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5,805 results for “Data model”
Data from: Experiments and modelling of rate-dependent transition delay in a stochastic subcritical bifurcation
Complex systems exhibiting critical transitions when one of their governing parameters varies are ubiquitous in nature and in engineering applications. Despite a vast literature focusing on this topic, there are few studies dealing with the effect of the rate of change of the bifurcation parameter on the tipping points. In this work, we consider a subcritical stochastic Hopf bifurcation under two scenarios: the bifurcation parameter is first changed in a quasi-steady manner and then, with a finite ramping rate. In the latter case, a rate-dependent bifurcation delay is observed and exemplified experimentally using a thermoacoustic instability in a combustion chamber. This delay increases with the rate of change. This leads to a state transition of larger amplitude compared to the one that would be experienced by the system with a quasi-steady change of the parameter. We also bring experimental evidence of a dynamic hysteresis caused by the bifurcation delay when the parameter is ramped back. A surrogate model is derived in order to predict the statistic of these delays and to scrutinise the underlying stochastic dynamics. Our study highlights the dramatic influence of a finite rate of change of bifurcation parameters upon tipping points and it pinpoints the crucial need of considering this effect when investigating critical transitions.
Data from: Accurate estimation of substitution rates with neighbour-dependent models in a phylogenetic context
Most models and algorithms developed to perform statistical inference from DNA data make the assumption that substitution processes affecting distinct nucleotide sites are stochastically independent. This assumption ensures both mathematical and computational tractability, but is in disagreement with observed data in many situations -- one well-known example being CpG dinucleotide hypermutability in mammalian genomes. In this paper, we consider the class of RN95+YpR substitution models, which allows neighbour-dependent effects -- including CpG hypermutability -- to be taken into account, through transitions between pyrimidine-purine dinucleotides. We show that it is possible to adapt inference methods originally developed under the assumption of independence between sites to RN95+YpR models, using a mathematically rigorous framework provided by specific structural properties of this class of models. We assess how efficient this approach is at inferring the CpG hypermutability rate from aligned DNA sequences. The method is tested on simulated data and compared against several alternatives; the results suggest that it delivers a high degree of accuracy at a low computational cost. We then apply our method to an alignment of ten DNA sequences from primate species. Model comparisons within the RN95+YpR class show the importance of taking into account neighbour-dependent effects. An application of the method to the detection of hypomethylated islands is discussed.
Data from: A multi-state dynamic occupancy model to estimate local colonization-extinction rates and patterns of co-occurrence between two or more interacting species
1. Although ecology is rife with theory that explores how multiple species co-occur through space and time, the field lacks robust statistical models to parameterize this theory with empirical data, particularly when species are detected imperfectly and data are collected as a time-series. 2. We address this need by developing an occupancy model that estimates local colonization and extinction rates for two or more interacting species when data are collected across multiple sampling occasions. This model estimates how community composition at a site may change across sampling occasions by assuming the latent occupancy state is a categorical random variable. We used a multinomial-logit model to parameterize species-specific parameters and pairwise interactions between species, both of which can be made a function of covariates. These transition probabilities between community states can then be converted to occupancy or co-occurrence probabilities to determine how community composition varies along an environmental gradient or through time. 3. As an example, we estimate patterns of co-occurrence between coyote (Canis latrans), Virginia opossum (Didelphis virginiana), and raccoon (Procyon lotor) in Chicago, Illinois, USA with data from a multi-year camera trapping study. Models with pairwise interactions between species greatly out performed models that assumed independence between species. Opossum and raccoon, for example, were far less likely to go extinct in habitat patches where coyotes were present. 4. Community composition at a site depends on species interactions and the local environment. Our model can separate such effects by estimating the underlying processes that define species occurrence patterns. As a result, our model can more explicitly quantify a wide range of ecological dynamics and therefore be used to empirically test ecological theory, such as estimating priority effects at a site or turnover rates between species, both of which can be made to vary as a function of covariates.
