cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12993346.v1
title: A first phylogenomic hypothesis for Eulophidae (Hymenoptera, Chalcidoidea)

description: Eulophidae is a hyper-diverse family of chalcidoid wasps with 324 genera, about 5300 described species and probably thousands of others to be described. Until now, the absence of unequivocal morphological apomorphies and the low resolution provided by the handful of Sanger sequenced genes have hampered the reconstruction of phylogenetic relationships within the family. Here, we used ultra-conserved elements and their flanking regions to resolve relationships among 84 species of eulophids included in 63 genera representing all subfamilies and most tribes, plus 15 outgroups. Our analyses recover all traditional Eulophidae subfamilies and tribes with high support and globally agree with the traditional classification of the family. Our results confirm that Eulophinae + Tetrastichinae is the sister group of (Opheliminae + Entiinae) + Entedoninae. At the generic level, our analyses provide high support for intergeneric relationships for which morphology and Sanger markers previously failed to provide resolution. Our results also confirm that <i>Trisecodes</i> does not group with Eulophidae and may not belong to this family; however, its correct classification still awaits a large-scale phylogenomic hypothesis for Chalcidoidea. This work opens new avenues towards a better understanding of the evolutionary history, biogeography and evolution of host–parasitoid associations in this hyper-diverse family of chalcidoid wasps.
DOI: 10.6084/m9.figshare.12993346
title: A first phylogenomic hypothesis for Eulophidae (Hymenoptera, Chalcidoidea)

description: Eulophidae is a hyper-diverse family of chalcidoid wasps with 324 genera, about 5300 described species and probably thousands of others to be described. Until now, the absence of unequivocal morphological apomorphies and the low resolution provided by the handful of Sanger sequenced genes have hampered the reconstruction of phylogenetic relationships within the family. Here, we used ultra-conserved elements and their flanking regions to resolve relationships among 84 species of eulophids included in 63 genera representing all subfamilies and most tribes, plus 15 outgroups. Our analyses recover all traditional Eulophidae subfamilies and tribes with high support and globally agree with the traditional classification of the family. Our results confirm that Eulophinae + Tetrastichinae is the sister group of (Opheliminae + Entiinae) + Entedoninae. At the generic level, our analyses provide high support for intergeneric relationships for which morphology and Sanger markers previously failed to provide resolution. Our results also confirm that <i>Trisecodes</i> does not group with Eulophidae and may not belong to this family; however, its correct classification still awaits a large-scale phylogenomic hypothesis for Chalcidoidea. This work opens new avenues towards a better understanding of the evolutionary history, biogeography and evolution of host–parasitoid associations in this hyper-diverse family of chalcidoid wasps.
DOI: 10.6084/m9.figshare.12993343.v2
title: On the horns of a dilemma: toward a better understanding of the <i>Monacon</i> species (Hymenoptera: Perilampidae) of Borneo

description: A new species of <i>Monacon</i> is described from Borneo based on digital images, COI sequences and X-ray microtomography. The natural history of this primary parasitoid is documented and its host, a pinhole borer or ambrosia beetle, is documented with digital images and a COI sequence. The X-ray microtomography revealed that the frontal horn that is characteristic of the genus <i>Monacon</i> is not simply a cuticular process, but is associated with tissues, and may have a sensory function.
DOI: 10.6084/m9.figshare.12993343.v1
title: On the horns of a dilemma: toward a better understanding of the <i>Monacon</i> species (Hymenoptera: Perilampidae) of Borneo

description: A new species of <i>Monacon</i> is described from Borneo based on digital images, COI sequences and X-ray microtomography. The natural history of this primary parasitoid is documented and its host, a pinhole borer or ambrosia beetle, is documented with digital images and a COI sequence. The X-ray microtomography revealed that the frontal horn that is characteristic of the genus <i>Monacon</i> is not simply a cuticular process, but is associated with tissues, and may have a sensory function.
DOI: 10.5519/qd.bqs1aife
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (365829 records)
DOI: 10.21227/7mq1-kn40
title: Battery and Heating Data in Real Driving Cycles

