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3,472 results for “Balancing”
Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model
<p>Database corresponding to the research work submitted to Water Resources Research :<br> "Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model"<br> Fanny Picourlat (fanny.picourlat@lsce.ipsl.fr), Emmanuel Mouche, Claude Mugler</p> <p> </p> <p>"Geomorphic_Analysis" directory ------------------------------------------------------------------------------</p> <p>Little Washita geomophic analysis results. Analysis conducted on the 100 m resolution DEM (from USGS datadase, accessible at https://www.usgs.gov/core-science-systems/national-geospatial-program/small-scale-data) using a flow paths modeling algorithm developped by Maquin (2016).</p> <p> - Hillslopes_Length.csv : List of hillslopes lengths [m]<br> <br> - Hillslopes_MeanSlopes.csv : List of hillslopes mean slopes [%]<br> <br> <br> "3DREF_model_19981999" directory ------------------------------------------------------------------------------<br> Three-dimensional reference model files : exemple of the 1998-1999 water year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.</p> <p> - parameters_summary.pdf : parameters summary table.<br> <br> - "Outputs" directory ---------------------------------------------<br> Output files that form paper's figures.<br> <br> - LWo.hydrograph.Hydrographe_USGS1_07327550.dat : Streamflow at USGS1<br> - LWo.water_balance.dat : Water balance<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used for plotting depth to water table.<br> - LWo.olf.dat : Surface domain variables for all nodes for all output times. File used for plotting evapotranspiration at the closest node from the Ameriflux station.<br> <br> <br> "Hillslope_Model" directory -----------------------------------------------------------------------------------<br> Equivalent hillslope model files for the 20-year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.<br> <br> - "Outputs" directory ----------------------------------------------<br> <br> - LWo.water_balance.dat : Water balance. File used for plotting hillslope discharge and evapotranspiration.<br> - LWo.observation_well_flow.Well_1.dat : Outputs at nodes located at x=100m. File used for plotting depth to water table.<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used to extract the "post-processed" files described below.</p> <p> - "Post_processed" directory -------------------------------<br> Data extracted from the output file LWo.pm.dat.</p> <p> - TAB_xzy_sat.csv : Saturation for all nodes for all output times. Used for defining the seepage face extension Xs(t).<br> - x(t).csv : x coordinate (for all output times) of the intersection point between roots ending limit and water table. Used for defining the water table slope tan(i(t)).</p> <p><br> "Params" directory --------------------------------------------------------------------------------------------<br> Parameters files for both 3DREF model (1998-1999 simulation) and hillslope model (20-year simulation).<br> <br> - "Topography" directory --------------------------------------------<br> - maillage_hgs_corr3_riv.2dm : Horizontal 3D mesh file<br> - fichier_altitude_grok_corr3_riv_b.txt : 3D surface nodes elevation [m]<br> <br> - "Props" directory -------------------------------------------------<br> - LW.mprops : Subsurface material parameters<br> - LW.oprops : Surface domain parameters<br> - LW_LAI.etprops : Vegetation parameters<br> <br> - "Zones_vg" directory ----------------------------------------------<br> - zone_1_ele.txt : Bare soil 3D elements IDs<br> - zone_2_ele.txt : Deciduous forest 3D elements IDs<br> - zone_3_ele.txt : Evergreen forest 3D elements IDs<br> - zone_4_ele.txt : Shrubs 3D elements IDs<br> - zone_5_ele.txt : Grassland 3D elements IDs<br> - zone_6_ele.txt : Pasture 3D elements IDs<br> - zone_7_ele.txt : Crops 3D elements IDs<br> <br> - "Forcings" directory ----------------------------------------------<br> - NARR_day_9899.csv : Daily rainfall [m/s] on 1998-1999<br> - NARR_day_etp_9899.csv : Daily Potential Evapotranspiration [m/s] on 1998-1999<br> - NARR_day_9313.csv : Daily rainfall [m/s] on 1993-2013<br> - NARR_day_etp_9313.csv : Daily Potential Evapotranspiration [m/s] on 1993-2013<br> <br> - "Output_times" directory ------------------------------------------<br> - output_times_365d.csv : Output times [s] for 365 days<br> - output_times_20y.csv : Output times [s] for 20 