Address Data Management Research Articles

Designing cost-effective data solutions: Best practices from multi-million dollar projects involving cross-functional teams and diverse data sources

This paper explores the design and implementation of cost-effective data solutions, focusing on multi-million dollar projects that involve cross-functional teams and diverse data sources. The study aims to identify best practices and technological advancements that enhance data management efficiency, ensuring scalability, security, and compliance. Employing a comprehensive review of existing literature and case studies, the paper examines the challenges and opportunities in integrating technologies such as AI, machine learning, cloud computing, and blockchain. Key findings highlight the importance of cross-functional collaboration in addressing data management complexities and the adoption of agile methodologies to enhance adaptability and efficiency. The study also underscores the significance of advanced data integration techniques and the application of circular economy principles to promote sustainability in data management. The conclusion provides actionable recommendations for organizations seeking to optimize their data solutions, emphasizing the need for strategic investment in emerging technologies and a robust data governance framework. By offering insights into future trends and research directions, this paper contributes to the ongoing discourse on developing cost-effective, secure, and sustainable data solutions in an increasingly digitalized world. Keywords: Cost-Effective Data Solutions, Cross-Functional Teams, Data Integration, Cybersecurity, Artificial Intelligence, Circular Economy Principles

Blockchain-based cloud-edge collaborative data management for human-robot collaboration digital twin system

Human-robot collaboration demonstrates broad application prospects in product customization. Digital twin represents an advanced real-virtual interaction technology that plays an essential role in enhancing perception and interaction for human-robot collaboration. A digital twin-based human-robot collaboration system has been proposed to devise collaborative strategies, simulate collaborative processes, and ensure human safety. However, there exist research gaps in implementing human-robot collaboration digital twin systems. A significant challenge lies in constructing data models for describing data types and content in human-robot collaboration digital twin systems. Additionally, addressing data management aspects, including data sharing and storage, is crucial for the effective operation of human-robot collaboration digital twin systems. To bridge existing deficiencies, a novel approach is introduced for managing data in human-robot collaboration digital twin systems through a blockchain-based cloud-edge collaborative method. Initially, a conceptualization of the human-robot collaboration digital twin system alongside a cloud-edge data management framework is introduced. Subsequently, a data model is delineated to outline data categories and contents of human-robot collaboration digital twin systems. Following this, an exploration is conducted on methodologies for data sharing and storage utilizing blockchain and cloud technologies. Ultimately, the efficacy of the proposed approaches is validated through a case study.

Social Robot Privacy Concern (SRPC): Rethinking privacy concerns within the hospitality domain

Understanding privacy needs has become paramount in the evolving landscape of social robots in the hospitality industry. Traditional privacy concerns, dominated by transactional interactions, fail to encapsulate the complexities of human-social robot interaction on service frontlines. Drawing from the communication management theory and the media equation theory, this study conceptualized "Social Robot Privacy Concerns" (SRPC) and discovered its multidimensionality. The SRPC construct is characterized by three key dimensions: (1) Information Privacy Concern, addressing data management and collection; (2) Interpersonal Privacy Concern, focusing on the robot's physical presence; and (3) Environmental Privacy Concern, encompassing broader contexts of government regulations, media influence, and surveillance mechanisms. The findings unveil that these dimensions are pivotal in navigating the challenges posed by service robots implemented in the hospitality industry, necessitating a nuanced approach to understanding and addressing emergent privacy needs. This study also empirically validated the scale to measure the SRPC construct, providing a foundational tool for future research.

Reference architecture design for developing data management systems in smart farming

The traditional data management systems prove inadequate to handle the volume, velocity, and variety of the data within farm business processes. Smart farming technologies offer advanced data management systems as a practical solution to these challenges. However, data is complex and originates from many sources; hence many aspects of data must be considered during the data management design of smart farming systems. This study proposes a reference architecture for data management in smart farming, developed through domain analysis and architecture modeling approaches. The domain analysis provides insights into the common and variant features and modules of the smart farming system, resulting in a blueprint representing family features across various smart farming domains. The effectiveness of the proposed reference architecture has been evaluated through two case studies, demonstrating its efficacy in designing data management systems for smart farming. The study found that the percentage of reused modules in the case studies, compared to the provided reference architecture, was 82.6%. The outcomes of this research will pave the way for further exploration in smart farming, particularly addressing data management issues within smart farming systems.

