Checklist - NSF Data Management Plans

This page contains expand/collapse [underlined] items that list questions that are addressed in data management plans. Any single data management plan does need to address every question as each discipline will develop different standards.

Checklist items

Raw data

What will you be producing?

NSF's list of items

Digital Data	Software	Samples
Curricular Materials	Physical Collections	Other

Types of Data

Observational data captured around the time of the event

Examples: Sensor readings, telemetry, survey results, neuroimages

Usually irreplaceable

Experimental data from lab equipment

Examples: gene sequences, chromatograms, toroid magnetic field readings

Often reproducible, but can be lengthy and expensive

Simulation data generated from test models

Examples: climate models, economic models

Models and metadata (inputs) more important than output data.

Reproducible, but possibly expensive

Derived or compiled data

Examples: text and data mining, compiled database, 3D models

Reproducible, but possibly expensive

Samples and other non-digital data forms

Samples, physical collections, notebooks

All may be considered data for the purposes of presenting a dmp

How are you generating the data?

Examples	Software	Equipment
	Drilling	Imaging

File formats?

File types:

Text	e.g. ASCII, Word, PDF
Numerical	e.g. ASCII, SAS, Stata, Excel, netCDF, HDF
Database	e.g. MySQL, MS Access, Oracle
Multimedia	e.g. JPEG, TIFF, Dicom, MPEG, Quicktime
Models	e.g. 3D VRML, X3D
Software	e.g. Java, C
Domain Specific	e.g. FITS in Astronomy, CIF in Chemistry
Vendor Specific	e.g. Varian NMR data format, LeCroy digital oscilloscope format.

Are you using a sustainable digital format - one that is compatible, for the foreseeable future, with software needed to open and read it?

Will these file types be long-lived?

Are there tools or software you will need to process or view the data that need to be archived along with the data?

How fast will the data be growing?

Where will the data be stored?

Personal computer

Cloud storage

Lab server

ThayerFS

Webserver

rSTor

What about backups (or copies of) your data?

Frequency - How often?

Location(s) of backups or file copies - Office, building, off-site

What kind of system or software - College backup (NetBackup), Retrospect, Online: Mozy or Carbonite

Testing procedures - will you test the restore process to make sure backups are working correctly.

Levels of Data

What are levels of data?

Raw data -> Cleaned data -> Processed data -> Summary Level data -> Publication data

Metadata. Information about the data.

What will you keep?

How long will you keep the data?

What are the procedures envisioned for long-term archiving and preservation of the data, including succession plans for the data should the expected archiving entity go out of existence.

Retention?

Destruction?

How will you document your data?

Is there good project and data documentation?

What directory and file naming convention will be used?

Will you be using versioning controls?

What metadata will you include to describe contextual information about who collected/created it, the date, instrument settings, etc.

Title

Name of the dataset or research project that produced it

Creator

Names and addresses of the organization or people who created the data

Identifier

Number used to identify the data, even if it is just an internal project reference number

Subject

Keywords or phrases describing the subject or content of the data

Funders

Organizations or agencies who funded the research

Dates

Key dates associated with the data, including: project start and end date; release date; time period covered by the data; and other dates associated with the data lifespan, e.g., maintenance cycle, update schedule

Location

Where the data relates to a physical location, record information about its spatial coverage

Methodology

How the data was generated, including equipment or software used, experimental protocol, other things one might include in a lab notebook

Sources

Citations to material for data derived from other sources, including details of where the source data is held and how it was accessed

List of file names

List of all data files associated with the project, with their names and file extensions (e.g. 'NWPalaceTR.WRL', 'stone.mov')

File formats

Format(s) of the data, e.g. FITS, SPSS, HTML, JPEG, and any software required to read the data

File Structure

Organization of the data file(s) and the layout of the variables, when applicable

Variable List

List of variables in the data files, when applicable

Code Lists

Explanation of codes or abbreviations used in either the file names or the variables in the data files (e.g. '999 indicates a missing value in the data')

Versions

Date/time stamp for each file, and use a separate ID for each version (see organizing your files)

Checksums

To test if your file has changed over time.

Is there an ontology or other community standard for data sharing/integration?

Who will assign the metadata?

Data quality issues

Are there any data quality issues?

How will the (technical) quality of the data be assured?

How will adherence to this data management plan will be checked or demonstrated?

Access and Security

Audience: Who are the potential secondary users of the data?