cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2019-10.1871926/1916121
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1859040/1916122
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858912/1916123
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858716/1916124
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871919/1916125
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874584/1916126
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874593/1916127
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858711/1916128
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858722/1916129
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858755/1916130
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858970/1916131
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871924/1916132
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1859074/1916133
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874598/1916134
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1872435/1916135
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1872436/1916136
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1872396/1916137
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858750/1916138
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858725/1916139
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871922/1916140
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858712/1916141
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871918/1916142
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871928/1916143
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1872438/1916144
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874596/1916145
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858703/1916146
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871875/1916147
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871914/1916148
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858723/1916149
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858733/1916150
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1859002/1916151
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1872428/1916152
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858713/1916153
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874601/1916154
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858940/1916155
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858714/1916156
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1872433/1916157
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871915/1916158
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874602/1916159
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858715/1916160
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1859016/1916161
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871930/1916162
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858754/1916163
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858929/1916164
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858997/1916165
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1859010/1916166
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858789/1916167
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871923/1916168
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874597/1916169
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871921/1916170
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858718/1916171
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871874/1916172
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1859000/1916173
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1859034/1916174
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858852/1916175
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858917/1916176
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1859020/1916177
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871911/1916178
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874582/1916179
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874603/1916180
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871927/1916181
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871925/1916182
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874583/1916183
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874595/1916184
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871929/1916185
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858707/1916186
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858717/1916187
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858706/1916188
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858991/1916189
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858995/1916190
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1872437/1916191
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858924/1916192
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858729/1916193
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858708/1916194
title: Data for EMSL Project 50717 from October 2019
DOI: 10.6084/m9.figshare.21952883
title: TIMM17B.rar

description: The BC tissue microarrays (F151Br01, Bioaitech, Xi'an, China) collected osteosarcoma tissue samples from 140 patients. For IHC (Immunohistochemistry) staining, BC tissue microarrays were incubated with anti-TIMM17B (1:300, 11062-1-AP, rabbit, ProteinTech,Wuhan,China).
DOI: 10.6084/m9.figshare.21952883.v1
title: TIMM17B.rar

description: The BC tissue microarrays (F151Br01, Bioaitech, Xi'an, China) collected osteosarcoma tissue samples from 140 patients. For IHC (Immunohistochemistry) staining, BC tissue microarrays were incubated with anti-TIMM17B (1:300, 11062-1-AP, rabbit, ProteinTech,Wuhan,China).
DOI: 10.25582/data.2019-10.1871920/1916195
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874591/1916196
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874592/1916197
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1859001/1916198
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858730/1916199
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871876/1916200
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871916/1916201
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871931/1916202
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874604/1916203
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1859038/1916204
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871912/1916205
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858726/1916206
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1874599/1916207
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1871913/1916208
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1872430/1916209
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1872431/1916210
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1856091/1916213
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1856514/1916214
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1856558/1916215
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1856492/1916216
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-11.1890408/1916217
title: Data for EMSL Project 50717 from November 2019
DOI: 10.25582/data.2019-11.1890416/1916218
title: Data for EMSL Project 50717 from November 2019
DOI: 10.25582/data.2019-10.1858700/1916219
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1858696/1916220
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-11.1890397/1916221
title: Data for EMSL Project 50717 from November 2019
DOI: 10.25582/data.2019-10.1856293/1916222
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1856499/1916223
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1856516/1916224
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1856519/1916225
title: Data for EMSL Project 50717 from October 2019
DOI: 10.25582/data.2019-10.1856510/1916226
title: Data for EMSL Project 50717 from October 2019
DOI: 10.6084/m9.figshare.19445102
title: Additional file 1 of Prosthetic embodiment: systematic review on definitions, measures, and experimental paradigms

description: Additional file 1. Results of systematic embodiment search.
DOI: 10.6084/m9.figshare.19445102.v1
title: Additional file 1 of Prosthetic embodiment: systematic review on definitions, measures, and experimental paradigms

description: Additional file 1. Results of systematic embodiment search.
DOI: 10.6084/m9.figshare.19445105
title: Additional file 2 of Prosthetic embodiment: systematic review on definitions, measures, and experimental paradigms

