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477 results for “input data”
Data and modeling input and parameter files for the 2021 Acapulco, Mexico earthquake and tsunami
<p>Raw and processed strong motion, GNSS, InSAR and tide gauge data for the event. Also includes MudPy slip inversion parameter files as well as GeoClaw input files.</p>
Sample Input Data and Supporting Files for the SELECT Model of Urbanization
<p>Sample Input Data and Supporting Files for the SELECT Model of Urbanization</p> <p>Code available at: https://github.com/IMMM-SFA/select</p>
AURES II, WP8, Dataset Input Data
<p>Input Dataset for power system modelling with the model BALMOREL conducted in the course of the AURES II project.<br> Workpackage 8; TU Wien</p> <p> </p>
Data from: Between the Cape Fold Mountains and the deep blue sea: comparative phylogeography of selected codistributed ectotherms reveals asynchronous cladogenesis. Sampling locations and MaxEnt input files
<p><span>We compare the phylogeographic structure of thirteen codistributed ectotherms including four reptiles (a snake, a legless skink and two tortoise species) and nine invertebrates (six freshwater crabs and three velvet worm species) to test the presence of congruent evolutionary histories. </span><span>Phylogenies were estimated and dated using maximum likelihood and Bayesian methods with combined mitochondrial and nuclear DNA sequence datasets. </span><span>All taxa demonstrated a marked east/west phylogeographic division, separated by the Cape Fold Mountain range.</span><span> <span>Phylogeographic concordance factors were calculated to assess the degree of evolutionary congruence among the study species and </span></span><span>generally supported a shared pattern of diversification along the east/west longitudinal axis</span><span>. Testing simultaneous divergence between the eastern and western phylogeographic regions indicated </span><span>pseudo-congruent evolutionary histories among the study taxa, with at least three separate divergence events throughout the Mio/Plio/Pleistocene epochs.</span><span> <span>Climatic refugia were identified for each species using climatic niche modeling, </span></span><span>demonstrating taxon-specific responses to climatic fluctuations. Climate and the Cape Fold Mountain barrier explained the highest proportion of genetic diversity in all taxa, while climate was the most significant individual abiotic variable. </span><span>This study highlights the complex interactions between the Cape Fold Mountains and past climatic oscillations during the Mio/Plio/Pleistocene. The congruent east/west phylogeographic division observed in all taxa lends support to the conclusion that the longitudinal climatic gradient within the Greater Cape Floristic Region, mediated in part by the barrier to dispersal posed by the Cape Fold Mountains, plays a major role in lineage diversification and population differentiation.</span></p>
Archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil
<p>This is the archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil; submitted to the Journal Science of the Total Environment.</p>
Climate change effects on deep-water corals – habitat suitability model input data
<p>Deep-water corals are protected in the seas around New Zealand by legislation that prohibits intentional damage and removal, and by marine protected areas where bottom trawling is prohibited. However, these measures do not protect them from the impacts of a changing climate and ocean acidification. To enable adequate future protection from these threats we require knowledge of the present distribution of corals and the environmental conditions that determine their preferred habitat, as well as the likely future changes in these conditions, so that we can identify areas for potential refugia.</p> <p>In this study, we built habitat suitability models for 12 taxa of deep-water corals using a comprehensive set of sample data and predicted present and future seafloor environmental conditions from an earth system model specifically tailored for the South Pacific. These models predicted that for most taxa there will be substantial shifts in the location of the most suitable habitat and decreases in the area of such habitat by the end of the 21st century, driven primarily by decreases in seafloor oxygen concentrations, shoaling of aragonite and calcite saturation horizons, and increases in nitrogen concentrations. The current network of protected areas in the region appear to provide little protection for most coral taxa, as there is little overlap with areas of highest habitat suitability, either in the present or the future. We recommend an urgent re-examination of the spatial distribution of protected areas for deep-water corals in the region, utilising spatial planning software that can balance protection requirements against value from fishing and mineral resources, take into account the current status of the coral habitats after decades of bottom trawling, and consider connectivity pathways for colonisation of corals into potential refugia.</p>
