cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc276j66
title: CCDC 2061847: Experimental Crystal Structure Determination

description: Related Article: Jinxiang Chen|2021|CSD Communication|||
description: Related Article: Yu Hou, Cai-Zhu Luo, Deng-Hui Xie, Jing-Jing Hu, Jin-Xiang Chen, Nai-Han Huang, Hong Wang, Shu-Qing Zhang, Qun Zhang|2021|Int.J.Pharm.|608|121083|doi:10.1016/j.ijpharm.2021.121083
DOI: 10.5517/ccdc.csd.cc27h6ts
title: CCDC 2070205: Experimental Crystal Structure Determination

description: Related Article: Yin-Qiang Zhang, Min Li, Guan-Cheng Xu|2021|Eur.J.Inorg.Chem.|2021|3771|doi:10.1002/ejic.202100547
DOI: 10.5517/ccdc.csd.cc27h6vt
title: CCDC 2070206: Experimental Crystal Structure Determination

description: Related Article: Yin-Qiang Zhang, Min Li, Guan-Cheng Xu|2021|Eur.J.Inorg.Chem.|2021|3771|doi:10.1002/ejic.202100547
DOI: 10.25505/fiz.icsd.cc27hy6x
title: CSD 2070899: Experimental Crystal Structure Determination

description: Related Article: Tuba Iftikhar, Natalya V. Izarova, Jan Leusen, Paul Kögerler|2021|Chem.-Eur.J.|27|13376|doi:10.1002/chem.202101474
DOI: 10.25505/fiz.icsd.cc27j2ql
title: CSD 2071039: Experimental Crystal Structure Determination

description: Related Article: Tuba Iftikhar, Natalya V. Izarova, Jan Leusen, Paul Kögerler|2021|Chem.-Eur.J.|27|13376|doi:10.1002/chem.202101474
DOI: 10.5517/ccdc.csd.cc27l2yw
title: CCDC 2072968: Experimental Crystal Structure Determination

description: Related Article: Yang Zhang, Dan Ye, Lei Shen, Kangjiang Liang, Chengfeng Xia|2021|Org.Lett.|23|7112|doi:10.1021/acs.orglett.1c02510
DOI: 10.5517/ccdc.csd.cc27lngz
title: CCDC 2073511: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.5517/ccdc.csd.cc27lnj1
title: CCDC 2073513: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.5517/ccdc.csd.cc27lnk2
title: CCDC 2073514: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.5517/ccdc.csd.cc27lnl3
title: CCDC 2073515: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.5517/ccdc.csd.cc27lnm4
title: CCDC 2073516: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.5517/ccdc.csd.cc27lnn5
title: CCDC 2073517: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.5517/ccdc.csd.cc27lnp6
title: CCDC 2073518: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.5517/ccdc.csd.cc27lnq7
title: CCDC 2073519: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.5517/ccdc.csd.cc27lnr8
title: CCDC 2073520: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.5517/ccdc.csd.cc27lns9
title: CCDC 2073521: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.5517/ccdc.csd.cc27lntb
title: CCDC 2073522: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.5517/ccdc.csd.cc27lnvc
title: CCDC 2073523: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.5517/ccdc.csd.cc27lnwd
title: CCDC 2073524: Experimental Crystal Structure Determination

description: Related Article: Maxime Tricoire, Luca Münzfeld, Jules Moutet, Nolwenn Mahieu, Léo La Droitte, Eufemio Moreno-Pineda, Frédéric Gendron, Jeremy D. Hilgar, Jeffrey D. Rinehart, Mario Ruben, Boris Le Guennic, Olivier Cador, Peter W. Roesky, Grégory Nocton|2021|Chem.-Eur.J.|27|13558|doi:10.1002/chem.202101599
DOI: 10.6084/m9.figshare.16704958
title: Supplementary Material for: Changes in Stroke Patients’ Health-Seeking Behavior by COVID-19 Epidemic Regions: Data from the Korean Stroke Registry

