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1,600 results for “input”
The potential of decentral heat pumps as flexibility option for decarbonised energy systems: Balmorel input data
<p><strong>Description</strong></p> <p>This dataset holds all Balmorel model input data as well as the Balmorel code used for the scenarios of the paper 'The potential of decentral heat pumps as a flexibility option for Austria's electricity system in 2030' submitted to 'Applied Energy'.</p> <p>The original Balmorel source code is available under https://github.com/balmorelcommunity/Balmorel under the ISC license. It was adapted in the course of this paper.</p> <p><strong>Data format</strong></p> <p>We provide the data in form of the data folders holding the .inc files for all scenarios.</p>
MAgPIE model input data sets: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>These MAgPIE input data sets include harmonized crop yield projections from several crop models (9 crop models and 5 climate models). Additionally, regional, validation, and calibration data sets are also reported.</p>
ICESat, ERS1, ERS2, Envisat Laser and Radar Altimetry Datasets for the Cryosphere model Comparison Tool (CmCt) Input for Greenland and Antarctica
<div> <p>These datasets contain the ICESat, ERS1, ERS2, Envisat Laser and Radar Altimetry Datasets for CmCt Input data for Greenland and Antarctica. These reference observational datasets are used in the CmCt to compare ice sheet models with.</p> <p>The <strong>ICESat/GLAS</strong> instrument was a lidar altimeter and the primary instrument on the NASA ICESat mission. It took point elevation measurements approximately every 170 meters along its track, and each shot had a footprint of approximately 70 meters in diameter.</p> <p>The GLAS instrument contained 3 lasers, but due to some instrumentation issues, it was decided to turn the lasers on and off during predetermined time periods. For more detailed information about GLAS and the ICESat mission, visit the <a href="http://icesat.gsfc.nasa.gov/icesat/" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">ICESat website</a>.</p> <p>For use with the CmCt project, the Greenland elevation data from ICESat/GLAS (Zwally et al, 2002) were preprocessed. The data were cleaned and limited to the ice sheets. At the time of creation the 634 release of the <a href="https://nsidc.org/data/GLA12/versions/34" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">GLAS12</a> product was used (<em>Zwally et al, 2014</em>). </p> <p>The processing was accomplished by:</p> <ol> <li>restricting the data to GLAS data points only on the ice surface</li> <li>applying two data quality filters we required the GLAS surface reflectivity to be > 0.0375 and we required the uncertainty associated with the GLAS fitting procedure to be < 0.0375 (the numerical coincidence is in fact a coincidence). These are the same quality criteria that were used for <a href="http://imbie.org/" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">IMBIE2</a> and thus are being implemented for the CmCt.</li> <li>checking the data against the reerence DEM (GIMP 90-m DEM for Greenland or Bamber 1-km DEM for Antarctica), requiring the elevation difference to be < 200m.</li> </ol> <p>Please find more details on the data preprocessing in the Supporting Docs tab.</p> <p>The<a href="https://www.esa.int/Applications/Observing_the_Earth/Envisat" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer"> <strong>Envisat</strong></a> (Environmental Satellite), <strong><a href="https://eoportal.org/web/eoportal/satellite-missions/e/ers-1" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">ERS1</a></strong>, <strong><a href="https://eoportal.org/web/eoportal/satellite-missions/e/ers-2" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">ERS2</a></strong> (European Remote Sensing Satellites 1 and 2) radar altimeter datasets were also preprocessed to prepare the data to generate a comparison data set for the CmCt. Several filters were used to remove data that are not on the ice sheet or have questionable elevations. Please see the detailed processing descriptions in the Supporting Docs.</p> <p>Radar and laser altimeters measure similar parameters. They measure the time of flight of photons from the spacecraft to the reflection point and back to the spacecraft. The time of flight is then used to calculate an elevation. Accurate elevations require precise knowledge of the spacecraft orbit, corrections for atmospheric scattering, and other factors.</p> <p>There are several differences between the radar and laser altimetry data available here that should be noted:</p> <ul> <li>The accuracy of the elevations calculated from the radar data is generally lower than the accuracy of elevations based on laser data, primarily because <ul> <li>the radar beam is much broader (several km by the time it reaches the ground vs < 100 m for the laser beam).</li> <li>the radar photons penetrate snow and ice a significant amount (cm to m), whereas the laser photons from ICESat penetrate minimally if at all.</li> </ul> </li> <li>ERS and Envisat worked at a lower pulse rate than ICESat, and had a shorter repeat period, so the data are sparser on the ground (but repeat approximately monthly). On the other hand, the radar satellites worked continuously, whereas ICESat only operated for 2-3 months per year. </li> <li>The radar data collectively cover a longer period of time, starting more than a decade earlier and extending past the end of the ICESat data.