LaMEM source code and input files corresponding to Present‐day upper‐mantle architecture of the Alps: Insights from data‐driven dynamic modelling
<p>This repository contains LaMEM source code and input files for the models presented in Kumar, A., Cacace, M., Scheck-Wenderoth, M., Götze, H.-J., & Kaus, B. J. P. (2022). Present-day upper-mantle architecture of the Alps: Insights from data-driven dynamic modeling. Geophysical Research Letters, 49, e2022GL099476. https://doi. org/10.1029/2022GL099476</p>
Magnetique: input data and PostgreSQL database
<p>Magnetique: An interactive web application to explore transcriptome signatures of heart failure</p> <p>Supplementary dataset.</p> <p>- <a href="https://zenodo.org/api/files/200e02bb-a112-4ccb-a20f-a49dca9e579a/db_dump.sql.gz?versionId=f87954dd-af1e-47b2-be76-c5268ce63c4c">db_dump.sql.gz</a>: This is a daily [backup-dump](https://www.postgresql.org/docs/current/backup-dump.html) of the Magnetique database obtained on 18.07.2022 and shared for reproducibility purposes</p> <p>- Other files are required as input for the modeling steps detailed at https://github.com/dieterich-lab/magnetiqueCode2022</p> <p>Refer to https://shiny.dieterichlab.org/app/magnetique or contact the authors for details.</p>
Supplementary Data: Code, Input Data and Result Summaries: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the scripts to build the model, input data and result summaries for the model PyPSA-Eur-Sec-30 described in the above publication.</p> <p>The full results files (which include the post-processed input data) can be found in a <a href="https://zenodo.org/record/1146649">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p><strong>WARNING:</strong> A newer, improved version of this model, <a href="https://github.com/PyPSA/pypsa-eur-sec">PyPSA-Eur-Sec</a>, is under construction on GitHub.</p> <p><strong>Scripts</strong></p> <p>To use the scripts, you need the following free software Python libraries:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">PyPSA</a> for the modelling framework</li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a> for various helper functions to build the model instance</li> <li><a href="https://github.com/FRESNA/atlite">atlite</a> to process weather data into power system data</li> <li><a href="https://snakemake.readthedocs.io/en/latest/">snakemake</a> to organise the execution of the software</li> </ul> <p>and other standard libraries from the <a href="https://pypi.python.org/pypi">Python Package Index</a> (PyPI), such as pandas, pyomo, countrycode, etc.</p> <p>snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work. If you insist on using the latest versions, please be aware that you'll need to make at least the following changes:</p> <p>i) To accommodate changes in pandas versions 0.22 and higher, in scripts/prepare_network.py change "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby("technology").sum()" to "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby(level="technology").sum(min_count=1)".</p> <p>ii) In later versions of PyPSA the component groups like "pypsa.components.one_port_components" have become network-specific and are stored instead at "network.one_port_components".</p> <p>To solve the optimisation problem the scripts are coded to use the commercial solver <a href="http://www.gurobi.com/">Gurobi</a>. To solve the problems in a reasonable time, you will need <a href="http://www.gurobi.com/">Gurobi</a> or an equivalently fast solver such as <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a>. <a href="http://www.gurobi.com/">Gurobi</a> and <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a> both have cost-free licences for academic users.</p> <p>You will also need a computer with at least 64 GB of RAM, since pyomo and the solver are memory intensive.</p> <p>The Python scripts in this repository (in the directory scripts/) are released under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GNU General Public Licence Version 3.0</a> (GPL 3.0).</p> <p>The scripts build_*.py process all raw input data into a form where it can be used in the model.</p> <p>make_options.py prepares the options.yml file for each model run.