University of New South Wales, Australia
LinkedIn Profile: https://www.linkedin.com/in/ted-rohr-85745418/
Dr. Ted Rohr is Director of Research Ethics & Compliance Support at UNSW Sydney. In this role he is responsible for the university’s operations of research involving humans, animals, gene technology, radiation, controlled goods and technology, drones and quarantine. Ted has held senior roles in the fields of research integrity and ethics at La Trobe University and RMIT University in Melbourne since 2006. He is closely involved with regulators in the development of national Codes and guidelines and presents on the topics of research integrity and ethics at national and international conferences. He has extensive academic and research experience as lecturer, postgraduate supervisor and environmental consultant in both university and private sectors. Ted is actively engaged with the Australasian Research Management Society as convenor of the Ethics & Research Integrity Special Interest Group.
Good Practices for Data Management
Few aspects of research management can critically impact on the integrity of research outputs like the inappropriate management of research data. Indeed, ‘sloppy’ research practice has been identified in main stream newspapers as being responsible for the waste of large amounts of public funding as it appears that a significant amount of research cannot be reproduced. While some research data are manipulated and falsified, it appears that a much larger amount of data, and hence the associated research output, is compromised by the lack of proper data management or wrong interpretation. As technology advances rapidly, data sets increase in size as well thus creating challenges even for those who are meticulous in their data management practices.
To speak broadly about research data belies the diversity of media that are used to create data. Research data may be recorded on documents or spreadsheets or in laboratory note books, either in hard copy or electronically. Other data are created and stored in the form of digitally recorded images, videos or their scanned reproductions or transcriptions. More complex data include algorithms, scripts and software, and technologies such as medical imaging or spectrometry can create large data sets that are expensive to store. Classification of research data is an important step in developing a system to manage research data.
Research data management plans are built on the life cycle of research data, beginning with the preparation for data acquisition, to data entry and quality review, using the data to create metadata and analysis per se, through to the research output and final archiving. Depending on the type of research data, these management plans vary in complexity and may include additional requirements such as set retention periods and access restrictions. Increasingly, funding bodies demand that the data are made publicly available, and some journals now ask for the raw data to be supplied at the time of manuscript submission.
This presentation will cover the diversity of research data and review practical solutions to their management, with emphasis on the importance of good practice in research data management for the integrity of research. The presentation will also cover new opportunities arising through recent developments in technology that allow good data management practice to be implemented effectively.