Data Science - Extracting Insights Editor- In-Chief : Mr. Mandeep Title proper : Data-Science-Extracting Insights Subject : Computer Science Publisher : Python Publishing Press Dates of publication : 2022 Frequency : Quarterly Language : English Country : Belgium Medium : Online Indexed by : CROSSREF DOI Prifix: 10.70705
Lost Science: Protecting Data Through Improved Archiving
Mike Carr wrote recently Of the wOnderful planetary geOlOgy accOmplished by NASA Over the past 50 years Of space explOration, with images and descriptions from the early Venus, Mars, and lunar missions as well as mOre recent missions, which evOked gOOd memOries (M. H. Carr, GeOlOgic explOration Of the planets: The first 50 years, Eos, 94(3), 29–30, doi:10.1029/2013EO030001, 2013).
Tracking Reviews and Scraping the Web. Gaining Understanding from Business Records
There are a lot of uses for web scraping. To get more out of web pages, you may utilize it in conjunction with APIs. As an example, there is a lot of commercial data out there, but it may not be relevant in its current form on websites. Our proposal in this paper is to analyze the reviews and derive valuable insights from the data using web scraping techniques (specifically, BeautifulSoup and Selenium, two popular libraries) and other Python libraries and techniques (vaderSentiment, Sentiment Intensity Analyzer, nltk, n consecutive words). We constructed a web scraper to extract prices and track their fluctuations. In addition, the evaluations are culled and examined to find pertinent comments, including consumer concerns.
Visualizations of Data Created to Aid in the Detection of Laboratory Data
Research and service activities in fundamental and clinical pharmacology rely heavily on the assessment of pharmacological and endogenous chemical concentrations. Using data science visualizations of laboratory data, it is shown on a real-life example that recommended standard visual techniques of data inspection or basic statistical investigation of laboratory test findings may fail to discover systemic laboratory mistakes. For instance, conventional data quality control approaches could miss data pathologies like an experiment run where all probes provide the same result. We find that sys- tematic laboratory mistakes may be better detected when using data visualizations that highlight diverse perspectives on the data. To better understand the data range, outliers, and a specific kind of systematic error—where identical values are incorrectly obtained in all probes—a dotplot of individual data organized by assay is suggested.
The Role of Big Data, Analytics and Understanding in Cardiology
Wearable, smart, and portable technology, among many others, aid the healthcare system in treating patients. The massive amounts of data generated by these devices need some sort of shaping in order to get actionable insights that may benefit both researchers and practitioners. When it comes to healthcare, there are a lot of different ways to accomplish this and get useful data. Additional data analysis for early patient care and treatment planning is being informed by this information. When patients receive treatment and care early on, it can improve their healthcare experience, which in turn can reduce healthcare costs, improve the quality of care they receive, and lessen the likelihood of errors and waste. This suggested study aims to help healthcare in general and cardiology in particular by surveying the literature on big data, analytics, and insights. Researchers and practitioners in the fields of healthcare and cardiology might use the provided study as a starting point for new ideas for early care.
Tools for Business Analytics and their Evaluation
Business analytics and its applications in technology are the focus of this article. A quick overview, definition, and breakdown of business analytics are provided at the outset of the article. The following section provides a synopsis of the several forms of business analytics. The next section will provide a comparison between business analytics and business intelligence, allowing the reader to grasp the difference between the two. Various methods utilized in the field and prominent organizations employing business analytics for growth are included in the paper’s conclusion.
Data Science’s Significant Role in Enhancing Cyber Defenses
Data science is the engine that is propelling the current technological and operational revolution in cybersecurity inside the realm of computers. The secret to an automated and intelligent security system is to construct a data-driven model that can extract patterns or insights from cybersecurity data regarding security incidents. Data science is the applied study of real-world events via the use of data in conjunction with different scientific methodologies, machine learning approaches, procedures, and systems. In this paper, I have provided a brief overview of data science, its evolution, and its applications in cloud security. I have also discussed the origins of cybersecurity data science (CSDS), the benefits it offers, and the steps involved in gathering data from relevant cybersecurity sources. Next, I will go over how analytics complement the latest data-driven patterns to provide more effective security solutions. By using the principles of cybersecurity data science, conventional cybersecurity computing processes may be superseded by more intelligent and actionable ones. Afterwards, I’ve gone over the several potential problems that may arise as a result of CSDS’s widespread use, as well as the ways in which machine learning and deep learning may be useful in this context, as well as the algorithms that could be employed. The study delves into the history of data science and its current applications in cybersecurity. It explains how data-driven intelligent decision-making systems can safeguard our system from both known and undiscovered cyber threats.
