cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/sjtjwjdchhqjhe-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C21H21F3O3/c1-14-15(5-4-6-19(14)25)8-9-16-10-11-17(13-18(16)21(22,23)24)27-20-7-2-3-12-26-20/h10-11,13,20H,2-7,12H2,1H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (300 MHz, CDCl3, ppm) δ = 1.54–1.79 (m, 3H), 1.83–1.91 (m, 2H), 1.91–2.08 (m, 3H), 2.03 (t, J = 1.8 Hz, 3H), 2.46 (t, J = 6.7 Hz, 2H), 2.53–2.61 (m, 2H), 3.62 (dt, J = 11.3 Hz, 1.4 Hz, 1H), 3.81 (ddd, J = 11.3 Hz, 10.3 Hz, 3.0 Hz, 1H), 5.49 (t, J = 2.8 Hz, 1H), 7.19 (dd, J = 8.6 Hz, 2.4 Hz, 1H), 7.35 (d, J = 2.4 Hz, 1H), 7.52 (d, J = 8.6 Hz, 1H). Analysis taken from the literature: ISBN-10: 3867271291
DOI: 10.14272/sjtjwjdchhqjhe-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C21H21F3O3/c1-14-15(5-4-6-19(14)25)8-9-16-10-11-17(13-18(16)21(22,23)24)27-20-7-2-3-12-26-20/h10-11,13,20H,2-7,12H2,1H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (75.5 MHz, CDCl3, ppm) δ = 13.8, 18.2, 22.6, 24.9, 29.9, 30.8, 37.9, 61.9, 91.8, 96.4, 98.8, 113.0 (q, J = 2.2 Hz), 114.7 (q, J = 5.2 Hz), 118.9, 123.1 (q, J = 274 Hz), 132.7 (q, J = 31 Hz), 135.6, 137.2, 139.1, 157.4, 198.5. Analysis taken from the literature: ISBN-10: 3867271291
DOI: 10.14272/sjtjwjdchhqjhe-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C21H21F3O3/c1-14-15(5-4-6-19(14)25)8-9-16-10-11-17(13-18(16)21(22,23)24)27-20-7-2-3-12-26-20/h10-11,13,20H,2-7,12H2,1H3

description: dataset for mass spectrometry (MS) MS (EI, 70 eV): m/z (%) = 378.2 (2) [M]+, 284.1 (98) [M – C5H8O]+, 85.0 (100) [C5H9O]+. HRMS (C21H21F3O3): calcd 378.1443 , found 378.1446. Analysis taken from the literature: ISBN-10: 3867271291
DOI: 10.17632/w3n9xp5dnp.2
title: DNA organization along pachytene chromosome axes and its relationship with crossover frequencies 

description: MLH1- focus map along SC1
The construction of crossover frequency histograms from MLH1 foci in birds and other vertebrates is fully explained in several publications (Pigozzi and Solari 1999; Borodin et al 2008). Briefly, individual SC lengths, centromeric signals, and MLH1 foci were scored using version 3.3 of the MicroMeasure program [53] which records absolute and relative distances on digitized images. To generate the recombination maps of the SC1, we calculated the absolute position of each MLH1 focus by multiplying the relative position of each focus by the average absolute length of the chromosome arm. These data were pooled for each arm and graphed in histogram form. The genetic map length in cM of each interval in this histogram (bin size 0.25 µm) was calculated multiplying the average number of MLH1 foci by 50. To produce the cumulative cM distribution, the average number of MLH1 foci per interval was converted to cM and then added along the SC starting from the tip of the short arm.
Foci on microbivalentes 9-38. The number of foci is the average scored in 138 nuclei.
DOI: 10.14272/sjtjwjdchhqjhe-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C21H21F3O3/c1-14-15(5-4-6-19(14)25)8-9-16-10-11-17(13-18(16)21(22,23)24)27-20-7-2-3-12-26-20/h10-11,13,20H,2-7,12H2,1H3

