Presentation about stray dogs
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This document presents a literature review and proposed research plan to examine the resettlement of stray dogs in Beijing. It begins with an introduction noting the security and health issues caused by the large stray dog population. The literature review summarizes previous studies that found most stray dogs are not properly resettled and some are slaughtered, incidents of stray dogs wounding people in Beijing, and risks of rabies. The research objectives are listed as investigating disadvantages of stray dogs to society, their distribution, issues caused, and analyzing how to reduce numbers and improve resettlement. The document outlines methodology, limitations, ethical/legal considerations, and a proposed timescale.
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Big Data Research Trend and Forecast (2005-2015): An Informetrics Perspective
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Big data is prevalent in our daily life. Not surprisingly, big data becomes a hot topic discussedby commercial worlds, media, magazines, general publics and elsewhere. From academic point of view, isit a research area of potential worth being explored? Or it is just another hype? Are there only computer orIS related scholars suitable for big data research due to its nature? Or scholars from other research areas are alsosuitable for this subject? This study aims to answer these questions through the use of informetricsapproach and data source form the SSCI Journal database, leveraging informetric‟s robust natures ofquantitative power of analyze information in any form onto the data source of representativeness. This research shows that big data research is at its growth phase with an exponential growth patternsince 2012 and with great potential for years to come. And perhaps surprisingly, computer or IS relateddisciplinesare not on the top 5 research areas fromthis research results. In fact, the top five research disciplinesare more diversified then expected: business economics (#1), Government Law (#2), InformationScience/ Library Science (#3), Social Science (#4) and Computer Science (#5). Scholars from the USuniversities are the most productive in this subject while Asian countries, including Taiwan, are alsovisible. Besides, this study also identifies that big data publications from SSCI journal database during2005-2015 do fit Lotka‟s law. This study contributes tounderstand the current big data research trends and also show the ways toresearchers who are interested to conduct future research in big data regardless of their research backgrounds.Decomposing Social and Semantic Networks in Emerging “Big Data” Research
빅데이터가 학문으로 등장한 배경을 잘 정리한 논문
http://www.sciencedirect.com/science/article/pii/S1751157713000473
Park, H.W.@, & Leydesdorff, L. (2013). Decomposing Social and Semantic Networks in Emerging “Big Data” Research. Journal of Informetrics. 7 (3), 756-765. DOI information: 10.1016/j.joi.2013.05.004
Sci 2011 big_data(30_may13)2nd revised _ loet
This document summarizes a research paper that analyzes social and semantic networks related to big data research. It describes how the authors collected data on internationally co-authored papers from the 2011 SCI database using search terms related to big data. It then summarizes the two research questions addressed: 1) What is the structural pattern of international co-authorship networks in big data research? 2) What is the semantic structure of paper titles in this field? The authors analyzed the data using social network analysis and semantic network methods to address these questions and better understand patterns of collaboration and terminology use in emerging big data science.
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Meyer Big Data SDP13
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.
Data Science definition
Content:
Data Science and data scientist.
Statistics-
Knowledge domain-
Algoritmic data analysis-
Statistical data analysis
Computational Statistics
Let's talk about Data Science
Data Science is an interdisciplinary approach that combines computational science, statistics, and domain knowledge to extract meaningful insights from large and complex data. It aims to address challenges posed by the data revolution characterized by big data from diverse sources. There is no single agreed upon definition, but most definitions emphasize applying techniques from computer science, statistics, and the relevant domain area to discover patterns, make predictions, and support decision making from data. Key aspects include developing appropriate methodologies for knowledge discovery, forecasting and decisions using large and diverse data from sources like surveys, social media, sensors and more. The integration of domain knowledge representation with computational and statistical tools is seen as an important novelty that can enhance data analysis and interpretation.
1. Data Science overview - part1.pptx
This document provides an overview of data science including why it is an exciting field, where data comes from, what data science is, how to do data science, and who data scientists are. It discusses the history of data analysis and examples of exciting new applications of data analytics like Google Flu Trends. It also covers sources of big data, the five V's of big data, contrasting data science with databases and business intelligence, and contrasting data science with machine learning.
