According to Wikipedia (https://en.wikipedia.org/wiki/Raw_data), raw data is considered a relative term, because even once raw data have been “cleaned” and processed by one team of researchers, another team may consider these processed data to be “raw data” for another stage of research. An example I can think of is the authors of individual experimental research and meta-analysis. For experimental researchers, the “raw data” is all the variable data they collected from subjects, and for the authors of meta-analysis. For experimental researchers, the “raw data” is all the variable data they collected from subjects, and for the authors of meta-analysis, the “raw data” is all the processed data provided by the experimental researchers. The concept of “Raw data” is useful while misleading. On the one hand, the concept emphasizes that the data are raw and unprocessed for the data user, and that future processing is based on raw data. This concept establishes that subsequent data interpretation and visualization are eventually based on this version of data. In addition, the concept of “raw data” is misleading. In addition to the relative nature of “raw”, another major problem is the assumptions that exist in data collection and management. As mentioned in the article Why Data is Never Raw (https://www.thenewatlantis.com/publications/why-data-is-never-raw): “as we’ve seen, even the initial collection of data already involves intentions, assumptions, and choices that amount to a kind of pre-processing”. The purpose of collecting and managing data is to make interpretations and inferences, which inherently determines the methodology of data collection, including what is measured, how it is measured, how it is collected, where it is collected from, and how it is recorded. With these limitations, the inferences we can make are inherently limited, and there is the problem of not fully reflecting on reality. This problem leads to a contradiction in the objectivity and absoluteness implied by the concept of “raw data”.