GRDS Services Reference

This page describes the services the Global Research & Data Science (GRDS) team offers IPA research teams: the five service areas, the three ways teams can engage GRDS, and the typical outputs and timelines for each.

TipKey Takeaways
  • GRDS organizes its support into five service areas
    • Data Products & Infrastructure
    • Data Science & Engineering
    • Research Knowledge & Methods
    • Research Ethics & Responsible Data
    • Capacity Building
  • Teams can engage GRDS in three ways: Consultative Services, Direct Technical Support (DTS), or Project-Based Collaboration — each suited to a different project stage.
  • Typical turnaround ranges from a few days for a Knowledge Hub upload to six to eight weeks for a co-led, multi-country data science engagement.

What GRDS Does

The Global Research & Data Science (GRDS) team supports IPA research teams and partners in strengthening research quality and technical capacity. GRDS provides hands-on data science, analytics, and research management support that helps projects turn collected data into results teams can act on.

GRDS organizes its support into five service areas, described below.

Service Areas

Function: Design secure, interoperable, and scalable data systems for research teams and partners.

Capabilities:

  • Data pipelines and extract-transform-load (ETL) or extract-load-transform (ELT) automation
  • Cloud-based data warehouses and layered architectures
  • Dashboards and visualization systems, including DataSure quality-assurance dashboards
  • Application programming interface (API) integration between platforms, including SurveyCTO
  • Metadata management and version control

Applications:

  • Clean, merge, and validate research datasets
  • Automate SurveyCTO downloads and quality checks through DataSure
  • Integrate Poverty Probability Index (PPI) scorecards into SurveyCTO survey forms
  • Build Stata, R or Python pipelines for multi-round or multi-country data harmonization

Function: Apply data science and analytical methods to derive insights, automate workflows, and improve decision-making.

Capabilities:

  • Predictive modeling and forecasting
  • Natural language processing (NLP) for text, chat, or document data
  • Geospatial and image analysis
  • Automation and quality-assurance systems
  • Experimental design and adaptive trials

Applications:

  • Build predictive models for program targeting
  • Automate transcription of field-collected audio
  • Extract structured data from scanned documents, such as mobile-money statements, using an NLP pipeline
  • Automate PPI scoring and build a poverty-measurement dashboard

Function: Promote and generate methodological resources that strengthen rigor, reproducibility, and learning.

Capabilities:

  • Power calculations and sample design
  • Reproducibility checks and code review
  • Standardized templates and analytical pipelines
  • Methodological documentation and learning resources
  • Meta-analysis and synthesis of research findings

Applications:

  • Review sampling, randomization, and power calculations for a research design
  • Provide methodological feedback on a pre-analysis plan
  • Audit a replication package for reproducibility
  • Curate and tag resources for the Knowledge Hub

Function: Integrate ethics, privacy, and responsible data practices into every stage of research.

An Institutional Review Board (IRB) provides independent ethical oversight for studies conducted, managed, or funded by IPA.

Capabilities:

  • IRB coordination and reliance agreements
  • Data protection and privacy risk assessments
  • Governance and compliance frameworks
  • Responsible data audits and ethical reviews
  • Training on responsible data collection and use

Applications:

  • Review IRB submission packages and consent forms
  • Coordinate reliance agreements between IPA and partner institutions
  • Assess privacy risk for digital or administrative data use

Function: Strengthen institutional and local capacity through training, technical mentorship, and system-strengthening initiatives.

Capabilities:

  • Data science and analytics training programs
  • Mentorship and technical support for research staff
  • Knowledge-sharing tools and documentation
  • Institutional data governance and sustainability support
  • Learning pathways for technical upskilling

Applications:

  • Deliver Python, GitHub, or data-management training for research and policy teams
  • Develop onboarding resources for new projects and partners
  • Mentor field and data teams through project-based learning

Ways to Engage GRDS

GRDS support falls into three categories, suited to different stages of a project.

Service Type What It Is When to Engage Typical Duration
Consultative Services Advisory or design-oriented support to guide study design, data planning, or methods refinement. Early project stages or proposal development. 1-2 weeks
Direct Technical Support (DTS) Targeted, short-term technical assistance for immediate needs in ongoing research. During project implementation or analysis. 2-4 weeks
Project-Based Collaboration Long-term, embedded support in which GRDS co-leads a technical component or tool. Multi-month projects or cross-country initiatives. 4-8 weeks

Typical Support by Service Area and Engagement Type

The table below gives illustrative focus, example requests, and turnaround time for each combination of service area and engagement type.

Service Area Consultative Services Direct Technical Support Project-Based Collaboration
Data Science & Engineering Guidance on applying AI, NLP, or modeling methods (1-2 weeks) Execution of modeling, transcription, or NLP tasks (2-4 weeks) Collaborative data science solutions at scale (6-8 weeks)
Data Products & Infrastructure Advising on a data-management setup (1-2 weeks) DataSure automation, dashboarding (2-3 weeks) Multi-country data architecture and workflow tools (4-6 weeks)
Research Knowledge & Methods Reviewing power calculations or standard operating procedures (1-2 weeks) Knowledge Hub resource curation (3-5 days) Joint training-material development (2-3 weeks)
Ethics & Responsible Data Advisory on IRB readiness (1 week) Rapid IRB or reliance-agreement support (3-5 days) Harmonizing IRB processes across studies (3-4 weeks)
NotePPI as an Example, Not a Separate Area

The Poverty Probability Index (PPI) is a tool GRDS applies within the existing service areas. Requests to integrate a PPI scorecard into a survey fall under Data Products & Infrastructure; requests to automate PPI scoring or build a poverty dashboard fall under Data Science & Engineering.

Illustrative Examples

The following requests are drawn from actual GRDS engagements and illustrate the range of support available.

Nature of Request What GRDS Did Output Timeline
Transcribe field-collected audio from a SurveyCTO survey Evaluated AI transcription tools for accuracy and efficiency; piloted a speech-to-text model on sample audio Python code that transcribes audio files to a CSV of transcriptions 3-4 weeks
Automate routine data-quality checks and daily reports from SurveyCTO Used SurveyCTO’s API to speed up data download and quality checks within IPA’s DataSure system Enhanced DataSure features for automated data download 3-5 weeks
Include PPI data collection in a baseline survey for cross-site comparison Integrated a country-specific PPI scorecard into SurveyCTO forms; trained field teams on scoring PPI-integrated baseline survey tool and enumerator guide 2-3 weeks
Review a research design’s sampling, randomization, and power calculations Reviewed sampling design, random assignment plan, and simulation-based power calculations in Stata and R Technical review memo with simulation code and recommendations 2 weeks

Related Page

For the request process itself — when to engage, what to include in a request, and what to expect at each stage — see How to Request Support from GRDS.

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