Find research datasets worth reusing

opencc-by-4.0Jul 2024View details →

Supporting materials for 'Building quantitative skills with a simplified physical model of coastal storm deposition'

Supporting Materials for Lazarus (2024): "Building quantitative skills with a simplified physical model of coastal storm deposition" (preprint <a href="https://doi.org/10.31223/X56H5B">here</a>). Files include: <ul> <li>DEM_washover_final_demo.tif – "final" DEM for a bare back-barrier floodplain in a physical laboratory experiment of coastal barrier overwash</li> <li>Lazarus_2024_experimental_washover_exercise_instructions_Zenodo_release.pdf – step-by-step instructions for a classroom exercise that guides students through using the 'DEM_washover_final_demo' file to digitise, measure, and plot washover deposits with QGIS and Python</li> <li>experiments_plotting_simple.ipynb – Python notebook for plotting results from classroom exercise</li> <li>Lazarus_2024_washover_exercise_figs.ipynb – Python notebook for plotting Figs. 3 & 4 in the accompanying manuscript (Lazarus, 2024)</li> <li>GGES2021_S24_data_all_release.csv – dataset of morphometric measurements presented and discussed in the accompanying manuscript (<a href="https://doi.org/10.31223/X56H5B">Lazarus, 2024</a>)</li> </ul>

opencc-by-4.0Jul 2019View details →

T2* and quantitative susceptibility mapping in an equine model of post-traumatic osteoarthritis: prediction of mechanical and structural properties

Dataset for the manuscript titled "T2* and quantitative susceptibility mapping in an equine model of post-traumatic osteoarthritis: assessment of mechanical and structural properties"

opencc-by-4.0Nov 2022View details →

UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation

This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density.  Below are the description of each data file: <ul> <li>high_density_both_lane_raw_AI_speed.csv <ul> <li>Description: extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li>Data Fields: <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li>high_density_inner_lane_traffic_AI_count_1min_bin.csv <ul> <li>Description: AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li>high_density_inner_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>high_density_inner_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li>high_density_outer_lane_traffic_AI_count_1min_bin.csv <ul> <li>Description: AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li>high_density_outer_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>high_density_outer_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li>low_density_inner_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>low_density_inner_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li>low_density_outer_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>low_density_outer_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Jan 2023View details →

KidDO project update - quantitative proteomic and metabolomic analysis of five mouse models with chronic kidney disease

Chronic kidney disease (CKD) is one of the most deadly diseases faced by patients and is a major global health and socioeconomic burden.CKD increases cardiovascular morbidity and premature mortality and decreases quality of life. Hypertension (HTN) and type 2 diabetes mellitus (T2DM), which are reaching epidemic levels, are major risk factors for CKD. CKD diagnosis and progression is based on estimated GFR (eGFR) and urinary albumin excretion. However, eGFR only has a predictive value in advanced disease and there is risk of progressive CKD in non-albuminuric individuals. Thus, there is an urgent need for new approaches for early detection of the most “at risk” individuals and identification of CKD signatures to aid in designing novel drugs and preventive measures that could ameliorate progression of CKD. Our overarching goal is to identify metabolites that predict kidney cell phenotypes during CKD and how crosstalk of these metabolites with the proteome drive CKD progression. We will integrate metabolomics and proteomic information from animal models of CKD with human CKD patient biopsies to identify common signatures in the tubulointerstitium that correlate with human pathophysiology. Here we provide quantitative proteomic and metabolomic datasets, as well as plasma and urine electrolyte measurements on five CKD mouse models.

publicApr 2021View details →

Quantitative variables related to color, territory, behavior, and morphology for male lesser prairie-chickens used in discrete choice models in mate choice study

Open the record for dataset details and reuse information.

publicOct 2025View details →

Data and code from: Wide-Angle Lung Experiment Segmentation (WALES): A novel methodology for quantitative assessment of lung pathology in model systems

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publicJul 2020View details →

Data from: Estimating fish population abundance by integrating quantitative data on environmental DNA and hydrodynamic modeling

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publicJul 2024View details →

Induced pluripotent stem cell-derived cardiomyocyte in vitro models: tissue fabrication protocols, assessment methods, and quantitative maturation metrics for benchmarking progress