description: High-voltage batteries in battery electric vehicles face significant load fluctuations due to driving behavior. High accelerations cause high discharge currents, while regenerative braking leads to charging currents. This dynamic performance of the powertrain is contrasted by the almost constant load of the auxiliary consumers. The highest auxiliary consumption is generated by the heating and air conditioning system, which decreases the vehicles range significantly. This data set contains real driving data and simulated data. The real driving was recorded with a BMW i3 (60 Ah) and serves for the model development and validation of a full vehicle model consisting of the powertrain and the heating circuit. Each ride contains: Environmental data (temperature, elevation, etc.)Vehicle data (speed, throttle, etc.)Battery data (voltage, current, temperature, SoC)Heating circuit data (indoor temperature, heating power, etc.) Using the validated model, strategies for electrothermal recuperation, which describes the direct use of power in regenerative braking operations for heating, were implemented and the range advantage was determined. This approach was further developed to a peak power shaving. Depending on the drive power, the heating power is controlled. The simulation data* show this approach and the resulting improvement in range and battery life. *The corresponding paper was submitted.
DOI: 10.21227/cs1k-ya91
title: Battery and Heating Data in Real Driving Cycles

description: High-voltage batteries in battery electric vehicles face significant load fluctuations due to driving behavior. High accelerations cause high discharge currents, while regenerative braking leads to charging currents. This dynamic performance of the powertrain is contrasted by the almost constant load of the auxiliary consumers. The highest auxiliary consumption is generated by the heating and air conditioning system, which decreases the vehicles range significantly. This data set contains real driving data and simulated data. The real driving was recorded with a BMW i3 (60 Ah) and serves for the model development and validation of a full vehicle model consisting of the powertrain and the heating circuit. Each ride contains:Environmental data (temperature, elevation, etc.)Vehicle data (speed, throttle, etc.)Battery data (voltage, current, temperature, SoC)Heating circuit data (indoor temperature, heating power, etc.)Using the validated model, strategies for electrothermal recuperation, which describes the direct use of power in regenerative braking operations for heating, were implemented and the range advantage was determined. This approach was further developed to a peak power shaving. Depending on the drive power, the heating power is controlled. The simulation data* show this approach and the resulting improvement in range and battery life.*The corresponding paper was submitted.
DOI: 10.21227/ja0e-ev19
title: Battery and Heating Data in Real Driving Cycles

description: High-voltage batteries in battery electric vehicles face significant load fluctuations due to driving behavior. High accelerations cause high discharge currents, while regenerative braking leads to charging currents. This dynamic performance of the powertrain is contrasted by the almost constant load of the auxiliary consumers. The highest auxiliary consumption is generated by the heating and air conditioning system, which decreases the vehicles range significantly. This data set contains real driving data and simulated data. The 72 real driving trips was recorded with a BMW i3 (60 Ah) and serves for the model development and validation of a full vehicle model consisting of the powertrain and the heating circuit. Each ride contains:Environmental data (temperature, elevation, etc.)Vehicle data (speed, throttle, etc.)Battery data (voltage, current, temperature, SoC)Heating circuit data (indoor temperature, heating power, etc.)Using the validated model, strategies for electrothermal recuperation, which describes the direct use of power in regenerative braking operations for heating, were implemented and the range advantage was determined. This approach was further developed to a peak power shaving. Depending on the drive power, the heating power is controlled. The simulation data* show this approach and the resulting improvement in range and battery life.*The corresponding paper was submitted.
DOI: 10.21227/ra8b-bx91
title: Battery and Heating Data in Real Driving Cycles

description: High-voltage batteries in battery electric vehicles face significant load fluctuations due to driving behavior. High accelerations cause high discharge currents, while regenerative braking leads to charging currents. This dynamic performance of the powertrain is contrasted by the almost constant load of the auxiliary consumers. The highest auxiliary consumption is generated by the heating and air conditioning system, which decreases the vehicles range significantly. This data set contains real driving data and simulated data. The 72 real driving trips was recorded with a BMW i3 (60 Ah) and serves for the model development and validation of a full vehicle model consisting of the powertrain and the heating circuit. Each trip contains:Environmental data (temperature, elevation, etc.)Vehicle data (speed, throttle, etc.)Battery data (voltage, current, temperature, SoC)Heating circuit data (indoor temperature, heating power, etc.)Using the validated model, strategies for electrothermal recuperation, which describes the direct use of power in regenerative braking operations for heating, were implemented and the range advantage was determined. This approach was further developed to a peak power shaving. Depending on the drive power, the heating power is controlled. The simulation data* show this approach and the resulting improvement in range and battery life.*The corresponding paper was submitted.
DOI: 10.21227/5cgv-4s17
title: Battery and Heating Data in Real Driving Cycles

