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activity guide big open and crowdsourced data

Activity Guide: Big Open and Crowdsourced Data – Unlocking the Potential of Collective Intelligence

Author: Dr. Anya Sharma, PhD in Data Science, specializing in open data initiatives and crowdsourcing methodologies. Dr. Sharma has over 10 years of experience working with large-scale datasets, developing innovative applications using open and crowdsourced data, and publishing research in leading data science journals.

Publisher: Open Data Institute (ODI), a global leader in promoting the use of open data for social good.

Editor: Mr. Ben Carter, MSc in Data Analytics, experienced editor with a focus on technical and scientific publications.

Keywords: activity guide, big open data, crowdsourced data, open data initiatives, data analysis, data visualization, data ethics, data privacy, collaborative data science, citizen science, data governance, open data platforms

Introduction:

This activity guide – big open and crowdsourced data serves as a comprehensive resource for individuals and organizations seeking to leverage the immense potential of openly accessible and collaboratively generated data. We’ll explore various methodologies, practical applications, and ethical considerations associated with working with these powerful datasets. This guide aims to equip you with the knowledge and tools to effectively engage with big open and crowdsourced data, fostering innovation and positive societal impact.

I. Understanding Big Open and Crowdsourced Data

Big open data refers to massive datasets that are publicly available and freely accessible for anyone to use and analyze. These datasets often come from diverse sources such as government agencies, research institutions, and private companies. Crowdsourced data, on the other hand, is generated through the collective efforts of a large number of individuals, often via online platforms. Examples include contributions to Wikipedia, OpenStreetMap, or citizen science projects. The synergy between these two data types presents incredible opportunities but also demands careful consideration.

II. Methodologies for Working with Big Open and Crowdsourced Data

This activity guide – big open and crowdsourced data outlines several core methodologies:

A. Data Acquisition and Cleaning: Identifying relevant open data repositories (e.g., data.gov, opendata.europa.eu) is crucial. Crowdsourced data requires careful validation and cleaning to address inconsistencies and biases. Techniques like data deduplication, outlier detection, and imputation are essential.

B. Data Exploration and Visualization: Understanding the structure and characteristics of the data is paramount. Exploratory data analysis (EDA) techniques, along with data visualization tools (e.g., Tableau, Power BI), help uncover patterns, trends, and potential biases.

C. Data Analysis and Modeling: A variety of analytical approaches can be employed, ranging from simple descriptive statistics to complex machine learning algorithms. The choice of method depends on the research question and the nature of the data.

D. Data Interpretation and Communication: Drawing meaningful insights from the analysis is crucial. Effective communication of findings, often through visualizations and reports, is key to disseminating knowledge and influencing decision-making.

III. Applications of Big Open and Crowdsourced Data

The applications of this activity guide – big open and crowdsourced data are vast:

Environmental Monitoring: Crowdsourced data on air quality, water pollution, and biodiversity can complement official data, providing a more comprehensive picture.
Urban Planning: Open data on traffic patterns, crime rates, and public transportation can inform urban planning and infrastructure development.
Public Health: Crowdsourced health data can be used to monitor disease outbreaks, track health trends, and improve healthcare services.
Disaster Response: Open data and crowdsourced information can aid in disaster relief efforts by providing real-time situational awareness.
Social Science Research: Open and crowdsourced data provide rich sources for studying social trends, behaviors, and opinions.

IV. Ethical Considerations and Data Governance

Working with big open and crowdsourced data necessitates careful consideration of ethical implications:

Data Privacy: Protecting the privacy of individuals whose data is being used is paramount. Anonymization and data aggregation techniques are important safeguards.
Data Bias: Crowdsourced data can reflect existing societal biases. Understanding and mitigating these biases is crucial for ensuring fair and equitable outcomes.
Data Transparency: Openly sharing data and methodologies promotes trust and reproducibility.
Data Security: Protecting data from unauthorized access and misuse is essential.

V. Tools and Technologies

This activity guide – big open and crowdsourced data highlights several useful tools:

OpenRefine: For data cleaning and transformation.
R and Python: Programming languages for data analysis and modeling.
Tableau and Power BI: Data visualization tools.
GitHub: For collaborative data projects.

Conclusion:

This activity guide – big open and crowdsourced data underscores the transformative potential of harnessing these powerful data sources. By combining rigorous methodologies, ethical considerations, and appropriate technologies, we can unlock valuable insights and drive positive change across diverse sectors. This collaborative approach, leveraging collective intelligence, is essential for addressing complex challenges and building a more informed and equitable world.

