Abstract
Classically, scientific research has been driven by hypotheses based on personal inspiration and intuition against the background of personal knowledge. In contrast, scientists have recently proposed that scientific research should basically be driven by data, meaning big data yielded by preliminary omic analyses in this context. A genuine hypothesis-driven strategy is usually exciting but occasionally ends up with negative conclusions, whereas a data-driven approach is less exciting and cost-consuming but produces significant outcomes in most cases. Here, we should be aware that a number of bioscientific resources provide a variety of big data free of charge. Therefore, one of the most effective research strategies is to construct a research question based on comprehensive knowledge derived not only from inside information, but also from the analysis of data available to everybody. However, a classical scientist without a sufficient bioinformatic background may hesitate in dealing with information supplied through the Internet. This chapter is aimed at CCN family researchers who do not possess specific bioinformatic knowledge and/or huge grants-in-aid, in order to assist them in developing their research by taking advantage of the scientific treasury open to the public.
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Acknowledgments
We gratefully thank Ms. Yoshiko Miyake for secretary assistance.
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This study was supported by JSPS KAKENHI Grant Numbers 21H03105 and 21K19603.
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Kubota, S. (2023). Utilizing Public Molecular Biological Databases for CCN Family Research. In: Takigawa, M. (eds) CCN Proteins. Methods in Molecular Biology, vol 2582. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2744-0_12
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DOI: https://doi.org/10.1007/978-1-0716-2744-0_12
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