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Background There is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various ‘big data’ efforts. While these methods are necessary, they are insufficient to achieve the full personalized medicine promise. A rigorous, complementary ‘small data’ paradigm that can function both autonomously from and in collaboration with big data is also needed. By ‘small data’ we build on Estrin’s formulation and refer to the rigorous use of data by and for a specific N-of-1 unit (i.e., a single person, clinic, hospital, healthcare system, community, city, etc.) to facilitate improved individual-level description, prediction and, ultimately, control for that specific unit. Main body The purpose of this piece is to articulate why a small data paradigm is needed and is valuable in itself, and to provide initial directions for future work that can advance study designs and data analytic techniques for a small data approach to precision health. Scientifically, the central value of a small data approach is that it can uniquely manage complex, dynamic, multi-causal, idiosyncratically manifesting phenomena, such as chronic diseases, in comparison to big data. Beyond this, a small data approach better aligns the goals of science and practice, which can result in more rapid agile learning with less data. There is also, feasibly, a unique pathway towards transportable knowledge from a small data approach, which is complementary to a big data approach. Future work should (1) further refine appropriate methods for a small data approach; (2) advance strategies for better integrating a small data approach into real-world practices; and (3) advance ways of actively integrating the strengths and limitations from both small and big data approaches into a unified scientific knowledge base that is linked via a robust science of causality. Conclusion Small data is valuable in its own right. That said, small and big data paradigms can and should be combined via a foundational science of causality. With these approaches combined, the vision of precision health can be achieved.

It is irresponsible to support research but not data stewardship, says Barend Mons. It is irresponsible to support research but not data stewardship, says Barend Mons. Funders hold the stick: they should disburse no further funding without a data-stewardship plan.

The pandemic has shed harsh light on the need for a live minimum dataset

To promote effective sharing, we must create an enduring link between the people who generate data and its future uses, urge Heather H. Pierce and colleagues. To promote effective sharing, we must create an enduring link between the people who generate data and its future uses, urge Heather H. Pierce and colleagues. Fluorescence immunohistochemistry and confocal microscopy images of normal and cancerous human tissue samples

One of the best ways for a neuroscientist like me to keep up to date with what colleagues are working on is to attend conferences. But on recent trips I have noticed a problem. Too few researchers are consulting and using publicly available data - my own included. What is going on?

Letter to the Editor

Repositories like INA-Rxiv and IndiaRxiv boost regional science, but finding cash to run them is proving difficult. Repositories like INA-Rxiv and IndiaRxiv boost regional science, but finding cash to run them is proving difficult.

In 2020, the number of internet users surpassed 4.6 billion. Individuals who create and share digital data can leave a trail of information about their habits and preferences that collectively generate a digital footprint. Studies have shown that digital footprints can reveal important information regarding an individual's health status, ranging from diet and exercise to depression. Uses of digital applications have accelerated during the COVID-19 pandemic where public health organizations have utilized technology to reduce the burden of transmission, ultimately leading to policy discussions about digital health privacy. Though US consumers report feeling concerned about the way their personal data is used, they continue to use digital technologies. This study aimed to understand the extent to which consumers recognize possible health applications of their digital data and identify their most salient concerns around digital health privacy. We conducted semistructured interviews with a diverse national sample of US adults from November 2018 to January 2019. Participants were recruited from the Ipsos KnowledgePanel, a nationally representative panel. Participants were asked to reflect on their own use of digital technology, rate various sources of digital information, and consider several hypothetical scenarios with varying sources and health-related applications of personal digital information. The final cohort included a diverse national sample of 45 US consumers. Participants were generally unaware what consumer digital data might reveal about their health. They also revealed limited knowledge of current data collection and aggregation practices. When responding to specific scenarios with health-related applications of data, they had difficulty weighing the benefits and harms but expressed a desire for privacy protection. They saw benefits in using digital data to improve health, but wanted limits to health programs' use of consumer digital data. Current privacy restrictions on health-related data are premised on the notion that these data are derived only from medical encounters. Given that an increasing amount of health-related data is derived from digital footprints in consumer settings, our findings suggest the need for greater transparency of data collection and uses, and broader health privacy protections.

Repositories let researchers store, share and access life‑science images — and maybe even extract new findings. Repositories let researchers store, share and access life‑science images — and maybe even extract new findings.

Creating large genome/phenome collections can require consortium-scale resources. DNA.Land is a digital biobank that collects genetic data from individuals tested by consumer genomic companies using a fraction of the resources of traditional studies.