Data Driven Societies
Digital & Computational Studies
Bowdoin College
February 10, 2014
Professors Gieseking & Gaze
Lecture Slides "Defining Data & Redefining Privacy"
Grounded theory meets big data: One way to marry ethnography and digital methodsCitizens in the Making
This document discusses how grounded theory can be applied to analyze large datasets from social media platforms like Twitter. It proposes combining both qualitative and computational methods like machine learning. Specifically, it suggests using machine learning like latent Dirichlet allocation to identify topic clusters in Twitter data, which can then inform the qualitative coding categories used to analyze content, profiles and other metadata. The document advocates an emergent approach to coding to build conceptual knowledge from Twitter data, and emphasizes the importance of being reflexive and considering multiple perspectives.
Cyber Summit 2016: Establishing an Ethics Framework for Predictive Analytics ...Cybera Inc.
This document summarizes a presentation about establishing an ethics framework for predictive analytics using student data in higher education. It discusses how technology has enabled more data collection and predictive modeling of student behavior. However, few guidelines exist for these practices. The presentation advocates developing an ethics framework that safeguards student privacy, promotes transparency, considers unintended consequences, and involves consultation. It also examines existing principles and discusses challenges like opaque predictive models that work against students' interests. The presenter argues universities should internalize norms of respecting trust and serving students, not just avoiding legal issues.
Cyber Summit 2016: Privacy Issues in Big Data Sharing and ReuseCybera Inc.
This document summarizes a presentation on big data and data reuse given by Bart Custers. It discusses:
1) The Eudeco project which examines big data and data reuse from legal, societal, economic, and technological perspectives across multiple European countries.
2) Issues with data sharing and reuse, including potential privacy violations, discrimination, lack of transparency, and unintended consequences from new uses of data or placing it in new contexts.
3) Potential solutions discussed, including privacy impact assessments, privacy by design, and new approaches focusing more on transparency and responsibility than restricting data access and use.
This tutorial provides a framework for identifying and managing confidential information in research. It is most appropriate for mid-late career graduate students, faculty, and professional research staff who actively engage in the design/planning of research. The course will provide an overview of the major legal requirements governing confidential research data; and the core technological measures used to safeguard data. And it will provide an introduction to the statistical methods and software tools used to analyze and limit disclosure risks.
Matching Uses and Protections for Government Data Releases: Presentation at t...Micah Altman
In the work included below, and presented at the Simons Institute, we describe work-in progress that aims to align emerging methods of data protections with research uses.
Privacy Gaps in Mediated Library Services: Presentation at NERCOMP2019Micah Altman
Libraries enable patrons to access a wide range of information, but much of the access to this information is now directly managedy publishers. This has lead to a significant gap across library values, patrons perception of privacy, and effective privacy protection for access to digital resources.
In the work included below, and presented at NERCOMP 2019, we review privacy principles based on ALA, IFLA, and NISO policies. We then organizing and comparing high level privacy protections required by ALA checklist, NISO, and GDPR. This framework of principles and controls is then used to score the privacy policies and practices of major vendors of research library content. We evaluate each element of the vendors privacy policy, and use instrumented browsers to identify the types of tracking mechanisms used by different vendors. We use this set of privacy scores to support analyses of change over time, and of potential gaps between patron expectations and privacy policies and practices.
With big data research all the rage, how are librarians being asked to engage with data? As big data research takes off across Business, Science, and the Humanities, librarians need to understand big data and the issues around its storage and curation. How can it be made accessible? What tools and resources are required to use and analyze big data? In this webinar, panelists Caroline Muglia and Jill Parchuck share how big data is being used on their campuses and how they, as librarians, are supporting the sourcing and storage of this data.
Data Driven Societies
Digital & Computational Studies
Bowdoin College
January 29, 2014
Professors Gieseking & Gaze
Lecture Slides "How We Make Sense of Data in Visualization"
Grounded theory meets big data: One way to marry ethnography and digital methodsCitizens in the Making
This document discusses how grounded theory can be applied to analyze large datasets from social media platforms like Twitter. It proposes combining both qualitative and computational methods like machine learning. Specifically, it suggests using machine learning like latent Dirichlet allocation to identify topic clusters in Twitter data, which can then inform the qualitative coding categories used to analyze content, profiles and other metadata. The document advocates an emergent approach to coding to build conceptual knowledge from Twitter data, and emphasizes the importance of being reflexive and considering multiple perspectives.
