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78 results for “generation time”
An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times
<p>Catalog of 440,697 earthquakes of the 2016-2017 Central Italy seismic sequence semi-automatically generated by Spallarossa et al. (2020). The catalogue covers one year of aftershocks following the first mainshock of the sequence (from 08242016 to 08312017).</p> <p>The catalog has been generated using the Complete Automatic Seismic Processor (CASP) procedure (Scafidi et al., 2019) to detect the events and an advanced picker engine (RSNI-Picker<sub>2</sub>; Scafidi et al., 2018; Spallarossa et al., 2014) to determine their phase arrival times. The final set of about 7 million P- and 10 million S-wave arrival times have been used to locate the events using a non-linear location algorithm (NonLinLoc; Lomax et al. 2000), with a 1D velocity model calibrated for the area (De Luca et al., 2009) and station corrections. For each event, also local magnitudes (M<sub>L</sub>) has been calculated as well as a locations quality.</p> <p>Earthquake locations quality has been classified by means of the procedure proposed by Michele et al., (2019) consisting of the combination of diverse uncertainty parameters provided by the NonLinLoc location code. Locations quality is provided in terms of a unique numeric normalized value, named quality factor, varying between qf=0 (best quality location) and qf=1 (worst quality location). Then locations have been assigned to a quality class depending on the qf parameter value according to the following scheme: A-class (0 < qf ≤ 0.25), B-class (0.25 < qf ≤ 0.50), C-class (0.50 < qf ≤ 0.75), and D-class (0.75 < qf < 1.00). The earthquake locations are distributed between the quality classes as A-30.6%, B-31.4%, C-18.6%, and D-19.4% (details in Spallarossa et al., 2020).</p> <p>We accompanied the catalogue with the 30 events with M>3.5 missed by our procedure (bring the total number of events to 440,727), including the first Amatrice mainshock (M<sub>W</sub>6.0; see Spallarossa et al., 2020). These 30 missing events recognisable by the ID starting with ISI), have been taken from INGV bulletin (<a href="http://terremoti.ingv.it">http://terremoti.ingv.it</a>; ISIDe Working Group., 2007), manually generated. These additional events report INGV locations and magnitude parameters while are missing related quality factors and quality class, being generated by a different procedure.</p> <p>We added to the larger events, the available moment magnitudes (M<sub>W</sub>) from Time Domain Moment Tensor catalogue (<a href="http://terremoti.ingv.it/tdmt">http://terremoti.ingv.it/tdmt</a>; Scognamiglio et al., 2006).</p> <p>The catalog is in csv format, semicolon separator, ordered by origin time and the header content is the following:</p> <ul> <li>Id-event – ID</li> <li>Latitude (°) expressed in decimal degrees - LAT</li> <li>Longitude (°) expressed in decimal degrees - LON</li> <li>Depth(km) hypocentral depth expressed in kilometres - DEP</li> <li>Year of origin time in the format yyyy - YR</li> <li>Month of origin time in the format mo - MON</li> <li>Day of origin time in the format dd - DY</li> <li>Hour of origin time in the format hh - HR</li> <li>Minute of origin time in the format mi - MIN</li> <li>Second of origin time in the format XX.XXX s - SEC</li> <li>Local Magnitude - ML</li> <li>Standard deviation of the Local Magnitude – STD</li> <li>Moment Magnitude – Mw (from TDMT)</li> <li>Horizontal Error (from NLL output) (km) expressed in kilometres - ERH</li> <li>Vertical Error (from NLL output) (km) expressed in kilometres - ERZ</li> <li>RMS (from NLL output) (s) expressed in seconds - RMS</li> <li>Number of Phases – NPHS</li> <li>Stations Azimuthal GAP (°) expressed in decimal degrees - GAP</li> <li>Quality factor - Qf</li> <li>Quality class - Qc</li> </ul> <p> </p> <p>De Luca G., M. Cattaneo, G. Monachesi and A, Amato (2009). Seismicity in the Umbria-Marche region from the integration of national and regional seismic networks. Tectonophysics, 476(1), 219-231. doi: 10.1016/j.tecto.2008.11.032.