Conference Agenda (All times are shown in Mountain Daylight Time (MDT) unless otherwise noted)

Member engagement can benefit professional associations, their members, and the profession. Rarely any studies adopt the "information practices" approach to identify the factors influencing professional associations' member engagement. The experiences, epiphanies, and the frequency of 11 information practices of six SIG-III officers and volunteers when planning and implementing 184 activities of eight initiatives from 2020 to 2023 helped this autoethnography study identify 99 sub-factors influencing the member engagement online. Information production, dissemination, recording, use, and discovery emerge as the top 5 information practices of officers and volunteers, in the same order, for influencing the SIG member engagement. Managing member attendance, Sharing knowledge, Managing member attention, Meeting member needs, and Building trust serve as the top 5 factors, in the same order, for affecting the member engagement. We propose a theoretical model and provide guidance to associations to enhance and sustain member engagement.

In the era of big and ubiquitous data, professionals and students alike are finding themselves needing to perform a number of textual analysis tasks. Historically, the general lack of statistical expertise and programming skills has stopped many with humanities or social sciences backgrounds from performing and fully benefiting from such analyses. Thus, we introduce Coconut Libtool (www.coconut-libtool.com/), an open-source, web-based application that utilizes state-of-the-art natural language processing (NLP) technologies. Coconut Libtool analyzes text data from customized files and bibliographic databases such as Web of Science, Scopus, and Lens. Users can verify which functions can be performed with the data they have. Coconut Libtool deploys multiple algorithmic NLP techniques at the backend, including topic modeling (LDA, Biterm, and BERTopic algorithms), network graph visualization, keyword lemmatization, and sunburst visualization. Coconut Libtool is the people-first web application designed to be used by professionals, researchers, and students in the information sciences, digital humanities, and computational social sciences domains to promote transparency, reproducibility, accessibility, reciprocity, and responsibility in research practices.

This paper introduces TORCHLITE, an innovative HathiTrust Research Center (HTRC) open analytical and computational framework designed to offer efficient, open, and approachable access to the HTRC Extracted Features (EF) dataset via a well-documented web-based API. This poster will summarize project goals and progress, and discuss community engagement, which has played a pivotal role in this project. A hackathon event held in spring 2024, TORCHLITE fostered collaboration among digital humanities and information science scholars to develop widgets and notebooks utilizing the EF API. Through the hackathon, participants explored the API's capabilities, leading to the creation of over a dozen analytical widgets and interactive programming notebooks (e.g., Jupyter).

Open government information systems offer great potential for advancing civic life and democracy, but they also reflect and reinforce the biases and systematic inequalities faced by members of socially marginalized groups. We present results from a critical data modeling project that uses a data quality framework to examine open datasets published by police departments in order to understand how data modeling choices shape the social impact of these datasets. Using an arrest record dataset published by the Los Angeles Police Department as a case study, we present we present results detailing the representation of racial data and the presence of children in the dataset. We argue that current data quality frameworks for open government data are insufficient for critical data studies due to an orientation around institutional and computational interests. Incorporating feminist data ethics into data quality analysis provides an approach to data quality that centers people and communities. We propose a definition for data quality of open government datasets based on an ethics of care that centers the needs of vulnerable populations and accountability of institutions toward their communities.