Find research datasets worth reusing

opencc-by-4.0Jun 2023View details →

MGD+: An Enhanced Music Genre Dataset with Success-based Networks

This dataset is built by using data from Spotify. It provides a daily chart of the 200 most streamed songs for each country and territory it is present, as well as an aggregated global chart.  Considering that countries behave differently when it comes to musical tastes, we use chart data from global and regional markets from January 2017 to March 2022 (downloaded from CSV files), considering 68 distinct markets. We also provide information about the hit songs and artists present in the charts, such as all collaborating artists within a song (since the charts only provide the main ones) and their respective genres, which is the core of this work. MGD+ also provides data about musical collaboration, as we build collaboration networks based on artist partnerships in hit songs. Therefore, this dataset contains: <ul> <li>Genre Networks: Success-based genre collaboration networks</li> <li>Artist Networks: Success-based artist collaboration networks</li> <li>Artists: Some artist data</li> <li>Hit Songs: Hit Song data and features</li> <li>Charts: Enhanced data from Spotify Daily Top 200 Charts</li> </ul>

opencc-by-4.0Nov 2021View details →

SARS-CoV-2 Viral Samples and Reference Genome for Galaxy Training Network SARS with Galaxy on AnVIL Tutorial

In the lab activity, we'll see if there are genomic differences in the collected sample compared to the original SARS-CoV-2 genome (the reference). We need three files to do this: <ul> <li>SARS-CoV-2_reference_genome.fasta : the reference genome</li> <li>VA_sample_forward_reads.fastq.gz: 1 of 2 raw read data files</li> <li>VA_sample_reverse_reads.fastq.gz: 2 of 2 raw read data files</li> </ul> The sample for this activity was derived from data collected at Virginia Commonwealth University in Richmond, VA. The researchers collected the sample with the goal of being able to track the spread and evolution of this virus state-wide, nationally, and internationally. You can download the original data <a href="https://www.ncbi.nlm.nih.gov/sra/?term=XGTK449087">here</a>. You can download the reference genome <a href="https://www.ncbi.nlm.nih.gov/nuccore/1798174254">here</a>.

Dataset for Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem

This file consists all the data used in the manuscript " Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem "

Supplemental networks of cowords of the paper Measuring the impact of Big Data in the scientific research in Agriculture and allied fields

Supplemental networks of cowords of the paper Measuring the impact of Big Data in the scientific research in Agriculture and allied fields.

Dataset for "An Accuracy Study of Emulation Daemons for IEEE 802.11 Networks"

This is the dataset used in "An Accuracy Study of Emulation Daemons for IEEE 802.11 Networks" presented at the 48th IEEE Conference on Local Computer Networks (LCN), October 1-5, 2023, Daytona Beach, Florida, USA. `raw_results` contains all the raw results redacted from the experiment hosts, including log files. The `tikz` files used to generate the figures presented in the paper can be found in `tex/figures`. Executing `make` in the `tex` directory generates the `pdf` plots in the `figures` folder. The `figures` folder also contains pre-processed results from the raw data set.

Dataset for: Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE

Based on the Github respostitory: <a href="https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main">https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main</a> This repository contains the data used for the paper "Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE" You'll find the csv files for the three simulated (Hydrus 1D) scenarios explained in the paper. These files were processed from the 'Nod_Inf.out' files to csv format. Acknowledgments The publicly available data used for this study (scenario 1 & 2) as well as the code for the second PINN architecture (based on Dr. Maziar Raissi PINN code) and the code used to transform “Nod_inf.out” files from Hydrus 1D to csv files created by Dr. Toshiyuki Bandai and Dr. Teamrat A. Ghezzehei were helpfulfor this study.

Appendix for the paper "Assessment of Aging and Rejuvenation for Resiliency in Heterogeneous Network Clusters"

Appendix for the paper "Alberto Avritzer, Andrea Janes, Andrea Marin, Andre van Hoorn, Matteo Camilli, Catia Trubiani, Daniel S. Menasché: Assessment of Aging and Rejuvenation for Resiliency in Heterogeneous Network Clusters" This repository contains the complete set of charts for all core machines (in the paper, figures 3-8 contain only the results of one machine) and the collected data for each strategy. Contents The files contained in this appendix are: <ul> <li>18 files of the format "experiment(strategy)(granularity).pdf, where "strategy" goes from -1 to 4 and granularity is one of i, dc, or dp (see table I in the paper).</li> <li>18 files of the format "output-(strategy)(granularity).csv, with the same format as the files above, containing the raw data.</li> </ul>

Data repository of the paper "Quantum-noise-limited optical neural networks operating at a few quanta per activation"

