cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2cxjsm
title: CCDC 2202171: Experimental Crystal Structure Determination

description: Related Article: Charles J. McMonagle, Gemma F. Turner, Isabelle Jones, David R. Allan, Mark R. Warren, Konstantin V. Kamenev, Simon Parsons, Paul A. Wright, Stephen A. Moggach|2022|Chem.Commun.|58|11507|doi:10.1039/D2CC04649K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cxjtn
title: CCDC 2202172: Experimental Crystal Structure Determination

description: Related Article: Charles J. McMonagle, Gemma F. Turner, Isabelle Jones, David R. Allan, Mark R. Warren, Konstantin V. Kamenev, Simon Parsons, Paul A. Wright, Stephen A. Moggach|2022|Chem.Commun.|58|11507|doi:10.1039/D2CC04649K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cxjvp
title: CCDC 2202173: Experimental Crystal Structure Determination

description: Related Article: Charles J. McMonagle, Gemma F. Turner, Isabelle Jones, David R. Allan, Mark R. Warren, Konstantin V. Kamenev, Simon Parsons, Paul A. Wright, Stephen A. Moggach|2022|Chem.Commun.|58|11507|doi:10.1039/D2CC04649K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cxjwq
title: CCDC 2202174: Experimental Crystal Structure Determination

description: Related Article: Charles J. McMonagle, Gemma F. Turner, Isabelle Jones, David R. Allan, Mark R. Warren, Konstantin V. Kamenev, Simon Parsons, Paul A. Wright, Stephen A. Moggach|2022|Chem.Commun.|58|11507|doi:10.1039/D2CC04649K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cxjxr
title: CCDC 2202175: Experimental Crystal Structure Determination

description: Related Article: Charles J. McMonagle, Gemma F. Turner, Isabelle Jones, David R. Allan, Mark R. Warren, Konstantin V. Kamenev, Simon Parsons, Paul A. Wright, Stephen A. Moggach|2022|Chem.Commun.|58|11507|doi:10.1039/D2CC04649K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cxjys
title: CCDC 2202176: Experimental Crystal Structure Determination

description: Related Article: Charles J. McMonagle, Gemma F. Turner, Isabelle Jones, David R. Allan, Mark R. Warren, Konstantin V. Kamenev, Simon Parsons, Paul A. Wright, Stephen A. Moggach|2022|Chem.Commun.|58|11507|doi:10.1039/D2CC04649K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7195660
title: solidago_caesia_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7195659
title: solidago_caesia_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.14331554
title: Code and Data for Scharf et al 2022 Behav Ecol Soc

description: Data collected in 2020 and 2021 including size measurements and fledging metrics for prothonotary warbler nestlings in nests parasitized with a cowbird (heterospecific), warbler (conspecific), or left non-parasitized.
DOI: 10.6084/m9.figshare.14331554.v2
title: Code and Data for Scharf et al 2022 Behav Ecol Soc

description: Data collected in 2020 and 2021 including size measurements and fledging metrics for prothonotary warbler nestlings in nests parasitized with a cowbird (heterospecific), warbler (conspecific), or left non-parasitized.
DOI: 10.6084/m9.figshare.14331554.v1
title: Code and Data for Scharf et al 2022 Behav Ecol Soc

description: Data collected in 2020 and 2021 including size measurements and fledging metrics for prothonotary warbler nestlings in nests parasitized with a cowbird (heterospecific), warbler (conspecific), or left non-parasitized.
DOI: 10.11570/20.0007
title: The JCMT Gould Belt Survey: radiative heating by OB stars.

description: This contains data and other information related to the publication 'The JCMT Gould Belt Survey: radiative heating by OB stars.' by Rumble et al., Monthly Notices of the Royal Astronomical Society, Volume 505, Issue 2, August 2021, Pages 2103–2110, May 27 2021
DOI: 10.6084/m9.figshare.21330411.v1
title: Remote consent approaches for mobile phone surveys of non-communicable disease risk factors in Colombia and Uganda: A randomized study

