cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5497258
title: Yields from paper: ALUMINIUM-26 FROM MASSIVE BINARY STARS II. ROTATING SINGLE STARS UP TO CORE-COLLAPSE AND THEIR IMPACT ON THE EARLY SOLAR SYSTEM

description: Title: Aluminium-26 From Massive Binary Stars II: ROTATING SINGLE STARS UP TO CORE-COLLAPSE AND THEIR IMPACT ON THE EARLY SOLAR SYSTEM<br> Authors: Brinkman H.E., den Hartogh J. W., Doherty C.L., Pignatari M., Lugaro M.<br> ================================================================================<br> Description of contents: A .tar.gz package containing three files with the complete set<br> of yields from the models presented in this paper. YieldsNR.txt contains the yields for the<br> non-rotating models and Yields150.txt and Yields300.txt the yields for the models<br> rotating at an initial velocity of 150 and 300 km/s, respectively. ================================================================================
description: This work is supported by the ERC via CoG-2016 RADIOSTAR (Grant Agreement 724560). We also acknowledge support from "ChETEC" COST Action(CA16117), supported by COST (European Cooperation in Science andTechnology). MP acknowledges significant support to NuGrid from STFC (through the University of Hull's Consolidated Grant ST/R000840/1), from the National Science Foundation (NSF, USA) under grant No. PHY-1430152 (JINA Center for the Evolution of the Elements) and from the "Lendulet-2014" Program of the Hungarian Academy of Sciences (Hungary). MP thanks for the support from the US IReNA Accelnet network
DOI: 10.5281/zenodo.5497257
title: Yields from paper: ALUMINIUM-26 FROM MASSIVE BINARY STARS II. ROTATING SINGLE STARS UP TO CORE-COLLAPSE AND THEIR IMPACT ON THE EARLY SOLAR SYSTEM

description: Title: Aluminium-26 From Massive Binary Stars II: ROTATING SINGLE STARS UP TO CORE-COLLAPSE AND THEIR IMPACT ON THE EARLY SOLAR SYSTEM<br> Authors: Brinkman H.E., den Hartogh J. W., Doherty C.L., Pignatari M., Lugaro M.<br> ================================================================================<br> Description of contents: A .tar.gz package containing three files with the complete set<br> of yields from the models presented in this paper. YieldsNR.txt contains the yields for the<br> non-rotating models and Yields150.txt and Yields300.txt the yields for the models<br> rotating at an initial velocity of 150 and 300 km/s, respectively. ================================================================================
description: This work is supported by the ERC via CoG-2016 RADIOSTAR (Grant Agreement 724560). We also acknowledge support from "ChETEC" COST Action(CA16117), supported by COST (European Cooperation in Science andTechnology). MP acknowledges significant support to NuGrid from STFC (through the University of Hull's Consolidated Grant ST/R000840/1), from the National Science Foundation (NSF, USA) under grant No. PHY-1430152 (JINA Center for the Evolution of the Elements) and from the "Lendulet-2014" Program of the Hungarian Academy of Sciences (Hungary). MP thanks for the support from the US IReNA Accelnet network
DOI: 10.6084/m9.figshare.15094539.v3
title: SSRGT_ExampleData.tar.gz

description: SSRGT is an easy-to-use and powerful tool to genotype and SSR calling across a hybrid population with genome resequencing data. This is the test data of SSRGT.<br>
DOI: 10.6084/m9.figshare.16592585
title: Supplementary Material for: Effects of Smoking on Short-Term and Long-Term Mortality after Aneurysmal Subarachnoid Hemorrhage

