Find research datasets worth reusing

opencc-by-4.0Oct 2022View details →

NUMAC: Description of the Nested Unified Model with Aerosols and Chemistry, and evaluation with KORUS-AQ data: supporting data

Simulated data presented in manuscript with title above, generated with the Met Office Unified Model, together with observations from KORUS-AQ and MODIS that are used in the model evaluation. The Terra/MODIS aerosol datasets were acquired from the Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC), located in the Goddard Space Flight Center in Greenbelt, Maryland https://ladsweb.nascom.nasa.gov/. All surface and aircraft observation data is freely and publicly available at https://www-air.larc.nasa.gov/cgi-bin/ArcView/korusaq, last access 10 June 2022.

opencc-by-4.0Apr 2023View details →

Proactive Conflict Detection for Collaborative Model-driven Software Engineering (Evaluation Data)

Results of the evaluation for the paper "Proactive Conflict Detection for Collaborative Model-driven Software Engineering"

opencc-zeroApr 2023View details →

Code and data for: Modeling the interaction between salmon management and consumption by coastal brown bears

Harvest management policy for species with strong trophic connections can reverberate through food webs and cause unintended consequences such as altering the abundance of a harvested species' predators or prey. Pacific salmon (Oncorhynchus spp.), a key food for many predators and an economically valuable harvested species, is generally managed for maximum sustained harvests without explicit consideration for the freshwater and terrestrial food webs which they support. The density of brown bear (Ursus arctos) populations in Alaska, USA is correlated with the amount of salmon they can access and consume, so it seems likely their populations are inadvertently affected by salmon management. We simulated the effect of salmon management policy on brown bears by customizing a general bear-salmon model using empirical data from three watersheds in southwest Kodiak, Alaska. Our goal was to quantify the effect of current salmon management policy (i.e., escapement goals and early/late run allocations) on salmon consumption by brown bears. Bears in the individually based model evaluated the value of each foraging site based on salmon abundance, salmon vulnerability, and competition with other bears and made movement decisions (among salmon spawning sites) accordingly. The two code files provided here contain the brown bear salmon simulation, the structure for setting and adjusting the parameters of the model, and two empirical datasets needed to run simulations.

opencc-by-4.0Apr 2023View details →

Data for Coupling Covariance Matrix Adaptation with Continuum Modeling for Determination of Kinetic Parameters Associated with Electrochemical CO2 Reduction

This data set contains digitized and tagged polarization and partial current density data for 18 datasets of CO2 reduction to H2 and CO over Ag catalysts, as well as 8 datasets of CO2 reduction to HCOO-, CO, and H2 over Sn catalysts. We analyze this data using a coupled continuum modeling and covariance matrix adaptation approach for which the codebase is provided in DOI: 10.5281/zenodo.7866195.

opencc-zeroApr 2023View details →

Code and initial metapopulation data for model construction and simulation analyses for: Genetic rescue from protected areas is modulated by migration, hunting rate and timing of harvest

Migrants from protected areas may buffer the risk of harvest-induced evolutionary changes in exploited populations that face strong selective harvest pressures in both terrestrial and marine ecosystems. Understanding the mechanisms favouring genetic rescue through migration could help ensure sustainable harvest outside protected areas and conserve genetic diversity inside those areas. We developed a stochastic individual-based metapopulation model to evaluate the potential for migration from protected areas to mitigate the evolutionary consequences of selective harvest. We parameterized the model with detailed data from individual monitoring of two populations of bighorn sheep subjected to trophy hunting. We tracked horn length through time in a metapopulation including large protected and trophy-hunted populations connected through male breeding migrations. We quantified and compared declines in horn length and rescue potential under various combinations of migration rate, hunting rate in hunted areas and temporal overlap in timing of harvest and migrations, which affects the migrants' survival and chances to breed within exploited areas. Our simulations suggest that the effects of size-selective harvest on male horn length in hunted populations can be dampened or avoided if harvest pressure is low, migration rate is substantial, and migrants have a low risk of being shot. Intense size-selective harvest impacts the phenotypic and genetic diversity in horn length, and population structure through changes in proportions of large-horned males, sex ratio and age structure. When hunting pressure is high and overlaps with male migrations, effects of selective removal also emerge in the protected population, so that instead of a genetic rescue of hunted populations, our model predicts undesirable effects inside protected areas. Our results stress the importance of a metapopulational approach to management, to promote genetic rescue from protected areas and limit ecological and evolutionary impacts of harvest on both harvested and protected populations.

opencc-zeroApr 2023View details →

Data from: Hydrodynamic analysis of bioinspired vortical cross-step filtration by computational modelling

