Data Analysis Skills Research Articles

PlateletSeq: A novel method for discovery of blood-based biomarkers.

Platelets are small circulating fragments of cells that play important roles in thrombosis, haemostasis, immune response, inflammation and cancer growth. Although anucleate, they contain a rich RNA repertoire which offers an opportunity to characterise changes in platelet gene expression in health and disease. Whilst this can be achieved with conventional RNA sequencing, a large input of high-quality RNA, and hence blood volume, is required (unless a pre-amplification step is added), along with specialist bioinformatic skills for data analysis and interpretation. We have developed a transcriptomics next-generation sequencing-based approach that overcomes these limitations. Termed PlateletSeq, this method requires very low levels of RNA input and does not require specialist bioinformatic analytical skills. Here we describe the methodology, from sample collection to processing and data analysis. Specifically, blood samples can be stored for up to 8days at 4°C prior to analysis. Platelets are isolated using multi-step centrifugation and a purity of≤1 leucocyte per 0.26x106 platelets is optimal for gene expression analysis. We have applied PlateletSeq to normal adult blood samples and show there are no age-associated variations and only minor gender-associated differences. In contrast, platelets from patients with myeloproliferative neoplasms show differences in platelet transcript profiles from normal and between disease subtypes. This illustrates the potential applicability of PlateletSeq for biomarker discovery and studying platelet biology in patient samples. It also opens avenues for assessing platelet quality in other fields such as transfusion research.

Yes We Can (But Maybe Not Yet): The Impact of Data Poverty on Realising the Potential of Learning Analytics

Learning analytics (LA), as research and practice, has been committed to contributing to understanding students’ learning and the contexts in which their learning occurs since its emergence in 2011. Core to learning analytics is the measurement, collection, analysis and use of student data. As higher education becomes increasingly digitised and datafied, institutions have access to not only more data, but also a variety, velocity and granularity of data. However, there is also increasing evidence of data poverty - referring not only to students’ unable to afford data, but also institutions without, inter alia, the data infrastructure and analytical skills to realise the potential of LA. In both cases, the fiduciary duty of higher education institutions to provide enabling and supportive learning environments are severely compromised. In this conceptual paper, we provide an introduction to the notion of data poverty as it relates to both students and institutions before mapping key dimensions of student data poverty and their implications for LA. We also discuss institutional data poverty before concluding with a number of pointers to move from ‘not yet’ to ‘yes we can’ in providing evidence-led teaching and student support.

Escaping the Environmental Crises: Online Escape Rooms for Evaluating Student Data Analysis Skills.

Summative lab assessments probe student mastery over concepts, but conventional ones often result in decreased student engagement and confidence. If conventional summative lab assessments are replaced by accessible gamified evaluations, such as online escape rooms, this leads to improved student engagement and confidence. In this work, we adapted two sustainability themed online escape room activities to increase student engagement and confidence in data analyses in Integrated Chemistry I (CHEM 381) over three semesters at CSU, Chico. Over 89.7% of students earned full credit. Further, 80.0% of the written comments included positive feedback. After the online escape room assessments, 60.0% of the students rated their confidence as "high" or "very high" in all categories assessed, compared to 25.6% before the experience. Students found that the online escape room assessments were more engaging than the traditional assessment and increased their confidence as they worked toward solving two sustainability crises and competed for the quickest time to complete the escape rooms.

Performance based assessment of LIS professionals toward digital data sets management in university libraries: A TAM model approach

In recent times, computer systems have acquired the capacity to gather, process and analyze massive quantities of data, as well as techniques for distributing, transmitting, and computing estimates. Therefore, the study is conducted with an objective to ascertain the performance-based assessment of LIS professionals toward digital data sets management DDSM in university libraries. A six-factor measurement model was tested based on the 28-valid items obtained from the literature. The hypothesized research model was validated through the application of confirmatory factor analysis (CFA), and structural equation modeling (SEM) technique. LIS professionals in research-oriented organizations possess the necessary data analytical skills and technical expertise to extract insights from the data. The findings indicated that analytical skills have a significant positive impact on PEOU (β =246, SE = 0.54, p < 0.000). Perceived usefulness (PU) of digital data management practices is significantly influenced by analytical skills (AS) (β =0.443, SE = 0.077, p ⩽ 0.000), PEOU significantly influence the attitude of LIS professionals toward digital data management practices (β =0.215, SE = 0.214, p ⩽ 0.000), and attitude of LIS professionals has a positive impact on their performance in managing digital data sets in university libraries (β =0.584, SE = 0.178, p ⩽ 0.005). The study reports primary data gathered through survey on constructs given in technology acceptance (TAM) model. The study provides a valuable insight for organizational leaders, stakeholders, and planners to enhance and refine DDSM practices and methodologies in academic libraries. This will enable them to effectively utilize library resources and services through the implementation of DDSM techniques.