This data repository includes the requisite data and code for deriving the primary results from the paper, "Quantum-noise-limited optical neural networks operating at a few quanta per activation". The repository is structured to provide everything needed to reproduce the figures included in the main manuscript, along with the source code for training the neural network models and the collected experimental data mentioned in the paper. The code in this repository is primarily intended for reproducing the results discussed in the paper. Those interested in developing their own applications may refer to our Github repository: https://github.com/mcmahon-lab/Single-Photon-Detection-Neural-Networks. Where to Start The directory 'main_figures' includes Jupyter notebooks to generate each panel in Figure 3 and Figure 4 in the main text, using the data from the directory 'results', which can be generated by notebooks in the directory 'test'.  The simulations, experiments, and figure generation were all conducted in Python. As certain parts of the code require specific versions of Python packages, the necessary packages are listed in the 'requirements.txt' file. For more information, please refer to 'README.txt'.

Data of "Dissipative quantum many-body dynamics in (1+1)D quantum cellular automata and quantum neural networks"

The uploaded files contain the data of the simulations presented in the figures in <a href="https://doi.org/10.48550/arXiv.2304.11209">https://doi.org/10.48550/arXiv.2304.11209</a>.

openmit-licenseJul 2023View details →

PANDA cell-type-specific networks for S858R and WT mouse cerebral cortex

The below files are from the data/results directory of this associated project and include the following: <ul> <li>PANDA networks : cell-type-specific TF-gene regulatory networks constructed using multi-omic inputs (snRNA-seq, TF-motif, and PPI) for all cell types in S858R and WT mouse cerebral cortex tissues (n = 16). All the files included for PANDA networks indicate the condition (heterozygous or control), tissue (cerebral cortex) followed by _PANDA.Rdata. (example: astrocytes_heterozygouscortexexpression_PANDA.Rdata)</li> </ul>

openmit-licenseJul 2023View details →

PANDA cell-type-specific networks for S858R and WT mouse kidney

The below files are from the data/results directory of this associated project and include the following: <ul> <li>PANDA networks : cell-type-specific TF-gene regulatory networks constructed using multi-omic inputs (snRNA-seq, TF-motif, and PPI) for all cell types in S858R and WT kidney tissues (n = 34). All the files included for PANDA networks indicate the condition (heterozygous or control), tissue (kidney) followed by _PANDA.Rdata. (example: pericytes_heterozygouskidneyexpression_PANDA.Rdata)</li> </ul>

Emission-Constrained Optimization of Gas Networks: Input-Convex Neural Network Approach

# Emission-Aware Optimization of Gas Networks   This repository collects the Belgium gas network dataset, details on the training procedure, and codes to replicate the results reported in the following paper:   *Emission-Constrained Optimization of Gas Networks: Input-Convex Neural Network Approach *   accepted for presentation at the 62nd IEEE Conference on Decision and Control, Dec. 13-15, 2023, Singapore.   Materials are released with the Attribution 4.0 International (CC BY 4.0) license.   The repository contains two folders: * ```operation_planning``` folder containing data and codes for neural network training and operation planning optimization  * ```long_term_planning``` folder containing data and codes for neural network training and long-term planning optimization    The models are implemented in ```Julia-1.6``` Language, using ```JuMP.jl``` using ```Flux.jl``` library for machine learning and JuMP.jl library for mathematical programming. Before running the code, make sure to activate the virtual environment from ```Project.toml``` files stored in each folder, e.g., by running  ``` julia> ] (@v1.6) pkg> activate . (operation_planning) pkg> instantiate ``` For experiment settings, refer to ```exp_settings``` dictionary in file ```main.jl```. For network data, refer to ```.../data/case_BE```.

opencc-by-4.0Apr 2023View details →

Understanding the impact of host networking elements on traffic bursts: Raw measurement data

This record contains the raw trace files gathered by the Valinor network traffic burst measurement framework in Redis dump (rdb) format. Please refer to the artifact repository for instructions on how to parse and use the datasets: <a href="https://github.com/hopnets/valinor-rawdata">hopnets/valinor-rawdata: Raw Redis datasets containing the measurement results of Valinor NSDI '23 paper (github.com)</a>

Competitive branching: the morphogenesis of tidal creek networks

Data and codes used in the paper entitled "Competitive branching: the morphogenesis of tidal creek networks".