Data Analytics Expert Research throughout Several Sectors
The importance of domain expertise in improving data analysis across different businesses is explored in this article. By thoroughly analyzing case studies from the retail, healthcare, and financial industries, we show how domain knowledge greatly enhances data analysis, metric choice, and the generation of useful insights. Experts in a particular field are in a prime position to spot significant trends, create reliable prediction models, and provide stakeholders with useful insights because of the complementary nature of technical analytical abilities and in-depth knowledge of business contexts, as discussed in the article.We also provide data analysts concrete ways to learn domain expertise and use it into their work, such as partnering with experts in the field, doing industry-specific research, and attending industry-specific training. This essay highlights the significance of domain knowledge in the ever-changing data analysis landscape, while recognizing certain obstacles including cognitive biases and the fast development of businesses. Our research has important ramifications for businesses looking to improve their analytical capacities and for aspiring data analysts who want to focus on a particular industry.
Data Visualizations to Detect Systematic Errors in Laboratory Assay Results
The measurement of concentrations of drugs and endogenous substances is widely used in basic and clinical pharmacology research and service tasks. Using data science-derived visualizations of laboratory data, it is demonstrated on a real-life example that basic statistical exploration of laboratory assay results or advised stan- dard visual methods of data inspection may fall short in detecting systematic labora- tory errors. For example, data pathologies such as generating always the same value in all probes of a particular assay run may pass undetected when using standard methods of data quality check. It is shown that the use of different data visualiza- tions that emphasize different views of the data may enhance the detection of sys- tematic laboratory errors. A dotplot of single data in the order of assay is proposed that provides an overview on the data range, outliers and a particular type of sys- tematic errors where similar values are wrongly measured in all probes.
The Plant Journal Actively Supports Open Science and Data Transparency
An essential principle of academic research is that results of research and the resources developed are shared with the wider community. In the past, publishing the work in a peer-reviewed journal and adhering to journal guidelines with respect to sharing of materials such as plasmids, antibodies and seeds has been sufficient to ensure that this principle operates. However, technological advances have turned biology into a much more data-intensive science with very large datasets often a key part of the research.
A Guide for Social Science Journal Editors on Easing into Open Science
Journal editors have a large amount of power to advance open science in their respective fields by incentivising and mandating open policies and practices at their journals. The Data PASS Journal Editors Discussion Interface (JEDI, an online community for social science journal editors: www.dpjedi.org) has collated several resources on embed‑ ding open science in journal editing (www.dpjedi.org/resources). However, it can be overwhelming as an editor new to open science practices to know where to start. For this reason, we created a guide for journal editors on how to get started with open science. The guide outlines steps that editors can take to implement open policies and practices within their journal, and goes through the what, why, how, and worries of each policy and practice. This manuscript introduces and summarizes the guide (full guide: https://doi. org/10.31219/osf.io/hstcx).
Science Overview of the Europa Clipper Mission
The goal of NASA’s Europa Clipper mission is to assess the habitability of Jupiter’s moon Europa. After entering Jupiter orbit in 2030, the flight system will collect science data while flying past Europa 49 times at typical closest approach distances of 25–100 km. The mis- sion’s objectives are to investigate Europa’s interior (ice shell and ocean), composition, and geology; the mission will also search for and characterize any current activity including possible plumes. The science objectives will be accomplished with a payload consisting of remote sensing and in-situ instruments. Remote sensing investigations cover the ultraviolet, visible, near infrared, and thermal infrared wavelength ranges of the electromagnetic spec- trum, as well as an ice-penetrating radar. In-situ investigations measure the magnetic field, dust grains, neutral gas, and plasma surrounding Europa. Gravity science will be achieved using the telecommunication system, and a radiation monitoring engineering subsystem will provide complementary science data. The flight system is designed to enable all science in- struments to operate and gather data simultaneously. Mission planning and operations are guided by scientific requirements and observation strategies, while appropriate updates to the plan will be made tactically as the instruments and Europa are characterized and dis- coveries emerge. Following collection and validation, all science data will be archived in NASA’s Planetary Data System. Communication, data sharing, and publication policies pro- mote visibility, collaboration, and mutual interdependence across the full Europa Clipper science team, to best achieve the interdisciplinary science necessary to understand Europa.