</p> <p>prepare_network.py populates the PyPSA network for each model run with the input data.</p> <p>solve_network.py solves the optimisation problem with <a href="http://www.gurobi.com/">Gurobi</a> or the solver of your choice (this step takes several hours).</p> <p>make_summary.py aggregates the results into CSV files in the directory results/ (also provided in this repository).</p> <p>The scripts plot_*.py and paper_graphics*.py prepare graphical output.</p> <p>All scripts are managed with the <a href="http://snakemake.readthedocs.io/en/latest/">snakemake</a> workflow management tool.</p> <p>To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute</p> <pre><code>snakemake</code></pre> <p>for the rule you want to run.</p> <p>Since the jobs are computationally intensive you may want to run them on a cluster. To run the jobs on a cluster with <a href="https://slurm.schedmd.com/">Slurm</a>, then execute e.g.</p> <pre><code>./snakemake_cluster --jobs 6</code></pre> <p>The cluster is configured in cluster.yaml. You will need to create the directory for the logs, i.e. logs/cluster/, before running the script.</p> <p><strong>Data</strong></p> <p>All input data (in the directory scripts/) and results summaries (in the directory results/) are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0), except those where explicit sources and licences are mentioned in the data folders.</p> <p>The input data include:</p> <ul> <li>Electricity sector data, which largely follows the <a href="https://doi.org/10.5281/zenodo.804337">Zenodo repository</a> for <strong><a href="https://doi.org/10.1016/j.energy.2017.06.004">The Benefits of Cooperation in a Highly Renewable European Electricity Network</a></strong>, except the current repository uses the <a href="https://data.open-power-system-data.org/time_series/2017-07-09/">Open Power System Data Time Series Data Package</a> for load data and <a href="http://renewables.ninja/">Renewables.ninja</a> for solar time series.</li> <li>Heating time series based on the degree-day approximation, constructed with the library <a href="https://github.com/FRESNA/atlite">atlite</a>.</li> <li>Hourly traffic statistics for a week from the German Federal Highway Research Institute (BASt).</li> <li>Yearly energy per country per sector from the <a href="http://www.indicators.odyssee-mure.eu/energy-efficiency-database.html">Odyssee database</a> and <a href="http://ec.europa.eu/eurostat/web/energy/data/energy-balances">Eurostat</a>.</li> <li>A cost database with literature sources.</li> </ul>
Pilot 1 Model-based decision support for testing drought-related adaptation strategies in the Aa of Weerijs river basin, the Netherlands: Hydrological model description, input data sources and model results
<p>This dataset contains: the report with the description of the model structure, the input data sources and the spatial locations within the catchment for which surface and groundwater results data are provided.</p>
Data for: Multimodal convergence in the pedunculopontine tegmental nucleus: motor, sensory, and theta-frequency inputs influence the activity of single neurons
<p>The pedunculopontine tegmental nucleus of the brainstem (PPTg) has extensive interconnections and neuronal-behavioural correlates. It is implicated in movement control and sensorimotor integration. We investigated whether single neuron activity in freely moving rats is correlated with components of skilled forelimb movement and whether individual neurons respond to both motor and sensory events. We found that individual PPTg neurons showed changes in firing rate at different times during the reach. This type of temporally specific modulation is like activity seen elsewhere in voluntary movement control circuits, such as the motor cortex, and suggests that PPTg neural activity is related to different specific events occurring during the reach. In particular, many neuronal modulations were time-locked to the end of the extension phase of the reach, when fine distal movements related to food grasping occur, indicating strong engagement of PPTg in this phase of skilled individual forelimb movements. In addition, some neurons showed brief periods of apparent oscillatory firing in the theta range at specific phases of the reach-to-grasp movement. When movement-related neurons were tested with tone stimuli, many also responded to this auditory input, allowing for sensorimotor integration at the cellular level. Together, these data extend the concept of the PPTg as an integrative structure in the generation of complex movements, by showing that this function extends to the highly coordinated control of the forelimb during skilled reach to grasp movement and that sensory and motor-related information converges on a single neuron, allowing for direct integration at the cellular level.</p>
