Researchers who use quantitative methods in the social sciences often publish out their data sets into online repositories at the time they publish the work based on the data.  These datasets are original data.  They have a clear provenance. They are placed in a format that is easily ingestible into any number of quantitative data analysis software tools.  Some universities host dataverses or data repositories based on the work of their faculty.  Such datasets are often scrubbed of identifiers and “noise” before their release into public spaces.  They are shared generally for two reasons:  (1) to enable other researchers to test the findings of the prior researcher (who originated the dataset)…and (2) to enable other researchers to surface new findings from the released data (possibly using new methods or new technologies, or new combined methods and technologies).  

Open-source data repositories tend to mostly share quantitative data—such as those specializing in government data and in map data.  

Currently, there is not an online space per se for the release and distribution of NVivo datasets, which are based on qualitative and mixed methods research.  Part of the concern is that there are privacy issues with using raw qualitative data, which may be used to re-identify participants to the research.  Another issue is the difficulty of trying to “replicate” findings from qualitative or mixed methods data given the differences in methodologies and their underlying theories.  NVivo does not generally enable scrubbing of ingested data, so if identifiers were included initially, it is possible that those would remain throughout the use of the software.  Also, the integrated secondary sources (already-published articles) would be potentially “re-published” with the release of NVivo projects, which may contravene intellectual property and copyright issues.  Also, NVivo datasets would require NVivo to open and access given that these are proprietary .nvp files.  

A current movement in quantitative-based research is to enable authors to present research online as a stream of human-readable text and machine-readable code which are woven to enable readers to access the analytical data (at minimum) as well as the modeling code--so that the data analysis may be verified.  It is possible that such approaches may flow over to qualitative and mixed methods research.  One popular engine for such dynamic report creation is Knitr (pronounced "knitter").

One of the first articles to address this was by Dr. Lisa Cliggett in "Qualitative Data Archiving in the Digital Age: Strategies for Data Preservation and Sharing." This was published in The Qualitative Report (TQR, Vol. 18, 1 - 11) in 2013. 

Cornell University links to a number of Internet data sources for social scientists through the Cornell Institute for Social and Economic Research.  The Inter-university Consortium for Political and Social Science is another source.  The Pew Research Center offers datasets on social and demographic trends.