Antitrust laws only go so far when addressing companies that don’t produce any physical goods. It is time to negotiate a new set of rules. Otherwise, our future economy will be dominated by just a few companies.
There are still people out there who think that Amazon is nothing more than an online version of a department store. But it’s much more than that: It is a rapidly growing, global internet giant that is changing the way we shop, conquering more and more markets, using Alexa to suck up our personal data straight out of our living rooms and currently seeking access to our front door keys so it can deliver packages even when nobody’s home.
It wasn’t that long ago that EU efforts to limit the power of Google and Amazon on the European market were decried in the U.S. as protectionism, as an attempt by the Europeans to protect their own inferior digital economy. Now, though, politicians and economists in the U.S. have even begun discussing the prospect of breaking up the internet giants. The mood has shifted.
The digital economy, by contrast, is based on algorithms and its most powerful companies don’t produce any physical products. Customers receive their services free of charge, paying only with their data. The more customers a service provider attracts, the more attractive it becomes to new customers, who then deliver even more data – which is why Google and Facebook need not fear new competition.
first of all, the power of a company, and the abuse of that power, must be redefined. We cannot allow a situation in which these extremely large companies can swallow up potential rivals before they can even begin to develop. As such, company acquisitions must be monitored much more strictly than they currently are and, if need be, blocked.
Second, it must be determined who owns the data collected – whether, for example, it should also be made available to competitors or whether consumers should receive more in exchange than simply free internet search results.
Third, those disseminating content cannot be allowed to reject responsibility for that content. Demonstrably false claims and expressions of hate should not be tolerated.
And finally, those who earn lots of money must also pay lots of taxes – and not just back home but in all the countries where they do business.
Vicky Steeves (@VickySteeves) is the first Research Data Management and Reproducibility Librarian
Reproducibility is made so much more challenging because of computers, and the dominance of closed-source operating systems and analysis software researchers use. Ben Marwick wrote a great piece called ‘How computers broke science – and what we can do to fix it’ which details a bit of the problem. Basically, computational environments affect the outcome of analyses (Gronenschild et. al (2012) showed the same data and analyses gave different results between two versions of macOS), and are exceptionally hard to reproduce, especially when the license terms don’t allow it. Additionally, programs encode data incorrectly and studies make erroneous conclusions, e.g. Microsoft Excel encodes genes as dates, which affects 1/5 of published data in leading genome journals.
technology to capture computational environments, workflow, provenance, data, and code are hugely impactful for reproducibility. It’s been the focus of my work, in supporting an open source tool called ReproZip, which packages all computational dependencies, data, and applications in a single distributable package that other can reproduce across different systems. There are other tools that fix parts of this problem: Kepler and VisTrails for workflow/provenance, Packrat for saving specific R packages at the time a script is run so updates to dependencies won’t break, Pex for generating executable Python environments, and o2r for executable papers (including data, text, and code in one).
a plugin for Jupyter notebooks), and added a user interface to make it friendlier to folks not comfortable on the command line.
The proliferation of mobile devices and the adoption of learning applications in higher education simplifies formative assessment. Professors can, for example, quickly create a multi-modal performance that requires students to write, draw, read, and watch video within the same assessment. Other tools allow for automatic grade responses, question-embedded documents, and video-based discussion.
Multi-Modal Assessments – create multiple-choice and open-ended items that are distributed digitally and assessed automatically. Student responses can be viewed instantaneously and downloaded to a spreadsheet for later use.
Formative (http://www.goformative.com) allows professors to upload charts or graphic organizers that students can draw on with a stylus. Formative also allows professors to upload document “worksheets” which can then be augmented with multiple-choice and open-ended questions.
Nearpod (http://www.nearpod.com) allows professors to upload their digital presentations and create digital quizzes to accompany them. Nearpod also allows professors to share three-dimensional field trips and models to help communicate ideas.
Video-Based Assessments – Question-embedded videos are an outstanding way to improve student engagement in blended or flipped instructional contexts. Using these tools allows professors to identify if the videos they use or create are being viewed by students.
Playposit (http://www.playposit.com) are two leaders in this application category. A second type of video-based assessment allows professors to sustain discussion-board like conversation with brief videos.
Flipgrid (http://www.flipgrid.com), for example, allows professors to posit a video question to which students may respond with their own video responses.
Quizzing Assessments – ools that utilize close-ended questions that provide a quick check of student understanding are also available.
Kahoot (http://www.kahoot.com) are relatively quick and convenient to use as a wrap up to instruction or a review of concepts taught.
Integration of technology is aligned to sound formative assessment design. Formative assessment is most valuable when it addresses student understanding, progress toward competencies or standards, and indicates concepts that need further attention for mastery. Additionally, formative assessment provides the instructor with valuable information on gaps in their students’ learning which can imply instructional changes or additional coverage of key concepts. The use of tech tools can make the creation, administration, and grading of formative assessment more efficient and can enhance reliability of assessments when used consistently in the classroom. Selecting one that effectively addresses your assessment needs and enhances your teaching style is critical.
