Once the agreement is signed, the work is not finished. The researcher should develop a plan to ensure compliance with the terms of the agreement and implement measures to demonstrate compliance with the AEA requirements. Controlling computer controls, authorized user lists, site updates, upcoming publications, and verifying publications require coordination across the entire search team. Even if the data provider does not follow these things, the researcher should. Limited records can only contain the following identifiers: a refusal may also come from a major decision maker who feels that the risks of data sharing overburden the potential benefits. They may have concerns about unauthorized uses, violations, negative advertisements or data protection issues raised by their legislators or customers. Policymakers may fear that problems may be discovered in the data or may be afraid, as the results of the study will show. These concerns are in “Why data providers say no… and Why they Should Say Yes” (National Neighborhood Indicators Partnership (NNIP) 2018). The Ingabia matrix and the transparency checklist, described in the field of breaking communication tools for the integration of subjects and the public, can be useful in this area. Secure projects have project-based governance measures and sensitivities, with review and approval procedures involving colleges of institutional experts (IRBs) or ethics committees. Data providers must determine who are safe people through policy, screening and training, and may require membership in an educational institution or non-profit organization to demonstrate research competence (. B for example, scholarships received, resumes) and nationality or affiliation in the country concerned.
Secure settings and data include the researcher`s interface and work environment and can limit what an analyst can see, what an analyst can do, the analyst`s computer environment, and the analyst`s physical location (see also the physical security chapter). Safe data and expenses protect the privacy of those affected by reducing the risk of re-identification, both during access and after publication. This protection is provided by methods of limiting statistical disclosure, such as rounding, aggregation and oppression (obscuring clear observations in tables, graphs or maps) or by formal protection of mathematical data (see chapter on methods of preventing disclosure and differential privacy). Researchers may question whether the data needed for the project has been successfully shared by the data provider. In relevant cases, it may be useful to rely on a copy of the prior data use agreement provided by the Agency or by researchers who have accessed data in the past.25 When the researcher addresses an agency with a defined process for data sharing, they should check the process and forms and find out which office in the organization has approved the requirements. If you request an unusual extract or go to an agency that has never authorized access to the search results, researchers should identify a few examples of data exchange within their department or in other locations to verify the conditions in their agreements. On the other hand, data providers can ask researchers about the results obtained so far on quantitative research projects. This may include the use of administrative data or examples of their data management plans and approaches for the processing of confidential data. This information can help the data provider determine whether the researcher is able to protect the data, provide the results it offers, and whether they have been good partners in the past (or if they have been involved in data breaches).