</p> <p>ISIDe Working Group. (2007). Italian Seismological Instrumental and Parametric Database (ISIDe). Istituto Nazionale di Geofisica e Vulcanologia (INGV); https://doi.org/10.13127/ISIDE.</p> <p>Lomax, A., J. Virieux, P. Volant, and C. Berge-Thierry (2000). Probabilistic earthquake location in 3D and layered models: introduction of a Metropolis–Gibbs method and comparison with linear locations. In: Advances in seismic event location, ed. C. H. Thurber and N. Rabinowitz, 101–134. Dordrecht and Boston: Kluwer Academic Publishers.</p> <p>Michele, M., Latorre, D., Emolo, A. (2019). An Empirical Formula to Classify the Quality of Earthquake Locations. Bulletin of the Seismological Society of America. Vol. 109, No. 6, pp. 2755–2761, December 2019, doi: 10.1785/0120190144.</p> <p>Scafidi, D., Viganò A., Ferretti G., and Spallarossa D. (2018). Robust picking and accurate location with RSNI-Picker2: real-time automatic monitoring of earthquakes and non-tectonic events, Seismol. Res. Lett, Vol. 89 (4), pp. 1478-1487, doi: 10.1785/0220170206.</p> <p>Scafidi D, Spallarossa D, Ferretti G, Barani S, Castello B, Margheriti L (2019). A complete automatic procedure to compile reliable seismic catalogs and travel-time and strong-motion parameters datasets. Seismol Res Lett 90(3):1308–1317.</p> <p>Scognamiglio, L., Tinti, E., Quintiliani, M. (2006). Time Domain Moment Tensor [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/TDMT.</p> <p>Spallarossa, D., G. Ferretti, D. Scafidi, C. Turino, and M. Pasta (2014). Performance of the RSNI-Picker, Seismol. Res. Lett. 85, 1243–1254.</p> <p>Spallarossa D., Cattaneo M., Scafidi D., Michele M., Chiaraluce L., Segou M. and I. G. Main (2020). An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times. Geophys. J. Int. doi: 10.1093/gji/ggaa604.</p>
Load and renewable generation time series for selected regions in Africa and Eurasia
<p>The data set consist of three groups of yearly time series for 2003-2012 used in grid modelling. All time series are presented in .csv format. The data covers wind and PV power generation and load of 12 regions. The regions have 2 letter identifier and include the following:</p> <p>EU - EU countries</p> <p>NA - Algeria, Egypt, Libya, Morocco, Tunisia</p> <p>WA - Cameroon, Ghana, Nigeria</p> <p>EA - Ethiopia, Kenya, Tanzania, Uganda</p> <p>SA - South Africa</p> <p>RU - European part of Russia, Belarus, Ukraine</p> <p>MS - Arabic countries, Israel, Turkey</p> <p>IP - Bangladesh, India, Pakistan, Sri Lanka</p> <p>SB - Asian part of Russia, Kazakhstan, Turkmenistan, Uzbekistan</p> <p>IC - Indonesia, Malaysia, Philippines, Thailand, Vietnam</p> <p>CN - China</p> <p>FE - South Korea, Japan</p> <p> </p> <p>Wind power time series are derived from MERRA wind speed data for Enercon E-126 wind turbine.</p> <p>PV power time series are derived from MERRA solar surface irradiance data.</p> <p>Load time series are generated with the periodical function.</p> <p> </p> <p>Data for EU was derived within the project RESTORE 2050 as described in</p> <p>Kies, A., Chattopadhyay, K., von Bremen, L., Lorenz, E., & Heinemann, D. (2016). Simulation of renewable feed-in for power system studies.</p> <p>Data for all other nodes was derived as described in</p> <p>Krutova, M. et al., The smoothing effect for renewable resources in an Afro-Eurasian power grid, Adv. Sci. Res., 2017</p> <p>Load for EU is obtained from ENTSO-E and is not included in this data set.</p>
The Effect of a New Generation of Ankle Foot Orthoses on Sloped Walking in Children with Hemiplegia Using the Gait Real Time Analysis Interactive Lab (GRAIL)
<div>The dataset includes the kinematics and kinetics of gait uphill (+10 deg), level ground (0 deg), and downhill (-5 deg) in children with unilateral cerebral palsy who underwent a single session of walking using immersive virtual reality (GRAIL system by Motek), while wearing traditional ankle-foot orthosis (oldAFOs) and a new generation AFO (newCAMOt1).</div> <div>In column A of the file, you will find the patient ID, the type of orthosis used, and the walking condition (e.g., P01_oldAFO_flat0001 indicates that patient 01 performed the walking trial on level ground with the traditional orthosis).</div> <div>In column B of the file, you will find the laterality (R = right, L = left), the name of the analyzed variable, and the stride number (e.g., L_Moment Ankle Flex_step1 means that we are considering the moment at the left ankle of the first left stride).</div> <div>From column C to column CY, you will find the values at the 101 temporal instants of the analyzed variable.</div>
