Atlas of the Historical Geography of the United States
more on maps in this IMS blog
more on maps in this IMS blog
M W F at 9am in SH 303. My class is GEOG 361: Tourism Transportation. Instructor Stacey Olson
how/where can you find us: http://scsu.mn/TechInstruct
Consider requesting an instruciton session http://web.stcloudstate.edu/pmiltenoff/info/
Kahoot quiz at the end: https://kahoot.it/#/
* why do I need to know this: it is a trending quick and effective way to visualize numbers (stats)
* what is my assignment: create a MEANINGFUL infographic
* how I will be evaluated: assess the infographic and your strategy to make it public
– why infographics matter. Why is it more then just another alternative to PowerPoint
– what is an infographic: a portmanteau of information + graphic
– what are the cloud-based tools: Pictochart, Easilly and Infogram
– why are stats so important for infographics
– where are the stats to be found for the infographics
– how much stats and math needs one to know, to create a meaningful infographic
– how can infographics be promoted effectively, inexpensively and quickly
An Opportunity for Interdisciplinary Research
When: Friday, September 28, 8:30am-3:00pm
Where: Wilson Research Collaboration Studio, Wilson Library
Cost: Free; advanced registration is required
1968 was one of the most turbulent years of the 20th century. 2018 marks the 50th anniversary of that year’s landmark political, social and cultural events–events that continue to influence our world today.
Focusing on the importance of this 50 year anniversary we are calling out to all faculty, staff, students, and community partners to participate the workshop ‘Mapping 1968, Conflict and Change’. This all-day event is designed to bring people together into working groups based on common themes. Bring your talent and curiosity to apply an interdisciplinary approach to further explore the spatial context of these historic and/or current events. Learn new skills on mapping techniques that can be applied to any time in history. To compliment the expertise that you bring to the workshop, working groups will also have the support of library, mapping, and data science experts to help gather, create, and organize the spatial components of a given topic.
To learn more and to register for the workshop, go here.
Workshop sponsors: Institute for Advanced Studies (IAS), U-Spatial, Liberal Arts Technologies & Innovation Services (LATIS), Digital Arts, Science & Humanities (DASH), and UMN Libraries.
https://www.goodreads.com/book/show/5114403-early-thematic-mapping-in-the-history-of-cartography – symbolization methods, cartographers and statisticians.
Kevin Ehrman-Solberg firstname.lastname@example.org PPT on Mapping Prejudice. https://www.mappingprejudice.org/
Henneping County scanned the deeds, OCR, Python script to search. Data in an open source. covenant data. Local historian found microfishes, the language from the initial data. e.g. eugenics flavor: arian, truncate.
Dan Milz. Public Affairs. geo-referencing, teaching a class environmental planning, spatial analysis, email@example.com @dcmlz
Chris ancient historian. The Tale of Mediterranean, City: Mapping the history of Premodern Carthage and Tunis.
College of Liberal Arts
from archives to special resources. archaeological data into GIS layers. ESRI https://www.esri.com/en-us/home how interactive is ESRI.
mapping for 6 months. finding the maps in the archeological and history reports was time consuming. once that data was sorted out, exciting.
Posted by InforMedia Services on Friday, September 28, 2018
Kate Carlson, U-Spatial Story Maps, An Intro
patters, we wouldn’t see if we did not bring it up spatially. interactivity and data visualization, digital humanities
making an argument, asking questions, crowdsourcing, archival and resources accessibility, civitates orbis terrarum http://historic-cities.huji.ac.il/mapmakers/braun_hogenberg.html
storymaps.arcgis.com/en/gallery https://storymaps.arcgis.com/en/gallery/#s=0 cloud-based mapping software. ArcGIS Online. organizational account for the U, 600 users. over 700 storymaps creates within the U, some of them are not active, share all kind of data: archive data on spreadsheet, but also a whole set of data within the software; so add the data or use the ArcGIS data and use templates. web maps into the storymap app, Living Atlas: curated set of data: hunderd sets of data, from sat images, to different contents. 846 layers of data, imagery, besides org account, one can create maps within the free account with limited access. data browser to use my own data – Data Enrichment to characterized my data. census data from 2018 and before,
make plan, create a storyboard, writing for the web, short and precise (not as writing for a journal), cartographic style, copyright, citing the materials, choosing the right map scale for each page. online learning materials, some only thru org account ESRI academy has course catalogue. Mapping 101, Dekstop GIS 101, Collector 101, Imagery 101, SQL 101, Story Maps 101,
Awards for UMN undergrad and grad students, $1000
history, anthropology, political science,
Melinda, Kernik, Spatial Data Curator firstname.lastname@example.org Jenny McBurney email@example.com
University Digital COnservancy
civil rights information from the U (migrants blog)
DASH Digital Arts, Sciences and Humanities. text mining data visualization,
data repository for the U (DRUM)
DASH director, https://dash.umn.edu/. Ben Wiggins
more on GIS in this IMS blog
Wednesday, May 30, 2018
Last summer Google added the option for students to explore the VR expeditions on their own.