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opencc-by-4.0Jun 2020View details →

Evaluation of articular cartilage with quantitative MRI in an equine model of post-traumatic osteoarthritis

This dataset contains raw qMRI data and the corresponding calculated relaxation time maps, reference data and an example MATLAB script demonstrating how to access the data, comprising study: Evaluation of articular cartilage with quantitative MRI in an equine model of post-traumatic osteoarthritis Journal of Orthopaedic Research | DOI: https://doi.org/10.1002/jor.24780 Kajabi Abdul Wahed*(1,2), Casula Victor(1,2), Sarin Jaakko K.(3,4), Ketola Juuso H.(1), Nykänen Olli(3), te Moller Nikae C.R.(5), Mancini Irina A.D.(5), Visser Jetze(6), Brommer Harold(5), van Weeren P. René(5), Malda Jos(5,6), Töyräs Juha(3,4,7), Nieminen Miika T.(1,2,8), Nissi Mikko J.*(1,3) <ol> <li>Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland</li> <li>Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland </li> <li>Department of Applied Physics, University of Eastern Finland, Kuopio, Finland</li> <li>Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland</li> <li>Department of Equine Sciences, Faculty of Veterinary Medicine, Utrecht University, The Netherlands</li> <li>Department of Orthopaedics, University Medical Center Utrecht, The Netherlands </li> <li>School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia</li> <li>Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland </li> </ol>   *Corresponding authors: Mikko J. Nissi Department of Applied Physics, University of Eastern Finland POB 1627, FI-70211, Kuopio, Finland mikko.nissi@uef.fi +358-50-5955517 Abdul Wahed Kajabi Research Unit of Medical Imaging, Physics and Technology, University of Oulu POB 50, FI-90029, Oulu, Finland abdul.kajabi@oulu.fi +358-50-3037425 Keywords: cartilage, post-traumatic, osteoarthritis, quantitative MRI, relaxation times The file "Pony_AllData.mat" contains all the data of the study. The main struct variable in the file ("all_data") contains several fields storing all the data per sample. In the following, brief descriptions for the main subfields are given: <ul> <li>name: contains generic name of the sample</li> <li>qMRI_Data: contains qMRI raw data, the corresponding relaxation time maps and normalized cartilage full-thickness ROI profiles</li> <li>calcROI_Data: contains ROIs used to calculate the relaxation time maps</li> <li>analysisROI_Data: ROIs used for the analysis</li> <li>DD_prof and interpDD_prof: Original and interpolated profiles for proteoglycan content (Optical Density)</li> <li>PLM_prof and interpPLM_prof: Original and interpolated profiles for collagen fiber orientation (Polarized Light Microscopy)</li> <li>E_eq and E_dyn: Equilibrium and dynamic moduli of articular cartilage.</li> </ul> The example script, "Analysis_script.m" contains a short demonstration on how the data in the struct can be accessed. The script assumes that Aedes (http://aedes.uef.fi) analysis software is available for matlab. (Abdul Wahed Kajabi, 17 Jun 2020)

opencc-by-4.0Oct 2020View details →

Towards a Quantitative Model to Deal with Uncertainty Management in Software Projects

The evolution of thinking in project management has raised interest in areas not yet explored by researchers and practitioners of project management, including the management of uncertainties associated with risk management. The correct risk and uncertainty management in software projects can represent a competitive differential for the software development industry. Despite the increasing use of uncertainty management strategies, many projects still fail. Some recent studies show that the current techniques used to manage uncertainties organize the project's known information, but give little or no indication of the unknown information or uncertainties associated with the project. These techniques do not take into account the impact of existing dependency and interdependence relationships between the various sources of uncertainties in the project. This work will apply Action Research to develop a model with a focus on uncertainty quantification techniques. This work aims to present a model with a focus on uncertainty quantification techniques that take into account the relationships of dependencies and interdependence that exist between the sources of risks and uncertainties in software projects and as a result, contribute with the advance of state of the art in the practice of risk and uncertainty management in project software.

opencc-by-4.0Feb 2024View details →

Modelling Cell Shape in 3D Structured Environments: A Quantitative Comparison with Experiments