FAQs:

    • What is the difference between open data and crowdsourced data? Open data is publicly available, while crowdsourced data is generated by the public.
    • How can I find reliable sources of big open data? Data.gov, opendata.europa.eu, and similar government portals are good starting points.
    • What are the common challenges in working with crowdsourced data? Inconsistency, bias, and the need for validation are major challenges.
    • What are the ethical implications of using big open data? Privacy, bias, and transparency are key ethical considerations.
    • What tools can help with analyzing big open and crowdsourced data? R, Python, Tableau, and Power BI are useful tools.
    • How can I ensure the accuracy of crowdsourced data? Implementing validation mechanisms and employing data cleaning techniques are vital.
    • What are the legal considerations when using open data? Licensing agreements and terms of use should be carefully reviewed.
    • How can I contribute to crowdsourced data initiatives? Participate in citizen science projects or contribute to open data platforms.
    • Where can I learn more about big open and crowdsourced data? Explore online courses, workshops, and academic resources.

Related Articles:

    • "Data Cleaning Techniques for Crowdsourced Datasets": A detailed guide on data cleaning methods specific to crowdsourced data, addressing inconsistencies and biases.
    • "Ethical Considerations in Open Data Initiatives": An in-depth exploration of the ethical dilemmas associated with open data usage, including privacy and bias.
    • "Visualizing Big Data: Best Practices and Tools": A guide on effective data visualization techniques for large datasets, focusing on clarity and insight.
    • "Introduction to Machine Learning for Open Data Analysis": An introductory guide to applying machine learning algorithms to open datasets.
    • "Case Studies: Successful Applications of Crowdsourced Data": Real-world examples of successful applications of crowdsourced data across various fields.
    • "Open Data Governance Frameworks: Best Practices": A discussion of best practices for establishing robust data governance frameworks for open data initiatives.
    • "Building a Crowdsourced Data Platform: A Step-by-Step Guide": A practical guide to designing and implementing a platform for collecting and managing crowdsourced data.
    • "The Future of Big Open and Crowdsourced Data": A forward-looking perspective on the evolving landscape of big open and crowdsourced data and its implications.
    • "Combating Bias in Crowdsourced Data: Strategies and Techniques": A detailed exploration of methods to identify and mitigate biases in crowdsourced data.