Supplemental data for characterization of mixing in nanoparticle hetero-aggregates using convolutional neural networks

This is the supplemental data for the manuscript titled Characterization of mixing in nanoparticle hetero-aggregates using convolutional neural networks submitted to Nano Select. Motivation: Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network is trained to do this task for STEM-images of TiO2-WO3 nanoparticle hetero-aggregates. The present dataset contains the training data and some jupyter-notebooks that can be used after installation of the MMDetection toolbox (<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>) to train the CNN. Details are provided in the manuscript submitted to Nano Select and in the comments of the jupyter-notebooks. Authors and funding: The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1. Dataset description: Four jupyter-notebooks are provided, which can be used for different tasks, according to their names. Details can be found within the comments and markdowns. These notebooks can be run after installation of MMDetection within the mmdetection folder. <ul> <li>particle_detection_training.ipynb: This notebook can be used for network training.</li> <li>particle_detection_evaluation.ipynb: This notebook is for evaluation of a trained network with simulated test images.</li> <li>particle_detection_evaluation_experiment.ipynb: This notebook is for evaluation of a trained network with experimental test images.</li> <li>particle_detection_measurement_experiment.ipynb: This notebook is for application of a trained network to experimental data.</li> </ul> In addition, a script titled particle_detection_functions.py is provided which contains functions required by the notebooks. Details can be found within the comments. The zip archive training_data.zip contains the training data. The subfolder HAADF contains the images (sorted as training, validation and test images), the subfolder json contains the annotation (sorted as training, validation and test images). Each file within the json folder provides for each image the following information: <ul> <li>aggregat_no: image id, the number of the corresponding image file</li> <li>particle_position_x: list of particle position x-coordinates in nm</li> <li>particle_position_y: list of particle position y-coordinates in nm</li> <li>particle_position_z: list of particle position z-coordinates in nm</li> <li>particle_radius: list of volume equivalent particle radii in nm</li> <li>particle_type: list of material types, 1: TiO2, 2: WO3</li> <li>particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron</li> <li>rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)</li> <li>deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation).</li> <li>cluster_index: list of cluster indices for each particle</li> <li>initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged</li> <li>fractal_dimension: the intended fractal dimension of the aggregate</li> <li>fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)</li> <li>fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)</li> <li>fractal_prefactor: fractal prefactor</li> <li>mixing_ratio_intended: the intended mixing ratio (fraction of WO3 particles)</li> <li>mixing_ratio_true: the realised mixing ratio (fraction of WO3 particles)</li> <li>mixing_ratio_volume: the realised mixing ratio (fraction of WO3 volume)</li> <li>mixing_ratio_weight: the realised mixing ratio (fraction of WO3 weight)</li> <li>particle_1_rho: density of TiO2 used for the calculations</li> <li>particle_1_size_mean: mean TiO2 radius</li> <li>particle_1_size_min: smallest TiO2 radius</li> <li>particle_1_size_max: largest TiO2 radius</li> <li>particle_1_size_std: standard deviation of TiO2 radii</li> <li>particle_1_clustersize: average TiO2 cluster size</li> <li>particle_1_clustersize_init: average TiO2 cluster size of primary clusters (before merging into larger clusters)</li> <li>particle_1_clustersize_init_intended: intended TiO2 cluster size of primary clusters</li> <li>particle_2_rho: density of WO3 used for the calculations</li> <li>particle_2_size_mean: mean WO3 radius</li> <li>particle_2_size_min: smallest WO3 radius</li> <li>particle_2_size_max: largest WO3 radius</li> <li>particle_2_size_std: standard deviation of WO3 radii</li> <li>particle_2_clustersize: average WO3 cluster size</li> <li>particle_2_clustersize_init: average WO3 cluster size of primary clusters (before merging into larger clusters)</li> <li>particle_2_clustersize_init_intended: intended WO3 cluster size of primary clusters</li> <li>number_of_primary_particles: number of particles within the aggregate</li> <li>gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)</li> <li>gyration_radius_weighted: gyration radius of the aggregate (including particle densities)</li> <li>mean_coordination: mean total coordination number (particle contacts)</li> <li>mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)</li> <li>mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)</li> <li>radius_equiv: list of area equivalent particle radii (in projection)</li> <li>k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)</li> <li>polygons: list of polygons that surround the particle (COCO annotation)</li> <li>bboxes: list of particle bounding boxes</li> <li>aggregate_size: projected area of the aggregate translated into the radius of a circle in nm</li> <li>n_pix: number of pixel per image in horizontal and vertical direction (squared images)</li> <li>pixel_size: pixel size in nm</li> <li>image_size: image size in nm</li> <li>add_poisson_noise: 1 if poisson noise was added, 0 otherwise</li> <li>frame_time: simulated frame time (required for poisson noise)</li> <li>dwell_time: dwell time per pixel (required for poisson noise)</li> <li>beam_current: beam current (required for poisson noise)</li> <li>electrons_per_pixel: number of electrons per pixel</li> <li>dose: electron dose in electrons per Å2</li> <li>add_scan_noise: 1 if scan noise was added, 0 otherwise</li> <li>beam misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)</li> <li>scan_noise: parameter that describes how far the beam can be misplaced in pixel (required for scan noise)</li> <li>add_focus_dependence: 1 if a focus effect is included, 0 otherwise</li> <li>data_format: data format of the images, e.g. uint8</li> </ul> There are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder COCO. For training, validation and test data there is a file annotation_COCO.json that includes all information required for the CNN training. The zip archive experiment_test_data.zip includes manually annotated experimental images. All experimental images were filtered as explained in the main work. The subfolder HAADF includes thirteen images. The subfolder json includes an annotation file for each image in COCO format. A single file concluding all annotations is stored in json/COCO/annotation_COCO.json. The zip archive experiment_measurement.zip includes the experimental images investigated in the manuscript. It contains four subfolders corresponding to the four investigated samples. All experimental images were filtered as explained in the manuscript. The zip archive particle_detection.zip includes the network, that was trained, evaluated and used for the investigation in the manuscript. The network weights are stored in the file particle_detection/logs/fit/20230622-222721/iter_60000.pth. These weights can be loaded with the jupyter-notebook files. Furthermore, a configuration file, which is required by the notebooks, is stored as particle_detection/logs/fit/20230622-222721/config_file.py. There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening. The dataset was generated to discriminate between TiO2 and WO3 nanoparticles in STEM-images. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO2 and WO3 but there is no guarantee.

