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Public DMPs

Public DMPs are plans created using the DMPonline service and shared publicly by their owners. They are not vetted for quality, completeness, or adherence to funder guidelines.

Project title  Template  Organisation Owner Download
Relationship between Physical Qualities and Sprint Kinematics in Team Sport Athletes DCC Template University of Salford Achmed Kevin Syahlintang (new window) Opens in new window
Smart thermosensitive systems based on hydrogels and biocompatible nanomaterials DCC Template Universidade Estadual Paulista Julio de Mesquita Filho Nayrim Brizuela Guerra (new window) Opens in new window
Translating the Science of Reading to the Classroom: Can Teacher Development Workshops in the Science of Reading Improve Spelling in Mainstream Classrooms? (DEMSI Part 2) DCC Template University of York Cameron Downing (new window) Opens in new window
Monitoring Earth’s Evolution and Tectonics, WP11 - IPSES - Italian Platform for Solid Earth Science, Activity 11.9a - Services and interoperability layers for distributing earthquake faulting data in 4D DCC Template Other Roberto Basili (new window) Opens in new window
Exploring the gaps and opportunities for development of an AMR educational package for higher education: An online survey of higher education professionals DCC Template University of Plymouth Delphine Kayem (new window) Opens in new window
Applying Linked Data and Semantic Web Standards to Improving Interoperability of GBIF Datasets DCC Template Other Marcos Zárate (new window) Opens in new window
Legal Challenges of Gene Editing DCC Template Other Monika Nogel (new window) Opens in new window
Análise do repositório de dados de investigação do INESC TEC de acordo com os Core Trust Seal requisitos DCC Template University of Porto (Universidade do Porto) Pedro Duarte (new window) Opens in new window
The Influence of AI Integration In Human Resource Management: Assessing The Adoption And Impact On The Future Of Work DCC Template University of Plymouth Victoria Agbe (new window) Opens in new window
FiL-Hyper: Tackling Federated Incremental Learning With Label Noise Via Hypergraph Guided Knowledge Distillation DCC Template City St George's, University of London Rui Zhu (new window) Opens in new window