cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/evzj-k510
title: Coronavirus (COVID-19) Global Sentiment Trend

description: This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.A quick overview of the datasetThe sentiment scores are defined in the range [-1,0), 0, and (0,+1] for negative sentiment, neutral sentiment, and positive sentiment. Since the number of negative sentiment tweets is always less than the combined number of neutral and positive sentiment tweets, the majority of the time, the average sentiment falls pretty close to +0.05. So if we consider +0.05 as the neutral point for average sentiment score, then any score greater than +0.1 (peaks) and smaller than 0 (drops) can be regarded as a point of interest for further scrutinizing. Following are the dates when the Twitter stream (based on the tweets present in the Coronavirus (COVID-19) Tweets Dataset) experienced those peaks and drops:Positive peaks: In 2020 (April 30, May 3, May 23, May 24, May 25, May 26, June 2, June 22, June 28, July 3, July 12, July 26, August 15, August 16, August 18, August 21, August 24, August 31, September 1, September 2, September 4, September 5, September 9, September 21, September 23)Negative Peaks: In 2020 (May 28, May 30, May 31, June 1, June 2, June 7, June 8, June 12, June 13, June 14, June 15, June 21, June 24, june 25, July 6, July 7, July 10, August 26, September 1, September 3, September 13, September 17, September 25, September 26, September 28)What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format. There are 44 instances where the average sentiment score is NULL because of networking issues with the cloud service.
DOI: 10.21227/fa9v-nn55
title: Coronavirus (COVID-19) Global Sentiment Trend

description: This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.A quick overview of the datasetThe sentiment scores are defined in the range [-1,0), 0, and (0,+1] for negative sentiment, neutral sentiment, and positive sentiment. Since the number of negative sentiment tweets is always less than the combined number of neutral and positive sentiment tweets, the majority of the time, the average sentiment falls pretty close to +0.05. So if we consider +0.05 as the neutral point for average sentiment score, then any score greater than +0.1 (peaks) and smaller than 0 (drops) can be regarded as a point of interest for further scrutinizing. Following are the dates when the Twitter stream (based on the tweets present in the Coronavirus (COVID-19) Tweets Dataset) experienced those peaks and drops:Positive peaks: In 2020 (April 30, May 3, May 23, May 24, May 25, May 26, June 2, June 22, June 28, July 3, July 12, July 26, August 15, August 16, August 18, August 21, August 24, August 31, September 1, September 2, September 4, September 5, September 9, September 21, September 23)Negative Peaks: In 2020 (May 28, May 30, May 31, June 1, June 2, June 7, June 8, June 12, June 13, June 14, June 15, June 21, June 24, june 25, July 6, July 7, July 10, August 26, September 1, September 3, September 13, September 17, September 25, September 26, September 28)What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format. There are 44 instances where the average sentiment score is NULL because of networking issues with the cloud service.
DOI: 10.21227/yd60-t491
title: Coronavirus (COVID-19) Global Sentiment Trend

description: This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.A quick overview of the datasetThe sentiment scores are defined in the range [-1,0), 0, and (0,+1] for negative sentiment, neutral sentiment, and positive sentiment. Since the number of negative sentiment tweets is always less than the combined number of neutral and positive sentiment tweets, the majority of the time, the average sentiment falls pretty close to +0.05. So if we consider +0.05 as the neutral point for average sentiment score, then any score greater than +0.1 (peaks) and smaller than 0 (drops) can be regarded as a point of interest for further scrutinizing. Following are the dates when the Twitter stream (based on the tweets present in the Coronavirus (COVID-19) Tweets Dataset) experienced those peaks and drops:Positive peaks: In 2020 (April 30, May 3, May 23, May 24, May 25, May 26, June 2, June 22, June 28, July 3, July 12, July 26, August 15, August 16, August 18, August 21, August 24, August 31, September 1, September 2, September 4, September 5, September 9, September 21, September 23)Negative Peaks: In 2020 (May 28, May 30, May 31, June 1, June 2, June 7, June 8, June 12, June 13, June 14, June 15, June 21, June 24, june 25, July 6, July 7, July 10, August 26, September 1, September 3, September 13, September 17, September 25, September 26, September 28)What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format. There are 44 instances where the average sentiment score is NULL because of networking issues with the cloud service.
DOI: 10.15468/637gcx
title: Diplodiscus latifii (Malvaceae - Brownlowioideae), a new species from Sabah, Malaysia