NAVIGATING ETHICAL CHALLENGES IN DATA MANAGEMENT FOR U.S. PROGRAM DEVELOPMENT: BEST PRACTICES AND RECOMMENDATIONS

In the landscape of U.S. program development, the ethical management of data plays a crucial role in ensuring the integrity, privacy, and trustworthiness of information. This review outlines best practices and recommendations for navigating ethical challenges inherent in data management within this context. Understanding ethical challenges involves recognizing the complexities of data collection, storage, usage, and sharing, and the potential dilemmas they pose. Best practices entail implementing robust procedures for informed consent, privacy protection, encryption, access control, and compliance with relevant regulations. Additionally, ethical decision-making requires the establishment of frameworks that prioritize stakeholder interests, risk assessment, and the implementation of guidelines through staff training and regular audits. Through case studies, real-world examples illustrate the practical application of ethical principles in addressing data management challenges. This review emphasizes the critical role of ethical considerations in safeguarding data integrity and fostering trust among stakeholders in U.S. program development initiatives. It underscores the need for continued vigilance and adherence to ethical guidelines to mitigate risks and uphold the ethical standards essential for responsible data management in this domain.
 Keywords: Ethical Challenges, Data Management, U.S. Program Development, Best Practices, Recommendations.

Addressing Policy Challenges of Disruptive Technologies

ABSTRACT This special issue examines the policy challenges and government responses to disruptive technologies. It explores the risks, benefits, and trade-offs of deploying disruptive technologies, and examines the efficacy of traditional governance approaches and the need for new regulatory and governance frameworks. Key themes include the need for government stewardship, taking adaptive and proactive approaches, developing comprehensive policies accounting for technical, social, economic, and political dimensions, conducting interdisciplinary research, and addressing data management and privacy challenges. The findings enhance understanding of how governments can navigate the complexities of disruptive technologies and develop policies to maximize benefits and mitigate risks.

Data Management Challenges and Resolutions in the Telecommunication Industry

This paper addresses data management threats, challenges, and measures for mitigation of such data threats in the telecommunication industry. The telecommunications industry handles tonnes of essential, personal and sensitive data on staff, clients and customers. This data is critical in developing product lines and marketing strategies for telecom companies, opening them up to opportunities that otherwise would have been beyond their reach. As a result, telecom companies’ databases are rendered vulnerable to violation and unauthorised access from insiders, third-party entities, as well as hackers. They have become premium target for cyberattacks, malware, interference, data theft and loss of vital data. While systems for the management of data in the telecom industry are crucial in gathering, storing, and analysing data, it is equally important for such systems to be efficient enough to provide a safe and secure mode of accessing, analysing and storing data assets. This is to prevent illegitimate use of sensitive data, cyberattacks, malware and data infringements on telecom data assets.

Sustainability Reporting in Cambodia: Challenges and Prospects

ABSTRACT This study investigates the challenges and prospects of sustainability reporting in Cambodia, filling a research gap in the existing literature. The study’s objectives focus on identifying the challenges faced by Cambodian companies when adopting sustainability reporting practices, investigating the benefits these practices provide, and making recommendations for improvement. The study utilized a purposive sampling technique to select participants from diverse sectors in Cambodia. The data collection methods involve conducting semi-structured interviews and online surveys. The data is then analyzed using thematic analysis and triangulation techniques. The research findings provide valuable insights into the challenges faced by organizations when implementing sustainability reporting practices in Cambodia. The challenges identified in the study include limited awareness of sustainability reporting, the absence of a common standard for reporting, and data management issues. In response to the challenges, the study offers recommendations to enhance sustainability reporting in Cambodia. These recommendations include raising awareness among companies, developing clear guidelines for reporting, promoting collaboration among stakeholders, addressing data management challenges, and involving SMEs in sustainability reporting. In addition, the study acknowledges its limitations, precisely the limited number of interviews and surveys used as the basis for analysis. Therefore, it calls for future research with a larger sample size and longitudinal studies to examine Cambodia’s sustainability reporting challenges and prospects further. Overall, this research contributes to improving sustainability reporting in Cambodia and provides valuable insights for companies, policymakers, and stakeholders. Keywords: Sustainability reporting; Sustainable development; ESG; Cambodia; Challenges; Prospects

