cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8271123
title: SuperDARN Grid data in netCDF format (2017-Aug)

description: 2017-Aug SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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.8271090
title: SuperDARN Grid data in netCDF format (2017-Oct)

description: 2017-Oct SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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.8271091
title: SuperDARN Grid data in netCDF format (2017-Oct)

description: 2017-Oct SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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.8271110
title: SuperDARN Grid data in netCDF format (2017-Sep)

description: 2017-Sep SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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.8271111
title: SuperDARN Grid data in netCDF format (2017-Sep)

description: 2017-Sep SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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.8271074
title: SuperDARN Grid data in netCDF format (2017-Nov)

description: 2017-Nov SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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.8271075
title: SuperDARN Grid data in netCDF format (2017-Nov)

description: 2017-Nov SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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: 10.48366/r609253
title: NLP and Knowledge Graph Representations and Reasoning for Question Answering Modeling

description: Knowledge Representations and reasoning for Question Answering Modeling are done using Natural Language Processing and Knowledge Graphs. Evaluations of various datasets are done.
DOI: 10.17632/k72dntnfgv.2
title: 2022-2023 Quantitative and qualitative survey of telecoms network operators

description: This dataset consists of:
(a) a spreadsheet containing responses to a questionnaire published on the following network operator group mailing lists: AFNOG, APOPS, AUSNOG, DENOG, ENOG, FRNOG, GORE, IDNOG, INNOG, ITNOG, JANOG, LACNOG, NANOG, SAFNOG, SANOG, SWINOG, UKNOF;
(b) a spreadsheet containing responses to a questionnaire distributed by SG Analytics to screened respondents;
(c) data collected through qualitative survey, to assess the results obtained by assessing the data in (a) and (b). The qualitative data consist of emails, transcripts and recordings. All data in (c) has been approved by respondents for publication, or anonymized if not approved.
DOI: 10.17632/k72dntnfgv.1
title: 2022-2023 Quantitative and qualitative survey of telecoms network operators

description: This dataset consists of:
(a) a spreadsheet containing responses to a questionnaire published on the following network operator group mailing lists: AFNOG, APOPS, AUSNOG, DENOG, ENOG, FRNOG, GORE, IDNOG, INNOG, ITNOG, JANOG, LACNOG, NANOG, SAFNOG, SANOG, SWINOG, UKNOF;
(b) a spreadsheet containing responses to a questionnaire distributed by SG Analytics to screened respondents;
(c) data collected through qualitative survey, to assess the results obtained by assessing the data in (a) and (b). The qualitative data consist of emails, transcripts and recordings. All data in (c) has been approved by respondents for publication, or anonymized if not approved.
DOI: 10.17632/k72dntnfgv.3
title: 2022-2023 Quantitative and qualitative survey of telecoms network operators

description: This dataset consists of:
(a) a spreadsheet containing responses to a questionnaire published on the following network operator group mailing lists: AFNOG, APOPS, AUSNOG, DENOG, ENOG, FRNOG, GORE, IDNOG, INNOG, ITNOG, JANOG, LACNOG, NANOG, SAFNOG, SANOG, SWINOG, UKNOF;
(b) a spreadsheet containing responses to a questionnaire distributed by SG Analytics to screened respondents;
(c) data collected through qualitative survey, to assess the results obtained by assessing the data in (a) and (b). The qualitative data consist of emails, transcripts and recordings. All data in (c) has been approved by respondents for publication, or anonymized if not approved.
DOI: 10.5281/zenodo.8306854
title: VCF Files (Annotated and Non-Annotated)

description: VCF files, plus annotations using SNPEff.
DOI: 10.5281/zenodo.8306853
title: VCF Files (Annotated and Non-Annotated)

description: VCF files, plus annotations using SNPEff.
DOI: 10.17632/5dfrzx359s.1
title: PICTOGRAPHIC ELEMENTS (EMOJIS) IN SOCIAL COMMERCE 

description: The study that used this base proposed the integration of pictographic elements (emojis) as a novel dimension within the realm of social commerce. This database served as the foundation for conducting three distinct experiments, each aimed at exploring various facets of the relationship between emojis and consumer behavior.

