cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25646/7494
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: 10.25646/7552
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: 10.25646/7558
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: 10.25646/7563
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: 10.25646/7559
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: 10.25646/7537
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: 10.25646/7569
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: 10.25646/7572
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.12600440.v1
title: Raw Data

description: - Three datasets of one structure parameters for all specimens included in the analysis: whole humerus, whole vertebra, and vertebral centrum (individual CSV files).<br>- Lifestyle classification for all extant mammal genera (CSV file).<br>- Timetree modified from TimeTree.org to match our sampling (see also Supplementary Online Material).<br>
DOI: 10.6084/m9.figshare.12600440.v2
title: Raw Data

description: - Three datasets of one structure parameters for all specimens included in the analysis: whole humerus, whole vertebra, and vertebral centrum (individual CSV files).Abbreviations:
DOI: 10.17632/bvbzbh65nz.1
title: Data for: Evolution of the Indochina Block from its formation to amalgamation with Asia: constraints from protoliths in the Kontum Massif, Vietnam

description: Chemical and isotopic data for metamorphic rocks in the Kontum Massif
DOI: 10.5281/zenodo.4268341
title: Automated design of synthetic microbial communities

description: In naturally occurring microbial systems, species rarely exist in isolation. There is strong ecological evidence for a positive relationship between species diversity and the functional output of communities. The pervasiveness of these communities in nature highlights that there may be advantages for engineered strains to exist in cocultures as well. Building synthetic microbial communities allows us to create distributed systems that mitigates issues often found in engineering a monoculture, especially when functional complexity is increasing. Here, we demonstrate a methodology for designing robust synthetic communities that use quorum sensing to control amensal bacteriocin interactions in a chemostat environment. We explore model spaces for two and three strain systems, using Bayesian methods to perform model selection, and identify the most robust candidates for producing stable steady state communities. Our findings highlight important interaction motifs that provide stability, and identify requirements for selecting genetic parts and tuning the community composition.
DOI: 10.5281/zenodo.4268347
title: High density SNP genotypes (Infinium Human CytoSNP-850K v1.2 BeadChip) of ERAP2-WT and ERAP2-KO Birdshot LCL

description: SNP genotype data was performed on DNA isolated from WT and CRISPR-Cas9 edited LCLs (ERAP2-KO) according to standard procedures using the Infinium Human CytoSNP-850K v1.2 BeadChip (Illumina, San Diego, CA, USA). SNP-array results and data analysis were carried out using NxClinical software v5.1 (BioDiscovery, Los Angeles, CA, USA). Human genome build Feb. 2009 GRCh37/hg19 was used.
DOI: 10.5281/zenodo.4268346
title: High density SNP genotypes (Infinium Human CytoSNP-850K v1.2 BeadChip) of ERAP2-WT and ERAP2-KO Birdshot LCL

description: SNP genotype data was performed on DNA isolated from WT and CRISPR-Cas9 edited LCLs (ERAP2-KO) according to standard procedures using the Infinium Human CytoSNP-850K v1.2 BeadChip (Illumina, San Diego, CA, USA). SNP-array results and data analysis were carried out using NxClinical software v5.1 (BioDiscovery, Los Angeles, CA, USA). Human genome build Feb. 2009 GRCh37/hg19 was used.
DOI: 10.5281/zenodo.4266229
title: Body measurements of human adults, with repeated readings

description: This data file contains body measurements of participants of a course in morphometrics. The data are entirely anonymized and the compliance for publication of the data in the present form was obtained from each person. The participants were asked to measure themselves twice at intervals of one or two days, which resulted in reading a and reading b. The following measurements were taken (in mm): arm.l -&gt; arm length<br> bod.h -&gt; total body height<br> foo.l -&gt; foot length<br> hea.c -&gt; head circumference<br> leg.l -&gt; leg length The dataset may be considered for demonstration of measurement error.
DOI: 10.5281/zenodo.4266228
title: Body measurements of human adults, with repeated readings

