cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8128597
title: Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation

description: A highly granular calorimeter, similar to CALICE is simulated in Geant4. The calorimeters showers are represented as either images or point clouds. Two state-of-the-art score based diffusion models, on image based and the other point cloud based, are trained on the same set of calorimeter simulations and directly compared to each other.<br> <br> These files include the original Geant4 simulation, an intermediate form of the data required for training, and the samples generated by the image and point cloud models.
DOI: 10.21227/b8gk-b727
title: Phishing Website Detection Via Shuffle Net V2 And Exemplar Pyramid Deep Feature Extraction Network (EPDFE)

description: Customers are moving from traditional retail to online shopping due to the Internet's tremendous growth. Instead of robbing banks or shops anymore, modern criminals use a variety of unique strategies to locate their victims online. Due to the invisibility of the Internet, hackers have developed new strategies, such as phishing, to trick victims into entering sensitive information, such as usernames, passwords, and account IDs, on fake websites. Detecting if a web page is accurate or phishing is a very challenging task because of the semantics-based assault structure of phishing, which primarily preys on the vulnerabilities of computer users. Software developers frequently create new anti-phishing innovations, however, even when these tools employ blacklists, heuristics, visual and machine learning-based techniques, they can't stop all phishing attacks. Several ML based phishing detection methods have been proposed due to the development and use of ML technology. As a result of specific solutions depending on features extracted by rules and others needing to rely on other services, the prediction service may encounter instability and time-consuming issues. This paper provides the deep learning-based shuffle Net V2 approach for phishing website detection, which is optimized by an enhanced crow search optimization algorithm (ECSO). The feature extractor receives a list of website URLs and uses an exemplar pyramid deep feature extraction network (EPDFE) to extract the crucial features. The collected features are given to the Enhanced rat swarm optimization (ERSO), which chooses the features. The proposed method performs well in actual phishing discovery, indicating its great applicability in performances and enhancing detection outcomes while significantly reducing execution time.
DOI: 10.5281/zenodo.8129501
title: Glacial lake inventory over Bhutan Himalaya in 2021

description: The dataset include shape files of glacial lake inventory 2021 covering Bhutan Himalaya delineated by deep learning from multi-source satellite images with manual refinement
DOI: 10.5281/zenodo.8129500
title: Glacial lake inventory over Bhutan Himalaya in 2021

description: The dataset include shape files of glacial lake inventory 2021 covering Bhutan Himalaya delineated by deep learning from multi-source satellite images with manual refinement
DOI: 10.11583/dtu.17033267
title: Data associated to the publication "Including the power regulation strategy in aerodynamic optimization of wind turbines for increased design freedom"

description: This dataset contains wind turbine optimization results related to the publication "Iori J, McWilliam MK, Stolpe M. Including the power regulation strategy in aerodynamic optimization of wind turbines for increased design freedom. Wind Energy. 2022. https://doi.org/10.1002/we.2769 " <br> There are 3 types of optimization results in this dataset: - Type A: AEP maximization - Type B: AEP maximization with thrust penalty - Type C: AEP maximization with thrust penalty and constraints on the rotational speed and thrust. <br> The detailed description of each optimization type can be found in the related paper. <br> For the optimization type A, the same optimization is done for 5 different number of wind speeds: 11, 22, 33, 44 and 88. In addition, the optimization is done in 3 possible ways: using a nested formulation, using a one-level formulation with analytic gradients, and using a one-level formulation with finite-difference gradients. The prefix of each file describes the formulation and the number of wind speed used. <br> For the optimization type B, the optimization is ran for the one-level and nested formulation. <br> For the optimization type C, the optimization is ran for the one-level formulation only. The characteristics of the initial design are also reported. <br> <br> There are 5 types of file, depending on the postfix: - XXX_planform.dat : reports the planform design (radius, chord, twist, relative thickness) - XXX_reg_strategy.dat: reports the regulation strategy and the power and thrust evolution in the operational range - XXX_cp_ct.dat: reports the data to draw the power and thrust coefficient surfaces for the design - XXX_opt.dat: reports the optimization characteristics at each iteration. It corresponds to the output given by IPOPT. Note that the objective function is the AEP (with or without thrust penalty) with a scaling factor. - XXX_vr.dat: reports the value of the rated wind speed. This only relevant for the optimization type A, and the optimization type B when the nested formulation is used.
DOI: 10.11583/dtu.17033267.v1
title: Data associated to the publication "Including the power regulation strategy in aerodynamic optimization of wind turbines for increased design freedom"

