Teaching with Research Data by the Australian National Data Service (ANDS) provides nine case studies of courses that incorporate data usage in teaching. These case studies cover various subject areas. Two examples activities are:

History at the University of Melbourne: data creation and text analysis in the humanities
The course includes an introduction to big data. The creation of vast textural databases means that it is possible not just to find information but to analyse and interpret in ways not previously available for teaching or for the general public. Students are encouraged to use databases of text such as Google Books (using NGram) or the NLA’s Trove newspapers (using QueryPic) to generate graphs of word use or phrase use frequency to see whether their findings accord with other evidence or to otherwise add depth to their assignments. Students are required to open a Zotero account, and use it to collect and present information they have collected. Use of Omeka is currently under investigation: this is an open source web publishing platform to display online exhibitions of scholarly materials and has been one of those resources publicised by the University of Melbourne Research Bazaar.

Approaches to Research in Education at Flinders University: learning social science methodology using real data
David Curtis is Associate Professor, Educational Research at Flinders University where he also teaches a number of subjects to do with statistical methods and research techniques as part of masters degree courses in education. One subject in particular, Approaches to Research (EDUC9761) has a focus on using authentic data, most often PISA data from the OECD19 and LSAY data collected by ACER20. The primary object of the course is to introduce both qualitative and quantitative approaches to research, including the identification of problems, literature review, developing questions and hypotheses, collecting and analysing data and reporting and evaluating research. In this context, use of authentic data provides an opportunity to explore real issues within the field and to integrate the students’ studies with their own research interest. Using real world data means that students confront issues such as incompleteness, measures which are ‘not quite as good as you might wish they were’ and other problems usually not encountered with manufactured data sets.

THIS TEMPLATE WILL HELP YOU TO SHARE A READY-MADE ACTIVITY: HOW TO SUBMIT A LINK AND FILL IN THE METADATA FIELDS

THIS TEMPLATE WILL HELP YOU TO SHARE A READY-MADE ACTIVITY: HOW TO SUBMIT A LINK AND FILL IN THE METADATA FIELDS

This article offers a short guide to the steps scientists can take to ensure that their data and associated analyses continue to be of value and to be recognized. In just the past few years, hundreds of scholarly papers and reports have been written on questions of data sharing, data provenance, research reproducibility, licensing, attribution, privacy, and more—but our goal here is not to review that literature. Instead, we present a short guide intended for researchers who want to know why it is important to “care for and feed” data, with some practical advice on how to do that. The final section at the close of this work (Links to Useful Resources) offers links to the types of services referred to throughout the text.

Journal policy on research data and code availability is an important part of the ongoing shift toward publishing reproducible computational science. This article extends the literature by studying journal data sharing policies by year (for both 2011 and 2012) for a referent set of 170 journals. We make a further contribution by evaluating code sharing policies, supplemental materials policies, and open access status for these 170 journals for each of 2011 and 2012. We build a predictive model of open data and code policy adoption as a function of impact factor and publisher and find higher impact journals more likely to have open data and code policies and scientific societies more likely to have open data and code policies than commercial publishers. We also find open data policies tend to lead open code policies, and we find no relationship between open data and code policies and either supplemental material policies or open access journal status. Of the journals in this study, 38% had a data policy, 22% had a code policy, and 66% had a supplemental materials policy as of June 2012. This reflects a striking one year increase of 16% in the number of data policies, a 30% increase in code policies, and a 7% increase in the number of supplemental materials policies. We introduce a new dataset to the community that categorizes data and code sharing, supplemental materials, and open access policies in 2011 and 2012 for these 170 journals.

This case study is retrieved from the open book Open Data as Open Educational Resources. Case studies of emerging practice.

It explores why and how open data can be used as a material with which to produce engaging challenges for students as they are introduced to programming. Through describing the process of producing the assignments, and learner responses to them, we suggest that open data is a powerful material for designing learning activities because of its qualities of ease of access and authenticity.

In two successive years, forms of open data were used to construct coursework assignments for postgraduate students at the University of Nottingham, UK. The rationale for using open data was to shift the focus towards an outward-looking approach to coding with networks, files and data structures, and to engage students in constructing applications that had real-world relevance.

Python was chosen as the programming language.

The assignment in the first year utilised e-book text files from Project Gutenberg1, and required students to build an e-reader application. In the next year, car park status data, which was made available in a regularly updated form by the city council through their open data initiative2 was used as the basis for an assignment in which students developed a city-wide car park monitoring application.

Il volume rappresenta la tappa finale della prima stagione di implementazione del Progetto ABACUS (giugno 2019 - settembre 2020), sostenuta dal finanziamento pubblico garantito dalla Regione Siciliana e dalla Presidenza del Consiglio dei Ministri. In tal senso, la pubblicazione raccoglie sia una sezione di materiali di discussione critica sul percorso progettuale e sui primi esiti maturati, sia una ricca parte di contributi tematici offerti da referenti istituzionali, studiosi ed esperti, docenti accademici e ricercatori, professionisti e rappresentanti di organismi del Terzo settore Sono state così affrontate ed esaminate differenti tematiche e problematiche socio-culturali e socio-economiche, e prospettive e approcci metodologico-operativi tra loro affini e convergenti, che si sviluppano a cavallo delle politiche sociali, giovanili e culturali, della progettazione sociale e culturale, dell'innovazione sociale e della diversità culturale, in differenti contesti socio-territoriali siciliani e italiani, con una particolare attenzione per quelle iniziative che rappresentano casi paradigmatici in cui le istanze istituzionali, della ricerca, dell'educazione e della formazione si incontrano con le aspettative dei pubblici differenziati e, specialmente, delle giovani generazioni, anche sull'orizzonte della innovazione dell'occupazione giovanile.