years<br> <br> - "Rivers" directory ------------------------------------------------<br> - no_rivers_ele.txt : No river 3D elements IDs<br> - rivers_ele.txt : River 3D elements IDs<br> <br> - "LAI" directory ---------------------------------------------------<br> LAI files (1st column : time [s], 2nd column : LAI [-])<br> - LAI_2_hydro.csv : Deciduous forest LAI over one water year<br> - LAI_4_5_6_hydro.csv : Shrubs, grassland and pasture LAI over one water year<br> - LAI_winterwheat_OAlaoui.csv : Crops LAI over one water year<br> - LAI_eq_20y.csv : Equivalent LAI (for hillslope model) over 20 years<br> <br> "Analytical_Model" directory -----------------------------------------------------------------------------------</p> <p> - Analytical_model.py : Analytical model code for the 20-year simulation of the equivalent hillslope water balance.</p> <p> </p>
data sets for the article Short‑term impact of crop diversifcation on soil carbon fuxes and balance in rainfed and irrigated woody cropping systems under semiarid Mediterranean conditions
<p>Diversifcation practices such as intercropping in woody cropping systems have recently been proposed as a promising management strategy for addressing problems related to soil degradation, climate change mitigation and food security. In this study, we assess the impact of several diversifcation practices in diferent management regimes on the main carbon fuxes regulating the soil carbon balance under semiarid Mediterranean conditions.</p>
The Unit Re-Balancing Problem
<p>The unit re-balancing problem is about a number of defensive military units distributed over a geographic area. Each unit consists of a number of components (e.g., people, armor, or equipment). A value between 0 and 1 describes the current rating of each component. By a nonlinear function this value is converted into a nominal status assessment. This allows a comparison of different components of all units. The lowest of the statuses determines the efficiency of a unit, and the highest status its cost. An unbalanced unit has a gap between these two. When too many units are unbalanced, the entire system is costly and inefficient. To re-balance the units, people and material can be transferred. The goal is to have all units equally well equipped at the lowest possible cost. On a secondary level, the cost for the re-balancing should also be minimal. We present a mixed-integer nonlinear programming formulation for this problem, which describes the potential movement of components as a multi-commodity flow. Nonlinear constraints are needed to obtain the lowest and the highest status. Since we assume that these functions are piecewise linear, we reformulate them using inequalities and binary variables. This results in a mixed-integer linear program, and numerical standard solvers are able to compute proven optimal solutions for instances with up to 100 units. The dataset consists of the models and test instances that were presented at the (virtual) 6th IMA Conference on Mathematics in Defence and Security, March 30-31, 2021. </p>
Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data from two monitoring stations Bertha Ganter – Fort McKay and Barge Landing for 20 August 2013 to 2 September 2013. This data was used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125) for model output and observational data comparisons. The same data can be accessed and downloaded from "<a href="https://wbea.org/historical-monitoring-data/">https://wbea.org/historical-monitoring-data/</a>".</p>
Balancing selection at a wing pattern locus is associated with major shifts in genome-wide patterns of diversity and gene flow
<p>Selection shapes genetic diversity around target mutations, yet little is known about how selection on specific loci affects the genetic trajectories of populations, including their genome-wide patterns of diversity and demographic responses. Here we study the patterns of genetic variation and geographic structure in a neotropical butterfly, <em>Heliconius numata</em>, and its closely related allies in the so-called melpomene-silvaniform clade. <em>H. numata</em> is known to have evolved an inversion supergene which controls variation in wing patterns involved in mimicry associations with distinct groups of co-mimics. Butterflies show disassortative mate preferences and heterozygote advantage at this locus. We contrasted patterns of genetic diversity and structure 1) among extant polymorphic and monomorphic populations of <em>H. numata</em>, 