cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4892729
title: Dataset for the publication: "Aerosol properties and aerosol-radiation interactions in clear sky conditions over Germany"

description: This repository contains the data to reproduce the analysis conducted in the publication: Witthuhn, J., Hünerbein, A., Filipitsch, F., Wacker, S., Meilinger, S., and Deneke, H., Aerosol properties and aerosol-radiation interactions in clear sky conditions over Germany.
DOI: 10.24341/tethys.87
title: Geodaten - Blatt 196 Obertilliach (1:50.000)
title: Geodata - sheet 196 Obertilliach (1:50.000)

description: Die Geodaten wurden aus dem Kartenwerk GK50 „Geologische Karte der Republik Österreich 1:50.000“ abgeleitet. Die Datengrundlage basiert auf einer umfassenden Datenerhebung im Rahmen der gesetzlich verankerten geologischen Landesaufnahme von Österreich, welche eine umfangreiche Literaturrecherche, die Geländekartierung, sowie die Probenahme mit wissenschaftlichen Analysen und Messungen beinhaltet. Die im BMN-Blattschnitt veröffentlichten Geodaten entsprechen dem aktuellen Wissensstand zum Zeitpunkt des Kartendruckes und können geringfügig von den Originalen abweichen.
description: Die vorliegende Datenpublikation ist ein Bestandteil der Kartenserie „Geologische Karte der Republik Österreich 1:50.000“ (GK50), die im Zeitraum zwischen 1977 und 2017 veröffentlicht wurden und aus insgesamt 89 Geodatensätzen besteht. Eine ausführliche Datenbeschreibung wird mit den Kartendaten bereitgestellt und kann direkt zu jeder Publikation heruntergeladen werden.
description: This data publication is part of the map series "Geological Map of the Republic of Austria 1:50.000" (GK50), published between 1977 and 2017 consisting of 89 spatial data sets. A detailed data description is provided with the map data and can be downloaded together with the data publication.
DOI: 10.15468/wwbr57
title: Antarctic cryosphere fungal diversity (2015)

description: Amplicon sequencing dataset (Illumina MiSeq) targeting Fungi (ITS) in ice samples (n=8) from the Antarctic Peninsula.
DOI: 10.5281/zenodo.4972467
title: Analysis of the effect of transcription inhibitors on the shapes of RNA polymerase II clusters

description: Data and scripts for the analysis of the effect of transcription inhibitors on the shape of RNA polymerase II clusters. The data set includes data obtained from zebrafish embryos, THP-1 cell cultures, and lattice simulations. CellProfiler pipelines for the initial analysis of images are provided, along with Python scripts to export data into CSV format. Lattice simulation results are provided in the form of CSV files. MatLab scripts for the statistical analysis of the different data sets are included.
DOI: 10.5281/zenodo.4972466
title: Analysis of the effect of transcription inhibitors on the shapes of RNA polymerase II clusters

description: Data and scripts for the analysis of the effect of transcription inhibitors on the shape of RNA polymerase II clusters. The data set includes data obtained from zebrafish embryos, THP-1 cell cultures, and lattice simulations. CellProfiler pipelines for the initial analysis of images are provided, along with Python scripts to export data into CSV format. Lattice simulation results are provided in the form of CSV files. MatLab scripts for the statistical analysis of the different data sets are included.
DOI: 10.6084/m9.figshare.14797875
title: Setting up the Early Permian Beishan Paleomagnetic ‘Bridge’ for the southern Central Asian Orogenic Belt

description: This dataset is only for peer review. Corresponding work has been submitted to Journal of Geophysical Research: Solid Earth.
DOI: 10.6084/m9.figshare.14797875.v1
title: Setting up the Early Permian Beishan Paleomagnetic ‘Bridge’ for the southern Central Asian Orogenic Belt

description: This dataset is only for peer review. Corresponding work has been submitted to Journal of Geophysical Research: Solid Earth.
DOI: 10.5281/zenodo.4972223
title: LMT test database - 20201005_test_3days_Experiment 3495

description: This is a test data file from a Live Mouse Tracker (LMT) experiment. This file is in the SQLite database format. I create this ressource to test an application to manage LMT data and analysis.
description: Test LMT SQLite file.
description: {"references": ["de Chaumont, F., Ey, E., Torquet, N. et al. Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning. Nat Biomed Eng 3, 930\u2013942 (2019). https://doi.org/10.1038/s41551-019-0396-1"]}
DOI: 10.5281/zenodo.4972222
title: LMT test database - 20201005_test_3days_Experiment 3495