The foundation of health and medical research is data. Data sharing facilitates the progress of research and strengthens science. Data sharing in research is widely discussed in the literature; however, there are seemingly no evidence-based incentives that promote data sharing. Methods A systematic review (registration: doi.org/10.17605/OSF.IO/6PZ5E) of the health and medical research literature was used to uncover any evidence-based incentives, with pre- and post-empirical data that examined data sharing rates. We were also interested in quantifying and classifying the number of opinion pieces on the importance of incentives, the number observational studies that analysed data sharing rates and practices, and strategies aimed at increasing data sharing rates. Results Only one incentive (using open data badges) has been tested in health and medical research that examined data sharing rates. The number of opinion pieces (n = 85) out-weighed the number of article-testing strategies (n = 76), and the number of observational studies exceeded them both (n = 106). Conclusions Given that data is the foundation of evidence-based health and medical research, it is paradoxical that there is only one evidence-based incentive to promote data sharing. More well-designed studies are needed in order to increase the currently low rates of data sharing.

Open data is a vital pillar of open science and a key enabler for reproducibility, data reuse, and novel discoveries. Enforcement of open-data policies, however, largely relies on manual efforts, which invariably lag behind the increasingly automated generation of biological data. To address this problem, we developed a general approach to automatically identify datasets overdue for public release by applying text mining to identify dataset references in published articles and parse query results from repositories to determine if the datasets remain private. We demonstrate the effectiveness of this approach on 2 popular National Center for Biotechnology Information (NCBI) repositories: Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA). Our Wide-Open system identified a large number of overdue datasets, which spurred administrators to respond directly by releasing 400 datasets in one week.

Growth of the open science movement has drawn significant attention to data sharing and availability across the scientific community. In this study, we tested the ability to recover data collected under a particular funder-imposed requirement of public availability. We assessed overall data recovery success, tested whether characteristics of the data or data creator were indicators of recovery success, and identified hurdles to data recovery. Overall the majority of data were not recovered (26% recovery of 315 data projects), a similar result to journal-driven efforts to recover data. Field of research was the most important indicator of recovery success, but neither home agency sector nor age of data were determinants of recovery. While we did not find a relationship between recovery of data and age of data, age did predict whether we could find contact information for the grantee. The main hurdles to data recovery included those associated with communication with the researcher; loss of contact with the data creator accounted for half (50%) of unrecoverable datasets, and unavailability of contact information accounted for 35% of unrecoverable datasets. Overall, our results suggest that funding agencies and journals face similar challenges to enforcement of data requirements. We advocate that funding agencies could improve the availability of the data they fund by dedicating more resources to enforcing compliance with data requirements, providing data-sharing tools and technical support to awardees, and administering stricter consequences for those who ignore data sharing preconditions.

Integrated Digitized Biocollections (iDigBio https://www.idigbio.org) is the National Resource for Advancing Digitization of Biodiversity Collections (ADBC) funded by the National Science Foundation. Through ADBC, data and images for millions of biological specimens are being made available in electronic format for the research community, government agencies, students, educators, and the general public.

The iDigBio specimen portal (https://www.idigbio.org/portal) provides access to millions of records about neontological and paleontological specimens curated at museums and other institutions in the US. Records might include information about the specimen, when, where and by whom it was collected, the institution providing the data, images and other media related to the specimen.

From January 2014, Psychological Science introduced new submission guidelines that encouraged the use of effect sizes, estimation, and meta-analysis (the “new statistics”), required extra detail of methods, and offered badges for use of open science practices. We investigated the use of these practices in empirical articles published by Psychological Science and, for comparison, by the Journal of Experimental Psychology: General, during the period of January 2013 to December 2015. The use of null hypothesis significance testing (NHST) was extremely high at all times and in both journals. In Psychological Science, the use of confidence intervals increased markedly overall, from 28% of articles in 2013 to 70% in 2015, as did the availability of open data (3 to 39%) and open materials (7 to 31%). The other journal showed smaller or much smaller changes. Our findings suggest that journal-specific submission guidelines may encourage desirable changes in authors’ practices.

Open access to research data has been described as a driver of innovation and a potential cure for the reproducibility crisis in many academic fields. Against this backdrop, policy makers are increasingly advocating for making research data and supporting material openly available online. Despite its potential to further scientific progress, widespread data sharing in small science is still an ideal practised in moderation. In this article, we explore the question of what drives open access to research data using a survey among 1564 mainly German researchers across all disciplines. We show that, regardless of their disciplinary background, researchers recognize the benefits of open access to research data for both their own research and scientific progress as a whole. Nonetheless, most researchers share their data only selectively. We show that individual reward considerations conflict with widespread data sharing. Based on our results, we present policy implications that are in line with both individual reward considerations and scientific progress.