an XY table format, with the number of days post immunisation with low virulent virus indicated.</p>
ASTRI Mini-Array Instrument Response Functions (Prod2, v1.0)
<p><strong>Aim:</strong></p> <p>This data repository provides access to a set of Instrument Response Functions (IRFs) of the ASTRI Mini-Array, saved in a FITS data file. The IRFs can be used as input to science analysis tools for high-level scientific analysis purposes.</p> <p><strong>Citations:</strong></p> <p>In the case the present ASTRI Mini-Array Instrument Response Functions (IRFs) are used in a research project, we kindly ask to add the following acknowledgement in any resulting publication:</p> <p>"This research has made use of the ASTRI Mini-Array Instrument Response Functions (IRFs) provided by the ASTRI Project [citation]."</p> <p>Please use the following BibTex Entry for [citation] in the reference section of your publication: <a href="https://zenodo.org/record/6827882/export/hx">https://zenodo.org/record/6827882/export/hx</a></p> <p><strong>Instrument:</strong></p> <p>The ASTRI Mini-Array is an international project led by the Italian National Institute for Astrophysics (INAF) to build and operate an array of nine 4-m class Imaging Atmospheric Cherenkov Telescopes (IACTs) at the <em>Observatorio del Teide</em> (Tenerife, Spain) [1]. The telescopes are an evolution of the dual-mirror ASTRI-Horn telescope, successfully installed and tested since 2014 at the INAF “M.C. Fracastoro” observing station in Serra La Nave (Mt. Etna, Italy) [2][3].</p> <p>The ASTRI Mini-Array is designed to perform deep observations of the galactic and extragalactic gamma-ray sky in the TeV and multi-TeV energy band, with a differential sensitivity that surpass the one of current Cherenkov telescope facilities above a few TeV, extending the energy band well above hundreds of TeV [4].</p> <p>The main science goals of the ASTRI Mini-Array in the very high-energy (VHE) gamma-ray band encompass both galactic and extragalactic science [5][6][7]. Important synergies with other ground-based gamma-ray facilities in the Northern Hemisphere and space-borne telescopes are foreseen.</p> <p><strong>Monte Carlo Simulations:</strong></p> <p>The IRFs of the ASTRI Mini-Array were obtained from a dedicated Monte Carlo (MC) production (dubbed ASTRI Mini-Array Prod2, version 1.0). Air showers initiated by gamma rays, protons and electrons were simulated using the CORSIKA package [8] (version 6.99), while the response of the array telescopes was simulated using the sim_telarray package [9] (version 2018-11-07).</p> <p>The layout of the ASTRI Mini-Array telescopes considered in the MC simulations is based on the actual telescope positions at the Teide Observatory site (28.30°N, 16.51°W, 2390 m a.s.l.). The nominal telescope pointing configuration, in which all telescopes point to the same sky position, was assumed in all MC simulations. Air showers produced by the primaries were simulated as coming from a zenith angle of 20° and an azimuth angle of 0° and 180° (corresponding to telescope pointing directions toward the geomagnetic North and South, respectively). Although not-negligible differences in performance (on the order of ≤15% at a zenith angle of 20°) are found between the two azimuthal pointing directions, the final IRFs were obtained by averaging between the two directions. Finally, all MC simulations were generated with a night sky background (NSB) level corresponding to dark sky conditions at the Teide Observatory site.</p> <p><strong>Monte Carlo data reduction and analysis:</strong></p> <p>The MC simulations were reduced and analysed with A-SciSoft [10][11] (version 0.3.1), the scientific software package of the ASTRI Project. The calibration and reconstruction of the MC events were achieved with the standard methods implemented in the data reduction pipeline (see [10][11] for more details). In particular, the background rejection and energy reconstruction were achieved with a procedure based on the Random Forest method [12], while the arrival direction of each shower was estimated from a weighted intersection of the major axes of the images from different telescopes. After the full reconstruction of the MC events, the background (proton and electron) events were re-weighted according to recent experimental measurements of their spectra, while gamma-ray events with a power-law gamma-ray spectrum with a photon index of 2.62. This approach follows a similar procedure adopted in [13].</p> <p>The final analysis cuts were based on the background rejection, shower arrival direction, and event multiplicity parameters. They were defined, in each considered energy bin and off-axis bin, by optimising the flux sensitivity for 50 hr exposure time. Then, five standard deviations (5σ, with σ defined as in Eq. 17 of [14]) were required for a detection in each energy bin and off-axis bin, considering the same exposure time (as in the cut optimization procedure) and a ratio of the off-source to on-source exposure equal to 5. In addition, the signal excess was required to be larger than 10 and at least 5 times the expected systematic uncertainty in the background estimation (assumed to be ∼1%). It should be noted that these analysis cuts, based on the best flux sensitivity, do not provide the best angular and energy resolution achievable by the system. Other analysis cuts, which take into account both differential flux sensitivity and angular/energy resolution in the cut optimization process, may actually provide better performance [4].