cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2024-01.3006463/2297180
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003936/2297181
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003834/2297182
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003751/2297183
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3004350/2297184
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3004352/2297185
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003693/2297186
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003664/2297187
title: Data for EMSL Project 60923 from January 2024
DOI: 10.57745/8u1dxc
title: train.zip

description: Training data containing normal, load switching, and capacitor switching states.
DOI: 10.57745/iyijsq
title: test.zip

description: Testing data containing HIF, load switching, capacitor switching, and normal states.
DOI: 10.25582/data.2024-01.3003718/2297188
title: Data for EMSL Project 60923 from January 2024
DOI: 10.57745/i3qajf
title: IEEE_34_node_HIF.pdf

description: Simulink diagram for IEEE 34 nodes test feeder
DOI: 10.57745/0yxkp3
title: data_read.py

description: Import data using this python demo
DOI: 10.57745/uha7bx
title: data_explanation.pdf

description: Data description
DOI: 10.25582/data.2024-01.3004354/2297189
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003735/2297190
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003620/2297191
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003554/2297192
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003588/2297193
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003690/2297194
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003782/2297195
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003819/2297196
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003710/2297197
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003639/2297198
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003575/2297199
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003842/2297200
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003890/2297201
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003871/2297202
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003901/2297203
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003857/2297204
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003638/2297205
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003913/2297206
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003676/2297207
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003697/2297208
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003915/2297209
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003877/2297210
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003534/2297211
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003882/2297212
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003884/2297213
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003757/2297214
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003938/2297215
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003560/2297216
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003602/2297217
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003741/2297218
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003779/2297219
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003556/2297220
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3004756/2297221
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003879/2297222
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003688/2297223
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003726/2297224
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003898/2297225
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003910/2297226
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003764/2297227
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3004528/2297228
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003894/2297229
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003569/2297230
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003832/2297231
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003729/2297232
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3008059/2297233
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2024-01.3003776/2297234
title: Data for EMSL Project 60923 from January 2024
DOI: 10.25582/data.2023-02.2737069/2297235
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737043/2297236
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737042/2297237
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737071/2297238
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737025/2297239
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737000/2297240
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737057/2297241
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737079/2297242
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737009/2297243
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737049/2297244
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737078/2297245
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737076/2297246
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737081/2297247
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737075/2297248
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2736980/2297249
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737063/2297250
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737030/2297251
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737083/2297252
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2736979/2297253
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737061/2297254
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737006/2297255
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737119/2297256
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737002/2297257
title: Data for EMSL Project 60171 from February 2023
DOI: 10.6084/m9.figshare.12229448
title: Research data: Understanding the luminescent nature of organic radicals for efficient doublet emitters and pure-red light-emitting diodes

description: Source data for figures in 'Understanding the luminescent nature of organic radicals for efficient doublet emitters and pure-red light-emitting diodes' as published in Nature Materials (https://www.nature.com/articles/s41563-020-0705-9).<br>Use standard software for analysis (Excel, Origin, MATLAB etc).
DOI: 10.25582/data.2023-02.2737058/2297258
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2736976/2297259
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737086/2297260
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2736971/2297261
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737050/2297262
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737048/2297263
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737074/2297264
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2736983/2297265
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737135/2297266
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737070/2297267
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737035/2297268
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737163/2297269
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737082/2297270
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737032/2297271
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2736970/2297272
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2736981/2297273
title: Data for EMSL Project 60171 from February 2023
DOI: 10.6084/m9.figshare.14850863.v1
title: Additional file 2 of Genome-Wide Association Study of the Genetic Basis of Effective Tiller Number in Rice

description: Additional file 2: Table S2. Detailed information of significant SNPs and ETN-associated genes mapped by six GWAS assays.
DOI: 10.25582/data.2023-02.2737037/2297274
title: Data for EMSL Project 60171 from February 2023
DOI: 10.6084/m9.figshare.14850860
title: Additional file 1 of Genome-Wide Association Study of the Genetic Basis of Effective Tiller Number in Rice

