Some of the most frequent mistakes can be avoided by understanding data policies. Many researchers focus only on the information contained in the data and the results which can be derived from it.
This comes naturally as we want to focus on research. But the success of research also depends on avoiding data policy mistakes that could hinder research. In this post, we discuss how to avoid various data policy mistakes.
It's important to check for any rules that may apply to data collection. Data consent is most often required in the biomedical field, where ethical committees often must ensure that human subjects approve of their data being collected and used in research. There are also rules on how often it can be used.
There is a tendency to make data as open as possible to the academic community, and researchers can use that to their advantage; but it's still important to pay attention to terms.
For example: Let’s say ethical committees must review and pass information to ensure sensitive data of subjects has been protected or that different types of institutional requirements for authorization were applied before using the data.
When making sure the terms of compliance are documented, it's always a good idea to think of documenting any sensitive data to the lowest extent of detail possible. This can be done, for example, by protecting personal data with initials or codes instead of real subject names.
Following data sharing policies: your key to advancing research
Sharing data is one of the best ways researchers who publish can contribute to the development of their research disciplines. This not only makes your data available for others to use in their own research, but it also makes your own research more credible and transparent.
A data contributor is a researcher contributing a certain amount of data to the scientific community. Data contributors and the practice of sharing data are becoming ever more important . Many journals, institutions, and data repositories encourage authors to contribute their data. They all have their own data contribution policies, which should be read in detail.
- Common Data contributor policies require that:
- The data is complete and original.
- The data - and the context surrounding it - is as complete as possible.
- The data is accurate and credible.
Contact details for data contributors are always a good thing to add as well, just in case any additional or new information is asked of the contributor.
Data Users should always make sure to respect the contributors’ intellectual property rights, so make sure to cite any data used and carefully read the license associated with the contributed data, just in case any additional terms are defined by the contributor.
Finally, research journals should always define these licenses, making them clearly visible for users. They should also provide any useful information that facilitates the compliance process.
Data Integrity policies - keys to avoiding mistakes related to research quality
Data integrity policies are often commonly used by research institutions. Some of the most frequent policies relate to:
- Data quality
- Data completeness
- Data consistency
- Data Accuracy.
Data integrity policies in many institutions have to do with the overall quality of the data. Making sure the data is accurate is one of the most frequently used policies.
‘Accurate’ is a term that can be used in many different situations, so reading the data terms for research projects and institutions should generally have a section that explains what accuracy means.
Accuracy could mean instrument accuracy. It could mean measurement accuracy, observation accuracy, method accuracy, statistical model accuracy, analysis accuracy, software implementation accuracy, interpretation accuracy, simulation accuracy and more. Defining these is essential for research project protocols.
Finally, it is highly advisable for any researcher/author to read and comply with the requested terms and conditions of your journal or institution of interest to avoid potential setbacks in both the research and publication phases.
Some important documents generally found are:
- Best practice guidance on publishing excellence,
- Document and citation policies.
- Related to research data, principles of stewardship and management (eg. FAIR principles)
For further reading
The following are some data policies from different institutions and journals around the world.
- WHO - Data policies: https://www.who.int/about/policies/publishing/data-policy
- Elsevier journal policies associated with the research data: https://www.elsevier.com/about/policies/research-data
- Springer Nature journal policies associated with the research data: https://www.springernature.com/gp/authors/research-data
- Guiding principles for scientific data management and stewardship: https://www.go-fair.org/fair-principles/
- Lancet publishing excellence document with some aspects on data access and validity: https://www.thelancet.com/publishing-excellence
- Citation policies - UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/citation_policy.html
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