APPLICATION OF STATISTICAL DATA AND METHODS TO ESTABLISH RPN RATINGS OF FMEA METHOD FOR CONSTRUCTION PROJECTS

The Failure Mode and Effects Analysis (FMEA) is paramount for analytical skills of reliability design in dynamic prevention. The FMEA model is a significant method which can simultaneously reduce the operating errors or delays as well as improve the construction quality. In particular, the Risk Priority Number (RPN) in the FMEA model is a vital tool which helps construction managers prioritize problem-solving. As the Internet of Things and big data analytical skills have become progressively widespread and mature, among the three risk indicators of RPN, the number of operating errors or delays per unit time can be estimated by the data collected from the analysis of statistical methods and regarded as the basis of 10-level classification. In addition, when the loss is larger, then the severity is higher. This paper proposed three evaluation criteria, including Occurrence, Severity, and Detection of RPN in construction engineering, and a 10-level classification model. To assist the construction managers, priority for construction improvement can be identified based on RPN calculations.

Skills Analysis of Employes by Using LinkedIn Profiles of Global and Türkiye Audit Firms

Purpose –The study aims to reveal the current status of the skills of those working in audit firms in today's changing business world. In addition, the aim of the study is to reveal the differences between Global audit firms and local audit firms in Türkiye.Design/methodology/approach –In this study, descriptive analysis method were used to reveal the existing situation. It is aimed to reach the top 20 audit firms with the highest turnover in the world and 364 audit firms registered with the Public Oversight Agency in Türkiye as of January 2022. LinkedIn corporate pages of 19 of the top 20 audit firms with the highest turnover in the world and 58 of 364 audit firms in Turkey have been reached.Findings –It has been revealed that the soft skills of employees in Big4 firms are an important complement to their technical skills. This situation is more limited in local audit firms. While Data Analytics (DA) skills are also observed in employees of Big4 firms,no one stated skills with the word “data analytics” in local audit firms. It has been determined that Python, R, SQL, Power BI and Tableau tools are used as DA skills. Engineering education stands out for those who indicate Python, R, and SQLskills.Discussion –The study's empirical evidence shows that Big4 firms need DA skills more and more as they move from the accounting and auditing fields to consulting, technology, and digital services. It shows the lack of both soft and DA skills in local audit firms. This study develops empirical knowledge of what employee skills are and should be for global and local audit firms

Early Engagement in HIV Research: Evaluation of the Penn CFAR Scholars Program Aimed at Increasing Diversity of the HIV/AIDS Workforce.

Demographic diversity is not represented in the HIV/AIDS workforce. Engagement of underrepresented trainees as early as high school may address this disparity. We established the Penn Center for AIDS Research (CFAR) Scholars Program, a mentored research experience for underrepresented minority (URM) trainees spanning educational stages from high school to medical school. The program provides participants with tailored educational programming, professional skill building, and mentored research experiences. We conducted qualitative interviews with scholar, mentor, and leadership groups to evaluate the program's impact. Eleven participants were selected to partake in 1 of 5 existing mentored research programs as CFAR scholars. Scholars attended an 8-week HIV Seminar Series that covered concepts in the basic, clinical, behavioral, and community-based HIV/AIDS research. Program evaluation revealed that scholars' knowledge of HIV pathophysiology and community impact increased because of these seminars. In addition, they developed tangible skills in literature review, bench techniques, qualitative assessment, data analysis, and professional network building. Scholars reported improved academic self-efficacy and achieved greater career goal clarity. Areas for improvement included clarification of mentor-mentee roles, expectations for longitudinal mentorship, and long-term engagement between scholars. Financial stressors, lack of social capital, and structural racism were identified as barriers to success for URM trainees. The Penn CFAR Scholars Program is a novel mentored research program that successfully engaged URM trainees from early educational stages. Barriers and facilitators to sustained efforts of diversifying the HIV/AIDS workforce were identified and will inform future program planning.

Literasi Statistik Dalam Pembelajaran Matematika SD Melalui Kurikulum 2013

This research aims to investigate the implementation of statistical literacy at the elementary schools level within the mathematics curriculum. Statistical literacy is a essential skill that enables students to understand, analyze, and interpret data and information critically. In an era where information and data are abundant, having statistical literacy skills has become increasingly relevant and crucial for developing students' understanding of the world, which involves various data-based information. This study uses a qualitative approach by analyzing government regulation documents concerning the mathematics curriculum used at the elementary school level. The results of the research indicate that statistical literacy has not been fully integrated effectively into the elementary school mathematics curriculum. While there have been some efforts to incorporate statistical concepts into mathematics learning, there are still challenges in teaching data analysis skills and interpreting statistical information more deeply.

Increasing representation and diversity in health research: A protocol of the MYHealth research training program for high school students.

Despite decades of calls for increased diversity in the health research workforce, disparities exist for many populations, including Black, Indigenous, and People of Color individuals, those from low-income families, and first-generation college students. To increase representation of historically marginalized populations, there is a critical need to develop programs that strengthen their path toward health research careers. High school is a critically important time to catalyze interest and rebuild engagement among youth who may have previously felt excluded from science, technology, engineering, and mathematics (STEM) and health research careers. The overall objective of the MYHealth program is to engage high school students in a community-based participatory research program focused on adolescent health. Investigators will work alongside community partners to recruit 9th through 12th graders who self-identify as a member of a group underrepresented in STEM or health research careers (e.g., based on race and ethnicity, socioeconomic status, first generation college student, disability, etc.). MYHealth students are trained to be co-researchers who work alongside academic researchers, which will help them to envision themselves as scientists capable of positively impacting their communities through research. Implemented in three phases, the MYHealth program aims to foster a continuing interest in health research careers by developing: 1) researcher identities, 2) scientific literacy, 3) scientific self-efficacy, and 4) teamwork and leadership self-efficacy. In each phase, students will build knowledge and skills in research, ethics, data collection, data analysis, and dissemination. Students will directly collaborate with and be mentored by a team that includes investigators, community advisors, scientific advisors, and youth peers. Each year, a new cohort of up to 70 high school students will be enrolled in MYHealth. We anticipate the MYHealth program will increase interest and persistence in STEM and health research among groups that have been historically excluded in health research careers.

Data Analytical Skills in Agricultural Science

Data Analytical Skills in Agricultural Science

The Value of Data‐driven Category Management: A Case for Teaching Data Analytics to Purchasing and Supply Management Students

AbstractAs companies look to differentiate themselves with the help of their suppliers, the need for robust and sophisticated purchasing and supply management (PSM) processes that allow for informed decision‐making becomes increasingly important. These processes often rely on sophisticated data analytics to inform the design of a company’s category management strategy, such as its supply network design, its supplier relationship management, and its supplier performance management. Therefore, data analytics skills are crucial for PSM professionals. To foster these skills, we developed an innovative approach for teaching students how to use data analytics tools and techniques in PSM through the use of a teaching case called “Savingtools.” This case, developed in collaboration with a company undergoing a PSM transformation, illustrates the value of data‐driven category management in PSM. The case further demonstrates the principles and tools related to PSM data management, spend analysis, and classification, and allows for in‐depth data analysis, including visualizations. Our approach has been shown to effectively enhance student learning and comprehension, and we believe that it prepares future supply chain leaders while advancing PSM pedagogy.

Digital health care and data work: Who are the data professionals?

This article reports on a study that investigated data professionals in health care. The topic is interesting and relevant because of the ongoing trend towards digitisation of the healthcare domain and efforts for it to become data driven, which entail a wide variety of work with data. Despite an interest in data science and more broadly in data work, we know surprisingly little about the people who work with data in healthcare. Therefore, we investigated data work at a large national healthcare data organisation in Denmark. An explorative mixed method approach combining a non-probability technique for design of an open survey with a target population of 300+ and 11 semi-structured interviews, was applied. We report findings relevant to educational background, work identity, work tasks, and how staff acquired competences and knowledge, as well as what these attributes comprised. We found recurring themes of healthcare knowledge, data analytical skills, and information technology, reflected in education, competences and knowledge. However, there was considerable variation within and beyond those themes, and indeed most competences were learned "on the job" rather than as part of formal education. Becoming a professional working with data in health care can be the result of different career paths. The most recurring work identity was that of "data analyst"; however, a wide variety of responses indicated that a stable data worker identity has not yet developed. The findings present implications for educational policy makers and healthcare managers.

