cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34808/b4dk-5t68
title: The generalization by simplification operator with Sester’s method of objects representing groups of buildings in Kartuzy district - scale 1:25000

description: The process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the national geodesy and cartography resource from BDOT10k (10k topographic database) [1].
The input data are building groups obtained as a result of the aggregation operator activity (KAR_BuildingAggregation). The Sester's simplifying operator is designed to simplify buildings, eliminating sections that do not meet the minimum length [2, 3]. As a result of the simplification process, 5.3% of building groups have been removed.
Literature
[1] Regulation of the Ministry of Administration and Digitization, W sprawie bazy danych obiektów topograficznych oraz bazy danych obiektów ogólnogeograficznych, a także standardowych opracowań kartograficznych [Regarding the database of topographic objects and the database of general geographic objects, as well as standard cartographic studies.]
[2] Sester, Monika, and Claus Brenner. "Continuous generalization for fast and smooth visualization on small displays." International Archives of Photogrammetry and Remote Sensing 34.part 4 (2004): 1293-1298.
[3] Sester, Monika, and Claus Brenner. "A vocabulary for a multiscale process description for fast transmission and continuous visualization of spatial data." Computers & Geosciences 35.11 (2009): 2177-2184.
DOI: 10.34808/tvfp-m429
title: The generalization of objects representing groups of buildings in the Kartuzy district by simplification operator with the Simplify Building tool - scale 1:25000

description: The process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the national geodesy and cartography resource from BDOT10k (10k topographic database) [1].
The input data are building groups obtained as a result of the aggregation operator activity (KAR_25k_BuildingAggregation). The employed Simplify Building tool is described by the manufacturer Esri Inc. in the documentation [2]. As a result of the simplification process, several building groups have been removed.
Literature
[1] Regulation of the Ministry of Administration and Digitization, W sprawie bazy danych obiektów topograficznych oraz bazy danych obiektów ogólnogeograficznych, a także standardowych opracowań kartograficznych [Regarding the database of topographic objects and the database of general geographic objects, as well as standard cartographic studies.]
[2] www.pro.arcgis.com [access: 15.11.2020]
DOI: 10.34808/3c9r-0e45
title: CFD analysis of a flow in commutation window in PWK pump

description: The included data is a result of CFD analysis of a flow in a commutation window in PWK pump. The results were obtained for different pressures, viscosities and geometrical parameters of the gap.  All the rows and columns have descriptions similar as in PhD Thesis: "Kompensacja skoków ciśnienia w pompie tłoczkowej o zmiennej wydajności z rozrządem krzywkowym", therefore it is advised to use the dataset with mentioned publication.
DOI: 10.34808/a2qq-tj35
title: Data from pizoelectric pressure sensor placed inside PWK pump chamber

description: The dataset contains raw data acquired from a piezoelectric pressure sensor placed inside PWK pump chamber. Pumping pressure was 10MPa, displacement was set to 100%, oil temperature was 33C.  Columns "Czas" stand for a time in seconds and milliseconds, "Napięcie" stands for Voltage in Volts
DOI: 10.5517/ccdc.csd.cc21fl03
title: CCDC 1889884: Experimental Crystal Structure Determination

description: Related Article: Ryohei Akiyoshi, Yuki Komatsumaru, Masaki Donoshita, Shun Dekura, Yukihiro Yoshida, Hiroshi Kitagawa, Yasutaka Kitagawa, Leonard F. Lindoy, Shinya Hayami|2021|Angew.Chem.,Int.Ed.|60|12717|doi:10.1002/anie.202015322
DOI: 10.5517/ccdc.csd.cc21fl14
title: CCDC 1889885: Experimental Crystal Structure Determination

description: Related Article: Ryohei Akiyoshi, Yuki Komatsumaru, Masaki Donoshita, Shun Dekura, Yukihiro Yoshida, Hiroshi Kitagawa, Yasutaka Kitagawa, Leonard F. Lindoy, Shinya Hayami|2021|Angew.Chem.,Int.Ed.|60|12717|doi:10.1002/anie.202015322
DOI: 10.5517/ccdc.csd.cc21fl25
title: CCDC 1889886: Experimental Crystal Structure Determination

