High-quality data are critical to the entire scientific enterprise, yet the complexity and effort involved in data curation are vastly under-appreciated. This is especially true for large observational, clinical studies because of the amount of multimodal data that is captured and the opportunity for addressing numerous research questions through analysis, either alone or in combination with other data sets. However, a lack of details concerning data curation methods can result in unresolved questions about the robustness of the data, its utility for addressing specific research questions or hypotheses and how to interpret the results. We aimed to develop a framework for the design, documentation and reporting of data curation methods in order to advance the scientific rigour, reproducibility and analysis of the data.

Forty-six experts participated in a modified Delphi process to reach consensus on indicators of data curation that could be used in the design and reporting of studies.

We identified 46 indicators that are applicable to the design, training/testing, run time and post-collection phases of studies.

The Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) Guidelines are the first comprehensive set of data quality indicators for large observational studies. They were developed around the needs of neuroscience projects, but we believe they are relevant and generalisable, in whole or in part, to other fields of health research, and also to smaller observational studies and preclinical research. The DAQCORD Guidelines provide a framework for achieving high-quality data; a cornerstone of health research.

Biomedical researchers need skills in innovation and entrepreneurship (I&E) to efficiently translate scientific discoveries into products and services to be used to improve health.

In 2016, the European Union identified and published 15 entrepreneurial competencies (EntreComp) for the general population. To validate the appropriateness of these competencies for I&E training for biomedical researchers and to identify program content, we conducted six modified Delphi panels of 45 experts (6–9 per panel). Participating experts had diverse experience, representing such fields as entrepreneurship, academic research, venture capital, and industry.

The experts agreed that all 15 EntreComp competencies were important for biomedical research trainees and no additional competencies were identified. In a two-round Delphi process, the experts identified 120 topics to be included in a training curriculum. They rated the importance of each topic using a 5-point scale from not at all important (1) to extremely important (5) for two student groups: entrepreneurs (those interested in starting their own ventures) and intrapreneurs (those wanting to be innovative and strategic within academia or industry). Consensus (mean importance score >4) was reached that 85 (71%) topics were of high importance for the curriculum. Four topics were identified by multiple panels for both student groups: resiliency, goal setting, team management, and communication skills.

I&E training for biomedical trainees should address all 15 EntreComp competencies, including “soft skills,” and be flexible to accommodate the needs of trainees on different career trajectories.