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195 results for “publication models”
plan4res - public dataset for case study 1 part MIM-1: time series used for multi-modal investment pathway modelling
<p>Public data set which is used within the plan4res project for performing case study 1 "Multi-modal European energy concept for achiving COP21" - Multi-modal Investment modelling (MIM) Part 1: Time series for the reference year 2015</p> <p>The related documentation is included in plan4res' deliverable D4.5 chapter 3.2 (see 10.5281/zenodo.3785010) </p> <p>The data set includes the following data:</p> <p>a) characteristic annual load profiles for large industrial heat demand for chemical, iron & steel, food & beverage and pulp & paper industries for the reference year 2015</p> <p>HOTMAPS__TD_OUT_D_CHEM__20200608T160653__20200422T120000Z__v01.csv <br> HOTMAPS__TD_OUT_D_FOOD__20200608T160724__20200422T120000Z__v01.csv <br> HOTMAPS__TD_OUT_D_IRON__20200608T160705__20200422T120000Z__v01.csv HOTMAPS__TD_OUT_D_PAPER__20200608T160715__20200422T120000Z__v01.csv </p> <p>b) characteristic demand profiles for road-side car passenger transport and availability of cars for charging while (home) parking for the reference year 2015 </p> <p>SIEMENS__TD_OUT_D_RoadCar__20200608T160627__20200401T120000Z__v01.csv SIEMENS__TD_CAP_CarPark__20200608T160637__20200401T120000Z__v01.csv </p> <p>c) load profiles for exogeneous demand of electricity for the reference year 2015. The exogenous demand includes all electricity consumptions not explicitly modeled within MIM modeling.</p> <p>HRE4__TD_OUT_ElectricityExo__20200608T160732__20200401T120000Z__v01.csv </p> <p>c) regionally resolved demand profiles for (individual) space heating and space cooling for the reference year 2015</p> <p>HRE4__TRD_CAP_Cool_2015__20200608T160051__20200401T120000Z__v01.csv <br> HRE4__TRD_CAP_HeatInd_2015__20200608T155849__20200401T120000Z__v01.csv</p> <p>d) regionally resolved generation profiles of electricity from photovoltaic, wind onshore, wind offshore, hydro run-of-river, and for heat generation from solar thermal for the reference year 2015</p> <p>NINJA__TRD_CAP_PV_2015__20200608T160440__20191104T120000Z__v01.csv <br> NINJA__TRD_CAP_WindOFF_2015__20200608T155422__20191104T120000Z__v01.csv <br> NINJA__TRD_CAP_WindON_2015__20200608T155251__20191104T120000Z__v01.csv HRE4__TRD_CAP_HydroRoR_2015__20200608T155550__20200401T120000Z__v01.csv <br> HRE4__TRD_CAP_SolarThermal_2015__20200608T155718__20200401T120000Z__v01.csv </p> <p>e) regionally resolved generation profile of electricity from wind offshore transformed in a way to represent potential capacity factors in future as stated by doi:10.2760/041705. Data based on reference year 2015</p> <p>SIEMENS__TRD_CAP_WindOFF_2040__20200608T155127__20200401T120000Z__v01.csv </p> <p>x) A list of geographical description of the zone hierarchy data used in MIM for the EU33 region set.:</p> <p>SIEMENS__ZoneHierarchy_MIM_EU33__20181231T120000Z___20200131T1200000Z__v001.csv </p> <p>Further info:</p> <p>Time series are based on historical data for the reference year 2015. </p> <p>Values are normalized over one reference year in a way that either the maximum = 1 (CAP) or the integral = 1 (OUT).</p> <p>All values are listed in arbitrary units. </p> <p>All country names are according to ISO 3166-1 alpha-2.</p>
Data for publication: Autoadaptive motion modelling for MR-based respiratory motion estimation
<p>This repository contains four T1-weighted 2D MR slice datasets from multiple slice positions covering the entire thorax during free breathing and breath holds. The data was used to evaluate our novel autoadaptive respiratory motion model which we proposed in [1]. In particular, the datasets contain the following:</p> <ol> <li>Acquisition of all sagittal slice positions covering the thorax and one coronal slice position acquired during a breath hold.</li> <li>Results of registration between adjacent sagittal slice positions [control point displacements (cpp) and displacement fields (dfs)]</li> <li>40 dynamic acquisitions of each slice position also present in the breath-hold acquired during free breathing. </li> <li>Results of registration of the dynamic acquisitions to the respective breath-holds slices (cpp's and dfs's). </li> </ol> <p>The data is divided into 4 zip files, each containing the data of one volunteer. The folder structure for each is as follows:</p> <blockquote> <p>|-- bhs (breath hold data)<br /> | |-- images (images)<br /> | | |-- cor<br /> | | `-- sag<br /> | `-- mfs_slpos2slpos (registration results)<br /> | `-- sag<br /> `-- dyn (dynamic free-breathing data)<br /> |-- images (images)<br /> | |-- cor<br /> | `-- sag<br /> `-- mfs_tpos2tpos (registration results)<br /> |-- cor<br /> `-- sag</p> </blockquote> <p>Please, see our publication [1] for details on the acquisition sequence and registration used. </p> <p>--</p> <p>[1]: CF Baumgartner, C Kolbitsch, JR McClelland, D Rueckert, AP King, <em>Autoadaptive motion modelling for MR-based respiratory motion estimation</em>, Medical Image Analysis (2016), http://dx.doi.org/10.1016/j.media.2016.06.005</p>