Coordinated Distributed Experiments in Higher Education: A Low Barrier to Entry and Potential for Student Projects

AbstractThis mixed‐methods research focused on the implementation of a coordinated distributed experiment (CDE) investigating local adaptation in common milkweed (Asclepias syriaca), a host plant for the monarch butterfly population. Faculty participants were recruited from the Ecological Research as Education Network (EREN) who recruited their former undergraduate students. Quantitative data were drawn from the Milkweed Local Adaptation (MLA) CDE database across the three project years. Qualitative data included faculty survey responses, semi‐structured interviews of faculty and former undergraduates, and review of undergraduate research posters, papers, and curricula using rubrics aligned with 4DEE and Next Generation Science Standards (NGSS) benchmarks. Analysis of the MLA CDE database illustrates a decline in both participating institutions and in counts of milkweed stems over the project (2018–2020). Qualitative data analysis revealed that CDEs: (1) offer opportunities for higher education faculty and their students to be part of research including developing skills of data collection, analysis, and interpretation; (2) have unexpected challenges; and (3) can inspire undergraduate students to develop independent research projects or curricular modules for use in formal 6–12 classrooms. Broader ecological educational implications of our outcomes for higher education faculty and their undergraduate students include: (1) recommendation that faculty members involved ought to be proactively informed about potential challenges and provided with guidance on how to mitigate them; (2) mitigating challenges with model studies to try to estimate the sample size and redundancy likely to produce robust data; and (3) proactive use of the educational network to understand institutional use of the CDE project with undergraduates.

The data-driven leader: developing a big data analytics leadership competency framework

PurposeThis study focuses on leadership in organizations where big data analytics (BDA) is an essential component of corporate strategy. While leadership researchers have conducted promising studies in the field of digital transformation, the impact of BDA on leadership is still unexplored.Design/methodology/approachThis study is based on semi-structured interviews with 33 organizational leaders and subject-matter experts from various industries. Using a grounded theory approach, a framework is provided for the emergent field of BDA in leadership research.FindingsThe authors present a conceptual model comprising foundational competencies and higher order roles that are data analytical skills, data self-efficacy, problem spotter, influencer, knowledge facilitator, visionary and team leader.Research limitations/implicationsThis study focuses on BDA competency research emerging as an intersection between leadership research and information systems research. The authors encourage a longitudinal study to validate the findings.Practical implicationsThe authors provide a competency framework for organizational leaders. It serves as a guideline for leaders to best support the BDA initiatives of the organization. The competency framework can support recruiting, selection and leader promotion.Originality/valueThis study provides a novel BDA leadership competency framework with a unique combination of competencies and higher order roles.

Preparing Accounting Students for Careers Utilizing Data Analytics: Curriculum Innovation Steps and Challenges

Abstract Data analytics has become increasingly important in the accounting profession. To prepare accounting students for careers that require data analytic skills, accounting departments in institutions of higher education need to integrate data analytics into existing curricula. While the curriculum development process has been described extensively in prior literature, adding data analytics to accounting curriculum proves challenging for several reasons, including the complexity of the subject of data analytics, insufficient faculty resources, and the limited credit hours accounting students have available for new courses. In this paper, we describe our business school’s seven-step process of incorporating data analytics into the accounting curriculum. Since analytics learning objectives cannot be taught within the accounting curriculum by accounting faculty alone, this curriculum innovation required collaboration with other faculty. This analytics curriculum update required buy-in from various stakeholders, all done despite notoriously limited time and resources. We were able to see accounting students succeed in their internships and careers using analytics learned from this new curriculum. In addition to describing the steps, we discuss the challenges faced and lessons learned from integrating accounting and analytics education.

Assessment of data demand for informed-decisions among health facility and department heads in public health facilities of Amhara Region, northwest Ethiopia

BackgroundEvidence-based decision-making is a foundation of health information systems; however, routine health information is not mostly utilized by decision makers in the Amhara region. Therefore, this study aimed to explore the facility and department heads' perceptions towards the demand for and use of routine health information for decision making.MethodsA phenomenological qualitative study was done in eight districts of the Amhara region from June 10/2019 to July 30/2019. We obtained written informed consent and recruited 22 key informants purposively. The research team prepared a codebook, assigned codes to ideas, identified salient patterns, grouped similar ideas, and developed themes from the data. Thus, data were analyzed thematically using OpenCode software.ResultsThe study revealed that health workers collected many data, but little was demanded and utilized to inform decisions. The majority of respondents perceived that data were collected merely for reporting. Lack of skills in data management, analysis, interpretation, and use were the technical attributes. Individual attributes included low staff motivation, carelessness, and lack of value for data. Poor access to data, low support for Health Information System, limited space for archiving, and inadequate finance were related to organizational attributes. The contextual (social-political) factors also influenced the use of eHealth applications for improved data demand and use among health care providers.ConclusionIn this study, health workers collect routine health data merely for reporting, and they did not demand and use it mostly to inform decisions and solve problems. Technical, individual, organizational, and contextual attributes were contributors to low demand and use of routine health data. Thus, we recommend building the technical capacity of health workers, introducing motivation mechanisms and ensuring accountability systems for better data use.

