Research data are facts, observations or experiences on which an argument or theory is based | Cited in ANDS, 2017
Essentials 4 data support is an introductory course for data supporters, those who (want to) support researchers in storing, managing, archiving and sharing their research data. But what do we actually mean by research data? In this section you will find different definitions and ways of looking at research data.
What a researcher understands by 'research data' depends on the significance of these data in the research process. And that will vary from one scientific discipline to another. Research data exist in many formats, which can be read with just as many different types of software. In the slideshow below you can see a number of definitions of research data.
1. The way in which data is collected or obtained
Data can be collected or obtained in various ways, for example through experiments, simulations, observations, derived data or source research.
2. The forms that data take
Research data are often defined by the form in which they are recorded. Examples include text documents, spreadsheets, electronic lab journals, field notebooks and diaries, questionnaires, transcriptions and code books, audio and video tapes, photographs and films, artefacts, slides, database schemes, models, algorithms and scripts, workflows, protocols, metadata and other data files such as reports from literature research and e-mail archives.
3. De formats in which data are stored
A third way of thinking about data is the data format in which different data types (textual, numerical, multimedia, structured, software code, etc.) are stored. Statistical data can be stored, for example, as SPSS (* .sav) or STATA file formats, films such as * .mpg or * .avi, structured data such as * .xml or in a relational MySQL database and text files such as * .docx, * .pdf or * .rtf.
4. The size of the data files
The size of the data files is important, as is their complexity. Managing a relatively small and simple dataset poses different challenges than managing large, complex databases.
5. The phase in the research lifecycle
The different life stages of research data each have their own challenges for (supporting) research data management.
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ANDS (2017). ANDS Guides and Resources. What is research data. https://www.ands.org.au/guides/what-is-research-data (PDF https://www.ands.org.au/__data/assets/pdf_file/0006/731823/Whatis-research-data.pdf)
CESSDA (2017). Data Management Expert Guide. Research Data. https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide/1.-Plan/Research-data
OECD (2007). Principles and Guidelines for Access to Research Data from Public Funding, OECD Publishing, Paris. http://www.oecd.org/sti/inno/38500813.pdf
Queensland University of Technology. (2013). Management of Research data. http://www.mopp.qut.edu.au/D/D_02_08.jspRDM Rose (2015). RDM Rose Learning Materials. http://rdmrose.group.shef.ac.uk/?page_id=10#session-51-researchers-and-their-data
Utrecht University (2016). University policy framework for research data Utrecht University. https://www.uu.nl/sites/default/files/university_policy_framework_for_research_data_utrecht_university_-_january_2016.pdf
University of Southampton. (2016). Introducing Research Data. 4th Edition. https://eprints.soton.ac.uk/403440/1/introducing_research_data.pdf
Van Berchum, M. & Grootveld, M. (2017). Research data management. An overview of recent developments in the Netherlands. http://hdl.handle.net/20.500.11755/a9539a60-ecef-4e62-a998-0fda190b303b