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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).
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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.
Stanford – Folio, Cornell, Duke and several others. https://www.folio.org/ Alma too locked up for Stanford.
Easy Proxy for Alma Primo
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.
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Using Design Thinking — Do we really want a makerspace?
Anne Shelley, Illinois State University, Milner Library
Julie Murphy, Illinois State University, Milner Library
Paul Unsbee, Illinois State University, Milner Library
makerbot replicator 3d printer
one touch studio 4 ready record studio. data analytics + several rooms to schedule.
Are you considering switching an operating system (OS)? Do you have an old computer (hardware), but you don’t want to through it out yet (environment)?
These and other questions discussed as comments to the following article:
I can’t afford to upgrade my Vista PC. What can I do?
Firefox is about to stop supporting Windows Vista and websites are not working. Is there a cheap or preferably free solution?
selected comments under the article (practical, funny, for pundits and novices):
Ujjwal Dey Fedora is nice but it’s for more experienced users. Setting it up for everyday use is no rocket science, but still requires a bit of work with bash or whatever shell Fedora provides these days. For easy migration Mint is the best IMO.
Fraser McCabe Maybe Linux Mint or Manjaro.
If you want to test them first, you can create a bootable USB thumb drive first
Ubuntu is ideal for older machines. I run Kali and Lubuntu on an old P4 for basic pen tests and VM. I use Tor and Private VPN. No probs. Slow compared to modern systems. Yet functional. I have a lot of old Machines that I collect for Free to network and simulate environments to penetrate with virtual emulators. Works perfectly fine.
The article doesn’t mention the excellent Microsoft Office alternative – Libre Office.
It’s completely free, comes ready-installed with Ubuntu Linux, and in most cases can read and write to Microsoft Office format documents.
(there’s also a version of Libre Office that runs on Windows – again, an excellent free alternative)
Easiest way is to install a pirate version of Win 7 for free, then you buy a Win 7 activation key on Ebay for like $5. Where there’s a will, there’s a way 👌👌
Buy a second hand laptop from somewhere like CeX for about £60 with Windows 7 or 10 installed, assuming you can afford that.
Stop using Firefox. Switch to Chrome or Internet Explorer.
I’ve been “off the line” for years now. My advice: read a bloody book!
says David Greenfield, a psychologist and assistant clinical professor of psychiatry at the University of Connecticut:When we hear a ding or little ditty alerting us to a new text, email or Facebook post, cells in our brains likely release dopamine — one of the chemical transmitters in the brain’s reward circuitry. That dopamine makes us feel pleasure
“It’s a spectrum disorder,” says Dr. Anna Lembke, a psychiatrist at Stanford University, who studies addiction. “There are mild, moderate and extreme forms.” And for many people, there’s no problem at all.
Signs you might be experiencing problematic use, Lembke says, include these:
Interacting with the device keeps you up late or otherwise interferes with your sleep.
It reduces the time you have to be with friends or family.
It interferes with your ability to finish work or homework.
It causes you to be rude, even subconsciously. “For instance,” Lembke asks, “are you in the middle of having a conversation with someone and just dropping down and scrolling through your phone?” That’s a bad sign.
It’s squelching your creativity. “I think that’s really what people don’t realize with their smartphone usage,” Lembke says. “It can really deprive you of a kind of seamless flow of creative thought that generates from your own brain.”
Consider a digital detox one day a week
Tiffany Shlain, a San Francisco Bay Area filmmaker, and her family power down all their devices every Friday evening, for a 24-hour period.
“It’s something we look forward to each week,” Shlain says. She and her husband, Ken Goldberg, a professor in the field of robotics at the University of California, Berkeley, are very tech savvy.
A recent study of high school students, published in the journal Emotion, found that too much time spent on digital devices is linked to lower self-esteem and a decrease in well-being.
Malware, Phishing, Hacking, Ransomware – oh my! Learn about the threats to you, your users and your library. During this session, we will explore the threats to online security and discuss solutions that can be implemented at any level. Most importantly, we will look at how we can educate our users on current threats and safety
Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.
Title:
Learning to Harness Big Data in an Academic Library
Abstract (200)
Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions. Introduction to the problem. Limitations
The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.
Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.
In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).
The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).
Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).
De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).
The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics. The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).
Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).
Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?
Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.
Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success. Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).
Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.
Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).
The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.
Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.
