Find research datasets worth reusing

opencc-by-4.0Jan 2023View details →

Code and dataset for publication "Laser Wakefield Accelerator modelling with Variational Neural Networks"

Data and code for reproducing figures in published work.   High Power Laser Science and Engineering <a href="https://doi.org/10.1017/hpl.2022.47">https://doi.org/10.1017/hpl.2022.47</a> Code used various python packages including tensorflow. Conda environment was created with (on 6th Jan 2022) conda create --name tf tensorflow notebook tensorflow-probability pandas tqdm scikit-learn matplotlib seaborn protobuf opencv scipy scikit-image scikit-optimize Pillow PyAbel libclang flatbuffers gast --channel conda-forge

opencc-by-4.0Jan 2023View details →

Key sulphur species predicted by photochemical models and their employed chemical networks

The volume mixing ratio output of the key sulphur species computed by photochemical models for producing Fig. 1 in Tsai et al. 2023 and the chemical networks used in each model.

opencc-by-4.0Jan 2023View details →

Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data

Numerical codes and results for the article: Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data

Synthetic plant modelling: creating plants in 3D to train neural networks

The following video describes how virtual plant modeling can be used to create synthetic datasets that inform machine learning and computer vision. funded by EU Grant 773875 Videos are available in: <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> Video script: (MINCHIN) At INRIA in Lyon in France we were also looking at virtual modelling of plants. This creates synthetic data sets that can then help us train machine learning algorithms to identify plants in the field or particular traits in real life. (GODIN) So this is the current bottleneck where we are, we need absolutely massive ground truth data if we want to train this algorithm, this new algorithm, these new families of algorithms to produce this 3d structures and segment it in the the correct botanical way. So how to do this? There are two options basically; the first option would be to acquire a real plant data by photograph, by scanner laser, and then the expert segment by hand different parts of the plant, saying that this is a leaf, this part of the point cloud is a leaf, this part of the point cloud is a stem et cetera. And you can imagine that this is extremely time consuming, this is extremely heavy task and we are blocked at this point because of the of the ability of humans to do such complicated tasks. And there is another option that is to create artificial plants and then say whether with this segmentation of the virtual plant. Whether we are right or not because as we designed the virtual plant we know that this part of the point cloud corresponds to a leaf, this part of the point cloud corresponds to a stem and because of this it is possible to automatise the training of the system. So what we want to do in the context of such a phenotyping approach is to use a virtual pipeline where we would produce the virtual plant in the computer. Then we would create point clouds out of these virtual plants, so this would be virtual point clouds. Then we would use training algorithms in the context of this machine learning construction process, and then we would get as an output the trained machine learning system. Then once we have this, it is possible to get back to the original pipeline and use here this trained machine learning system in order to recognise identify segment the different organs on the plant. So L-py is the programming language to simulate plants and from this it is possible to create full databases of plants by a stochastic simulation of this, of plant populations, to produce massive data, and then this massive data, give them to machine learning systems in order to train this machine learning based on this large amount of input data. The first thing to know is that how plants are growing. You have leaves like this and then you have a stem like this. This is growing due to this small part of the tip that is called the apex, and the apex is producing all the organs that are being built on the plant. So for the leaves and also the fruits here or the flowers. So if you look at this in a more detailed manner you you make a close-up on this. What you observe is that it is like a small dome like this, that is producing lateral organs and this is the stem, and these are the young organs. Like this can be a leaf or a flower or anything else that is produced by the by the meristem. And this part here, it is the place where all the stem cells are living and they are dividing and they are producing the small organs here one after the other at the tip of the plant. So if we want to model this we need to model how this small part of the plant which is built, which is made up of a small amount of stem cells, undifferentiated cells, how this is growing. an apex let's call it ‘a producing a piece of stem’, (that I call for the internet) and literally produces an apex that will in turn be able to grow on it on its own. We formalise this rule of growth by this, let's say, a mathematical expression. (BESNARD) In the case of ROMI, so this European project we are involved in, we are using virtual plants for a precise objective which is to use virtual plants to create a data set, virtual dataset that we can use for a training deep learning algorithm very efficiently and costless, and as I told you. Then, once you have an objective you have to question yourself whether realism and the realistic rendering of the plant is useful or not for your objective. In the program. We use pipelines that are able to detect automatically to segment the plant and to detect automatically the organs, okay so here basically it will be the branching point that will be discovered and this branching point can be either branches that are cut here or the helix here. And once it is detected we use a representation where we highlight these branching points so this or branches, and you can see that here you have another layer and this highlight overlays perfectly with the branches. So you can say wow