cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7507722
title: The effect of element intensity that distinguishes flavour in a food packaging on the memory and perception of Peruvian millennials and centennials

description: <em>ABSTRACT</em><br> Background: Globalization has greatly facilitated access to all kinds of products and brands from different parts of the world, increasing consumer choices and, consequently, competition among packaged products. For this reason, a brand's packaging design to attract consumers must stand out among its competitors, especially if it is a new product or a new flavour. In this context, it becomes necessary to explore dimensions of packaging design that allow a high degree of differentiation. In that way, there are very efficient strategies and approaches used in the literature to perform this exploration in packagings, such as perception evaluations measured with attributes and eye-tracking technology that records direct information about visual attention on the most distinctive elements. Thus, the present study aims to determine the impact of the presentation of a new flavour on packaging by varying the intensity of the stimulus that represents it according to the Weber-Fechner law, using three main elements of the packaging: text, colour, and image.<br> Methods: The experiment exposed three groups of participants (n=30) to 3 stimuli, which were based on adding an element as the intensity of the flavour description of three products from three different food categories was intended to increase. This study used the RealEye webcam eye-tracking platform to measure attention, and a self-administered questionnaire was used to examine perception and recall. <br> Results: First, the results show that greater intensity expressed in a more significant number of visual elements facilitates flavour distinction. Second, the results reveal that perceived difficulty in flavour differentiation correlates strongly with the presence of colour and image. <br> Conclusions: Therefore, this study provides an approach to ways to optimize the intensity of flavour distinctiveness elements that may be useful, especially for less recognized brands.
DOI: 10.6084/m9.figshare.21831054
title: Brazilian school teachers' knowledge regarding dental trauma in children and adolescents: a systematic review and meta-analysis

description: studies included in this systematic review
DOI: 10.6084/m9.figshare.21831054.v1
title: Brazilian school teachers' knowledge regarding dental trauma in children and adolescents: a systematic review and meta-analysis

description: studies included in this systematic review
DOI: 10.5281/zenodo.4302133
title: Dry Bench Skills for the Researcher

description: The practice of biological inquiry has evolved so that a biological researcher's training must include both wet-lab and dry lab experimental and computational approaches. This course seeks to lay a broad foundation for the dry bench researcher. It is clear that "data literacy is a key skill in the modern world." This course will introduce the trainee to the computational techniques that enable data analysis locally on their laptop and as well as how to enable these same analysis techniques in the cloud. In this workshop, we will also introduce the learner to the concept of team science. The learner will obtain critical skills in effective collaboration that is imperative in a world where the amount of data available is beyond a single lab's walls. Over 10 hours of instruction, spread over two weeks, we seek to introduce the learner to RNAseq analysis and demystify concepts in RNAseq analysis. By the end of the course, the learner can successfully use tools involved in these analyses collaboratively and independently. This course aims to go broad and point the learner to learn more and take their knowledge deeper. Through their participation in the class, learners will acquire experience in a full suite of tools essential to any bioinformatics analysis. They will learn and understand FAIR best practices. They will obtain and use their GitHub, Docker, and ORCID ids. They will know where to find Nextflow workflows and run them to generate results that are further explored through interactive Jupyter Lab notebooks. They will learn about publicly available datasets downloadable from cloud resources and the ever-growing datasets that may aid their research. They will learn about using flexible, powerful, and community-based workflow languages such as Nextflow and how the resulting workflows use containerization to modularize and simplify bioinformatic analysis. Such workflows will be run using cloud-accessible data in the cloud and on their laptop. The course's final day will focus on pulling together all these resources into a single end-to-end example to empower the trainee to apply new knowledge towards their specific use case.
DOI: 10.5281/zenodo.4317239
title: Dry Bench Skills for the Researcher