description: <strong>Introduction:</strong> Automated mobile phone surveys (MPS) can be used to collect public health data of various types to inform health policy and programs globally. One challenge in administering MPS is identification of an appropriate and effective participant consent process. This study investigated the impact of different survey consent approaches on participant disposition (response characteristics and understanding of the purpose of the survey) within the context of an MPS that measured noncommunicable disease (NCD) risk factors across Colombia and Uganda. <strong>Methods:</strong> Participants were randomized to one of five consent approaches, with consent modules varying by the consent disclosure and mode of authorization. The control arm consisted of a standard consent disclosure and a combined opt-in/opt-out mode of authorization. The other four arms consist of a modified consent disclosure and one of four different forms of authorization (i.e., opt-in, opt-out, combined opt-in/opt-out, or implied). Data related to respondent disposition and respondent understanding of the survey purpose were analyzed. <strong>Results:</strong> Among 1889 completed surveys in Colombia, differences in contact, response, refusal, and cooperation rates by study arms were found. About 68% of respondents correctly identified the survey purpose, with no significant difference by study arm. Participants reporting higher levels of education and urban residency were more likely to identify the purpose correctly. Participants were also more likely to accurately identify the survey purpose after completing several survey modules, compared to immediately following the consent disclosure (78.8% vs 54.2% correct, p&lt;0.001). In Uganda, 1890 completed surveys were collected. Though there were differences in contact, refusal, and cooperation rates by study arm, response rates were similar across arms. About 37% of respondents identified the survey purpose correctly, with no difference by arm. Those with higher levels of education and who completed the survey in English were able to more accurately identify the survey purpose. Again, participants were more likely to accurately identify the purpose of the survey after completing several NCD modules, compared to immediately following the consent module (42.0% vs 32.2% correct, p = 0.013). <strong>Conclusion: </strong>This study contributes to the limited available evidence regarding consent procedures for automated MPS. Future studies should develop and trial additional interventions to enhance consent for automated public health surveys, and measure other dimensions of participant engagement and understanding.
DOI: 10.6084/m9.figshare.21330411
title: Remote consent approaches for mobile phone surveys of non-communicable disease risk factors in Colombia and Uganda: A randomized study

description: <strong>Introduction:</strong> Automated mobile phone surveys (MPS) can be used to collect public health data of various types to inform health policy and programs globally. One challenge in administering MPS is identification of an appropriate and effective participant consent process. This study investigated the impact of different survey consent approaches on participant disposition (response characteristics and understanding of the purpose of the survey) within the context of an MPS that measured noncommunicable disease (NCD) risk factors across Colombia and Uganda. <strong>Methods:</strong> Participants were randomized to one of five consent approaches, with consent modules varying by the consent disclosure and mode of authorization. The control arm consisted of a standard consent disclosure and a combined opt-in/opt-out mode of authorization. The other four arms consist of a modified consent disclosure and one of four different forms of authorization (i.e., opt-in, opt-out, combined opt-in/opt-out, or implied). Data related to respondent disposition and respondent understanding of the survey purpose were analyzed. <strong>Results:</strong> Among 1889 completed surveys in Colombia, differences in contact, response, refusal, and cooperation rates by study arms were found. About 68% of respondents correctly identified the survey purpose, with no significant difference by study arm. Participants reporting higher levels of education and urban residency were more likely to identify the purpose correctly. Participants were also more likely to accurately identify the survey purpose after completing several survey modules, compared to immediately following the consent disclosure (78.8% vs 54.2% correct, p&lt;0.001). In Uganda, 1890 completed surveys were collected. Though there were differences in contact, refusal, and cooperation rates by study arm, response rates were similar across arms. About 37% of respondents identified the survey purpose correctly, with no difference by arm. Those with higher levels of education and who completed the survey in English were able to more accurately identify the survey purpose. Again, participants were more likely to accurately identify the purpose of the survey after completing several NCD modules, compared to immediately following the consent module (42.0% vs 32.2% correct, p = 0.013). <strong>Conclusion: </strong>This study contributes to the limited available evidence regarding consent procedures for automated MPS. Future studies should develop and trial additional interventions to enhance consent for automated public health surveys, and measure other dimensions of participant engagement and understanding.
DOI: 10.6084/m9.figshare.21330411.v3
title: Remote consent approaches for mobile phone surveys of non-communicable disease risk factors in Colombia and Uganda: A randomized study

description: <strong>Introduction:</strong> Automated mobile phone surveys (MPS) can be used to collect public health data of various types to inform health policy and programs globally. One challenge in administering MPS is identification of an appropriate and effective participant consent process. This study investigated the impact of different survey consent approaches on participant disposition (response characteristics and understanding of the purpose of the survey) within the context of an MPS that measured noncommunicable disease (NCD) risk factors across Colombia and Uganda. <strong>Methods:</strong> Participants were randomized to one of five consent approaches, with consent modules varying by the consent disclosure and mode of authorization. The control arm consisted of a standard consent disclosure and a combined opt-in/opt-out mode of authorization. The other four arms consist of a modified consent disclosure and one of four different forms of authorization (i.e., opt-in, opt-out, combined opt-in/opt-out, or implied). Data related to respondent disposition and respondent understanding of the survey purpose were analyzed. <strong>Results:</strong> Among 1889 completed surveys in Colombia, differences in contact, response, refusal, and cooperation rates by study arms were found. About 68% of respondents correctly identified the survey purpose, with no significant difference by study arm. Participants reporting higher levels of education and urban residency were more likely to identify the purpose correctly. Participants were also more likely to accurately identify the survey purpose after completing several survey modules, compared to immediately following the consent disclosure (78.8% vs 54.2% correct, p&lt;0.001). In Uganda, 1890 completed surveys were collected. Though there were differences in contact, refusal, and cooperation rates by study arm, response rates were similar across arms. About 37% of respondents identified the survey purpose correctly, with no difference by arm. Those with higher levels of education and who completed the survey in English were able to more accurately identify the survey purpose. Again, participants were more likely to accurately identify the purpose of the survey after completing several NCD modules, compared to immediately following the consent module (42.0% vs 32.2% correct, p = 0.013). <strong>Conclusion: </strong>This study contributes to the limited available evidence regarding consent procedures for automated MPS. Future studies should develop and trial additional interventions to enhance consent for automated public health surveys, and measure other dimensions of participant engagement and understanding.
DOI: 10.6084/m9.figshare.21330411.v2
title: Remote consent approaches for mobile phone surveys of non-communicable disease risk factors in Colombia and Uganda: A randomized study

