cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3902653
title: Australia Rainfall Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict the total daily rainfall using 24 hours of temperature measurements. This is useful as temperature sensors are much cheaper and easy to maintain as compared to rain gauges. This dataset contains 160,267 time series obtained from a dataset released by the Australian Bureau of Meteorology (BOM).The time series has 3 dimensions, measuring the average hourly temperature, minimum hourly temperature and maximum hourly temperature from 518 weather stations throughout all of Australia. <br> Please refer to https://data.gov.au/data/dataset/weather-forecasting-verification-data-2015-05-to-2016-04 for more details
DOI: 10.5281/zenodo.3902667
title: Beijing PM10 Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict PM10 air quality in the city of Beijing. This dataset contains 17532 time series with 9 dimensions. This includes hourly air pollutants measurments (SO2, NO2, CO and O3), temperature, pressure, dew point, rainfall and windspeed measurments from 12 nationally controlled air quality monitoring sites. The air-quality data are from the Beijing Municipal Environmental Monitoring Center. The meteorological data in each air-quality site are matched with the nearest weather station from the China Meteorological Administration. The time period is from March 1st, 2013 to February 28th, 2017. <br> <br> Please refer to https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data for more details<br> <br> Relevant Papers <br> Zhang, S., Guo, B., Dong, A., He, J., Xu, Z. and Chen, S.X. (2017) Cautionary Tales on Air-Quality Improvement in Beijing. Proceedings of the Royal Society A, Volume 473, No. 2205, Pages 20170457 <br> <br> Citation Request <br> Zhang, S., Guo, B., Dong, A., He, J., Xu, Z. and Chen, S.X. (2017) Cautionary Tales on Air-Quality Improvement in Beijing. Proceedings of the Royal Society A, Volume 473, No. 2205, Pages 20170457
DOI: 10.5281/zenodo.3902666
title: Beijing PM10 Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict PM10 air quality in the city of Beijing. This dataset contains 17532 time series with 9 dimensions. This includes hourly air pollutants measurments (SO2, NO2, CO and O3), temperature, pressure, dew point, rainfall and windspeed measurments from 12 nationally controlled air quality monitoring sites. The air-quality data are from the Beijing Municipal Environmental Monitoring Center. The meteorological data in each air-quality site are matched with the nearest weather station from the China Meteorological Administration. The time period is from March 1st, 2013 to February 28th, 2017. <br> <br> Please refer to https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data for more details<br> <br> Relevant Papers <br> Zhang, S., Guo, B., Dong, A., He, J., Xu, Z. and Chen, S.X. (2017) Cautionary Tales on Air-Quality Improvement in Beijing. Proceedings of the Royal Society A, Volume 473, No. 2205, Pages 20170457 <br> <br> Citation Request <br> Zhang, S., Guo, B., Dong, A., He, J., Xu, Z. and Chen, S.X. (2017) Cautionary Tales on Air-Quality Improvement in Beijing. Proceedings of the Royal Society A, Volume 473, No. 2205, Pages 20170457
DOI: 10.5517/ccdc.csd.cc25bwjy
title: CCDC 2006460: Experimental Crystal Structure Determination

description: Related Article: Jordan J. Dotson, Ieva Liepuoniute, J. Logan Bachman, Vince M. Hipwell, Saeed I. Khan, K. N. Houk, Neil K. Garg, Miguel A. Garcia-Garibay|2021|J.Am.Chem.Soc.|143|4043|doi:10.1021/jacs.1c01100
DOI: 10.5517/ccdc.csd.cc25bwkz
title: CCDC 2006461: Experimental Crystal Structure Determination

description: Related Article: Jordan J. Dotson, Ieva Liepuoniute, J. Logan Bachman, Vince M. Hipwell, Saeed I. Khan, K. N. Houk, Neil K. Garg, Miguel A. Garcia-Garibay|2021|J.Am.Chem.Soc.|143|4043|doi:10.1021/jacs.1c01100
DOI: 10.5517/ccdc.csd.cc25bwl0
title: CCDC 2006462: Experimental Crystal Structure Determination

description: Related Article: Jordan J. Dotson, Ieva Liepuoniute, J. Logan Bachman, Vince M. Hipwell, Saeed I. Khan, K. N. Houk, Neil K. Garg, Miguel A. Garcia-Garibay|2021|J.Am.Chem.Soc.|143|4043|doi:10.1021/jacs.1c01100
DOI: 10.5517/ccdc.csd.cc25bwm1
title: CCDC 2006463: Experimental Crystal Structure Determination

