cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc27mfht
title: CCDC 2074256: Experimental Crystal Structure Determination

description: Related Article: Ruth Weller, Lutz Ruppach, Alena Shlyaykher, Frank Tambornino, C. Gunnar Werncke|2021|Dalton Trans.|50|10947|doi:10.1039/D1DT01543E
DOI: 10.5517/ccdc.csd.cc27mhgv
title: CCDC 2074317: Experimental Crystal Structure Determination

description: Related Article: Ruth Weller, Lutz Ruppach, Alena Shlyaykher, Frank Tambornino, C. Gunnar Werncke|2021|Dalton Trans.|50|10947|doi:10.1039/D1DT01543E
DOI: 10.5517/ccdc.csd.cc27xqzw
title: CCDC 2083199: Experimental Crystal Structure Determination

description: Related Article: Lars Preißing, Elias Behling, Andreas Schnepf|2021|Dalton Trans.|50|10789|doi:10.1039/D1DT02016A
DOI: 10.5517/ccdc.csd.cc27xr1z
title: CCDC 2083201: Experimental Crystal Structure Determination

description: Related Article: Lars Preißing, Elias Behling, Andreas Schnepf|2021|Dalton Trans.|50|10789|doi:10.1039/D1DT02016A
DOI: 10.5517/ccdc.csd.cc27xr20
title: CCDC 2083202: Experimental Crystal Structure Determination

description: Related Article: Lars Preißing, Elias Behling, Andreas Schnepf|2021|Dalton Trans.|50|10789|doi:10.1039/D1DT02016A
DOI: 10.5517/ccdc.csd.cc285dyt
title: CCDC 2090576: Experimental Crystal Structure Determination

description: Related Article: Antony P. Y. Chan, Martin Jakoobi, Chenxu Wang, Yancong Tian, Nathan Halcovitch, Roman Boulatov, Alexey G. Sergeev|2021|Chem.Commun.|57|7894|doi:10.1039/D1CC03445F
DOI: 10.5517/ccdc.csd.cc1zrwj4
title: CCDC 1840207: Experimental Crystal Structure Determination

description: Related Article: Wen-Yan Li, Chao-Wei Zhao, Yue-Feng Zhang, Qun Guan, Jing-Jing Wan, Jian-Ping Ma, Yan-An Li, Yu-Bin Dong|2021|Chem.Commun.|57|7954|doi:10.1039/D1CC02629A
DOI: 10.5517/ccdc.csd.cc27t70b
title: CCDC 2079821: Experimental Crystal Structure Determination

description: Related Article: Kanokon Upitak, Worawat Wattanathana, Tanin Nanok, Pitak Chuawong, Pimpa Hormnirun|2021|Dalton Trans.|50|10964|doi:10.1039/D1DT01470F
DOI: 10.5517/ccdc.csd.cc27mdfq
title: CCDC 2074223: Experimental Crystal Structure Determination

description: Related Article: Ruth Weller, Lutz Ruppach, Alena Shlyaykher, Frank Tambornino, C. Gunnar Werncke|2021|Dalton Trans.|50|10947|doi:10.1039/D1DT01543E
DOI: 10.5517/ccdc.csd.cc27mdny
title: CCDC 2074230: Experimental Crystal Structure Determination

description: Related Article: Ruth Weller, Lutz Ruppach, Alena Shlyaykher, Frank Tambornino, C. Gunnar Werncke|2021|Dalton Trans.|50|10947|doi:10.1039/D1DT01543E
DOI: 10.5517/ccdc.csd.cc2844g1
title: CCDC 2089352: Experimental Crystal Structure Determination

description: Related Article: Chifeng Li, Kazuya Yamaguchi, Kosuke Suzuki|2021|Chem.Commun.|57|7882|doi:10.1039/D1CC03099J
DOI: 10.5517/ccdc.csd.cc27xqyv
title: CCDC 2083198: Experimental Crystal Structure Determination

description: Related Article: Lars Preißing, Elias Behling, Andreas Schnepf|2021|Dalton Trans.|50|10789|doi:10.1039/D1DT02016A
DOI: 10.3886/icpsr35382.v2
title: Survey of Consumer Attitudes and Behavior, February 2006
title: Version 2

