cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25442/hku.20085970
title: Supporting data for "Establishing Human Respiratory Epithelial Organoid Culture Systems for Modeling SARS-CoV-2 Infection"

description: This is the complete dataset for the thesis "Establishing Human Respiratory Epithelial Organoid Culture Systems for Modeling SARS-CoV-2 Infection". <br> The file directory and description can be found in the README.txt file. Both raw data and processed data were included in this dataset. <br> In this study, human tissues were collected for the generation of organoids after approval by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW13-364 and UW21-695). All experiments with live viruses were conducted in biosafety level 3 laboratories after approval by the Faculty of Medicine, The University of Hong Kong. Other detailed methodology can be found in the "Chapter 2 - Materials and Methods" of the thesis. <br> Part of the data has been published in PNAS (10.1073/pnas.1806308115) and Cell Discovery (10.1038/s41421-022-00422-1). <br> <br>
DOI: 10.25442/hku.20085970.v2
title: Supporting data for "Establishing Human Respiratory Epithelial Organoid Culture Systems for Modeling SARS-CoV-2 Infection"

description: This is the complete dataset for the thesis "Establishing Human Respiratory Epithelial Organoid Culture Systems for Modeling SARS-CoV-2 Infection". <br> The file directory and description can be found in the README.txt file. Both raw data and processed data were included in this dataset. <br> In this study, human tissues were collected for the generation of organoids after approval by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW13-364 and UW21-695). All experiments with live viruses were conducted in biosafety level 3 laboratories after approval by the Faculty of Medicine, The University of Hong Kong. Other detailed methodology can be found in the "Chapter 2 - Materials and Methods" of the thesis. <br> Part of the data has been published in PNAS (10.1073/pnas.1806308115) and Cell Discovery (10.1038/s41421-022-00422-1). <br> <br>
DOI: 10.25442/hku.20085970.v1
title: Supporting data for "Establishing Organoid Culture System of the Human Respiratory Epithelium for Modeling SARS-CoV-2 Infection"

description: This is the complete dataset for the thesis "Establishing Organoid Culture System of the Human Respiratory Epithelium for Modeling SARS-CoV-2 Infection". <br> The file directory and description can be found in the README.txt file. Both raw data and processed data were included in this dataset. <br> In this study, human tissues were collected for the generation of organoids after approval by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW13-364 and UW21-695). All experiments with live viruses were conducted in biosafety level 3 laboratories after approval by the Faculty of Medicine, The University of Hong Kong. Other detailed methodology can be found in the "Chapter 2 - Materials and Methods" of the thesis. <br> Part of the data has been published in PNAS (10.1073/pnas.1806308115) and Cell Discovery (10.1038/s41421-022-00422-1). <br> <br>
DOI: 10.5281/zenodo.6628271
title: Data set for "A neuronal mechanism for motivational control of behavior"

description: Acting to achieve goals depends on the ability to motivate specific behaviors based on their predicted consequences given an individual’s internal state. However, the underlying neuronal mechanisms that encode and maintain such specific motivational control of behavior are poorly understood. Here, we used Ca2+ imaging and optogenetic manipulations in the basolateral amygdala of freely moving mice performing non-cued, self-paced instrumental goal-directed actions to receive and consume rewards. We found that distinct neuronal activity patterns sequentially represent the entire action-consumption behavioral sequence. Whereas action-associated patterns integrated the identity, value, and expectancy of pursued goals, consumption-associated patterns reflected the identity and value of experienced outcomes. Thus, the interplay between these patterns allows the maintenance of specific motivational states necessary to adaptively direct behavior toward prospective rewards.
DOI: 10.5281/zenodo.6628270
title: Data set for "A neuronal mechanism for motivational control of behavior"

