Integers: A signed or unsigned whole number running from -32,768 to 32,768 or from 0 to 65,535 if not signed. Integers are used anytime something needs to be counted.
Long Integer: Any whole number outside the above range. Python doesn’t distinguish between the two though many languages do. Practically, Python’s integers range from −2,147,483,648 to 2,147,483,648 or 0 to 0 to 4,294,967,295. Most of us will be very happy with this many whole numbers to choose from.
Real and Floating Point Numbers: Real numbers are signed or unsigned numbers including decimals. The numbers 2,3,4 are Integers and Real Numbers. The numbers 2.1, 2.9,3.9 are Real Numbers, but not Integers. Real Numbers can include representations of irrational numbers such as pi. Real numbers must be rational, that is a decimal number that terminates after a finite number of decimals. You will sometimes encounter the term Floating Point Numbers. This is a technical term referring to the way that large Real Numbers are represented in a computer. Python hides this detail from you so Real and Floating Point are used intercangeably in this language.
Binary Numbers: And Octal and Hexadecimal. These are numbers used internally by computers. You will run into these values fairly often. For instance, when you see color values in HTML such as “FFFFFF” or “0000FF”,
Hexadecimal and Octal are used because humans can read them without too much trouble and they are compromise between what computers process and what we can read. Any time you see something in Octal or Hexadecimal, you are looking at something that interfaces with the lower levels of a computer. You will most commonly use Hexadecimal numbers when dealing with Unicode character encodings. Python will interpret any number which begins with a leading zero as binary unless formatting commands have been used.
Numbers such as 7i are referred to as complex. They have a real part, the 7, and an imaginary part, i. Chance are you won’t use complex numbers unless you’re working with scientific data.
A String consists of a sequence of characters. The term String refers to how this data type is represented internally. You store text in Strings. Text can by anything, letters, words, sentences, paragraphs, numbers, just about anything.
Lists are close cousins to Strings, though you may never need to think of them that way. A list is just that, a list of things. Lists may contain any number of numbers or any number of strings. List may even contain any number of other lists. Lists are compared to arrays, but they are not the same thing. In most uses, the function the same so the difference, for our purposes, is moot. Strings are like lists in that, internally, the computer works with strings in an identical manner to lists. This is why the operations on Strings are so different from numbers.
The last main data type in the Python programming language is the dictionary. Dictionaries are map types, known in other languages as hashes, and in computer science as Associative Arrays. The best way to think of what the dictionary does is to consider a Library of Congress Call Number(something this audience is familiar with). The call number is what’s called a Key. It connects to a record which contains information about a book. The combination of keys and records, called values, comprises a dictionary. A single key will connect to a discrete group of values such as the items in this record. Dictionaries will be touched on in the next lesson in some detail in the next course. These are fairly advanced data structures and require a solid understanding a programming fundamentals in order to be used properly.
Statements, an Overview
Programs consist of statements. A statement is a unit of executable code. Think of a statement like a sentence. In a nutshell, statements are how you do things in a program. Writing a program consists of breaking down a problem you want to solve into smaller pieces that you can represent as mathematical propositions and then solve. The statement is where this process gets played out. Statements themselves consist of some number of expressions involving data. Let’s see how this works.
An expression would be something like 2+2=4. This expression, however is not a complete statements. Ask Python to evaluate it and you will get the error “SyntaxError: can’t assign to operator”. What’s going on here? Basically we didn’t provide a complete statement. If we want to see the sum of 2+2 we have to write a complete statement that tells the interpreter what to do and what to do it with. The verb here is ‘print’ and the object is ‘2+2’. Ask Python to evaluate ‘print 2+2’ and it will show ‘4’. We could also throw in subject and do something a bit more detailed: ‘Sum=2+2’. In this case we are assigning the value of 2+2 to the variable, Sum. We can then do all sorts of things with Sum. We can print it. We can add other numbers to it, hand it off to a function and so on. For instance, might want to know the root of Sum. In which case we might write something like ‘print sqrt(sum)’ which will display ‘2’.