description: The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2006 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding respondents' appraisals of present market conditions for purchasing houses, automobiles, and other durables. Also explored in this survey were respondents' types of savings and financial investments, loan use, family income, retirement planning, mortgage refinancing, general feelings and mental health, willingness to give to charity, and television news viewing. Other topics in this series typically include ownership, lease, and use of automobiles, and respondents' familiarity with and use of the Internet. Demographic information includes ethnic origin, sex, age, marital status, and education.
description: The purpose of this survey series is to forecast changes in aggregate consumer behavior.
description: The data are not weighted, however, this collection contains four weight variables; WT (HOUSEHOLD HEAD WEIGHT), WT_HH (HOUSEHOLD WEIGHT), WT_ADHD (ADULT HEAD WEIGHT), and WT_AD (ADULT WEIGHT) that must be used in any analysis. For more information on weights and sampling, please refer to the documentation and/or visit the Surveys of Consumers Web site.
description: National sampling of dwelling units selected by area probability sampling.
description: Datasets: DS1: Survey of Consumer Attitudes and Behavior, February 2006
description: Persons aged 18 years or older living in households with telephones within the United States. Smallest Geographic Unit: Census division
description: computer-assisted telephone interview (CATI)Information on the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations and how they were created can be found in the ICPSR Codebook.Additional information on the Survey of Consumers can be found by visiting the Surveys of Consumers Web site.
DOI: 10.6084/m9.figshare.15145041.v1
title: eukrep_training_genomes.tar.gz

description: Genomes in fasta format used to train EukRep
DOI: 10.6084/m9.figshare.15145041
title: eukrep_training_genomes.tar.gz

description: Genomes in fasta format used to train EukRep
DOI: 10.3886/e112670v1-93700
title: Replication data for: The China Syndrome: Local Labor Market Effects of Import Competition in the United States

description: We analyze the effect of rising Chinese import competition between 1990 and 2007 on US local labor markets, exploiting cross- market variation in import exposure stemming from initial differences in industry specialization and instrumenting for US imports using changes in Chinese imports by other high-income countries. Rising imports cause higher unemployment, lower labor force participation, and reduced wages in local labor markets that house import-competing manufacturing industries. In our main specification, import competition explains one-quarter of the contemporaneous aggregate decline in US manufacturing employment. Transfer benefits payments for unemployment, disability, retirement, and healthcare also rise sharply in more trade-exposed labor markets.
DOI: 10.3886/icpsr35379.v2
title: Survey of Consumer Attitudes and Behavior, November 2005
title: Version 2

description: The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2005 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding respondents' appraisals of present market conditions for purchasing houses, automobiles, and other durables. Also explored in this survey were respondents' types of savings and financial investments, loan use, family income, and retirement planning. Other topics in this series typically include ownership, lease, and use of automobiles, respondents' use of personal computers at home and in the office, general feelings, and respondents' familiarity with and use of the Internet. Demographic information includes ethnic origin, sex, age, marital status, and education.
description: The purpose of this survey series is to forecast changes in aggregate consumer behavior.
description: The data are not weighted, however, this collection contains four weight variables; WT (HOUSEHOLD HEAD WEIGHT), WT_HH (HOUSEHOLD WEIGHT), WT_ADHD (ADULT HEAD WEIGHT), and WT_AD (ADULT WEIGHT) that must be used in any analysis. For more information on weights and sampling, please refer to the documentation and/or visit the Surveys of Consumers Web site.
description: National sampling of dwelling units selected by area probability sampling.
description: Datasets: DS1: Survey of Consumer Attitudes and Behavior, November 2005
description: Persons aged 18 years or older living in households with telephones within the United States. Smallest Geographic Unit: Census divison
description: computer-assisted telephone interview (CATI)Information on the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations and how they were created can be found in the ICPSR Codebook. Additional information on the Survey of Consumers can be found by visiting the Surveys of Consumers Web site.
DOI: 10.25493/wa7v-6c6
title: GapMap Temporal to Parietal (v11.3)

