cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6520465
title: Aquila spilogaster SPAdes preassembly

description: .
DOI: 10.6084/m9.figshare.13286942
title: Flower transcriptome of three Rhytidophyllum species (Gesneriaceae)

description: Floral transcriptome of three Rhytidophyllum species (Gesneriaceae): <i>Rhytidophyllum auriculatum</i>, <i>Rhytidophyllum rupincola</i>, and <i>Rhytidophyllum vernicosum</i>. Also provided are an annotation of transcripts, list of orthologous transcripts among the transcriptome, and results of selection analyses (dn/ds) with PAML. The raw sequence data has been archived as a sequence read archive at NCBI under project PRJNA732582.
DOI: 10.5281/zenodo.6323785
title: Data and Code for: Validating ATLAS: a regional-scale high-throughput tracking system

description: Data and code for the manuscript: <strong>Validating ATLAS: a regional-scale high-throughput tracking system</strong>
DOI: 10.5281/zenodo.4527613
title: Data and Code for: Validating ATLAS: a regional-scale high-throughput tracking system

description: Data and code for the manuscript: <strong>Validating ATLAS: a regional-scale high-throughput tracking system</strong>
DOI: 10.5281/zenodo.6521297
title: Dataset_TheoryOfChainWalkingCatalysis

description: This dataset contains processed data (data) and plotting scripts (plots) related to the paper:<br> "Theory of Chain Walking Catalysis: From Disordered Dendrimers to Dendritic Bottle-Brushes (2022)" Please consult the README.md and RUN.md in the zip.
description: This work was supported by the Deutsche Forschungsgemeinschaft (DFG) under Contract No. SO-277/13. We thank the Center for Information Services and High Performance Computing (ZIH) at TU Dresden for generous allocations of CPU and GPU time.
DOI: 10.5281/zenodo.6520205
title: SuperDARN data in netCDF format (2012-Jun)

description: 2012-Jun SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6519335
title: SuperDARN data in netCDF format (2012-Oct)

description: 2012-Oct SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6519517
title: SuperDARN data in netCDF format (2012-Sep)

description: 2012-Sep SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6519837
title: SuperDARN data in netCDF format (2012-Aug)

description: 2012-Aug SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6490377
title: SuperDARN data in netCDF format (2013-Jan)

description: 2013-Jan SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6520026
title: SuperDARN data in netCDF format (2012-Jul)

description: 2012-Jul SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6519247
title: SuperDARN data in netCDF format (2012-Nov)

description: 2012-Nov SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6519152
title: SuperDARN data in netCDF format (2012-Dec)

description: 2012-Dec SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.25678/0005ds
title: Data for: Host-associated bacterial communities vary between genotypes but not by host genetic distance in Daphnia galeata

description: Host genotype may shape host-associated bacterial communities (commonly referred to as microbiomes). We sought to determine (a) whether bacterial communities vary among host genotypes in the water flea Daphnia galeata and (b) if this difference is driven by the genetic distance between host genotypes, by using D. galeata genotypes hatched from sediments of different time periods. We used 16S amplicon sequencing to profile the gut and body bacterial communities of eight D. galeata genotypes hatched from resting eggs; these were isolated from two distinct sediment layers (dating to 1989 and 2009) of a single sediment core of the lake Greifensee, and maintained in a common garden in laboratory cultures for five years. In general, bacterial community composition varied in both the Daphnia guts and bodies; but not between genotypes from different sediment layers. Specifically, genetic distances between host genotypes did not correlate with beta diversity of bacterial communities in Daphnia guts and bodies. Our results indicate that Daphnia bacterial community structure is to some extent determined by a host genetic component, but that genetic distances between hosts do not correlate with diverging bacterial communities.
DOI: 10.34740/kaggle/dsv/3581215
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.6521603
title: Analysed data for poster presentation "Dielectric barrier discharge plasma reduction of iron oxides"

