Scholarship & Research
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Item An analysis of use and performance data aggregated from 35 institutional repositories(2020-11) Arlitsch, Kenning; Wheeler, Jonathan; Pham, Minh Thi Ngoc; Parulian, Nikolaus NovaPurpose This study demonstrates that aggregated data from the Repository Analytics and Metrics Portal (RAMP) have significant potential to analyze visibility and use of institutional repositories (IR) as well as potential factors affecting their use, including repository size, platform, content, device and global location. The RAMP dataset is unique and public. Design/methodology/approach The webometrics methodology was followed to aggregate and analyze use and performance data from 35 institutional repositories in seven countries that were registered with the RAMP for a five-month period in 2019. The RAMP aggregates Google Search Console (GSC) data to show IR items that surfaced in search results from all Google properties. Findings The analyses demonstrate large performance variances across IR as well as low overall use. The findings also show that device use affects search behavior, that different content types such as electronic thesis and dissertation (ETD) may affect use and that searches originating in the Global South show much higher use of mobile devices than in the Global North. Research limitations/implications The RAMP relies on GSC as its sole data source, resulting in somewhat conservative overall numbers. However, the data are also expected to be as robot free as can be hoped. Originality/value This may be the first analysis of aggregate use and performance data derived from a global set of IR, using an openly published dataset. RAMP data offer significant research potential with regard to quantifying and characterizing variances in the discoverability and use of IR content.Item Using the Balanced Scorecard as a Framework for Strategic Planning and Organizational Change(2020) Johnson, Kris; Arlitsch, Kenning; Kyrillidou, Martha; Swedman, DavidStrategic planning processes offer an opportunity to connect foundational practices with a vision for future change. In this chapter, Kotter’s eight stages of change are mapped to the Montana State University Library’s strategic planning effort (September 2017– February 2018). Montana State University (MSU) is a land-grant public research university located in Bozeman, Montana. It is listed in the Carnegie Classification as a doctoral-granting university with “Higher Research Activity,” and with a head count of nearly 17,000 students in Fall 2018, it is by far the largest institution of higher education in Montana. The university’s annual budget is $201 million, and research and development expenditures exceeded $126 million in 2018. In addition to having its teaching and research missions, MSU is also one of 359 universities in the US awarded Carnegie’s community engagement classification.Item Data-Driven Improvement to Institutional Repository Discoverability and Use(Institute of Museum and Library Services, 2018-09) Arlitsch, Kenning; Kahanda, Indika; OBrien, Patrick; Shanks, Justin D.; Wheeler, JonathanThe Montana State University (MSU) Library, in partnership with the MSU School of Computing, the University of New Mexico Library and DuraSpace, seeks a $49,998 Planning Grant from the Institute of Museum and Library Services through its National Leadership Grant program under its National Digital Platform project category to develop a sustainability plan for the Repositories Analytics & Metrics Portal that will keep its dataset open and available to all researchers. The proposal also includes developing a preliminary institutional repositories (IR) reporting model; a search engine optimization (SEO) audit and remediation plan for IR; and exploring whether machine learning can improve the quality of IR content metadata.The project team expects work conducted in this planning grant to make the case for advanced research projects that will be high-impact and worthy of funding.Item Protecting privacy on the web: A study of HTTPS and Google Analytics(Emerald, 2018-09) OBrien, Patrick; Young, Scott W. H.; Arlitsch, Kenning; Benedict, KarlThe purpose of this paper is to examine the extent to which HTTPS encryption and Google Analytics services have been implemented on academic library websites and to discuss the privacy implications of free services that introduce web tracking of users. The home pages of 279 academic libraries were analyzed for the presence of HTTPS, Google Analytics services and privacy-protection features. Results indicate that HTTPS implementation on library websites is not widespread, and many libraries continue to offer non-secured connections without an automatically enforced redirect to a secure connection. Furthermore, a large majority of library websites included in the study have implemented Google Analytics and/or Google Tag Manager, yet only very few connect securely to Google via HTTPS or have implemented Google Analytics IP anonymization. Librarians are encouraged to increase awareness of this issue and take concerted and coherent action across five interrelated areas: implementing secure web protocols (HTTPS), user education, privacy policies, informed consent and risk/benefit analyses.Item Wikipedia and Wikidata Help Search Engines Understand Your Organization: Using Semantic Web Identity to Improve Recognition and Drive Traffic(ALA Editions, the American Library Association, 2018) Arlitsch, Kenning; Shanks, Justin D.Semantic Web Identity (SWI) is the condition in which search engines formally recognize entities and their relationships. Entities can be people, places, organizations, landmarks, or other “things” but in this chapter, entities will be defined as academic organizations: libraries, but also other academic units such as colleges, departments, centers, and institutes. An entity can be said to have achieved SWI if a formal display known as a Knowledge Graph Card (KC) appears for it in search engine results pages (SERP). The KC offers information about the entity directly in the search engine window, including such elements as address, phone number, hours of operation, description, link to the website, user reviews, etc. More importantly, the KC is an indicator that the search engine has achieved a machine-based comprehension of the existence and nature of the entity. With this understanding, the search engine can be more precise in its referrals and can hand off information about the entity to other semantic technologies. Far from being an end in itself, the display of an accurate and robust KC should simply be considered a positive indicator of SWI. Unfortunately, most academic organizations have not achieved SWI at this time. A search engine displays a KC when it has gathered enough verifiable facts about an entity. Search engines gather some facts organically while indexing website documents. But verifiable facts are more likely to be harvested from proprietary knowledge bases such as Google My Business, and from knowledge bases on the Linked Open Data (LOD) cloud, such as Wikipedia and Wikidata. Academic organizations have the best chance of controlling their SWI by proactively creating and curating records in these knowledge bases. This chapter will: (1) explain the significance