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This chapter analyzes the compliance of some category of Open Data in Politics with EU General Data Protection Regulation (GDPR) requirements. After clarifying the legal basis of this framework, with specific attention to the processing procedures that conform to the legitimate interests pursued by the data controller, including open data licenses or anonymization techniques, that can result in partial application of the GDPR, but there is no generic guarantee, and, as a consequence, an appropriate process of analysis and management of risks is required.

This paper aims to design a secure and seamless system that ensures quick sharing of health-care data to improve the privacy of sensitive health-care data, the efficiency of health-care infrastructure, effective treatment given to patients and encourage the development of new health-care technologies by researchers. These objectives are achieved through the proposed system, a “privacy-aware data tagging system using role-based access control for health-care data.”

Health-care data must be stored and shared in such a manner that the privacy of the patient is maintained. The method proposed, uses data tags to classify health-care data into various color codes which signify the sensitivity of data. It makes use of the ARX tool to anonymize raw health-care data and uses role-based access control as a means of ensuring only authenticated persons can access the data.

The system integrates the tagging and anonymizing of health-care data coupled with robust access control policies into one architecture. The paper discusses the proposed architecture, describes the algorithm used to tag health-care data, analyzes the metrics of the anonymized data against various attacks and devises a mathematical model for role-based access control.

The paper integrates three disparate topics – data tagging, anonymization and role-based access policies into one seamless architecture. Codifying health-care data into different tags based on International Classification of Diseases 10th Revision (ICD-10) codes and applying varying levels of anonymization for each data tag along with role-based access policies is unique to the system and also ensures the usability of data for research.

This study aims to report on an automatic deidentification system for labeling and obfuscating personally identifiable information (PII) in student-generated text.

The authors evaluate the performance of their deidentification system on two data sets of student-generated text. Each data set was human-annotated for PII. The authors evaluate using two approaches: per-token PII classification accuracy and a simulated reidentification attack design. In the reidentification attack, two reviewers attempted to recover student identities from the data after PII was obfuscated by the authors’ system. In both cases, results are reported in terms of recall and precision.

The authors’ deidentification system recalled 84% of student name tokens in their first data set (96% of full names). On the second data set, it achieved a recall of 74% for student name tokens (91% of full names) and 75% for all direct identifiers. After the second data set was obfuscated by the authors’ system, two reviewers attempted to recover the identities of students from the obfuscated data. They performed below chance, indicating that the obfuscated data presents a low identity disclosure risk.

The two data sets used in this study are not representative of all forms of student-generated text, so further work is needed to evaluate performance on more data.

This paper presents an open-source and automatic deidentification system appropriate for student-generated text with technical explanations and evaluations of performance.

Previous study on text deidentification has shown success in the medical domain. This paper develops on these approaches and applies them to text in the educational domain.

Anonymous recruitment seeks to limit managers’ reliance on stereotypes in employment decisions, thereby reducing discrimination. This paper aims to explore how managers interpret the information embedded in anonymised job applications and how they interpret the organisational priorities driving the adoption of anonymous recruitment.

Semi-structured interviews with 30 managers in two Australian public sector organisations were analysed.

The results showed that managers used implicit signals and cues to infer the gender identities of applicants in anonymised applications, reintroducing the possibility of bias. Managers perceived that anonymous recruitment sent positive external signals to prospective employees but were sceptical about its effectiveness.

The results showed that removing applicants’ names and identifying information from applications may not be sufficient to reduce bias. In organisations where managers are sympathetic to equity and diversity issues, use of anonymous recruitment may provoke resentment if managers perceive organisational distrust or inconsistent objectives. Limitations regarding the size and nature of the sample are acknowledged.

Organisations seeking to reduce gender discrimination in recruitment may consider adopting standardised application procedures or training managers to understand how stereotypes affect evaluations. Organisations should also assess managerial support for, and understanding of, anonymous recruitment prior to implementation.

The findings add to existing knowledge regarding the effects of implicit gender signals in managers’ assessments and the effectiveness of anonymous recruitment in reducing gender bias. It also contributes to signalling theory by examining how managers interpret the signals conveyed in organisational policies.

The chapter deliberates on research ethics and the unanticipated side effects that technological developments have brought in the past decades. It looks at data protection and privacy through the prism of ethics and focuses on the need for safeguarding the fundamental rights of the research participants in the new digital era. Acknowledging the benefits of data analytics for boosting scientific process, the chapter reflects on the main principles and specific research derogations, introduced by the EU General Data Protection Regulation. Further on, it discusses some of the most pressing ethics concerns, related to the use, reuse, and misuse of data; the distinction between publicly available and open data; ethics challenges in online recruitment of research participants; and the potential bias and representativeness problems of Big Data research. The chapter underscores that all challenges should be properly addressed at the outset of research design. Highlighting the power asymmetries between Big Data studies and individuals’ rights to data protection, human dignity, and respect for private and family life, the chapter argues that anonymization may be reasonable, yet not the ultimate ethics solution. It asserts that while anonymization techniques may protect individual data protection rights, the former may not be sufficient to prevent discrimination and stigmatization of entire groups of populations. Finally, the chapter suggests some approaches for ensuring ethics compliance in the digital era.