description: <strong>Introduction:</strong> Automated mobile phone surveys (MPS) can be used to collect public health data of various types to inform health policy and programs globally. One challenge in administering MPS is identification of an appropriate and effective participant consent process. This study investigated the impact of different survey consent approaches on participant disposition (response characteristics and understanding of the purpose of the survey) within the context of an MPS that measured noncommunicable disease (NCD) risk factors across Colombia and Uganda. <strong>Methods:</strong> Participants were randomized to one of five consent approaches, with consent modules varying by the consent disclosure and mode of authorization. The control arm consisted of a standard consent disclosure and a combined opt-in/opt-out mode of authorization. The other four arms consist of a modified consent disclosure and one of four different forms of authorization (i.e., opt-in, opt-out, combined opt-in/opt-out, or implied). Data related to respondent disposition and respondent understanding of the survey purpose were analyzed. <strong>Results:</strong> Among 1889 completed surveys in Colombia, differences in contact, response, refusal, and cooperation rates by study arms were found. About 68% of respondents correctly identified the survey purpose, with no significant difference by study arm. Participants reporting higher levels of education and urban residency were more likely to identify the purpose correctly. Participants were also more likely to accurately identify the survey purpose after completing several survey modules, compared to immediately following the consent disclosure (78.8% vs 54.2% correct, p&lt;0.001). In Uganda, 1890 completed surveys were collected. Though there were differences in contact, refusal, and cooperation rates by study arm, response rates were similar across arms. About 37% of respondents identified the survey purpose correctly, with no difference by arm. Those with higher levels of education and who completed the survey in English were able to more accurately identify the survey purpose. Again, participants were more likely to accurately identify the purpose of the survey after completing several NCD modules, compared to immediately following the consent module (42.0% vs 32.2% correct, p = 0.013). <strong>Conclusion: </strong>This study contributes to the limited available evidence regarding consent procedures for automated MPS. Future studies should develop and trial additional interventions to enhance consent for automated public health surveys, and measure other dimensions of participant engagement and understanding.
DOI: 10.35111/8gvn-bj05
title: Rime-Cantonese: A Normalized Cantonese Jyutping Lexicon

description: <h3>Introduction</h3><br>
<p>Rime-Cantonese: A Normalized Cantonese Jyutping Lexicon was developed by the Cantonese Computational Linguistics Infrastructure Working Group. It contains approximately 130,000 Cantonese character, word, and phrase entries paired with their corresponding romanized pronunciations in <a href="https://jyutping.org/en/">Jyutping</a>, a scheme created by The Linguistic Society of Hong Kong.</p><br>
<h3>Data</h3><br>
<p>Data was collected from a variety of physical and online sources. The character collection was subjected to a normalization process for differences between traditional and simplified Chinese, regional differences and other variants in Chinese characters, and differences in orthography. Additional information about this process and the lexicon in general is available in the documentation included with this release.</p><br>
<p>The corpus data is presented in a collection of UTF-8 encoded csv files.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2022L01.txt">word sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2023 Trustees of the University of Pennsylvania
DOI: 10.6084/m9.figshare.21330465
title: Unfiltered proteomics data and DIDAR filtered proteomics data

description: We analyzed the proteomic data and DIDAR filtered/QC'ed proteomic data from a recent study of 270 single human cells divided between control and sotorasib treatments. The data included here is the processed results using Proteome Discoverer 2.4 using the same search parameters. This data is in support of Figure 2 of Jenkins and Orsburn 2022.
DOI: 10.6084/m9.figshare.21330465.v1
title: Unfiltered proteomics data and DIDAR filtered proteomics data