A shell is essentially a user interface that provides you access to a system’s features. Normally, this means access to an Operating System. In cases like this, the shell provides you access to the Python programming environment.
Anything preceed by a “#” is not interpreted or executed by the programming shell. Comments are used widely to document programs. One school of programming holds that code should be so clear that comments are uncessary.
Operations on Numbers
Expressions are discrete statements in programming that do something. They typically occupy one line of code, though programmers will sometimes squeeze more in. This is generally bad form and can really make your program a mess. Expressions consist of operations and data or rather data and operations on them. So, what can you do with numbers? Here is a concise list of the basic operations for integers and real numbers of all types:
Arithemetic:
Addition: z= x + y
Subtraction: z = x – y
Multiplication: z = x * y. Here the asterisk serves as the ‘X’ multiplication symbol from grade school.
Division: z = x/y. Division.
Exponents: z = x ** y or xy, x to the y power.
Operations have an order of precedence which follows the algebraic order of precedence. The order can be remembered by the old Algebra mnenomic, Please Excuse My Dear Aunt Sally which is remeinds you that the order of operations is:
Parentheses
Exponents
Multiplication
Division
Addition
Subtraction
Operations on Strings
Strings are strange creatures as I’ve noted before. They have their own operations and the arithmetic operations you saw earlier don’t behave the same way with strings.
Putting Expressions Together to Make Statements
As I noted earlier, all computer languages, and natural languages, possess pragmatics, larger scale structures which reduce ambiguity by providing context. This is a fancy way of saying just as sentences posses rules of syntax to make able to be comprehended, larger documents have similar rules. Computer Programs are no different. Here’s a break down of the structure of programs in Python, in a general sense.
Programs consist of one or more modules.
Modules consist of one or more statements.
Statements consist of one or more expressions.
Expressions create and/or manipulate objects(and variables of all kinds).
Modules and Programs are for the next class in the series, though we will survey these larger structures next lesson. For now, we’ll focus on statements and expressions. Actually, we’ve already started with expressions above. In Python, statements can do three things.
Assign a variable
Change a variable
Take an action
Variable Names and Reserved Words
Now that we’ve seen some variable assignments, let’s talk about best practices. First off, aside from reserved words, variable names can be almost any combination of letters, numbers and punctuation marks. You, however, should never ever, use the following punctuation marks in variable names:
+
–
!
@
^
%
(
)
.
“
?
/
:
;
*
These punctuation marks tends to be operators and characters that have special meanings in most computer languages. The other issue is reserved words. What are “reserved words”? They are words that Python interprets as commands. Pythons reservers the following words.:
True: A special value set aside for boolean values
False: The other special value set aside for boolean vaules
None: The logical equivalent of 0
and: a way of combining logical conditions
as: describes how modules are imported
assert: a way of forcing something to take on a certain value. Used in debugging of large programs
break: breaks out of a loop and goes on with the rest of the program
class: declares a class for object oriented design. For now, just remember not to use this variable name
continue: returns to the top of the loop and keeps on going again
def: declares functions which allow you to modularize your code.
elif: else if, a cotnrol structure we’ll see next lesson
else: as above
except: another control structure
finally: a loop control structure
for: a loop control structure
from: used to import modules
global: a scoping statement
if: a control structure/li>
in: used in for each loops
is: a logical operator
lamda: like def, but weird. It defines a function in a single line. I will not teach this becuase it is icky. If you ever learn Perl you will see this sort of thing a lot and you will hate it, but that’s just my personal opinion.
nonlocal: a scoping command
not: a logical operator
or: another logical operator
pass: does nothing. Used as placeholder
raise: raises an error. This is used to write custom error messages. Your programs may have conditions which would be considered invalid based on our business situation. The interpreter may not consider them errors, but you might not want your user to do something so you ‘raise’ an exception and stop the program.
return: tells a function to return a value
try: this is part of an error testing statement
while: starts a while loop
with: a context manager. This will be covered in the course after the next one in this series
yield: works like return
Variable names should be meaningful. Let’s say I have to track a person’s driver license number. explanatory names like ‘driverLicenseNumber’.