Data from: Metabolomic perfusate analysis during kidney machine perfusion: the pig provides an appropriate model for human studies
Introduction: Hypothermic machine perfusion offers great promise in kidney transplantation and experimental studies are needed to establish the optimal conditions for this to occur. Pig kidneys are considered to be a good model for this purpose and share many properties with human organs. However it is not established whether the metabolism of pig kidneys in such hypothermic hypoxic conditions is comparable to human organs. Methods: Standard criteria human (n = 12) and porcine (n = 10) kidneys underwent HMP using the LifePort Kidney Transporter 1.0 (Organ Recovery Systems) using KPS-1 solution. Perfusate was sampled at 45 minutes and 4 hours of perfusion and metabolomic analysis performed using 1-D 1H-NMR spectroscopy. Results: There was no inter-species difference in the number of metabolites identified. Of the 30 metabolites analysed, 16 (53.3%) were present in comparable concentrations in the pig and human kidney perfusates. The rate of change of concentration for 3-Hydroxybutyrate was greater for human kidneys (p<0.001). For the other 29 metabolites (96.7%), there was no difference in the rate of change of concentration between pig and human samples. Conclusions: Whilst there are some differences between pig and human kidneys during HMP they appear to be metabolically similar and the pig seems to be a valid model for human studies.
Data from: Metabolite profile of a mouse model of Charcot-Marie-Tooth type 2D neuropathy: implications for disease mechanisms and interventions
Charcot-Marie-Tooth disease encompasses a genetically heterogeneous class of heritable polyneuropathies that result in axonal degeneration in the peripheral nervous system. Charcot-Marie-Tooth type 2D neuropathy (CMT2D) is caused by dominant mutations in glycyl tRNA synthetase (GARS). Mutations in the mouse Gars gene result in a genetically and phenotypically valid animal model of CMT2D. How mutations in GARS lead to peripheral neuropathy remains controversial. To identify putative disease mechanisms, we compared metabolites isolated from the spinal cord of Gars mutant mice and their littermate controls. A profile of altered metabolites that distinguish the affected and unaffected tissue was determined. Ascorbic acid was decreased fourfold in the spinal cord of CMT2D mice, but was not altered in serum. Carnitine and its derivatives were also significantly reduced in spinal cord tissue of mutant mice, whereas glycine was elevated. Dietary supplementation with acetyl-L-carnitine improved gross motor performance of CMT2D mice, but neither acetyl-L-carnitine nor glycine supplementation altered the parameters directly assessing neuropathy. Other metabolite changes suggestive of liver and kidney dysfunction in the CMT2D mice were validated using clinical blood chemistry. These effects were not secondary to the neuromuscular phenotype, as determined by comparison with another, genetically unrelated mouse strain with similar neuromuscular dysfunction. However, these changes do not seem to be causative or consistent metabolites of CMT2D, because they were not observed in a second mouse Gars allele or in serum samples from CMT2D patients. Therefore, the metabolite 'fingerprint' we have identified for CMT2D improves our understanding of cellular biochemical changes associated with GARS mutations, but identification of efficacious treatment strategies and elucidation of the disease mechanism will require additional studies.
Data from: State-space reduction and equivalence class sampling for a molecular self-assembly model
Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving 'target information' from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of a Markov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.
Data from: Fruiting strategies of perennial plants: a resource budget model to couple mast seeding to pollination efficiency and resource allocation strategies
Masting, a breeding strategy common in perennial plants, is defined by seed production that is highly variable over years and synchronized at the population level. Resource budget models (RBMs) proposed that masting relies on two processes: (i) the depletion of plant reserves following high fruiting levels, which leads to marked temporal fluctuations in fruiting; and (ii) outcross pollination that synchronizes seed crops among neighboring trees. We revisited the RBM approach to examine the extent to which masting could be impacted by the degree of pollination efficiency, by taking into account various logistic relationships between pollination success and pollen availability. To link masting to other reproductive traits, we split the reserve depletion coefficient into three biological parameters related to resource allocation strategies for flowering and fruiting. While outcross pollination is considered to be the key mechanism that synchronizes fruiting in RBMs, our model counterintuitively showed that intense masting should arise under low-efficiency pollination. When pollination is very efficient, medium-level masting may occur, provided that the costs of female flowering (relative to pollen production) and of fruiting (maximum fruit set and fruit size) are both very high. Our work highlights the powerful framework of RBMs, which include explicit biological parameters, to link fruiting dynamics to various reproductive traits and to provide new insights into the reproductive strategies of perennial plants.
