Possibility and prevention of inappropriate data manipulation in Polar Data Journal. Stakeholders in the scientific field must always maintain transparency in the process of publishing research results in journals. Unfortunately, although research misconduct has stopped, certain forms of manipulation continue to appear in other forms. As new techniques of scientific publishing develop, science stakeholders need to examine the possibility of inappropriate activity in these new platforms. The National Institute of Polar Research in Japan launched a new data journal Polar Data Journal (PDJ) in 2017 to review the quality of data obtained in the polar region. To maintain transparency in this new data journal, we investigated the possibility of inappropriate data manipulation in peer reviews before the inception of this journal. We clarified inappropriate activity for the data in the peer review and considered preventive measures. We designed a specific workflow for PDJ. This included two measures: (i) the comparison of hash values in the review process and (ii) open peer review report publishing. Using the hash value comparison, we detected two instances of inappropriate data manipulation after the start of the journal. This research will help improve workflow in data journals and data repositories.

Unformatted, Certified Scientific Objects We present an approach for scientific data management systems to apply certificates to scientific objects, which are typically unformatted datasets, to facilitate analysis by climate scientists. Typically, for a program to process data, the program requires cleansed data in a form that supports automatic manipulation. Most systems require that data must adhere to a specific format to achieve that goal. The technique described in this paper takes the opposite approach; instead, any dataset may be imported and manipulated in the system. But upon initial import, however, only a subset of system functions may work with any given dataset. As the data is refined and transformed by system functions, more functions may become compatible. Certificates are associated with objects that pass constraint validation within the system to ensure that they conform to function requirements. The attached object constraints represent invariant properties of the object, which may be used by functions in the system as function preconditions. Furthermore, the functions defined in the system may associate certificates with the newly generated results. Certificates related to function results are effectively function postconditions, which in turn are used to associate certificates with the objects generated in the system. Additionally, attached object certificates reflect the refinement of data into a more pristine version. This paper describes the technique for modeling and enforcing the constraints for data scientists that have similar requirements.