Raw fMRI data from 12 rats used in the manuscript "Mapping of hemodynamic responses to the sensorymotor stimulation in a rodent model: a BOLD fMRI study" submitted to PlosOne for publication
<p>Raw data are from twelve male adult Wistar rats (Charles River Laboratories, Paris-France) weighing 300 ± 20g.</p> <p>Rats were initially anesthetized (induction) with 3% isoflurane and were maintained under 0,7-0.8% (sedation along with a muscle relaxation) during fMRI session. </p> <p>Each rat was submitted to two fMRI sessions: one with TE of 30 ms and and other with TE of 40 ms.</p> <p>For fMRI acquisition, electrodes were inserted subcutaneously in the palmar surface of the right hindpaw of each rat and electrical stimulation (current pulses with a 1.7 mA amplitude, 10 ms duration and 8 Hz frequency) was applied in a block-design starting with a resting period of 25s as a baseline followed by 25s stimulation, repeated 8 times.</p> <p>Ten 1-mm thick contiguous axial slices, from -6.36 mm to +2.64 mm to Bregma, were acquired with a two-shot gradient echo planar imaging (GE EPI) pulse sequence (2.56 cm2 FOV; 64x64 matrix size; a TR of 1000 ms; a flip angle of 50°) resulting in the pixel size of 0.4 mm.</p> <p>All imaging experiments were performed on a 4.7T Bruker (Biospec 47/40, Bruker, GmbH, Ettlingen,Germany) with a horizontal bore magnet equipped with a 12 cm gradient coil (Bruker BGA12, 400 mT/m) and interfaced to AVANCE III console. Two actively decoupled RF coils were used: a 7.2-cm diameter volume coil for transmission and a 2-cm diameter surface coil (Rapid Biomedical, Rimpar, Germany) positioned on the top of the animal's head for reception.</p>
Data for the publication "Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model" - part 2
<p>The data is split into two datasets, for each to be smaller than 50 GB.</p>
Dataset for the publication titlted "A computational mechanics model for producing molecular assembly using molecularly woven pantographs" in the journal Cell Reports Physical Science, authored by Byeonghwa Goh and Joonmyung Choi.
<p>Dataset for the publication titlted "A computational mechanics model for producing molecular assembly using molecularly woven pantographs" in the journal Cell Reports Physical Science, authored by Byeonghwa Goh and Joonmyung Choi.</p>
Dataset and R-codes for Publication: "Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" (accepted version)
<p><span>In this upload you can find the R-codes and dataset for the Publication: </span></p> <p><span>"Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" by Simon Oberholzer Laura Summerauer, Markus Steffens and Chinwe Ifejika Speranza accepted in SOIL (https://doi.org/10.5194/egusphere-2023-1087)</span></p> <p><span>To reproduce the results of the manuscript, start with the R-file “ResampleandPreprocess.R” to prepare spectral data and then continue with the R-file “PLSRmodelling_GroupedCV.R” for the modelling.</span></p> <p><span>The R-files “control_train.R” and “rep_grouped_kfold_CV.R” are helper-functions for the grouped cross-validation. The file metadata.csv explains the column names in the spectral data (spcdata.RDS).</span></p>
Data used in the publication: Sensitivity of modeled microphysics to stochastically perturbed parameters
<p>These data support the results presented in the manuscript titled "Sensitivity of modeled microphysics to stochastically perturbed parameters". They consist of results from an idealized single vertical column atmospheric model run for a number of experiments that explore methods of representing model uncertainty. </p>
Data to support publication figures and animation scripts at GitHub: Modeling weather-driven long-distance dispersal of spruce budworm moths (Choristoneura fumiferana)
<p>Long-term studies of insect populations in the North American boreal forest have shown the vital importance of long-distance dispersal to the maintenance and expansion of insect outbreaks. In this work, we extend several concepts established previously in an empirically-based dispersal flight model with recent work on the physiology and behavior of the adult eastern spruce budworm (SBW) moth, Choristoneura fumiferana (Clem.). An outbreak of defoliating SBW in Quebec, ongoing since the mid-2000s, already covers millions of hectares of forests in eastern Canada and threatens to spread into neighboring areas through annual summertime episodes of long-distance dispersal. Such flight events in favorable conditions frequently include billions of SBW moths dispersing in the warm atmospheric boundary layer, typically starting around sunset and often lasting through several hours of wind-driven transport over hundreds of kilometers. Successful SBW dispersal to possibly distant host forest areas depends acutely on the weather. Here we describe the components