Data from: Keeping pace with climate change: stage-structured moving-habitat models
Life cycles can limit the abilities of species to track changing climatic conditions. We combined age or stage structure and a moving-habitat model to explore the effects of life history on the persistence of populations in the presence of climate change. We studied four dissimilar plant species in moving patches and found that (1) population growth rates, (2) elasticities with respect to the survival (stasis and shrinkage) components of the projection matrix, and (3) the evenness of the elasticities with respect to the components of the projection matrix all decreased as we increased the translational speeds of the patches. In addition, the value of long-distance dispersal increased with patch speed for three of the four species. Our analyses confirm that rapid growth, high fecundity, and long-distance dispersal can benefit species in moving patches. Thus, species with long generation times and limited dispersal ability are especially vulnerable to habitat movement. Stage-structured moving-habitat models can easily incorporate spatial complexity and can help us predict the effects of shifting climatic conditions.
On The Accuracy of Cache Sharing Models (Data)
<p>Data to accompany http://dx.doi.org/10.1145/2188286.2188294.</p>
Data and Analysis Scripts for the Application of the Sprat Marine Ecosystem Model to the Eastern Scotian Shelf
<p>Data and Analysis Scripts for the Application of the Sprat Marine Ecosystem Model to the Eastern Scotian Shelf</p>
Data and Analysis Scripts for the Application of the Sprat Marine Ecosystem Model to the Eastern Scotian Shelf
<p>Data and Analysis Scripts for the Application of the Sprat Marine Ecosystem Model to the Eastern Scotian Shelf</p>
Nordic44 - 2015 Powerflow Data: An Open Data Repository of an Equivalent Nordic Grid Model Matched to Historical Electricity Market Data for 2015
<p>This repository is used to provide documentation related to the model and data development process, provide source (raw) data for the model in different forms (i.e. Modelica, CIM 14, and PSS/E) for an equivalent Nordic grid model that has been matched to historical power flow data.</p> <p>The repository is documented in the paper below, see [Ref00].</p> <p><strong>Using this model, data or related software = cite our publications!</strong></p> <p>We are happy to contribute with this dataset, however, if you use any of the data or software provided, we will appreciate if you cite the following publications, as follows:</p> <p>A) Cite that "the raw and processed data files corresponding to the model are available as an open data set and documented in [Ref00]."</p> <p>B) Cite that the first appearance of the model, i.e. "the model is first presented in [Ref01]"</p> <p>[Ref00] L. Vanfretti, S.H. Olsen, V. S. Narasimham Arava, G. Laera, A. Bibadafar, T. Rabuzin, H. Jackobsen, J. Lavenius, and M. Baudette, "An Open Data Repository and a Data Processing Software Toolset of an Equivalent Nordic Grid Model Matched to Historical Electricity Market Data," submitted for publication, Data in Brief, 2016.</p> <p>[Ref01] L. Vanfretti, T. Rabuzin, M. Baudette, M. Murad, iTesla Power Systems Library (iPSL): A Modelica library for phasor time-domain simulations, SoftwareX, Available online 18 May 2016, ISSN 2352-7110, http://dx.doi.org/10.1016/j.softx.2016.05.001.</p> <p><strong>Acknowledgment:</strong></p> <p>This model was originally developed in the context of the FP7 iTesla project, and further extended within the ITEA3 openCPSproject.</p> <p>Structure of the repository:</p> <p><strong>01_PSSE_Resources</strong>:</p> <ol> <li> <p><strong>Models</strong> :</p> <ul> <li> <p>A folder with PSS/E files of the base case</p> </li> <li> <p>A folder with a 7zip archive containing files of the original N44 system that has been modified to have the PSS/E base case</p> </li> </ul> </li> <li> <p><strong>Snapshots</strong> :</p> <ul> <li> <p><strong>N44_2015xxxx</strong> are folders named according to the day they refer to (for example <em>N44_20150401</em> refers to the 1st of April 2015). In each folder there are Excel files (<em>Consumption_xx.xlsx</em>, <em>Exchange_xx.xlsx</em>, <em>Production_xx.xlsx</em>) with data downloaded from Nord Pool website, an Excel file (<em>PSSE_in_out.xlsx</em>) summarizing the results from the Python script <em>Nordic44.py</em> in the folder <strong>04_Python_Resources</strong>, PSS/E snapshots for each hour before solving the power flow (<em>hx_before_PF.raw</em>) and after solving the power flow (<em>hx_after_PF.raw</em>)</p> </li> <li> <p><em>N44_BC.sav</em> is the PSS/E solved base case that Python script <em>Nordic44.py</em> (put the reference)</p> </li> </ul> </li> </ol> <p><strong>02_CIM14_Snapshots</strong>:</p> <ul> <li> <p><strong>N44_2015xxxx</strong> are folders named according to the day they refer to (e.g. <strong>N44_20150401</strong> refers to the 1st of April 2015). In each folder there are CIM files for each hour (<em>N44_hx_EQ.xml</em>, <em>N44_hx_SV.xml_, _N44_hx_TP.xml</em>)</p> </li> <li> <p><strong>N44_noOL_RDFIDMAP.xml</strong> is the file with IDs mapping of those cases (<em>N44_hx_noOL_EQ.xml</em>, <em>N44_hx_noOL_SV.xml</em>, <em>N44_hx_noOL_TP.xml</em>) with fixed overloading problems.</p> </li> <li> <p><strong>N44_RDFIDMAP_2015-1.xml</strong> and <strong>N44_RDFIDMAP_2015-2.xml</strong> are the files with IDs mapping of the remaining snapshots from 2015</p> </li> </ul> <p><strong>03_Modelica</strong>:</p> <ol> <li> <p><strong>iTesla_Platform</strong></p> <ul> <li> <p><strong>iPSL</strong> folder contains the version of the library which can be used to simulate snapshots generated from the iTesla Platform</p> </li> <li> <p><strong>Modelica_snapshots</strong> Modelica models generated from the snapshots by iTesla Platform</p> </li> </ul> </li> <li> <p><strong>SmarTSLab</strong></p> <ul> <li> <p><strong>OpenIPSL</strong> folder contains the version of the forked iPSL library which can be used to simulate the manually generated Modelica model of N44 with the record structures corresponding to the snapshots</p> </li> <li> <p><strong>Snapshots</strong> folder contains Modelica records automatically generated from the PSS/E records</p> </li> <li> <p><em>N44_Base_Case.mo</em> is the handmade N44 model with the loaded record of the power flow results from the PSS/E base case. It can be used to load other PF results from the folder <strong>03_Modelica/Snapshots</strong></p> </li> </ul> </li> </ol>
R scripts for data simulation and model assessment in ShuqingNTeng MEE 2017
<p>R scripts for reproducing results in ShuqingNTeng MEE 2017</p>
Gut Analysis Toolbox: Training data and 2D models for segmenting enteric neurons, neuronal subtypes and ganglia
<p>This upload is associated with the software, <a href="https://github.com/pr4deepr/GutAnalysisToolbox">Gut Analysis Toolbox</a> (GAT).</p> <p>If you use it please cite:</p> <p><strong><em>Sorensen et al. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950. doi: <a href="https://doi.org/10.1242/jcs.261950" target="_blank" rel="noopener">https://doi.org/10.1242/jcs.261950</a></em></strong></p> <p>The upload contains<strong> StarDist models for segmenting enteric neurons in 2D, enteric neuronal subtypes in 2D and FPN+ResNet101 model for enteric ganglia in 2D in gut wholemount tissue.</strong> GAT is implemented in Fiji, but the models can be used in any software that supports StarDist and the use of 2D UNet models. The files here also consist of <strong>Python notebooks (Google Colab)</strong>, training and test data as well as reports on model performance.</p> <p>Note: The enteric ganglia model is has been updated to v3 which uses pytorch and is a different architecture (FPN+ResNet101).</p> <p>The model files are located in the respective folders as zip files. The folders have also been zipped:</p> <ul> <li>Neuron (Hu; <a href="https://github.com/stardist/stardist">StarDist</a> model): <ul> <li>Main folder: 2D_enteric_neuron_model_QA.zip</li> <li>StarDist Model File:2D_enteric_neuron_v4_1.zip </li> <li>DeepImageJ compatible model: 2D_enteric_neuron.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> <li>Neuronal subtype (<a href="https://github.com/stardist/stardist">StarDist</a> model): <ul> <li>Main folder: 2D_enteric_neuron_subtype_model_QA.zip</li> <li>Model File: 2D_enteric_neuron_subtype_v4.zip</li> <li>DeepImageJ compatible model: 2D_enteric_neuron_subtype.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> <li>Enteric ganglia (2D FPN_ResNet101; Use in FIJI with <a href="https://deepimagej.github.io/deepimagej/">deepImageJ</a>) <ul> <li>Main folder: 2D_enteric_ganglia_v3_training.zip</li> <li>Model File: 2D_Ganglia_RGB_v3.