Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
855
datasets available to search
ShareScore release 0.9.0
Dataset results
855 results for “model system”
Dataset for Analysis of Various Spatial Resolutions for Modelling Sector-Coupled Energy Systems
<p>Dataset for preprocessing Balmorel data in this Danish case study.</p>
Sector-coupled model for the German energy system in 2019
<p>This repository contains input data for the open-source Python tool <a href="https://github.com/openego/eTraGo">eTraGo</a> (<strong>e</strong>lectricity <strong>Tra</strong>nsmission <strong>G</strong>rid <strong>o</strong>ptimization) version 0.10.0.<br>This data will be uploaded to the <a href="https://openenergy-platform.org/">OpenEnergy Platform</a> which can be accessed by eTraGo. This dataset is an intermediate solution until the data is uploaded.</p> <p>The published data includes the sector-coupled transmission grid data for the scenario <em>status2019</em>. It was created with the open-source tool <a href="https://github.com/openego/powerd-data">powerd-data</a> within the research project <a href="https://h2-powerd.de/">PoWerD</a>. All input data sets as well as the code are available under open source licenses.</p> <p>We thank the Federal Ministry for Economic Affairs and Climate Action for funding the research project PoWerD (grant number: 03EI1042C)</p> <p>The data is stored as a PostgreSQL database in the attached backup file. First, the required schemas and extensions have to be created within the database by running the following SQL statements:</p> <p><code>CREATE EXTENSION postgis;</code></p> <p>Afterwards, the data can be restored by using e.g. pgAdmin or via PostgreSQL's <a href="https://www.postgresql.org/docs/current/app-pgrestore.html">pg_restore</a> command (replace <code>HOST</code>, <code>DATABASE_NAME</code>, <code>PORT</code> and <code>USER</code> by your settings):</p> <p><code>pg_restore --host HOST --port PORT --username USER --no-password --dbname </code><code>DATABASE_NAME --no-owner --no-privileges --verbose "PoWerD_status2019_v3.backup"</code></p>
U-Surf: a global 1km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
<p>High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth System Models (ESMs) and ultra-high-resolution urban climate modeling, particularly at large scales. Here, we present a first-of-its-kind 1km-resolution present-day (circa-2020) global continuous urban surface parameter dataset – U-Surf. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for developing dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet- and canopy-level. Our high-resolution U-Surf dataset significantly improves the representation of the urban land heterogeneity both within and across cities globally. U-Surf provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs, enables detailed city-to-city comparisons across the globe, and supports the next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf are also relevant as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to promote the research frontier on urban systems science, climate-sensitive urban design, and coupled human-Earth systems in the future.</p> <p>The complete list of parameters is presented in the table below.</p> <table> <tbody> <tr> <td>Category</td> <td>Parameter</td> <td>Notes</td> </tr> <tr> <td>Radiative</td> <td>Roof | Impervious | Pervious canyon floor | Wall emissivity</td> <td> </td> </tr> <tr> <td> </td> <td>Roof | Impervious | Pervious canyon floor | Wall albedo</td> <td> </td> </tr> <tr> <td>Morphological</td> <td>Roof | Pervious fraction</td> <td>Roof fraction is w.r.t. urban horizontal surface, and pervious fraction is w.r.t. canyon floor (i.e. pervious and impervious canyon floor).</td> </tr> <tr> <td> </td> <td>Building height</td> <td>Unit: m; Height of wind in the canyon is simply set as half of the building height in CLMU.</td> </tr> <tr> <td> </td> <td>Canyon height-to-width ratio</td> <td> </td> </tr> <tr> <td> </td> <td>Urban percentage</td> <td> </td> </tr> <tr> <td>Thermal</td> <td>Roof | Wall thickness</td> <td>Unit: m</td> </tr> <tr> <td> </td> <td>Roof | Impervious canyon floor | Wall thermal conductivity</td> <td>Unit: W/m*K</td> </tr> <tr> <td> </td> <td>Roof | Impervious canyon floor | Wall volumetric heat capacity</td> <td>Unit: J/m^3*K</td> </tr> <tr> <td> </td> <td>Number of impervious canyon floor layer</td> <td> </td> </tr> <tr> <td> </td> <td>Minimum | Maximum interior building temperature</td> <td>Unit: K</td> </tr> <tr> <td> </td> <td>Air conditioning adoption rate</td> <td> </td> </tr> </tbody> </table> <p> </p> <p>Radiative and morphological parameters are presented in the format of both .tif and .nc to accommodate different needs for the urban climate modeling community. Thermal parameters adapted from CLMU are available in a single .nc file. A CESM-compatiable surface dataset and a time-variant urban dataset (including P_AC and T_BUILDING_MAX; Li et al., 2024) at standard resolution (0.9375°x1.25°) are included for direct simulation use. Note that the urban percentage used to create the surface dataset comes from the PCT_URBAN parameter calculated in U-Surf, but users can input their own urban extent data to generate a customized surface dataset. The raw 1-km data can be easily aggregated/regridded to other resolution as needed.