Method
The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data. Send survey URL to (academic?) libraries around the world.
Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.
The study will include the use of closed-ended survey response questions and open-ended questions. The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.
Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17). Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).
Sampling design
Online survey of 10-15 question, with 3-5 demographic and the rest regarding the use of tools.
1-2 open-ended questions at the end of the survey to probe for follow-up mixed method approach (an opportunity for qualitative study)
data analysis techniques: survey results will be exported to SPSS and analyzed accordingly. The final survey design will determine the appropriate statistical approach.
Project Schedule
Complete literature review and identify areas of interest – two months
Prepare and test instrument (survey) – month
IRB and other details – month
Generate a list of potential libraries to distribute survey – month
Contact libraries. Follow up and contact again, if necessary (low turnaround) – month
Collect, analyze data – two months
Write out data findings – month
Complete manuscript – month
Proofreading and other details – month
Significance of the work
While it has been widely acknowledged that Big Data (and its handling) is changing higher education (https://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (https://blog.stcloudstate.edu/ims/2016/03/29/analytics-in-education/), it remains nebulous how Big Data is handled in the academic library and, respectively, how it is related to the handling of Big Data on campus. Moreover, the visualization of Big Data between units on campus remains in progress, along with any policymaking based on the analysis of such data (hence the need for comprehensive visualization).
This research will aim to gain an understanding on: a. how librarians are handling Big Data; b. how are they relating their Big Data output to the campus output of Big Data and c. how librarians in particular and campus administration in general are tuning their practices based on the analysis.
Based on the survey returns (if there is a statistically significant return), this research might consider juxtaposing the practices from academic libraries, to practices from special libraries (especially corporate libraries), public and school libraries.
References:
Adams Becker, S., Cummins M, Davis, A., Freeman, A., Giesinger Hall, C., Ananthanarayanan, V., … Wolfson, N. (2017). NMC Horizon Report: 2017 Library Edition.
Andrejevic, M., & Gates, K. (2014). Big Data Surveillance: Introduction. Surveillance & Society, 12(2), 185–196.
Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125
Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.
Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD Skills: New Analysis Practices for Big Data. Proc. VLDB Endow., 2(2), 1481–1492. https://doi.org/10.14778/1687553.1687576
Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., … Chuang, I. (2014). Privacy, Anonymity, and Big Data in the Social Sciences. Commun. ACM, 57(9), 56–63. https://doi.org/10.1145/2643132
De Mauro, A. D., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122–135. https://doi.org/10.1108/LR-06-2015-0061
De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644(1), 97–104. https://doi.org/10.1063/1.4907823
Eaton, M. (2017). Seeing Library Data: A Prototype Data Visualization Application for Librarians. Publications and Research. Retrieved from http://academicworks.cuny.edu/kb_pubs/115
Emanuel, J. (2013). Usability testing in libraries: methods, limitations, and implications. OCLC Systems & Services: International Digital Library Perspectives, 29(4), 204–217. https://doi.org/10.1108/OCLC-02-2013-0009
Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261. https://doi.org/10.1177/2043820613513121
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47(Supplement C), 98–115. https://doi.org/10.1016/j.is.2014.07.006
Laney, D. (2001, February 6). 3D Data Management: Controlling Data Volume, Velocity, and Variety.
Miltenoff, P., & Hauptman, R. (2005). Ethical dilemmas in libraries: an international perspective. The Electronic Library, 23(6), 664–670. https://doi.org/10.1108/02640470510635746
Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(Supplement C), 314–347. https://doi.org/10.1016/j.ins.2014.01.015
Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
Reyes, J. (2015). The skinny on big data in education: Learning analytics simplified. TechTrends: Linking Research & Practice to Improve Learning, 59(2), 75–80. https://doi.org/10.1007/s11528-015-0842-1
Schroeder, R. (2014). Big Data and the brave new world of social media research. Big Data & Society, 1(2), 2053951714563194. https://doi.org/10.1177/2053951714563194
Sugimoto, C. R., Ding, Y., & Thelwall, M. (2012). Library and information science in the big data era: Funding, projects, and future [a panel proposal]. Proceedings of the American Society for Information Science and Technology, 49(1), 1–3. https://doi.org/10.1002/meet.14504901187
Tene, O., & Polonetsky, J. (2012). Big Data for All: Privacy and User Control in the Age of Analytics. Northwestern Journal of Technology and Intellectual Property, 11, [xxvii]-274.
van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society; Newcastle upon Tyne, 12(2), 197–208.
Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010
West, D. M. (2012). Big data for education: Data mining, data analytics, and web dashboards. Governance Studies at Brookings, 4, 1–0.
Willis, J. (2013). Ethics, Big Data, and Analytics: A Model for Application. Educause Review Online. Retrieved from https://docs.lib.purdue.edu/idcpubs/1
Wixom, B., Ariyachandra, T., Douglas, D. E., Goul, M., Gupta, B., Iyer, L. S., … Turetken, O. (2014). The current state of business intelligence in academia: The arrival of big data. CAIS, 34, 1.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. https://doi.org/10.1109/TKDE.2013.109
Wu, Z., Wu, J., Khabsa, M., Williams, K., Chen, H. H., Huang, W., … Giles, C. L. (2014). Towards building a scholarly big data platform: Challenges, lessons and opportunities. In IEEE/ACM Joint Conference on Digital Libraries (pp. 117–126). https://doi.org/10.1109/JCDL.2014.6970157
More data doesn’t automatically lead to better decisions. A shortage of skilled data scientists has hindered progress towards translation of information into actionable business insights. In addition, traditionally dense spreadsheets and linear slideshows are ineffective to present discoveries when dealing with Big Data’s dynamic nature. We need to evolve how we capture, analyze and communicate data.
Large-scale visualization platforms have several advantages over traditional presentation methods. They blur the line between the presenter and audience to increase the level of interactivity and collaboration. They also offer simultaneous views of both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets.
Visualization walls enable presenters to target people’s preferred learning methods, thus creating a more effective communication tool. The human brain has an amazing ability to quickly glean insights from patterns – and great visualizations make for more efficient storytellers.
Grant: Visualizing Digital Scholarship in Libraries and Learning Spaces
Award amount: $40,000
Funder: Andrew W. Mellon Foundation
Lead institution: North Carolina State University Libraries
Due date: 13 August 2017
Notification date: 15 September 2017
Website: https://immersivescholar.org
Contact: immersivescholar@ncsu.edu
Project Description
NC State University, funded by the Andrew W. Mellon Foundation, invites proposals from institutions interested in participating in a new project for Visualizing Digital Scholarship in Libraries and Learning Spaces. The grant aims to 1) build a community of practice of scholars and librarians who work in large-scale multimedia to help visually immersive scholarly work enter the research lifecycle; and 2) overcome technical and resource barriers that limit the number of scholars and libraries who may produce digital scholarship for visualization environments and the impact of generated knowledge. Libraries and museums have made significant strides in pioneering the use of large-scale visualization technologies for research and learning. However, the utilization, scale, and impact of visualization environments and the scholarship created within them have not reached their fullest potential. A logical next step in the provision of technology-rich, visual academic spaces is to develop best practices and collaborative frameworks that can benefit individual institutions by building economies of scale among collaborators.
The project contains four major elements:
An initial meeting and priority setting workshop that brings together librarians, scholars, and technologists working in large-scale, library and museum-based visualization environments.
Scholars-in-residence at NC State over a multi-year period who pursue open source creative projects, working in collaboration with our librarians and faculty, with the potential to address the articulated limitations.
Funding for modest, competitive block grants to other institutions working on similar challenges for creating, disseminating, validating, and preserving digital scholarship created in and for large-scale visual environments.
A culminating symposium that brings together representatives from the scholars-in-residence and block grant recipient institutions to share and assess results, organize ways of preserving and disseminating digital products produced, and build on the methods, templates, and tools developed for future projects.
Work Summary
This call solicits proposals for block grants from library or museum systems that have visualization installations. Block grant recipients can utilize funds for ideas ranging from creating open source scholarly content for visualization environments to developing tools and templates to enhance sharing of visualization work. An advisory panel will select four institutions to receive awards of up to $40,000. Block grant recipients will also participate in the initial priority setting workshop and the culminating symposium. Participating in a block grant proposal does not disqualify an individual from later applying for one of the grant-supported scholar-in-residence appointments.
Applicants will provide a statement of work that describes the contributions that their organization will make toward the goals of the grant. Applicants will also provide a budget and budget justification.