  activity guide big open and crowdsourced data: The Book of Alternative Data Alexander Denev, Saeed Amen, 2020-07-21 The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject. This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors—leading experts in financial modeling, machine learning, and quantitative research and analytics—employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book: Provides an integrated modeling approach to extract value from multiple types of datasets Treats the processes needed to make alternative data signals operational Helps investors and risk managers rethink how they engage with alternative datasets Features practical use case studies in many different financial markets and real-world techniques Describes how to avoid potential pitfalls and missteps in starting the alternative data journey Explains how to integrate information from different datasets to maximize informational value The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users.
  activity guide big open and crowdsourced data: The GIS Guide to Public Domain Data Joseph J. Kerski, Jill Clark, 2012 Readers will understand how to find, evaluate, and analyze data to solve location-based problems. This guide covers practical issues such as copyrights, cloud computing, online data portals, volunteered geographic information, and international data with supplementary exercises.
  activity guide big open and crowdsourced data: The Analytics Lifecycle Toolkit Gregory S. Nelson, 2018-03-07 An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results.
  activity guide big open and crowdsourced data: Crowdsourcing our Cultural Heritage Ms Mia Ridge, 2014-10-28 Crowdsourcing, or asking the general public to help contribute to shared goals, is increasingly popular in memory institutions as a tool for digitising or computing vast amounts of data. This book brings together for the first time the collected wisdom of international leaders in the theory and practice of crowdsourcing in cultural heritage. It features eight accessible case studies of groundbreaking projects from leading cultural heritage and academic institutions, and four thought-provoking essays that reflect on the wider implications of this engagement for participants and on the institutions themselves. This book will be essential reading for information and cultural management professionals, students and researchers in universities, corporate, public or academic libraries, museums and archives.
  activity guide big open and crowdsourced data: Big Data for Twenty-First-Century Economic Statistics Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, Matthew D. Shapiro, 2022-03-11 Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.
  activity guide big open and crowdsourced data: The Data Science Design Manual Steven S. Skiena, 2017-07-01 This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)
  activity guide big open and crowdsourced data: GIS and Machine Learning for Small Area Classifications in Developing Countries Adegbola Ojo, 2020-12-30 Since the emergence of contemporary area classifications, population geography has witnessed a renaissance in the area of policy related spatial analysis. Area classifications subsume geodemographic systems which often use data mining techniques and machine learning algorithms to simplify large and complex bodies of information about people and the places in which they live, work and undertake other social activities. Outputs developed from the grouping of small geographical areas on the basis of multi- dimensional data have proved beneficial particularly for decision-making in the commercial sectors of a vast number of countries in the northern hemisphere. This book argues that small area classifications offer countries in the Global South a distinct opportunity to address human population policy related challenges in novel ways using area-based initiatives and evidence-based methods. This book exposes researchers, practitioners, and students to small area segmentation techniques for understanding, interpreting, and visualizing the configuration, dynamics, and correlates of development policy challenges at small spatial scales. It presents strategic and operational responses to these challenges in cost effective ways. Using two developing countries as case studies, the book connects new transdisciplinary ways of thinking about social and spatial inequalities from a scientific perspective with GIS and Data Science. This offers all stakeholders a framework for engaging in practical dialogue on development policy within urban and rural settings, based on real-world examples. Features: The first book to address the huge potential of small area segmentation for sustainable development, combining explanations of concepts, a range of techniques, and current applications. Includes case studies focused on core challenges that confront developing countries and provides thorough analytical appraisal of issues that resonate with audiences from the Global South. Combines GIS and machine learning methods for studying interrelated disciplines such as Demography, Urban Science, Sociology, Statistics, Sustainable Development and Public Policy. Uses a multi-method approach and analytical techniques of primary and secondary data. Embraces a balanced, chronological, and well sequenced presentation of information, which is very practical for readers.
  activity guide big open and crowdsourced data: Data Literacy in the Real World Kristin Fontichiaro, Amy Lennex, Jo Angela Oehrli, Tyler Hoff, Kelly Hovinga, 2017 Knowing how to recognize the role data plays in our lives is critical to navigating today's complex world. In this volume, you'll find two kinds of professional development tools to support that growth. Part I contains pre-made professional development via links to webinars from the 2016 and 2017 4T Virtual Conference on Data Literacy, along with discussion questions and activities that can animate conversations around data in your school. Part II explores data in the wild with case studies pulled from the headlines, along with provocative discussion questions, professionals and students alike can explore multiple perspectives at play with Big Data, data privacy, personal data management, ethical data use, and citizen science.
