MERIT supports the quality- and impact-oriented assessment of research and researchers, to increase the robustness and fairness of decision-making. The portal follows these principles:

• Quality over quantity: multi-dimensional, criteria-based assessment; Journal Impact Factor and h-index are neither requested nor generated.
• Explicit criteria: assessment is not based on implicit notions of quality or excellence.
• Transparent, structured criteria: aligned with key phases of research and careers.
• Diversity of criteria: accounting for varied research outputs and career paths.
• Structured assessment aids: based on the application template; no additional, hidden criteria.
• Context-sensitive assessment: criteria are selected and weighed according to the call, research area, career stage, academic age and DEI aspects; not all criteria apply equally to all applicants.
• Qualitative ratings (e.g. strong – medium – weak) rather than numerical scores, to avoid false precision; no ranking or cut-offs from vague scores.
• Reflexive, content-oriented peer review: supported by structured procedures, training materials and bias-reduction strategies (e.g. anonymization, academic age).
• Open feedback to applicants: to improve assessment quality and support knowledge exchange.
• Transparent AI use: applicants and reviewer disclose use of generative AI in application preparation or assessments, in line with the DFG statement on the Influence of Generative Models of Text and Image Creation on Science and the Humanities (2023).