Data Science - Extracting Insights
type: channel
description: 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
publisher: Python Publishing Press
language: en
publication name: Data Science - Extracting Insights
items: Lost Science: Protecting Data Through Improved Archiving
items: Tracking Reviews and Scraping the Web. Gaining Understanding from Business Records
items: Visualizations of Data Created to Aid in the Detection of Laboratory Data
items: The Role of Big Data, Analytics and Understanding in Cardiology
items: Tools for Business Analytics and their Evaluation
items: Data Science’s Significant Role in Enhancing Cyber Defenses
items: Data Analytics Expert Research throughout Several Sectors
items: Data Visualizations to Detect Systematic Errors in Laboratory Assay Results
items: The Plant Journal Actively Supports Open Science and Data Transparency
items: A Guide for Social Science Journal Editors on Easing into Open Science
items: Science Overview of the Europa Clipper Mission
items: Special Issue on Responsible Data Management and Data Science
items: Retracted: An Architectural Framework for Healthcare Dashboards Design
items: Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review
items: Big Data, Extracting Insights, Comprehension and Analytics in Cardiology: An Overview
items: Small Data Machine Learning in Materials Science
items: SciSciNet: A Large-Scale Open Data Lake for the Science of Science Research
items: Data Science for Next-Generation Recommender Systems
items: Analysing Trends of Computational Urban Science and Data Science Approaches for Sustainable Development
items: Two Journals and a Pandemic: Reflections on Being a Data Science Editor-in-Chief
items: The Launch of the Data Science Journal in 2002
items: Insights on Extracting Bio-Active Compounds from Plants that Act Against Bacteria
items: Data Analysis in Social Science Research
items: The Synergy of Data Science, GIS Spatial Analysis and Knowledge Management as a Path to Sustainability Insights
items: Data Science in Marketing: How Analytics are Reshaping Consumer Insight
items: Knowledge Mapping in the Field of Library and Information Science in Scopus-Indexed Q1 Library Science Journals, 2018-2022
items: Quantum Insights: Revolutionizing International Business and Financial Services through Data Analytics, Big Data and Data Science
items: Data Science in the 21st Century: Evolution, Challenges and Future Directions
items: Medical Library and Information Science: Do We Need New Journal?

Lost Science: Protecting Data Through Improved Archiving
type: item
description: 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).
subject: Articles
  topic: section
creator: Karen E. Simmons
rights: Copyright (c) 2024
date: 2024-08-17
publication date: 2024-08-17
starting page: 147
ending page: 148
doi: 10.70705/

Tracking Reviews and Scraping the Web. Gaining Understanding from Business Records
type: item
description: 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.
subject: Articles
  topic: section
subject: Web scraping
  topic: keywords
subject: Price tracking
  topic: keywords
subject: Sentiment analysis
  topic: keywords
subject: Alerts
  topic: keywords
creator: Julio C. Fernández-Travieso
creator: José Illnait-Ferrer
creator: Lilia Fernández-Dorta
creator: Sarahi Mendoza-Castaño
rights: Copyright (c) 2024
date: 2024-11-26
publication date: 2024-11-26
starting page: 153
ending page: 156
doi: 10.70705/ppp.dsei.2024.v02.i02.pp153-156

Visualizations of Data Created to Aid in the Detection of Laboratory Data
type: item
description: 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.
subject: Articles
  topic: section
subject: Data quality check
  topic: keywords
subject: Data science
  topic: keywords
subject: R programming language
  topic: keywords
creator: Akmal Djamaan
creator: Reni Mayerni
creator: Betna Dewi
rights: Copyright (c) 2024
date: 2024-10-14
publication date: 2024-10-14
starting page: 151
ending page: 152
doi: 10.70705/ppp.dsei.2024.v02.i02.pp151-152

The Role of Big Data, Analytics and Understanding in Cardiology
type: item
description: 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.
subject: Articles
  topic: section
creator: Sapra Palak
creator: Vasava S. Mahesh
creator: Solanki A. Hitesh
creator: Hitesh Patel
rights: Copyright (c) 2024
date: 2024-04-30
publication date: 2024-04-30
starting page: 84
ending page: 87
doi: 10.70705/ppp.dsei.2024.v02.i01.pp84-87

Tools for Business Analytics and their Evaluation
type: item
description: 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.
subject: Articles
  topic: section
subject: Advanced analytics for businesses
  topic: keywords
subject: Including descriptive
  topic: keywords
subject: Predictive
  topic: keywords
subject: Prescriptive
  topic: keywords
subject: Diagnostic
  topic: keywords
subject: Business intelligence
  topic: keywords
creator: Sony E. Nugraha
creator: Agatha Christie
creator: Gamit B. Sandip
rights: Copyright (c) 2024
date: 2024-04-26
publication date: 2024-04-26
starting page: 80
ending page: 83
doi: 10.70705/ppp.dsei.2024.v02.i01.pp80-83

