This paper is a part of the presentation, "Automating the Authority Control Process," for the Innovative Users Group Conference in 2020. The presentation introduces ideas on how to handle authority control using a variety of tools, both paid and free. The paper details the specific procedures described in the presentation.

Notes to accompany a presentation given as part of the session "Music encoding use-cases in US libraries: aims, pedagogy, and workflows" at the International Association of Music Libraries, Archives and Documentation Centres (IAML) Congress held virtually on July 26-30, 2021.

Notes accompanying a presentation on the Crazy Water radio broadcasts in the UNT Music Library. This presentation was selected for the 2020 Best of Chapters presentations at the Music Library Association conference in Norfolk, VA held on February 26-March 1, 2020.

This workflow template was provided as a supporting document as part of the presentation, "E-Resource Cataloging: Challenges and Solutions." In the presentation, catalogers from a large academic library share how they make e-resources discoverable through their approach to record quality assessment, batch editing with regular expressions in MarcEdit, and record load tracking.

This list of guides was provided as a supporting document as part of the presentation, "E-Resource Cataloging: Challenges and Solutions." In the presentation, catalogers from a large academic library share how they make e-resources discoverable through their approach to record quality assessment, batch editing with regular expressions in MarcEdit, and record load tracking.

Script notes for the presentation, "E-Resource Cataloging: Challenges and Solutions." In the presentation, catalogers from a large academic library share how they make e-resources discoverable through their approach to record quality assessment, batch editing with regular expressions in MarcEdit, and record load tracking.

This tracking sheet was provided as a supporting document as part of the presentation, "E-Resource Cataloging: Challenges and Solutions." In the presentation, catalogers from a large academic library share how they make e-resources discoverable through their approach to record quality assessment, batch editing with regular expressions in MarcEdit, and record load tracking.

The University of North Texas (UNT) Libraries aim to create safe, welcoming, and respectful spaces for all members of the campus community. This resource presents guidelines created by the Library Council on Diversity and Inclusion (LCDI) for Inclusion, Diversity, Equity, and Accessibility (IDEA) when planning library-wide internal events and for external events and conferences hosted by the UNT Libraries.

This handout was presented along with a poster that presented the preliminary results from a survey of library employees on their experiences with toxic workplaces in libraries. This handout details the survey questions and the sources used to develop the survey. It was presented at the Electronic Resources & Libraries 2020 conference held in Austin, Texas.

This handout was presented along with a poster that presented the preliminary results from a survey of library employees on their experiences with toxic workplaces in libraries. This handout details signs to look for during interviews and suggested questions to ask to determine if their is a toxic workplace environment. It was presented at the Electronic Resources & Libraries 2020 conference held in Austin, Texas.

Notes to accompany a presentation on the life and career of Willis Conover for the Willis Conover Centennial Symposium held virtually on December 11, 2020.

Notes to accompany a presentation on the friendship and collaboration between Willis Conover and Duke Ellington for the 26th International Duke Ellington Study Group Conference. Due to the Covid pandemic, this conference was cancelled early in the morning before it was set to begin.

Notes accompanying a presentation on the contents and historical context of the Rhodes Baker Collection at the University of North Texas Music Library. It was presented at the 55th Annual Association for Recorded Sound Collections (ARSC) Conference held virtually May 12-15, 2021.

Notes accompanying a presentation on the interviews present in the John Gilliland Collection, and how they can inform rock music historiography. It was presented at the joint Texas Chapter of the Music Library Association and the Southwest Chapter of the American Musicology Society meeting held on September 24-26, 2020.

Grant narrative for the grant, "Connecting Communities with Libraries, Archives, and Historians Through Oral Histories." The University of North Texas (UNT) Department of Information Science, partnering with the UNT Oral History Program, will host a series of forums to identify best practices and strategies to respond to challenges around building, implementing, preserving, and accessing community oral history projects. The project team will convene librarians, archivists, oral history practitioners, public historians, community-based memory workers, and others working in oral history to identify good practices, share challenges and lessons learned, and prepare an actionable roadmap for building, implementing, preserving, and sustaining community oral history projects.

Grant narrative for the grant, "Students Data Literacy Needs in Community Colleges: Perspectives of Libraries, Students and Faculty." The University of North Texas will conduct an 18-month planning project to examine the current perspectives of community college librarians, faculty, and students regarding data literacy; identify the data literacy competencies needed for community college students; and develop data literacy action plans for community college libraries to assist community college librarians in assessing their capability and creating a road map to incorporate data literacy into their existing literacy programs. The findings of this project will identify the role and position of community college libraries in facilitating and enhancing the development of the data literacy competencies of students.

Precision medicine is an emerging approach for disease treatment and prevention that considers individual variability in genes, environment, and lifestyle for each person. Big data analytics (BDA) using cutting-edge technologies helps to design models that can diagnose, treat and predict diseases. In Ethiopia, healthcare service delivery faces many challenges specifically in relation to prescribing the right medicine to the right patient at the right time. Thus, patients face challenges ranging from staying on treatment plans longer, and then leaving treatment, and finally dying of complications. Therefore, the aim of this paper is to explore the trends, challenges, and opportunities of applying BDA in precision medicine globally and take lessons for Ethiopia through a systematic literature review of 19 peer reviewed articles from five databases. The findings indicated that cancer in general, epilepsy, and systemic diseases altogether are areas currently getting big attention. The challenges are attributed to the nature of health data, failure in collaboration for data sharing, ethical and legal issues, interoperability of systems, poor knowledge skills and culture, and poor infrastructure. Development of modern technologies, experimental technologies and methods, cloud computing, Internet of Things, social networks and Ethiopia’s government initiative to promote private technological firms could be an …

