cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21523970.v1
title: TreeS5_MOTU_tree_Rasborinae_CladeI from Cryptic diversity impacts model selection and macroevolutionary inferences in diversification analyses

description: Species persist in landscapes through ecological dynamics but proliferate at wider spatial scales through evolutionary mechanisms. Disentangling the contribution of each dynamic is challenging, but the increasing use of dated molecular phylogenies opened new perspectives. First, the increasing use of DNA sequences in biodiversity inventory shed light on a substantial amount of cryptic diversity in species-rich ecosystems. Second, explicit diversification models accounting for various eco-evolutionary models are now available. Integrating both advances, we explored diversification trajectories among 10 lineages of freshwater fishes in Sundaland, for which time-calibrated and taxonomically rich phylogenies are available. By fitting diversification models to dated phylogenies and incorporating DNA-based species delimitation methods, the impact of cryptic diversity on diversification model selection and related inferences is explored. Eight clades display constant speciation rate model as the most likely if cryptic diversity is accounted, but nine display a signature of diversification slowdowns when cryptic diversity is ignored. Cryptic diversification occurs during the last 5 Myr for most groups, and palaeoecological models received little support. Most cryptic lineages display restricted range distribution, supporting geographical isolation across homogeneous landscapes as the main driver of diversification. These patterns question the persistence of cryptic diversity and its role during species proliferation.
DOI: 10.6084/m9.figshare.21523973
title: TreeS1_MOTU_tree_Channa from Cryptic diversity impacts model selection and macroevolutionary inferences in diversification analyses

description: Species persist in landscapes through ecological dynamics but proliferate at wider spatial scales through evolutionary mechanisms. Disentangling the contribution of each dynamic is challenging, but the increasing use of dated molecular phylogenies opened new perspectives. First, the increasing use of DNA sequences in biodiversity inventory shed light on a substantial amount of cryptic diversity in species-rich ecosystems. Second, explicit diversification models accounting for various eco-evolutionary models are now available. Integrating both advances, we explored diversification trajectories among 10 lineages of freshwater fishes in Sundaland, for which time-calibrated and taxonomically rich phylogenies are available. By fitting diversification models to dated phylogenies and incorporating DNA-based species delimitation methods, the impact of cryptic diversity on diversification model selection and related inferences is explored. Eight clades display constant speciation rate model as the most likely if cryptic diversity is accounted, but nine display a signature of diversification slowdowns when cryptic diversity is ignored. Cryptic diversification occurs during the last 5 Myr for most groups, and palaeoecological models received little support. Most cryptic lineages display restricted range distribution, supporting geographical isolation across homogeneous landscapes as the main driver of diversification. These patterns question the persistence of cryptic diversity and its role during species proliferation.
DOI: 10.6084/m9.figshare.21523973.v1
title: TreeS1_MOTU_tree_Channa from Cryptic diversity impacts model selection and macroevolutionary inferences in diversification analyses

description: Species persist in landscapes through ecological dynamics but proliferate at wider spatial scales through evolutionary mechanisms. Disentangling the contribution of each dynamic is challenging, but the increasing use of dated molecular phylogenies opened new perspectives. First, the increasing use of DNA sequences in biodiversity inventory shed light on a substantial amount of cryptic diversity in species-rich ecosystems. Second, explicit diversification models accounting for various eco-evolutionary models are now available. Integrating both advances, we explored diversification trajectories among 10 lineages of freshwater fishes in Sundaland, for which time-calibrated and taxonomically rich phylogenies are available. By fitting diversification models to dated phylogenies and incorporating DNA-based species delimitation methods, the impact of cryptic diversity on diversification model selection and related inferences is explored. Eight clades display constant speciation rate model as the most likely if cryptic diversity is accounted, but nine display a signature of diversification slowdowns when cryptic diversity is ignored. Cryptic diversification occurs during the last 5 Myr for most groups, and palaeoecological models received little support. Most cryptic lineages display restricted range distribution, supporting geographical isolation across homogeneous landscapes as the main driver of diversification. These patterns question the persistence of cryptic diversity and its role during species proliferation.
DOI: 10.6084/m9.figshare.21523976.v1
title: TreeS10 from Cryptic diversity impacts model selection and macroevolutionary inferences in diversification analyses

