Add publish data page

Author: kabilar

docs/mkdocs.yaml

@@ -67,6 +67,7 @@ nav:
     - Populate: compute/populate.md
     - Key Source: compute/key-source.md
     - Distributed Computing: compute/distributed.md
+  - Publish Data: publish-data.md
   - Internals:
     - SQL Transpilation: internal/transpilation.md
   - Reproducibility:

docs/src/publish-data.md

@@ -0,0 +1,34 @@
+# Publishing Data
+
+DataJoint is a framework for building data pipelines that support rigorous flow of 
+structured data between experimenters, data scientists, and computing agents *during* 
+data acquisition and processing within a centralized project.  
+Publishing final datasets for the outside world may require additional steps and 
+conversion.
+
+## Provide access to a DataJoint server
+
+One approach for publishing data is to grant public access to an existing pipeline.  
+Then public users will be able to query the data pipelines using DataJoint's query 
+language and output interfaces just like any other users of the pipeline.
+For security, this may require synchronizing the data onto a separate read-only public 
+server. 
+
+## Containerizing as a DataJoint pipeline
+
+Containerization platforms such as [Docker](https://www.docker.com/) allow convenient 
+distribution of environments including database services and data.  
+It is convenient to publish DataJoint pipelines as a docker container that deploys the 
+populated DataJoint pipeline.
+One example of publishing a DataJoint pipeline as a docker container is 
+> Sinz, F., Ecker, A.S., Fahey, P., Walker, E., Cobos, E., Froudarakis, E., Yatsenko, D., Pitkow, Z., Reimer, J. and Tolias, A., 2018. Stimulus domain transfer in recurrent models for large scale cortical population prediction on video. In Advances in Neural Information Processing Systems (pp. 7198-7209).  https://www.biorxiv.org/content/early/2018/10/25/452672
+
+The code and the data can be found at https://github.com/sinzlab/Sinz2018_NIPS
+
+## Exporting into a collection of files
+
+Another option for publishing and archiving data is to  export the data from the 
+DataJoint pipeline into a collection of files.
+DataJoint provides features for exporting and importing sections of the pipeline. 
+Several ongoing projects are implementing the capability to export from DataJoint 
+pipelines into [Neurodata Without Borders](https://www.nwb.org/) files.