Welcome to the registration page for Digitorium 2018. The conference will be held at The University of Alabama in Tuscaloosa, AL, from Thursday, October 4, to Saturday, October 6, in the Hotel Capstone on campus. We look forward to welcoming you here. All participants in Digitorium, including presenters, must register and pay fees by September 28, 2018. To register, please complete the Online Registration process. Thank you again for your interest in and support for this event. We are excited to meet you in Tuscaloosa.
If you have any questions about registration, please contact Thomas C. Wilson (tcwilson@ua.edu).
!*!*!*!*! — this article was pitched by Mark Vargas in the fall of 2013, back then dean of LRS and discussed at a faculty meeting at LRS in the same year—- !*!*!*!
New Roles for New Times: Transforming Liaison Roles in Research Libraries
(p. 4) Building strong relationships with faculty and other campus professionals, and establishing collaborative partnerships within and across institutions, are necessary building blocks to librarians’ success. In a traditional liaison model, librarians use their subject knowledge to select books and journals and teach guest lectures.
“Liaisons cannot be experts themselves in each new capability, but knowing when to call in a colleague, or how to describe appropriate expert capabilities to faculty, will be key to the new liaison role.
six trends in the development of new roles for library liaisons
user engagement is a driving factor
what users do (research, teaching, and learning) rather than on what librarians do (collections, reference, library instruction). In addition, an ALA-accredited master’s degree in library science is no longer strictly required.
In a networked world, local collections as ends in themselves make learning fragmentary and incomplete. (p. 5)
A multi-institutional approach is the only one that now makes sense.
Scholars already collaborate; libraries need to make it easier for them to do so.
but they also advise and collaborate on issues of copyright, scholarly communication, data management, knowledge management, and information literacy. The base level of knowledge that a liaison must possess is much broader than familiarity with a reference collection or facility with online searching; instead, they must constantly keep up with evolving pedagogies and research methods, rapidly developing tools, technologies, and ever-changing policies that facilitate and inform teaching, learning, and research in their assigned disciplines.
In many research libraries, programmatic efforts with information literacy have been too narrowly defined. It is not unusual for libraries to focus on freshman writing programs and a series of “one-shot” or invited guest lectures in individual courses. While many librarians have become excellent teachers, traditional one-shot, in-person instructional sessions can vary in quality depending on the training librarians have received in this arena; and they neither scale well nor do they necessarily address broader curricular goals. Librarians at many institutions are now focusing on collaborating with faculty to develop thoughtful assignments and provide online instructional materials that are built into key courses within a curriculum and provide scaffolding to help students develop library research skills over the course of their academic careers.
And many libraries stated that they lack instructional designers and/or educational technologists on their staff, limiting the development of interactive online learning modules and tutorials. (my note: or just ignore the desire by unites such as IMS to help).
(p. 7). This move away from supervision allows the librarians to focus on their liaison responsibilities rather than on the day-to-day operations of a library and its attendant personnel needs.
effectively support teaching, (1.) learning, and research; (2.) identify opportunities for further development of tools and services; (3.) and connect students, staff, and faculty to deeper expertise when needed.
At many institutions, therefore, the conversation has focused on how to supplement and support the liaison model with other staff.
At many institutions, therefore, the conversation has focused on how to supplement and support the liaison model with other staff.
the hybrid exists within the liaison structure, where liaisons also devote a portion of their time (e.g., 20% or more) to an additional area of expertise, for example digital humanities and scholarly communication, and may work with liaisons across all disciplinary areas. (my note: and at the SCSU library, the librarians firmly opposed the request for a second master’s degree)
functional specialists who do not have liaison assignments to specific academic departments but instead serve as “superliaisons” to other librarians and to the entire campus. Current specialist areas of expertise include copyright, geographic information systems (GIS), media production and integration, distributed education or e-learning, data management, emerging technologies, user experience, instructional design, and bioinformatics. (everything in italics is currently done by IMS faculty).
divided into five areas of functional specialization: information resources and collections management; information literacy, instruction, and curriculum development; discovery and access; archival and special collections; scholarly communication and the research enterprise.