Influence of Altitudinal Gradient on the Voltinism and Generation Time of Cydia pomonella (Lepidoptera: Tortricidae) in walnut trees in northwestern Argentina
<p><span><span>11-<span> </span></span></span><em><span>Cydia pomonella L</span></em><span>. causes significant economic loss in the global fruit-growing industry. Its biology is influenced by environmental factors which have an impact on its voltinism in relation to its habitat.</span></p> <p><span>22- <span>The influence of the altitudinal gradient on the number and duration of <em>C. pomonella</em> populations on walnut trees grown at latitudes below 29° was determined. The relation between temperature and population curves was evaluated by an altitudinal gradient.</span></span></p> <p><span><span>33- </span></span><span><span><span> </span></span></span><span>A higher number of generations was quantitatively recorded in a shorter time on orchards located at lower altitudes. Walnut trees grown at higher altitudes, with lower temperatures, showed less pest pressure. The “Predictive Extension Timing Estimator”, used in several regions for the management of <em>C. pomonella</em>, does not accurately predict the length of the generation time in plantation crops at lower altitudes, requiring further studies in this area.</span></p> <p><span>44- </span><span>The results are relevant for designing of management strategies for walnut trees grown at latitudes below 29°, where environmental conditions differ considerably from other latitudes that were studied, impacting on the population dynamics of <em>C. pomonella</em>.</span></p> <p> </p>
Load and generation time series for German federal states: Static vs. dynamic regionalization factors (data)
<p>This dataset contains regionalization factors for electricity generation and demand time series in Germany for the years 2019 - 2022. The factors can be used to distribute national generation and demand time series available from SMARD or ENTSO-E to federal state level. The methods underlying the regionalization factors are described in [1], with a focus on the year 2021. However, an extended version of the dataset covering the years 2019-2022 is also included for comprehensive analysis. Moreover, the dataset comprises the corresponding regionalized generation and demand time series at the federal state level of Germany. This time series has been generated using the provided distribution factors for the years 2019-2022 and corresponding generation and demand time series from SMARD [2]. Addtionally, the regionalization methodology for the distributed generation and demand data for the year 2021 has been supplemented with validation data, as described in [1]. This data has been cross-checked against the available SMARD Transmission System Operator (TSO) data. A description of the preprocessing required to obtain the TSO data comparison is provided in a separate .txt file. A PDF document has been prepared, which includes scatter plots that illustrate a comparison between actual and allocated generation per production type or demand data for TSOs on an hourly basis for the year 2021.</p> <p><strong>"static_regionalization_factors.2021[csv, xlsx]"</strong></p> <p>Each column corresponds to one factor per federal state and per production type or demand. Regionalization factors are based on share of generation capacity in each state (generation) or population and GDP (demand).</p> <p><strong>"dynamic_regionalization_factors_2021.[csv, xlsx]"<br> “dynamic_regionalization_factors_all.