Using your in-depth understanding of data-driven research methods you are able to plan the development of analytic applications, evaluate the range of data management challenges to be expected, and identify solutions that fit the research purpose and level of complexity involved. Your plan articulates the potential insights and risks of the data intensive research you perform, relating these to ethical and FAIR principles, and funders’ policies. In writing your Data Management Plan you seek supervision from your Principal Investigator or other established data scientist. You also liaise with professional services e.g. data stewards, and help peer review plans produced by others.
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You demonstrate expert knowledge and creativity to find, access, integrate and reuse data from novel sources, leading to excellent research, teaching, or non-academic applications. Datasets may come from publicly available repositories in your own domain or from other reputable sources, including your network of collaborators. Supporting research students in your team, you also liaise with other professionals where appropriate, to identify new opportunities to assemble data, analytic tools or pipelines from a range of sources.
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You initiate and deliver novel data analytics applications, algorithms and tools, using machine learning technologies appropriate to the data and domains your research focuses on. You can develop predictive statistical methods to exploit novel data types and sources and offer new insights. Aware of the dependencies of your results on specific software code or environments, you ensure results and code are as open as possible, or as closed as necessary to comply with legal obligations. You contribute to community standards for recognising excellent tools or services and exceed these standards locally.
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You demonstrate excellence in making the data and code that you use FAIR, and contribute to community guidelines in applying FAIR criteria to these outputs. You develop novel approaches to improving efficiency in data preparation and pre-processing, and to the application of provenance standards in your domains. You contribute to standards in data or metadata formats and apply FAIR principles creatively to the integration and reuse of diverse data and computational sources.
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