Cyber Summit 2016: Establishing an Ethics Framework for Predictive Analytics ...Cybera Inc.
This document summarizes a presentation about establishing an ethics framework for predictive analytics using student data in higher education. It discusses how technology has enabled more data collection and predictive modeling of student behavior. However, few guidelines exist for these practices. The presentation advocates developing an ethics framework that safeguards student privacy, promotes transparency, considers unintended consequences, and involves consultation. It also examines existing principles and discusses challenges like opaque predictive models that work against students' interests. The presenter argues universities should internalize norms of respecting trust and serving students, not just avoiding legal issues.
Cyber Summit 2016: Privacy Issues in Big Data Sharing and ReuseCybera Inc.
This document summarizes a presentation on big data and data reuse given by Bart Custers. It discusses:
1) The Eudeco project which examines big data and data reuse from legal, societal, economic, and technological perspectives across multiple European countries.
2) Issues with data sharing and reuse, including potential privacy violations, discrimination, lack of transparency, and unintended consequences from new uses of data or placing it in new contexts.
3) Potential solutions discussed, including privacy impact assessments, privacy by design, and new approaches focusing more on transparency and responsibility than restricting data access and use.
This tutorial provides a framework for identifying and managing confidential information in research. It is most appropriate for mid-late career graduate students, faculty, and professional research staff who actively engage in the design/planning of research. The course will provide an overview of the major legal requirements governing confidential research data; and the core technological measures used to safeguard data. And it will provide an introduction to the statistical methods and software tools used to analyze and limit disclosure risks.
Matching Uses and Protections for Government Data Releases: Presentation at t...Micah Altman
In the work included below, and presented at the Simons Institute, we describe work-in progress that aims to align emerging methods of data protections with research uses.
Privacy Gaps in Mediated Library Services: Presentation at NERCOMP2019Micah Altman
Libraries enable patrons to access a wide range of information, but much of the access to this information is now directly managedy publishers. This has lead to a significant gap across library values, patrons perception of privacy, and effective privacy protection for access to digital resources.
In the work included below, and presented at NERCOMP 2019, we review privacy principles based on ALA, IFLA, and NISO policies. We then organizing and comparing high level privacy protections required by ALA checklist, NISO, and GDPR. This framework of principles and controls is then used to score the privacy policies and practices of major vendors of research library content. We evaluate each element of the vendors privacy policy, and use instrumented browsers to identify the types of tracking mechanisms used by different vendors. We use this set of privacy scores to support analyses of change over time, and of potential gaps between patron expectations and privacy policies and practices.
With big data research all the rage, how are librarians being asked to engage with data? As big data research takes off across Business, Science, and the Humanities, librarians need to understand big data and the issues around its storage and curation. How can it be made accessible? What tools and resources are required to use and analyze big data? In this webinar, panelists Caroline Muglia and Jill Parchuck share how big data is being used on their campuses and how they, as librarians, are supporting the sourcing and storage of this data.
Data Driven Societies
Digital & Computational Studies
Bowdoin College
January 29, 2014
Professors Gieseking & Gaze
Lecture Slides "How We Make Sense of Data in Visualization"
Data Driven Societies
Digital & Computational Studies
Bowdoin College
February 17, 2014
Professors Gieseking & Gaze
Lecture Slides "On Digital Publics of Opening…or Not"
Digital Image of the City - Housing
Bowdoin College
Fall 2014
Annie Chen, Emma Chow, Jenny Ibsen, Eva Sibinga, Jackie Sullivan, Libby Szuflita
Presentation given on 12/10/14
Digital Image of the City - Infrastructure
Bowdoin College
Fall 2014
Roya Moussapour, Alex N'Diaye, Karl Reinhardt, Alexi Robbins, James Wang, Max Wolf
Presentation given on 12/10/14
We had a rousing conversation about the merits of open access (#OA) during Open Access Week at Trinity College. My presentation focused on how I came into OA and the key resources that make a busy faculty member or graduate student's entrée into sharing their research publicly as part of the open education movement. See jgieseking.org for the complementary handout. After an introduction from our digital librarian Amy Harrell, I was joined by my colleagues Jack Doughtery in Urban Education Studies, and Charles Lebel in Language and Culture Studies in brief individual presentations followed by a conversation with our faculty.
