BioCollect (Advanced field data collection)

software | ENG

The BioCollect app aims to provide a user-friendly tool for data collection in biodiversity science. Users can not only collect data for their own projects, but also easily share it with other interested parties or make use of existing databases. In this way, BioCollect supports a wide range of initiatives, from biodiversity monitoring and scientific research projects to activities focused on vegetation restoration, landscape rehabilitation and other related areas.

Citizen science and the use of data collected by public participation in research

training | CZ

This course is held in the Czech language.

The course aims to introduce the possibilities of using citizen science in data acquisition. Participants will learn the basic principles of citizen science, including the typology of citizen science projects. The possibilities of data collection, data processing, and publication will be discussed, with emphasis on the participatory research component.

Compilation of data sources per research discipline

list of recommended resources | ENG

Southern Methodist University has compiled a list of recommended resources for finding existing data, along with a list of resources categorized by research field.

From classroom to impact: Addressing societal challenges with Citizen Science

Recommended | Training | EN

This course aims to introduce readers to the basics of citizen science and its use in education. Participants will learn what citizen science is, what projects exist in this field, and what benefits it brings to working with students. The course also includes practical teaching experience and sample materials that can be used immediately in the classroom, along with tips and best practices for creating new activities.

Towards a data quality framework for EOSC

Report | ENG

The document “Towards a Data Quality Framework for EOSC” highlights the pivotal role of data quality and outlines standards and processes that ensure trustworthiness, legitimacy, and practical usability of data. It emphasizes establishing compliance mechanisms and fostering trust within the research community, thereby strengthening EOSC as a reliable data management framework.

The recommendations are based on systematic literature reviews and consultations with the expert community. The framework aims to:

• Support community consensus on standards for FAIR data in research,
• Improve the structure and documentation of datasets for enhanced usability,
• Engage data producers and providers in actively enhancing data quality.

These efforts strive to raise awareness of the importance of data quality across scientific disciplines and propose a governance model that can significantly influence data management practices and policies within and beyond EOSC.