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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)
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.
what is shall and what does it do. language close to computers, fast.
what is “bash” . cd, ls
shell job is a translator between the binory code, the middle name. several types of shells, with slight differences. one natively installed on MAC and Unix. born-again shell
bash commands: cd change director, ls – list; ls -F if it does not work: man ls (manual for LS); colon lower left corner tells you can scrool; q for escape; ls -ltr
arguments is colloquially used with different names. options, flags, parameters
cd .. – move up one directory . pwd : see the content cd data_shell/ – go down one directory
cd ~ – brings me al the way up . $HOME (universally defined variable
the default behavior of cd is to bring to home directory.
the core shall commands accept the same shell commands (letters)
$ du -h . gives me the size of the files. ctrl C to stop
$ clear . – clear the entire screen, scroll up to go back to previous command
man history $ history $! pwd (to go to pwd . $ history | grep history (piping)
$ cat (and the file name) – standard output
$ cat ../
+++++++++++++++
how to edit and delete files
to create new folder: $ mkdir . – make directory
text editors – nano, vim (UNIX text editors) . $ nano draft.txt . ctrl O (save) ctr X (exit) .
$ vim . shift esc (key) and in command line – wq (write quit) or just “q”
$ mv draft.txt ../data . (move files)
to remove $ rm thesis/: $ man rm
copy files $cp $ touch . (touches the file, creates if new)
C and C++. scripting purposes in microbiology (instructor). libraries, packages alongside Python, which can extend its functionality. numpy and scipy (numeric and science python). Python for academic libraries?
going out of python $ quit () . python expect beginning and end parenthesis
new terminal needed after installation. anaconda 5.0.1
python 3 is complete redesign, not only an update.
python is object oriented and i can define the objects
python creates its own types of objects (which we model) and those are called “DataFrame”
method applied it is an attribute to data that already exists. – difference from function
data.info() . is function – it does not take any arguments
whereas
data.columns . is a method
print (data.T) . transpose. not easy in Excel, but very easy in Python
data = pandas.read_csv(‘/Users/plamen_local/Desktop/data/gapminder_gdp_oceania.csv’ , index_col=’country’)
data.loc[‘Australia’].plot()
plt.xticks(rotation=10)
GD plot 2 is the most well known library.
xelatex is a PDF engine. reST restructured text like Markdown. google what is the best PDF engine with Jupyter
four loops . any computer language will have the concept of “for” loop. In Python: 1. whenever we create a “for” loop, that line must end with a single colon
2. indentation. any “if” statement in the “for” loop, gets indented
An introduction to digital badges and a brief history
Simply put, a digital badge is an indicator of accomplishment or skill that can be displayed, accessed, and verified online. These badges can be earned in a wide variety of environments, an increasing number of which are online.
The anatomy of digital badges
In addition to the image-based design we think of as a digital badge, badges have meta-data to communicate details of the badge to anyone wishing to verify it, or learn more about the context of the achievement it signifies.
The many functions of digital badges
Just like their real-world counterparts, digital badges serve a wide variety of purposes depending on the issuing body and the individual. For the most part, badges’ functions can be bucketed into one of five categories.
Badges are issued by individual organizations who set criteria for what constitutes earning a badge. They’re most often issued through an online credential or badging platform.
Criticism of digital badges
There are various arguments to be made against the implementation of digital badges, including the common issuance of seemingly “meaningless” badges.
The future of digital badges
With the rise of online education and the increasing availability of high quality massive open online courses, there will be an increasing need for verifiable digital badges and digital credentials.
Gary Hunter. copyright. movies, public performance rights, youtube videos. up
the compliance of the terms of service of the web site. Contract law. copyright law. system procedure – copyright clearance, clearing the copyright means using it without violating the copyright law.
clearing copyright:
determine if materials are or are not protected
use your own original materials
perform fair use analysis with fair use checklist to usitify use
use in compliance with sections 110 (1) & (2) of copyright act
use materials avaialble through an open or CC license
get permission (letter, email, subscription, license, etc.)
fair use >> . transformation: 1. add / subtract from original 2. use for different purpose; >> parody songs – using enough of music and words to recognize the song, but not enough to it to be copyright infrigement. memes.
students’ use of copyrighted works. students may: use the entire copyrighted work but not publish openly
copyright act #110 (1) applies to F2F teaching.
copyright act #110 (2) applies to Hybrid/Online teaching. exception one digital copy can made and uploaded on D2L. reasonable and limited portions of dramatic musical or audiovisual works
if people identifiable ask them to sign a media release form
plagiarism v copyright infringement.
Creative Commons (CC). search engine for content available through cc licenses. https://creativecommons.org/ CC BY – attribution needed; CC BY-SA may remix, tweak CC BY-ND can redistribute, but not alter CC BY-NC for non profit. CC BY-NC-SA
copyright questions
book chapters: one is a rule of thumb
PDF versions of the eassays textbook acceptable, if the students purchased it
music performance licenses: usually cover – educational activities on campus; ed activities at off-campus locations that are outreach
augmented reality takeover. It’s played out at Snapchat and Facebook, at Google and Apple. Companies are using AR to design cars, sell furniture, make little digital sharks swim around your breakfast table. What if Prezi could apply that same technology to make better presentations?
the product isn’t ready for a public launch yet. Prezi has enlisted a select group of influencers to try out the AR tools and offer feedback before the company releases a beta version.
This 90 minute webinar will bring participants up to speed on the current state of altmetrics, and focus in on changes across the scholarly ecosystem. Through sharing of use cases, tips, and open discussion, this session will help participants to develop a nuanced, strategic framework for incorporating and promoting wider adoption of altmetrics throughout the research lifecycle at their institution and beyond.
Definition by National Information Standards Organization NISO (http://www.niso.org/home/): Altmetrics is a broad term that encapsulates the digital collection, creation, and use of multiple forms of assessment that are derived from activity and engagement among diverse stakeholders and scholarly outputs in the research ecosystem.”
Altmetrics are data that help us understand how often and by whom research objects are discussed, shared, and used on the social Web.”