cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/tu.the.2019.727
title: ทัศนคติของ ROV user ที่มีต่อ e-sport marketing: กรณีศึกษา KFCxROV ชุดขุมทรัพย์เดอะบอกซ์

description: การศึกษาทัศนคติของ RoV User ที่มีต่อ E-Sport Marketing กรณีศึกษา “KFC x RoV ชุดขุมทรัพย์เดอะบอกซ์” มีวัตถุประสงค์ 1.) เพื่อศึกษาการใช้กลยุทธ์การตลาดผ่าน E-Sport Marketing ของ KFC x RoV 2.) เพื่อศึกษาความสัมพันธ์ระหว่างปัจจัยทางประชากรศาสตร์ กับกลยุทธ์การตลาดผ่าน E-Sport Marketing ของ KFC x RoVการศึกษาครั้งนี้เป็นการศึกษารูปแบบการวิจัยเชิงปริมาณ (Quantitative Approach) โดยใช้แบบสอบถาม (Questionnaires) เป็นเครื่องมือที่ใช้ในการวิจัย โดยกลุ่มตัวอย่างคือ ผู้บริโภคที่อยู่ในกลุ่ม Facebook “ROV : Garena Thailand” มีทั้งสิ้น 400 ตัวอย่าง วิเคราะห์ผลโดยการใช้สถิติเชิงพรรณนา (Descriptive Statistic) อธิบายผลการวิเคราะห์ ได้แก่ การแจกแจงความถี่ ร้อยละ ค่าเฉลี่ย ค่าเบี่ยงเบนมาตรฐาน ทดสอบสมมติฐานโดยใช้ Independence Sample t-Test และ One-way ANOVAผลการศึกษาพบว่า กลุ่มตัวอย่าง RoV User ส่วนใหญ่มีอายุระหว่าง 21 -25 ปี มีการศึกษาในระดับมัธยมศึกษา โดยมีอาชีพนักเรียน / นักศึกษา ที่มีระดับรายได้ต่อเดือนต่ำกว่า 10,000 บาท และมีความถี่ในการเล่นเกม RoV วันละครั้ง ผลวิเคราะห์ข้อมูลปัจจัยส่วนผสมทางการตลาดของ KFC x RoV พบว่า RoV User ให้ความสำคัญต่อปัจจัยส่วนผสมทางการตลาดโดยรวมอยู่ในระดับมาก โดยเรียงลำดับจากมากไปน้อย ได้แก่ ด้านช่องทางการจัดจำหน่าย ด้านผลิตภัณฑ์ ด้านราคา และด้านการส่งเสริมการตลาด ตามลำดับ และผลวิเคราะห์พฤติกรรมการเล่มเกม ROV และการบริโภค KFC พบว่า ผู้ตอบแบบสอบถามส่วนใหญ่มีระยะเวลาที่เล่นเกม RoV มากกว่า 2 ปี มีเวลาเฉลี่ยในการเล่น RoV อยู่ที่ 1- 3 ชั่วโมง มีการบริโภค KFC อยู่เป็นประจำ และรู้จัก KFC x RoV มาจากช่องทาง Facebook เมื่อทดสอบสมมติฐาน พบว่า อายุ ระดับการศึกษา อาชีพ รายได้ และความถี่ในการเล่น RoV ที่แตกต่างกัน นั้นให้ความคิดเห็นที่มีต่อปัจจัยส่วนผสมการตลาดอย่างมีนัยสำคัญทางสถิติที่ .05 และ .01
DOI: 10.5281/zenodo.3249379
title: SFB genomes and annotations

