By IQ Editor | October 2, 2022
Welcome to the second issue of IASSIST Quarterly for the year 2022 - (IQ vol. 46(2) 2022) .
At last - a really real conference took place. I am of course referring to the IASSIST 2022 conference in June in Göteborg, Sweden. Many IASSISTers saw each other after a long time. That is typical for yearly conferences, but this was the first since 2019, after delays from Covid-19 in 2020 and 2021. Great work by the organizers and the participants! Also, thanks to the people who participated virtually in this hybrid conference. If you missed some presentations, then hopefully you will be able to find the missing information in issues of the IASSIST Quarterly.
Before sharing data, the data must be deposited! That is the focus of this issue’s first article on repository success. The second article finds that ‘others’ often only focus on quantitative data when addressing the issue of data deposited and registered at repositories and available from libraries. My presumption is that many of us are ‘others’ sometimes. The use of the data is the subject of the third article that presents successful design and implementation of workshops and internships that raise the number of social science students capable of data-driven research. Quantitative data research has the focus here, but I have confidence that in all areas qualitative data are also being recognized and used - including among big data.
The first article is Factors contributing to repository success in recruiting data deposits by Michele Hayslett and Matthew Jansen from the University of North Carolina at Chapel Hill. Repositories are often found at universities and similar institutions with a focus on deposit of publications and pre-prints. However, few repositories enable the preservation of datasets. The authors refer to some studies that outline researchers’ data management needs and how repositories can meet those needs, but few have assessed the success of various approaches, and the literature yields very few assessments of data repositories. This study examines infrastructure for accepting data into repositories and identifies factors influencing recruiting data deposits. In the IASSIST community the concepts of data sharing and open data are per se positive. Researchers often have intentions on becoming depositors, not least because data with a persistent identifier is a plus or demanded for publication. However, the authors present many references for obstacles experienced by researchers who intend to share their data. The study consists of a survey with an undetermined number of probable participants where a small number responded to the survey which naturally limits the conclusions. However, the authors were able to formulate several recommendations. I noticed especially ‘offering more advanced curation services’ like code checking and offering encryption, or the quality of the offering, and good relationships with researchers - that could be interpreted as establishing a relationship early in the research process.
Jessica Hagman, University of Illinois at Urbana-Champaign, and Hilary Bussell, Ohio State University, are the authors of the second article Going qual in: Towards methodologically inclusive data work in academic libraries. Qualitative research is seldom central to the support for research data in academic libraries. This article is a report on data literacy, qualitative research, and academic library infrastructure around qualitative research as experienced by practicing academic librarians. Specific support for qualitative research connected to the libraries is often limited to NVivo workshops that also include faculty. Participants for the survey were identified using the authors’ personal social media accounts, relevant email lists, and targeted outreach combined with snowball sampling. In-depth interviews were performed with 13 librarians in the United States. The results show how the interviewees define qualitative data and data literacy. The general understanding was that qualitative research is under-valued because others typically are understanding data as being solely quantitative. You can even find this expectation of others among qualitative researchers who believe that academic libraries do not support services for their qualitative data. Better information and building of relationships are needed.
In the third article the geography moves from North America to the United Kingdom. Vanessa Higgins and Jackie Carter from the University of Manchester present Developing data literacy: how data services and data fellowships are creating data skilled social researchers. In the abstract, the authors promise to describe two successful approaches to data literacy training within the social sciences. Not an offer we can easily refuse. The first, being delivered by the UK Data Service, is an extensive training programme of events and web-based materials that focuses on essential foundational data literacy skills. There is a need for quantitative data skills in the UK and this has long been recognized by influential bodies like government and business. The second is a Data Fellows programme - delivered by the University of Manchester Q-Step - that has been developed to help undergraduate social science students gain real-world experience by applying their classroom skills in the workplace. The focus is on data analysis and Data Fellows should become able to critically evaluate and use numerical data. The UK Data Service training contains events and on-demand web-based training, for instance a session on ‘Getting Started with Secondary Analysis’. Furthermore, it is mostly online, involves no financial cost, and has thousands of attendees; some positive feedback quotes are included in the article. Data Fellows work in organizations on data-driven research for a two-month paid internship. Two case studies of Data Fellows are presented in the article. Both programmes are led by the University of Manchester. The paper also discusses next steps in the global development of data literacy skills via the EmpoderaData project, which is trialling the Data Fellows programme in Latin America.
Submissions of papers for the IASSIST Quarterly are always very welcome. We welcome input from IASSIST conferences or other conferences and workshops, from local presentations, or papers especially written for the IQ. 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 https://www.iassistquarterly.com (our Open Journal System application). We permit authors to have ‘deep links’ into the IQ 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 IQ 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 IASSIST Quarterly website. Authors are very welcome to take a look at the instructions and layout: https://www.iassistquarterly.com/index.php/iassist/about/submissions.
Authors can also contact me directly via e-mail: kbr [ at ] sam.sdu.dk. Should you be interested in compiling a special issue for the IQ as guest editor(s) I will also be delighted to hear from you.
Karsten Boye Rasmussen - March 2022
In Volume 41 (cumulative 1-4 issue, 2017) of the IASSIST Quarterly, the following appears uncited on page 2 of the article by Vlaeminck and Podkrajac, “Journals in Economic Sciences: Paying Lip Service to Reproducible Research?”:
“While in other sciences replicability is regarded as a fundamental principle of research and a prerequisite for the publication of results, in economic sciences it is not treated as a top priority. In 2006…”
This should instead have been cited as follows:
“According to Höffler (2017), replicability does not take a high priority in economics. In his opinion, this is in sharp contrast to other sciences where replicability is “regarded as a fundamental principle of research and a prerequisite for the publication of results” (Höffler, 2017, p.1). Already in 2006…”
At the authors’ request we have posted a revised version of the entire article, to include this erratum at the beginning to note the changed version, the change in the text on page 2, and the new citation in the References list.