</li> <li>ERS and Envisat were in orbits that left larger holes at the poles than ICESat (8.5 degrees for the radar satellites vs 4 degrees for ICESat).</li> <li>Radar beams penetrate clouds, whereas the ICESat laser beam was scattered by clouds, with returns becoming unusable if the optical depth was much greater than 1.</li> </ul> <h4> </h4> <h4>Laser and Radar Altimetry Available Data Time Range:</h4> <h4> </h4> <table> <tbody> <tr> <td>ERS1:</td> <td>1991-1995</td> </tr> <tr> <td>ERS2:</td> <td>1996-2002</td> </tr> <tr> <td>Envisat:</td> <td>2003-2012</td> </tr> <tr> <td>ICESat/GLAS:</td> <td>2003-2009</td> </tr> </tbody> </table> <h4> </h4> <h4>Downloading Data</h4> <p><a href="https://theghub.org/resources?id=4737"><strong>The data can be downloaded from the Globus GHub-CmCt endpoint. Please log in and click on the Download tab to receive the Download instructions.</strong></a></p> </div> <h4>References</h4> <div> <p>Howat, I. M., A. Negrete, and B. E. Smith, The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets, The Cryosphere 8.4 (2014): 1509-1518.</p> <p> </p> <p>Howat, I. M., A. Negrete, and B. E. Smith. MEaSUREs Greenland Ice Sheet Mapping Project (GIMP) Digital Elevation Model, Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center (2015).</p> <p> </p> <p>Zwally, H. J., et al. ICESat's laser measurements of polar ice, atmosphere, ocean, and land, Journal of Geodynamics 34.3 (2002):405-445.</p> <p> </p> <p>Zwally, H. J., et al. GLAS/ICESat L2 Antarctic and Greenland ice sheet altimetry data V034, National Snow and Ice Data Center, Boulder, Colorado (2014).</p> </div>
BovReg_eqTL RNAseq demo input data as counts and aligned bam files
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DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: input files (3 of 3)
<p>DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes the files necessary for nudging the currents towards Copernicus GLORYS12V1 reanalysis values in a simulation from 1 September to 31 December 2013.</p> <p>The remaining input files for this period are available at <a href="https://doi.org/10.5281/zenodo.12752190" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752190</a> and <a href="https://doi.org/10.5281/zenodo.12734049" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12734049</a>.</p> <p>Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request.</p> <p>Sample output files (from the physics and biogeochemistry modules) are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a> and <a href="https://doi.org/10.5281/zenodo.12746262" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12746262</a>.</p>
Model input data, simulation output data and processed data presented in "Modelling the three-dimensional, diagnostic anisotropy field of an ice rise"
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Supplementary Data: Code, Input Data and Model data: PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p>Supplementary Data (preliminary version)</p> <p>PyPSA-Eur: An Open Optimisation Model of the European Transmission System</p> <p>Authors: J. Hörsch, F. Hofmann, D. Schlachtberger, T. Brown</p> <p>and</p> <p>The role of spatial scale in joint optimisations of generation and transmission for European highly renewable scenarios</p> <p>Authors: J. Hörsch, T. Brown</p> <p>The files in this record contain the scripts to build a <a href="http://pypsa.org/">PyPSA</a> model of the European Electricity System including renewable feed-in from wind, solar and hydro installations derived from reanalysis weather data satellite irradiation. The model PyPSA-Eur is described in the above publication.</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.</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>base_network.py creates the initial PyPSA network topology.</p> <p>add_electricity.py adds generators and storage units to the models, it generates the detailed resolved model described in the PyPSA-Eur paper.</p> <p>simplify_network.py removes stub ac-buses from network topology and simplifies long dc lines.</p> <p>cluster_network.py creates clustered representations of the electricity network for a given number of buses following the topology described in the "spatial scale" paper.</p> <p>prepare_network.py adds parameters like the CO2 limit and the transmission expansion volume relevant for the optimization to the model.</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><strong>Data</strong></p> <p>The input data include:</p> <ul> <li>Electricity sector data</li> <li>Topology derived from the analysis of an extract of the <a href="https://www.entsoe.eu/data/map/">ENTSO-E online map</a> using <a href="https://github.com/bdw/GridKit">GridKit</a> .</li> <li>A cost database with literature sources.</li> </ul> <p> </p>
ATSUI input files
<p>This dataset contains example input files required to run the ATSUI method.</p>
Input data related to article under revision
<p>Input data related to article under revision. Only reviewers can download the dataset.</p>
InVEST model inputs and key outputs for the SESMO journal manuscript "Where should livestock graze?"