description: This dataset contains sequence files for a Metagenome Assembled Genome (MAG) from human metagenomes, as well as 5 SFB reference genomes: GCF_000270205 Candidatus Arthromitus sp. SFB-mouse-Japan GCF_000283555 Candidatus Arthromitus sp. SFB-rat-Yit GCF_000284435 Candidatus Arthromitus sp. SFB-mouse-Yit GCF_000709435 Candidatus Arthromitus sp. SFB-mouse-NL GCF_001655775 Candidatus Arthromitus sp. SFB-turkey isolate UMNCA01 The dataset consists of 8 gzipped tar archives. Here's brief summary of their contents: <strong>sfb_abundance</strong>: Counts of mapped reads and normalized counts for each contig in 825 samples (see <strong>sfb_map</strong>) Files named 'raw_counts' are number of reads assigned to each contig while files named 'tpm' are counts normalized to Transcripts Per Million. The 'percontig' files show numbers per contig while raw_counts.tab and tpm.tab files have counts summed for each genome. <strong>sfb_abundance.cds</strong>: Counts of mapped reads and normalized counts as above but only for reads mapping to protein-coding regions. <strong>sfb_annotations</strong>: Annotation files, from running the prokka pipeline on the genomes and subsequently eggnog-mapper, pfam_scan and dbCAN. <strong>sfb_checkm</strong>: Results from running 'checkm lineage_wf' on the genomes. <strong>sfb_collated</strong>: Collated counts of annotations in each genome. <strong>sfb_fastani</strong>: Results from running fastANI on the genomes, with subsequent clustering of genomes based on 75% overlap and 95% ANI. <strong>sfb_gtdb</strong>: Results from the 'gtdbtk classify_wf' on the genomes. This shows how the genomes are classified against the Genome Taxonomy Database (release86). <strong>sfb_gtdb_denovo</strong>: Phylogeny as created using the following command on the genomes. <pre><code class="language-bash">gtdbtk de_novo_wf --bac120_ms --outgroup_taxon p__Patescibacteria -x .fna --cpus 20 --rnd_seed 123</code></pre> <strong>sfb_map</strong>: Results from mapping reads from 825 samples to the 6 genomes. Reads were aligned using bowtie2 with '--very-sensitive --no-unal' settings and '--score-min C,0,0' to only report reads aligning without mismatches.Output was sorted by position and duplicates removed using MarkDuplicates of the picard tools suite. The archive contains a single merged bam file ('sfb.bam') where each sample has been assigned a ReadGroup inferred from its file name. Note that this mapping step was performed to investigate the presence of the SFB MAG in other metagenomes and was not part of the actual binning step. <strong>SFB.unoise.vsearch.tsv: </strong>Count table of amplified 16S sequence variants with one sample per column and one Amplicon Sequence Variant (ASV) per row. The sixth column shows the assigned taxonomy, and the seventh, the sequence. Total DNA was amplified with the universal bacterial 16S primer pair 341f-805r. Primer sequences and low quality bases were removed from the raw reads with Cutadapt. ASVs were picked using Unoise3 with standard parameters. Taxonomy was assigned by the SINA classifier, based on the SILVA database v132.
DOI: 10.6084/m9.figshare.13233911
title: crossing data

description: data of pedestrian crossing street
DOI: 10.25592/uhhfdm.638
title: Iceland-Lofotes-Difference-Index (ILD) 1957-2017

description: The weather and climate change in the North Atlantic has a strong influence on Europe's weather and especially the position of steering lows is of great interest. In the long-term mean of atmospheric pressure in the North Atlantic appeared two regions with increased activity. These lows have a significant impact on the meridional heat flows to Europe. The two activity centres are located on the one hand over the Irminger Sea near Iceland and on the other over the Norwegian Sea near the Lofoten Islands. There is also a primary and secondary minimum in the long-term mean air pressure field at sea level. To study these minima, a climate index of the Iceland-Lofoten pressure difference (ILD index) was defined [Jahnke-Bornemann 2008, 2010]. The calculation of the index was carried out, analogous to the station-based NAO index [Hurrell, 1995], as the difference of the standardized atmospheric pressure anomalies of a defined Iceland and Lofoten region. This data set contains the monthly ILD-Index, calculated from mean sea level pressure (MSLP) fields from ECMWF Reanalysis data ERA 40 (9/1957-12/1978) and ERA Interim (1/1979-1/2017) for the period September 1957 to January 2017, with the reference period 01/1979-12/2000. The areas used for the calculation of pressure means are 70N to 72.5N, 13.5E to 15.75 E for Lofoten, and 60N to 63N, 35W to 38W for Iceland.
DOI: 10.25592/uhhfdm.636
title: Iceland-Lofotes-Difference-Index (ILD) 1957-2017

description: The weather and climate change in the North Atlantic has a strong influence on Europe's weather and especially the position of steering lows is of great interest. In the long-term mean of atmospheric pressure in the North Atlantic appeared two regions with increased activity. These lows have a significant impact on the meridional heat flows to Europe. The two activity centres are located on the one hand over the Irminger Sea near Iceland and on the other over the Norwegian Sea near the Lofoten Islands. There is also a primary and secondary minimum in the long-term mean air pressure field at sea level. To study these minima, a climate index of the Iceland-Lofoten pressure difference (ILD index) was defined [Jahnke-Bornemann 2008, 2010]. The calculation of the index was carried out, analogous to the station-based NAO index [Hurrell, 1995], as the difference of the standardized atmospheric pressure anomalies of a defined Iceland and Lofoten region. This data set contains the monthly ILD-Index, calculated from mean sea level pressure (MSLP) fields from ECMWF Reanalysis data ERA 40 (9/1957-12/1978) and ERA Interim (1/1979-1/2017) for the period September 1957 to January 2017, with the reference period 01/1979-12/2000. The areas used for the calculation of pressure means are 70N to 72.5N, 13.5E to 15.75 E for Lofoten, and 60N to 63N, 35W to 38W for Iceland.
DOI: 10.17632/yszpf7pfcd.1
title: Data for: Biogenic volatile organic compounds in different ecosystems in southeastern Kenya

description: Biogenic VOC concentrations from forests, bushlands, grasslands, and agricultural lands in Taita Taveta County, Kenya. Measurements carried out at the start of the rainy season in 2019. For more information contact Yang Liu (yang.z.liu@helsinki.fi).
DOI: 10.5281/zenodo.4271143
title: Kenya Cropland Map and Non-Crop Labelled dataset

description: This dataset provides a 10m resolution map of cropland in Kenya for the 2019-2020 growing season (kenya_cropland_binary_2019.tif.zip) and a 10m resolution map of cropland in Busia County, Kenya for the 2020-2021 growing season (busia_cropland_binary_2020.tif.zip). Each pixel has a binary value, 0 if it does not contain crops and 1 if it does. These values were obtained by thresholding the predictions of an LSTM classifier trained on multi-spectral time series of Sentinel-2 satellite observations. A thresholding value of 0.5 was used. This dataset also provides the hand-labelled non-crop points used for training, which were created by labelling high-resolution satellite imagery in QGIS and Google Earth Pro. For more information, or if you use any part of this dataset, please refer to / cite the following paper: Gabriel Tseng, Hannah Kerner, Catherine Nakalembe and Inbal Becker-Reshef. 2020. Annual and in-season mapping of cropland at field scale with sparse labels. Tackling Climate Change with Machine Learning workshop at NeurIPS ’20: December 11th, 2020
DOI: 10.5517/ccdc.csd.cc232tyz
title: CCDC 1939141: Experimental Crystal Structure Determination

description: Related Article: Yi Gong|2019|CSD Communication|||
description: Related Article: Yong-Jia Hao, Yi Gong, Ying Zhou, Jian Zhou, Jin-Sheng Yu|2020|Org.Lett.|22|8516|doi:10.1021/acs.orglett.0c03123
DOI: 10.5517/ccdc.csd.cc24fktz
title: CCDC 1979251: Experimental Crystal Structure Determination

description: Related Article: Pianpian Wang, Jian Huang, Tibor Kurtán, Attila Mándi, Hongli Jia, Wei Cheng, Wenhan Lin|2020|Org.Lett.|22|8215|doi:10.1021/acs.orglett.0c02645
DOI: 10.5517/ccdc.csd.cc25pmtb
title: CCDC 2016792: Experimental Crystal Structure Determination

description: Related Article: Maciej Majdecki, Agata Tyszka-Gumkowska, Janusz Jurczak|2020|Org.Lett.|22|8687|doi:10.1021/acs.orglett.0c03272
DOI: 10.5517/ccdc.csd.cc261mws
title: CCDC 2027365: Experimental Crystal Structure Determination