description: <b><i>Introduction:</i></b> The coronavirus disease 2019 (COVID-19) pandemic has led to changes in stroke patients’ healthcare use. This study evaluated changes in Korean stroke patients’ health-seeking behaviors and stroke care services using data from the Korean Stroke Registry (KSR). <b><i>Methods:</i></b> We reviewed data from patients with acute stroke and transient ischemic attack (TIA) during 2019 (before COVID-19 period) and 2020 (COVID-19 period). Outcomes included patient characteristics, time from stroke onset to hospital arrival, and in-hospital stroke pathways. Subgroup analyses were performed for an epidemic region (Daegu city and Gyeongsangbuk-do region, the D-G region). <b><i>Results:</i></b> The study included 1,792 patients from the pre-COVID-19 period and 1,555 patients from the COVID-19 period who visited hospitals that contribute to the KSR. During the COVID-19 period, the D-G region had two-thirds the number of cases (vs. the pre-CO­VID-19 period) and a significant decrease in the proportion of patients with TIA (9.97%–2.91%). Unlike other regions, the median onset-to-door time increased significantly in the D-G region (361 min vs. 526.5 min, <i>p</i> = 0.016), and longer onset-to-door times were common for patients with mild symptoms and who were in their 60s or 70s. The number of patients who underwent intravenous thrombolysis also decreased during the COVID-19 period, although the treatment times were not significantly different between the 2 periods. <b><i>Discussion/Conclusion:</i></b> Korean stroke patients in a CO­VID-19 epidemic region exhibited distinct changes in health-seeking behaviors. Appropriate triage system and public education regarding the importance of early treatment are needed during the COVID-19 pandemic.
DOI: 10.6084/m9.figshare.16692271
title: Supplementary material: Health promotion through monetary incentives: Evaluating the impact of different reinforcement schedules on engagement levels with an mHealth app

description: <b>Introduction</b>mHealth apps can be employed to promote health behavior, for example through financial rewards. However, the optimal reinforcement schedule (with maximum impact on health behavior change, at lowest costs) remains unclear.<br><b>Methods</b>We have evaluated the impact of different reinforcement schedules on engagement levels with an mHealth app in a six-week, three-arm randomized intervention trial. Participants (i.e., university staff members and students, N = 61) were awarded virtual points for performing health-related activities. They kept track of their performance via an individual dashboard, social leaderboard, and newsfeed. Additionally, participants could win financial rewards. These rewards were distributed according to a fixed ratio schedule in the first study arm, and a variable schedule in the other arms. The payouts of the first and second study arm were immediate, whereas the payout of the third arm was delayed.<br><b>Results</b>No single reinforcement schedule had a more profound impact on engagement levels. However, we found that a preference for monetary incentives in general seems to be personal. Particularly, individuals that were triggered by financial rewards could better defer gratification and were less intrinsically motivated.<br><b>Conclusion</b>Monetary incentives can stimulate mHealth app engagement and health behavior, particularly in individuals that are generally triggered by financial incentives.<br><b>List of files</b>`0-intervention-design.pdf` includes visualisations of the mHealth application.<br>`0-list-of-variables.pdf` includes an overview of different variables within the dataset.<br>`1a-intervention-data.csv` includes data that was recorded from the intervention mHealth tool.<br>`1b-pre-test-survey-data.csv` includes data that was recorded form a pre-test survey.<br>`1c-post-test-survey-data.csv` includes data that was recorded form a post-test survey.<br><br>`1d-monetary-incentives-data.csv` includes data on the financial rewards that were distributed throughout the program.<br>`2-data-analysis.Rmd` includes the R-script that was used to perform the data analyses.<br>`2-data-analysis.pdf` is the PDF output that is produced when the R Markdown script is run.<br>`3-figures.zip` is a folder with figures (in PDF format) that are output by the R-script, and that are used in the article.<br>`4-email-templates.zip` is a folder with email templates that were used throughout the health promotion program.<br>
DOI: 10.6084/m9.figshare.16692271.v1
title: Supplementary material: Health promotion through monetary incentives: Evaluating the impact of different reinforcement schedules on engagement levels with an mHealth app

