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477 results for “input data”
FLEXIES Input Data Central Europe
<p><strong>If you use this data, please cite the following publication:</strong></p> <p>Maeder, M., Weiss, O., & Boulouchos, K. (2021). Assessing the need for flexibility technologies in decarbonized power systems: A new model applied to Central Europe. <em>Applied Energy</em>, <em>282</em>, 116050. https://doi.org/10.1016/j.apenergy.2020.116050</p> <p><strong>Description:</strong></p> <p>FLEXibility from Interconnections and Electricity Storages (FLEXIES) is a power system optimization model that investigates flexibility technologies for fully decarbonized power systems.</p> <ul> <li>Authors: Mattia Maeder & Olga Weiss (June 2020)</li> <li>Developed in the Master Thesis "The role of energy flexibility measures in the energy transition" by Mattia Maeder and supervised by Olga Weiss, ETH Zurich (April 2020)</li> <li>Introduced and used in the manuscript "Assessing the need for flexibility technologies in decarbonized power systems: A new model applied to Central Europe" by Mattia Maeder, Olga Weiss, and Konstantinos Boulouchos (June 2020)</li> </ul> <p>This upload contains the <strong>input datasets on energy generation and flexibility technologies for the case study of Central Europe</strong> (Switzerland, Austria, France, Germany, and Italy). Furthermore, it contains the files that <strong>read the data into MATLAB</strong>. The FLEXIES code can be accessed here: 10.5281/zenodo.3881499</p>
Key input and output data for the multi-model analysis "Open Source Energiewende"
<p>This repository contains key input and output data of the multi-model analysis carried out in the project "Open Source Energiewende", financed by the German Federal Ministry for Economic Affairs and Energy.</p> <p>The results are presented and discussed in the paper "Power sector effects of cheaper stationary batteries: insights from an open multi-model analysis".</p> <p>The model codes are availabe in individual repositories, which are provided in the paper.</p>
Input data for the Monin-Obukhov Similarity Theory (MOST) benchmark
<p>This is the input data used in Wakebench MOST benchmark.</p> <p> </p>
Input data for performing chemistry coupled PALM model system 6.0 simulations with different chemical mechanisms
<p>The data presented here comprised of input files that have been used to run chemistry coupled PALM model system 6.0 simulations for the article entitled "Development of an atmospheric chemistry model coupled to the PALM model system 6.0: Implementation and first applications". In this article we describe the implementation of an online-coupled gas-phase chemistry model in the turbulence resolving PALM model system 6.0.</p> <p>List of the input data required for performing chemistry model simulations with different chemical mechanisms is given below. A text file comprised of measured concentrations of NO, NO<sub>2</sub> and O<sub>3</sub> is also added.</p> <ol> <li>Fortran parameter (PARIN) files for four mechanisms and one meteorology-only simulation.</li> <li>Static file</li> <li>Dynamic file</li> <li>Two files (shortwave and longwave input data) for rrtmg radiation model</li> <li>Observation from two air quality stations in Berlin, Germany .</li> <li>PALM model source code revision 4450 (palm_trunk_rev-4450.tar.gz)</li> <li>PALM model source code revision 4601 (palm_trunk_rev-4601.tar.gz)</li> </ol> <p>The PALM model system 6.0 revision 4451 and 4601 (for chemistry flux profiles only) have been used for these simulations. </p>
quishqa/MUNICH_VEIN_SP: MUNICH and VEIN input and output data for Sao Paulo
<p>Here are the input and output data to create the results for the paper Simulation of O3 and NOX in Sao Paulo street urban canyons with VEIN (v0.2.2) and MUNICH (v1.0)</p>
Input data of manuscript "CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells"
<p>This is the directory containing input data necessary to reproduce analyses presented in the manuscript:</p> <blockquote> <p><strong>CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells</strong><br> Shadi Darvish Shafighi, Szymon M Kiełbasa, Julieta Sepúlveda Yáñez, Ramin Monajemi, Davy Cats, Hailiang Mei, Roberta Menafra, Susan Kloet, Hendrik Veelken, Cornelis A.M. van Bergen, Ewa Szczurek</p> </blockquote>
Estimating individual level plant traits at scale: input data