description: Related Article: Jingya Yang, Menghui Song, Hongyan Zhou, Ganggang Wang, Ben Ma, Yuanyuan Qi, Congde Huo|2020|Org.Lett.|22|8407|doi:10.1021/acs.orglett.0c03039
DOI: 10.23729/161d467b-e35b-49b1-b9c2-e4cf70cdf5a7
title: RSA Iron preview

description: This sample is a spectacular example of a banded iron formation or BIF - where by fine grains of iron ore have been deposited in to a shallow water environment and preserving micro-scale depositional events. The significance is that these rocks containing Fe-oxides is that they show there must have been an atmosphere on Earth at these times (Archaean) , otherwise oxides could not form (and did not before these times). This is the so-called great oxygenation event in geological history. Exact location is unknown but it is from South Africa. From the Butcher Collection. Preview of PAS-dataset https://doi.org/10.23729/21561fb3-6544-4b3a-9b17-4a46d6406c17
DOI: 10.21227/abp4-py44
title: MDD Dataset

description: That's a dataset.
DOI: 10.17632/r9t9dgg57k.2
title: Cell-to-cell and genome-to-genome variability of Adenovirus transcription tuned by the cell cycle

description: The dataset contains source files for figures in the manuscript "Cell-to-cell and genome-to-genome variability of Adenovirus transcription tuned by the cell cycle"
DOI: 10.6084/m9.figshare.12640460
title: RNA sequencing data set of dlPFC grey matter from schizophrenia patients and controls

description: RNA sequencing data set of the grey matter of the dorsolateral prefrontal cortex of four schizophrenia patients and four controls. Detailed methods as well as patient and tissue characteristics can be found in the publication "<b>Maas, D.A., Martens, M.B., Priovoulos, N. <i>et al.</i> Key role for lipids in cognitive symptoms of schizophrenia. <i>Transl Psychiatry</i> 10, </b><b>399 (2020). https://doi.org/10.1038/s41398-020-01084-x</b>" and any questions about this data set may be addressed to Dr. Dorien A. Maas at d.a.maas@outlook.com.
DOI: 10.5286/isis.e.rb2070002-2
title: quantum criticality derived from the valence fluctuations of YbCu4Ni

description: In the previous works, many good crystals and nice experimental method provided a lot of subjects of the quantum critical phenomena originated from the spin. In these days, the quantum critical phenomena from the novel freedom such as the electric-quadrupole, rattling, and valence are much attracted. The prime candidate is valence. However, to study of quantum critical phenomena derived from valences, there is a large experimental problem of finding actual candidate materials. In YbCu4Ni, the valence fluctuation of Yb ion occurs from the our NQR measurement and the development of quantum fluctuations was observed with decreasing low temperatures. However, the reason for the rapid relaxation is that the intensity of the resonance line is small and it is difficult to perform ultra-low temperature measurement. muSR is suitable because the time scale is faster.
DOI: 10.6084/m9.figshare.13207424
title: Nature Post-Doctoral Survey

description: Anonymised raw data, cleaned data analysis and the questionnaire for Nature's inaugural survey of post-doctoral researchers, which attracted more than 7,600 respondents from across the world.
DOI: 10.6084/m9.figshare.13207424.v1
title: Nature Post-Doctoral Survey

description: Anonymised raw data, cleaned data analysis and the questionnaire for Nature's inaugural survey of post-doctoral researchers, which attracted more than 7,600 respondents from across the world.
DOI: 10.6084/m9.figshare.12666446.v2
title: SSR screening and morphological characterizathion of ‘Galega vulgar’, a very diverse Mediterranean olive variety

description: SSR screening of 595 individuals of a Portuguese olive tree variety, 'Galega vulgar', plus three representative samples of 'Picual', 'Arbequina' and 'Galega' varieties from WOGB.Morphological characterization of endocarps and the endocarp profiles found in the 629 prospected olive trees in 10 traditional Portuguese regions of olive production.<br><br>
DOI: 10.6084/m9.figshare.13153346
title: Data matrices MTBLS606

description: Data matrix generated by SLAW and XCMS used in the SLAW article.<br>
DOI: 10.6084/m9.figshare.13153346.v1
title: Data matrices MTBLS606

description: Data matrix generated by SLAW and XCMS used in the SLAW article.<br>
DOI: 10.6084/m9.figshare.13153358
title: Data matrices MTBLS1129