and results of SBW–pyATM, an open-source individual-based modeling framework developed in Python for the simulation of these weather-driven SBW dispersal events. Using seasonal SBW phenology results from BioSIM at known outbreak locations and high-resolution Weather Research and Forecasting (WRF) model output, we focus on modeling dispersal flights over two successive nights in July 2013 in southern Quebec. Our flight model closely reproduces the SBW spatial patterns and motions observed by weather surveillance radar over the St. Lawrence estuary. With SBW–pyATM we can estimate landing locations for both male and female SBW and the resulting spatial patterns of egg distribution, allowing us eventually to forecast future larval defoliation activity in new locations where immigration could help overcome local limitations on SBW populations. This information could then support forest management decisions where SBW outbreaks threaten valuable resources.</p>
Data publication for "First-principles derivation and properties of density-functional average-atom models"
<p>Data for the pre-print "First-principles derivation and properties of density-functional average-atom models", https://arxiv.org/abs/2103.09928.</p> <p>Each data folder is named according to the corresponding figure in the paper. For any questions, please contact the authors.</p>
Unified Model GA 7.0 Nudging Experiments used in JGR-A publication "Investigating Tropical versus Extratropical Influences on the Southern Hemisphere Tropical Edge in the Unified Model", Freisen et al. (2022)
<p><strong>UK Met Office Unified Model (UM) Global Atmosphere version 7.0 (GA7.0) Nudging Experiments </strong><strong>used in </strong><strong>"Investigating Tropical versus Extratropical Influences on the Southern Hemisphere Tropical Edge in the Unified Model" published in Journal of Geophysical Research: Atmospheres (doi.org/10.1029/2021JD036106).</strong></p> <p>Please refer to UM_nudging_experiments_readme.txt.</p>
Publication release: How well do species distribution models predict occurrences in exotic ranges?
<div class="record-description"> <p>Species distribution models (SDMs) are widely used predictive tools to forecast potential biological invasions. However, the reliability of SDMs extrapolated to exotic ranges remains understudied, with most analyses restricted to few species and equivocal results. We examined the spatial transferability of SDMs for 647 non-indigenous species extrapolated across 1,867 invaded ranges, and identify what factors may help differentiate predictive success from failure. We performed a large-scale assessment of the transferability of SDMs using two modelling approaches: generalized additive models (GAMs) and MaxEnt. We fitted SDMs on the native ranges of species and extrapolated them to exotic ranges. We examined the influence of general factors and factors related to biological invasions on spatial transferability.</p> <p>Here, we provide the code and data for publication in Global Ecology and Biogeography as part of Nguyen and Leung 2022 "How well do species distribution models predict occurrences in exotic ranges?". Provided are the files and scripts necessary to fit and validate the SDMs using distirbutional data from their native and exotic ranges, respectively, formulated as generalized additive models (GAMs) or MaxEnt models. Additionally, provided is a script to validate the SDMs on their native fitting range using 10-fold cross-validation, and to fit the transferability model, as a linear mixed model (LMM), with a provided cleaned data.frame. The dataset provided includes a full species list with GBIF occurrence records, target-group background (TGB) records to use with model fitting and validation, as well as environmental data associated with the sightings.</p> </div>
FESOM model data and particle tracking data used in publication 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean'
<p>FESOM model data and particle tracking data used in the paper 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean" by Andreas Rogge at al.</p> <p>1) FESOM velocity fields averaged over the top 200 m water depth, averaged over the time period 2015-2018.</p> <p>2) FESOM transect at 95°E in the Arctic Ocean (temperature, salinity and velocity), averaged over the time period 2015-2018.</p> <p>3a) Particle back-tracking data based on daily FESOM velocity fields in netcdf format. Particles were released at 95°E every 14 days during the year 2018 and tracked until they reached the surface. Three different constant sinking velocities were used, representative for small and large non-ballasted particles and small ballasted particles.</p> <p>3b) Distribution of particles at the surface for the experiments with three different sinking velocities as mat files. </p>
Simulation results with the EULAG research model for the publication: "Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost"
