Activity one What does Open Data mean to you? (10 mins) Using post-its participants please note down three terms you relate to openness in data and research. Using the space at the back, please group your post-its according to themes

Openness, sharing and data Louise Bezuidenhout University of Notre Dame

What is open science?

Benefits of increasingly open data Speed up research Enhance quality of research, avoid duplication of studies Enhance accountability to public and safeguard returns on public investment in research Improve visibility of research teams Enhance local research and facilitate capacity-building – particularly by providing increased access to research resources

So, what does this mean for data? How does one operationalize openness and data sharing? What concerns need to be addressed: Data – scientist Inherent in data In data sharing

Relationship between Data and the scientist Ownership of data Credit and motivations to share Rewards for sharing - criteria for promotion Problematic implementation: the idea of Open Data is relatively new. Research ethos and practices, career structures and incentives have not yet adapted. Resources – time, financial, support

Information Inherent in data Sharing human data is associated with particular concerns Ownership Privacy Limits of informed consent Commercialization of personal data Stigmatization, vulnerability, persecution

Effective data sharing Data-intensive science requires ensemble of methods, skills, infrastructures, technologies and institutions Re-use Sustainability Size Openness

Effective data sharing (2) Re-use: Habitual data donation Adequate standards and guidelines for data formatting Well-organised databases Sharing of related materials Sustainability: In space – intelligent curation to avoid data dumps In time – continual re-evaluation of data infrastructures, dedicated finances and expertise towards supporting these infrastructures.

Effective data sharing (3) Size – balancing size and completeness (“big data”) with issues of sampling, metadata, accuracy and reliability. “More” is not always “better” – correlation does not trump causality Openness – openness means different things to different people, even within the same discipline. There are considerable ethical, social, legal, political and economic obstacles to effective data sharing Data sharing more difficult in highly competitive fields Data sharing not currently a recognized measure of research excellence

Really opening up data Further questions: Re-use: what counts as ”data”? How should data be shared? Who sets standards for sharing? How are data curated? What metadata are necessary for data to be effectively shared? Who designs the algorithms that mediate sharing and re-use of data? How can the act of sharing be balanced against the fallacy of reciprocity? Who should control access to data? Who should fund data sharing?

Activity two How can openness be fostered in research? (20 mins) Select one of the key themes identified in activity one. Working as a group identify activities that could promote openness in research and also how to address some of the issues identified in the lecture. Groups report back on their discussions.

What do you think? Do we all have a responsibility towards openness in research? Is openness in research something that works for you? Are you a contributor towards openness in research? If anyone would like to discuss further please find me at dinner or at the bar after dinner. Email: louise.m.bezuidenhout.1@nd.edu