description: High-voltage batteries in battery electric vehicles face significant load fluctuations due to driving behavior. This dynamic performance of the powertrain is contrasted by the almost constant load of the auxiliary consumers. The highest auxiliary consumption is generated by the heating and air conditioning system, which decreases the vehicles range significantly. This data set contains real driving data and simulated data. The 72 real driving trips was recorded with a BMW i3 (60 Ah) and serves for the model development and validation of a full vehicle model consisting of the powertrain and the heating circuit. Each trip contains:Environmental data (temperature, elevation, etc.)Vehicle data (speed, throttle, etc.)Battery data (voltage, current, temperature, SoC)Heating circuit data (indoor temperature, heating power, etc.)Using the validated model, strategies for electrothermal recuperation, which describes the direct use of power in regenerative braking operations for heating, were implemented and the range advantage was determined. This approach was further developed to a peak power shaving. Depending on the drive power, the heating power is controlled. The simulation data show this approach and the resulting improvement in range and battery life. The corresponding paper was submitted to IEEE Transactions on Transportation Electrification.
DOI: 10.24378/exe.2763
title: What Hope Lies Buried Here: Differential Mortality and Mortuary Treatment of Adolescents in Dubuque's Third Street Cemetery (Appendix C Osteodatabase)

description: Appendix for the Doctoral thesis "What Hope Lies Buried Here: Differential Mortality and Mortuary Treatment of Adolescents in Dubuque's Third Street Cemetery".
description: Osteodatabase, All individuals from Third Street Cemetery, Microsoft Access file
description: The thesis associated with this dataset is located in ORE at: http://hdl.handle.net/10871/123293
DOI: 10.21227/ccyx-r392
title: Battery and Heating Data in Real Driving Cycles

description: High-voltage batteries in battery electric vehicles face significant load fluctuations due to driving behavior. This dynamic performance of the powertrain is contrasted by the almost constant load of the auxiliary consumers. The highest auxiliary consumption is generated by the heating and air conditioning system, which decreases the vehicles range significantly. This data set contains real driving data and simulated data. The 72 real driving trips were recorded with a BMW i3 (60 Ah) and serves for the model development and validation of a full vehicle model consisting of the powertrain and the heating circuit. Each trip contains:Environmental data (temperature, elevation, etc.)Vehicle data (speed, throttle, etc.)Battery data (voltage, current, temperature, SoC)Heating circuit data (indoor temperature, heating power, etc.)Using the validated model, strategies for electrothermal recuperation, which describes the direct use of power in regenerative braking operations for heating, were implemented and the range advantage was determined. This approach was further developed to a peak power shaving. Depending on the drive power, the heating power is controlled. The simulation data show this approach and the resulting improvement in range and battery life. The corresponding paper was submitted to IEEE Transactions on Transportation Electrification.
DOI: 10.21227/7ej4-r721
title: Battery and Heating Data in Real Driving Cycles

description: High-voltage batteries in battery electric vehicles face significant load fluctuations due to driving behavior. This dynamic performance of the powertrain is contrasted by the almost constant load of the auxiliary consumers. The highest auxiliary consumption is generated by the heating and air conditioning system, which decreases the vehicles range significantly. This data set contains real driving data and simulated data. 72 real driving trips were recorded with a BMW i3 (60 Ah), serving for model validation of a full vehicle model consisting of the powertrain and the heating circuit. Each trip contains:Environmental data (temperature, elevation, etc.)Vehicle data (speed, throttle, etc.)Battery data (voltage, current, temperature, SoC)Heating circuit data (indoor temperature, heating power, etc.)Using the validated model, strategies for electrothermal recuperation, which describes the direct use of power in regenerative braking operations for heating, were implemented and the range advantage was determined. This approach was further developed to a peak power shaving. Depending on the drive power, the heating power is controlled. The simulation data show this approach and the resulting improvement in range and battery life. The corresponding paper was submitted to IEEE Transactions on Transportation Electrification.
DOI: 10.21227/5qx4-7z61
title: Battery and Heating Data in Real Driving Cycles

