cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23735455
title: New specimen and redescription of <i>Anisodontosaurus greeri</i> (Moenkopi Formation: Middle Triassic) and the spatiotemporal origins of Trilophosauridae

description: <i>Anisodontosaurus greeri</i> is an enigmatic small-bodied tetrapod with a heterodont dentition from the Holbrook Member of the Moenkopi Formation (Middle Triassic) of Arizona (U.S.A.). The evolutionary relationships of this taxon have long been debated and remain uncertain. Using micro-computed tomography (µCT) scans we redescribe the holotype of <i>Anisodontosaurus greeri</i> (UCMP 37804), as well as an additional specimen (MNA.V.1478) that had been informally referred to this taxon. Our new data reveal hidden details of the dentition (i.e., ankylothecodonty and absence of replacement teeth) that, in combination with the tricuspid and mediolaterally expanded crowns, support a referral of both specimens to the archosauromorph clade Trilophosauridae. The referral of MNA.V.1478 to <i>Anisodontosaurus greeri</i> is supported by the unique anatomy of the highly differentiated dentition (i.e., ‘figure of 8’-shaped premolariforms in occlusal views; mesiodistally short tooth row; presence of a distal ‘molariform’ crown). Comparison of <i>Anisodontosaurus greeri</i> with other trilophosaurids highlights marked similarities with <i>Variodens inopinatus</i> from the Upper Triassic deposits of the U.K. Our cladistic analyses confirm these observations, and recover, for the first time, two distinct lineages within Trilophosauridae: one geologically long-lived and comprising <i>Anisodontosaurus</i> and <i>Variodens</i>; and one comprising <i>Tricuspisaurus</i>, <i>Trilophosaurus</i> spp., and <i>Spinosuchus</i>. These results imply that: (i) <i>Anisodontosaurus</i> is one of the oldest known trilophosaurids worldwide and the oldest in North America; (ii) trilophosaurids achieved a broad distribution at low latitudes within western Pangea by the Middle Triassic; (iii) small body sizes were more common than previously thought among trilophosaurids.
DOI: 10.6084/m9.figshare.23735455.v1
title: New specimen and redescription of <i>Anisodontosaurus greeri</i> (Moenkopi Formation: Middle Triassic) and the spatiotemporal origins of Trilophosauridae

description: <i>Anisodontosaurus greeri</i> is an enigmatic small-bodied tetrapod with a heterodont dentition from the Holbrook Member of the Moenkopi Formation (Middle Triassic) of Arizona (U.S.A.). The evolutionary relationships of this taxon have long been debated and remain uncertain. Using micro-computed tomography (µCT) scans we redescribe the holotype of <i>Anisodontosaurus greeri</i> (UCMP 37804), as well as an additional specimen (MNA.V.1478) that had been informally referred to this taxon. Our new data reveal hidden details of the dentition (i.e., ankylothecodonty and absence of replacement teeth) that, in combination with the tricuspid and mediolaterally expanded crowns, support a referral of both specimens to the archosauromorph clade Trilophosauridae. The referral of MNA.V.1478 to <i>Anisodontosaurus greeri</i> is supported by the unique anatomy of the highly differentiated dentition (i.e., ‘figure of 8’-shaped premolariforms in occlusal views; mesiodistally short tooth row; presence of a distal ‘molariform’ crown). Comparison of <i>Anisodontosaurus greeri</i> with other trilophosaurids highlights marked similarities with <i>Variodens inopinatus</i> from the Upper Triassic deposits of the U.K. Our cladistic analyses confirm these observations, and recover, for the first time, two distinct lineages within Trilophosauridae: one geologically long-lived and comprising <i>Anisodontosaurus</i> and <i>Variodens</i>; and one comprising <i>Tricuspisaurus</i>, <i>Trilophosaurus</i> spp., and <i>Spinosuchus</i>. These results imply that: (i) <i>Anisodontosaurus</i> is one of the oldest known trilophosaurids worldwide and the oldest in North America; (ii) trilophosaurids achieved a broad distribution at low latitudes within western Pangea by the Middle Triassic; (iii) small body sizes were more common than previously thought among trilophosaurids.
DOI: 10.6084/m9.figshare.23833596
title: A new chigutisaurid (Brachyopoidea, Temnospondyli) with soft tissue preservation from the Triassic Sydney Basin, New South Wales, Australia