description: This dataset contains wind turbine optimization results related to the publication "Iori J, McWilliam MK, Stolpe M. Including the power regulation strategy in aerodynamic optimization of wind turbines for increased design freedom. Wind Energy. 2022. https://doi.org/10.1002/we.2769 " <br> There are 3 types of optimization results in this dataset: - Type A: AEP maximization - Type B: AEP maximization with thrust penalty - Type C: AEP maximization with thrust penalty and constraints on the rotational speed and thrust. <br> The detailed description of each optimization type can be found in the related paper. <br> For the optimization type A, the same optimization is done for 5 different number of wind speeds: 11, 22, 33, 44 and 88. In addition, the optimization is done in 3 possible ways: using a nested formulation, using a one-level formulation with analytic gradients, and using a one-level formulation with finite-difference gradients. The prefix of each file describes the formulation and the number of wind speed used. <br> For the optimization type B, the optimization is ran for the one-level and nested formulation. <br> For the optimization type C, the optimization is ran for the one-level formulation only. The characteristics of the initial design are also reported. <br> <br> There are 5 types of file, depending on the postfix: - XXX_planform.dat : reports the planform design (radius, chord, twist, relative thickness) - XXX_reg_strategy.dat: reports the regulation strategy and the power and thrust evolution in the operational range - XXX_cp_ct.dat: reports the data to draw the power and thrust coefficient surfaces for the design - XXX_opt.dat: reports the optimization characteristics at each iteration. It corresponds to the output given by IPOPT. Note that the objective function is the AEP (with or without thrust penalty) with a scaling factor. - XXX_vr.dat: reports the value of the rated wind speed. This only relevant for the optimization type A, and the optimization type B when the nested formulation is used.
DOI: 10.5281/zenodo.8129515
title: Glacial lake inventory over Boshula in 2021

description: This dataset include the shape files of glacial lake inventory over Boshula mountain ranges in 2021 delineated by DeepLabv3+ from multi-source satellite images with manual refinement.
DOI: 10.5281/zenodo.8129514
title: Glacial lake inventory over Boshula in 2021

description: This dataset include the shape files of glacial lake inventory over Boshula mountain ranges in 2021 delineated by DeepLabv3+ from multi-source satellite images with manual refinement.
DOI: 10.57745/waqz0m
title: Attached Repository - Without Raw data.zip

description: This file contains the attached repository for the paper "Half of global agricultural soil phosphorus fertility derived from anthropogenic sources" published in the Journal Nature Geoscience (https://doi.org/10.1038/s41561-022-01092-0). It contains 4 repositories. 1. Figures. with all the figures in the main text and supplementary information. 2. Inputs Data. To compute the input data for the model. 3. Scripts. Contains all the python scripts to run the model and tests. 4.Results. Contains outputs from the model.
DOI: 10.6084/m9.figshare.22933073
title: A deep learning analysis of <i>Drosophila</i> body kinematics during magnetically tethered flight

description: Flying <i>Drosophila</i> rely on their vision to detect visual objects and adjust their flight course. Despite their robust fixation on a dark, vertical bar, our understanding of the underlying visuomotor neural circuits remains limited, in part due to difficulties in analyzing detailed body kinematics in a sensitive behavioral assay. In this study, we observed the body kinematics of flying <i>Drosophila</i> using a magnetically tethered flight assay, in which flies are free to rotate around their yaw axis, enabling naturalistic visual and proprioceptive feedback. Additionally, we used deep learning-based video analyses to characterize the kinematics of multiple body parts in flying animals. By applying this pipeline of behavioral experiments and analyses, we characterized the detailed body kinematics during rapid flight turns (or saccades) in two different visual conditions: spontaneous flight saccades under static screen and bar-fixating saccades while tracking a rotating bar. We found that both types of saccades involved movements of multiple body parts and that the overall dynamics were comparable. Our study highlights the importance of sensitive behavioral assays and analysis tools for characterizing complex visual behaviors.
DOI: 10.6084/m9.figshare.22933073.v1
title: A deep learning analysis of <i>Drosophila</i> body kinematics during magnetically tethered flight