description: High-voltage batteries in battery electric vehicles face significant load fluctuations. This dynamic performance of the powertrain is contrasted by the almost constant load of the auxiliary consumers. The highest auxiliary consumption is generated by the heating and air conditioning system, which decreases the vehicles range significantly. 72 real driving trips were recorded with a BMW i3 (60 Ah), serving for model validation of a full vehicle model consisting of the powertrain and the heating circuit. Each trip contains:Environmental data (temperature, elevation, etc.)Vehicle data (speed, throttle, etc.)Battery data (voltage, current, temperature, SoC)Heating circuit data (indoor temperature, heating power, etc.)Using the validated model, strategies for electrothermal recuperation, which describes the direct use of power in regenerative braking operations for heating, were implemented and the range advantage was determined. This approach was further developed to a peak power shaving. Depending on the drive power, the heating power is controlled. The simulation data show this approach and the resulting improvement in range and battery life. The corresponding paper was submitted to IEEE Transactions on Transportation Electrification.
DOI: 10.6084/m9.figshare.13083983.v1
title: Lung regeneration

description: scRNA-seq time-series dataset of lung epithelial regeneration past bleomycin injury in mice assayed using Dropseq. Contains 13 time points spanning days 2-15 past injury with two replicates each. Total cell number is 24,882.<br>This dataset contains raw spliced and unspliced counts estimated using the velocyto pipeline (La Manno et al., Nature 2018) that can be used to compute RNA velocity. <br> Please cite the original publication: https://doi.org/10.1038/s41467-020-17358-3
DOI: 10.6084/m9.figshare.13079543
title: Pancreas development preprocessed

description: Preprocessed scRNA-seq dataset of pancreatic development at E15.5 in mice assayed using 10x genomics. Total cell number is 2531. Contains RNA velocity (La Manno et al., Nature 2018) computed using scVelo (Bergen et al., Nature Biotechnology 2020).<br><br>Please cite the original publication: https://doi.org/10.1242/dev.173849
DOI: 10.6084/m9.figshare.13105448
title: Pancreas MAGIC imputed data

description: scRNA-seq dataset of pancreatic development at E15.5 in mice assayed using 10x genomics. Total cell number is 2531. <br><br>This contains MAGIC (van Dijk et al., Cell 2018) imputed data for 12,987 genes which are expressed in at least 10 cells. <br>Please cite the original publication: https://doi.org/10.1242/dev.173849
DOI: 10.5517/ccdc.csd.cc212hpb
title: CCDC 1879241: Experimental Crystal Structure Determination

description: Related Article: Anima Bose,  Sudip Sau,  Prasenjit Mal|2019|Eur.J.Org.Chem.|2019|4105|doi:10.1002/ejoc.201900732
DOI: 10.6084/m9.figshare.13078955
title: Pancreas development

description: scRNA-seq dataset of pancreatic development at E15.5 in mice assayed using 10x genomics. Total cell number is 2531. <br><br>This dataset contains raw spliced and unspliced counts estimated using the velocyto pipeline (La Manno et al., Nature 2018) that can be used to compute RNA velocity. <br><br>Please cite the original publication: https://doi.org/10.1242/dev.173849
DOI: 10.6084/m9.figshare.13083983
title: Lung regeneration

description: scRNA-seq time-series dataset of lung epithelial regeneration past bleomycin injury in mice assayed using Dropseq. Contains 13 time points spanning days 2-15 past injury with two replicates each. Total cell number is 24,882.<br>This dataset contains raw spliced and unspliced counts estimated using the velocyto pipeline (La Manno et al., Nature 2018) that can be used to compute RNA velocity. <br> Please cite the original publication: https://doi.org/10.1038/s41467-020-17358-3
DOI: 10.5517/ccdc.csd.cc21sfz8
title: CCDC 1900330: Experimental Crystal Structure Determination

description: Related Article: Lucas H. G. KalinkeDanielle CangussuFrancesc LloretRosaria BrunoDonatella ArmentanoEmilio PardoJesus Ferrando-Soria, Jesus Ferrando-Soria|2019|Cryst.Growth Des.|19|3905|doi:10.1021/acs.cgd.9b00324
DOI: 10.5517/ccdc.csd.cc21vjv9
title: CCDC 1902341: Experimental Crystal Structure Determination

description: Related Article: Huanyu Ding, Li Wu, Tao Guo, Zaiyong Zhang, Bello Mubarak Garba, Ge Gao, Siyu He, Wei Zhang, Yizhi Chen, Yangjing Lin, Hewen Liu, Jamshed Anwar, Jiwen Zhang|2019|Cryst.Growth Des.|19|3888|doi:10.1021/acs.cgd.9b00319
DOI: 10.5517/ccdc.csd.cc21vkk1
title: CCDC 1902363: Experimental Crystal Structure Determination

