Find research datasets worth reusing

opencc-by-4.0Dec 2022View details →

6TiSCH traces in real-world network deployment

Introduction This data set includes the detailed logs of the 6TiSCH network created with the Contiki-NG OS running on LOG-a-TEC testbed, which is located in the park area of the Jožef Stefan Institute, Ljubljana. The data set can be used to analyze the operation of the 6TiSCH network in a real-world outdoor environment, to detect network anomalies or environmental changes, to classify network uptime, throughput and traffic volume, etc. The generated logs contain all metrics (RSSI, LQI, SQN, ASN, IPv6 address, frequency channel, timestamp) for in-depth analysis. Network setup 6TiSCH network was set up with 15 testbed devices which and operated for 5 days. To generate additional traffic, the devices sent a UDP packet (size 10B) to the root of the network at intervals of 10 seconds (+ random jitter). Besides application packets (UDP), the devices also exchanged packets for network maintenance - UDP packets can be identified by searching for a packet length. Data set structure The included folder contains the logs from all devices used. The suffix of the folder name represents the last 3 digits of the device's IP address. Each folder contains several files. The prefix of a file stands for the order of the collected logs - after 1 hour elapsed, the measurements are saved in a new file.

opencc-by-4.0Dec 2022View details →

Dataset for "Root Length Estimation: Automated Minirhizotron Image Analysis with Convolutional Networks without Segmentation"

This data contains 4015 root images, splitted into 4 datasets, acquired using two minirhizotron (MR) system types - manual (Dataset 1 & Dataset 4) and automated (Dataset 2 & Dataset 3).  It includes four crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. The data was acquired by researchers from Ben-Gurion University of the Negev, Beer Sheva, Israel, and used for research of automated TRL estimation with Convolutional Neural Networks. The annotations were conducted manually using the Rootfly software (Wells and Birchfield, Clemson University, South Carolina, USA), and data were transformed as CSV formats. In this software, the annotator must draw a root by marking points along the selected root. These points usually correspond to the coordinates at the start and the end of the root, and curving points along the root. These points are then connected in a line, the length of which reflects the real length of the selected root. The annotations has been done for all roots within an image, and for all images in the provided dataset. The provided annotations include the total root length (TRL) per image (mm) and the coordinates of annotated points. The annotations are given in two types of files: "TRL.csv" files: contain the image names and corresponding TRL values (mm). "pointsOutput.csv" files: contain the annotated image names and the coordinates of the points of the roots in the image (if the image contains roots) in the form of x1, y1, x2, y2, x3, y3, etc. It the image doesn't have roots, the file contains only its name.

opencc-by-4.0Jan 2018View details →

Visual Genome - Visual Relationship Detection - Scene Graph Generation using Message Passing Neural Networks and Graph Convolutional Networks

This repository contains a processed version of Visual Genome for Visual Relationship Detection, from the Diploma (MSc) thesis Scene Graph Generation using Message Passing Neural Networks and Graph Convolutional Networks by Miltiadis Kofinas, supervised by Christos Diou and Anastasios Delopoulos. The original thesis is written in Greek <blockquote> Νευρωνικά Δίκτυα Ανταλλαγή&sigmaf; Μηνυμάτων και Συνελικτικά Δίκτυα Γράφων για Εξαγωγή Γράφου Σκηνή&sigmaf; Εικόνων Μιλτιάδη&sigmaf; Κοφινά&sigmaf; <a href="https://ikee.lib.auth.gr/record/300900">https://ikee.lib.auth.gr/record/300900</a> </blockquote> A summarized English version of the thesis can be accessed <a href="https://www.dropbox.com/s/m87ixw8c8ecrswm/mkofinas_thesis_english_scene_graph_generation.pdf?dl=0">here</a>. It contains region proposals for VGG-16 for all images, and metadata about the bounding box distribution and the predicate classes.

Source Data for "Phosphorescent extensophores expose elastic nonuniformity in polymer networks"

This source data is for the manuscript "Phosphorescent extensophores expose elastic nonuniformity in polymer networks".

