Data Management   Tags: data, data_management, data_services  

This guides discusses several stages of data management:

• Creating the Plan
• Documenting Your Data
• Publishing Your Data 
• Data Mandates
• Preservation and Open Data

And there are 

• More Links to further resources on data management

Ryan Womack, Research Data Manager for RUcore, is offering presentations at different locations on the New Brunswick campus. The presentation will discuss:

• Elements of the NSF Data Management Plan and resources to help with creating a plan.

• RUresearch, the new research data service of RUcore, which provides sophisticated contextual presentation of research data and all associated documentation, software, media, and more

• Services that the RUresearch Data Team can provide for any research project whether or not it is submitted to the NSF, including:
  • consulting on data management for your research,
  • creation of metadata (structured description of data content)
  • permanent data archiving

• Presenting your data in a portal customized to your data and your research audience

The presentation will last 30 minutes, followed by a 30 minute Q&A period. Please come to discuss your specific data needs, and we can devise solutions together.

For a preview, see RUresearch at http://rucore.libraries.rutgers.edu/research

The presentation will be offered on the following dates and times:

Can’t make a meeting? Please contact Ryan and the RUresearch team at rucore_research@email.rutgers.edu

Meetings at Newark and Camden will be scheduled later in the spring. Time and place TBA.

This guide is heavily modeled on the excellent data management guides at

MIT Libraries and the

University of Minnesota Libraries

If you are generating and/or using data in your research, a well-thought out approach to data management will save you time and frustration, maximize the impact of your work, and is an important component of the responsible conduct of scholarship.  A data management plan can help you: 

• Conduct research efficiently by analysing your data practices
• Simplify the use and reuse of your data through proper documentation and application of standards
• Increase your research visibility by publishing your datasets and documentation in repositories
• Meet funding agency, legal and ethical requirements for dissemination and documentation of your research
• Preserve and provide access to your data in the long term, allowing future scholars to build on your work

Research data can be any systematic collection of information that is used by the researcher for their analysis.  Typical examples of data include sensor readings, experimental result recordings, survey results, or simulation output.  Numeric data in various formats abounds, but research data can also include video, sound, or text data, if it is used for systematic analysis.  For example, while a feature film would usually not be considered research data, a corpus of video interviews that had been tagged to identify gesture and facial expression would be considered research data.

Research data must be appropriately structured and documented in order for it to be used effectively for analysis.  Any unique programs or models needed to analyse the data stream should be preserved as well.

Data archiving services are available through the RUresearch portal of RUcore, the Libraries' Institutional Repository.  RUcore is a robust, long-term storage system will full, standards-based metadata, that can support complex relationships between data and supporting documentation, software and media. Support for data management is also available through ORSP's data.rutgers.edu.

In addition, there are many Centers at Rutgers with major data collections, such as:

• Center for Remote Sensing (New Jersey GIS data)
• Protein Data Bank

Please comment on the pages in this guide so that we can improve them!