Research on the suspension-feeding apparatus of fishes has led recently to the identification of novel filtration mechanisms involving vortices. Structures inside fish mouths form a series of 'backward-facing steps' by protruding medially into the mouth cavity. In paddlefish and basking shark mouths, porous gill rakers lie inside 'slots' between the protruding branchial arches. Vortical flows inside the slots of physical models have been shown to be important for the filtration process, but the complex flow patterns have not been visualized fully. Here we resolve the three-dimensional hydrodynamics by computational fluid dynamics simulation of a simplified mouth cavity including realistic flow dynamics at the porous layer. We developed and validated a modelling protocol in ANSYS Fluent software that combines a porous media model and permeability direction vector mapping. We found that vortex shape and confinement to the medial side of the gill rakers result from flow resistance by the porous gill raker surfaces. Anteriorly directed vortical flow shears the porous layer in the centre of slots. Flow patterns also indicate that slot entrances should remain unblocked, except for the posterior-most slot. This new modelling approach will enable future design exploration of fish-inspired filters.

opencc-by-4.0Apr 2023View details →

Ultrasound Stochastic Tomography simulation data for In-silico 2D Breast Phantom model with tumour

An anatomically realistic numerical breast phantom model (with realistic acoustic properties of speed of sound, density, and attenuation coefficient of tissues) derived from [1] is presented with details of a ultrasound tomography experiment in simulation. Details of source wavelets, geometry of transducer set, observed data at each transducer for each shots are provided with phantom model. References [1] <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a>

MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region. Article data

Regional coupled and Earth System models are fundamental numerical tools for climate investigations, downscaling of predictions and projections, process-oriented understanding of regional extreme events, and many more applications. Here we introduce a newly developed coupled regional modeling framework for the Mediterranean region, called MESMAR (Mediterranean Earth System model at ISMAR) version 1, which is composed of the WRF atmospheric model, the NEMO oceanic 15 model, and the HD hydrological discharge model, coupled via the OASIS coupler. The model is implemented at moderate resolution (about 1/12° for the ocean and river routing, while twice coarser for the atmosphere) for long-term investigations. The gzipped tarball contains data files contained in the manuscript associated with the MESMARv1 description and submitted to Geoscientific Model Developments: MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region by Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, Chunxue Yang

Echo from noise: synthetically generated cardiac ultrasound data using semantic diffusion models

This is the data repository for the paper: "Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation", available at: https://arxiv.org/abs/2305.05424. The corresponding code is available at: https://github.com/david-stojanovski/echo_from_noise   This is the first work to utilize Denoising Diffusion Probabilistic Models (DDPMs) for generating medical images using semantic label maps as a source image for conditioning the generated image. Each of the 400+50 CAMUS patients contributes with 4 labelled frames (ED and ES for 2 chamber and 4 chamber), totalling 1800 initial semantic maps, to which we added the sector label. These semantic maps then had five random deformations applied (a combination of random affine and elastic deformation) to produce, 9000 transformed semantic maps (8000 for training and 1000 for validation).  Affine transformation ranges for rotation degrees, translate, scale and shear were: (-5, 5), (0, 0.05), (0.8, 1.05) and 5 respectively. This was implemented using the torchvision python package. Elastic deformation was implemented using the TorchIO package. The settings for number of control points and max displacement were (10, 10, 4) and (0, 30, 30) respectively. Using these 9000 semantic maps as input to the generative models, we produced 9000 synthetic ultrasound images. Each echo view folder contains 3 folders: 1) annotations: augmented labels, with no sector label and no clipping due to sector 2) images: semantic diffusion model inferenced images 3) sector_annotations: label maps which contain ultrasound cone sector, which were used to generate corresponding semantic diffusion model images ema_0.9999_050000_2ch_ed_256.pt and ema_0.9999_050000_4ch_ed_256.pt are the saved checkpoints for the 2 and 4 chamber diffusion models respectively. The pretrained segmentation networks are provided within the <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/final_models.zip">final_models.zip</a> file. A diagram of image numbers is shown in <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/Data%20diagram.png">Data diagram.png</a>

SECURES-Met - A European wide meteorological data set suitable for electricity modelling (supply and demand) for historical climate and climate change projections

For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.   The SECURES-Met dataset provides variables visible in the table. <table> <tbody><tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Aggregation methods</th> <th>Temporal resolution</th> </tr> </tbody><tbody> <tr> <th>Temperature (2m)</th> <td>T2M</td> <td> °C °C </td> <td> spatial mean population weighted mean (recommended) </td> <td>hourly</td> </tr> <tr> <th>Radiation</th> <td> GLO (mean global radiation) BNI (direct normal irradiation) </td> <td> Wm-2 Wm-2 </td> <td> spatial mean population weighted mean (recommended) </td> <td>hourly</td> </tr> <tr> <th>Potential Wind Power </th> <td>WP</td> <td>1</td> <td>normalized with potentially available area</td> <td>hourly</td> </tr> <tr> <th>Hydro Power Potential</th> <td> HYD-RES (reservoir) HYD-ROR (run-of-river) </td> <td> MW 1 </td> <td> summed power production summed power production normalized with average daily production </td> <td>daily</td> </tr> </tbody> </table>                           SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (RCP 4.5 and RCP 8.5, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM:  ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the spatial aggregation level <ul> <li>NUTS0 (country-wide),</li> <li>NUTS2 (province-wide),</li> <li>NUTS3 (Austria only),</li> <li>and EEZ (Exclusive Economic Zones, offshore only).</li> </ul> The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized,  and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As population weighted temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only. The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.