International Collaboration Networks in the Emerging (Big) Data Science
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.
Lecture series: Using trace data or subjective data, that is the question dur...
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
Scott Edmunds: Channeling the Deluge: Reproducibility & Data Dissemination in...
Scott Edmunds: Channeling the Deluge: Reproducibility & Data Dissemination in...GigaScience, BGI Hong Kong
Scott Edmunds talk at the 7th Internation Conference on Genomics: "Channeling the Deluge: Reproducibility & Data Dissemination in the “Big-Data” Era. ICG7, Hong Kong 1st December 2012
"Big data analysis of news and social media content
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Lecture #01
This document provides an overview of the introductory lecture to the BS in Data Science program. It discusses key topics that were covered in the lecture, including recommended books and chapters to be covered. It provides a brief introduction to key terminologies in data science, such as different data types, scales of measurement, and basic concepts. It also discusses the current landscape of data science, including the difference between roles of data scientists in academia versus industry.
"Reproducibility from the Informatics Perspective"
Dr. Altman will provide expert comment on the need for informatics modeling as part of the National Academies workshop: Statistical Challenges in Assessing and Fostering the Reproducibility of Scientific Results
This workshop focuses on the topic of addressing statistical challenges in assessing and fostering the reproducibility of scientific results by examining three issues from a statistical perspective: the extent of reproducibility, the causes of reproducibility failures, and potential remedies.
Accessing and Using Big Data to Advance Social Science Knowledge
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.
Ralph schroeder and eric meyer
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.
BROWN BAG TALK WITH MICAH ALTMAN, SOURCES OF BIG DATA FOR SOCIAL SCIENCES
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.
Roger hoerl say award presentation 2013
This document discusses how statistical engineering principles can help address challenges with "Big Data" projects. It argues that simply having powerful algorithms and large datasets does not guarantee good models or results. The leadership challenge for statisticians is to ensure Big Data projects are built on sound modeling foundations rather than hype. Statistical engineering principles like understanding data quality, using sequential approaches, and integrating subject matter knowledge can help improve the success of Big Data analyses and provide the statistical profession an opportunity for leadership in this area. Statistical engineering provides a framework to structure Big Data projects and incorporate fundamentals of good science that are sometimes overlooked.
Practical Issues in Social Research Methods
1. The lecture discusses formulating a good research problem and theoretical model, which requires an extensive literature review to identify key findings, unresolved issues, and influential studies.
2. Selecting a research question should avoid implying answers and involve ethical procedures. Good questions proceed from description to explanation or propose new research, challenge prior work, or extend existing research.
3. Developing hypotheses requires specifying at minimum an independent and dependent variable to explain outcomes rather than constants.
Identifying psychological research data in the digital environment.
Identifying psychological research data in the digital environment. Leibniz-Zentrum für Psychologische Information & Dokumentation
Weichselgartner, E. (2011, Februar).
Identifying psychological research data in the digital environment. (PDF) IDSC of IZA/Gesis/RatSWD Workshop: Persistent Identifiers for the Social Sciences, Bonn.Similar to Presentation about stray dogs (20)
Big Data Research Trend and Forecast (2005-2015): An Informetrics Perspective
Big Data Research Trend and Forecast (2005-2015): An Informetrics Perspective
Decomposing Social and Semantic Networks in Emerging “Big Data” Research
Decomposing Social and Semantic Networks in Emerging “Big Data” Research
DigiCCurr 2013 PhD Workshop - Citizen Science and Data Curation: Who needs what?
DigiCCurr 2013 PhD Workshop - Citizen Science and Data Curation: Who needs what?
International Collaboration Networks in the Emerging (Big) Data Science
International Collaboration Networks in the Emerging (Big) Data Science
Lecture series: Using trace data or subjective data, that is the question dur...