E-Scholarship Collaborative, a Research Support Services Collaborative (p. 8).
p. 9. managing alerts and feeds, personal archiving, and using social networking for teaching and professional development
p. 10. new initiatives in humanistic research and teaching are changing the nature and frequency of partnerships between faculty and the Libraries. In particular, cross-disciplinary Humanities Laboratories (http://fhi.duke.edu/labs), supported by the John Hope Franklin Humanities Institute and the Andrew W. Mellon Foundation-funded Humanities Writ Large project, have allowed liaisons to partner with faculty to develop and curate new forms of scholarship.
consultations on a range of topics, such as how to use social media to effectively communicate academic research and how to mark up historical texts using the Text Encoding Initiative (TEI) guidelines
p. 11. Media literacy, and facilitating the integration of media into courses, is an area in which research libraries can play a lead role at their institutions. (my note: yet still suppressed or outright denied to IMS to conducts such efforts)
Purdue Academic Course Transformation, or IMPACT (http://www.lib.purdue.edu/infolit/impact). The program’s purpose is to make foundational courses at Purdue more student-centered and participatory. Librarians are key members of interdepartmental teams that “work with Purdue instructors to redesign courses by applying evidence-based educational practices” and offer “learning solutions” that help students engage with and critically evaluate information. (my note: as offered by Keith and myself to Miguel, the vice provost for undergrads, who left; then offered to First Year Experience faculty, but ignored by Christine Metzo; then offered again to Glenn Davis, who bounced it back to Christine Metzo).
p. 15. The NCSU Libraries Fellows Program offers new librarians a two-year appointment during which they develop expertise in a functional area and contribute to an innovative initiative of strategic importance. NCSU Libraries typically have four to six fellows at a time, bringing in people with needed skills and working to find ongoing positions when they have a particularly good match. Purdue Libraries have experimented with offering two-year visiting assistant professor positions. And the University of Minnesota has hired a second CLIR fellow for a two-year digital humanities project; the first CLIR fellow now holds an ongoing position as a curator in Archives and Special Collections. The CLIR Fellowship is a postdoctoral program that hires recent PhD graduates (non-librarians), allowing them to explore alternative careers and allowing the libraries to benefit from their discipline-specific expertise.
Preliminary Information and Literature. Please do not hesitate to share in the comments section your ideas, suggestions and questions
предварителна информация и литература по дискусията. Не се колебайте да споделите мнения, препоръки и въпроси в “Comment” секцията:
++++++++++++++++
For more information and for backchanneling please use the following social media
за повече въпроси и информация, както и за споделяне на вашите идеи и мисли използвайте следните канали / социални медии:
social media – call centers in Iowa, where agriculture is expected. not an awesome job. http://sched.co/D7pQ
Caleris as featured in New York Times.
Sarah Roberts talk about psychological effects of working at Caleris; it resembles the effect of air strikes on the drone pilots
Drupal based. Google Analytics like. Bookmarks. objects list can be shared through social media, email, etc. Pachyderm used to have timeline like Islandora. still images, audio, video
The ACM/IEEE Joint Conference on Digital Libraries in 2018 (JCDL 2018L: https://2018.jcdl.org/) will be held in conjunction with UNT Open Access
Symposium 2018 (https://openaccess.unt.edu/symposium/2018) on June 3 – 6, 2018
in Fort Worth, Texas, the rustic and artistic threshold into the American
West. JCDL welcomes interesting submissions ranging across theories, systems,
services, and applications. We invite those managing, operating, developing,
curating, evaluating, or utilizing digital libraries broadly defined, covering
academic or public institutions, including archives, museums, and social
networks. We seek involvement of those in iSchools, as well as working in
computer or information or social sciences and technologies. Multiple tracks
and sessions will ensure tailoring to researchers, practitioners, and diverse
communities including data science/analytics, data curation/stewardship, information retrieval, human-computer interaction, hypertext (and Web/network science), multimedia, publishing, preservation, digital humanities, machine learning/AI, heritage/culture, health/medicine, policy, law, and privacy/ intellectual property.