"Personal/Political/Feminist Maps: Reflections on Spatial Methods for Action Research"
Talk given at Feminist Social Justice Conference, a Workshop on Participatory and Feminist Research Methods
March 16, 2015
San Diego State University, San Diego, CA
In _The Practice of Everyday Life_, de Certeau writes that "What the map cuts up, the story cuts across." But what if the everyday stories you seek are already cut up by centuries of structural inequality and oppression, such as those of lesbians and queer women? In this talk I investigate what can be gained for the study of women’s lives and spaces by bringing together the isolated but overlapping stories of lesbians and queer women in maps, from the hand-drawn to the most technologically advanced and interactive. Drawing upon qualitative and quantitative work on lesbians' and queer women's spaces and economies in New York City from 1983 to 2008—including multi-generational focus groups and mental maps, archival research and GIS—I work through three different types of mapping methods and platforms within a participatory action research framework. Through a close analysis of mental maps and GIS maps created using QGIS and TileMill/Mapbox, I suggest that while the spatial and verbal can both obfuscate and illuminate understandings of everyday life. It is the queer practice of holding these seeming binaries in tension that reveals the most rich and complicated knowledge.
This document summarizes several research projects related to big data and social science knowledge. It discusses projects that analyzed large social media platforms like Facebook, Twitter, and Wikipedia to study information diffusion and social influences. It also discusses challenges like securing access to commercial data and ensuring replicability of findings. Examples demonstrate how big data can provide novel insights but are limited by the objects studied and incomplete representation of populations. The document discusses debates around the implications of big data for privacy, prediction, exclusion, and manipulation. It argues that knowledge depends on how research technologies advance knowledge within ethical and legal frameworks.
“Big data” in human services organisations: Practical problems and ethical di...husITa
“Big data” initiatives that aim to bring together and mine data from multiple databases across government and non-government agencies promise new insights into human service delivery. Specifically they aim to provide information about what services are being used, how, by whom and with what outcome. However, the process of achieving such insights poses both practical problems and ethical dilemmas. In this presentation, drawing from an extensive literature review and research with government and non-government human service organisations focussing on the design and redevelopment of electronic information systems, the most significant problems and dilemmas will be explored. It will be argued that current frameworks for ethical social work and human service practice will need to be expanded to accommodate developments in technology which have made ‘Big data’ projects possible.
This document discusses the opportunities and challenges of big data and data science over the next decade. It outlines three key points:
1. Big data is opening doors to accelerating scientific discovery through generating hypotheses from data and using ensemble models to gain multiple perspectives. However, challenges around efficacy and efficiency remain.
2. Data science can be viewed as applying the scientific method to data through discovering correlations from data-driven models and seeking causation through empirical verification, similar to traditional scientific discovery.
3. For data science to fulfill its potential, its laws and best practices around ensuring meaningful correlations and determining causation through verification must be followed, although they are not always common in practice currently. The limits of data science also
Ethical and Legal Issues in Computational Social Science - Lecture 7 in Intro...Lauri Eloranta
Seventh lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
This document provides an overview of data science including its importance, what data scientists do, how the field has emerged, and how to become a data scientist. It notes that by 2018 the US could face shortages of people with data analytics skills. It then discusses how LinkedIn's early growth in 2006 exemplifies the data science process of framing questions, collecting and processing data, exploring patterns, and communicating results. Finally, it outlines the tools used in data science like SQL, analytics software, and machine learning and discusses getting started in the field through education, curiosity, and ongoing learning with mentorship support.
Accessing and Using Big Data to Advance Social Science KnowledgeJosh Cowls
This document summarizes a project investigating the use of big data to advance social science knowledge. It introduces the project leaders and discusses data sources and scope. It then focuses on defining big data, discussing how digital data represents real-world objects and phenomena, and the opportunities and limits this presents. Challenges of using big data to gauge public opinion are also examined, such as issues of representativeness, reliability, and replicability. The document concludes by listing project papers on this topic.
This document provides an overview of data science including its importance, what data scientists do, how the field has emerged, and how to become a data scientist. It discusses how data science can help answer important business questions using LinkedIn in 2006 as a case study. It also outlines the typical data science process of framing questions, collecting and cleaning data, exploring patterns, and communicating results. Finally, it introduces some common data science tools like SQL, analytics software, and machine learning algorithms and discusses options for continuing education in data science.
