Find research datasets worth reusing

Datasets used in a Transformer network for image inversion of multi-dimensional nonuniform aperture synthesis radiometers

该数据集于 2023 年 11 月在中南大学生成,并通过 matlab 仿真软件进行仿真和收集。主要用于图像重建网络的训练和测试。该数据集由原始场景亮度数据、能见度数据和一维、二维和三维非均匀天线阵列图像重建的能见度函数对应的频域采样点位置数据,以及使用其他一些常规方法进行图像重建获得的亮度数据组成。此外,为了验证所提方法的有效性,在工作频率为 33.5 Ghz 的原型 8 元一维非均匀天线阵列上进行了室内实验,并生成了测量数据集。 具体来说,名为 Tb_in、V2_noise、T3_AAF 和 T2_idft 的四个仿真数据集存储在名为 1d 的 zip 包中。 Tb_IN_1d存储了一维非均匀天线对应的原始场景亮温数据,该数据选自西北工业大学制作的遥感影像场景分类公共数据集。 V2_noise存储了包含各种误差的能见度函数值,主要是通过将原始场景亮温图像输入到运行在接收频率为 33.5 GHz 的非均匀积分孔径辐射计模拟程序中得到的。 T2_idft 和 T3_AAF 分别是使用逆离散傅里叶变换和阵列因子形成方法进行图像重建获得的明亮温度数据。这两组数据都可用于后续的比较实验。 名为 Tb_IN_2d、Tb_out_2d、VS_2d 和 VS_P_2d 的四个数据集存储在名为 2d 的 zip 包中。 Tb_IN_2d内部存储的是二维非均匀天线对应的原始场景亮温数据,该数据选自西北工业大学制作的遥感影像场景分类公共数据集。 VS_2d为二维非均匀天线阵列对应的能见度函数值,主要是将原始场景亮温图像输入到接收频率为 33.5 GHz 的非均匀集成孔径辐射计模拟程序中得到的。 VS_P_2d存储了二维非均匀天线阵列的能见度函数对应的频域采样点位置,该值主要通过计算能见度函数值得到。 Tb_out_2d文件存储了使用传统方法进行图像重建得到的亮温值,该数据也用于后续与所提方法获得的数据的比较实验。 名为 Tb_IN_3d、Tb_out_3d、VS_3d 和 VS_P_3d 的四个数据集存储在名为 3d 的 zip 包中。 Tb_IN_3d内部存储的是 3D 非均匀天线对应的原始场景亮温数据,该数据选自西北工业大学制作的遥感图像场景分类公共数据集。 VS_3d是三维非均匀天线阵列对应的能见度函数值,是将原始场景亮温图像输入到接收频率为 33.5 GHz 的非均匀积分孔径辐射计仿真程序中得到的。 VS_P_3d存储了三维非均匀天线阵列的能见度函数对应的频域采样点位置,该位置是通过计算能见度函数值得到的。 Tb_out_3d文件存储了使用常规方法进行图像重建得到的亮温值,该数据也用于后续与所提方法获得的数据的比较实验。 名为 Array2_R、Array3_R、V2_noise 和 Tb_out 的四个数据集存储在名为 8mm8 channel 的 zip 包中。 存储在 Array2_R 和 Array3_R 中的是系统在不同位置测量的目标点源的相关矩阵,矩阵中元素的值反映了测试点对目标点源的检测能力。根据此相关矩阵,可以计算可见性值。 存储在 V2_noise 内部的是包含各种误差的测量可见性函数的样本。对这些数据进行实验主要是为了验证所提方法的有效性。 存储在 Tb_out 中的是使用测试数据集获得的亮温结果数据,用于测试训练的网络。

Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"

Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading". Name/institution/contact information: Dr. Michal Barto&scaron;ák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz. Date of data collection: The data were collected between 2021 and 2024. File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt." See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.

opencc-by-4.0Jun 2024View details →

Intermediate-mass black hole binary parameter estimation with next-generation ground-based detector networks

