Searching for "data"

Borgman data

book reviews:
https://bobmorris.biz/big-data-little-data-no-data-a-book-review-by-bob-morris
“The challenge is to make data discoverable, usable, assessable, intelligible, and interpretable, and do so for extended periods of time…To restate the premise of this book, the value of data lies in their use. Unless stakeholders can agree on what to keep and why, and invest in the invisible work necessary to sustain knowledge infrastructures, big data and little data alike will become no data.”
http://www.cjc-online.ca/index.php/journal/article/view/3152/3337
he premise that data are not natural objects with their own essence, Borgman rather explores the different values assigned to them, as well as their many variations according to place, time, and the context in which they are collected. It is specifically through six “provocations” that she offers a deep engagement with different aspects of the knowledge industry. These include the reproducibility, sharing, and reuse of data; the transmission and publication of knowledge; the stability of scholarly knowledge, despite its increasing proliferation of forms and modes; the very porosity of the borders between different areas of knowledge; the costs, benefits, risks, and responsibilities related to knowledge infrastructure; and finally, investment in the sustainable acquisition and exploitation of data for scientific research.
beyond the six provocations, there is a larger question concerning the legitimacy, continuity, and durability of all scientific research—hence the urgent need for further reflection, initiated eloquently by Borgman, on the fact that “despite the media hyperbole, having the right data is usually better than having more data”
o Data management (Pages xviii-xix)
o Data definition (4-5 and 18-29)
p. 5 big data and little data are only awkwardly analogous to big science and little science. Modern science, or big science inDerek J. de Solla Price  (https://en.wikipedia.org/wiki/Big_Science) is characterized by international, collaborative efforts and by the invisible colleges of researchers who know each other and who exchange information on a formal and informal basis. Little science is the three hundred years of independent, smaller-scale work to develop theory and method for understanding research problems. Little science is typified by heterogeneous methods, heterogeneous data and by local control and analysis.
p. 8 The Long Tail
a popular way of characterizing the availability and use of data in research areas or in economic sectors. https://en.wikipedia.org/wiki/Long_tail

o Provocations (13-15)
o Digital data collections (21-26)
o Knowledge infrastructures (32-35)
o Open access to research (39-42)
o Open technologies (45-47)
o Metadata (65-70 and 79-80)
o Common resources in astronomy (71-76)
o Ethics (77-79)
o Research Methods and data practices, and, Sensor-networked science and technology (84-85 and 106-113)
o Knowledge infrastructures (94-100)
o COMPLETE survey (102-106)
o Internet surveys (128-143)
o Internet survey (128-143)
o Twitter (130-133, 138-141, and 157-158(
o Pisa Clark/CLAROS project (179-185)
o Collecting Data, Analyzing Data, and Publishing Findings (181-184)
o Buddhist studies 186-200)
o Data citation (241-268)
o Negotiating authorship credit (253-256)
o Personal names (258-261)
o Citation metrics (266-209)
o Access to data (279-283)

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more on big data in education in this IMS blog
http://blog.stcloudstate.edu/ims?s=big+data

academic library collection data visualization

Finch, J. f., & Flenner, A. (2016). Using Data Visualization to Examine an Academic Library Collection. College & Research Libraries77(6), 765-778.

http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dllf%26AN%3d119891576%26site%3dehost-live%26scope%3dsite

p. 766
Visualizations of library data have been used to: • reveal relationships among subject areas for users. • illuminate circulation patterns. • suggest titles for weeding. • analyze citations and map scholarly communications

Each unit of data analyzed can be described as topical, asking “what.”6 • What is the number of courses offered in each major and minor? • What is expended in each subject area? • What is the size of the physical collection in each subject area? • What is student enrollment in each area? • What is the circulation in specific areas for one year?

libraries, if they are to survive, must rethink their collecting and service strategies in radical and possibly scary ways and to do so sooner rather than later. Anderson predicts that, in the next ten years, the “idea of collection” will be overhauled in favor of “dynamic access to a virtually unlimited flow of information products.”  My note: in essence, the fight between Mark Vargas and the Acquisition/Cataloguing people

The library collection of today is changing, affected by many factors, such as demanddriven acquisitions, access, streaming media, interdisciplinary coursework, ordering enthusiasm, new areas of study, political pressures, vendor changes, and the individual faculty member following a focused line of research.

subject librarians may see opportunities in looking more closely at the relatively unexplored “intersection of circulation, interlibrary loan, and holdings.”

Using Visualizations to Address Library Problems

the difference between graphical representations of environments and knowledge visualization, which generates graphical representations of meaningful relationships among retrieved files or objects.