Use case to make your variable names readable. Python is case sensitive, meaning a variable named ‘cat’ is different from named ‘Cat’. If you use more than one word to name variable, start of lower case the change case on the second word. For instance “bigCats = [‘Tiger’,’Lion’,’Cougar’, ‘Desmond’]”. The common practice used by programmers in many settings is that variables start with lowercase and functions(methods and so on) start with upper case. This is called “Camel Case” for its lumpy, the humpy appearance. Now, as it happens, there is something of a religious debate over this. Many Python programmers prefer to keep everything lower case and join words in a name by underscores such as “big_cats”. Use whichever is easiest or looks the nicest to you.
Variable names should be unique. Do not reuse names. This will cause confusion later on.
Python conventions. Python, as with any other programming language, has culture built up around it. That means there are some conventions surrounding variable naming. Two leading underscores, __X, denote system variables which have special meaning to the interpreter. So avoid using this for your own variables. There may be a time and place, but that’s for an advanced prorgramming course. A single underscore _X indicates to other programmers that this a fundamental variable and that they mess with it at their own peril.
Avoid starting variable names with a number. This may or may not return an error. It can also mislead anyone reading your program.
“A foolish consistency is the hobgoblin of little minds”. But not to programming minds. Consistency helps the readability of code a great deal. Once you start a system, stick with it.
Statement Syntax
Putting together valid statements can be a little hard at first. There’s a grammar to them. Thus far, we’ve mainly been workign with expressions such as “x = x+1”. You can think of expression as nouns. We’ve clearly defined x, but how do we look inside? For that we need to give it a verb, the print command. We would then write “print x”. However we can skip the middle statement and print an expression such as “print x + 1”. The interpreter evaluates this per the order of operations I laid out earlier. However, once that expression is evaluated, it then applies the verb, “print”, to that expression.
Print is a function that comes with the Python distribution. There are many more and you can create your own. We’ll cover that a bit in next lesson. Let’s look at little more at the grammar of a statement. Consider:
x = sin(b)
Assume that b has been defined elsewhere. x is the subject, b is the object and sin is the verb. Python will go to the right side of the equal sign first. It will then go to the inside of the function and evaluate what’s there first. It then evaluates the value of the function and finishes by setting x to that value. What about something like this?
x=sin(x+3/y)
Python evaluates from the inside out according to the rules of operation. Very complex statements can be built up this way.
x = sin(log((x + 3)/(e**2)))
Regardless of what this expression evaluates to (I don’t actually know), Python starts with the innermost parentheses, then works through the value of e squared then adds 3 to x and divides the result by e squared. With that worked out, it takes the logarithm of the result and takessthe sine of that before setting x to the final result.What you cannot do is execute more than one statement on a line. No more than one verb on a line. In this context, a verb is an assignment, or a command acting on an expression
markdown cell
code cell
Call up your copy of Think Python or go to the website at http://www.greenteapress.com/thinkpython/html/. Read Chapter 2. This will reiterate much of what I’ve presnted here, but this will help cement the content into you minds. Skip section 2.6 because IPython treats everything as script mode. IPyton provides you with the illusion of interactive, but everything happens asynchronously. This means that any action you type in will not instantaneously resolve as it would if you were running Python interactively on your computer. You will have to use print statements to see the results of your work.
Your assignment consists of the following:
Exercise 1 from Chapter 2 of Think Python. If you type an integer with a leading zero, you might get a confusing error:
<<< zipcode = 02492
SyntaxError: invalid token
Other numbers seem to work, but the results are bizarre:
<<< zipcode = 02132
<<< zipcode
1114
Can you figure out what is going on? Hint: display the values 01, 010, 0100 and 01000.