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> </ul> <p>For the all models, files included are:</p> <ol> <li>Model for segmenting cells or ganglia in 2D FIJI. StarDist or 2D UNet.</li> <li>Training and Test datasets used for training.</li> <li>Google Colab notebooks used for training and quality assurance (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">ZeroCost DL4Mic notebooks</a>).</li> <li>Python notebook and code for training ganglia model with QA.</li> <li>Quality assurance reports generated from above notebooks.</li> <li>StarDist model exported for use in QuPath.</li> </ol> <p>The model files can be used within can be used within the software, <a href="https://github.com/stardist/stardist">StarDist</a>. They are intended to be used within FIJI or QuPath, but can be used in any software that supports the implementation of StarDist in 2D.</p> <p><strong>Data:</strong></p> <p>All the images were collected from 4 different research labs and a public database (<a href="https://sparc.science/data?type=dataset">SPARC database</a>) to account for variations in image acquisition, sample preparation and immunolabelling.</p> <p>For enteric neurons the pan-neuronal marker, Hu has been used and the 2D wholemounts images from mouse, rat and human tissue.</p> <p>For enteric neuronal subtypes, 2D images for nNOS, MOR, DOR, ChAT, Calretinin, Calbindin, Neurofilament, CGRP and SST from mouse tissue have been used..</p> <p>25 images were used from the following entries in the <a href="https://sparc.science/data?type=dataset">SPARC database</a>:</p> <ul> <li><a href="https://doi.org/10.26275/9FFG-482D">Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium. </a></li> <li><a href="https://doi.org/10.26275/PZEK-91WX">Graham, K. D., Huerta-Lopez, S., Sengupta, R., Shenoy, A., Schneider, S., Wright, C. M., Feldman, M., Furth, E., Lemke, A., Wilkins, B. J., Naji, A., Doolin, E., Howard, M., & Heuckeroth, R. (2020). Robust 3-Dimensional visualization of human colon enteric nervous system without tissue sectioning (Version 1) [Data set]. SPARC Consortium.</a></li> <li>Wang, L., Yuan, P.-Q., Gould, T. and Tache, Y. (2021). Antibodies Tested in theColon – Mouse (Version 1) [Data set]. SPARC Consortium. doi:10.26275/i7dl-58h</li> </ul> <p>Additional images for new ganglia model:</p> <ul> <li>Hamnett, R., Dershowitz, L. B., Sampathkumar, V., Wang, Z., Gomez-Frittelli, J., De Andrade, V., Kasthuri, N., Druckmann, S. and Kaltschmidt, J. A. (2022b). Regional cytoarchitecture of the adult and developing mouse enteric nervous system. Curr. Biol. 32, 4483-4492.e5.</li> </ul> <p>The images have been acquired using a combination different microscopes. The images for the mouse tissue were acquired using: </p> <ul> <li> <p>Leica TCS-SP8 confocal system (20x HC PL APO NA 1.33, 40 x HC PL APO NA 1.3) </p> </li> <li> <p>Leica TCS-SP8 lightning confocal system (20x HC PL APO NA 0.88) </p> </li> <li> <p>Zeiss Axio Imager M2 (20X HC PL APO NA 0.3) </p> </li> <li> <p>Zeiss Axio Imager Z1 (10X HC PL APO NA 0.45) </p> </li> </ul> <p>Human tissue images were acquired using: </p> <ul> <li> <p>IX71 Olympus microscope (10X HC PL APO NA 0.3) </p> </li> </ul> <p>For more information, visit the <a href="https://gut-analysis-toolbox.gitbook.io/docs" target="_blank" rel="noopener">Documentation</a> website.</p> <p><strong>NOTE:</strong> The images for enteric neurons and neuronal subtypes have been rescaled to 0.568 µm/pixel for mouse and rat. For human neurons, it has been rescaled to 0.9 µm/pixel . This is to ensure the neuronal cell bodies have similar pixel area across images. The area of cells in pixels can vary based on resolution of image, magnification of objective used, animal species (larger animals -> larger neurons) and potentially how the tissue is stretched during wholemount preparation </p> <p>Average neuron area for neuronal model: 701.2 ± 195.9 pixel<sup>2 </sup>(Mean ± SD, 6267 cells)</p> <p>Average neuron area for neuronal subtype model: 880.9 ± 316 pixel<sup>2 </sup>(Mean ± SD, 924 cells)</p> <p><strong>Software References:</strong></p> <p><strong><a href="https://github.com/stardist/stardist">Stardist</a></strong></p> <p>Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018, September). Cell detection with star-convex polygons. In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em> (pp. 265-273). Springer, Cham.