description: Native surface oxides, which usually contaminate metals in ambient atmosphere, limit the performance in industrial applications like coating or welding. Different deoxidation techniques were developed to remove such oxide layers. Promising results were obtained for dielectic barrier discharge (DBD) hydrogen plasmas in deoxidation of copper at 20 °C and 100 – 1000 hPa. In this study, we report the application of a DBD plasma for deoxidation of natively oxidized iron surfaces in an Ar/H2 atmosphere at 100 and 1000 hPa. Plasma treatments were performed at a discharge voltage of 10 kV and a frequency of 8.8 kHz at room temperature (RT) as well as in the combination with heating<br>
DOI: 10.5281/zenodo.6521602
title: Analysed data for poster presentation "Dielectric barrier discharge plasma reduction of iron oxides"

description: Native surface oxides, which usually contaminate metals in ambient atmosphere, limit the performance in industrial applications like coating or welding. Different deoxidation techniques were developed to remove such oxide layers. Promising results were obtained for dielectic barrier discharge (DBD) hydrogen plasmas in deoxidation of copper at 20 °C and 100 – 1000 hPa. In this study, we report the application of a DBD plasma for deoxidation of natively oxidized iron surfaces in an Ar/H2 atmosphere at 100 and 1000 hPa. Plasma treatments were performed at a discharge voltage of 10 kV and a frequency of 8.8 kHz at room temperature (RT) as well as in the combination with heating<br>
DOI: 10.6084/m9.figshare.19714216
title: Regression_data.csv

description: Questionnaire data. Variables: Engagement with peer review tasks, Planning behaviours, Perceptions of change over the year, Perceptions of improvement over the year, Perceptions of value of the peer review process, Language proficiency and Behaviours associated with being a ‘conscientious’ student
DOI: 10.5281/zenodo.4531394
title: Smart Home Automation Survey

description: Dataset of smart home automation scenarios gather from a survey with 20 participants.
DOI: 10.5281/zenodo.16706
title: CVS-Vintage: A dataset of 14 CVS repositories of java software

description: CVS-Vintage is a dataset of 14 CVS repositories of Java applications. This dataset aims at supporting the replication of early papers in the field of software evolution and mining software repositories. By building this dataset, we saved some CVS repositories from a probable death by deletion. <pre>@techreport{monperrus:hal-00769121, title = {CVS-Vintage: A Dataset of 14 CVS Repositories of Java Software}, author = {Monperrus, Martin and Martinez, Matias}, url = {https://hal.archives-ouvertes.fr/hal-00769121/file/paper.pdf}, year = {2012}, number = {hal-00769121}, institution = {HAL}, }</pre> A Git version is available at https://github.com/monperrus/cvs-vintage/
description: {"references": ["Martin Monperrus, Matias Martinez. CVS-Vintage: A Dataset of 14 CVS Repositories of Java Software. 2012. "]}
DOI: 10.14278/rodare.1558
title: Data Publication: Protein Enrichment by Foam Fractionation: Experiment and Modeling

description: This work investigates the enrichment of bovine serum albumin (BSA) protein through foam fractionation. Here, we performed experiments using BSA and measured the recovery and grade of the extract. Additionally, an unsteady-state simulation of the protein foam fractionation process was carried out by numerically solving the liquid drainage equation in the foam. Thereby, the extracted liquid volume and protein concentration were calculated. Required quantities such as foam stability, interface coverage or bubble size distribution were measured in corresponding experiments and were fed into the model. The experiments showed that the foam coalescence accelerates the liquid drainage leading to dryer extract and higher protein enrichment. The modeling also reproduced the liquid recovery and extract concentration of the foam fractionation tests within a reasonable error range. The modeling solely relies on experimental inputs and does not require any tuning parameters. It can be further used for optimization or up-scaling of protein foam fractionation.
DOI: 10.14278/rodare.1557
title: Data Publication: Protein Enrichment by Foam Fractionation: Experiment and Modeling

