Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

Data: Non-utopian optical properties computed of a tomographically reconstructed real photonic band gap crystal

This repository contains the scripts and data used for the manuscript "Non-utopian optical properties computed of a tomographically reconstructed real photonic band gap crystal'' by LJ Corbijn van Willenswaard, S Smeets, N Renaud, M Schlottbom, JJW van der Vegt and WL Vos This dataset contains the following: <ul> <li>The starting slice of the X-ray holotomagraphy dataset</li> <li> All data generated that is used in the paper including: <ul> <li>X-ray processing results</li> <li>Raw results from the computations</li> <li>Post processed results that are the basis of the manuscript</li> </ul> </li> <li>All scripts that were used to automate this process</li> <li>A copy of: <ul> <li>Nanomesh repository (for x-ray processing)</li> <li>hpgem & DGMax source code (for the computations)</li> </ul> </li> </ul> A more detailed readme is included with the data.

openmit-licenseMar 2024View details →

A multi-omics systems vaccinology resource to develop and test computational models of immunity: 1st challenge dataset and submissions

The goal of the CMI-PB prediction contest is to foster a collaborative research community that can collectively tackle challenges and accelerates scientific progress beyond the capabilities of individual researchers or groups. The CMI-PB consortium has curated multi-source data from multiple individuals, encompassing Ab titers (around four antibodies/features), cell frequency (approximately 20 cell types/features), gene expression (roughly 50,000 RNA transcripts/features), and plasma proteomics (around 50 proteins/features). The challenge requires integrating these diverse data sources to predict different immune responses or tasks. Specifically, you will utilize multi-source data from several individuals on day 0 (baseline) to predict specific immune responses at later time points (1, 3, 7, and  14 days post-booster vaccination). The first CMI-PB challenge, which is an internal challenge, was conducted using datasets from 2020 (train) and 2021 (test). In the following sections, we provide detailed information on the datasets, challenge tasks, submission format, descriptions, and access to the necessary data files for participants to develop their models and make predictions. A) Multiomics CMI-PB dataset: We propose a study design that enables a systems-level understanding of the immune responses through computational modeling. Our cohort comprises aP vs. wP infancy-primed subjects boosted with Tdap. We recruit individuals born before 1995 (wP) and after 1996 (aP), collect baseline plasma and blood samples, and then at 1, 3, 7 and  14 days post booster vaccination. With the obtained samples processed, we generated omics data by: <ul> <li> Bulk PBMCs transcriptomics, </li> <li> Plasma proteomics using Olink, which provides a quantitative readout of cytokines, chemokines, and other immune factors, </li> <li> Cell frequency in PBMCs using flow cytometry, </li> <li> Tdap-specific antibodies levels </li> </ul> B) List of tasks can be accessed using the “List of tasks for challenge 1.docx” file, and submissions need to submit in provided format here: “submission template challenge 1.tsv” C) Datasets for model building and making predictions:     Data files are divided into two categories: 1) raw dataset and 2) computable matrices. <ol> <li> Raw dataset: This raw-most dataset is divided into training and test datasets.  </li> <li> Computable matrices: There are three different types of computable matrices. a) Full: These files are generated by dividing raw files into sub-files specific to planned days specific to vaccination. b) harmonized: These are generated by preserving only overlapping features between train and test datasets. b) imputed: MICE imputation is performed to impute missing values in the dataset. </li> </ol> D) Submission evaluation This folder contains all submitted models with ranking files and code for evaluating these models. To learn more about the CMI-PB prediction challenge, visit our website at www.cmi-pb.org.

opencc-by-4.0Oct 2023View details →

Voltage controlled iontronic switches: a computational method to predict electrowetting in hydrophobically gated nanopores

Folder with numbered names ("0", "0.1", "n0.5") have the data from restrained molecular dynamics (".dat") and some trajectories (".xyz") at the applied voltage corresponding to the name of the folder.  "n" in the folder name correspond to the negative voltages. Folders "wetting" and "drying" contain the data used to compute the wetting and drying rates at different applied voltages using Molecular Dynamics.

opencc-by-4.0Nov 2023View details →

Thermal conductivity analysis of polymer-derived nano-composite via image-base structure reconstruction, computational homogenization and machine learning