</p> <p><strong><a href="https://deepimagej.github.io/deepimagej/">deepImageJ</a></strong></p> <p>Gómez-de-Mariscal, E., García-López-de-Haro, C., Ouyang, W., Donati, L., Lundberg, E., Unser, M., Muñoz-Barrutia, A. and Sage, D., 2021. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. <em>Nature Methods</em>, <em>18</em>(10), pp.1192-1195.</p> <p><strong><a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">ZeroCost DL4Mic</a></strong></p> <p>von Chamier, L., Laine, R.F., Jukkala, J., Spahn, C., Krentzel, D., Nehme, E., Lerche, M., Hernández-Pérez, S., Mattila, P.K., Karinou, E. and Holden, S., 2021. Democratising deep learning for microscopy with ZeroCostDL4Mic. <em>Nature communications</em>, <em>12</em>(1), pp.1-18.</p>
Data for "Improvements in wintertime surface temperature variability in the Community Earth System Model version 2 (CESM2) related to the representation of snow density"
<p>This dataset contains all the postprocessed data required to reproduce the figures in the publication Simpson et al (2022) "Improvements in wintertime surface temperature variability in the Community Earth System Model version 2 (CESM2) related to the representation of snow density", in the Journal of Advances in Modelling the Earth System.</p>
Data Bundle for PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>It contains</strong> alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a>, since git is not suited for handling large changing files. Instead we provide separate <strong>data bundles</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-eur.readthedocs.io/en/latest/installation.html">documentation</a>.</p> <p>This is the <strong>full</strong> data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets.</p> <p>While the <a href="https://github.com/PyPSA/PyPSA-eur">code</a> in PyPSA-Eur is released as free software under the MIT, <strong>different licenses and terms of use</strong> apply to the various input data, which are summarised below:</p> <p><strong>corine/*</strong></p> <ul> <li>CORINE Land Cover (CLC) database</li> <li><strong>Source:</strong> <a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/">https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/</a></li> <li><strong>Terms of Use: </strong><a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata">https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata</a></li> </ul> <p><strong>natura/*</strong></p> <ul> <li>Natura 2000 natural protection areas</li> <li><strong>Source:</strong> <a href="https://www.eea.europa.eu/data-and-maps/data/natura-10">https://www.eea.europa.eu/data-and-maps/data/natura-10</a></li> <li><strong>Terms of Use:</strong><a href="https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata"> https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata</a></li> </ul> <p><strong>gebco/GEBCO_2014_2D.nc</strong></p> <ul> <li>GEBCO bathymetric dataset</li> <li><strong>Source:</strong> <a href="https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/">https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/</a></li> <li><strong>Terms of Use: </strong><a href="https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf">https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf</a></li> </ul> <p><strong>je-e-21.03.02.xls</strong></p> <ul> <li>Population and GDP data for Swiss Cantons</li> <li><strong>Source:</strong> <a href="https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html">https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html</a></li> <li><strong>Terms of Use: <br></strong></li> <li><a href="https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html">https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html</a></li> <li><a href="https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html">https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html</a></li> </ul> <p><strong>nama_10r_3popgdp.tsv.gz</strong></p> <ul> <li>Population by NUTS3 region</li> <li><strong>Source:</strong> <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en">http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en</a></li> <li><strong>Terms of Use:</strong></li> <li><a href="https://ec.europa.eu/eurostat/about/policies/copyright">https://ec.europa.eu/eurostat/about/policies/copyright</a></li> </ul> <p><strong>GDP_per_capita_PPP_1990_2015_v2.nc</strong></p> <ul> <li>Gross Domestic Product per capita (PPP) from years 1999 to 2015</li> <li>Rectangular cutout for European countries in PyPSA-Eur, including a 10 km buffer</li> <li>Kummu et al. "Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015"</li> <li><strong>Source:</strong> https://doi.org/10.1038/sdata.2018.4 and associated dataset https://doi.org/10.1038/sdata.2018.4</li> </ul> <p><strong>ppp_2019_1km_Aggregated.tif</strong></p> <ul> <li>The spatial distribution of population in 2020: Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.