<p>This dataset includes the input data to ensure replicability of the results. This dataset include the (1) reflectance for individual tree crowns, extracted from field and algorithmically delineated crowns in 2 NEON sites (OSBS and TALL); (2) the associated measures of tree traits (N%, C%, P%, LMA). The folder should be decompressed inside of the root directory of the project in https://github.com/MarconiS/Estimating-individual-level-plant-traits-at-scale/ for correctly replicate the analysis.</p>
Input data for SPLOC test drivers
<p>Files used for running the test drivers included with the SPLOC code. </p> <p>PNGs of expected results included.</p>
Data Documentation Erdgas-BRidGE – Input data for modeling the power, building, and gas sector
<p>This data documentation provides data representing parts of the power, the building and gas sector, which have been compiled within the research project Erdgas-BRidGE (Erdgas - Bedeutung und zukünftige Rolle in der deutschen (German) Energiewende). The aim of this documentation is to increase the transparency of input data for energy modeling in the German context.</p> <p>Therefore, the report Erdgas-BRidGE_Data_Documentation_Report (2021).pdf documents the data collected and processed in the course of the project. Furthermore, the data set Erdgas-BRidGE_Dataset (2021).xlsx provides the compiled or further processed data with separate table sheets. The script available under the file name PythonCodeToProcessCapacityBookings.zip has been used to process historical capacity bookings.</p> <p>The modifications of the version 1.1.0 – compared to the previous version 1.0.0 – include the following updates:</p> <ul> <li>Minor formal corrections in the report</li> <li>An adjustment in the calculation basis of the district heating profiles</li> <li>Correction of an error in the calculation of the absolute number of individual type buildings in the database for the German building stock.</li> </ul> <p>Erdgas-BRidGE is a joined effort by the Energiewirtschaftliches Insitut an der Universität zu Köln (ewi) and the Chair of Energy Economics at the Technische Universität Dresden (TUD-EE2). The project was funded by the Federal Ministry for Economic Affairs and Energy through the grant "Erdgas-BRidGE", FKZ: 03ET4055A and FKZ: 03ET4055B.</p> <ul> </ul>
Data from: Seedling recruitment correlates with seed input across seed sizes: implications for coexistence
Understanding controls on recruitment is critical to predicting community assembly, diversity and coexistence. Theory posits that at mean fecundity, recruitment of highly fecund small-seeded plants should be primarily microsite limited, which is indicated by a saturating recruitment function. In contrast, species that produce fewer large seeds are more likely to be seed-limited, which is characterized by a linear recruitment function. If these patterns hold in nature, seed predation that disproportionately affects larger-seeded species can limit their establishment. We tested these predictions by comparing recruitment functions among 16 co-occurring perennial forb species that vary by over two orders of magnitude in seed size. We also assessed how post-dispersal seed predation by mice influenced recruitment. We added seeds at densities from zero to three times natural fecundity of each species to undisturbed plots and examined spatial variation in recruitment by conducting experiments across ten grassland sites that varied in productivity and resource availability. Consistent across two replicated years, most species had linear recruitment functions across the range of added seed densities, indicative of seed-limited recruitment. Depending on year, the recruitment functions of only 19-37% of target species saturated near their average fecundity, and this was not associated with seed size. Recruitment was strongly inhibited by rodent seed predation for large-seeded species but not for smaller-seeded species. Proportional recruitment was more sensitive to spatial variation in recruitment conditions across sites for some small-seeded species than for large-seeded species. These results contradict the common belief that highly fecund small-seeded species suffer from microsite-limited recruitment. Rather, they imply that, at least episodically, recruitment can be strongly correlated to plant fecundity. However, proportional recruitment of small-seeded species was inhibited at productive sites to a greater extent than large-seeded species. Results also show that in a system where the dominant granivore prefers larger seeds, low-fecundity large-seeded species can suffer from even greater seed-limited recruitment than would occur in the absence of predators.