Reading the data Each of the alldetectors and CE40CE20ET .zip file contains a folder with .pickle files. Each of these .pickle files corresponds to a point on the respective grid. The files contain dictionaries structured as follows: <ul> <li>In the skyareas and m1m2grid folders, each dictionary stores the source-frame component masses, redshift, angular parameters, network SNR, network covariance matrix and 90% sky area.</li> <li>In the Mzgrid folders, each dictionary stores source-frame total mass, redshift, angular parameters, individual SNR and Fisher matrix for each detector in the network.</li> </ul> The grids, detector network and parameters used to perform the Fisher calculations are described in the companion paper. The converted_errors folder contains network SNR, 1-sigma errors on source-frame masses, redshift, 90% sky areas for all the networks considered in the companion paper. These quantities are all angle-averaged. The full_pe .zip files cointain data comparing our Fisher results with full Bayesian PE runs for a few selected cases, as well as Jupyter notebooks to read those results (see Appendix B of the companion paper).

Public Procurement: a systemic approach to sustainability and technology – SAPIENS Network Webinar Series

On the 5th of June 2024, the SAPIENS Network Early Stage Researchers <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/sap-in-construction/">Alexandru Buftic</a> (Babeș-Bolyai University), <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/delivering-sap-through-collaboration/">Felippe Vilaça</a> (University of Gävle), <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/measuring-lcc/">Haitham Ghaida</a> (Hasselt University) and <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/industry-4-for-sp/">Nadia Sava</a> (Babeș-Bolyai University) delivered a scientific webinar on the topic of Public Procurement: a systemic approach to sustainability and technology. The webinar was moderated by <a href="https://sapiensnetwork.eu/dacian-dragos/">Dacian C. Dragoș</a>, Professor of Administrative and European Law with the Public Administration and Management Department at Babes-Bolyai University, <a href="https://sapiensnetwork.eu/sebastien-lizin/">Sebastien Lizin</a>, Assistant Professor at the Faculty of Business Economics and vice-chair of the Department of Economics at Hasselt University and <a href="https://www.linkedin.com/in/roxana-vornicu-phd-49470347/">Roxana Vornicu</a>, PhD, Senior Lecturer at King´s College London, Centre of Construction Law, and Managing Partner of Sirbu & Vornicu Law. The webinar tackled various current challenges related to sustainability in public procurement. The presentations covered different topics, united by the concepts of sustainability and digitalisation in public procurement. The speakers highlighed the value of taking a systemic approach to sustainability in public procurement, the role of monitoring green and sustainable considerations in the process, the importance of EU legislation in the uptake of sustainability as well as effect of life cycle assessment reserch for sustainable building design.

Datasets used in "Assesing the quality of random number generators through neural networks", Machine Learning: Science and Technology 5 (2024) 025072

Datasets corresponding to the bits generated by different random number generators used in J. L. Crespo et al, Machine Learning: Science and Technology 5 (2024) 025072. VCSEL_QRNG_postprocessed_bits.txt: postprocessed bits from the random generator based on gain-switching of VCSELs  EC_LCG_bits.txt:  bits from the linear congruential generator on elliptic curves LCG_32_bits.txt:: bits from the linear congruential generator with 32 bits VCSEL_QRNG_raw_bits.txt: raw bits from the random generator based on gain-switching of VCSELs

NYC Bike Sharing Network: Time-Series Enhanced Nodes and Edges Dataset

This dataset presents a comprehensive graph representation of the New York City Bike Sharing system, structured with nodes representing stations and edges delineating trips between these stations. The dataset is distinctive in integrating dynamic properties as time series data, which are meticulously updated using historical records (csv files) and live data feeds (gbfs files) provided by<a href="https://citibikenyc.com/system-data" target="_blank" rel="noopener"> NYC Bike sharing system</a>.  <ul> <li> Nodes: <ul> <li>Source: Data is collected from the New York City Bike Station Information API.</li> <li>Attributes: <ul> <li>ID: Unique identifier for each station.</li> <li>Name: Name of the station.</li> <li>Capacity: Number of bikes the station can accommodate.</li> <li>Short ID: A condensed identifier used internally.</li> </ul> </li> <li>Time Series Data: <ul> <li>Updated every 5 minutes from the Station Status API.</li> <li>Captures changes in bike availability, recording values only when they differ from previous data points.</li> </ul> </li> </ul> </li> <li> Edges: <ul> <li>Source: Compiled from trip data provided in CSV format specific to NYC Bike Sharing.</li> <li>Attributes: <ul> <li>Trip Counter: Total number of trips recorded.</li> <li>Bike Type Counter: Counts trips made with electric versus classic bikes.</li> <li>Trip Type Counter: Separates trips made by members versus casual riders.</li> <li>Active Trips Tracker: Tracks the number of active trips at any given moment.</li> </ul> </li> <li>Aggregation: Trip data between identical start and end points, in the same direction, are aggregated into a single edge, with time-series tracking the frequency of these trips.</li> </ul> </li> </ul>