description: The practice of biological inquiry has evolved so that a biological researcher's training must include both wet-lab and dry lab experimental and computational approaches. This course seeks to lay a broad foundation for the dry bench researcher. It is clear that "data literacy is a key skill in the modern world." This course will introduce the trainee to the computational techniques that enable data analysis locally on their laptop and as well as how to enable these same analysis techniques in the cloud. In this workshop, we will also introduce the learner to the concept of team science. The learner will obtain critical skills in effective collaboration that is imperative in a world where the amount of data available is beyond a single lab's walls. Over 10 hours of instruction, spread over two weeks, we seek to introduce the learner to RNAseq analysis and demystify concepts in RNAseq analysis. By the end of the course, the learner can successfully use tools involved in these analyses collaboratively and independently. This course aims to go broad and point the learner to learn more and take their knowledge deeper. Through their participation in the class, learners will acquire experience in a full suite of tools essential to any bioinformatics analysis. They will learn and understand FAIR best practices. They will obtain and use their GitHub, Docker, and ORCID ids. They will know where to find Nextflow workflows and run them to generate results that are further explored through interactive Jupyter Lab notebooks. They will learn about publicly available datasets downloadable from cloud resources and the ever-growing datasets that may aid their research. They will learn about using flexible, powerful, and community-based workflow languages such as Nextflow and how the resulting workflows use containerization to modularize and simplify bioinformatic analysis. Such workflows will be run using cloud-accessible data in the cloud and on their laptop. The course's final day will focus on pulling together all these resources into a single end-to-end example to empower the trainee to apply new knowledge towards their specific use case.
DOI: 10.5281/zenodo.4317260
title: Dry Bench Skills for the Researcher

description: The practice of biological inquiry has evolved so that a biological researcher's training must include both wet-lab and dry lab experimental and computational approaches. This course seeks to lay a broad foundation for the dry bench researcher. It is clear that "data literacy is a key skill in the modern world." This course will introduce the trainee to the computational techniques that enable data analysis locally on their laptop and as well as how to enable these same analysis techniques in the cloud. In this workshop, we will also introduce the learner to the concept of team science. The learner will obtain critical skills in effective collaboration that is imperative in a world where the amount of data available is beyond a single lab's walls. Over 10 hours of instruction, spread over two weeks, we seek to introduce the learner to RNAseq analysis and demystify concepts in RNAseq analysis. By the end of the course, the learner can successfully use tools involved in these analyses collaboratively and independently. This course aims to go broad and point the learner to learn more and take their knowledge deeper. Through their participation in the class, learners will acquire experience in a full suite of tools essential to any bioinformatics analysis. They will learn and understand FAIR best practices. They will obtain and use their GitHub, Docker, and ORCID ids. They will know where to find Nextflow workflows and run them to generate results that are further explored through interactive Jupyter Lab notebooks. They will learn about publicly available datasets downloadable from cloud resources and the ever-growing datasets that may aid their research. They will learn about using flexible, powerful, and community-based workflow languages such as Nextflow and how the resulting workflows use containerization to modularize and simplify bioinformatic analysis. Such workflows will be run using cloud-accessible data in the cloud and on their laptop. The course's final day will focus on pulling together all these resources into a single end-to-end example to empower the trainee to apply new knowledge towards their specific use case.
DOI: 10.5281/zenodo.4317512
title: Dry Bench Skills for the Researcher

description: The practice of biological inquiry has evolved so that a biological researcher's training must include both wet-lab and dry lab experimental and computational approaches. This course seeks to lay a broad foundation for the dry bench researcher. It is clear that "data literacy is a key skill in the modern world." This course will introduce the trainee to the computational techniques that enable data analysis locally on their laptop and as well as how to enable these same analysis techniques in the cloud. In this workshop, we will also introduce the learner to the concept of team science. The learner will obtain critical skills in effective collaboration that is imperative in a world where the amount of data available is beyond a single lab's walls. Over 10 hours of instruction, spread over two weeks, we seek to introduce the learner to RNAseq analysis and demystify concepts in RNAseq analysis. By the end of the course, the learner can successfully use tools involved in these analyses collaboratively and independently. This course aims to go broad and point the learner to learn more and take their knowledge deeper. Through their participation in the class, learners will acquire experience in a full suite of tools essential to any bioinformatics analysis. They will learn and understand FAIR best practices. They will obtain and use their GitHub, Docker, and ORCID ids. They will know where to find Nextflow workflows and run them to generate results that are further explored through interactive Jupyter Lab notebooks. They will learn about publicly available datasets downloadable from cloud resources and the ever-growing datasets that may aid their research. They will learn about using flexible, powerful, and community-based workflow languages such as Nextflow and how the resulting workflows use containerization to modularize and simplify bioinformatic analysis. Such workflows will be run using cloud-accessible data in the cloud and on their laptop. The course's final day will focus on pulling together all these resources into a single end-to-end example to empower the trainee to apply new knowledge towards their specific use case.
DOI: 10.5281/zenodo.7025300
title: Dry Bench Skills for the Researcher