description: Species persist in landscapes through ecological dynamics but proliferate at wider spatial scales through evolutionary mechanisms. Disentangling the contribution of each dynamic is challenging, but the increasing use of dated molecular phylogenies opened new perspectives. First, the increasing use of DNA sequences in biodiversity inventory shed light on a substantial amount of cryptic diversity in species-rich ecosystems. Second, explicit diversification models accounting for various eco-evolutionary models are now available. Integrating both advances, we explored diversification trajectories among 10 lineages of freshwater fishes in Sundaland, for which time-calibrated and taxonomically rich phylogenies are available. By fitting diversification models to dated phylogenies and incorporating DNA-based species delimitation methods, the impact of cryptic diversity on diversification model selection and related inferences is explored. Eight clades display constant speciation rate model as the most likely if cryptic diversity is accounted, but nine display a signature of diversification slowdowns when cryptic diversity is ignored. Cryptic diversification occurs during the last 5 Myr for most groups, and palaeoecological models received little support. Most cryptic lineages display restricted range distribution, supporting geographical isolation across homogeneous landscapes as the main driver of diversification. These patterns question the persistence of cryptic diversity and its role during species proliferation.
DOI: 10.6084/m9.figshare.21523976
title: TreeS10 from Cryptic diversity impacts model selection and macroevolutionary inferences in diversification analyses

description: Species persist in landscapes through ecological dynamics but proliferate at wider spatial scales through evolutionary mechanisms. Disentangling the contribution of each dynamic is challenging, but the increasing use of dated molecular phylogenies opened new perspectives. First, the increasing use of DNA sequences in biodiversity inventory shed light on a substantial amount of cryptic diversity in species-rich ecosystems. Second, explicit diversification models accounting for various eco-evolutionary models are now available. Integrating both advances, we explored diversification trajectories among 10 lineages of freshwater fishes in Sundaland, for which time-calibrated and taxonomically rich phylogenies are available. By fitting diversification models to dated phylogenies and incorporating DNA-based species delimitation methods, the impact of cryptic diversity on diversification model selection and related inferences is explored. Eight clades display constant speciation rate model as the most likely if cryptic diversity is accounted, but nine display a signature of diversification slowdowns when cryptic diversity is ignored. Cryptic diversification occurs during the last 5 Myr for most groups, and palaeoecological models received little support. Most cryptic lineages display restricted range distribution, supporting geographical isolation across homogeneous landscapes as the main driver of diversification. These patterns question the persistence of cryptic diversity and its role during species proliferation.
DOI: 10.6084/m9.figshare.21559161
title: Additional file 1 of Early changes in gene expression profiles in AML patients during induction chemotherapy

description: Additional file 1.
DOI: 10.6084/m9.figshare.21559161.v1
title: Additional file 1 of Early changes in gene expression profiles in AML patients during induction chemotherapy

description: Additional file 1.
DOI: 10.6084/m9.figshare.21559164
title: Additional file 2 of Early changes in gene expression profiles in AML patients during induction chemotherapy

description: Additional file 2.
DOI: 10.6084/m9.figshare.21559164.v1
title: Additional file 2 of Early changes in gene expression profiles in AML patients during induction chemotherapy

description: Additional file 2.
DOI: 10.6084/m9.figshare.21559167
title: Additional file 3 of Early changes in gene expression profiles in AML patients during induction chemotherapy

description: Additional file 3.
DOI: 10.6084/m9.figshare.21559167.v1
title: Additional file 3 of Early changes in gene expression profiles in AML patients during induction chemotherapy

