H2020
Europeo
ExpectedOutcome:Project results are expected to contribute to all the following expected outcomes:
enable a rewards and recognition system based on a new generation of (qualitative or quantitative) metrics and indicators[1], leading to a culture and system change that increases the quality and impact, the creativity and the transparency of and trust in science;establish a system of qualitative information based on community-led curation and annotations of research outcomes that feeds into a revamped rewards and recognition system;contribute to the Horizon Europe EOSC Partnership.
Scope:A coherent corpus of reports and recommendations[2]shows a broad consensus among researchers and policy makers that changes in the evaluation of research and researchers’ performance are necessary in order to incentivise higher quality research, collaboration and open science practices.
This topic supports the development of EOSC-federated services and tools that allow the gathering and monitoring of information and data on the use and uptake of research outputs and of open science practices across borders and disciplines. Such tools and services are essential to col...
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ExpectedOutcome:Project results are expected to contribute to all the following expected outcomes:
enable a rewards and recognition system based on a new generation of (qualitative or quantitative) metrics and indicators[1], leading to a culture and system change that increases the quality and impact, the creativity and the transparency of and trust in science;establish a system of qualitative information based on community-led curation and annotations of research outcomes that feeds into a revamped rewards and recognition system;contribute to the Horizon Europe EOSC Partnership.
Scope:A coherent corpus of reports and recommendations[2]shows a broad consensus among researchers and policy makers that changes in the evaluation of research and researchers’ performance are necessary in order to incentivise higher quality research, collaboration and open science practices.
This topic supports the development of EOSC-federated services and tools that allow the gathering and monitoring of information and data on the use and uptake of research outputs and of open science practices across borders and disciplines. Such tools and services are essential to collect the information to be used for next generation metrics[3], together with qualitative indicators, in an assessment system that valorises open science.
Services and tools should collect data on the different usages of research outputs such as data sets, models, software, etc., on the usage of EOSC services, research infrastructures, data platforms, etc., and on open science practices such as those identified in the context of the Open Science Policy Platform registry of pilots and implementations of responsible metrics[4] and the RDA Interest Group on Open Science Graphs for FAIR Data[5].
Proposals should also aim to promote the adoption of community-led curation and annotation systems to foster qualitative aspects of a new generation research assessment system. Related services should be developed, considering for example FAIRness evaluation and the use of machine learning algorithms and AI, to provide qualitative information that will enrich the meta-information of all research outputs.
The tools and services may support research-performing and/or research-funding organisations in measuring the usage, relevance, quality and impact of research outputs, research infrastructures and open science practices, thereby providing the necessary data and information for next-generation metrics and indicators for the implementation of a new research assessment system.
In developing the services and the tools, it is important to integrate a level of flexibility that allows research-performing and research-funding organisations to set their own recruitment and evaluation policies, respecting also the differences among scientific disciplines, taking into account the specificities of the different career stages and allowing for diversity in practices.
Proposals should take into account existing services, tools and infrastructures in order not to duplicate efforts, e.g. on data collection, on discipline based metadata schemas, on AAI and on Persistent Identifiers developed by projects resulting from the topic HORIZON-INFRA-2021-EOSC-01-03.
To ensure complementarity of outcomes, proposals are expected to cooperate and align with activities of the EOSC Partnership and to coordinate with relevant initiatives and projects contributing to the development of EOSC.
In this topic the integration of the gender dimension (sex and gender analysis) in research and innovation content is not a mandatory requirement.
Cross-cutting Priorities:EOSC and FAIR dataDigital AgendaCo-programmed European PartnershipsArtificial Intelligence
[1]Indicator frameworks for fostering open knowledge practices in science and scholarship: https://op.europa.eu/en/publication-detail/-/publication/b69944d4-01f3-11ea-8c1f-01aa75ed71a1/language-en/format-PDF/source-108756824
[2]For example, 2017 Commission report “Evaluation of research careers fully acknowledging Open Science practices” https://doi.org/10.2777/75255; 2018 “Open Science Policy Platform recommendations” https://doi.org/10.2777/958647; 2019 Commission report “Indicator frameworks for fostering open knowledge practices in science and scholarship” https://doi.org/10.2777/445286; 2018 LERU report “Open Science and its role in Universities” https://www.leru.org/files/LERU-AP24-Open-Science-full-paper.pdf; 2020 Final Report of the Open Science Policy Platform https://ec.europa.eu/research/openscience/pdf/ec_rtd_ospp-final-report.pdf.
[3]https://ec.europa.eu/research/openscience/pdf/report.pdf
[4]https://ec.europa.eu/research/openscience/pdf/ec_rtd_ospp-final-report.pdf
[5]https://www.rd-alliance.org/groups/open-science-graphs-fair-data-ig
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