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.
Presentation for the 2018 International Conference on Knowledge Management. This handout accompanies a workshop describing a faculty IT liaison program that uses the Bonded Design methodology to encourage interaction and communication between faculty members and information technology professionals.
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 opportunity to use BDA for precision medicine in Ethiopia.
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 data - and their prediction performances were compared. The results indicated that: (1) random forest outperforms GBM with an accuracy of 63.3%, and (2) the change in the prediction performance after further learning from climate data was marginal since the accuracy reached to 64.9%.
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.
Considerable research has been conducted on the topic of information overload using different approaches, from marketing and customer demand to information technologies and sciences, and even among mental health professionals. In business the critical question is how does information overload impact processes, operations, and profitability, and how can data visualization help to solve issues with data management and consumption in organizations. The ability to quickly and effectively process information and make decisions equates to organizational survival in a dynamic, knowledge-based economy where all segments of society are heavily affected by information technologies and systems and data management industries. The growing number of systems apparatuses challenges both individuals and organizations, resulting in reports of fatigue and experiences that compromise successful performance. The objective of this literature review is to discuss how data visualization tools help address information overload and optimize decision making and the business intelligence process in organizations. It concludes that data visualization, indeed, is critical in helping individuals capture, manage, organize, visualize, and present understandable data, but that decision making is affected by cognitive factors that interfere with data processing and interpretation in decision makers.
This abstract describes a poster about the Apiary Project. The Apiary Project, a collaboration of the Texas Center for Digital Knowledge at the University of North Texas and the Botanical Research Institute of Texas, is building a framework and web-based workflow for the extraction and parsing of herbarium specimen data. The workflow will support the transformation of written or printed specimen data into a high-quality machine-processable XML format. This poster describes an event model that informed the development of the Apiary XML Application Schema
This document is an extensive, but not comprehensive, bibliography of articles pertaining to social tagging and library catalogs between 2006-2012, mostly peer-reviewed sources, arranged chronologically.
This paper proposes a project on experience-based learning for information professionals and disciplinary researchers. Proposal written for the Institute of Museum and Library Services' (IMLS) Laura Bush 21st Century Librarian Program.
This document is a draft version of the Z-Interop 2 project search and record data requirements for Z39.50 interoperability testing using radioactive MARC records.
This document is part of a Government Information Quarterly Special Issue. The author serves as the editor of this issue focusing on the use of metadata as a strategy to improve access to and management of electronic government information. Contributions by writers address federal and state metadata activities and issues.
This document discusses decomposing MARC 21 records for analysis. To prepare the test dataset of the 1% sample of MARC 21 records from the WorldCat database for use in the Z39.50 Interoperability Testbed, the authors need to be able to efficiently analyze the records to determine relevant records to be returned for a set of test searches. The first step in that analysis is to determine the occurrence of test search terms in specific records. This document describes the general approach for this analysis and identifies specifications for the analysis.
This document provides guidelines for indexing MARC 21 records to support a set of searches using Z39.50. The Z39.50 Interoperability Testbed Project (Z-Interop) uses these guidelines to index the 400,000 MARC 21 records that comprise the Z-Interop reference implementation of the Z39.50 server and online catalog.
This document discusses a Z39.50 Interoperability Testing framework. In a first phase of the Z39.50 Interoperability Testbed, a large dataset of MARC records was used. In this work, the authors are exploring how a set of special, diagnostic MARC records can be developed and used to identify interoperability problems between a Z39.50 client and a Z39.50 server providing access to a database of bibliographic records supporting the search and retrieval functions of an online library catalog. The authors refer to these special, diagnostic records as radioactive MARC records. It discusses the various components and identifies the tasks related to developing and implementing the components.
This document describes how the authors can extend a relational database of MARC documentation to store the appropriate information that will support the automatic generation of the special, diagnostic MARC records the authors will call radioactive MARC (RadMARC) records. The information contained in the database will also support the generation of the Z queries used in the interoperability testing.
In this document, the authors present some aspects of data normalization of the decomposed records to improve the results of analysis. The data normalization processes use pattern-matching techniques to eliminate and/or generalize anomalous characters and terms. Since the unit of analysis in preparing the test dataset of 400,000 MARC 21 records is a "word," there was a need for data normalization to provide reliability in the subsequent analysis.
This document provides an overview and the details of policies and procedures of the Z39.50 Interoperability Testbed Project (referred to as Z-Interop). Specifically, the document lays out the responsibilities and obligations of the Z-Interop testbed and the organizations that participate in interoperability testing. For purposes of this document, Z-Interop staff refers to all members of the Z39.50 Interoperability Testbed Project. Z-Interop participant refers to an individual or organization who tests its Z39.50 client or Z39.50 server through the Z39.50 Interoperability Testbed.
This document describes the procedures that participants in the Z39.50 Interoperability Testbed (Z-Interop Participant) are to use when testing Z39.50 client (Z-client) applications. The testing of a Z-Interop reference implementation Z39.50 server. Specifically, the attribute combination and other query components (e.g., Boolean operators) are reviewed, and a report of the results will be prepared for each Z-Interop participant.
This document provides the preliminary specifications for the different RadMARC records to be created for use in the Z-Interop2 interoperability testbed. Experience with these records may result in revisions to the specifications.
This document describes the data analysis procedures developed to create the Aggregate and Candidate Record Groups using SQL statements. This is the preliminary version of these procedures tested and validated on a sample of decomposed MARC records. (For a description of how the MARC records were decomposed see the Z-Interop document, Decomposing MARC 21 Records for Analysis. A subsequent version may be necessary as the authors move to the procedures for the entire file of decomposed records.
This dialog allows you to filter your current search.
Each of the Collections listed note their name and the number of records that will be limited down to if you choose that option.
This dialog allows you to filter your current search.
Each of the Serial/Series Titles listed note their name and the number of records that will be limited down to if you choose that option.
The list can be sorted by name or the count.
This dialog allows you to filter your current search.
Each of the World Regions listed note their name and the number of records that will be limited down to if you choose that option.
This dialog allows you to filter your current search.
Each of the Countries listed note their name and the number of records that will be limited down to if you choose that option.
This dialog allows you to filter your current search.
Each of the U.S. States listed note their name and the number of records that will be limited down to if you choose that option.
This dialog allows you to filter your current search.
Each of the Decades listed note their name and the number of records that will be limited down to if you choose that option.
This dialog allows you to filter your current search.
Each of the Years listed note their name and the number of records that will be limited down to if you choose that option.
The list can be sorted by name or the count.
This dialog allows you to filter your current search.
Each of the Months listed note their name and the number of records that will be limited down to if you choose that option.
The list can be sorted by name or the count.
This dialog allows you to filter your current search.
Each of the Days listed note their name and the number of records that will be limited down to if you choose that option.
The list can be sorted by name or the count.
This dialog allows you to filter your current search.
Each of the Department listed note their name and the number of records that will be limited down to if you choose that option.