How has Data Quality Changed in Science?

Data quality within science has improved over the years due to many factors. These have ultimately enabled data to become more reliable and valid. The science field has progressed towards a more open state that includes sharing data and allowing a higher level of transparency.

A few contributing factors to the improvement in the quality of data within science can include, but are not limited to, the peer-review process, ethical review boards, and the incorporation of artificial intelligence.

Scientific Data

Image Credit: Gorodenkoff/

Peer-Review Process

The sharing of scientific studies and an open scientific community, with the publishing of research in journals, has allowed scientists to hold other scientists accountable, which has elevated the field as a whole. Furthermore, peer-review panels and ethical review boards have also contributed to the increase in data quality, as researchers are more likely to demonstrate their ability to reproduce results through detailing techniques and provide reasoning and rationale for their study.

The peer-review process consists of an author’s research and work being analyzed and scrutinized by expert scientists in the same field. This can control the quality of work being performed and published as it can encourage authors to reach for a higher standard, ensuring there are no unwarranted claims and all interpretations can be supported with evidence.

This process has become a critical part of academic writing and can ensure all published work within scientific journals aim to answer or solve significant research questions. The peer-review process also ensures low-quality research does not reach the scientific community, thus preventing the work from impacting the standard set for all.

Ethical Review Boards for Improving Data Quality

The use of ethical review boards and ethical guidelines is a significant factor in improving scientific data and the field of science due to the protection of participants as a priority. Ensuring ethical conduct within research also enhances scientific honesty and prevents misconduct, including conflict of interest.

An infamous example of scientific research without ethical guidelines includes the research of Edward Jenner, a British physician who is perceived to have started the science of immunology during the early modern times through testing a smallpox vaccine on his son and neighborhood children.

The use of ethical guidelines and review boards can impact the quality of research before being published, influencing the ability of the research to be published at all. This has increased the standard of practice, as researchers are required to explain the requirement of human participants and animal models with as much transparency as possible.

This ensures all living creatures are being provided with rights, with research being conducted ensuring the necessity of participants and care during trials. Additionally, the 3R principle is also included in ensuring ethical use of animals with ensuring any possibility of replacing animal models with non-animal methods is considered, reducing the number of animals necessary for the study and that the study has been refined to minimize the stress on animals.

Artificial Intelligence and Data

Additionally, the growth of data quality within science can also be ascribed to supportive fields such as artificial intelligence (AI), which has aided in accelerating science, engineering, and medicine. This is due to AI providing researchers with the ability to analyze large datasets that can demonstrate the results of their study; an example of this can be clinical trials, which can have a large sample size, and so using AI, the large volume of results can be gathered and validated in support of a novel drug treatment.

AI can also identify faint patterns within large datasets that may not be apparent to human analysis; this can be helpful when a dataset is too large for trial-and-error analytic techniques when looking for a pattern that cannot be defined in a particularly noisy dataset.

Overall, analyzing large datasets using AI has been a tremendous advancement in science as it can save time and allow more accurate results due to the lack of human error. It can also aid in understanding predictive models, which can be the basis of a hypothesis and start a revolutionary research study.

AI and Data

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Future Outlook

The quality of data over the years within the field of science can be said to have increased tremendously, and with the advancement of technology and innovative notions, this quality can be further improved. This progression may include the advancement of supercomputers that can further the capacity of analysis of research and datasets with speed, accuracy, and reliability, reducing human error and increasing productivity.

Additionally, the use of 3D organoids that can mimic the architecture of tissues within the human body may mark the start of research that possibly moves away from animal use, with higher compatibility of tested drugs being translated to use in vivo.

The future of science may be full of intangible advancements, with continuous innovative ideas being produced by researchers that may enable the quality of research to be stronger and higher than ever before.


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  • Kelly J, Sadeghieh T, Adeli K. Peer Review in Scientific Publications: Benefits, Critiques, & A Survival Guide. EJIFCC. 2014;25(3):227-243. Published 2014 Oct 24. Available at:,draw%20accurate%20conclusions%20based%20on%20professionally%20executed%20experimentation.
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Last Updated: Jul 28, 2022

Marzia Khan

Written by

Marzia Khan

Marzia Khan is a lover of scientific research and innovation. She immerses herself in literature and novel therapeutics which she does through her position on the Royal Free Ethical Review Board. Marzia has a MSc in Nanotechnology and Regenerative Medicine as well as a BSc in Biomedical Sciences. She is currently working in the NHS and is engaging in a scientific innovation program.


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