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Dataset results
477 results for “input data”
Input forcing data for CLM-ml at Soltis Center in Costa Rica
<p>Forcing data for Modeling profiles of micrometeorological variables in a tropical premontane rainforest using multi-layered CLM (CLM-ml)</p>
Input data for Isca radiative kernel offline calculation
<p>Input data for calculating the radiative kernels (offline) for Isca, including:</p> <ul> <li>three hourly climatology from Isca with Byrne & O'Gorman (BOG) radiaiton scheme</li> <li>three hourly climatology from Isca with RRTM radiaiton scheme</li> <li>ozone file for RRTM</li> </ul>
The influence of dynamic topography, climate, and tectonics on the Nile River source-to-sink system – Model input data
<p>Input data for Badlands models used in 2020 Honours thesis at the University of Sydney.</p>
Input and output model data for Horvath et al. 2020 (https://doi.org/10.5194/bg-2020-149)
<p>Please see "readme" file. </p>
Demeter - Input and Output Data
<p>This dataset includes input and output data for 20 combinations of GCM/RCP scenarios for the Demeter model. The GCM and RCP selected for the study of Argentina Energy-Water-Land Systems are MIROC-ESM-CHEM and RCP 6.0.</p>
Data from: Insect brain plasticity: effects of olfactory input on neuropil size
Insect brains are known to express a high degree of experience-dependent structural plasticity. One brain structure in particular, the mushroom body, has been attended to in numerous studies as it is implicated in complex cognitive processes such as olfactory learning and memory. It is, however, poorly understood to what extent sensory input per se affects plasticity of the mushroom bodies. By performing unilateral blocking of olfactory input on immobilized butterflies, we were able to measure the effect of passive sensory input on the volumes of antennal lobes and mushroom body calyces. We showed that the primary and secondary olfactory neuropils respond in different ways to olfactory input. Antennal lobes show absolute experience-dependency and increase in volume only if receiving direct olfactory input from ipsilateral antennae, while mushroom body calyx volumes were unaffected by the treatment and instead show absolute age-dependency in this regard. We therefore propose that cognitive processes related to behavioural expressions are needed in order for the calyx to show experience-dependent volumetric expansions. Our results indicate that such experience-dependent volumetric expansions of calyces observed in other studies may have been caused by cognitive processes rather than by sensory input, bringing some causative clarity to a complex neural phenomenon.
Data from: Increased nitrogen input enhances Kandelia obovata seedling growth in the presence of invasive Spartina alterniflora in subtropical regions of China
Mangroves in China are severely affected by the rapid invasion of the non-native species Spartina alterniflora. Although many studies have addressed the possible impacts of S. alterniflora on the performance of mangrove seedlings, how excessive nitrogen (N) input due to eutrophication affects the interactions between mangrove species and S. alterniflora remains unknown. Here, we report the results from a mesocosm experiment using seedlings of the native mangrove species Kandelia obovata and the exotic S. alterniflora grown in monoculture and mixed culture under no nitrogen addition and nitrogen (N) addition treatments for 18 months. Without N addition, the presence of S. alterniflora inhibited the growth of K. obovata seedlings. Excessive N addition significantly increased the growth rate of K. obovata in both cultures. However, the positive and significantly increasing relative interaction intensity index under excessive N input suggested that the invasion of S. alterniflora could favour the growth of K. obovata under eutrophication conditions. Our results imply that excessive N input in southeastern China can increase the competitive ability of mangrove seedlings against invasive S. alterniflora.
Input data of the IAP-CAS S2S system
<div> <div> <div> <p>This is the boundary conditions and input data for the IAP-CAS S2S system.</p> </div> </div> </div>
AGATNet Input Data
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Input data of Ham et al. (2019)
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Input data for Vahmani et al 2021 AH
<p>Input data for Vahmani et al 2021 AH</p>
TracBEV input data
<p>This data set is used with the software tool <a href="https://github.com/rl-institut/tracbev">TracBEV</a> to calculate locations for charging infrastructure from <a href="https://github.com/rl-institut/simbev">SimBEV</a> results. The following files are included in this data set:</p> <p><strong>boundaries.gpkg</strong><br> Boundaries for regions in Germany, split by <a href="https://de.wikipedia.org/wiki/Amtlicher_Gemeindeschl%C3%BCssel">AGS</a>.</p> <p><strong>housing_data.gpkg</strong><br> Number of houses per type (EFH, MFH) in a 100x100m grid.<br> Modified data set based on <a href="https://www.zensus2011.de/DE/Home/home_node.html">Zensus 2011</a>.</p> <p><strong>hpc_positions.gpkg</strong><br> Possble HPC charging points with weights.<br> Own creation based on <a href="https://www.openstreetmap.org/copyright">OSM</a> data, <a href="https://www.zensus2011.de/DE/Home/home_node.html">Zensus 2011</a> data and the <a href="https://www.bundesnetzagentur.de/DE/Fachthemen/ElektrizitaetundGas/E-Mobilitaet/start.html">BNetzA Ladesäulenregister</a>.</p> <p><strong>landuse.gpkg</strong><br> Filtered <a href="https://www.openstreetmap.org/copyright">OSM</a> data for landuse.</p> <p><strong>poi_cluster.gpkg</strong><br> Possible public charging points with weights.<br> Own creation based on <a href="https://www.openstreetmap.org/copyright">OSM</a> data.</p> <p><strong>public_positions.gpkg</strong><br> Existing public charging infrastructure.<br> Filtered version of the <a href="https://www.bundesnetzagentur.de/DE/Fachthemen/ElektrizitaetundGas/E-Mobilitaet/start.html">BNetzA Ladesäulenregister</a>.</p> <p> </p>
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>
BovReg_eqTL RNAseq demo input data as counts and aligned bam files
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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>
Input data related to article under revision
<p>Input data related to article under revision. Only reviewers can download the dataset.</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>
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.