2) between <em>H. numata</em> and its close relatives, and 3) between ancestral lineages. We show that <em>H. numata</em> populations which carry the inversions as a balanced polymorphism show markedly distinct patterns of diversity compared to all other taxa. They show the highest genetic diversity and effective population size estimates in the entire clade, as well as a low level of geographic structure and isolation by distance across the entire Amazon basin. By contrast, monomorphic populations of <em>H. numata</em> as well as its sister species and their ancestral lineages all show lower effective population sizes and genetic diversity, and higher levels of geographical structure across the continent. One hypothesis is that the large effective population size of polymorphic populations could be caused by the shift to a regime of balancing selection due to the genetic load and disassortative preferences associated with inversions. Testing this hypothesis with forward simulations supported the observation of increased diversity in populations with the supergene. Our results are consistent with the hypothesis that the formation of a supergene triggered a change in gene flow, causing a general increase in genetic diversity and the homogenisation of genomes at the continental scale.</p>
Glacial meltwater determines the balance between autotrophic and heterotrophic processes in a Greenland fjord
<p>Raw data for the summer CTD transect, the annual CTD mooring, data from the seasonal sediment trap, data from the oxygen and 14C incubations and nutrients data presented in the paper</p>
Vertex balanced dataset (protein ligand-binding sites)
<p>Vertex balanced dataset.</p> <p>This dataset consists in 676 pairs of binding-sites<br> (338 similar pairs, 338 dissimilar pairs).</p> <p>The original Vertex dataset was describbed in (but is not downloadable from):<br> ---<br> Chen, Y. C., Tolbert, R., Aronov, A. M., McGaughey, G., Walters, W. P., & Meireles, L. (2016).<br> Prediction of protein pairs sharing common active ligands using protein sequence,<br> structure, and ligand similarity.<br> Journal of chemical information and modeling, 56(9), 1734-1745.<br> https://doi.org/10.1021/acs.jcim.6b00118<br> ---</p> <p>Its balanced version was recently describbed in (but is not downloadable from):<br> ---<br> Eguida, M., & Rognan, D. (2020).<br> A computer vision approach to align and compare protein cavities:<br> application to fragment-based drug design.<br> Journal of Medicinal Chemistry, 63(13), 7127-7142.<br> https://doi.org/10.1021/acs.jmedchem.0c00422<br> ---<br> </p>
CAP1 - Planning and control of asphalt production - Thermal balance data (.xlsx)
<p>CAP1- Planning and Optimization of Asphalt Production</p> <p>The cognitive planning solution for asphalt production consists of a planning decision tool that, from the sensors data installed in the plant and contributing to the planning reference implementation layer of the CAP, allows to decide the right moment to start the production and the necessary adjustments to get the asphalt mix to leave the production plant to the asphalt application area in the optimal conditions (temperature mainly). It takes into account the industrial data streams coming from both the local control system located at the asphalt use case and the data coming from the new different sensors that have been connected to the local datalogger also available at the asphalt production plant and as part of this project development.</p> <p>The implementation of the cognitive system of production planning and optimization gathers all the data coming from the cognitive sensors developed in the project (as the content of bitumen or filler present in the asphalt mix) and any other sensors already installed alongside, with data coming from the laboratory if needed.</p> <p>It is needed to perform two types of calculations:</p> <ul> <li>A <strong>mass balance</strong> both at the dryer and mixing process on a daily basis and for each type of asphalt mix design (a recipe containing the proportions of each ingredient, the aggregates, bitumen and recycled asphalt).</li> <li>A <strong>thermal balance</strong> also both at the dryer (heating up the cold aggregates) and mixing of the hot aggregates, the bitumen and the cold RAP (recycled asphalt). Both processes have a temperature set point. For the drying, there is a temperature safety limit to not damage the baghouse filter. For the mixing, the temperature is set by the asphalt mix design so the final mix is transported and laid out at the job site at a minimum temperature.