Dataset: Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks

Associated publication [1] Samuel Ponting*, Takuma Morimoto*, Hannah E. Smithson, “Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks”, *equal contribution, bioRxiv, <a href="https://www.google.com/url?q=https%3A%2F%2Fdoi.org%2F10.1101%2F2022.11.02.514864&sa=D&sntz=1&usg=AOvVaw3KwSo7KmqPzBR1UMc1MHmk">https://doi.org/10.1101/2022.11.02.514864</a> [2] Takuma Morimoto, and Hannah E. Smithson, “Discrimination of spectral reflectance under complex environmental illumination,” Journal of the Optical Society of America A, 35, 4, B244-B255 (2018) https://doi.org/10.1364/JOSAA.35.00B244   Datasets contain 2 folders and 1 mat file.   (Folder 1) Stimuli (Folder 2) Psychophysics_data (Mat file) stimulusMagnitudeToMacLeodBoynton.mat   Details are described below.   ---------------------------------------------------------------------------------------------------------------------------------------- (Folder 1) Stimuli   Overview of datasets This Image dataset includes 57,600 images (2 gloss levels * 3 environments * 100 stimulus magnitudes * 8 hue directions * 12 camera angles from 0 to 330 degree in 30 degree step) in .mat format.   The half of images were used in psychophysical experiment (camera angles: 0, 60, 120, 180, 240, 300 degrees). Other half images were used for testing chromatic statistics models and CNN-based models [1] (camera angles: 30, 90, 150, 210, 270, 330 degrees).   Each image file Filename denotes a condition name and the camera angle as formatted in a following way.   stim_”environment” _”glossiness”_”hueAngle”_”magnitude”_”cameraAngle”.mat e.g. “stim_en1_glossy_hue45_n45_cameraAngle90.mat”   Stimulus magnitude 100 is a maximum saturation, and 1 corresponds to equal energy white (which was used as a distractor object).   Each image file contains two valuables : MacLeodBoynton, XYZ   Each variable contains an image of 128*128*3 pixels (height*width*channel).   MacLeod-Boynton: MacLeod-Boynton chromaticity image (1st channel: L/(L+M), 2nd channel: S/(L+M), and 3rd channel L+M) XYZ: XYZ coordinates calculated based on 2-degree CIE 1931 xyz color matching function (1st channel: X, 2nd channel: Y, and 3rd channel Z)   Luminance and L+M are both relative (normalised by the maximum luminance across all 57,600 images).   ---------------------------------------------------------------------------------------------------------------------------------------- (Folder 2) Psychophysics_data Filename denotes the condition and observers formatted in a following way.   data_”environment” _”specularities”_”sessionNumber”_”obsever”.mat e.g. data_en2_matte_session4_JH.mat or .csv   Each file includes following variables:   (Variable 1) threshold Thresholds are stored in MacLeod-Boynton (MB) chromaticity coordinates for all 8 hue directions (from 0 to 315 degree in 45 degree step).   MacLeod-Boynton chromaticity coordinates were calculated in a following way. These scalings are in accordance with description in CVRL main site (Chromaticity coordinates tab ).   First of all, L, M, and S cone signals were calculated based on Stockman & Sharpe cone fundamentals (energy in linear scale available at at http://www.cvrl.org). Each sensitivity curve was normalised to have 1.0 at the peak.   Then, MB coordinates were calculated using equation (1-3).   L/(L+M) = Lw*L/(Lw*L+Mw*M) - (1) S/(L+M) = Sw*S/(Lw*L+Mw*M) - (2) L+M = Lw*L+Mw*M - (3)   where Lw = 0.689903; Mw = 0.348322;Sw = 1.93540.   L, M and S denote L-cone, M-cone, S-cone excitations, respectively.   Under this calculation, equal energy white becomes L/(L+M) = 0.7078 and S/(L+M) = 1.   (Variable 2) staircase   Since we ran 8 interleaved staircase (for 8 hue angles), information about 8 staircases are stored in this single variable. (staircase(1) corresponds to 0 degree, and staircase(8) corresponds to 315 degree)   There are 5 fields: (i) groundtruth, (ii) response, (iii) correct, (iv) magnitude, (v) cameraAngle   For each trial, the location of objects was defined in a following way. | 1 3 | | 2 4 |   And each field stores following information for all trials in the staircase.   (i) groundtruth Location of the target object   (ii) response Location that the participant chose   (iii) correct If the response was correct (1) or incorrect (0)   (iv) magnitude Stimulus magnitude of target object in each trial from 1 to 100 (1 for equal energy white and 100 for maximum saturation).   (v) Camera angle Camera angles assigned for four objects in each trial. This data and (i) groundtruth allow reconstruct of the exact image for each trial.   ---------------------------------------------------------------------------------------------------------------------------------------- (Mat file) stimulusMagnitudeToMacLeodBoynton.mat This file stores a variable ‘stimulusMagnitudeToMacLeodBoynton’ (8*100*2) which describes correspondence map between stimulus magnitude and MacLeod-Boynton chromaticity.   1st channel: hue direction from 0 degree to 315 degree, 45 degree step 2nd channel: magnitude from 1 to 100 3rd channel: MacLeod-Boynton coordinate, 1 being L/(L+M) and 2 being S/(L+M)