[csv, xlsx]”</strong></p> <p>Each column corresponds to one factor per federal state and per production type or demand. Each row corresponds to a specific hour of the years 2019 through 2022. Regionalization factors are based on a combination of per unit generation data and share of generation capacity in each state, simulated renewable generation data based on spatio-temporal weather data and distribution of wind and solar generation capacities, and a regionalized load dataset for 2015 [3].</p> <p><strong>“time_series_federal_states_all.[csv, xlsx]”</strong></p> <p>Each column corresponds to the allocated electricity generation or demand per federal state per production type or demand in units of MWh. Each row corresponds to a specific hour of the years 2019 through 2022. The regionalized generation and demand time series has been created by utilizing the dynamic regionalization factors provided in the dataset, in conjunction with the national electricity generation and demand data of Germany as provided by SMARD [2].</p> <p><strong>“TSO_actual.[csv, xlsx]”<br> “TSO_allocated.[csv, xlsx]”</strong></p> <p>Each column corresponds to the spatially aggregated electricity generation per type or demand per TSO in units of GWh. Each row corresponds to one hour of the year 2021. The TSOs in Germany do not hold direct responsibility for individual federal states, but rather for specific regions. In order to assess the validity of the regionalization methodology employed, it was necessary to generate data at the NUTS3 level and subsequently aggregate it to correspond with the relevant TSOs. The data is pre-processed at NUTS3 level and then undergoes the same methodology as outlined in [1]. The preprocessing steps required to map the installed capacity to the TSO level are explained in the accompanying .txt file. The allocated generation and demand data are aggregated to correspond to the TSO level using a shapefile of mapped regions in Germany that correspond to the TSOs [4]. The actual TSO data is generation and demand as published by SMARD [2]. The accompanying PDF presents scatter plots that showcase the actual vs allocated hourly generation types or demand per TSO, expanding on the information provided in the article.</p> <p>[1] M. Sundblad, T. Fürmann, A. Weidlich and M. Schäfer, "<a href="https://arxiv.org/abs/2304.02951">Load and generation time series for German federal states: Static vs. dynamic regionalization factors</a>," <em>2023 Open Source Modelling and Simulation of Energy Systems (OSMSES)</em>, Aachen, Germany, 2023, pp. 1-6, doi: 10.1109/OSMSES58477.2023.10089686.</p> <p>[2] Bundesnetzagentur | <a href="https://www.smard.de/home">SMARD.de</a></p> <p>[3] Matthias Kühnbach, Anke Bekk, and Anke Weidlich (2021). <a href="https://www.forecast-model.eu/forecast-en/content/publications.php">Prepared for regional self-supply? On the regional fit of electricity demand and supply in Germany</a>. Energy Strategy Reviews, 34:100609, 20</p> <p>[4] Frysztacki, Martha Maria. (2023). Mapping of districts to control zones of German Transmission System Operators (TSOs) (v0.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7530196">https://doi.org/10.5281/zenodo.7530196</a></p>
Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses- Dataset
<p>This folder contains raw data and information to reproduce the findings of the article titled 'Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses' . Each folder corresponds to a figure in the article.</p> <p>Raw data is provided for the calculations along with the input file and output log of the calculation. When raw data is too large, it is possible to reproduce the calculation from the provided input file. Calculations are performed with <a href="https://octopus-code.org/documentation/12/">Octopus code</a> , the related version and commit number of the code can be retrieved from output log file provided in calculation folder.</p>
Data and analysis code of "Forestation at the right time with the right species can generate persistent carbon benefits in China"