description: Additional file 3.
DOI: 10.6084/m9.figshare.21559170
title: Additional file 4 of Early changes in gene expression profiles in AML patients during induction chemotherapy

description: Additional file 4.
DOI: 10.6084/m9.figshare.21559170.v1
title: Additional file 4 of Early changes in gene expression profiles in AML patients during induction chemotherapy

description: Additional file 4.
DOI: 10.5517/cctz21w
title: CCDC 773668: Experimental Crystal Structure Determination

description: Related Article: Jiao Feng, Jing-Lin Wang, Bin Liu, Bin-Sheng Yang|2013|Jiegou Huaxue|32|1787|
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.ccvb3s1
title: CCDC 784293: Experimental Crystal Structure Determination

description: Related Article: Xiao-Wen Li, Xi-Hui Zhao, Yan-Tuan Li, Zhi-Yong Wu|2019|Inorg.Chim.Acta|488|219|doi:10.1016/j.ica.2019.01.026
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/cc10hcjd
title: CCDC 938293: Experimental Crystal Structure Determination

description: Related Article: Loganathan Nagarajan,Andreas Roodt|2014|Jiegou Huaxue|33|19|
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/cc13t266
title: CCDC 1036987: Experimental Crystal Structure Determination

description: Related Article: Ulrich Flörke|2014|CSD Communication|||
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.cc1q1zxn
title: CCDC 1580842: Experimental Crystal Structure Determination

description: Related Article: Yan-Hong Zhang, Gui-Ru Deng, Yuan-Yuan Gao|2019|Transition Met.Chem.|44|737|doi:10.1007/s11243-019-00343-1
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.cc1z2zwx
title: CCDC 1820130: Experimental Crystal Structure Determination

description: Related Article: Diana Chisca, Anatolii V. Siminel, Marina S. Fonari, Lilia Croitor|2019|Polyhedron|170|245|doi:10.1016/j.poly.2019.05.052
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.cc1zzks9
title: CCDC 1846632: Experimental Crystal Structure Determination

description: Related Article: Isaac A. Cuadra, Francisco J. Martínez-Casado, José A. R. Cheda, M. I. Redondo, Concepción Pando, Albertina Cabañas|2019|Cryst.Growth Des.|19|620|doi:10.1021/acs.cgd.8b01034
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.cc20v2zy
title: CCDC 1872120: Experimental Crystal Structure Determination

description: Related Article: Yuanfeng Hou, Zhongshu Li, Yaqi Li, Peng Liu, Cheng-Yong Su, Florian Puschmann, Hansjörg Grützmacher|2019|Chemical Science|10|3168|doi:10.1039/C8SC05281F
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.cc21h9wr
title: CCDC 1891554: Experimental Crystal Structure Determination

description: Related Article: Sonu Gupta, Pooja Dubey, Ajai K. Singh, Nidhi Jain|2019|Dalton Trans.|48|10129|doi:10.1039/C9DT01766F
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.cc21n8nn
title: CCDC 1896321: Experimental Crystal Structure Determination

description: Related Article: Hamid Golchoubian, Delaram Mehrbanian, Ehsan Rezaee, Zong-Xiang Xu|2019|Transition Met.Chem.|44|671|doi:10.1007/s11243-019-00332-4
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.cc229c0s
title: CCDC 1915614: Experimental Crystal Structure Determination

description: Related Article: Katja Dankhoff, Madeleine Gold, Luisa Kober, Florian Schmitt, Lena Pfeifer, Andreas Dürrmann, Hana Kostrhunova, Matthias Rothemund, Viktor Brabec, Rainer Schobert, Birgit Weber|2019|Dalton Trans.|48|15220|doi:10.1039/C9DT02571E
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.cc1zzktb
title: CCDC 1846633: Experimental Crystal Structure Determination

description: Related Article: Isaac A. Cuadra, Francisco J. Martínez-Casado, José A. R. Cheda, M. I. Redondo, Concepción Pando, Albertina Cabañas|2019|Cryst.Growth Des.|19|620|doi:10.1021/acs.cgd.8b01034
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.cc20v2xw
title: CCDC 1872118: Experimental Crystal Structure Determination