description: Data matrix generated by SLAW and XCMS used in the SLAW article
DOI: 10.6084/m9.figshare.13153358.v1
title: Data matrices MTBLS1129

description: Data matrix generated by SLAW and XCMS used in the SLAW article
DOI: 10.15161/oar.it/73255
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.4271341
title: A new set of analytical formulae for the computation of the bootstrap current and the neoclassical conductivity in tokamaks

description: Provided data includes data sets that have been used to develop a new set of analytical formulas for calculating the bootstrap current and the neoclassical conductivity.
DOI: 10.6084/m9.figshare.13234196
title: Appendix 3: Apatite fission track data. Footprints of the Alice Springs Orogeny preserved in far northern Australia: An application of multi-kinetic thermochronology in the Pine Creek Orogen and Arnhem Province

description: Appendix 3: Apatite fission track data
DOI: 10.6084/m9.figshare.13234196.v1
title: Appendix 3: Apatite fission track data. Footprints of the Alice Springs Orogeny preserved in far northern Australia: An application of multi-kinetic thermochronology in the Pine Creek Orogen and Arnhem Province

description: Appendix 3: Apatite fission track data
DOI: 10.6084/m9.figshare.13234202
title: Appendix 4: Apatite (U–Th–Sm)/He data. Footprints of the Alice Springs Orogeny preserved in far northern Australia: An application of multi-kinetic thermochronology in the Pine Creek Orogen and Arnhem Province

description: Appendix 4: Apatite (U–Th–Sm)/He data
DOI: 10.6084/m9.figshare.13234202.v1
title: Appendix 4: Apatite (U–Th–Sm)/He data. Footprints of the Alice Springs Orogeny preserved in far northern Australia: An application of multi-kinetic thermochronology in the Pine Creek Orogen and Arnhem Province

description: Appendix 4: Apatite (U–Th–Sm)/He data
DOI: 10.6084/m9.figshare.13234205
title: Appendix 2: Apatite geochemistry data. Footprints of the Alice Springs Orogeny preserved in far northern Australia: An application of multi-kinetic thermochronology in the Pine Creek Orogen and Arnhem Province

description: Appendix 2: Apatite geochemistry data
DOI: 10.5281/zenodo.4268064
title: Au38Q MBTR-K3

description: <strong>Purpose</strong><br> The purpose of Au38Q MBTR-K3 is to test the scalability of a machine learning regression model when the number of observations and the number of features change. <strong>Background</strong><br> The Au38Q MBTR-K3 was created from a trajectory file regarding the density functional theory simulation of Au38Q hybrid nanoparticle performed by Juarez-Mosqueda et al. in their paper <em>Ab initio molecular dynamics studies of Au38(SR)24 isomers under heating</em> using the MBTR descriptor by Himanen et al. as presented in paper <em>DScribe: Library of descriptors for machine learning in materials science</em>. The MBTR was used with the default parameters for K=3 (angles between atoms) presented at the website of Dscribe version 0.4.0. The dataset was first used to probe the properties of Minimal Learning Machine in paper <em>Do Randomized Algorithms Improve the Efficiency of<br> Minimal Learning Machine?</em> by Linja et al. <strong>Description</strong><br> The dataset contains nine variants of the same idea. In each, an observation refers to a MBTR description of the structural angles of the Au38Q hybrid nanoparticle of a single timestep in a DFT simulation and the potential energy of the said nanoparticle at the timestep. The input space is the MBTR description and the output space is the potential energy. Features refer to the output of the MBTR descriptor, here used as the input. We used three different numbers of observations and three different numbers of descriptor accuracies. Regarding the the number of observations, we used <em>RS-maximin</em> to find out the most different observations available and used the first 4000 and first 8000 as the selections in 4k and 8k variants. Regarding the number of features, we used different descriptor accuracy values [2,10,100] that produced descriptors of lengths [80,400,4000]. This allowed the number of features to represent the data description resolution. Downsampling of the number of features from 4000 to lower numbers was not used. Further details are presented in paper <em>Do Randomized Algorithms Improve the Efficiency of<br> Minimal Learning Machine?</em> by Linja et al.
description: {"references": ["Juarez-Mosqueda, R.; Sami, M.; Hannu, H. Ab initio molecular dynamics studies of Au38(SR)24 isomers under heating. The European Physical Journal D 2019, 73. doi:10.1140/epjd/e2019-90441-5", "Himanen, L.; J\u00e4ger, M.O.J.; Morooka, E.V.; Federici Canova, F.; Ranawat, Y.S.; Gao, D.Z.; Rinke, P.; Foster, A.S. DScribe: Library of descriptors for machine learning in materials science. Computer Physics Communications 2020, 247, 106949. doi:10.1016/j.cpc.2019.106949", "Pihlajam\u00e4ki, A.; H\u00e4m\u00e4l\u00e4inen, J.; Linja, J.; Nieminen, P.; Malola, S.; K\u00e4rkk\u00e4inen, T.; H\u00e4kkinen, H. Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods. The Journal of Physical Chemistry A 2020. doi:10.1021/acs.jpca.0c01512"]}
DOI: 10.6084/m9.figshare.13234205.v1
title: Appendix 2: Apatite geochemistry data. Footprints of the Alice Springs Orogeny preserved in far northern Australia: An application of multi-kinetic thermochronology in the Pine Creek Orogen and Arnhem Province

