Data security and responsible data handling

Attention must be paid to opportunities and challenges arising along the entire data lifecycle chain. This chain consists of the four elements of a) acquisition and generation of data, b) data storage and data handling, c) data analysis and knowledge accumulation, and d) use of data for economic purposes (services or product development). Each of these elements may concern all partners involved.

A responsible use of data means that research partners must respect key ethical principles both externally towards stakeholders (such as local communities, funding partners, the scientific community, and society), as well as internally among the research partners themselves. Such key principles include transparency – for instance, information about data origin and data ownership –, protection of data and prevention of data losses or autonomy.

The latter requires guaranteeing informational self-determination – i.e., the right of individuals to decide independently and freely about the data they wish to release and the use that is made thereof – as well as the respect of privacy, necessarily associated with the principle of informed consent. Informed consent represents a key concept that research partners must deal with and be trained and sensitised in from the outset. To protect individual rights, researchers must abide to the principle that not all potentially available data must actually be collected and they should raise awareness of the fact that consent must not necessarily be granted and may be withdrawn at any time.

Training and capacity building in data-related issues should be considered as an opportunity rather than an unpopular obligation. North-South research partnerships provide frequent mutual learning opportunities, and data collection, analysis, handling and storage, as well as data valorisation are important fields in which mutual benefit can be created.

Research partners should be encouraged by their institutions and funders to set up proper data governance structures and data sharing policies. Funding should be available not only for data infrastructure, but also for digital human resources, i.e. support staff being able to provide the required knowledge to the respective research partners.

Sharing of data also encompasses the sharing of infrastructures and capacities. (Big) data analysis is a resource-intensive activity, with significant requirements in terms of both hardware (storage capacity) and software infrastructures. In order to allow all responsible partners involved in a North-South research partnership to reap benefits of (big) data analysis, such infrastructure must be accessible to all partners, and the corresponding knowledge should be shared.

Particular attention must be paid in this regard to agreements with actors from the private sector. Data sharing with private companies is a common feature in many research contexts, but represents a sensitive issue that must be dealt with in clear agreements specifying which data are shared.

Attention still needs to be paid to the limits associated with new methods of collecting and analysing data. They may apply both to the Global South and the Global North. Such new methods – e.g., using social media posts, carrying out interviews and discussions with video-calls, using digital data collected by large companies or on individual smartphones (photos, GPS data, health data, etc.) – have become broadly available, but might not entirely replace actual field research that enables spontaneous interaction and observation in the study area. In addition, not all data can be meaningfully interpreted and analysed through computer software. Research partners must take these limits of data-driven methods into account while designing and implementing their joint research projects.