Research data

For AgroParisTech, research data is defined as a set of factual recordings (digital, textual, visual, or audio information, etc.) that is collected, observed, or created as part of a research activity. This data is necessary to organizing research, and to establishing and validating research findings. Raw data is data that has not yet been processed or contextualized. Between raw data and final results, intermediary data from successive transformations of raw data can become the basis for more research, and as a result, can also be considered as research data. 

As in most fields, the world of research is influenced by the expansion of technology: more and more data are produced, and at a faster rate. The evolution is occurring in the context of open science, in which knowledge must be as accessible as possible to all, while keeping to legal constraints and time frames to transfer data into formalized results. Managing data properly for it to express their full scientific potential and unlock it as effectively and confidently as possible is therefore essential for research stakeholders and their partners. 

• Making sure that AgroParisTech’s scientific digital heritage is preserved and viable 

• Making data understandable and reusable to avoid having to reproduce it, especially if it is costly, rare, non-replicable, or if it required the use of living materials on which the impact of research should be limited 

• Being able to support the validity of results produced by the school at any time. 

Because AgroParisTech is involved in public research and is deeply anchored in major societal issues, the school, following on an instruction launched in 2018, adopted a policy for managing and opening research data, which came into force on January 1, 2021. It reflects a continuation of the school’s openscience policy which came into force on January 1, 2020. The policy applies to all doctoral students and agents involved in research activities at AgroParisTech. 

• Formalizing data management using the FAIR principles application for research projects and research units, alongside partners. Using data management plans is highly encouraged. 
• Open data: formal training on the issue of privacy and open data for all research projects. Recommended open data strategies depend on filing data in data banks (preferred theme) and on publishing data papers. 
• Courses: course enrichment based on research with open data, recommendations during internships, training doctoral students to apply management and open data principles as part of their research curriculum. 
• Data valorization and reuse: as part of research projects, in coordination with economic development mechanisms. 

AgroParisTech is developing incentive means and schemes around IT, support, and training infrastructures, funding an assessing research, traceability, and indicators. 

More information about internal organization is available here.