opencc-by-4.0Nov 2022View details →

Interpersonal lending network

The dataset of this paper originated from quantitative surveys from May 2014 to June 2014. The data collection was embedded in a Participatory Action Research (PAR) which aim was to investigate the financial survival strategies of low-income households, in a Hungarian village in a disadvantaged region. The directed networks of lending and borrowing households is a unique dataset that empirically captures a hidden and informal finical activity between households. The network contains 158 households and 281 ties between them. A unique feature of the networks is that it indicates the social relations and the spatial distance between the households and also their financial situations. Therefore, the data describes not only the informal, interpersonal credit relations between the households but also the socioeconomic factors of the actors as well.

Multichannel Displacement measurement via self mixing interferometry and neural network : training and test datasets

Self mixing interferometry is a simple and robust sensing method which can be used (among other things) to measure the displacement of a target along the light propagation axis. While conceptually simple, the actual use of this method is less straightforward than originally envisioned because reconstructing the target displacement from the interferometric signal is often tricky. A small neural network can do this task very well after proper training, as described in [10.1364/OE.419844], with dataset [10.5281/zenodo.7303745].  Here, the dataset is composed by a training set and a test set, in a specific configuration in which 3 self-mixing sensors measure simultaneously the same target displacement. Both datasets contain the displacement itself and the 3 interferometric signals (1 per sensing channel) The training set relies on two python/numpy data files corresponding to harmonic displacements for different frequencies ranging from 53 and 93 Hz and amplitudes from 3.5 to 7.5 µm :  <ul> <li>Training_set_2_lostchannel_displacement.npy : 93744-elements long numpy array containing the target's displacement in units of µm/ms with a 1.024 ms time step. </li> <li>Training_set_2_lostchannel_signal.npy : numpy array of shape (3, 93744, 256, 1) containing the interferometric signals. The first dimension refers to the channel (1, 2 or 3), the second dimension is the number of segments of 256 points. Each segment of 256 points correspond to a 1.024 ms window of signal, matching one element of the displacement. For instance, the displacement value in `displacement[416]` corresponds to the interferometric signal segment `signal[0,416,:,0]` for channel 1, `signal[1,416,:,0]` for channel 2 and `signal[2,416,:,0]` for channel 3. </li> </ul> The test set follows the same architecture and format as the training set, but contains only random displacements generated by a delta-correlated signal, which we Fourier filter with a fifth order Butterworth filter between 10 and 100 Hz : <ul> <li>"Displacement_test.npy" :  with shape (246078, 1)</li> <li>"Signal_test.npy" : with shape (3, 246078, 256, 1)</li> <li>Only the displacement type has changed from harmonic to random, from the training to the test datasets.</li> </ul> These datasets have been used to train and test a 3 channel neural network (after data augmentation) in order to emphasize the high availability potential of multichannel schemes, against backscattered power fluctuations.

Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty

CEA covered the scenario of UAV navigation through a set of gates with unknown locations using a DNN-based navigation model. The implemented navigation model uses two DL components (perception and control), and uses (Bayesian) uncertainty estimation methods to capture the uncertainty (confidence) associated with the predictions of each component. The safety requirements in the UAV mission are related to the confidence (uncertainty) associated with the predictions from these components. CEA observed and analysed the uncertainty from each DNN under specific situations that can pose a risk to the UAV mission. Then, the observations were used to define STL rules to track the confidence of the DNN-based navigation system. Finally, mitigation behaviours (e.g., hover, land, DNN-based autonomous flight) are triggered depending on the satisfaction (or violation) of the STL rules. Moreover, the proposed ROS2-based architecture for safe navigation contributed to the definition and improvement of the COMP4DRONES reference architecture, showing in practice how the proposed safety monitoring architecture relates and integrates with the components from other system functions.

MHG4SNA: Images and videos of social networks on arthurian romances

Description This dataset contains images and videos of social networks (static and dynamic graphs) based on character annotations of middle high german arthurian romances. All images and videos are part of my dissertation. The networks can be created in gephi using the gefx files provided in the following repositories: <ul> <li>zenodo: <a href="https://doi.org/10.5281/zenodo.7544004">10.5281/zenodo.7544004</a> </li> <li>and github: <a href="https://github.com/NoraKet/MHG4SNA/tree/v1.0.0">https://github.com/NoraKet/MHG4SNA/tree/v1.0.0</a></li> </ul>   Texts <ul> <li>Wolfram von Eschenbach: 'Parzival', in: Wolfram von Eschenbach: Werke, ed. by Karl Lachmann, 5th edition, Berlin 1891, pp. 11–388.</li> <li>Hartmann von Aue: 'Erec', ed. by Albert Leitzmann continued by Ludwig Wolff, 7th edition by Kurt Gärtner, Tübingen 2006 (Altdeutsche Textbibliothek 39).</li> <li>Hartmann von Aue: 'Iwein', ed. by G. F. Benecke and K. Lachmann, revised by Ludwig Wolff, 7th edition, part 1: Text, Berlin 1968.</li> </ul>   Files The network graphs are created using gephi. The filenames indicate: <ul> <li>the name of the text ("Erec", "Iwein", "Parzival"; "Parzival" consists of 16 books; it can be devided in the story of Parzival ("PARZ") and the story of Gawan ("GAW")); </li> <li>the filters "EK" and "EK2" which indicate if narrator's comments are included or excluded from the data;</li> <li>the filter "DS" which indicates if direct speech is included or excluded from the data;</li> <li>additional filters performed in gephi, for example removed characters ("noErec"), frequence filters ("filterfreq1": all characters that only occur one time are removed), selection of books ("Parzival_3-6": contains only books 3 to 6 from "Parzival"), "Egozentriert" indicates an egocentric network;</li> <li>for videos: the playback speed which can be set in gephi's settings.</li> </ul>   For information about the annotations please see <a href="https://doi.org/10.5281/zenodo.7544004">10.5281/zenodo.7544004</a>.

