Open MERIT: Responsible research assessment, done right.

Responsible Research Assessment Practice

The main goal of MERIT is to support the quality and impact-oriented assessment of research and researchers and to increase the robustness and fairness of the decision-making process. 

The following principles apply for the institutional usage of MERIT: 

Quality over quantity by applying multi-dimensional quality-oriented criteria and indicators supported by metrics or quantitative information where applicable and informative. The Journal Impact Factor and H-Index are neither requested from the applicants nor generated by the tool.

Assessment based on explicit criteria  to avoid decision-making based on implicit notions of quality or excellence.

Transparent and structured application criteria  that correspond to key phases of research and careers.

Offer of a diversity of potential criteria, in particular to consider the diversity of research outputs and career paths. 

Structured assessment aids  based on the application template to support consistent and transparent assessments, no additional (hidden) criteria are applied. 

Context-sensitive assessments: Candidates are not expected to answer to all criteria equally. Applications are assessed context-specific with respect to goal of the call or program, research question, area of research, career stage, academic age as well as aspects of DEI. 

Qualitative assessments (e.g., strong – medium – weak) are preferred over numerical grading to avoid a false sense of precision and objectivity. Rankings and cut-offs based on vague numerical scores are avoided in decision-making.

Reflexive, content-oriented peer review, supported by structured procedures, educational materials to strengthen peer-review, built in strategies to reduce risk of bias (e.g. anonymization, academic age)

Open feedback to applicants (open peer review) to increase quality of assessments and to promote knowledge exchange. 

Transparent AI use: applicants are required to disclose the specific uses of generative AI in the preparation of the application, following the statement of the DFG.