description: This data file contains body measurements of participants of a course in morphometrics. The data are entirely anonymized and the compliance for publication of the data in the present form was obtained from each person. The participants were asked to measure themselves twice at intervals of one or two days, which resulted in reading a and reading b. The following measurements were taken (in mm): arm.l -&gt; arm length<br> bod.h -&gt; total body height<br> foo.l -&gt; foot length<br> hea.c -&gt; head circumference<br> leg.l -&gt; leg length The dataset may be considered for demonstration of measurement error.
DOI: 10.2905/f97e4216-e81c-4dfb-b42d-d0c19070d029
title: Potential biophysical effects of vegetation cover change

description: Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. This dataset provides such estimations of potential local changes of the surface energy balance for multiple vegetation transitions derived from satellite remote sensing products.
DOI: 10.6084/m9.figshare.12635837.v1
title: Data from: Higher temperatures worsen the effects of mutations on protein stability

description: <b>CONTENTS OF THIS DATASET</b><br><b>1) ADK_tree.phy:</b> The ADK gene tree reconstructed for this study.<br><br><b>2) calibrated_species_tree.phy:</b> The relative time-calibrated phylogeny reconstructed for this study.<br><br><b>3) ddG_vs_Temp_interspecific.csv:</b> Weighted median ΔΔG estimates and temperature values for each of the 70 prokaryotic ADKs.<br><br><b>4) ddG_vs_Temp_intraspecific.csv:</b> Median ΔΔG estimates and temperature values for the 6 selected prokaryotic ADKs.<br><b>5) ln_K_t_gen_vs_Temp_and_Cell_Volume.csv:</b> Generation time-corrected substitution rate estimates (using a natural logarithm transformation), minimum/maximum cell volume estimates, and temperature values.
DOI: 10.6084/m9.figshare.12525701
title: Results for Report on Evaluation Experiments Using Different Machine Learning Techniques for Defect Prediction

description: Preprocessed dataset for software defect prediction using process metrics.Result of application of software defect prediction on these data presented in the Paper "Report on Evaluation Experiments Using Different Machine Learning Techniques for Defect Prediction"
DOI: 10.6084/m9.figshare.12525701.v1
title: Results for Report on Evaluation Experiments Using Different Machine Learning Techniques for Defect Prediction

description: Preprocessed dataset for software defect prediction using process metrics.Result of application of software defect prediction on these data presented in the Paper "Report on Evaluation Experiments Using Different Machine Learning Techniques for Defect Prediction"
DOI: 10.6073/pasta/50e2f7b297046aaf01b77b46a011b6da
title: North Temperate Lakes LTER Pelagic Macroinvertebrate Abundance 1983 - current

description: Five vertical tows are done at the deepest point of each of the seven primary lakes in the Trout Lake area (Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes and bog lakes 27-02 [Crystal Bog], and 12-15 [Trout Bog]) using a 1-mm mesh net with a 1-m wide mouth. On Trout Lake four additional sites are sampled, where depths are approximately at 10 m, 15 m, 20 m, and 25 m respectively, with three tows done at each site. Trout Lake was the only lake sampled in 2020. All samples are taken in darkness. Samples are preserved and counted, yielding numbers caught. These night tows target the large invertebrate planktivore component of the pelagic zooplankton community. Sampling Frequency: annually Number of sites: 11
DOI: 10.5281/zenodo.4268345
title: Environmental Data of Geneva in Hectometric cells

description: The database is built using the Landsat dataset, which enabled the characterisation of inhabited Hectometric cells of Geneva. Bands 4 (R), 5 (NIR) and 11 (thermal) from Landsat 8. images taken on 26.06.2018 were used in this short paper. Bands 11 covers a wavelength from 11.50 to 12.51 micrometers, has a 100m resolution and is considered as an improved thermal mapping. Bands 4 (R) covers a wavelength from 0.64 to 0.67 micrometers and is a discrimination of the vegetation slopes. Regarding Bands 5 (NIR) puts an emphasis on the biomass content and shorelines and covers a wavelength from 0.85 to 0.88 micrometers. Moreover, a Hectometric grid of inhabited areas of Geneva is used. This grid is obtained from STATPOP 2016 and has a resolution of 100m by 100m. Each cell is later characterised with NDVI and thermal values, as described in the methods section.
DOI: 10.5281/zenodo.4268344
title: Environmental Data of Geneva in Hectometric cells