description: The practice of biological inquiry has evolved so that a biological researcher's training must include both wet-lab and dry lab experimental and computational approaches. This course seeks to lay a broad foundation for the dry bench researcher. It is clear that "data literacy is a key skill in the modern world." This course will introduce the trainee to the computational techniques that enable data analysis locally on their laptop and as well as how to enable these same analysis techniques in the cloud. In this workshop, we will also introduce the learner to the concept of team science. The learner will obtain critical skills in effective collaboration that is imperative in a world where the amount of data available is beyond a single lab's walls. Over 10 hours of instruction, spread over two weeks, we seek to introduce the learner to RNAseq analysis and demystify concepts in RNAseq analysis. By the end of the course, the learner can successfully use tools involved in these analyses collaboratively and independently. This course aims to go broad and point the learner to learn more and take their knowledge deeper. Through their participation in the class, learners will acquire experience in a full suite of tools essential to any bioinformatics analysis. They will learn and understand FAIR best practices. They will obtain and use their GitHub, Docker, and ORCID ids. They will know where to find Nextflow workflows and run them to generate results that are further explored through interactive Jupyter Lab notebooks. They will learn about publicly available datasets downloadable from cloud resources and the ever-growing datasets that may aid their research. They will learn about using flexible, powerful, and community-based workflow languages such as Nextflow and how the resulting workflows use containerization to modularize and simplify bioinformatic analysis. Such workflows will be run using cloud-accessible data in the cloud and on their laptop. The course's final day will focus on pulling together all these resources into a single end-to-end example to empower the trainee to apply new knowledge towards their specific use case.
DOI: 10.5281/zenodo.7025773
title: Dry Bench Skills for the Researcher

description: The practice of biological inquiry has evolved so that a biological researcher's training must include both wet-lab and dry lab experimental and computational approaches. This course seeks to lay a broad foundation for the dry bench researcher. It is clear that "data literacy is a key skill in the modern world." This course will introduce the trainee to the computational techniques that enable data analysis locally on their laptop and as well as how to enable these same analysis techniques in the cloud. In this workshop, we will also introduce the learner to the concept of team science. The learner will obtain critical skills in effective collaboration that is imperative in a world where the amount of data available is beyond a single lab's walls. Over 10 hours of instruction, spread over two weeks, we seek to introduce the learner to RNAseq analysis and demystify concepts in RNAseq analysis. By the end of the course, the learner can successfully use tools involved in these analyses collaboratively and independently. This course aims to go broad and point the learner to learn more and take their knowledge deeper. Through their participation in the class, learners will acquire experience in a full suite of tools essential to any bioinformatics analysis. They will learn and understand FAIR best practices. They will obtain and use their GitHub, Docker, and ORCID ids. They will know where to find Nextflow workflows and run them to generate results that are further explored through interactive Jupyter Lab notebooks. They will learn about publicly available datasets downloadable from cloud resources and the ever-growing datasets that may aid their research. They will learn about using flexible, powerful, and community-based workflow languages such as Nextflow and how the resulting workflows use containerization to modularize and simplify bioinformatic analysis. Such workflows will be run using cloud-accessible data in the cloud and on their laptop. The course's final day will focus on pulling together all these resources into a single end-to-end example to empower the trainee to apply new knowledge towards their specific use case.
DOI: 10.5281/zenodo.7510563
title: Dry Bench Skills for the Researcher