Special Issue on Responsible Data Management and Data Science
We, the guest editors of the VLDB Journal special issue on Responsible Data Management and Data Science, are excited to present this special issue to the readership of the VLDB Journal. This special issue was conceived almost two years ago when it was becoming increasingly apparent to our community that while data management and data science technologies enable tremendous opportunities for individuals and organizations to make use of their data assets to create value, they also have the potential, if used without proper care, to have serious adverse consequences on these same individuals and organizations.
Retracted: An Architectural Framework for Healthcare Dashboards Design
In today’s competitive environment, one of the new tools in the field of information technology is business or organizational dashboards that are a backup in the process of strategic management of organizations. The purpose of the current research is to provide a framework to design the healthcare dashboard through technical architecture with fulfilling the decision-makers’ requirements. In this study, a common qualitative research method, metasynthesis, is applied, including a seven-step set of research questions, conducting systematic literature search and selection of suitable papers, data extraction, analysis and findings of the qualitative composition, quality control, and presentation of findings. During this process, 102 articles were found by saturation of information resources and then 12 articles were selected for extracting data using acceptance and rejection criteria. A critical evaluation method was used to evaluate the quality of selected articles. After investigating the selected articles and scoring them, in terms of quality, one article was very good, 10 articles were good, and one article was moderate. Then, with regard to the principles and guidelines of technical architecture, the required information was extracted from the selected articles and was analyzed with the method of open, axial, and selective coding. Following the steps of metasynthesis methods, the principles extracted with major and minor titles principles and guidelines in the form of multilayered system architecture including presentation layer, application layer, data layer, and technical infrastructure layer were classified. In the obtained framework, 15 indicators as the main principles and 66 subcriteria as the subsidiary principles for the design and technical architecture of enterprise dashboards were identified.
Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review
According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.
Big Data, Extracting Insights, Comprehension and Analytics in Cardiology: An Overview
Healthcare system facilitates the treatment of patients with the support of wearable, smart, and handheld devices, as well as many other devices. These devices are producing a huge bulk of data that need to be moulded for extracting meaningful insights from them for the useful use of researchers and practitioners. Various approaches, methods, and tools are in use for doing so and to extract meaningful information in the field of healthcare. This information is being used as evidence to further analyze the data for the early care of patient and to devise treatment. Early care and treatment can facilitate healthcare and the treatment of the patient and can have immense potentiality of dropping the care cost and quality refining of care and can decrease waste and chances of error. To facilitate healthcare in general and cardiology in specific, the proposed study presents an overview of the available literature associated with big data, its insights, and analytics. The presented report will help practitioners and researchers to devise new solutions for early care in healthcare and in cardiology.
Small Data Machine Learning in Materials Science
This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small data were introduced, including data extraction from publications, materials database construction, high-throughput computations and experiments from the data source level; modeling algorithms for small data and imbalanced learning from the algorithm level; active learning and transfer learning from the machine learning strategy level. Finally, the future directions for small data machine learning in materials science were proposed.
SciSciNet: A Large-Scale Open Data Lake for the Science of Science Research
The science of science has attracted growing research interests, partly due to the increasing availability of large-scale datasets capturing the innerworkings of science. These datasets, and the numerous linkages among them, enable researchers to ask a range of fascinating questions about how science works and where innovation occurs. Yet as datasets grow, it becomes increasingly difficult to track available sources and linkages across datasets. Here we present SciSciNet, a large-scale open data lake for the science of science research, covering over 134M scientific publications and millions of external linkages to funding and public uses. We offer detailed documentation of pre-processing steps and analytical choices in constructing the data lake. We further supplement the data lake by computing frequently used measures in the literature, illustrating how researchers may contribute collectively to enriching the data lake. Overall, this data lake serves as an initial but useful resource for the field, by lowering the barrier to entry, reducing duplication of efforts in data processing and measurements, improving the robustness and replicability of empirical claims, and broadening the diversity and representation of ideas in the field.
