Find research datasets worth reusing

Microseismic source imaging using physics-informed neural networks with hard constraints

Locating subsurface seismic sources is crucial to both seismic monitoring and seismology. We propose a novel direct source imaging framework based on physics-informed neural networks with hard constraints. In this letter, we present the relevant dataset to the paper.

opencc-zeroJan 2023View details →

dryad32/100

Arginine-vasopressin expressing neurons in the murine suprachiasmatic nucleus exhibit a circadian rhythm in network coherence in vivo

The suprachiasmatic nucleus (SCN) is composed of functionally distinct subpopulations of GABAergic neurons which form a neural network responsible for synchronizing most physiological and behavioral circadian rhythms in mammals. To date, little is known regarding which aspects of SCN rhythmicity are generated by individual SCN neurons, and which aspects result from neuronal interaction within a network. Here, we utilize in vivo miniaturized microscopy to measure fluorescent GCaMP-reported calcium dynamics in arginine vasopressin (AVP)-expressing neurons in the intact SCN of awake, behaving mice. We report that SCN AVP neurons exhibit periodic, slow calcium waves which we demonstrate, using in vivo electrical recordings, likely reflect burst firing. Further, we observe substantial heterogeneity of function in that AVP neurons exhibit unstable rhythms and relatively weak rhythmicity at the population level. Network analysis reveals that correlated cellular behavior, or coherence, among neuron pairs also exhibited stochastic rhythms with about 33% of pairs rhythmic at any time. Unlike single-cell variables, coherence exhibited a strong rhythm at the population level with time of maximal coherence among AVP neuronal pairs at CT/ZT 6 and 9, coinciding with the timing of maximal neuronal activity for the SCN as a whole. These results demonstrate robust circadian variation in the coordination between stochastically rhythmic neurons and interactions between AVP neurons in the SCN may be more influential than single-cell activity in the regulation of circadian rhythms. Furthermore, they demonstrate that cells in this circuit, like those in many other circuits, exhibit profound heterogenicity of function over time and space.

opencc-by-4.0Sep 2022View details →

Quality-aware Analysis and Optimisation of Virtual Network Functions

To watch it in Youtube: <a href="https://youtu.be/RGwIVCgANcU">https://youtu.be/RGwIVCgANcU</a> This is the live presentation of a conference paper. Please, access and cite the published version: <a href="https://doi.org/10.1145/3546932.3547007">https://doi.org/10.1145/3546932.3547007</a> The softwarisation and virtualisation of network functionality is the last milestone in the networking industry. Software-Defined Networks (SDN) and Network Function Virtualization (NFV) offer the possibility of using software to manage computer and mobile networks and build novel Virtual Network Functions (VNFs) deployed in heterogeneous devices. To reason about the variability of network functions and especially about the quality of a software product defined as a set of VNFs instantiated as part of a service (i.e., Service Function Chaining), a variability model along with a quality model is required. However, this domain imposes certain challenges to quality-aware reasoning of service function chains, such as numerical features or configuration-level Quality Attributes (QAs) (e.g., energy consumption). Incorporating numerical reasoning with quality data into SPL analyses is challenging and tool support is rare. In this work, we present 3 groups of operations: model report, aggregate functions to dynamically convert QAs at the feature-level into the configuration-level, and quality-aware optimisation. Our objective is to test the most complete reasoning tools to exploit the extended variability with quality attributes needed for VNFs.

Metabolic networks paper CoCoMiCo

root.tgz contains all metabolic network from root microbiota (10.1038/nature16192) leaf.tgz contains all metabolic network from the leaf microbiota (10.1038/nature16192) soil.tgz contains all metabolic network from the RefSoil database (10.1038/ismej.2016.168) gut.tgz contains all metabolic network from the gut microbiota (10.1038/s41587-018-0008-8)

Geomagnetic datasets of BJI station reconstructed through Artificial Neural Network improved by Genetic Algorithm in 2021

Beijing station established in 1954 is one of the oldest geomagnetic observatories in China, which plays an important role in data exchange, and further provide data or standardization for satellite observation and geomagnetic model construction. With the development of urbanization, the observed data are greatly disturbed by subways, and data disturbed are almost unavailable. The dataset was reconstructed through Artificial Neural Network improved by Genetic Algorithm, including minutely data of three components (D, H and Z) in 2021. This reconstruction method has been proved to be effective.

Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Super-Resolution Convolutional Neural Networks

Datasets and source codes for the manuscript "Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Super-Resolution Convolutional Neural Networks" submitted to the journal "Artificial Intelligence for the Earth Systems" of the American Meteorological Society.

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

Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics [Dataset]

Supplementary data for 'Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics'.

opencc-by-4.0Dec 2022View details →

Supplementary information files: Gene co-expression network and differential expression analyses of subcutaneous white adipose tissue reveal novel insights into the pathological mechanisms underlying ketosis in dairy cows

Supplementary information files: Gene co-expression network and differential expression analyses of subcutaneous white adipose tissue reveal novel insights into the pathological mechanisms underlying ketosis in dairy cows

dryad32/100

Social network matrices data from sociology university classrooms

Hypothetically, a student could attend a class, listen to lectures, and pass the class without knowing or interacting with other students. What happens to the network when the classroom expectations change? For example, there is a coursework expectation that students exchange contact information, or the instructor uses collaborative learning practices. Or what if the principal investigator (PI) of a scientific team goes on a sabbatical? This study uses the framework of classrooms because of their relatability across science. We asked how do different instructor coursework expectations change network structures within a classroom or other learning environments. A social network survey was administered at the start and end of the semester (pre- and post-test) in six university sociology classrooms to explore how expectations impacted the communication and learning networks. We found practical changes in course expectations impact the communication and learning networks, suggesting that instructors, facilitators, and others could be the archintorÔ (architect+instructor+facilitator) of the network. Understanding that expectations can impact a network's structure marks a paradigm shift in educational assessment approaches. If the archintorÔ has identified the "optimal" network structure, then their task is to design expectations that result in specific interactions that ultimately improve student achievement and success. This work provides recommendations for classroom archintorsÔ to create the most impactful classroom networks. Future research should extend beyond education and classroom networks and identify the best or desired networks in other areas like public policy, urban planning, and more. If these "optimal" networks were identified, an archintorÔ could design a social network to solve wicked problems, manage a crisis, and create social change.

opencc-zeroFeb 2023View details →

Botnet Data - Computer Networks Lab 2022/2

Dataset created for the Computer Network Laboratory class at UFES. Reproduction in a different experimental setup of the botnet attack presented in: "An empirical comparison of botnet detection methods" Sebastian Garcia, Martin Grill, Jan Stiborek and Alejandro Zunino. Computers and Security Journal, Elsevier. 2014. Vol 45, pp 100-123. http://dx.doi.org/10.1016/j.cose.2014.05.011 More specifically, the scenario https://mcfp.felk.cvut.cz/publicDatasets/CTU-Malware-Capture-Botnet-48/ For more information, https://github.com/VitorSpa/LabRedes-2023

Neuromorphic Learning, Working Memory and Metaplasticity in Nanowire Networks

Code + Data files for Neuromorphic Learning, Working Memory and Metaplasticity in Nanowire Networks, Science Advances

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".

MDS Network Plots

Two sources: <ul> <li>Video of network change</li> <li>Individual network plot, which compose the video</li> </ul>

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'

Data used in the paper titled 'Reconstructing and Nowcasting the Rainfall Field by a CML Network'

The dataset contains the CML, OTT disdrometer, rain gauge and radar data as well as some of the scripts used in the paper titled 'Reconstructing and Nowcasting the Rainfall Field by a CML Network'

Data: Upsamling Monte Carlo Neutron Transport Simulation Tallies Using a Convolutional Neural Network

This repository contains: <ul> <li>openmc-data-XXXX.tar.gz - Training Data generated with the OpenMC Monte Carlo code representing neutron flux tallies in 4,400 unique light water reactor fuel assemblies in HDF5 format. Training samples consist of tallies in 64x64 pixels and 8 neutron energy groups, and tallies in 128x128 pixels and 16 neutron energy groups. Folders 0008 to 0023 contain training and validation data. Folder 0024 contains test data.</li> <li>out.mat - Upsampling results using a Convolutional Neural Network for 300 testing data samples in MATLAB format. These data include OpenMC tally uncertainties in low and high resolution tallies, scaling values used in data pre-processing, low resolution inputs to the CNN, and high resolution upsampled results as well as high resolution ground truth values.</li> </ul>

opencc-by-4.0Mar 2023View details →

A Network-of-Networks Coordination for Cross-Industry Supply Chain Adaptation

Data and code for reproducing the results in paper entitled    "A Network-of-Networks Coordination for Cross-Industry Supply Chain Adaptation"