</li> </ul> <p>The advanced calculation of the mass balance throughout all the production chain is performed including the continuous part of it (aggregates drying process) and the batch one (mix tower). This mass balance is made up of the different calculations that can be performed using all the available data and taking into consideration both, stationary and dynamic (transitory) mass balances like mass balance of aggregates in the dryer, mass balance in the baghouse filter, mass balance in the bucket elevator to the mixing tower, mass balance in the upper sieves and in the hot aggregates hoppers and eventually the mass balance in the mixer taking into account the different additives (including RAP, bitumen, etc.). Also, the different recipes production historical data is used as a basis for the calculations of this tool.</p>
A striatal circuit balances learned fear in the presence and absence of sensory cues
<p>This repository continues the dataset accessible by doi 10.5281/zenodo.7530512. The latter contains all the relevant metadata description.</p>
Data from: Balanced replacement of fish meal with Hermetia illucens meal allows efficient hepatic nutrient metabolism and increased fillet lipid quality in gilthead sea bream (Sparus aurata) juveniles
<p>In the present study, gilthead sea bream (<em>Sparus aurata</em>) juveniles were reared using sustainable feeds containing insect meal from <em>Hermetia illucens</em> larvae and poultry by-products meal. Proteomics and Proton Nuclear Magnetic Resonance-based metabolomics analysis were used to assess the metabolic impact of tested dietary formulations in sea bream liver, whereas the composition of muscle fillet was characterized by means of metabolomics and gas chromatography of fatty acids methyl esters. Replacing fish meal with insect meal in a 5% fish meal diet did not substantially alter metabolism of dietary nutrients, leading to small but statistically detectable effects solely on lauric acid content of sea bream fillet, and few alterations in some markers of immune response, such as leukocyte elastase inhibitor-like, granzyme B (G,H)-like, and two associated ortholog groups namely serpin B, and chymase). Liver morphology confirmed the absence of structural damage or inflammation in the insect meal-fed group, which showed a lower amount of hepatic lipid deposition and accumulation, too.</p>
Surface mass balance of the Monte Sarmiento Massif (2000-2022), Tierra del Fuego, Chile.
<p>This dataset contains the climatological input and the modelled surface mass balance of the Monte Sarmiento Massif, Tierra del Fuego, calculated with four different surface mass balance models. The climatological input is calculated by statistically downscaling ERA5 reanalysis data to a weather station at Schiaparelli Glacier via Quantile Mapping. The precipitation is simulated with an orographic precipitation model. The four applied surface mass balance models are a) a positive degree day model (PDD), b) a simplified energy balance model with potential radiation (SEBGpot) and c) with actual radiation including cloud cover and shading (SEBG), and d) a coupled snowpack and ice surface energy and mass balance model (COSIPY).</p>
Dataset - "Ice core evidence for a 20th century increase in surface mass balance in coastal Dronning Maud Land, East Antarctica"
<p>We provide in this dataset the isotopic and ionic records from a 120 m-long ice core drilled at the summit of Derwael ice rise (70°14'44.88'' S, 26°20'5.64'' E) situated in coastal Dronning Maud Land, East Antarctica. Ions concentrations (Na<sup>+</sup>, MSA, Cl<sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and NO<sub>3</sub><sup>-</sup>) and water stable isotopes (δ<sup>18</sup>O, δD and d-excess) are presented for the top 103 meters (corresponding to 1815 and the Tambora eruption) with a continuous record for the water stable isotopes and discontinuous sections for ions concentrations. A complementary database “Annual layer thicknesses and age-depth (oldest estimate) of Derwael Ice Rise (IC12), Dronning Maud Land, East Antarctica” with age model and annual layer thickness is available at https://doi.org/10.1594/PANGAEA.857574 and the companion paper is Philippe et al., 2016 (“Ice core evidence for a 20th century increase in surface mass balance in coastal Dronning Maud Land, East Antarctica”, https://doi.org/10.5194/tc-10-2501-2016).</p>
