Tableau (version 2020.3)

Beard, Lynly; Aghassibake, Negeen

doi:10.5195/jmla.2021.1135

Cited by 20 publications

(17 citation statements)

References 4 publications

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“…The pre-survey and post-survey responses to each question were counted and collected into groups based on response type, then displayed using Tableau Software (Beard & Aghassibake, 2021). All de-identified audio recordings were stored in one folder containing their 4-digit codes without any indication of being from the pre-recordings or the post-recordings to help eliminate bias that could arise since student attempts in the post-recordings may be perceived as more proficient.…”

Section: Discussionmentioning

confidence: 99%

Impact of the “DrugSpeak” programme on drug name pronunciation skills and perceptions in a pharmacy student cohort

Cheesman

Alcorn

et al. 2022

Pharm Educ

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Introduction: Difficulties in pronouncing many drug names can lead to medication errors in pharmacy practice. A pilot study called DrugSpeak was devised to provide educational scaffolding to enable students to pronounce drug names correctly. Method: Student participants (n = 26) accessed online videos on phonetics and audio files of drug names, and then undertook a workshop that provided them with basic phonetics training to assist them in pronouncing drug names correctly. Surveys and audio recordings of student pronunciations of drug names were conducted before and after intervention with DrugSpeak. Results: Significant increases (p<0.01) in student drug pronunciation and accuracy were observed following DrugSpeak. Students reported reductions in anxiety and enhancements in their confidence levels relevant to drug pronunciation. Conclusion: The DrugSpeak education package yielded promising outcomes in the improvement of student drug pronunciation skills and in providing students with the confidence to tackle drug names unfamiliar to them.

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Section: Discussionmentioning

confidence: 99%

Impact of the “DrugSpeak” programme on drug name pronunciation skills and perceptions in a pharmacy student cohort

Cheesman

Alcorn

et al. 2022

Pharm Educ

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“…Our choice to focus on REST API implementation instead of classical graphical user interface implementation is primarily motivated by a desire to give users a free choice to construct search requests whether they are simple or complex without limitations of standard use-cases that are typical for the latter. This approach allows the use of tools for data analysis [19], including the tools for building summary charts, which have recently become more and more widely used.…”

Section: Database Contentmentioning

confidence: 99%

ICBrainDB: An Integrated Database for Finding Associations between Genetic Factors and EEG Markers of Depressive Disorders

Ivanov

Kazantsev

Zavarzin

et al. 2022

JPM

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In this study, we collected and systemized diverse information related to depressive and anxiety disorders as the first step on the way to investigate the associations between molecular genetics, electrophysiological, behavioral, and psychological characteristics of people. Keeping that in mind, we developed an internet resource including a database and tools for primary presentation of the collected data of genetic factors, the results of electroencephalography (EEG) tests, and psychological questionnaires. The sample of our study was 1010 people from different regions of Russia. We created the integrated ICBrainDB database that enables users to easily access, download, and further process information about individual behavioral characteristics and psychophysiological responses along with inherited trait data. The data obtained can be useful in training neural networks and in machine learning construction processes in Big Data analysis. We believe that the existence of such a resource will play an important role in the further search for associations of genetic factors and EEG markers of depression.

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“…Users can vote for questions and answers, both upvotes (positive feedback) and down votes (negative feedback) are allowed. The number of the upvotes minus the number of downvotes represent the score.It is the largest Software development Q&A community, according to the SO Annual developer survey 2019 8 which reported that SO had 80 million visit, of which 25% are developer professionals and university students, and more than 80% rely on SO for educational purposes and also 65% of Stack Overflow's professional developers contribute to open source projects.…”

Section: A Stack Overflow and Its Tagging Systemmentioning

confidence: 99%

“…To apply the approach and analyze data, we turned to the most useful and popular library for machine learning in Python Scikit-learn [7] and the full-featured AI and ML integrated tool that supports multiple scripting languages and is easy to work with huge datasets Tableau [8]. Scikit-learn ML is a high-level API built on data frames and datasets that allows pipelines and is easier to build.…”

Section: Introductionmentioning

confidence: 99%

Identifying Community-Supported Technologies and Software Developments Concepts by K-means Clustering

Almansoury¹,

Kpodjedo²,

Boussaidi³

2022

IJACSA

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Working on technologies that have community support is one of the most important factors in software development. Software developers often face difficulties during software development, and community support from other software developers help them significantly. This paper presents an approach based on K-mean clustering technique to identify the level of community support for software technologies and development concepts using Stack Overflow discussion forums. To test the approach, a case study was performed by gathering data from SO and preparing a dataset that contains over a million of Java developers' questions. Then, K-mean clustering was applied to identify the community support levels. The goal is to find the best features that group community-supported software technologies and development concepts and identify the number of groups to determine the community support levels. Statistical error, clustering and classification evaluation metrics were applied. The results indicate that the best features to formulate community supported technologies and development concept levels are Failure Rate and Wait Time. The results show that the approach identifies two groups of community supported and development concept levels based on the best silhouette index value of 97%. According to the results the majority of Java technologies and development concepts are labeled with less community supported technologies and development concepts (Cluster 2). Random Forest classifier was applied to indirectly evaluate the approach to detect the identified community support class. The result shows that RF classifier presents a good performance and shows high accuracy value of 99.49% which indicates that the identified groups improve the performance of the classifier. The approach can be utilized to assist software developers and researchers in utilizing the SO platform in developing SO-based recommendation systems.

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Tableau (version 2020.3)

Abstract: This review analyzes the new features in Tableau and how it can be used both by librarians and the patrons they serve.

Cited by 20 publications

References 4 publications

Impact of the “DrugSpeak” programme on drug name pronunciation skills and perceptions in a pharmacy student cohort

Impact of the “DrugSpeak” programme on drug name pronunciation skills and perceptions in a pharmacy student cohort

ICBrainDB: An Integrated Database for Finding Associations between Genetic Factors and EEG Markers of Depressive Disorders

Identifying Community-Supported Technologies and Software Developments Concepts by K-means Clustering

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