cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6369654
title: SuperDARN data in netCDF format (2017-May)

description: 2017-May 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.6363429
title: SuperDARN data in netCDF format (2017-Jul)

description: 2017-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.6347056
title: SuperDARN data in netCDF format (2017-Aug)

description: 2017-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.6348016
title: SuperDARN data in netCDF format (2016-Dec)

description: 2016-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: 10.5281/zenodo.6344019
title: SuperDARN data in netCDF format (2016-Nov)

description: 2016-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: 10.34820/fk2/1pic9a/unnyx0
title: listapps_playstore_exportumkm.xlsx

description: dataset of Analysis of Profile Small and Medium Enterprise (SME) Software from Playstore
DOI: 10.34820/fk2/1pic9a
title: Analysis of Profile Small and Medium Enterprise (SME) Software from Playstore

description: This dataset comes from the results of crawling data from the Playstore application about the analysis profile of Small and Medium Enterprises (SMEs) software from the Playstore. The dataset contains data on the name of the SME application, rating, number of reviews, number of installs, capacity, url, and description of the results of the profile analysis of the SME application.
DOI: 10.34740/kaggle/dsv/3340987
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.6368041
title: Tubulanus cf. lutescens

description: Costa Rican deep-sea palaeonemertean, SIO-BIC N233 Cross sections of anterior part, serial sections, 5µm, Azan
DOI: 10.5281/zenodo.6368040
title: Tubulanus cf. lutescens

description: Costa Rican deep-sea palaeonemertean, SIO-BIC N233 Cross sections of anterior part, serial sections, 5µm, Azan
DOI: 10.25966/7123-ab12
title: Companion dataset for "On the North-South Asymmetry of Co-seismic Ionospheric Disturbances During the 16 September 2015 Illapel M8.3 Earthquake"
DOI: 10.6084/m9.figshare.17825273.v1
title: Data of trajectory optimization of dual-arm coordination based on arm angle constraint

description: Data of trajectory optimization of dual-arm coordination based on arm angle constraint
DOI: 10.6084/m9.figshare.17825273.v2
title: Data of trajectory optimization of dual-arm coordination based on arm angle constraint

description: Data of trajectory optimization of dual-arm coordination based on arm angle constraint
DOI: 10.6084/m9.figshare.18857654.v4
title: Data from: Sociality and body size determine potential and realized bee foraging ranges

description: This dataset consists of 432 foraging range measurements for 101 bee species. 390 measurements are classifiable into typical or maximum foraging ranges, which are presented and analysed in relation to body size, sociality and measurement type in Kendall et al. (2022). 42 additional measurements, which were considered unclassifiable and not formally analysed, are included here out of general interest. A reference list for all range measurements and species' life history trait information is also included.<br>All R code for conducting our analyses is also provided.
DOI: 10.3886/e165661
title: Data for: Inequality in Early Care Experienced by U.S. Children

description: PSID data used in "Inequality in Early Care Experienced by U.S. Children"<br>
DOI: 10.3886/e165661v1
title: Data for: Inequality in Early Care Experienced by U.S. Children

description: PSID data used in "Inequality in Early Care Experienced by U.S. Children"<br>
DOI: 10.5281/zenodo.6376141
title: SciExpeM - 10.5281/zenodo.6376141

description: Stranic, Ivo; Chase, Deanna P.; Harmon, Joseph T.; Yang, Sheng; Davidson, David F.; Hanson, Ronald K. - Combustion And Flame, 2012, (159), 516-527
DOI: 10.5281/zenodo.6376140
title: SciExpeM - 10.5281/zenodo.6376141

description: Stranic, Ivo; Chase, Deanna P.; Harmon, Joseph T.; Yang, Sheng; Davidson, David F.; Hanson, Ronald K. - Combustion And Flame, 2012, (159), 516-527
DOI: 10.6084/m9.figshare.19398014
title: Raw data of 287 cities.xlsx

description: health economic
DOI: 10.6084/m9.figshare.19398014.v1
title: Raw data of 287 cities.xlsx

description: health economic
DOI: 10.5281/zenodo.6376153
title: SciExpeM - 10.5281/zenodo.6376153

description: Stranic, Ivo; Chase, Deanna P.; Harmon, Joseph T.; Yang, Sheng; Davidson, David F.; Hanson, Ronald K. - Combustion And Flame, 2012, (159), 516-527
DOI: 10.5281/zenodo.6376152
title: SciExpeM - 10.5281/zenodo.6376153

description: Stranic, Ivo; Chase, Deanna P.; Harmon, Joseph T.; Yang, Sheng; Davidson, David F.; Hanson, Ronald K. - Combustion And Flame, 2012, (159), 516-527
DOI: 10.5281/zenodo.6360825
title: Using social media and personality traits to assess software developers' emotions