  activity guide big open and crowdsourced data: The Crowdsourced Performance Review: How to Use the Power of Social Recognition to Transform Employee Performance Eric Mosley, 2013-05-29 Praise for The Crowdsourced Performance Review: Take advantage of the technology and data available to you and turn the dreaded performance review into a powerful force for decision-making and culture-building by using the methods outlined in this clear and clever guide. --Daniel H. Pink, author of To Sell Is Human and Drive Social technologies aren't just changing how people interact, they're fundamentally changing how businesses must engage with people inside and outside their organization. In The Crowdsourced Performance Review, Mosley shows HR and business leaders why a 'groundswell' approach for employee recognition is the key to driving better employee performance. This is one of the most innovative enterprise uses of crowdsourcing I've seen. --Charlene Li, founder of Altimeter Group, author of Open Leadership, and coauthor of Groundswell In what is easily the most comprehensive and provocative Globoforce book to date, Mosley lays out a clear vision for how modern recognition systems can be integrated with performance management. This is one of the most interesting, innovative, and potentially important new approaches to performance management that I have seen in many years of working on this topic. --Gerald Ledford, Senior Research Scientist, Center for Effective Organizations, Marshall School of Business, University of Southern California The Crowdsourced Performance Review should be at the top of every HR professional's reading list. It shows convincingly why the traditional performance review doesn't work and how social recognition is the key to a performance system that actually makes an impact. --Kevin Kruse, Forbes Leadership columnist and bestselling author of Employee Engagement 2.0 As a pioneer in multirater feedback, I love Eric's new application! Social media comes to visit the performance appraisal. Many minds can be better than one! Read this and find out how. --Marshall Goldsmith, author of New York Times bestsellers MOJO and What Got You Here Won't Get You There Fix the Performance Review with the Wisdom of Crowds! Today's most successful companies are transforming their predictable one-way review processes into dynamic, collaborative systems that apply the latest social technologies. Instead of a one-time annual evaluation of performance, managers and employees receive collective feedback from everyone across their company. It's all achieved through crowdsourcing, and it generates more accurate, actionable results than traditional methods. With The Crowdsourced Performance Review, you'll create a review system that gathers the feedback of many, so you can make better, more informed decisions. And this new model is simpler than you think. It's based on three innovations: CROWDSOURCING: Applying the same techniques that companies like Apple, Angie's List, and Zagat use to inform customers, you can gather the same kind of data to inform managers. SOCIAL MEDIA TECHNOLOGIES: The most revolutionary communication tools since the telephone, these technologies have singlehandedly created a new language of business. ORGANIZATIONAL CULTURE: When managed well, it's one of the most effective tools for building and maintaining a competitive advantage. These three assets come together for the purpose of evaluating performance in the practice of social recognition--a system in which all employees recognize each other's great work on a daily basis. Social recognition creates engagement, energy, and even happiness in a company--leading to the ultimate goal of a Positivity-Dominated Workplace.
  activity guide big open and crowdsourced data: Crowdsourcing Jeff Howe, 2008-08-26 “The amount of knowledge and talent dispersed among the human race has always outstripped our capacity to harness it. Crowdsourcing ­corrects that—but in doing so, it also unleashes the forces of creative destruction.” —From Crowdsourcing First identified by journalist Jeff Howe in a June 2006 Wired article, “crowdsourcing” describes the process by which the power of the many can be leveraged to accomplish feats that were once the province of the specialized few. Howe reveals that the crowd is more than wise—it’s talented, creative, and stunningly productive. Crowdsourcing activates the transformative power of today’s technology, liberating the latent potential within us all. It’s a perfect meritocracy, where age, gender, race, education, and job history no longer matter; the quality of work is all that counts; and every field is open to people of every imaginable background. If you can perform the service, design the product, or solve the problem, you’ve got the job. But crowdsourcing has also triggered a dramatic shift in the way work is organized, talent is employed, research is conducted, and products are made and marketed. As the crowd comes to supplant traditional forms of labor, pain and disruption are inevitable. Jeff Howe delves into both the positive and negative consequences of this intriguing phenomenon. Through extensive reporting from the front lines of this revolution, he employs a brilliant array of stories to look at the economic, cultural, business, and political implications of crowdsourcing. How were a bunch of part-time dabblers in finance able to help an investment company consistently beat the market? Why does Procter & Gamble repeatedly call on enthusiastic amateurs to solve scientific and technical challenges? How can companies as diverse as iStockphoto and Threadless employ just a handful of people, yet generate millions of dollars in revenue every year? The answers lie within these pages. The blueprint for crowdsourcing originated from a handful of computer programmers who showed that a community of like-minded peers could create better products than a corporate behemoth like Microsoft. Jeff Howe tracks the amazing migration of this new model of production, showing the potential of the Internet to create human networks that can divvy up and make quick work of otherwise overwhelming tasks. One of the most intriguing ideas of Crowdsourcing is that the knowledge to solve intractable problems—a cure for cancer, for instance—may already exist within the warp and weave of this infinite and, as yet, largely untapped resource. But first, Howe proposes, we need to banish preconceived notions of how such problems are solved. The very concept of crowdsourcing stands at odds with centuries of practice. Yet, for the digital natives soon to enter the workforce, the technologies and principles behind crowdsourcing are perfectly intuitive. This generation collaborates, shares, remixes, and creates with a fluency and ease the rest of us can hardly understand. Crowdsourcing, just now starting to emerge, will in a short time simply be the way things are done.
  activity guide big open and crowdsourced data: Computer Vision – ECCV 2016 Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling, 2016-09-16 The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action, activity and tracking; 3D; and 9 poster sessions.
  activity guide big open and crowdsourced data: The Science of Citizen Science Katrin Vohland, Anne Land-zandstra, Luigi Ceccaroni, Rob Lemmens, Josep Perelló, Marisa Ponti, Roeland Samson, Katherin Wagenknecht, 2021 This open access book discusses how the involvement of citizens into scientific endeavors is expected to contribute to solve the big challenges of our time, such as climate change and the loss of biodiversity, growing inequalities within and between societies, and the sustainability turn. The field of citizen science has been growing in recent decades. Many different stakeholders from scientists to citizens and from policy makers to environmental organisations have been involved in its practice. In addition, many scientists also study citizen science as a research approach and as a way for science and society to interact and collaborate. This book provides a representation of the practices as well as scientific and societal outcomes in different disciplines. It reflects the contribution of citizen science to societal development, education, or innovation and provides and overview of the field of actors as well as on tools and guidelines. It serves as an introduction for anyone who wants to get involved in and learn more about the science of citizen science.