description: Related Article: Ryohei Akiyoshi, Yuki Komatsumaru, Masaki Donoshita, Shun Dekura, Yukihiro Yoshida, Hiroshi Kitagawa, Yasutaka Kitagawa, Leonard F. Lindoy, Shinya Hayami|2021|Angew.Chem.,Int.Ed.|60|12717|doi:10.1002/anie.202015322
DOI: 10.5517/ccdc.csd.cc21vykf
title: CCDC 1902735: Experimental Crystal Structure Determination

description: Related Article: Rim Zarroug, Anissa Haj Abdallah, Philippe Guionneau, Albert Masip-Sánchez, Xavier López, Brahim Ayed|2021|J.Mol.Struct.|1241|130677|doi:10.1016/j.molstruc.2021.130677
DOI: 10.5517/ccdc.csd.cc24l5zx
title: CCDC 1983689: Experimental Crystal Structure Determination

description: Related Article: Alexandria M. McCollum, Angela M. Longo, Amanda E. Stahl, Abigail S. Butler, Arnold L. Rheingold, Thomas R. Cundari, David B. Green, Kelsey R. Brereton, Joseph M. Fritsch|2021|Polyhedron|204|115233|doi:10.1016/j.poly.2021.115233
DOI: 10.5517/ccdc.csd.cc24l60z
title: CCDC 1983690: Experimental Crystal Structure Determination

description: Related Article: Alexandria M. McCollum, Angela M. Longo, Amanda E. Stahl, Abigail S. Butler, Arnold L. Rheingold, Thomas R. Cundari, David B. Green, Kelsey R. Brereton, Joseph M. Fritsch|2021|Polyhedron|204|115233|doi:10.1016/j.poly.2021.115233
DOI: 10.5517/ccdc.csd.cc24l610
title: CCDC 1983691: Experimental Crystal Structure Determination

description: Related Article: Alexandria M. McCollum, Angela M. Longo, Amanda E. Stahl, Abigail S. Butler, Arnold L. Rheingold, Thomas R. Cundari, David B. Green, Kelsey R. Brereton, Joseph M. Fritsch|2021|Polyhedron|204|115233|doi:10.1016/j.poly.2021.115233
DOI: 10.5517/ccdc.csd.cc24l621
title: CCDC 1983692: Experimental Crystal Structure Determination

description: Related Article: Alexandria M. McCollum, Angela M. Longo, Amanda E. Stahl, Abigail S. Butler, Arnold L. Rheingold, Thomas R. Cundari, David B. Green, Kelsey R. Brereton, Joseph M. Fritsch|2021|Polyhedron|204|115233|doi:10.1016/j.poly.2021.115233
DOI: 10.5517/ccdc.csd.cc24l632
title: CCDC 1983693: Experimental Crystal Structure Determination

description: Related Article: Alexandria M. McCollum, Angela M. Longo, Amanda E. Stahl, Abigail S. Butler, Arnold L. Rheingold, Thomas R. Cundari, David B. Green, Kelsey R. Brereton, Joseph M. Fritsch|2021|Polyhedron|204|115233|doi:10.1016/j.poly.2021.115233
DOI: 10.5517/ccdc.csd.cc24l643
title: CCDC 1983694: Experimental Crystal Structure Determination

description: Related Article: Alexandria M. McCollum, Angela M. Longo, Amanda E. Stahl, Abigail S. Butler, Arnold L. Rheingold, Thomas R. Cundari, David B. Green, Kelsey R. Brereton, Joseph M. Fritsch|2021|Polyhedron|204|115233|doi:10.1016/j.poly.2021.115233
DOI: 10.5517/ccdc.csd.cc24s86f
title: CCDC 1989524: Experimental Crystal Structure Determination