The Role of Institutions of Higher Education in Preparing Students to Write in the Workplace: Suggestions for Writing Curricula and Pedagogy

Employers have expressed discontent with the workplace skill sets and overall professionalism of recent college graduates. Employers specifically report that graduates lack important work-related writing skills such as editing, research, and data analysis skills. The literature continually provides metrics on recent graduates’ weaknesses as workplace writers. If graduates are to maximize their marketability to employers, perceived writing gaps must be addressed specifically in the context of higher education writing curricula and pedagogy. Based on employer reports on the specific writing skill sets lacking in recent college graduates, this article is a primer on the topic of meeting employers’ workplace writing needs and puts forth (1) the context behind and causes of a workplace writing skills gap in recent college graduates, (2) curricular and pedagogical suggestions for institutions of higher education (IHEs) to strengthen work-related writing competencies in college students, and (3) recommendations for future research pertaining to meeting the writing needs of the workplace.

Analyzing Position, Velocity and Acceleration Graphs using Arduino

Motion graphics and their relationships within the scope of the kinematics unit are difficult topics for students to learn. In this study, an Arduino-based in-class physics activity was developed, in which students can analyze position-time, velocity-time and acceleration-time graphs practically. Within the scope of the study, Arduino UNO and HC-SR04 distance sensors, which are very low cost and easily obtainable, were used in the material development process. Data analyzes were also carried out using the Excel program. The position values and time values of a simple car moving with constant acceleration were collected with the help of Arduino and then these data were transferred to the Excel program. Instant velocity and acceleration values were determined on the Excel platform. Then, graph types and relationships between graphs were analyzed by drawing time-dependent graphs of position, velocity and acceleration with the help of Excel program. The motion acceleration was determined as 2.388 m/s2 both by taking the second derivative of the position-time equation with respect to time and by using the slope of the time-dependent graph of the instantaneous velocity values found. Furthermore, the displacement was calculated as 0.363 m using first and last position values that were directly read from the distance sensor and 0.348 m using the area under the velocity-time graph. The two values are very close to each other, with a difference of +4.1. The change in velocity was calculated as 0.845 m/s by taking the difference of the initial and final velocity and calculating the area under the acceleration-time graph. In addition to providing students with important educational gains in graphic subjects, it is expected that such a study carried out with students in the classroom will also be effective in the development of technological literacy and data analysis skills.

Uso y percepción de ChatGPT en la educación superior

This article aims to analyze higher education students' management and perception regarding using ChatGPT in their academic activities. To accomplish this, a descriptive study with a qualitative approach was employed to analyze the data obtained through a questionnaire administered to students from various majors at the Universidad Autónoma de Sinaloa. The instrument consisted of two sections with closed-ended questions and a Likert scale to measure the learners' perception. The results revealed that a minority of the respondents (33%) had used ChatGPT in their school practices. Additionally, it was found that a significant proportion (75%) did not consider the use of this tool suitable for their educational tasks, and a similar percentage (79%) did not perceive improvements in their research and data analysis skills. A low dependence on using this tool for school assignments was observed (4%), along with a lack of confidence in teachers' preparedness to effectively incorporate this technology into their classes (83%). In conclusion, educational institutions are recommended to carefully consider integrating artificial intelligence tools in didactic exercises, taking into account the concerns expressed by the students.

Functionality of nutrition steering and technical committees and quality of child growth monitoring program in northwest Ethiopia

Malnutrition in Ethiopia is still a major public health problem. Effective growth monitoring activities are difficult to integrate with local circumstances and a national program and are also given less weight. Therefore, this study aimed to assess the functionality of the nutrition steering and technical committees on the quality of the growth monitoring program in the central Gondar zone of northwest Ethiopia. A mixed-methods study was employed among 51 committee members using purposive sampling and 8 months data. The participation rate and the proportion of faltering rates were computed. A data extraction sheet and an in-depth interview guide were prepared, respectively. The overall participation rate was 44.94% in Gondar Zuria and 50.62% in East Demiba districts, respectively. Similarly, the percentage of children faltering was significantly higher in Gondar Zuria district (2.85%) than in East Dembia district (0.48%). Neither the nutrition steering committee nor the technical committee were fully functional to support the program. In addition, a lack of materials, knowledge, and skills in data analysis and interpretation, as well as refresher training, are barriers to program implementation. The overall participation rate is very low in the study area. Program materials were not available, and the technical and steering committees are not fully functional. As a result, preparing working documents, assigning a specific task to strengthen the existing steering and technical committees, providing refresher training, and ensuring the availability of important program materials are very crucial.