Leverage global public goods

Like many networks and groups around the world, ESN-CA is keen to contribute to and leverage the game-changing Evidence Synthesis Infrastructure Collaborative (ESIC).

ESIC is a ‘community of communities’ committed to a collective-impact approach to learning from others – using evidence synthesis – to improve lives. Its communities range from 45 UN entities (through the Global SDG Synthesis Coalition) and the big producers of evidence synthesis (e.g., Campbell and Cochrane) to the big networks of evidence intermediaries and science advisors and 35+ funders (with US$141 million raised).

ESIC will be the ‘go to’ place for four types of outputs:

  • interoperable data that are synthesis and AI ‘ready’ (drawing from ‘continuous evidence surveillance’ mechanisms) and that are synthesis-derived (e.g., findings from and risk-of-bias assessments of empirical studies)
  • living evidence maps
  • policy-scale living evidence syntheses
  • decision-relevant evidence products.

The data will be available through ESIC’s open-data system and the three other types of outputs will be available through ESIC’s sectoral and regional hubs. Many of these developments are being made possible through an ongoing methods and process transformation and additional ESIC infrastructure. ESIC is currently in its first learning and improvement cycle – running from September 2026 to February 2027 – with a focus on creating 4-6 policy-scale living evidence syntheses, as well as complementary living evidence maps and decision-relevant evidence products.

Here are six important things to know about ESIC:

  1. enabling impact
    • by underpinning timely, demand-driven evidence support when ‘windows of opportunity’ open in national (and local) advisory and decision-making processes and in multilateral processes (with policy-scale living evidence syntheses)
    • by identifying scientific shifts that have agenda-setting potential (with continuous evidence surveillance)
    • by creating feedback loops for next-generation primary research (and identifying the research that tipped the balance in what we know, and hence supporting research-impact assessment)
    • by reducing research waste (in both primary and secondary research)
  2. addressing the big sectoral and cross-sectoral questions of our time (moving us to full coverage)
  3. synthesizing what we know – including how it varies by groups and contexts – and with what confidence and caveats (and this ‘global evidence’ can then be put alongside the many needed forms of local evidence about problems, options, implementation considerations, and monitoring and evaluation plans)
  4. using a collective-impact approach, with a common agenda (ESIC roadmap), shared measurement system, mutually reinforcing activities, continuous communications, and backbone support, as well as being agile in all it does
  5. incorporating AI safely and responsibly: 1) in existing evidence-synthesis workflows; 2) in transformed workflows; and 3) by shifting the trajectory of ‘AI for policy’ large-language model-derived tools (e.g., evidence synthesis, localization assessments, and mixing and matching needed forms of evidence)
  6. shifting power to the Global South and foregrounding equity in all it does.

The ESIC roadmap – infographic, 11-page version, and full (56-page) version – and periodic stock-takes help ESIC to keep adapting and growing for maximum impact.