description: Companion DATA of the paper "Using social media and personality traits to assess software developers’ emotions" submitted to the IEEE Access journal, 2022. The folders contain: <br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications <br> /dataset<br> alldata.json: contains the dataset used in the paper <br> /notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results Statistics - Polynomial Regression: notebook file with the polynomial regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period <br> /surveys<br> Demographic_Survey.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI <br> ------------------------------------------------------------ <br> We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.
DOI: 10.5281/zenodo.6360824
title: Using social media and personality traits to assess software developers' emotions

description: Companion DATA of the paper "Using social media and personality traits to assess software developers’ emotions" submitted to the IEEE Access journal, 2022. The folders contain: <br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications <br> /dataset<br> alldata.json: contains the dataset used in the paper <br> /notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results Statistics - Polynomial Regression: notebook file with the polynomial regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period <br> /surveys<br> Demographic_Survey.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI <br> ------------------------------------------------------------ <br> We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.
DOI: 10.5281/zenodo.6376156
title: SciExpeM - 10.5281/zenodo.6376156

description: Stranic, Ivo; Chase, Deanna P.; Harmon, Joseph T.; Yang, Sheng; Davidson, David F.; Hanson, Ronald K. - Combustion And Flame, 2012, (159), 516-527
DOI: 10.5281/zenodo.6376155
title: SciExpeM - 10.5281/zenodo.6376156

description: Stranic, Ivo; Chase, Deanna P.; Harmon, Joseph T.; Yang, Sheng; Davidson, David F.; Hanson, Ronald K. - Combustion And Flame, 2012, (159), 516-527
DOI: 10.5281/zenodo.6376158
title: SciExpeM - 10.5281/zenodo.6376158

description: Stranic, Ivo; Chase, Deanna P.; Harmon, Joseph T.; Yang, Sheng; Davidson, David F.; Hanson, Ronald K. - Combustion And Flame, 2012, (159), 516-527
DOI: 10.5281/zenodo.6376157
title: SciExpeM - 10.5281/zenodo.6376158

description: Stranic, Ivo; Chase, Deanna P.; Harmon, Joseph T.; Yang, Sheng; Davidson, David F.; Hanson, Ronald K. - Combustion And Flame, 2012, (159), 516-527
DOI: 10.25403/upresearchdata.13022435
title: Decentralised cognitive radio networks

description: Python code used for the belief propagation algorithm and simulation in a CRN environment. Contains the core code used to generate the results used in the master's dissertation. The file "mc_results_learned.py" contains all the high-level testing code. The folder "decentralised_crn" contains the utilities for simulating the crn. The folder "factor_graph" contains the implementation of the belief propagation algorithm. The results datasets and image generation code used for all results in the dissertation. The datasets are stored as Python pickle data objects, and can be parsed into data structures using the code "results_plot.py". All the images are stored as LaTeX .pgf format.<br>The flow diagram of the algorithm that is the core output of the research project. The key results from the master's dissertation. A monte carlo simulation was performed over the maliciousness and proportion of agents introduced to a cognitive radio network. The spectrum sensing accuracy of the proposed algorithm was evaluated against a control, and was shown to be much more effective than the control in mitigating these attacks. The malicious user detection performance, algorithm learning speed, number of iterations to convergence, and effect of increasing trusted users on the network was also analysed.<br>The mathematical model underpinning the research into the secure sensing algorithm. Belief propagation is applied on this model to generate messages to marginalise the joint distribution represented by this graph. The subgraphs, along with annotated messages, are also attached.
DOI: 10.25403/upresearchdata.13022435.v2
title: Decentralised cognitive radio networks

description: Python code used for the belief propagation algorithm and simulation in a CRN environment. Contains the core code used to generate the results used in the master's dissertation. The file "mc_results_learned.py" contains all the high-level testing code. The folder "decentralised_crn" contains the utilities for simulating the crn. The folder "factor_graph" contains the implementation of the belief propagation algorithm. The results datasets and image generation code used for all results in the dissertation. The datasets are stored as Python pickle data objects, and can be parsed into data structures using the code "results_plot.py". All the images are stored as LaTeX .pgf format.<br>The flow diagram of the algorithm that is the core output of the research project. The key results from the master's dissertation. A monte carlo simulation was performed over the maliciousness and proportion of agents introduced to a cognitive radio network. The spectrum sensing accuracy of the proposed algorithm was evaluated against a control, and was shown to be much more effective than the control in mitigating these attacks. The malicious user detection performance, algorithm learning speed, number of iterations to convergence, and effect of increasing trusted users on the network was also analysed.<br>The mathematical model underpinning the research into the secure sensing algorithm. Belief propagation is applied on this model to generate messages to marginalise the joint distribution represented by this graph. The subgraphs, along with annotated messages, are also attached.
DOI: 10.5281/zenodo.6376164
title: SciExpeM - 10.5281/zenodo.6376164

