Find research datasets worth reusing

opencc-by-4.0May 2022View details →

Semi-empirical methods SPT inputs for bearing capacity prediction

These datasets presents inputs for bearing capacity prediction methods. These methods are four well-known semi-empirical models for predicting bearing capacity of piles. The data was collected from the works of Lobo (2005), Vianna (2000) and Jr. (1988) and includes 168 load tests and SPT measures taken from severam Brazilian regions. . The file Data.csv is composed only with the numeric values used in each method and the file Data_with_soils.csv includes the soil types for the piles. The suffix 'Dq', 'Mey', 'Av' and 'Tx' represents which method this input was obtained from, corresponding respectively to Decourt and Quaresma (1978), Meyerhof (1983), Aoki and Velloso (1975) and Teixeira (1996). The columns indexes represents: N_pile - Pile number (for reference); SPT_L - SPT result for the pile lenght; SPT_P - SPT result for the pile tip Soil_L - Predominant soil type along the pile lenght; Soil_P - Predominant soil type in the pile tip; L - pile lenght; D - pile diameter; Qu - Pile bearing capacity, obtained through NBR 6122 load test. When using this dataset, please cite the following paper: //<a href="http://soilsandrocks.com/sr-2021-074921">soilsandrocks.com/sr-2021-074921</a> DOI: 10.28927/SR.2021.074921

opencc-by-4.0Jun 2022View details →

An Effective Activation Method for Industrially Produced TiFeMn Powder for Hydrogen Storage [Dataset related to publication]

Data type: XRD patterns; SEM micrographs and EDX maps; particle size distributions; atomic concentrations; hydrogen loading profiles; kinetic models; volume expansions.   Data format: *.opj; *.tif. Origin of the data: laboratory equipment from Hereon (XRD, SEM, PSD Analyzer, BET, XPS, Sievert apparatus) and UniPV (SEM). Software needed to plot the data: folders need to be unzipped, Origin.

opencc-by-4.0Jul 2022View details →

NURBS Enhanced Virtual Element Methods for the Spatial Discretisation of the Multigroup Neutron Diffusion Equation on Curvilinear Polygonal Meshes

This repository holds all of the raw data generated by my C++ code for a paper "NURBS Enhanced Virtual Element Methods for the Spatial Discretisation of the Multigroup Neutron Diffusion Equation on Curvilinear Polygonal Meshes". The C++ code solves the neutron diffusion equation using a novel spatial discretisation called the Virtual Element Method. Alongside the raw data (stored in VTK and HDF5 files) are post-processing python scripts which read the raw data, compute meaningful quantities of interest and generate plots/figures.

opencc-by-4.0Nov 2021View details →

Data used in ECLIPSER methods paper and GTEx snRNA-seq cross-tissue reference map analysis

The tables were used in the papers: Rouhana*, Wang* et al., ECLIPSER: identifying causal cell types and genes for complex traits through single cell enrichment of e/sQTL-mapped genes in GWAS loci, bioRxiv 2021, doi: https://doi.org/10.1101/2021.11.24.469720; and Eraslan et al., Single-nucleus cross-tissue molecular reference maps to decipher disease gene function, bioRxiv 2021, doi: https://doi.org/10.1101/2021.07.19.452954. '<a href="https://zenodo.org/api/files/1f8d48d0-6bf7-4bec-b6ef-5c7a9ead8079/GTEx_v8_HG38_all_variants.tsv.gz">GTEx_v8_HG38_all_variants.tsv.gz</a>' is an input file for running GWASvar2gene on GTEx v8 eQTLs and sQTLs, and all other files are input files for ECLIPSER.

Synthetic cryo electron microscopy single particle images containing biomolecular complexes with continuous conformational variability used for validating DeepHEMNMA method and validation results