</p> <p> </p> <p><strong>Version 1.1 updates:</strong></p> <p>1. Fill part of the data gaps in Asia. </p> <p>2. Change the aggregation method of some parameters to be facet-area weighted in the 1deg surfdata.</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>
Dataset related to article "Development of a 3D ex vivo model of brain-leukemia interaction to study the role of Activin A in the Central Nervous System microenvironment"
<p>Excel file related to the article</p>
Model's configuration for paper "Dynamic and Thermodynamic coupling between the Atmosphere and Ocean near the Kuroshio Current and Extension System"
<p>Here are the data used for the SKRIPS simulations. </p>
A systems biology approach to investigate glucocorticoid response in a cellular model of human bronchial epithelium - Supplementary material
<p>Electronic appendices of PhD thesis: "A systems biology approach to investigate glucocorticoid response in a cellular model of human bronchial epithelium".</p>
Figure 1 from: Sánchez-Fernández D, Rizzo V, Bourdeau C, Cieslak A, Comas J, Faille A, Fresneda J, Lleopart E, Millán A, Montes A, Pallares S, Ribera I (2018) The deep subterranean environment as a model system in ecological, biogeographical and evolutionary research. Subterranean Biology 25: 1-7. https://doi.org/10.3897/subtbiol.25.23530
Figure 1 Relationship between the temperature inside the cave and the surface (Mean Annual Temperature (°C) of each pixel (0.08° cells).
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>
Supplemental material for article "An analytical approach for bacteria modeling in an estuarine system and in-situ die-off rate estimation"
<p>It contains several supplemental figures and text. </p>
Moving-block System: Requirements and Formal Models
<p>A moving-block system (cf. <a href="https://en.wikipedia.org/wiki/Moving_block">https://en.wikipedia.org/wiki/Moving_block</a>) is a railway signalling and distancing system aimed at reducing the headways between trains along a track, therefore increasing line capacity. In railways, a block is intended as a segment of track that is assigned to a train: a train cannot enter a block that is occupied by another train. With traditional fixed-block systems, each moving train is assigned a fixed block, regardless of its speed and position within the block itself. With moving-block systems, the block is dynamically computed based on the position and speed of the train, which is continuously computed onboard and communicated to the wayside control systems. This enables the possibility of routing more trains along the same track. </p> <p>For more details, please refer to: <a href="https://en.wikipedia.org/wiki/Communications-based_train_control">https://en.wikipedia.org/wiki/Communications-based_train_control</a></p> <p>The package includes a set of <strong>requirements</strong> and <strong>models</strong> for a railway moving-block system:</p> <p>(a) a PDF document named Moving-block Model and Requirements.pdf, which includes a UML model of a moving-block system together with a set of requirements for the system;</p> <p>(b) a set of 9 folders, each one associated to a formal or semi-formal development tool. Each folder contains one or more models of the moving-block system from (a), developed by means of the tool. </p> <p>The models were developed using the following tool versions. Other versions may still open and verify the models.</p> <ul> <li>Simulink (2017b)</li> <li>UMC (4.7)</li> <li>UPPAAL SMC (4.1.4) </li> <li>Atelier B (4.2.1)</li> <li>ProB (1.10.2018)</li> <li>NuSMV (2.6.0)</li> <li>SPIN (6.4.9)</li> <li>CADP (2019-a)</li> <li>FDR4 (4.2.3)</li> </ul>
Fig. 6 in Outcome of within-host competition demonstrates that parasite virulence doesn't equal success in a myxozoan model system
Fig. 6. Log fold change in immunogloblulin expression relative to controls on day 14 in intestine samples. Parasite copy numbers measured in intestine samples are overlaid on IgM intestine plot (parasite copy number data are the same for the IgT plot). Letters denote treatments that differed (Tukey's HSD tests, α = 0.05).