</p> <p><strong>Instrument Response Functions (IRFs):</strong></p> <p>The IRFs are saved in a FITS data file [15] which contains the following quantities (FITS tables): effective collection area ("EFFECTIVE AREA" table), angular resolution ("POINT SPREAD FUNCTION" table), energy resolution ("ENERGY DISPERSION" table), and residual background rate ("BACKGROUND" table). These quantities are provided as a function of the energy and the off-axis. The energy bins are logarithmic and range between 10<sup>-0.7 </sup>~ 0.2 TeV and 10<sup>2.5 </sup>~ 316 TeV. Five energy bins per decade are used for the angular resolution and residual background rate, while ten energy bins per decade for the effective collection area. In the case of energy resolution, the energy migration matrix is provided with a much finer energy binning. The off-axis bins are linearly spaced between 0° and 6°, with a bin width equal to 1°. In the case of the residual background rate, a 2-dimensional squared spatial binning is used, which ranges between 0° and 6° with a bin width equal to 0.2° in each direction.</p> <p>The IRFs can be used as input to science analysis tools and, in particular, are compliant with the input/output (I/O) data format requested by the science analysis tools Gammapy [16] and ctools [17].</p> <p><strong>Dataset:</strong></p> <p>The dataset consists of one file: "astri_100_43_008_0502_C0_20_AVERAGE_50h_SC_v1.0.lv3.fits".</p> <p>The naming convention is: astri_[ARRAY_ID]_[ORIG_ID]_[REL_ID]_[PACKET_TYPE]_[CLASS_CUT]_[ZENITH]_[AZIMUTH]_[ EXPOSURE_TIME]_[AIM]_[VERSION].lv3.fits</p> <p>where:</p> <ul> <li>[ARRAY_ID] = 100 (100 = ASTRI Mini-Array with 9 telescopes)</li> <li>[ORIG_ID] = 43 (4 = INAF-OAR; 3 = AIV/AIT MC simulations)</li> <li>[REL_ID] = 008 (008 = MC prod2, v1.0)</li> <li>[PACKET_TYPE] = 0502 (0502 = IRF3)</li> <li>[CLASS_CUT] = C0 (C0 = cuts based on sensitivity maximisation)</li> <li>[ZENITH] = 20 [deg]</li> <li>[AZIMUTH] = AVERAGE [deg]</li> <li>[EXPOSURE_TIME] = 50h</li> <li>[AIM] = SC (SC = SCience)</li> <li>[VERSION]= v1.0</li> </ul> <p><strong>Acknowledgments:</strong></p> <p>This work was conducted in the context of the ASTRI Project thanks to the support of the Italian Ministry of University and Research (MUR) as well as the Ministry for Economic Development (MISE) with funds specifically assigned to the Italian National Institute of Astrophysics (INAF). We acknowledge support from the Brazilian Funding Agency FAPESP (Grant 2013/10559-5) and from the South African Department of Science and Technology through Funding Agreement 0227/2014 for the South African Gamma-Ray Astronomy Programme. The Instituto de Astrofisica de Canarias (IAC) is supported by the Spanish Ministry of Science and Innovation (MICIU). This work has also been partially supported by H2020-ASTERICS, a project funded by the European Commission Framework Programme Horizon 2020 Research and Innovation action under grant agreement n. 653477. This work has gone through the internal ASTRI review process.</p> <p>We would also like to thank the computing centres that provided resources for the generation of the Monte Carlo (MC) simulations used to produce the ASTRI Mini-Array Instrument Response Functions (IRFs) released in this work:</p> <ul> <li>CAMK, Nicolaus Copernicus Astronomical Center, Warsaw, Poland</li> <li>CIEMAT-LCG2, CIEMAT, Madrid, Spain</li> <li>CYFRONET-LCG2, ACC CYFRONET AGH, Cracow, Poland</li> <li>DESY-ZN, Deutsches Elektronen-Synchrotron, Standort Zeuthen, Germany</li> <li>GRIF, Grille de Recherche d’Ile de France, Paris, France</li> <li>IN2P3-CC, Centre de Calcul de l’IN2P3, Villeurbanne, France</li> <li>IN2P3-CPPM, Centre de Physique des Particules de Marseille, Marseille, France</li> <li>IN2P3-LAPP, Laboratoire d'Annecy de Physique des Particules, Annecy, France</li> <li>INFN-FRASCATI, INFN Frascati, Frascati, Italy</li> <li>INFN-T1, CNAF INFN, Bologna, Italy</li> <li>INFN-TORINO, INFN Torino, Torino, Italy</li> <li>MPIK, Heidelberg, Germany</li> <li>OBSPM, Observatoire de Paris Meudon, Paris, France</li> <li>PIC, port d’informacio cientifica, Bellaterra, Spain</li> <li>prague_cesnet_lcg2, CESNET, Prague, Czech Republic</li> <li>praguelcg2, FZU Prague, Prague, Czech Republic</li> <li>UKI-NORTHGRID-LANCS-HEP, Lancaster University, United Kingdom</li> </ul> <p><strong>References:</strong></p> <ol> <li>Scuderi, S. et al., "The ASTRI Mini-Array of Cherenkov telescopes at the Observatorio del Teide", Journal of High Energy Astrophysics 35, 52–68 (2022).</li> <li>Giro, E. et al., "First optical validation of a Schwarzschild Couder telescope: the ASTRI SST-2M Cherenkov telescope", A&A 608, A86 (Sept. 2017).</li> <li>Lombardi, S. et al., "First detection of the Crab Nebula at TeV energies with a Cherenkov telescope in a dual-mirror Schwarzschild-Couder configuration: the ASTRI-Horn telescope", A&A 634, A22 (Feb. 2020).</li> <li>Lombardi, S. et al., "Performance of the ASTRI Mini-Array at the Observatorio del Teide", in [37th International Cosmic Ray Conference. 12-23 July 2021. Berlin], 884 (Mar. 2022).</li> <li>Vercellone, S. et al., "ASTRI Mini-Array core science at the Observatorio del Teide", Journal of High Energy Astrophysics 35, 1–42 (2022).</li> <li>D’Aì, A. et al., "Galactic Observatory Science with the ASTRI Mini-Array at the Observatorio del Teide", Journal of High Energy Astrophysics 35, 139–175 (2022).</li> <li>Saturni, F. et al., "Extragalactic Observatory Science with the ASTRI Mini-Array at the Observatorio del Teide", Journal of High Energy Astrophysics 35, 91–111 (2022).</li> <li>Heck, D. et al., [CORSIKA: a Monte Carlo code to simulate extensive air showers.], Report FZKA 6019 (1998).</li> <li>Bernlöhr, K., "Simulation of imaging atmospheric Cherenkov telescopes with CORSIKA and sim_telarray", Astropart. Phys. 30, 149–158 (Oct. 2008).</li> <li>Lombardi, S. et al., "ASTRI SST-2M prototype and mini-array data reconstruction and scientific analysis software in the framework of the Cherenkov Telescope Array", in [Software and Cyberinfrastructure for Astronomy IV], Chiozzi, G. and Guzman, J. C., eds., Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series 9913, 991315 (July 2016).