description: This is a test data file from a Live Mouse Tracker (LMT) experiment. This file is in the SQLite database format. I create this ressource to test an application to manage LMT data and analysis.
description: Test LMT SQLite file.
description: {"references": ["de Chaumont, F., Ey, E., Torquet, N. et al. Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning. Nat Biomed Eng 3, 930\u2013942 (2019). https://doi.org/10.1038/s41551-019-0396-1"]}
DOI: 10.5517/ccdc.csd.cc2796nf
title: CCDC 2064434: Experimental Crystal Structure Determination

description: Related Article: MUhammad Usman Anwar, Ahmed Al-Harrasi|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2798mg
title: CCDC 2064495: Experimental Crystal Structure Determination

description: Related Article: Iman Abdelmoty, Bruce M. Foxman|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc279cjg
title: CCDC 2064585: Experimental Crystal Structure Determination

description: Related Article: Dieter Schollmeyer, Enquan Jin|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2798sm
title: CCDC 2064500: Experimental Crystal Structure Determination

description: Related Article: TAO SHENG|2021|CSD Communication|||
description: Related Article: Tao Sheng, Mengmeng Kong, Yujie Wang, HuiJun Wu, Qin Gu, Anita Shyying Chuang, Shengkun Li, Xuewen Gao|2021|Eur.J.Med.Chem.|222|113563|doi:10.1016/j.ejmech.2021.113563
DOI: 10.5517/ccdc.csd.cc2798wq
title: CCDC 2064503: Experimental Crystal Structure Determination

description: Related Article: TAO SHENG|2021|CSD Communication|||
description: Related Article: Tao Sheng, Mengmeng Kong, Yujie Wang, HuiJun Wu, Qin Gu, Anita Shyying Chuang, Shengkun Li, Xuewen Gao|2021|Eur.J.Med.Chem.|222|113563|doi:10.1016/j.ejmech.2021.113563
DOI: 10.5517/ccdc.csd.cc1jq5q4
title: CCDC 1421496: Experimental Crystal Structure Determination

description: Related Article: SharmilaTagore. S, Swaminathan.J, Manikandan.D, Gomathi.S, Sabarinathan.N, Ramalingam.M, Sethuraman.V|2021|CSD Communication|||
description: Related Article: S. Sharmila Tagore, J. Swaminathan, D. Manikandan, S. Gomathi, N. Sabarinathan, M. Ramalingam, V. Sethuraman|2021|Heliyon|7|e06593|doi:10.1016/j.heliyon.2021.e06593
DOI: 10.24341/tethys.88
title: Geodaten - Blatt 198 Weißbriach (1:50.000)
title: Geodata - sheet 198 Weißbriach (1:50.000)

description: Die Geodaten wurden aus dem Kartenwerk GK50 „Geologische Karte der Republik Österreich 1:50.000“ abgeleitet. Die Datengrundlage basiert auf einer umfassenden Datenerhebung im Rahmen der gesetzlich verankerten geologischen Landesaufnahme von Österreich, welche eine umfangreiche Literaturrecherche, die Geländekartierung, sowie die Probenahme mit wissenschaftlichen Analysen und Messungen beinhaltet. Die im BMN-Blattschnitt veröffentlichten Geodaten entsprechen dem aktuellen Wissensstand zum Zeitpunkt des Kartendruckes und können geringfügig von den Originalen abweichen.
description: Die vorliegende Datenpublikation ist ein Bestandteil der Kartenserie „Geologische Karte der Republik Österreich 1:50.000“ (GK50), die im Zeitraum zwischen 1977 und 2017 veröffentlicht wurden und aus insgesamt 89 Geodatensätzen besteht. Eine ausführliche Datenbeschreibung wird mit den Kartendaten bereitgestellt und kann direkt zu jeder Publikation heruntergeladen werden.
description: This data publication is part of the map series "Geological Map of the Republic of Austria 1:50.000" (GK50), published between 1977 and 2017 consisting of 89 spatial data sets. A detailed data description is provided with the map data and can be downloaded together with the data publication.
DOI: 10.5281/zenodo.4779097
title: Supplementary Data and Code: A subterranean adaptive radiation of amphipods in Europe