Fig. 5 in Outcome of within-host competition demonstrates that parasite virulence doesn't equal success in a myxozoan model system
Fig. 5. Log-foldchange in cytokine expression relative to controls at day-14 in spleen and intestine samples. Parasite copy numbers measured in gill tissues is overlaid on IFN-gamma spleen plot and parasite copy numbers measured in intestine samples are overlaid on IFN-gamma intestine plot, but parasite copy number data are the same for, and apply to, all cytokine plots underneath. Letters denote treatments that differed (Tukey's HDS tests, α = 0.05).
Multi-Robot Cell Use Case - Prism System Model
Open the record for dataset details and reuse information.
Intercomparison of Two Model Climates Simulated by a Unified Weather-Climate Model System (GRIST)
<p>Part I and other scripts.</p>
Spatially explicit re-harmonized terrestrial carbon densities for calibrating Integrated human-Earth System Models
<p>Soil and vegetation carbon densities play a critical role in global and regional human-Earth system models. These densities affect variables such as land use change emissions and also influence land use change pathways under climate mitigation scenarios where terrestrial carbon is assigned a carbon price. Recently, more spatially explicit, fine resolution data have become available for both soil and vegetation carbon. However, for models to effectively use these data the fine resolution data need to be reharmonized to initial land use and land cover conditions represented by these models. Without such reharmonization the carbon values may be very inaccurate for particular land types and places where the source data and the model disagree on the land use/cover type. Here we present reharmonized soil and vegetation carbon densities both at the grid cell level at 5 arcmin resolution and also aggregated to 235 water sheds for 4 different land use and 15 land cover types. These data are particularly useful as initial land carbon conditions for global Multisectoral Dynamic Models (MSD). Moreover, these data include six different statistical states calculated using distinct resampling methods for each of the land use, land cover types. These statistical states are used to define a range of possible carbon values for each land classification, and any state can be used for defining initial conditions of soil and vegetation carbon in MSD models. We make use of these statistical states to calculate spatially distinct uncertainties in the carbon densities by land type. We have implemented these data in a state-of-the-art multi sector dynamics model, namely the Global Change Analysis Model (GCAM), and show that these new data improve several land use responses in the model, especially when terrestrial carbon is assigned a carbon price. The statistical states in our data are validated against similar estimates in the literature both at a grid cell level and at a regional level. </p> <p>This is a data record which corresponds to the paper "Spatially explicit re-harmonized terrestrial carbon densities for calibrating Integrated Multisectoral Models" (Narayan et al. 2023, under review)</p> <p>We have now also added a tabular version of the dataset aggregated to GTAP's AEZ definitions as opposed to GCAM's GLUs</p> <p> </p>
Anatomical models and scripts for conducting simulations of conduction delays in the ventricular conduction system in human post myocardial infarction using MonoAlg3D
<p>The repository contains:<br><br>1. Video example (example_LBBB.mp4). Simulation of accelerating sinus rhythm (120 to 171 bpm) in myocardial infarction (MI) and left bundle branch block (LBBB) conditions leading to arrhythmia.</p> <p>2. Collection of anatomical models (ventricles and conduction system) used to create the population of models in the study. It includes variability in the infarct size (none, small, large) and multiple conduction delay conditions (LBBB, RBBB and left ventricle) in different sizes, locations and combinations. Models can be visualised in Paraview using the script paraview-alg-plugin.py. The file simulation_plan.xlsx provides further details on the population of models.</p> <p>3. Simulation scripts, defining the scenario and model conditions of the simulations are described in these files, to be used in MonoAlg3D (<span><a href="https://github.com/LLRiebel/MonoAlg3D_C-2023">https://github.com/LLRiebel/MonoAlg3D_C-2023</a></span>). </p>
Data from: Using probability modelling and genetic parentage assignment to test the role of local mate availability in mating system variation.
