Knowledge transfer refers to sharing or disseminating of knowledge and providing inputs to problem solving. When employees leave, they take their skills and institutional knowledge with them. Capturing their knowledge and research outputs is essential to the success of future users.
This session will present a document to be used in conjunction with the RDM Offboarding Checklist in order to guide the knowledge...
Data sharing is becoming an increasingly prevalent and expected part of the research process. Researchers may be hesitant to share datasets about human subjects, and rightfully so. While some data can be shared respecting Institutional Review Board (IRB) and federal restrictions, other data is ultimately not publicly shareable.
This webinar will address conflicts that can arise when attempting to balance the protection of data with expectations for open data, such as restrictive language in data use agreements, IRB protocols, and consent forms.
When employees leave, they take their skills and institutional knowledge with them. It is important to record essential informative information related to projects and datasets to ensure the success of future users.
This session will review the important steps for offboarding employees/trainees from a lab or project. We will provide an overview of the various elements that should be addressed before closing out a project or someone leaves the institution.
Publishing research data within a trusted repository helps you comply with funder and journal data sharing policies, supports the discovery of and access to data, and can result in more visibility and higher impact for research projects.
This webinar will look at strategies to effectively publish data and provide strategies for “curating” a dataset for formal sharing.
Whether you’re motivated by the joy-sparking tidying up of Marie Kondo, or get inspired by a good old-fashioned spring-cleaning spree, now is a great time to clean up your data. The time you spend doing a good data cleanup will more than pay off in increased efficiency in your workflows and project time. This session will review the top practices for cleaning and organizing your data and research files.
Come ready to apply research data management best practices to your files and projects and walk away with a "Spring...
Managing research data involves many tasks, people, tools, and policies. Therefore, a simple tool to help you understand all elements involved with RDM is the checklist. A checklist is simply a standardized list of the required steps developed for a repetitive task.
In this session, we share key guides for getting you started with research data management throughout the entire lifecycle.
Data Availability Statements provide information about where data supporting the results reported in a published article can be found. They are a key resource for the future credence of a manuscript. These statements include links to archived datasets, and help facilitate FAIR Data Principles–findable, accessible, interoperable, and reusable–so that other researchers can locate and meaningfully use the data.
In this session, we will create useful Data Availability Statements and apply FAIR Data Principles for ensuring...
A data management plan (DMP) is a document that outlines what you will do with your data during and after a research project. Many funding agencies require a DMP as part of their application processes. Even if you are not seeking funding for your research, documenting a plan for your data is a best practice and will help your data comply with Harvard's policies for responsible data management. The DMPTool is web-based and provides basic templates that can help you construct data management plans. The DMPTool provides you with...
Are you looking for datasets to help you in your research? Identifying and locating sources of existing data can be important for a variety of reasons, including: asking new questions or providing a new analysis of the data, comparing results from various studies, replicating and validating previous results, developing or testing computational models, and extending a study by incorporating data from multiple datasets.
In this session, we will explore the repository Harvard Dataverse to help you find, reuse, and cite...