Archive of ‘information technology’ category

digital darwinism

We Need New Rules for the Internet Economy

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.

A DER SPIEGEL Editorial by Armin Mahler  November 03, 2017  06:12 PMhttp://www.spiegel.de/international/business/editorial-time-for-new-rules-for-the-ditigal-economy-a-1176403.html

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.

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

Reproducibility Librarian

Reproducibility Librarian? Yes, That Should Be Your Next Job

https://www.jove.com/blog/2017/10/27/reproducibility-librarian-yes-that-should-be-your-next-job/
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).
plugin for Jupyter notebooks), and added a user interface to make it friendlier to folks not comfortable on the command line.

I would also recommend going to conferences:

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more on big data in an academic library in this IMS blog
academic library collection data visualization

https://blog.stcloudstate.edu/ims/2017/10/26/software-carpentry-workshop/

https://blog.stcloudstate.edu/ims?s=data+library

more on library positions in this IMS blog:
https://blog.stcloudstate.edu/ims?s=big+data+library
https://blog.stcloudstate.edu/ims/2016/06/14/technology-requirements-samples/

on university library future:
https://blog.stcloudstate.edu/ims/2014/12/10/unviersity-library-future/

librarian versus information specialist

 

digital assessment

Unlocking the Promise of Digital Assessment

By Stacey Newbern Dammann, EdD, and Josh DeSantis October 30, 2017

https://www.facultyfocus.com/articles/teaching-with-technology-articles/unlocking-promise-digital-assessment/

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.
    • (socrative.com) and
    • Poll Everywhere (http://www.pollev.com).
    • 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.
    • EdPuzzle (edpuzzle.com) and
    • 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.
    • Quizizz (quizizz.com) and
    • 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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more on digital assessment in this IMS blog
https://blog.stcloudstate.edu/ims/2017/03/15/fake-news-bib/

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
https://blog.stcloudstate.edu/ims?s=data+visualization

Software Carpentry Workshop

Minnesota State University Moorhead – Software Carpentry Workshop

https://www.eventbrite.com/e/minnesota-state-university-moorhead-software-carpentry-workshop-registration-38516119751

Reservation code: 680510823  Reservation for: Plamen Miltenoff

Hagen Hall – 600 11th St S – Room 207 – Moorhead

pad.software-carpentry.org/2017-10-27-Moorhead

http://www.datacarpentry.org/lessons/

https://software-carpentry.org/lessons/

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Friday

Jeff – certified Bash Python, John

http://bit.do/msum_swc

https://ntmoore.github.io/2017-10-27-Moorhead/

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 ../

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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)

remove $ rm .    anything PSEUDO is dangerous   Bash profile: cp -i

*- wild card, truncate       $ ls analyzed      (list of the analyized directory)

stackoverflow web site .

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head command .  $head basilisk.day (check only the first several lines of a large file

$ for filename in basilisk.dat unicorn.dat . (making a loop = multiline)

> do (expecting an action) do

> head -n 3 $filename . (3 is for the first three line of the file to be displayed and -n is for the number)

> done

for doing repetitive functions

also

$ for filename in *.dat ; do head -n 3$x; done

$ for filename in *.dat ; do echo $filename do head -n 3$x; done

$ echo $filename (print statement)

how to loop

$ for filename in *.dat ; do echo $filename ; echo head -n 3 $filename ; done

ctrl c or apple comd dot to get out of the loop

http://swcarpentry.github.io/shell-novice/02-filedir/

also

$ for filename in *.dat

> do

> $filename

> head -n  10 (first ten files ) $filename | tail  -n 20 (last twenty lines)

$ for filename  in *.dat

do
>> echo  $filename
>> done

$ for filename in *.dat
>> do
>> cp $filename orig_$filename
>>done\

history > something else

$ head something.else

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another function: word count

$ wc *.pdb  (protein databank)

$ head cubane.pdb

if i don;t know how to read the outpun $ man wc

the difference between “*” and “?”

$ wc -l *.pdb

$

wc -l *.pdb > lenghts.txs

cat lenghts.txt

$ for fil in *.txt
>>> do
>>> wc -l $fil

by putting a $ sign use that not the actual text.

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nano middle.sh . The entire point of shell is to automate

$ bash (exectubale) to run the program middle.sh

rwx – rwx – rwx . (owner – group -anybody)

bash middle.sh

$ file middle.sh

$path .