description: Related Article: Xiao-Ting Hu, Zheng Yin, Xiao-Peng Luo, Chang-Hao Shen, Ming-Hua Zeng|2021|Inorg.Chem.Commun.|129|108664|doi:10.1016/j.inoche.2021.108664
DOI: 10.5517/ccdc.csd.cc25kzyp
title: CCDC 2013293: Experimental Crystal Structure Determination

description: Related Article: Huan Luo, Rong-Gui Yang, Zhao-Hui Chen, Guo-Qing Zhong|2021|J.Solid State Chem.|300|122256|doi:10.1016/j.jssc.2021.122256
DOI: 10.5517/ccdc.csd.cc25kzzq
title: CCDC 2013294: Experimental Crystal Structure Determination

description: Related Article: Huan Luo, Rong-Gui Yang, Zhao-Hui Chen, Guo-Qing Zhong|2021|J.Solid State Chem.|300|122256|doi:10.1016/j.jssc.2021.122256
DOI: 10.5517/ccdc.csd.cc25w4z6
title: CCDC 2022098: Experimental Crystal Structure Determination

description: Related Article: Ryohei Akiyoshi, Yuki Komatsumaru, Masaki Donoshita, Shun Dekura, Yukihiro Yoshida, Hiroshi Kitagawa, Yasutaka Kitagawa, Leonard F. Lindoy, Shinya Hayami|2021|Angew.Chem.,Int.Ed.|60|12717|doi:10.1002/anie.202015322
DOI: 10.5517/ccdc.csd.cc25zysx
title: CCDC 2025750: Experimental Crystal Structure Determination

description: Related Article: Huan Luo, Rong-Gui Yang, Zhao-Hui Chen, Guo-Qing Zhong|2021|J.Solid State Chem.|300|122256|doi:10.1016/j.jssc.2021.122256
DOI: 10.5517/ccdc.csd.cc26jr1k
title: CCDC 2041878: Experimental Crystal Structure Determination

description: Related Article: Kangrui Yuan|2021|CSD Communication|||
description: Related Article: Kangrui Yuan, Xiaomei Ye, Wei Liu, Kun Liu, Dudu Wu, Wenlong Zhao, Zebin Qian, Shan Li, Chaobo Huang, Zhiqiang Yu, Zhi Chen|2021|J.Mol.Struct.|1241|130624|doi:10.1016/j.molstruc.2021.130624
DOI: 10.5517/ccdc.csd.cc26mmt9
title: CCDC 2044661: Experimental Crystal Structure Determination

description: Related Article: Rim Zarroug, Anissa Haj Abdallah, Philippe Guionneau, Albert Masip-Sánchez, Xavier López, Brahim Ayed|2021|J.Mol.Struct.|1241|130677|doi:10.1016/j.molstruc.2021.130677
DOI: 10.5517/ccdc.csd.cc26z6cs
title: CCDC 2054815: Experimental Crystal Structure Determination

description: Related Article: Terrence-Thang H. Nguyen, Connor J. O’Brien, Minh L. N. Tran, Steven H. Olson, Nicholas S. Settineri, Stanley B. Prusiner, Nick A. Paras, Jay Conrad|2021|Org.Lett.|23|3823|doi:10.1021/acs.orglett.1c00871
DOI: 10.5517/ccdc.csd.cc270jh9
title: CCDC 2056090: Experimental Crystal Structure Determination

description: Related Article: Chao-Huan Zhang, Qing Gao, Meng Li, Jian-Fei Wang, Chuan-Ming Yu, Bin Mao|2021|Org.Lett.|23|3949|doi:10.1021/acs.orglett.1c01110
DOI: 10.5517/ccdc.csd.cc273fld
title: CCDC 2058883: Experimental Crystal Structure Determination

description: Related Article: Jiraporn Buasakun, Phakinee Srilaoong, Kittipong Chainok, Chadchalerm Raksakoon, Ramida Rattanakram, Tanwawan Duangthongyou|2021|Polyhedron|204|115265|doi:10.1016/j.poly.2021.115265
DOI: 10.5517/ccdc.csd.cc273fmf
title: CCDC 2058884: Experimental Crystal Structure Determination