Site characterization, water balance modeling, and regeneration attributes of managed and unmanaged ponderosa pine sites in the southwestern United States
<p>This dataset contains biotic and abiotic site characterization data and SOILWAT2 water balance model simulation outputs (two daily outputs: 1915-2011, 1980-2020) for 77 ponderosa pine forest sites in the southwestern United States. Data were collected in summer 2019 and summer 2021. Overviews of the sampling and modeling methodologies are detailed in the following publications:</p> <p>Pirtel NL, Bradford JB, Hubbard RM, Abella SR, Kolb TE, Litvak ME, Porter SL and Petrie MD. 2021. The aboveground and belowground growth characteristics of juvenile conifers in the southwestern United States, Ecosphere 12: e03839, doi:10.1002/ecs2.3839.</p> <p>Petrie MD, Hubbard RM, Bradford JB, Kolb TE, Moser WK, Noel A, Schlaepfer DR, Bowen MA, Fuller LR and Moser WK. 2023. Widespread regeneration failure in ponderosa pine forests of the southwestern United States, Forest Ecology and Management: in press.</p> <p> </p>
Global continental discharge estimates from ocean mass balance
<p>These files include a time series of global continental discharge estimated from ocean mass balance following Chandanpurkar et al., 2017. </p> <p>The ocean mass balance is obtained from these components:</p> <p>dM/dt (change in ocean mass): From altimetry and from GRACE/FO. When derived from altimetry, steric level change is subtracted from the GMSL using EN4.2.2 temperature and salinity data. </p> <p>E-P: Here, two methods are used:</p> <p>1. Directly, using estimates of ocean E and P, using OAFlux for E and GPCP and CMAP separately for P</p> <p>2. Indirectly, using atmospheric moisture balance using vertically integrated horizontal moisture flux divergence, and change in the total column water vapor. These are obtained using ERA5 and MERRA-2 reanalyses products.</p> <p>The eight discharge estimates are combinations of the above, and the exact combination is mentioned in the filename. </p>
Distinct frequencies balance segregation with interaction between different memory types within a prefrontal circuit
<p>2023-05-30</p> <p>This archive holds the original data used to derive the results reported in the publication:<br> 'Distinct frequencies balance segregation with interaction between different memory types within a prefrontal circuit'</p> <p>Authors: Martina Bracco, Tuomas P. Mutanen, Domenica Veniero, Gregor Thut & Edwin M. Robertson<br> Corresponding Author: Edwin M. Robertson<br> Contact: edwin.robertson@glasgow.ac.uk</p> <p>Please refer to the License for the correct use of the data. <br> If the use of this data leads to any public dissemination, including (but not restricted to) scientific articles, conference presentations, and posters, please remember to cite the original article.</p> <p><br> <strong>General Description</strong></p> <p>The dataset is shared in BIDS format. To facilitate the upload of files to Zenodo, each subject folder was compressed separately. To ensure a valid bids structure of the dataset, extract the contents of each zip file and place them in the same directory as the other files from the dataset. By doing so, you will have all the necessary files in the correct structure for the BIDS format.</p> <p>Data type: raw TMS-EEG data organized in BIDS format, and epoched ±800 ms around the pulse.<br> preprocessed motor skill and word recall data organized in BIDS format (.tsv = data file; .json = data description).</p> <p>Key to Filenames: sub = Participant<br> ses-preLearning = Session 1 data folder<br> ses-postLearnng = Session 2 data folder<br> eeg = TMS-EEG data folder<br> beh = behaviral data folder</p> <p>Participants: 45 healthy participants (30 females, 23.9±3.6 years; mean ± std) <br> <br> Groups: Participants were randomly assigned to three different groups with different behavioural conditions:<br> - In the 'motorSkill'-group, participants performed a sequential motor-skill task followed by a wordlist memory task. <br> (sub-01 to sub-15)<br> - In the 'wordRecall'-group, participants performed a wordlist memory followed by a sequential task motor-skill task.<br> (sub-16 to sub-30)<br> - In the 'control'-group, participants performed a random motor-skill task followed by a wordlist memory task.<br> (sub-31 to sub-45)</p> <p><strong>TMS-EEG data description</strong></p> <p>Stimulation sites: Right dorsolateral prefrontal cortex (rDLPFC). <br> Left primary motor cortex (lM1).