description: <b>Introduction</b>mHealth apps can be employed to promote health behavior, for example through financial rewards. However, the optimal reinforcement schedule (with maximum impact on health behavior change, at lowest costs) remains unclear.<br><b>Methods</b>We have evaluated the impact of different reinforcement schedules on engagement levels with an mHealth app in a six-week, three-arm randomized intervention trial. Participants (i.e., university staff members and students, N = 61) were awarded virtual points for performing health-related activities. They kept track of their performance via an individual dashboard, social leaderboard, and newsfeed. Additionally, participants could win financial rewards. These rewards were distributed according to a fixed ratio schedule in the first study arm, and a variable schedule in the other arms. The payouts of the first and second study arm were immediate, whereas the payout of the third arm was delayed.<br><b>Results</b>No single reinforcement schedule had a more profound impact on engagement levels. However, we found that a preference for monetary incentives in general seems to be personal. Particularly, individuals that were triggered by financial rewards could better defer gratification and were less intrinsically motivated.<br><b>Conclusion</b>Monetary incentives can stimulate mHealth app engagement and health behavior, particularly in individuals that are generally triggered by financial incentives.<br><b>List of files</b>`0-intervention-design.pdf` includes visualisations of the mHealth application.<br>`0-list-of-variables.pdf` includes an overview of different variables within the dataset.<br>`1a-intervention-data.csv` includes data that was recorded from the intervention mHealth tool.<br>`1b-pre-test-survey-data.csv` includes data that was recorded form a pre-test survey.<br>`1c-post-test-survey-data.csv` includes data that was recorded form a post-test survey.<br><br>`1d-monetary-incentives-data.csv` includes data on the financial rewards that were distributed throughout the program.<br>`2-data-analysis.Rmd` includes the R-script that was used to perform the data analyses.<br>`2-data-analysis.pdf` is the PDF output that is produced when the R Markdown script is run.<br>`3-figures.zip` is a folder with figures (in PDF format) that are output by the R-script, and that are used in the article.
DOI: 10.25934/00005847.0
title: Available IPD datapackage for study 'Randomised, Double-blind, Parallel-group, Placebo-controlled, Fixed Dose Study on the Efficacy of [Vortioxetine] Lu AA21004 on Cognitive Dysfunction in Adult Patients With Major Depressive Disorder (MDD)'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.25934/00005847.1
title: IPD datapackage for study 'Randomised, Double-blind, Parallel-group, Placebo-controlled, Fixed Dose Study on the Efficacy of [Vortioxetine] Lu AA21004 on Cognitive Dysfunction in Adult Patients With Major Depressive Disorder (MDD)' and research request: 'Choosing the Right Antidepressant for Depressive Disorder in Adults: Individual Patient Data Network Meta-Analysis'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.5281/zenodo.4915931
title: Wave Parameters - North Atlantic Ocean - Period 1996-2005 - MODEL: Wavewatch III - Driver: HadGEM

description: <strong>Wave Model:</strong> WAVEWATCH_III - version number 5.16 <strong>Global driver: </strong> HadGEM (Hadley Centre Global Environmental Model) <strong>Variables:</strong> Significant Wave Height Mean period, peak frequency Mean wave direction 0.25° x 0.25° horizontal resolution - 3h time resolution <strong>Region: </strong> southernmost latitude = 10° northernmost latitude = 42° westernmost longitude = -70° easternmost longitude = -5° <strong>360-day calendar</strong> <strong>NetCDF format</strong>
description: The data can also be downloaded from the SOCLIMPACT FTP Repository hosted at https://giotto.casaccia.enea.it/soclimpact/index.php/
DOI: 10.5281/zenodo.4915930
title: Wave Parameters - North Atlantic Ocean - Period 1996-2005 - MODEL: Wavewatch III - Driver: HadGEM

description: <strong>Wave Model:</strong> WAVEWATCH_III - version number 5.16 <strong>Global driver: </strong> HadGEM (Hadley Centre Global Environmental Model) <strong>Variables:</strong> Significant Wave Height Mean period, peak frequency Mean wave direction 0.25° x 0.25° horizontal resolution - 3h time resolution <strong>Region: </strong> southernmost latitude = 10° northernmost latitude = 42° westernmost longitude = -70° easternmost longitude = -5° <strong>360-day calendar</strong> <strong>NetCDF format</strong>
description: The data can also be downloaded from the SOCLIMPACT FTP Repository hosted at https://giotto.casaccia.enea.it/soclimpact/index.php/
DOI: 10.5281/zenodo.4005572
title: FYRE Climate: Precipitation

description: <strong>FYRE Climate</strong> (French hYdrometeorological REanalysis Climate dataset) is a 25-member ensemble of 142-year high-resolution reanalysis of precipitation and temperature over France, from 1 January 1871 to 29 December 2012. FYRE Climate results from the assimilation of historical daily station observations of temperature and precipitation into the SCOPE Climate reconstructions (Caillouet et al., 2019) through a Kalman filter ensemble approach (Devers et al., 2020). FYRE Climate provides an ensemble of 25 equally-plausible spatially-coherent gridded bivariate time series. Data are available at a daily time step on a 8 km grid over France as 25 files in NetCDF format. Values cover grid cells located only within metropolitan France national borders (including Corsica). The FYRE Climate dataset is fully described by Devers et al. (2021).
description: This work was funded by Irstea/INRAE and CNR (Compagnie Nationale du Rhône).
description: {"references": ["Devers, A., Vidal, J.-P., Lauvernet, C., Graff, B. & Vannier, O. (2020) A framework for high-resolution meteorological surface reanalysis through offline data assimilation in an ensemble of downscaled reconstructions. Quarterly Journal of the Royal Meteorological Society, 146, 153-173. https://doi.org/10.1002/qj.3663", "Devers, A., Vidal, J.-P., Lauvernet, C. & Vannier, O. (2021) FYRE Climate: A high-resolution reanalysis of daily precipitation and temperature in France from 1871 to 2012, Climate of the Past, 17, 1857-1879. https://doi.org/10.5194/cp-17-1857-2021"]}
DOI: 10.5281/zenodo.4006471
title: FYRE Climate: Temperature