description: dataset for infrared absorption spectroscopy (IR) IR (KBr): ṽ = 2947, 2198, 1665, 1594, 1499, 1438, 1381, 1357, 1313, 1258, 1230, 1133, 1076, 1038, 960, 933, 755 cm–1.Analysis taken from the literature: ISBN-10: 3867271291
DOI: 10.14272/nuymhlvxvldfhg-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C20H20Cl2O3/c1-13-14(5-4-6-19(13)23)8-9-16-17(21)11-15(12-18(16)22)25-20-7-2-3-10-24-20/h11-12,20H,2-7,10H2,1H3

description: dataset for mass spectrometry (MS) MS (EI, 70 eV): m/z (%) = 378.2/380.2 (2/1) [M]+, 294.1/296.1 (58/38) [M – C5H8O]+, 85.1 (100) [C5H9O]+.HRMS (C20H20Cl2O3): Calcd 379.08623 [M + H]+, Found 379.08605 [M + H]+. Analysis taken from literature: ISBN-10: 3867271291
DOI: 10.14272/nuymhlvxvldfhg-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C20H20Cl2O3/c1-13-14(5-4-6-19(13)23)8-9-16-17(21)11-15(12-18(16)22)25-20-7-2-3-10-24-20/h11-12,20H,2-7,10H2,1H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (75.5 MHz, CDCl3, ppm) δ = 14.1, 18.1, 22.7, 24.9, 29.8, 30.8, 37.9, 61.9, 96.6, 96.6, 96.9, 114.7, 116.2 (2C), 137.2, 138.0 (2C), 139.3, 157.6, 198.5. Analysis taken from literature: ISBN-10: 3867271291
DOI: 10.14272/nuymhlvxvldfhg-uhfffaoysa-n.1
title: 3-((2,6-dichloro-4-((tetrahydro-2H-pyran-2-yl)oxy)phenyl)ethynyl)-2-methylcyclohex-2-en-1-one
title: InChI=1S/C20H20Cl2O3/c1-13-14(5-4-6-19(13)23)8-9-16-17(21)11-15(12-18(16)22)25-20-7-2-3-10-24-20/h11-12,20H,2-7,10H2,1H3

description: This dataset contains experimental data from a sample (InChI=1S/C20H20Cl2O3/c1-13-14(5-4-6-19(13)23)8-9-16-17(21)11-15(12-18(16)22)25-20-7-2-3-10-24-20/h11-12,20H,2-7,10H2,1H3). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) mass spectrometry (MS) infrared absorption spectroscopy (IR)
DOI: 10.14272/nuymhlvxvldfhg-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C20H20Cl2O3/c1-13-14(5-4-6-19(13)23)8-9-16-17(21)11-15(12-18(16)22)25-20-7-2-3-10-24-20/h11-12,20H,2-7,10H2,1H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (300 MHz, CDCl3, ppm) δ = 1.54–1.79 (m, 3H), 1.81–2.09 (m, 5H), 2.09 (t, J = 1.8 Hz, 3H), 2.49 (t, J = 6.7 Hz, 2H), 2.59–2.67 (m, 2H), 3.59–3.69 (m, 1H), 3.79 (ddd, J = 11.2 Hz, 10.3 Hz, 3.0 Hz, 1H), 5.43 (t, J = 2.7 Hz, 1H), 7.09 (s, 2H). Analysis taken from literature: ISBN-10: 3867271291
DOI: 10.14272/nuymhlvxvldfhg-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C20H20Cl2O3/c1-13-14(5-4-6-19(13)23)8-9-16-17(21)11-15(12-18(16)22)25-20-7-2-3-10-24-20/h11-12,20H,2-7,10H2,1H3

description: dataset for infrared absorption spectroscopy (IR) IR (KBr): ṽ) = 2948, 2196, 1666, 1585, 1539, 1474, 1357, 1289, 1231, 1200, 1120, 1050, 972, 901, 858, 799 cm–1. Analysis taken from literature: ISBN-10: 3867271291
DOI: 10.17632/8jtm577zzk.1
title: Código CNC