This repository contains experimental data and computer scripts for the following publication: Link R, Jaggy M, Bastmeyer M, Schwarz US (2024) Modelling cell shape in 3D structured environments: A quantitative comparison with experiments. PLoS Comput Biol 20(4): e1011412. https://doi.org/10.1371/journal.pcbi.1011412 There are two directories, “data” and “scripts”.  1)     Directory data  WRL-files for experimental data generated with Imaris from Zeiss image files. The WRL-files can be converted to STL-files with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>). The STL-files can be converted to FE-files for the SurfaceEvolver with our script CreateFeFile.py.  The WRL-files are named according to the scaffolds: L*.wrl cells in L-shaped scaffolds (n=6). V*.wrl cells in V-shaped scaffolds (n=7). TRight*.wrl cells in right-triangle scaffolds (n=3). TEqui*.wrl cells in equilateral-triangle scaffolds (n=4). 2)     Directory scripts ClusterSurfaceLinearPlugin: New CompuCell3D plugin needed to calculate linear surface energy functional for cells with nucleus (using the cluster concept). CompuCell3DScript: Hamiltonian_Comparison.cc3d is the main script for our simulations, uses the directory “Simulation”. CreateFeFile.py: generates surface evolver FE-file from STL-file. A STL-file can be generated from a WRL-file e.g. with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>).  SphericalHarmonicsAnalysis.ipynb: Python notebook that calculates the Fourier spectrum and Delta_30, needs WRL-file as input.

dryad32/100

Data from: Human judgment vs. quantitative models for the management of ecological resources

Despite major advances in quantitative approaches to natural resource management, there has been resistance to using these tools in the actual practice of managing ecological populations. Given a managed system and a set of assumptions, translated into a model, optimization methods can be used to solve for the most cost-effective management actions. However, when the underlying assumptions are not met, such methods can potentially lead to decisions that harm the environment and economy. Managers who develop decisions based on past experience and judgment, without the aid of mathematical models, can potentially learn about the system and develop flexible management strategies. However, these strategies are often based on subjective criteria and equally invalid and often unstated assumptions. Given the drawbacks of both methods, it is unclear whether simple quantitative models improve environmental decision making over expert opinion. In this study, we explore how well students, using their experience and judgment, manage simulated fishery populations in an online computer game and compare their management outcomes to the performance of model-based decisions. We consider harvest decisions generated using four different quantitative models: (1) the model used to produce the simulated population dynamics observed in the game, with the values of all parameters known (as a control), (2) the same model, but with unknown parameter values that must be estimated during the game from observed data, (3) models that are structurally different from those used to simulate the population dynamics, and (4) a model that ignores age structure. Humans on average performed much worse than the models in cases 1–3, but in a small minority of scenarios, models produced worse outcomes than those resulting from students making decisions based on experience and judgment. When the models ignored age structure, they generated poorly performing management decisions, but still outperformed students using experience and judgment 66% of the time.

opencc-zeroDec 2015View details →

opencc-by-4.0Aug 2022View details →

Supplementary Data for the Project Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome

Supplementary Tables for the Project "Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome" Supplementary Table 1. List of 18 genome-sequenced human gut bacteria with metabolic characteristics that were used for community assembly. Supplementary Table 2. Diet composition. Supplementary Table 3. Species relative abundance. Supplementary Table 4. Gene abundance and expression changes. Supplementary Table 5. Gene pathway enrichment results. Supplementary Table 6. Metabolomics data. Supplementary Table 7. Metabolite fold changes, clustering and model parameters. Supplementary Table 8. Description of the intestinal flux model. Supplementary Table 9. Enzymatic paths between substrates and products. Supplementary Table 10. Pearson's correlation coefficients between potential substrates and products, and metagenomics and metatranscriptomic measurements.

dryad32/100

Quantitative and qualitative methods complementing: Bridging modelling and policy-making efforts to realise the European bioeconomy

