Creating a complete inventory is an important step even though you may only be able to provide a summary in a Federal DMP due to length ( most less than two pages some only one paragraph) .
What is research purpose?
What type of data is it?
Examples: Spatial, temporal, observational, experimental, survey responses, etc.
How will the data be collected?
Examples: recorded interviews, surveys, models, sensors, image analysis, DNA sequencing, word counts, camera traps, etc.
How will the data be saved and opened?
(i.e. file types and file formats)
Example file types: text, spreadsheets, databases, digital images, sound files, digital video, computer code, algorithms, etc.
Example file formats: PDF (portable document format), .csv (comma separated values), .docx (Microsoft Word), .txt (plain text), etc.
How much data will be generated for this research?
How long will the data be collected and how often will it change?
Are you using data that someone else produced? If so, where is it from?
Who is responsible for managing the data? Who will ensure that the data management plan is carried out?
Various methods can be used to keep data organized. Some of these are simple things we do every day such as saving a new version of a draft using "Save As..." instead of "Save" while others require commitment and planning to implement. File management systems are a trade-off: while they take time to set up, they will save you time later by reducing errors.
While data organization and quality control methods are very important, they should not take up a significant portion of your federal DMP. As with all sections of the Federal DMP you will need to summarize.
Version control, also known as "file versioning", is when you save an updated file as a new file instead of overwriting the old file. This lets you "revert" back to an earlier version if needed. While this process can be done by hand it is much easier (and safer) to have a machine assist you. ( e.g. Git)
What documentation will you be creating in order to make the data understandable by other researchers?
Are you using metadata that is standard to your field? How will the metadata be managed and stored?
What file formats will be used? Do these formats conform to an open standard and/or are they proprietary?
Are you using a file format that is standard to your field? If not, how will you document the alternative you are using?
What directory and file naming convention will be used?
What are your local storage and backup procedures? Will this data require secure storage?
It is important to understand that data sharing is an expected part of every data management plan requirement.
There are a few cases when data sharing may not be appropriate. In general data which may compromise research subjects or be tied to intellectual property such as patents, are acceptable exceptions.
Data sharing is expected by both federal funders and an increasing number of journals. Requirements vary widely but there are some universal best practices to use as a starting point.
Data repositories are the best choice for research data sharing, distribution, and preservation.
The Data sharing tab on this guide provides a basic overview of the topic, links to some of the largest and well known data repositories, and tools to help you select a repository.
Data papers are a new type of scholarly publication that provide a citable manuscript and data documentation in one package. The tab on data sharing provides an overview of topic and a selective list of journals that publish these manuscripts.
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