description: Additional file 1: Table S1. Summary of the 490 rice accessions and their ETN phenotypes and information of 28 accessions used for OsPILS6b transcription analysis.
DOI: 10.6084/m9.figshare.7510949
title: The Prediction of the Man-Hour in Aircraft Assembly Based on Support Vector Machine Particle Swarm Optimization

description: ABSTRACT: As the representative of manufacturing industry, aircraft assembly lacks of effective method to forecast man-hour. The forecasting accuracy of existing methods is universally pretty low. On the basis of full analysis of aircraft assembly's feature, this study proposes a forecasting model based on support vector machine (SVM), which is optimized by particle swarm optimization. It can carry out quantitative prediction of the process' man-hour during aircraft's assembly. Firstly, we decompose aircraft's assembly work by the concept of work breakdown structure. Further, the process parameters related to man-hour were listed and we made necessary correlation analysis of these historical data. Parameters with high contribution are then used as input of forecasting model. A new forecasting model utilizing SVM is proposed, which carries out the process as the minimum research granularity. Its performance is compared with back propagation neural network. The process of automatic drilling & riveting is adopted as an example in order to present and validate the model. Experimental results reflect that SVM has high forecast precision and good fitness, so that it is suitable for small sample prediction. Through the optimization, it can effectively predict man-hour of assembly work in a short time while maintaining sufficient accuracy.
DOI: 10.6084/m9.figshare.7510949.v1
title: The Prediction of the Man-Hour in Aircraft Assembly Based on Support Vector Machine Particle Swarm Optimization

description: ABSTRACT: As the representative of manufacturing industry, aircraft assembly lacks of effective method to forecast man-hour. The forecasting accuracy of existing methods is universally pretty low. On the basis of full analysis of aircraft assembly's feature, this study proposes a forecasting model based on support vector machine (SVM), which is optimized by particle swarm optimization. It can carry out quantitative prediction of the process' man-hour during aircraft's assembly. Firstly, we decompose aircraft's assembly work by the concept of work breakdown structure. Further, the process parameters related to man-hour were listed and we made necessary correlation analysis of these historical data. Parameters with high contribution are then used as input of forecasting model. A new forecasting model utilizing SVM is proposed, which carries out the process as the minimum research granularity. Its performance is compared with back propagation neural network. The process of automatic drilling & riveting is adopted as an example in order to present and validate the model. Experimental results reflect that SVM has high forecast precision and good fitness, so that it is suitable for small sample prediction. Through the optimization, it can effectively predict man-hour of assembly work in a short time while maintaining sufficient accuracy.
DOI: 10.25582/data.2023-02.2737010/2297275
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737148/2297276
title: Data for EMSL Project 60171 from February 2023
DOI: 10.6084/m9.figshare.14850869.v1
title: Additional file 4 of Genome-Wide Association Study of the Genetic Basis of Effective Tiller Number in Rice

description: Additional file 4: Table S4. Potential transcription factors and their binding sites in the selected regions of OsPILS6b promoter.
DOI: 10.25582/data.2023-02.2736975/2297277
title: Data for EMSL Project 60171 from February 2023
DOI: 10.6084/m9.figshare.14850866.v1
title: Additional file 3 of Genome-Wide Association Study of the Genetic Basis of Effective Tiller Number in Rice

description: Additional file 3: Table S3. Expression data of 27 ETN-associated genes.
DOI: 10.25582/data.2023-02.2737066/2297278
title: Data for EMSL Project 60171 from February 2023
DOI: 10.6084/m9.figshare.14850869
title: Additional file 4 of Genome-Wide Association Study of the Genetic Basis of Effective Tiller Number in Rice