The European Bioeconomy Strategy aims to facilitate the transition from a take-make-dispose fossil economy into one fostering circular bio-based value chains linking sustainable land use with cutting-edge products. Optimised designs, implementation and monitoring rely on continuous interactions between policymakers and modellers who run multiple scenarios for environmentally, economically and socially desirable futures. "Bridging modelling and policy-making efforts to realise the European bioeconomy" leverages a multi-layered framework that cross-references 39 policies and 32 models to assess how they address the five principle objectives of the Bioeconomy Strategy in terms of accompanying sectors, value-chains, and multi-dimensional indicators. The framework identifies gaps in bioeconomy knowledge both in policy and modelling. The analysis stemming from this framework matching policies and models based on their scope and aim, and reviewing how each addresses bioeconomy objectives, sectors, value chain stages and indicators, was built using mixed methods. The files complement the Supplementary Files of "Bridging modelling and policy-making efforts to realise the European bioeconomy" to produce a comprehensive compendium of the key findings extracted from the main publication.

opencc-zeroAug 2022View details →

opencc-by-nc-4.0May 2016View details →

Tree species recognition with quantitative structure models

A quantitative structure model (QSM) contains the geometric and topological structure of a reconstructed tree. As such, QSMs enable computation of detailed tree properties that have been laborious or impossible to measure before. The computed tree properties can be used as classification features for tree species recognition. The first half of this video illustrates how we define the 15 classification features our research group has used for a species recognition study. An example QSM is used to visualize the relevant tree parts and key steps in the feature computations. The second half shows how the feature values of over a thousand Finnish trees of three different species, Silver birch, Scots pine and Norway Spruce, are distributed, and how well the species separate in the defined feature dimensions. One example QSM of each tree species shown on the right-hand-side with the only the tree parts visible that are related to the current feature. Table of contents: 0:01 Feature illustration 3:46 Viewer guide on screen elements 5:20 Feature value distributions 9:07 Credits The contents of this video link directly to the paper titled "Automatic tree species recognition with quantitative structure models" published in Remote Sensing of Environment (http://dx.doi.org/10.1016/j.rse.2016.12.002). For more information about QSMs, please visit the groups homepage, or watch the other videos on the topic: "3D Forest Information" (https://www.youtube.com/watch?v=wANRdliE1zQ) and "Cylinder reconstruction" (https://www.youtube.com/watch?v=j0Emjwp-fmU). This animation was produced by the Inverse Problems research group in the Department of Mathematics at Tampere University of Technology (http://math.tut.fi/inversegroup). Animation created using Blender 2.77a (http://www.blender.org). Music: "Life of Riley" "Thinking of you" "Jarvic 8" by Kevin MacLeod (http://incompetech.com) Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0/

opencc-by-nc-4.0Jan 2016View details →

Quantitative Structure Models

Summary of how Quantitative Structure Models are reconstructed, and how they are being utilized. The process starts with the laser scanning of a forest. The produced point cloud contains millions of points, and numerous trees. The point cloud is segmented automatically into trees and then into branches. Each branch is reconstructed by fitting several cylinders to the data. The branching topology is stored together with the geometric structure to a single tree model, which is called a Quantitative Structure Model (QSM). QSMs can be used in many applications, as they allow easy access to tree properties, such as, volume, area, branch count, crown shape, taper curve, and size distribution. These properties can be used by forest scientists, forest industry, and forest owners to, e.g., evaluate the current or future state of a forest, or its value. The model properties can also be used for automatic species recognition. Furthermore, QSMs can be visualized in various ways to produce realistic virtual representations of locations, such as, national or city parks, for the travel industry. The models can be textured and augmented with leaves to achieve either a realistic or a fantasy look, to suite the needs of game developers. For more information about QSMs, please visit the groups homepage, or watch the other videos on the topic: "3D Forest Information" (https://www.youtube.com/watch?v=wANRdliE1zQ) and "Cylinder reconstruction" (https://www.youtube.com/watch?v=j0Emjwp-fmU). This animation was produced by the Inverse Problems research group in the Department of Mathematics at Tampere University of Technology (http://math.tut.fi/inversegroup). Animation created using Blender (http://www.blender.org). Music: "Delay Rock" by Kevin MacLeod (http://incompetech.com) Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0/ Textures: "Bark 0007" xoio (xoio.de) "Cherry Leaf" BrianHanson2nd (deviantart.com) Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0/ The animation builds upon but does not directly feature the "Prunus avium - Cherry Tree" point cloud data by Jan Hackenberg (http://www.simpletree.uni-freiburg.de/openData.html) shared under the Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International license http://creativecommons.org/licenses/by-nc-sa/4.0/