description: Related Article: Jiraporn Buasakun, Phakinee Srilaoong, Kittipong Chainok, Chadchalerm Raksakoon, Ramida Rattanakram, Tanwawan Duangthongyou|2021|Polyhedron|204|115265|doi:10.1016/j.poly.2021.115265
DOI: 10.6084/m9.figshare.14703684
title: Analisis competencias

description: The experience of digital accessibility in developed countries raises legislation and its applicability in virtual education
DOI: 10.6084/m9.figshare.14703684.v1
title: Analisis competencias

description: The experience of digital accessibility in developed countries raises legislation and its applicability in virtual education
DOI: 10.5517/ccdc.csd.cc2771q8
title: CCDC 2062359: Experimental Crystal Structure Determination

description: Related Article: Wei Cheng, Ren Sheng, Yuting Liu, Song Wang, Ping Chen, Bihai Tong|2021|Inorg.Chem.Commun.|129|108667|doi:10.1016/j.inoche.2021.108667
DOI: 10.5517/ccdc.csd.cc277kpr
title: CCDC 2062854: Experimental Crystal Structure Determination

description: Related Article: Arun Sridharan, Alexandra C. Brown, Daniel L. M. Suess|2021|Angew.Chem.,Int.Ed.|60|12802|doi:10.1002/anie.202102603
DOI: 10.5517/ccdc.csd.cc277kqs
title: CCDC 2062855: Experimental Crystal Structure Determination

description: Related Article: Arun Sridharan, Alexandra C. Brown, Daniel L. M. Suess|2021|Angew.Chem.,Int.Ed.|60|12802|doi:10.1002/anie.202102603
DOI: 10.34808/a796-9h86
title: Results of experimental research of hydraulic satellite motor

description: The study of the flow rate in the motor and the torque on the motor shaft at low constant speed were carried out.The file contains measurement data of the torque on the motor shaft and the flow rate in the motor as a function of the angle of shaft rotation at various constant pressure drops in the motor.
The flow rate is not constant as a function of the angle of rotation of the motor shaft. Rapid changes in the flow rate through the death chamber and through the minimum volume chamber are observed.
DOI: 10.34808/98n4-k804
title: The Weibull model of the multipath fading channel

description: The dataset contains the results of simulations that are part of the research on modelling the multipath fading in the communication channel. The Weibull fading envelope is generated using the Monte-Carlo simulation (MCS) in the LabVIEW programming environment.
The term small-scale fading denotes the rapid variations of the envelope of a received signal. The statistics of the received signal in a mobile radio environment are obtained by using the scattering model and the assumption of large number of randomly phased components. Then the probability density function of the signal envelope has a Rayleigh distribution. Another distribution used to fading model in a multipath environment is the Weibull distribution. This distribution seems to exhibit good fit to the experimental fading channel measurements, for indoor and outdoor environments. The probability density function of the envelope is given by Equation (1). In the special case when a shape parameter is equal to 2, the equation (1) describes the Rayleigh distribution. The parameter of the Weibull distribution r0 is explain by equation (2). The mean value and variance for Weibull fading envelope are given by Equations (3) and (4), respectively. The Weibull envelope can be also written down as a function of the in-phase and quadrature components that specifying the model applied in MCS (Equations 5).
The MCS provides a general approach to numerically approximating the distribution function for the value of the output quantity R(t) = f(X) (where R(t) is a single (scalar) output quantity. X represents the N input quantities (X1(t), . . . ,XN(t))T .
The inputs to the Monte Carlo simulation procedure are:
- the model R(t) = f(X),
- the probability density functions for input quantities X1(t), . . . ,XN (t),
- the number M of Monte Carlo trials.
The probability density functions for X=X1(t) Y=X2(t) are a Gaussian type. The number of Monte Carlo trials is equal to 100 000. The exemplary simulation results are shown in Fig. 1.
The simulations were carried out for the Weibull variance equal to 0.5 (Equation 6), and different values of the shape parameter of the Weibull distribution.
The attached files contain two columns related to fading envelope: the number of samples and the amplitude of fading envelope in [dB] and two columns related to the histogram: the number of bins and the histogram results in percents of total amplitude. Files can be opened in both the LabVIEW software and Windows Office Notepad.
The files are described as follows: MCS_Weibull_value of the shape parameter.
DOI: 10.34808/d7mr-zm26
title: The generalized Suzuki model of the multipath fading channel

