cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc25bt8m
title: CCDC 2006390: Experimental Crystal Structure Determination

description: Related Article: Yuelu Zhu, Feng Chen, Donghui Cheng, Ying Chen, Xinyang Zhao, Wei Wei, Yi Lu, Jing Zhao|2020|Org.Lett.|22|8786|doi:10.1021/acs.orglett.0c03126
DOI: 10.5517/ccdc.csd.cc25f863
title: CCDC 2008744: Experimental Crystal Structure Determination

description: Related Article: Caixia Xie, Di Yang, Xinfeng Wang, Chen Ma|2020|J.Org.Chem.|85|14937|doi:10.1021/acs.joc.0c01662
DOI: 10.5517/ccdc.csd.cc25kztk
title: CCDC 2013289: Experimental Crystal Structure Determination

description: Related Article: Long-Sheng Zheng, Fangyuan Wang, Xiang-Yu Ye, Gen-Qiang Chen, Xumu Zhang|2020|Org.Lett.|22|8882|doi:10.1021/acs.orglett.0c03261
DOI: 10.5517/ccdc.csd.cc25j0h7
title: CCDC 2011388: Experimental Crystal Structure Determination

description: Related Article: Meng-Yu Rong, Jin-Shan Li, Yin Zhou, Fa-Guang Zhang, Jun-An Ma|2020|Org.Lett.|22|9010|doi:10.1021/acs.orglett.0c03406
DOI: 10.5517/ccdc.csd.cc25v90c
title: CCDC 2021262: Experimental Crystal Structure Determination

description: Related Article: Sijia Yan, Junxin Rao, Cong-Ying Zhou|2020|Org.Lett.|22|9091|doi:10.1021/acs.orglett.0c03493
DOI: 10.5517/ccdc.csd.cc25xb2j
title: CCDC 2023217: Experimental Crystal Structure Determination

description: Related Article: Kevin Baumgärtner, Marvin Hoffmann, Frank Rominger, Sven M. Elbert, Andreas Dreuw, Michael Mastalerz|2020|J.Org.Chem.|85|15256|doi:10.1021/acs.joc.0c02100
DOI: 10.5517/ccdc.csd.cc25xb3k
title: CCDC 2023218: Experimental Crystal Structure Determination

description: Related Article: Kevin Baumgärtner, Marvin Hoffmann, Frank Rominger, Sven M. Elbert, Andreas Dreuw, Michael Mastalerz|2020|J.Org.Chem.|85|15256|doi:10.1021/acs.joc.0c02100
DOI: 10.5517/ccdc.csd.cc25xb4l
title: CCDC 2023219: Experimental Crystal Structure Determination

description: Related Article: Kevin Baumgärtner, Marvin Hoffmann, Frank Rominger, Sven M. Elbert, Andreas Dreuw, Michael Mastalerz|2020|J.Org.Chem.|85|15256|doi:10.1021/acs.joc.0c02100
DOI: 10.5517/ccdc.csd.cc25xb5m
title: CCDC 2023220: Experimental Crystal Structure Determination

description: Related Article: Kevin Baumgärtner, Marvin Hoffmann, Frank Rominger, Sven M. Elbert, Andreas Dreuw, Michael Mastalerz|2020|J.Org.Chem.|85|15256|doi:10.1021/acs.joc.0c02100
DOI: 10.5517/ccdc.csd.cc25xb6n
title: CCDC 2023221: Experimental Crystal Structure Determination

description: Related Article: Kevin Baumgärtner, Marvin Hoffmann, Frank Rominger, Sven M. Elbert, Andreas Dreuw, Michael Mastalerz|2020|J.Org.Chem.|85|15256|doi:10.1021/acs.joc.0c02100
DOI: 10.5517/ccdc.csd.cc25xb7p
title: CCDC 2023222: Experimental Crystal Structure Determination

description: Related Article: Kevin Baumgärtner, Marvin Hoffmann, Frank Rominger, Sven M. Elbert, Andreas Dreuw, Michael Mastalerz|2020|J.Org.Chem.|85|15256|doi:10.1021/acs.joc.0c02100
DOI: 10.5517/ccdc.csd.cc25xb8q
title: CCDC 2023223: Experimental Crystal Structure Determination

description: Related Article: Kevin Baumgärtner, Marvin Hoffmann, Frank Rominger, Sven M. Elbert, Andreas Dreuw, Michael Mastalerz|2020|J.Org.Chem.|85|15256|doi:10.1021/acs.joc.0c02100
DOI: 10.5517/ccdc.csd.cc25xb9r
title: CCDC 2023224: Experimental Crystal Structure Determination

description: Related Article: Kevin Baumgärtner, Marvin Hoffmann, Frank Rominger, Sven M. Elbert, Andreas Dreuw, Michael Mastalerz|2020|J.Org.Chem.|85|15256|doi:10.1021/acs.joc.0c02100
DOI: 10.5281/zenodo.4305137
title: Dataset de Primera Ejecución BiFeO3 Antiferromagnético Tipo G

description: Scripts de ejecución de Quantum ESPRESSO para la perovskita BiFeO3 con arreglo antiferromagnético tipo G. Primera simulación.
DOI: 10.5517/ccdc.csd.cc25xbbs
title: CCDC 2023225: Experimental Crystal Structure Determination

description: Related Article: Kevin Baumgärtner, Marvin Hoffmann, Frank Rominger, Sven M. Elbert, Andreas Dreuw, Michael Mastalerz|2020|J.Org.Chem.|85|15256|doi:10.1021/acs.joc.0c02100
DOI: 10.5517/ccdc.csd.cc25xbct
title: CCDC 2023226: Experimental Crystal Structure Determination

description: Related Article: Kevin Baumgärtner, Marvin Hoffmann, Frank Rominger, Sven M. Elbert, Andreas Dreuw, Michael Mastalerz|2020|J.Org.Chem.|85|15256|doi:10.1021/acs.joc.0c02100
DOI: 10.5281/zenodo.4305136
title: Dataset de Primera Ejecución BiFeO3 Antiferromagnético Tipo G

description: Scripts de ejecución de Quantum ESPRESSO para la perovskita BiFeO3 con arreglo antiferromagnético tipo G. Primera simulación.
DOI: 10.5517/ccdc.csd.cc25xbdv
title: CCDC 2023227: Experimental Crystal Structure Determination

description: Related Article: Kevin Baumgärtner, Marvin Hoffmann, Frank Rominger, Sven M. Elbert, Andreas Dreuw, Michael Mastalerz|2020|J.Org.Chem.|85|15256|doi:10.1021/acs.joc.0c02100
DOI: 10.5517/ccdc.csd.cc261l95
title: CCDC 2027316: Experimental Crystal Structure Determination

description: Related Article: Hamada Mohamed Ibrahim, Haider Behbehani|2020|J.Org.Chem.|85|15368|doi:10.1021/acs.joc.0c02186
DOI: 10.5517/ccdc.csd.cc261lb6
title: CCDC 2027317: Experimental Crystal Structure Determination

description: Related Article: Hamada Mohamed Ibrahim, Haider Behbehani|2020|J.Org.Chem.|85|15368|doi:10.1021/acs.joc.0c02186
DOI: 10.5517/ccdc.csd.cc261lc7
title: CCDC 2027318: Experimental Crystal Structure Determination

description: Related Article: Hamada Mohamed Ibrahim, Haider Behbehani|2020|J.Org.Chem.|85|15368|doi:10.1021/acs.joc.0c02186
DOI: 10.5517/ccdc.csd.cc261ld8
title: CCDC 2027319: Experimental Crystal Structure Determination

description: Related Article: Hamada Mohamed Ibrahim, Haider Behbehani|2020|J.Org.Chem.|85|15368|doi:10.1021/acs.joc.0c02186
DOI: 10.5517/ccdc.csd.cc261lf9
title: CCDC 2027320: Experimental Crystal Structure Determination

description: Related Article: Hamada Mohamed Ibrahim, Haider Behbehani|2020|J.Org.Chem.|85|15368|doi:10.1021/acs.joc.0c02186
DOI: 10.5517/ccdc.csd.cc261spr
title: CCDC 2027514: Experimental Crystal Structure Determination

description: Related Article: Lu Yang, Ilya D. Gridnev, Masahiro Terada, Tienan Jin|2020|Org.Lett.|22|8920|doi:10.1021/acs.orglett.0c03283
DOI: 10.5517/ccdc.csd.cc265fjc
title: CCDC 2031012: Experimental Crystal Structure Determination

description: Related Article: Yuji Sumii, Takato Nagasaka, Jiandong Wang, Hiroto Uno, Norio Shibata|2020|J.Org.Chem.|85|15699|doi:10.1021/acs.joc.0c02201
DOI: 10.5281/zenodo.4305148
title: Dataset de Segunda Ejecución BiFeO3 Antiferromagnético Tipo A

description: Scripts de ejecución de Quantum ESPRESSO para la perovskita BiFeO3 con arreglo antiferromagnético tipo A. Segunda simulación.
DOI: 10.5281/zenodo.4305147
title: Dataset de Segunda Ejecución BiFeO3 Antiferromagnético Tipo A

description: Scripts de ejecución de Quantum ESPRESSO para la perovskita BiFeO3 con arreglo antiferromagnético tipo A. Segunda simulación.
DOI: 10.21258/1383432
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897183
DOI: 10.5281/zenodo.4305152
title: Dataset de Segunda Ejecución BiFeO3 Antiferromagnético Tipo G

description: Scripts de ejecución de Quantum ESPRESSO para la perovskita BiFeO3 con arreglo antiferromagnético tipo G. Segunda simulación.
DOI: 10.5281/zenodo.4305151
title: Dataset de Segunda Ejecución BiFeO3 Antiferromagnético Tipo G

description: Scripts de ejecución de Quantum ESPRESSO para la perovskita BiFeO3 con arreglo antiferromagnético tipo G. Segunda simulación.
DOI: 10.21258/1384357
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897815
DOI: 10.21258/1384385
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897955
DOI: 10.21258/1383447
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897282
DOI: 10.6084/m9.figshare.13336565
title: Hanford Area 2016-2019 Flux Data

description: This dataset includes eddy covariance measurements from the US-Hn1 and US-Hn2 flux towers from 2016-2019. It also includes water surface elevation measurements measured upstream, downstream, and in the slough near the US-Hn2 flux tower.
DOI: 10.6084/m9.figshare.13336565.v1
title: Hanford Area 2016-2019 Flux Data

description: This dataset includes eddy covariance measurements from the US-Hn1 and US-Hn2 flux towers from 2016-2019. It also includes water surface elevation measurements measured upstream, downstream, and in the slough near the US-Hn2 flux tower.
DOI: 10.21258/1422592
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891670
DOI: 10.21258/1422590
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891677
DOI: 10.21258/1424596
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891991
DOI: 10.21258/1383886
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 899638
DOI: 10.21258/1383435
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897198
DOI: 10.21258/1422084
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891943
DOI: 10.21258/1384336
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897750
DOI: 10.21258/1424378
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891664
DOI: 10.21258/1425609
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891849
DOI: 10.18141/1673604
title: Controlled source electromagnetic data from Bell Creek, MT, USA

description: Time-lapse charged well casing controlled source electromagnetic data collected at the Bell Creek oil field in southeastern Montana between October 2017 and July 2019.
DOI: 10.21258/1425627
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891948
DOI: 10.21258/1424618
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891953
DOI: 10.21227/77d8-yf84
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related datasets: (a) Coronavirus (COVID-19) Tweets Sentiment Trend (Global)(b) Tweets Originating from India During COVID-19 Lockdowns-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 274,838 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.21258/1422118
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891875
DOI: 10.21258/1413447
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 897241
DOI: 10.21258/1384410
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 898036
DOI: 10.21227/15tq-q722
title: Tweets Originating from India During COVID-19 Lockdowns

description: This India-specific COVID-19 tweets dataset has been developed using the large-scale Coronavirus (COVID-19) Tweets Dataset, which currently contains more than 700 million COVID-19 specific English language tweets. This dataset contains tweets originating from India during the first week of each four phases of nationwide lockdowns initiated by the Government of India. For more information on filtering keywords, please visit the primary dataset page.— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z-------------------------------------Related datasets: (a) Coronavirus (COVID-19) Tweets Dataset(b) Coronavirus (COVID-19) Geo-tagged Tweets Dataset(c) Coronavirus (COVID-19) Tweets Sentiment Trend (Global)-------------------------------------What's inside the dataset? The files in the dataset contain IDs of the tweets present in the Coronavirus (COVID-19) Tweets Dataset. Note: Below, (all files) means that all the files mentioned and in-between have been considered to develop the ID file, while (only even-numbered files) suggests that only the even-numbered files have been considered.Lockdown period tweets: (all files)Lockdown1.zip: March 25, 2020 - April 02, 2020; corona_tweets_08.csv to corona_tweets_14.csvLockdown2.zip: April 14, 2020 - April 21, 2020; corona_tweets_27.csv to corona_tweets_33.csvLockdown3.zip: May 01, 2020 - May 07, 2020; corona_tweets_44.csv to corona_tweets_49.csvLockdown4.zip: May 18, 2020 - May 23, 2020; corona_tweets_61.csv to corona_tweets_66.csvExtras: (all files)extras_june1_june7.zip: corona_tweets_75.csv to corona_tweets_80.csvExtras: (only even-numbered files)extras_june24_july1.zip: corona_tweets_96.csv to corona_tweets_104.csvextras_july2_july15.zip: corona_tweets_106.csv to corona_tweets_118.csvextras_july16_august4.zip: corona_tweets_120.csv to corona_tweets_138.csvextras_august5_august18.zip: corona_tweets_140.csv to corona_tweets_152.csvextras_august19_september1.zip: corona_tweets_154.csv to corona_tweets_166.csvextras_september2_september15.zip: corona_tweets_168.csv to corona_tweets_180.csv
DOI: 10.21258/1384228
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 899089
DOI: 10.21258/1422589
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891687
DOI: 10.21258/1422347
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891785
DOI: 10.21258/1419335
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 891907
DOI: 10.21258/1384056
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 899475
DOI: 10.21258/1383882
title: Schweizerisches Landesforstinventar - Ergebnistabelle Nr. 899615
DOI: 10.6084/m9.figshare.13336604
title: Summary for meta-analysis from How general is cognitive ability in non-human animals? A meta-analytical and multi-level reanalysis approach

description: General intelligence has been a topic of high interest for over a century. Traditionally, research on general intelligence was based on principal component analyses and other dimensionality reduction approaches. The advent of high-speed computing has provided alternative statistical tools that have been used to test predictions of human general intelligence. In comparison, research on general intelligence in non-human animals is in its infancy and still relies mostly on factor-analytical procedures. Here, we argue that dimensionality reduction, when incorrectly applied, can lead to spurious results and limit our understanding of ecological and evolutionary causes of variation in animal cognition. Using a meta-analytical approach, we show, based on 555 bivariate correlations, that the average correlation among cognitive abilities is low (<i>r</i> = 0.185; 95% CI: 0.087–0.287), suggesting relatively weak support for general intelligence in animals. We then use a case study with relatedness (genetic) data to demonstrate how analysing traits using mixed models, without dimensionality reduction, provides new insights into the structure of phenotypic variance among cognitive traits, and uncovers genetic associations that would be hidden otherwise. We hope this article will stimulate the use of alternative tools in the study of cognition and its evolution in animals.
DOI: 10.6084/m9.figshare.13336604.v1
title: Summary for meta-analysis from How general is cognitive ability in non-human animals? A meta-analytical and multi-level reanalysis approach

description: General intelligence has been a topic of high interest for over a century. Traditionally, research on general intelligence was based on principal component analyses and other dimensionality reduction approaches. The advent of high-speed computing has provided alternative statistical tools that have been used to test predictions of human general intelligence. In comparison, research on general intelligence in non-human animals is in its infancy and still relies mostly on factor-analytical procedures. Here, we argue that dimensionality reduction, when incorrectly applied, can lead to spurious results and limit our understanding of ecological and evolutionary causes of variation in animal cognition. Using a meta-analytical approach, we show, based on 555 bivariate correlations, that the average correlation among cognitive abilities is low (<i>r</i> = 0.185; 95% CI: 0.087–0.287), suggesting relatively weak support for general intelligence in animals. We then use a case study with relatedness (genetic) data to demonstrate how analysing traits using mixed models, without dimensionality reduction, provides new insights into the structure of phenotypic variance among cognitive traits, and uncovers genetic associations that would be hidden otherwise. We hope this article will stimulate the use of alternative tools in the study of cognition and its evolution in animals.
DOI: 10.6084/m9.figshare.13336610.v1
title: Code for case study analysis from How general is cognitive ability in non-human animals? A meta-analytical and multi-level reanalysis approach

description: General intelligence has been a topic of high interest for over a century. Traditionally, research on general intelligence was based on principal component analyses and other dimensionality reduction approaches. The advent of high-speed computing has provided alternative statistical tools that have been used to test predictions of human general intelligence. In comparison, research on general intelligence in non-human animals is in its infancy and still relies mostly on factor-analytical procedures. Here, we argue that dimensionality reduction, when incorrectly applied, can lead to spurious results and limit our understanding of ecological and evolutionary causes of variation in animal cognition. Using a meta-analytical approach, we show, based on 555 bivariate correlations, that the average correlation among cognitive abilities is low (<i>r</i> = 0.185; 95% CI: 0.087–0.287), suggesting relatively weak support for general intelligence in animals. We then use a case study with relatedness (genetic) data to demonstrate how analysing traits using mixed models, without dimensionality reduction, provides new insights into the structure of phenotypic variance among cognitive traits, and uncovers genetic associations that would be hidden otherwise. We hope this article will stimulate the use of alternative tools in the study of cognition and its evolution in animals.
DOI: 10.6084/m9.figshare.13336610
title: Code for case study analysis from How general is cognitive ability in non-human animals? A meta-analytical and multi-level reanalysis approach

description: General intelligence has been a topic of high interest for over a century. Traditionally, research on general intelligence was based on principal component analyses and other dimensionality reduction approaches. The advent of high-speed computing has provided alternative statistical tools that have been used to test predictions of human general intelligence. In comparison, research on general intelligence in non-human animals is in its infancy and still relies mostly on factor-analytical procedures. Here, we argue that dimensionality reduction, when incorrectly applied, can lead to spurious results and limit our understanding of ecological and evolutionary causes of variation in animal cognition. Using a meta-analytical approach, we show, based on 555 bivariate correlations, that the average correlation among cognitive abilities is low (<i>r</i> = 0.185; 95% CI: 0.087–0.287), suggesting relatively weak support for general intelligence in animals. We then use a case study with relatedness (genetic) data to demonstrate how analysing traits using mixed models, without dimensionality reduction, provides new insights into the structure of phenotypic variance among cognitive traits, and uncovers genetic associations that would be hidden otherwise. We hope this article will stimulate the use of alternative tools in the study of cognition and its evolution in animals.
DOI: 10.6084/m9.figshare.13336613.v1
title: Correlations for meta-analysis from How general is cognitive ability in non-human animals? A meta-analytical and multi-level reanalysis approach

description: General intelligence has been a topic of high interest for over a century. Traditionally, research on general intelligence was based on principal component analyses and other dimensionality reduction approaches. The advent of high-speed computing has provided alternative statistical tools that have been used to test predictions of human general intelligence. In comparison, research on general intelligence in non-human animals is in its infancy and still relies mostly on factor-analytical procedures. Here, we argue that dimensionality reduction, when incorrectly applied, can lead to spurious results and limit our understanding of ecological and evolutionary causes of variation in animal cognition. Using a meta-analytical approach, we show, based on 555 bivariate correlations, that the average correlation among cognitive abilities is low (<i>r</i> = 0.185; 95% CI: 0.087–0.287), suggesting relatively weak support for general intelligence in animals. We then use a case study with relatedness (genetic) data to demonstrate how analysing traits using mixed models, without dimensionality reduction, provides new insights into the structure of phenotypic variance among cognitive traits, and uncovers genetic associations that would be hidden otherwise. We hope this article will stimulate the use of alternative tools in the study of cognition and its evolution in animals.