Digital Twin (DT) Smart City for Air Quality Management

Air pollution is a pressing issue in cities, and managing air quality poses a challenge for urban designers and decision-makers. This study proposes a Digital Twin (DT) Smart City integrated with Mixed Reality technology to enhance visualization and collaboration for addressing urban air pollution. The research adopts an applied research approach, with a focus on developing a DT framework. A use case of DT development for Jakarta, the capital of Indonesia, is presented. By integrating air quality data, meteorological information, traffic patterns, and urban infrastructure data, the DT provides a comprehensive understanding of air pollution dynamics. The visualization capabilities of the DT, utilizing Mixed Reality technology, facilitate effective decision-making and the identification of strategies for managing air quality. However, further research is needed to address data management challenges to build a DT for Smart City at scale.

Earth and Space Science Informatics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

AbstractThis article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman, et al., 2021b, https://doi.org/10.1029/2021EO153180) in Earth and Space Science Informatics (ESSI) and includes discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Global collaboration, cyberinfrastructure, and data sharing; (Section 3) Machine learning for multiscale modeling; (Section 4) Aerial and satellite remote sensing for advancing Earth system model development by integrating field and ancillary data. ESSI addresses data management practices, computation and analysis, and hardware and software infrastructure. Our role in ICON science therefore involves collaborative work to assess, design, implement, and promote practices and tools that enable effective data management, discovery, integration, and reuse for interdisciplinary work in Earth and space science disciplines. Networks of diverse people with expertise across Earth, space, and data science disciplines are essential for efficient and ethical exchanges of findable, accessible, interoperable, and reusable (FAIR) research products and practices. Our challenge is then to coordinate the development of standards, curation practices, and tools that enable integrating and reusing multiple data types, software, multi‐scale models, and machine learning approaches across disciplines in a way that is as open and/or FAIR as ethically possible. This is a major endeavor that could greatly increase the pace and potential of interdisciplinary scientific discovery.

A Recent Regulatory Update on Consequences of Data Integrity Issues and its Management in Pharmaceutical Scenario

Purpose: Pharmaceutical industry ensures that data entered for various steps of drug development is accurate, which gives us confidence that the drugs produced by the industry are within specified parameters. Data integrity indicates sustaining and assuring the accuracy and reliability of data throughout the life cycle of the product. Over the years,numerous leading regulatory authorities have communicated their expectations in the form of regulations and guidance documents from the US FDA, MHRA, EMA, PIC/S and WHO,which address data management and data integrity issues. However, with an increase in digitalisation and the role of global markets, data integrity failures have increased.This results in recalls of products, warning letters, seizures, legal action and ultimately the potential for patient harm. Materials and Methods: Over the last few years, several regulatory agencies have acted against data integrity deficiencies in the pharmaceutical industry. In 2016, more than 50% of MHRA warning letters involved data integrity lapses for computerized systems were observed compared to year 2015. Broadly, the U.S. approximately has received 15 percent of the warning letters, European countries have received approximately 8 percent and the rest of the world claims approximately 15 percent from FDA in the years 2008-2018. MHRA published a guidance document on data integrity in the March 2015 and its revised draft copy was published in March 2018 which applies to GxP systems. Results: From a quality standpoint, data integrity plays a pivotal role in a company’s quality system. Data management and data governance should be efficiently integrated into the quality management system. Conclusion: This article represents the evaluation of warning letters from the last ten years regarding data integrity deficiencies

Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters.

The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop.

Data Stewardship Addressing Disciplinary Data Management Needs

One of the biggest challenges for multidisciplinary research institutions which provide data management support to researchers is addressing disciplinary differences (Akers and Doty,2013). Centralised services need to be general enough to cater for all the different flavours of research conducted in an institution. At the same time, focusing on the common denominator means that subject-specific differences and needs may not be effectively addressed. In 2017, Delft University of Technology (TU Delft) embarked on an ambitious Data Stewardship project, aiming to comprehensively address data management needs across a multi-disciplinary campus. In this article we describe the principles behind the Data Stewardship project at TU Delft, the progress so far, identify the key challenges and explain our plans for the future.