Data Science for Next-Generation Recommender Systems
Data science has been the foundation of recommender systems for a long time. Over the past few decades, various recommender systems have been developed using different data science and machine learning methodologies and techniques. However, no existing work systematically discusses the significant relationships between data science and recommender systems. To bridge this gap, this paper aims to systematically investigate recommender systems from the perspective of data science. Firstly, we introduce the various types of data used for recommendations and the corresponding machine learning models and methods that effectively represent each type. Next, we provide a brief outline of the representative data science and machine learning models utilized in building recommender systems. Subsequently, we share some preliminary thoughts on next-generation recommender systems. Finally, we summarize this special issue on data science for next-generation recommender systems.
Analysing Trends of Computational Urban Science and Data Science Approaches for Sustainable Development
Urban computing with a data science approaches can play a pivotal role in understaning and analyzing the potential of these methods for strategic, short-term, and sustainable planning. The recent development in urban areas have progressed towards the data-driven smart sustainable approaches to resolve the complexities around urban areas. The urban system faces severe challenges and these are complicated to capture, predict, resolve and deliver. The cur- rent study advances an unconventional decision-support framework to integrate the complexities of science, urban sustainability theories, and data science, with a data-intensive science to incorporate grassroots initiatives for a top- down policies. This work will influence the urban data analytics to optimize the designs and solutions to enhance sustainability, efficiency, resilience, equity, and quality of life. This work emphasizes the significant trends of data- driven and model-driven decision support systems. This will help to address and create an optimal solution for mul- tifaceted challenges of an urban setup within the analytical framework. The analytical investigations includes the research about land use prediction, environmental monitoring, transportation modelling, and social equity analysis. The fusion of urban computing, intelligence, and sustainability science is expected to resolve and contribute in shap- ing resilient, equitable, and future environmentally sensible eco-cities. It examines the emerging trends in the domain of computational urban science and data science approaches for sustainable development being utilized to address urban challenges including resource management, environmental impact, and social equity. The analysis of recent improvements and case studies highlights the potential of data-driven insights with computational models for promoting resilient sustainable urban environments, towards more effective and informed policy-making. Thus, this work explores the integration of computational urban science and data science methodologies to advance sustainable development.
Two Journals and a Pandemic: Reflections on Being a Data Science Editor-in-Chief
This essay is a collection of reflections from my experiences in the past few years, first as editor-in-chief (EiC) of the Data Science Journal and then launching Patterns as its first EiC during the global COVID-19 pandemic. I discuss what I learned and how I worked with these journals, and I close with some hopes for the next 20 years of data science.
The Launch of the Data Science Journal in 2002
The Committee on Data for Science and Technology of the International Council of Scientific Unions (CODATA) decided in February, 2001 to publish a new on-line peer reviewed journal, freely available to all. It would be called the Data Science Journal. The subsequent steps taken to launch this journal are described, leading to the publication of the first issue of 10 papers in April 2002 and of the first volume of 19 papers by December 2002. Some necessary corrections and improvements before the publication of a second volume of 19 papers in 2003 are also described.
Insights on Extracting Bio-Active Compounds from Plants that Act Against Bacteria
Green technology is the worldwide urgent demand all over the world since various bioactive compounds are in use daily effecting by their composition man’s health that led to the optimization for a most beneficial technique for extracting essential materials and compounds, the need for safe and effective methods in extracting essential compounds were raised among these novel methods: extracting for Bioactive Materials by Supercritical and subcritical fluid Extraction, by Ultrasound assisted Extraction, by Negative Pressure Cavitation Assisted Extraction, by Microwave Assisted Extraction, by Pressurized Liquid and Hot Water Extraction, by High Pressure Assisted Extraction, by Enzyme Assisting and many other methods which are all green and environmentally friendly. Researches proceed in inventing new effective green methods of novelty. This could be aided by the previous techniques and their yields quality and amount with less time, cost, energy, human interference. Our review highlights how these environmentally friendly techniques can enhance the recovery of bioactive compounds, with the goal of influencing future extraction procedures and industrial uses in food, cosmetics, and pharmaceuticals.