Data Science’s Significant Role in Enhancing Cyber Defenses
type: item
description: 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.
subject: Articles
  topic: section
subject: Bitcoin
  topic: keywords
subject: Cryptocurrency
  topic: keywords
subject: Deep neural networks
  topic: keywords
subject: Severe gradient boosting
  topic: keywords
creator: Ramlan Sinaga
creator: Siti M. Sinaga
rights: Copyright (c) 2023
date: 2023-12-12
publication date: 2023-12-12
starting page: 54
ending page: 65
doi: 10.70705/ppp.doaj.2023.v01.i01.pp54-65

Data Analytics Expert Research throughout Several Sectors
type: item
description: 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.
subject: Articles
  topic: section
subject: Data analysis
  topic: keywords
subject: Domain knowledge
  topic: keywords
subject: Insights into a specific industry
  topic: keywords
subject: Analytical efficacy
  topic: keywords
subject: Analytics applicable to several sectors
  topic: keywords
creator: Jeffrey Archer
creator: Marline Nainggolan
rights: Copyright (c) 2023
date: 2023-11-24
publication date: 2023-11-24
starting page: 47
ending page: 53
doi: 10.70705/ppp.doaj.2023.v01.i01.pp47-53

Data Visualizations to Detect Systematic Errors in Laboratory Assay Results
type: item
description: 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.
subject: Articles
  topic: section
subject: Data quality check
  topic: keywords
subject: Data science
  topic: keywords
subject: R programming language
  topic: keywords
creator: Jo€rn Lo€tsch
rights: Copyright (c) 2024
date: 2024-09-20
publication date: 2024-09-20
starting page: 150
ending page: 150
doi: 10.70705/ppp.dsei.2024.v02.i02.pp149

The Plant Journal Actively Supports Open Science and Data Transparency
type: item
description: 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.
subject: Articles
  topic: section
creator: Alisdair Fernie
creator: Lee Sweetlove
rights: Copyright (c) 2024
date: 2024-09-06
publication date: 2024-09-06
starting page: 149
ending page: 149
doi: 10.70705/ppp.dsei.2024.v02.i02.pp147-148

A Guide for Social Science Journal Editors on Easing into Open Science
type: item
description: 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).
subject: Articles
  topic: section
subject: Open science
  topic: keywords
subject: Journal editing
  topic: keywords
subject: Scholarly publishing
  topic: keywords
subject: Peer review
  topic: keywords
creator: Priya Silverstein
creator: Colin Elman
creator: Amanda Montoya
creator: Barbara McGillivray
creator: Charlotte R. Pennington
creator: Chase H. Harrison
rights: Copyright (c) 2024
date: 2024-06-04
publication date: 2024-06-04
starting page: 135
ending page: 146
doi: 10.70705/ppp.dsei.2024.v02.i02.pp135-146

Science Overview of the Europa Clipper Mission
type: item
description: 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.
subject: Articles
  topic: section
subject: Europa
  topic: keywords
subject: Europa clipper
  topic: keywords
subject: Ocean
  topic: keywords
subject: Habitability
  topic: keywords
subject: Icy moon
  topic: keywords
subject: Jupiter
  topic: keywords
creator: Robert T. Pappalardo
creator: Bonnie J. Buratti
creator: Haje Korth
creator: David A. Senske
creator: Diana L. Blaney
creator: Donald D. Blankenship
rights: Copyright (c) 2024
date: 2024-05-31
publication date: 2024-05-31
starting page: 107
ending page: 134
doi: 10.70705/ppp.dsei.2024.v02.i02.pp107-134

Special Issue on Responsible Data Management and Data Science
type: item
description: 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.
subject: Articles
  topic: section
creator: Zi Huang
creator: Yanyan Shen
creator: Divesh Srivastava
rights: Copyright (c) 2024
date: 2024-05-28
publication date: 2024-05-28
starting page: 106
ending page: 106
doi: 10.70705/ppp.dsei.2024.v02.i02.pp106

Retracted: An Architectural Framework for Healthcare Dashboards Design
type: item
description: 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.
subject: Articles
  topic: section
subject: Information technology
  topic: keywords
subject: Organizational
  topic: keywords
subject: Framework
  topic: keywords
creator: Saeed Rouhani
creator: Shooka Zamenian
rights: Copyright (c) 2024
date: 2024-05-13
publication date: 2024-05-13
starting page: 98
ending page: 105
doi: 10.70705/ppp.dsei.2024.v02.i02.pp98-105

Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review
type: item
description: 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.
subject: Articles
  topic: section
subject: American Cancer Society
  topic: keywords
subject: Computer-Aided Diagnosis (CAD)
  topic: keywords
subject: United States
  topic: keywords
subject: CAD system
  topic: keywords
creator: Saleem Z. Ramadan
rights: Copyright (c) 2024
date: 2024-05-01
publication date: 2024-05-01
starting page: 80
ending page: 97
doi: 10.70705/ppp.dsei.2024.v02.i02.pp80-97

Big Data, Extracting Insights, Comprehension and Analytics in Cardiology: An Overview
type: item
description: 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.
subject: Articles
  topic: section
subject: Healthcare
  topic: keywords
subject: Handheld devices
  topic: keywords
subject: Cardiology
  topic: keywords
creator: Hui Xiao
creator: Sikandar Ali
creator: Zhen Zhang
creator: Muhammad S. Sarfraz
creator: Fang Zhang
creator: Mohammad Faisal
rights: Copyright (c) 2024
date: 2024-04-24
publication date: 2024-04-24
starting page: 74
ending page: 79
doi: 10.70705/ppp.dsei.2024.v02.i01.pp74-79

Small Data Machine Learning in Materials Science
type: item
description: 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.
subject: Articles
  topic: section
subject: Machine learning
  topic: keywords
subject: Small data
  topic: keywords
subject: Materials database
  topic: keywords
creator: Pengcheng Xu
creator: Xiaobo Ji
creator: Minjie Li
creator: Wencong Lu
rights: Copyright (c) 2024
date: 2024-04-01
publication date: 2024-04-01
starting page: 58
ending page: 73
doi: 10.70705/ppp.dsei.2024.v02.i01.pp58-73

SciSciNet: A Large-Scale Open Data Lake for the Science of Science Research
type: item
description: 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.
subject: Articles
  topic: section
subject: SciSciNet
  topic: keywords
subject: Science
  topic: keywords
subject: Data processing
  topic: keywords
subject: Science research
  topic: keywords
creator: Zihang Lin
creator: Yian Yin
creator: Lu Liu
creator: Dashun Wang
rights: Copyright (c) 2024
date: 2024-03-22
publication date: 2024-03-22
starting page: 46
ending page: 57
doi: 10.70705/ppp.dsei.2024.v02.i01.pp46-57

Data Science for Next-Generation Recommender Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Data science
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Recommender systems
  topic: keywords
subject: Recommendation
  topic: keywords
creator: Shoujin Wang
creator: Yan Wang
creator: Fikret Sivrikaya
creator: Sahin Albayrak
creator: Vito W. Anelli
rights: Copyright (c) 2024
date: 2024-03-08
publication date: 2024-03-08
starting page: 35
ending page: 45
doi: 10.70705/ppp.dsei.2024.v02.i01.pp35-45

Analysing Trends of Computational Urban Science and Data Science Approaches for Sustainable Development
type: item
description: 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 comp…
subject: Articles
  topic: section
subject: Data science
  topic: keywords
subject: Sustainable urban development
  topic: keywords
subject: Resource management
  topic: keywords
subject: Computational urban science
  topic: keywords
subject: Environmental challenges
  topic: keywords
creator: Deepak Kumar
creator: Nick P. Bassill
rights: Copyright (c) 2024
date: 2024-02-26
publication date: 2024-02-26
starting page: 20
ending page: 34
doi: 10.70705/ppp.dsei.2024.v02.i01.pp20-34

Two Journals and a Pandemic: Reflections on Being a Data Science Editor-in-Chief
type: item
description: 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.
subject: Articles
  topic: section
subject: Launch
  topic: keywords
subject: Data science
  topic: keywords
subject: On-line journal
  topic: keywords
creator: Sarah Callaghan
rights: Copyright (c) 2024
date: 2024-01-27
publication date: 2024-01-27
starting page: 4
ending page: 6
doi: 10.70705/ppp.dsei.2024.v02.i01.pp4-6

The Launch of the Data Science Journal in 2002
type: item
description: 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.
subject: Articles
  topic: section
subject: Launch
  topic: keywords
subject: Data science
  topic: keywords
subject: On-line journal
  topic: keywords
creator: Francis J. Smith
rights: Copyright (c) 2024
date: 2024-01-04
publication date: 2024-01-04
starting page: 1
ending page: 3
doi: 10.70705/ppp.dsei.2024.v02.i01.pp1-3

Insights on Extracting Bio-Active Compounds from Plants that Act Against Bacteria
type: item
description: 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.
subject: Articles
  topic: section
subject: Extraction method
  topic: keywords
subject: Green technology
  topic: keywords
subject: Essential plant oil
  topic: keywords
subject: Bioactive compounds
  topic: keywords
creator: Mariam H. Abdulkareem
rights: Copyright (c) 2023
date: 2023-10-19
publication date: 2023-10-19
starting page: 43
ending page: 46
doi: 10.70705/ppp.dsei.2023.v01.i01.pp43-46