Dataset for the Galaxy Training Network (GTN) Tutorial "Viewing Cancer Alignments in a Genome Browser"

Datasets for the Galaxy Training Network (GTN) Tutorial "Viewing Cancer Alignments in a Genome Browser"

opencc-zeroFeb 2023View details →

Data for: Induction of C4 genes during de-etiolation of Gynandropsis gynandra evolved through changes in cis allowing integration into ancestral C3 gene regulatory networks

C4 photosynthesis has evolved repeatedly and in doing so repurposed existing enzymes to drive a carbon pump that limits the oxygenation reaction of RuBisCO. C4 proteins accumulate to levels matching those of the photosynthetic apparatus, and to allow this gene expression must be modified over evolutionary time. To better understand this rewiring of gene expression we undertook RNA-SEQ and DNaseI-SEQ on de-etiolating seedlings of C4 Gynandropsis gynandra which is evolutionarily proximate to C3 A. thaliana. Changes in chloroplast ultrastructure and C4 gene expression in G. gynandra were coordinated and rapid. C3 and C4 photosynthesis genes showed similar induction patterns, but C4 genes from G. gynandra were more strongly induced than orthologs from A. thaliana. The cistrome of G. gynandra was enriched in TGA, TCP and homeodomain binding sites. Furthermore, in vivo binding data in G. gynandra highlighted TGA and homeodomain as well as light responsive elements such as G- and I-box motifs as being associated with the rapid increase in transcripts derived from C4 genes. Although promoters of PPDK and ASP1 from G. gynandra contained distinct light responsive elements, promoters from both A. thaliana and G. gynandra allowed high expression. Deletion analysis of the Ppa6 gene from G. gynandra showed that regions containing G- and I-boxes were necessary for high expression. The data support a model in which accumulation of transcripts derived from C4 genes in leaves of G. gynandra is enhanced compared with homologs in A. thaliana because a variety of modifications in cis allowed integration into ancestral transcriptional networks.

opencc-by-4.0Oct 2022View details →

SensEURCity: A multi-city air quality dataset collected using networks of open low-cost sensor systems

We provide a unique curated dataset of urban air quality measurements acquired using dense networks of low-cost sensor systems in three European cities for the years 2020 and 2021. The dataset includes the raw sensor data of quality-controlled sensor networks along with co-located reference data sets. Sensor data are collected using the AirSensEUR sensor system, including sensors to monitor NO, NO2, O3, CO, PM2.5, PM10, PM1, CO2, and meteorological parameters. In total, 85 sensor systems were deployed throughout the years 2020 and 2021 in three European cities (Antwerp , Oslo  and Zagreb), resulting in a dataset comprising different meteorological and ambient conditions. The main data collection included two co-location campaigns in different seasons at an air quality monitoring station in each city and a deployment at different locations in each city (including also locations at other air quality monitoring stations). The dataset consists of data files with sensor and reference data, and metadata files with description of locations, deployment dates and description of sensors and reference instruments.

opencc-zeroFeb 2023View details →

Shifts from non-obligate generalists to obligate specialists in simulations of mutualistic network assembly

Understanding ecosystem recovery after perturbation is crucial for ecosystem conservation. Mutualisms contribute key functions for plants such as pollination and seed dispersal. We modelled the assembly of mutualistic networks based on trait matching between plants and their animal partners that have different degrees of specialization on plant traits. Additionally, we addressed the role of non-obligate animal mutualists, including facultative mutualists or non-resident species that have their main resources outside the target site. Our computer simulations show that non-obligate animals facilitate network assembly during the early stages, furthering colonization by an increase in niche space and reduced competition. While non-obligate and generalist animals provide most of the fitness benefits to plants in the early stages of the assembly, obligate and specialist animals dominate at the end of the assembly. Our results thus demonstrate the combined occurrence of shifts from diet, trait, and habitat generalists to more specialised animals.

opencc-by-4.0Dec 2023View details →

EpimiRNA network analysis

Rcode and data (in R dataset) for EpimiRNA network analysis

opencc-zeroMar 2023View details →

Data for: Simulation and social network analysis provide insight into the acquisition of tool behavior in hybrid macaques