This document discusses the rise of big data and data science. It notes that while data volumes are growing exponentially, data alone is just an asset - it is data scientists that create value by building data products that provide insights. The document outlines the data science workflow and highlights both the tools used and challenges faced by data scientists in extracting value from big data.
Getting started in Data Science (April 2017, Los Angeles)Thinkful
The document discusses the rise of data science and the skills needed for data scientists. It defines data science as the intersection of engineering, statistics, and communication. Data scientists analyze large datasets to answer important business questions. The document uses LinkedIn in 2006 as a case study, outlining how a data scientist there framed questions, collected and processed user data, explored patterns, and communicated results to improve the user experience and growth. It highlights tools like SQL, analytics software, and machine learning that data scientists use and stresses the importance of curiosity, technical skills, and strong communication for those interested in the field.
This document discusses open data and privacy concerns in the humanities. It outlines that while open data has benefits, some humanities and social science data contains personal details that require protections. Three examples show challenges with medical records, subscriber lists, and student work. The document examines how data can be anonymized but still useful, and questions if IRB rules are too strict. Overall, it argues that fully open or closed access are sometimes false dichotomies, and more nuanced policies are needed to both protect privacy and enable collaborative research.
This document provides an overview of a presentation on big data in the social sciences given by Ralph Schroeder and Eric Meyer at the Oxford Internet Institute Summer Doctoral Programme. The presentation discusses how big data relates to advancing social science research, including opportunities for unprecedented insights but also challenges regarding data quality and replicability. Three case studies are summarized that demonstrate novel uses of big data: analyzing search engine queries, text analysis of digitized books, and identifying Twitter bots. The presentation concludes by considering debates around whether new technologies are outpacing social science and the threats and opportunities of new data sources.
A Revolution in Open Science: Open Data and the Role of Libraries (Professor ...LIBER Europe
This document discusses the opportunities and challenges of open science and open data. It argues that openly sharing scientific data and findings has significant benefits, including enabling faster scientific progress, deterring fraud, and supporting citizen science. However, for data to be truly open and useful to others, it needs to be accessible, intelligible, assessable, and reusable. The document also examines the roles and responsibilities of different stakeholders in working towards more open and reproducible science. This includes changing incentives for scientists, strategic funding for technical solutions from funders, and exploring how institutions like libraries and learned societies can help address the challenges of managing and making sense of the growing volume of research data.
International Collaboration Networks in the Emerging (Big) Data Sciencedatasciencekorea
This document summarizes research on international collaboration networks in emerging big data science. It finds that while global scientific collaboration is widespread, collaboration specifically in big data research is still relatively limited. The United States, Germany, United Kingdom, France, and other developed countries form the most central hubs in the big data collaboration network. The study aims to build on previous descriptive analyses by applying social network analysis and examining collaboration patterns and trends over time.
This document summarizes a presentation on research into how personal online reputations are built, maintained, and evaluated. The research examines both how individuals manage their own online reputations and how people assess others' reputations based on available online information. The presentation outlines the research questions, literature review, theoretical framework, methods, and early findings. Interviews and diaries were used to collect data from a sample of UK participants of different generations. Preliminary analysis found social media is an extension of everyday life, with varying levels of self-censorship. Evaluating others' reputations proved difficult for some.
Personal online reputations: Managing what you can’t controlFrances Ryan
This talk for the 5th annual Discover Academic Research, Training, and Support (DARTS) conference discusses the role of online information in the building, management, and evaluation of personal reputation. It considers the existing literature surrounding reputation and social media use, as well as some early findings from Frances’ information science doctoral investigation on the same topics. A short interactive element encourages participants to think about their own social media use, online information behaviours, and digital footprints—as well as some practical advice on managing a reputation that you can’t fully control.
Data Driven Societies
Digital & Computational Studies
Bowdoin College
February 17, 2014
Professors Gieseking & Gaze
Lecture Slides "On Digital Publics of Opening…or Not"
Digital Image of the City - Housing
Bowdoin College
Fall 2014
Annie Chen, Emma Chow, Jenny Ibsen, Eva Sibinga, Jackie Sullivan, Libby Szuflita
Presentation given on 12/10/14
Digital Image of the City - Infrastructure
Bowdoin College
Fall 2014
Roya Moussapour, Alex N'Diaye, Karl Reinhardt, Alexi Robbins, James Wang, Max Wolf
Presentation given on 12/10/14
We had a rousing conversation about the merits of open access (#OA) during Open Access Week at Trinity College. My presentation focused on how I came into OA and the key resources that make a busy faculty member or graduate student's entrée into sharing their research publicly as part of the open education movement. See jgieseking.org for the complementary handout. After an introduction from our digital librarian Amy Harrell, I was joined by my colleagues Jack Doughtery in Urban Education Studies, and Charles Lebel in Language and Culture Studies in brief individual presentations followed by a conversation with our faculty.
