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This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS.; International audience; Profound changes in our world are exacerbating data availability challenges at the global level, in particular between scientists and other knowledge workers from regions separated by various features including historical, financial, cultural, political aspects, aside from time and space . Very few, if any, of our present problems such as biodiversity decline, climate change, and viral pandemics stop at national, disciplinary and linguistic boundaries, yet our most vital responses to the shared problems, the information generated to analyze and derive solutions, is still siloed in different languages and locations throughout the world. It is clear that in order for us to effectively respond, we need to collaborate globally and communicate information more effectively. Globalization of research requires interoperability of our observations and experimentation systems.The use of common FAIR vocabularies, that are both human and machine readable, is a key criterion in the FAIR principles (Principle I2 of Wilkinson et al 2016 specifies ‘(meta)data use vocabularies that follow FAIR principles’). Using common FAIR vocabularies will enable data interoperability and the necessary meta-analyses even when data have different origins and are based on multiple vocabularies. The objective of this poster is to offer an overview of the many multi-language challenges for effective Data Stewardship. For instance, some bottlenecks are highly dependent on community approval processes, because they are linked to data dictionary understandability, and/or related training challenges.The discrepancies between regions (cultural, data content, means and translation) are numerous and occur both at the global level and for end users. We must anticipate issues such as choosing a preferred language, polysemy (1 term, multiple meanings), confusion (multiple terms for 1 meaning or ‘false friends’ between 2 languages), plus existing and evolving nuances (not an exact match between languages and during time). Furthermore, terms are often adopted from another language with different contexts and disciplinary realms (that might decrease interoperability) and impedes translation of all versions at the same time. Specifically regarding translation, a key point is that it occurs at the concept level, not as a simple one-on-one translation of (consecutive) words. Care must be taken to ensure that translation to indigenous languages results in datasets that can be used by the affected communities as part of projects that practice co-creation and co-evolution of knowledge.Taking these challenges into account, we have to consider human efforts and the level of translation, e.g. a low or minimum yet sustainable level, that is legally allowable. How would these minimal objectives be linked with FAIR principle compliance? In several case studies, translation was voluntary. One of the sustainability challenges is how to keep interested groups involved, and the need for ongoing engagement. Finally, we bring up the need for expert translators, to maintain the quality level critical to achieve effective harmonization among languages.

Presentation slides ofthe DSCC-IN National Chapters Meeting, held online on October 4th-5th 2021.

The Plenary of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), in section II of its decision IPBES-2/5, established a task force on knowledge and data for the period of its first work programme 2014‒2018. In decision IPBES-3/1, section II, the Plenary approved the data and information management plan set out in annex II to the same decision. At its seventh session, in decision IPBES-7/1, section II, the Plenary adopted the rolling work programme of the Platform up to 2030, which included among its six objectives objective 3 (a), advanced work on knowledge and data. In section IV of its decision IPBES-7/1, the Plenary recalled the establishment of the task force and extended its mandate for the implementation of objective 3 (a) of the rolling work programme of the Platform up to 2030, in accordance with the revised terms of reference set out in annex II to the same decision, and requested the Bureau and Multidisciplinary Expert Panel, through the IPBES secretariat to constitute the task force in accordance with the terms of reference. According to its terms of reference, the task force on knowledge and data oversees and takes part in the implementation of objective 3 (a) of the rolling work programme up to 2030 and acts in accordance with relevant decisions by the Plenary and its subsidiary bodies. Its mandate includes, among other things, to guide the secretariat, including the dedicated technical support unit, in the management of the data, information and knowledge used in IPBES products, including the development of the web-based infrastructure, to ensure their long-term availability and data interoperability. In line with this mandate, the task force on knowledge and data drafted and, upon approval by the Multidisciplinary Expert Panel and Bureau at their 14th meetings in January 2020, published the first version of the IPBES data management policy (version 1.0). The task force revised the policy in September 2020 and published the revision (version 1.1) after approval by the Multidisciplinary Expert Panel and Bureau at their 15th meetings in November 2020. The data management policy was presented to the Plenary in document IPBES-8/INF/12. The Plenary, in decision IPBES-8/1 section IV, took note of the data management policy, as presented. The data management policy was further revised in 2021 and the task force on knowledge and data published the IPBES data and knowledge management policy (version 2.0) after approval by the Multidisciplinary Expert Panel and Bureau at their 18th meetings in February 2022. The purpose of the policy is to provide overarching guidance on the management of data and knowledge to current assessments and the work of task forces regarding IPBES products. The policy is grounded in the principles of open science, accessibility, and building knowledge through partnerships. To get the latest version of the policy visit http:/doi.org/10.5281/zenodo.3551078 Inquiries should be directed to tsu.data@ipbes.net

IPBES DATA MANAGEMENT POLICY Version: 1.1 - December 2020 Revision authors version 1.1:Rainer M. Krug, Joy A. Kumagai, Dave Thau, Debora Pignatari Drucker, Aidin Niamir This revision has been reviewed by IPBES task force on knowledge and data, IPBES Secretariat, and has been approved by the IPBES Multidisciplinary Expert Panel, and IPBES Bureau. Version: 1.0 - January 2020 Drafting authors of version 1.0:Rainer M. Krug, Benedict A. Omare, Wouter Addink, Gregoire Dubois, Cornelia Krug, Howard Nelson, Fatima Parker-Allie, Debora Pignatari Drucker, David Thau, Aidin Niamir Editors: Rainer M. Krug, Benedict Aboki Omare, and Aidin Niamir The version 1.0 has been reviewed by:IPBES task force on knowledge and data, IPBES task force on capacity-building, IPBES task force on indigenous and local knowledge, IPBES task force on policy tools and methodologies, IPBES task force on scenarios and models, and IPBES Secretariat To get the latest version of the policy visit http:/doi.org/10.5281/zenodo.3551078 Inquiries should be directed to tsu.data@ipbes.net