cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2022-02.2442075/1937286
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442281/1937291
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439762/1937296
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439208/1937312
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439218/1937313
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439398/1937336
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442144/1937345
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439093/1937346
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439826/1937353
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439081/1937357
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439896/1937362
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439040/1937388
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439079/1937397
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442299/1937410
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439558/1937423
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442274/1937435
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442135/1937445
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439901/1937454
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439740/1937476
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442166/1937489
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442301/1937515
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439769/1937521
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439085/1937525
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439179/1937584
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439905/1938023
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442112/1938030
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439838/1938034
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442188/1938036
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439820/1938041
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439869/1938075
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442155/1938078
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442236/1938127
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442303/1938128
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439090/1938135
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442300/1938141
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439800/1938147
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442069/1938148
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439070/1938194
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439238/1938197
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442240/1938202
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442102/1938218
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439031/1938221
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442157/1938223
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442206/1938245
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442226/1938278
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442203/1938287
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439214/1938291
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442293/1938293
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442096/1938296
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439524/1938299
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442132/1938300
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439038/1938304
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442164/1938312
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439083/1938318
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442095/1938321
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442111/1938332
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442184/1938338
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442081/1938342
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442228/1938350
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442139/1938353
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442205/1938363
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442264/1938373
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442259/1939130
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439415/1939132
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439833/1939144
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442158/1939154
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439790/1939169
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442116/1939179
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439749/1939191
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442179/1939197
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442280/1939205
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439735/1939213
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442167/1939223
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439294/1939227
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442219/1939234
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439589/1939252
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442292/1939262
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439731/1939270
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442080/1939314
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442269/1939316
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442174/1939323
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442138/1939333
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439242/1939345
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442124/1939348
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442282/1939359
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439810/1939360
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439113/1939380
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439844/1939391
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442239/1939402
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442227/1939411
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439128/1939421
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439894/1939447
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442059/1939450
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439560/1939458
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439895/1940140
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439181/1940153
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442229/1940165
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439842/1940177
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442185/1940194
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442265/1940196
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442242/1940203
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439082/1940207
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442066/1940221
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439756/1940222
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439715/1940257
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439809/1940265
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439163/1940273
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442178/1940281
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442065/1940283
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439881/1940288
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439595/1941074
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442204/1941078
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442097/1941095
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439650/1941132
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439778/1941133
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442271/1941135
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442121/1941162
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439100/1941182
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439350/1941186
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442074/1941210
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439909/1941212
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442279/1941214
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442202/1941216
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2442180/1941220
title: Data for EMSL Project 51132 from February 2022
DOI: 10.25582/data.2022-02.2439025/1951442
title: Data for EMSL Project 49771 from February 2022
DOI: 10.5517/ccdc.csd.cc2cknf1
title: CCDC 2191713: Experimental Crystal Structure Determination

description: Related Article: Andreas Luxenburger, Lawrence D. Harris, Elizabeth M. Ure, Wanting Jiao, Anthony D. Woolhouse, Scott A. Cameron, Alex Weymouth-Wilson, Richard H. Furneaux, Janet L. Pitman, Simon F.R. Hinkley|2023|Eur.J.Med.Chem.|250|115143|doi:10.1016/j.ejmech.2023.115143
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2ckng2
title: CCDC 2191714: Experimental Crystal Structure Determination

description: Related Article: Andreas Luxenburger, Lawrence D. Harris, Elizabeth M. Ure, Wanting Jiao, Anthony D. Woolhouse, Scott A. Cameron, Alex Weymouth-Wilson, Richard H. Furneaux, Janet L. Pitman, Simon F.R. Hinkley|2023|Eur.J.Med.Chem.|250|115143|doi:10.1016/j.ejmech.2023.115143
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cknh3
title: CCDC 2191715: Experimental Crystal Structure Determination

description: Related Article: Andreas Luxenburger, Lawrence D. Harris, Elizabeth M. Ure, Wanting Jiao, Anthony D. Woolhouse, Scott A. Cameron, Alex Weymouth-Wilson, Richard H. Furneaux, Janet L. Pitman, Simon F.R. Hinkley|2023|Eur.J.Med.Chem.|250|115143|doi:10.1016/j.ejmech.2023.115143
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d5kw1
title: CCDC 2209893: Experimental Crystal Structure Determination

description: Related Article: Rolivhuwa Mahwasane, Luccile Mbonzhe, Nabanita Chatterjee, Clive Oliver, Eustina Batisai|2023|Inorg.Chem.Commun.|150|110477|doi:10.1016/j.inoche.2023.110477
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d5ktz
title: CCDC 2209891: Experimental Crystal Structure Determination

description: Related Article: Rolivhuwa Mahwasane, Luccile Mbonzhe, Nabanita Chatterjee, Clive Oliver, Eustina Batisai|2023|Inorg.Chem.Commun.|150|110477|doi:10.1016/j.inoche.2023.110477
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d5kv0
title: CCDC 2209892: Experimental Crystal Structure Determination