This dataset includes supplementary data and utilities for validating simulation results and training machine learning models as outlined in the publication titled "Thermal Conductivity Analysis of Polymer-Derived Nanocomposite via Image-Based Structure Reconstruction, Computational Homogenization, and Machine Learning" (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>). This dataset containes the microstructure images (identified by particle diameters size $D_1$ and $D_2$ volume fraction $V_\mathrm{f}$ and aspect ratio $A_\mathrm{r}$) (see Table 1) and their corresponding homogenized thermal conductivity. these images resemble the microstructure of the monolithic $\mathrm{(Hf,Ta)C/SiC}$ ceramic following FAST sintering, the material system of this work (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>). White and black colors within the images represent distinct regions of the material system,  respectively referring to former powder particles (FPPs) and sinter necks (SNs), which is explained in this work. Table 1. Parameterized descriptors extracted from the mesoscale SEM image analysis <table> <tbody> <tr> <td>Param.</td> <td>Mean [unit]</td> <td>Std.</td> </tr> <tr> <td>$D_{1}$</td> <td>40, 50, 60 [μm]</td> <td>20%</td> </tr> <tr> <td>$D_{2}$</td> <td>20, 25, 26, 30, 33, 40 [μm]</td> <td>30%</td> </tr> <tr> <td>$V_\mathrm{f}$</td> <td>1.5, 2.0</td> <td>-</td> </tr> <tr> <td>$A_\mathrm{r}$</td> <td>35, 40, 45, 55, 60 [%]</td> <td>-</td> </tr> </tbody> </table> This dataset contains: <ul> <li>dataset.csv: containing a summary of data including the names of microstructure images, their corresponding geometric details, as well as the first and third principal components of two-point statistics for all images, along with the effective thermal conductivity of the corresponding microstructures. Further details can be found in the associated publication.</li> <li>microstructures_images.zip: containing binary cross-section images of the RVEs from synthetic microstructures。</li> <li>results.zip: contains all the simulation results based on digitized diffuse-interface microstructures, which can be opened by the post-processing software, such as ParaView.</li> </ul>

Role of Quantum Computing in Shaping the Future of 6G Technology

The dataset is provided for the data collected to understand the role of quantum cmputing in shaping the future of 6G technology. The dataset consist of all the raw data and analysed results with respect to the research questions.

opencc-by-4.0Jan 2024View details →

Computational data for The odd-number cyclo[13]carbon and its dimer cyclo[26]carbon

This dataset contains the computational data associated with "The odd-number cyclo[13]carbon and its dimer cyclo[26]carbon".

Applying innovative cloud computing technology for the effective management of Groundwater resources to promote SUStainable food security within the Sokoto Basin, Nigeria (AGSUS)

Open the record for dataset details and reuse information.

Dataset for the SFmodel, applied in Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches

Dataset used for the SFmodel, applied in the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches". For units and nomenclature of the variables refer to Units_and_Nomenclature_for_SFmodel_in_Evapotranspiration_dynamics_and_partitioning_in_a_grassed_vineyard.pdf.

Dataset for "Application of Generalized - Aurora Computed Tomography to the EISCAT_3D project"

Dataset for "Application of Generalized - Aurora Computed Tomography to the EISCAT_3D project"

opengpl-3.0-or-laterMar 2024View details →

Code, benchmarks and experiment data for the ICAPS 2024 paper "Merging or Computing Saturated Cost Partitionings? A Merge Strategy for the Merge-and-Shrink Framework"

This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper. Finally, it contains an appendix with some more detailed results for the paper. appendix.pdf: document with more detailed results ommitted in the paper. sievers-et-al-icaps2024-fast-downward.zip contains the implementation based on Fast Downward. It also contains the experiment scripts compatible with Lab 7.1 for reproducing all experiments of the paper, under experiments/scp-ms. The scripts 2024-03-* contain configurations for running the experiments and the script paper-crc.py gathers the data and produces plots and tables. (Note that some adjustments to the scripts would need to be done because, e.g., the entire tree is not a repository anymore.) sievers-et-al-icaps2024-ipc-benchmarks.zip contains the IPC benchmarks. It consists of the STRIPS IPC benchmarks used in all optimal sequential tracks of IPCs up to 2023 (suite optimal_strips from https://github.com/aibasel/downward-benchmarks). sievers-et-al-icaps2024-lab.tar.gz contains a copy of Lab 7.1 (https://github.com/aibasel/lab). sievers-et-al-icaps2024-raw-data.zip and sievers-et-al-icaps2024-processed-data.zip contain the experimental data. Directories without the "-eval" ending (sievers-et-al-icaps2024-raw-data.zip) contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" suffix (sievers-et-al-icaps2024-processed-data.zip) contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run. Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.