Experiment 1 - Presence vs. Absence of Emojis:
The initial dataset comprises two primary conditions: one involving the presence of emojis and the other lacking them. Alongside this, key variables were taken into consideration, including participants' levels of trust, perception of social presence, gender, and age. 

Experiment 2 - Positive and Negative Comment Integration:
Building upon the findings of the first experiment, the second dataset expanded to incorporate an additional layer of complexity: the inclusion of positive and negative comments alongside emojis. 

Experiment 3 - Emojis in Utilitarian vs. Hedonic Contexts:
The third and final dataset delved into the nuanced interrelationship between emojis and the type of service offered. Here, two primary dimensions were considered: the presence or absence of emojis and the categorization of service as either utilitarian or hedonic. This experiment sought to unravel how these factors could impact variables such as purchase intention, shedding light on whether the effects of emojis varied across different service contexts.

Conclusion:
The database developed for this research initiative represents a comprehensive exploration of the integration of emojis as a multifaceted dimension within social commerce. Through a series of three experiments, the impact of emojis on consumer behavior was scrutinized from various angles. The findings of these experiments contribute to understand how emojis can be strategically employed to enhance user engagement and purchase intentions in the ever-evolving landscape of social commerce.
DOI: 10.17632/yfw8786fgz.1
title: Peer review: Pre-feasibility analysis framework for the integration of renewable energy sources: incorporating technical, economic, and environmental aspects 

description: Abstract: Nowadays, the energy landscape of many countries faces transformational challenges driven by sustainable development objectives supported by the implementation of clean technologies, such as renewable energy sources, to meet the flexibility and diversification needs of the traditional energy mix. However, integrating these technologies requires a thorough study of the context in which they are developed. Furthermore, it is necessary to carry out an analysis from a sustainable approach that quantifies the impact of proposals on multiple objectives established by stakeholders. This article presents a framework for analysis that integrates a method for evaluating the technical feasibility of the primary generation resource for photovoltaic solar, wind, small hydroelectric, and biomass alternatives. These alternatives are evaluated using a hybrid multi-criteria method FAHP-TOPSIS. FAHP is used as a comparison technique among a set of proposed criteria for technical, economic, and environmental dimensions. TOPSIS ranks the resource alternatives considering their level of trade-off between criteria. The framework is applied in a case study in Valle del Cauca - Colombia, based on the available resources and their analysis. The results offer a wide range of alternatives to assist in decision-making for the correct use of available resources and maximize the benefit of all stakeholders.

The provided data includes historical information on renewable generation resources in Jamundí, Valle del Cauca, CO. This information has been obtained from publicly available databases, aggregated from PowerNasa tools, the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM), and the Colombian Agricultural Institute.  Additional available data: stakeholders survey, indicator data, and obtained results.

The processing of this information was carried out using a module developed in Python. For more information, please contact the author or visit https://github.com/MateoBarrera.
DOI: 10.17632/yfw8786fgz.2
title: Peer review: Pre-feasibility analysis framework for the integration of renewable energy sources: incorporating technical, economic, and environmental aspects 

description: Abstract: Nowadays, the energy landscape of many countries faces transformational challenges driven by sustainable development objectives supported by the implementation of clean technologies, such as renewable energy sources, to meet the flexibility and diversification needs of the traditional energy mix. However, integrating these technologies requires a thorough study of the context in which they are developed. Furthermore, it is necessary to carry out an analysis from a sustainable approach that quantifies the impact of proposals on multiple objectives established by stakeholders. This article presents a framework for analysis that integrates a method for evaluating the technical feasibility of the primary generation resource for photovoltaic solar, wind, small hydroelectric, and biomass alternatives. These alternatives are evaluated using a hybrid multi-criteria method FAHP-TOPSIS. FAHP is used as a comparison technique among a set of proposed criteria for technical, economic, and environmental dimensions. TOPSIS ranks the resource alternatives considering their level of trade-off between criteria. The framework is applied in a case study in Valle del Cauca - Colombia, based on the available resources and their analysis. The results offer a wide range of alternatives to assist in decision-making for the correct use of available resources and maximize the benefit of all stakeholders.