General Instructions on submissions of full papers, short papers, posters and
demonstrations, doctoral consortium, tutorials, workshops, and panels can be
found at https://2018.jcdl.org/general_instructions. Below are the submission
deadlines:
• Jan. 15, 2018 – Tutorial and workshop proposal submissions
• Jan. 15, 2018 – Full paper and short paper submissions
• Jan. 29, 2018 – Panel, poster and demonstration submissions
• Feb. 1, 2018 – Notification of acceptance for tutorials and workshops
• Mar. 8, 2018 – Notification of acceptance for full papers, short papers,
panels, posters, and demonstrations
• Mar. 25, 2018 – Doctoral Consortium abstract submissions
• Apr. 5, 2018 – Notification of acceptance for Doctoral Consortium
• Apr. 15, 2018 – Final camera-ready deadline for full papers, short papers,
panels, posters, and demonstrations
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
According to the email below, library faculty are asked to provide their feedback regarding the qualifications for a possible faculty line at the library.
In the fall of 2013 during a faculty meeting attended by the back than library dean and during a discussion of an article provided by the dean, it was established that leading academic libraries in this country are seeking to break the mold of “library degree” and seek fresh ideas for the reinvention of the academic library by hiring faculty with more diverse (degree-wise) background.
Is this still the case at the SCSU library? The “democratic” search for the answer of this question does not yield productive results, considering that the majority of the library faculty are “reference” and they “democratically” overturn votes, who see this library to be put on 21st century standards and rather seek more “reference” bodies for duties, which were recognized even by the same reference librarians as obsolete.
It seems that the majority of the SCSU library are “purists” in the sense of seeking professionals with broader background (other than library, even “reference” skills).
In addition, most of the current SCSU librarians are opposed to a second degree, as in acquiring more qualification, versus seeking just another diploma. There is a certain attitude of stagnation / intellectual incest, where new ideas are not generated and old ideas are prepped in “new attire” to look as innovative and/or 21st
Last but not least, a consistent complain about workforce shortages (the attrition politics of the university’s reorganization contribute to the power of such complain) fuels the requests for reference librarians and, instead of looking for new ideas, new approaches and new work responsibilities, the library reorganization conversation deteriorates into squabbles for positions among different department.
Most importantly, the narrow sightedness of being stuck in traditional work description impairs most of the librarians to see potential allies and disruptors. E.g., the insistence on the supremacy of “information literacy” leads SCSU librarians to the erroneous conclusion of the exceptionality of information literacy and the disregard of multi[meta] literacies, thus depriving the entire campus of necessary 21st century skills such as visual literacy, media literacy, technology literacy, etc.
Simultaneously, as mentioned above about potential allies and disruptors, the SCSU librarians insist on their “domain” and if they are not capable of leading meta-literacies instructions, they would also not allow and/or support others to do so.
Considering the observations above, the following qualifications must be considered:
According to the information in this blog post: https://blog.stcloudstate.edu/ims/2016/06/14/technology-requirements-samples/
for the past year and ½, academic libraries are hiring specialists with the following qualifications and for the following positions (bolded and / or in red). Here are some highlights: Positions
digital humanities
Librarian and Instructional Technology Liaison
library Specialist: Data Visualization & Collections Analytics
Qualifications
Advanced degree required, preferably in education, educational technology, instructional design, or MLS with an emphasis in instruction and assessment.
Programming skills – Demonstrated experience with one or more metadata and scripting languages (e.g.Dublin Core, XSLT, Java, JavaScript, Python, or PHP)
Data visualization skills
multi [ meta] literacy skills
Data curation, helping students working with data
Experience with website creation and design in a CMS environment and accessibility and compliance issues
Demonstrated a high degree of facility with technologies and systems germane to the 21st century library, and be well versed in the issues surrounding scholarly communications and compliance issues (e.g. author identifiers, data sharing software, repositories, among others)
Bilingual
Provides and develops awareness and knowledge related to digital scholarship and research lifecycle for librarians and staff.
Experience developing for, and supporting, common open-source library applications such as Omeka, ArchiveSpace, Dspace,
Responsibilities Establishing best practices for digital humanities labs, networks, and services
Assessing, evaluating, and peer reviewing DH projects and librarians
Actively promote TIGER or GRIC related activities through social networks and other platforms as needed.