Activities that can be funded through block grants include, but are not limited to:
Commissioning work by a visualization expert
Hosting a visiting scholar, artist, or technologist residency
Software development or adaptation
Development of templates and methodologies for sharing and scaling content utilizing open source software
Student or staff labor for content or software development or adaptation
Curricula and reusable learning objects for digital scholarship and visualization courses
Travel (if necessary) to the initial project meeting and culminating workshop
User research on universal design for visualization spaces
Funding for operational expenditures, such as equipment, is not allowed for any grant participant.
Application
Send an application to immersivescholar@ncsu.edu by the end of the day on 13 August 2017 that includes the following:
Statement of work (no more than 1000 words) of the project idea your organization plans to develop, its relationship to the overall goals of the grant, and the challenges to be addressed.
List the names and contact information for each of the participants in the funded project, including a brief description of their current role, background, expertise, interests, and what they can contribute.
Project timeline.
Budget table with projected expenditures.
Budget narrative detailing the proposed expenditures
Selection and Notification Process
An advisory panel made up of scholars, librarians, and technologists with experience and expertise in large-scale visualization and/or visual scholarship will review and rank proposals. The project leaders are especially keen to receive proposals that develop best practices and collaborative frameworks that can benefit individual institutions by building a community of practice and economies of scale among collaborators.
Awardees will be selected based on:
the ability of their proposal to successfully address one or both of the identified problems;
the creativity of the proposed activities;
relevant demonstrated experience partnering with scholars or students on visualization projects;
whether the proposal is extensible;
feasibility of the work within the proposed time-frame and budget;
whether the project work improves or expands access to large-scale visual environments for users; and
the participant’s ability to expand content development and sharing among the network of institutions with large-scale visual environments.
Awardees will be required to send a representative to an initial meeting of the project cohort in Fall 2017.
If you go to the Facebook Live Map and browse the live feeds, you’ll often see people talking about nothing in particular, with unflattering close-up camera angles and scratchy audio. People often shift their phones from hand to hand when they tire of holding them, and brush the mic without realizing it.
#2: Invest in a Mobile Phone Setup Budget: $150-$300
iPhone Setup When choosing a mount for an iPhone, consider the iOgrapher ($60), shown below. Attach the 37mm wide angle lens ($40) if you want to get more people or surroundings in the video. Android and Windows Phone Setup The Saramonic SmartMixer ($149) fits any phone (including the iPhone) and incorporates both audio and video stabilization in one piece of gear. The mics are stereo, and you can angle them however you want to capture multiple people talking.
#3: Broadcast From Your Desktop
Budget: Free-$600 Going live from your computer allows you to bring in guests to interview, add pre-recorded video, graphics, titles (so people know who the hosts are), and more.
You can use the built-in camera on your computer or a USB camera, like the Logitech C920 ($99).
OBS OBS (Open Broadcaster Software) is open-source software, which means it’s available for free.
OBS is a great option, but it doesn’t have all of the bells and whistles of paid software to make it intuitive or easy to use. You’ll need to do a bit of setup and testing before you go live.
Wirecast Wirecast ($495) has been around for years and has come a long way in the last few months as Facebook Live has exploded in popularity. The interface is a little more intuitive than OBS, but still requires some setup and experimentation.
The reputations of Asian universities, and Chinese universities in particular, are on the rise. China’s World Class 2.0 project, announced in August 2015, aims to strengthen the research performance of China’s nine top-ranked universities, with the goal of having six of those institutions ranked within the world’s top 15 universities by 2030.
After two decades in which China has been largely an exporter of students to Australia, Canada, the US and the UK, it is now increasingly attracting international students to study at its universities. And what is true of China is true of other countries too. Global flows of students are an increasing feature of the world’s higher education systems.
You can see the recruitment of international students as an exercise in soft power, in global engagement , in global citizenship, a great exercise in language learning , the practical application of a challenge thrown down by the great American social anthropologist Clifford Geertz.
Certainly, my friends who lead universities in Australia, Canada and New Zealand are delighted when they read politicians’ rhetoric about making it harder for international students to come to the UK.(my note, this is a Guardian article, but applies perfectly with Bush Junior politics and with the rhetoric of Trump)
peer-reviewed http://scsu.mn/2e8mdNh – permanent link to the SCSU online database search (Arduino + Education)
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Does music help learn better? get smarter? advance in life?
keywords: music, education, intelligence.
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keywords: music, education, multimedia.
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ALLISON AUBREY