The international BeeXML Standardization Workshop took place December 16 – 17 2019 in Munich.
On the agenda for the first day was a technical session to define the specifics of the standard as well as the procedure for expanding the scope of BeeXML. The second day was focused on steps to encourage the adoption of the standard.
The workshop was hosted by Arno Bruder, Beekeeping Advisor of the District of Upper Bavaria and chaired by Joseph Cazier, Ph.D Director of the Center for Analytics Research and Education Appalachian State University in conjunction with Walter Haefeker, President of the European Professional Beekeepers Association (EPBA).
Minutes were taken by Max Rünzel, Associate Research Fellow at the Center of Analytics Research and Education, Appalachian State University.
- Dr Agnès Rortais, Scientific Officer, Scientific Committee and Emerging Risks Unit, Risk Assessment and Scientific Assistance Department, European Food Safety Authority, Parma, Italy
- Giuseppe Antonio Triacchini, Evidence Management Unit, European Food Safety Authority, Parma, Italy
- Edgar E. Hassler III, Ph.D. Assistant Professor Computer Information Systems & Supply Chain Management Walker College of Business.
- Awad Hassan, Ph.D, Associate Reserach Fellow, Center for Analtyics Research and Educat, Appalachian State University
- Noa Delso, BeeLife, Brussels, Belgium
- Mag. dr. Michael Rubinigg, Scientific Staff, Biene Österreich
- Gregor Sušanj, beeHub Project, CEO, Software Engineer, ZIP Solutions, Maribor, Slovenia
- Janis Kronbergs – Latvian Beekeeper association council member and member of Nordic-Baltic Beekeepers council (NBBC)
- Magdalena Sperl – SAMS Smart Apiculture Management Services
- Konstanza Jochim – SAMS Smart Apiculture Management Services
- Marten Schoonman, www.beep.nl open source observation + automatic monitoring platform
- Alexis Gkantiragas – Molecular Biology student at University College London, BeeLab
- Peter He – Department of Computing. Imperial College London, BeeLab
- Gerard Glowacki – BeeLab (Unsupervised machine learning algorithm applied to pollen analysis)