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. These may be publicly available trustworthy repositories in your own domain, or other reputable sources, including your network of collaborators. You help students and colleagues to translate secondary data or code from its original context, to address new questions or problems. You seek advice from other professionals where appropriate, to help broaden the opportunities available, or to make data actionable, whether it is from external sources or within your own team.
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You can describe the opportunities that electronic lab notebooks, virtual research environments and other online services should offer your research. If your research analyses depend on specific software code or scripts these are made as open as possible, or as closed as necessary to comply with legal obligations. You.can show you understand how to give and get attribution for any 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 understand also that the value of data for reuse, and as evidence for published research claims, depends on there being a traceable path of documentation from creation to analysis. You can apply provenance concepts in your community, and use standard formats and identifiers for metadata and data. Using these you help yourself and others to find and get further practical value from research data, making it accessible, and recording how data is managed to ensure it is interoperable and reusable in different contexts.
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Building on your awareness of research integrity principles and codes of professional research conduct you show you are able to properly cite any data, code and methods that you reuse. When you publish results you also acknowledge your collaborators, technicians or others who have contributed to results, as co-authors where appropriate. You apply 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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