</li> <li>Lombardi, S. et al., "ASTRI data reduction software in the framework of the Cherenkov Telescope Array", in [Software and Cyberinfrastructure for Astronomy V], Guzman, J. C. and Ibsen, J., eds., Society of Photo- Optical Instrumentation Engineers (SPIE) Conference Series 10707, 107070R (July 2018).</li> <li>Breiman, L., "Random Forests", Machine Learning 45, 5–32 (Jan. 2001).</li> <li>Cherenkov Telescope Array Observatory, & Cherenkov Telescope Array Consortium. (2021). CTAO Instrument Response Functions - prod5 version v0.1 (v0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5499840</li> <li>Li, T.-P. and Ma, Y.-Q., "Analysis methods for results in gamma-ray astronomy", ApJ, 272, 317 (1983)</li> <li>Pence, W. D. et al., "Definition of the Flexible Image Transport System (FITS), version 3.0", A&A 524, A42 (Dec. 2010).</li> <li>Deil, C. et al., "Gammapy - A prototype for the CTA science tools", in [35th International Cosmic Ray Conference (ICRC2017)], International Cosmic Ray Conference 301, 766 (Jan. 2017).</li> <li>Knödlseder, J. et al., "GammaLib and ctools. A software framework for the analysis of astronomical gamma- ray data", A&A 593, A1 (Aug. 2016).</li> </ol>
Harmonized data and code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age"
<p>Harmonized data and R code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age" by Tonke Strack, Lukas Jonkers, Marina C. Rillo, Helmut Hillebrand and Michal Kucera (in <em>Nature Ecology & Evolution</em>, 2022, https://doi.org/10.1038/s41559-022-01888-8).</p> <p>Analyse planktonic foraminifera species assemblages from the North Atlantic Ocean over the past 24,000 years.</p> <p>Scripts written by Tonke Strack</p> <p>DATA SOURCES<br>* WOA18: Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov, Technical Editor. NOAA Atlas NESDIS 81, 52 (2019).<br>* LGMR: Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. Nature 599, 239-244, doi:10.1038/s41586-021-03984-4 (2021).<br>* MARGO: Kucera, M., Rosell-Melé, A., Schneider, R., Waelbroeck, C. & Weinelt, M. Multiproxy approach for the reconstruction of the glacial ocean surface (MARGO). Quat. Sci. Rev. 24, 813-819, doi:10.1016/j.quascirev.2004.07.017 (2005). Kucera, M. et al. Reconstruction of sea-surface temperatures from assemblages of planktonic foraminifera: multi-technique approach based on geographically constrained calibration data sets and its application to glacial Atlantic and Pacific Oceans. Quat. Sci. Rev. 24, 951-998, doi:10.1016/j.quascirev.2004.07.014 (2005).<br>* planktonic foraminifera assemblage data: individual citations provided in CoreList_PlanktonicForaminifera.csv</p> <p>DATA<br>1. Harmonized assemblage data*: FullDataTable_PF_harmonized.txt<br>2. Core list with additional information to time series: CoreList_PlanktonicForaminifera.csv<br>3. Reference list for PF names: ReferenceList_PlanktonicForaminifera.csv</p> <p>CODE<br>1. 01_DataAnalysis_PCA.R: principal component analysis on assemblage data of individual time series as well as on whole dissimilarity matrix (results shown in Fig. 1 and 2)<br>2. 02_DataAnalysis_LocalBiodiversityChange.R: local biodiversity change analysis of individual time series (results shown in Fig. 3 and Extended Data Fig. 1); also recalculates resolution of time-series<br>3. 03_DataAnalysis_NoAnalogueAssemblages.R: calculates compositional dissimilarity to the nearest LGM sample to analyse existence of no-analogues (results shown in Fig. 4, as well as Extended Data Fig. 3 and 4)<br>4. 04_DataAnalysis_LDG_LGMresiduals.R: visualises latitudinal diversity gradient through time and the difference between richness and Shannon diversity to their respective LGM mean values (results shown in Fig. 5)</p> <p>*Assemblage data of individual time series were manually downloaded, checked and harmonized following the taxonomy of Siccha and Kucera (2017) and combined into one data file. Species not reported in the time series data were assumed to be absent (i.e., zero abundance). We merged <em>Globigerinoides ruber ruber</em> and <em>Globigerinoides ruber albus</em>, because some studies only reported them together as <em>Globigerinoides ruber</em>. Also, P/D intergrades (an informal category of morphological intermediates between <em>Neogloboquadrina incompta</em> and <em>Neogloboquadrina dutertrei</em>) were merged with <em>Neogloboquadrina incompta</em>. In total, 41 species of planktonic foraminifera were included in this study.</p> <p>Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. <em>Sci. Data</em> 4, 170109, doi:10.1038/sdata.2017.109 (2017).</p>
Dataset: High emission rates and strong temperature response make boreal wetlands a large source of isoprene and terpenes
<p>Dataset used in the article "High emission rates and strong temperature response make boreal wetlands a large source of isoprene and terpenes"</p> <p>The tab-delimited file contains direct surface-atmosphere Volatile Organic Compound fluxes, measured by Eddy Covariance with a Vocus- proton transfer reaction mass spectrometer (Vocus-PTR) at a subarctic fen during 2021. It also contains PAR (Photosynthetic Active Radiation), temperature and flux quality criteria.</p>
Raw Data - Photo-Responsive Doped 3D-Printed Copper Electrodes for Water Splitting: Refractory One-Pot Doping Dramatically Enhances the Performance
<p>The dataset contains raw data that complements the article:</p> <p>Photo-Responsive Doped 3D-Printed Copper Electrodes for Water Splitting: Refractory One-Pot Doping Dramatically Enhances the Performance</p> <p>Christian Iffelsberger, Daniel Rojas, and Martin Pumera<strong>*</strong></p> <p>https://doi.org/10.1021/acs.jpcc.1c10686</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Machine Breakdown Event