236-Bus Low Voltage Distribution Network Data

The data describes a realistic low-voltage distribution network with 236 buses, 235 underground cables, 96 residential customers supplied by a transformer with 1000 kVA and 10 kV/420V.The network characteristics, such as cable resistances, reactances, and thermal limits, are available.Demand, EV demand, and photovoltaic generation profiles are presented for 8640 total 15-minute periods.

Genetic networks datasets: ten books by Gustave Roud, "En mer" by Bernard Comment

In genetic criticism, scholarly editing, authorial philology and, more generally, for the study of authorial manuscripts and writing processes it is essential to order and classify the textual witnesses and their relationships. The two datasets documents so-called ‘genetic networks’, representations of the genetic entities (witnesses, publications, dossiers) and of their relationships, modelled according to the GENO 1.0 ontology. The datasets contain genetic networks of the works of two Swiss authors: the main publications of Gustave Roud (1897-1976) and the short story “En mer” by Bernard Comment (1960).

opencc-by-4.0Sep 2022View details →

Inferring microbial co-occurrence network from amplicon data: a systematic evaluation

Supporting data for the manuscript "Inferring microbial co-occurrence network from amplicon data: a systematic evaluation".

dryad36/100

AutoCore: network-based definition of the core module of human autoimmunity and autoinflammation

Although research on rare autoimmune and autoinflammatory diseases has enabled definition of non-redundant regulators of homeostasis in human immunity, due to the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and non-coordinated individual studies. Here, we used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation which we termed "AutoCore". The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multi-disease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale towards systematic targeting for therapeutic purposes. Our study provides a proof-of-concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.

opencc-zeroSep 2023View details →

Fruits important for network cohesiveness may not be the same as for bird diets

The present database contains 6 data frames used in "Fruits important for network cohesiveness may not be the same as for bird diets" simulations and analyses divided into two scripts. The first script contains the HMSC-F fitting that generates almost all the inputs needed for the second script in which  is placed in the procedure for network simulations, rewiring, replaceability estimations, and data analysis described in the main text.  To perform run correctly the methodology applied is important to use the scripts sequentially.  The data frames are listed as follows: - phylbird_sorted.csv contains the phylogenetic correlation matrix of bird species. - phylplant_sorted.csv contains the phylogenetic correlation matrix of plant species. - interacciones.csv contains the interactions between plants and birds in six communities. "pajaro" column contains bird species;    "planta" column contains fruit species; "sitio" column contains community site information. - pabund_sorted1.txt list the fruit abundance in each community measured in grams/hectare. "species" column contains fruit species;    "abundancia" column is the abundance of each fruit in grams/hectare; "sitio" column means the site from which each community was sampled. - plant_traits.txt contains the fruit traits of the species under study. This data frame is to be used in the 1st part of the analysis (HMSC-F fitting).    It contains "t.carb" column that shows the amount of non-structural carbohydrates of each fruit in mg/g; "c.tann" is condensed tannins in ug/g;   "width" shows the equatorial width of fruits in mm.  - ptraits_sorted1.txt depicts plant traits to be used in the estimation of diet descriptors (nutritional state and functional dispersion). This   data frame is to be used in part 2 of the analysis. "t.carb" column shows the amount of non-structural carbohydrates of each fruit in mg/g;   "lipid" column that shows the number of lipids of each fruit in mg/g; "protein" column that shows the number of proteins of each fruit in mg/g. - btraits_sorted.csv contain birds traits obtained from the Elton traits (Wilman et al. 2014). "pajaro" column shows bird species; "gape" depict gape size   in mm; "Diet.Inv" shows the proportion of invertebrates in bird diets; "Diet.Fruit" shows the proportion of fruits in birds' diets; "BodyMass.Value" contains the body mass of birds in g.