The Natural Resources Information Management Toolkit (the Toolkit) has been prepared by National Land & Water Resources Audit and ANZLIC – the Spatial Information Council to assist regional natural resources management groups discover, access, visualise and manage their data and information. The Toolkit has been developed in close cooperation with all jurisdictions and has been targeted at regional groups to build capacity and facilitate the two-way flow of data and information. The Toolkit can be used as a text book document, teaching aid or as a series of stand-alone modules.

The Toolkit has three components:

1. Executive Summary
   Information on high-level issues for managers to consider and build into business processes (available in hardcopy, CD and web versions).
2. Concise Guide for Technical Managers
   A short version guide containing essential check lists (available in hardcopy, CD and web versions).
3. Technical Modules
   The how-to guide, comprising ten modules, for regional groups involved within natural resources data and information management. Each module is a stand-alone document and ordered hierarchically, from general principles to detailed practices (available in CD and web versions only).

It is acknowledged that when dealing with capacity building and the issue of data and information management at a regional level that there can be no one-size-fits-all product. It is also important to recognise that in many cases each State and Territory may have its own initiatives including governance guidelines and protocols related to data and information management. In this respect, the Toolkit has been designed to complement activities being carried out by respective State and Territory jurisdictions.

Users of the Toolkit are encouraged to communicate with their respective State and Territory coordination contacts to ensure they obtain the correct information for their jurisdiction–this includes point-of-truth and authoritative datasets, and any additional detailed information on methodologies for data collection such as classification schemes and data content standards.