<p>This collection contains the datasets used in our study ‘<strong><em>Forestation at the right time with the right species can generate persistent carbon benefits in China</em></strong>’.</p> <p> </p> <p><strong><em>Part A: Data</em></strong></p> <p>Most of the data presented here are after pre-processing, such as transforming the projection, extracting variables, clipping to the study region (70<sup>o</sup>E-140<sup>o</sup>E,15<sup>o</sup>N-55<sup>o</sup>N), and resampling to 1-km.</p> <p>The original source of these data sets (usually global, at different resolutions) is given in 'data_original.txt' as well as in the 'Data availability' of the main text.</p> <p>1. Climate_china_1km.7z: This compressed file contains the annual precipitation and temperature from Peng et al. 2019 at 1km.</p> <p>2. Soil_china_1km.7z: This compressed file contains the soil properties derived from Soilgrid250m for China at 1-km resolution.</p> <p>3. Topography_china_1km.7z: This compressed file contains the topographic properties derived from Global Multi-resolution Terrain Elevation Data 2010 for China at 1-km resolution.</p> <p>4. MaxEnt_process.7z: This compressed file contains all the input data and model results of the MaxEnt model: 1) environment layers in ’.asc’, 2) rarefied occurrence points for the 15 forest types, 3) MaxEnt results in ’.tif’ (average of the 10-folds results)</p> <p>5. Potential_china_forest_1km.7z: contains the potential forest distributions for China at 1-km resolution from multiple source (Random Forest, WRI and ORCHIDEE). Note: the forest distribution is in the form of logical variables in the .mat file, where a value of 1 or true means that the grid point is potentially forestable, and a value of 0 or false means this grid is not suitable for forest.</p> <p>6. Existing_china_forest_1km.7z: contains the existing forest distributions for China at 1-km resolution from multiple source (FI2013-2017, Hansen, MODIS, ESA-CCI, CNLUCC, GLC-FCS30 and GlobeLand30). Note: the existing forest distribution is in the form of logical variables in the .mat file, where a value of 1 or true means there is forest distribution, and a value of 0 or false means there is currently no forest distribution.</p> <p>7. Crop_urban_china_1km.7z: similar to the Existing_china_forest_1km.zip, but stores the distribution of cropland and urban.</p> <p>8. Masks_area_china_1km.7z: area mask and the shp files of the national and provincial boundaries of China.</p> <p>9. CMIP6_outputs.7z: contains historical (1970-2014) and future (2015-2100) climate and CO2 fertilization factor simulated by Earth System Models participating in CMIP6.</p> <p>10. Ori_carbon_all_grid_1km.mat: Living biomass carbon densities in 2010 for China at 1-km (unit: Mg C ha<sup>-1</sup>). Both aboveground and belowground biomass carbon values are included. The original biomass map is from Spawn et al. 2020.</p> <p>11. Forest_inventory_data_5th_9th.xlsx: 1) The forest area reported in 5th to 9th national forest inventory 2) The forest area of different age classes derived from the 9th national forest inventory.</p> <p>12. Forest_age_CN2019.7z: the forest stand age map for China updated to 2019.</p> <p>13. data_original.txt: the original source of these data sets</p> <p><strong>Part B: MATLAB Code</strong></p> <p>This file (Matlab_code.7z) contains the code, functions and parameters for our analysis of the data, mainly MATLAB files (.m or mat)</p> <p><strong>Part C: Demo/Example data and code</strong></p> <p>This file (Demo.7z) contains the demo of our code running, which includes the demo code along with code comments, input data for the demo, and the expected output results.</p> <p><strong>Part D: Docs</strong></p> <p>Reference and guidelines (Docs.7z).</p> <p>If you have any questions or suggestions, please contact xuhaotony@pku.edu.cn</p>
Data from: Interacting effects of genetic variation for seed dormancy and flowering time on phenology, life history, and fitness of experimental Arabidopsis thaliana populations over multiple generations in the field
Open the record for dataset details and reuse information.
Genomic vulnerability to rapid climate warming in a tree species with a long generation time
Open the record for dataset details and reuse information.