Data and Code for Blaszczak et al. 2023, Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience

Data and code for analyses in Blaszczak et al. 2023, Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience. See publication and ReadMe file for analysis description and further details.  bioRxiv pre-print: Blaszczak, J.R., Yackulic, C., Shriver, R., & R.O. Hall, Jr. 2023. Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience. https://doi.org/10.1101/2023.04.11.535773

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)

The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)

U-T training and test data for Saltblock model

Here are the  training and testing data sets involved in the numerical experiments in the article that has been submitted to the journal “Journal of Geophysical Research: Solid Earth”, named “Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density”: Saltblock model. Each dataset consists of two parts: a training dataset and a testing dataset. Both training and testing data sets contain three parts: seismic data, velocity model and density model.

opencc-zeroMay 2023View details →

Discretized U.S. drought data to support statistical modeling

Drought is a costly and disruptive natural disaster, with widespread implications for agriculture, wildfire, and urban planning. We present a novel data set on US drought built to enable computationally efficient spatio-temporal statistical and probabilistic models of drought. We converted drought data obtained from the widely-used US Drought Monitor (USDM) from continuous shape files to a 0.5-degree regular lattice. These data cover the Continental US from 2000 to mid-2022. Known environmental drivers of drought include those obtained from the North American Land Data Assimilation System (NLDAS-2), US Geological Survey (USGS) streamflow data, and National Oceanic and Atmospheric Administration (NOAA) teleconnections data. The USGS streamflow data is itself a new gridded data product, aggregating point-referenced stream discharges from across the US to a common lattice using watersheds to combine nearby stream data. The resulting data set permits statistical and probabilistic modeling of drought with explicit spatial and/or temporal dependence. Such models could be used to forecast short-range and even season-to-season future droughts with uncertainty, extending the reach and value of the current US Drought Outlook produced by the National Weather Service Climate Prediction Center.

opencc-zeroMay 2023View details →

Experimental data analyzed in: Signal detection models as contextual bandits

Signal detection theory (SDT) has been widely applied to identify the optimal discriminative decisions of receivers under uncertainty. However, the approach assumes that decision-makers immediately adopt the appropriate acceptance threshold, even though the optimal response must often be learned. Here we recast the classical normal-normal (and power-law) signal detection model as a contextual multi-armed bandit (CMAB). Thus, rather than starting with complete information, decision-makers must infer how the magnitude of a continuous cue is related to the probability that a signaller is desirable, while simultaneously seeking to exploit the information they acquire. We explain how various CMAB heuristics resolve the trade-off between better estimating the underlying relationship and exploiting it. Next, we determined how naïve human volunteers resolve signal detection problems with a continuous cue. As anticipated, a model of choice (accept/reject) that assumed volunteers immediately adopted the SDT-predicted acceptance threshold did not predict volunteer behaviour well. The Softmax rule for solving CMABs, with choices based on a logistic function of the expected payoffs, best explained the decisions of our volunteers but a simple midpoint algorithm also predicted decisions well under some conditions. CMABs offer principled parametric solutions to solving many classical SDT problems when decision-makers start with incomplete information.

opencc-zeroMay 2023View details →

Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases.

Supporting data (MEG and fMRI) for brain model

Supporting data (MEG and fMRI) for the brain model published here: https://doi.org/10.5281/zenodo.7988965.

opencc-by-4.0Jun 2023View details →

Data set used in "Effect of turbulence and viscosity models on wall shear stress derived biomarkers for aorta simulations"

Data set used in "Effect of turbulence and viscosity models on wall shear stress derived biomarkers for aorta simulations" Includes the data for 20 heartbeats. Divided into external and internal walls regions.

opencc-by-4.0Jun 2023View details →

Pyrolysis Model Data Set Contribution for the MaCFP-3 Workshop October 2023

Pyrolysis model data set contribution for the <a href="https://iafss.org/macfp/">MaCFP-3 workshop October 2023</a> The contributions are also available via the materials database <a href="https://github.com/MaCFP/matl-db">MaCFP git repository.</a> For the inverse modelling in general, please see the manuscript <a href="https://doi.org/10.48550/arXiv.2303.17446">"PMMA Pyrolysis Simulation -- from Micro- to Real-Scale"</a>   There are three different ZIP archives provided: <ul> <li>"MaCFP_Gasification" contains the simulations reproducing the NIST Gasification Apparatus for validation of the parameter sets, as requested per MaCFP. There are three different runs conducted, two for the originally submitted parameter sets (BUW-FZJ Approach A/B). No changes to the parameter sets are conducted. A new parameter set is provided to the MaCFP repository (BUW-FZJ PMMA01) and its validation simulations are provided as well.  Furthermore, Jupyter notebooks are provided to process the simulation data.</li> <li>"PyrolysisKinetics_PMMA01" contains the inverse modelling run to determine the pyrolysis reaction kinetics.</li> <li>"Thermophysical_PMMA01" contains the inverse modelling run to determine the thermophysical parameters.</li> </ul>