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In January 2020 the first Data Management Policy of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) was approved (https://doi.org/10.5281/zenodo.3551079). This milestone was presented at the last WBF in 2020 (https://doi.org/10.5281/zenodo.3669004). But very soon after its approval, it was realised, that the policy does not tell the whole story: The policy lays a solid foundation for FAIR data management, but it does not address specific requirements of the handling and usage of Indigenous and Local Knowledge (ILK), which is used extensively in IPBES and follows strict rules (see the Talk by Peter Bates in this session). The CARE Principles for Indigenous Data Governance do provide a framework which supplements the FAIR data management principles (see the talk by Stephanie Russo Carroll in this session for a discussion of FAIR and CARE). The IPBES Task Force on Knowledge and Data therefore decided to develop a Data Management Policy v2.0 which incudes CARE as well as FAIR and which pays attention to the special role of Indigenous People and Local Communities (IPLCs). In this talk, I will discuss the motivation behind this decision, the considerations during the process, and the problems and challenges faced. Please also see the e-poster by A. Niamir (https://doi.org/10.5281/zenodo.3551078) about the data and knowledge management policy (https://doi.org/10.5281/zenodo.3551078 after approval).

IPBES, [the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services] (https://ipbes.net) has the overall mission “to strengthen the science-policy interface for biodiversity and ecosystem services for the conservation and sustainable use of biodiversity, long-term human well-being and sustainable development”. In this capacity, IPBES is integrating existing data and knowledge and provides assessments on topics it is mandated to by the member governments. Based on these assessments, Summaries for Policy Makers are produced, which are the documents used for policy support. This whole process does not involve the generation of new data. So why does an organisation like this need a Data Management Policy? Which lessons did we learn from developing this DM Policy? And are there areas, in which a DM Policy by an orginisation like IPBES should develop, in contrast to other data generating organisations? I will discuss these issues, provide some answers, but will leave many questions open for discussion, as not much existing experiences in this area is available.

This is a general presentation of the IPBES data management policy prepared for the experts of the ongoing assessments.

A poster promoting the IPBES Data Management Policy presented at the World Biodivereiry Forum 2020 in Davos, Switzerland. The Multidisciplinary Expert Panel and the Bureau of theIntergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), at their 13th meeting in January 2020, has approved the IPBES data management policy.This is an important step for IPBES to further improve the accessibilityof its products and the transparency of the underlying procedures. The IPBES data management policy is based on the principles of Open Science and FAIR data. It provides a framework to make the products (e.g. statements, maps, and tables) traceable from the original data layers, through the processing steps, and up to the final status in the product. The IPBES data management policy also provides guidelineson long-term repositories, metadata, and file formats. Management, handling, and delivery of the materials from the indigenous peopleand local communities are also covered by the policy. The IPBES data management policy will pave the road towards reproducible assessments over time and scalable at the national or regional scale.In this contribution, we aim to inform the community with the highlights of the policy and to present an outlook of projects emerging from the implementation of the policy. Here is the link to the IPBES data management policy: https://doi.org/10.5281/zenodo.3551078

A poster promoting the IPBES Data Management Policy presented at the World Biodivereiry Forum 2020 in Davos, Switzerland. The Multidisciplinary Expert Panel and the Bureau of theIntergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), at their 13th meeting in January 2020, has approved the IPBES data management policy.This is an important step for IPBES to further improve the accessibilityof its products and the transparency of the underlying procedures. The IPBES data management policy is based on the principles of Open Science and FAIR data. It provides a framework to make the products (e.g. statements, maps, and tables) traceable from the original data layers, through the processing steps, and up to the final status in the product. The IPBES data management policy also provides guidelineson long-term repositories, metadata, and file formats. Management, handling, and delivery of the materials from the indigenous peopleand local communities are also covered by the policy. The IPBES data management policy will pave the road towards reproducible assessments over time and scalable at the national or regional scale.In this contribution, we aim to inform the community with the highlights of the policy and to present an outlook of projects emerging from the implementation of the policy. Here is the link to the IPBES data management policy: https://doi.org/10.5281/zenodo.3551078

To adress questions about the future of biodiversity, it is essential to use a wide range of data from different sources. This is only possible, if the relevant datasets can be found, accessed, and understood through the metadata accompanying them. To promote this essential open data culture, the FAIR data principle was launched (https://www.force11.org/group/fairgroup/fairprinciples), which is gaining traktion throughout the scientific community. With this increasing need and use for FAIR data, metadata, and it's quality, plays an ever increasing role. Nevertheless, providing metadata is not easily accomplished. One of the reasons for this is the cumbersome way of entering metadata in formats or webforms needed by repositories. Also, there is little or no quality control. Finally, the metadata schemes used by the repositories are either restricted to bibliometric metadata (e.g. DataCite) or are using metadata schemes which have to cover a vast diversity of different data (e.g. EML or Darwin Core). I will present an approach which uses domain specific metadata schemes to overcome these problems, by developing these schemes together with scientists of their domain. It can be employed without detailed technical knowledge, e.g. using spreadsheets and a web browser. This will help researchers recognise the value of metadata for their own research and sharing. The provided validation tool increases quality of metadata.

MetaData can be Easy! GfÖ Annual Meeting 2019 09. to 13. September, Münster, Germany

Der Einsatz prozeßnaher Meßeinrichtungen in der automatisierten Fertigung, insbesondere der Einsatz von Meßtastern in CNC-Maschinen, zielt längst nicht nur auf Maßnahmen zur Qualitätskontrolle und -sicherung, sondern zunehmend auch auf Effekte zur Rationalisierung und Verbesserung der Wirtschaftlichkeit. Dies ergab eine am Institut für Werkzeugmaschinen der Universität Stuttgart (IfW) im Kreise von Anwendern und Anbietern durchgeführte Studie im Rahmen des Forschungsprogramms "Prozeßüberwachung". Der folgende Bericht gibt eine erste Zusammenfassung der wichtigsten Ergebnisse.

Untersuchungen und Vergleiche mit anderen Meßverfahren ergaben, daß mit dem "Quick-Check-System" Geometriefehler schnell zu messen und gut zu erkennen sind. Im Rahmen der vorbeugenden Instandhaltung sind somit regelmäßige Genauigkeitsüberwachungen leicht durchführbar: Maschinenabweichungen lassen sich frühzeitig erkennen, weiter beobachten und bei Bedarf mit entsprechenden Maßnahmen korrigieren.