Data from: Feeding specialisation and longer generation time are associated with relatively larger brains in bees
<p>Despite their miniature brains, insects exhibit substantial variation in brain size. Although the functional significance of this variation is increasingly recognized, research on whether differences in insect brain sizes are mainly the result of constraints or selective pressures has hardly been performed. Here, we address this gap by combining prospective and retrospective phylogenetic-based analyses of brain size for a major insect group, bees (superfamily Apoidea). Using a brain dataset of 93 species from North America and Europe, we found that body size was the single best predictor of brain size in bees. However, the analyses also revealed that substantial variation in brain size remained even when adjusting for body size. We consequently asked whether such variation in relative brain size might be explained by adaptive hypotheses. We found that ecologically specialized species with single generations have larger brains —relative to their body size— than generalist or multi-generation species, but we did not find an effect of sociality on relative brain size. Phylogenetic reconstruction further supported the existence of different adaptive optima for relative brain size in lineages differing in feeding specialisation and reproductive strategy. Our findings shed new light on the evolution of the insect brain, highlighting the importance of ecological pressures over social factors and suggesting that these pressures are different from those previously found to influence brain evolution in other taxa.</p>
Assemblies generated in the manuscript "Time and memory efficient genome assembly with Raven"
<p>This collection contains assemblies generated for the benchmark in the manuscript "Time and memory efficient genome assembly with Raven".</p>
Data from: Cancer cell lines show high heritability for motility but not generation time
<p>Tumour evolution depends on heritable differences between cells in traits affecting cell survival or replication. It is well-established that cancer cells are genetically and phenotypically heterogeneous; however, the extent to which this phenotypic variation is heritable is far less well-explored. Here we estimate the broad-sense heritability (H2) of two cell traits related to cancer hallmarks - cell motility and generation time - within populations of four cancer cell lines in vitro, and find that motility is strongly heritable. This heritability is stable across multiple cell generations, with heritability values at the high end of those measured for a range of traits in natural populations of animals or plants. These findings confirm a central assumption of cancer evolution, provide a first quantification of the evolvability of key traits in cancer cells, and indicate that there is ample raw material for experimental evolution in cancer cell lines. Generation time, a trait directly affecting cell fitness, shows substantially lower values of heritability than cell speed, consistent with its having been under directional selection removing heritable variation.</p>
Continuous Wavelet Transform and Short-Time Fourier Transform used to generate S1_Table2
<p>Continuous Wavelet Transform and Short-Time Fourier Transform true vs. false recognition accuracies: the t-test was used to compare CWT and STFT recognition accuracies with mean <em>f</em>NIRS classification accuracy. The result was used to generate S1_Table2.</p>
Dataset for paper "All-Optical Generation and Time-Resolved Polarimetry of Magnetoacoustic Resonances via Transient Grating Spectroscopy" by Carrara P. et al.
<p>This dataset complements the publication "All-Optical Generation and Time-Resolved Polarimetry of Magnetoacoustic Resonances via Transient Grating Spectroscopy" by Carrara P. et al.</p><p>The data hierarchy is explained in the file "Readme.docx", which also reports relevant metadata.</p>
Time-bin entanglement in the deterministic generation of linear photonic cluster states
<p>We investigate strategies for the efficient deterministic creation of trains of time-bin entangled photons using an individual quantum emitter described by a $\Lambda$-type electronic system. We explicitly demonstrate generation of high-quality linear cluster states of substantial length in our full microscopic numerical simulations. The underlying scheme is based on the manipulation of ground state coherences through precise optical driving. One important finding is that the most easily accessible quality metrics, the achievable rotation fidelities, fall short in assessing the actual quantum correlations of the emitted photons in the face of losses. To address this, we explicitly calculate stabilizer generator expectation values as a superior gauge for the quantum properties of the many-photon state. Our results illustrate that with controlled minimization of losses and realistic system parameters for quantum-dot type systems, useful linear cluster states of significant lengths can be generated, showcasing promise of scalability for quantum information processing endeavors.</p>
Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 2