Data Analysis in Social Science Research
Data analysis in social science provides the statistical concepts and programming skills to conduct and evaluate social science research studies. Data analysis provides social science researchers with the tools to unlock insights and understand complex social phenomena. Researchers can interpret the data and uncover relationships and patterns to address human behaviour and social experiences. Data analysis summarizes the collected data. It involves the interpretation of data gathered through the use of analytical and logical reasoning to determine patterns, relationships, or trends. Social science researchers focus on expanding their knowledge of social dynamics. Data analysis in social science research provides empirical evidence to dig deeper and explore human experience, attitudes, interactions, and social structures. The social science data seeks researchers with training or a demonstrable aptitude for social science work and programming to refine and extend their skills through the generation, analysis, and critique of large-scale social data. The tools for such an approach are multifaceted and evolve quickly. Data analysis encompasses both quantitative and qualitative methods. Quantitative methods in social science research allow objective insights with the help of statistical analysis. Qualitative methods in social science research provide exploratory insights with the help of textual analysis. The goal of data analysis in social science is to interpret and summarise findings. Statistical analysis is playing a major role in social science research. In the present era, the top tier journal editors expect the researchers to use a scientific approach to analyse the data collected through primary or secondary sources. This knowledge will help social science researchers and teachers understand the process of analysing the data and reporting the results in their research projects using different software.
The Synergy of Data Science, GIS Spatial Analysis and Knowledge Management as a Path to Sustainability Insights
This chapter focuses on data science, GIS-based spatial analysis, and knowledge management (KM) threesome and its potential contributions to sustainability insights. Discussion commenced with tracing the historical evolution and essen- tiality of geography for comprehending environmental and social dimensions of sustainability. Then, argumentation delved into the interplay among the three- some’s domains and their contributions to sustainability achievement. A prolonged elaboration was provided on geospatial data analytics, visualization, geospatial data mining, and predictive models and their significance for extracting informative and meaningful insights. Since data science has transformed and enriched most scientific disciplines, its empowering implications on GIS were explained. Spatial analysis, therefore, occupied a central position and enabled advanced GIS technique utiliza- tion to reveal patterns, relationships, and trends in geospatial data. Furthermore, the chapter explained the interdependent relationships between GIS and KM. Integrating GIS and KM techniques has revolutionized geospatial data organization, visualization, and dissemination and enhanced applications of decision support, environmental planning, and others. The Nexus of this threesome, therefore, serves as a roadmap for solving issues of intricate spatial problems via modeling and informed decisions. The chapter stressed and concluded that the integrated fusion of data science, GIS, and KM provides a robust framework and ideal tools supporting sustainability.
Data Science in Marketing: How Analytics are Reshaping Consumer Insight
Purpose: The purpose of this study is to explore the transformative impact of data science on marketing strategies and consumer insights. Research Design and Methodology: Employing a qualitative research design, the study utilizes in-depth case studies and interviews with industry experts to uncover how data-driven approaches enhance traditional marketing practices. Findings and Discussion: The findings reveal that data science significantly improves customer segmentation, dynamic pricing, customer relationship management (CRM), and personalized marketing. By leveraging behavioral data, companies like Amazon achieve more granular and dynamic segmentation, leading to higher engagement and conversion rates. Dynamic pricing, as implemented by companies like Uber, optimizes revenue and enhances customer satisfaction through real-time adjustments based on demand and competition. The study also highlights the importance of predictive analytics in CRM, allowing businesses to identify at-risk customers and implement targeted retention strategies. Furthermore, ethical considerations such as data privacy and algorithmic bias are critical for maintaining consumer trust. Implications: The research underscores the need for integrating data science with traditional marketing frameworks and adapting models to diverse cultural contexts. The implications for practice include adopting data-driven segmentation, dynamic pricing, and CRM strategies while ensuring ethical data practices. This study contributes to the broader discourse on data-driven marketing, offering valuable insights for both academic research and practical applications, ultimately advocating for responsible and effective use of data science in marketing.