Trauma, and its associated effects, can be conceptualized as a period of information uncertainty. The natural psychological response to trauma is a period of post-traumatic stress. Trauma occurs when an existing knowledge base has been challenged. Any event that challenges important components of an individual’s assumptive world is said to be traumatic. This post-traumatic period is akin to many theories and concepts in information science including uncertainty reduction, Everyday Life Information Seeking, Sensemaking Theory, Making Meaning and Anomalous States of Knowledge. One possible outcome after the post- traumatic period is post-traumatic growth. Researchers agree post-traumatic growth primarily occurs across one or more of the following domains: personal strength, new possibilities, relating to others, appreciation of life and spiritual change. That is, people affected by trauma tend to grow when they find new or additional paths of information certainty.

Understanding trends is helpful to identify future behaviors in the field, and the roles of people, places, and institutions in setting those trends. Although traditional clustering strategies can group articles into topics, these techniques do not focus on topics over limited timescales; additionally, even when articles are grouped, the generated results are extensive and difficult to navigate. To address these concerns, we create an interactive dashboard that helps an expert in the field to better understand and quantify trends in their area of research. Trend detection is performed using the time-biased document clustering introduced in Behpour et al. (2021) study. The developed and freely available web application enables users to detect well defined trending topics in financial journals by experimenting with various levels of temporal bias - from detecting short-timescale trends to allowing those trends to spread over longer times. Experts can readily drill down into the identified topics to understand their meaning through keywords, example articles, and time range. Overall, the interactive dashboard will allow experts in the field to sift through the vast literature to identify the concepts, people, places, and institutions most critical to the field.

Developing, implementing, and managing metadata is crucial to successful knowledge management, and academic libraries have traditionally played a central role in these activities. The Arabian Gulf countries are underrepresented in the existing research into library metadata practices. This exploratory study used semi-structured interviews of metadata managers at 8 universities with the goal of developing understanding of the current state of metadata practices, including descriptive cataloging, identity management, and knowledge organization in academic libraries of three Arabian Gulf countries (Kuwait, Oman, and Qatar), as well as potential future developments to facilitate discovery of resources. Findings provide insights into this previously under-researched area and contribute to understanding of knowledge management and risks on a global scale.

Evaluating the impact of learning from climate data, in addition to bridge data, on the performance of concrete deck condition rating prediction is critical for identifying the right data needed to enhance bridge maintenance decision making. Few studies have considered such an evaluation and utilized a small size of samples that prevent revealing the knowledge hidden within the big size of data. Although, such evaluation over big data seems quite necessary, class imbalance problem makes it challenging. To alleviate such a problem, five states, including Alabama, Iowa, New York, Pennsylvania, and South Carolina, were selected as the case study. Not only are the states located in three different climatically consistent regions defined by the National Ocean and Atmospheric Administration (NOAA), but also their concrete deck conditions ratings are somewhat balanced. To conduct the evaluation, this research developed the bridge data set pertaining to 56,288 bridges across the afore-mentioned states through employing the GIS technology. The bridge data set contains bridge data derived from National Bridge Inventory (NBI), and climate data derived from Parameter-elevation Relationships on Independent Slopes Model (PRISM) climate maps and NOAA. Then, two machine learning algorithms, including random forest and GBM, were trained - with and without climate …

This workshop is presented by members of a University of North Texas research team. First, the team will overview their experience as members of the research team and share experience in areas such as trust formation, team roles, productivity, work-life balance, faculty-students interaction, peer and faculty mentorship, dissertation preparation, and job seeking. Second, the workshop will discuss and brainstorm how this format can be implemented for organizations both with faculty-student teams and with peer-directed teams. Finally, successes and challenges are openly discussed with audience.

Global warming is the term used to describe critical environmental issues and concerns. Social media such as Twitter provides a platform for people to share information, exchange ideas, and express their opinions about current and timely issues. This study utilized contextual analysis to analyze data collected from Twitter for the hashtag "global warming" during the period 2010 & 2011. Using sentiment analysis and topic modeling, the study aimed first at assessing people's perception towards global warming issues, and second study the impact of misinformation from the standpoint of social noise on people's perception of global warming during critical environmental events. The outcome of this study helps create a better understanding of the environmental issues discussed on social media. The sentiment analysis from the data analyzed so far shows that most of the tweets were based on Twitter users' personal opinions and not science. The topic modeling results suggest that Twitter users typically tweeted when a major environmental event occurred due to global warming. Topic modeling also aids in the identification of terms that is associated with social noise. The presence of social noise suggests that misinformation does exist and spreads faster.

Building predictive models for companies often relies on inference using historical data of companies in the same industry sector. However, companies are similar across a variety of dimensions that should be leveraged in relevant prediction problems. This is particularly true for large, complex organizations which may not be well defined by a single industry and have no clear peers. To enable prediction using company information across a variety of dimensions, we create an embedding of company stocks, Stock2Vec, which can be easily added to any prediction model that applies to companies with associated stock prices. We describe the process of creating this rich vector representation from stock price fluctuations and characterize what the dimensions represent. We then conduct comprehensive experiments to evaluate this embedding in applied machine learning problems in various business contexts. Our experiment results demonstrate that the four features in the Stock2Vec embedding can readily augment existing cross-company models and enhance cross-company predictions.