Exercise 3 from Chapter 2 of Think Python.Assume that we execute the following assignment statements:
width = 17
height = 12.0
delimiter = ‘.’
For each of the following expressions, write the value of the expression and the type (of the value of the expression).
width/2
width/2.0
height/3
1 + 2 5
delimiter 5
Exercise 4 from Capter 2 of Think Python. Practice using the Python interpreter as a calculator:
1. The volume of a sphere with radius r is 4/3 π r3. What is the volume of a sphere with radius 5? Hint: 392.7 is wrong!
2. Suppose the cover price of a book is $24.95, but bookstores get a 40% discount. Shipping costs $3 for the first copy and 75 cents for each additional copy. What is the total wholesale cost for 60 copies?
3/ If I leave my house at 6:52 am and run 1 mile at an easy pace (8:15 per mile), then 3 miles at tempo (7:12 per mile) and 1 mile at easy pace again, what time do I get home for breakfast?
In your IPython notebook Create a markdown cell and write up your exercise in there. Just copy it from the textbook or from the above write up. Next ceate a code cell and do your work in there. Please, comment your work thoroughly. You cannot provide too many comments. Use print statements to see the outcome of your work.
Our beginning programming course, CNA 267, is now using Python as the programming language. Students learn to work with decision and loop control structures, variables, lists (arrays) and procedures, etc. Python is becoming one of the most widely-accepted languages for business professionals and scientists.
Please inform your students (who need to learn programming) of this course. It is being offered during spring semester, as well as next fall.
Part of the Debates in the Digital Humanities Series A book series from the University of Minnesota Press Matthew K. Gold and Lauren F. Klein, Series Editors
Defintion
Critical infrastructure studies has emerged as a framework for linking thought on the complex relations between society and its material structures across fields such as science and technology studies, design, ethnography, media infrastructure studies, feminist theory, critical race and ethnicity studies, postcolonial studies, environmental studies, animal studies, literary studies, the creative arts, and others (see the CIstudies.org Bibliography )
An informal consensus seems to have emerged that if students in the humanities are going to make use of quantitative methods, they should probably first learn to program. Introductions to this dimension of the field are organized around programming languages: The Programming Historian is built around an introduction to Python; Matthew Jockers’s Text Analysis with R is at its heart a tutorial in the R language; Taylor Arnold and Lauren Tilton’s Humanities Data in R begins with chapters on the language; Folgert Karsdorp’s Python Programming for the Humanities is a course in the language with examples from stylometry and information retrieval.[11] “On the basis of programming,” writes Moretti in “Literature, Measured,” a recent retrospective on the work of his Literary Lab, “much more becomes possible”
programming competence is not equivalent to competence in analytical methods. It might allow students to prepare data for some future analysis and to produce visual, tabular, numerical, or even interactive summaries; Humanities Data in R gives a fuller survey of the possibilities of exploratory data analysis than the other texts.[15]Yet students who have focused on programming will have to rely on their intuition when it comes to interpreting exploratory results. Intuition gives only a weak basis for arguing about whether apparent trends, groupings, or principles of variation are supported by the data.
From Humanities to Scholarship: Librarians, Labor, and the Digital
First hired as a “digital humanities librarian,” I saw my title changed within less than a year to “digital scholarship librarian,” with a subject specialty later appended (American History). Some three-plus years later at a different institution, I now find myself a digital-less “religion and theology librarian.” At the same time, in this position, my experience and expertise in digital humanities (or “digital scholarship”) are assumed, and any associated duties are already baked into the job description itself.
Jonathan Senchyne has written about the need to reimagine library and information science graduate education and develop its capacity to recognize, accommodate, and help train future library-based digital humanists in both computational research methods and discipline-focused humanities content (368–76). However, less attention has been paid to tracking where these digital humanities and digital scholarship librarians come from, the consequences and opportunities that arise from sourcing librarians from multiple professional and educational stations, and the more ontological issues associated with the nature of their labor—that is, what is understood as work for the digital humanist in the library and what librarians could be doing.
https://sched.co/JAqk
the type of data: wikipedia. the dangers of learning from wikipedia. how individuals can organize mitigate some of these dangers. wikidata, algorithms.
