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Opening the black box of retractions: Exploring the causes and consequences of data management errors
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Description: In the present study, we plan to collect background information about the research data management mistakes (RDM) of retracted papers by surveying their authors. We will use an RDM taxonomy to categorize the different mistakes and their causes. The results of the proposed study could provide valuable information on what types of these honest mistakes could lead to such a serious consequence as a retraction. We plan to suggest useful tools and practices to prevent these mistakes for those researchers who wish to minimize the risk of a retraction.