"Personal/Political/Feminist Maps: Reflections on Spatial Methods for Action Research"
Talk given at Feminist Social Justice Conference, a Workshop on Participatory and Feminist Research Methods
March 16, 2015
San Diego State University, San Diego, CA
In _The Practice of Everyday Life_, de Certeau writes that "What the map cuts up, the story cuts across." But what if the everyday stories you seek are already cut up by centuries of structural inequality and oppression, such as those of lesbians and queer women? In this talk I investigate what can be gained for the study of women’s lives and spaces by bringing together the isolated but overlapping stories of lesbians and queer women in maps, from the hand-drawn to the most technologically advanced and interactive. Drawing upon qualitative and quantitative work on lesbians' and queer women's spaces and economies in New York City from 1983 to 2008—including multi-generational focus groups and mental maps, archival research and GIS—I work through three different types of mapping methods and platforms within a participatory action research framework. Through a close analysis of mental maps and GIS maps created using QGIS and TileMill/Mapbox, I suggest that while the spatial and verbal can both obfuscate and illuminate understandings of everyday life. It is the queer practice of holding these seeming binaries in tension that reveals the most rich and complicated knowledge.
This document summarizes several research projects related to big data and social science knowledge. It discusses projects that analyzed large social media platforms like Facebook, Twitter, and Wikipedia to study information diffusion and social influences. It also discusses challenges like securing access to commercial data and ensuring replicability of findings. Examples demonstrate how big data can provide novel insights but are limited by the objects studied and incomplete representation of populations. The document discusses debates around the implications of big data for privacy, prediction, exclusion, and manipulation. It argues that knowledge depends on how research technologies advance knowledge within ethical and legal frameworks.
“Big data” in human services organisations: Practical problems and ethical di...husITa
“Big data” initiatives that aim to bring together and mine data from multiple databases across government and non-government agencies promise new insights into human service delivery. Specifically they aim to provide information about what services are being used, how, by whom and with what outcome. However, the process of achieving such insights poses both practical problems and ethical dilemmas. In this presentation, drawing from an extensive literature review and research with government and non-government human service organisations focussing on the design and redevelopment of electronic information systems, the most significant problems and dilemmas will be explored. It will be argued that current frameworks for ethical social work and human service practice will need to be expanded to accommodate developments in technology which have made ‘Big data’ projects possible.
This document discusses the opportunities and challenges of big data and data science over the next decade. It outlines three key points:
1. Big data is opening doors to accelerating scientific discovery through generating hypotheses from data and using ensemble models to gain multiple perspectives. However, challenges around efficacy and efficiency remain.
2. Data science can be viewed as applying the scientific method to data through discovering correlations from data-driven models and seeking causation through empirical verification, similar to traditional scientific discovery.
3. For data science to fulfill its potential, its laws and best practices around ensuring meaningful correlations and determining causation through verification must be followed, although they are not always common in practice currently. The limits of data science also
Ethical and Legal Issues in Computational Social Science - Lecture 7 in Intro...Lauri Eloranta
Seventh lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
This document provides an overview of data science including its importance, what data scientists do, how the field has emerged, and how to become a data scientist. It notes that by 2018 the US could face shortages of people with data analytics skills. It then discusses how LinkedIn's early growth in 2006 exemplifies the data science process of framing questions, collecting and processing data, exploring patterns, and communicating results. Finally, it outlines the tools used in data science like SQL, analytics software, and machine learning and discusses getting started in the field through education, curiosity, and ongoing learning with mentorship support.
Accessing and Using Big Data to Advance Social Science KnowledgeJosh Cowls
This document summarizes a project investigating the use of big data to advance social science knowledge. It introduces the project leaders and discusses data sources and scope. It then focuses on defining big data, discussing how digital data represents real-world objects and phenomena, and the opportunities and limits this presents. Challenges of using big data to gauge public opinion are also examined, such as issues of representativeness, reliability, and replicability. The document concludes by listing project papers on this topic.