Exhaustive lists of data visualization tools include: • the DIRT Directory (http://dirtdirectory.org/categories/visualization) • Kathy Schrock’s educating through infographics (www.schrockguide.net/ infographics-as-an-assessment.html) • Dataviz list of online tools (www.improving-visualisation.org/case-studies/id=5)

Visualization tools explored for this study include Plotly, Microsoft Excel, Python programming language, and D3.js, a javascript library for creating documents based on data. Tableau Public©

Eugene O’Loughlin, National College of Ireland, is very helpful in composing the charts and is found here: https://youtu.be/4FyImh2G7N0.

p. 771 By looking at the data (my note – by visualizing the data), more questions are revealed,  The visualizations provide greater comprehension than the two-dimensional “flatland” of the spreadsheets, in which valuable questions and insights are lost in the columns and rows of data.

By looking at data visualized in different combinations, library collection development teams can clearly compare important considerations in collection management: expenditures and purchases, circulation, student enrollment, and course hours. Library staff and administrators can make funding decisions or begin dialog based on data free from political pressure or from the influence of the squeakiest wheel in a department.

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more on data visualization for the academic library in this IMS blog
http://blog.stcloudstate.edu/ims?s=data+visualization

data visualization for librarians

Eaton, M. E. (2017). Seeing Seeing Library Data: A Prototype Data Visualization Application for Librarians. Journal of Web Librarianship, 11(1), 69–78. Retrieved from http://academicworks.cuny.edu/kb_pubs

Visualization can increase the power of data, by showing the “patterns, trends and exceptions”

Librarians can benefit when they visually leverage data in support of library projects.

Nathan Yau suggests that exploratory learning is a significant benefit of data visualization initiatives (2013). We can learn about our libraries by tinkering with data. In addition, handling data can also challenge librarians to improve their technical skills. Visualization projects allow librarians to not only learn about their libraries, but to also learn programming and data science skills.

The classic voice on data visualization theory is Edward Tufte. In Envisioning Information, Tufte unequivocally advocates for multi-dimensionality in visualizations. He praises some incredibly complex paper-based visualizations (1990). This discussion suggests that the principles of data visualization are strongly contested. Although Yau’s even-handed approach and Cairo’s willingness to find common ground are laudable, their positions are not authoritative or the only approach to data visualization.

a web application that visualizes the library’s holdings of books and e-books according to certain facets and keywords. Users can visualize whatever topics they want, by selecting keywords and facets that interest them.

Primo X-Services API. JSON, Flask, a very flexible Python web micro-framework. In addition to creating the visualization, SeeCollections also makes this data available on the web. JavaScript is the front-end technology that ultimately presents data to the SeeCollections user. JavaScript is a cornerstone of contemporary web development; a great deal of today’s interactive web content relies upon it. Many popular code libraries have been written for JavaScript. This project draws upon jQuery, Bootstrap and d3.js.

To give SeeCollections a unified visual theme, I have used Bootstrap. Bootstrap is most commonly used to make webpages responsive to different devices

D3.js facilitates the binding of data to the content of a web page, which allows manipulation of the web content based on the underlying data.

 

JSON and Structured Data

JSON and Structured Data

https://www.w3schools.com/js/js_json_intro.asp

JSON replace XML. lightweight data-interchange format. Often used with AJAX (send data forth back client, server, without refresh)

Data types:
number: no dfference between integer and floats
string: string of unicode characters “”
Boolean: true and false
array: ordered list of 0 and more values
Object: unordered collection of key/value pairs
Null: empty value

JSON Syntax Rules:
uses key/value pairs – {“name”;”brad”} .     uses double quotes around Key and value .     must use the specific data type .   file type is “.json” .   MIME type is “application/json”

http://www.json.org/

https://code.google.com/archive/p/json-simple/

https://www.linkedin.com/learning/learn-api-documentation-with-json-and-xml/json-basics

strings: text enclosed in single or double quotation marks
numbers: integer or decimal, positive or negative
booleans: true or false, no quot marks
null: means “nothing,” no quot marks

arrays are lists in square brackets, comma separated, can mix data types

objects are JSON dictionaries in curly brackets, keys and values are separated by a colon, pairs are separated by commas. keys and values can be any data type, but string is the most common value for a key

nesting : arrays and objects inside each other
can put arrays inside objects, objects inside

 

anonymous browsing data

‘Anonymous’ browsing data can be easily exposed, researchers reveal

https://www.theguardian.com/technology/2017/aug/01/data-browsing-habits-brokers

A similar strategy was used in 2008, Dewes said, to deanonymise a set of ratings published by Netflix to help computer scientists improve its recommendation algorithm: by comparing “anonymous” ratings of films with public profiles on IMDB, researchers were able to unmask Netflix users – including one woman, a closeted lesbian, who went on to sue Netflix for the privacy violation.