description: Compared with other Mesozoic tetrapod groups, chigutisaurid fossils from Australia are rare, with only three named taxa described from the continent. From Queensland, <i>Keratobrachyops australis</i> is known from the Triassic, and <i>Siderops kehli</i> from the Jurassic. <i>Koolasuchus cleelandi</i>, from the Cretaceous of Victoria, represents the youngest-known temnospondyl globally. Here we describe the first chigutisaurid from New South Wales, from the Early–Middle Triassic Terrigal Formation. The specimen (AM F125866) comprises an articulated, near-complete skeleton, presented in ventral aspect, as well as outlines of soft tissue. The new taxon preserves features that indicate an affinity with Chigutisauridae, confirming previous hypotheses on the presence of large-bodied chigutisaurids in the Triassic of Australia. The new Terrigal chigutisaurid is only the second chigutisaurid known from the Lower Triassic and fourth from Australia overall. The distribution of brachycephalic temnospondyls throughout the Mesozoic suggests specific adaptations led to the long-term survival of chigutisaurids, especially across the end-Triassic extinction event.
DOI: 10.6084/m9.figshare.23833596.v1
title: A new chigutisaurid (Brachyopoidea, Temnospondyli) with soft tissue preservation from the Triassic Sydney Basin, New South Wales, Australia

description: Compared with other Mesozoic tetrapod groups, chigutisaurid fossils from Australia are rare, with only three named taxa described from the continent. From Queensland, <i>Keratobrachyops australis</i> is known from the Triassic, and <i>Siderops kehli</i> from the Jurassic. <i>Koolasuchus cleelandi</i>, from the Cretaceous of Victoria, represents the youngest-known temnospondyl globally. Here we describe the first chigutisaurid from New South Wales, from the Early–Middle Triassic Terrigal Formation. The specimen (AM F125866) comprises an articulated, near-complete skeleton, presented in ventral aspect, as well as outlines of soft tissue. The new taxon preserves features that indicate an affinity with Chigutisauridae, confirming previous hypotheses on the presence of large-bodied chigutisaurids in the Triassic of Australia. The new Terrigal chigutisaurid is only the second chigutisaurid known from the Lower Triassic and fourth from Australia overall. The distribution of brachycephalic temnospondyls throughout the Mesozoic suggests specific adaptations led to the long-term survival of chigutisaurids, especially across the end-Triassic extinction event.
DOI: 10.6084/m9.figshare.23617346
title: Intraspecific craniomandibular and dental analysis of <i>Pseudotypotherium exiguum</i> (Mesotheriidae, Notoungulata) from Monte Hermoso, late Neogene, Buenos Aires Province, Argentina

description: Mesotheriid taxonomy for Monte Hermoso, Buenos Aires province, Argentina is extremely complex, with much of the controversy centered on the Late Miocene–Pliocene mesotheriine notoungulate type species <i>Pseudotypotherium pulchrum</i>, which is a subjective junior synonym of <i>P. exiguum</i>. Confusion around <i>P. exiguum</i> intraspecific variation is exacerbated by specimens of the Pleistocene taxon <i>Mesotherium cristatum</i> originally defined in Toscas del Río de La Plata. As a result, remains of Monte Hermoso mesotheres have been referred to six species and four genera (<i>Typotherium</i>, <i>Pseudotypotherium</i>, <i>Xenotherium</i>, and <i>Typotheriodon</i>) during the past 100 + years. The current consensus is that <i>Pseudotypotherium</i> is the correct genus for Monte Hermoso mesotheres, but detailed analyses of variation, especially ontogenetic, have been lacking. We rectify that deficiency in this work through bibliographic and firsthand study of type materials and comparison of molar size (PCA of linear dimensions) and i1 ontogenetic variation (via CT) using a broad ontogenetic series. We conclude that the variation observed in Monte Hermoso mesotheres is consistent with ontogenetic and individual variation within a single species. As previously suggested, <i>Pseudotypotherium</i> (<i>=Typotherium</i>) <i>exiguum</i> is the valid senior synonym for Monte Hermoso mesotheres. We provide a detailed diagnosis and description of <i>P. exiguum</i>, including a rather complete ontogenetic series, the first for any species of <i>Pseudotypotherium</i>.
DOI: 10.6084/m9.figshare.23617346.v1
title: Intraspecific craniomandibular and dental analysis of <i>Pseudotypotherium exiguum</i> (Mesotheriidae, Notoungulata) from Monte Hermoso, late Neogene, Buenos Aires Province, Argentina