This archive contains a synthetic dataset used for validating DeepHEMNMA method and the validation results. DeepHEMNMA is a deep learning extension of HEMNMA approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron (cryo-EM) microscopy single particle images. We provide a training set of 20,000 images and an inference set of 50,000 images. The training images were used (1) to estimate the conformational and rigid-body parameters with HEMNMA and (2) to train the neural network using the parameters previously estimated with HEMNMA (the file with the HEMNMA-estimated parameters is provided). The inference images were used to infer the parameters with the trained neural network. Also, we provide (1) the input PDB structure, its normal modes, and the conformational and rigid-body parameters used to synthesize the 20,000 training images (ground-truth parameters) and (2) the conformational and rigid-body parameters inferred from the set of 50,000 inference images. The DeepHEMNMA method and the method for synthesizing images have been fully described in the following article: "Hamitouche I and Jonic S (2022), DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images. Front Mol Biosci 9, 965645. <a href="https://doi.org/10.3389/fmolb.2022.965645">https://doi.org/10.3389/fmolb.2022.965645</a> (in press)". Additionally, this article describes a test of DeepHEMNMA using one experimental cryo-EM dataset (available in EMPIAR database under the accession code EMPIAR-10016).

SMTLIB2.6 translation of proof obligations from the B formal method

These files have been produced from the <a href="https://doi.org/10.5281/zenodo.7050797">POG files</a> using the program ppTransSmt, distributed with Atelier B. The source code of this version of ppTransSmt correspond to git commit `abb8ad04003803ea3683d8069363681e92308ebf` (in the private repository of Atelier B).

Proof obligations from the B formal method

Proof obligations generated with Atelier B, an IDE for the B method and Event-B. The files are in the POG format.

Experimental characterization of transversal-heterogeneous clay mixtures by the spectral induced polarization method

In this folder you will find multiple datasets (*.txt) from SIP measurements of transversal-heterogeneous clay mixtures using spectral induced polarization acquired between April and May 2022. Additionally, we include two python codes to read and process the data. SIP_Plot_ReWrite.py is a python program aimed to process a .res file from a SIP Fuchs III. It gives a .txt file with the frequency, the resistivity, the phase and the associated errors. In order for the program to give the resistivity, you will need to enter the geometric factor of the studied sample. The six text files in the folder (excluding README.txt) were created using SIP_Plot_ReWrite.py. IL_1by1.txt and IL_1by1_V2.txt are from two different homogeneous mixtures of illite and water with a concentration of initially 0.01 mol/L of NaCl. MtR_1by1.txt is from a homogeneous mixture of red montmorillonite and water with a concentration of initially 0.01 mol/L of NaCl. IL_MtR_1by2.txt, IL_MtR_1by4.txt and IL_MtR_1by8.txt are from three transversal-heterogeneous mixtures of illite and red montmorillonite with water containing a concentration of initially 0.01 mol/L of NaCl. For IL_MtR_1by2.txt, there was one portion of each clay types, occupying a half of the cylindrical container each. For IL_MtR_1by4.txt, there was two portions of each clay types, occupying a quarter of the cylindrical container each. These two samples were made using the same mixtures as for IL_1by1.txt and MtR_1by1.txt. For IL_MtR_1by8.txt, there was four portions of each clay types, occupying an eighth of the cylindrical container each. This sample was made using the same mixtures as for IL_1by1_V2.txt and MtR_1by1.txt. TestDoubleColeColeFit.py is a python program which optimizes a double Cole-Cole model by multiplication on SIP data. This program needs a file with the same structure as the .txt file made by SIP_Plot_ReWrite.py.

The dataset for publication "Characterization of scintillating materials in use for brachytherapy fiber based dosimeters" by S. Commeti, et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2022.

This dataset is related to paper journal paper with DOI: <a href="http://dx.doi.org/10.1016/j.nima.2022.167083">10.1016/j.nima.2022.167083</a>. The dataset contains raw txt file and matlab files on the transmittance and the attenuation of Gadox and YVO specimens.  Data files were prepared by agnieszka.gierej@vub.be

opencc-by-4.0Oct 2022View details →

NLO QCD Track Evolution Kernels for Fourier and Wavelet Methods

Two datasets of LO and NLO evolution numerical kernels for track functions, one for the Fourier series method and the other for the Legendre wavelet method. We also include the corresponding Julia code to numerically solve the track evolution equation based on these kernels. These datasets are used to build docker images for <a href="https://hub.docker.com/r/haochern/qcd-track-evolution-fourier">Fourier</a> and <a href="https://hub.docker.com/r/haochern/qcd-track-evolution-wavelet">wavelet</a> approaches. More details of instructions, as well as the moment method to the track evolution, can be found on <a href="https://github.com/HaoChern14/Track-Evolution">https://github.com/HaoChern14/Track-Evolution</a>.