Like any augmented reality app, the new AR content in Google Expeditions lets students view and manipulate digital content in a physical world context. The new AR content can be used as components in science, math, geography, history, and art lessons. Some examples of the more than 100 AR tours that you’ll now find in the app include landforms, the skeletal system, dinosaurs, ancient Egypt, the brain, and the Space Race.
To use the AR content available through Google Expeditions you will need to print marker or trigger sheets that students scan with their phones or tablets. Once scanned the AR imagery appears on the screen. (You can actually preview some of the imagery without scanning a marker, but the imagery will not be interactive or 3D). Students don’t need to look through a Cardboard viewer in order to see the AR imagery.
You can get the Google Expeditions Android app here and the iOS version here.
more on GOogle Expeditions in this IMS blog
Play the game!
more on geography in this IMS blog
more on geography and history in this IMS blog
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.
Learning to Harness Big Data in an Academic Library
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 like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…”
– Dan Ariely, 2013 https://www.asist.org/publications/bulletin/aprilmay-2017/big-datas-impact-on-privacy-for-librarians-and-information-professionals/
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.
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).
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 (http://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (http://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.
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
Bail, C. A. (2014). The cultural environment: measuring culture with big data. Theory and Society, 43(3–4), 465–482. https://doi.org/10.1007/s11186-014-9216-5
Borgman, C. L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press.
Bruns, A. (2013). Faster than the speed of print: Reconciling ‘big data’ social media analysis and academic scholarship. First Monday, 18(10). Retrieved from http://firstmonday.org/ojs/index.php/fm/article/view/4879
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.
Chen, X. W., & Lin, X. (2014). Big Data Deep Learning: Challenges and Perspectives. IEEE Access, 2, 514–525. https://doi.org/10.1109/ACCESS.2014.2325029
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
Dumbill, E. (2012). Making Sense of Big Data. Big Data, 1(1), 1–2. https://doi.org/10.1089/big.2012.1503
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
Harper, L., & Oltmann, S. (2017, April 2). Big Data’s Impact on Privacy for Librarians and Information Professionals. Retrieved November 7, 2017, from https://www.asist.org/publications/bulletin/aprilmay-2017/big-datas-impact-on-privacy-for-librarians-and-information-professionals/
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
Hwangbo, H. (2014, October 22). The future of collaboration: Large-scale visualization. Retrieved November 7, 2017, from http://usblogs.pwc.com/emerging-technology/the-future-of-collaboration-large-scale-visualization/
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
Power, D. J. (2014). Using ‘Big Data’ for analytics and decision support. Journal of Decision Systems, 23(2), 222–228. https://doi.org/10.1080/12460125.2014.888848
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
Reilly, S. (2013, December 12). What does Horizon 2020 mean for research libraries? Retrieved November 7, 2017, from http://libereurope.eu/blog/2013/12/12/what-does-horizon-2020-mean-for-research-libraries/
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
Weiss, A. (2018). Big-Data-Shocks-An-Introduction-to-Big-Data-for-Librarians-and-Information-Professionals. Rowman & Littlefield Publishers. Retrieved from https://rowman.com/ISBN/9781538103227/Big-Data-Shocks-An-Introduction-to-Big-Data-for-Librarians-and-Information-Professionals
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
The Department of Geography & Planning, School of Public Affairs and Northland Community and Technical College announce, Dronetech Roundtable on Wednesday, October 11 from 1-2:30 p.m. in the Atwood Alumni Room.
Learn about Unmanned Aircraft Systems UAS (Drones) and their future impact for tomorrow’s workforce from leading industry partners. See how you could use drones and geospatial technology in your career and enhance your competitive edge. Various drone platforms will be on display.
