cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/w6n7-k856
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related: Coronavirus (COVID-19) Tweets Sentiment Trend (Global), Coronavirus (COVID-19) Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 264,646 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.5281/zenodo.4280224
title: Supplementary Figures and Tables

description: Supplementary Figures and Tables
DOI: 10.5281/zenodo.4280223
title: Supplementary Figures and Tables

description: Supplementary Figures and Tables
DOI: 10.25959/5fb601c69eeb9
title: Diversity and abundance of soil microbial communities decline and community compositions change, with severity of post-logging fire
DOI: 10.5517/ccdc.csd.cc25xq61
title: CCDC 2023593: Experimental Crystal Structure Determination

description: Related Article: Ai-Ju Liu, Yue Han, Fei Xu, Song-De Han, Jie Pan, Guo-Ming Wang|2020|Dalton Trans.|49|14598|doi:10.1039/D0DT03010D
DOI: 10.5517/ccdc.csd.cc25xq50
title: CCDC 2023592: Experimental Crystal Structure Determination

description: Related Article: Ai-Ju Liu, Yue Han, Fei Xu, Song-De Han, Jie Pan, Guo-Ming Wang|2020|Dalton Trans.|49|14598|doi:10.1039/D0DT03010D
DOI: 10.5517/ccdc.csd.cc25p9fm
title: CCDC 2016470: Experimental Crystal Structure Determination

description: Related Article: So Yokomori, Shun Dekura, Tomoko Fujino, Mitsuaki Kawamura, Taisuke Ozaki, Hatsumi Mori|2020|J.Mater.Chem.C|8|14939|doi:10.1039/D0TC04280C
DOI: 10.25505/fiz.icsd.cc26bxp5
title: CSD 2036287: Experimental Crystal Structure Determination

description: Related Article: Liang Long |2020|ICSD Communication|||
DOI: 10.25505/fiz.icsd.cc26bxq6
title: CSD 2036288: Experimental Crystal Structure Determination

description: Related Article: Liang Long |2020|ICSD Communication|||
DOI: 10.5517/ccdc.csd.cc26gncq
title: CCDC 2039873: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gndr
title: CCDC 2039874: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gnfs
title: CCDC 2039875: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gngt
title: CCDC 2039876: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gnhv
title: CCDC 2039877: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gnjw
title: CCDC 2039878: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gnkx
title: CCDC 2039879: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gnly
title: CCDC 2039880: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gnmz
title: CCDC 2039881: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gnn0
title: CCDC 2039882: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gnp1
title: CCDC 2039883: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gnq2
title: CCDC 2039884: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gnr3
title: CCDC 2039885: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gns4
title: CCDC 2039886: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gnt5
title: CCDC 2039887: Experimental Crystal Structure Determination

description: Related Article: C. Harish Chinthal, C.N. Kavitha, H.S. Yathirajan, S. Foro, R.S. Rathore, C. Glidewell|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1779|doi:10.1107/S2056989020014097
DOI: 10.5517/ccdc.csd.cc26gqp3
title: CCDC 2039945: Experimental Crystal Structure Determination

description: Related Article: M. Gogoi, B.K. Das|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1775|doi:10.1107/S2056989020014103
DOI: 10.5517/ccdc.csd.cc26j6sr
title: CCDC 2041374: Experimental Crystal Structure Determination

description: Related Article: Yehao Qiu, John F. Hartwig|2020|J.Am.Chem.Soc.|142|19239|doi:10.1021/jacs.0c09157
DOI: 10.5517/ccdc.csd.cc26j6ts
title: CCDC 2041375: Experimental Crystal Structure Determination

description: Related Article: Yehao Qiu, John F. Hartwig|2020|J.Am.Chem.Soc.|142|19239|doi:10.1021/jacs.0c09157
DOI: 10.5517/ccdc.csd.cc26j6vt
title: CCDC 2041376: Experimental Crystal Structure Determination

description: Related Article: Yehao Qiu, John F. Hartwig|2020|J.Am.Chem.Soc.|142|19239|doi:10.1021/jacs.0c09157
DOI: 10.5517/ccdc.csd.cc26j6wv
title: CCDC 2041377: Experimental Crystal Structure Determination

description: Related Article: Yehao Qiu, John F. Hartwig|2020|J.Am.Chem.Soc.|142|19239|doi:10.1021/jacs.0c09157
DOI: 10.5281/zenodo.4280030
title: A 15-year full-coverage daily PM1 dataset across China based on a data-fusion model