it's really good, but when you dig into these plants sometimes algorithms, machine learning algorithms are not working well and here is when you proceed with the same algorithm, the leaf is recognised partly as a leaf at the beginning okay, but the tip of the leaf is recognised as a fruit or a branch. So it shows you here, that you have some issues so those machine learning algorithms are not perfect and we need to improve them to increase our performances in terms of fragmentation. So I will proceed in the natural plant, and you can see that definitely the the leaves here are in the current model with which we trained this network, they are like quiet simple structure so it's a flat very simple shape that are not like really the shape of the current leaf. So here's an important question if we make a new plant modified here leaf shapes, would this help in this the algorithm to make a better plan segmentation? (GODIN) To make more realistic plants we decided to grow real Arabidopsis plants in growth chamber and to measure them in a systematic manner. We then used these detailed measurements to refine our virtual Arabidopsis plant in different ways. First we refined the modelling of the different organs for example the coiling of leaves. With time the leaf would be produced laterally by the stem in a sort of straight way like this, or like this, and then with time it would curl and change the shape. Instead of having one single curve. Now I will have a set of curves, I would just put the curve, it would be straight in the beginning then i would fold a bit the curve fall a bit the curve fold a bit like this, even I can go in this way. So I would define the curve, zero curve, one curve, six. Okay actually this is what I did here. And then I say all this bunch, create me an object that is able, given a time . So the Curve, Function, Object, ‘curve function object’, it is able to take a time too ‘Tao’ (T) and then to return a curve object as at the time term. And then so this curve object is provided by L-pi. You just call it with a series of functions, then once you have this you can call curve of ’T’ and then if I know this, I can compute the current curve at time, and then display it and and actually while it is doing this, it is able to compute all the intermediate curves by interpolation. Like this. Detailed models of organs were then assembled to model groups of organs, such as flowers or at an even more integrated level the dynamics of inferences development. The difficulty here is to synchronise the growth dynamics of each part, for this we use the new strategy that we developed in the course of the project and that is called ‘hierarchical timeline warping’. This strategy makes it possible to synchronise the growth dynamics of the different plant parts onto each other in a hierarchical and non-linear manner similarly still based on real plant measurements. We modelled also the observed variability in the dimensions and orientation of the different organs. The gravitropism and the mechano-perception of the different axes resulting in complex and dynamic bending of their parts. The final model provides a realistic rendering of the plant growth that can be used as a faithful reference to train ROMI’s machine learning algorithms. Then the technology to construct virtual plants developed for Arabidopsis was used to generate other virtual plants with different levels of complexity and accuracy, we first updated our tomato model by introducing stochasticity in the development of the plant here you can observe two tomato plants that were generated using the same stochastic model. The model varies the number of organs their size their dimensions their orientation with respect to their parent stem, their bending and so on. Another virtual model was made of a Canopodium this plant is a weed that can be easily found in crop fields similarly to Arabidopsis although with less details, we grew several plants in growth chamber and observed how it grows. We used these observations to construct a virtual model model of a Canopodium, the model is very different from that of a tomato, it grows with a main axis that dominates over secondary axis that grows in turn with slight delay with respect to the leader. Here also stochasticity was introduced in the model so that one can generate different individuals with a simple click. Finally based on the training course participants could start to produce their own virtual plants. Here is the first version of a model of a pepper plant showing the step-by-step approach of the student based on real plant observations. Here is a 3d model of a carrot plant with fractal leaves and stochasticity. Several instances of this stochastic model can be generated to create a virtual field of carrots. So by the way we will learn on Friday, Ayan will make a presentation showing how we can use these systems and to scan them make a sample of points, a 3d cloud sample of points, so that we can train systems, machine learning systems, that take as an input sample points, in order to produce the right segmentation of the point cloud. Parts of this video were extracted from a week-long course given in April 2022 and covering every aspect of how to virtually model plants in three dimensions, with L systems in Python. If you are interested don't hesitate to go and check it out on the ROMI youtube channel.

Video simulations for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"

Videos of the comparison between numerical and deep learning simulations for test datasets 1, 2, and 3 for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks".

Model output publication 'Mechanisms of salt overspill at estuarine network junctions explained with an idealized model'

The model output for the publication in JGR: Oceans titled 'Mechanisms of salt overspill at estuarine network junctions explained with an idealized model'

opencc-by-4.0Feb 2022View details →

Artificial neural network model and metabolomics data of selected microbial strains

Metabolomics data, metadata, sample R code, and a pre-trained artificial neural network model to predict group memberships of the bacterial strains in the dataset.