description: Código G e Setup de moldes utilizados na máquina de reciclagem de material compósito.
DOI: 10.14272/jvynufuqtohcqs-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C15H12Cl2O2/c1-9-10(3-2-4-15(9)19)5-6-12-13(16)7-11(18)8-14(12)17/h7-8,18H,2-4H2,1H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (75.5 MHz, [D6]-DMSO, ppm) δ = 13.8, 22.2, 30.1, 37.2, 95.7, 96.5, 111.8, 115.6 (2C), 136.3, 137.1 (2C), 137.9, 159.4, 197.1. Analysis from the PhD thesis of IG
DOI: 10.14272/jvynufuqtohcqs-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C15H12Cl2O2/c1-9-10(3-2-4-15(9)19)5-6-12-13(16)7-11(18)8-14(12)17/h7-8,18H,2-4H2,1H3

description: dataset for mass spectrometry (MS) MS (EI, 70 eV): m/z (%) = 294.0/296.0 (100/61) [M]+, 266.0/268.0 (38/23) [M – CO]+.HRMS (C15H12Cl2O2): Calcd 295.02871 [M + H]+, Found 295.02880 [M + H]+. Analysis from the PhD thesis of IG
DOI: 10.14272/jvynufuqtohcqs-uhfffaoysa-n.1
title: 3-((2,6-dichloro-4-hydroxyphenyl)ethynyl)-2-methylcyclohex-2-en-1-one
title: InChI=1S/C15H12Cl2O2/c1-9-10(3-2-4-15(9)19)5-6-12-13(16)7-11(18)8-14(12)17/h7-8,18H,2-4H2,1H3

description: This dataset contains experimental data from a sample (InChI=1S/C15H12Cl2O2/c1-9-10(3-2-4-15(9)19)5-6-12-13(16)7-11(18)8-14(12)17/h7-8,18H,2-4H2,1H3). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) mass spectrometry (MS) infrared absorption spectroscopy (IR)
DOI: 10.14272/jvynufuqtohcqs-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C15H12Cl2O2/c1-9-10(3-2-4-15(9)19)5-6-12-13(16)7-11(18)8-14(12)17/h7-8,18H,2-4H2,1H3

description: dataset for infrared absorption spectroscopy (IR) IR (KBr): ṽ) = 3261, 2193, 1639, 1583, 1432, 1384, 1361, 1299, 1226, 1051, 860, 810 cm–1. Analysis from the PhD thesis of IG
DOI: 10.14272/jvynufuqtohcqs-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C15H12Cl2O2/c1-9-10(3-2-4-15(9)19)5-6-12-13(16)7-11(18)8-14(12)17/h7-8,18H,2-4H2,1H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (300 MHz, [D6]-DMSO, ppm) δ = 1.89–2.00 (m, 2H), 1.94 (t, J = 1.6 Hz, 3H), 2.40 (t, J = 6.7 Hz, 2H), 2.50–2.58 (m, 2H), 6.94 (s, 2H), 11.00 (s br., 1H). Analysis from the PhD thesis of IG.
DOI: 10.17632/xmcg82mx9k.1
title: MonkeyPox2022Tweets: The First Public Twitter Dataset on the 2022 MonkeyPox Outbreak

description: Please cite the following paper when using this dataset:
N. Thakur, “MonkeyPox2022Tweets: The first public Twitter dataset on the 2022 MonkeyPox outbreak,” Preprints, 2022, DOI: 10.20944/preprints202206.0172.v1

Abstract
The world is currently facing an outbreak of the monkeypox virus, and confirmed cases have been reported from 28 countries. Following a recent “emergency meeting”, the World Health Organization is considering whether the outbreak should be assessed as a “potential public health emergency of international concern”, as was done for the COVID-19 and Ebola outbreaks in the past. During this time, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, a dataset of Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description
The dataset consists of a total of 102,452 tweet IDs of the same number of tweets about monkeypox that were posted on Twitter from 7th May 2022 to 26th June 2022 (the most recent date at the time of dataset upload). The Tweet IDs are presented in 5 different files based on the timelines of the associated tweets. The following are the details of these dataset files