description: This dataset contains the digitized treatments in Plazi based on the original journal article Chung, Richard Cheng Kong, Soepadmo, Engkik (2020): Diplodiscus latifii (Malvaceae - Brownlowioideae), a new species from Sabah, Malaysia. PhytoKeys 161: 99-106, DOI: http://dx.doi.org/10.3897/phytokeys.161.55781, URL: http://dx.doi.org/10.3897/phytokeys.161.55781
DOI: 10.5517/ccdc.csd.cc20c0gw
title: CCDC 1857627: Experimental Crystal Structure Determination

description: Related Article: Khatera Hazin, Brian O. Patrick, Derek P. Gates|2019|Inorg.Chem.|58|188|doi:10.1021/acs.inorgchem.8b02174
DOI: 10.5517/ccdc.csd.cc20c0hx
title: CCDC 1857628: Experimental Crystal Structure Determination

description: Related Article: Khatera Hazin, Brian O. Patrick, Derek P. Gates|2019|Inorg.Chem.|58|188|doi:10.1021/acs.inorgchem.8b02174
DOI: 10.5517/ccdc.csd.cc20c0jy
title: CCDC 1857629: Experimental Crystal Structure Determination

description: Related Article: Khatera Hazin, Brian O. Patrick, Derek P. Gates|2019|Inorg.Chem.|58|188|doi:10.1021/acs.inorgchem.8b02174
DOI: 10.5517/ccdc.csd.cc20c0kz
title: CCDC 1857630: Experimental Crystal Structure Determination

description: Related Article: Khatera Hazin, Brian O. Patrick, Derek P. Gates|2019|Inorg.Chem.|58|188|doi:10.1021/acs.inorgchem.8b02174
DOI: 10.5517/ccdc.csd.cc20c89z
title: CCDC 1857870: Experimental Crystal Structure Determination

description: Related Article: Justin K. Pagano, David S. J. Arney, Brian L. Scott, David E. Morris, Jaqueline L. Kiplinger, Carol J. Burns|2019|Dalton Trans.|48|50|doi:10.1039/C8DT02932F
DOI: 10.5517/ccdc.csd.cc20cdrk
title: CCDC 1858008: Experimental Crystal Structure Determination

description: Related Article: Kuan Gao, Chao Huang, Yisen Yang, Hong Li, Jie Wu, Hongwei Hou|2019|Cryst.Growth Des.|19|976|doi:10.1021/acs.cgd.8b01527
DOI: 10.5517/ccdc.csd.cc20cxbn
title: CCDC 1858491: Experimental Crystal Structure Determination

description: Related Article: Jiong-Peng Zhao, Song-De Han, Fu-Chen Liu|2019|Inorg.Chem.|58|1184|doi:10.1021/acs.inorgchem.8b02587
DOI: 10.5517/ccdc.csd.cc20d5yk
title: CCDC 1858758: Experimental Crystal Structure Determination

description: Related Article: E. Echenique-Errandonea, A. Zabala-Lekuona, J. Cepeda, A. Rodríguez-Diéguez, J. M. Seco, I. Oyarzabal, E. Colacio|2019|Dalton Trans.|48|190|doi:10.1039/C8DT03800G
DOI: 10.5517/ccdc.csd.cc20d5zl
title: CCDC 1858759: Experimental Crystal Structure Determination

description: Related Article: E. Echenique-Errandonea, A. Zabala-Lekuona, J. Cepeda, A. Rodríguez-Diéguez, J. M. Seco, I. Oyarzabal, E. Colacio|2019|Dalton Trans.|48|190|doi:10.1039/C8DT03800G
DOI: 10.5517/ccdc.csd.cc20d62q
title: CCDC 1858762: Experimental Crystal Structure Determination

description: Related Article: E. Echenique-Errandonea, A. Zabala-Lekuona, J. Cepeda, A. Rodríguez-Diéguez, J. M. Seco, I. Oyarzabal, E. Colacio|2019|Dalton Trans.|48|190|doi:10.1039/C8DT03800G
DOI: 10.5517/ccdc.csd.cc20d63r
title: CCDC 1858763: Experimental Crystal Structure Determination

description: Related Article: E. Echenique-Errandonea, A. Zabala-Lekuona, J. Cepeda, A. Rodríguez-Diéguez, J. M. Seco, I. Oyarzabal, E. Colacio|2019|Dalton Trans.|48|190|doi:10.1039/C8DT03800G
DOI: 10.5517/ccdc.csd.cc20dbh8
title: CCDC 1858899: Experimental Crystal Structure Determination

description: Related Article: Rajesh Manne, Maya Miller, Andrew Duthie, M. Fátima C. Guedes da Silva, Edit Y. Tshuva, Tushar S. Basu Baul|2019|Dalton Trans.|48|304|doi:10.1039/C8DT03747G
DOI: 10.5517/ccdc.csd.cc20dbj9
title: CCDC 1858900: Experimental Crystal Structure Determination

description: Related Article: Rajesh Manne, Maya Miller, Andrew Duthie, M. Fátima C. Guedes da Silva, Edit Y. Tshuva, Tushar S. Basu Baul|2019|Dalton Trans.|48|304|doi:10.1039/C8DT03747G
DOI: 10.5517/ccdc.csd.cc20dbkb
title: CCDC 1858901: Experimental Crystal Structure Determination

description: Related Article: Rajesh Manne, Maya Miller, Andrew Duthie, M. Fátima C. Guedes da Silva, Edit Y. Tshuva, Tushar S. Basu Baul|2019|Dalton Trans.|48|304|doi:10.1039/C8DT03747G
DOI: 10.5517/ccdc.csd.cc20dblc
title: CCDC 1858902: Experimental Crystal Structure Determination

description: Related Article: Rajesh Manne, Maya Miller, Andrew Duthie, M. Fátima C. Guedes da Silva, Edit Y. Tshuva, Tushar S. Basu Baul|2019|Dalton Trans.|48|304|doi:10.1039/C8DT03747G
DOI: 10.5517/ccdc.csd.cc20dbmd
title: CCDC 1858903: Experimental Crystal Structure Determination

description: Related Article: Rajesh Manne, Maya Miller, Andrew Duthie, M. Fátima C. Guedes da Silva, Edit Y. Tshuva, Tushar S. Basu Baul|2019|Dalton Trans.|48|304|doi:10.1039/C8DT03747G
DOI: 10.5517/ccdc.csd.cc20dbnf
title: CCDC 1858904: Experimental Crystal Structure Determination

description: Related Article: Rajesh Manne, Maya Miller, Andrew Duthie, M. Fátima C. Guedes da Silva, Edit Y. Tshuva, Tushar S. Basu Baul|2019|Dalton Trans.|48|304|doi:10.1039/C8DT03747G
DOI: 10.5517/ccdc.csd.cc20dbpg
title: CCDC 1858905: Experimental Crystal Structure Determination

description: Related Article: Rajesh Manne, Maya Miller, Andrew Duthie, M. Fátima C. Guedes da Silva, Edit Y. Tshuva, Tushar S. Basu Baul|2019|Dalton Trans.|48|304|doi:10.1039/C8DT03747G
DOI: 10.5517/ccdc.csd.cc20dbqh
title: CCDC 1858906: Experimental Crystal Structure Determination

description: Related Article: Rajesh Manne, Maya Miller, Andrew Duthie, M. Fátima C. Guedes da Silva, Edit Y. Tshuva, Tushar S. Basu Baul|2019|Dalton Trans.|48|304|doi:10.1039/C8DT03747G
DOI: 10.5517/ccdc.csd.cc20dbrj
title: CCDC 1858907: Experimental Crystal Structure Determination

description: Related Article: Rajesh Manne, Maya Miller, Andrew Duthie, M. Fátima C. Guedes da Silva, Edit Y. Tshuva, Tushar S. Basu Baul|2019|Dalton Trans.|48|304|doi:10.1039/C8DT03747G
DOI: 10.5517/ccdc.csd.cc20dgzw
title: CCDC 1859038: Experimental Crystal Structure Determination

description: Related Article: Wulan Zeng, Xia Wang|2019|J.Chem.Cryst.|49|139|doi:10.1007/s10870-018-0746-y
DOI: 10.5517/ccdc.csd.cc20dh0y
title: CCDC 1859039: Experimental Crystal Structure Determination

description: Related Article: Wulan Zeng, Xia Wang|2019|J.Chem.Cryst.|49|139|doi:10.1007/s10870-018-0746-y
DOI: 10.5517/ccdc.csd.cc20dvpz
title: CCDC 1859401: Experimental Crystal Structure Determination

description: Related Article: Li-Ping Li, He-Long Peng, Lian-Qiang Wei, Bao-Hui Ye|2019|Inorg.Chem.|58|785|doi:10.1021/acs.inorgchem.8b02934
DOI: 10.5517/ccdc.csd.cc20dvq0
title: CCDC 1859402: Experimental Crystal Structure Determination

description: Related Article: Li-Ping Li, He-Long Peng, Lian-Qiang Wei, Bao-Hui Ye|2019|Inorg.Chem.|58|785|doi:10.1021/acs.inorgchem.8b02934
DOI: 10.5517/ccdc.csd.cc20dvr1
title: CCDC 1859403: Experimental Crystal Structure Determination

description: Related Article: Li-Ping Li, He-Long Peng, Lian-Qiang Wei, Bao-Hui Ye|2019|Inorg.Chem.|58|785|doi:10.1021/acs.inorgchem.8b02934
DOI: 10.5517/ccdc.csd.cc1yxqp8
title: CCDC 1815071: Experimental Crystal Structure Determination

description: Related Article: Mark Bispinghoff, Zoltán Benkő, Hansjörg Grützmacher, Fuencisla Delgado Calvo, Maria Caporali, Maurizio Peruzzini|2019|Dalton Trans.|48|3593|doi:10.1039/C8DT01840E
DOI: 10.5517/ccdc.csd.cc20f0s8
title: CCDC 1859559: Experimental Crystal Structure Determination

description: Related Article: Tao He, Yong-Zheng Zhang, Xiang-Jing Kong, Xiu-Liang Lv, Lin-Hua Xie, Jian-Rong Li|2019|Cryst.Growth Des.|19|430|doi:10.1021/acs.cgd.8b01528
DOI: 10.5517/ccdc.csd.cc20f0t9
title: CCDC 1859560: Experimental Crystal Structure Determination

description: Related Article: Tao He, Yong-Zheng Zhang, Xiang-Jing Kong, Xiu-Liang Lv, Lin-Hua Xie, Jian-Rong Li|2019|Cryst.Growth Des.|19|430|doi:10.1021/acs.cgd.8b01528
DOI: 10.5517/ccdc.csd.cc20f0vb
title: CCDC 1859561: Experimental Crystal Structure Determination

description: Related Article: Tao He, Yong-Zheng Zhang, Xiang-Jing Kong, Xiu-Liang Lv, Lin-Hua Xie, Jian-Rong Li|2019|Cryst.Growth Des.|19|430|doi:10.1021/acs.cgd.8b01528
DOI: 10.5517/ccdc.csd.cc20f0wc
title: CCDC 1859562: Experimental Crystal Structure Determination

description: Related Article: Tao He, Yong-Zheng Zhang, Xiang-Jing Kong, Xiu-Liang Lv, Lin-Hua Xie, Jian-Rong Li|2019|Cryst.Growth Des.|19|430|doi:10.1021/acs.cgd.8b01528
DOI: 10.5517/ccdc.csd.cc20dxq2
title: CCDC 1859464: Experimental Crystal Structure Determination

description: Related Article: Maxime Dufaye, Sylvain Duval, Grégory Stoclet, Xavier Trivelli, Marielle Huvé, Alain Moissette, Thierry Loiseau|2019|Inorg.Chem.|58|1091|doi:10.1021/acs.inorgchem.8b02210
DOI: 10.5517/ccdc.csd.cc1zzxc7
title: CCDC 1846960: Experimental Crystal Structure Determination

description: Related Article: Tao Wang, Kun Huang, Mengni Peng, Xianglin Li, Defang Han, Linhai Jing, Dabin Qin|2019|CrystEngComm|21|494|doi:10.1039/C8CE01868E
DOI: 10.5517/ccdc.csd.cc20fsy6
title: CCDC 1860308: Experimental Crystal Structure Determination

description: Related Article: Chao Bai, Chuan-Ti Li, Huai-Ming Hu, Bin Liu, Jin-Dian Li, Ganglin Xue|2019|Dalton Trans.|48|814|doi:10.1039/C8DT03617A
DOI: 10.5517/ccdc.csd.cc1zyt7z
title: CCDC 1845902: Experimental Crystal Structure Determination

description: Related Article: Tao Wang, Kun Huang, Mengni Peng, Xianglin Li, Defang Han, Linhai Jing, Dabin Qin|2019|CrystEngComm|21|494|doi:10.1039/C8CE01868E
DOI: 10.5517/ccdc.csd.cc1zyt80
title: CCDC 1845903: Experimental Crystal Structure Determination

description: Related Article: Tao Wang, Kun Huang, Mengni Peng, Xianglin Li, Defang Han, Linhai Jing, Dabin Qin|2019|CrystEngComm|21|494|doi:10.1039/C8CE01868E
DOI: 10.5517/ccdc.csd.cc20g7yp
title: CCDC 1860742: Experimental Crystal Structure Determination

description: Related Article: Philipp Kitschke, Ana-Maria Preda, Alexander A. Auer, Sebastian Scholz, Tobias Rüffer, Heinrich Lang, Michael Mehring|2019|Dalton Trans.|48|220|doi:10.1039/C8DT03695K
DOI: 10.5517/ccdc.csd.cc20cdsl
title: CCDC 1858009: Experimental Crystal Structure Determination

description: Related Article: Kuan Gao|2018|CSD Communication|||
description: Related Article: Kuan Gao, Chao Huang, Yisen Yang, Hong Li, Jie Wu, Hongwei Hou|2019|Cryst.Growth Des.|19|976|doi:10.1021/acs.cgd.8b01527
DOI: 10.5517/ccdc.csd.cc20gdpl
title: CCDC 1860889: Experimental Crystal Structure Determination

description: Related Article: Yaqin Cui,  Jinxing Jiang,  Xiaoyang Mao,  Jincai Wu|2019|Inorg.Chem.|58|218|doi:10.1021/acs.inorgchem.8b02290
DOI: 10.5517/ccdc.csd.cc13p262
title: CCDC 1033143: Experimental Crystal Structure Determination

description: Related Article: Iryna Olyshevets, Nataliia Kariaka, Kateryna Znovjyak, Nikolay Gerasimchuk, Sergey Lindeman, Sergii Smola, Maksym Seredyuk, Tetiana Yu. Sliva, Vladimir M. Amirkhanov|2020|Inorg.Chem.|59|76|doi:10.1021/acs.inorgchem.8b02846
DOI: 10.5517/ccdc.csd.cc13p251
title: CCDC 1033142: Experimental Crystal Structure Determination

description: Related Article: Iryna Olyshevets, Nataliia Kariaka, Kateryna Znovjyak, Nikolay Gerasimchuk, Sergey Lindeman, Sergii Smola, Maksym Seredyuk, Tetiana Yu. Sliva, Vladimir M. Amirkhanov|2020|Inorg.Chem.|59|76|doi:10.1021/acs.inorgchem.8b02846
DOI: 10.5517/ccdc.csd.cc20jcc9
title: CCDC 1862770: Experimental Crystal Structure Determination

description: Related Article: María del Carmen Mancilla-González, Uvaldo Hernádez-Balderas, Mónica Mercedes Moya-Cabrera, Lillian G. Ramírez-Palma, Diego Martínez-Otero, Fernando Cortés-Guzmán, Vojtech Jancik|2019|Inorg.Chem.|58|265|doi:10.1021/acs.inorgchem.8b02347
DOI: 10.6084/m9.figshare.12952058
title: Hydro-climate data.xlsx

description: These datasets help in conducting the rainfall-runoff modeling to evaluate water balance of the lake.<br>
DOI: 10.6084/m9.figshare.12952058.v2
title: Hydro-climate data.xlsx

description: These datasets help in conducting the rainfall-runoff modeling to evaluate water balance of the lake.<br>
DOI: 10.6084/m9.figshare.12952058.v1
title: Hydro-climate data.xlsx

description: These datasets help in conducting the rainfall-runoff modeling to evaluate water balance of the lake.<br>
DOI: 10.25583/1669350
title: RAS Protein Molecular Dynamics Simulations

description: This dataset contains 6401 files that describe parameters and other inputs for running molecular dynamics computer simulations of Ras proteins associated with a lipid-based cell membrane model in water, as well as resulting time-trajectories of atomic motion in these simulations. Specifically, the content includes: (a) 21 files containing generalized force field parameters, and (b) 22 files of similar format and content for each of 290 separate molecular dynamics simulations (22 ´ 290 = 6,380). The total size of this data set is 1.0 TB.
DOI: 10.6084/m9.figshare.13047557
title: Data from: Laboratory evaluation of Orthocentrus brachycerus (Hymenoptera: Ichneumonidae), as a potential biological control agent in mushroom cultivation

description: The data supporting the results of Fig. 2, 3, S1, S2 and S3 (Mukai & Kitajima) in submitted paper for Journal of Applied Entomology.
DOI: 10.6084/m9.figshare.13047557.v1
title: Data from: Laboratory evaluation of Orthocentrus brachycerus (Hymenoptera: Ichneumonidae), as a potential biological control agent in mushroom cultivation

description: The data supporting the results of Fig. 2, 3, S1, S2 and S3 (Mukai & Kitajima) in submitted paper for Journal of Applied Entomology.
DOI: 10.6084/m9.figshare.13019417
title: (Confidential ver.) Data from: Laboratory evaluation of Orthocentrus brachycerus (Hymenoptera: Ichneumonidae), as a potential biological control agent in mushroom cultivation

description: The data supporting the results of Fig. 2, 3, S1, S2 and S3 (Mukai & Kitajima) in submitted paper for Journal of Applied Entomology.
DOI: 10.6084/m9.figshare.13019417.v6
title: (Confidential ver.) Data from: Laboratory evaluation of Orthocentrus brachycerus (Hymenoptera: Ichneumonidae), as a potential biological control agent in mushroom cultivation

description: The data supporting the results of Fig. 2, 3, S1, S2 and S3 (Mukai & Kitajima) in submitted paper for Journal of Applied Entomology.
DOI: 10.6084/m9.figshare.13019417.v5
title: Data from: Laboratory evaluation of Orthocentrus brachycerus (Hymenoptera: Ichneumonidae), as a potential biological control agent in mushroom cultivation

description: The data supporting the results of Fig. 2, 3, S1, S2 and S3 (Mukai & Kitajima) in submitted paper for Journal of Applied Entomology.
DOI: 10.5281/zenodo.4064616
title: E3SM - Drake Passage Transport, 26.5 AMOC, Northward Heat Transport at 26.5N, from Hewitt et al 2020

description: E3SM monthly output has been annually averaged and processed to derive the maximum overturning at 26.5 N, the northward heat transport at 26.5N and the drake passage transport. The model output includes the low resolution configuration and high resolution configuration. Both runs use the 1950 control from the HighResMIP protocol.
DOI: 10.5281/zenodo.4064615
title: E3SM - Drake Passage Transport, 26.5 AMOC, Northward Heat Transport at 26.5N, from Hewitt et al 2020

description: E3SM monthly output has been annually averaged and processed to derive the maximum overturning at 26.5 N, the northward heat transport at 26.5N and the drake passage transport. The model output includes the low resolution configuration and high resolution configuration. Both runs use the 1950 control from the HighResMIP protocol.