IBM Watson is using wikipedia by algorythms making sense, AI system
youtube videos debunked of conspiracy theories by using wikipedia.
semantic relatedness, Word2Vec
how does algorithms work: large body of unstructured text. picks specific words
lots of AI learns about the world from wikipedia. the neutral point of view policy. WIkipedia asks editors present as proportionally as possible. Wikipedia biases: 1. gender bias (only 20-30 % are women).
conceptnet. debias along different demographic dimensions.
citations analysis gives also an idea about biases. localness of sources cited in spatial articles. structural biases.
geolocation on Twitter by County. predicting the people living in urban areas. FB wants to push more local news.
danger (biases) #3. wikipedia search results vs wkipedia knowledge panel.
collective action against tech: Reddit, boycott for FB and Instagram.
data labor: what the primary resources this companies have. posts, images, reviews etc.
boycott, data strike (data not being available for algorithms in the future). GDPR in EU – all historical data is like the CA Consumer Privacy Act. One can do data strike without data boycott. general vs homogeneous (group with shared identity) boycott.
the wikipedia SPAM policy is obstructing new editors and that hit communities such as women.
how to access at different levels. methods and methodological concerns. ethical concerns, legal concerns,
tweetdeck for advanced Twitter searches. quoting, likes is relevant, but not enough, sometimes screenshot
engagement option
social listening platforms: crimson hexagon, parsely, sysomos – not yet academic platforms, tools to setup queries and visualization, but difficult to algorythm, the data samples etc. open sources tools (Urbana, Social Media microscope: SMILE (social media intelligence and learning environment) to collect data from twitter, reddit and within the platform they can query Twitter. create trend analysis, sentiment analysis, Voxgov (subscription service: analyzing political social media)
graduate level and faculty research: accessing SM large scale data web scraping & APIs Twitter APIs. Jason script, Python etc. Gnip Firehose API ($) ; Web SCraper Chrome plugin (easy tool, Pyhon and R created); Twint (Twitter scraper)
Facepager (open source) if not Python or R coder. structure and download the data sets.
TAGS archiving google sheets, uses twitter API. anything older 7 days not avaialble, so harvest every week.
social feed manager (GWUniversity) – Justin Litman with Stanford. Install on server but allows much more.
legal concerns: copyright (public info, but not beyond copyrighted). fair use argument is strong, but cannot publish the data. can analyize under fair use. contracts supercede copyright (terms of service/use) licensed data through library.
methods: sampling concerns tufekci, 2014 questions for sm. SM data is a good set for SM, but other fields? not according to her. hashtag studies: self selection bias. twitter as a model organism: over-represnted data in academic studies.
methodological concerns: scope of access – lack of historical data. mechanics of platform and contenxt: retweets are not necessarily endorsements.
ethical concerns. public info – IRB no informed consent. the right to be forgotten. anonymized data is often still traceable.
table discussion: digital humanities, journalism interested, but too narrow. tools are still difficult to find an operate. context of the visuals. how to spread around variety of majors and classes. controversial events more likely to be deleted.
takedowns, lies and corrosion: what is a librarian to do: trolls, takedown,
development kit circulation. familiarity with the Oculus Rift resulted in lesser reservation. Downturn also.
An experience station. clean up free apps.
question: spherical video, video 360.
safety issues: policies? instructional perspective: curating,WI people: user testing. touch controllers more intuitive then xbox controller. Retail Oculus Rift
app Scatchfab. 3modelviewer. obj or sdl file. Medium, Tiltbrush.
College of Liberal Arts at the U has their VR, 3D print set up.
Penn State (Paul, librarian, kiniseology, anatomy programs), Information Science and Technology. immersive experiences lab for video 360.