description: Related Article: Jordan J. Dotson, Ieva Liepuoniute, J. Logan Bachman, Vince M. Hipwell, Saeed I. Khan, K. N. Houk, Neil K. Garg, Miguel A. Garcia-Garibay|2021|J.Am.Chem.Soc.|143|4043|doi:10.1021/jacs.1c01100
DOI: 10.5517/ccdc.csd.cc25bwp3
title: CCDC 2006465: Experimental Crystal Structure Determination

description: Related Article: Jordan J. Dotson, Ieva Liepuoniute, J. Logan Bachman, Vince M. Hipwell, Saeed I. Khan, K. N. Houk, Neil K. Garg, Miguel A. Garcia-Garibay|2021|J.Am.Chem.Soc.|143|4043|doi:10.1021/jacs.1c01100
DOI: 10.5517/ccdc.csd.cc25bwq4
title: CCDC 2006466: Experimental Crystal Structure Determination

description: Related Article: Jordan J. Dotson, Ieva Liepuoniute, J. Logan Bachman, Vince M. Hipwell, Saeed I. Khan, K. N. Houk, Neil K. Garg, Miguel A. Garcia-Garibay|2021|J.Am.Chem.Soc.|143|4043|doi:10.1021/jacs.1c01100
DOI: 10.5517/ccdc.csd.cc25bwr5
title: CCDC 2006467: Experimental Crystal Structure Determination

description: Related Article: Jordan J. Dotson, Ieva Liepuoniute, J. Logan Bachman, Vince M. Hipwell, Saeed I. Khan, K. N. Houk, Neil K. Garg, Miguel A. Garcia-Garibay|2021|J.Am.Chem.Soc.|143|4043|doi:10.1021/jacs.1c01100
DOI: 10.5517/ccdc.csd.cc25bws6
title: CCDC 2006468: Experimental Crystal Structure Determination

description: Related Article: Jordan J. Dotson, Ieva Liepuoniute, J. Logan Bachman, Vince M. Hipwell, Saeed I. Khan, K. N. Houk, Neil K. Garg, Miguel A. Garcia-Garibay|2021|J.Am.Chem.Soc.|143|4043|doi:10.1021/jacs.1c01100
DOI: 10.5517/ccdc.csd.cc25bwt7
title: CCDC 2006469: Experimental Crystal Structure Determination

description: Related Article: Jordan J. Dotson, Ieva Liepuoniute, J. Logan Bachman, Vince M. Hipwell, Saeed I. Khan, K. N. Houk, Neil K. Garg, Miguel A. Garcia-Garibay|2021|J.Am.Chem.Soc.|143|4043|doi:10.1021/jacs.1c01100
DOI: 10.5517/ccdc.csd.cc25gmgr
title: CCDC 2010054: Experimental Crystal Structure Determination

description: Related Article: Jun-Qi Zhang, Dandan Hu, Jinyu Song, Hongjun Ren|2021|J.Org.Chem.|86|4646|doi:10.1021/acs.joc.0c03041
DOI: 10.5517/ccdc.csd.cc26q616
title: CCDC 2047117: Experimental Crystal Structure Determination

description: Related Article: Yan Gao, Niannian Chen, Yiming Tian, Jiahua Zhang, Dingxian Jia|2021|Inorg.Chem.|60|3761|doi:10.1021/acs.inorgchem.0c03528
DOI: 10.5517/ccdc.csd.cc26q627
title: CCDC 2047118: Experimental Crystal Structure Determination

description: Related Article: Yan Gao, Niannian Chen, Yiming Tian, Jiahua Zhang, Dingxian Jia|2021|Inorg.Chem.|60|3761|doi:10.1021/acs.inorgchem.0c03528
DOI: 10.5517/ccdc.csd.cc26q638
title: CCDC 2047119: Experimental Crystal Structure Determination

description: Related Article: Yan Gao, Niannian Chen, Yiming Tian, Jiahua Zhang, Dingxian Jia|2021|Inorg.Chem.|60|3761|doi:10.1021/acs.inorgchem.0c03528
DOI: 10.6084/m9.figshare.14061119.v3
title: Supporting information for Late Paleozoic Evolution of Solonker Ocean: Implications from the Constitution and Kinematics of the Ondor Sum Thrust System