description: Additional file 4: Table S4. Potential transcription factors and their binding sites in the selected regions of OsPILS6b promoter.
DOI: 10.25582/data.2023-02.2737067/2297279
title: Data for EMSL Project 60171 from February 2023
DOI: 10.25582/data.2023-02.2737031/2297280
title: Data for EMSL Project 60171 from February 2023
DOI: 10.6084/m9.figshare.14850866
title: Additional file 3 of Genome-Wide Association Study of the Genetic Basis of Effective Tiller Number in Rice

description: Additional file 3: Table S3. Expression data of 27 ETN-associated genes.
DOI: 10.5281/zenodo.5940139
title: Supporting dataset for the publication "The Crucial Role of Solvation Forces in the Steric Stabilization of Nanoplatelets"

description: Here, we provide an example of our umbrella sampling simulations (35.6 nm²×1.5 nm, 4 ligand beads, 250k solvent molecules), the input and output data of our WHAM calculations, free energy curves, and simulation data from the comparison of the MARTINI force field to the TraPPE-UA force field. Additional, we provide a modified file of the HOOBAS molecular builder. This file includes the "platelet" class we use to create nonoplatelets in our simulations.
DOI: 10.5281/zenodo.6832532
title: Supporting dataset for the publication "The Crucial Role of Solvation Forces in the Steric Stabilization of Nanoplatelets"

description: Here, we provide an example of our umbrella sampling simulations (35.6 nm²×1.5 nm, 4 ligand beads, 250k solvent molecules), the input and output data of our WHAM calculations, and free energy curves. Additional, we provide a modified file of the HOOBAS molecular builder. This file includes the "platelet" class we use to create nonoplatelets in our simulations.
DOI: 10.5281/zenodo.5940140
title: Supporting dataset for the publication "The crucial role of solvation forces in inter-nanoplatelet interactions and stack formation"

description: Here, we provide an example of our umbrella sampling simulations (35.6 nm²×1.5 nm, 4 ligand beads, 250k solvent molecules), the input and output data of our WHAM calculations, and free energy curves. Additional, we provide a modified file of the HOOBAS molecular builder. This file includes the "platelet" class we use to create nonoplatelets in our simulations.
DOI: 10.21227/dt6h-6873
title: CRAWDAD roma/taxi

description: User Mobility Characterization&lt;/purpose&gt;&lt;purpose&gt;Network Performance Analysis&lt;/purpose&gt;&lt;purpose&gt;Routing Protocol for DTNs (Disruption Tolerant Networks)&lt;/purpose&gt;&lt;purpose&gt;Positioning Systems&lt;/purpose&gt;&lt;purpose&gt;Human Behavior Modeling&lt;/purpose&gt;&lt;purpose&gt;Localization&lt;/purpose&gt;&lt;purpose&gt;Opportunistic ConnectivityDataset of mobility traces of taxi cabs in Rome, Italy.Dataset of mobility traces of taxi cabs in Rome, Italy.This dataset contains mobility traces of taxi cabs in Rome, Italy. It contains GPS coordinates of approximately 320 taxis collected over 30 days. Related publication: Marco Bonola, Lorenzo Bracciale, Pierpaolo Loreti, Raul Amici, Antonello Rabuffi, and Giuseppe Bianchi. 'Opportunistic communication in smart city: Experimental insight with small-scale taxi fleets as data carriers'. Ad Hoc Networks, February 2016.date/time of measurement start: 2014-02-01date/time of measurement end: 2014-03-02collection environment: 320 taxi drivers that work in the center of Rome. Traces present the positions of drivers, collected every 7 seconds.sanitization: Driver names have been replaced with an id.limitation: To retrieve the position, we used the getAccuracy function of Android LocationManager objects. We filter out the positions if their precision is less than 20m.Tracesetroma/taxi/taxicabsDataset of mobility traces of taxi cabs in Rome, Italy.file: taxi_february.tar.gzdescription: Dataset of mobility traces of taxi cabs in Rome, Italy.measurement purpose: User Mobility Characterization, Network Performance Analysis, Routing Protocol for DTNs (Disruption Tolerant Networks), Positioning Systems, Human Behavior Modeling, Opportunistic Connectivityroma/taxi/taxicabs Tracetaxi-rome1: 392 Mb of compressed data regarding the position of taxi cabs working for a month in the center of Rome, Italy.format: The trace is a txt file formatted as: DRIVER_ID;TIMESTAMP;POSITION Where: - Driver_id is an integer. - Timestamp includes date and time. - Position is formatted as POINT(latitude, longitude) The trace is sorted on the timestamp.
DOI: 10.6084/m9.figshare.21644321
title: Figure 1