description: Mesotheriid taxonomy for Monte Hermoso, Buenos Aires province, Argentina is extremely complex, with much of the controversy centered on the Late Miocene–Pliocene mesotheriine notoungulate type species <i>Pseudotypotherium pulchrum</i>, which is a subjective junior synonym of <i>P. exiguum</i>. Confusion around <i>P. exiguum</i> intraspecific variation is exacerbated by specimens of the Pleistocene taxon <i>Mesotherium cristatum</i> originally defined in Toscas del Río de La Plata. As a result, remains of Monte Hermoso mesotheres have been referred to six species and four genera (<i>Typotherium</i>, <i>Pseudotypotherium</i>, <i>Xenotherium</i>, and <i>Typotheriodon</i>) during the past 100 + years. The current consensus is that <i>Pseudotypotherium</i> is the correct genus for Monte Hermoso mesotheres, but detailed analyses of variation, especially ontogenetic, have been lacking. We rectify that deficiency in this work through bibliographic and firsthand study of type materials and comparison of molar size (PCA of linear dimensions) and i1 ontogenetic variation (via CT) using a broad ontogenetic series. We conclude that the variation observed in Monte Hermoso mesotheres is consistent with ontogenetic and individual variation within a single species. As previously suggested, <i>Pseudotypotherium</i> (<i>=Typotherium</i>) <i>exiguum</i> is the valid senior synonym for Monte Hermoso mesotheres. We provide a detailed diagnosis and description of <i>P. exiguum</i>, including a rather complete ontogenetic series, the first for any species of <i>Pseudotypotherium</i>.
DOI: 10.6084/m9.figshare.24408181
title: Genome annotation of <i>Thrips tabaci</i>

description: This is a project to annotate the genome of <i>Thrips tabaci</i>. In this project, the genes and proteins of <i>Thrips tabaci</i> have been annotated, which is a complement to the genome.
DOI: 10.5281/zenodo.7439943
title: Atlas of virus specific CD8+ Tcells

description: Code and Data accompanying the manuscript " "


TS logistic regression model:



	
TargetScape_Model_FCS_Input.zip contains the TS data in pickled format to be imported in python
	
targetscape-model-paper-20221001.html contains the python code to perform data analysis, model training, testing and interpretation



TAP logistic regression model:



	
Generate_Pseudobulk_Markers_ADT_TAP_Cohort.R to generate candidate features by pseudo-bulking surface markers across donors.
	
TAP_ParameterBenchmarking_Model_Training_Evaluation.R to perform model training and parameter benchmark.
	
TAP_DiscoveryCohort_Epitopes.rds file containing a Seurat object of the TAP-Cohort.
	
Cohort_Pseudobulk_ADT_Virus_markers.RDS RDS file containing the candidate markers and their fold-changes/p-values, generated by Generate_Pseudobulk_Markers_ADT_TAP_Cohort.R
	
Leave-one-epitope-out-TAP.R: R script to train a model using one epitope as a validation set.



Figures related to Machine Learning approaches:



	
Model_Analysis_Figure_Table_Generation.R script to generate Figures 3 and Figure 4 of the main manuscript as well as corresponding Supplementary Figures
	
TargetScapeModel_Feature_Importance.csv contains the regression coefficients of the model learned on TargetScape (TS) data.
	
TargetScape_Cohort_Logicle.csv contains the actual data of the TS cohort after logicle transformation.
	
TargetScape_Validation_Cohort_Logicle.csv contains the actual data of the TS validation cohort after logicle transformation
	
TargetScape_Model_Predictions.csv contains predictions of the TS model made on the TS validation cohort
	
Performance_Benchmarking_TAP.zip contains all parameter benchmarking performance files generated by the TAP model above while executing TAP_ParameterBenchmarking_Model_Training_Evaluation.R.
	
Performance_Selected_Model.txt contains model performance information on the selected TAP model
	
TAP_Model_MultinomialElaNetLogRegModel_minCellsPerPatient_30_minCellsPerVirus_200_ResourcePaper.RDS contains the best model trained on TAP data.
	