description: <strong>FYRE Climate</strong> (French hYdrometeorological REanalysis Climate dataset) is a 25-member ensemble of 142-year high-resolution reanalysis of precipitation and temperature over France, from 1 January 1871 to 29 December 2012. FYRE Climate results from the assimilation of historical daily station observations of temperature and precipitation into the SCOPE Climate reconstructions (Caillouet et al., 2019) through a Kalman filter ensemble approach (Devers et al., 2020). FYRE Climate provides an ensemble of 25 equally-plausible spatially-coherent gridded bivariate time series. Data are available at a daily time step on a 8 km grid over France as 25 files in NetCDF format. Values cover grid cells located only within metropolitan France national borders (including Corsica). The FYRE Climate dataset is fully described by Devers et al. (2021).
description: This work was funded by Irstea/INRAE and CNR (Compagnie Nationale du Rhône).
description: {"references": ["Devers, A., Vidal, J.-P., Lauvernet, C., Graff, B. & Vannier, O. (2020a) A framework for high-resolution meteorological surface reanalysis through offline data assimilation in an ensemble of downscaled reconstructions. Quarterly Journal of the Royal Meteorological Society, 146, 153-173. https://doi.org/10.1002/qj.3663", "Devers, A., Vidal, J.-P., Lauvernet, C. & Vannier, O. (2021) FYRE Climate: A high-resolution reanalysis of daily precipitation and temperature in France from 1871 to 2012, Climate of the Past, 17, 1857-1879. https://doi.org/10.5194/cp-17-1857-2021"]}
DOI: 10.5281/zenodo.5060503
title: Dataset - mouse spontaneous ultrasonic vocalizations

description: This datasets includes all data used in our study " LMT USV Toolbox, a novel methodological approach to place mouse ultrasonic vocalizations in their behavioral contexts – A study in female and male C57BL/6J mice and in Shank3 mutant females". A "readme" file describes the type of analyses based on each data file.
DOI: 10.5281/zenodo.5060502
title: Dataset - mouse spontaneous ultrasonic vocalizations

description: This datasets includes all data used in our study " LMT USV Toolbox, a novel methodological approach to place mouse ultrasonic vocalizations in their behavioral contexts – A study in female and male C57BL/6J mice and in Shank3 mutant females". A "readme" file describes the type of analyses based on each data file.
DOI: 10.6084/m9.figshare.16705813
title: Methylation Data (Illumina EPIC array)

description: The data (raw Illumina HumanMethylation EPIC data) accompanying our manuscript "Methylation subgroup and molecular heterogeneity is a hallmark of glioblastoma: Implications for biopsy targeting, classification and therapy" is available upon request and signing of a data-transfer agreement. Please get in contact with b.wiestler@tum.de for optaining this data. Thank you.
DOI: 10.6084/m9.figshare.16705813.v1
title: Methylation Data (Illumina EPIC array)

description: The data (raw Illumina HumanMethylation EPIC data) accompanying our manuscript "Methylation subgroup and molecular heterogeneity is a hallmark of glioblastoma: Implications for biopsy targeting, classification and therapy" is available upon request and signing of a data-transfer agreement. Please get in contact with b.wiestler@tum.de for optaining this data. Thank you.
DOI: 10.5281/zenodo.5539414
title: Interference suppression techniques for OPM-based MEG: Opportunities and challenges

description: Datasets to run the example data analysis tutorials presented in: Seymour et al., (2021). Interference suppression techniques for OPM-based MEG: Opportunities and challenges.<em> In Prep.</em>
DOI: 10.5281/zenodo.5539413
title: Interference suppression techniques for OPM-based MEG: Opportunities and challenges

description: Datasets to run the example data analysis tutorials presented in: Seymour et al., (2021). Interference suppression techniques for OPM-based MEG: Opportunities and challenges.<em> In Prep.</em>