description: Flying <i>Drosophila</i> rely on their vision to detect visual objects and adjust their flight course. Despite their robust fixation on a dark, vertical bar, our understanding of the underlying visuomotor neural circuits remains limited, in part due to difficulties in analyzing detailed body kinematics in a sensitive behavioral assay. In this study, we observed the body kinematics of flying <i>Drosophila</i> using a magnetically tethered flight assay, in which flies are free to rotate around their yaw axis, enabling naturalistic visual and proprioceptive feedback. Additionally, we used deep learning-based video analyses to characterize the kinematics of multiple body parts in flying animals. By applying this pipeline of behavioral experiments and analyses, we characterized the detailed body kinematics during rapid flight turns (or saccades) in two different visual conditions: spontaneous flight saccades under static screen and bar-fixating saccades while tracking a rotating bar. We found that both types of saccades involved movements of multiple body parts and that the overall dynamics were comparable. Our study highlights the importance of sensitive behavioral assays and analysis tools for characterizing complex visual behaviors.
DOI: 10.34934/dvn/8exlam
title: EPRES_BE2019 Service Voucher

description: This data are a subsample of the Employment Precariousness Survey Belgium (EPRES-BE) data from 2019. The sample is composed of domestic workers employed within the Service Voucher System and contains information on well-being and work quality.
DOI: 10.15129/ebd1459f-9d20-4806-b384-a2f116d5b61e
title: Data for "Three Oxidative Addition Routes of Alkali Metal Aluminyls to Dihydridoaluminates and Reactivity with CO2"

description: The dataset contains the following 3 files: 1. X-Ray data (.cif file) 2. Checkcif (.docx file) 3. IR Data (zip file) 4. NMR Data (zip file) The dataset relates to research reported in the manuscript in the title, carried out as part of the Leverhulme Trust funded (award no: RPG-2019-264) project, in collaboration with Prof. Martyn Coles group in Victoria University of Wellington, New Zealand (Funding for the Martyn Coles group: The Royal Society Te Apārangi (Grant Number: MFP-VUW2020) and the MacDiarmid Institute for Advanced Materials and Nanotechnology). Please note that the NMR data requires a specialist program such as Topspin or Mestrelab, and the X-Ray data (.cif) requires a program such as Olex or Mercury to view. The IR data can be viewed using Origin software.
DOI: 10.25919/9d98-0951
title: Parkes observations for project P1176 semester 2023APRS_02

description: Pulsars and their winds are among nature's most powerful particle accelerators, producing particles with energies up to a few PeVatrons. In addition to high-energy radiation from the magnetosphere, the rotational energy of a neutron star is carried away in the form of magnetized ultra-relativistic pulsar winds. Investigation of pulsar wind nebula (PWN) offers an excellent chance to determine the interaction details of relativistic flows with their surroundings that may never be possible in other classes of source. Our recent analyses of ASKAP observations of Galactic Plane uncovered previously known X-ray PWN CXOU J163802.6-471358 (nicknamed as Potoroo). Recently, we have successfully detected a pulsar candidate associated with Potoroo with Parkes telescope. It is detected at 3 GHz with a dispersion measure of DM = 1553 pc cm-3 and a rotation period of 65.74 ms. Here we propose timing observations for this pulsar with the Parkes Ultra-Wideband Low (UWL) receiver. Through pulsar timing, we will be able to obtain precise measurements of the pulsar position and proper motions, and the spin-down of the pulsar which enables us to independently estimate the age of the system. Long-term timing will also enable us to measure the parallax of the system and search for pulsation at high energies (i.e. X-ray and gamma-ray)
DOI: 10.5281/zenodo.7957234
title: Data underlying the article: 3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors

description: This repository contains the datasets and results supporting the conclusions of the manuscript "<strong>3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors</strong>". Publicly available data is not included in this repository. The source code to generate the results gathered here can be found on GitHub (https://github.com/CDDLeiden/3ddpd).
DOI: 10.5281/zenodo.7957235
title: Data underlying the article: 3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors

description: This repository contains the datasets and results supporting the conclusions of the manuscript "<strong>3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors</strong>". Publicly available data is not included in this repository. The source code to generate the results gathered here can be found on GitHub (https://github.com/CDDLeiden/3ddpd).
DOI: 10.5281/zenodo.8129614
title: Data underlying the article: 3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors

description: This repository contains the datasets and results supporting the conclusions of the manuscript "<strong>3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors</strong>". Publicly available data is not included in this repository. The source code to generate the results gathered here can be found on GitHub (https://github.com/CDDLeiden/3ddpd).
DOI: 10.11583/dtu.10289417
title: E. coli chemical tolerance raw resequencing reads