**NCTC, in partnership with St. Cloud State’s Geography & Planning Department, earned a $600,000 grant from the National Science Foundation to advance education in geospatial information technology and unmanned aircraft systems.**
more on drones in this IMS blog
The Honor System:
A Comparison Between the U.S. South and the Mediterranean World
Plamen Miltenoff, MLIS, Ph.D.
Meeting Times & Places
5:00 pm – 7:30 pm Wednesdays Miller Center 206
|Back to Top|
The best way to contact me is through email, but you can use any of the options below.
|Office Location:||Miller Center, 204-J|
The Honor system is a phenomenon well known in many cultures across the globe and strongly presented in cultures since Ancient Greece and Rome. The concepts of honor and shame have long been associated with cultures in the Mediterranean region mostly because the first scholars to study the social impact of these concepts did so in Southern Europe. Honor has two fundamental components: birth and morality. People could gain or lose their honor by the morality of their conduct. Despite the scholarly emphasis on the Mediterranean, the concept of honor influenced social systems all over the world, and historians are beginning to detect its traces in places as different as China and Africa. The Southern Honor system can firmly be traced back in the European roots and determined to a great degree the American history of the 19th century.
This course will study the geography, history, sociology and religions, cultural and political systems of two worlds and learn to compare the findings. Based on those comparisons, lessons in gender, culture and politics will be drawn.
Students in this course will
|Back to Top|
All assignments should be submitted by midnight of the date on which they are due. Ten percent of an assignment’s point value will be removed for each day an assignment is late. This policy will be adjusted on a case-by-case basis if emergencies prevent you from submitting an assignment on time. In these situations, contact me as soon as is reasonable to determine how this policy can be adjusted in a way that meets your needs and is still fair to other students.
|Back to Top|
The grade book in D2L will be used to show detailed information about grades in this course. The table below shows the value of each assignment and the total number of points available.
|94% – 100% = A|
|90 % – 93.99% = A-|
|86% – 89.99% = B+|
|83% – 85.99% = B|
|80% – 82.99% = B-|
|70% – 79.99% = C|
|60% – 69.99% = D|
|59.99% or lower = F|
|WEEK 1. August 28
Peruse through all articles in the D2L content area. Choose one article to your liking and be ready to reflect on it.
|Introduction. Orientation, class parameters and familiarizing with the syllabus. Questions and issues. Course goals||What is an/the Honor System? Entry Interview (D2L survey is completed and analyzed). Why explore this topic and these vastly different geographic entities (US South and the Mediterranean). Define interest in this class and interest for a project; how this class can help your studies? Your career? All over as a human being?|
|WEEK 2.Sept 4
Assignment[s]: 1. Find an article on Honor and Shame. 2. Outline in two paragraphs the content of one of the three articles and in a third paragraph compare to your findings; use academic style to log your responses. If you have hesitation about your style, please check with the Write Place, your peers and me.
|Why research? Work on the reading material for class
Find articles for the course.
|What is academic research? What is a peer-review article? When and how research the Internet. How do I access and keep track of resources.
RefWorks versus Zotero and Mendeley
What is an academic paper. How do I write an academic paper. The Write place.
Making plans: final project
|WEEK 3. Sept 11
|Honors and Shame from a historical perspective||Do we have a robust theory/notion about the Honor/Shame system through the centuries? Do you think tracking that model through centuries helps in the 21st century? If yes, how and if no, why?|
|WEEK 4. Sept 18
Reading[s]: Fernand Braudel (http://en.wikipedia.org/wiki/Fernand_Braudel) and the Annales School
|Honors and Shame from a geographic perspective||Is there a “southern” connection (Mediterranean is the European South)? Can be Annale School be right (geography and relief determines history)? To what degree geography and geographical conditions determine such models (Honor/Shame)?|
|WEEK 5. Sept 25
Reading[s]: Crook, Z. (2009). Honor, Shame, and Social Status Revisited. Journal of Biblical Literature, 128(3), 591–611.