description: The database is built using the Landsat dataset, which enabled the characterisation of inhabited Hectometric cells of Geneva. Bands 4 (R), 5 (NIR) and 11 (thermal) from Landsat 8. images taken on 26.06.2018 were used in this short paper. Bands 11 covers a wavelength from 11.50 to 12.51 micrometers, has a 100m resolution and is considered as an improved thermal mapping. Bands 4 (R) covers a wavelength from 0.64 to 0.67 micrometers and is a discrimination of the vegetation slopes. Regarding Bands 5 (NIR) puts an emphasis on the biomass content and shorelines and covers a wavelength from 0.85 to 0.88 micrometers. Moreover, a Hectometric grid of inhabited areas of Geneva is used. This grid is obtained from STATPOP 2016 and has a resolution of 100m by 100m. Each cell is later characterised with NDVI and thermal values, as described in the methods section.
DOI: 10.34740/kaggle/dsv/1637562
title: Turkey Covid-19 Dataset
DOI: 10.6084/m9.figshare.13089434
title: Data: erp and dip files from cue visual task

description: Data from 31 subjects (15 ADHD and 16 TDC) recorded with ASA EEG/ERP System (ANT Software, the Netherlands) with 128 Ag/AgCI sintered ring electrodes embedded in a active-shield cap (10-20 system) and shielded co-axial cables. Off-line data treatment with EEGlab software (Delorme & Makeig, 2004) and in house MATLAB-based tools (Cheron et al. 2014). More detailed available in related study undersubmission. Wait for publication.<br>
DOI: 10.34740/kaggle/dsv/1637590
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5517/ccdc.csd.cc24q49b
title: CCDC 1987481: Experimental Crystal Structure Determination

description: Related Article: Thirupathi Gogula, Jinquan Zhang, Madhava Reddy Lonka, Shuaizhong Zhang, Hongbin Zou|2020|Chemical Science|11|11461|doi:10.1039/D0SC02328K
DOI: 10.5517/ccdc.csd.cc24q4bc
title: CCDC 1987482: Experimental Crystal Structure Determination

description: Related Article: Thirupathi Gogula, Jinquan Zhang, Madhava Reddy Lonka, Shuaizhong Zhang, Hongbin Zou|2020|Chemical Science|11|11461|doi:10.1039/D0SC02328K
DOI: 10.5517/ccdc.csd.cc24q4cd
title: CCDC 1987483: Experimental Crystal Structure Determination

description: Related Article: Thirupathi Gogula, Jinquan Zhang, Madhava Reddy Lonka, Shuaizhong Zhang, Hongbin Zou|2020|Chemical Science|11|11461|doi:10.1039/D0SC02328K
DOI: 10.5517/ccdc.csd.cc24q4df
title: CCDC 1987484: Experimental Crystal Structure Determination

description: Related Article: Thirupathi Gogula, Jinquan Zhang, Madhava Reddy Lonka, Shuaizhong Zhang, Hongbin Zou|2020|Chemical Science|11|11461|doi:10.1039/D0SC02328K
DOI: 10.5517/ccdc.csd.cc25gsct
title: CCDC 2010206: Experimental Crystal Structure Determination

description: Related Article: Fang Wang, Austin T. Cannon, Moumita Bhattacharya, Robert Baumgarten, Ryan T. VanderLinden, Caroline T. Saouma|2020|Chem.Commun.|56|12142|doi:10.1039/D0CC04310A
DOI: 10.5517/ccdc.csd.cc25hjz6
title: CCDC 2010938: Experimental Crystal Structure Determination

description: Related Article: Raquel Mato, Efraim Reyes, Luisa Carrillo, Uxue Uria, Liher Prieto, Ruben Manzano, Jose L. Vicario|2020|Chem.Commun.|56|13149|doi:10.1039/D0CC05981A
DOI: 10.5517/ccdc.csd.cc25hk08
title: CCDC 2010939: Experimental Crystal Structure Determination

description: Related Article: Raquel Mato, Efraim Reyes, Luisa Carrillo, Uxue Uria, Liher Prieto, Ruben Manzano, Jose L. Vicario|2020|Chem.Commun.|56|13149|doi:10.1039/D0CC05981A
DOI: 10.5517/ccdc.csd.cc25jqbs
title: CCDC 2012065: Experimental Crystal Structure Determination