TAP-Validation.RDS contains the Seurat object of the TAP validation cohort.
DOI: 10.5281/zenodo.7439944
title: Atlas of virus specific CD8+ Tcells

description: Code and Data accompanying the manuscript " " <strong>TS logistic regression model:</strong> TargetScape_Model_FCS_Input.zip contains the TS data in pickled format to be imported in python targetscape-model-paper-20221001.html contains the python code to perform data analysis, model training, testing and interpretation <strong>TAP logistic regression model:</strong> Generate_Pseudobulk_Markers_ADT_TAP_Cohort.R to generate candidate features by pseudo-bulking surface markers across donors. TAP_ParameterBenchmarking_Model_Training_Evaluation.R to perform model training and parameter benchmark. TAP-Cohort_WNN-CT.RDS RDS file containing a Seurat object of the TAP-Cohort. Cohort_Pseudobulk_ADT_Virus_markers.RDS RDS file containing the candidate markers and their fold-changes/p-values, generated by<em> Generate_Pseudobulk_Markers_ADT_TAP_Cohort.R</em> <strong>Figures related to Machine Learning approaches:</strong> Model_Analysis_Figure_Table_Generation.R script to generate Figures 3 and Figure 4 of the main manuscript as well as corresponding Supplementary Figures TargetScapeModel_Feature_Importance.csv contains the regression coefficients of the model learned on TargetScape (TS) data. TargetScape_Cohort_Logicle.csv contains the actual data of the TS cohort after logicle transformation. TargetScape_Validation_Cohort_Logicle.csv contains the actual data of the TS validation cohort after logicle transformation TargetScape_Model_Predictions.csv contains predictions of the TS model made on the TS validation cohort Performance_Benchmarking_TAP.zip contains all parameter benchmarking performance files generated by the TAP model above while executing TAP_ParameterBenchmarking_Model_Training_Evaluation<em>.R.</em> Performance_Selected_Model.txt contains model performance information on the selected TAP model TAP_Model_MultinomialElaNetLogRegModel_minCellsPerPatient_30_minCellsPerVirus_200_ResourcePaper.RDS contains the best model trained on TAP data. TAP-Validation.RDS contains the Seurat object of the TAP validation cohort.
DOI: 10.5281/zenodo.8053965
title: Atlas of virus specific CD8+ Tcells

description: Code and Data accompanying the manuscript " " <strong>TS logistic regression model:</strong> TargetScape_Model_FCS_Input.zip contains the TS data in pickled format to be imported in python targetscape-model-paper-20221001.html contains the python code to perform data analysis, model training, testing and interpretation <strong>TAP logistic regression model:</strong> Generate_Pseudobulk_Markers_ADT_TAP_Cohort.R to generate candidate features by pseudo-bulking surface markers across donors. TAP_ParameterBenchmarking_Model_Training_Evaluation.R to perform model training and parameter benchmark. TAP-Cohort_WNN-CT.RDS RDS file containing a Seurat object of the TAP-Cohort. Cohort_Pseudobulk_ADT_Virus_markers.RDS RDS file containing the candidate markers and their fold-changes/p-values, generated by<em> Generate_Pseudobulk_Markers_ADT_TAP_Cohort.R</em> <strong>Figures related to Machine Learning approaches:</strong> Model_Analysis_Figure_Table_Generation.R script to generate Figures 3 and Figure 4 of the main manuscript as well as corresponding Supplementary Figures TargetScapeModel_Feature_Importance.csv contains the regression coefficients of the model learned on TargetScape (TS) data. TargetScape_Cohort_Logicle.csv contains the actual data of the TS cohort after logicle transformation. TargetScape_Validation_Cohort_Logicle.csv contains the actual data of the TS validation cohort after logicle transformation TargetScape_Model_Predictions.csv contains predictions of the TS model made on the TS validation cohort Performance_Benchmarking_TAP.zip contains all parameter benchmarking performance files generated by the TAP model above while executing TAP_ParameterBenchmarking_Model_Training_Evaluation<em>.R.</em> Performance_Selected_Model.txt contains model performance information on the selected TAP model TAP_Model_MultinomialElaNetLogRegModel_minCellsPerPatient_30_minCellsPerVirus_200_ResourcePaper.RDS contains the best model trained on TAP data. TAP-Validation.RDS contains the Seurat object of the TAP validation cohort.
DOI: 10.5281/zenodo.8330231
title: Atlas of virus specific CD8+ Tcells

description: Code and Data accompanying the manuscript " "


TS logistic regression model:



	
TargetScape_Model_FCS_Input.zip contains the TS data in pickled format to be imported in python
	
targetscape-model-paper-20221001.html contains the python code to perform data analysis, model training, testing and interpretation



TAP logistic regression model:



	
Generate_Pseudobulk_Markers_ADT_TAP_Cohort.R to generate candidate features by pseudo-bulking surface markers across donors.
	
TAP_ParameterBenchmarking_Model_Training_Evaluation.R to perform model training and parameter benchmark.
	
TAP_DiscoveryCohort_Epitopes.rds file containing a Seurat object of the TAP-Cohort.
	
Cohort_Pseudobulk_ADT_Virus_markers.RDS RDS file containing the candidate markers and their fold-changes/p-values, generated by Generate_Pseudobulk_Markers_ADT_TAP_Cohort.R
	
Leave-one-epitope-out-TAP.R: R script to train a model using one epitope as a validation set.



Figures related to Machine Learning approaches:



	
Model_Analysis_Figure_Table_Generation.R script to generate Figures 3 and Figure 4 of the main manuscript as well as corresponding Supplementary Figures
	
TargetScapeModel_Feature_Importance.csv contains the regression coefficients of the model learned on TargetScape (TS) data.
	
TargetScape_Cohort_Logicle.csv contains the actual data of the TS cohort after logicle transformation.
	
TargetScape_Validation_Cohort_Logicle.csv contains the actual data of the TS validation cohort after logicle transformation
	
TargetScape_Model_Predictions.csv contains predictions of the TS model made on the TS validation cohort
	
Performance_Benchmarking_TAP.zip contains all parameter benchmarking performance files generated by the TAP model above while executing TAP_ParameterBenchmarking_Model_Training_Evaluation.R.
	
Performance_Selected_Model.txt contains model performance information on the selected TAP model
	
TAP_Model_MultinomialElaNetLogRegModel_minCellsPerPatient_30_minCellsPerVirus_200_ResourcePaper.RDS contains the best model trained on TAP data.
	
TAP-Validation.RDS contains the Seurat object of the TAP validation cohort.
DOI: 10.6073/pasta/312f45d8d2ceaecf0c02e791f5fd9a63
title: Long-term lake dissolved oxygen and temperature data, 1941-2018

description: The concentration of oxygen dissolved in water regulates many aspects of aquatic ecosystems, including habitat suitability for biota, greenhouse gas emissions, nutrient cycling, and metal toxicity. However, little is known about how dissolved oxygen (DO) concentrations in lakes are changing through time. The solubility of oxygen in water declines with increasing water temperatures, but other temperature-sensitive processes may suppress or amplify trends through time, making long-term observations essential to understanding DO dynamics and trends. This dataset includes measurements of dissolved oxygen and temperature from greater than 400 widely geographically-distributed lakes, as well as a suite of associated measurements in or around many of the lakes, including watershed land use, water clarity, limiting nutrient concentrations, and chlorophyll concentrations. Analysis of these data reveal widespread losses in dissolved oxygen through time in both surface and deep waters.
DOI: 10.5517/ccdc.csd.cc29j5vw
title: CCDC 2130718: Experimental Crystal Structure Determination

description: Related Article: M.S.S. Vinod Mouli, Ashutosh Kumar Mishra|2023|Inorg.Chim.Acta|558|121752|doi:10.1016/j.ica.2023.121752
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.cc2dlj6r
title: CCDC 2222334: Experimental Crystal Structure Determination

description: Related Article: Anatolii S. Burlov, Valery G. Vlasenko, Leonid D. Popov, Yurii V. Koshchienko, Dmitrii A. Garnovskii, Alexandra A. Kolodina, Evgeniya V. Korshunova, Yan V. Zubavichus, Vladimir A. Lazarenko|2023|Inorg.Chim.Acta|558|121765|doi:10.1016/j.ica.2023.121765
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.cc2dlj7s
title: CCDC 2222335: Experimental Crystal Structure Determination

description: Related Article: Anatolii S. Burlov, Valery G. Vlasenko, Leonid D. Popov, Yurii V. Koshchienko, Dmitrii A. Garnovskii, Alexandra A. Kolodina, Evgeniya V. Korshunova, Yan V. Zubavichus, Vladimir A. Lazarenko|2023|Inorg.Chim.Acta|558|121765|doi:10.1016/j.ica.2023.121765
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.cc2dlj8t
title: CCDC 2222336: Experimental Crystal Structure Determination

description: Related Article: Anatolii S. Burlov, Valery G. Vlasenko, Leonid D. Popov, Yurii V. Koshchienko, Dmitrii A. Garnovskii, Alexandra A. Kolodina, Evgeniya V. Korshunova, Yan V. Zubavichus, Vladimir A. Lazarenko|2023|Inorg.Chim.Acta|558|121765|doi:10.1016/j.ica.2023.121765
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.cc2fynvx
title: CCDC 2262840: Experimental Crystal Structure Determination

description: Related Article: Sharmila Pandey, Tanmoy Mandal|2023|Inorg.Chim.Acta|558|121755|doi:10.1016/j.ica.2023.121755
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.