description: We analyzed the proteomic data and DIDAR filtered/QC'ed proteomic data from a recent study of 270 single human cells divided between control and sotorasib treatments. The data included here is the processed results using Proteome Discoverer 2.4 using the same search parameters. This data is in support of Figure 2 of Jenkins and Orsburn 2022.
DOI: 10.14469/hpc/11434
title: Test of ImageCIF media type

description: test
DOI: 10.6084/m9.figshare.20601231
title: Identification of exhaled metabolites in children with cystic fibrosis: data

description: MS2 data in mgf format
DOI: 10.6084/m9.figshare.20601231.v1
title: Identification of exhaled metabolites in children with cystic fibrosis: data

description: MS2 data in mgf format
DOI: 10.5281/zenodo.7195724
title: iGEM-Leiden-2022-Supplementary Experiments 10 [INTERLAB] Calibration Experiment Raw Data

description: Raw data Supplementary Experiment 10 [INTERLAB] Calibration Experiment iGEM Leiden 2022
DOI: 10.5281/zenodo.7195723
title: iGEM-Leiden-2022-Supplementary Experiments 10 [INTERLAB] Calibration Experiment Raw Data

description: Raw data Supplementary Experiment 10 [INTERLAB] Calibration Experiment iGEM Leiden 2022
DOI: 10.5281/zenodo.7195730
title: iGEM-Leiden-2022-Supplementary Experiments 10 [INTERLAB] Calibration Experiment Submitted Data

description: Submitted data Supplementary Experiment 10 [INTERLAB] Calibration Experiment iGEM Leiden 2022
DOI: 10.5281/zenodo.7195729
title: iGEM-Leiden-2022-Supplementary Experiments 10 [INTERLAB] Calibration Experiment Submitted Data

description: Submitted data Supplementary Experiment 10 [INTERLAB] Calibration Experiment iGEM Leiden 2022
DOI: 10.5281/zenodo.7195737
title: iGEM-Leiden-2022-Supplementary Experiments 10 [INTERLAB] Experiment 1 Raw Data

description: Raw data Supplementary Experiment 10 [INTERLAB] Experiment 1 iGEM Leiden 2022
DOI: 10.5281/zenodo.7195736
title: iGEM-Leiden-2022-Supplementary Experiments 10 [INTERLAB] Experiment 1 Raw Data

description: Raw data Supplementary Experiment 10 [INTERLAB] Experiment 1 iGEM Leiden 2022
DOI: 10.5281/zenodo.7195745
title: iGEM-Leiden-2022-Supplementary Experiments 10 [INTERLAB] Experiment 1 Submitted Data

description: Submitted data Supplementary Experiment 10 [INTERLAB] Experiment 1 iGEM Leiden 2022
DOI: 10.5281/zenodo.7195744
title: iGEM-Leiden-2022-Supplementary Experiments 10 [INTERLAB] Experiment 1 Submitted Data

description: Submitted data Supplementary Experiment 10 [INTERLAB] Experiment 1 iGEM Leiden 2022
DOI: 10.5281/zenodo.7195752
title: iGEM-Leiden-2022-Supplementary Experiments 10 [INTERLAB] Experiment 2 Raw Data

description: Raw data Supplementary Experiment 10 [INTERLAB] Experiment 2 iGEM Leiden 2022
DOI: 10.5281/zenodo.7195751
title: iGEM-Leiden-2022-Supplementary Experiments 10 [INTERLAB] Experiment 2 Raw Data

description: Raw data Supplementary Experiment 10 [INTERLAB] Experiment 2 iGEM Leiden 2022
DOI: 10.5281/zenodo.7094729
title: SolarWalk Dataset: Occupant Identification using Indoor Photovoltaic Harvester Output Voltage

description: We present the dataset containing time-series open circuit output voltage traces of indoor photovoltaic cell corresponding to occupant door crossing events to perform smart home occupant identification. We collect shadow patterns of five participants from two different doors in two rooms of a building. We collect a total of 900 door entry and exit events during different hours of the day. We sample the voltage at 50 hz and provide the raw timestamped data. We also pre-process the data to filter the event of interest and label the data with occupant id and type of door events. We provide two example scripts to demonstrate how to process raw data and apply machine learning models for occupant identification using solar cell voltage samples.
DOI: 10.5281/zenodo.7094730
title: SolarWalk Dataset: Occupant Identification using Indoor Photovoltaic Harvester Output Voltage

description: We present the dataset containing time-series open circuit output voltage traces of indoor photovoltaic cell corresponding to occupant door crossing events to perform smart home occupant identification. We collect shadow patterns of five participants from two different doors in two rooms of a building. We collect a total of 900 door entry and exit events during different hours of the day. We sample the voltage at 50 hz and provide the raw timestamped data. We also pre-process the data to filter the event of interest and label the data with occupant id and type of door events. We provide two example scripts to demonstrate how to process raw data and apply machine learning models for occupant identification using solar cell voltage samples.