Helping with many aspects of the research data lifecycle including research data management, finding data, recommendations for cleaning and understanding data, mapping and data visualisation.
Our expert staff are available to help with many aspects of the research data lifecycle including research data management, finding data, recommendation for cleaning and understanding data, mapping and visualizing your data.
We present a few examples here that show how adoption of these technologies can improve the data management lifecycle.
Emphasis on ontological annotations and standards makes it a resource valuable beyond the limits of the project and, in particular, we have shown how the Directory can support important aspects of the data management lifecycle, providing a resource-efficient way to integrate the information content with external resources, such as tools and knowledge bases.
Although there are promising information systems to manage product lifecycle and product-related data, including product data management (PDM) and product lifecycle management (PLM), significant limitations still exist, where information required to make decisions may not be available, may be lacking consistency, and may not be expressed in a general way for sharing between systems.
Product data management (PDM) and product lifecycle management (PLM) systems offer support by automating and managing some of the operational complexity of modular design activities.
It also makes it feasible to consider a long-term archiving strategy for raw experimental data, and to extend a coherent workflow for data management across the whole data lifecycle, from experiment to publication.
Data management practices are integral to the entire research lifecycle, from planning for what kind of data you will collect to depositing your dataset in a repository.
Attendees will learn about data management best practices throughout the research lifecycle.
A principled approach to data management involves modeling information through the lifecycle to assess stakeholder requirements at each stage, and then tracking management, use and impact of that information.
Traditional product lifecycle management (PLM) solutions focus mostly on the product data management and design house-keeping aspects to maintain complete and consistent product information during product R&D.