description: The practice of biological inquiry has evolved so that a biological researcher's training must include both wet-lab and dry lab experimental and computational approaches. This course seeks to lay a broad foundation for the dry bench researcher. It is clear that "data literacy is a key skill in the modern world." This course will introduce the trainee to the computational techniques that enable data analysis locally on their laptop and as well as how to enable these same analysis techniques in the cloud. In this workshop, we will also introduce the learner to the concept of team science. The learner will obtain critical skills in effective collaboration that is imperative in a world where the amount of data available is beyond a single lab's walls. Over 10 hours of instruction, spread over two weeks, we seek to introduce the learner to RNAseq analysis and demystify concepts in RNAseq analysis. By the end of the course, the learner can successfully use tools involved in these analyses collaboratively and independently. This course aims to go broad and point the learner to learn more and take their knowledge deeper. Through their participation in the class, learners will acquire experience in a full suite of tools essential to any bioinformatics analysis. They will learn and understand FAIR best practices. They will obtain and use their GitHub, Docker, and ORCID ids. They will know where to find Nextflow workflows and run them to generate results that are further explored through interactive Jupyter Lab notebooks. They will learn about publicly available datasets downloadable from cloud resources and the ever-growing datasets that may aid their research. They will learn about using flexible, powerful, and community-based workflow languages such as Nextflow and how the resulting workflows use containerization to modularize and simplify bioinformatic analysis. Such workflows will be run using cloud-accessible data in the cloud and on their laptop. The course's final day will focus on pulling together all these resources into a single end-to-end example to empower the trainee to apply new knowledge towards their specific use case.
DOI: 10.5281/zenodo.4302132
title: Dry Bench Skills for the Researcher

description: The practice of biological inquiry has evolved so that a biological researcher's training must include both wet-lab and dry lab experimental and computational approaches. This course seeks to lay a broad foundation for the dry bench researcher. It is clear that "data literacy is a key skill in the modern world." This course will introduce the trainee to the computational techniques that enable data analysis locally on their laptop and as well as how to enable these same analysis techniques in the cloud. In this workshop, we will also introduce the learner to the concept of team science. The learner will obtain critical skills in effective collaboration that is imperative in a world where the amount of data available is beyond a single lab's walls. Over 10 hours of instruction, spread over two weeks, we seek to introduce the learner to RNAseq analysis and demystify concepts in RNAseq analysis. By the end of the course, the learner can successfully use tools involved in these analyses collaboratively and independently. This course aims to go broad and point the learner to learn more and take their knowledge deeper. Through their participation in the class, learners will acquire experience in a full suite of tools essential to any bioinformatics analysis. They will learn and understand FAIR best practices. They will obtain and use their GitHub, Docker, and ORCID ids. They will know where to find Nextflow workflows and run them to generate results that are further explored through interactive Jupyter Lab notebooks. They will learn about publicly available datasets downloadable from cloud resources and the ever-growing datasets that may aid their research. They will learn about using flexible, powerful, and community-based workflow languages such as Nextflow and how the resulting workflows use containerization to modularize and simplify bioinformatic analysis. Such workflows will be run using cloud-accessible data in the cloud and on their laptop. The course's final day will focus on pulling together all these resources into a single end-to-end example to empower the trainee to apply new knowledge towards their specific use case.
DOI: 10.5281/zenodo.7510573
title: Dry Bench Skills for the Researcher

description: The practice of biological inquiry has evolved so that a biological researcher's training must include both wet-lab and dry lab experimental and computational approaches. This course seeks to lay a broad foundation for the dry bench researcher. It is clear that "data literacy is a key skill in the modern world." This course will introduce the trainee to the computational techniques that enable data analysis locally on their laptop and as well as how to enable these same analysis techniques in the cloud. In this workshop, we will also introduce the learner to the concept of team science. The learner will obtain critical skills in effective collaboration that is imperative in a world where the amount of data available is beyond a single lab's walls. Over 10 hours of instruction, spread over two weeks, we seek to introduce the learner to RNAseq analysis and demystify concepts in RNAseq analysis. By the end of the course, the learner can successfully use tools involved in these analyses collaboratively and independently. This course aims to go broad and point the learner to learn more and take their knowledge deeper. Through their participation in the class, learners will acquire experience in a full suite of tools essential to any bioinformatics analysis. They will learn and understand FAIR best practices. They will obtain and use their GitHub, Docker, and ORCID ids. They will know where to find Nextflow workflows and run them to generate results that are further explored through interactive Jupyter Lab notebooks. They will learn about publicly available datasets downloadable from cloud resources and the ever-growing datasets that may aid their research. They will learn about using flexible, powerful, and community-based workflow languages such as Nextflow and how the resulting workflows use containerization to modularize and simplify bioinformatic analysis. Such workflows will be run using cloud-accessible data in the cloud and on their laptop. The course's final day will focus on pulling together all these resources into a single end-to-end example to empower the trainee to apply new knowledge towards their specific use case.