IASSIST Quarterly 2022-12-01T11:47:43-07:00 Karsten Boye Rasmussen Open Journal Systems <p class="p1">The <strong>IASSIST Quarterly</strong> at 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> We talk data. We do data. 2022-12-01T07:50:00-07:00 Karsten Boye Rasmussen <p>Welcome to the third issue of <em>IASSIST Quarterly</em> for the year 2022 - <em>IQ</em> vol. 46(3).&nbsp;</p> <p>In Denmark we sometimes retrieve an old quote from a member of the Danish Parliament: 'If those are the facts, then I deny the facts'. We have laughed at that for more than a hundred years, but now fact denial is apparently the new normal in many places. And we are not amused. Data can become dangerous as facts can be fabricated. Therefore, a critical approach to data is fundamental to producing reliable information: facts. The articles in this issue are about teaching students good data behavior, and how researchers with great care and attention can carry out the task of fact production.</p> <p>The first article is about improvement in teaching data: 'Investigating teaching practices in quantitative and computational Social Sciences: a case study' by Rebecca Greer and Renata G. Curty. The authors are both at the University of California, Santa Barbara Library, where Rebecca Greer is director of Teaching &amp; Learning and Renata Curty is social science research facilitator. They are investigating data education and present some of the findings from a local report - part of a national project - into how instructors adapt curricula and pedagogy to advance undergraduates computational and statistical knowledge in the social sciences. The core goal of the instructors concerns 'data thinking' - the critical understanding and evaluation of data. Many students have a preconceived fear of mathematics that influences other areas. Personally, I feel that data thinking is essential to live and participation in society, and I believe that it should be achievable even with a background of math fear. However, for social science students I also expect they have acquired some level of 'data doing'. I agree with the authors that the necessary support for data is more often found in the areas of Science, Technology, Engineering and Mathematics than it is in Social Sciences. However, many IASSIST members successfully work to relate data to social science students. And the implicit relationship via data to STEM areas will furthermore often improve job success for social science students. The local study interviewed instructors and the article presents among other things the learning goals and the explicit skills contained in these goals. The study uses many quotations from the interviewees, including quotes on sharing among the instructors. This leads to how the instructors can be further supported and how the library can support them, including a partnership between the library's Research Data Services and Teaching &amp; Learning.</p> <p>With the second article we continue at a university. Now the focus shifts from teaching to research - the other main area of university work, and more specifically the data in research. The article 'Research data integrity: A cornerstone of rigorous and reproducible research' is by Patricia B. Condon, Julie F. Simpson and Maria E. Emanuel. All three are in positions at the University of New Hampshire, Durham, USA. The article starts with the foundation of the four Rs of research: rigor, reproducibility, replication, and reuse. The interest in data integrity came from a question at a graduate seminar on the difference between data integrity and data quality.&nbsp; When exploring the data quality component, they found that research data integrity is closely associated with data management as well as with data security. The aims of the article are several, but the first is to establish practical explanations of research data integrity and its components. Training and documentation are fundamental and form the surroundings in the proposed Research Data Integrity Model that also graphically presents the overlapping areas between the components: data quality, data management, and data security. I find this focus on the sharing between components a structurally clear approach, and with good outcome too. When juggling concepts that often are regarded as being more or less identical, it is clearly positive to make these relationships and distinctions. This positive structural approach is continued as the authors relate research data integrity to the research data lifecycle to produce an implementation schema. The last section is relating research data integrity to the four Rs.