description: Acting to achieve goals depends on the ability to motivate specific behaviors based on their predicted consequences given an individual’s internal state. However, the underlying neuronal mechanisms that encode and maintain such specific motivational control of behavior are poorly understood. Here, we used Ca2+ imaging and optogenetic manipulations in the basolateral amygdala of freely moving mice performing non-cued, self-paced instrumental goal-directed actions to receive and consume rewards. We found that distinct neuronal activity patterns sequentially represent the entire action-consumption behavioral sequence. Whereas action-associated patterns integrated the identity, value, and expectancy of pursued goals, consumption-associated patterns reflected the identity and value of experienced outcomes. Thus, the interplay between these patterns allows the maintenance of specific motivational states necessary to adaptively direct behavior toward prospective rewards.
DOI: 10.5281/zenodo.6805409
title: Funding Covid-19 research: Insights from an exploratory analysis using open data infrastructures - Supplementary material

description: This dataset contains supplementary material for the paper 'Funding Covid-19 research: Insights from an exploratory analysis using open data infrastructures' by Alexis-Michel Mugabushaka, Nees Jan van Eck, and Ludo Waltman. supplementary_material_1_dataset.ods: Dataset of Covid-19 publications. supplementary_material_2_sample.ods: Samples of publications used to assess the accuracy of funding data in the different databases. supplementary_material_3_tables_and_figures.ods: Statistics underlying the tables and figures presented in the paper.
DOI: 10.5281/zenodo.6112761
title: Funding Covid-19 research: Insights from an exploratory analysis using open data infrastructures - Supplementary material

description: This dataset contains supplementary material for the paper 'Funding Covid-19 research: Insights from an exploratory analysis using open data infrastructures' by Alexis-Michel Mugabushaka, Nees Jan van Eck, and Ludo Waltman. supplementary_material_1_dataset.ods: Dataset of Covid-19 publications. supplementary_material_2_sample.ods: Samples of publications used to assess the accuracy of funding data in the different databases. supplementary_material_3_tables_and_figures.ods: Statistics underlying the tables and figures presented in the paper.
DOI: 10.5281/zenodo.6805415
title: Data set "Narrow, fast, and "cool" mantle plumes caused by strain-weakening rheology in Earth's lower mantle" (G-cubed)

description: This archive contains data related to the manuscript “Narrow, fast, and “cool” mantle plumes caused by strain-weakening rheology in Earth’s lower mantle” by Gülcher et al., (G-cubed). It contains output quantities and selected fields for the numerical models shown and discussed in the paper (M###). Moreover, it contains the input parametric files implemented in StagYY. Please read the README.dat file for more information.
DOI: 10.5281/zenodo.6805414
title: Data set "Narrow, fast, and "cool" mantle plumes caused by strain-weakening rheology in Earth's lower mantle" (G-cubed)

description: This archive contains data related to the manuscript “Narrow, fast, and “cool” mantle plumes caused by strain-weakening rheology in Earth’s lower mantle” by Gülcher et al., (G-cubed). It contains output quantities and selected fields for the numerical models shown and discussed in the paper (M###). Moreover, it contains the input parametric files implemented in StagYY. Please read the README.dat file for more information.
DOI: 10.6084/m9.figshare.20252855
title: Additional file 10 of Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

description: Additional file 10. “Simulation-Code3.R”. R code used to define the confidence intervals for each statistic and each recombination model. We ran 10,000 simulations with no QTL effect. The 95% quantiles were used as significant threshold values to define the confidence intervals of each statistic.
DOI: 10.6084/m9.figshare.20252855.v1
title: Additional file 10 of Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

description: Additional file 10. “Simulation-Code3.R”. R code used to define the confidence intervals for each statistic and each recombination model. We ran 10,000 simulations with no QTL effect. The 95% quantiles were used as significant threshold values to define the confidence intervals of each statistic.
DOI: 10.6084/m9.figshare.20252878
title: Additional file 1 of Microplastic exposure across trophic levels: effects on the host–microbiota of freshwater organisms

description: Additional file1 (CSV 25 KB) The unprocessed data that was used to perform the predation analysis over time. The damselfly larvae were exposed to the following treatment: microplastics, deltamethrin pesticide, deltamethrin & microplastics, and control. The data showed damselfly survival counts (Survival), dragonfly predation counts (Predation), predation times in minutes (Time), dragonfly IDs (Number), categorical predation times (TimeC), categorical dragonfly IDs (NumberC), damselfly survival percentages (SurvivalP), microplastics as a binomial variable (MP), deltamethrin as a binomial variable (Delta), and the damselflies' survival (SurvivalDam1, SurvivalDam2, and SurvivalDam2).
DOI: 10.6084/m9.figshare.20252878.v1
title: Additional file 1 of Microplastic exposure across trophic levels: effects on the host–microbiota of freshwater organisms