Filename: TweetIDs_Part1.txt (No. of Tweet IDs: 13926, Date Range of the associated Tweet IDs: May 7, 2022 to May 21, 2022)
Filename: TweetIDs_Part2.txt (No. of Tweet IDs: 17705, Date Range of the associated Tweet IDs: May 21, 2022 to May 27, 2022)
Filename: TweetIDs_Part3.txt (No. of Tweet IDs: 17585, Date Range of the associated Tweet IDs: May 27, 2022 to June 5, 2022)
Filename: TweetIDs_Part4.txt (No. of Tweet IDs: 19718, Date Range of the associated Tweet IDs: June 5, 2022 to June 11, 2022)
Filename: TweetIDs_Part5.txt (No. of Tweet IDs: 33518, Date Range of the associated Tweet IDs: June 12, 2022 to June 26, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used.
DOI: 10.5281/zenodo.6778751
title: KPIs to measure the impact of Food loss and Waste prevention strategies

description: These data correspond to the process of KPIs definition to measure the impact of Food Loss and Waste prevention strategies in FOODRUS. Both the initial long list of KPIs that started the process, and the results of 3 different surveys delivered to experts and stakeholders are included here.
DOI: 10.5281/zenodo.6778750
title: KPIs to measure the impact of Food loss and Waste prevention strategies

description: These data correspond to the process of KPIs definition to measure the impact of Food Loss and Waste prevention strategies in FOODRUS. Both the initial long list of KPIs that started the process, and the results of 3 different surveys delivered to experts and stakeholders are included here.
DOI: 10.14272/jmwdkefugjjktf-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C21H22Cl2O3/c1-2-16-14(6-5-7-20(16)24)9-10-17-18(22)12-15(13-19(17)23)26-21-8-3-4-11-25-21/h12-13,21H,2-8,11H2,1H3

description: dataset for mass spectrometry (MS) MS (ESI, MeOH/H2O/HCOOH): m/z (%) = 309.0/311.0 (100/65) [M + H – C5H8O]+, 393.1/395.1 (94/60) [M + H]+, 785.2/787.2 (36/44) [2M + H]+. HRMS (C21H22Cl2O3): Calcd 393.10167 [M + H]+, Found 393.10188 [M + H]+. Analysis taken from the literature: ISBN-10: 3867271291
DOI: 10.14272/jmwdkefugjjktf-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C21H22Cl2O3/c1-2-16-14(6-5-7-20(16)24)9-10-17-18(22)12-15(13-19(17)23)26-21-8-3-4-11-25-21/h12-13,21H,2-8,11H2,1H3

description: dataset for infrared absorption spectroscopy (IR) IR (KBr): ṽ= 2945, 2195, 1663, 1583, 1539, 1454, 1361, 1299, 1256, 1199, 1120, 1061, 1037, 972, 944, 899, 875, 801 cm–1. Analysis taken from the literature: ISBN-10: 3867271291
DOI: 10.14272/jmwdkefugjjktf-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C21H22Cl2O3/c1-2-16-14(6-5-7-20(16)24)9-10-17-18(22)12-15(13-19(17)23)26-21-8-3-4-11-25-21/h12-13,21H,2-8,11H2,1H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (75.5 MHz, CDCl3, ppm) δ = 13.5, 18.1, 21.7, 22.7, 24.8, 29.8, 30.8, 38.2, 61.9, 96.2, 96.4 (2C), 96.6, 114.6, 116.2, 136.8, 138.0 (2C), 145.2, 157.6, 198.0. Analysis taken from the literature: ISBN-10: 3867271291
DOI: 10.14272/jmwdkefugjjktf-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C21H22Cl2O3/c1-2-16-14(6-5-7-20(16)24)9-10-17-18(22)12-15(13-19(17)23)26-21-8-3-4-11-25-21/h12-13,21H,2-8,11H2,1H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (300 MHz, CDCl3, ppm) δ = 1.05 (t, J = 7.5 Hz, 3H), 1.53–1.79 (m, 3H), 1.80–2.10 (m, 5H), 2.47 (t, J = 6.7 Hz, 2H), 2.55–2.66 (m, 4H), 3.63 (dt, J = 11.1 Hz, 1.4 Hz, 1H), 3.73–3.84 (m, 1H), 5.43 (t, J = 2.8 Hz, 1H), 7.08 (s, 2H). Analysis taken from the literature: ISBN-10: 3867271291
DOI: 10.17632/gnnzjdfrg4.1
title: Complementary data: Reproductive parameters of male crab-eating fox (Cerdocyon thous) subjected to pharmacological semen collection by urethral catheterization