<p>The dataset named SEBAL evapotranspiration in China (SEBAL ET) characterized the daily evapotranspiration (in millimeter) of vegetation in China from 2001 to 2018, the spatial resolution is 1 km × 1km and the temporal resolution is 1 day with the coordinate system of GCS_WGS_1984. The products were generated using Surface Energy Balance Algorithm of Land (SEBAL) and multi-sources remote sensing data, including MOD43A1 daily surface albedo, MOD11A1 daily surface temperature and MOD13 vegetation indices (obtained from NASA: https://ladsweb.modaps.eosdis.nasa.gov/search/), the meteorological data obtained from GMAO (https://gmao.gsfc.nasa.gov/research/highlights/2013-2015.php), the input variables were all aggregated of resampled to 1 km × 1km. The products were evaluated using the eight flux towers observation data for point validation and water balance method for regional validation and showed R value of 0.79 and 0.98, respectively, which indicated the products have a great performance. SEBAL ET can be used for several geoscience studies, especially for global change, water resources mangement and agricultural drought monitoring, etc.</p>
Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 1
<p>The dataset named SEBAL evapotranspiration in China (SEBAL ET) characterized the daily evapotranspiration (in millimeter) of vegetation in China from 2001 to 2018, the spatial resolution is 1 km × 1km and the temporal resolution is 1 day with the coordinate system of GCS_WGS_1984. The products were generated using Surface Energy Balance Algorithm of Land (SEBAL) and multi-sources remote sensing data, including MOD43A1 daily surface albedo, MOD11A1 daily surface temperature and MOD13 vegetation indices (obtained from NASA: https://ladsweb.modaps.eosdis.nasa.gov/search/), the meteorological data obtained from GMAO (https://gmao.gsfc.nasa.gov/research/highlights/2013-2015.php), the input variables were all aggregated of resampled to 1 km × 1km. The products were evaluated using the eight flux towers observation data for point validation and water balance method for regional validation and showed R value of 0.79 and 0.98, respectively, which indicated the products have a great performance. SEBAL ET can be used for several geoscience studies, especially for global change, water resources mangement and agricultural drought monitoring, etc.</p> <p> </p>
Climate change impacts on seabirds and marine mammals: the importance of study duration, thermal tolerance and generation time
Understanding climate change impacts on top predators is fundamental to marine biodiversity conservation, due to their increasingly threatened populations and their importance in marine ecosystems. We conducted a systematic review of the effects of climate change (prolonged, directional change) and climate variability on seabirds and marine mammals. We extracted data from 484 studies (4808 published studies were reviewed), comprising 2215 observations on demography, phenology, distribution, diet, behaviour, body condition and physiology. The likelihood of concluding that climate change had an impact increased with study duration. However, the temporal thresholds for the effects of climate change to be discernible varied from 10 to 29 years depending on the species, the biological response and the oceanic study region. Species with narrow thermal ranges and relatively long generation times were more often reported to be affected by climate change. This provides an important framework for future assessments, with guidance on response- and region-specific temporal dimensions that need to be considered when reporting effects of climate change. Lastly, we found that tropical regions and non-breeding life stages were poorly covered in the literature, a concern that should be addressed to enable a better understanding of the vulnerability of marine predators to climate change.
RAW DATA: FullThrOTTLE-trIR: Time resolved IR Spectroscopy of electrochemically generated species using a Full Throughput Optically Transparent Thin Layer Electrochemical Cell
<p><span>Data behind the figures in the Manuscript: FullThrOTTLE-trIR: Time resolved IR Spectroscopy of electrochemically generated species using a Full Throughput Optically Transparent </span><span>Thin Layer </span><span>Electrochemical Cell submitted to J. Phys. Chem. </span></p>
Generation-based and consumption-based grid emission intensity time series for German federal states 01/2020 - 07/2024
<p>The dataset contains time series estimating the consumption mix, generation-based and consumption-based grid emission intensity of German federal states for the period January 2020 to end of June 2024 as shown on the <a href="https://co2map.de">CO2Map</a> website. For an explanation of the underlying methodology see the <a href="https://co2map.de/methodology.html">CO2Map Methology</a> section, a detailed scientific article currently is in preparation.