  activity guide big open and crowdsourced data: Handbook of e-Tourism Zheng Xiang, Matthias Fuchs, Ulrike Gretzel, Wolfram Höpken, 2022-09-01 This handbook provides an authoritative and truly comprehensive overview both of the diverse applications of information and communication technologies (ICTs) within the travel and tourism industry and of e-tourism as a field of scientific inquiry that has grown and matured beyond recognition. Leading experts from around the world describe cutting-edge ideas and developments, present key concepts and theories, and discuss the full range of research methods. The coverage accordingly encompasses everything from big data and analytics to psychology, user behavior, online marketing, supply chain and operations management, smart business networks, policy and regulatory issues – and much, much more. The goal is to provide an outstanding reference that summarizes and synthesizes current knowledge and establishes the theoretical and methodological foundations for further study of the role of ICTs in travel and tourism. The handbook will meet the needs of researchers and students in various disciplines as well as industry professionals. As with all volumes in Springer’s Major Reference Works program, readers will benefit from access to a continually updated online version.
  activity guide big open and crowdsourced data: Digital Classics Outside the Echo-Chamber Gabriel Bodard, Matteo Romanello, 2016-04-28 Edited by organisers of “Digital Classicist” seminars in London and Berlin, this volume explores the impact of computational approaches to the study of antiquity on audiences other than the scholars who conventionally publish it. In addition to colleagues in classics and digital humanities, the eleven chapters herein concern and are addressed to students, heritage professionals and “citizen scientists”. Each chapter is a scholarly contribution, presenting research questions in the classics, digital humanities or, in many cases, both. They are all also examples of work within one of the most important areas of academia today: scholarly research and outputs that engage with collaborators and audiences not only including our colleagues, but also students, academics in different fields including the hard sciences, professionals and the broader public. Collaboration and scholarly interaction, particularly with better-funded and more technically advanced disciplines, is essential to digital humanities and perhaps even more so to digital classics. The international perspectives on these issues are especially valuable in an increasingly connected, institutionally and administratively diverse world. This book addresses the broad range of issues scholars and practitioners face in engaging with students, professionals and the public, in accessible and valuable chapters from authors of many backgrounds and areas of expertise, including language and linguistics, history, archaeology and architecture. This collection will be of interest to teachers, scientists, cultural heritage professionals, linguists and enthusiasts of history and antiquity.
  activity guide big open and crowdsourced data: The Origin of Consciousness in the Breakdown of the Bicameral Mind Julian Jaynes, 2000-08-15 National Book Award Finalist: “This man’s ideas may be the most influential, not to say controversial, of the second half of the twentieth century.”—Columbus Dispatch At the heart of this classic, seminal book is Julian Jaynes's still-controversial thesis that human consciousness did not begin far back in animal evolution but instead is a learned process that came about only three thousand years ago and is still developing. The implications of this revolutionary scientific paradigm extend into virtually every aspect of our psychology, our history and culture, our religion—and indeed our future. “Don’t be put off by the academic title of Julian Jaynes’s The Origin of Consciousness in the Breakdown of the Bicameral Mind. Its prose is always lucid and often lyrical…he unfolds his case with the utmost intellectual rigor.”—The New York Times “When Julian Jaynes . . . speculates that until late in the twentieth millennium BC men had no consciousness but were automatically obeying the voices of the gods, we are astounded but compelled to follow this remarkable thesis.”—John Updike, The New Yorker “He is as startling as Freud was in The Interpretation of Dreams, and Jaynes is equally as adept at forcing a new view of known human behavior.”—American Journal of Psychiatry
  activity guide big open and crowdsourced data: Rise to the Occasion Brad Ross, 2017-01-15 The story of a crisis of epic proportion and the lessons of leadership, innovation, motivation, and teamwork that effectively saved lives and the mine. Rise to the Occasion tells the dramatic story of the men and women who safely led Utah’s 107-year-old Bingham Canyon Mine through the largest mining highwall failure in history. The Manefay failure resulted in 144.4 million tons of rock plummeting more than 2,000 feet and traveling 1.5 miles within 90 seconds—without a single death or injury. The story is told through the eyes of an insider, as the author was brought into the mine just six short weeks before the failure and was a key member of the management team. It’s a Story Only He Can Tell. Illustrated with 160 full-color aerial and ground photos, charts, and illustrations, Rise to the Occasion details the unfolding events of the preparation, failure, and recovery efforts in moment-by-moment accounts. The author then leads the reader to valuable lessons that were learned and how to apply these lessons to any organization that faces risks. The reader will learn to manage a crisis or normal operations by: • Understanding, measuring, and acting on the greatest risks facing the organization. • Creating a culture, based on communication, that inspires dedication, trust, and success. • Wearing a “Black Hat” to challenge thinking that can blind an organization. • Setting “impossible” goals that will not only be met but exceeded. • Breaking down silos to improve teamwork and solve problems. • Reducing bureaucracy and empowering people to increase innovation and expedite solutions. • Using independent experts to provide different points of view and audit the processes.