opencc-by-4.0Jul 2024View details →

Italian TEN-T road network and Hydrogen Refueling Station nodes

This folder contains three ESRI-shapefiles (representing respectively the network edges, the network nodes and the resulting positions found with an optimization framework to site hydrogen refueling stations (HRS) in Italy) and a CSV-file used to characterize the vehicle traffic flow on each origin-destination path on the represented network.  network_nodes.shp There are different type of nodes: <ul> <li>NUTS-3 nodes, being the possible origin or destination nodes. They represent the weighted centroid of the NUTS-3 region they refer to. </li> <li>Fuel areas centroids: obtained as the centroids of the dissolved buffer areas of the conventional fuel stations on the road network.</li> <li>Intersection nodes: manually added on the network nodes intersections.</li> <li>Main road entrances.</li> <li>Port nodes.</li> </ul> network_edges.shp The edges represent the Trans-European Network (TEN-T) core and comprehensive layers. Some edges are manually added: <ul> <li>Edges connecting NUTS-3 nodes to the main road network;</li> <li>Ferry edges, which have "road length" equal to 0, as they do not imply fuel consumption for road trucks.</li> </ul> italian_paths_OD_NUTS3_vR1.csv Containing O-D paths thorugh the TEN-T road network from each origin to each destination. Each path is represented as an ordered succession of nodes and of their positions with respect to the origin node. For each path an estimation of the tons and trucks expected on it by 2030 is given.  HRS_nodes.shp HRS nodes position and specifications obtained with an optimization framework to serve the national freight transport system if vehicles paths with dist_ROAD > 100 km, Traffic_flow_trucks_2030 >= 2600, initial vehicle range = 300 and maximum vehicle range = 600 km are considered. The fuel cell hydrogen electric vehicles share over the total number of circulating vehicles is assumed equal to 10%.

Dog neuroimaging data from: Action observation reveals a network with divergent temporal and parietal cortex engagement in dogs compared to humans

Action observation is a fundamental pillar of social cognition. Neuroimaging research has revealed a human and non-human primate action observation network (AON) encompassing fronto-temporo-parietal areas with links to the species’ imitation tendencies and relative lobe expansion. Dogs (Canis familiaris) have good action perception and imitation skills and a less expanded parietal than temporal cortex, but their AON remains unexplored. We conducted a functional MRI study with 28 dogs and 40 humans and found functionally analogous involvement of somatosensory and temporal brain areas of both species’ AONs and responses to transitive and intransitive action observation in line with their imitative skills. Employing a functional localizer, we also identified functionally analogous agent-responsive areas within both species’ AONs. However, activation and task-based functional connectivity measures suggested significantly less parietal cortex involvement in dogs than in humans. These findings advance our understanding of the neural bases of action understanding and the convergent evolution of social cognition, with analogies and differences resulting from similar social environments and divergent brain expansion, respectively. This data set contains: <ul> <li>raw functional neuroimaging data of N = 28 dogs</li> <li>structural scans of the same dogs including brain masks & skull-stripped versions</li> <li>eventfiles containing the condition names, onsets and durations for each dog and task run</li> </ul> Data of the comparative human neuroimaging sample will be made available by the first author upon reasonable request. Please also visit our Open Science Framework project site for group-level imaging data of both species (https://osf.io/z479k/).

Character Networks of 'A Song of Ice and Fire' Adaptations Across Media

Description. This work aims at comparing three crossmedia adaptations of the same story: George R. R. Martin’s A Song of Ice an Fire original novels, the comics directly adapted from this series, and the Game of Throne TV show. To perform our analysis, we used two representations of these stories: textual vs. graph-based. The textual one corresponds to summaries, whereas the graphs are character networks, representing the interactions between characters. We performed a descriptive analysis of the three adaptations, and tackle a task consisting in automatically aligning the three stories. This dataset contains the input files used by our scripts (various versions of the character networks) as well as the files produced during the processing. <ul> <li>The archives starting with `nets_` correspond to the network files used as input during processing.</li> <li>Those starting with `out_` are the files (mainly stats and plots) produced by our scripts.</li> </ul> The networks are available in several versions, using the `graphml` format: <ul> <li>Folder `cumul`: cumulative networks, i.e. dynamic networks that grow from the beginning to the end of the story. The very last network corresponds to the static network, i.e. the network representing the whole timeline.</li> <li>Folder `instant`: also a dynamic network, but this time each instant is not considered as an increment. Each `graphml` file only focuses on a specific temporal subdivision of the story.</li> </ul> The temporal subdivisions depend on the considered medium: <ul> <li>Novels: only chapter.</li> <li>Comics: chapter or scene.</li> <li>TV show: episode, block or scene.</li> </ul> TV show blocks were defined ad hoc to get a subdivision larger than a scene but smaller than a whole episode. We experimented with two versions: blocs based on the location persistence vs. character similarity. Software. The scripts are publicly available online: <a href="https://github.com/CompNet/Sachan">https://github.com/CompNet/Sachan</a> References. This work was published in the following article: <ul> <li> <div> <div>A. Amalvy, M. Janickyj, S. Mannion, P. MacCarron, and V. Labatut, “Interconnected Kingdoms: Comparing ‘A Song of Ice and Fire ́Crossmedia Adaptations Using Complex Networks,” Social Network Analysis and Mining, vol. 14, p. 199, 2024. DOI: <a href="https://doi.org/10.1007/s13278-024-01365-z">10.1007/s13278-024-01365-z</a> ⟨<a href="https://hal.science/hal-04722579">hal-04722579</a>⟩</div> </div> </li> </ul> Citation. If you use these scripts, please cite the above article: <pre><code>@Article{Amalvy2024c, author = {Amalvy, Arthur and Janickyj, Madeleine and Mannion, Shane and MacCarron, Pádraig and Labatut, Vincent}, title = {Interconnected Kingdoms: Comparing `A Song of Ice and Fire' Crossmedia Adaptations Using Complex Networks}, journal = {Social Network Analysis and Mining}, year = {2024}, volume = {14}, pages = {199}, doi = {10.1007/s13278-024-01365-z}, }</code> </pre>

A combined NMR and Deep Neural Network approach for enhancing the spectral resolution of aromatic side chains in proteins

Nuclear magnetic resonance (NMR) spectroscopy has become an important technique in structural biology for characterising the structure, dynamics and interactions of macromolecules. While a plethora of NMR methods are now available to inform on backbone and methyl-bearing side-chains of proteins, a characterisation of aromatic side chains is more challenging and often requires specific labelling or 13C-detection. Here we present a deep neural network (DNN) named FID-Net-2, which transforms NMR spectra recorded on simple uniformly 13C labelled samples to yield high-quality 1H-13C correlation spectra of the aromatic side chains. Key to the success of the DNN is the design of a complementary set of NMR experiments that produce spectra with unique features to aid the DNN produce high-resolution aromatic 1H-13C correlation spectra with accurate intensities. The reconstructed spectra can be used for quantitative purposes as FID-Net-2 predicts uncertainties in the resulting spectra. We have validated the new methodology experimentally on protein samples ranging from 7 to 40 kDa in size. We demonstrate that the method can accurately reconstruct high resolution two-dimensional aromatic 1H-13C correlation maps, high resolution three-dimensional aromatic-methyl NOESY spectra to facilitate aromatic 1H-13C assignments, and that the intensities of peaks from the reconstructed aromatic 1H-13C correlation maps can be used to quantitatively characterise the kinetics of protein folding.  More generally, we believe that this strategy of devising new NMR experiments specifically for analysis using customised DNNs represents a substantial advance that will have a major impact on the study of molecules using NMR in the years to come.

opencc-by-4.0Aug 2024View details →

Dataset for publication: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform

This dataset provides the necessary data to get the images and results shown in the paper "Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform".  Source Data Raw.zip has the entire data set used to generate the images. Source Data.zip contains the processed data  from "Source Data Raw.zip".    Files with extension .dream3d are accompained by a file with extension .xdmf. This files can be opened with Paraview. And their data can be accesible using python or matlab. For more information contact Proffesor Somnath Ghosh at Johns Hopkins University, Civil and Systems Engineering Department.

