Data requires citations for the same reasons journal articles and other types of publications require citations: to acknowledge the original author/producer and to help other researchers find the resource.
Data collection is the systematic recording of information;
Data analysis involves working to uncover patterns and trends in datasets;
Data interpretation involves explaining those patterns and trends. Scientists interpret data based on their background knowledge and experience; thus, different scientists can interpret the same data in different ways. By publishing their data and the techniques they used to analyze and interpret those data, scientists give the community the opportunity to both review the data and use them in future research.
Below is a list of available software, to help you analyze, manage, and visualize data. Contact your Data Services Librarian for assistance.
Results can be presented in textual and non-textual form. See the resources below, for best practices on non-textual data presentation (i.e. charts, tables, etc.).
Dozens of federal funding agencies, many non-profit granting agencies, and some corporate funders now require grant applicants to submit a "data management plan" document.
See Yale's Research Data Management guide for more information.