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A hacker explains the best way to browse the internet anonymously.
https://www.facebook.com/techinsider/videos/824655787732779/ 

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more on privacy in this IMS blog
https://blog.stcloudstate.edu/ims?s=privacy

big data in ed

New Report Examines Use of Big Data in Ed

By Dian Schaffhauser  05/17/17

https://campustechnology.com/articles/2017/05/17/new-report-examines-use-of-big-data-in-ed.aspx

new report from the National Academy of Education “Big Data in Education,” summarizes the findings of a recent workshop held by the academy

three federal laws: Family Educational Rights and Privacy Act (FERPA), the Children’s Online Privacy Protection Act (COPPA) and the Protection of Pupil Rights Amendment (PPRA).

over the last four years, 49 states and the District of Columbia have introduced 410 bills related to student data privacy, and 36 states have passed 85 new education data privacy laws. Also, since 2014, 19 states have passed laws that in some way address the work done by researchers.

researchers need to get better at communicating about their projects, especially with non-researchers.

One approach to follow in gaining trust “from parents, advocates and teachers” uses the acronym CUPS:

  • Collection: What data is collected by whom and from whom;
  • Use: How the data will be used and what the purpose of the research is;
  • Protection: What forms of data security protection are in place and how access will be limited; and
  • Sharing: How and with whom the results of the data work will be shared.

Second, researchers must pin down how to share data without making it vulnerable to theft.

Third, researchers should build partnerships of trust and “mutual interest” pertaining to their work with data. Those alliances may involve education technology developers, education agencies both local and state, and data privacy stakeholders.

Along with the summary report, the results of the workshop are being maintained on a page within the Academy’s website here.

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more on big data in education in this IMS blog
http://blog.stcloudstate.edu/ims?s=big+data

Analytics and Data Mining in Education

https://www.linkedin.com/groups/934617/934617-6255144273688215555

Call For Chapters: Responsible Analytics and Data Mining in Education: Global Perspectives on Quality, Support, and Decision-Making

SUBMIT A 1-2 PAGE CHAPTER PROPOSAL
Deadline – June 1, 2017

Title:  Responsible Analytics and Data Mining in Education: Global Perspectives on Quality, Support, and Decision-Making

Synopsis:
Due to rapid advancements in our ability to collect, process, and analyze massive amounts of data, it is now possible for educators at all levels to gain new insights into how people learn. According to Bainbridge, et. al. (2015), using simple learning analytics models, educators now have the tools to identify, with up to 80% accuracy, which students are at the greatest risk of failure before classes even begin. As we consider the enormous potential of data analytics and data mining in education, we must also recognize a myriad of emerging issues and potential consequences—intentional and unintentional—to implement them responsibly. For example:

· Who collects and controls the data?
· Is it accessible to all stakeholders?
· How are the data being used, and is there a possibility for abuse?
· How do we assess data quality?
· Who determines which data to trust and use?
· What happens when the data analysis yields flawed results?
· How do we ensure due process when data-driven errors are uncovered?
· What policies are in place to address errors?
· Is there a plan for handling data breaches?

This book, published by Routledge Taylor & Francis Group, will provide insights and support for policy makers, administrators, faculty, and IT personnel on issues pertaining the responsible use data analytics and data mining in education.

Important Dates:

· June 1, 2017 – Chapter proposal submission deadline
· July 15, 2017 – Proposal decision notification
· October 15, 2017 – Full chapter submission deadline
· December 1, 2017 – Full chapter decision notification
· January 15, 2018 – Full chapter revisions due
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more on data mining in this IMS blog
http://blog.stcloudstate.edu/ims?s=data+mining

more on analytics in this IMS blog
http://blog.stcloudstate.edu/ims?s=analytics

big data as big success

Big Data като Big Success

Анализът на масивите данни може да помогне на редица бизнеси да решават проблеми и да намаляват загубите и пропуснатите ползи, твърди Александър Ефремов

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more on big data in this IMS blog
http://blog.stcloudstate.edu/ims?s=big+data

student data mining

Beyond the Horizon Webinar on Student Data

March 29, 2017 @ 12-1pm US Central Time

NMC Beyond the Horizon > Integrating Student Data Across Platforms

The growing use of data mining software in online education has great potential to support student success by identifying and reaching out to struggling students and streamlining the path to graduation. This can be a challenge for institutions that are using a variety of technology systems that are not integrated with each other. As institutions implement learning management systems, degree planning technologies, early alert systems, and tutor scheduling that promote increased interactions among various stakeholders, there is a need for centralized aggregation of these data to provide students with holistic support that improves learning outcomes. Join us to hear from an institutional exemplar who is building solutions that integrate student data across platforms. Then work with peers to address challenges and develop solutions of your own.

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more on altmetrics in this IMS blog
http://blog.stcloudstate.edu/ims?s=altmetrics

more on big data in this IMS blog
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