description: The long-term (2004-2018) full-coverage daily PM<sub>1</sub> dataset across China was developed based on a stacking decision tree model assimilating satellite data, meteorological variables, and other geographical covariates. The PM<sub>1</sub> dataset captured a strong prediction capability with a high cross-validation (CV) R<sup>2</sup> value (0.64), and the low root-mean-square error (RMSE: 18.60 μg/m<sup>3</sup>) and moderate absolute error (MAE: 11.96 μg/m<sup>3</sup>). The long-term PM<sub>1</sub> dataset obtained here provide a key scientific basis and data support for epidemiological research and air pollution mitigation.
DOI: 10.5281/zenodo.4280029
title: A 15-year full-coverage daily PM1 dataset across China based on a data-fusion model

description: The long-term (2004-2018) full-coverage daily PM<sub>1</sub> dataset across China was developed based on a stacking decision tree model assimilating satellite data, meteorological variables, and other geographical covariates. The PM<sub>1</sub> dataset captured a strong prediction capability with a high cross-validation (CV) R<sup>2</sup> value (0.64), and the low root-mean-square error (RMSE: 18.60 μg/m<sup>3</sup>) and moderate absolute error (MAE: 11.96 μg/m<sup>3</sup>). The long-term PM<sub>1</sub> dataset obtained here provide a key scientific basis and data support for epidemiological research and air pollution mitigation.
DOI: 10.6084/m9.figshare.13258589
title: Changes in visual attentional behavior in complex regional pain syndrome: A preliminary study

description: <b>Purpose:</b> The purpose of the present study was to investigate the visual attentional behavior toward a pain-affected area and face/body images using eye tracking in complex regional pain syndrome (CRPS) patients. Moreover, we investigated the relationship between visual attentional behavior and clinical symptoms. <b>Patients and methods:</b> Eight female patients with CRPS type 1 in their upper limb and 8 healthy adult women participated in this study. First, the participants were asked to watch videoclips in a relaxed manner (Videoclip 1 featured young adults who introduced themselves; Videoclip 2 featured young adults touching the hand of the other person sitting across from them with their hand.) Eye movement data were tracked with eye-tracking glasses. <b>Results: </b>In video clip 1, the fixation duration (FD) and fixation count (FC) on faces tended to be lower in CRPS patients than in healthy controls. This tendency was found in patients with low body cognitive distortions. In video clip 2, CRPS patients displayed significantly lower FD and FC on the unaffected hand while watching a video of the unaffected hand being touched compared with healthy controls. Moreover, patients with low body cognitive distortion displayed significantly longer FD on the affected hand. <b>Conclusion:</b> Some CRPS patients differed in visual attentional behavior toward the face and body compared with healthy controls. In addition, our findings suggest that patients with lower body cognitive distortion may have a high visual attention for the affected hand, while patients with higher distortion may be neglecting the affected hand.
DOI: 10.6084/m9.figshare.13258589.v1
title: Changes in visual attentional behavior in complex regional pain syndrome: A preliminary study

description: <b>Purpose:</b> The purpose of the present study was to investigate the visual attentional behavior toward a pain-affected area and face/body images using eye tracking in complex regional pain syndrome (CRPS) patients. Moreover, we investigated the relationship between visual attentional behavior and clinical symptoms. <b>Patients and methods:</b> Eight female patients with CRPS type 1 in their upper limb and 8 healthy adult women participated in this study. First, the participants were asked to watch videoclips in a relaxed manner (Videoclip 1 featured young adults who introduced themselves; Videoclip 2 featured young adults touching the hand of the other person sitting across from them with their hand.) Eye movement data were tracked with eye-tracking glasses. <b>Results: </b>In video clip 1, the fixation duration (FD) and fixation count (FC) on faces tended to be lower in CRPS patients than in healthy controls. This tendency was found in patients with low body cognitive distortions. In video clip 2, CRPS patients displayed significantly lower FD and FC on the unaffected hand while watching a video of the unaffected hand being touched compared with healthy controls. Moreover, patients with low body cognitive distortion displayed significantly longer FD on the affected hand. <b>Conclusion:</b> Some CRPS patients differed in visual attentional behavior toward the face and body compared with healthy controls. In addition, our findings suggest that patients with lower body cognitive distortion may have a high visual attention for the affected hand, while patients with higher distortion may be neglecting the affected hand.
DOI: 10.6084/m9.figshare.13258580
title: Donia_Hypertension_data.sav

description: Dataset for the manuscript, entitled "Prevalence and determinants of hypertension: A cross-sectional study in an urban slum area of Bangladesh"
DOI: 10.6084/m9.figshare.13258580.v1
title: Donia_Hypertension_data.sav