</li> <li>Rectangular cutout for non-NUTS3 countries in PyPSA-Eur, i.e. MD and UA, including a 10 km buffer</li> <li>WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647</li> <li><strong>Source:</strong> https://data.humdata.org/dataset/worldpop-population-counts-for-world and https://hub.worldpop.org/geodata/summary?id=24777</li> <li><strong>License: </strong>Creative Commons Attribution 4.0 International Licens</li> </ul> <p><strong>data/bundle/era5-HDD-per-country.csv</strong></p> <p>- Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875<br>- License: CC-BY 4.0<br>- Contains country-level heating degree days in Europe for<br> 1941-2023. Used for rescaling heat demand in weather years not covered by<br> energy balance statistics.</p> <p><strong>data/bundle/era5-runoff-per-country.csv</strong></p> <p>- Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875<br>- License: CC-BY 4.0<br>- Contains country-level daily sum of runoff in Europe for<br> 1941-2023. Used for rescaling hydro-electricity availability in weather years<br> not covered by EIA hydro-generation statistics.</p> <p><strong>shipdensity_global.zip</strong></p> <ul> <li>Global Shipping Traffic Density</li> <li>Creative Commons Attribution 4.0</li> <li><a href="https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density">https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density</a></li> </ul> <p><strong>seawater_temperature.nc</strong></p> <ul> <li>Global Ocean Physics Reanalysis</li> <li>Seawater temperature at 5m depth</li> <li>Link: https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/services</li> <li>License: https://marine.copernicus.eu/user-corner/service-commitments-and-licence</li> </ul> <p><strong>hera_be_2013-03-01_to_2013-03-08.zip</strong></p> <ul> <li>Tilloy, A., Paprotny, D., Luc, F., Grimaldi, S., Goncalo, G., Hylcke, B., Lange, S., Bianchi, A. (2024): HERA: a high-resolution pan-European hydrological reanalysis (1950-2020).</li> <li>6-hourly river discharge and ambient temperature for 2019 at 1-arc minute spatial resolution. Subset to the PyPSA-Eur test cutout (2013-03-01 to 2013-03-08 for longitude 1.5 to 7 and latitude 49 to 52)</li> <li>Link: <a href="https://publications.pik-potsdam.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_29543">https://publications.pik-potsdam.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_29543</a></li> <li>License: <a href="https://data.jrc.ec.europa.eu/licence/com_reuse">https://data.jrc.ec.europa.eu/licence/com_reuse</a></li> </ul>
Calibration Data for Kinematic Stellar Age Models
<p>https://github.com/ssagear/KinematicAgePredictor</p>
Data and code from: Large language models can segment narrative events similarly to humans.
<p>Data and code supporting the paper: Large language models can segment narrative events similarly to humans.</p>
Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) - Data Set
<p>GENERAL INFORMATION</p> <p>The data and scripts used for the analysis of the paper "Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – Towards an improved representation of mountain water resources in global assessments"</p> <p><strong>When using this dataset, please refer to the original publication in addition to this Zenodo repository.</strong></p> <p><strong>Hanus, S., Schuster, L., Burek, P., Maussion, F., Wada, Y., and Viviroli, D.: Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – towards an improved representation of mountain water resources in global assessments, Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, 2024.</strong></p> <p>DATA & FILE OVERVIEW</p> <p>please have a look at readme.txt </p> <p>Don't hesitate to contact us in case of any questions (sarah.hanus@geo.uzh.ch)</p>
Data and R code for cluster analysis and machine learning modelling of favourite places for outdoor recreation
Open the record for dataset details and reuse information.
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