$ echo $PATH | tr “:” “\n”

/usr/local/bin

/usr/bin

/bin

/usr/sbin

/sbin

/Applications/VMware Fusion.app/Contents/Public

/usr/local/munki

$ export PATH=$PWD:$PATH

(this is to make sure that the last version of Python is running)

$ ls ~ . (hidden files)        

$ ls -a ~

$ touch .bach_profile .bashrc

$history | grep PATH

   19   echo $PATH

   44  echo #PATH | tr “:” “\n”

   45   echo $PATH | tr “:” “\n”

   46   export PATH=$PWD:$PATH

   47  echo #PATH | tr “:” “\n”

   48   echo #PATH | tr “:” “\n”

   55  history | grep PATH

 

wc -l “$@” | sort -n ($@  – encompasses eerything. will process every single file in the list of files

 

$ chmod (make it executable)

 

$ find . -type d . (find only directories, recursively, ) 

$ find . -type f (files, instead of directories)

$ find . -name ‘*.txt’ . (find files by name, don’t forget single quotes)

$ wc -l $(find . -name ‘*.txt’)  – when searching among direcories on different level

$ find . -name ‘*.txt’ | xargs wc -l    –  same as above ; two ways to do one and the same

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Saturday

Python

Link to the Python Plotting : https://swcarpentry.github.io/python-novice-gapminder

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.

http://swcarpentry.github.io/python-novice-gapminder/setup/

jupyter crashes in safari. open in chrome. spg engine maybe

https://swcarpentry.github.io/python-novice-gapminder/01-run-quit/

to start python in the terminal $ python

>> variable = 3

>> variable +10

several data types.

stored in JSON format.

command vs edit code.  code cell is the gray box. a text cell is plain text

markdown syntax. format working with git and github .  search explanation in https://swcarpentry.github.io/python-novice-gapminder/01-run-quit/

hackMD https://hackmd.io/ (use your GIthub account)

PANDOC – translates different data formats. https://pandoc.org/

print is a function

in what cases i will run my data trough Python instead of SPSS?

python is a 0 based language. starts counting with 0 – Java, C, P

atom_name = ‘helium ‘
print(atom_name[0])                  string slicing and indexing is tricky

atom_name = ‘helium ‘
print(atom_name[0:6])
vs
atom_name = ‘helium ‘
print(atom_name[7])                python does not know how to slice it
synthax of python is        start : end : countby/step
string versus list .   string is in a single quote, list will have brakets
strings allow me to work not only w values, revers the string
atom_name = ‘helium lithium beryllium’
print(atom_name[::-1])
muillyreb muihtil muileh
Atom_name = ‘helium’
len (atom_name)                                     6 .             case sensitive
to clean the memory, restart the kernel
objects in Python have different types. adopt a class, value may have class inherent in its defintion
print (type(’42’)) .   Python tells me that it is a string
print (type(42)) .    tells e it is a string
LaTex
to combine integer and letter: print (str(1) + ‘A’)
converting a string to integer . : print (1 + int(’55’)) .    all the same type
translation table. numerical representation of a string
float
print (‘half is’, 1 / 2.0)
built in functions and help
print is a function, lenght is a function (len); type, string, int, max, round,
Python does not explain well why the code breaks
ASCI character set – build in Python conversation
libraries – package: https://swcarpentry.github.io/python-novice-gapminder/06-libraries/
function “import”
 Saturdady afternoon
reading .CSV in Python
http://swcarpentry.github.io/python-novice-gapminder/files/python-novice-gapminder-data.zip
**For windows users only: set up git https://swcarpentry.github.io/workshop-template/#git 
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
print (data.describe()) .
/Users/plamen_local/anaconda3/lib/python3.6/site-packages/pandas/__init__.py
%matplotlib inline teling Jupyter notebook

import pandas

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

intellectual property

When:  October 24, 2017    2:00-3:00pm
Where: Adobe Connect meeting:  https://webmeeting.minnstate.edu/oercommunityconversations

Who: Karen Pikula, Psychology faculty, Central Lakes College, and Minnesota State OER Faculty Development Coordinator

Special Guest: Gary Hunter System Director for Intellectual Property

Questions?  

Feel free to contact Kimberly Johnson, Director of Faculty and Instructional Development at kimberly.johnson@minnstate.edu or Karen Pikula, Minnesota State OER Faculty Development Coordinator, at karen.pikula@minnstate.edu.