Coordinates the transmission of online workshops through Google HangoutsScript metadata transformations and digital object processing using BASH, Python, and XSLT
liaison consults with faculty and students in a wide range of disciplines on best practices for teaching and using data/statistical software tools such as R, SPSS, Stata, and MatLab.
In response to the form attached to the Friday, September 29, email regarding St. Cloud State University Library Position Request Form:
Title
Digital Initiatives Librarian
Responsibilities:
TBD, but generally:
– works with faculty across campus on promoting digital projects and other 21st century projects. Works with the English Department faculty on positioning the SCSU library as an equal participants in the digital humanities initiatives on campus
Works with the Visualization lab to establish the library as the leading unit on campus in interpretation of big data
Works with academic technology services on promoting library faculty as the leading force in the pedagogical use of academic technologies.
Quantitative data justification
this is a mute requirement for an innovative and useful library position. It can apply for a traditional request, such as another “reference” librarian. There cannot be a quantitative data justification for an innovative position, as explained to Keith Ewing in 2015. In order to accumulate such data, the position must be functioning at least for six months.
Qualitative justification: Please provide qualitative explanation that supports need for this position.
Numerous 21st century academic tendencies right now are scattered across campus and are a subject of political/power battles rather than a venue for campus collaboration and cooperation. Such position can seek the establishment of the library as the natural hub for “sandbox” activities across campus. It can seek a redirection of using digital initiatives on this campus for political gains by administrators and move the generation and accomplishment of such initiatives to the rightful owner and primary stakeholders: faculty and students.
Currently, there are no additional facilities and resources required. Existing facilities and resources, such as the visualization lab, open source and free application can be used to generate the momentum of faculty working together toward a common goal, such as, e.g. digital humanities.
– Current initiatives and progress on sustainable models for making monographs openly accessible. Webinar for Open Access Week, Tuesday, October 24, 4 p.m Eastern (10 a.m. HAST; 1 p.m. Pacific; 2 p.m. Mountain; 3 p.m. Central)
Registration is free. Please sign up with this registration form
with a growing number of initiatives, publishers, and economic models, the question is sustainability. There are a number of different models, including Open Book Publishers, Open Humanities Press, and numerous university and commercial publishers who have open monograph publications, thus more initiatives than we could include for this one-hour webinar. We have invited a selected number of representatives from various open monograph publishing initiatives to participate in a panel discussion about their current economic models and future of open access monographs. Each panelist will give a brief statement about their initiative, their editorial review process, their funding model, and their perspectives on the future of open access monographs. Following their brief statements, we will have a question and answer period moderated by Kevin Smith, the Dean of Libraries at the University of Kansas.
Participants for the panel include:
AAUP Open Access Monograph Publishing Initiative– Wendy Pradt Lougee, University Librarian and McKnight Presidential Professor, University of Minnesota, Twin Cities. The Association of American Universities (AAU), Association of Research Libraries (ARL), and Association of American University Presses (AAUP) are implementing a new initiative with 13 universities and 60 university presses participating. Universities will provide subventions for open digital monographs, to be published by university presses.
Lever Pressand Knowledge Unlatched – Charles Watkinson, Associate University Librarian for Publishing, University of Michigan Library, and Director, University of Michigan Press. University of Michigan Press and Amherst Press are partners in the Lever Press which is supported by pledging institutions. University of Michigan Press has also been an active participant in Knowledge Unlatched, which uses a crowd -source funding model to make previously published works openly available. Charles is also a Board Member of Knowledge Unlatched Research and will compare Lever Press with KU.
Luminos– Erich van Rijn, Assistant Director, Director of Publishing Operations at University of California Press. The financial model is shared costs between author, institution, publisher, and libraries.
University of Ottawa Press– Lara Mainville, Director of University of Ottawa Press. OA publications are funded by the University of Ottawa libraries.
Moderator: Kevin Smith, Dean of Libraries at the University of Kansas. Prior to joining the University of Kansas, Kevin served as Director of Copyright and Scholarly Communications at the Duke University Libraries.
My note: it is NOT about creating masses of programmers and driving the salaries down, as the author claims; it is about fostering a generation, which is technology literate. A doctor, knowing how to code will be a better doctor in the era of IoT; a philosopher knowing how to code will be better in the era of digital humanities.