description: Supplementary Data, R code, alignments and settings for phylogenetic analyses for manuscript entitled <em>A subterranean adaptive radiation of amphipods in Europe.</em> <strong>The dataset contains</strong> <strong>Alignments and settings for phylogenetic analyses</strong><br> <em>BEAST2_subset_alignment.nex</em>→ BEAST2 analysis on a subset of MOTUs with at least two markers<br> <em>BEAST2_subset_settings.xml</em>→ BEAST2 analysis on a subset of MOTUs with at least two markers <em>BEAST2_whole_alignment.nex </em>→ BEAST2 analysis on a whole dataset of 377 Niphargus MOTUs (+ outgroup <em>Niphargellus</em>)<br> <em>BEAST2_whole_settings.xml </em>→ BEAST2 analysis on a whole dataset of 377 Niphargus MOTUs (+ outgroup <em>Niphargellus</em>) <em>IQ-TREE_alignment.phy </em>→ IQ-TREE analysis<br> <em>IQ-TREE_settings.nex </em>→ IQ-TREE analysis <em>MrBayes.nex</em> → MrBayes analysis <em>analyses.R </em>→ code for analyses on whole dataset<br> <em>analyses_small.R</em>→ code for repeated LTT, DTT, CTT analyses on subset <em>beast.trees</em> → data for import into R: all BEAST2 trees<br> <em>beast_meanH.tre</em> → data for import into R: maximum credibility tree<br> <em>beast_meanH_small.tre</em> → data for import into R: small maximum credibility tree<br> <em>beast_small.trees</em> → data for import into R: all small BEAST2 trees<br> <em>ecology.xlsx</em> → data for import into R: ecological data<br> <em>morphology.xlsx</em> → data for import into R: morphometric data
DOI: 10.5281/zenodo.4779096
title: Supplementary Data and Code: A subterranean adaptive radiation of amphipods in Europe

description: Supplementary Data, R code, alignments and settings for phylogenetic analyses for manuscript entitled <em>A subterranean adaptive radiation of amphipods in Europe.</em> <strong>The dataset contains</strong> <strong>Alignments and settings for phylogenetic analyses</strong><br> <em>BEAST2_subset_alignment.nex</em>→ BEAST2 analysis on a subset of MOTUs with at least two markers<br> <em>BEAST2_subset_settings.xml</em>→ BEAST2 analysis on a subset of MOTUs with at least two markers <em>BEAST2_whole_alignment.nex </em>→ BEAST2 analysis on a whole dataset of 377 Niphargus MOTUs (+ outgroup <em>Niphargellus</em>)<br> <em>BEAST2_whole_settings.xml </em>→ BEAST2 analysis on a whole dataset of 377 Niphargus MOTUs (+ outgroup <em>Niphargellus</em>) <em>IQ-TREE_alignment.phy </em>→ IQ-TREE analysis<br> <em>IQ-TREE_settings.nex </em>→ IQ-TREE analysis <em>MrBayes.nex</em> → MrBayes analysis <em>analyses.R </em>→ code for analyses on whole dataset<br> <em>analyses_small.R</em>→ code for repeated LTT, DTT, CTT analyses on subset <em>beast.trees</em> → data for import into R: all BEAST2 trees<br> <em>beast_meanH.tre</em> → data for import into R: maximum credibility tree<br> <em>beast_meanH_small.tre</em> → data for import into R: small maximum credibility tree<br> <em>beast_small.trees</em> → data for import into R: all small BEAST2 trees<br> <em>ecology.xlsx</em> → data for import into R: ecological data<br> <em>morphology.xlsx</em> → data for import into R: morphometric data
DOI: 10.24341/tethys.89
title: Geodaten - Blatt 199 Hermagor (1:50.000)
title: Geodata - sheet 199 Hermagor (1:50.000)

description: Die Geodaten wurden aus dem Kartenwerk GK50 „Geologische Karte der Republik Österreich 1:50.000“ abgeleitet. Die Datengrundlage basiert auf einer umfassenden Datenerhebung im Rahmen der gesetzlich verankerten geologischen Landesaufnahme von Österreich, welche eine umfangreiche Literaturrecherche, die Geländekartierung, sowie die Probenahme mit wissenschaftlichen Analysen und Messungen beinhaltet. Die im BMN-Blattschnitt veröffentlichten Geodaten entsprechen dem aktuellen Wissensstand zum Zeitpunkt des Kartendruckes und können geringfügig von den Originalen abweichen.
description: Die vorliegende Datenpublikation ist ein Bestandteil der Kartenserie „Geologische Karte der Republik Österreich 1:50.000“ (GK50), die im Zeitraum zwischen 1977 und 2017 veröffentlicht wurden und aus insgesamt 89 Geodatensätzen besteht. Eine ausführliche Datenbeschreibung wird mit den Kartendaten bereitgestellt und kann direkt zu jeder Publikation heruntergeladen werden.
description: This data publication is part of the map series "Geological Map of the Republic of Austria 1:50.000" (GK50), published between 1977 and 2017 consisting of 89 spatial data sets. A detailed data description is provided with the map data and can be downloaded together with the data publication.
DOI: 10.5281/zenodo.4972491
title: Analysis of RNA polymerase II phosphorylation in two-color STED microscopy images