opencc-by-4.0Nov 2017View details →

Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods

EEG signals recorded from seven healthy subjects. On average, Seventy-three minutes of EEG data were recorded from 31 electrodes placed according to the extended 10-20 system. Signals are used in the paradigm-agnostic post-hoc labeled dataset generation framework for benchmarking of oscillatory neural decoding methods.

opencc-by-4.0Dec 2017View details →

Data archive accompanying "A new method of physics-based data assimilation for the quiet and disturbed thermosphere" [Sutton, 2018, doi:10.1002/2017SW001785]

This archive contains the data used to create the plots presented in "A new method of physics-based data assimilation for the quiet and disturbed thermosphere" [Sutton, 2018, SWx, doi:10.1002/2017SW001785]. Format: MATLAB save file Contents: 1. CHAMP and GRACE-A accelerometer-derived densities and ephemeris; 2. TIE-GCM GPI model output sampled on both satellites; 3. IRIDEA prior and posterior model output sampled on both satellites; 4. Short description and units for all variables

Images of article "Sexy ways: the methodical approaches to study plant sex chromosomes"

Figure 1. Schematic diagram of sex chromosome evolution in dioecious plants. Species are shown according to their level of sex chromosome differentiation and Y chromosome asynapsis. In S. oleracea, A. officinalis and C. papaya, the sex chromosomes are mostly homomorphic with recently formed non-recombining regions (region with suppressed recombination). The non recombining region is largely extended almost to entire chromosomal length in species with heteromorphic sex chromosomes, namely in S. latifolia, R. hastatulus (XY cytotype), R. acetosa, H. lupulus, H. japonicus and M. polymorpha. The position of the centromere, the PAR length and the ratio between X and Y is illustrative.  Figure 2. Laser microdissection as a tool to reduce genome complexity. Sex chromosomes in metaphase are isolated from plant cells (mostly pollen mother cells or root tips) and subsequently spread on a special microscopic slide covered with the membrane. After microdissection, chromosomes are transferred into a tube and processed to other applications. In case of chromosome sorting, the chromosome suspension is stained with a DNA-specific dye and introduced into a flow chamber. Within this chamber, individual chromosomes interact with a laser beam, and the scattered light and emitted fluorescence are measured. Through this process, a histogram of fluorescence intensity (known as a flow karyotype) is generated. Sorting is accomplished by breaking the liquid stream into droplets and electrically charging the droplets containing the chromosomes of interest. Figure 3. Cytogenetic tools to study sex chromosome origin and evolution. Cytogenetics nowadays combine genomic tools to study repeat fraction including TEs and satellites (a), design  unique barcodes to distinguish particular chromosome or chromosomal domain using chromosome oligo-painting probe design (b), and bioinformatic tools to dissect single chromosomes or genome parts (c). The combination of above methods helps to understand sex chromosome evolution regarding their autosomal origin, chromosomal rearrangements, and Y(W) chromosome differentiation. Arrows represent evolutionary steps during sex chromosome divergence (d). The sex chromosome barcoding allows understanding of meiotic pairing which in turn supports chromosomal fusions and inversion/translocations. To chromosomes belong to species with references, from the top to the bottom as follows: S. latifolia Ogre retroelement (Kubat et al., 2014), R. hastatulus XY cytotype satellite Cl135 (Sacchi et al., 2023, Preprint), S. latifolia PAR oligo-painting probe with the subtelomeric satellite X43.1 and centromeric satellite STAR-C (Bačovský et al., 2020), and the same DNA probes on chromosomes in metaphase I in S. latifolia (Bernasconi et al., 2009; Bačovský et al., 2022).  Figure 4. Methodical strategies to assess the function of sex chromosomes in plants. Experimental assays with polyploids (alternatively aneuploids) represent the classical way to determine the role of individual sex chromosomes (a). These assays with plants of various ploidy levels were usually supported by analyses of deletion lines (plants carrying short-chromosomal  deletions or microdeletions) (b) that allowed researchers to identify sex-linked regions involved in sex determination and floral development. Modern assays using reverse genetics, such as CRISPR/Cas9, virus-induced gene silencing (VIGS) or peptide treatment of shoot apical meristem (c) provide direct evidence of the gene function and its contribution to the development  of reproductive organs. Parasite infected (d) or chemically induced (e) hermaphrodites from either female or male individuals, e.g. in Silene or kaki, let to the identification of key mechanisms and genes that regulate sexual phenotypes, and to understand the regulatory networks leading to separate sexes.