description: Additional file 2. Summary of embodiment thematic coding process.
DOI: 10.6084/m9.figshare.19445111
title: Additional file 4 of Prosthetic embodiment: systematic review on definitions, measures, and experimental paradigms

description: Additional file 4. Summary of ownership and agency experiments and measures.
DOI: 10.6084/m9.figshare.19445108
title: Additional file 3 of Prosthetic embodiment: systematic review on definitions, measures, and experimental paradigms

description: Additional file 3. Results of systematic ownership and agency search.
DOI: 10.6084/m9.figshare.19445111.v1
title: Additional file 4 of Prosthetic embodiment: systematic review on definitions, measures, and experimental paradigms

description: Additional file 4. Summary of ownership and agency experiments and measures.
DOI: 10.6084/m9.figshare.19445105.v1
title: Additional file 2 of Prosthetic embodiment: systematic review on definitions, measures, and experimental paradigms

description: Additional file 2. Summary of embodiment thematic coding process.
DOI: 10.6084/m9.figshare.19445108.v1
title: Additional file 3 of Prosthetic embodiment: systematic review on definitions, measures, and experimental paradigms

description: Additional file 3. Results of systematic ownership and agency search.
DOI: 10.6084/m9.figshare.19445110.v1
title: Additional file 1 of In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

description: Additional file 1 Read depth (X) and breadth (%) coverages estimated with BBMap (version February 13, 2020) or INNUca (version 4.2.2) of Listeria monocytogenes paired-end reads from reference genomes ATCC19114, ATCC19115 and ATCCBAA679 (n = 420) downsampled at different targeted read (Dr: 10X, 20X, 30X, 40X, 50X, 60X, 70X, 80X, 90X and 100X) and kmer (Dk: 8X, 15X, 23X, 30X, 38X, 45X, 52X, 60X, 67X, 75X) depth (X) with BBNorm (read length R = 150 and kmer size K = 30). (TSV 65 kb)
DOI: 10.6084/m9.figshare.19445110
title: Additional file 1 of In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

description: Additional file 1 Read depth (X) and breadth (%) coverages estimated with BBMap (version February 13, 2020) or INNUca (version 4.2.2) of Listeria monocytogenes paired-end reads from reference genomes ATCC19114, ATCC19115 and ATCCBAA679 (n = 420) downsampled at different targeted read (Dr: 10X, 20X, 30X, 40X, 50X, 60X, 70X, 80X, 90X and 100X) and kmer (Dk: 8X, 15X, 23X, 30X, 38X, 45X, 52X, 60X, 67X, 75X) depth (X) with BBNorm (read length R = 150 and kmer size K = 30). (TSV 65 kb)
DOI: 10.6084/m9.figshare.19445113.v1
title: Additional file 2 of In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

description: Additional file 2 Standardized matrices of the cgMLST workflows BIGSdb (n = 423), INNUENDO (n = 339), GENPAT (n = 423), SeqSphere (n = 423), BioNumericsAB (n = 423), BioNumericsAF (n = 423) and MentaLiST (n = 423) applied to downsampled paired-end reads from 3 reference genomes of Listeria monocytogenes (i.e. ATCC19114, ATCC19115 and ATCCBAA679). The terms ATCC19114, ATCC19115 and ATCCBAA679 from the field FILE correspond to cgMLST profiles of the corresponded circular de novo assemblies from ATCC company. The empty sets represent mismatches. Because of internal firewall, the INNUca assembler integrated into the cgMLST workflow INNUENDO cannot not perform assemblies of paired-end reads with read depth of coverage of 20X (n = 42) and 10X (n = 42). (TSV 12262 kb)
DOI: 10.6084/m9.figshare.19445113
title: Additional file 2 of In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

description: Additional file 2 Standardized matrices of the cgMLST workflows BIGSdb (n = 423), INNUENDO (n = 339), GENPAT (n = 423), SeqSphere (n = 423), BioNumericsAB (n = 423), BioNumericsAF (n = 423) and MentaLiST (n = 423) applied to downsampled paired-end reads from 3 reference genomes of Listeria monocytogenes (i.e. ATCC19114, ATCC19115 and ATCCBAA679). The terms ATCC19114, ATCC19115 and ATCCBAA679 from the field FILE correspond to cgMLST profiles of the corresponded circular de novo assemblies from ATCC company. The empty sets represent mismatches. Because of internal firewall, the INNUca assembler integrated into the cgMLST workflow INNUENDO cannot not perform assemblies of paired-end reads with read depth of coverage of 20X (n = 42) and 10X (n = 42). (TSV 12262 kb)
DOI: 10.6084/m9.figshare.19445116
title: Additional file 3 of In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

description: Additional file 3 Standardized outcomes of the cgMLST workflows BIGSdb (n = 420), INNUENDO (n = 336), GENPAT (n = 420), SeqSphere (n = 420), BioNumerics (n = 420) and MentaLiST (n = 420) applied to downsampled paired-end reads from 3 reference genomes of Listeria monocytogenes (i.e. ATCC19114, ATCC19115 and ATCCBAA679) with associated de novo assembly parameters assessed with Quast (version 5.0.2) and MultiQC (version 1.9). The targeted read depth (Dr: 10X, 20X, 30X, 40X, 50X, 60X, 70X, 80X, 90X and 100X) were prepared according to kmer depth (Dk): 8X, 15X, 23X, 30X, 38X, 45X, 52X, 60X, 67X, 75X) setting of BBNorm (read length R = 150 and kmer size K = 30). Because of internal firewall, the INNUca assembler integrated into the cgMLST workflow INNUENDO cannot not perform assemblies of paired-end reads with read depth of coverage of 20X (n = 42) and 10X (n = 42). (TSV 1207 kb)
DOI: 10.6084/m9.figshare.19445116.v1
title: Additional file 3 of In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

description: Additional file 3 Standardized outcomes of the cgMLST workflows BIGSdb (n = 420), INNUENDO (n = 336), GENPAT (n = 420), SeqSphere (n = 420), BioNumerics (n = 420) and MentaLiST (n = 420) applied to downsampled paired-end reads from 3 reference genomes of Listeria monocytogenes (i.e. ATCC19114, ATCC19115 and ATCCBAA679) with associated de novo assembly parameters assessed with Quast (version 5.0.2) and MultiQC (version 1.9). The targeted read depth (Dr: 10X, 20X, 30X, 40X, 50X, 60X, 70X, 80X, 90X and 100X) were prepared according to kmer depth (Dk): 8X, 15X, 23X, 30X, 38X, 45X, 52X, 60X, 67X, 75X) setting of BBNorm (read length R = 150 and kmer size K = 30). Because of internal firewall, the INNUca assembler integrated into the cgMLST workflow INNUENDO cannot not perform assemblies of paired-end reads with read depth of coverage of 20X (n = 42) and 10X (n = 42). (TSV 1207 kb)
DOI: 10.34740/kaggle/dsv/3286750
title: US Accidents (2016 - 2021)
DOI: 10.5281/zenodo.6349897
title: Preprocessed Data and Pretrained Models for Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings

description: This is preprocessed data and pretrained models from two of our papers: "Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings," by Erica Cooper, Cheng-I Lai, Yusuke Yasuda, Fuming Fang, Xin Wang, Nanxin Chen, and Junichi Yamagishi. (ICASSP 2020)<br> https://arxiv.org/abs/1910.10838 "Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis," by Erica Cooper, Xin Wang, Yi Zhao, Yusuke Yasuda, and Junichi Yamagishi. (arXiv) https://arxiv.org/abs/2011.04839 This data is meant to be used with our open-source implementation, which can be found here: https://github.com/nii-yamagishilab/multi-speaker-tacotron More information about the directory structure and how to use the data can be found in the READMEs on GitHub.
description: {"references": ["Cooper, Erica et al. (2020). Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings. ICASSP 2020. https://arxiv.org/abs/1910.10838", "Cooper, Erica et al. (2020). Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis. https://arxiv.org/abs/2011.04839"]}
DOI: 10.5281/zenodo.6349896
title: Preprocessed Data and Pretrained Models for Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings

description: This is preprocessed data and pretrained models from two of our papers: "Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings," by Erica Cooper, Cheng-I Lai, Yusuke Yasuda, Fuming Fang, Xin Wang, Nanxin Chen, and Junichi Yamagishi. (ICASSP 2020)<br> https://arxiv.org/abs/1910.10838 "Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis," by Erica Cooper, Xin Wang, Yi Zhao, Yusuke Yasuda, and Junichi Yamagishi. (arXiv) https://arxiv.org/abs/2011.04839 This data is meant to be used with our open-source implementation, which can be found here: https://github.com/nii-yamagishilab/multi-speaker-tacotron More information about the directory structure and how to use the data can be found in the READMEs on GitHub.
description: {"references": ["Cooper, Erica et al. (2020). Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings. ICASSP 2020. https://arxiv.org/abs/1910.10838", "Cooper, Erica et al. (2020). Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis. https://arxiv.org/abs/2011.04839"]}
DOI: 10.6084/m9.figshare.19445842.v1
title: Additional file 2 of Species and genotypes belonging to Echinococcus granulosus sensu lato complex causing human cystic echinococcosis in Europe (2000–2021): a systematic review

description: Additional file 2: Table S2. Summary of data extracted from publications reviewed in this study.
DOI: 10.6084/m9.figshare.19445881.v1
title: Additional file 5 of Risk factors for long-term survival in patients with ypN+ M0 rectal cancer after radical anterior resection

description: Additional file 5. Dataset.
DOI: 10.6084/m9.figshare.19445935.v1
title: Additional file 1 of Widely used, short 16S rRNA mitochondrial gene fragments yield poor and erratic results in phylogenetic estimation and species delimitation of amphibians

description: Additional file 1: Table S1. List of sequences used in this study and their associated GenBank accession numbers.
DOI: 10.6084/m9.figshare.19445792.v1
title: Additional file 1 of Transgenesis in parasitic helminths: a brief history and prospects for the future

description: Additional file 1: Table S1. Extended details in the historical advances in the transgenesis of parasitic helminths.
DOI: 10.6084/m9.figshare.19445980.v1
title: Additional file 1 of Retrospective analysis of radiographic signs in feline pleural effusions to predict disease aetiology

description: Additional file 1.
DOI: 10.6084/m9.figshare.19445981.v1
title: Additional file 10 of Genomic architecture of 5S rDNA cluster and its variations within and between species

description: Additional file 10.
DOI: 10.6084/m9.figshare.19445984.v1
title: Additional file 11 of Genomic architecture of 5S rDNA cluster and its variations within and between species

description: Additional file 11.
DOI: 10.6084/m9.figshare.19445990.v1
title: Additional file 2 of Genomic architecture of 5S rDNA cluster and its variations within and between species

description: Additional file 2.
DOI: 10.6084/m9.figshare.19445993.v1
title: Additional file 3 of Genomic architecture of 5S rDNA cluster and its variations within and between species

description: Additional file 3.
DOI: 10.6084/m9.figshare.19445996.v1
title: Additional file 4 of Genomic architecture of 5S rDNA cluster and its variations within and between species

description: Additional file 4.
DOI: 10.6084/m9.figshare.19445999.v1
title: Additional file 5 of Genomic architecture of 5S rDNA cluster and its variations within and between species

description: Additional file 5.
DOI: 10.6084/m9.figshare.19446005.v1
title: Additional file 6 of Genomic architecture of 5S rDNA cluster and its variations within and between species

description: Additional file 6.
DOI: 10.6084/m9.figshare.19446017.v1
title: Additional file 8 of Genomic architecture of 5S rDNA cluster and its variations within and between species

description: Additional file 8.
DOI: 10.6084/m9.figshare.19446011.v1
title: Additional file 7 of Genomic architecture of 5S rDNA cluster and its variations within and between species

description: Additional file 7.
DOI: 10.6084/m9.figshare.19446023.v1
title: Additional file 9 of Genomic architecture of 5S rDNA cluster and its variations within and between species

description: Additional file 9.
DOI: 10.6084/m9.figshare.19445774.v1
title: Additional file 6 of BarWare: efficient software tools for barcoded single-cell genomics

description: Additional file 6: Table S6. Antibodies used for Cell Hashing and FACS. Manufacturer: Reagent manufacturer. Catalog No.: Manufacturer catalog number. Conjugate: Moiety (fluorophore or oligonucleotides) conjugated to each antibody. Target: Antibody binding target(s). Clone: Antibody clone. Vol per M cells (µL): Volume of antibody added per million cells for staining.