NEW! Computational Support
Meet one-on-one with an expert in Python, R, ArcGIS, data visualization, and many other data science/programming packages.
For more information or to set up an appointment, contact the Research Data Management Librarian.
Need assistance with study design or data manipulation/analysis? The Statistical Consulting Center may be able to help.
Are you looking for massive parallel computing power? Consider the High Performance Computing Cluster.
There are several ways to publish data:
What about providing data to colleagues by request?
Sharing data by request is a well-established scholarly practice. Data sharing is not the same as data publication, because data that is shared directly between researchers cannot be cited as easily and will not be indexed by search tools. Some repositories formally publish data but also implement access controls (ICPSR is a good example). Thus, data publication should not be confused with allowing unrestricted public access to that data. However, data publication is not appropriate in every case. Informal data exchange among colleagues is and will continue to be an important form of data sharing.
A major benefit to publishing data is that it can increase the impact of your research. Datasets can and should be cited like any other scholarly source. In order to ensure research is cited properly, data providers need to associate datasets with a stable URL and assign a permanent identifier like a DOI. Good research repositories do this automatically at the time of deposit.
Suggested data citation style:
Author 1, Author 2, (etc.). Year. Dataset title. Data repository/archive. Version (if applicable). Permanent identifier.
When publishing data, check that you have provided enough information for others to generate a complete citation. Some repositories provide the suggested citation alongside the dataset so that it can be copied by users. For more information, see data citation best practices.