This paper examines how mathematics teacher educators (MTEs) learn to enact equitable mathematics instruction using technology through lesson study (LS).

A LS team with three MTEs conducted three iterations of LS on teaching the Pythagorean Theorem in an in-person, technology-mediated environment. Many forms of data were collected: Desmos activities, videos of research lessons (RLs), videos of MTE RL debriefings, artifacts of student learning in the Desmos Dashboard, and MTEs' written self-reflection. The authors investigate the teacher educators' learning through LS by analyzing the MTE debriefings of the RLs using Bannister’s (2015) framework for teacher learning in communities of practice.

The MTEs learned to enact equitable mathematics instruction using technology through addressing emerging issues related to intellectual authority and use of student thinking. Throughout the LS, the MTEs sought ways of promoting students' mathematical authority and using student thinking through features of the Desmos platform.

This study focuses on MTEs' learning without examining participating preservice teachers' learning. It demonstrates the benefits of LS for MTEs' professional learning.

This study showcases how a research-based Desmos activity is used and refined to promote MTE learning how to implement equitable mathematics instruction.

The study contributes to better understanding of how LS could be used to develop MTEs' professional learning. Moreover, the dual process of participation and reification was concretized through diagnostic and prognostic frames in the LS context, which enriches the concept of community of practice.

This paper aims to explore the issues, the role of research data management (RDM) as a mechanism for implementing open research data and the role and opportunities for records managers. The open data agenda is premised on making as much data as possible open and available. However, in the context of open research data there are methodological, ethical and practical issues with this premise.

Two collaborative research projects focusing on qualitative health data were conducted. “DATUM for Health” designed and delivered a tailored RDM skills training programme for postgraduate research students in health studies. “DATUM in Action” was an action research project between researchers from information sciences, health, mathematics and computing, looking at planning and implementing RDM.

Three key issues emerged about what research data is appropriate to make open/accessible for sharing and reuse: re-using qualitative data conflicts with some of the epistemological and methodological principles of qualitative research; there are ethical concerns about making data obtained from human participants open, which are not completely addressed by consent and anonymisation; many research projects are small scale and the costs of preparing and curating data for open access can outweigh its value. In exploring these issues, the authors advocate the need for effective appraisal skills and researcher-focused RDM with records managers playing a useful role.

The findings come from two small-scale qualitative projects in health studies. Further exploration of these issues is required.

Records managers have new crucial opportunities in the open data and RDM contexts, bringing their expertise and experience in managing a wider range of data and information. They can help realise the benefits of multiple perspectives (researcher, data manager, records manager and archivist) on open research data.

Researcher-focused RDM offers a mechanism for implementing open research data.

It raises complex issues around open research data not found in the records management literature, highlights the need for researcher-focussed RDM and research data appraisal skills and a not yet fully recognised role for records managers.

The purpose of this paper is to examine the extent to which HTTPS encryption and Google Analytics services have been implemented on academic library websites, and discuss the privacy implications of free services that introduce web tracking of users.

The home pages of 279 academic libraries were analyzed for the presence of HTTPS, Google Analytics services and privacy-protection features.

Results indicate that HTTPS implementation on library websites is not widespread, and many libraries continue to offer non-secured connections without an automatically enforced redirect to a secure connection. Furthermore, a large majority of library websites included in the study have implemented Google Analytics and/or Google Tag Manager, yet only very few connect securely to Google via HTTPS or have implemented Google Analytics IP anonymization.

Librarians are encouraged to increase awareness of this issue and take concerted and coherent action across five interrelated areas: implementing secure web protocols (HTTPS), user education, privacy policies, informed consent and risk/benefit analyses.

Third-party tracking of users is prevalent across the web, and yet few studies demonstrate its extent and consequences for academic library websites.

Research data management (RDM) is gaining a lot of momentum in the present day and rightly so. Research data are the core of any research study. The findings and conclusions of a study are entirely dependent on the research data. Traditional publishing did not focus on the presentation of data, along with the publications such as research monographs and especially journal articles, probably because of the difficulties involved in managing the research data sets. The current day technology, however, has helped in making this task easier. The purpose of this paper is to present a conceptual framework for managing research data at the institutional level.

This paper discusses the significance and advantages of sharing research data. In the spirit of open access to publications, freeing research data and making it available openly, with minimal restrictions, will help in not only furthering research and development but also avoiding duplication of efforts. The issues and challenges involved in RDM at the institutional level are discussed.

A conceptual framework for RDM at the institutional level is presented. A model for a National Repository of Open Research Data (NRORD) is also proposed, and the workflow of the functioning of NRORD is also presented.

The framework clearly presents the workflow of the data life-cycle in its various phases right from its creation, storage, organization and sharing. It also attempts to address crucial issues in RDM such as data privacy, data security, copyright and licensing. The framework may help the institutions in managing the research data life-cycle in a more efficient and effective manner.