description: Results of the zircon U-Pb dating in "<b>Late Paleozoic Evolution of Solonker Ocean: Implications from the Constitution and Kinematics of the Ondor Sum Thrust System</b>" have been uploaded in this item.Zircons of sample JN-1were analyzed for U-Pb isotopes using SHRIMP Ⅱ at Beijing SHRIMP Center, Chinese Academy of Geological Sciences. Isotopic dating of zircons from sample ZR19-2 and ZR19-8 were performed using an Agilent 7500a ICP-MS instrument equipped with a GeoLasPro 193 nm laser ablation (LA) system at the Key Laboratory of Mineral Resources Evaluation in Northeast Asia, Ministry of Natural Resources, Jilin University, China. Argon was used as the make-up gas and standard zircon 91500 was used as an external standard. <br>
DOI: 10.6084/m9.figshare.14061119
title: Supprting date for Late Paleozoic Evolution of the Solonker Ocean: Implications from the Composition and Kinematics of the Ondor Sum Thrust System

description: Results of the zircon U-Pb dating in "<b>Late Paleozoic Evolution of Solonker Ocean: Implications from the Constitution and Kinematics of the Ondor Sum Thrust System</b>" have been uploaded in this item.Zircons of sample JN-1were analyzed for U-Pb isotopes using SHRIMP Ⅱ at Beijing SHRIMP Center, Chinese Academy of Geological Sciences. Isotopic dating of zircons from sample ZR19-2 and ZR19-8 were performed using an Agilent 7500a ICP-MS instrument equipped with a GeoLasPro 193 nm laser ablation (LA) system at the Key Laboratory of Mineral Resources Evaluation in Northeast Asia, Ministry of Natural Resources, Jilin University, China. Argon was used as the make-up gas and standard zircon 91500 was used as an external standard. <br>
DOI: 10.6084/m9.figshare.14061119.v5
title: Supprting date for Late Paleozoic Evolution of the Solonker Ocean: Implications from the Composition and Kinematics of the Ondor Sum Thrust System

description: Results of the zircon U-Pb dating in "<b>Late Paleozoic Evolution of Solonker Ocean: Implications from the Constitution and Kinematics of the Ondor Sum Thrust System</b>" have been uploaded in this item.Zircons of sample JN-1were analyzed for U-Pb isotopes using SHRIMP Ⅱ at Beijing SHRIMP Center, Chinese Academy of Geological Sciences. Isotopic dating of zircons from sample ZR19-2 and ZR19-8 were performed using an Agilent 7500a ICP-MS instrument equipped with a GeoLasPro 193 nm laser ablation (LA) system at the Key Laboratory of Mineral Resources Evaluation in Northeast Asia, Ministry of Natural Resources, Jilin University, China. Argon was used as the make-up gas and standard zircon 91500 was used as an external standard. <br>
DOI: 10.6084/m9.figshare.14061119.v4
title: Supprting date for Late Paleozoic Evolution of the Solonker Ocean: Implications from the Composition and Kinematics of the Ondor Sum Thrust System

description: Results of the zircon U-Pb dating in "<b>Late Paleozoic Evolution of Solonker Ocean: Implications from the Constitution and Kinematics of the Ondor Sum Thrust System</b>" have been uploaded in this item.Zircons of sample JN-1were analyzed for U-Pb isotopes using SHRIMP Ⅱ at Beijing SHRIMP Center, Chinese Academy of Geological Sciences. Isotopic dating of zircons from sample ZR19-2 and ZR19-8 were performed using an Agilent 7500a ICP-MS instrument equipped with a GeoLasPro 193 nm laser ablation (LA) system at the Key Laboratory of Mineral Resources Evaluation in Northeast Asia, Ministry of Natural Resources, Jilin University, China. Argon was used as the make-up gas and standard zircon 91500 was used as an external standard. <br>
DOI: 10.6084/m9.figshare.14214509
title: mcpdata_homerangepaper_plosone.csv

description: This is a raw data set containing echidna locations at Fowlers Gap Arid Zone Research Station, NSW. The data includes the identity of all echidnas used in this study (E1-E13) who were numbered according to when they were tagged (E1 was the first tagged). The data includes both decimal degrees and UTM coordinates as well as the UTM zone.
DOI: 10.6084/m9.figshare.14214509.v1
title: mcpdata_homerangepaper_plosone.csv

description: This is a raw data set containing echidna locations at Fowlers Gap Arid Zone Research Station, NSW. The data includes the identity of all echidnas used in this study (E1-E13) who were numbered according to when they were tagged (E1 was the first tagged). The data includes both decimal degrees and UTM coordinates as well as the UTM zone.
DOI: 10.15468/vja3r3
title: Coenoflora of Larici sibiricae-Abietetum sibiricae Ermakov 2014, communities recorded along the Abaza-Ak-Dovurak route