description: Dataset for the manuscript, entitled "Prevalence and determinants of hypertension: A cross-sectional study in an urban slum area of Bangladesh"
DOI: 10.6084/m9.figshare.13258574
title: Experimental datasets

description: Experimental datasets (MCBJ, IVs, Raman) accompanying the manuscript 'Benchmark and application of unsupervised classification approaches for univariate data'.<br><br>
DOI: 10.6084/m9.figshare.13258574.v1
title: Experimental_datasets.zip

description: Experimental datasets (MCBJ, IVs, Raman) accompanying the manuscript 'Benchmark and application of unsupervised classification approaches for univariate data'.<br><br>
DOI: 10.5281/zenodo.4280372
title: Observational signal of the interaction between mountain–plain wind and urban breeze under weak synoptic systems

description: The dataset contains all material involved in figure4a, figure4b, figure5, figure6, figure7 and figure8. There are six data sheets in this xlsx file. Meteorological data acquired between 2013 and 2015 were used to investigate at Hangzhou. there are three key points: (1)Criteria to identify valley wind episodes from ground-based observations were proposed and evaluated. (2)Interactions between mountain–plain wind and urban breeze were found from surface observations.(3)The interaction can modulate the reversal times of diurnal mountain-plain winds. The data is including wind direction, wind speed and temperature with 5-min sampling. Every data sheet has a readme for a more detailed explanation, and you need to analyse them in combination with figures.
DOI: 10.5281/zenodo.4280371
title: Observational signal of the interaction between mountain–plain wind and urban breeze under weak synoptic systems

description: The dataset contains all material involved in figure4a, figure4b, figure5, figure6, figure7 and figure8. There are six data sheets in this xlsx file. Meteorological data acquired between 2013 and 2015 were used to investigate at Hangzhou. there are three key points: (1)Criteria to identify valley wind episodes from ground-based observations were proposed and evaluated. (2)Interactions between mountain–plain wind and urban breeze were found from surface observations.(3)The interaction can modulate the reversal times of diurnal mountain-plain winds. The data is including wind direction, wind speed and temperature with 5-min sampling. Every data sheet has a readme for a more detailed explanation, and you need to analyse them in combination with figures.
DOI: 10.6084/m9.figshare.13258595
title: Generated MCBJ datasets

description: Generated MCBJ datasets accompanying the manuscript 'Benchmark and application of unsupervised classification approaches for univariate data' and used for the benchmark in the main text
DOI: 10.6084/m9.figshare.13256750
title: Supplementary files

description: Data for the following paper; Comparison of alemtuzumab, anti-thymocyte globulin, and post-transplant cyclophosphamide for graft-versus-host disease and graft-versus-leukemia in murine models.
DOI: 10.6084/m9.figshare.13256750.v1
title: Supplementary files

description: Data for the following paper; Comparison of alemtuzumab, anti-thymocyte globulin, and post-transplant cyclophosphamide for graft-versus-host disease and graft-versus-leukemia in murine models.
DOI: 10.6084/m9.figshare.13258640.v1
title: Experimental datasets

description: Experimental datasets (MCBJ, IVs, Raman) accompanying the manuscript 'Benchmark and application of unsupervised classification approaches for univariate data'.<br>
DOI: 10.5281/zenodo.4280384
title: Natural Lake Database in China in 2015 (NLD_China2015)

description: The first database of natural lakes with an area &gt; 0.01 km<sup>2</sup> in China in 2015 (NLD_China2015). The database was manually developed in reference to the existing lake inventories, reservoir datasets, and high-resolution images from Google Earth. The details of the NLD_China2015 is described in "LakeDatabase_China2015_Readme_V1.0.docx".
DOI: 10.5281/zenodo.4280383
title: Natural Lake Database in China in 2015 (NLD_China2015)

description: The first database of natural lakes with an area &gt; 0.01 km<sup>2</sup> in China in 2015 (NLD_China2015). The database was manually developed in reference to the existing lake inventories, reservoir datasets, and high-resolution images from Google Earth. The details of the NLD_China2015 is described in "LakeDatabase_China2015_Readme_V1.0.docx".
DOI: 10.17632/j9cv8bsndy.1
title: Data for: Household survey data on income, food security and dietary behavior among women and children from urban and per urban regions of Nairobi metropolitan

description: This data contains analysed odds tables of consuming food items from for groups data collected.
DOI: 10.17632/b7nh7zrtf4.1
title: Data for: Micro-textures and Mechanical Properties of Fe-21Cr-15Ni-6Mn-Mo-Nb Austenitic Stainless Steel Plate in Asymmetric Hot Rolling

