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Intellectual honesty is an applied method of problem solving characterised by a nonpartisan and honest attitude, which can be demonstrated in a number of different ways: One's personal beliefs or politics do not interfere with the pursuit of truth;
Research integrity became a major debated topic in biological sciences after 1970, due to a combination of factors: the development of advanced data analysis methods, the growing commercial relevancy of fundamental research, [7] and the increased focus of federal funding agencies in the context of big science. [8]
Neo-colonial research or neo-colonial science, [36] [37] frequently described as helicopter research, [36] parachute science [38] [39] or research, [40] parasitic research, [41] [42] or safari study, [43] is when researchers from wealthier countries go to a developing country, collect information, travel back to their country, analyze the data ...
Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, including physical and social sciences, humanities, [2] and business ...
In scientific inquiry and academic research, data fabrication is the intentional misrepresentation of research results. As with other forms of scientific misconduct, it is the intent to deceive that marks fabrication as unethical, and thus different from scientists deceiving themselves. There are many ways data can be fabricated.
Research integrity or scientific integrity is an aspect of research ethics that deals with best practice or rules of professional practice of scientists.. First introduced in the 19th century by Charles Babbage, the concept of research integrity came to the fore in the late 1970s.
First, 'big data' is an important aspect of twenty-first century society, and the analysis of 'big data' allows for a deeper understanding of what is happening and for what reasons. [1] Big data is important to critical data studies because it is the type of data used within this field.
[2] [3] [4] However, researcher degrees of freedom can lead to data dredging and other questionable research practices where the different interpretations and analyses are taken for granted [5] [6] Their widespread use represents an inherent methodological limitation in scientific research, and contributes to an inflated rate of false-positive ...