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> an announcement of a machine breakdown event was simulated on Friday at 6:00, describing that machine MAQ119 could breakdown at any moment, detected using a predictive maintenance system. Accordingly, the proposed scheduler imposed a machine available frames constraint, during its event, of 0 usable frames, thus removing the machine from production. The proposed genetic algorithm was executed for 1 hour at period 769 (Friday at 7:00) until the remainder of the schedule’s time window. Also, the predefined optimization weights were 1 for total cost and 0 for machine occupancy deviation.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Machine_Breakdown_Optimization - JSON input data for the machine breakdown event</li> <li>Output_JSON_Machine_Breakdown_Optimization - JSON output data for the machine breakdown event</li> <li>Output_Statistics_Machine_Breakdown_Optimization - Excel output machine breakdown event statistics</li> </ul>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Maintenance Optimization
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> a maintenance optimization scenario was formulated to validate the scheduler's ability to schedule tasks as well as maintenance activities while also minimizing the total costs. Accordingly, it was considered an optimization weight of 1 for the total cost and 0 for machine occupancy deviation, as well as a 2-hour execution time for the genetic algorithm. Each maintenance activity, for every machine, has a duration of 6 hours and 10 minutes, with a labor cost of 3,22 EUR/hour during the stipulated maintenance hours and a monetary penalty, that doubles the cost (i.e., 6.44 EUR/hour) if done out of maintenance hours.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Maintenance_Optimization - JSON input data for the maintenance optimization</li> <li>Output_JSON_Maintenance_Optimization - JSON output data for the maintenance optimization</li> <li>Output_Statistics_Maintenance_Optimization - Excel output maintenance optimization statistics</li> </ul>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Total Cost and Machine Occupancy Deviation Optimization
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> a total cost and machine occupancy deviation optimization scenario was formulated that aims to demonstrate how the proposed scheduler is able to balance tasks between machines while also reducing overall costs. For this scenario, it was considered an optimization weight of 0.5 for both the total costs and machine occupancy deviation objectives and the genetic algorithm was executed for 2 hours.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON input data for the total cost and machine occupancy deviation optimization</li> <li>Output_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON output data for the total cost and machine occupancy deviation optimization</li> <li>Output_Statistics_Total_Cost_Machine_Occupancy_Deviation_Optimization - Excel output total cost and machine occupancy deviation optimization statistics</li> </ul>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Energy Cost Optimization with Energy Selling
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> an energy cost optimization considering the presence of an energy buyer is proposed to validate the scheduler’s ability to maximize profits while also minimizing energy costs. The scenario considers an added sales value corresponding to 50% of the buying. For this scenario, the genetic algorithm was executed for 2 hours, with 1 and 0 for the optimization weights total cost and machine occupancy deviation, respectively.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Energy_Cost_Energy_Selling_Optimization - JSON input data for the energy cost optimization with energy selling</li> <li>Output_JSON_Energy_Cost_Energy_Selling_Optimization - JSON output data for the energy cost optimization with energy selling</li> <li>Output_Statistics_Energy_Cost_Energy_Selling_Optimization - Excel output energy cost optimization with energy selling statistics</li> </ul>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Joint Optimization of Production and Maintenance
<p>Using the previous datasets at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>>, <<a href="https://zenodo.org/record/7055698">https://zenodo.org/record/7055698</a>>, <<a href="https://zenodo.org/record/7055580">https://zenodo.org/record/7055580</a>>, and <<a href="https://zenodo.org/record/7055573">https://zenodo.org/record/7055573</a>> a joint optimization of production and maintenance scenario was formulated which aims at combining all the features from the cited scenarios. For energy selling, it was considered an added sales value corresponding to 50% of the buying. Regarding maintenance activities, it was simulated an announcement of a maintenance activity for MAQ118 from Monday at 07:00 (i.e., period 1) to Monday at 17:00 (i.e., period 120), and another for MAQ120 which can be done at any time. These maintenance activities take 6 hours and 10 minutes to complete and have an associated labor cost of 3,22 EUR/hour in maintenance hours, and a double cost penalty (i.e., 6.44 EUR/hour) if done out of maintenance hours. The scenario was executed in 2 hours, with the corresponding optimization weights of 0.8 and 0.2 for the total cost and machine occupancy deviation, respectively.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Joint_Optimization_Production_Maintenance - JSON input data for the joint optimization of production and maintenance</li> <li>Output_JSON_Joint_Optimization_Production_Maintenance - JSON output data for the joint optimization of production and maintenance</li> <li>Output_Statistics_Joint_Optimization_Production_Maintenance - Excel output joint optimization of production and maintenance statistics</li> </ul>
Data set for "The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices"