description: Related Article: Yuanfeng Hou, Zhongshu Li, Yaqi Li, Peng Liu, Cheng-Yong Su, Florian Puschmann, Hansjörg Grützmacher|2019|Chemical Science|10|3168|doi:10.1039/C8SC05281F
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.cc229c1t
title: CCDC 1915615: Experimental Crystal Structure Determination

description: Related Article: Katja Dankhoff, Madeleine Gold, Luisa Kober, Florian Schmitt, Lena Pfeifer, Andreas Dürrmann, Hana Kostrhunova, Matthias Rothemund, Viktor Brabec, Rainer Schobert, Birgit Weber|2019|Dalton Trans.|48|15220|doi:10.1039/C9DT02571E
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.cc229c2v
title: CCDC 1915616: Experimental Crystal Structure Determination

description: Related Article: Katja Dankhoff, Madeleine Gold, Luisa Kober, Florian Schmitt, Lena Pfeifer, Andreas Dürrmann, Hana Kostrhunova, Matthias Rothemund, Viktor Brabec, Rainer Schobert, Birgit Weber|2019|Dalton Trans.|48|15220|doi:10.1039/C9DT02571E
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.cc229c3w
title: CCDC 1915617: Experimental Crystal Structure Determination

description: Related Article: Katja Dankhoff, Madeleine Gold, Luisa Kober, Florian Schmitt, Lena Pfeifer, Andreas Dürrmann, Hana Kostrhunova, Matthias Rothemund, Viktor Brabec, Rainer Schobert, Birgit Weber|2019|Dalton Trans.|48|15220|doi:10.1039/C9DT02571E
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.cc22jhpt
title: CCDC 1922486: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington, A.S. Richardson|2019|CSD Communication|||
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.cc22kdmp
title: CCDC 1923352: Experimental Crystal Structure Determination

description: Related Article: Hana Oh, Suhyuk Choi, Joo Yeon Kim, Hyun S. Ahn, Seungwoo Hong|2019|Chem.Commun.|55|12659|doi:10.1039/C9CC06956A
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.cc235l2z
title: CCDC 1941780: Experimental Crystal Structure Determination

description: Related Article: C. Elamathi, R. J. Butcher, A. Mohankumar, P. Sundararaj, A. Madankumar, P. Kalaivani, R. Prabhakaran|2019|Inorg.Chem.Front.|6|3237|doi:10.1039/C9QI00992B
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: 10.5519/qd.agvnpqgf
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (29 records)
DOI: 10.5066/p9xss01v
title: A reference genome assembly for the endangered Aga or Mariana Crow (Corvus kubaryi)

description: The Aga, also known as the Mariana Crow, is an endangered endemic crow of the Northern Mariana Islands, a Commonwealth of the United States of America. The only known population currently extant is on the island of Rota and has been in decline over the past two decades. Unknown pathogens are among the factors that may be contributing to this decline. To support metagenomic and transcriptomic analysis of potential disease agents, a reference genome was generated and gene features comprehensively annotated. The methods used and data availability are described. This data release consists of: 1. Gene annotations for the endangered Aga or Mariana Crow, including summary statistics, that are available from the National Center for Biotechnology Information (NCBI) at https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Corvus_kubaryi/100/ 2. Raw.data.accessions.txt. A summary of raw data accessions deposited in NCBI databases used to complete the reference genome and gene annotation. The format is tab-delimited text, available at https://doi.org/10.5066/P9XSS01V. 3. Supernova.assembly.report.txt. A table describing the input data for genome assembly and output statistics of the final assembly, as computed by the supernova assembly program, in tab-delimited text and available at https://doi.org/10.5066/P9XSS01V. Additional information about these assembly parameters, including the algorithms used to compute them and expected ranges, are described in Weisenfeld et al. 2017.