description: The dataset includes the list of plant species registered in 27 releves of association Larici sibiricae-Abietetum sibiricae made along the Abaza-Ak-Dovurak route. At the beginning of the XX century, these tall-herb dark-coniferous forests covered main area in the low part of the forest belt of the northern macroslope of West Sayan. Now they are almost cut down, only small plots still exist on the mountain slopes and in the river valleys. Abies sibirica prevail; Pinus sibirica, Betula pubescens and Picea obovata are common. Lonicera altaica, Padus avium, Rubus idaeus, Sorbus sibirica, and Spiraea chamaedrifolia form the shrub layer. The upper sub-layer of herb layer is formed by Calamagrosis obtusata, Aconitum septentrionale, Athyrium filix - femina, Diplazium sibiricum; the low sub-layer - by Aegopodium alpestre, Cerastium pauciflorum, Cruciata krylovii, Linnea borealis, Maianthemum bifolium, Trientalis europaea. Mosses occur mainly on brushwood; Hylocomium splendens, Pleurozium schreberi, Ptilium crista-castrensis are common. *For mosses and lichens, only the most widespread species are included in the list.
DOI: 10.17608/k6.auckland.14273630
title: Supplementary data 2. Hi-C datasets used in the study

description: Details of the Hi-C datasets from 70 cell-lines/tissues used for the analysis is provided in the table. Hi-C contact data was used to find the target genes of the autoimmune disease-associated SNPs.
DOI: 10.17608/k6.auckland.14273630.v2
title: Supplementary data 2. Hi-C datasets used in the study

description: Details of the Hi-C datasets from 70 cell-lines/tissues used for the analysis is provided in the table. Hi-C contact data was used to find the target genes of the autoimmune disease-associated SNPs.
DOI: 10.17608/k6.auckland.14273630.v1
title: Hi-C datasets used in the study

description: Details of the Hi-C datasets from 70 cell-lines/tissues used for the analysis is provided in the table. Hi-C contact data was used to find the target genes of the autoimmune disease-associated SNPs.
DOI: 10.5281/zenodo.4619128
title: Replication data for "Outsize Influence of Central American Orography on Global Climate"

description: This repository contains output from the two different coupled atmosphere-ocean Global Climate Models (GCMs) used in the related study (Baldwin et al 2021, "Outsize Influence of Central American Orography on Global Climate" <em>AGU Advances</em>): GFDL CM2.5-FLOR (hereafter FLOR), and NCAR CCSM4 (hereafter CCSM4). Also included are earth observations used to compare to the GCM output. The purpose of the GCM experiments is to test the influence of orographic height on climate, with a particular focus on Central America. The purpose of the observations is to calculate GCM biases. Details for the methods used to create this data can be found in the related publication linked below. The data is in netCDF format, with dimensions latitude, longitude, and (in most cases) time. Due to storage limitations, the diagnostics included here are only those that support the analyses included in the related paper. For other diagnostics from these experiments please contact the data authors. <em>Experiments/ File categories:</em> flor.ctrl and ccsm4.ctrl = topography averaged onto model grid (standard method) flor.hitopo = topography interpolated to model grid by taking gricell max flor.cam = hitopo method only over Central America ccsm4.ideal_cam = topography elevated to particular height over only Central America obs = observational datasets from a variety of sources (see paper for details) <em>Diagnostics</em> (Note: these labels correspond to the file names; the variables within the files follow the specific GCM-- eg CCSM4 or FLOR--conventions.): ucomp = near-surface zonal winds vcomp = near-surface meridional winds precip = precipitation taux = zonal surface wind stress tauy = meridional surface wind stress sst = surface temperature (used only for sea-surface temperature in the accompanying code and paper) u = zonal ocean currents at 15 m depth zsurf = surface height land_mask = land-sea mask land = includes land-sea mask and surface height
DOI: 10.5281/zenodo.4619127
title: Replication data for "Outsize Influence of Central American Orography on Global Climate"

