Annual mean sea level in the Dutch Wadden Sea 2009-2011

Model data of mean sea level in the Dutch Wadden Sea, 2009-2011, to study inter-annual and regional variability of annual mean sea level

Relativistic Lattice Boltzmann Method

Numerical solver for a relativistic gas in flat space time. See for details the preprint at https://doi.org/10.21203/rs.3.rs-1558550/v

Crowdflow – diluted pedestrian dynamics in the Metaforum building of Eindhoven University of Technology

This is a dataset of pedestrian trajectories recorded on a nearly 24/7 schedule in a landing in the Metaforum building at Eindhoven University of Technology. The data acquisition spanned over a year and, overall, about 250.000 trajectories have been collected. Depth imaging data has been first obtained via an overhead Microsoft Kinect sensor, then ad hoc localization algorithms and PTV-like tracking have been employed to estimate the trajectory of individual heads (cf. publication). The current dataset includes 20.000 trajectories from pedestrians walking undisturbed i.e. in diluted conditions (individuals are walking alone in the facility). There are 10.000 trajectories of pedestrians crossing the landing entering from the left hand side (file: "left-to-right.ssv") and 10.000 trajectories of pedestrians entering in the opposite side (file: "right-to-left.ssv", right-left reference is given according to the publication linked). The purpose of the dataset is to enable ensemble analyses of diluted pedestrian motion. The trajectories are in the following table format: Pid Rstep X Y X_SG Y_SG U_SG V_SG (Pid: unique identifier of a trajectory - Rstep: identifier of the timestep (starts from zero, the first 5 and last 5 samples are eliminated as typically less precise)) - X,Y: position in Cartesian coordinates (in meters) - X_SG,Y_SG: position in Cartesian coordinates after Savizky-Golay smoothing (in meters, cf. paper) - U_SG, V_SG: velocity in Cartesian coordinates after Savizky-Golay smoothing (in meters per second, cf. paper)

Lattice Boltzmann simulations of drying suspensions of soft particles - numerical data

LB3D configuration files (in the folder config) and raw output data (in the folder data) used in the manuscript "Lattice Boltzmann simulations of drying suspensions of soft particles". The file "input-file" contains the simulation parameters. The file "parm_meshes.dat" contains the parameters related to the particles. A "Readme.txt" file is provided for more details

Data of laboratory experiments on sediment transport and morphodynamics by a translating monopolar vortex

Data obtained from experiments on the sediment transport and morphodynamics induced by a translating monopolar vortex. The volume occupied by the suspended sediment was dynamically measured using a vertically moving horizontal laser sheet which illuminated cross-sections of the region where particles were suspended. Changes in the particle bed were measured using a single camera photogrammetric technique. The vortex characteristics (size, strength, trajectory and velocity) where obtained using Particle Image Velocimetry (PIV). Two types of particles were used. The first type of particles (referred to as PS particles) were translucent, spherical, polystyrene particles with an average diameter 580 μm and a density equat to g/cm^3. The second type of particles (referred to as PMMA particles) were smaller and heavier, with a diameter between 250 μm and 300 μm and a density equal to 1.20 g/cm^3. Experiments without a sediment bed were also performed and compared to their counterparts with sediment to determine possible effects of the changes in the sediment bed and particle loading on the vortex

3D high resolution numerical simulations of the hydrodynamics of the Dutch Wadden Sea (hindcasting 2009-2011)

At the Royal NIOZ, during the proyect PACE, 3D high resolution numerical simulations of the hydrodynamics of the Dutch Wadden Sea where perfored. As a by-product of these simulations, we have created a three-year long (2009-2011) hindcast database of the hydrodynamic conditions of the Dutch Wadden Sea with a resolution of 200m for further use by sediment transport, and especially, ecological studies

Data from numerical simulations of topological structure and dynamics of three-dimensional active nematics

The data in this dataset are the result of numerical simulations aimed at understanding the topology and dynamics of active nematics. In the manuscript a comparison between numerical and experimental results is also presented

Dataset underlying the publication: "Efficient deep learning surrogate method for predicting the transport of particle patches in coastal environments"

The data provided in this repository can be used to run the surrogate and optimal prediction experiments described in the manuscript "Efficient deep learning surrogate method for predicting the transport of particle patches in coastal environments". This paper introduces a revolutionary tool for forecasting the spread of tracers or pollutants in our oceans. We have developed a unique surrogate modeling method that combines the power of deep learning with physical oceanographic understanding. This translates to accurate forecasts that achieve at least two orders of magnitude faster than traditional systems – once the deep learning model is trained. In our paper, the experiment "surrogate prediction" is used to assess the performance of our current deep learning approach, whereas the experiment "optimal prediction" shows what can be achieved if a perfect deep learning prediction is obtained. A small sample of the data is also stored in the GitHub repository (https://github.com/JeancarloFU/paper_Efficient_Deep_Learning_Surrogate_Method_For_Lagrangian_Transport). Here, scripts, and notebooks (based on Python v3.8) used to run the surrogate and optimal prediction experiments described in the manuscript are archived

Representation of crowd accidents in popular media

This repository contains results related to the analysis of a corpus of news reports covering the topic of crowd accidents. To facilitate online visualization and offline analysis, the files are organized by assigning a number to each. The number system and the details of each set of files are described as follows: Class 0 – This contains the same files provided in this repository, but they are organized into folders to make analysis easier. If you intend to analyze the data from our lexical analysis, we suggest using this file since it is better organized and can be directly downloaded. Class 1 – This contains the sources and relevant information for people who are interested in replicating our dataset or accessing the news reports used in our analysis. Please note that due to copyright regulations, the texts cannot be shared. However, you can refer to the links provided in these files to access the news articles and Wikipedia pages. Some links have stopped working during the time we were working on this study, and others may be unreachable in the future. Class 2 – This contains the results from a lexical analysis of the corpus. The HTML page allows you to visualize each result interactively through the online VOSviewer app (you need to download the file and open it using a browser since Zenodo does not recognize this as a link). It is possible that this service (VOSviewer app) may be discontinued at some point in the future. PNG images of lexical maps are, therefore, available for download through the ZIP archive, although they do not allow interactive access. If you plan to read our results using the offline VOSviewer software or perform a more systematic analysis, JSON files are available for each category (time period, geographical area of the reporting institution, and purpose of gathering). The same files can be also find in the ZIP archive in class 0. Class 3 – These are the results of the sentiment analysis. For each report, a single result is generated for the title. However, for the body, the text is divided into parts, which are analyzed independently. The format of CSV and JSON files should be self-explanatory after reading our publication. For specific questions or queries, please contact one of the authors, and we will try to assist you

Data underlying the publication: Pattern formation of spherical particles in an oscillating flow

This data set contains all the data to reproduce the results presented in the manuscript titled 'Pattern formation of spherical particles in an oscillating flow'. All data processing is done in Jupyter-Lab and Paraview. FigureX.ipynb uses the preprocessed data and functions to generate Figure X from the original manuscript. See the file README.txt for more information about data structure and processing