Many funding bodies expect research projects to start with a plan of how the outputs will be made FAIR for future use. Some expectations may also apply to PhD projects. You can show you understand why funders have these expectations, why scientific organisations and research institutions support them, and what should be covered in the Data Management Plan (DMP). The planning should include databases, software code, and physical samples where appropriate. You can articulate how planning benefits yourself as the ‘first reuser’ of your outputs, and seek help where needed from a research supervisor, and from your institution.
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From the beginning of your career you need to know the regulations, ethical principles, and community expectations affecting research, and how to apply them. Responsibilities will apply to you, your colleagues, your organisation, and to any service providers used. Making research data and code FAIR helps meet your responsibility to be able to base published claims on evidence, and share that evidence. If the research involves public concerns, risks to individuals, or impacts on society you also have an ethical responsibility to engage with representatives of those affected. You are confident discussing with senior colleagues how you have not only complied with relevant requirements but conducted the research professionally.
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To apply principles of research integrity and professional conduct you can show that you properly cite any data, code and methods that you reuse. When you publish your thesis or dissertation you also acknowledge your collaborators, technicians or others who have contributed to results, as co-authors where appropriate. You use standards to credit those who helped with collection, management, documentation, publication and archiving of research outputs, so that everyone’s expertise is appropriately rewarded. By using standard output identifiers (e.g. DOIs) researcher identifiers (e.g. ORCID) and contributor roles (e.g. the CRediT taxonomy) you also help to make your outputs findable by others.
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You can demonstrate knowledge of research funding sources, including for open science and innovation. You are aware of what funders in your field typically look for in an application. That will include a realistic budget, to include the costs of making outputs FAIR, as open as possible, and as closed as necessary. You know where to find specialist advice e.g. on storage costs, or data and metadata formats. You can also describe how to apply for dedicated funding to promote open science activities, events, training or other professional development activities. You are confident in discussing research questions and the steps needed to turn ideas into a grant application, with help from a research supervisor, and from your organisation’s professional advisors.
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