  activity guide big open and crowdsourced data: Open Access Peter Suber, 2012-07-20 A concise introduction to the basics of open access, describing what it is (and isn't) and showing that it is easy, fast, inexpensive, legal, and beneficial. The Internet lets us share perfect copies of our work with a worldwide audience at virtually no cost. We take advantage of this revolutionary opportunity when we make our work “open access”: digital, online, free of charge, and free of most copyright and licensing restrictions. Open access is made possible by the Internet and copyright-holder consent, and many authors, musicians, filmmakers, and other creators who depend on royalties are understandably unwilling to give their consent. But for 350 years, scholars have written peer-reviewed journal articles for impact, not for money, and are free to consent to open access without losing revenue. In this concise introduction, Peter Suber tells us what open access is and isn't, how it benefits authors and readers of research, how we pay for it, how it avoids copyright problems, how it has moved from the periphery to the mainstream, and what its future may hold. Distilling a decade of Suber's influential writing and thinking about open access, this is the indispensable book on the subject for researchers, librarians, administrators, funders, publishers, and policy makers.
  activity guide big open and crowdsourced data: UGC NET Sociology Paper II Chapter Wise Note Book | Complete Preparation Guide EduGorilla Prep Experts, 2022-09-15 • Best Selling Book in English Edition for UGC NET Sociology Paper II Exam with objective-type questions as per the latest syllabus given by the NTA . • Increase your chances of selection by 16X. • UGC NET Sociology Paper II Kit comes with well-structured Content & Chapter wise Practice Tests for your self evaluation • Clear exam with good grades using thoroughly Researched Content by experts.
  activity guide big open and crowdsourced data: The Network Reshapes the Library Lorcan Dempsey, 2014-08-18 Since he began posting in 2003, Dempsey has used his blog to explore nearly every important facet of library technology, from the emergence of Web 2.0 as a concept to open source ILS tools and the push to web-scale library management systems.
  activity guide big open and crowdsourced data: New Technologies for Human Rights Law and Practice Molly K. Land, Jay D. Aronson, 2018-04-19 Provides a roadmap for understanding the relationship between technology and human rights law and practice. This title is also available as Open Access.
  activity guide big open and crowdsourced data: Data and the City Rob Kitchin, Tracey P. Lauriault, Gavin McArdle, 2017-08-15 There is a long history of governments, businesses, science and citizens producing and utilizing data in order to monitor, regulate, profit from and make sense of the urban world. Recently, we have entered the age of big data, and now many aspects of everyday urban life are being captured as data and city management is mediated through data-driven technologies. Data and the City is the first edited collection to provide an interdisciplinary analysis of how this new era of urban big data is reshaping how we come to know and govern cities, and the implications of such a transformation. This book looks at the creation of real-time cities and data-driven urbanism and considers the relationships at play. By taking a philosophical, political, practical and technical approach to urban data, the authors analyse the ways in which data is produced and framed within socio-technical systems. They then examine the constellation of existing and emerging urban data technologies. The volume concludes by considering the social and political ramifications of data-driven urbanism, questioning whom it serves and for what ends. This book, the companion volume to 2016’s Code and the City, offers the first critical reflection on the relationship between data, data practices and the city, and how we come to know and understand cities through data. It will be crucial reading for those who wish to understand and conceptualize urban big data, data-driven urbanism and the development of smart cities.
  activity guide big open and crowdsourced data: Encyclopedia of Information Science and Technology Mehdi Khosrow-Pour, Mehdi Khosrowpour, 2009 This set of books represents a detailed compendium of authoritative, research-based entries that define the contemporary state of knowledge on technology--Provided by publisher.
  activity guide big open and crowdsourced data: High-Performance Modelling and Simulation for Big Data Applications Joanna Kołodziej, Horacio González-Vélez, 2019-03-25 This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications.
  activity guide big open and crowdsourced data: Crowdsourcing in Computer Vision Adriana Kovashka, Olga Russakovsky, Li Fei-Fei, Kristen Grauman, 2016-11-30 An overview of how crowdsourcing has been used in computer vision, enabling a computer vision researcher who has previously not collected non-expert data to devise a data collection strategy. It will also be of help to researchers who focus broadly on crowdsourcing to examine how the latter has been applied in computer vision.
  activity guide big open and crowdsourced data: Crowdsourcing Daren C. Brabham, 2013-05-10 A concise introduction to crowdsourcing that goes beyond social media buzzwords to explain what crowdsourcing really is and how it works. Ever since the term “crowdsourcing” was coined in 2006 by Wired writer Jeff Howe, group activities ranging from the creation of the Oxford English Dictionary to the choosing of new colors for M&Ms have been labeled with this most buzz-generating of media buzzwords. In this accessible but authoritative account, grounded in the empirical literature, Daren Brabham explains what crowdsourcing is, what it is not, and how it works. Crowdsourcing, Brabham tells us, is an online, distributed problem solving and production model that leverages the collective intelligence of online communities for specific purposes set forth by a crowdsourcing organization—corporate, government, or volunteer. Uniquely, it combines a bottom-up, open, creative process with top-down organizational goals. Crowdsourcing is not open source production, which lacks the top-down component; it is not a market research survey that offers participants a short list of choices; and it is qualitatively different from predigital open innovation and collaborative production processes, which lacked the speed, reach, rich capability, and lowered barriers to entry enabled by the Internet. Brabham describes the intellectual roots of the idea of crowdsourcing in such concepts as collective intelligence, the wisdom of crowds, and distributed computing. He surveys the major issues in crowdsourcing, including crowd motivation, the misconception of the amateur participant, crowdfunding, and the danger of “crowdsploitation” of volunteer labor, citing real-world examples from Threadless, InnoCentive, and other organizations. And he considers the future of crowdsourcing in both theory and practice, describing its possible roles in journalism, governance, national security, and science and health.