|Honors and Shame from a cultural perspective. Gender roles, Masculinity||Does the Honor/Shame model help understand gender roles, social status, masculinity etc.?|
|WEEK 6. Oct 2
|Honors and Shame from a political and social perspective||Can Honor/Shame be connected with the current political situation in Egypt, Syria, Turkey? Did Honor/Shame system influence decision in American history?|
|WEEK 7. Wednesday Oct 9
Assignment[s]: final project details
|Start working on the final project||Present and discuss your final project: 1. Finalized title 2. Outline 3. Plan 4. Clear work distribution among group members 5. Clear way for peer assessment.|
| WEEK 8. Wednesday Oct 16
Assignment[s]: details on final project
|Final brainstorming and start working on the project||Meeting as a whole: 1. Present group’s plan to class. 2. Share group’s ideas with class. 3. Share technology 4. Share sources 5. Share means for peer assessment|
|WEEK 9. Wednesday Oct 23
Assignment[s]: draft of bibliography
|Class as a whole: peer review and brainstorming||Meeting as a whole: 1. Are sources reliable? 2. Are sources of academic origin (peer-reviewed)? 3. Is the bibliography adhering correctly to the formats (APA, Chicago, ALA)|
|WEEK 10. Wednesday Oct 30
Assignment[s]: details on presentation
|Work on the final project||Meeting as a whole: 1. Presentation format 2. Share technology 3. Share ideas|
|WEEK 11. Wednesday Nov 6
Assignment[s]: paper draft due in D2L dropbox
|Work on final project||Meeting as a whole: share group’s progress and seek other group’s feedback|
|WEEK 12. Wednesday Nov 13
Assignment[s]: paper draft and presentation
|Work on project||Meeting as a whole: share group’s progress and seek other group’s feedback|
|WEEK 13. Wednesday Nov 20
Assignment[s]: paper draft due in D2L dropbox
|Work on project||Meeting as a whole: share group’s progress and seek other group’s feedback|
|WEEK 13. Wednesday Nov 27|
|Work on project||Meeting as a whole: share group’s progress and seek other group’s feedback|
|WEEK 13. Wednesday Dec 4
Assignment[s]: paper final draft due in D2L dropbox
|presentations||Class presentations of the final projects|
|WEEK 13. Wednesday Dec 11|
|presentations||Class presentations of the final projects|
Bertram Wyatt-Brown. (n.d.). Retrieved from http://personal.tcu.edu/swoodworth/Wyatt-Brown.htm
Brayford, S. A. (1999). TO SHAME OR NOT TO SHAME: SEXUALITY IN THE MEDITERRANEAN DIASPORA. Semeia, (87), 163.
BUSATTA, S. (2006). Honour and Shame in the Mediterranean. Antrocom, 2(2). 75-78. Retrieved March 19, 2013, from http://www.academia.edu/524890/Honour_and_Shame_in_the_Mediterranean
Cohen, D. (n.d.). Insult, Aggression, and the Southern Culture of Honor: An “Experimental Ethnography.” Journal of Personality and Social Psychology, 70(5), 945–960.
Crook, Z. (2009). Honor, Shame, and Social Status Revisited. Journal of Biblical Literature, 128(3), 591–611.
Culture of honor (Southern United States). (n.d.). Retrieved from http://en.wikipedia.org/wiki/Culture_of_honor_(Southern_United_States)
Dussere, E. (2001). The Debts of History: Southern Honor, Affirmative Action, and Faulkner’s Intruder in the Dust. Faulkner Journal, 17(1), 37–57.
Esmer, T. U. (n.d.). Honor in Ottoman and Contemporary Mediterranean Societies: Controversies, Continuities, and New Directions. conference announcement. Retrieved from http://www.h-net.org/announce/show.cgi?ID=196551
Family, Patronage, and Social Contests.pdf. (n.d.).
Hall, J. L. (1907). Half-hours in southern history. B. F. Johnson publishing co.
Harrell, L. A. (2009, December 4). It’s an honorable choice: Rebellions Against Southern Honor in William Styron’s The Confessions of Nat Turner. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/2614
Harris, J. W. (2002). Honor, Grace, and War (But Not Slavery?) in Southern Culture. Reviews in American History, 30(1), 1–7. doi:10.2307/30031707
Hellerman. (n.d.). Reconstructing Honor in Roman Philippi. Cambridge University Press.
Herzfeld, M. (1980). Honour and Shame: Problems in the Comparative Analysis of Moral Systems. Man, 15(2), 339–351. doi:10.2307/2801675
Honor, Shame, and Social Status.pdf. (n.d.).
honor-04-Antrocom_Honour and Shame in the Mediterranean_S.pdf. (n.d.).
Honors and Shame and the Unity of the Mediterranean. (n.d.). Retrieved from http://www.jstor.org/stable/3317790
Honour and shame (Anthropology). (n.d.). Retrieved from http://what-when-how.com/social-and-cultural-anthropology/honour-and-shame-anthropology/
Lever, A. (1986). Honour as a Red Herring. Critique of Anthropology, 6(3), 83–106. doi:10.1177/0308275X8600600305
Manly Honor Part V: Honor in the American South. (n.d.). The Art of Manliness. Retrieved August 15, 2013, from http://www.artofmanliness.com/2012/11/26/manly-honor-part-v-honor-in-the-american-south/
Moxnes, V. (1996). Honor and Shame. In R. L. Rohrbaugh (Ed.). The Social Sciences and New Testament Interpretation (pp. 19-40). Peabody, Mass.: Hendrickson. http://tinyurl.com/qdvc499
Murder in Jerba_ Honour, Shame and.pdf. (n.d.).
Osiek, C. (2008). Women, honor, and context in Mediterranean antiquity, 64(1), 323–337. doi:10.4102/hts.v64i1.2
Peoples and Cultures of the Mediterranean. (n.d.). Retrieved March 19, 2013, from http://www.academia.edu/2437701/Peoples_and_Cultures_of_the_Mediterranean
Rabichev, R. (n.d.). The Mediterranean concepts of honour and shame as seen in the depiction of the biblical women. Retrieved from http://prophetess.lstc.edu/~rklein/Doc6/renata.htm
Santos, N. F. (2008). Family, Patronage, and Social Contests: Narrative Reversals in the Gospel of Mark. S&J, (2).
Slavery and Southern Honor. (n.d.). StudyMode. Education. Retrieved from http://www.studymode.com/essays/Slavery-Southern-Honor-72644.html
Smith, A. (2004). Murder in Jerba: Honour, Shame and Hospitality among Maltese in Ottoman Tunisia. History and Anthropology Routledge, 15(2), 107–132.
Stewart,, Y. (n.d.). Mursi: A Study in Honor-Shame dynamics. CATEGORY ARCHIVES: HONOR-SHAME CULTURE. Retrieved from http://www.theaugeanstables.com/category/honor-shame-culture/
TO SHAME OR NOT TO SHAME_ SEXUALITY IN THE MEDITERRANEAN DIASPORA..pdf. (n.d.).
Weir, D. (n.d.). Honour and Shame. Islam Watch. Retrieved from http://www.islam-watch.org/Others/Honour-and-Shame-in-Islam.htm
Women, honor, and context in Mediterranean antiquity.pdf. (n.d.).
Wyatt-Brown, B. & Milbauer, Richard J. (2004). Honor, Shame, and Iraq in American Foreign Policy. In Note prepared for the Workshop on Humiliation and Violent Conflict, Columbia University, New York, November 18-19, 2004. Presented at the Workshop on Humiliation and Violent Conflict, Columbia University, New York,. Retrieved from http://www.humiliationstudies.org/documents/WyattBrownNY04meeting.pdf
Pricing: Free, paid
Concepts: Digital creation, programming and coding, game design
GameMaker: Studio is a robust game-making tool that appeals to both entry-level novices and game-development pros alike.
Concepts: Music theory, memorization, listening, part-whole relationships
The Orchestra is an interactive iPad app for exploring classical music, the orchestra and orchestral instruments.
Concepts: Design, geography, curiosity, imagination, making new creations
As its name suggests, WonderBox is an app that piques kids’ natural curiosity through video, drawing, taking pictures, messaging with family and friends and engaging in multistep challenges.
A.D.A.M. Interactive Anatomy Online
Pricing: Free to try, paid
Concepts: Anatomy, biology, memorization, part-whole relationships
A.D.A.M. Interactive Anatomy Online is a 3D visualization and curriculum-development tool all about the human body. Teachers can select and create assignments that allow students to manipulate 3D images of the human body.
Pricing: Free, paid
Concepts: Digital creation, programming and coding, game design
Construct 2 is a Web-based 2D game-creation tool for students and teachers who want to get into game design without the need to know programming languages.
Concepts: Fractions, part-whole relationships
Set in a tropical jungle full of brightly colored fruit and animated birds, Fruity Fractions teaches fractions concepts to kids in first through third grades.
1 2 Next