<p>This data set was used for the modelling in the article M. Kölbach, O. Höhn, K. Rehfeld, M. Finkbeiner, J. Barry, and M. M. May, “The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices”<strong><em>,</em></strong> <em>Sustainable Energy Fuels</em>, <strong>2022</strong>, <strong>6</strong>, 4062-4074, <a href="https://doi.org/10.1039/D2SE00561A">https://doi.org/10.1039/D2SE00561A</a>.</p> <p>It contains the External Quantum Efficiency (EQE) data of a wafer-bonded AlGaAs//Si dual-junction solar cell for several top absorber compositions, angle of incidences, and temperatures modelled using the OPTOS formalism (see <a href="https://doi.org/10.1364/OE.24.0A1083">https://doi.org/10.1364/OE.24.0A1083</a> , <a href="https://doi.org/10.1364/OE.23.0A1720">https://doi.org/10.1364/OE.23.0A1720</a> , and <a href="http://doi.org/10.1109/JPHOTOV.2021.3064562"> https://doi.org/10.1109/JPHOTOV.2021.3064562</a>). Moreover, the data set includes hourly resolved direct and diffuse solar spectra for a location near the Neumayer station in Antarctica (-70.67°/-8.28°) that were modelled using the libRadtran software package for the year 2021 (see <a href="https://doi.org/10.1140/epjconf/e2009-00912-1">https://doi.org/10.1140/epjconf/e2009-00912-1</a> and <a href="http://doi.org/10.5194/acp-5-1855-2005">https://doi.org/10.5194/acp-5-1855-2005</a>). The modelling of the spectra was performed employing the predefined “subarctic summer” and “subarctic winter” atmosphere datasets assuming a tilt angle of 70° and 1-axis tracking. For the sake of simplicity, no cloud cover was assumed over the course of the whole year. Finally, the input files required for modelling the climatic response of solar water splitting devices for the selected location in Antarctica using the “climatic_response_function” of YaSoFo (see <a href="http://doi.org/10.5281/zenodo.5257492">https://doi.org/10.5281/zenodo.5257492</a> for an extended example) are included in the data set.</p>
Dataset for the ``Fast atmospheric response to a cold oceanic mesoscale patch in the north-western tropical Atlantic" publication
<p>The dataset presented here contains the files needed to produce the results presented in the publication "Fast atmospheric response to a SST mesoscale cold patch in the north-western subtropical Atlantic" submitted to the <em>Journal of Geophysical Research: Atmospheres</em>. The scripts that read and produce these files are publicly available at <a href="https://github.com/ClauClouds/SST-impact/">https://github.com/ClauClouds/SST-impact/</a> and can also be found in this repository (code_python.zip). This Zenodo data repository includes the following datasets:</p> <ul> <li> <p>Radiosonde data from 2-3 February 2020 (Stephan et al., 2021)</p> </li> <li> <p>Doppler lidar, and ARTHUS Raman lidar variables data from 2-3 February 2020,</p> </li> <li> <p>GOES-East (Geostationary Operational Environmental Satellite - East) Binary Cloud Mask (BCM) and Cloud Optical Depth (COD) products, provided at 2 km grid spacing every 10 minutes. They come from the GOES-R Advanced Baseline Imager (ABI) (Schmit et al., 2017), available at <a href="https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data">https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data</a> and they are provided for the 2-3 February 2020.</p> </li> <li> <p>Multi-scale Ultra-high Resolution (MUR) product (JPL MUR MEaSUREs Project, 2015,183 (Chin et al., 2017)) averaged between the 2nd and 3rdfor the 2nd of February 2020. The MUR product is an analysis product provided on a daily basis that combines different satellite (infrared at high and medium resolutions and microwave products) and in-situ data (Chin et al., 2017).</p> </li> <li> <p>W-band radar data post-processed for the purposes of the publication. The original W-band radar data used are publicly accessible at <a href="https://howto.eurec4a.eu/merian_cloudradar.html">https://howto.eurec4a.eu/merian_cloudradar.html</a> and can be downloaded via <a href="https://eurec4a.aeris-data.fr/">AERIS data portal</a>. See more details and specific DOI below.</p> </li> </ul> <p>The present dataset is structured as follows:</p> <ul> <li> <p>diurnal_cycle_removed_vars: files containing the time series of the variables without noise and diurnal cycle (filenames with extended dates 20200202 and 20200203)</p> </li> <li> <p>diurnal_cycle: files containing the diurnal cycle of each variable used in the publication</p> </li> <li> <p>binned_sst_vars: files containing variables binned in terms of SST, used to derive the plots in the paper.</p> </li> <li> <p>satellite_data: a folder containing all satellite data used in the publication</p> </li> </ul> <p>Additional data used in the publication, that are processed via the scripts contained in the link mentioned above, are available online at the following urls:</p> <ul> <li> <p>cloud radar observations can be directly obtained from the public dataset identifiable via DOI: <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a> (Acquistapace et al., 2022)</p> </li> <li> <p>ASCAT wind field data and corresponding MUR SST data are available from the NASA JPL PODAAC platform (<a href="https://podaac.jpl.nasa.gov/">https://podaac.jpl.nasa.gov/</a>)</p> </li> <li> <p>hourly ERA5 (Hersbach et al., 2020) gridded fields (available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, last accessed March 2022) of the following variables: SST, water vapor mixing ratio, air temperature, and horizontal wind components. </p> </li> </ul> <p><br> </p> <p>References;</p> <p>Acquistapace et al., 2022, ESSD, <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a>.</p> <p>Schmit, T. et al., 2017, QJRMS, <a href="https://doi.org/10.1175/BAMS-D-15-00230.1">https://doi.org/10.1175/BAMS-D-15-00230.1</a></p> <p>Hersbach et al., 2020, QJRMS, <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803">https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803</a></p> <p>Stephan et al., 2021, ESSD, <a href="https://doi.org/10.5194/essd-13-491-2021">https://doi.org/10.5194/essd-13-491-2021</a></p> <p>Chin, T. M. et al., (2017), RS, <a href="https://doi.org/10.1016/j.rse.2017.07.029">https://doi.org/10.1016/j.rse.2017.07.029</a></p>