description: Microscopic characterization and mechanical properties
DOI: 10.17632/bjmpmz998x.1
title: Data for: Kinetic and mechanistic study of the gas-phase reaction of γ-terpinene with ozone

description: The 4 first files are dedicated to the rate coefficients determination and the 6 others to the investigation of the products formed in the gaseous phase.
DOI: 10.6084/m9.figshare.13258661
title: Supplementary Material for: Brief Online Cognitive Behavioural Intervention for Dysfunctional Worry Related to the COVID-19 Pandemic: A Randomised Controlled Trial

description: <b><i>Introduction:</i></b> Worries about the immediate and long-term consequences of the COVID-19 pandemic may for some individuals develop into pervasive worry that is disproportionate in its intensity or duration and significantly interferes with everyday life. <b><i>Objective:</i></b> The aim of this study was to investigate if a brief self-guided, online psychological intervention can reduce the degree of dysfunctional worry related to the COVID-19 pandemic and associated symptoms. <b><i>Methods:</i></b> 670 adults from the general Swedish general population reporting daily uncontrollable worry about CO­VID-19 and its possible consequences (e.g., illness, death, the economy, one’s family) were randomised (1:1 ratio) to a 3-week self-guided, online cognitive behavioural intervention targeting dysfunctional COVID-19 worry and associated symptoms, or a waiting list of equal duration. The primary outcome measure was a COVID-19 adapted version of the Generalised Anxiety Disorder 7-item scale administered at baseline and weeks 1–3 (primary endpoint). Follow-up assessments were conducted 1 month after treatment completion. The trial was registered on ClinicalTrials.gov (NCT04341922) before inclusion of the first participant. <b><i>Results:</i></b> The main pre-specified intention-to-treat analysis indicated significant reductions in COVID-19-related worry for the intervention group compared to the waiting list (β = 1.14, <i>Z</i> = 9.27, <i>p</i> &lt; 0.001), corresponding to a medium effect size (bootstrapped <i>d</i> = 0.74 [95% CI: 0.58–0.90]). Improvements were also seen on all secondary measures, including mood, daily functioning, insomnia, and intolerance of uncertainty. Participant satisfaction was high. No serious adverse events were recorded. <b><i>Conclusions:</i></b> A brief digital and easily scalable self-guided psychological intervention can significantly reduce dysfunctional worry and associated behavioural symptoms related to the COVID-19 pandemic.
DOI: 10.5281/zenodo.4280460
title: Mapping the age of ice of Gauligletscher combining surface radionuclide contamination and ice flow modeling: Radionuclide data

description: The data corresponds to 239-Pu and 236-U concentrations of 220 samples of ice collected along 5 flowlines at the surface of Gauligletscher in July 2019. Details about the data sampling and analysis can be found in the paper "Mapping the age of ice of Gauligletscher combining surface radionuclide contamination and ice flow modeling", by G. Jouvet, S. Röllin, H. Sahli, J. Corcho, L. Gnägi, L. Compagno, D. Sidler, M. Schwikowski, A. Bauder, and M. Funk. The Cryosphere, 2020.
description: {"references": ["Mapping the age of ice of Gauligletscher combining surface radionuclide contamination and ice flow modeling, by G. Jouvet, S. R\u00f6llin, H. Sahli, J. Corcho, L. Gn\u00e4gi, L. Compagno, D. Sidler, M. Schwikowski, A. Bauder, and M. Funk. The Cryosphere, 2020."]}
DOI: 10.5281/zenodo.4280459
title: Mapping the age of ice of Gauligletscher combining surface radionuclide contamination and ice flow modeling: Radionuclide data

description: The data corresponds to 239-Pu and 236-U concentrations of 220 samples of ice collected along 5 flowlines at the surface of Gauligletscher in July 2019. Details about the data sampling and analysis can be found in the paper "Mapping the age of ice of Gauligletscher combining surface radionuclide contamination and ice flow modeling", by G. Jouvet, S. Röllin, H. Sahli, J. Corcho, L. Gnägi, L. Compagno, D. Sidler, M. Schwikowski, A. Bauder, and M. Funk. The Cryosphere, 2020.
description: {"references": ["Mapping the age of ice of Gauligletscher combining surface radionuclide contamination and ice flow modeling, by G. Jouvet, S. R\u00f6llin, H. Sahli, J. Corcho, L. Gn\u00e4gi, L. Compagno, D. Sidler, M. Schwikowski, A. Bauder, and M. Funk. The Cryosphere, 2020."]}