Case study input data set for article "Stochastic planning of energy system transformation pathways under uncertain industry demands"
<p>The data set contains input data for the model EMPRISE of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. </p>
Input data files for Dietrich et al. Chl-a and nutrient random forest modeling
<p>Input data for the models originally from:</p> <p>EPA, U. S. <em>WSIO Indicator Data Library</em>, <<a href="https://www.epa.gov/wsio/wsio-indicator-data-library">https://www.epa.gov/wsio/wsio-indicator-data-library</a>> (2023).</p> <p>Platt, L. R., Spaulding, S.A., Covert, A., Murphy, J.C., and Raynor, N. A national harmonized dataset of discrete chlorophyll from lakes and streams (2005-2022). (2023). <a href="https://doi.orghttps">https://doi.org:https://doi.org/10.5066/P9J0ZIOF</a></p> <p>Saad, D. A., Argue, D.M., Schwarz, G.E., Anning, D.W., Ator, S.W., Hoos, A.B., Preston, S.D., Robertson, D.M., and Wise, D.R., 2019. Water-quality and streamflow datasets used for estimating long-term mean daily streamflow and annual loads to be considered for use in regional streamflow, nutrient and sediment SPARROW models, United States, 1999-2014. (2019). <a href="https://doi.orghttps">https://doi.org:https://doi.org/10.5066/F7DN436B</a></p> <p> </p>
Input data for 1d EVP model
<p>Data here is the input needed for running the 1D EVP model referenced here:</p> <p>Rasmussen, T. A. S., Poulsen, J., Ribergaard, M. H., & Rethmeier, S. (2024). dmidk/cice-evp1d: Unit test refactorization of EVP solver CICE (refactorevp1d_v0.1). EGU V. Zenodo. https://doi.org/10.5281/zenodo.10782548.</p> <p>This is based on a restart on the 2020030100 from the NAAg domain. </p> <p>Ponsoni Leandro, Ribergaard Mads Hvid, Nielsen-Englyst Pia, Wulf Tore, Buus-Hinkler Jørgen, Kreiner Matilde Brandt, Rasmussen Till Andreas Soya; Greenlandic sea ice products with a focus on an updated operational forecast system; Frontiers in Marine Science, Volume 10,2023;10.3389/fmars.2023.979782 </p> <p> </p>
Data from: Nitrifier controls on soil NO and N2O emissions in three chaparral ecosystems under contrasting atmospheric N inputs
<p>Nitrogen saturation theory predicts high rates of atmospheric N deposition can increase ecosystem N availability and stimulate ecosystem N losses via soil nitric oxide (NO; an air pollutant at high concentrations) and nitrous oxide (N<sub>2</sub>O; a strong greenhouse gas) emissions. However, it remains unclear whether theories developed in mesic ecosystems apply to drylands, where plant and soil N availability are not always coupled in dry soils. NO and N<sub>2</sub>O are often produced in soils during the oxidation of ammonia by ammonia oxidizing archaea (AOA) or ammonia oxidizing bacteria (AOB) during nitrification. AOB are thought to emit more NO and N<sub>2</sub>O during nitrification than AOA and may be favored in N-rich relative to N-limited environments, suggesting high rates of atmospheric N deposition might produce positive feedback sending more of the N to the atmosphere. To assess how high rates of atmospheric N deposition affect AOB- and AOA-derived N trace gas emissions, we selectively inhibited AOA and AOB nitrifiers and measured NO and N<sub>2</sub>O emissions from soils collected from three dryland sites exposed to relatively low (3.8 kg ha<sup>-1 </sup>= Low N) or high (11.8 kg ha<sup>-1</sup> = High N1; 15.6 kg ha<sup>-1</sup> = High N2) rates of atmospheric N inputs. We found that while the High N2 deposition site had the lowest AOA:AOB ratio (2.33 ± 0.57), consistent with expectations, this site did not emit the most NO and N<sub>2</sub>O. Rather, AOA emitted between 21–78% of the NO from our sites, with higher AOA-derived NO emissions from relatively coarse-textured soils in the Low N deposition site. In addition to nitrification, denitrification also contributed to NO and N<sub>2</sub>O emissions, especially in the Moderate N deposition site (where denitrification-derived NO and N<sub>2</sub>O emissions were 2.0 – 3.7 time greater than the other sites), which had finer textured soils that may favor denitrification. Interactions between soil texture and N availability, therefore, appears to be the primary mechanism determining whether atmospheric N deposition is retained in the ecosystem or reemitted to the atmosphere as NO or N<sub>2</sub>O.</p>
Modelling input data for the case studies of the paper "Strategic bidding in light-robust day-ahead electricity markets".