description: This data set includes the raw image data and MatLab analysis scripts to assess the relative distribution of RNA polymerase II C-terminal domain serine 5 and serine 2 phosphorylation in zebrafish embryos. Phosphorylated polymerase II was labeled by immunofluorescence, microscopy images were acquired by STED microscopy and analyzed using MatLab scripts.
description: Steps needed to carry out the analysis: 1. Install bfmatlab from OME, add it to the MatLab path 2. Extract the image data by running the script STED_extraction_lif.m 3. Analyze the extracted data by running the script ClusterAnalysis.m 4. Generate example images by running the script ExampleImages.m
DOI: 10.15161/oar.it/73854
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.6084/m9.figshare.14797884
title: Supplementary Material for: Prevalence of Cognitive Impairment and Dementia in Older Adults Living at High Altitude: A Systematic Review and Meta-Analysis

description: <b><i>Introduction:</i></b> Dementia is a chronic disease with a variable prevalence throughout the world; however, this could be higher at high-altitude populations. We aimed to summarize the prevalence of cognitive impairment and dementia in older adults living at high altitude. <b><i>Methods:</i></b> We searched in PubMed, Medline, Scopus, Web of Science, and Embase and included the studies published from inception to July 20, 2020, with no language restriction, which reported the frequency of cognitive impairment or dementia in older adults living at high-altitude populations. Random-effects meta-analyses were performed to calculate the overall prevalence and 95% confidence intervals (95% CI) of cognitive impairment and dementia. The risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS) adapted for cross-sectional studies. <b><i>Results:</i></b> Six studies were included (3,724 participants), and 5 of the 6 included studies were carried out in Latin America. The altitude ranged from 1,783 to 3,847 m, the proportion of women included varied from 38.7 to 65.6%, and the proportion of participants with elementary or illiterate educational level ranged from 71.7 to 97.6%. The overall prevalence of cognitive impairment was 22.0% (95% CI: 8–40, <i>I</i><sup>2</sup>: 99%), and the overall prevalence of dementia was 11.0% (95% CI: 6–17, <i>I</i><sup>2</sup>: 92%). In a subgroup analysis according to the instrument used to evaluate cognitive impairment, the prevalence of cognitive impairment was 21.0% (95% CI: 5–42, <i>I</i><sup>2</sup>: 99%) in the MMSE group while the prevalence was 29.0% (95% CI: 0–78) in the non-MMSE group. <b><i>Conclusions:</i></b> The prevalence of cognitive impairment and dementia in older adults living at high altitude is almost twice the number reported in some world regions.
DOI: 10.6084/m9.figshare.14797884.v1
title: Supplementary Material for: Prevalence of Cognitive Impairment and Dementia in Older Adults Living at High Altitude: A Systematic Review and Meta-Analysis

description: <b><i>Introduction:</i></b> Dementia is a chronic disease with a variable prevalence throughout the world; however, this could be higher at high-altitude populations. We aimed to summarize the prevalence of cognitive impairment and dementia in older adults living at high altitude. <b><i>Methods:</i></b> We searched in PubMed, Medline, Scopus, Web of Science, and Embase and included the studies published from inception to July 20, 2020, with no language restriction, which reported the frequency of cognitive impairment or dementia in older adults living at high-altitude populations. Random-effects meta-analyses were performed to calculate the overall prevalence and 95% confidence intervals (95% CI) of cognitive impairment and dementia. The risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS) adapted for cross-sectional studies. <b><i>Results:</i></b> Six studies were included (3,724 participants), and 5 of the 6 included studies were carried out in Latin America. The altitude ranged from 1,783 to 3,847 m, the proportion of women included varied from 38.7 to 65.6%, and the proportion of participants with elementary or illiterate educational level ranged from 71.7 to 97.6%. The overall prevalence of cognitive impairment was 22.0% (95% CI: 8–40, <i>I</i><sup>2</sup>: 99%), and the overall prevalence of dementia was 11.0% (95% CI: 6–17, <i>I</i><sup>2</sup>: 92%). In a subgroup analysis according to the instrument used to evaluate cognitive impairment, the prevalence of cognitive impairment was 21.0% (95% CI: 5–42, <i>I</i><sup>2</sup>: 99%) in the MMSE group while the prevalence was 29.0% (95% CI: 0–78) in the non-MMSE group. <b><i>Conclusions:</i></b> The prevalence of cognitive impairment and dementia in older adults living at high altitude is almost twice the number reported in some world regions.
DOI: 10.6084/m9.figshare.11890278.v1
title: PRJNA398031_normalized_gene_count_matrix