The input sounding and synthetic AMV data for the idealized case study
<p>This is a data repository in support of the article "Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts. Part I: Observing System Simulation Experiment (OSSE)Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts. Part I: Observing System Simulation Experiment (OSSE)" submitted to AGU <em>J. of Advances in Modeling of Earth Systems. </em></p> <p>The data set consists of</p> <ul> <li>The input sounding data for the truth simulation of an idealized supercell strom (input_sounding).</li> <li>The simulated AMV observations with the resolutions of 10km, 20km, 30km and 40km are contained in the zipped files.</li> </ul>
Data from: Organic matter and nutrient inputs from large wildlife influence ecosystem function in the Mara River, Africa
Animals can be important vectors for the movement of resources across ecosystem boundaries. Animals add resources to ecosystems primarily through egestion, excretion and carcasses, and the stoichiometry and bioavailability of these inputs likely interacts with characteristics of the recipient ecosystem to determine their effects on ecosystem function. We studied the influence of hippopotamus excretion/egestion and wildebeest carcasses, and their interactions with discharge, in the Mara River, Kenya. We measured nutrient dissolution and decomposition rates of wildlife inputs, the influence of inputs on nutrient concentrations and nutrient limitation in the river and the influence of inputs on biofilm growth and function in both experimental streams and along a gradient of inputs in the river. We found that hippopotamus excretion/egestion increase ammonium and coarse particulate organic matter in the river, and wildebeest carcasses increase ammonium, soluble reactive phosphorus, and total phosphorus. Concentrations of dissolved carbon and nutrients in the water column increased along a gradient of wildlife inputs and during low discharge, although concentrations of particulate carbon decreased during low discharge due to deposition on the river bottom. Autotrophs were nitrogen-limited, and heterotrophs were carbon-limited and nitrogen- and phosphorus- co-limited, upstream of animal inputs, but there was no nutrient limitation downstream of inputs. In experimental streams, hippo and wildebeest inputs together increased biofilm GPP and R. These results differed in the river, where low concentrations of hippo inputs increased gross primary production (GPP) and respiration (R) of biofilms, but high concentrations of hippo inputs in conjunction with wildebeest inputs decreased GPP. Our research shows that inputs from large wildlife alleviate nutrient limitation and stimulate ecosystem metabolism in the Mara River, and that the extent to which these inputs subsidize the ecosystem is mediated by the quantity and quality of inputs and discharge of the river ecosystem. Thus, animal inputs provide an important ecological subsidy to this river, and animal inputs were likely important in many other rivers prior to the widespread extirpation of large wildlife.
Data Supplement: Inputs and Outputs for Rahman et al. 2021
<p><strong>Contents:</strong></p> <p><strong>prototype_elec:</strong> Input Data Files (IDFs) generated by tempset for a small office with electric resistance heating. Only heating setpoints are modified.</p> <p><strong>output_elec:</strong> Output csvs corresponding to the IDFs generated in prototype_elec</p> <p><strong>prototype_gas:</strong> IDFs generated by tempset for a small office with gas furnace heating. Only heating setpoints are modified.</p> <p><strong>output_gas:</strong> Output CSVs generated corresponding to the IDFs generated in prototype_gas</p> <p><strong>prototype_cool:</strong> IDFs generated by tempset for a small office with vapor compression cooling. Only cooling setpoints are modified.</p> <p><strong>output_cool:</strong> Output CSVs generated corresponding to the IDFs generated in prototype_cool</p> <p><strong>Summary</strong>:</p> <p>Input and Output data for tradeoffs resulting from specific changes to those schedules in terms of three quantities: total electricity consumption per day; maximum hourly electricity consumption per day; and predicted percentage dissatisfied (occupant thermal comfort). The method for generating schedules requires a single base schedule as the starting point and, using a few clearly defined parameters, transforms it into a set of schedules that can be used for modeling an existing building stock or for performing parametric studies. Temperature setpoint schedules are generated and simulated for a small office building in a cool-dry climate in three different case studies pertaining to changing temperature setpoint schedules. </p> <p><strong>Accompanying software and workflow described here:</strong></p> <p>https://github.com/IMMM-SFA/rahman-etal_2021_tbd</p> <p> </p>
Data from: Field-scale experiments reveal persistent yield gaps in low-input and organic cropping systems
