Software integration between external game APIs and the GameBus repository

Many games exist to serve a unique purpose for different domains, so is the case with healthcare. However, when it comes to integrating such games to achieve healthcare benefits, and an integrated, enjoyable experience, not enough has been done due to factors such as shortage of resources, time to market and others. This report describes the design and implementation of the GameBus data model to realize such an integrated platform. This model is the technology independent core of the solution. Moreover, the model is a common language for different developers and partners, which contributes to bringing the functional design closer to the software design. This report encompasses and explains all the different aspects of the development throughout this project. These include problem analysis, system architecture, design and its implementation

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed

Mobile Communication & Accessibility for Blind Users

Abstract-Throughout the world, legislations accessibility guidelines have been adopted. However, the technical solutions that have been proposed mainly focus to provide support when users access the Web applications through desktop interfaces. Recent technological evolutions in mobile devices can provide a number of interesting and fruitful opportunities for disabled people in order to improve the quality of their life and social interactions. We need to identify solutions for disabled users when they are on the move and they want to carry out important social activities (such as visiting museums). For these reasons we believe that emerging mobile technologies should introduce represent new opportunities for all, especially for users with special needs. In this regard, research should better investigate on how mobile devices, such as PDAs or smart phones, can be used to aid in daily life of blind users. To better understand the accessibility issues experienced by users of current mobile devices, we will engage in qualitative observations and interviews with mobile device users having disabilities. Our analysis will explore the types of mobile devices currently used by people with disabilities, common accessibility issues that these people encounter, and adaptation strategies that they use to overcome accessibility issues. We are also considering focus groups and diary studies to cover additional mobile accessibility corners. We will analyze a framework of accessible interaction techniques that can be used to increase the accessibility of mainstream mobile devices. This framework will be designed by qualitative investigation and participatory design for mobile device users having disabilities, and will be implemented as user-installable software for current and future mobile devices

A Conceptual Model to Support the Transmuters in Acquiring the Desired Knowledge of a Data Scientist

Recently, data science has emerged as the most attractive profession. This is mainly because data scientists are currently in high demand in the business and healthcare industry, and are also a high-paying profession and several career options. Inspired by this, the transmuter (i.e. a person who wants to change his/her profession as per job trends) having different educational backgrounds focuses on acquiring the required level of knowledge and skills regarding the data scientist. However, to the best of our knowledge, the current state-of-the-art lacks in providing any information/roadmap useful for the transmuters to gain the required set of data scientist’s knowledge and skills. Based on this, the main objective of this work is to identify the skills and knowledge required for data scientists keeping in view of their educational background. To achieve this, we have conducted an exploratory study and received responses from 134 data scientists of different educational backgrounds from 31 countries. The conducted study suggests the six different types of data scientists, which help different organizations to build an effective team for data scientists. Moreover, this study also proposed a conceptual model for transmuters as well to adopt data science according to the respective data scientist’s category. Moreover, the current study also aims to reduce the gap between industry and academic. The proposed framework is validated using an expert opinion-based technique. We believe that current work effectively supports both transmuters and industry; for transmuters, it helps them to propose the appropriate tools and required knowledge, and for the industry, it provides the basic strengths and weaknesses of different categories of transmuters

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed

Same data, different conclusions : radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed