cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1285/i15900932v40n1p115
title: Frame measures for infinitely many measures

description: For every frame spectral measure $ \mu $, there exists a discrete measure $ \nu $ as a frame measure. If $ \mu $ is not a frame spectral measure, then there is not any general statement about the existence of frame measures $ \nu $ for $ \mu $. This motivated us to examine Bessel and frame measures. We construct infinitely many measures $ \mu $ which admit frame measures $ \nu $, and we show that there exist infinitely many frame spectral measures $ \mu $ such that besides having a discrete frame measure, they admit continuous frame measures too.
DOI: 10.1285/i15900932v40n1p129
title: Index
DOI: 10.5285/afd20113-bfa6-46ee-9116-2bb1ec9408ed
title: Campaign GNSS results on rocky outcrops west of former Larsen B ice shelf, 2014-2015

description: During the 2014/2015 season six temporary GNSS stations were deployed on rocky outcrops west of the former Larsen B ice shelf by the University of Leeds and BAS, in the region of the Flask and Leppard Glaciers. This was carried out as part of the NERC funded UKANET project. This is an area we have targeted with a space-based technique called radar interferometry (InSAR), which can provide dense measurements of uplift rates, and the temporary GNSS network were deployed to better understand the contribution of atmospheric noise to the InSAR results. Four were taken out in the same season, while the other two were pulled in the 2015/2016 season. Funding was provided by NERC grant NE/L006065/1.
description: GPS and GLONASS Data were gathered using Leica GS10 receivers and Leica AS10 antennas connected to a car battery with a solar panel attached. The antennas were fixed on 30cm masts screwed onto a cemented pin. The sample rate was set to 15s. The instruments ran until power ran out due to lack of insolation during winter. The GPS data only were processed using the NASA JPL GPS- Inferred Positioning System (GIPSY) software.
description: Leica GS10 receivers and Leica AS10 antennas
description: High quality for the most part, but missing data towards end of the time series for UKA3 and UKA4, when batteries reached a low charge. Uncertainties are included in the data files.
DOI: 10.1285/i15900932v40n1p130
title: Colophon
DOI: 10.6084/m9.figshare.12947528
title: Metadata record for: Long-term surveys of age structure in 13 ungulate and one ostrich species in the Serengeti, 1926-2018

description: This dataset contains key characteristics about the data described in the Data Descriptor Long-term surveys of age structure in 13 ungulate and one ostrich species in the Serengeti, 1926-2018. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.12947528.v1
title: Metadata record for: Long-term surveys of age structure in 13 ungulate and one ostrich species in the Serengeti, 1926-2018

description: This dataset contains key characteristics about the data described in the Data Descriptor Long-term surveys of age structure in 13 ungulate and one ostrich species in the Serengeti, 1926-2018. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.12994508
title: Metadata record for: Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain

description: This dataset contains key characteristics about the data described in the Data Descriptor Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.12994508.v1
title: Metadata record for: Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain

description: This dataset contains key characteristics about the data described in the Data Descriptor Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.34777/hs2y-q453
title: KeyLemon FARGO

description: KeyLemon FARGO is a dataset for mobile face recognition. The dataset is an RGB-D dataset containing recordings of 75 identities which were captured at three different sites during Summer 2016. Each identity was captured twice with two sensor setups, mobile and laptop, and in three 	different lighting conditions, namely controlled, dark, and outdoor.
DOI: 10.5281/zenodo.4106127
title: [ITU-T AI Challenge] Input/Output of project "Improving the capacity of IEEE 802.11 WLANs through Machine Learning"

description: This data set will be used by participants of the ITU-T AI Challenge. The data set contains: Input files: contain information such as nodes labels, nodes position, or channels used. These files have been used to simulate the behavior of random WLAN deployments under different channel bonding conditions. Output files: contain the output of the simulations - throughput per STA, RSSI that each STA receives from its AP, interference map from APs' point of view, average SINR experienced by each device during packet receptions. More details can be found on the official website of the challenge: https://www.upf.edu/web/wnrg/ai_challenge <strong>[Update - 28 July 2020] </strong>A script (script_process_dataset.sh) has been added to process the output files. In particular, the results of each deployment are separated into different files. Besides, different files are created according to the type of label/feature (throughput, airtime, RSSI map, and interference list). <strong>[Update - 22 September 2020] </strong>A new feature has been added to all the files in the data set. In particular, we have added the average Signal-to-Interference-plus-Noise Ration (SINR) experienced by each STA during packet receptions (including data and control packets). The SINR values in APs are marked as Inf because we focus on downlink transmissions only. <strong>[Update - 30 September 2020] </strong>The test data set has been released, which corresponds to the simulations of a set of deployments with different characteristics. Input node files are contained in input_node_files_test.zip, while output_simulator_test.zip includes the output generated by the simulator. The label (i.e., the throughput) of the test data set will not be included in this repository until the next update (estimated date: 15 October 2020). <strong>[Update - 19 October 2020] </strong>After participants have submitted their solutions, we provide the entire test data set, including the actual throughput obtained by each AP and STA in the test deployments.
DOI: 10.5281/zenodo.3879457
title: [ITU-T AI Challenge] Input/Output of project "Improving the capacity of IEEE 802.11 WLANs through Machine Learning"