description: Related Article: Rolivhuwa Mahwasane, Luccile Mbonzhe, Nabanita Chatterjee, Clive Oliver, Eustina Batisai|2023|Inorg.Chem.Commun.|150|110477|doi:10.1016/j.inoche.2023.110477
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.22152380
title: VCF genotype of four maize DH populations

description: ALL lines were genotyped with the GenoBaits Maize 1K marker panel that was developed by Mol Breeding Biotechnology Co., Ltd., Shijiazhuang, China (http://www.molbreeding.com/), based on genotyping by target sequencing platform in maize.
DOI: 10.7278/s50d-vsvt-ecjx
title: Dataset for: electric field obtained from FDTD simulations at different resolutions during space weather hazards
DOI: 10.6084/m9.figshare.22152839
title: P.brasierii_TUB-ITS-COX_clade6_Combined_P532.fasta

description: Phylogenetic relationships of <em>Phytophthora brasierii </em>sp. nov.
DOI: 10.6084/m9.figshare.22152839.v1
title: P.brasierii_TUB-ITS-COX_clade6_Combined_P532.fasta

description: Phylogenetic relationships of <em>Phytophthora brasierii </em>sp. nov.
DOI: 10.6084/m9.figshare.22153037
title: Reproducibility code

description: Reproducibility code for scAce
DOI: 10.6084/m9.figshare.22153037.v1
title: Reproducibility code

description: Reproducibility code for scAce
DOI: 10.21227/v0ze-vy88
title: Spectral Efficiency

description: In-building wireless solutions, such as distributed antenna systems and small cells, appear as alternatives for mobile operators to provide coverage, capacity and data traffic to subscribers in indoor scenarios where service from the macro network is poor, due to RF propagation losses, capacity limitations and signal interference. Each indoor scenario presents unique challenges in terms of throughput and spectral efficiency. This paper presents a statistical analysis of the influence that in-building wireless solutions, indoor scenarios and antenna technology (factors of the experiment) have on the spectral efficiency of a mobile network, using ANOVA to determine statistical significance. Results show that the aforementioned factors have a significant impact on the spectral efficiency, revealing the importance to ponder which in-building wireless solution provides better performance based on the indoor scenario under consideration.
DOI: 10.6084/m9.figshare.22153244
title: hindsii_Btub_clade6_Final.fasta

description: <em><strong>Phylogenetic analysis</strong></em>
DOI: 10.6084/m9.figshare.22153244.v1
title: hindsii_Btub_clade6_Final.fasta

description: <em><strong>Phylogenetic analysis</strong></em>
DOI: 10.5281/zenodo.7102601
title: VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

description: The <em>VivesDebate-Speech</em> corpus contains the acoustic information of 29 different argumentative debates and the annotations of the segmentation (i.e., BIO tags) of the Argumentative Discourse Units identified in the spoken natural language discourse.
DOI: 10.5281/zenodo.7102602
title: VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

description: The <em>VivesDebate-Speech</em> corpus contains the acoustic information of 29 different argumentative debates and the annotations of the segmentation (i.e., BIO tags) of the Argumentative Discourse Units identified in the spoken natural language discourse.
DOI: 10.5281/zenodo.7672241
title: VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

description: The <em>VivesDebate-Speech</em> corpus contains the acoustic information of 29 different argumentative debates and the annotations of the segmentation (i.e., BIO tags) of the Argumentative Discourse Units identified in the spoken natural language discourse.
DOI: 10.6084/m9.figshare.22153289
title: hindsii_cox_clade6_Final.fasta

description: Phylogenetic analysis
DOI: 10.6084/m9.figshare.22153289.v1
title: hindsii_cox_clade6_Final.fasta

description: Phylogenetic analysis
DOI: 10.6084/m9.figshare.22153303
title: Uncertainty quantification of reference-based cellular deconvolution algorithms

description: The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g., whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm; however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterize a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a sample’s DNA methylation profile and its expected profile given the estimated cellular proportions and cell type reference profiles. We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to &gt;6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using a common reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO).
DOI: 10.6084/m9.figshare.22153303.v1
title: Uncertainty quantification of reference-based cellular deconvolution algorithms

description: The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g., whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm; however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterize a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a sample’s DNA methylation profile and its expected profile given the estimated cellular proportions and cell type reference profiles. We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to &gt;6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using a common reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO).
DOI: 10.6084/m9.figshare.22153301
title: P.brasierii_Btub_clade6.fasta

description: Phylogenetic analysis result
DOI: 10.6084/m9.figshare.22153301.v1
title: P.brasierii_Btub_clade6.fasta

description: Phylogenetic analysis result
DOI: 10.6084/m9.figshare.22153307
title: P.brasierii_cox_clade6.fasta

description: Phylogenetic analysis
DOI: 10.6084/m9.figshare.22153307.v1
title: P.brasierii_cox_clade6.fasta

description: Phylogenetic analysis