</p> <p>Sessions: Session 1: TMS-EEG recordings before the behavioural tasks.<br> Session 2: TMS-EEG recordings after the behavioural tasks.</p> <p>Number of TMS pulses: 126 pulses delivered to either DLPFC or M1 before, before and after behavioural tasks (a total of 252 pulses for Session 1 and Session 2, each).</p> <p>Inter-Stimulus Interval: 4-6 s (4.9±0.6 s, mean ± std).</p> <p>TMS intensity: 80% of participants' individual motor threshold.</p> <p>TMS machine: Magstim Rapid2, Magstim Company.</p> <p>Coil: figure-of-eight coil (Double 70-mm Alpha Coil).</p> <p>Number of channels: 62-channel EEG channels (AFz and TP9 are used as reference and ground, respectively) <br> + 1 FDI EMG channel<br> + 1 electrode on the outer canthus of the left eye to monitor eye movements.</p> <p>Sampling rate: 5000 Hz. </p> <p>Voltage resolution: 0.1 uV.</p> <p>EEG system: TMS-EEG compatible BrainAmp (Brain Products).</p> <p>Noise masking during TMS-EEG recordings: Yes. </p> <p> </p> <p><strong>Memory tasks data description</strong></p> <p><br> <em>Motor skill task</em></p> <p>'motorSkill'-group (sub-01 to sub-15): TestSerial; averaged response time of the last 50 serial trials immediately after learning.<br> TestRandom; averaged response time for the 50 subsequent random trials immediately after learning. <br> RetestSerial; averaged response time for the last 50 serial trials 10h after learning.<br> RetestRandom; averaged response time for the 50 subsequent random trials 10h after learning.</p> <p>'wordRecall'-group (sub-16 to sub-30): TestSerial; averaged response time for the last 50 serial trials immediately after learning.<br> TestRandom; averaged response time for the 50 subsequent random trials immediately after learning. </p> <p>'control'-group (sub-31 to sub-45): TestRandom; averaged response time of the last 100 random trials at testing.<br> RetestRandom; averaged response time of the last 100 random trials 10h after the tasks.</p> <p><em>Word recall task</em></p> <p>'motorSkill'-group (sub-01 to sub-15): Test; number of words recalled immediately after learning.</p> <p>'wordRecall'-group (sub-16 to sub-30): Test; number of words recalled immediately after learning.<br> Retest; number of words recalled 10h after learning. </p> <p>'control'-group (sub-31 to sub-45): Test; number of words recalled immediately after learning.</p>
Total Load Profiles by Balancing Authority in the Western United States for GODEEEP
<p>This dataset contains time-series of total load profiles across Balancing Authorities (BAs) in the western United States (U.S.) electricity grid interconnection for the years 2025, 2030, 2035, 2040, 2045, and 2050. The data is provided for two different socioeconomic pathways and one climate scenario. The socioeconomic pathways -- Business-As-Usual (BAU_Climate) and Net-Zero without CCS (NetZeroNoCCS_Climate) -- are described by <a href="https://doi.org/10.5281/zenodo.7838871">https://doi.org/10.5281/zenodo.7838871</a>. The climate scenario -- Representative Concentration Pathway 8.5 hotter (rcp85hotter) -- is described by <a href="https://doi.org/10.57931/1885756">https://doi.org/10.57931/1885756</a>. Transportation loads in this dataset are derived from <a href="https://doi.org/10.5281/zenodo.8065137">https://doi.org/10.5281/zenodo.8065137</a>. Non-transportation loads are derived using the Total Electricity Loads (TELL) model which is available <a href="https://github.com/IMMM-SFA/tell">here</a>. The data was post-processed using a Jupyter notebook available <a href="https://github.com/GODEEEP/load_analysis/blob/main/notebooks/process_load_data.ipynb">here</a>.</p> <p>A brief summary of the files and directories in this data package is provided below. Each file in the "<em>total_loads</em>" subdirectory is a comma-separated-value (CSV) format with the following columns:</p> <ul> <li><strong>BA</strong> - Acronym of the balancing authority (BA) for this data point</li> <li><strong>Time_UTC</strong> - Timestamp of the hourly data in UTC format</li> <li><strong>Non-Transportation_Load_MWh</strong> - Total hourly load in the BA from non-transportation sources in megawatt hours</li> <li><strong>Transportation_Load_MWh</strong> - Total hourly load in the BA from transportation sources in megawatt hours</li> <li><strong>Total_Load_MWh</strong> - Sum of the load from non-transportation and transportation sources in megawatt hours</li> </ul> <p>Data in the "<em>gridview_ready_total_loads</em>" subdirectory contains the hourly total loads by BA in a format that is ready for ingestion into the GridView production cost model.</p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Data from: Dynamic balancing of risks and rewards in a large herbivore: Further extending predator-prey concepts to road ecology