description: This data set will be used by participants of the ITU-T AI Challenge. The data set contains: Input files: contain information such as nodes labels, nodes position, or channels used. These files have been used to simulate the behavior of random WLAN deployments under different channel bonding conditions. Output files: contain the output of the simulations - throughput per STA, RSSI that each STA receives from its AP, interference map from APs' point of view, average SINR experienced by each device during packet receptions. More details can be found on the official website of the challenge: https://www.upf.edu/web/wnrg/ai_challenge <strong>[Update - 28 July 2020] </strong>A script (script_process_dataset.sh) has been added to process the output files. In particular, the results of each deployment are separated into different files. Besides, different files are created according to the type of label/feature (throughput, airtime, RSSI map, and interference list). <strong>[Update - 22 September 2020] </strong>A new feature has been added to all the files in the data set. In particular, we have added the average Signal-to-Interference-plus-Noise Ration (SINR) experienced by each STA during packet receptions (including data and control packets). The SINR values in APs are marked as Inf because we focus on downlink transmissions only. <strong>[Update - 30 September 2020] </strong>The test data set has been released, which corresponds to the simulations of a set of deployments with different characteristics. Input node files are contained in input_node_files_test.zip, while output_simulator_test.zip includes the output generated by the simulator. The label (i.e., the throughput) of the test data set will not be included in this repository until the next update (estimated date: 15 October 2020). <strong>[Update - 19 October 2020] </strong>After participants have submitted their solutions, we provide the entire test data set, including the actual throughput obtained by each AP and STA in the test deployments.
DOI: 10.21227/mz0n-k933
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: 10.6084/m9.figshare.13027220
title: Metadata record for: Density functional theory-based electric field gradient database

description: This dataset contains key characteristics about the data described in the Data Descriptor Density functional theory-based electric field gradient database. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.13027220.v1
title: Metadata record for: Density functional theory-based electric field gradient database

description: This dataset contains key characteristics about the data described in the Data Descriptor Density functional theory-based electric field gradient database. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.17632/nbzpn4fkmc.5
title: Data for: The Reinvestment Risk Premium in the Valuation of British and Russian Government Coupon Bonds

description: Data and computation for the research
DOI: 10.5517/ccdc.csd.cc13zddw
title: CCDC 1042108: Experimental Crystal Structure Determination

description: Related Article: Jian Zhang, Seyede Rahele Nabavi, Dehua Zhu, SSarah Karbalaei Khani, Xianglei Zhang, Fahime Bigdeli, Ali Morsali|2019|Mater.Lett.|251|140|doi:10.1016/j.matlet.2019.05.054
DOI: 10.5517/ccdc.csd.cc1n3k5h
title: CCDC 1522756: Experimental Crystal Structure Determination

description: Related Article: Zhen Cao, Lian Chen, Feilong Jiang, Kang Zhou, Muxin Yu, Tan Jing, Shengchang Li, Zhijia Li, Maochun Hong|2019|Cryst.Growth Des.|19|3860|doi:10.1021/acs.cgd.9b00295
DOI: 10.5517/ccdc.csd.cc1pmbl7
title: CCDC 1567719: Experimental Crystal Structure Determination

description: Related Article: Assem Barakat, Fardous F. El-Senduny, Zainab Almarhoon, Hessa H. Al-Rasheed, Farid A. Badria, Abdullah Mohammed Al-Majid, Hazem A. Ghabbour, Ayman El-Faham|2019|J.Chem.|2019|9403908|doi:10.1155/2019/9403908
DOI: 10.5517/ccdc.csd.cc1z07t1
title: CCDC 1817493: Experimental Crystal Structure Determination

description: Related Article: Pengcheng Zheng, Shuquan Wu, Chengli Mou, Wei Xue, Zhichao Jin, Yonggui Robin Chi|2019|Org.Lett.|21|5026|doi:10.1021/acs.orglett.9b01624
DOI: 10.5517/ccdc.csd.cc20zv51
title: CCDC 1876683: Experimental Crystal Structure Determination

description: Related Article: Wei-Li Lee, Tse-Wei Hsu, Wei-Cheng Hung, Jim-Min Fang|2019|Dalton Trans.|48|8026|doi:10.1039/C9DT01339C
DOI: 10.5517/ccdc.csd.cc21619m
title: CCDC 1882639: Experimental Crystal Structure Determination

