Formalizing Privacy Laws for License Generation and Data Repository Decision Automation

Authors : Micah Altman, Stephen Chong, Alexandra Wood

In this paper, we summarize work-in-progress on expert system support to automate some data deposit and release decisions within a data repository, and to generate custom license agreements for those data transfers.

Our approach formalizes via a logic programming language the privacy-relevant aspects of laws, regulations, and best practices, supported by legal analysis documented in legal memoranda.

This formalization enables automated reasoning about the conditions under which a repository can transfer data, through interrogation of users, and the application of formal rules to the facts obtained from users.

The proposed system takes the specific conditions for a given data release and produces a custom data use agreement that accurately captures the relevant restrictions on data use.

This enables appropriate decisions and accurate licenses, while removing the bottleneck of lawyer effort per data transfer.

The operation of the system aims to be transparent, in the sense that administrators, lawyers, institutional review boards, and other interested parties can evaluate the legal reasoning and interpretation embodied in the formalization, and the specific rationale for a decision to accept or release a particular dataset.


Evaluating and Promoting Open Data Practices in Open Access Journals

Authors : Eleni Castro, Mercè Crosas, Alex Garnett, Kasey Sheridan, Micah Altman

In the last decade there has been a dramatic increase in attention from the scholarly communications and research community to open access (OA) and open data practices.

These are potentially related, because journal publication policies and practices both signal disciplinary norms, and provide direct incentives for data sharing and citation. However, there is little research evaluating the data policies of OA journals.

In this study, we analyze the state of data policies in open access journals, by employing random sampling of the Directory of Open Access Journals (DOAJ) and Open Journal Systems (OJS) journal directories, and applying a coding framework that integrates both previous studies and emerging taxonomies of data sharing and citation.

This study, for the first time, reveals both the low prevalence of data sharing policies and practices in OA journals, which differs from the previous studies of commercial journals’ in specific disciplines.

URL : Evaluating and Promoting Open Data Practices in Open Access Journals