Global forest cover loss tipping points leading to changing hydrologic responses
<p>This dataset describes the methods used to develop the results for study entitled: Global forest cover loss tipping points leading to changing hydrologic responses.</p> <p>EVENTS_List_45.docx is a table describing each deforestation event used for the study</p> <p>MATLAB Script 1: Plotting Hydrologic Sensitive Area against Tree cover loss every 10 % tree cover loss for all 45 events and adjusting Richard's curve function to obtain the parameters. This script uses EXCEL SHEET: HSiaresults.xlsx</p> <p>MATLAB Script 2: Computing the critical points of acceleration based on the Richards curve parameters. This script uses the parameters or results obtained in Script one.</p> <p>MATLAB Script 3: Plotting the climate and water yield direction against tree cover loss. This script used EXCEL SHEET: direction.xlsx</p>
Dataset: Auditory brainstem responses to varying stimulus presentation rates of 12 bat species in the wild and captivity
<p>Dataset for the Dataset Publication: Auditory brainstem responses to varying stimulus presentation rates of 12 bat species in the wild and captivity</p> <p>There are two datasets available: 1) the measured ABRs from Experiments 1 and 2 and 2) the extracted IOIs:</p> <ol> <li>ABR measurements:</li> </ol> <p>The filename of the ABR recordings from Experiment 1 include the species name, individual ID, sex, stimulus presentation rate (indicated as “modrate”) and recording day and time (yyyyddmm). Each recording file contains 256 measurements of the same stimulus and stimulus presentation rate in columns. An exemplary filename would be “Carollia_perspicillata_cp6male_modrate6_20190905T125903”, meaning that this is a recording of <em>Carollia perspicillata</em> individual cp6 of sex male, tested with a stimulus presentation rate of 6 Hz on the 09.05.2019, and the file was saved at 12:59:03 (the T between date and time stands for “Time”).</p> <p>The filename of the ABR recordings from Experiment 2 include the place of the Experiments (Bad Segeberg) and species name (<em>C. perspicillata</em>), individual ID, sex, stimulus presentation rate (indicated as “modrate”) and recording day and time (yyyymmdd; be aware, that the date format is different between Experiment 1 and 2). Each recording file contains 256 measurements of the same stimulus and stimulus presentation rate in columns. An exemplary filename would be “BadSegeberg_cper_1_male_modrate6_20200622T140952_stimulus_ST01_short ”, meaning that this is a recording of <em>Carollia perspicillata</em> individual cp6 of sex male, tested with a stimulus presentation rate of 6 Hz on the 09.05.2019, and the file was saved at 12:59:03 (the T between date and time stands for “Time”), the individual was presented with stimulus example 01 of the short stimuli.</p> <ol> <li>IOI recordings</li> </ol> <p>The recordings of Inter-Onset-Intervals are all in one single csv file and species and sequence ID is given per row, to be able to analyze the data further.</p> <p> </p>
Datasets for the paper "Finite element modelling of the vibro-acoustic response in dielectric elastomer membranes"
<p>This upload contains the numerical datasets used for the numerical plots reported in the paper "Finite element modelling of the vibro-acoustic response in dielectric elastomer membranes" by G. Moretti et al., In <em>Electroactive Polymer Actuators and Devices (EAPAD) XXIV</em> (https://doi.org/10.1117/12.2612784).</p> <p>Please refer to the readme file for information on the files structure and content. </p>
Earthquake Response of Reinforced Concrete Frames with Infill and Active External Confinement: Tests and Dataset
<p>One option to retrofit reinforced concrete (RC) frames is the construction of infill walls. Many studies have shown that infill increases lateral strength and stiffness but tends to reduce drift capacity relative to bare frames. Fewer studies have quantified reductions in drift demand attributed to infills prior to failure. This report summarizes experiments designed to compare drift demands of frames with and without infill. Included data comes from two theses completed at Purdue University which focused on the dynamic response of one-third scale, non-ductile RC frames to uniaxial simulated earthquake ground motions. Tests were conducted on bare frames, frames with masonry infill walls, and frames with timber infill walls. In 11 of 14 test series, active confinement was applied to columns using external post-tensioned reinforcement</p> <p> </p> <p>This dataset summarizes two experimental programs that studied the dynamic, in-plane response of one-third scale RC frames with full-height infills and active external column confinement. Theses summarized were written by Monical (2021) and Kerby (2022), and included data from 254 in-plane dynamic tests of non-ductile RC frames with various seismic retrofits.</p>
Diallel analysis reveals Mx1-dependent and Mx1-independent effects on response to influenza A virus in mice
<p>Data and analysis files for diallel analysis of weight loss in 8-12 week old male and female mice (n=1,043), mock treated or infected with influenza A virus (H1N1, PR8) across 4 days post-infection, as well as founder haplotype effect analysis at Mx1 for pre-CC and CC-RIX.</p>
dataset: Responses of the structure and function of the understory plant communities to precipitation reduction across forest ecosystems in Germany