description: Appendix 2: Apatite geochemistry data
DOI: 10.5281/zenodo.4268063
title: Au38Q MBTR-K3

description: <strong>Purpose</strong><br> The purpose of Au38Q MBTR-K3 is to test the scalability of a machine learning regression model when the number of observations and the number of features change. <strong>Background</strong><br> The Au38Q MBTR-K3 was created from a trajectory file regarding the density functional theory simulation of Au38Q hybrid nanoparticle performed by Juarez-Mosqueda et al. in their paper <em>Ab initio molecular dynamics studies of Au38(SR)24 isomers under heating</em> using the MBTR descriptor by Himanen et al. as presented in paper <em>DScribe: Library of descriptors for machine learning in materials science</em>. The MBTR was used with the default parameters for K=3 (angles between atoms) presented at the website of Dscribe version 0.4.0. The dataset was first used to probe the properties of Minimal Learning Machine in paper <em>Do Randomized Algorithms Improve the Efficiency of<br> Minimal Learning Machine?</em> by Linja et al. <strong>Description</strong><br> The dataset contains nine variants of the same idea. In each, an observation refers to a MBTR description of the structural angles of the Au38Q hybrid nanoparticle of a single timestep in a DFT simulation and the potential energy of the said nanoparticle at the timestep. The input space is the MBTR description and the output space is the potential energy. Features refer to the output of the MBTR descriptor, here used as the input. We used three different numbers of observations and three different numbers of descriptor accuracies. Regarding the the number of observations, we used <em>RS-maximin</em> to find out the most different observations available and used the first 4000 and first 8000 as the selections in 4k and 8k variants. Regarding the number of features, we used different descriptor accuracy values [2,10,100] that produced descriptors of lengths [80,400,4000]. This allowed the number of features to represent the data description resolution. Downsampling of the number of features from 4000 to lower numbers was not used. Further details are presented in paper <em>Do Randomized Algorithms Improve the Efficiency of<br> Minimal Learning Machine?</em> by Linja et al.
description: {"references": ["Juarez-Mosqueda, R.; Sami, M.; Hannu, H. Ab initio molecular dynamics studies of Au38(SR)24 isomers under heating. The European Physical Journal D 2019, 73. doi:10.1140/epjd/e2019-90441-5", "Himanen, L.; J\u00e4ger, M.O.J.; Morooka, E.V.; Federici Canova, F.; Ranawat, Y.S.; Gao, D.Z.; Rinke, P.; Foster, A.S. DScribe: Library of descriptors for machine learning in materials science. Computer Physics Communications 2020, 247, 106949. doi:10.1016/j.cpc.2019.106949", "Pihlajam\u00e4ki, A.; H\u00e4m\u00e4l\u00e4inen, J.; Linja, J.; Nieminen, P.; Malola, S.; K\u00e4rkk\u00e4inen, T.; H\u00e4kkinen, H. Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods. The Journal of Physical Chemistry A 2020. doi:10.1021/acs.jpca.0c01512"]}