<p>Archives of InVEST model data related to optimization of livestock- and water-related ecosystem services. (SESMO journal manuscript "Where should livestock graze?")</p> <p>1. InVEST inputs: Includes all the inputs to run the open source models InVEST (Seasonal Water Yield and Sediment Delivery Ratio)</p> <p>2. InVEST marginal values: key outputs derived from the InVEST models that are used in the optimizer (cf. manuscript). Includes baseflow marginal values (MV_SWY) and sediment marginal values (MV_SDR)</p> <p>3. InVEST outputs figures: key outputs derived from the InVEST models and optimizer that were used to create the figures in the manuscript</p>
Input data
<p>Input data for case studies. </p>
Input data and Supplementary Results for "Community-level signatures of ecological succession in natural bacterial communities"
<p> </p> <p><strong>README<br> ======</strong></p> <p><br> This file describes the content of the different files included in this repository to<br> reproduce results from [1] and some of its supplementary results.</p> <p> </p> <p><strong>## Input files ##</strong></p> <p><strong>* 20151016_Functions_remainder.csv</strong></p> <p> Functions measured in [2]. The relevant quantities used in [1] are labelled with "7", and include:<br> <br> * Community: Id of the sample<br> * Replicate<br> * Plate<br> * mgCO2.7: CO2 measured along 7 days of experiment<br> * CPM7: Cell counts at the end of the experiment<br> * pgRPC.7: CO2 per cell<br> * ATP7: ATP measured (nM)<br> * mG7: beta glucosidase (mM)<br> * mN7: beta chitinase (mM)<br> * mX7: xylosidase (mM)<br> * mP7: phosphatase (mM)<br> <br> <strong>* samples_metadata_time0.tsv</strong></p> <p> * Samples: Id of the sample <br> * Part.dates: Date of sampling<br> * Part.GPS.PAM: Optimal sampling sites <br> * Part.SparCC.PAM.t0: Optimal partition using SparCC<br> * Part.SJD.PAM.t0: Optimal partition using Jensen-Shannon Divergence<br> * Part.Dir.t0: Optimal partition using Dirichlet mixtures<br> * Part.month: Month in which the community was sampled<br> <strong>* Dist_GPS-Haversine.dat</strong></p> <p> Haversine (spatial) distances between samples</p> <p><strong>* corMat-SparCC_20151016_OTU_remainder.clean.samples.txt</strong></p> <p> Matrix of correlations between samples computed with SparCC<br> <br> <strong>* distMat_ShannonJensen_Samples_Time0.clean.dat</strong></p> <p> Distance matrix computed with Jensen-Shannon divergence.</p> <p> </p> <p><br> <strong>## Supplementary results ##</strong></p> <p><strong>* SEMmodels.zip</strong></p> <p> Results for the Structural Equation Models analysed. The structure of the folders follows the one<br> available at the repository of the [project ](https://github.com/apascualgarcia/TreeHoles_descriptive).<br> <br> <strong>* TaxaSummaries.zip</strong></p> <p> The file contains one folder for each community-class, with matrices in different formats (biom and txt) computing the relative abundances of the OTUs at different taxonomic levels (labelled L2 being the proxy for Phylum to L6, the proxy of species). These matrices can be visualized interactively opening with a web browser the files area_charts.html.</p> <p><strong>#### References ####</strong></p> <blockquote> <p> [1] Pascual-García, A., & Bell, T. (2019). Community-level signatures of ecological succession in natural bacterial communities. Nature Communications (In press)</p> </blockquote> <blockquote> <p> [2] Rivett, Damian W., and Thomas Bell. Abundance determines the functional role of bacterial phylotypes in complex communities." Nature microbiology 3.7 (2018): 767.</p> </blockquote> <p> </p>
Input for QCxMS prediction tutorial
<p>Input table for QCxMS prediction tutorial available on Galaxy Training Network. </p>
Gas Temperature and Plasma Discharge Region Temperature at Different Input Power Levels
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Ar (750.4 nm) and OH free radical (309 nm) change along the discharge gap intensity (microwave frequency 2.45 GHz, flow 3 L/min input power 70W and 100W, respectively).
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Electron Density at Different Input Power (Gas Flow: 3 L/min, Microwave Frequency: 2.45 GHz)}
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High benthic community respiration and ecosystem response to phytodetrital input in a sub-polar fjord on the West Antarctic Peninsula
<p><span>Glaciomarine fjords dominate the coastal margin of the West Antarctic Peninsula. Studies in similar habitats in the Arctic have shown that benthic biodiversity and ecosystem functioning in inner and middle fjord basins are reduced by turbidity and sedimentation disturbance caused by climate-warming-enhanced glacial melting. In contrast, the inner and middle fjord basins along the West Antarctic Peninsula are characterised as productivity and biodiversity hotspots, but benthic ecosystem functions remain unevaluated.</span> <span>In 2015-2016, we conducted sediment-respiration and <sup>13</sup>C pulse-chase experiments to assess benthic ecosystem functions along a five-station transect at ~500-600 m depths from the inner Andvord Bay fjord, through to Gerlache Strait, and onto the open continental shelf. Incubation samples from the inner and middle basins of Andvord Bay showed peaks in background seafloor respiration, benthic biomass, and uptake of labeled algal biomass compared to more outlying stations; the continental shelf exhibited the lowest levels of these variables, as well as dissolved inorganic carbon production. Macrofaunal community uptake was responsible for most of the C processing in the inner and middle parts of the fjord (>45%) while dissolved inorganic carbon was the dominant repository of processed C near the fjord mouth and on the continental shelf (>80%). The inner parts of Andvord Bay are hotspots of benthic C-cycling and metabolism, in addition to biodiversity. Ongoing climate warming is likely to negatively impact these inner-fjord hotspots by increasing meltwater input and sedimentation disturbance, yielding a reduction in the input and recycling of labile detritus at the seafloor in the inner-middle fjord.</span></p>
RFM atmospheric profiles input
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D1.002 space Input dataset
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B1.002 space Input dataset
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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.