<p><strong>Context</strong>: Understory plant communities play a central role in forest biogeochemistry and the recruitment of trees making up the future forest. It is so far poorly understood how climate change will affect understory structure and functions in forest of different management intensity.</p> <p> </p><p><strong>Aims</strong>: We monitored understory functional traits including transpiration and carbon isotope discrimination, community structure and diversity during two growing seasons as affected by drought in forests subjected to different management intensities. We hypothesized that drought would affect ecophysiological traits such as transpiration but not species richness and diversity. Moreover, we assumed that stand-specific characteristics and forest management intensity modify the drought-resistance of the understory community.</p> <p></p> <p><strong>Methods</strong>: We set up roofs in beech and conifer stands with different management intensity in three different regions across Germany and a drought event close to the 2003 drought was imposed in two consecutive years.</p> <p><strong>Results</strong>: Precipitation reduction decreased soil water content by 2 to 8%, depending on stand and region, in comparison to the control subplots. In the first year, leaf level transpiration was reduced for different functional groups, which scaled to community transpiration modified by additional effects of drought on functional group specific leaf area. Acclimation effects in most functional groups were observed in the second year. We did not observe a significant reduction of plant diversity or a consistent management effect upon drought.</p> <p><strong>Conclusion</strong>: Our results indicate high plasticity and acclimation responses of the forest understory vegetation to changing climate conditions and recurrent drought events.</p> <p><strong>Abbreviations:</strong></p> <p>sp12 - campaign spring 2012; ls12 - campaign late summer 2012; es13 - campaign early summer 2013; ls13-campaign late summer 2013</p> <p>SEW16 - Schorfheide plot 16; SEW49 - Schorfheide plot 49; SEW48 - Schorfheide plot 48;HEW03 - Hainich plot 03; HEW12 - Hainich plot 12; HEW47- Hainich plot 47; AEW13 - Alb plot 13; AEW29 - Alb plot 29; AEW08 - Alb plot 08<br> explo - exploratory<br> SEW - Schorfheide; HEW - Hainich; AEW - Schwäbische Alb<br> in - conifer intensive managed; ma - beech managed; un - beech unmanaged<br> c- control; r - roof<br> LAIs - community leaf area index m<sup>2</sup>/m<sup>2</sup>; H - Shannon´s diversity index; Ts - community transpiration rate (weighted by LAI) mmol H<sub>2</sub>O m-<sup>2</sup> leaf area s-<sup>1</sup>; Ets - Evapotranspiration (mmol/m2/sec); E - Evaporation (mmol/m2/sec); C - leaf photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982); Cs - community photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982) (weighted by LAI)</p> <p> </p>
Dataset for: Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses
<p>This repository contains supplementary material for the paper titled `Application of Machine Learning for the Spatial<br> Analysis of Binaural Room Impulse Responses' Available at: <a href="http://dx.doi.org/10.3390/app8010105">dx.doi.org/10.3390/app8010105</a> . These programs and audio files are distributed in the hopes that they will prove useful under the Creative Commons Attribution 4.0, with no warranty; or the implied warranty of merchantability or fitness for a particular problem. Please give appropriate credit for use of the material provided in this repository back to the author. </p> <p>In order to use the MatLab code the Auditory Toolbox by Malcolm Slaney [1] and the Cochleagram function distributed by Bin Gao [2] are required.</p> <p>The python scrips require the following Python libraries to be installed: Numpy[3], SciPy[4] and Tensorflow [5].</p> <p>The MatLab code was tested using MatLab R2017a on a Computer running windows 7.</p> <p>The python code was tested using Python 3.2.5, using an anaconda Python environment - in windows command line.</p> <p>--</p> <p>The repository contains:</p> <p>Folders:</p> <p><br> 1.) neg90 - This folder contains the gaussian normalisation parameters stored as text files and the weights and biases for the trained neural network - these are all for the -90° rotation neural network.</p> <p>2.) pos90 - This folder contains the gaussian normalisation parameters stored as text files and the weights and biases for the trained neural network - these are all for the +90° rotation neural network.</p> <p>3.) testData - this folder contains pre-generated test data for the different binaural dummy head microphones, speaker, and signal type combinations.</p> <p>Python Scripts:</p> <p><br> 1.) AnalyseDoA.py - A python script that can be run to test the neural network using the pre-generated test data - running the script will allow the user to input the binaural dummy head, speaker, and signal type. The important variables generated by this script are DoA - the direction of arrival for each signal in the feature vector, and yDiff - the difference between the predicted DoA and the expected direction of arrival</p> <p>2.) DirectionAnalysis.py - This python file contains a set of function that are used to define the neural network, and run it. The function called DoAPrediction takes the feature vector generated by the MatLab code as its input argument, these features will then be passed to the neural network, and the output of this function is the direction of arrival predicted by the neural network for each signal. The functions: DoAAnalysis_neg90 and DoAAnalysis_pos90 are called by the DoAPrediction function, these functions create the neural network using the NN function, import the weights and biases, and passes the feature matrix (provided as input) through the neural network - the output of these functions are the predicted direction of arrival.