<p><span>This data package includes the modelling input data to replicate the results of the case studies included in the paper "Strategic bidding in light-robust day-ahead electricity markets". </span></p> <p><span>This supplementary data package includes the following files:</span></p> <p><span><span>-<span> </span></span></span><span>Meta Data – Input data: Dataset containing the input data for the strategic bidding behavior problem. It includes bids from conventional, demand and stochastic players and the scenarios for system imbalance and real-time production.</span></p> <p><span><span>-<span> </span></span></span><span>Readme.txt: Includes a detailed description of the data packages</span></p> <p><span> </span></p> <p><span>Sources of data:</span></p> <p><span>* Ordoudis, C., Pinson, P., Morales, J. M., & Zugno, M. (2016). An updated version of the IEEE RTS 24-bus system for electricity market and power system operation studies. Technical University of Denmark.</span></p> <p><span>* Silva-Rodriguez, L., Sanjab, A., Fumagalli, E., Virag, A., & Gibescu, M. (2022). A light robust optimization approach for uncertainty-based day-ahead electricity markets. Electric Power Systems Research, 212, 108281. https://doi.org/10.1016/J.EPSR.2022.108281<span> </span></span></p> <p><span>* Derived (scaled down) from Elia. (2024). Open data. Retrieved from https://www.elia.be/en/grid-data/open-data<span> </span></span></p> <p><span>* Own data<span> </span></span></p> <p><span><span> </span></span></p>
Combined data file for Jokinen et al. "Terrestrial organic matter input drives sedimentary trace metal sequestration in a human-impacted boreal estuary", Science of the Total Environment 717, 2020
<p>The datafile contains all the new raw data presented in the figures in the publication.</p>
Raw data and simulation input file for friction experiment "crack28" performed at Geoazur on 2023/06/30.
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Inputs, results data and analysis script for the evaluation of the PDG-Arena forest growth model on beech-fir stands
<p>Supplementary files for simulations in Rouet et al. (2024): PDG-Arena: An eco-physiological model for characterizing tree-tree interactions in heterogeneous and mixed stands (doi: <a href="https://doi.org/10.1101/2024.02.09.579667" target="_blank" rel="noopener">10.1101/2024.02.09.579667</a>).</p> <p>This repository is an archive of the github repository PDG-Arena-extra (release v1.0.3), accessible at <a href="https://github.com/camille-rouet/PDG-Arena-extra/tree/v1.0.3" target="_blank" rel="noopener">https://github.com/camille-rouet/PDG-Arena-extra/tree/v1.0.3</a>.</p>
Input data of the multi-patch geometries used in: A. Farahat, H. M. Verhelst, J. Kiendl, M. Kapl, Isogeometric analysis for multi-patch structured Kirchhoff–Love shells, Computer Methods in Applied Mechanics and Engineering 411 (2023) 116060 DOI: 10.1016/j.cma.2023.116060
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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.