Raw datasets for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"

Raw datasets for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks". The zip folder comprises 4 subfolders (DEM, WD, VX, VY), containing the elevation, water depths in time, and velocities (in x and y directions) in time for all training and testing simulations. The overview.csv file provides the runtime of the numerical model on each different simulation, identified by its id. The simulations ids are divided as follows: - 1-80: Training and validation - 501-520: Testing dataset 1 - 10001-10020: Testing dataset 2 - 15001-15020: Testing dataset 3

opencc-by-4.0Apr 2023View details →

Supplementary dataset for paper: "Approximate non-linear model predictive control with safety-augmented neural networks"

Supplementary dataset for paper Henrik Hose and Johannes Koehler and Melanie N. Zeilinger and Sebastian Trimpe "Approximate non-linear model predictive control with safety-augmented neural networks". The code to use this dataset is publicly available at <a href="https://github.com/hshose/soeampc">https://github.com/hshose/soeampc</a> The dataset contains training and testing data to train an NN controller for three standard benchmark systems, a stir tank reactor, a quadcopter, and a chain mass system. For each system, there are initial conditions as comma separated value in the `x0.txt` file, the MPC input trajectory in the `U.txt` file and the corresponding predicted state sequence in the `X.txt` file. MPC parameters are provided for each system. The dataset was computed using acados for SQP solving. The dataset also contains pretrained neural network approximations of the dataset.These are provided in the `pretrained_models.zip` file. The neural networks were trained with tensorflow.

opencc-by-4.0Dec 2022View details →

Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution

Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European marine species based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.5° Resolution. The data report, for each 0.5° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell.

opencc-by-4.0May 2022View details →

Benchmark data for "Model-X knockoffs reveal data-dependent limits on regulatory network identification"

This collection of data was used in our manuscript tentatively entitled "Model-X knockoffs reveal data-dependent limits on regulatory network identification". It is entirely from public sources, but to enable easy repetition of our analyses, we collect it all here in the exact format we used. Links to related papers and code can be found at the <a href="https://github.com/ekernf01/knockoffs_paper">knockoffs paper</a> homepage.

opencc-by-4.0Dec 2023View details →

Detecting Transient Deformation at the Active Volcano Ol Doinyo Lengai in Tanzania with the TZVOLCANO Network: Supplementary software, data, model files

These are supplementary data, code, and model files associated with the manuscript "Detecting Transient Deformation at the Active Volcano Ol Doinyo Lengai in Tanzania with the TZVOLCANO Network" in consideration for publication in the Geophysical Research Letters. tzvolcano_code_and_models.zip contains all necessary Targeted Projection Operator (TPO) software, input, and output files for the GNSS inversions presented in our manuscript necessary to reproduce the results. The TPO program is a Unix/Linux code developed by Kang-Hyeun Ji working at the Korea Institute for Geoscience and Mineral Resources, Daejeon, South Korea. The source code is available in the supplementary Zenodo repository. We also include input and output model files for the USGS code dMODELS for reproducibility. Please see the README.txt file for more details. This study was funded by the US National Science Foundation grant number EAR-1943681 to Virginia Tech, internal university funds via Ardhi University, and Ministry of Science and ICT of Korea Basic Research Project GP2021-006 to the Korea Institute of Geosciences and Mineral Resources. We acknowledge and thank the EarthScope Consortium for archiving and making TZVOLCANO GNSS datasets freely available, supported by the National Science Foundation’s Seismological Facility for the Advancement of Geoscience (SAGE) Award under Cooperative Support Agreement EAR-1851048 and Geodetic Facility for the Advancement of Geoscience (GAGE) Award under NSF Cooperative Agreement EAR-1724794.

restrictedIPD-UNDECIDEDFeb 2026View details →

Multi-scale Modeling of Sleep Behaviors in Social Networks

ClinicalTrials.gov study NCT02846077. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.

closedIPD-NOFeb 2026View details →

Swiss Sarcoma Network: Prediction Model for Patient Selection in Sarcoma Care

ClinicalTrials.gov study NCT04300257. IPD Sharing: NO. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →

Modelling Tau Distribution From DTI With Generative Adversarial Network for Alzheimer's Disease Diagnosis

ClinicalTrials.gov study NCT05020626. IPD Sharing: Not stated. Countries: 1. Publications: 33.

closedIPD-NOFeb 2026View details →

TORNADO-Omics Techniques and Neural Networks for the Development of Predictive Risk Models

ClinicalTrials.gov study NCT06372054. IPD Sharing: NO. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →

The Establishment of Multiple Point Prediction Model About Tacrolimus Metabolic Differences Based on Genetic Network After Liver Transplantation and the Application of Multicenter

ClinicalTrials.gov study NCT02752529. IPD Sharing: Not stated. Countries: 1. Publications: 2.