The formal testing of mating system theories with empirical data is important for evaluating the relative importance of different processes in shaping mating systems in wild populations. Here we present a generally applicable probability modelling framework to test the role of local mate availability in determining a population's level of genetic monogamy. We provide a significance test for detecting departures in observed mating patterns from model expectations based on mate availability alone, allowing the presence and direction of behavioural effects to be inferred. The assessment of mate availability can be flexible and in this study it was based on population density, sex ratio and spatial arrangement. This approach provides a useful tool for (1) isolating the effect of mate availability in variable mating systems and (2) in combination with genetic parentage analyses, gaining insights into the nature of mating behaviours in elusive species. To illustrate this modelling approach, we have applied it to investigate the variable mating system of the mountain brushtail possum (Trichosurus cunninghami) and compared the model expectations with the outcomes of genetic parentage analysis over an 18 year study. The observed level of monogamy was higher than predicted under the model. Thus, behavioural traits, such as mate guarding or selective mate choice, may increase the population level of monogamy. We show that combining genetic parentage data with probability modelling can facilitate an improved understanding of the complex interactions between behavioural adaptations and demographic dynamics in driving mating system variation.
Parasitism and host dispersal plasticity in an aquatic model system
<p>Dispersal is a central determinant of spatial dynamics in communities and ecosystems, and various ecological factors can shape the evolution of constitutive and plastic dispersal behaviours. One important driver of dispersal plasticity is the biotic environment. Parasites, for example, influence the internal condition of infected hosts and define external patch quality. Thus state-dependent dispersal may be determined by infection status and context-dependent dispersal by the abundance of infected hosts in the population. A prerequisite for such dispersal plasticity to evolve is a genetic basis on which natural selection can act. Using interconnected microcosms, we investigated dispersal in experimental populations of the freshwater protist <i>Paramecium caudatum</i> in response to the bacterial parasite <i>Holospora undulata</i>. For a collection of 20 natural host strains, we found substantial variation in constitutive dispersal, and to a lesser degree in dispersal plasticity. First, infection tended to increase or decrease dispersal relative to uninfected controls, depending on strain identity, potentially indicative of state-dependent dispersal plasticity. Infection additionally decreased host swimming speed compared to the uninfected counterparts. Second, for certain strains, there was a weak negative association between dispersal and infection prevalence, such that uninfected hosts tended to disperse less when infection was more frequent in the population, indicating context-dependent dispersal plasticity. Future experiments may test whether the observed differences in dispersal plasticity are sufficiently strong to react to natural selection. The evolution of dispersal plasticity as a strategy to mitigate parasite effects spatially may have important implications for epidemiological dynamics.</p>
Data from: Challenges and solutions for analyzing dual RNA-seq data for non-model host/pathogen systems
1. Dual RNA-seq simultaneously profiles the transcriptomes of a host and pathogen during infection and may reveal the mechanisms underlying host-pathogen interactions. Dual RNA-seq is inherently a mixture of transcripts from at least two species (host and pathogen), so this mixture must be computationally sorted into host and pathogen components. Sorting relies on aligning reads to respective reference genomes, which may be unavailable for both species in non-model host-pathogen pairs. This lack of genomic resources may present challenges to applying dual RNA-seq to non-model systems. 2. We assessed the accuracy of alignments of dual RNA-seq when using the genomic resources of a closely-related species to the species of interest by simulating datasets of mixed transcripts from a host and pathogen. Specifically, we compared how different aligners performed across different proportions of pathogen to host transcripts and across variation in the genetic distance between the pathogen genome and reference genome. We performed extensive analyses for a host plant with fungal pathogen, and then we extended the plant-fungus results by repeating key analyses in vertebrate (human)-fungus and vertebrate-bacterium systems. 3. Aligners that were able to map pathogen transcripts to the reference genome of a species closely related to the pathogen (a "related reference genome") also mismapped transcripts originating from the host to the pathogen's related reference genome, which results in regions where this occurred being quantified as overexpressed. If a host reference genome was available, we show that to minimize host transcript mismapping while retaining the ability to map pathogen transcripts, one could concatenate it with the pathogen's related reference genome, then map transcripts to the concatenated genomes. If a host genome was unavailable, assembling reads de novo prior to aligning substantially decreased host read mismapping, while retaining the ability to map pathogen transcripts to a related reference genome. 4. The application of dual RNA-seq to organisms without reference genomes is currently limited. We propose an analytical workflow that leverages the genomic resources of species closely related to species of interest to facilitate application of dual RNA-seq to reveal the mechanisms of host-pathogen interactions across a wider array of systems.
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.