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)
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.
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
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/
counting how many times students use electronic library resources or visit in person, and comparing that to how well the students do in their classes and how likely they are to stay in school and earn a degree. And many library leaders are finding a strong correlation, meaning that students who consume more library materials tend to be more successful academically.
carefully tracking how library use compares to other metrics, and it has made changes as a result—like moving the tutoring center and the writing lab into the library. Those moves were designed not only to lure more people into the stacks, but to make seeking help more socially-acceptable for students who might have been hesitant.
a partnership between the library, which knows what electronic materials students use, and the technology office, which manages other campus data such as usage of the course-management system. The university is doing a study to see whether library usage there also equates to student success.
Inclusion of 3D Artifacts into a Digital Library: Exploring Technologies and Best Practice Techniques
The IUPUI University Library Center for Digital Scholarship has been digitizing and providing access to community and cultural heritage collections since 2006. Varying formats include: audio, video, photographs, slides, negatives, and text (bound, loose). The library provides access to these collections using CONTENTdm. As 3D technologies become increasingly popular in libraries and museums, IUPUI University Library is exploring the workflows and processes as they relate to 3D artifacts. This presentation will focus on incorporating 3D technologies into an already established digital library of community and cultural heritage collections.
On behalf of the 2018 LITA Library Technology Forum Committee, I am pleased to notify you that your proposal, “Virtual Reality (VR) and Augmented Reality (AR) for Library Orientation: A Scalable Approach to Implementing VR/AR/MR in Education”, has been accepted for presentation at the 2018 LITA Library Technology Forum in Minneapolis, Minnesota (November 8-10).
Mark Gill and Plamen Miltenoff will participate in a round table discussion Friday. November 9, 3:30PM at Haytt Regency, Minneapolis, MN. We will stream live on Facebook: https://www.facebook.com/InforMediaServices/
U of MN has a person, whose entire job is to read and negotiate contracts with vendors. No resources, not comfortable to negotiate contracts and vendors use this.
If you can’t open it, you don’t own it. if it is not ours… we don’t get what we don’t ask for.
libraries are now developing plenty, but if something is brought in, so stop analytics over people. Google Analytics collects data, which is very valuable for students. bring coherent rink of services around students and show money saving. it is not possible to make a number of copyright savings. collecting such data must be in the library, not outside. Data that is collected, will be put to use. Data that is collected, will be put to uses that challenge library values. Data puts people at risk. anonymized data is not anonymous. rethink our relationship to data. data sensitivity is contextual.
stop requiring MLSs for a lot of position. not PhDs in English, but people with specific skills.
perspective taking does not help you understand what others want. connection to tech. user testing – personas (imagining one’s perspective). we need to ask, better employ the people we want to understand. in regard of this, our profession is worse then other professions.
pay more is important to restore value of the profession.
Voyager to OCLC. Archive space from in-house to vendor. Migration
Polaris, payments, scheduling, PC sign up. Symphony, but discussing migration to Polaris to share ILS. COntent Diem. EasyProxy, from Millenium no Discovery Layer to Koha and EDS. ILL.
WMS to Alma. Illinois State – CARLY – from Voyager to Alma Primo. COntent Diem, Dynex to Koha.
Princeton: Voyager, migrating Alma and FOlio. Ex Libris. Finances migrate to PeopleSoft. SFX. Intota
RFPs – Request for Proposals stage. cloud and self-hosted bid.
Data Preparation. all data is standard, consistent. divorce package for vendors (preparing data to be exported (~10K). the less to migrate, the better, so prioritize chunks of data (clean up the data)
Data. overwhelming for the non-tech services. so a story is welcome. Design and Admin background, not librarian background, big picture, being not a librarian helps not stuck with the manusha (particular records)
teams and committees – how to compile a great team. who makes the decision. ORCHID integration. Blog or OneNote place to share information. touch base with everyone before they come to the meeting. the preplanning makes large meetings more productive.
Using Design Thinking — Do we really want a makerspace?
The 100-page study presents data from 1,140 college students from 4-year colleges in the United States concerning their use of specialized library technology, group and individual study rooms. The report enables its end users to answer questions such as: which students use individual and group study rooms? Which use specialized technology rooms? How often do they use them?
Data in the report is presented in the aggregate and then broken out separately for sixteen different variables including but not limited to: college grades, gender, income level, year of college standing, SAT/ACT scores, regional origin, age, sexual orientation, race & ethnicity, college major and other personal variables, and by Carnegie class, enrollment size and public/private status of the survey participants institutions of higher education.
Session Title: Measuring Learning Outcomes of New Library Initiatives Coordinator: Professor Plamen Miltenoff, Ph.D., MLIS, St. Cloud State University, USA Contact: firstname.lastname@example.org Scope & rationale: The advent of new technologies, such as virtual/augmented/mixed reality, and new pedagogical concepts, such as gaming and gamification, steers academic libraries in uncharted territories. There is not yet sufficiently compiled research and, respectively, proof to justify financial and workforce investment in such endeavors. On the other hand, dwindling resources for education presses administration to demand justification for new endeavors. As it has been established already, technology does not teach; teachers do; a growing body of literature questions the impact of educational technology on educational outcomes. This session seeks to bring together presentations and discussion, both qualitative and quantitative research, related to new pedagogical and technological endeavors in academic libraries as part of education on campus. By experimenting with new technologies such as Video 360 degrees and new pedagogical approaches such as gaming and gamification, does the library improve learning? By experimenting with new technologies and pedagogical approaches, does the library help campus faculty to adopt these methods and improve their teaching? How can results be measured, demonstrated?
Our library is gearing up to create a virtual reality demonstration station using either VTC Hive or Oculus Rift. We want to make sure that we at least a small suite of educational VR products.
If your library runs a VR workstation, could you share one or two educational titles that you’re especially happy with? We are planning on getting Mission:ISS, a simulation of the International Space Station.
OWL Program Manager / Internet and Technology Consultant