  activity guide big open and crowdsourced data: Beyond Transparency Brett Goldstein, Lauren Dyson, 2013-09-30 The rise of open data in the public sector has sparked innovation, driven efficiency, and fueled economic development. While still emerging, we are seeing evidence of the transformative potential of open data in shaping the future of our civic life, and the opportunity to use open data to reimagine the relationship between residents and government, especially at the local level. As we look ahead, what have we learned so far from open data in practice and how we can apply those lessons to realize a more promising future for America's cities and communities? Edited by Brett Goldstein, former Chief Data Officer for the City of Chicago, with Code for America, this book features essays from over twenty of the world's leading experts in a first-of-its-kind instructive anthology about how open data is changing the face of our public institutions. Contributors include: Michael Flowers, Chief Analytics Officer, New York City Beth Blauer, former director of Maryland StateStat Jonathan Feldman, CIO, City of Asheville Tim O'Reilly, founder & CEO, O'Reilly Media Eric Gordon, Director of Engagement Game Lab, Emerson College Beth Niblock, CIO, Louisville Metro Government Ryan & Mike Alfred, Co-Founders, Brightscope Emer Coleman, former director of the London Datastore Mark Headd, Chief Data Officer, City of Philadelphia As an essential volume for anyone interested in the future of governance, urban policy, design, data-driven policymaking, journalism, or civic engagement, Beyond Transparency combines the inspirational glow and political grit of Profiles in Courage with the clarity of an engineer's calm explanation of how something technical actually works. Here are the detailed how-to stories of many members of the first generation of open government pioneers, written in a generous, accessible style; this compilation presents us with a great deal to admire, ample provocation, and wise guidance from a group of remarkable individuals. -Susan Crawford, author of Captive Audience Just as he did during his time in my administration, Goldstein has brought together industry leaders to discuss issues of relevance in the open data movement and the practical implications of implementing these policies... This book will help continue the work to make open government a reality across the country. - Mayor Rahm Emanuel, City of Chicago A must-read for anyone who is passionate about what open data can do to transform city living. - Boris Johnson, Mayor of London
  activity guide big open and crowdsourced data: Frontiers in Massive Data Analysis National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on Applied and Theoretical Statistics, Committee on the Analysis of Massive Data, 2013-09-03 Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
  activity guide big open and crowdsourced data: We Are Smarter Than Me Barry Libert, Jon Spector, 2008 Wikinomics and The Wisdom of Crowds identified the phenomena of emerging social networks, but they do not confront how businesses can profit from the wisdom of crowds. WE ARE SMARTER THAN ME by Barry Libert and Jon Spector, Foreword by Wikinomics author Don Tapscott, is the first book to show anyone in business how to profit from the wisdom of crowds. Drawing on their own research and the insights from an enormous community of more than 4,000 people, Barry Libert and Jon Spector have written a book that reveals what works, and what doesn't, when you are building community into your decision making and business processes. In We Are Smarter Than Me, you will discover exactly how to use social networking and community in your business, driving better decision-making and greater profitability. The book shares powerful insights and new case studies from product development, manufacturing, marketing, customer service, finance, management, and beyond. You'll learn which business functions can best be accomplished or supported by communities; how to provide effective moderation, balance structure with independence, manage risk, define success, implement effective metrics, and much more. From tools and processes to culture and leadership, We Are Smarter than Me will help you transform the promise of social networking into a profitable reality.
  activity guide big open and crowdsourced data: The Open Innovation Marketplace Alpheus Bingham, Dwayne Spradlin, 2011-03-25 Many technical obstacles to effective innovation no longer exist: today, companies possess global networks that can connect with knowledge from virtually any source. Today’s challenge is to collaboratively transform that knowledge into higher-value innovation. Their book introduces groundbreaking strategies and models for consistently achieving this goal. Authors Alpheus Bingham and Dwayne Spradlin draw on their own experience building InnoCentive, the pioneering global platform for open innovation (a.k.a. crowdsourcing). Writing for business executives, R&D leaders, and innovation strategists, Bingham and Spradlin demonstrate how to dramatically increase the flow of high-value ideas and innovative solutions both within enterprises and beyond their boundaries. They show: Why open innovation works so well. How to use open innovation to become more agile and entrepreneurial. How to access Idea Markets more quickly, and get more value from them. How to overcome new forms of Not Invented Here syndrome. How to implement cultural, organizational, and management changes that lead to greater innovation. New trends in open innovation–and the opportunities they present. The authors present many new open innovation case studies, from P&G and Eli Lilly to NASA and the City of Chicago.
  activity guide big open and crowdsourced data: Building the Digital Enterprise Mark Skilton, 2016-04-29 The digital economy is at a tipping point. This practical book defines digital ecosystems, discusses digital design using converging technologies of social networking, mobility, big data and cloud computing, and provides a methods for linking digital technologies together to meet the challenges of building a digital enterprise in the new economy.
  activity guide big open and crowdsourced data: New Horizons for a Data-Driven Economy José María Cavanillas, Edward Curry, Wolfgang Wahlster, 2016-04-04 In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.
  activity guide big open and crowdsourced data: Digital Humanitarians Patrick Meier, 2015-01-06 The overflow of information generated during disasters can be as paralyzing to humanitarian response as the lack of information. This flash flood of information‘social media, satellite imagery and more is often referred to as Big Data. Making sense of this data deluge during disasters is proving an impossible challenge for traditional humanitarian