description: Related Article: Rafał Orłowski, Grzegorz Cichowicz, Olga Staszewska‐Krajewska, Wojciech Schilf, Michał K. Cyrański, Daniel T. Gryko|2019|Chem.-Eur.J.|25|9658|doi:10.1002/chem.201901254
DOI: 10.5517/ccdc.csd.cc21yqc3
title: CCDC 1905395: Experimental Crystal Structure Determination

description: Related Article: Wenjie Tao, Jamey K. Bower, Curtis E. Moore, Shiyu Zhang|2019|J.Am.Chem.Soc.|141|10159|doi:10.1021/jacs.9b03635
DOI: 10.5517/ccdc.csd.cc2228k0
title: CCDC 1908811: Experimental Crystal Structure Determination

description: Related Article: Neha Choudhary, Alexander Dimmling, Xiaozhu Wang, Lily Southcott, Valery Radchenko, Brian O. Patrick, Peter Comba, Chris Orvig|2019|Inorg.Chem.|58|8685|doi:10.1021/acs.inorgchem.9b01016
DOI: 10.5517/ccdc.csd.cc222q30
title: CCDC 1909231: Experimental Crystal Structure Determination

description: Related Article: Letao Huang, Shiwu Li, Xia Ling, Jun Zhang, Anjun Qin, Jian Zhuang, Meng Gao, Ben Zhong Tang|2019|Chem.Commun.|55|7458|doi:10.1039/C9CC02782C
DOI: 10.5517/ccdc.csd.cc2247q6
title: CCDC 1910707: Experimental Crystal Structure Determination

description: Related Article: Maria Nicolaou, Chryssoula Drouza, Anastasios D. Keramidas|2019|New J.Chem.|43|17595|doi:10.1039/C9NJ01999E
DOI: 10.5517/ccdc.csd.cc224n1y
title: CCDC 1911089: Experimental Crystal Structure Determination

description: Related Article: Andrew W. Cook, Zachary R. Jones, Guang Wu, Simon J. Teat, Susannah L. Scott, Trevor W. Hayton|2019|Inorg.Chem.|58|8739|doi:10.1021/acs.inorgchem.9b01140
DOI: 10.5517/ccdc.csd.cc2247p5
title: CCDC 1910706: Experimental Crystal Structure Determination

description: Related Article: Maria Nicolaou, Chryssoula Drouza, Anastasios D. Keramidas|2019|New J.Chem.|43|17595|doi:10.1039/C9NJ01999E
DOI: 10.5517/ccdc.csd.cc22hwfx
title: CCDC 1921889: Experimental Crystal Structure Determination

description: Related Article: Ezzat Khan, Abdullah Khan, Zarif Gul, Farhat Ullah, Muhammad Nawaz Tahir, Muhammad Khalid, Hafiz Muhammad Asif, Sumreen Asim, Ataualpa Albert Carmo Braga|2020|J.Mol.Struct.|1200|127126|doi:10.1016/j.molstruc.2019.127126
DOI: 10.5517/ccdc.csd.cc22hwgy
title: CCDC 1921890: Experimental Crystal Structure Determination

description: Related Article: Ezzat Khan, Abdullah Khan, Zarif Gul, Farhat Ullah, Muhammad Nawaz Tahir, Muhammad Khalid, Hafiz Muhammad Asif, Sumreen Asim, Ataualpa Albert Carmo Braga|2020|J.Mol.Struct.|1200|127126|doi:10.1016/j.molstruc.2019.127126
DOI: 10.5517/ccdc.csd.cc22hwhz
title: CCDC 1921891: Experimental Crystal Structure Determination

description: Related Article: Ezzat Khan, Abdullah Khan, Zarif Gul, Farhat Ullah, Muhammad Nawaz Tahir, Muhammad Khalid, Hafiz Muhammad Asif, Sumreen Asim, Ataualpa Albert Carmo Braga|2020|J.Mol.Struct.|1200|127126|doi:10.1016/j.molstruc.2019.127126
DOI: 10.5517/ccdc.csd.cc22hwj0
title: CCDC 1921892: Experimental Crystal Structure Determination

description: Related Article: Ezzat Khan, Abdullah Khan, Zarif Gul, Farhat Ullah, Muhammad Nawaz Tahir, Muhammad Khalid, Hafiz Muhammad Asif, Sumreen Asim, Ataualpa Albert Carmo Braga|2020|J.Mol.Struct.|1200|127126|doi:10.1016/j.molstruc.2019.127126