description: Additional file1 (CSV 25 KB) The unprocessed data that was used to perform the predation analysis over time. The damselfly larvae were exposed to the following treatment: microplastics, deltamethrin pesticide, deltamethrin & microplastics, and control. The data showed damselfly survival counts (Survival), dragonfly predation counts (Predation), predation times in minutes (Time), dragonfly IDs (Number), categorical predation times (TimeC), categorical dragonfly IDs (NumberC), damselfly survival percentages (SurvivalP), microplastics as a binomial variable (MP), deltamethrin as a binomial variable (Delta), and the damselflies' survival (SurvivalDam1, SurvivalDam2, and SurvivalDam2).
DOI: 10.6084/m9.figshare.20252870
title: Additional file 5 of Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

description: Additional file 5. “OgOb-all-merged.DPech.recode.vcf”. vcf file containing high coverage sequencing data from 250 African rice genomes 32.
DOI: 10.6084/m9.figshare.20252870.v1
title: Additional file 5 of Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

description: Additional file 5. “OgOb-all-merged.DPech.recode.vcf”. vcf file containing high coverage sequencing data from 250 African rice genomes 32.
DOI: 10.6084/m9.figshare.20252873
title: Additional file 6 of Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

description: Additional file 6. “Simulation-Code1a.R”. R code used to compare the mapping of a single QTL using different BSA statistics in three recombination models. The QTL was placed in the middle of the chromosome and sequencing noise was simulated according to a binomial distribution.
DOI: 10.6084/m9.figshare.20252876
title: Additional file 7 of Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

description: Additional file 7. “Simulation-Code1b.R”. R code used to compare the mapping of a single QTL using different BSA statistics in three recombination models. The QTL was placed in the middle of the chromosome and real data from rice were used in the simulation of sequencing noise.
DOI: 10.6084/m9.figshare.20252873.v1
title: Additional file 6 of Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

description: Additional file 6. “Simulation-Code1a.R”. R code used to compare the mapping of a single QTL using different BSA statistics in three recombination models. The QTL was placed in the middle of the chromosome and sequencing noise was simulated according to a binomial distribution.
DOI: 10.6084/m9.figshare.20252876.v1
title: Additional file 7 of Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

description: Additional file 7. “Simulation-Code1b.R”. R code used to compare the mapping of a single QTL using different BSA statistics in three recombination models. The QTL was placed in the middle of the chromosome and real data from rice were used in the simulation of sequencing noise.
DOI: 10.6084/m9.figshare.20252879
title: Additional file 8 of Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

description: Additional file 8. “Simulation-Code2a.R”. R code used to evaluate the accuracy of QTL detection using different BSA statistics. In this case we ran 1,000 simulations. Each loop defines a random QTL position. The absolute difference between the initial position of the QTL and the position retrieved with each method was used to compare the accuracy of the different methods. This code uses binomial distribution in the simulation of sequencing noise.
DOI: 10.6084/m9.figshare.20252879.v1
title: Additional file 8 of Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

description: Additional file 8. “Simulation-Code2a.R”. R code used to evaluate the accuracy of QTL detection using different BSA statistics. In this case we ran 1,000 simulations. Each loop defines a random QTL position. The absolute difference between the initial position of the QTL and the position retrieved with each method was used to compare the accuracy of the different methods. This code uses binomial distribution in the simulation of sequencing noise.
DOI: 10.6084/m9.figshare.20252882
title: Additional file 9 of Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

description: Additional file 9. “Simulation-Code2b.R”. R code used to evaluate the accuracy of QTL detection using different BSA statistics. In this case we ran 1,000 simulations. Each loop defines a random QTL position. The absolute difference between the initial position of the QTL and the position retrieved with each method was used to compare the accuracy of the different methods. This code uses rice real data in the simulation of sequencing noise.
DOI: 10.6084/m9.figshare.20252882.v1
title: Additional file 9 of Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