description: Raw genomic resequencing data (fastq) for all the 224 resequenced strains.
DOI: 10.11583/dtu.10289417.v1
title: E. coli chemical tolerance raw resequencing reads

description: Raw genomic resequencing data (fastq) for all the 224 resequenced strains.
DOI: 10.6084/m9.figshare.23112776
title: FHP Model - SAI

description: DESCRIPTION There are 2 files: -"Sintax1.txt" to run in Stata and obtain the simulated data and the regressions -"1.dta" The specific random dataset I obtained
DOI: 10.6084/m9.figshare.23112776.v2
title: FHP Model - SAI

description: DESCRIPTION There are 2 files: -"Sintax1.txt" to run in Stata and obtain the simulated data and the regressions -"1.dta" The specific random dataset I obtained
DOI: 10.11583/dtu.16794500
title: QM9 Charge Densities and Energies Calculated with VASP

description: QM9 molecules calculated with VASP using Atomic Simulation Environment with the following parameters: Vasp(xc='PBE', istart=0, algo='Normal', icharg=2, nelm=180, ispin=1, nelmdl=6, isym=0, lcorr=True, potim=0.1, nelmin=5, kpts=[1,1,1], ismear=0, ediff=0.1E-05, sigma=0.1, nsw=0, ldiag=True, lreal='Auto', lwave=False, lcharg=True, encut=400)<br> The resulting CHGCAR files have been compressed with lz4 compression and packed in non-compressed tar archives with up to 1000 structures in each. <br> The datasplits json files contain the indices (0-index) of the train, validation and test sets used in the paper "Graph neural networks for fast electron density estimation of molecules, liquids, and solids" <br> The QM9 molecule structures were obtained from https://doi.org/10.6084/m9.figshare.c.978904.v5
DOI: 10.6084/m9.figshare.19778566
title: NEW1

description: DATA
DOI: 10.6084/m9.figshare.19778566.v3
title: NEW1

description: DATA
DOI: 10.6084/m9.figshare.19778566.v2
title: A cautionary note on the use of SAI - 2022 May

description: This is the data created with a Monte Carlo simulation (1.dta) and the sintax (Sintax1.txt) you can run in Stata in order to get similar results as mine.
DOI: 10.11583/dtu.8143352
title: Exercise3.mat

description: Velocity measurements in turbulent air jet flow. For full description, see Section 10.6 (Exercise 3: Statistical, correlation, and spectral analysis of turbulent air jet flow) in:<br>Sumer, B.M. and Fuhrman, D.R. (2020) Turbulence in Coastal and Civil Engineering. World Scientific. DOI: 10.1142/10829<br>The data is provided as a MAT-file, intended to be accessed in the Matlab environment.<br>
DOI: 10.11583/dtu.8143352.v1
title: Exercise3.mat

description: Velocity measurements in turbulent air jet flow. For full description, see Section 10.6 (Exercise 3: Statistical, correlation, and spectral analysis of turbulent air jet flow) in:<br>Sumer, B.M. and Fuhrman, D.R. (2020) Turbulence in Coastal and Civil Engineering. World Scientific. DOI: 10.1142/10829<br>The data is provided as a MAT-file, intended to be accessed in the Matlab environment.<br>
DOI: 10.6084/m9.figshare.17000071.v6
title: Bankuptcy

description: This dataset includes stata sintax: Sintax of November 22 version.docx stata data: FILING BBDD2.dta
DOI: 10.6084/m9.figshare.16963954
title: New2

description: New data 2024
DOI: 10.6084/m9.figshare.16963954.v2
title: New2

description: New data 2024
DOI: 10.6084/m9.figshare.16963954.v1
title: Journal of Mathematics: A cautionary note on the use of Semi Accounting Identity-based models

description: DESCRIPTION0 - README1 - STATA SINTAXIS to run in Stata and obtain the simulated data and the regressions2 - DATASET The specific random dataset I obtained
DOI: 10.6084/m9.figshare.23652606
title: Normative Reference Values, Determinants and Regression Equations for the Incremental Shuttle Walk Test (ISWT) in Healthy Asian Population Aged 21 to 80 Years.pdf

description: This is the
DOI: 10.6084/m9.figshare.23652606.v1
title: Normative Reerence Values, Determinants and Regression Equations for the Incremental Shuttle Walk Test (ISWT) in Healthy Asian Population Aged 21 to 80 Years.pdf

description: This is the
DOI: 10.11583/dtu.13395953.v1
title: Dataset and input files for "Evaluation of idealized large-eddy simulations performed with the Weather Research and Forecasting model using turbulence measurements from a 250-m meteorological mast"

