Discovery science
[1] Discovery-based methodologies are commonly contrasted with traditional scientific practice, the latter involving hypothesis formation before experimental data is closely examined.
Discovery science involves the process of inductive reasoning or using observations to make generalisations, and can be applied to a range of science-related fields, e.g., medicine, proteomics, hydrology, psychology, and psychiatry.
For example, in the early years of water resources research, the use of discovery science was demonstrated by seeking to elucidate phenomena that was, until that point, unexplained.
In this sense, discovery science is based on the attitude that ‘‘we must not allow our concepts of the earth, in so far as they transcend the reach of observation, to root themselves so deeply and so firmly in our minds that the process of uprooting them causes mental discomfort" (as stated by Davis in 1926).
[1] Ultimately, discovery science is currently enabling a transition to the era of personalised medicine for treating complex syndromes, e.g., sepsis and ARDS.
[6] The process of moving from cancer genomic discoveries to personalised medicine involves some major scientific, logistical and regulatory hurdles.
[6] This includes patient consent, sample acquisition, clinical annotation and study design, all of which can lead to data generation and computational analyses.
[6] Delivering personalised cancer medicine benefits from traditional, unconstrained and non-directed academic exploration, with the goal of directing scientific inquiry to convert genomic discovery to diagnostic and therapeutic targets.
Proteome analysis as a discovery science is applicable in biotechnology, e.g., it assists in 1) the discovery of biochemical pathways which can identify targets for therapies, 2) developing new processes for manufacturing biological materials, 3) monitoring manufacturing processes for the purpose of quality control, and 4) developing diagnostic tests and efficacious treatment strategies for clinical diseases.
These include: identifying spatial patterns of soil moisture and how they relate to topography; interrogating such data through the use of geostatistics; and discovering the importance of macropore flow and hydrological connectivity.
[10] Traditionally, the neuroimaging community within psychology has focused on task-based and hypothesis-driven approaches, however, a powerful tool for discovery science has emerged in the form of resting-state functional MRI (R-fMRI).
[10] Among the scientific community, recruiting participation and achieving collaboration from the broad population is essential for successfully implementing discovery-based science in the context of human brain function.
[10] Discovery-based methodologies are often viewed in contrast to traditional scientific practice, where hypotheses are formed before close examination of experimental data.
[4] Normally, discovery-based approaches for research are initially hypothesis-free, however, hypothesis testing can be elevated to a new level that effectively supports traditional hypothesis-driven studies.
[11] Researchers hope that combining integrative analyses of data from a range of different levels can result in new classification approaches to enable personalised interventions.
[9] However, combining broad and deep data and storing them in large-scale databases makes it practically impossible to rely on traditional statistical approaches.