description: Stranic, Ivo; Chase, Deanna P.; Harmon, Joseph T.; Yang, Sheng; Davidson, David F.; Hanson, Ronald K. - Combustion And Flame, 2012, (159), 516-527
DOI: 10.5281/zenodo.6376163
title: SciExpeM - 10.5281/zenodo.6376164

description: Stranic, Ivo; Chase, Deanna P.; Harmon, Joseph T.; Yang, Sheng; Davidson, David F.; Hanson, Ronald K. - Combustion And Flame, 2012, (159), 516-527
DOI: 10.6084/m9.figshare.19397948
title: Single-cell sequencing of shrimp

description: single-cell sequencing in shrimp<br>
DOI: 10.5281/zenodo.6376169
title: SciExpeM - 10.5281/zenodo.6376169

description: Stranic, Ivo; Chase, Deanna P.; Harmon, Joseph T.; Yang, Sheng; Davidson, David F.; Hanson, Ronald K. - Combustion And Flame, 2012, (159), 516-527
DOI: 10.5281/zenodo.6376168
title: SciExpeM - 10.5281/zenodo.6376169

description: Stranic, Ivo; Chase, Deanna P.; Harmon, Joseph T.; Yang, Sheng; Davidson, David F.; Hanson, Ronald K. - Combustion And Flame, 2012, (159), 516-527
DOI: 10.6084/m9.figshare.19397861
title: Supplementary table 2-1.xlsx

description: The raw dates are the supplementary of manuscript titled '<b>Screening novel vaccine candidates for Leishmania donovani by combining differential proteomics and immunoinformatics analysis</b>.' MHC Ⅱbinding predictions include supplementary table 2-1,2-2 and 2-3. The average percentages of 27 allele binding peptides are shown in table 2-1 sheet: MHC II percentage. The raw dates of MHC Ⅱbinding predictions of LeIF, LmST1, TSA, Leish-111f and different expression proteins are shown in the corresponding sheets in supplementary table 2-1, 2-2 and 2-3.
DOI: 10.6084/m9.figshare.19397861.v1
title: Supplementary table 2-1.xlsx

description: The raw dates are the supplementary of manuscript titled '<b>Screening novel vaccine candidates for Leishmania donovani by combining differential proteomics and immunoinformatics analysis</b>.' MHC Ⅱbinding predictions include supplementary table 2-1,2-2 and 2-3. The average percentages of 27 allele binding peptides are shown in table 2-1 sheet: MHC II percentage. The raw dates of MHC Ⅱbinding predictions of LeIF, LmST1, TSA, Leish-111f and different expression proteins are shown in the corresponding sheets in supplementary table 2-1, 2-2 and 2-3.
DOI: 10.5281/zenodo.6364704
title: Teaser Local Climate Zone maps extracted from the global map of Local Climate Zones.

description: Local Climate Zone teaser maps for the 15 largest functional urban areas stratified by urban ecoregion. The data is extracted from the global 100 m spatial resolution LCZ map, and is used to plot Figures 5, 6 and 7 of Demuzere et al. (2022a). The full global LCZ map is available via Demuzere et al. (2022b).<br> <br> See readme.txt for more details. <em>Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., Vliet, J. van, & Bechtel, B. (2022a). A global map of Local Climate Zones to support earth system modelling and urban scale environmental science. Earth Syst. Sci. Data Discuss.<br> Demuzere M, Kittner J, Martilli A, et al. Global map of Local Climate Zones. Zenodo. 2022b. doi:10.5281/zenodo.6364594</em>
description: See readme.txt for additional information.
DOI: 10.5281/zenodo.6376191
title: SciExpeM - 10.5281/zenodo.6376191

description: Heufer, K.A.; Fernandes, R.X.; Olivier, H.; Beeckmann, J.; Röhl, O.; Peters, N. - Proceedings Of The Combustion Institute, 2011, (33), 359-366
DOI: 10.5281/zenodo.6376190
title: SciExpeM - 10.5281/zenodo.6376191

description: Heufer, K.A.; Fernandes, R.X.; Olivier, H.; Beeckmann, J.; Röhl, O.; Peters, N. - Proceedings Of The Combustion Institute, 2011, (33), 359-366
DOI: 10.5281/zenodo.6376193
title: SciExpeM - 10.5281/zenodo.6376193

description: Heufer, K.A.; Fernandes, R.X.; Olivier, H.; Beeckmann, J.; Röhl, O.; Peters, N. - Proceedings Of The Combustion Institute, 2011, (33), 359-366
DOI: 10.5281/zenodo.6376192
title: SciExpeM - 10.5281/zenodo.6376193

description: Heufer, K.A.; Fernandes, R.X.; Olivier, H.; Beeckmann, J.; Röhl, O.; Peters, N. - Proceedings Of The Combustion Institute, 2011, (33), 359-366