description: This work investigates the enrichment of bovine serum albumin (BSA) protein through foam fractionation. Here, we performed experiments using BSA and measured the recovery and grade of the extract. Additionally, an unsteady-state simulation of the protein foam fractionation process was carried out by numerically solving the liquid drainage equation in the foam. Thereby, the extracted liquid volume and protein concentration were calculated. Required quantities such as foam stability, interface coverage or bubble size distribution were measured in corresponding experiments and were fed into the model. The experiments showed that the foam coalescence accelerates the liquid drainage leading to dryer extract and higher protein enrichment. The modeling also reproduced the liquid recovery and extract concentration of the foam fractionation tests within a reasonable error range. The modeling solely relies on experimental inputs and does not require any tuning parameters. It can be further used for optimization or up-scaling of protein foam fractionation.
DOI: 10.25678/0005kz
title: Dataset: Daphnia galeata genomic variants - resurrected clones from Greifensee

description: This data package contains a variant call format file (vcf) of single nucleotide polymorphisms (SNPs) for *Daphnia galeata* clones resurrected from sediment cores from Lake Greifensee. See README.md for more information
DOI: 10.34740/kaggle/dsv/3581344
title: WallStreetBets 2022
DOI: 10.5281/zenodo.6521774
title: Example data for PlotsOfData

description: Example data for PlotsOfData
DOI: 10.5281/zenodo.6521773
title: Example data for PlotsOfData

description: Example data for PlotsOfData
DOI: 10.34740/kaggle/dsv/3581143
title: Data for HR Analytics
DOI: 10.5281/zenodo.6520412
title: Solutions of the mass continuity equation in hollow fibers for fully developed flow with some notes on the Lévêque correlation

description: <strong>Solutions of the mass continuity equation in hollow fibers for fully developed flow with some notes on the </strong><strong>Lévêque</strong><strong> correlation</strong> The file includes: Appendix A. Code in SageMath and Maple Appendix B. Semi-analytical method of lines for solving the linear-BC case The complete scientific article that can be found at https://zenodo.org/record/5675723#.YhNn8JPMIeY
DOI: 10.5281/zenodo.6520411
title: Solutions of the mass continuity equation in hollow fibers for fully developed flow with some notes on the Lévêque correlation

description: <strong>Solutions of the mass continuity equation in hollow fibers for fully developed flow with some notes on the </strong><strong>Lévêque</strong><strong> correlation</strong> The file includes: Appendix A. Code in SageMath and Maple Appendix B. Semi-analytical method of lines for solving the linear-BC case The complete scientific article that can be found at https://zenodo.org/record/5675723#.YhNn8JPMIeY
DOI: 10.5281/zenodo.6517107
title: Dataset for 'Experimental Quantification of Gas Dispersion in 3D-Printed Logpile Structures Using a Noninvasive Infrared Transmission Technique'

description: This dataset contains the infrared images of tracer flow that were taken in the investigations of transverse dispersion in 3D-printed logpile structures. Accompanying the files (which are labelled according to the convention of the camera software) is a Python script which can be used to link the images to the operating conditions at which they were obtained. Documentation of this script can be found in the file at the very top. <br> This dataset was used as basis for the journal article 'Experimental Quantification of Gas Dispersion in 3D-Printed Logpile Structures Using a Noninvasive Infrared Transmission Technique', published in ACS Engineering Au under DOI:10.1021/acsengineeringau.1c00040. This paper can also be found in this repository at https://zenodo.org/record/6517082
DOI: 10.5281/zenodo.6517106
title: Dataset for 'Experimental Quantification of Gas Dispersion in 3D-Printed Logpile Structures Using a Noninvasive Infrared Transmission Technique'

description: This dataset contains the infrared images of tracer flow that were taken in the investigations of transverse dispersion in 3D-printed logpile structures. Accompanying the files (which are labelled according to the convention of the camera software) is a Python script which can be used to link the images to the operating conditions at which they were obtained. Documentation of this script can be found in the file at the very top. <br> This dataset was used as basis for the journal article 'Experimental Quantification of Gas Dispersion in 3D-Printed Logpile Structures Using a Noninvasive Infrared Transmission Technique', published in ACS Engineering Au under DOI:10.1021/acsengineeringau.1c00040. This paper can also be found in this repository at https://zenodo.org/record/6517082