description: This repository contains output from the two different coupled atmosphere-ocean Global Climate Models (GCMs) used in the related study (Baldwin et al 2021, "Outsize Influence of Central American Orography on Global Climate" <em>AGU Advances</em>): GFDL CM2.5-FLOR (hereafter FLOR), and NCAR CCSM4 (hereafter CCSM4). Also included are earth observations used to compare to the GCM output. The purpose of the GCM experiments is to test the influence of orographic height on climate, with a particular focus on Central America. The purpose of the observations is to calculate GCM biases. Details for the methods used to create this data can be found in the related publication linked below. The data is in netCDF format, with dimensions latitude, longitude, and (in most cases) time. Due to storage limitations, the diagnostics included here are only those that support the analyses included in the related paper. For other diagnostics from these experiments please contact the data authors. <em>Experiments/ File categories:</em> flor.ctrl and ccsm4.ctrl = topography averaged onto model grid (standard method) flor.hitopo = topography interpolated to model grid by taking gricell max flor.cam = hitopo method only over Central America ccsm4.ideal_cam = topography elevated to particular height over only Central America obs = observational datasets from a variety of sources (see paper for details) <em>Diagnostics</em> (Note: these labels correspond to the file names; the variables within the files follow the specific GCM-- eg CCSM4 or FLOR--conventions.): ucomp = near-surface zonal winds vcomp = near-surface meridional winds precip = precipitation taux = zonal surface wind stress tauy = meridional surface wind stress sst = surface temperature (used only for sea-surface temperature in the accompanying code and paper) u = zonal ocean currents at 15 m depth zsurf = surface height land_mask = land-sea mask land = includes land-sea mask and surface height
DOI: 10.5517/ccdc.csd.cc25y9xc
title: CCDC 2024173: Experimental Crystal Structure Determination

description: Related Article: Zequn Ma, Chaojun Jing, Deyu Hang, Hongtao Fan, Lumeng Duan, Shuqing Fang, Li Yan|2021|RSC Advances|11|11004|doi:10.1039/D0RA10584H
DOI: 10.5281/zenodo.3902670
title: Beijing PM2.5 Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict PM2.5 air quality in the city of Beijing. This dataset contains 17532 time series with 9 dimensions. This includes hourly air pollutants measurments (SO2, NO2, CO and O3), temperature, pressure, dew point, rainfall and windspeed measurments from 12 nationally controlled air quality monitoring sites. The air-quality data are from the Beijing Municipal Environmental Monitoring Center. The meteorological data in each air-quality site are matched with the nearest weather station from the China Meteorological Administration. The time period is from March 1st, 2013 to February 28th, 2017. <br> <br> Please refer to https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data for more details<br> <br> Relevant Papers <br> Zhang, S., Guo, B., Dong, A., He, J., Xu, Z. and Chen, S.X. (2017) Cautionary Tales on Air-Quality Improvement in Beijing. Proceedings of the Royal Society A, Volume 473, No. 2205, Pages 20170457 <br> <br> Citation Request <br> Zhang, S., Guo, B., Dong, A., He, J., Xu, Z. and Chen, S.X. (2017) Cautionary Tales on Air-Quality Improvement in Beijing. Proceedings of the Royal Society A, Volume 473, No. 2205, Pages 20170457
DOI: 10.5281/zenodo.3902673
title: Benzene Concentration Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ This goal of this dataset is to predict benzene concentration in an Italian city. This dataset contains 8878 time series obtained from the Air Quality dataset from the UCI repository. The time series has 8 dimensions which consists of hourly averaged responses from an array of 5 metal oxide chemical sensors embedded in an Air Quality Chemical Multisensor Device, as well as temperature, relative humidity and absolute humidity. The Air Quality Chemical Multisensor device was located on the field in a significantly polluted area, at road level, within an Italian city. Data were recorded from March 2004 to February 2005 (one year) representing the longest freely available recordings of on field deployed air quality chemical sensor devices responses. Ground Truth hourly averaged concentrations for CO, Non Metanic Hydrocarbons, Benzene, Total Nitrogen Oxides (NOx) and Nitrogen Dioxide (NO2) and were provided by a co-located reference certified analyzer.<br> <br> Please refer to https://archive.ics.uci.edu/ml/datasets/Air+Quality for more details. Relevant papers<br> S. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005.<br> Saverio De Vito, Marco Piga, Luca Martinotto, Girolamo Di Francia, CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization, Sensors and Actuators B: Chemical, Volume 143, Issue 1, 4 December 2009, Pages 182-191, ISSN 0925-4005.<br> S. De Vito, G. Fattoruso, M. Pardo, F. Tortorella and G. Di Francia, Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction, in IEEE Sensors Journal, vol. 12, no. 11, pp. 3215-3224, Nov. 2012. doi: 10.1109/JSEN.2012.2192425 <br> Citation request<br> S. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005