This document provides an overview of data science including its importance, what data scientists do, how the field has emerged, and how to become a data scientist. It discusses how data science can help answer important business questions using LinkedIn in 2006 as a case study. It also outlines the typical data science process of framing questions, collecting and cleaning data, exploring patterns, and communicating results. Finally, it introduces some common data science tools like SQL, analytics software, and machine learning algorithms and discusses options for continuing education in data science.
This document discusses the rise of big data and data science. It notes that while data volumes are growing exponentially, data alone is just an asset - it is data scientists that create value by building data products that provide insights. The document outlines the data science workflow and highlights both the tools used and challenges faced by data scientists in extracting value from big data.
Getting started in Data Science (April 2017, Los Angeles)Thinkful
The document discusses the rise of data science and the skills needed for data scientists. It defines data science as the intersection of engineering, statistics, and communication. Data scientists analyze large datasets to answer important business questions. The document uses LinkedIn in 2006 as a case study, outlining how a data scientist there framed questions, collected and processed user data, explored patterns, and communicated results to improve the user experience and growth. It highlights tools like SQL, analytics software, and machine learning that data scientists use and stresses the importance of curiosity, technical skills, and strong communication for those interested in the field.
This document discusses open data and privacy concerns in the humanities. It outlines that while open data has benefits, some humanities and social science data contains personal details that require protections. Three examples show challenges with medical records, subscriber lists, and student work. The document examines how data can be anonymized but still useful, and questions if IRB rules are too strict. Overall, it argues that fully open or closed access are sometimes false dichotomies, and more nuanced policies are needed to both protect privacy and enable collaborative research.
This document provides an overview of a presentation on big data in the social sciences given by Ralph Schroeder and Eric Meyer at the Oxford Internet Institute Summer Doctoral Programme. The presentation discusses how big data relates to advancing social science research, including opportunities for unprecedented insights but also challenges regarding data quality and replicability. Three case studies are summarized that demonstrate novel uses of big data: analyzing search engine queries, text analysis of digitized books, and identifying Twitter bots. The presentation concludes by considering debates around whether new technologies are outpacing social science and the threats and opportunities of new data sources.
A Revolution in Open Science: Open Data and the Role of Libraries (Professor ...LIBER Europe
This document discusses the opportunities and challenges of open science and open data. It argues that openly sharing scientific data and findings has significant benefits, including enabling faster scientific progress, deterring fraud, and supporting citizen science. However, for data to be truly open and useful to others, it needs to be accessible, intelligible, assessable, and reusable. The document also examines the roles and responsibilities of different stakeholders in working towards more open and reproducible science. This includes changing incentives for scientists, strategic funding for technical solutions from funders, and exploring how institutions like libraries and learned societies can help address the challenges of managing and making sense of the growing volume of research data.
International Collaboration Networks in the Emerging (Big) Data Sciencedatasciencekorea
This document summarizes research on international collaboration networks in emerging big data science. It finds that while global scientific collaboration is widespread, collaboration specifically in big data research is still relatively limited. The United States, Germany, United Kingdom, France, and other developed countries form the most central hubs in the big data collaboration network. The study aims to build on previous descriptive analyses by applying social network analysis and examining collaboration patterns and trends over time.
This document summarizes a presentation on research into how personal online reputations are built, maintained, and evaluated. The research examines both how individuals manage their own online reputations and how people assess others' reputations based on available online information. The presentation outlines the research questions, literature review, theoretical framework, methods, and early findings. Interviews and diaries were used to collect data from a sample of UK participants of different generations. Preliminary analysis found social media is an extension of everyday life, with varying levels of self-censorship. Evaluating others' reputations proved difficult for some.
Personal online reputations: Managing what you can’t controlFrances Ryan
This talk for the 5th annual Discover Academic Research, Training, and Support (DARTS) conference discusses the role of online information in the building, management, and evaluation of personal reputation. It considers the existing literature surrounding reputation and social media use, as well as some early findings from Frances’ information science doctoral investigation on the same topics. A short interactive element encourages participants to think about their own social media use, online information behaviours, and digital footprints—as well as some practical advice on managing a reputation that you can’t fully control.