CALIPHA part of it is xrlibraries. libraries equal education. content provider LifeLiqe STEM library of AR and VR objects. https://www.lifeliqe.com/
libraians, IT staff, IDs. help faculty with course design, primarily online, master courses. Concordia is GROWING, mostly because of online students.
solve issues (putting down fires, such as “gradebook” on BB). Librarians : research and resources experts. Librarians helping with LMS. Broadening definition of Library as support hub.
Many educational institutions maintain their own data centers. “We need to minimize the amount of work we do to keep systems up and running, and spend more energy innovating on things that matter to people.”
what’s the difference between machine learning (ML) and artificial intelligence (AI)?
Jeff Olson: That’s actually the setup for a joke going around the data science community. The punchline? If it’s written in Python or R, it’s machine learning. If it’s written in PowerPoint, it’s AI.
machine learning is in practical use in a lot of places, whereas AI conjures up all these fantastic thoughts in people.
What is serverless architecture, and why are you excited about it?
Instead of having a machine running all the time, you just run the code necessary to do what you want—there is no persisting server or container. There is only this fleeting moment when the code is being executed. It’s called Function as a Service, and AWS pioneered it with a service called AWS Lambda. It allows an organization to scale up without planning ahead.
How do you think machine learning and Function as a Service will impact higher education in general?
The radical nature of this innovation will make a lot of systems that were built five or 10 years ago obsolete. Once an organization comes to grips with Function as a Service (FaaS) as a concept, it’s a pretty simple step for that institution to stop doing its own plumbing. FaaS will help accelerate innovation in education because of the API economy.
If the campus IT department will no longer be taking care of the plumbing, what will its role be?
I think IT will be curating the inter-operation of services, some developed locally but most purchased from the API economy.
As a result, you write far less code and have fewer security risks, so you can innovate faster. A succinct machine-learning algorithm with fewer than 500 lines of code can now replace an application that might have required millions of lines of code. Second, it scales. If you happen to have a gigantic spike in traffic, it deals with it effortlessly. If you have very little traffic, you incur a negligible cost.
Remember that a blockchain is an immutable, sequential chain of records called Blocks. They can contain transactions, files or any data you like, really. But the important thing is that they’re chained together using hashes.
reading and writing some basic Python, as well as have some understanding of how HTTP requests work, since we’ll be talking to our Blockchain over HTTP.
When: Friday, September 28, 8:30am-3:00pm Where: Wilson Research Collaboration Studio, Wilson Library Cost: Free; advanced registration is required
1968 was one of the most turbulent years of the 20th century. 2018 marks the 50th anniversary of that year’s landmark political, social and cultural events–events that continue to influence our world today.
Focusing on the importance of this 50 year anniversary we are calling out to all faculty, staff, students, and community partners to participate the workshop ‘Mapping 1968, Conflict and Change’. This all-day event is designed to bring people together into working groups based on common themes. Bring your talent and curiosity to apply an interdisciplinary approach to further explore the spatial context of these historic and/or current events. Learn new skills on mapping techniques that can be applied to any time in history. To compliment the expertise that you bring to the workshop, working groups will also have the support of library, mapping, and data science experts to help gather, create, and organize the spatial components of a given topic.
Workshop sponsors: Institute for Advanced Studies (IAS), U-Spatial, Liberal Arts Technologies & Innovation Services (LATIS), Digital Arts, Science & Humanities (DASH), and UMN Libraries.
Henneping County scanned the deeds, OCR, Python script to search. Data in an open source. covenant data. Local historian found microfishes, the language from the initial data. e.g. eugenics flavor: arian, truncate.
