From Measure to Leisure: Extending Theory on Technology in the Workplace

The values present both in modern organizations and in research on these organizations reflect the organizational culture that has developed gradually over time. For example, research on organizations regularly focuses on the aspects of work that can be most easily quantified, such as the hierarchy within the organization or the physical arrangement of the office. Less defined aspects of organizations, such as the support for visibility and reflection, are more difficult to study and potentially less valued by the organizational culture. Similarly, the scientific management movement that spurred the Industrial Revolution is a very visible example of the high value that has been assigned to quantifiable efficiency within the workplace itself. Though the scientific management movement was soon contradicted by findings that showed the importance of psychological factors such as individual recognition, the ultimate response within organizations was to quantify additional aspects of the work environment, to varying degrees of success. The values that give efficiency and quantification this prominence in the workplace and in organizational research also impact the design and use of computing technology in the workplace. Computing has become a significant element in the modern organization, but the accepted role for computing technologies is often restricted to the automation of analytic tasks formerly accomplished by workers. In this way, computing technology becomes a surrogate for a human brain, attempting to model the way a specific type of work has traditionally been done. The mental processes involved in work, however, are not simply analytical. David Levy (2005) contends that the excess of information available for analysis in contemporary work environments cannot be meaningfully processed without allowing workers time for reflection and contemplation. This time may help workers draw connections that are still difficult for computers, or it may provide workers with opportunities for collaboration and diversification. The elevation of the importance of visibility and reflection within the workplace may have more success if undertaken in conjunction with the installation of technology designed for this purpose. Because current organizational studies typically omit activities with complex motivations, initial studies on the subject must gather data for the purpose of grounded (inductive) theory generation. The study described herein addresses traditional organizational research topics as well as the presence and use of non-task-based activities in the workplace. The study takes a broad look at a university department encompassing approximately 60 individuals, utilizing surveys and interviews to collect a variety of background information. As an additional intervention, a prototype technology devise with ludic intentions was introduced to the department, and its use provided further insight into the role of technology in the workplace. Ultimately, a series of testable hypotheses are proposed to guide further research into visibility and reflection in the workplace

Engaging Researchers in Data Dialogues: Designing Collaborative Programming to Promote Research Data Sharing

A range of regulatory pressures emanating from funding agencies and scholarly journals increasingly encourage researchers to engage in formal data sharing practices. As academic libraries continue to refine their role in supporting researchers in this data sharing space, one particular challenge has been finding new ways to meaningfully engage with campus researchers. Libraries help shape norms and encourage data sharing through education and training, and there has been significant growth in the services these institutions are able to provide and the ways in which library staff are able to collaborate and communicate with researchers. Evidence also suggests that within disciplines, normative pressures and expectations around professional conduct have a significant impact on data sharing behaviors (Kim and Adler 2015; Sigit Sayogo and Pardo 2013; Zenk-Moltgen et al. 2018). Duke University Libraries\u27 Research Data Management program has recently centered part of its outreach strategy on leveraging peer networks and social modeling to encourage and normalize robust data sharing practices among campus researchers. The program has hosted two panel discussions on issues related to data management—specifically, data sharing and research reproducibility. This paper reflects on some lessons learned from these outreach efforts and outlines next steps

Design and update of a classification system : the UCSD map of science

Global maps of science can be used as a reference system to chart career trajectories, the location of emerging research frontiers, or the expertise profiles of institutes or nations. This paper details data preparation, analysis, and layout performed when designing and subsequently updating the UCSD map of science and classification system. The original classification and map use 7.2 million papers and their references from Elsevier’s Scopus (about 15,000 source titles, 2001–2005) and Thomson Reuters’ Web of Science (WoS) Science, Social Science, Arts & Humanities Citation Indexes (about 9,000 source titles, 2001–2004)–about 16,000 unique source titles. The updated map and classification adds six years (2005–2010) of WoS data and three years (2006–2008) from Scopus to the existing category structure–increasing the number of source titles to about 25,000. To our knowledge, this is the first time that a widely used map of science was updated. A comparison of the original 5-year and the new 10-year maps and classification system show (i) an increase in the total number of journals that can be mapped by 9,409 journals (social sciences had a 80% increase, humanities a 119% increase, medical (32%) and natural science (74%)), (ii) a simplification of the map by assigning all but five highly interdisciplinary journals to exactly one discipline, (iii) a more even distribution of journals over the 554 subdisciplines and 13 disciplines when calculating the coefficient of variation, and (iv) a better reflection of journal clusters when compared with paper-level citation data. When evaluating the map with a listing of desirable features for maps of science, the updated map is shown to have higher mapping accuracy, easier understandability as fewer journals are multiply classified, and higher usability for the generation of data overlays, among others

Designing Public Visualizations of Library Data

As in many other organizations and fields of inquiry, the data generated by libraries becomes ever more complex, and the need to communicate trends both internally and externally has also been increasing. As visualizations become increasingly embedded in library assessment and outreach, it is crucial to take into consideration the audience of the visualizations and to design visualizations that are easy to interpret. This chapter will walk readers through the process of selecting a visualization based on a particular data representation need, designing that visualization to be optimized to its specific purpose, and combining visualizations into larger narratives to engage a public audience.<p>pre-print of:<br>Zoss, Angela M. “Designing Public Visualizations of Library Data.” In <i>Data Visualization: A Guide to Visual Storytelling for Librarians</i>, edited by Lauren Magnuson. Lanham, MD: Rowman & Littlefield Publishers, Inc., forthcoming.</p

Subject headings and beyond: Mapping the HathiTrust Digital Library content for wider use

SUBJECT HEADINGS and BEYOND: Mapping the HathiTrust Digital Library content for wider us

Data quality, transparency and reproducibility in large bibliographic datasets

Increasingly, large bibliographic databases are hosted by dedicated teams that commit to database quality, curation, and sharing, thereby providing excellent sources of data. Some databases, such as PubMed or HathiTrust Digital Library, offer APIs and describe the steps to retrieve or process their data. Others of comparable size and importance to bibliographic scholarship, such as the ACM digital library, still forbid data mining. The additional cleaning and expansion steps required to overcome barriers to data acquisition must be reproducible and incorporated into the curation pipeline, or the use of large bibliographic databases for analysis will remain costly, time-consuming, and inconsistent. In this presentation, we will describe our efforts to create reproducible workflows to generate datasets from three large bibliographic databases: PubMed, DBLP (as a proxy for the ACM digital library), and HathiTrust. We will compare these sources of bibliographic data and address the following: initial download and setup, gap analysis, supplemental sources for data retrieval and integration. By sharing our workflows and discussing both automated and manual steps of data enhancements, we hope to encourage researchers and data providers to think about sharing the responsibility of openness, transparency and reproducibility in re-using large bibliographic database