description: <b><i>Objective:</i></b> The relationship between smoking and clinical outcomes after aneurysmal subarachnoid hemorrhage (aSAH) is poorly clarified, and current pieces of evidence are inconsistent. The purpose of this multicenter cohort study is therefore to explore the relationship between smoking and mortality as well as several complications after aSAH. <b><i>Methods:</i></b> Databases of patient records were from 4 tertiary hospitals. We assessed the impact of tobacco use and tobacco dose (categorized based on smoking index [SI]) on several complication and overall outcome variables. The primary outcome was mortality within the longest follow-up. Logistic models were used to investigate univariate and multivariate relationships between predictors and outcomes. We also developed a propensity score matching for smoking status by using all known confounders. <b><i>Results:</i></b> A total of 6,578 patients with aSAH were analyzed. Current smoking and former smoking did not show association with mortality within the longest follow-up (odds ratio [OR], 0.95, 95% confidence interval [CI]: 0.69–1.30, <i>p</i> = 0.726; OR, 0.66, 95% CI: 0.38–1.15, <i>p</i> = 0.139, respectively). In addition, patients who were current smokers showed an independent association with the decreased occurrence of hydrocephalus (OR, 0.60; 95% CI: 0.41–0.88; <i>p</i> = 0.009) after matching all known confounders. We also found moderate smoking (SI between 384 and 625) was associated with reduced mortality in hospital. <b><i>Conclusions:</i></b> Our results indicated that in patients with aSAH, current smoking or former smoking was not associated with all-cause mortality up to 7-year follow-up.
DOI: 10.6084/m9.figshare.16592546
title: Supplementary Material for: Spinal Cord Diffuse Leptomeningeal Glioneuronal Tumor Presenting without Leptomeningeal Dissemination

description: <b><i>Background and Importance:</i></b> Diffuse leptomeningeal glioneuronal tumor (DLGNT) represents a provisional entity in the 2016 World Health Organization classification of tumors; it is characterized by a widespread leptomeningeal growth and oligodendroglial-like cytology. To this day, 4 pediatric patients have been reported to present with an isolated spinal cord tumor in the absence of leptomeningeal dissemination. Gross total resection (GTR) was achieved in only 1 patient. We present the clinical and technical nuances of this unique type of tumor, as well as the second reported case of GTR in a patient with DLGNT. <b><i>Clinical Presentation:</i></b> A 4-year-old boy presented to the emergency department after an episode of flaccid paralysis of bilateral lower extremities. MRI showed an intramedullary spinal cord tumor centered at T8. The patient was taken to the operative room, where a laminectomy and tumor resection were performed; cystic and solid tumor components were identified. Pathology report was consistent with DLGNT. After achieving GTR, patient is free of recurrence after a 15-month follow-up. <b><i>Conclusion:</i></b> No standard treatment for DLGNT has been identified. Current literature report surgery and chemotherapy with variable success rates. DLGNT presenting as an isolated intramedullary tumor is an uncommon condition which progression appears to be halted when treated promptly. Identifying solid and cystic components of this tumor is crucial for achieving GTR.
DOI: 10.6084/m9.figshare.16592546.v1
title: Supplementary Material for: Spinal Cord Diffuse Leptomeningeal Glioneuronal Tumor Presenting without Leptomeningeal Dissemination

description: <b><i>Background and Importance:</i></b> Diffuse leptomeningeal glioneuronal tumor (DLGNT) represents a provisional entity in the 2016 World Health Organization classification of tumors; it is characterized by a widespread leptomeningeal growth and oligodendroglial-like cytology. To this day, 4 pediatric patients have been reported to present with an isolated spinal cord tumor in the absence of leptomeningeal dissemination. Gross total resection (GTR) was achieved in only 1 patient. We present the clinical and technical nuances of this unique type of tumor, as well as the second reported case of GTR in a patient with DLGNT. <b><i>Clinical Presentation:</i></b> A 4-year-old boy presented to the emergency department after an episode of flaccid paralysis of bilateral lower extremities. MRI showed an intramedullary spinal cord tumor centered at T8. The patient was taken to the operative room, where a laminectomy and tumor resection were performed; cystic and solid tumor components were identified. Pathology report was consistent with DLGNT. After achieving GTR, patient is free of recurrence after a 15-month follow-up. <b><i>Conclusion:</i></b> No standard treatment for DLGNT has been identified. Current literature report surgery and chemotherapy with variable success rates. DLGNT presenting as an isolated intramedullary tumor is an uncommon condition which progression appears to be halted when treated promptly. Identifying solid and cystic components of this tumor is crucial for achieving GTR.
DOI: 10.5281/zenodo.5167599
title: Anti-symmetric Compton scattering in LiNiPO$_4$: Towards a direct probe of the magneto-electric multipole moment

description: Data files and photos related to publication: 'Anti-symmetric Compton scattering in LiNiPO$_4$: Towards a direct probe of the magneto-electric multipole moment'
DOI: 10.5281/zenodo.5167598
title: Anti-symmetric Compton scattering in LiNiPO$_4$: Towards a direct probe of the magneto-electric multipole moment

description: Data files and photos related to publication: 'Anti-symmetric Compton scattering in LiNiPO$_4$: Towards a direct probe of the magneto-electric multipole moment'
DOI: 10.5517/ccdc.csd.cc284ctm
title: CCDC 2089580: Experimental Crystal Structure Determination

description: Related Article: Rui Guo, Tong Li, Rui Wei, Xin Zhang, Qianli Li, Liu Leo Liu, Chen-Ho Tung, Lingbing Kong|2021|J.Am.Chem.Soc.|143|13483|doi:10.1021/jacs.1c06152
DOI: 10.5517/ccdc.csd.cc284cvn
title: CCDC 2089581: Experimental Crystal Structure Determination

description: Related Article: Rui Guo, Tong Li, Rui Wei, Xin Zhang, Qianli Li, Liu Leo Liu, Chen-Ho Tung, Lingbing Kong|2021|J.Am.Chem.Soc.|143|13483|doi:10.1021/jacs.1c06152
DOI: 10.5517/ccdc.csd.cc284cwp
title: CCDC 2089582: Experimental Crystal Structure Determination

description: Related Article: Rui Guo, Tong Li, Rui Wei, Xin Zhang, Qianli Li, Liu Leo Liu, Chen-Ho Tung, Lingbing Kong|2021|J.Am.Chem.Soc.|143|13483|doi:10.1021/jacs.1c06152
DOI: 10.5517/ccdc.csd.cc284cxq
title: CCDC 2089583: Experimental Crystal Structure Determination

description: Related Article: Rui Guo, Tong Li, Rui Wei, Xin Zhang, Qianli Li, Liu Leo Liu, Chen-Ho Tung, Lingbing Kong|2021|J.Am.Chem.Soc.|143|13483|doi:10.1021/jacs.1c06152
DOI: 10.5517/ccdc.csd.cc284cyr
title: CCDC 2089584: Experimental Crystal Structure Determination

description: Related Article: Rui Guo, Tong Li, Rui Wei, Xin Zhang, Qianli Li, Liu Leo Liu, Chen-Ho Tung, Lingbing Kong|2021|J.Am.Chem.Soc.|143|13483|doi:10.1021/jacs.1c06152
DOI: 10.5517/ccdc.csd.cc28680s
title: CCDC 2091384: Experimental Crystal Structure Determination

description: Related Article: Ali Wang, Mingduo Lu, Yuanhong Liu|2021|Org.Lett.|23|6813|doi:10.1021/acs.orglett.1c02389
DOI: 10.5517/ccdc.csd.cc28681t
title: CCDC 2091385: Experimental Crystal Structure Determination

description: Related Article: Ali Wang, Mingduo Lu, Yuanhong Liu|2021|Org.Lett.|23|6813|doi:10.1021/acs.orglett.1c02389
DOI: 10.5517/ccdc.csd.cc288b52
title: CCDC 2093373: Experimental Crystal Structure Determination

description: Related Article: Robert W. Elliott, Ashley L. Sutton, Brendan F. Abrahams, Deanna M. D’Alessandro, Lars Goerigk, Carol Hua, Timothy A. Hudson, Richard Robson, Keith F. White|2021|Inorg.Chem.|60|13658|doi:10.1021/acs.inorgchem.1c01979
DOI: 10.5517/ccdc.csd.cc28bkz5
title: CCDC 2095537: Experimental Crystal Structure Determination

description: Related Article: Yu Peng, Xitao Liu, Lina Li, YunPeng Yao, Huang Ye, Xiaoying Shang, Xueyuan Chen, Junhua Luo|2021|J.Am.Chem.Soc.|143|14077|doi:10.1021/jacs.1c07183
DOI: 10.5517/ccdc.csd.cc28blt1
title: CCDC 2095563: Experimental Crystal Structure Determination

description: Related Article: Yu Peng, Xitao Liu, Lina Li, YunPeng Yao, Huang Ye, Xiaoying Shang, Xueyuan Chen, Junhua Luo|2021|J.Am.Chem.Soc.|143|14077|doi:10.1021/jacs.1c07183
DOI: 10.5517/ccdc.csd.cc28bxvd
title: CCDC 2095874: Experimental Crystal Structure Determination

description: Related Article: Ji-Lei Wang, Mu-Xiu Yang, Ning-Fang Li, Xiao-Mei Liu, Jia-Nian Li, Qing-Dong Ping, Yan Xu|2021|Inorg.Chem.|60|13748|doi:10.1021/acs.inorgchem.1c02181
DOI: 10.5517/ccdc.csd.cc28bxwf
title: CCDC 2095875: Experimental Crystal Structure Determination

description: Related Article: Ji-Lei Wang, Mu-Xiu Yang, Ning-Fang Li, Xiao-Mei Liu, Jia-Nian Li, Qing-Dong Ping, Yan Xu|2021|Inorg.Chem.|60|13748|doi:10.1021/acs.inorgchem.1c02181
DOI: 10.5517/ccdc.csd.cc28d8jh
title: CCDC 2097166: Experimental Crystal Structure Determination

description: Related Article: Tanmoy Mandal, Sudha Yadav, Joyanta Choudhury|2021|J.Organomet.Chem.|953|122047|doi:10.1016/j.jorganchem.2021.122047
DOI: 10.5517/ccdc.csd.cc28jnzg
title: CCDC 2101396: Experimental Crystal Structure Determination

description: Related Article: Alexey L. Trifonov, Alexander D. Dilman|2021|Org.Lett.|23|6977|doi:10.1021/acs.orglett.1c02603
DOI: 10.5517/ccdc.csd.cc28jp0j
title: CCDC 2101397: Experimental Crystal Structure Determination

description: Related Article: Alexey L. Trifonov, Alexander D. Dilman|2021|Org.Lett.|23|6977|doi:10.1021/acs.orglett.1c02603
DOI: 10.5517/ccdc.csd.cc1kdrxm
title: CCDC 1442241: Experimental Crystal Structure Determination

description: Related Article: Robert W. Elliott, Ashley L. Sutton, Brendan F. Abrahams, Deanna M. D’Alessandro, Lars Goerigk, Carol Hua, Timothy A. Hudson, Richard Robson, Keith F. White|2021|Inorg.Chem.|60|13658|doi:10.1021/acs.inorgchem.1c01979
DOI: 10.5517/ccdc.csd.cc1kdrvk
title: CCDC 1442239: Experimental Crystal Structure Determination

description: Related Article: Robert W. Elliott, Ashley L. Sutton, Brendan F. Abrahams, Deanna M. D’Alessandro, Lars Goerigk, Carol Hua, Timothy A. Hudson, Richard Robson, Keith F. White|2021|Inorg.Chem.|60|13658|doi:10.1021/acs.inorgchem.1c01979
DOI: 10.5517/ccdc.csd.cc288b41
title: CCDC 2093372: Experimental Crystal Structure Determination

description: Related Article: Robert W. Elliott, Ashley L. Sutton, Brendan F. Abrahams, Deanna M. D’Alessandro, Lars Goerigk, Carol Hua, Timothy A. Hudson, Richard Robson, Keith F. White|2021|Inorg.Chem.|60|13658|doi:10.1021/acs.inorgchem.1c01979
DOI: 10.5517/ccdc.csd.cc1kdrwl
title: CCDC 1442240: Experimental Crystal Structure Determination

description: Related Article: Robert W. Elliott, Ashley L. Sutton, Brendan F. Abrahams, Deanna M. D’Alessandro, Lars Goerigk, Carol Hua, Timothy A. Hudson, Richard Robson, Keith F. White|2021|Inorg.Chem.|60|13658|doi:10.1021/acs.inorgchem.1c01979
DOI: 10.5517/ccdc.csd.cc288b63
title: CCDC 2093374: Experimental Crystal Structure Determination

description: Related Article: Robert W. Elliott, Ashley L. Sutton, Brendan F. Abrahams, Deanna M. D’Alessandro, Lars Goerigk, Carol Hua, Timothy A. Hudson, Richard Robson, Keith F. White|2021|Inorg.Chem.|60|13658|doi:10.1021/acs.inorgchem.1c01979
DOI: 10.5517/ccdc.csd.cc288b74
title: CCDC 2093375: Experimental Crystal Structure Determination

description: Related Article: Robert W. Elliott, Ashley L. Sutton, Brendan F. Abrahams, Deanna M. D’Alessandro, Lars Goerigk, Carol Hua, Timothy A. Hudson, Richard Robson, Keith F. White|2021|Inorg.Chem.|60|13658|doi:10.1021/acs.inorgchem.1c01979
DOI: 10.22003/xfel.eu-data-002935-00
title: PES commissioning
DOI: 10.5281/zenodo.5497219
title: Dataset of "Nonlinear Wave-Particle Interaction is Suppressed by Realistic Properties of Chorus Waves"

description: Dataset of "Nonlinear Wave-Particle Interaction is Suppressed by Realistic Properties of Chorus Waves", including waveforms of the input PIC-generated chorus wave field (WF1) recorded at certain magnetic latitudes, and the evolution of electrons' equatorial pitch angle with magnetic latitude.
DOI: 10.6084/m9.figshare.15090462
title: Metadata record for: Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset

description: This dataset contains key characteristics about the data described in the Data Descriptor Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset. <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.15090462.v1
title: Metadata record for: Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset

description: This dataset contains key characteristics about the data described in the Data Descriptor Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.5281/zenodo.5483899
title: A computational intelligence approach to predict energy demand using Random Forest in a Cloudera cluster

description: Society’s energy consumption has shot up in recent years, making the prediction of its demand a current challenge to ensure an efficient and responsible use. Artificial intelligence techniques have proven to be potential tools in handling tedious tasks and making sense of large-scale data to make better business decisions in different areas of knowledge. In this article, the use of random forests algorithms in a Big Data environment is proposed for households energy demand forecasting. The predictions are based on the use of information from different sources, confirming a fundamental role of socioeconomic data in consumer’s behaviours. On the other hand, the use of Big Data architectures is proposed to perform horizontal and vertical scaling of the solution to be used in real environments. Finally, a tool for high-resolution predictions with great efficiency is introduced, which enables energy management in a very accurate way. Raw data is incuded in data.csv. This file contains half hourly home electricity consumption registers for 4404 households with fix tariffs (not subject to dynamic time of use) for a period between November 2011 and February 2014. Original information was acquired from the Low Carbon London project led by UK Power Networks (https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households) RFResults.zip contains the energy predictions for each ACORN group using the generated Random Forest algorithm. For this purpose, the first 613 days of a total of 818 observations of each group were considered for training and the last 205 days for testing. Meteorological data was adquired from the darksky app (https://darksky.net). These data are included in the weather_hourly_darksky.csv uk_bank_holidays. xlsx contains the dated of UK bank holidays for the studied period, used as additional variable related to occupancy
description: Funding: This research was partially supported by the Ministry of Economy and Competitiveness, project TIN2015-64776-C3-2-R, and by the Junta de Andalucia, under the Andalusian Plan for Research, Development and Innovation, TIC-239.
DOI: 10.5281/zenodo.5483898
title: A computational intelligence approach to predict energy demand using Random Forest in a Cloudera cluster

description: Society’s energy consumption has shot up in recent years, making the prediction of its demand a current challenge to ensure an efficient and responsible use. Artificial intelligence techniques have proven to be potential tools in handling tedious tasks and making sense of large-scale data to make better business decisions in different areas of knowledge. In this article, the use of random forests algorithms in a Big Data environment is proposed for households energy demand forecasting. The predictions are based on the use of information from different sources, confirming a fundamental role of socioeconomic data in consumer’s behaviours. On the other hand, the use of Big Data architectures is proposed to perform horizontal and vertical scaling of the solution to be used in real environments. Finally, a tool for high-resolution predictions with great efficiency is introduced, which enables energy management in a very accurate way. Raw data is incuded in data.csv. This file contains half hourly home electricity consumption registers for 4404 households with fix tariffs (not subject to dynamic time of use) for a period between November 2011 and February 2014. Original information was acquired from the Low Carbon London project led by UK Power Networks (https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households) RFResults.zip contains the energy predictions for each ACORN group using the generated Random Forest algorithm. For this purpose, the first 613 days of a total of 818 observations of each group were considered for training and the last 205 days for testing. Meteorological data was adquired from the darksky app (https://darksky.net). These data are included in the weather_hourly_darksky.csv uk_bank_holidays. xlsx contains the dated of UK bank holidays for the studied period, used as additional variable related to occupancy
description: Funding: This research was partially supported by the Ministry of Economy and Competitiveness, project TIN2015-64776-C3-2-R, and by the Junta de Andalucia, under the Andalusian Plan for Research, Development and Innovation, TIC-239.
DOI: 10.5281/zenodo.4905517
title: AppleLeafDamages

description: This dataset contains all pictures used for the publication "<strong>Applying deep neural networks to predict incidence and phenology of plant pests and diseases" </strong>published in Ecosphere. We included three main folders: The folders "standardized_pictures" and "field_pictures" contain the full dataset with all pictures taken during the sampling for the study. The folder "categorized" contains pictures of the six main categories of damages which were used to train deep neural networks. Leaves for standardized pictures and pictures in the field were sampled weekly between April 15<sup>th</sup> and August 28<sup>th</sup> 2019 in three apple orchards in central Switzerland, in Kleinwangen, Gelfingen and Waedenswil (split into Weadenswil and Gottshalde in the dataset). We used cameras of two different smartphones, an IPhone 6 (8 megapixel camera) and a Sony Xperia X (23 megapixel camera). Detailed methods can be found in the publication. Codes can be found under: <pre>https://doi.org/10.5281/zenodo.5497319</pre>
DOI: 10.5281/zenodo.4905516
title: AppleLeafDamages

description: This dataset contains all pictures used for the publication "<strong>Applying deep neural networks to predict incidence and phenology of plant pests and diseases" </strong>published in Ecosphere. We included three main folders: The folders "standardized_pictures" and "field_pictures" contain the full dataset with all pictures taken during the sampling for the study. The folder "categorized" contains pictures of the six main categories of damages which were used to train deep neural networks. Leaves for standardized pictures and pictures in the field were sampled weekly between April 15<sup>th</sup> and August 28<sup>th</sup> 2019 in three apple orchards in central Switzerland, in Kleinwangen, Gelfingen and Waedenswil (split into Weadenswil and Gottshalde in the dataset). We used cameras of two different smartphones, an IPhone 6 (8 megapixel camera) and a Sony Xperia X (23 megapixel camera). Detailed methods can be found in the publication. Codes can be found under: <pre>https://doi.org/10.5281/zenodo.5497319</pre>
DOI: 10.5258/soton/d1849
title: UHF RFID Ice Detection and Monitoring Data

description: Data supporting the article "Wireless Ice Detection and Monitoring usingFlexible UHF RFID Tags" submitted for publication in the IEEE Sensors Journal. Article DOI 10.1109/JSEN.2021.3087326