Data Analysis in Social Science Research
type: item
description: 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.
subject: Articles
  topic: section
subject: Social science
  topic: keywords
subject: Data
  topic: keywords
subject: Analysis
  topic: keywords
subject: Interpretation
  topic: keywords
creator: Chunuram Soren
rights: Copyright (c) 2023
date: 2023-09-06
publication date: 2023-09-06
starting page: 38
ending page: 42
doi: 10.70705/ppp.dsei.2023.v01.i01.pp38-42

The Synergy of Data Science, GIS Spatial Analysis and Knowledge Management as a Path to Sustainability Insights
type: item
description: 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.
subject: Articles
  topic: section
subject: Informatics
  topic: keywords
subject: Data science
  topic: keywords
subject: GIS-spatial analysis
  topic: keywords
subject: KM
  topic: keywords
subject: Sustainability
  topic: keywords
creator: Mohamed B.O. Osman
rights: Copyright (c) 2023
date: 2023-07-17
publication date: 2023-07-17
starting page: 30
ending page: 37
doi: 10.70705/ppp.dsei.2023.v01.i01.pp30-37

Data Science in Marketing: How Analytics are Reshaping Consumer Insight
type: item
description: 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.
subject: Articles
  topic: section
subject: Data science
  topic: keywords
subject: Marketing
  topic: keywords
subject: Customer segmentation
  topic: keywords
subject: Dynamic pricing
  topic: keywords
subject: Predictive analytics
  topic: keywords
subject: Ethical data practices
  topic: keywords
creator: Jaya Kusuma
rights: Copyright (c) 2023
date: 2023-05-20
publication date: 2023-05-20
starting page: 21
ending page: 29
doi: 10.70705/ppp.dsei.2023.v01.i01.pp21-29

Knowledge Mapping in the Field of Library and Information Science in Scopus-Indexed Q1 Library Science Journals, 2018-2022
type: item
description: 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.
subject: Articles
  topic: section
subject: Bibliometrics
  topic: keywords
subject: Knowledge mapping
  topic: keywords
subject: Library and information science research
  topic: keywords
creator: Salmah Kamalia
creator: Cecep Ibrahim
creator: Annisa Chafit
creator: Baiq S. Azahra
creator: Ririn Trimulyani
creator: Syahrani R. Sadino
creator: Keyza N. Qatrunnadine
creator: Bagas K. Akbar
creator: Danang D. Kangko
rights: Copyright (c) 2023
date: 2023-04-11
publication date: 2023-04-11
starting page: 14
ending page: 20
doi: 10.70705/ppp.dsei.2023.v01.i01.pp14-20

Quantum Insights: Revolutionizing International Business and Financial Services through Data Analytics, Big Data and Data Science
type: item
description: 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 algori…
subject: Articles
  topic: section
subject: Landscape
  topic: keywords
subject: Quantum computing
  topic: keywords
subject: Data analysis
  topic: keywords
subject: Quantum insights
  topic: keywords
subject: Data science
  topic: keywords
creator: Charles E
creator: Kaledio E
creator: Samuel Adedolapo
rights: Copyright (c) 2023
date: 2023-03-20
publication date: 2023-03-20
starting page: 9
ending page: 13
doi: 10.70705/ppp.dsei.2023.v01.i01.pp9-13

Data Science in the 21st Century: Evolution, Challenges and Future Directions
type: item
description: 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.
subject: Articles
  topic: section
subject: Data science
  topic: keywords
subject: 21st century
  topic: keywords
subject: Evolution
  topic: keywords
subject: Challenges
  topic: keywords
subject: Future directions
  topic: keywords
subject: Technology advancements
  topic: keywords
subject: Widespread adoption
  topic: keywords
subject: Data privacy
  topic: keywords
subject: Ethical considerations
  topic: keywords
subject: Skilled professionals
  topic: keywords
subject: Artificial intelligence integration
  topic: keywords
subject: Ethical data use
  topic: keywords
subject: Massive datasets
  topic: keywords
creator: K. K. Ramachandran
rights: Copyright (c) 2023
date: 2023-02-13
publication date: 2023-02-13
starting page: 2
ending page: 8
doi: 10.70705/ppp.dsei.2023.v01.i01.pp2-8

Medical Library and Information Science: Do We Need New Journal?
type: item
description: 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.
subject: Articles
  topic: section
creator: Maryam Shekofteh
rights: Copyright (c) 2023
date: 2023-01-21
publication date: 2023-01-21
starting page: 1
ending page: 1
doi: 10.70705/ppp.dsei.2023.v01.i01.pp1