description: PRJNA398031_normalized_gene_count_matrix
DOI: 10.6084/m9.figshare.11890287
title: PRJNA292861_normalized_gene_count_matrix

description: PRJNA292861_normalized_gene_count_matrix
DOI: 10.5281/zenodo.4972373
title: MTurk-MBTI

description: MTurk-MBTI was collected via Amazon Mechanical Turk platform. It contains, for a total of 347 Amazon Mechanical Turk workers, their self-reported MBTI personality types, and answers to two open-end questions: (1) What is your favourite type of vacations and why? (2) What hobbies do you prefer and why? Each answer to the open-end questions contains minimum 300 characters. The details of the procedure for data collection and the scientific background for choosing those exact two open-end questions can be found in [1]. The annotation reported in [1] was done on instances taken from this full dataset. Dataset can be used only for research non-commercial purposes. If you use this dataset, please reference the following paper: [1] Stajner, S., Yenikent, S. 2021. Why Is MBTI Personality Detection from Texts a Difficult Task? In Proceedings of the 16th conference of the European Chapter of the Association for Computational Linguistics (EACL), pp. 3580-3589. <br> Bibtex reference: @inproceedings{stajner-yenikent-2021-mbti,<br> title = "Why Is {MBTI} Personality Detection from Texts a Difficult Task?",<br> author = "\v{S}tajner, Sanja and<br> Yenikent, Seren",<br> booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",<br> month = apr,<br> year = "2021",<br> address = "Online",<br> publisher = "Association for Computational Linguistics",<br> url = "https://www.aclweb.org/anthology/2021.eacl-main.312",<br> pages = "3580--3589",<br> abstract = "Automatic detection of the four MBTI personality dimensions from texts has recently attracted noticeable attention from the natural language processing and computational linguistic communities. Despite the large collections of Twitter data for training, the best systems rarely even outperform the majority-class baseline. In this paper, we discuss the theoretical reasons for such low results and present the insights from an annotation study that further shed the light on this issue.",<br> }
DOI: 10.5281/zenodo.4972372
title: MTurk-MBTI

description: MTurk-MBTI was collected via Amazon Mechanical Turk platform. It contains, for a total of 347 Amazon Mechanical Turk workers, their self-reported MBTI personality types, and answers to two open-end questions: (1) What is your favourite type of vacations and why? (2) What hobbies do you prefer and why? Each answer to the open-end questions contains minimum 300 characters. The details of the procedure for data collection and the scientific background for choosing those exact two open-end questions can be found in [1]. The annotation reported in [1] was done on instances taken from this full dataset. Dataset can be used only for research non-commercial purposes. If you use this dataset, please reference the following paper: [1] Stajner, S., Yenikent, S. 2021. Why Is MBTI Personality Detection from Texts a Difficult Task? In Proceedings of the 16th conference of the European Chapter of the Association for Computational Linguistics (EACL), pp. 3580-3589. <br> Bibtex reference: @inproceedings{stajner-yenikent-2021-mbti,<br> title = "Why Is {MBTI} Personality Detection from Texts a Difficult Task?",<br> author = "\v{S}tajner, Sanja and<br> Yenikent, Seren",<br> booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",<br> month = apr,<br> year = "2021",<br> address = "Online",<br> publisher = "Association for Computational Linguistics",<br> url = "https://www.aclweb.org/anthology/2021.eacl-main.312",<br> pages = "3580--3589",<br> abstract = "Automatic detection of the four MBTI personality dimensions from texts has recently attracted noticeable attention from the natural language processing and computational linguistic communities. Despite the large collections of Twitter data for training, the best systems rarely even outperform the majority-class baseline. In this paper, we discuss the theoretical reasons for such low results and present the insights from an annotation study that further shed the light on this issue.",<br> }