</p> <p>All time series have an hourly resolution (time stamps are given in UTC) and contain values for the 13 federal territorial states in Germany, with the city states merged into them:<br><br>BW: Baden-Wuerttemberg<br>BY: Bavaria<br>BB: Brandenburg and Berlin<br>HE: Hesse<br>MV: Mecklenburg-Western Pomerania<br>NI: Lower Saxony and Bremen<br>NW: North Rhine-Westphalia<br>RP: Rhineland-Palatinate<br>SL: Saarland<br>SN: Saxony<br>ST: Saxony-Anhalt<br>SH: Schleswig-Holstein and Hamburg<br>TH: Thuringia</p> <p><strong>"Emission intensity"</strong><br>This folder contains the following files for 2020 to 2023:<br>"region_consumption_based_emission_intensity.csv": Consumption-based emission intensity in kgCO2/kWh<br>"region_generation_based_emission_intensity.csv": Generation-based emission intensity in kgCO2/kWh</p> <p><strong>"Import tables"</strong><br>The folder contains the raw data representing the hourly consumption and storage mix including imports for federal states in MWh. This data allows to calculate generation-based and consumption-based emission intensity time series using arbitrary technology-specific emission factors for the technology types Biomass, Gas, Hard coal, Hydro, Lignite, Nuclear, Other fossil, Other renewable, Solar, Storage, Wind Offshore, Wind Onshore. Each file contains time series for one region as given above and for one year. The columns represent the hourly amount of load/storage/load+storage originating from a certain region and technology type, with the regions including local generation (i.e. the same federal state), imports from other federal states in Germany, and imports from other European countries (the origin of the imports is determined using a flow-tracing algorithm, see the <a href="https://co2map.de/methodology.html">CO2Map Methology</a> section).</p> <p>For each year and each federal state there is one file for load (representing electricity consumption), one file for storage (representing storage charging) and one file for total (sum of load and storage). The file "2023_import_table_load_DE_BW.csv", for instance, contains the mix of hourly load in Baden-Wuerttemberg. The first entry 01.01.2023 00:00 (UTC) then contains the mix for the electricity consumption in the first hour (UTC) of the year 2023 in BW. The sum over all columns for this row yields 4667.80 MWh, which is the approximated total load in this hour in BW (based on a regionalization method). The largest entry in the row is from "DE_NI,HB / Wind Onshore" (614.57 MWh), which is the amount of onshore wind power generated in Lower Saxony and Bremen and consumed in Baden-Wuerttemberg (based on the flow-tracing method). The second largest entry is from "DE_BW / Wind Onshore", which is the amount of onshore wind power generated and consumed locally in Baden-Wuerttemberg. The largest import to BW from other countries in this hour has been 115.18 MWh of nuclear power from Belgium. The file "2023_import_table_load_DE_BW.csv" contains an aggregated version of the same file, with the originating regions corresponding to the same federal state, imports from other federal states in Germany, and imports from other countries (each separated per technology).</p> <p><strong>"Export tables"<br></strong>The folder "Export tables" is similar to the one with "Import tables", but gives data about the location where generation from German regions is used. It contains the raw data representing the hourly generation mix associated with the location where it is used (locally and in other regions). The columns represent the hourly amount of load/storage/load+storage originating from the given region. </p> <p>For each year and each federal state there is one file for load (representing electricity consumption), one file for storage (representing storage charging) and one file for total (sum of load and storage). The file "2023_export_table_total_DE_SH,HH.csv", for instance, contains the mix of hourly generation in Schleswig-Holstein and Hamburg which is associated with electricity consumption and storage charging both locally and abroad. The first entry 01.01.2023 00:00 (UTC) then contains the amount of generation from SH,HH used in each region in the first hour (UTC) of the year 2023. The sum over all columns for this row yields 5730.40 MWh, which is the approximated total generation in this hour in SH,HH (based on a regionalization method). Since SH,HH is a net exporter in this hour, the sum over all columns associated with SH,HH represents the local load+storage charging (1842.40 MWh). The sum over all columns with "Wind Onshore" yields the generation from onshore wind in SH,HH in this hour (4318.05 MWh) (again based on a regionalization method). The different entries for "Wind Onshore" then tell where this onshore wind is used (load and storage charging), with the main contributions locally in SH,HH (1388.33 MWh), and exports to Norway (1582.03 MWh) and UK (682.67 MWh). The destination of these exports is estimated based on the flow tracing method in the same way as the origin of imports are determined.</p> <p>For the data sources and methods used to calculate the time series contained in this dataset see the CO2Map <a href="https://co2map.de/methodology.html">Methodology </a>and <a href="https://co2map.de/about.html">About </a>sections. </p> <p>Please communicate any questions, corrections or comments to mirko.schaefer [at] inatech.uni-freiburg.de</p> <p>All authors acknowledge funding from Elektrizitätswerke Schönau (EWS) through the Sonnencent program, ID 00009280. Tim Fürmann acknowledges funding from DFG (SPP 1984), project ID 450860949. Ramiz Qussous and Robin L. Grether would like to thank the German Federal Government, the German State Governments, and the Joint Science Conference (GWK) for their funding and support as part of the NFDI4Energy consortium, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 501865131.</p>
ScienceDex guides
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