description: This dataset contains the “GapMap Temporal to Parietal" in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. In order to provide whole-brain coverage for the cortex within the Julich-Brain Atlas, yet uncharted parts of the frontal cortex have been combined to the brain region “GapMap Temporal to Parietal”. The distributions were modeled so that probabilistic gap maps were computed in analogy to other maps of the Julich-Brain Atlas. The probabilistic map of “GapMap Temporal to Parietal” is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. New maps are continuously replacing parts of “GapMap Temporal to Parietal” with progress in mapping. Other available data versions of Gap Map Temporal to Parietal: Amunts et al. (2020) [Data set, v9.0] [DOI: 10.25493/96J9-BVV](https://doi.org/10.25493%2F96J9-BVV) Amunts et al. (2020) [Data set, v9.1] [DOI: 10.25493/EZPF-Y21](https://doi.org/10.25493%2FEZPF-Y21) Amunts et al. (2020) [Data set, v9.2] [DOI: 10.25493/YXTZ-AB5](https://doi.org/10.25493%2FYXTZ-AB5) Amunts et al. (2020) [Data set, v10.0] [DOI: 10.25493/A0GE-1YJ](https://doi.org/10.25493%2FA0GE-1YJ) The most probable delineation of GapMap Temporal to Parietal derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0) Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.34943/e901c39e-7a64-40e5-9d3c-be82ca8837a2
title: Conception Bay Conductivity Temperature Depth Deployed 2021-02-14

description: The Sea-Bird Microcat SBE37SIP 5682 was deployed on 2021-02-14 at Conception Bay. Conception Bay is located west of the city St. John's, Newfoundland. This device is a Conductivity Temperature Depth. Conductivity Temperature Depth (CTD) is an instrument package that contains sensors for measuring the conductivity, temperature, and pressure of seawater. Salinity, sound velocity, depth and density are variables that can be derived from sensor measurements. CTDs can carry additional instruments and sensors such as oxygen sensors, turbidity sensors and fluorometers. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 2.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.5281/zenodo.5176277
title: CESM experiments for intraseasonal oscillation intensity changes in El Niño and La Niña developing summers

description: Experiments are conducted using version 5.2 of Community Atmospheric Model (CAM-5.2; https://www.cesm.ucar.edu/models/cesm1.1/cam/). CAM is the atmosphere component of Community Earth System Model (CESM) developed by the National Center for Atmospheric Research (NCAR). The model resolution used in this study is f19_g16, with f19 finite volume grid (1.9º latitude by 2.5º longitude) for the atmosphere and land components combined with the gx1v6 displaced pole grid (approximately 1º) for the ocean and ice components. Four sets of numerical experiments are performed in this study: Full SST forcing simulation (full-SST run): The CAM is forced by global monthly OI SST and integrated from January 1982 to December 2020. Climatological SST forcing simulation (CLM-SST run): The CAM is integrated for 30 years forced by global climatological mean monthly SST obtained from OI SST data for the period of January 1982 to December 2020. Tropical central-eastern Pacific SST forcing simulation (CEP-SST run): The SST forcing field is composed of two parts: total monthly SST in the domain of 15°S-15°N and 160°E-80°W and climatological mean monthly SST in the other regions. The CAM is integrated from January 1982 to December 2020. Sensitivity experiments of tropical central-eastern Pacific SST anomalies during ENSO events: Composite SST anomalies in El Niño and La Niña events in the region of 15°S-15°N and 160°E-80°W and the above SST anomaly fields multiplied by -1 are added to climatological mean monthly SST from May to December. Climatological mean monthly SST is used in the other regions and months. The CAM is integrated for 30 years in these experiments. These experiments are referred to as EPEL, EPLA, EPEL-reverse and EPLA-reverse, respectively.
DOI: 10.5281/zenodo.5176276
title: CESM experiments for intraseasonal oscillation intensity changes in El Niño and La Niña developing summers