storymaps.arcgis.com/en/gallery https://storymaps.arcgis.com/en/gallery/#s=0 cloud-based mapping software. ArcGIS Online. organizational account for the U, 600 users. over 700 storymaps creates within the U, some of them are not active, share all kind of data: archive data on spreadsheet, but also a whole set of data within the software; so add the data or use the ArcGIS data and use templates. web maps into the storymap app, Living Atlas: curated set of data: hunderd sets of data, from sat images, to different contents. 846 layers of data, imagery, besides org account, one can create maps within the free account with limited access. data browser to use my own data – Data Enrichment to characterized my data. census data from 2018 and before,
make plan, create a storyboard, writing for the web, short and precise (not as writing for a journal), cartographic style, copyright, citing the materials, choosing the right map scale for each page. online learning materials, some only thru org account ESRI academy has course catalogue. Mapping 101, Dekstop GIS 101, Collector 101, Imagery 101, SQL 101, Story Maps 101,
The “Mapping 1968, Conflict and Change” planning committee is very pleased with the amount of interest and the wonderful attendance at Friday’s gathering. Thank you for attending and actively participating in this interdisciplinary workshop!
To re-cap and learn more on your thoughts and expectations of the workshop we would be grateful if you can take a few moments to complete the workshop evaluation. Please complete the evaluation even if you were unable to attend last Friday, there are questions regarding continued communication and the possibility for future events of this kind.
Media literacy. Differentiated instruction. Media literacy guide.
Fake news as part of media literacy. Visual literacy as part of media literacy. Media literacy as part of digital citizenship.
Web design / web development
the roles of HTML5, CSS, Java Script, PHP, Bootstrap, JQuery, React and other scripting languages and libraries. Heat maps and other usability issues; website content strategy. THE MODEL-VIEW-CONTROLLER (MVC) design pattern
Social media for institutional use. Digital Curation. Social Media algorithms. Etiquette Ethics. Mastodon
I hosted a LITA webinar in the fall of 2016 (four weeks); I can accommodate any information from that webinar for the use of the IM students
OER and instructional designer’s assistance to book creators.
I can cover both the “library part” (“free” OER, copyright issues etc) and the support / creative part of an OER book / textbook
“Big Data.” Data visualization. Large scale visualization. Text encoding. Analytics, Data mining. Unizin. Python, R in academia.
I can introduce the students to the large idea of Big Data and its importance in lieu of the upcoming IoT, but also departmentalize its importance for academia, business, etc. From infographics to heavy duty visualization (Primo X-Services API. JSON, Flask).
NetNeutrality, Digital Darwinism, Internet economy and the role of your professional in such environment
I can introduce students to the issues, if not familiar and / or lead a discussion on a rather controversial topic
Digital assessment. Digital Assessment literacy.
I can introduce students to tools, how to evaluate and select tools and their pedagogical implications
Wikipedia
a hands-on exercise on working with Wikipedia. After the session, students will be able to create Wikipedia entries thus knowing intimately the process of Wikipedia and its information.
Effective presentations. Tools, methods, concepts and theories (cognitive load). Presentations in the era of VR, AR and mixed reality. Unity.
I can facilitate a discussion among experts (your students) on selection of tools and their didactically sound use to convey information. I can supplement the discussion with my own findings and conclusions.
eConferencing. Tools and methods
I can facilitate a discussion among your students on selection of tools and comparison. Discussion about the their future and their place in an increasing online learning environment
Digital Storytelling. Immersive Storytelling. The Moth. Twine. Transmedia Storytelling
I am teaching a LIB 490/590 Digital Storytelling class. I can adapt any information from that class to the use of IM students
VR, AR, Mixed Reality.
besides Mark Gill, I can facilitate a discussion, which goes beyond hardware and brands, but expand on the implications for academia and corporate education / world
Instructional design. ID2ID
I can facilitate a discussion based on the Educause suggestions about the profession’s development
Microcredentialing in academia and corporate world. Blockchain
IT in K12. How to evaluate; prioritize; select. obsolete trends in 21 century schools. K12 mobile learning
Podcasting: past, present, future. Beautiful Audio Editor.
a definition of podcasting and delineation of similar activities; advantages and disadvantages.
Gender, race and age in education. Digital divide. Xennials, Millennials and Gen Z. generational approach to teaching and learning. Young vs old Millennials. Millennial employees.
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.
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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.