of SWI; (2) describe a new library service developed at Montana State University that helps campus organizations implement SWI; and (3) demonstrate how SWI was successfully achieved in three case studies.Item Why So Many Repositories? Examining the Limitations and Possibilities of the Institutional Repositories Landscape(Taylor & Francis, 2018-03) Arlitsch, Kenning; Grant, CarlAcademic libraries fail to take advantage of the network effect because they manage too many digital repositories locally. While this argument applies to all manner of digital repositories, this article examines the fragmented environment of institutional repositories, in which effort and costs are duplicated, numerous software platforms and versions are managed simultaneously, metadata are applied inconsistently, users are served poorly, and libraries are unable to take advantage of collective data about content and users. In the meantime, commercial IR vendors and academic social networks have shown much greater success with cloud-based models. Collectively, the library profession has enough funding to create a national-level IR, but it lacks the willingness to abandon local control.Item RAMP - The Repository Analytics and Metrics Portal: A prototype Web service that accurately counts item downloads from institutional repositories(2016-11) OBrien, Patrick; Arlitsch, Kenning; Mixter, Jeff; Wheeler, Jonathan; Sterman, Leila B.Purpose – The purpose of this paper is to present data that begin to detail the deficiencies of log file analytics reporting methods that are commonly built into institutional repository (IR) platforms. The authors propose a new method for collecting and reporting IR item download metrics. This paper introduces a web service prototype that captures activity that current analytics methods are likely to either miss or over-report. Design/methodology/approach – Data were extracted from DSpace Solr logs of an IR and were cross-referenced with Google Analytics and Google Search Console data to directly compare Citable Content Downloads recorded by each method. Findings – This study provides evidence that log file analytics data appear to grossly over-report due to traffic from robots that are difficult to identify and screen. The study also introduces a proof-of-concept prototype that makes the research method easily accessible to IR managers who seek accurate counts of Citable Content Downloads. Research limitations/implications – The method described in this paper does not account for direct access to Citable Content Downloads that originate outside Google Search properties. Originality/value – This paper proposes that IR managers adopt a new reporting framework that classifies IR page views and download activity into three categories that communicate metrics about user activity related to the research process. It also proposes that IR managers rely on a hybrid of existing Google Services to improve reporting of Citable Content Downloads and offers a prototype web service where IR managers can test results for their repositories.Item Semantic Web Identity of Academic Libraries(Taylor & Francis, 2017-04-21) Arlitsch, KenningSemantic Web Identity (SWI) is proposed as the condition in which search engines recognize the existence and nature of entities. The display of a Knowledge Graph Card in Google search results is an indicator of SWI, as it demonstrates that Google has gathered verifiable facts about the entity. Such recognition is likely to improve the accuracy and relevancy of Google’s referrals to that entity. This article summarizes part of the research conducted for a recent doctoral dissertation, showing that SWI is poor for ARL libraries. The study hypothesizes that the failure to populate records in appropriate Linked Open Data and proprietary Semantic Web knowledge bases contributes to poor SWI.Item Semantic Web Identity of Academic Organizations: Search engine entity recognition and the sources that influence Knowledge Graph Cards in search results(Humboldt Universität zu Berlin, 2017) Arlitsch, KenningSemantic Web Identity (SWI) characterizes an entity that has been recognized as such by search engines. The display of a Knowledge Graph Card in Google search results for an academic organization is proposed as an indicator of SWI, as it demonstrates that Google has gathered enough verifiable facts to establish the organization as an entity. This recognition may in turn improve the accuracy and relevancy of its referrals to that organization. This dissertation presents findings from an in-depth survey of the 125 member libraries of the Association of Research Libraries (ARL). The findings show that these academic libraries are poorly represented in the structured data records that are a crucial underpinning of the Semantic Web and a significant factor in achieving SWI. Lack of SWI extends to other academic organizations, particularly those at the lower hierarchical levels of academic institutions, including colleges, departments, centers, and research institutes. A lack of SWI may affect other factors of interest to academic organizations, including ability to attract research funding, increase student enrollment, and improve institutional reputation and ranking. This study hypothesizes that the poor state of SWI is in part the result of a failure by these organizations to populate appropriate Linked Open Data (LOD) and proprietary Semantic Web knowledge bases. The situation represents an opportunity for academic libraries to develop skills and knowledge to establish and maintain their own SWI, and to offer SWI service to other academic organizations in their institutions. The research examines the current state of SWI for ARL libraries and some other academic organizations, and describes case studies that validate the effectiveness of proposed techniques to correct the situation. It also explains new services that are being developed at the Montana State University Library to address SWI needs on its campus, which could be adapted by other academic librariesItem Dataset supporting the dissertation “Semantic Web Identity of Academic Organizations: Search engine entity recognition and the sources that influence Knowledge Graph Cards in search results”(Montana State University ScholarWorks, 2016-11) Arlitsch, KenningThis dataset supports the dissertation “Semantic Web Identity in Academic Organizations: Search engine entity recognition and the sources that influence Knowledge Graph Cards in search results,” for a Ph.D. granted by the Institut für Bibliotheks- und Informationswissenschaft (IBI), Humboldt Universität zu Berlin. This dataset contains more than 1400 screen capture files of search results conducted in Google, Google My Business, Google+, Wikipedia, DBpedia, and Wikidata. The subjects of the searches were the 125 member organizations of the Association of Research Libraries (ARL). Searches were also conducted for the eleven colleges of Montana State University and for three libraries that served as case studies. Screenshots were captured in 2015 and 2016 to support the dissertation “Semantic Web Identity for Academic Organizations.” The dataset also includes the spreadsheet file (CSV format) that was used to record results of the searches, as well as the source files with statistical analysis equations used in R.