description: Experiments are conducted using version 5.2 of Community Atmospheric Model (CAM-5.2; https://www.cesm.ucar.edu/models/cesm1.1/cam/). CAM is the atmosphere component of Community Earth System Model (CESM) developed by the National Center for Atmospheric Research (NCAR). The model resolution used in this study is f19_g16, with f19 finite volume grid (1.9º latitude by 2.5º longitude) for the atmosphere and land components combined with the gx1v6 displaced pole grid (approximately 1º) for the ocean and ice components. Four sets of numerical experiments are performed in this study: Full SST forcing simulation (full-SST run): The CAM is forced by global monthly OI SST and integrated from January 1982 to December 2020. Climatological SST forcing simulation (CLM-SST run): The CAM is integrated for 30 years forced by global climatological mean monthly SST obtained from OI SST data for the period of January 1982 to December 2020. Tropical central-eastern Pacific SST forcing simulation (CEP-SST run): The SST forcing field is composed of two parts: total monthly SST in the domain of 15°S-15°N and 160°E-80°W and climatological mean monthly SST in the other regions. The CAM is integrated from January 1982 to December 2020. Sensitivity experiments of tropical central-eastern Pacific SST anomalies during ENSO events: Composite SST anomalies in El Niño and La Niña events in the region of 15°S-15°N and 160°E-80°W and the above SST anomaly fields multiplied by -1 are added to climatological mean monthly SST from May to December. Climatological mean monthly SST is used in the other regions and months. The CAM is integrated for 30 years in these experiments. These experiments are referred to as EPEL, EPLA, EPEL-reverse and EPLA-reverse, respectively.
DOI: 10.25493/2zbx-eam
title: GapMap Frontal to Occipital (v11.3)

description: This dataset contains the “GapMap Frontal to Occipital” in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. In order to provide whole-brain coverage for the cortex within the Julich-Brain Atlas, yet uncharted parts of the frontal cortex have been combined to the brain region “GapMap Frontal to Occipital”. The distributions were modeled so that probabilistic gap maps were computed in analogy to other maps of the Julich-Brain Atlas. The probabilistic map of “GapMap Frontal to Occipital” is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. New maps are continuously replacing parts of “GapMap Frontal to Occipital” with progress in mapping. Other available data versions of Gap Map Frontal to Occipital: Amunts et al. (2020) [Data set, v9.0] [DOI: 10.25493/PBP4-HE6](https://doi.org/10.25493%2FPBP4-HE6) Amunts et al. (2020) [Data set, v9.1] [DOI: 10.25493/8EHQ-XYY](https://doi.org/10.25493%2F8EHQ-XYY) Amunts et al. (2020) [Data set, v9.2] [DOI: 10.25493/AK8J-WK8](https://doi.org/10.25493%2FAK8J-WK8) Amunts et al. (2020) [Data set, v10.0] [DOI: 10.25493/15H9-NKC](https://doi.org/10.25493%2F15H9-NKC) The most probable delineation of GapMap Frontal to Occipital derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0) Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.5281/zenodo.5178165
title: CoVaxxy Tweet IDs data set

description: A collection of Tweet IDs related to Covid-19 Vaccines, gathered from Twitter since Jan 4, 2021. Please see https://arxiv.org/abs/2101.07694 for more information.
DOI: 10.25493/a0eq-wke
title: GapMap Frontal II (v11.3)

description: This dataset contains the “Gap Map Frontal II” in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. In order to provide whole-brain coverage for the cortex within the Julich-Brain Atlas, yet uncharted parts of the frontal cortex have been combined to the brain region “Gap Map Frontal II”. The distributions were modeled so that probabilistic gap maps were computed in analogy to other maps of the Julich-Brain Atlas. The probabilistic map of “Gap Map Frontal II” is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. New maps are continuously replacing parts of “Gap Map Frontal II” with progress in mapping. Other available data versions of Gap Map Frontal II: Amunts et al. (2020) [Data set, v9.0] [DOI: 10.25493/XCKH-NF9](https://doi.org/10.25493%2FXCKH-NF9) Amunts et al. (2020) [Data set, v9.1] [DOI: 10.25493/H54A-PEB](https://doi.org/10.25493%2FH54A-PEB) Amunts et al. (2020) [Data set, v9.2] [DOI: 10.25493/Q0MY-250](https://doi.org/10.25493%2FQ0MY-250) Amunts et al. (2020) [Data set, v10.0] [DOI: 10.25493/9M00-CJN](https://doi.org/10.25493%2F9M00-CJN) The most probable delineation of Gap Map Frontal II derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0) Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.25911/61131e84e594b
title: Nanangroe - butterfly tracking across edges