description: The dataset contains the results of simulations that are part of the research on modelling the multipath fading in the communication channel.  The generalized Suzuki fading envelope is generated using the Monte-Carlo simulation (MCS) in the LabVIEW programming environment.
The Suzuki distribution is used to modelling the envelope of the received land mobile channels in an urban environment. The mobile channels modelled by the Suzuki process include multipath propagation and shadowing. The generalized Suzuki model of communication channel is described by a composite distribution that combined the Weibull short-term fading and lognormal distribution that is usually used to represent the long-term characteristics of the mobile radio channel. Using the conditional probability rule, we obtain the generalized Suzuki distribution expressed by Equation (1). The parameter of the Weibull distribution r0 is explained by equation (2). The model of generation of the generalized Suzuki envelope is described by Equation (3).
The MCS provides a general approach to numerically approximating the distribution function for the value of the output quantity Y(t) = f(X)  (where Y(t) is a single (scalar) output quantity. X represents the N input quantities (X1(t), . . . ,XN(t))T .
The inputs to the Monte Carlo simulation procedure are:
- the model Y(t) = f(X),
- the probability density functions for input quantities X1(t), . . . ,XN (t),
- the number M of Monte Carlo trials.
The probability density functions for X1(t)=X2(t), and X3(t)= N(t) are Gaussian type. The number of Monte Carlo trials is equal to 100 000. The exemplary simulation results are shown in Fig. 1.
The simulations were carried out for the Weibull variance equal to 0.5 (Equation 4) and the mean of the lognormal distribution equal to 1. The variable parameters of simulations are the shape parameter of the Weibull distribution and the standard deviation of the lognormal distribution.
The attached files contain two columns related to fading envelope: the number of samples and the amplitude of fading envelope in [dB] and two columns related to the histogram: the number of bins and the histogram results in percents of total amplitude. Files can be opened in both the LabVIEW software and Windows Office Notepad.
The files are described as follows: MCS_gen_Suzuki_value of the standard deviation of the lognormal distribution expressed in [dB]_value of the shape parameter of the Weibull distribution.
DOI: 10.5281/zenodo.4876547
title: Supplementary Data for Basel Al-Barghouthi's dissertation

description: Supplementary Data for Basel Al-Barghouthi's dissertation
DOI: 10.5281/zenodo.4876546
title: Supplementary Data for Basel Al-Barghouthi's dissertation

description: Supplementary Data for Basel Al-Barghouthi's dissertation
DOI: 10.5517/ccdc.csd.cc207skm
title: CCDC 1854530: Experimental Crystal Structure Determination

description: Related Article: Jiraporn Buasakun, Phakinee Srilaoong, Kittipong Chainok, Chadchalerm Raksakoon, Ramida Rattanakram, Tanwawan Duangthongyou|2021|Polyhedron|204|115265|doi:10.1016/j.poly.2021.115265
DOI: 10.6084/m9.figshare.14703639
title: mnist+context

description: The dataset consists of MNIST digits set on either noisy backgrounds, with noise a Gaussian random variable, or MNIST digits set on a more naturalistic background from the CIFAR-10 dataset. A parameter that makes the task more difficult is digit transparency; as we increase transparency, the background interferes with the digit and the identity of the digit becomes more ambiguous. The goal is to perform image classification so that neural networks (NN) correctly identify the MNIST digit despite the different backgrounds. <br>
DOI: 10.6073/pasta/8b5fe65d95c46dc693dfd4429cf215dd
title: SBC LTER: Reef: Abundance, size and fishing effort for California Spiny Lobster (Panulirus interruptus), ongoing since 2012