</p> <p>MatLab files:</p> <p><br> 1.) runAnalysis.m - This MatLab script analyses the dataset provided as part of this repository. Users can change the variables head ('KEMAR' or 'KU100'), signalType ('directSound' or 'reflection'), and speaker ('EquatorD5' or 'Genelec8030'). This script will produce the gaussian normalised feature vector and expected direction of arrival for all signals with the defined head, signal type, and speaker combination. These variables are then saved in .mat files so they can be imported by the python scripts.</p> <p>2.) BinauralModelCochlea.m - This MatLab function analyses a given binaural signal and outputs the interaural cross-correlation, interaural level difference, interaural time difference, the cochlea output for the left and right channel and the centre frequencies of the gammatone filter band. The input variables are: IR - the signal to be analysed, N - the number of gammatone filters, freqLow - the lowest centre frequency of the gammatone filter bank (centre frequency of the first gammatone filter), and freqHigh - the highest centre frequency of the gammatone filter bank (the centre frequency of the Nth gammatone filter). This function requires Malcolm Slaney's Auditory Toolbox [1] and Bin Gao's Cochleagram function [2] in order to work.</p> <p>3.) generateFeatureVector.m - This MatLab function generates a feature vector from an input binaural signal x, and a version of the signal captured after the binaural dummy head has been rotated by either +90° or -90° degree (variables xPos90 and xNeg90 respectively). If the sampling frequency (Fs) isn't 44100, the signals are resampled to be at 44100. This file also contains a function 'gaussianNormalisationTestData' which gaussian normalises the data using the mean and standard deviation calculated from the data used to train the neural networks - the mean and standard deviation values are stored in the folder GMParams in the pos90 and neg90 folders.</p> <p>4.) generateTestData.m - This MatLab function analyses the included binaural dataset, it takes the input variables: head - the binaural dummy head used for the measurements either 'KEMAR' or 'KU100', speaker - the speaker used for the measurements either 'EquatorD5' or 'Genelec8030', and signalType - the type of signal being analysed either 'directSound' or 'reflection'.</p> <p>Text files:</p> <p><br> 1.) noLayers.txt - a text file containing the number of layers used when training the neural network - with the current version of the code the neural network contains only 1 layer.</p> <p>2.) README.txt - Read me file containing information about the repository.</p> <p>Audio files:</p> <p><br> This repository contains 1152 binaural signals half of which are direct sounds segmented from a binaural room impulse responses and the other half are reflections segmented from binaural room impulse responses (detailed in the paper this material supports) the direct sounds are recorded at angles from 0° to 357.5° in steps of 2.5° and the reflections are recorded at angles of 1° to 358.5° in steps of 2.5°. In the paper only recordings relating to signals recorded with the Equator D5 are analysed.</p> <p>The combination of audio files include:</p> <p>1.) 144 direct sound recordings captured with the KEMAR 45BC binaural dummy head microphone and the Equator D5 speaker<br> 2.) 144 reflection recordings captured with the KEMAR 45BC binaural dummy head microphone and the Equator D5 speaker<br> 3.) 144 direct sound recordings captured with the KU100 binaural dummy head microphone and the Equator D5 speaker<br> 4.) 144 reflection recordings captured with the KU100 binaural dummy head microphone and the Equator D5 speaker<br> 5.) 144 direct sound recordings captured with the KEMAR 45BC binaural dummy head microphone and the Genelec 8030 speaker<br> 6.) 144 reflection recordings captured with the KEMAR 45BC binaural dummy head microphone and the Genelec 8030 speaker<br> 7.) 144 direct sound recordings captured with the KU100 binaural dummy head microphone and the Genelec 8030 speaker<br> 8.) 144 reflection recordings captured with the KU100 binaural dummy head microphone and the Genelec 8030 speaker</p> <p>The files are stored using the following file naming convention:<br> head_Test3_speaker_signalType_000_0_Degrees.wav - where _000_0 defines the azimuth direction of arrival so for example for a direct sound measured with the KEMAR unit and the Genelec8030 at 5 degrees would be 'KEMAR_Test3_Genelec8030_directSound_005_0Degrees.wav' and for a reflection measured with the KU100 and the Equator D5 at 298.5 degrees would be 'KU100_Test3_EquatorD5_reflection_298_5Degrees.wav'</p> <p>--</p> <p>Bibliography:<br> [1] Slaney, M. (1998). Auditory Toolbox. Palo Alto, CA. [Online]. Available: https://engineering.purdue.edu/~malcolm/interval/1998-010/ [Accessed: Oct. 27, 2017]</p> <p>[2] Gao, B. (2014). Cochleagram and IS-NMF2D for Blind Source Separation. [Online] Available: http://uk.mathworks.com/matlabcentral/fileexchange/48622-cochleagram-and-is-nmf2d-for-blind-source-separation?focused=3855900&tab=function [Accessed: Oct. 27, 2017]</p> <p>[3] NumFocus. (n.d.). NumPy. [Online]. Available: http://www.numpy.org/ [Accessed: Oct. 27, 2017]</p> <p>[4] SciPy. (n.d.). SciPy. [Online]. Available: https://www.scipy.org/ [Accessed: Oct. 27, 2017]</p> <p>[5] Google. (n.d.). TensorFlow. [Online] Available: https://www.tensorflow.org/ [Accessed: Oct. 27, 2017]</p> <p>--</p> <p>All code and audio produced by: Michael Lovedee-Turner, PhD candidate in Music Technology at the Audio Lab, Department of Electronic Engineering, University of York</p> <p>Contact: mjlt500@york.ac.uk</p>
Data: Stability and biological response of PEGylated gold nanoparticles
<p><span>This dataset is focused on thermal stability of PEGylated Au NPs at 4 and 37 °C and after sterilization in autoclave.</span></p>
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.