description: Here you find the observational and simulated data, as well as the input files used for the WRF simulations presented in the manuscript "Evaluation of idealized large-eddy simulations performed with the Weather Research and Forecasting model using turbulence measurements from a 250-m meteorological mast"<br>The list of files are:<br>namelist_XXXX: the namelist.input files for the WRF simulationsinput_sounding_XXXX: the input sounding files used to initialize the simulationsXXXX_YYYY_table: comma separated values of the mean and standard deviation of a number of variables to reproduce the figures in the manuscript XXXX: atmospheric stability regime (unstable, neutral, stable)YYYY: observed or simulated data
DOI: 10.6084/m9.figshare.13567886
title: New3

description: New2025
DOI: 10.6084/m9.figshare.13567886.v2
title: New3

description: New2025
DOI: 10.6084/m9.figshare.13567886.v1
title: Investment-Cash Flow Sensitivities Are Very Probably Not Valid Measures of Financing Constraints: on the Semi Accounting Identities Problem

description: DESCRIPTION0 - README1 - STATA SINTAXIS to run in Stata and obtain the simulated data and the regressions2 - DATASET The specific random dataset I obtained
DOI: 10.14457/tu.the.2022.425
title: การศึกษาความเป็นไปได้ของการพัฒนาโครงการบริเวณถนนศรีนครินทร์ ซอยศรีด่าน 13 จังหวัดสมุทรปราการ

description: โครงการโรงแรม 107 โฮเทล แอนด์ เรสซิเดนซ์ ที่ตั้งโครงการอยู่บนถนนศรีนครินทร์ ภายในซอยสุขุมวิท107 แบริ่ง 68 และ ซอยศรีด่าน 13 ตำบลสำโรงเหนือ อำเภอเมือง จังหวัดสมุทรปราการ ห่างจากถนนศรีนครินทร์ 170 เมตร ขนาดพื้นที่ 0-2-99 ไร่ (1,196 ตารางเมตร) มีลักษณะเป็นรูปทรงสี่เหลี่ยมผืนผ้าในเบื้องต้นของการศึกษาความเป็นไปได้ ผู้จัดทำได้กลั่นกรองแนวคิด โดยเริ่มจากการวิเคราะห์ความเป็นไปได้ทางกฎหมาย ความเป็นไปได้ทางกายภาพ ความเป็นไปได้ทางการตลาด และความเป็นไปได้ทางการเงินเบื้องต้น พบว่าประเภทอสังหาริมทรัพย์ที่มีความเป็นไปได้มากที่สุดใน การพัฒนาบนที่ดินนี้ คือ อาคารอยู่อาศัยรวมชนิด เช่ารายวันประเภทโรงแรม ขนาดกลางแบบราคาประหยัด เนื่องจากพื้นที่ตั้งโครงการ มีระยะไม่ห่างจากการคมนาคม ถนนสายหลักสุขุมวิท 107 ถนนศรีนครินทร์ และสถานีรถไฟฟ้าสายสีเหลือง ประกอบกับความต้องการทางตลาด มีกลุ่มลูกค้ารองรับ มีอัตราเช่าในตลาดที่แข่งขันได้ โดยข้อกำหนดจากความเป็นไปได้ทางด้านผังกฎหมาย และกายภาพ พบว่าความกว้างถนนซึ่งเชื่อมต่อกับที่ดิน มีขนาดกว้าง 6 เมตร ทำให้มีความเป็นไปได้ที่จะสามารถสร้างอาคารขนาดใหญ่ที่มีพื้นที่ได้ถึง 10,000 ตารางเมตร หรือ อาคารสูงไม่เกิน 23 เมตร และเมื่อคำนึงถึงระยะร่นตามที่กำหนดพบว่าสามารถสร้างสิ่งปลูกสร้างได้สูงสุด 5382 ตารางเมตร ซึ่งเป็นข้อกำหนดที่สำคัญในการนำมาพิจารณาในการออกแบบพื้นที่อาคารในขั้นต่อไป การศึกษาโครงการจะดำเนินการโดยสัมภาษณ์กลุ่มลูกค้า เป้าหมายเพื่อกำหนดรูปแบบอาคารที่ตรงกับความต้องการของตลาด และเพื่อพัฒนาต่อยอดเพิ่มความแข็งแกร่งให้โครงการที่ศึกษาสามารถแข่งขันกับตลาดได้