description: Additional file 9. “Simulation-Code2b.R”. R code used to evaluate the accuracy of QTL detection using different BSA statistics. In this case we ran 1,000 simulations. Each loop defines a random QTL position. The absolute difference between the initial position of the QTL and the position retrieved with each method was used to compare the accuracy of the different methods. This code uses rice real data in the simulation of sequencing noise.
DOI: 10.25646/10041
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.6084/m9.figshare.20252917
title: Additional file 2 of Induction of mastitis by cow-to-mouse fecal and milk microbiota transplantation causes microbiome dysbiosis and genomic functional perturbation in mice

description: Additional file 2. Information on metagenomic data, microbial structure, composition and relative abundances in each metagenome.
DOI: 10.6084/m9.figshare.20252917.v1
title: Additional file 2 of Induction of mastitis by cow-to-mouse fecal and milk microbiota transplantation causes microbiome dysbiosis and genomic functional perturbation in mice

description: Additional file 2. Information on metagenomic data, microbial structure, composition and relative abundances in each metagenome.
DOI: 10.25646/10028
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.6084/m9.figshare.20252959
title: Additional file 1 of Spatial transcriptomics atlas reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment components in colorectal cancer

description: Additional file 1. Figures S1–S3, Tables S1–S9.
DOI: 10.6084/m9.figshare.20252959.v1
title: Additional file 1 of Spatial transcriptomics atlas reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment components in colorectal cancer

description: Additional file 1. Figures S1–S3, Tables S1–S9.
DOI: 10.6084/m9.figshare.20252664.v1
title: supplementary files

description: supplementary files
DOI: 10.6084/m9.figshare.20252664
title: supplementary files

description: supplementary files
DOI: 10.5281/zenodo.6651643
title: "Determinants of football players' valuation: a systematic review" datasets

description: 3 interdependent tables are available and are the materials used for a systematic review of the determinants of football players' valuation. - model_specifications is a table where each row represents one of the 111 model specifications included in the systematic review. Characteristics of the article from which specification was retrieved (title, year, authors, journal), attributes of the specification (sample size, population, econometric modeling, etc.), the significance levels of included variables, and the associated coefficients for significant variables. - model_specifications_dictionnary precise the names of the columns of the table model_specifications. - variables_definitions_and_classification is a table that details all the 471 variables used in the 29 articles analysed with definitions quoted from articles when possible and presents a classification of these variables into 6 categories and several subcategories.
DOI: 10.6084/m9.figshare.20252964
title: multiple comparisons

description: supplementary files-multiple comparisons
DOI: 10.6084/m9.figshare.20252964.v1
title: multiple comparisons

description: supplementary files-multiple comparisons
DOI: 10.25646/9870
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.25646/10046
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.5281/zenodo.6801310
title: giaIndoorLoc – Auto-labeled WLAN + IMU dataset generated via VI-SLAM2tag

description: This repository holds the data that belongs to the publication: M. Laska, T. Schulz, J. Grottke, C. Blut and J. Blankenbach, "VI-SLAM2tag: Low-Effort Labeled Dataset Collection for Fingerprinting-Based Indoor Localization", [arXiv:2207.02668] which is to appear at the 2022 IPIN conference. It is split into the following sub-parts:<br> - giaIndoorLoc_raw: Raw data recorded via the VI-SLAM2tag android app (https://github.com/laskama/VI-SLAM2tag_app)<br> - giaIndoorLoc: Annotated dataset (generated from giaIndoorLoc_raw)<br> - evaluation_data: Raw trajectory data that is used during evaluation of labeling accuracy of VI-SLAM2tag (Control-Point + Total Station (Tachymeter))<br> - model_evaluation: Model weights of fitted models used during baseline performance section (VII-B) of paper. Required for reproducing experiments with repo (https://github.com/laskama/mCELindoorLoc) For a detailed description, please refer to the given paper and the additional github repositories that host the implementations: - https://github.com/laskama/VI-SLAM2tag_post - https://github.com/laskama/VI-SLAM2tag_app - https://github.com/laskama/mCELindoorLoc