The pathways through which primates acquire skills are a central focus of cultural evolution studies. The roles of social and genetic inheritance processes in skill acquisition are often confounded by environmental factors. Hybrid macaques from Koram Island, Thailand provide an opportunity to examine the roles of inheritance and social learning to skill acquisition within a single ecological setting. These hybrids are a cross between tool-using Burmese long-tailed (Macaca fascicularis aurea) and non-tool-using common long-tailed macaques (Macaca fascicularis fascicularis). This population provides an opportunity to explore the roles of social learning and inheritance processes while being able to exclude underlying ecological factors. Here, we investigate the roles of social learning and inheritance in tool use prevalence within this population using social network analysis and simulation. Agent-based modeling (ABM) is used to generate expectations for how social/asocial learning and inheritance structure the patterning in a social network. The results of the simulation show that various transmission mechanisms can be differentiated based on associations between individuals in a social network. The results provide an investigative framework for discussing tool-use transmission pathways in the Koram social network. By combining ABM, network analysis, and behavioral data from the field we can investigate the roles social learning and inheritance play in tool acquisition in wild primates.

opencc-by-4.0Feb 2024View details →

Datasets and Codesets for "Wavelet Decomposition and Neural Networks: A Potent Combination for Short Term Wind Speed and Power Forecasting"

This is the datasets and codesets used in the paper: A. E. Kio, J. Xu, N. Gautam, and Y. Ding, 2024, “Wavelet decomposition and neural networks: A potent combination for short term wind speed and power forecasting,” Frontiers in Energy Research, section of Wind Energy, Vol. 12, pp. 1277464.  The PDF file, "Reproducibility Report," explains how to reproduce the results in the tables and figures.

opencc-by-4.0Mar 2023View details →

Wikipedia Academic Disciplines Network

This network is a result of scraping Wikipedia (using <a href="https://scrapy.org/">scrapy</a>), starting at the <a href="https://en.wikipedia.org/wiki/Outline_of_academic_disciplines">Outline of Academic Disciplines</a> and following every link until a page is reached (i.e. if an outline is reached, all the links there are followed, when a page is reached, the links there are recorded, but not followed). Each page/article visited is recorded as a node. If one page/article links to another, the nodes are connected with an edge. The edges are weighted by the number of links between the pages. An example of a weight greater than 1 is if two pages both link to each other, or if there are multiple links to the same page in an article.  This process resulted in 640,031 nodes and 4,503,438 edges. The network is undirected, so each edge only appears once.

opencc-zeroMar 2023View details →

Effects of temporal abiotic drivers on the dynamics of an allometric trophic network model

Current ecological research and ecosystem management call for improved understanding of the abiotic drivers of community dynamics, including temperature effects on species interactions and biomass accumulation. Allometric trophic network (ATN) models, which simulate material (carbon) transfer in trophic networks from producers to consumers based on mass-specific metabolic rates, provide an attractive framework to study consumer-resource interactions from organisms to ecosystems. However, the developed ATN models rarely consider temporal changes in some key abiotic drivers that affect e.g. consumer metabolism and producer growth. Here, we evaluate how temporal changes in carrying capacity and light-dependent growth rate of producers and in temperature-dependent mass-specific metabolic rate of consumers affect ATN model dynamics, namely seasonal biomass accumulation, productivity and standing stock biomass of different trophic guilds, including age-structured fish communities. Our simulations of the pelagic Lake Constance (LC) food web indicated marked effects of temporally changing abiotic parameters on seasonal biomass accumulation of different guild groups, particularly among the lowest trophic levels (primary producers and invertebrates). While the adjustment of average irradiance had a minor effect, increasing metabolic rate associated with 1–2˚C temperature increase led to a marked decline of larval (0-year age) fish biomass, but to a substantial biomass increase of 2- and 3-year-old fish that were not predated by ≥4-year-old top predator fish, European perch. However, when averaged across the 100 simulation years, the inclusion of seasonality in abiotic drivers caused only minor changes in standing stock biomasses and productivity of different trophic guilds. Our results demonstrate the potential of introducing seasonality in and adjusting the average values of abiotic ATN model parameters to simulate temporal fluctuations in food-web dynamics, which is an important step in ATN model development aiming to e.g. assess potential future community-level responses to ongoing environmental changes.

opencc-by-4.0Mar 2022View details →

Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease

This dataset repository corresponds to the project Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease.