cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc28lwh8
title: CCDC 2103520: Experimental Crystal Structure Determination

description: Related Article: E Liu, Xiaojing Liu, Zesen Jin, Fang Fang Jian|2021|J.Mol.Struct.|1252|132029|doi:10.1016/j.molstruc.2021.132029
DOI: 10.5517/ccdc.csd.cc282lyx
title: CCDC 2087879: Experimental Crystal Structure Determination

description: Related Article: E Liu, Xiaojing Liu, Zesen Jin, Fang Fang Jian|2021|J.Mol.Struct.|1252|132029|doi:10.1016/j.molstruc.2021.132029
DOI: 10.5517/ccdc.csd.cc28dgqw
title: CCDC 2097358: Experimental Crystal Structure Determination

description: Related Article: Davide Barreca, Lorenzo Bigiani, Max Klotzsche, Alberto Gasparotto, Roberta Seraglia, Christian Jandl, Alexander Pöthig, Ettore Fois, Laura Vanin, Gloria Tabacchi, Marco Roverso, Sara Bogialli, Emanuela Callone, Sandra Dirè, Chiara Maccato|2022|Mater.Chem.Phys.|277|125534|doi:10.1016/j.matchemphys.2021.125534
DOI: 10.5517/ccdc.csd.cc28mt6y
title: CCDC 2104410: Experimental Crystal Structure Determination

description: Related Article: Xiu-Rong Wu, Yi Shen, Hong-Mei Diao, Xiang Dong, Chao-Jiang Xiao, Bei Jiang|2021|Phytochemistry|193|112999|doi:10.1016/j.phytochem.2021.112999
DOI: 10.5517/ccdc.csd.cc28q99m
title: CCDC 2106800: Experimental Crystal Structure Determination

description: Related Article: Bo Ren, Cong Guo, Run-Ze Liu, Zhao-Yuan Bian, Rong-Chun Liu, Lan-Fang Huang, Jiang-Jiang Tang|2022|Eur.J.Med.Chem.|228|114031|doi:10.1016/j.ejmech.2021.114031
DOI: 10.5517/ccdc.csd.cc28q9bn
title: CCDC 2106801: Experimental Crystal Structure Determination

description: Related Article: Bo Ren, Cong Guo, Run-Ze Liu, Zhao-Yuan Bian, Rong-Chun Liu, Lan-Fang Huang, Jiang-Jiang Tang|2022|Eur.J.Med.Chem.|228|114031|doi:10.1016/j.ejmech.2021.114031
DOI: 10.5517/ccdc.csd.cc28qcp2
title: CCDC 2106874: Experimental Crystal Structure Determination

description: Related Article: Valentina Ferraro, Jesús Castro, Enrico Trave, Marco Bortoluzzi|2022|J.Organomet.Chem.|957|122171|doi:10.1016/j.jorganchem.2021.122171
DOI: 10.5517/ccdc.csd.cc28xrxw
title: CCDC 2113019: Experimental Crystal Structure Determination

description: Related Article: Tarlok Singh Lobana, Mani Kaushal, Ritu Bala, Isabel Garcia-Santos, Jerry P. Jasinski|2021|J.Indian Chem.Soc.|99|100298|doi:10.1016/j.jics.2021.100298
DOI: 10.5517/ccdc.csd.cc28xryx
title: CCDC 2113020: Experimental Crystal Structure Determination

description: Related Article: Tarlok Singh Lobana, Mani Kaushal, Ritu Bala, Isabel Garcia-Santos, Jerry P. Jasinski|2021|J.Indian Chem.Soc.|99|100298|doi:10.1016/j.jics.2021.100298
DOI: 10.5517/ccdc.csd.cc28xrzy
title: CCDC 2113021: Experimental Crystal Structure Determination

description: Related Article: Tarlok Singh Lobana, Mani Kaushal, Ritu Bala, Isabel Garcia-Santos, Jerry P. Jasinski|2021|J.Indian Chem.Soc.|99|100298|doi:10.1016/j.jics.2021.100298
DOI: 10.5517/ccdc.csd.cc28y48n
title: CCDC 2113371: Experimental Crystal Structure Determination

description: Related Article: Ya-Ming Tian, Huaiju Wang, Ritu, Burkhard König|2021|Chemical Science|13|241|doi:10.1039/D1SC06403G
DOI: 10.5517/ccdc.csd.cc28zxry
title: CCDC 2115091: Experimental Crystal Structure Determination

description: Related Article: Ariadni Zianna, Elena Geromichalou, George Geromichalos, Augusta-Maria Fiotaki, Antonios G. Hatzidimitriou, Stavros Kalogiannis, George Psomas|2021|J.Inorg.Biochem.|226|111659|doi:10.1016/j.jinorgbio.2021.111659
DOI: 10.5517/ccdc.csd.cc28zxsz
title: CCDC 2115092: Experimental Crystal Structure Determination

description: Related Article: Ariadni Zianna, Elena Geromichalou, George Geromichalos, Augusta-Maria Fiotaki, Antonios G. Hatzidimitriou, Stavros Kalogiannis, George Psomas|2021|J.Inorg.Biochem.|226|111659|doi:10.1016/j.jinorgbio.2021.111659
DOI: 10.5517/ccdc.csd.cc290mgf
title: CCDC 2115764: Experimental Crystal Structure Determination

description: Related Article: Yulia O. Edilova, Yulia S. Kudyakova, Mikhail A. Kiskin, Yanina V. Burgart, Victor I. Saloutin, Denis N. Bazhin|2021|J.Fluorine Chem.|253|109932|doi:10.1016/j.jfluchem.2021.109932
DOI: 10.5517/ccdc.csd.cc290mhg
title: CCDC 2115765: Experimental Crystal Structure Determination

description: Related Article: Yulia O. Edilova, Yulia S. Kudyakova, Mikhail A. Kiskin, Yanina V. Burgart, Victor I. Saloutin, Denis N. Bazhin|2021|J.Fluorine Chem.|253|109932|doi:10.1016/j.jfluchem.2021.109932
DOI: 10.5517/ccdc.csd.cc290mjh
title: CCDC 2115766: Experimental Crystal Structure Determination

description: Related Article: Yulia O. Edilova, Yulia S. Kudyakova, Mikhail A. Kiskin, Yanina V. Burgart, Victor I. Saloutin, Denis N. Bazhin|2021|J.Fluorine Chem.|253|109932|doi:10.1016/j.jfluchem.2021.109932
DOI: 10.5517/ccdc.csd.cc290mkj
title: CCDC 2115767: Experimental Crystal Structure Determination

description: Related Article: Yulia O. Edilova, Yulia S. Kudyakova, Mikhail A. Kiskin, Yanina V. Burgart, Victor I. Saloutin, Denis N. Bazhin|2021|J.Fluorine Chem.|253|109932|doi:10.1016/j.jfluchem.2021.109932
DOI: 10.5517/ccdc.csd.cc290mlk
title: CCDC 2115768: Experimental Crystal Structure Determination

description: Related Article: Yulia O. Edilova, Yulia S. Kudyakova, Mikhail A. Kiskin, Yanina V. Burgart, Victor I. Saloutin, Denis N. Bazhin|2021|J.Fluorine Chem.|253|109932|doi:10.1016/j.jfluchem.2021.109932
DOI: 10.5517/ccdc.csd.cc291q03
title: CCDC 2116804: Experimental Crystal Structure Determination

description: Related Article: F. Wieland Goetzke, Mireia Sidera, Stephen P. Fletcher|2021|Chemical Science|13|236|doi:10.1039/D1SC06035J
DOI: 10.5281/zenodo.5800156
title: DeepMSA2 alignments used by Zhang group in CASP14

description: Supporting data for Wei Zheng, Yang Li, Chengxin Zhang, Xiaogen Zhou, Robin Pearce, Eric W Bell, Xiaoqiang Huang, Yang Zhang<br> Protein structure prediction using deep learning distance and hydrogen‐bonding restraints in CASP14.<br> Proteins: Structure, Function, and Bioinformatics, 89: 1734-1751 (2021).<br> <br> Yang Li, Chengxin Zhang, Wei Zheng, Xiaogen Zhou, Eric W Bell, Dong‐Jun Yu, Yang Zhang<br> Protein inter‐residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14.<br> Proteins: Structure, Function, and Bioinformatics, 89: 1911-1921 (2021).
DOI: 10.5281/zenodo.5800155
title: DeepMSA2 alignments used by Zhang group in CASP14

description: Supporting data for Wei Zheng, Yang Li, Chengxin Zhang, Xiaogen Zhou, Robin Pearce, Eric W Bell, Xiaoqiang Huang, Yang Zhang<br> Protein structure prediction using deep learning distance and hydrogen‐bonding restraints in CASP14.<br> Proteins: Structure, Function, and Bioinformatics, 89: 1734-1751 (2021).<br> <br> Yang Li, Chengxin Zhang, Wei Zheng, Xiaogen Zhou, Eric W Bell, Dong‐Jun Yu, Yang Zhang<br> Protein inter‐residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14.<br> Proteins: Structure, Function, and Bioinformatics, 89: 1911-1921 (2021).
DOI: 10.25959/hqr3-9v39
title: The Tasmania Project - General Survey #1 (TTP1)

description: The Tasmania Project was established by the Institute for Social Change at the University of Tasmania to understand how Tasmanians are experiencing and adjusting to the social, political and economic responses to COVID-19. The project aims to support immediate and longer term social and economic decisions by providing useful, evidence-based and timely information, and share Tasmanian residents' experiences and ideas through this critical period and beyond. 

This dataset is for General Survey #1 (TTP1), which was open from 20 April - 10 May 2020 and had a final sample size of 1159 respondents. The survey consisted of ten sections: 1. Adjusting to life in the time of COVID-19, 2. Connections and support, 3. Staying informed, 4. Official responses to COVID-19, 5. Concerns, 6. The future in Tasmania, 7. Housing, 8. Work and employment, 9. School and learning, 10. Demographic information.
DOI: 10.25959/yzr3-7537
title: The Tasmania Project - General Survey #2 (TTP2)

description: The Tasmania Project was established by the Institute for Social Change at the University of Tasmania to understand how Tasmanians are experiencing and adjusting to the social, political and economic responses to COVID-19. The project aims to support immediate and longer term social and economic decisions by providing useful, evidence-based and timely information, and share Tasmanian residents' experiences and ideas through this critical period and beyond. 

This dataset is for General Survey #2 (TTP2), which was open from from 9  – 17 June 2020 and had a final sample size of 1258 respondents. The survey consisted of eight sections: 1. Official COVID-19 measures, 2. The future in Tasmania, 3. Wellbeing and concerns, 4. Arts and culture, 5. Work and employment, 6. Financial wellbeing, 7. Use of communications technologies, 8. Demographic information.
DOI: 10.25959/yp91-w234
title: The Tasmania Project - General Survey #3 (TTP3)

description: The Tasmania Project was established by the Institute for Social Change at the University of Tasmania to understand how Tasmanians are experiencing and adjusting to the social, political and economic responses to COVID-19. The project aims to support immediate and longer term social and economic decisions by providing useful, evidence-based and timely information, and share Tasmanian residents' experiences and ideas through this critical period and beyond. 

This dataset is for General Survey #3 (TTP3), which was open from 26 August  – 6 September 2020 and had a final sample size of 1133 respondents. The survey consisted of seven sections: 1. Restrictions and measures, 2. Wellbeing and concerns, 3. Food access and supply, 4. Tasmania’s connections to the world, 5. Where you live, 6. Work and employment, 7. Demographic information.
DOI: 10.25959/1mce-ez82
title: The Tasmania Project - General Survey #4 (TTP4)

description: The Tasmania Project was established by the Institute for Social Change at the University of Tasmania to understand how Tasmanians are experiencing and adjusting to the social, political and economic responses to COVID-19. The project aims to support immediate and longer term social and economic decisions by providing useful, evidence-based and timely information, and share Tasmanian residents' experiences and ideas through this critical period and beyond. 

This dataset is for General Survey #4 (TTP4), which was open from 29 April - 12 May 2021 and had a final sample size of 1176 respondents. The survey consisted of seven sections: 1. Restrictions and safety, 2. Vaccinations, 3. Wellbeing and concerns, 4. Employment and financial wellbeing, 5. Travel and events, 6. Food access and supply, 7. Demographic information.
DOI: 10.25959/562d-qr10
title: The Tasmania Project - General Survey #5 (TTP5)

description: The Tasmania Project was established by the Institute for Social Change at the University of Tasmania to understand how Tasmanians are experiencing and adjusting to the social, political and economic responses to COVID-19. The project aims to support immediate and longer term social and economic decisions by providing useful, evidence-based and timely information, and share Tasmanian residents' experiences and ideas through this critical period and beyond. 

This dataset is for General Survey #5 (TTP5), which was open from 24 September - 3 October 2021 and had a final sample size of 1200 respondents. The survey consisted of five sections: 1. Restrictions and safety, 2. Life in the second year of COVID, 3. Work and employment, 4. Volunteering, 5. Demographic information.
DOI: 10.25959/thj0-aq85
title: The Tasmania Project - Housing Survey

description: The Tasmania Project was established by the Institute for Social Change at the University of Tasmania to understand how Tasmanians are experiencing and adjusting to the social, political and economic responses to COVID-19. The project aims to support immediate and longer term social and economic decisions by providing useful, evidence-based and timely information, and share Tasmanian residents' experiences and ideas through this critical period and beyond. 

This dataset is for the Housing Survey, which was open from 23 June - 2 July 2020 and had a final sample size of 821 respondents. The survey consisted of four sections: 1. Living arrangements and housing circumstances, 2. Appropriateness of housing, 3. Property as a source of income, 4. Demographic information.

For this survey, we partnered with the Australian Housing and Urban Research Institute (AHURI) to generate data on regional housing and household experiences arising from the pandemic to manage the emerging housing needs of the community and support recovery.
DOI: 10.25959/h7m3-rf65
title: The Tasmania Project - Food Survey

description: The Tasmania Project was established by the Institute for Social Change at the University of Tasmania to understand how Tasmanians are experiencing and adjusting to the social, political and economic responses to COVID-19. The project aims to support immediate and longer term social and economic decisions by providing useful, evidence-based and timely information, and share Tasmanian residents' experiences and ideas through this critical period and beyond. 

This dataset is for the Food Survey, which was open from 20 May - 6 June 2020 and had a final sample size of 1170 respondents. The survey consisted of five sections: 1. Access to food, 2. Tasmanian produce, 3. Seafood consumption, 4. Food supply, 5. Demographic information.
DOI: 10.25959/47xd-wc30
title: The Tasmania Project - Wellbeing (PESRAC) Survey

description: The Tasmania Project was established by the Institute for Social Change at the University of Tasmania to understand how Tasmanians are experiencing and adjusting to the social, political and economic responses to COVID-19. The project aims to support immediate and longer term social and economic decisions by providing useful, evidence-based and timely information, and share Tasmanian residents' experiences and ideas through this critical period and beyond. 

This dataset is for the Wellbeing (PESRAC) Survey, which was open from 19 October - 22 November 2020 and had a final sample size of 2354 respondents. The survey consisted of three sections: 1. Wellbeing (adapting indicators from the OECD Better Life Index), 2. Tasmania’s recovery from COVID-19, 3. Demographic information.

This survey was part of Phase 2 of the consultation process conducted by the Premier's Economic and Social Advisory Council (PESRAC) and was designed to support PESRAC in developing advice to the Government on COVID-19 recovery strategies.
DOI: 10.25959/x4we-n532
title: The Tasmania Project - Borders and Community Health Survey

description: The Tasmania Project was established by the Institute for Social Change at the University of Tasmania to understand how Tasmanians are experiencing and adjusting to the social, political and economic responses to COVID-19. The project aims to support immediate and longer term social and economic decisions by providing useful, evidence-based and timely information, and share Tasmanian residents' experiences and ideas through this critical period and beyond. 

This dataset is for the Borders and Community Health Survey, which was open from 28 September - 12 October 2020 and had a final sample size of 1377 respondents. The survey consisted of six sections: 1. Border restrictions, 2. Compliance and responsibility, 3. Staying informed, 4. Employment, 5. Wellbeing, 6. Demographic information.

This survey was supported by the State Control Centre to inform the Tasmanian Government’s response to COVID-19 and help to develop public communications strategies and policy decisions as borders started to reopen.
DOI: 10.25959/1hht-ga79
title: The Tasmania Project - Creative and Cultural Industries Survey

description: The Tasmania Project was established by the Institute for Social Change at the University of Tasmania to understand how Tasmanians are experiencing and adjusting to the social, political and economic responses to COVID-19. The project aims to support immediate and longer term social and economic decisions by providing useful, evidence-based and timely information, and share Tasmanian residents' experiences and ideas through this critical period and beyond. 

This dataset is for the Creative and Cultural Industries (CCI) Survey, which was open from 6 - 31 August 2020 and had a final sample size of 291 individuals who were working (or had worked), either paid or unpaid, in the creative and cultural industries in Tasmania. The survey consisted of six sections: 1. Involvement in the sector, 2. Work in the sector, 3. Income, 4. COVID-19 recovery for the sector in Tasmania, 5. Concerns and wellbeing, 6. Demographic information.

For this survey, we partnered with the Tasmanian Government’s Department of State Growth to understand the impact of the pandemic on Tasmania’s creative and cultural industries and individuals who work in the sector.
DOI: 10.25910/sahc-v136
title: Claims Submitted to the Multilateral Development Bank Accountability Mechanisms –  1994-2021

description: Version 3.0 – data compiled between April 2016 and December 10 2021 
The dataset represents a summary depiction of grievance cases brought to the Accountability Mechanisms (AMs) of the six Multilateral Development Banks (MDBs) from 1994 to 2018: The Asian Development Bank, the African Development Bank, the Inter-American Development Bank, the European Bank for Reconstruction and Development, the World Bank (IBRD/IDA) and the World Bank Group (IFC and MIGA). These Accountability Mechanisms were created to enable people adversely or potentially adversely affected by a project or program financed by the MDBs to take their concerns to the Banks for recourse. The Accountability Mechanisms generally do not stop the project or provide material reparations. They seek to stop or prevent harm and mitigate the negative aspects of a development project. The objective of the database is to be able to garner a quick qualitative snapshot of any given case, as well as to be able to aggregate quantitative data for each AM as a whole and to identify trends over time. Each of the grievance mechanisms has a specific name, which may have changed over time. It may also be comprised of more than one office with separate functions (i.e. consulting with affected people versus a compliance investigation). Most of the Accountability Mechanisms now have separate functions, with the consultation process also called problem solving. The compliance phase is to investigate whether the MDBs have complied with their environmental and social policies and whether this has led to harm. The Accountability Mechanisms are detailed below. The dataset includes cases received by the AMs from the beginning of each mechanism until 22 August 2020. The last update for cases is August 22 2020. Access note: Please notify Susan Park for any changes made to the data set. Contact email: susan.park@sydney.edu.au
DOI: 10.5517/ccdc.csd.cc292wz8
title: CCDC 2117950: Experimental Crystal Structure Determination

description: Related Article: Yulia O. Edilova, Yulia S. Kudyakova, Mikhail A. Kiskin, Yanina V. Burgart, Victor I. Saloutin, Denis N. Bazhin|2021|J.Fluorine Chem.|253|109932|doi:10.1016/j.jfluchem.2021.109932
DOI: 10.5517/ccdc.csd.cc28z3n1
title: CCDC 2114313: Experimental Crystal Structure Determination

description: Related Article: Hassan A. Baalbaki, Kudzanai Nyamayaro, Julia Shu, Chatura Goonesinghe, Hyuk-Joon Jung, Parisa Mehrkhodavandi|2021|Inorg.Chem.|60|19304|doi:10.1021/acs.inorgchem.1c03123
DOI: 10.5517/ccdc.csd.cc28z3lz
title: CCDC 2114311: Experimental Crystal Structure Determination

description: Related Article: Hassan A. Baalbaki, Kudzanai Nyamayaro, Julia Shu, Chatura Goonesinghe, Hyuk-Joon Jung, Parisa Mehrkhodavandi|2021|Inorg.Chem.|60|19304|doi:10.1021/acs.inorgchem.1c03123
DOI: 10.5517/ccdc.csd.cc28z3ky
title: CCDC 2114310: Experimental Crystal Structure Determination

description: Related Article: Hassan A. Baalbaki, Kudzanai Nyamayaro, Julia Shu, Chatura Goonesinghe, Hyuk-Joon Jung, Parisa Mehrkhodavandi|2021|Inorg.Chem.|60|19304|doi:10.1021/acs.inorgchem.1c03123
DOI: 10.5281/zenodo.5800121
title: Pyrrhura cruentata decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5800120
title: Pyrrhura cruentata decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5800233
title: Sciades dowii decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5800232
title: Sciades dowii decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5793600
title: Cold storage of Spodoptera litura eggs and Telenomus remus adults for improving mass-rearing efficiency

description: This is the raw data of the performance of <em>Telenomus remus</em> adults after refrigeration and the its parasitism capacity on stored <em>Spodoptera litura</em> eggs.
DOI: 10.5281/zenodo.5793599
title: Cold storage of Spodoptera litura eggs and Telenomus remus adults for improving mass-rearing efficiency

description: This is the raw data of the performance of <em>Telenomus remus</em> adults after refrigeration and the its parasitism capacity on stored <em>Spodoptera litura</em> eggs.
DOI: 10.5281/zenodo.5800200
title: Emoji Gestures in Russian Tweets: Moscow

description: The dataset consists of 48 838 tweets each of them contains one of the 31 gesture emoji (different hand configurations) and its skin tone modifier options (e.g. 🙏🙏🏿🙏🏾🙏🏽🙏🏼🙏🏻), and posted within 50km from Moscow, Russia, in Russian, during May-August 2021. The dataset can be used to investigate the use of gesture emoji by Russian users of the Twitter platform. Python libraries used for collecting tweets and preprocessing: tweepy, re, preprocessor, emoji, regex, string, nltk. The dataset contains 12 columns: tweet_original original text of the tweet preprocessed preprocessed text of the tweet (4 steps) all_emoji lists all emoji in a given tweet hashtags lists all hashtags in a given tweet user_encoded encoded Twitter user name: the first 3 characters of the user name and the first 3 characters of the user's location location_encoded location of the user: "moscow", "moscow_region", or "other" mention_present checks whether each tweet contains url url_present checks whether each tweet contains url preprocess_tweet preprocessing step 1: tokenizing mentions, urls, and hashtags lowercase_tweet preprocessing step 2: lowercasing remove_punct_tweet preprocessing step 3: removing punctuation tokenize_tweet preprocessing step 4: tokenizing The further information on the research project can be found here: https://github.com/mzhukovaucsb/emoji_gestures/
DOI: 10.5281/zenodo.5800198
title: Emoji Gestures in English Tweets: California

description: The dataset consists of 479 193 tweets each of them contains one of the 31 gesture emoji (different hand configurations) and its skin tone modifier options (e.g. 🙏🙏🏿🙏🏾🙏🏽🙏🏼🙏🏻), posted within 250km from San Jose, CA and within 200km from Los Angeles, CA, in English, during May-August 2021. The dataset can be used to investigate the use of gesture emoji by English-speaking California Twitter users. Python libraries used for collecting tweets and preprocessing: tweepy, re, preprocessor, emoji, regex, string, nltk. The dataset contains 12 columns: tweet_original original text of the tweet preprocessed preprocessed text of the tweet (4 steps) all_emoji lists all emoji in a given tweet hashtags lists all hashtags in a given tweet user_encoded encoded Twitter user name: the first 3 characters of the user name and the first 3 characters of the user's location location_encoded location of the user: "los_angeles", "san_diego", "san_jose", "san_francisco", "fresno", "long_beach", "sacramento", "oakland", "bakersfield", "anaheim", or "other" mention_present checks whether each tweet contains url url_present checks whether each tweet contains url preprocess_tweet preprocessing step 1: tokenizing mentions, urls, and hashtags lowercase_tweet preprocessing step 2: lowercasing remove_punct_tweet preprocessing step 3: removing punctuation tokenize_tweet preprocessing step 4: tokenizing The further information on the research project can be found here: https://github.com/mzhukovaucsb/emoji_gestures/