Knowledge of production-system performance is largely based on observations at the experimental plot scale. Although yield gaps between plot-scale and field-scale research are widely acknowledged, their extent and persistence have not been experimentally examined in a systematic manner. At a site in southwest Michigan, we conducted a 6-y experiment to test the accuracy with which plot-scale crop-yield results can inform field-scale conclusions. We compared conventional versus alternative, that is, reduced-input and biologically based–organic, management practices for a corn–soybean–wheat rotation in a randomized complete block-design experiment, using 27 commercial-size agricultural fields. Nearby plot-scale experiments (0.02-ha to 1.0-ha plots) provided a comparison of plot versus field performance. We found that plot-scale yields well matched field-scale yields for conventional management but not for alternative systems. For all three crops, at the plot scale, reduced-input and conventional managements produced similar yields; at the field scale, reduced-input yields were lower than conventional. For soybeans at the plot scale, biological and conventional managements produced similar yields; at the field scale, biological yielded less than conventional. For corn, biological management produced lower yields than conventional in both plot- and field-scale experiments. Wheat yields appeared to be less affected by the experimental scale than corn and soybean. Conventional management was more resilient to field-scale challenges than alternative practices, which were more dependent on timely management interventions; in particular, mechanical weed control. Results underscore the need for much wider adoption of field-scale experimentation when assessing new technologies and production-system performance, especially as related to closing yield gaps in organic farming and in low-resourced systems typical of much of the developing world.
Input data files for simulation: top_hat_cg_supg
Input data files for simulation: top_hat_cg_supg
Input data for boreal tip analysis code
<p>Input data for analysis code of boreal tip repository.<br>Includes central European geographical subsets of CRU TS, MOD44B, GLC2000, MPI-ESM and CHELSA-TraCE21k data products as well as paths for download of complete data sets.</p>
Model version, input data, results, and processing scripts for the Speizer et al. zero emissions transport paper
<p>Includes the files needed to run the GCAM scenarios, analyze the outputs, and produce the figures for the Speizer et al. zero emissions transport paper.</p>
Data for: Predation and biophysical context control long-term carcass nutrient inputs in an Andean ecosystem
<p>Animal carcass decomposition is an often-overlooked component of nutrient cycles. The importance of carcass decomposition for increasing nutrient availability has been demonstrated in several ecosystems, but impacts in arid lands are poorly understood. In a protected high desert landscape in Argentina, puma predation of vicuñas is a main driver of carcass distribution. Here, we sampled puma kill sites across three habitats (plains, canyons, and meadows) to evaluate the impacts of vicuña carcass and stomach decomposition on soil and plant nutrients up to 5 years after carcass deposition. Soil beneath both carcasses and stomachs had significantly higher soil nutrient content than adjacent reference sites in arid, nutrient-poor plains and canyons, but not in moist, nutrient-rich meadows. Stomachs had greater effects on soil nutrients than carcasses. However, we did detect higher plant N concentrations at kill sites. The biogeochemical effects of puma kills persisted for several years and increased over time, indicating that kills do not create ephemeral nutrient pulses, but can have lasting effects on the distribution of soil nutrients. Comparison to broader spatial patterns of predation risk reveals that puma predation of vicuñas is more likely in nutrient-rich sites, but carcasses have the greatest effects on soil nutrients in nutrient-poor environments, such that carcasses increase localized heterogeneity by generating nutrient hotspots in less productive environments. Predation and carcass decomposition may thus be important overlooked factors influencing ecosystem functioning in arid environments.</p>
HANZE v2.2 flood impact model input data
<p>This dataset provides input data needed to run HANZE v2.2 model. The ZIP files need to be downloaded and unpacked in the same directory, which has to be defined in "get_file.py" of the HANZE model (variable "repo_path" at the beginning of the file).</p>
Supplementary supply chain (input) data
<p>This document provides supplementary information on the Conference Paper entitled 'Ensuring supply for emergency services –<br>modeling supply chains with incomplete sets of data'' submitted by Kippenberger et al. for the Simulation Notes Europe (SNE)</p> <p> </p>
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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.