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Data Management Support Pack

Welcome to the CIAT-CCAFS Data Management Support Pack. This pack is designed to help you produce high quality, reusable and open data from your research activities. It consists of documents, templates and videos covering the different aspects of data management and ranging from the overarching concepts and strategies through to the day-to-day activities. For each of the videos in the pack we have included a transcript of the narrative. The Data Management Support Pack was created to support the implementation of the CGIAR policy on Open Access and Data Management.

Structure of the pack

The pack is divided into eight areas:

1. PROTOCOLS

In this section we look at writing research protocols for the research activities, which detail exactly how the research activity will be carried out.  Producing a research protocol would be one of the first activities when you are planning your project. We provide a guide and a case study produced by the SSC as part of a series of guides funded by the Department for International Development of the United Kingdom. We have also included a couple of videos explaining what we mean by a “Research Protocol” and what such a document should contain.

2. POLICY DOCUMENTS

Developing a data management plan will help you to produce high quality data and more robust results By considering and detailing the data management tasks needed throughout the research project, you can ensure you have the necessary resources in place in terms of time, people skills, equipment, and finances. A data management checklist will help ensure that nothing is overlooked. This section of the pack includes a document and video explaining what we mean by a Data Management Plan and what it should contain. We suggest using a project level policy document and activity level plans which would detail the data steps that you intend to follow

3. BUDGETING & PLANNING FOR DATA MANAGEMENT

In this section we consider the allocation of resources to data management, including financial resources, time, skills and equipment.  In our years of experience we have had many requests for data management support for research projects only to find there were finances to cover just a day or two for all data management tasks.  This document, together with terms of reference for a data manager and technician, can help you to consider the resources needed from the beginning of the project.  As in the previous section we have also included a brief video highlighting the key points from the document.

4. DATA & DOCUMENT STORAGE

A Data Document Storage Facility (DDS) is basically an area where project data and documents are stored and the rules that enable a team to use it effectively.  This section includes videos introducing the concept and videos on DDS organisation and ownership.  We also have a short video introducing Dropbox as an example of cloud storage for your DDS.

5. FIELD WORK

Leading on from data ownership we next consider the principles of archiving and sharing data, including Open Access, which is now official CGIAR policy.  This guide lists some of the advantages of sharing data and also considers issues such as anonymity and confidentiality.  Before data are archived they need to be adequately documented; such documentation is often called the “metadata”.  In this section we include a video and document about metadata and also include the training manual, questionnaire and codebook from the CCAFS Household Baseline Survey as these fulfil the metadata requirements for this activity.

6. MANAGING DATA

This includes documents that describe the more hands-on technical data management tasks.  They include storing numerical and non-numerical data, converting “raw” data into “primary” data, and data quality assurance.

7. METADATA

This includes documents on how to document your data.

8. ARCHIVING AND SHARING

The final section includes documents that describe the more hands-on technical data management tasks.  They include storing numerical and non-numerical data, converting “raw” data into “primary” data, and data quality assurance.