opencc-by-4.0May 2024View details →

Quantitative evidence for modelling electric vehicles - Supplementary Data

Please cite as: Malte Jansen, Rob Gross, and Iain Staffell. ‘Quantitative Evidence for Modelling Electric Vehicles’. Renewable and Sustainable Energy Reviews 199 (1 July 2024): 114524. <a href="https://doi.org/10.1016/j.rser.2024.114524">https://doi.org/10.1016/j.rser.2024.114524</a>. Abstract: Electric vehicles are now a major contributor to decarbonising the transport sector. Their rollout has accelerated rapidly since 2020, reaching a global fleet of 40 million in 2023. This  presents both problems and opportunities for electricity systems, with charging increasing peak loads, but also providing a large new source of flexibility to help manage increased shares of wind and solar generation, shift peak demand and improve network management. While EV flexibility is widely discussed, there is uncertainty surrounding the magnitude to which EVs could help electricity systems, and a distinct lack of quantitative evidence around adoption, charging behaviour and technical capabilities for load shifting. This study employs the rapid evidence assessment method to synthesise recent information. We find that studies expect that EVs could provide 1–11 GW of flexible capacity per million vehicles (median: 3.7 GW), with the ability to shift demand by 1.5–5 hours (median: 4 hours) and a price elasticity of –0.77 to –0.10 (median: –0.15).  Diurnal profiles of charging demand and availability for providing flexibility are aggregated across multiple studies. The results are relevant for energy modellers and show that the interaction between EVs and electricity systems can be generalised on a widely-applicable basis.

opencc-by-4.0May 2024View details →

Ammonia and ammonia/hydrogen combustion: Comprehensive quantitative assessment of kinetic models and examination of critical parameters

This repository contains data on laminar burning velocity, ignition delay time, and species concentration measured in fundamental combustion experiments involving ammonia and ammonia/hydrogen mixtures. The data, compiled from literature sources, is used in the publication titled 'Ammonia and ammonia/hydrogen combustion: Comprehensive quantitative assessment of kinetic models and examination of critical parameters' in Combustion and Flame.

opencc-by-4.0May 2024View details →

A Modified Global Plate Model Including Quantitative Tomotectonic Plate Reconstruction of Western North America and the Eastern Pacific Basin

This plate model is based on that of Clennett et al. (2020) (<a href="../records/10348271" target="_blank" rel="noopener">https://zenodo.org/records/10348271</a>). The Clennett et al. (2020) model was an extension of the global model of Müller et al. (2019) (<a href="../records/10525287" target="_blank" rel="noopener">https://zenodo.org/records/10525287</a>), with a focus on producing a more detailed tectonic reconstruction of the North American Cordillera. Further minor modifications were made to the model for a paper by Alfonso et al. (2024), using the GPlates software (<a href="https://url.au.m.mimecastprotect.com/s/JVvZCE8wmrt1kqgwPtNcn8j?domain=gplates.org/" target="_blank" rel="noopener">https://www.gplates.org/</a>). Firstly, several subduction zones in the published plate model have no associated motions, instead remaining entirely stationary relative to the mantle. This was judged to be incompatible with basic geodynamic principles, as geodynamic models indicate that trenches and subducting slabs are almost always in motion relative to the mantle, exhibiting retreat or, more rarely, advance (e.g. Capitanio, 2013; Capitanio et al., 2010; Schellart et al., 2007; Stegman et al., 2010). As trenches of intermediate length generally display relatively slow retreat (Schellart, 2008), a small amount of trench rollback was imposed on the subduction zones in this model, whilst ensuring that they remained located above their associated tomographically-imaged slabs. Additionally, the Orcas plate (170–130 Ma), located within the Cordilleran archipelago, was split into two sections separated by an east-west trending mid-ocean ridge, consistent with the subsequent plate configuration after 130 Ma. This was done to improve the consistency of the plate motion model, which appeared to indicate divergence within the Orcas plate and convergence at its boundaries. Finally, the reconstruction’s absolute plate motion model was recalculated using the iterative optimisation workflow developed by Tetley et al. (2019).   The agegrids associated with this model can be accessed from: <a href="https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Alfonso_etal_2024_modClennettMuller/" target="_blank" rel="noopener">https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Alfonso_etal_2024_modClennettMuller/</a>