Data hazards in synthetic biology.

Data science is playing an increasingly important role in the design and analysis of engineered biology. This has been fueled by the development of high-throughput methods like massively parallel reporter assays, data-rich microscopy techniques, computational protein structure prediction and design, and the development of whole-cell models able to generate huge volumes of data. Although the ability to apply data-centric analyses in these contexts is appealing and increasingly simple to do, it comes with potential risks.

Methodologies for Monitoring Mental Health on Twitter: Systematic Review.

Background: The use of social media data to predict mental health outcomes has the potential to allow for the continuous monitoring of mental health and well-being and provide timely information that can supplement traditional clinical assessments. However, it is crucial that the methodologies used to create models for this purpose are of high quality from both a mental health and machine learning perspective. Twitter has been a popular choice of social media because of the accessibility of its data, but access to big data sets is not a guarantee of robust results.

Epicosm-a framework for linking online social media in epidemiological cohorts.

Motivation: Social media represent an unrivalled opportunity for epidemiological cohorts to collect large amounts of high-resolution time course data on mental health. Equally, the high-quality data held by epidemiological cohorts could greatly benefit social media research as a source of ground truth for validating digital phenotyping algorithms. However, there is currently a lack of software for doing this in a secure and acceptable manner.

The mental health and well-being profile of young adults using social media.

The relationship between mental health and social media has received significant research and policy attention. However, there is little population representative data about who social media users are which limits understanding of confounding factors between mental health and social media. Here we profile users of Facebook, Twitter, Instagram, Snapchat and YouTube from the Avon Longitudinal Study of Parents and Children population cohort (N=4,083).

Data Ethics Club: Creating a collaborative space to discuss data ethics.

Awareness and management of ethical issues in data science are becoming crucial skills for data scientists. Discussion of contemporary issues in collaborative and interdisciplinary spaces is an engaging way to allow data-science work to be influenced by those with expertise in sociological fields and so improve the ability of data scientists to think critically about the ethics of their work. However, opportunities to do so are limited.

Participant acceptability of digital footprint data collection strategies: an exemplar approach to participant engagement and involvement in the ALSPAC birth cohort study.

Introduction: Digital footprint records - the tracks and traces amassed by individuals as a result of their interactions with the internet, digital devices and services - can provide ecologically valid data on individual behaviours. These could enhance longitudinal population study databanks; but few UK longitudinal studies are attempting this. When using novel sources of data, study managers must engage with participants in order to develop ethical data processing frameworks that facilitate data sharing whilst safeguarding participant interests.

Investigating the transparency of reporting in two-sample summary data Mendelian randomization studies using the MR-Base platform.

Background: Two-sample Mendelian randomization (2SMR) is an increasingly popular epidemiological method that uses genetic variants as instruments for making causal inferences. Clear reporting of methods employed in such studies is important for evaluating their underlying quality. However, the quality of methodological reporting of 2SMR studies is currently unclear.

Mapping population vulnerability and community support during COVID-19: a case study from Wales.

Background: Disasters such as the COVID-19 pandemic pose an overwhelming demand on resources that cannot always be met by official organisations. Limited resources and human response to crises can lead members of local communities to turn to one another to fulfil immediate needs. This spontaneous citizen-led response can be crucial to a community's ability to cope in a crisis.

Views on social media and its linkage to longitudinal data from two generations of a UK cohort study.

Cohort studies gather huge volumes of information about a range of phenotypes but new sources of information such as social media data are yet to be integrated. Participant's long-term engagement with cohort studies, as well as the potential for their social media data to be linked to other longitudinal data, may give participants a unique perspective on the acceptability of this growing research area. Two focus groups explored participant views towards the acceptability and best practice for the collection of social media data for research purposes.