description: Related Article: Zhen Cao, Lian Chen, Feilong Jiang, Kang Zhou, Muxin Yu, Tan Jing, Shengchang Li, Zhijia Li, Maochun Hong|2019|Cryst.Growth Des.|19|3860|doi:10.1021/acs.cgd.9b00295
DOI: 10.5517/ccdc.csd.cc21bbvl
title: CCDC 1886779: Experimental Crystal Structure Determination

description: Related Article: Kovalenko Ekaterina, Andrienko Irina, Samsonenko Denis, Fedin Vladimir|2019|J.Mol.Struct.|1193|357|doi:10.1016/j.molstruc.2019.05.039
DOI: 10.5517/ccdc.csd.cc20r00t
title: CCDC 1869145: Experimental Crystal Structure Determination

description: Related Article: Mayank Gupta, Sunanda Sahana, Vivekanand Sharma, Parimal K. Bharadwaj|2019|Dalton Trans.|48|7801|doi:10.1039/C9DT00548J
DOI: 10.5517/ccdc.csd.cc20bl87
title: CCDC 1857218: Experimental Crystal Structure Determination

description: Related Article: X. Wang, Y. Shen, R. Liu, X. Liu, C. Lin, D. Shi, Y. Chen, F. Liao, J. Lin, J. Sun|2019|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|75|333|doi:10.1107/S2052520619003172
DOI: 10.5517/ccdc.csd.cc21v4wy
title: CCDC 1901970: Experimental Crystal Structure Determination

description: Related Article: Daniela Mazzier, Soumen De, Barbara Wicher, Victor Maurizot, Ivan Huc|2019|Chemical Science|10|6984|doi:10.1039/C9SC01322A
DOI: 10.5517/ccdc.csd.cc21v4xz
title: CCDC 1901971: Experimental Crystal Structure Determination

description: Related Article: Daniela Mazzier, Soumen De, Barbara Wicher, Victor Maurizot, Ivan Huc|2019|Chemical Science|10|6984|doi:10.1039/C9SC01322A
DOI: 10.5517/ccdc.csd.cc21w638
title: CCDC 1902969: Experimental Crystal Structure Determination

description: Related Article: Bertram Peters, Silke Santner, Carsten Donsbach, Pascal Vöpel, Bernd Smarsly, Stefanie Dehnen|2019|Chemical Science|10|5211|doi:10.1039/C9SC01358J
DOI: 10.5517/ccdc.csd.cc21wzlj
title: CCDC 1903728: Experimental Crystal Structure Determination

description: Related Article: Arindam Ghosh, Syamasrit Dash, A. Srinivasan, Cherumuttathu H. Suresh, Tavarekere K. Chandrashekar|2019|Chemical Science|10|5911|doi:10.1039/C9SC01633C
DOI: 10.6084/m9.figshare.13109627.v1
title: OD9 dataset part 21/28

description: 435 032 nwchem logfiles calculated thanks to the quchempedia BOINC project.DFT calculations (B3LYP / 321G)The first 184 158 molecules corresond to the recalculation of QM9 and PC9. The others are newly generated molecules with the EvoMol software.Beware the concaneted tar file is 141GB of compressed files.<br>
DOI: 10.6084/m9.figshare.13109627
title: OD9 dataset part 21/28

description: 435 032 nwchem logfiles calculated thanks to the quchempedia BOINC project.DFT calculations (B3LYP / 321G)The first 184 158 molecules corresond to the recalculation of QM9 and PC9. The others are newly generated molecules with the EvoMol software.Beware the concaneted tar file is 141GB of compressed files.<br>
DOI: 10.6084/m9.figshare.13109633
title: OD9 dataset part 22/28

description: 435 032 nwchem logfiles calculated thanks to the quchempedia BOINC project.DFT calculations (B3LYP / 321G)The first 184 158 molecules corresond to the recalculation of QM9 and PC9. The others are newly generated molecules with the EvoMol software.Beware the concaneted tar file is 141GB of compressed files.<br>
DOI: 10.6084/m9.figshare.13109633.v1
title: OD9 dataset part 22/28

description: 435 032 nwchem logfiles calculated thanks to the quchempedia BOINC project.DFT calculations (B3LYP / 321G)The first 184 158 molecules corresond to the recalculation of QM9 and PC9. The others are newly generated molecules with the EvoMol software.Beware the concaneted tar file is 141GB of compressed files.<br>
DOI: 10.6084/m9.figshare.13109636
title: OD9 dataset part 23/28

description: 435 032 nwchem logfiles calculated thanks to the quchempedia BOINC project.DFT calculations (B3LYP / 321G)The first 184 158 molecules corresond to the recalculation of QM9 and PC9. The others are newly generated molecules with the EvoMol software.Beware the concaneted tar file is 141GB of compressed files.<br>
DOI: 10.6084/m9.figshare.13109636.v1
title: OD9 dataset part 23/28