  activity guide big open and crowdsourced data: Flood Impact Mitigation and Resilience Enhancement Guangwei Huang, 2020-12-16 The concept of resilience has been gaining momentum in various fields in recent years and has been used in various ways from a catch phrase to a cornerstone in theoretic development or practical operation. No matter how it is used, it does contribute one way or another to the refinement and application of the concept. This book focuses on the application of the resilience concept to flood disaster management. This book is a collection of research works conducted across the world and across sectors. Therefore, it is a good example of how different perspectives can catalyze our insight into complex flood-related issues. It can be considered valuable reading material for students, researchers, policymakers and practitioners, because it provides both the fundamentals and new development of resilience-based approaches and delivers a message that the goal of resilience-based flood management goes beyond disaster reduction.
  activity guide big open and crowdsourced data: Advances in Crowdsourcing Fernando J. Garrigos-Simon, Ignacio Gil-Pechuán, Sofia Estelles-Miguel, 2015-05-08 ​​This book attempts to link some of the recent advances in crowdsourcing with advances in innovation and management. It contributes to the literature in several ways. First, it provides a global definition, insights and examples of this managerial perspective resulting in a theoretical framework. Second, it explores the relationship between crowdsourcing and technological innovation, the development of social networks and new behaviors of Internet users. Third, it explores different crowdsourcing applications in various sectors such as medicine, tourism, information and communication technology (ICT), and marketing. Fourth, it observes the ways in which crowdsourcing can improve production, finance, management and overall managerial performance. Crowdsourcing, also known as “massive outsourcing” or “voluntary outsourcing,” is the act of taking a job or a specific task usually performed by an employee of a company or contractors, and outsourcing it to a large group of people or a community (crowd or mass) via the Internet, through an open call. The term was coined by Jeff Howe in a 2006 issue of Wired magazine. It is being developed in different sciences (i.e., medicine, engineering, ICT, management) and is used in the most successful companies of the modern era (i.e., Apple, Facebook, Inditex, Starbucks). The developments in crowdsourcing has theoretical and practical implications, which will be explored in this book. Including contributions from international academics, scholars and professionals within the field, this book provides a global, multidimensional perspective on crowdsourcing.​
  activity guide big open and crowdsourced data: Social Media Mining Reza Zafarani, Mohammad Ali Abbasi, Huan Liu, 2014-04-28 Integrates social media, social network analysis, and data mining to provide an understanding of the potentials of social media mining.
  activity guide big open and crowdsourced data: The Academic Book of the Future Rebecca E. Lyons, Samantha Rayner, 2015-11-13 This book is open access under a CC-BY licence. Part of the AHRC/British Library Academic Book of the Future Project, this book interrogates current and emerging contexts of academic books from the perspectives of thirteen expert voices from the connected communities of publishing, academia, libraries, and bookselling.
  activity guide big open and crowdsourced data: Geek Girl Rising Heather Cabot, Samantha Walravens, 2017-05-23 This book isn't about the famous tech trailblazers you already know, like Sheryl Sandberg and Marissa Mayer. Instead, veteran journalists Heather Cabot and Samantha Walravens introduce readers to the ... female entrepreneurs and technologists fighting at the grassroots level for an ownership stake in the revolution that's changing the way we live, work and connect to each other--Amazon.com.
  activity guide big open and crowdsourced data: Big Data for Regional Science Laurie A Schintler, Zhenhua Chen, 2017-08-07 Recent technological advancements and other related factors and trends are contributing to the production of an astoundingly large and rapidly accelerating collection of data, or ‘Big Data’. This data now allows us to examine urban and regional phenomena in ways that were previously not possible. Despite the tremendous potential of big data for regional science, its use and application in this context is fraught with issues and challenges. This book brings together leading contributors to present an interdisciplinary, agenda-setting and action-oriented platform for research and practice in the urban and regional community. This book provides a comprehensive, multidisciplinary and cutting-edge perspective on big data for regional science. Chapters contain a collection of research notes contributed by experts from all over the world with a wide array of disciplinary backgrounds. The content is organized along four themes: sources of big data; integration, processing and management of big data; analytics for big data; and, higher level policy and programmatic considerations. As well as concisely and comprehensively synthesising work done to date, the book also considers future challenges and prospects for the use of big data in regional science. Big Data for Regional Science provides a seminal contribution to the field of regional science and will appeal to a broad audience, including those at all levels of academia, industry, and government.
  activity guide big open and crowdsourced data: Big Data and Global Trade Law Mira Burri, 2021-07-29 An exploration of the current state of global trade law in the era of Big Data and AI. This title is also available as Open Access on Cambridge Core.
  activity guide big open and crowdsourced data: Linked Data Visualization Laura Po, Nikos Bikakis, Federico Desimoni, George Papastefanatos, 2022-05-31 Linked Data (LD) is a well-established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from different scientific and commercial domains. The development of Linked Data Visualization techniques and tools has been followed as the primary means for the analysis of this vast amount of information by data scientists, domain experts, business users, and citizens. This book covers a wide spectrum of visualization issues, providing an overview of the recent advances in this area, focusing on techniques, tools, and use cases of visualization and visual analysis of LD. It presents the basic concepts related to data visualization and the LD technologies, the techniques employed for data visualization based on the characteristics of data techniques for Big Data visualization, use tools and use cases in the LD context, and finally a thorough assessment of the usability of these tools under different scenarios. The purpose of this book is to offer a complete guide to the evolution of LD visualization for interested readers from any background and to empower them to get started with the visual analysis of such data. This book can serve as a course textbook or a primer for all those interested in LD and data visualization.

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