https://iassistquarterly.com/index.php/iassist/issue/feed IASSIST Quarterly 2021-09-26T14:58:05-06:00 Karsten Boye Rasmussen editor.iassistquarterly@gmail.com Open Journal Systems <p class="p1">The <strong>IASSIST Quarterly</strong> at https://iassistquarterly.com is an international, peer-reviewed, indexed, open access quarterly publication of articles dealing with social science information and data services, including relevant societal, legal, and ethical issues.</p> <p class="p1">The <strong>IASSIST Quarterly</strong> represents an international cooperative effort on the part of individuals managing, operating, or using machine-readable data archives, data libraries, and data services. The <strong>IASSIST Quarterly </strong>reports on activities related to the production, acquisition, preservation, processing, distribution, and use of machine-readable data carried out by its members and others in the international social science community. </p> https://iassistquarterly.com/index.php/iassist/article/view/1011 Outside the R1: Equitable data management at the undergraduate level 2021-08-27T02:23:52-06:00 Elizabeth Blackwood lzbthblackwood@gmail.com <p>Universities within the California State University System are given the mandate to teach the students of the state, as is the case with many regional, public universities. This mandate places teaching first; however, research and scholarship are still required activities for reaching retention, tenure, and promotion, as well as important skills for students to practice. Data management instruction for both faculty and undergraduates is often omitted at these institutions, which fall outside of the R1 designation. This happens for a variety of reasons, including personnel and resource limitations. Such limitations disproportionately burden students from underrepresented populations, who are more heavily represented at these institutions. These students have pathways to graduate school and the digital economy, like their counterparts at R1s; thus, they are also in need of research data management skills. This paper describes and provides a scalable, low-resource model for data management instruction from the university library and integrated into a department’s capstone or final project curriculum. In the case study, students and their instructors participated in workshops and submitted data management plans as a requirement of their final project. The analysis will analyze the results of the project and focus on the broader implications of integrating research data management into undergraduate curriculum at public, regional universities. By working with faculty to integrate data management practices into their curricula, librarians reach both students and faculty members with best practices for research data management. This work also contributes to a more equitable and sustainable research landscape.</p> 2021-09-26T00:00:00-06:00 Copyright (c) 2021 Elizabeth Blackwood https://iassistquarterly.com/index.php/iassist/article/view/1010 Better data management, one nudge at a time 2021-06-21T09:20:26-06:00 Daria Orlowska daria.orlowska@wmich.edu Colleen Fallaw mfall3@illinois.edu Yali Feng yalifeng@illinois.edu Livia Garza liviag2@illinois.edu Ashley Hetrick ahetrick@illinois.edu Heidi Imker imker@illinois.edu Hoa Luong hluong2@illinois.edu <p>How do you help people improve their data management skills? For our team at the University of Illinois at Urbana-Champaign, we decided the answer was "one nudge at a time”.</p> <p>A study conducted by Wiley and Mischo (2016) found that Illinois researchers are aware of data services available but under-utilize them. Many researchers do not consider data management as a concern distinct from researching and producing scholarly work products. In 2017, the RDS piloted the Data Nudge – a monthly, opt-in email service to “nudge” Illinois researchers toward good data management practices, and towards utilizing data services on campus. The aim of the Data Nudge was to address the gap between knowing about a service and using it by highlighting best practices and campus resources.</p> <p>The topics covered in the Data Nudge center around data. Some topics are applicable to everyone, such as data back-up, documentation, and file naming conventions. Other topics are specific to Illinois, like storage options, events, and conferences.</p> <p>After four years, the Data Nudge has accumulated over 400 subscribers through word-of-mouth, marketing channels on campus and inclusion in subject liaisons' instructional workshops. It receives stable open rates averaging at 52% (compared to 19.44% average industry rate for Higher Education*) and many compliments from subscribers. We expect the Data Nudge to continue supplementing workshops and training as an effective means of communication to reach researchers on our campus. In the spirit of re-use, we are in the process of archiving the Data Nudge topics in a reusable format, readily adaptable by other institutions. </p> <p>Data Nudge link: https://go.illinois.edu/past_nudges</p> 2021-09-26T00:00:00-06:00 Copyright (c) 2021 Daria Orlowska, Colleen Fallaw, Yali Feng, Livia Garza, Ashley Hetrick, Heidi Imker, Hoa Luong https://iassistquarterly.com/index.php/iassist/article/view/989 DATABOOK : a standardised framework for dynamic documentation of algorithm design during Data Science projects 2021-07-22T03:27:26-06:00 Anna Nesvijevskaia anna.nesvijevskaia@gmail.com <p>This paper proposes a standard documentation framework for Data Science projects, called Databook. It is a result of five years of action-research on multiple projects in several sectors of activity in France, and of a confrontation of standard theoretical Data Science processes, such as CRISP_DM, with the reality of the field. As a vector for knowledge sharing and capitalisation, the Databook has been identified as one of the main facilitators of Human Data Mediation. Transformed into an operational prototype of simple and minimalist documentation, it has since been tested then on about a hundred Data Science projects, has proven its benefits for the internal and external efficiency of Data Science projects, and can be turned into a more ambitious standard framework for data patrimony valorisation and data quality governance.</p> 2021-09-26T00:00:00-06:00 Copyright (c) 2021 Anna Nesvijevskaia https://iassistquarterly.com/index.php/iassist/article/view/1018 Data management for students, researchers, and data science projects 2021-09-26T14:15:29-06:00 Karsten Boye Rasmussen kbr@sam.sdu.dk 2021-09-26T00:00:00-06:00 Copyright (c) 2021 Karsten Boye Rasmussen