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IEU DataLab

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The IEU DataLab aims to provide high-quality databases and data analyses to support the IEU in its rigorous, evidence-based evaluations. The IEU DataLab continually develops and maintains databases through extraction and update of quantitative and qualitative information from a variety of sources both internal and external to the GCF. Data sources come in various forms including, but not limited to, paper and digital documents, the GCF’s internal data management systems, and academically/publicly well-established databases. Extracted data are systematically reviewed for completeness and consistency and then stored in machine-readable formats. Such data are easily accessible for updates, analyses, and sharing for purposes of evaluations.
Moreover, the DataLab uses geospatial data to support the evaluation of the GCF’s performance in its geographical coverage. The DataLab also supports the IEU’s Learning-Oriented Real-time Impact Assessment (LORTA) in its impact assessments of the GCF-funded activities to provide to both the GCF’s task managers and the project implementers high-quality information on the activities’ implementations and the likelihood of impact. Overall, the IEU’s data-driven approach enables rigorous analyses of the GCF’s portfolio and processes and project case studies based on compelling evidence. 
Quantitative data: Quantitative data are essential and easy to manipulate for statistical analyses. The DataLab provides insightful analyses by exploring the relationships between quantitative variables, identifying relevant trends, and creating data visualizations.
Qualitative data: Data sources are many times rich in qualitative information that can make significant contributions to the evaluative evidence base. The DataLab categorizes large volumes of qualitative information into patterns and themes suitable for evaluative analyses. The organized qualitative data can further strengthen the rationale behind the trends emerging from quantitative analyses.
Geospatial data: The DataLab conducts geospatial analyses on the GCF’s projects at the super-country, country, and sub-country levels. The analyses examine environmental and socio-economic factors relevant to the GCF’s objectives and employ a wide spectrum of methodologies such as hotspot analysis, inverse distance weighting interpolation, and spatial autocorrelation.  
LORTA data: LORTA uses a mixed-methods approach to develop an impact evaluation design that measures the project’s quality of implementation and impact. The DataLab utilizes baseline, midline, and endline data of the project to measure causal changes attributable to the project. The real-time framework and qualitative data system aim to help the project’s implementation team measure the progress and learn quick lessons in the early stage of the project.