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"Data Action will offer a model for reading, collecting, visualizing, and putting data to work on civic change. Using arresting graphics and influential case studies, as well as incorporating cultural and historical context, Data Action presents a helpful corrective to standard practice. Historically, data has been used and manipulated to make policy decisions without input from the general public. Data Action asks advocates of big data to rethink how they work by laying out a methodology for more transparent and accountable data analysis. The tools outlined in this book will help anyone, not just government officials, but data scientists, civic activists and hackers, as well as all citizens reaching for more effective civic debates and policy reforms, to shape our environment, economy, public health, and quality of life, with greater transparency and democratic participation"--

Data exploration and visualization are a highly accessible gateway activity to learning data science. In this talk, we discuss our experience with "Data Scavenger Hunts" using web apps to democratize data science and make it accessible to a wide variety of audiences. In order to acheive this, we have developed an R package called `burro` that can enable public datasets to be explored together via a sharable web app. In this talk, we talk about our experience with using data scavenger hunts to teach each other interesting things about data. In particular, we share our experiences with exploring the NHANES (National Health Nutirition Examination Survey) data and the insights we have taught each other. We show that this guided and communal data exploration leads to increased confidence and curiosity about data science in Biodata-Club, our learning community. `burro` apps can be deployed by anyone to start conversations about data.

Welcome to the third issue of IASSIST Quarterly for the year 2022 - IQ vol. 46(3).
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.
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 & 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 & Learning.
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. 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.
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 at https://www.iassistquarterly.com. 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@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 - November 2022

Distributing Access to Data, not Data

Reducing data volume in big data: Parallel processing based data filtering techniques
Professor Shikharesh Majumdar from Carleton University examines reducing data volume in big data, focusing on parallel processing based data filtering techniques. Volume, velocity, and variety are three well-known characteristics of big data. The large data volumes often introduce formidable challenges to processing such data in a timely and economical manner. Research on data filtering is underway under the leadership of Shikharesh Majumdar at the Real Time and Distributed Systems Research Centre at Carleton University. Users are often interested only in a subset of the raw data.

The 2011 Canadian Research Data Summit was held at the Ottawa Convention Centre on September 14. About the Summit On September 14-15, 2011, The 2011 Canadian Research Data Summit brings together 100-150 senior researchers, high level policy makers, university administrators, and members of the private sector. Together, participants will work on formulating a shared strategy for addressing the challenges and opportunities for maximizing the benefits of our collective investment in research data in Canada. The Summit will act as a catalyst for the development of a made-inCanada approach for maximizing the availability and use of research data. About the Research Data Strategy Working Group The Research Data Strategy Working Group is a collaborative effort launched in 2008 to address the challenges and issues surrounding the access and preservation of data arising from Canadian research. This multi-disciplinary group of universities, institutes, libraries, operators of research infrastructure, granting agencies, governments, and individual researchers are united through a shared recognition of the pressing need to deal with Canadian data stewardship issues. Together, they are focussing on the necessary actions, next steps and leadership roles that researchers and institutions can take to ensure Canada's research data are accessible and usable for current and future generations of researchers

Contents of the dataset on country/macro level includes nine variables considering school and education system characteristics as well as country characteristics:
number of school types/tracks for 9th grade/15-year-olds; age at first selection; preschool obligation; compulsory school years/education years (with pre-primary school); government expenditure on education, total (% of GDP); mean years of schooling; Human Development Index (HDI); Gender Inequality Index (GII); women's share of seats in parliament (in %)

The data set contains information on 82 countries and regions that participated in the PISA study. Most of the data are for the school year period of 2017/18, however older and newer data is used as well if other sources were not available. The documents used to create the dataset (including European Commisssion, OECD, education ministries) can be found in the reference list in the excel file and can be requested from the author.