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The present study investigates Peer Review Ethics (PRE) in scientific journals in Iran, with a specific focus on compiling the PRE statement for the Journal of Information Processing and Management (JIPM). Moreover, qualitative analysis was conducted on the PRE statements of the journals published by the Ministry of Science, Research, and Technology (MSRT). The review process and documents of the JIPM were then analyzed. This evidence-based case study was conducted using a mixed-method approach, which included three research methods: researcher-based, data-based, and research-based. A focus group discussion (FGD) was also conducted to validate the research findings. The findings showed that PRE elements were classified such as "timeliness," "confidentiality," "bias," "conflict of interest," "research misconduct," "respectful and fair expressions," "constructive and objective feedback," and "accountability and responsibility." The analysis of ethical statements in MSRT journals found that only half of the journals published PRE statements. In addition, 102 instances of violations of PRE, accounting for 15% of each review, were identified in documents submitted for review in JIPM.  

Evidence-based policymaking stands at the forefront of contemporary governance, where data and evidence have emerged as indispensable allies in shaping effective and informed decisions. This article embarks on a comprehensive exploration of the challenges and critical issues encountered when data assumes the role of evidence in policy formulation. The foundation of this investigation is rooted in the extensive body of literature on evidence-based policy-making. We delve into the scholarly discourse, tracing the evolution of policy formulation from relying on intuition to being guided by empirical insights. As we navigate through this intellectual landscape, the crucial role of data as a catalyst for this transformation becomes apparent. Delving deeper, we will explore the intricacies of data and the rise of big data. Once regarded as mere numbers, data now represents the currency of the information age. Its volume, velocity, and variety characterize it, making it a powerful tool for generating evidence and formulating policies. As we explore its features, we uncover the potential of data to unlock unprecedented insights and inform governance with empirical precision. To conduct a comprehensive examination of the difficulties encountered when utilizing data as evidence in policy-making, we employ a rigorous scoping review methodology. Through meticulous screening, we have identified and analyzed 36 exemplary articles that offer invaluable insights into the multifaceted landscape of data-driven governance. These articles provide a comprehensive overview of the challenges, which can be grouped into three distinct clusters: technical challenges arising from data complexities, legal and privacy dilemmas intertwined with governance, and the formidable issues faced by policymakers. Our discussion unravels the intricate web of challenges, ranging from data quality and integration to confidentiality, ethics, and governance issues. We delve into the intricacies of data access, the fight against bias, and the challenges posed by data volume and complexity. Simultaneously, we explore the complex legal landscape of data ownership, security, sharing, and compliance. The challenges policymakers face in fostering data-driven cultures, navigating resource constraints, and communicating data-driven insights are brought to the forefront. In conclusion, our exploration sheds light on the complex challenges and crucial issues that underlie the use of data as evidence in evidence-based policy-making. This research underscores the transformative power of data in governance and emphasizes the challenges and pressing issues associated with using data as evidence in policymaking.

Data is considered the most crucial element in open banking processes and services. Therefore, it is necessary to pay attention to various aspects of the quality of this data in order to provide appropriate and expected services to customers. In this research, various dimensions representing different aspects of data quality were investigated in the field of open banking. This research has been conducted in two main steps: The Delphi method and the pairwise comparisons method. In the first step, various dimensions of data quality in open banking were extracted based on the Delphi method. In the next step, the importance of each of these dimensions was assessed relative to each other using the pairwise comparisons method, and the most crucial dimensions were determined. Based on the results obtained from these two methods, the significance of eleven dimensions of data quality in this field was determined. The best overall weighted averages were related to dimensions such as accuracy, accessibility, relevancy, timeliness, consistency, security, interpretability, reputation, believability, ease of understanding, and value-added, respectively. Banks and fintech companies offering open banking services can consider these dimensions when evaluating the quality of their data to enhance the provision of superior services

Decision-making processes are significantly influenced by a myriad of factors, with data quality emerging as a crucial determinant. Despite widespread awareness of the detrimental effects of poor-quality data on decisions, organizations struggle with persistent challenges because of the sheer volume of data within their systems. Existing literature advocates for providing Data Quality Metadata (DQM) to help decision-makers communicate data quality levels. However, concerns about potential cognitive overload induced by DQM may hinder decision-makers and negatively impact outcomes. To address this concern, we conducted an experimental exploration into the impact of Data Quality Management (DQM) on decision outcomes. Our study aimed to identify specific groups of decision-makers benefiting from DQM and uncover factors influencing its usage. Statistical analyses revealed that decision-makers with a heightened awareness of data quality demonstrated improved Data Quality Management (DQM) utilization, leading to increased decision accuracy. Nevertheless, a trade-off was observed as the efficiency of decision-makers suffered when employing Decision Quality Management (DQM). We propose that the positive impact of incorporating Data Quality Management (DQM) on decision outcomes is contingent on characteristics such as a high level of knowledge about data quality. However, we acknowledge that the inference of this positive impact could be more transparent and thoroughly explained. Our findings caution against a blanket inclusion of Data Quality Management (DQM) in data warehouses, emphasizing the need for tailored investigations into its utility and impact within specific organizational settings.

The term “cloud computing (CC)” has been extensively studied and utilized by major corporations since its inception. Within the realm of cloud computing, various research topics and perspectives have been explored, including resource management, cloud security, and energy efficiency. This paper explores the intersection of data quality and business process management within the context of cloud computing. Specifically, it examines how Quality of Service (QoS)-driven task scheduling algorithms in cloud environments can enhance data quality and optimize business processes. Cloud computing still faces the significant challenge of determining the most effective way to schedule tasks and manage available resources. We need effective scheduling strategies to manage these resources because of the scale and dynamic resource provisioning in modern data centers. The purpose of this work is to provide an overview of the various task scheduling methods that have been utilized in the cloud computing environment to date. An attempt has been made to categorize current methods, investigate issues, and identify important challenges present in this area. Our data reveals that 34% of researchers are focusing on makespan for QoS (Quality of Service) metrics, 17% on cost, 15% on load balancing, 10% on deadline, and 9% on energy usage. Other criteria for the Quality of Service (QoS) parameter contribute far less than the ones mentioned above. According to this study, scheduling algorithms commonly used by researchers include the genetic algorithm in bio-inspired systems and particle swarm optimization in swarm intelligence 80% of the time. According to the available literature, 70% of the studies have utilized CloudSim as their simulation tool of choice. Our findings suggest that current methodologies mainly employ genetic algorithms and particle swarm optimization, with CloudSim being a popular simulation tool. Ongoing work emphasizes refining scheduling strategies to enhance resource management in dynamic data center environments, providing crucial insights into future quality-of-service (QoS)-driven scheduling algorithms for cloud computing.