Understanding the opportunities that existing sources can offer as raw material for your own research, you can demonstrate the skills to find, access, integrate and reuse data from these sources. Datasets may come from publicly available repositories in your own domain or from others. They may also come from your network of collaborators. You seek advice from postgraduate data scientists, data steward, data librarian or archivist where appropriate, to broaden the opportunities available to you. You seek their assistance to make data actionable, whether it is sourced externally or from the research team.
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You demonstrate the ability to use and develop data analytics applications, algorithms and tools, using machine learning technologies appropriate to the data and domains your research focuses on. You can apply predictive statistical methods relevant to the unfolding nature of the data you derive from these analytic tools. 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 show you understand how to give and get attribution for the contributions that software authors make to published results.
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You can identify examples of data and code that offer insights to advance your field, and understand the importance of these being FAIR. You develop effective pipelines for data preparation and pre-processing, You apply provenance standards to ensure a traceable path throughout the data analysis. You use standard formats and identifiers for metadata and data. Using these you demonstrate the application of FAIR principles to gain new research insights and practical application from the integration and reuse of diverse data and computational sources.
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To apply principles of research integrity and professional conduct you properly cite data, code and methods that you reuse. You show that you understand attribution issues affecting text and data mining. You use appropriate methods to cite databases and other forms of dynamic data. When you publish your thesis or dissertation you also acknowledge your collaborators, technicians or others who have contributed to results, as co-authors. You use appropriate identifiers and standards to credit those who helped at all stages of the data lifecycle.
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