</p> <p>Submissions of papers for the <em>IASSIST Quarterly</em> are always very welcome. We welcome input from IASSIST conferences or other conferences and workshops, from local presentations or papers especially written for the <em>IQ</em>. When you are preparing such a presentation, give a thought to turning your one-time presentation into a lasting contribution. Doing that after the event also gives you the opportunity of improving your work after feedback. We encourage you to login or create an author profile at <a href=""></a> (our Open Journal System application). We permit authors to have 'deep links' into the <em>IQ</em> as well as deposition of the paper in your local repository. Chairing a conference session or workshop with the purpose of aggregating and integrating papers for a special issue <em>IQ</em> is also much appreciated as the information reaches many more people than the limited number of session participants and will be readily available on the <em>IASSIST Quarterly</em> website at <a href=""></a>. Authors are very welcome to take a look at the instructions and layout:</p> <p><a href=""></a></p> <p>Authors can also contact me directly via e-mail: <a href=""></a>. Should you be interested in compiling a special issue for the <em>IQ</em> as guest editor(s) I will also be delighted to hear from you.</p> <p>Karsten Boye Rasmussen - November 2022</p> 2022-12-01T00:00:00-07:00 Copyright (c) 2022 Karsten Boye Rasmussen Investigating teaching practices in quantitative and computational Social Sciences: A case study 2022-10-27T00:40:06-06:00 Rebecca Greer Renata Curty <p>Data education is gaining traction across disciplines and degree levels in higher education. Teaching data skills in the Social Sciences in today's data-driven world is vital for preparing the next generation of data literate and critical social scientists. The ability to identify, assess, analyze, and communicate well and responsibly with data is key for scholars and professionals to navigate dynamic and expansive information ecosystems. This paradigm shift demands instructors to adapt their curricula and pedagogy to advance students’ computational and statistical knowledge. This paper presents some of the findings from a local report of a larger national project which explored pedagogical techniques and instructional support needs for teaching undergraduates with quantitative data in the Social Sciences. Results revealed that the core learning goal of instructors is to develop students' critical thinking skills with data, including the conceptual understanding of the research methods employed in the field; the ability to critically evaluate research methodologies, findings, and data sets; and prowess using quantitative and computational tools and technologies. A recurring theme across interviews was students’ fear of math and technology and challenges these fears pose to data-related instruction. Instructors value participation in a community of practice and are eager for more institutional support to advance their computational skills. Based on these findings, we suggest avenues for academic libraries to further develop services, activities, and partnerships to aid data instruction efforts in the Social Sciences.</p> 2022-12-01T00:00:00-07:00 Copyright (c) 2022 Rebecca Greer, Renata Curty Research data integrity: A cornerstone of rigorous and reproducible research 2022-10-05T06:25:22-06:00 Patricia Condon Julie Simpson Maria Emanuel <p>Research data integrity provides a strong foundation for high quality research outcomes, and it is an essential part of the research data lifecycle due to its critical role in research rigor, reproducibility, replication, and data reuse (the four Rs). Understanding research data integrity is therefore imperative in collaborative interdisciplinary research and collaborative cross-sector research where different norms, procedures, and terminology regarding data exist.</p> <p>Research data integrity is closely associated with data management, data quality, and data security. Producing data that are reliable, trustworthy, valid, and secure throughout the research process requires purposefully planning for research data integrity and careful consideration of research data lifecycle actions like data acquisition, analysis, and preservation. In addition, purposeful planning enables researchers to conduct rigorous research and generate outcomes that are reproducible, replicable, and reusable. To advance this conversation, we developed two tools: a concept model that visually represents the relationship between data management, data quality, and data security as components of research data integrity, and a schema for implementing these components in practice. We contend that disentangling research data integrity and its components, developing a standardized way of describing their interplay, and intentionally addressing them in the research data lifecycle reduces threats to research data integrity.</p> <p>In this paper, we break down the complexity of research data integrity to make it more understandable and propose a practical process by which research data integrity can be achieved in a way that is useful for data producers, providers, users, and educators. We position our concept model and schema within the larger dialog around research integrity and data literacy and illuminate the role that research data integrity and its components (data management, data quality, and data security) play in the four Rs. In this paper, we present a concept model and schema for use as tools for instruction/training and practical implementation. Using these tools, we examine the role of research data integrity in rigorous and reproducible research and offer insight into ensuring research data integrity throughout the research process.</p> 2022-12-01T00:00:00-07:00 Copyright (c) 2022 Patricia Condon, Julie Simpson, Maria Emanuel