BROWN BAG TALK WITH MICAH ALTMAN, SOURCES OF BIG DATA FOR SOCIAL SCIENCESMicah Altman
This talk, is part of the MIT Program on Information Science brown bag series (http://informatics.mit.edu)
This talk reviews emerging big data sources for social scientific analysis and explores the challenges these present. Many of these sources pose distinct challenges for acquisition, processing, analysis, inference, sharing, and preservation.
Dr Micah Altman is Director of Research and Head/Scientist, Program on Information Science for the MIT Libraries, at the Massachusetts Institute of Technology. Dr. Altman is also a Non-Resident Senior Fellow at The Brookings Institution. Prior to arriving at MIT, Dr. Altman served at Harvard University for fifteen years as the Associate Director of the Harvard-MIT Data Center, Archival Director of the Henry A. Murray Archive, and Senior Research Scientist in the Institute for Quantitative Social Sciences.
Dr. Altman conducts research in social science, information science and research methods -- focusing on the intersections of information, technology, privacy, and politics; and on the dissemination, preservation, reliability and governance of scientific knowledge.
Lecture series: Using trace data or subjective data, that is the question dur...Bart Rienties
In this lecture series Bart Rienties (Professor of Learning Analytics, head of Academic Professional Development) will discuss how from the safety of your home you could use existing trace data to explore interactions between people (e.g., Twitter data, engagement data in a virtual learning environment, public data sets), and what the affordances and limitations of these trace data might be. Furthermore, he will discuss how other ways of collecting subjective data (e.g., surveys, interviews) might strengthen our understandings of complex interactions between people.
There are no prior requirements to join, and everyone is welcome. For those with a technical background you may enjoy this recent paper in PLOS ONE https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233977. For those with a non-technical background, you may enjoy this paper https://journals.sfu.ca/flr/index.php/journal/article/view/348
This document provides an overview and recap of key concepts relating to data driven societies and code as law. It begins with an outline of the typical structure for research papers. It then discusses publics and counterpublics, how social media has changed notions of publicness, and Lawrence Lessig's concept of "code is law" which argues that computer code and software regulate society in the same way laws do. The document notes Lessig advocated using code to help define law and regulate cyberspace. It closes by announcing the topics for the next class - continuing the discussion of the life of code and mapping Twitter data.
Similar to Bowdoin: Data Driven Socities 2014 - Defining Data & Redefining Privacy 2/10/14 (20)
This document discusses research on transgender communities on Tumblr. It begins by outlining the research questions around what can be learned from trans youth networks and how trans data can inform theory. It then describes the methods used to analyze over 1 million posts from the #ftm and #mtf hashtags on Tumblr. Key findings include Tumblr serving as an archive of experiences, a source of medical knowledge, and a site for cultural production and identity exploration. The document argues for an approach to trans data that recognizes its situated nature and holds binaries in tension. It suggests trans theory can benefit from understanding lived experiences and recognizing manifold identities.
This document summarizes Jen Jack Gieseking's presentation on queer spatial methods. It discusses using mixed qualitative and quantitative data to have conversations across datasets. It also discusses using multiple tools from digital humanities and social data sciences to allow for more expression. The presentation outlines using mental mapping, GIS, interactive online maps, and mixed analytics to hold tension between data. It provides a case study of mapping lesbian and queer women's spaces in New York City from 1983 to 2008. The goal is queer interventions in mapping that challenge norms.
Digital Image of the City - Infrastructure
Bowdoin College
Fall 2014
Rachel Barnes, Ezra Duplissie-Cyr, Ike May, Kote Mushegian, Luis Paniagua, Mingo Sanchez, Vivian Yang
Presentation given on 12/10/14
Digital Image of the City - Housing
Bowdoin College
Fall 2014
Ben Miller, Peter Nauffts, Hannah Rafkin, Claudia Villar, Jonah Watt
Presentation given on 12/10/14
"Sustaining Difference during Gentrification: NYC & Berlin Since 2008"
Dr. Jen Jack Gieseking
Digital & Computational Studies
Bowdoin College, Brunswick, ME, USA
BUKA 2010-2011, HU im Berlin
jgieseking.org
@jgieseking
Presentation from Alexander von Humboldt Foundation Bundeskanzler-Stipendium (BUKA) / German Chancellor Fellowship Kolloquium in Sankt-Petersburg, Russia.
Do not cite, reprint, or quote this presentation without express permission of Dr. Jen Jack Gieseking.