Knowledge Mapping in the Field of Library and Information Science in Scopus-Indexed Q1 Library Science Journals, 2018-2022
Knowledge mapping has spread beyond library and information science journals and into other fields. To give current insights into scientific literature, it is necessary to study the development of library and information science research. The goal of this study is to examine trends in research subjects in the field of library and information science using keyword occurrences from 2018 to 2022. The research method employs a bibliometric approach, conducting knowledge mapping based on co-occurrence using the VOSviewer application. Data collection techniques involve downloading metadata from international scientific journals categorized under “Library and Information Science” and indexed as Q1 on the Scimago Journal Rank website. Based on the downloaded metadata, the ISSN is used to obtain the metadata of each journal in CSV format from the Scopus. The results of the research indicate that library science studies have expanded into the fields of technology and health, as evidenced by the high occurrence of keywords such as “human” with 1310 occurrences and “social networking” with 732 occurrences.
Library and information science research
Quantum Insights: Revolutionizing International Business and Financial Services through Data Analytics, Big Data and Data Science
In the contemporary landscape of international business and financial services, the convergence of quantum computing, data analytics, big data, and data science is catalyzing a paradigm shift. This paper explores the transformative potential of Quantum Insights in revolutionizing decision-making processes, risk management, and strategic planning within these sectors. Quantum computing, with its ability to process vast datasets and perform complex calculations at unprecedented speeds, is poised to disrupt traditional approaches to data analysis. The integration of quantum algorithms with advanced data analytics techniques opens new avenues for uncovering patterns, trends, and correlations that were previously inaccessible. This synergy not only enhances the accuracy of predictive models but also enables real-time analysis, empowering businesses and financial institutions to make informed decisions swiftly in response to dynamic market conditions. Big Data, characterized by the exponential growth of information, presents both challenges and opportunities. Quantum computing, with its inherent parallelism, addresses the computational bottlenecks associated with handling massive datasets. This paper explores how Quantum Insights can efficiently extract actionable insights from big data, enabling organizations to gain a competitive edge through a deeper understanding of customer behavior, market trends, and operational dynamics. Moreover, the application of Quantum Insights in risk management is a critical aspect of this paradigm shift. By leveraging quantum computing’s computational prowess, financial institutions can simulate and assess complex risk scenarios with unprecedented precision. This not only enhances the resilience of the financial system but also contributes to the development of innovative financial products and services. Data science plays a pivotal role in unlocking the full potential of Quantum Insights. The integration of quantum machine learning algorithms with traditional data science methodologies expands the scope of predictive analytics, allowing for the discovery of novel insights and the optimization of business processes. This paper investigates how Quantum Insights can catalyze advancements in personalized financial services, fraud detection, and portfolio optimization. In conclusion, Quantum Insights represents a transformative force in international business and financial services. The convergence of quantum computing, data analytics, big data, and data science is reshaping the way organizations derive value from information. As quantum technologies continue to mature, businesses and financial institutions that embrace Quantum Insights will be better positioned to navigate the complexities of the global marketplace, foster innovation, and achieve sustainable growth.
Data Science in the 21st Century: Evolution, Challenges and Future Directions
Data Science has undergone a remarkable evolution in the 21st century, transforming from a niche field into an integral component of various industries. This article explores the dynamic journey of Data Science, highlighting its evolution, current challenges, and potential future directions. The evolution encompasses advancements in technology, methodologies, and applications, leading to its widespread adoption across diverse domains. Challenges such as data privacy, ethical considerations, and the need for skilled professionals are discussed, shedding light on the hurdles that the field faces. Looking ahead, the article envisions future directions for Data Science, including the integration of artificial intelligence, the ethical use of data, and the emergence of novel techniques to handle massive datasets. As Data Science continues to play a pivotal role in shaping the modern world, understanding its evolution, addressing challenges, and anticipating future trends are crucial for researchers, practitioners, and policymakers alike.
Medical Library and Information Science: Do We Need New Journal?
On behalf of the chairman, Prof. Abbas Haghparast, and the editorial board of the journal, I proudly announce Iran’s first English-language journal in the medical library and information science area.