Dataset for Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches

Data sets of the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches". You will find all data files needed for this work, organised by the figures of the paper. For the codes, refer to Flavio Bastos Campos. (2024). flaviobastoscampos/ET_dynamics_and_partitioning_vineyard: v2024.1 (v2024.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.10864169" target="_blank" rel="nofollow noopener">https://doi.org/10.5281/zenodo.10864169</a>.

opencc-by-4.0Apr 2023View details →

Data for Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization

Dataset substantiating the claims in <a href="https://arxiv.org/abs/2212.07957">[2212.07957] Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization (arxiv.org)</a>

The impact of deep learning aid on the workload and interpretation accuracy of radiologists on chest computed tomography: a cross-over reader study.

Data used to perform statistical analysis in "The impact of deep learning aid on the workload and interpretation accuracy of radiologists on chest computed tomography: a cross-over reader study.".

Dataset: Computational resources in the development of e-health IoT applications: A systematic mapping study

Files related to systematic mapping titled Computational resources in the development of e-health IoT applications: A systematic mapping study

Exploring the thermal and ionic transport of Cu+ conducting argyrodite Cu7PSe6 (Computational data)

This repository contains computational data for the manuscript titled “Exploring the thermal and ionic transport of Cu+ conducting argyrodite Cu7PSe6”. It consists of outputs and analysis scripts for bonding analysis through LOBSTER, harmonic phonon, and Grüneisen parameters calculations using VASP and phonopy.

Survey on Cloud Computing usage in Montenegrin SMEs, 2017-2023

Data collected among 100 SMEs in Montenegro related to their persepctvies on usage of cloud computing services in their businesses. Comprehesive questionnaire prepared on the bases of European Union Agency for Cybersecurity: Cloud Computing - SME Survey, conducted in 2017 and 2023

Features computed from physical exercises measurements

The data represents time series features from an accelerometer and gyroscope extracted from <a href="../records/10984138">Physical Exercise Measurements with Accelerometer and Gyroscope (zenodo.org)</a>. The data consists of 5 feature sets. Description of feature sets: 1.     RQA features set • "RR" - Recurrence rate • "DET" - Determinism, count recurrence points in diagonal lines of length >= lmin • "RATIO" - DET/RR • "AVG" - average length of diagonal lines of length >= lmin • "MAX" - maximal length of diagonal lines of length >= lmin • "DIV" - Divergence, 1/MAX • "LAM" - Laminarity, VLRP/TR • "TT" - Trapping time, average length of vertical lines of length >= lmin • "MAX_V" - maximal length of vertical lines of length >= lmin • "TR" - Total number of recurrence points • "DLRP" - Recurrence points on the diagonal lines of length of length >= lmin • "DLC" - Count of diagonal lines of length of length >= lmin • "VLRP" - Recurrence points on the vertical lines of length of length >= lmin • "VLC" - Count of vertical lines of length of length >= lmin Was calculated by Chaos01 R package. <a href="https://cran.r-project.org/package=Chaos01">https://CRAN.R-project.org/package=Chaos01</a> The parameters were chosen so that the embedding will create a vector of one value of each axis of the accelerometer/gyroscope measurements. Therefore the used parameters were: <table> <tbody> <tr> <td>Function argument</td> <td>Value</td> </tr> <tr> <td>embedding dimension (dim)</td> <td>3</td> </tr> <tr> <td>embedding lag (lag)</td> <td>time series length</td> </tr> <tr> <td>Minimal length of recurrence line (lmin)</td> <td>20</td> </tr> </tbody> </table> For Chaos01 we change eps argument and calculated it by following formula: <code># Calculate eps for acc and gyro Chaos 01----</code> <code>get_eps <- function(input_data, scale = 1) {</code> <code>  # Calculate eps for acc and gyro</code> <code>  eps_a <-</code> <code>    purrr::map_dbl(input_data$data,</code> <code>                   ~ .x |></code> <code>                     select(Ax, Ay, Az) |></code>                                              <code> as.matrix() |></code>                                              <code> as.vector() |></code>                                               <code> sd()) |></code>                                               <code> mean() * scale</code> <code>  eps_g <-</code> <code>    purrr::map_dbl(input_data$data,</code> <code>                   ~ .x |></code>                                             <code> select(Gx, Gy, Gz) |> </code>                                              <code> as.matrix() </code>                                             <code> as.vector() |></code>                                              <code> sd()) |></code>                                              <code> mean() * scale</code> <code>return(list(a = eps_a, g = eps_g))</code> <code>}</code> "TREND" - Trend of the number of recurrent points depending on the distance to the main diagonal. Was calculated by nonlinearTseries R package.  <a href="https://cran.r-project.org/package=nonlinearTseries">https://CRAN.R-project.org/package=nonlinearTseries</a>   All following feature sets was calculated by Python package https://tsfresh.readthedocs.io/en/latest/index.html   The used dictionary is included in file named tsfresh_autocorr_spectral_features.py.   2.     Autocorrelation features set #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.autocorrelation #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.agg_autocorrelation   3.     Spectral features set #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fft_aggregated #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.approximate_entropy #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fft_coefficient #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fourier_entropy #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.spkt_welch_density #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.ar_coefficient   4.     Mix RQA/Spectral/Autocorrelation features set   5.     Tsfresh all features set #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html   Versions of the software: Python (version 3.8.10) tsfresh = 0.20.2 R (version 4.3.2) Chaos01 = Version 1.2.1 nonlinearTseries = 0.3.0

opencc-by-4.0Aug 2024View details →

Data for "A combined experimental and computational exploration of heteroleptic cis-Pd2L2L'2 nanocages through geometric complementarity"

<div>In the following subdirectories are the input and output of GFN2-xTB and DFT calculations for this publication:</div> <div> </div> <div>chemrxiv: <a href="https://doi.org/10.26434/chemrxiv-2024-s0mmw">https://doi.org/10.26434/chemrxiv-2024-s0mmw</a></div> <div> </div> <div>Published: <a href="https://doi.org/10.1002/chem.202403336">https://doi.org/10.1002/chem.202403336</a></div> <div> </div> <div>Code repository: <a href="https://github.com/andrewtarzia/simple_het_construction">github.com/andrewtarzia/simple_het_construction</a></div> <div>Zenodo code DOI: <a href="https://doi.org/10.5281/zenodo.13649229">10.5281/zenodo.13649229</a></div> <div> </div> <div>data directory:</div> <div> <ul> <li>a spreadsheet with all final energy values and exchange energy calculations</li> <li>CSD Survey data, NPd_survey_data_261119.csv, for Pd centres</li> </ul> </div> <div>Naming convention for file conversions:</div> <div> <ul> <li>l1: 1DBF</li> <li>l2: 1Ph</li> <li>l3: 1Th</li> <li>la: 2DBF</li> <li>lb: 2Py</li> <li>lc: 2Ph</li> <li>ld: 2Th</li> </ul> </div> <div>Structure naming convention: </div> <div> <ul> <li> mX indicates a homoleptic cage with X Pd atoms, cis/trans are the cis/trans heteroleptic cages, respectively</li> </ul> </div> <div>   structures/xtb directory: </div> <div> <ul> <li>contains the structures from GFN2-xTB/ALPB(DMSO) optimisations of stk-generated structures   </li> </ul> </div> <div> </div> <div>structures/opt_*METHOD*_SP_*METHOD*_06-02-2024 directories:</div> <div> <ul> <li>All DFT was run by Victor Posligua</li> <li>contains the input files (.com), output files (.log) and structure files (.xyz/.mol) of DFT optimisations and single point energy calculations with each method</li> <li>When the opt method and SP method are the same, the final structure is included in .mol and .xyz formats</li> <li>However, if opt method is different from the SP method, the final structure is not included because only a single-point energy calculation was run. </li> <li>For example, there are no .mol or .xyz files for 'opt_PBE0_SP_B3LYP_06-02-2024’ since the structure is already in 'opt_PBE0_SP_PBE0_06-02-2024’.</li> <li>you’ll find 8 different folders: <ul> <li>opt_PBE0_SP_PBE0_06-02-2024</li> <li>opt_PBE0_SP_B3LYP_06-02-2024</li> <li>opt_PBE0_SP_B97D3_06-02-2024</li> <li>opt_PBE0_SP_HSE_06-02-2024</li> <li>opt_B3LYP_SP_B3LYP_06-02-2024</li> <li>opt_B97D3_SP_B97D3_06-02-2024</li> <li>opt_HSE_SP_HSE_06-02-2024</li> <li>opt_GFN2-xTB_SP_PBE0_06-02-2024</li> </ul> </li> </ul> </div>