This Data Management Plan outlines how a company will handle its data, both during
the research phase, and after the project is completed. The goal of a data management
plan is to consider the many aspects of data management, metadata generation, data
preservation, and analysis before the project begins. This tool can be used to provide
guidance and to establish policy and protocols regarding data management of a project.
This document should be used by a company when undertaking a project requiring a
data management plan.
Table of Contents
1 Data Management Policy ................................................................................................. 3
1.1 Research Context .................................................................................................................... 3
1.2 Key Terminology .................................................................................................................... 3
1.3 Information Model .................................................................................................................. 4
1.4 Intellectual Property ................................................................................................................ 4
1.5 Data Access and Distribution .................................................................................................. 4
1.6 Referencing and Citation ........................................................................................................ 5
1.7 Funding Arrangements............................................................................................................ 5
1.8 Other Responsibilities ............................................................................................................. 5
2 Research and Data Protocols ........................................................................................... 5
2.1 Data Collection, Deposit and Quality Control ........................................................................ 6
2.2 Access Protocols ..................................................................................................................... 6
2.3 Data Maintenance, Persistence and Archival Practice ............................................................ 6
2.4 Decommissioning/Destruction/Sanitisation ............................................................................ 8
3 Technical Requirements................................................................................................... 8
3.1 Current Infrastructure and Requirements ................................................................................ 8
3.2 Future Infrastructure Requirements ........................................................................................ 8
3.3 Interoperability ........................................................................................................................ 8
3.4 Data Security........................................................................................................................... 8
3.5 Availability, Reliability, Support and Response ..................................................................... 9
© Copyright 2011 Docstoc Inc. 2
1 Data Management Policy
1.1 Research Context
The section shall provide basic information about the research being conducted in the project,
group or department. It shall indicate the research discipline and briefly outline how the research
will be conducted and shall not go into details. It shall include the initial planning and decisions
of data management.
1.2 Key Terminology
This section shall list and briefly describe key terms or acronyms/abbreviations used throughout
this document. Consistent use of these terms will lead to a more readable plan. The following
terms are suggestions that can be removed or modified as per the context.
Data element type
Data elements that are collected for the same purpose, under similar methodology, and may have
the same file format and metadata schema, are considered to have the same “data element type.”
One such example is “survey data.”
Data element
This is an abbreviation of “data element type.” This may also refer to the actual data itself, such
as raw data and aggregate data (including files and collections) that fall under a specific “data
element type.” An example is the results of a particular survey, or the aggregate survey data.
Repository
A repository structure includes collections with multiple files. Each collection/file is usually
accompanied by metadata. Repositories are user-focused tools that allow for data entry
workflow, data management, search and online representation, and referencing.
Database
A database structure includes tables of information that have specific attributes. Each table
consists of multiple elements that conform to the same attribute schema (i.e., each element has the
same attributes.)
Content Management System (CMS)
A CMS is usually an online tool that allows document storage and management as well as
collaborative Web page editing.
Metadata
Information surrounding data that is not usually found within the data itself. Examples include
author, description, comments, and experimental parameters, references to subsequent updates, or
predecessor data. In most repositories, files/collections will be accompanied by metadata.
Metadata is often used for discovery of data by searching metadata, which are human readable