<p>Animal behavior is shaped by the ability to identify risks and profitably balance the levels of risks encountered with the payoffs experienced. Anthropogenic disturbances like roads generate novel risks and opportunities that wildlife must accurately perceive and respond to. Basic concepts in predator-prey ecology are often used to understand responses of animals to roads (e.g., increased vigilance, selection for cover in their vicinity). However, prey often display complex behaviors such as modulating space use given varying risks and rewards, and it is unclear if such dynamic balancing is used by animals in the context of road crossings.</p> <p>We tested whether animals dynamically balance risks and rewards relative to roads using extensive field -based and GPS collar data from elk in Yoho National Park (British Columbia, Canada) where a major highway completely bisects their range during most of the year.</p> <p>We analyzed elk behavior by combining hidden Markov movement models with a step-selection function framework. Rewards were indexed by a dynamic map of available forage biomass and risks were indexed by road crossings and traffic volumes.</p> <p>We found that elk generally selected intermediate and high forage biomass and avoided crossing the road. Most of the time, elk modulated their behavior given varying risks and rewards. When crossing the highway compared with not crossing, elk selected for greater forage biomass and this selection was stronger as the number of highway crossings increased. However, with traffic volume, elk only balanced foraging rewards when they crossed a single time during a travel sequence.</p> <p>Using a road ecology system, we empirically tested an important component of predator-prey ecology – the ability to dynamically modulate behavior in response to varying levels of risks and rewards. Such a test articulates how decision-making processes that consider the spatiotemporal variation in risks and rewards allow animals to successfully and profitably navigate busy roads. Applying well-developed concepts in predator-prey theory helps understand how animals respond to anthropogenic disturbances and anticipate the adaptive capacity for individuals and populations to adjust to rapidly changing environments.</p>
Balancing Innovation and Responsibility: The e-DIPLOMA Project's Approach to Ethical, Inclusive, Accessible, and Sustainable Research (video)
<p>This video includes project motivation, primary objective and methodology developed at e-DIPLOMA project and emphasize the Responsible Research and Innovation actions implemented in the project. e-DIPLOMA ensures the consideration of ethics, gender equality, legal aspects, and data privacy as well as it prioritizes transparency and accessibility.</p> <p>Project website: https://e-diplomaproject.eu/</p>
Dataset for the "Comparison of fiber interferometric sensor with a commercial interferometer for a Kibble balance velocity calibration"
<p>This dataset supports findings of the study presented in paper "Comparison of fiber interferometric sensor with a commercial interferometer for a Kibble balance velocity calibration".</p>
Balancing AI Advancements: Preserving Human Creativity Amidst Growing Artificial Ingenuity
<p>a recent empirical study in which tasks designed according to the principles of the grandomastery framework, specifically created to foster and evaluate skills in narrative and abstract thinking, were assigned to Chat GPT, Bing, and Google Bard. This experiment serves as a clear example of the abilities demonstrated by artificial intelligence (AI). By harnessing AI's skill in storytelling and introducing randomness through keywords and a predetermined thematic structure, the resulting outcomes were highly captivating. The visual representations of completed tasks related to comprehensive cognitive processes, organized within the grandomastery approach, effectively and eloquently depict AI's recent evolutionary path. This advancement spans from basic manipulation of data to the intricate art of crafting narratives that deeply resonate with audiences.</p>
ScienceDex guides
Understand access before you commit
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.