123: Using Eforms to Address Data Management Challenges: An IAEA Trial

123: Using Eforms to Address Data Management Challenges: An IAEA Trial

Librarians and Scientists Partner to Address Data Management: Taking Collaboration to the Next Level.

This study looks at the changing way in which the Information Services Office (ISO) at the National Institute of Standards and Technology (NIST) provides services to NIST scientific and technical staff throughout their research and publishing cycles. These services include the more traditional services of a research library as well as publishing NIST technical reports and The Journal of Research of NIST, and preserving and exhibiting scientific instruments and other artifacts. ISO has always prided itself on having a close relationship with its customers, providing a high level of service, and developing new services to stay in front of NIST researcher needs. Through a concerted, strategic effort since the late 1990s, ISO has developed and promoted relationships with its key customers through its Lab Liaison Program. This paper discusses the relationship ISO has developed with the Office of Data and Informatics (ODI), how this relationship was forged, and how this collaboration will serve as a model for working with the other labs and programs at NIST. It will also discuss the risks and opportunities of this new collaborative service model, how ISO positioned itself to become an equal partner with ODI in the exploration of solutions to data management issues, and the benefits of the relationship from ODI's perspective. A pattern of strategic changes to the services and activities offered by the Lab Liaison program has put ISO in the position to collaborate as peers with researchers at NIST. This study provides an overview of how ISO made strategic decisions to incorporate non-traditional services to support data management at NIST.

Smart data pricing models for the internet of things: a bundling strategy approach

The Internet of Things (IoT) has emerged as a new paradigm for the future Internet. In IoT, devices are connected to the Internet and thus are a huge data source for numerous applications. In this article, we focus on addressing data management in IoT through using a smart data pricing (SDP) approach. With SDP, data can be managed flexibly and efficiently through intelligent and adaptive incentive mechanisms. Moreover, data is a major source of revenue for providers and partners. We propose a new pricing scheme for IoT service providers to determine the sensing data buying price and IoT service subscription fee offered to sensor owners and service users, respectively. Additionally, we adopt the bundling strategy that allows multiple providers to form a coalition and offer their services as a bundle, attracting more users and achieving higher revenue. Finally, we outline some important open research issues for SDP and IoT.

Building an Engineering-Based Medical College: Is the Timing Ripe for the Picking?

The current US healthcare faces two major challenges in that while the cost is very high its overall outcome is not excellent. In its 2014 report, the Organization for Economic Cooperation and Development (OECD) informed us that healthcare expenditure of the USA costs 16.9 % gross domestic products (GDP) in 2012, the highest of all OECD countries [1]. Yet according to a 2014 study conducted by the Commonwealth Fund, the USA is ranked the very last in overall healthcare quality among 11 nations including Australia, Canada, France, Germany, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the USA [2]. Specifically, the USA is last or near last on measures of access, efficiency, and equity [2]. Furthermore, the current medical practice is outpaced by the rapid advancement of health science and technology. A recent survey on genetic curricula in USA and Canadian medical schools found that only 26 % responders reported formal genetic teaching during third and fourth year of school, and most responders felt the amount of time spent on genetics was insufficient for future clinical practice in this era of genomic medicine [3]. Not only medical educators felt the current medical education lags, students at Harvard Medical School expressed similar sentiment [4]. They felt that “our ability and capacity to train both new and experienced clinicians to manage the tremendous amount of data lag far behind the pace of the data revolution” and that “medical education at all levels must come to address data management and utilization issues as we enter the era of Big Data in the clinical domain” [4]. In addition, recent studies have pointed out that advanced technology useful to teach undergraduate medical knowledge and skills such as sonography and otolaryngology was underutilized [5, 6]. Since undergraduate medical education is the exclusive physician pipeline and the gateway to the future of medicine, what we educate the medical students today would have a profound influence what the physicians will practice in the 50 years ahead. The most effective way to transformmedicine of the future is, therefore, through medical education reform. Comparing the dynamic development of health science and technology and relative static medical education, one wonders if today’s medical education would be optimal in nurturing future physicians. Furthermore, the combined findings of low efficiency and high expenditure of the US health system has prompted the Institute of Medicine (IOM) to call for the implementation of three aims ofmedicine in the future: “better care, better health, and lower costs” [7, 8]. In his commencement speech delivered at the University of Illinois College of Medicine on May 8th, 2015, Dr. Victor Dzau, President of IOM, encouraged the medical graduates to “be innovative, challenge the status quo, think out of the box, and make a difference!” The news of first Engineeringbased medical school, Carle-University of Illinois College of Medicine, to be established at the University of Illinois Urbana/Champagne Campus has triggered various reactions within academic medicine community [9]. An engineeringbased medical education curriculum, which intends to promote innovation, creativity, and efficiency, could indeed be a roadmap to the future of medicine. In this commentary article, the goal of engineering education will be defined, followed by an attempt to solidify the meaning of engineering-based medical education, and then the rationale for the engineering* Lawrence S. Chan larrycha@uic.edu