description: Complementary data of the manuscript https://doi.org/10.1016/j.therwi.2022.100004
DOI: 10.14272/jmwdkefugjjktf-uhfffaoysa-n.1
title: 3-((2,6-dichloro-4-((tetrahydro-2H-pyran-2-yl)oxy)phenyl)ethynyl)-2-ethylcyclohex-2-en-1-one
title: InChI=1S/C21H22Cl2O3/c1-2-16-14(6-5-7-20(16)24)9-10-17-18(22)12-15(13-19(17)23)26-21-8-3-4-11-25-21/h12-13,21H,2-8,11H2,1H3

description: This dataset contains experimental data from a sample (InChI=1S/C21H22Cl2O3/c1-2-16-14(6-5-7-20(16)24)9-10-17-18(22)12-15(13-19(17)23)26-21-8-3-4-11-25-21/h12-13,21H,2-8,11H2,1H3). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) mass spectrometry (MS) infrared absorption spectroscopy (IR)
DOI: 10.17632/54x3jrp8wj.1
title: Data related to SHG

description: Data related to 336 respondents of SHG
DOI: 10.17632/8vgrh6dyfp.1
title: Amelioration of SARS-CoV-2 infection by ANO6 phospholipid scramblase inhibition (Sim et al)

description: Original datasets and Supplemental Videos : Amelioration of SARS-CoV-2 infection by ANO6 phospholipid scramblase inhibition
DOI: 10.17632/4g2xfsbmnr.2
title: Range map dataset for terrestrial vertebrates across Taiwan

description: This dataset provides up-to-date, high-precision species distribution maps for 379 terrestrial vertebrates in Taiwan. 
We used species distribution modeling as the base and then aggregated multiple open datasets describing species occurrence and environmental factors as data sources. Thereafter, we estimated the primary broad-scale and high spatial resolution species range maps using the MaxEnt modeling algorithm, and then consulted experts on each taxa to refine these maps.

There are three files in this dataset:
model_metadata.csv - metadata of models and information of species, including species taxonomic information, and model arguments.
range_maps.shp - species range maps in the shapefile format, each species has its own polygon.
DOI: 10.17632/5zcjm4mfn8.2
title: Tectonic erosion and deep subduction in Central Tibet: Evidence from the discovery of retrograde eclogites in the Amdo microcontinent 

description: This data shows the composition of representative minerals and geochemical data from the retrograde eclogites in Amdo microcontinent, wchic can be used to constrain the P-T conditions of eclogite. This data also shows the isotopic data of zircon and rutile, which can be used to constrain the metamorphism age of Amdo eclogite.
DOI: 10.6084/m9.figshare.19896211
title: Popularity Adjusted Block Models are Generalized Random Dot Product Graphs

description: We connect two random graph models, the Popularity Adjusted Block Model (PABM) and the Generalized Random Dot Product Graph (GRDPG), by demonstrating that the PABM is a special case of the GRDPG in which communities correspond to mutually orthogonal subspaces of latent vectors. This insight allows us to construct new algorithms for community detection and parameter estimation for the PABM, as well as improve an existing algorithm that relies on Sparse Subspace Clustering. Using established asymptotic properties of Adjacency Spectral Embedding for the GRDPG, we derive asymptotic properties of these algorithms. In particular, we demonstrate that the absolute number of community detection errors tends to zero as the number of graph vertices tends to infinity. Simulation experiments illustrate these properties. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19896211.v1
title: Popularity Adjusted Block Models are Generalized Random Dot Product Graphs

description: We connect two random graph models, the Popularity Adjusted Block Model (PABM) and the Generalized Random Dot Product Graph (GRDPG), by demonstrating that the PABM is a special case of the GRDPG in which communities correspond to mutually orthogonal subspaces of latent vectors. This insight allows us to construct new algorithms for community detection and parameter estimation for the PABM, as well as improve an existing algorithm that relies on Sparse Subspace Clustering. Using established asymptotic properties of Adjacency Spectral Embedding for the GRDPG, we derive asymptotic properties of these algorithms. In particular, we demonstrate that the absolute number of community detection errors tends to zero as the number of graph vertices tends to infinity. Simulation experiments illustrate these properties. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19969486
title: Analysis of Professional Basketball Field Goal Attempts via a Bayesian Matrix Clustering Approach

description: We propose a Bayesian nonparametric matrix clustering approach to analyze the latent heterogeneity structure in the shot selection data collected from professional basketball players in the National Basketball Association (NBA). The proposed method adopts a mixture of finite mixtures framework and fully uses the spatial information via a mixture of matrix normal distribution representation. We propose an efficient Markov chain Monte Carlo algorithm for posterior sampling that allows simultaneous inference on both the number of clusters and the cluster configurations. We also establish large-sample convergence properties for the posterior distribution. The compelling empirical performance of the proposed method is demonstrated via simulation studies and an application to shot chart data from selected players in the NBAs 2017–2018 regular season. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19969486.v2
title: Analysis of Professional Basketball Field Goal Attempts via a Bayesian Matrix Clustering Approach

description: We propose a Bayesian nonparametric matrix clustering approach to analyze the latent heterogeneity structure in the shot selection data collected from professional basketball players in the National Basketball Association (NBA). The proposed method adopts a mixture of finite mixtures framework and fully uses the spatial information via a mixture of matrix normal distribution representation. We propose an efficient Markov chain Monte Carlo algorithm for posterior sampling that allows simultaneous inference on both the number of clusters and the cluster configurations. We also establish large-sample convergence properties for the posterior distribution. The compelling empirical performance of the proposed method is demonstrated via simulation studies and an application to shot chart data from selected players in the NBAs 2017–2018 regular season. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19969486.v1
title: Analysis of professional basketball field goal attempts via a Bayesian matrix clustering approach

description: We propose a Bayesian nonparametric matrix clustering approach to analyze the latent heterogeneity structure in the shot selection data collected from professional basketball players in the National Basketball Association (NBA). The proposed method adopts a mixture of finite mixtures framework and fully utilizes the spatial information via a mixture of matrix normal distribution representation. We propose an efficient Markov chain Monte Carlo algorithm for posterior sampling that allows simultaneous inference on both the number of clusters and the cluster configurations. We also establish large-sample convergence properties for the posterior distribution. The compelling empirical performance of the proposed method is demonstrated via simulation studies and an application to shot chart data from selected players in the NBA’s 2017–2018 regular season.
DOI: 10.5281/zenodo.6779299
title: "Comparable Recipes": Cooking recipes that reveal differences in the writing style depending on the writer

description: Each of the recipes is described by more than one person for the same dish. You can see the differences in the writing style of each person and how the same person describes different recipes. 20 dishes 64 writers 同一の調理について、複数人が記載した料理レシピがそれぞれ記載されています。記載者による書き方の違いや、同じ記載者が異なるレシピをどう記載するか、がわかります。 20種類の調理について延べ64人の記載が公開されています。
DOI: 10.5281/zenodo.6411184
title: "Comparable Recipes": Cooking recipes that reveal differences in the writing style depending on the writer

description: Each of the recipes is described by more than one person for the same dish. You can see the differences in the writing style of each person and how the same person describes different recipes. 20 dishes 64 writers 同一の調理について、複数人が記載した料理レシピがそれぞれ記載されています。記載者による書き方の違いや、同じ記載者が異なるレシピをどう記載するか、がわかります。 20種類の調理について延べ64人の記載が公開されています。