Lecture series: Using trace data or subjective data, that is the question dur...
Scott Edmunds: Channeling the Deluge: Reproducibility & Data Dissemination in...
Scott Edmunds: Channeling the Deluge: Reproducibility & Data Dissemination in...
Big data analysis of news and social media content
Big data analysis of news and social media content
"Reproducibility from the Informatics Perspective"
"Reproducibility from the Informatics Perspective"
Accessing and Using Big Data to Advance Social Science Knowledge
Accessing and Using Big Data to Advance Social Science Knowledge
BROWN BAG TALK WITH MICAH ALTMAN, SOURCES OF BIG DATA FOR SOCIAL SCIENCES
BROWN BAG TALK WITH MICAH ALTMAN, SOURCES OF BIG DATA FOR SOCIAL SCIENCES
Identifying psychological research data in the digital environment.
Identifying psychological research data in the digital environment.
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Presentation about stray dogs
- 1. AN EXAMINATION OF RESETTLEMENT OF STRAY DOGS IN BEIJING ZHOU Zhou (Nigel) NCUK PMP 25th November 2011
- 2. INTRODUCTION There are a large number of various social contradictions with the huge number of stray dogs such as series of security issue, in particularly, the dangers of rabies. With the continued increase in the number of stray dogs, the problem will be caused by growing This research will conduct a survey on the Resettlement of stray.
- 3. LITERATURE REVIEW Li Chuan (Li, 2011) did a survey for the placement of stray dogs and found not only approximately 60% stray dogs has not been proper resettle but also slaughtered by others. Meng Yasheng and Jang De (Meng and Jang, 2010) released a report about stray dogs wounding event. Such incidents often occur in densely populated residential area of Beijing about more than 120000 times in 2009. Owing to stray dogs has not been standardized managed then seriously affect the safety of people's daily lives. Betty Dodet (Dodet,2001)written the article of Rabies Control in Asia ,the essay main introduced that the dangers of rabies, rabies occur each year, rabies distribution of city and the factors of rabies occur.
- 4. RESEARCH OBJECTIVES To investigate stray dogs cause disadvantages to society. To explain distribution of stray dogs. To examine issues caused by stray dogs. To analyze issues that how to reduce the number of stray dogs and the placement of stray dogs.
- 5. METHODOLOGY
- 6. LIMITATIONS Most of the survey data obtained from Beijing the authors don’t have enough time and relevant budget. Secondly, some of the papers and data statistics are confidential or charges. So the data in this research just include the public data. Most of the data can be found are 2007-2010, so the analysis of the results may not be very comprehensive.
- 7. ETHICAL AND LEGAL ISSUES Firstly, it is the issue of confidentiality data. Though the paper without any legal confidentiality of data. All of the data are public data. Secondly, there are possible legal issues for interviews. Because some of the interviewees are government officials who may reveal something important accidentally, that might drop them into legal issues. It is about the proper usage of data for the research and ensure that these data only for this research.
- 8. TIMESCALE search literatures Literature Review read literatures write literatures analysis Research Data Design design methods write methodology design questionnaires pilot study y Data Primary collect questionnaires Secondar search public laws,rules search public statistics Data Analysis analyze primary data analyze secondary data write finding finish draft dissertation Dissertation Finish discuss with tutors redraft and finish off 1 2 3 4 5 Week
- 9. BIBLIOGRAPHY Li Chuan (2009) A report about stray dogs killed by people. From: Beijing Times News http:news.qq.com/a/20091113/000771.htm Sheng Zhiming (2011) The stray dogs were driven From: New Beijing News http:www.chinanews.com/edu/2011/08-17/3263905.html Meng Yasheng and Jang De (2010) Stray dogs hurt people who is responsible for the incident. http:news.sina.com.cn/0/2010-02-25/0818171264285.shtn Mahendra B.J. (Mahendra, 2005) A community survey of dog bites From: Internet http://cat.inist.fr/?aModele=afficheN&cpsidt=14380663