Unlocking livestock development potential through science, influence and capacity

development ILRI APM, Addis Ababa, 15-17 May 2013

Efficient and effective data management for ILRI research projects: A holistic approach

This document is licensed for use under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported Licence May 2013

Karanja, Titus; Quiros, Carlos

Introduction Data are expensive to collect, manage and store. It is worthless putting a lot of effort into making sure that the methods used for obtaining the data are of high scientific relevance if the same stringent quality standards are not maintained throughout the project and CRP life-cycle. Thus, the whole data management process, including archiving and making it accessible, needs to be planned at the start of the research activity. The Present… • ILRI works in many regions and many CRPs • ILRI’s data management in research activities has been rather ad-hoc i.e. it happens ‘on the go’ • Data are decentralized across the institute, sat in separate databases in different locations (Database server, HPC, own computers) • No standard data management standards/protocols across research activities The “Very Near” Future… • Centralized data for CRP 3.7 and ‘key indicators’, systematic approach to the location of other databases i.e. not on own computers! • Standard Data Management protocols/standards e.g. software, etc. • Accessibility of data across the institute/regions/partners e.g. via online data portals • Enhanced capacity in new and emerging data management technologies • Getting data out in the public domain e.g. open access

Current Data Collection

Entry Platforms

• Direct data collection and entry via Android(ODK), CSPro(Netbooks & PDAs), SurveyBe(Netbooks)

• Paper questionnaires + PC data entry

• SMS-data collection (for M&E)

• Web-based data entry (CGP-Tanzania)

META

Template

Indicator information

(Name, data type, lookup source,

input type, etc.)

Data analysis

Sets of queries

Metadata

(About this template, example

of analysis, etc.)

Project

Metadata

(About this project)

Templates

...

CSPro, Access

Excel, Web, ...

MySQL Database

Common storage

Data entry

applications

Data processing

scripts

Generator

Software

Data entry plugins

...

Read

Create

Create Read data

Store

Use

Stage 1…the start • Standard data collection

technologies • Standard indicators

templates • New and open

technologies

Stage 2…data collection, relay, cleaning and analysis • Near or Real-time relay of data to

server • Real-time data quality assurance

checks • Automated Data cleaning • Data Audit trail

Stage 3…storage, access and reporting • Many data access

methods • Sufficient server

storage • Continued data back-

ups

Proposed Data Management Framework