description: This data was collected to answer two questions: (1) How do edge-effects between three land-cover types affect movement of adult male butterflies, and; (2) is response to edges influenced by perceptual range? We visually tracked the flight behaviour of the butterfly Heteronympha merope at edges between three land-cover types: native eucalypt woodland, farmland and exotic pine plantation. Using six replicates of each edge type, we released animals on both sides of the edges, and at two distances from the edge (5 m and 30 m).
DOI: 10.26180/15145224
title: Lidberg 2019 Information access administration survey Victoria pilot study_August 10, 2021_18.39.csv

description: CSV Data of Qualtrics survey - Pilot study OVIC 2919. Ethics project number19069 approved 02/04/2019
DOI: 10.26180/15145224.v1
title: Lidberg 2019 Information access administration survey Victoria pilot study_August 10, 2021_18.39.csv

description: CSV Data of Qualtrics survey - Pilot study OVIC 2919. Ethics project number19069 approved 02/04/2019
DOI: 10.5517/ccdc.csd.cc23k4g9
title: CCDC 1952890: Experimental Crystal Structure Determination

description: Related Article: Yingnan Zhao, Jie Ding, Xiao Han, Ting Geng, Xiuwen Zhou, Chen Hu, Yashu Wang, Guanjun Xiao, Bo Zou, Hongwei Hou|2020|Org.Chem.Front.|7|3853|doi:10.1039/D0QO00915F
DOI: 10.5517/ccdc.csd.cc23k4hb
title: CCDC 1952891: Experimental Crystal Structure Determination

description: Related Article: Yingnan Zhao, Jie Ding, Xiao Han, Ting Geng, Xiuwen Zhou, Chen Hu, Yashu Wang, Guanjun Xiao, Bo Zou, Hongwei Hou|2020|Org.Chem.Front.|7|3853|doi:10.1039/D0QO00915F
DOI: 10.18112/openneuro.ds003764.v1.0.1
title: Neural dynamics underlying the acquisition of distinct auditory category structures
DOI: 10.6084/m9.figshare.15145299
title: Thermoelectric potential and activation temperature of graphite, ferrosilicon alloy and magnetite.xlsx

description: 石墨、硅铁合金势和温度的热电和活化温度
DOI: 10.14457/cu.the.1968.2
title: ข้อคิดเห็นเกี่ยวกับการบริหารโรงเรียนของครูในโรงเรียนมัธยมศึกษาบางแห่งในจังหวัดพระนครและธนบุรี
DOI: 10.14457/cu.the.1965.1
title: การศึกษาทางด้านนิเวศน์วิทยาและชีววิทยาของหอยโข่งลาย (Achatina fulica)
DOI: 10.6084/m9.figshare.15145311
title: Raw Dada.zip

description: The raw data is to study the relationship between rhizosphere soil fungal diversity and wheat roots to understand the occurrence and development of wheat root rot disease.
DOI: 10.6084/m9.figshare.15145311.v1
title: Raw Dada.zip

description: The raw data is to study the relationship between rhizosphere soil fungal diversity and wheat roots to understand the occurrence and development of wheat root rot disease.
DOI: 10.5281/zenodo.5178755
title: Stochastic Simulation of the Suspended Sediment Deposition in the Channel with Vegetation and Its Relevance to Turbulent Kinetic Energy

description: This data deposit contains all the datasets needed to draw Figures 8 and 9 in the paper "Stochastic Simulation of the Suspended Sediment Deposition in the Channel with Vegetation and Its Relevance to Turbulent Kinetic Energy", which is now under review for potential publication in Water Resources Research.