Dataset from "A user-friendly method to get automated pollen analysis from environmental samples". New Phytologist.

Dataset used in publication "A user-friendly method to get automated pollen analysis from environmental samples". New Phytologist. This repository contains images from annual pollen trap samples mounted on slides and scanned under light microscopy; image annotation metadata; and the weights of the trained models from the YOLOv5 algorithm, saved after the last training epoch. More details can be found in the README file.

embargoedcc-by-4.0May 2024View details →

Dataset of "Comparison of Localization Methods for Internet of Things in 5G Cellular Networks: A Wide-scale Assessment"

As the 3rd generation partnership project (3GPP) organization pushes out new releases, positioning in heterogeneous mobile networks enables the achievement of the accuracy required in the majority of industrial applications without dependence on global navigation satellite systems (GNSS). This study presents the results gathered during an extensive measurement campaign related to the practical applicability of localization in next-generation heterogeneous networks. We present an accuracy comparison of basic timing advance (TA) localization with the k-nearest neighbor (KNN), decision tree-based random forest (RF), extreme gradient boosting (XGBoost), and long short-term memory (LSTM) recurrent neural network. Our results demonstrate that TA cannot be considered an optimal solution from the perspective of localization accuracy because the error roughly corresponds to the average separation distance from the base station (BS) to the end device (ED). In addition, we found that the LSTM approach is not optimal for the outdoor localization of moving ED because of the combination of multiple factors, with sparse deployment being the most important. The median value of the location error of the LSTM was more than 200m higher than that of the TA for the self-validation dataset. However, a simple KNN regression shows solid results for 5G New Radio (NR) operating in the non-standalone (NSA) mode. KNN provided the most accurate results of all methods, with median error values of approximately 12 (k=3) and 82 (k=5) m for the self-validated and cross-validated datasets, respectively.

Methods of promoting modern theater on social media / Методи популяризації сучасного театру у соціальних мережах

The dataset "Methods of promoting contemporary theatre on social media", based on a survey of 105 respondents, includes answers to the following questions: <ul> <li>How often do you go to the theatre?</li> <li>What social media do you use to find out about theatre events?</li> <li>What type of content on social media is most effective in drawing your attention to theatre events? - Has social media ever prompted you to buy theatre tickets?</li> <li>What factors influence your decision to attend a theatre performance you saw on social media?</li> <li>How do you assess the overall effectiveness of social media in promoting theatre events? Data downloaded in .csv format.</li> </ul>

Dataset for "A simple and accurate method to determine fluid-crystal phase boundaries from direct coexistence simulations"

This is a dataset for the article "A simple and accurate method to determine  fluid-crystal phase boundaries from direct coexistence simulations", available at https://arxiv.org/abs/2403.10891    (Full citation data will be added upon final publication of the article.) This package provides figure data and representative configuration files associated with the systems studied in the article above. Additionally, for the hard sphere system, this package includes direct coexistence data for all reported system sizes and crystal orientations.

Measurement data of a three-phase grid-side converter with a grid-forming synchronverter-based control method with current limitation

The data set was recorded for a publication currently undergoing the submission process. The published data correspond to the data presented in the figures. The first column represents the time vector. All other columns are linked to the corresponding scenario by an identifier in the column name. The column name also contains the name of the recorded signal and the associated unit. The naming convention is <identifier_to_figure>_<recorded_signal>_<unit>.

opencc-by-4.0Jun 2024View details →

Data for the paper "Particle method for the numerical simulation of the path-dependent McKean-Vlasov equation"

This deposit contains the data obtained by the method described in [A. Bernou, Y. Liu, Particle method for the numerical simulation of the path-dependent McKean-Vlasov equation, 2024]. The notebooks used to generate them through a suitable Euler scheme can be find at https://github.com/ArmdBrn/McKean_PathDep, along with files containing the estimated errors.  The two models considered are: - a modified Ornstein-Uhlenbeck model with path-dependency; - a model of neural masses with intrinsic potentiation leading to path-dependent dynamics.