The Digital Image of the City
Digital & Computational Studies
Bowdoin College
October 8, 2014
Professor Gieseking
Lecture Slides "Race, Ethnicity, Immigration"
The Digital Image of the City
Digital & Computational Studies
Bowdoin College
September 8, 2014
Professor Gieseking
Lecture Slides "An Introduction to The Digital Image of the City"
This document discusses the production of urban space and the concept of smart cities. It references works by James Merrill, Dolores Hayden, and Anthony Townsend on how space is produced and for whose benefit. Examples are provided of Hayden's work documenting spaces created by marginalized communities in East Harlem and Little Tokyo. The document also references a study visualizing taxi pickups and dropoffs in New York City. Readings by Debord, Bauman, and Lynch are assigned for an upcoming field trip, with a blog post due before the trip.
Ruben Martinez '16 is a student of Bowdoin College. He created this presentation as a part of the Data Driven Societies course (Spring 2014) taught by Drs. Gieseking and Gaze. His analysis draws upon one month of #wearable data scraped from Twitter in February of 2014.
Data Driven Societies
Digital & Computational Studies
Bowdoin College
April 14, 2014
Professor Gieseking
Lecture Slides "Visualizing Social Life (When They Let You)"
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
2. Data & Information (Recap)
✦
Information society
!
✦
Data vs. information
!
✦
Information-as-freedom
vs. information-as-control
3. Big Data & Privacy
✦
Ethical research
!
✦
Data sample and data
access
!
✦
Defining big data,
defining privacy
daily.captaindash.com
4. Social Scientific Approach
0. Identify an issue
1. Research question
2. Theoretical approach
3. Literature review
4. Methods
5. Analysis
6. Discussion
7. Conclusion
5. Social Scientific Approach
0. Identify an issue
1. Research question
2. Theoretical approach
3. Literature review
4. Methods
5. Analysis
6. Discussion
7. Conclusion
6. Social Scientific Approach
0. Identify an issue
1. Research question
2. Theoretical approach
3. Literature review
4. Methods
5. Analysis
6. Discussion
7. Conclusion
7. Social Scientific Approach
0. Identify an issue
1. Research question
2. Theoretical approach
3. Literature review
4. Methods
5. Analysis
6. Discussion
7. Conclusion
Ethics,
anyone?
8. The Future of Now
The Chronicle of Higher Ed
The White House
9. Visualize This
How we handle to emergence of Big Data is critical.
…it is still necessary to ask critical questions about
what all this data means, who gets access to what
data, how data analysis is deployed, and to what ends.
—danah boyd & Kate Crawford,
“Critical Questions for Big Data” (2012)
13. Data Access: Twitter
✦
API - application programming interface is the set of tools
developers can use to access structured data
!
✦
“Firehose” of access: GNIP, DataSifter
✦
“Gardenhose" of access: 10% of public tweets
✦
“Spritzer” of access: about 1% of public tweets
✦
White-listed accounts: allowed access to certain subject matter
14. Data Rich and the Data Poor
Manovich (2011) writes of three classes of people in the
realm of Big Data: “those who create data (both consciously
and by leaving digital footprints), those who have the means
to collect it, and those have expertise to analyze it.”
-boyd & Crawford (2012)
!
✦
Data rich and data poor - research insiders and
outsiders, respectively, who have varied degrees of
access to data and the means to analyze it
15. Defining Big Data
1. Large data sets that require supercomputers for analysis,
i.e., usually over 2gb (Manovich 2011)
!
2. A cultural, technological, and scholarly phenomenon that
depends on the interplay of the following:
✦
Technology: maximized computation power and
algorithmic accuracy
✦
Analysis: examining large data sets to identify patterns
to make claims
✦
Mythology: widespread brief that the larger the data set,
the more accurate the findings (boyd & Crawford 2012)
17. ScraperWiki Support
A clever and elegant solution to our problem of
accessing Twitter data with a limited number of calls:
!
1. Open ScraperWiki and view your table
!
2. Download EVERY MONDAY
!
3. Restart EVERY MONDAY (you will need to do this
the first Monday of break too)
18. Next Class: Feb. 12
Today: big data, privacy, research ethics,
data rich vs. data poor
✦
!
Quiz: terms / concepts coming via email
✦
!
Readings: Pariser, Stray
✦
!
✦
Next class/lab:
✦
filter bubbles
✦
correlation/causation
✦
work with Twitter datasets
✦
continue learning R