description: 435 032 nwchem logfiles calculated thanks to the quchempedia BOINC project.DFT calculations (B3LYP / 321G)The first 184 158 molecules corresond to the recalculation of QM9 and PC9. The others are newly generated molecules with the EvoMol software.Beware the concaneted tar file is 141GB of compressed files.<br>
DOI: 10.6084/m9.figshare.12924248
title: Metadata record for: Brain activity and transcriptional profiling in mice under chronic jet lag

description: This dataset contains key characteristics about the data described in the Data Descriptor Brain activity and transcriptional profiling in mice under chronic jet lag. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.12924248.v1
title: Metadata record for: Brain activity and transcriptional profiling in mice under chronic jet lag

description: This dataset contains key characteristics about the data described in the Data Descriptor Brain activity and transcriptional profiling in mice under chronic jet lag. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.12839735
title: Metadata record for: Reference exome data for a Northern Brazilian population

description: This dataset contains key characteristics about the data described in the Data Descriptor Reference exome data for a Northern Brazilian population. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.12839735.v1
title: Metadata record for: Reference exome data for a Northern Brazilian population

description: This dataset contains key characteristics about the data described in the Data Descriptor Reference exome data for a Northern Brazilian population. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.13102670.v1
title: 12S-DB-All_Sequences

description: 12S Gene part from MARE MAGE database<br>
DOI: 10.17632/nbzpn4fkmc.6
title: Data for: The Reinvestment Risk Premium in the Valuation of British and Russian Government Coupon Bonds

description: Data and computation for the research
DOI: 10.6084/m9.figshare.12993340
title: Data–rich description of a new genus of praying mantid egg parasitoids, <i>Lasallegrion</i> gen. n. (Hymenoptera: Torymidae: Podagrionini), with a re-examination of <i>Podagrion</i> species of Australia and New Caledonia

description: The genus <i>Lasallegrion</i> <b>gen. n.</b> is described, and three species, <i>Lasallegrion koebelei</i> (Crawford 1912), <b>comb. n.</b>, <i>Lasallegrion virescens</i> (Strand 1911), <b>comb. n.</b>, and <i>Lasallegrion washingtoni</i> (Girault 1915), <b>comb. n.</b>, are redescribed and transferred to it. <i>Podagrion holbeini</i> Girault 1923, <b>syn. n.</b>, and <i>Podagrion metatarsum</i> Girault 1929, <b>syn. n.</b>, are synonymised here with <i>Lasallegrion koebelei</i>. Further, 14 species from Australia and New Caledonia are confirmed as belonging to <i>Podagrion</i> Spinola, 1811. The classic morphological approaches are combined with multivariate ratio analysis and molecular and additional morphological methods for (re-)descriptions. A comprehensive set of morphometric data, COI barcode sequences, a fully sequenced transcriptome and detailed µCT data are provided. An updated key to the Australasian genera of Podagrionini and to the <i>Lasallegrion</i> species is given. Additionally, we present some discussion on the collisions of the concepts of modern taxonomy, and the restrictions caused by poorly conditioned old type material.
DOI: 10.6084/m9.figshare.12993340.v1
title: Data–rich description of a new genus of praying mantid egg parasitoids, <i>Lasallegrion</i> gen. n. (Hymenoptera: Torymidae: Podagrionini), with a re-examination of <i>Podagrion</i> species of Australia and New Caledonia

description: The genus <i>Lasallegrion</i> <b>gen. n.</b> is described, and three species, <i>Lasallegrion koebelei</i> (Crawford 1912), <b>comb. n.</b>, <i>Lasallegrion virescens</i> (Strand 1911), <b>comb. n.</b>, and <i>Lasallegrion washingtoni</i> (Girault 1915), <b>comb. n.</b>, are redescribed and transferred to it. <i>Podagrion holbeini</i> Girault 1923, <b>syn. n.</b>, and <i>Podagrion metatarsum</i> Girault 1929, <b>syn. n.</b>, are synonymised here with <i>Lasallegrion koebelei</i>. Further, 14 species from Australia and New Caledonia are confirmed as belonging to <i>Podagrion</i> Spinola, 1811. The classic morphological approaches are combined with multivariate ratio analysis and molecular and additional morphological methods for (re-)descriptions. A comprehensive set of morphometric data, COI barcode sequences, a fully sequenced transcriptome and detailed µCT data are provided. An updated key to the Australasian genera of Podagrionini and to the <i>Lasallegrion</i> species is given. Additionally, we present some discussion on the collisions of the concepts of modern taxonomy, and the restrictions caused by poorly conditioned old type material.
DOI: 10.6084/m9.figshare.12993343
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.