The provided data includes historical information on renewable generation resources in Jamundí, Valle del Cauca, CO. This information has been obtained from publicly available databases, aggregated from PowerNasa tools, the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM), and the Colombian Agricultural Institute.  Additional available data: stakeholders survey, indicator data, and obtained results.

The processing of this information was carried out using a module developed in Python. For more information, please contact the author or visit https://github.com/MateoBarrera.
DOI: 10.17632/yfw8786fgz.3
title: Peer review: A Framework for Evaluating Renewable Energy for Decision-Making Integrating a Hybrid FAHP-TOPSIS Approach: A Case Study in Valle del Cauca, Colombia

description: Abstract: Nowadays, the energy landscape of many countries faces transformational challenges driven by sustainable development objectives, supported by the implementation of clean technologies, such as renewable energy sources, to meet the flexibility and diversification needs of the traditional energy mix. However, integrating these technologies requires a thorough study of the context in which they are developed. Furthermore, it is necessary to carry out an analysis from a sustainable approach that quantifies the impact of proposals on multiple objectives established by stakeholders. This article presents a framework for analysis that integrates a method for evaluating the technical feasibility of resources for photovoltaic solar, wind, small-hydroelectric, and biomass generation. These resources are used to construct a set of alternatives and are evaluated using a hybrid FAHP-TOPSIS approach. FAHP-TOPSIS is used as a comparison technique among a collection of technical, economic, and environmental criteria, ranking the alternatives considering their level of trade-off between criteria. The framework is applied in a Valle del Cauca (Colombia) case study. The results offer a wide range of alternatives and indicate a combination of 50\% biomass, 25\% solar, 25\% hydro as the best, assisting in decision-making for the correct use of available resources and maximizing the benefits for stakeholders.

The provided data includes historical information on renewable generation resources in Jamundí, Valle del Cauca, CO. This information has been obtained from publicly available databases, aggregated from PowerNasa tools, the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM), and the Colombian Agricultural Institute.  Additional available data: stakeholders survey, indicator data, and obtained results.

The processing of this information was carried out using a module developed in Python. For more information, please contact the author or visit https://github.com/MateoBarrera.
DOI: 10.17632/yfw8786fgz.4
title: A Framework for Evaluating Renewable Energy for Decision-Making Integrating a Hybrid FAHP-TOPSIS Approach: A Case Study in Valle del Cauca, Colombia

description: Abstract: At present, the energy landscape of many countries faces transformational challenges driven by sustainable development objectives, supported by the implementation of clean technologies, such as renewable energy sources, to meet the flexibility and diversification needs of the traditional energy mix. However, integrating these technologies requires a thorough study of the context in which they are developed. Furthermore, it is necessary to carry out an analysis from a sustainable approach that quantifies the impact of proposals on multiple objectives established by stakeholders. This article presents a framework for analysis that integrates a method for evaluating the technical feasibility of resources for photovoltaic solar, wind, small hydroelectric power, and biomass generation. These resources are used to construct a set of alternatives and are evaluated using a hybrid FAHP-TOPSIS approach. FAHP-TOPSIS is used as a comparison technique among a collection of technical, economic, and environmental criteria, ranking the alternatives considering their level of trade-off between criteria. The results of a case study in Valle del Cauca (Colombia) offer a wide range of alternatives and indicate a combination of 50\% biomass, and 50\% solar as the best, assisting in decision-making for the correct use of available resources and maximizing the benefits for stakeholders.

The provided data includes historical information on renewable generation resources in Jamundí, Valle del Cauca, CO. This information has been obtained from publicly available databases, aggregated from PowerNasa tools, the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM), and the Colombian Agricultural Institute.  Additional available data: stakeholders survey, indicator data, and obtained results.

The processing of this information was carried out using a module developed in Python. For more information, please contact the author or visit https://github.com/MateoBarrera.