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notes from the webinar

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.)

http://www.minnstate.edu/system/asa/academicaffairs/policy/copyright/forms.html 

8 categories of copyright works

establishing copyright. eligibility requirements;

  • fixation
  • originality
  • minimal creativity

when these three criteria met, copyright arises automatically.

registering a copyright https://www.copyright.gov/ . $35. 70 years for individuals and 95 for corporations or 210 years

not protected by copyright

  • public domain (expired copyright/donated)
  • federal gov publications and web site info
  • works typically registered as a trademark
    • tag lines and slogans
      • just do it – nike 1988
      • got milk – 1993
  • math equations and formulas
  • recipes
  • blank forms
  • phone books

copyright holder exclusive rights

  1. make copies of the work
  2. prepare derivative works
  3. distribute copies
  4. perform the work – performing live (band concert); pre-record audio visual of the same items. DVD play of a movie is considered “performing”
  5. display the work

legality vs reality

legality – activity may be copyright infringement from a legal point of view.

reality – tolerated or ignored by the copyright holder for various reasons

limitations on copyright

  • fair use (#107). librarians use it a lot to copy. using copyright works in F2F teaching, scholarship, research and other non-profit ed purposes.
    1. criticism, comment, news reporting, teaching, scholarship, research

four factors to consider (not educational exception) ; it is a four part test to apply: 1. purpose and character if tge yse 2. nature of the copyirghted work (e.g. factual v creative) 3. amount
http://www.minnstate.edu/system/asa/academicaffairs/policy/copyright/docs/Fair_Use_Checklist1.pdf

http://www.minnstate.edu/system/asa/academicaffairs/policy/copyright/forms.html

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

http://www.minnstate.edu/system/asa/academicaffairs/policy/copyright/forms.html

personal use v public performance.

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

music licenses: BMI, ASCAP, SESAC

#201. Ownership of Copyright. Student ownership http://www.minnstate.edu/system/asa/academicaffairs/policy/copyright/forms.html

MnSCU board policy 3.26 intellectual policy. part 4, subpart A: institutional works; scholarly works; personal works; student works. MnSCU board policy 3.27.1: copyright clearance.

Gary.Hunter@so.mnscu.edu

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

effective presentations and AR

SORRY, POWERPOINT: THE SLIDE DECK OF THE FUTURE WILL BE IN AR

https://www.wired.com/story/prezi-augmented-reality/
augmented reality takeover. It’s played out at Snapchat and Facebook, at Google and Apple. Companies are using AR to design carssell 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.

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

altmetrics library Lily Troia

Taking Altmetrics to the Next Level in Your Library’s Systems and Services

Instructor: Lily Troia, Engagement Manager, Altmetric
October 31, 2017, 1:00 pm – 2:30 pm Central time

Register here, courses are listed by date

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.

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https://www.force11.org/sites/default/files/d7/presentation/1/fsci_mt9_altmetrics_day1.pdf

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.”

PlumX Metrics – Plum Analytics

Altmetric Explorer

https://www.altmetric.com/login.php

How are researchers & institutions using Altmetric?

  • Research and evaluation services – Identify & track influential research; assess impact & reach
  • Grants and reporting – Target new grants & grantees; demonstrate value to stakeholders
  • Communications and reputation management – Track press/social media; connect to opinion leaders
  • Marketing and promotion – Highlight vital findings; benchmark campaigns and outreach
  • Collaboration and partnerships – Discover disciplinary intersections & collaborative opportunities

DISCOVERY • Find trending research • Unearth conversations among new audiences • Locate collaborators & research opportunities • Identify key opinion leaders • Uncover disciplinary intersection

SHOWCASING • Identifying research to share • Share top mentions • Impact on public policy • Real-time tracking • Identifying key researchers • Recognizing early-career researchers

REPORTING • Grant applications • Funder reporting • Impact requirements • Reputation management • Benchmarking and KPIs (Key performance indicators) • Recruitment & review • Integration into researcher profiles/repositories

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https://www.force11.org/sites/default/files/d7/presentation/1/fsci_mt9_altmetrics_day_2.pdf

https://www.force11.org/sites/default/files/d7/presentation/1/fsci_mt9_altmetrics_fridaysummary.pptx

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more on altmetrics in the library in this IMS blog
https://blog.stcloudstate.edu/ims?s=altmetrics+library

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