description: Related Article: John J. Kiernicki, Emily E. Norwine, Myles A. Lovasz, Matthias Zeller, Nathaniel K. Szymczak|2020|Chem.Commun.|56|13105|doi:10.1039/D0CC05121G
DOI: 10.5517/ccdc.csd.cc25jqdv
title: CCDC 2012067: Experimental Crystal Structure Determination

description: Related Article: John J. Kiernicki, Emily E. Norwine, Myles A. Lovasz, Matthias Zeller, Nathaniel K. Szymczak|2020|Chem.Commun.|56|13105|doi:10.1039/D0CC05121G
DOI: 10.5517/ccdc.csd.cc25jqgx
title: CCDC 2012069: Experimental Crystal Structure Determination

description: Related Article: John J. Kiernicki, Emily E. Norwine, Myles A. Lovasz, Matthias Zeller, Nathaniel K. Szymczak|2020|Chem.Commun.|56|13105|doi:10.1039/D0CC05121G
DOI: 10.5517/ccdc.csd.cc25jqm2
title: CCDC 2012074: Experimental Crystal Structure Determination

description: Related Article: John J. Kiernicki, Emily E. Norwine, Myles A. Lovasz, Matthias Zeller, Nathaniel K. Szymczak|2020|Chem.Commun.|56|13105|doi:10.1039/D0CC05121G
DOI: 10.5517/ccdc.csd.cc25sj9t
title: CCDC 2019566: Experimental Crystal Structure Determination

description: Related Article: Jin-Tao Ma, Ying Cheng|2020|Org.Chem.Front.|7|3459|doi:10.1039/D0QO00877J
DOI: 10.5281/zenodo.4016916
title: EMUE-D4-3-QuantifyUncertaintiesInCalibration

description: Calibration of a sonic nozzle as an example for quantifying all uncertainties involved in straight-line regression When calibrating a sonic nozzle, it is recommended to estimate the straight-line relationship between the discharge coefficient of the nozzle and the square root of the inverse Reynolds number for a gas. The slope and intercept of this relation characterize the nozzle, and reliable estimates and uncertainties for this multivariate measurand are mandatory for its use as transfer or working standard. This example emphasizes the importance of accounting for correlation for a reliable uncertainty evaluation. The use of common reference standards and instruments causes correlation among and between the discharge coefficient and the Reynolds number, and impacts significantly on the uncertainty of the characteristic parameters of the nozzle. To show this, a measurement model based on the weighted total least-squares (WTLS) method is applied and its input quantities are fully characterized. In particular, we demonstrate in detail how to jointly evaluate the correlation, uncertainties and estimates for the input quantities of least-squares methods applying the Monte Carlo method. As a result, sonic nozzles can be characterized in line with the Guide to the expression of uncertainty in measurement (GUM). Files contained in the dataset are: Example_A223.pdf: Report “Calibration of a sonic nozzle as an example for quantifying all uncertainties involved in straight-line regression”; Example_A223.tex: LaTeX file to be compiled in order to produce Example_A223.pdf; Compendium_A223.bib: bibliography file; Fig1.pdf: PDF file needed to compile Example_A223.tex; Fig2.pdf: PDF file needed to compile Example_A223.tex; Fig2.png: PNG file needed to compile Example_A223.Rmd; Fig3a.pdf: PDF file needed to compile Example_A233.tex; Fig3b.pdf: PDF file needed to compile Example_A233.tex; Fig4.pdf: PDF file needed to compile Example_A223.tex; Fig5a.pdf: PDF file needed to compile Example_A223.tex; Fig5b.pdf: PDF file needed to compile Example_A223.tex; Fig6a.pdf: PDF file needed to compile Example_A223.tex; Fig6a.png: PNG file needed to compile Example_A223.Rmd; Fig6b.pdf: PDF file needed to compile Example_A223.tex; Fig6b.png: PNG file needed to compile Example_A223.Rmd; data_A223.csv: data file containing the estimates and associated standard uncertainties for the measurements; MC_data_A223.xslx: excel file containing the estimates and associated covariance matrix for the intermediate measurands; Example_A223.Rmd: R Markdown file for quantifying all uncertainties involved in straight-line regression. As output an HTML file similar to Example_A223.pdf is created with results dynamically produced by underlying R code; ieee.csl: CSL file needed to cite the reference in example_A223.Rmd according to the Institute of Electrical and Electronics Engineers style.