The China Patient-Centered Evaluative Assessment of Cardiac Events (China PEACE) Retrospective Study of Acute Myocardial Infarction: Study Design

Cardiovascular diseases are rising as a cause of death and disability in China. To improve outcomes for patients with these conditions, the Chinese government, academic researchers, clinicians, and >200 hospitals have created China Patient-Centered Evaluative Assessment of Cardiac Events (China PEACE), a national network for research and performance improvement. The first study from China PEACE, the Retrospective Study of Acute Myocardial Infarction (China PEACE-Retrospective AMI Study), is designed to promote improvements in acute myocardial infarction (AMI) quality of care by generating knowledge about the characteristics, treatments, and outcomes of patients hospitalized with AMI across a representative sample of Chinese hospitals during the past decade. The China PEACE-Retrospective AMI Study will examine >18 000 patient records from 162 hospitals identified using a 2-stage cluster sampling design within economic-geographic regions. Records were chosen from 2001, 2006, and 2011 to identify temporal trends. Data quality will be monitored by a central coordinating center and will, in particular, address case ascertainment, data abstraction, and data management. Analyses will examine patient characteristics, diagnostic testing patterns, in-hospital treatments, in-hospital outcomes, and variation in results by time and site of care. In addition to publications, data will be shared with participating hospitals and the Chinese government to develop strategies to promote quality improvement. The China PEACE-Retrospective AMI Study is the first to leverage the China PEACE platform to better understand AMI across representative sites of care and during the past decade in China. The China PEACE collaboration among government, academicians, clinicians, and hospitals is poised to translate research about trends and patterns of AMI practices and outcomes into improved care for patients. URL: http://www.clinicaltrials.gov. Unique identifier: NCT01624883.

Prepared to Plan? A Snapshot of Researcher Readiness to Address Data Management Planning Requirements

Objective: Cornell University’s Research Data Management Service Group (RDMSG) surveyed NSF principal investigators (PIs) at Cornell in order to understand how well-prepared researchers are to meet the new NSF data management planning requirement, to build our own understanding of the potential impact on campus services, and to identify service gaps. Methods: We administered a 43-question online survey, which included questions about the respondents’ research and research data, their interest in assistance with the creation of data management plans, and questions for each of the five general areas cited in the NSF’s Grant Proposal Guide (2011) section on data management plans. Results and Discussion: Respondents produce a wide variety of types and formats of data, although most expect to share relatively small amounts of data. Respondents are generally uncertain as to whether the data they produce conforms to disciplinary standards. The majority create no metadata; of those that do, most do not create metadata according to a particular standard. Most researchers do not express a need for advice regarding intellectual property issues. Researchers report using a variety of strategies (oncampus and commercial) for backing up and for providing access to their data sets. Conclusions: The overarching finding from our survey is that there is much uncertainty about what the new requirement means and how to meet it, and researchers welcome offers of assistance. To the extent that Cornell researchers are representative of NSF PIs, our findings reveal something about researchers’ readiness to meet the new requirement, and their attitudes towards it. Correspondence to Gail Steinhart: gss1@cornell.edu

Questions Arising about Emergence, Data Collection, and Its Interaction with Analysis in a Grounded Theory Study

There has been a strong call for increased clarity and transparency of method in qualitative research. Although qualitative data analysis has been detailed, data management has not been made as transparent in the literature. How do data collection and analysis interact in practical terms? What constitutes sufficient data? And can research be both planful and emergent? In this paper, the author highlights several methodological strategies for addressing data management challenges in a grounded theory study of preservice mathematics teachers.