An Advanced Cyberinfrastructure Research and Education Facilitator (ACI-REF) works directly with researchers to advance the computing- and data-intensive aspects of their research, helping them to make effective use of Cyberinfrastructure (CI). The University of Oklahoma (OU) is leading a national "virtual residency" program to prepare ACI-REFs to provide CI facilitation to the diverse populations of Science, Technology, Engineering and Mathematics (STEM) researchers that they serve. Until recently, CI facilitators have had no education or training program; the Virtual Residency program addresses this national need by providing: (1) training, specifically (a) summer workshops and (b) third party training opportunity alerts; (2) a community of facilitators, enabled by (c) a biweekly conference call and (d) a mailing list.
Computing in science and engineering is now ubiquitous: digital technologies underpin, accelerate, and enable new, even transformational, research in all domains. Access to an array of integrated and well-supported high-end digital services is critical for the advancement of knowledge. Driven by community needs, XSEDE (the Extreme Science and Engineering Discovery Environment) substantially enhances the productivity of a growing community of scholars, researchers, and engineers through access to advanced digital services that support open research. An XSEDE strategic goal is to extend use of high-end digital services to new communities by preparing current and next generation of scholars, researchers, and engineers in the use of advanced digital technologies via training, education, and outreach. The mission of XSEDE’s Under-Represented Community Engagement (URCE) program is to raise awareness of the value of advanced digital research services and recruit users from new communities. In collaboration with XSEDE training and education programs, the URCE program works with the faculty and students that are non-traditional users of XSEDE resources and helps them in utilizing XSEDE's advanced digital research services and ecosystem. The focus of this work are individual researchers, research teams, faculty, staff, and students who have limited or no exposure. These are first time users. The institutions that the URCE program works with are small, minority, and resource limited; and the individuals are under-represented minorities and women. In order for first time users to be successful, they need training, practice, user support, extended collaborative support, and software tools and environments including gateways to allow them to rapidly join the community and become productive. Over the past four years, the URCE program has organized and facilitated training across the country at a variety of institutions ranging from small private Historically Black Colleges and Universities (HBCUs) such as Philander Smith to the University of Texas at El Paso which is a large public Hispanic Serving Institution and in collaboration with research intuitions that have significant diversity initiatives on their campuses. Every URCE training workshop has included extensive post workshop evaluation and the participants progress in engaging with XSEDE services is tracked so we can identify deepening engagement and persistence. This type of success has been achieved because training has evolved due to our reflection on the post workshop feedback and data. The practices that have been incorporated include providing the motivation for using these types of services, promoting the simplest access through gateways, careful tailoring of the content to the audience, and developing persistence after the event.
This study employs latent Dirichlet allocation (LDA) algorithms and comparative text mining to search 800,000 periodicals in JSTOR (Journal Storage) and HathiTrust from 1746 to 2014 identify the types of conversations that emerge about Black women's shared experience over time and the resulting knowledge that developed. We used MALLET to interrogate various genres of text (poetry, science, psychology, sociology, African American Studies, policy, etc.). We also used comparative text mining (CTM) to explore latent themes across collections written in different time periods by analyzing the common and expert models. We used data visualization techniques, such as tree maps, to identify spikes in certain topics during various historical contexts such as slavery, reconstruction, Jim Crow, etc. We identified a subset of our corpus (20,000) comprised of known Black or Black women authors and compared patterns of words in the subset against the larger 8000,000 corpus. Preliminary findings indicate that when we pulled 300,000 volumes, about 80,000 (~25%) do not have subject metadata. This appears to suggest that if a researcher searched for volumes about Black women, they may not have access to a significant amount of data on the topic. When volumes are not tagged properly, researchers would have to know that it exists when they do their searches. The recovery nature of this project involves identifying these untagged volumes and making the corpus publicly available to librarians and others with copyright considerations.