<p>Supplementary material for the publication</p> <ul> <li>Mark Schlutow, Tobias Stacke, Tom Doerffel, et al. Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost. ESS Open Archive . January 24, 2024. <a href="https://doi.org/10.22541/essoar.170612558.81370785/v1">https://doi.org/10.22541/essoar.170612558.81370785/v1</a></li> </ul> <p>The material contains all simulation results and raw outputs that are necessary to reproduce the figures and statistics of the publication. </p>
Data for publication "A unified surface tension model for multi-component salt, organic and surfactant solutions"
<p>This repository contains the data of the publication:</p> <p>Title: "A unified surface tension model for multi-component<br>salt, organic and surfactant solutions"<br>Authors: Judith Kleinheins, Claudia Marcolli, Cari Dutcher, Nadia Shardt<br>Date: 2024</p>
Data for publication of "Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes"
<p>Implementation of Gaussian process regression-based Bayesian optimisation (G-BO) using the emcee package (<a href="https://emcee.readthedocs.io/en/stable/" rel="nofollow">https://emcee.readthedocs.io/en/stable/</a>).</p> <p>For more information about the implementation of G-BO in optimising the Weather Research and Forecasting (WRF) model parameters, please refer to the paper - <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes</a>.</p> <p><code>G-BO_script.ipynb</code> implements the GPR-based Bayesian optimisation using the Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler.</p> <ul> <li><strong>QMC_sobol_samples</strong>: This file contains the 128 parameter samples across the parameter space of three sensitive parameters utilizing the Quasi Monte-Carlo (QMC) Sobol sequence design.</li> <li><strong>nmae_all_128_ens_T_Rh</strong>: This file contains the normalised mean absolute error (NMAE) values of temperature (T) and relative humidity (Rh) of the 128 parameter sample WRF simulations. For more details, please refer to <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">this link</a>.</li> </ul>
Dataset to accompany publication "Precision of Radiation Chemistry Networks: Playing Jenga with Kinetic Models for Liquid-Phase Electron Microscopy"
<h2>Dataset description</h2> <p>This dataset displays the raw data for the manuscript "Precision of Radiation Chemistry Networks: Playing Jenga with Kinetic Models for Liquid-Phase Electron Microscopy"<strong> </strong>published in <em>Precision Chemistry </em>on 06 December 2023 (DOI: <a title="DOI URL" href="https://doi.org/10.1021/prechem.3c00078">10.1021/prechem.3c00078</a>).</p> <p> </p>
SDG Mapping Results for 1,000 Publications from Large Language Models: GPT-4o, Mixtral, Llama 2, Llama 3, Gemma 2, Qwen 2 and GPT-4o-mini
<p>Randomly selected 1,000 publications from the Swinburne University of Technology research bank were used for SDG mapping tasks with the large language model GPT-4o and the open-source models Mixtral, Llama 2, Llama 3, Gemma 2, Qwen 2 and GPT-4o-mini.</p> <p>The input to each model consisted of the publication’s title and abstract.</p> <p>The designed prompt is as follows: </p> <p>PROMPT = '''Analyze the publication and determine the SDGs it aligns with. Evaluate against all the 17 SDGs provide the reason for alignment. In the end summarise the confidence levels(%) for each assigned SDG in JSON format. For example: {example}. Title: {title} Description: {description}'''</p> <p>example = '''{ 'Goal 6': 0.67, 'Goal 11': 0.50, 'Goal 3': 0.25}'''</p>
PREPCLIM sample data set for publication in "Geoscientific Model Development" 2024
<p>Data set illustrating the functionality of software developed in the PREPCLIM project and used in the proposed paper:</p> <p>A Modeling System for Identification of Maize Ideotypes, optimal sowing dates and nitrogen<br>fertilization under climate change – PREPCLIM-v1</p> <p>https://doi.org/10.5194/gmd-2024-105<br>Preprint. Discussion started: 11 July 2024<br>c Author(s) 2024. CC BY 4.0 License.</p>
Supporting publication for 'Guidelines for reporting 2017 prevalence sample-based data in accordance with SSD2 data model'
<p>These two Excel documents help you to map terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes and offer you examples on how prevalence data can be reported using SSD2.</p>
Instance of the learning ecosystem metamodel: model of the ecosystem for knowledge management in Spanish Public Administration
<p>The conceptual model definition of the learning ecosystem for knowledge management in the Spanish Public Administration has been made by defining three views or packages from the ecosystems metamodel presented above. The views correspond to the three main parts identified in the learning ecosystem metamodel (<a href="http://doi.org/10.5281/zenodo.829859">http://doi.org/10.5281/zenodo.829859</a>): software components, human elements and the relationship among each other.</p>
ScienceDex guides
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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.