description: Data on abundance, size and fishing pressure of California spiny lobster (Panulirus interruptus) are collected along the mainland coast of the Santa Barbara Channel. Spiny lobsters are an important predator in giant kelp forests off southern California. Two SBC LTER study reefs are located in or near the California Fish and Game Network of Marine Protected Areas (MPA), Naples and Isla Vista, both established as MPAs on 2012-01-01. MPAs provide a unique opportunity to investigate the effects of fishing on kelp forest community dynamics. Sampling began in 2012 and is ongoing. This dataset contains two tables. 1) Abundance and size data collected annually by divers in late summer before the start of the fishing season at five SBC LTER long term kelp forest study sites: two within MPAs (Naples and Isla Vista) and three outside (Arroyo Quemado, Mohawk and Carpinteria). 2) Fishing pressure, as determined by counting the number of commercial trap floats. Data are collected every two to four weeks during the lobster fishing season (October to March) at nine sites along the mainland, eight of which are also SBC LTER long-term kelp forest study reefs. See Methods for more information.
DOI: 10.6084/m9.figshare.14703723
title: Whole Fin 1dpa ATACseq

description: For UCSC
DOI: 10.6084/m9.figshare.14703723.v1
title: Whole Fin 1dpa ATACseq

description: For UCSC
DOI: 10.6084/m9.figshare.14703744.v1
title: Evaluation of the Tendencies of Urology Residents in Different Training Centers in Turkey on the Use of Fluoroscopy in Operations

description: Datas of the study of Evaluation of the Tendencies of Urology Residents in Different Training Centers in Turkey on the Use of Fluoroscopy in Operations in Turkish language
DOI: 10.6084/m9.figshare.14703744
title: Evaluation of the Tendencies of Urology Residents in Different Training Centers in Turkey on the Use of Fluoroscopy in Operations

description: Datas of the study of Evaluation of the Tendencies of Urology Residents in Different Training Centers in Turkey on the Use of Fluoroscopy in Operations in Turkish language
DOI: 10.25919/4ksz-vj58
title: DCM datasets for corrosion pits development in SLM 3D-printed SS316 samples

description: This collection contains DCM dataset files for porosity clusters and corrosion pits in selective-laser-melting (SLM) 3D-printed low- and mid-density SS316L samples which have measured relative densities of 98.77% and 99.05% respectively. The corrosion pits were formed by immersing the samples in a 6 wt% ferric chloride solution. The samples were taken out of the solution after every 8 hours for X-ray CT and DCM analysis.
DOI: 10.5281/zenodo.4393108
title: Data from: Highly efficient and comprehensive identification of ethyl methanesulfonate-induced mutations in Nicotiana tabacum L. by whole-genome and whole-exome sequencing

description: Tobacco (<em>Nicotiana tabacum </em>L.) is a complex allotetraploid species with a large 4.5-Gb genome that carries duplicated gene copies. In this study, we describe the development of a whole-exome sequencing (WES) procedure in tobacco and its application to characterize a test population of ethyl methanesulfonate (EMS)-induced mutations. A probe set covering 50.3-Mb protein coding regions was designed from a reference tobacco genome. The EMS-induced mutations in 19 individual M<sub>2</sub> lines were analyzed using our mutation analysis pipeline optimized to minimize false positives/negatives. In the target regions, the on-target rate of WES was approximately 75%, and 61,146 mutations were detected in the 19 M<sub>2</sub> lines. Most of the mutations (98.8%) were single nucleotide variants, and 95.6% of them were C/G to T/A transitions. The number of mutations detected in the target coding sequences by WES was 93.5% of the mutations detected by whole-genome sequencing (WGS). The amount of sequencing data necessary for efficient mutation detection was significantly lower in WES (11.2 Gb), which is only 6.2% of the required amount in WGS (180 Gb). Thus, WES was almost comparable to WGS in performance but is more cost effective. Therefore, the developed target exome sequencing, which could become a fundamental tool in high-throughput mutation identification, renders the genome-wide analysis of tobacco highly efficient.