Position on Data Management
Data are the foundation of research and science. As such, their integrity is paramount. Designing experiments that create meaningful and unbiased data, and that do not waste resources and protect human and animal subjects, is the first step in good data management. Once an appropriate research topic is determined, proper data collection, retention, and sharing are vital to the research enterprise. If data are not recorded in a fashion that allows others to validate findings, results can be called into question. But there are some circumstances in which data need to be protected and not shared, such as with new inventions, protecting the confidentiality of human research subjects, and topics with national-security repercussions. Who actually owns data collected in an academic environment in a research project funded by the federal government is another issue often subject to misunderstanding.
Researchers spend much of their time collecting data. Data are used to confirm or reject hypotheses, to identify new areas of investigation, to guide the development of new investigative techniques, and more. We launch space probes to collect data that help us understand the origins of the universe and use gene databases as tools for understanding and curing disease. Science as we know and practice it today cannot exist without data. Data management practices are becoming increasingly complex and should be addressed before any data are collected by taking into consideration four important issues:
The integrity of data and, by implication, the usefulness of the research it supports, depends on careful attention to detail, from initial planning through final publication. While it might seem obvious that science and research are about collecting, storing, and sharing data, ethical considerations can arise for a researcher every step of the way.
NSF now requires that all proposals be submitted with a data management plan.