The efficacy of hemoglobin spray in wound management: a systematic review and network meta-analysis of comparative studies

PRISMA flow

Trade network dynamics and alien plant pest introductions: A global analysis

This is a supplement to "Trade network dynamics and alien plant pest introductions: A global analysis", Diversity and Distributions (<a href="https://doi.org/10.1111/ddi.13963">https://doi.org/10.1111/ddi.13963</a>). These data were partially derived from the following resources available in the public domain: <a href="http://www.cepii.fr/CEPII/fr/welcome.asp">www.cepii.fr/CEPII/fr/welcome.asp</a>; Seebens et al. (2017) (dataset DOI: <a href="https://doi.org/10.12761/sgn.2016.01.022">https://doi.org/10.12761/sgn.2016.01.022</a>); Fenn-Moltu et al. 2022 (dataset DOI: <a href="https://doi.org/10.5061/dryad.8931zcrrq">https://doi.org/10.5061/dryad.8931zcrrq</a>).

Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network - CAMS data for experiments

Contains data generated by the CAMS model (during a global reanalysis), used to train and evaluate the models presented article "Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network" - DOI of this article will be provided as soon as it is available. This data can be downloaded from the Copernicus Atmospheric Data Store (https://ads.atmosphere.copernicus.eu/#!/home), and is also hosted by the ICARE Data and Services Center (https://www.icare.univ-lille.fr/). This dataset only contains the specific data collection used for the experiments presented in aforementioned article. It is only a portion of the data available from these two websites.

The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK"

The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK". Data collector: Runda Zheng

opencc-zeroJan 2024View details →

Linked collectors and determiners for: New Zealand Biodiversity Recording Network.

Natural history specimen data linked to collectors and determiners held within, "New Zealand Biodiversity Recording Network". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/d3484430-8876-11dd-986c-b8a03c50a862">https://bionomia.net/dataset/d3484430-8876-11dd-986c-b8a03c50a862</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/d3484430-8876-11dd-986c-b8a03c50a862">https://gbif.org/dataset/d3484430-8876-11dd-986c-b8a03c50a862</a>. Formatted as a Frictionless Data package.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Time series of zooplankton abundance in the Florida Keys, collected by the South Florida Program (NOAA/AOML) and the Marine Biodiversity Observation Network (MBON).

Natural history specimen data linked to collectors and determiners held within, "Time series of zooplankton abundance in the Florida Keys, collected by the South Florida Program (NOAA/AOML) and the Marine Biodiversity Observation Network (MBON)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/ec0d2fe8-21b1-4ab1-8b91-67873e8ca912">https://bionomia.net/dataset/ec0d2fe8-21b1-4ab1-8b91-67873e8ca912</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/ec0d2fe8-21b1-4ab1-8b91-67873e8ca912">https://gbif.org/dataset/ec0d2fe8-21b1-4ab1-8b91-67873e8ca912</a>. Formatted as a Frictionless Data package.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Freshwater samples in MZNA-INV-FRW: Macroinvertebrate samples from the water quality monitoring network along the Ebro Basin.

Natural history specimen data linked to collectors and determiners held within, "Freshwater samples in MZNA-INV-FRW: Macroinvertebrate samples from the water quality monitoring network along the Ebro Basin". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/dfddad59-5bc5-4e35-8b35-334eed43bba9">https://bionomia.net/dataset/dfddad59-5bc5-4e35-8b35-334eed43bba9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/dfddad59-5bc5-4e35-8b35-334eed43bba9">https://gbif.org/dataset/dfddad59-5bc5-4e35-8b35-334eed43bba9</a>. Formatted as a Frictionless Data package.

Data from: Personality and social network structure influence cooperative dynamics across canid species

In canids, cooperative behaviour occurs in many scenarios. However, most studies focus on single-species observations, not accounting for variation beyond the species-level. We modelled cooperative behaviour using Eigenvalue centrality as well as boldness combined with biological traits such as kinship, sex, age, mating system and foraging strategy in multiple canid species with Bayesian inference, Tukey HSD and distance correlation.

Datasets for Manuscript: Neural-Network-Assisted Detection of Superconducting Topological Semimetals

This file contains datasets for an original machine-learning-approach that we developed for the identification of superconducting topological semimetals.