description: Figure 1 raw data in Graphpad prism format
DOI: 10.6084/m9.figshare.21644321.v1
title: Figure 1

description: Figure 1 raw data in Graphpad prism format
DOI: 10.5281/zenodo.7391820
title: Datasets for "Arctic sea ice loss weakens Northern Hemisphere summertime storminess due to ocean coupling"

description: The datasets contain post-processed model outputs and reanalysis data for creating figures in the paper. The data are npz files, which can be easily accessed using Python 3 and numpy package.
DOI: 10.5281/zenodo.7391819
title: Datasets for "Arctic sea ice loss weakens Northern Hemisphere summertime storminess due to ocean coupling"

description: The datasets contain post-processed model outputs and reanalysis data for creating figures in the paper. The data are npz files, which can be easily accessed using Python 3 and numpy package.
DOI: 10.5287/bodleian:e2x6bnggk
title: Top-down regulation by a reindeer herding system limits climate-driven Arctic vegetation change at a regional scale: data
DOI: 10.5517/ccdc.csd.cc292rlr
title: CCDC 2117814: Experimental Crystal Structure Determination

description: Related Article: Chao Xu, Qianqin Yuan, Xiao Wei, Hao Li, Honglei Shen, Xi Kang, Manzhou Zhu|2022|Chemical Science|13|1382|doi:10.1039/D1SC06002C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2931r7
title: CCDC 2118098: Experimental Crystal Structure Determination

description: Related Article: Utkurbek Makhmudov, T.Т.Тoshmurodov, А.А.Ziyaev, B.Tashkhodjaev|2021|Dokl.Akad.Nauk Uzbekskoi SSR|8|31|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2931s8
title: CCDC 2118099: Experimental Crystal Structure Determination

description: Related Article: Utkurbek Makhmudov, T.Т.Тoshmurodov, А.А.Ziyaev, B.Tashkhodjaev|2021|Dokl.Akad.Nauk Uzbekskoi SSR|8|31|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc294c72
title: CCDC 2119353: Experimental Crystal Structure Determination

description: Related Article: Shaofei Wu, Yong Ni, Yi Han, Xudong Hou, Congyong Wang, Wenping Hu, Jishan Wu|2022|Angew.Chem.,Int.Ed.|61|e202115571|doi:10.1002/anie.202115571
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7391371
title: Fake accounts activity

description: This dataset contains the IDs of about 5,458,582 tweets from 23,582 fake accounts for sale on buyaccs.com and active during 2020. Refer to the paper below for more details. Mazza, Michele, Guglielmo Cola, and Maurizio Tesconi. "Ready-to-(ab) use: From fake account trafficking to coordinated inauthentic behavior on Twitter." <em>Online Social Networks and Media</em> 31 (2022): 100224. --- Files ---------------------------------------------------------------------<br> * tweets.csv <br> List of tweets produced by monitored fake accounts, each with<br> "user.id_str" id of the user who posted the tweet;<br> "id_str" tweet id. <br> * users.csv<br> List of monitored fake accounts, each with<br> "user.id_str" user account id;<br> "cib" if present, this label identifies a specific coordinated <br> and inauthentic behavior, as described in the research article;<br> "user.meta.account_category" label indicating the category assigned by the merchant.