The Global Biodiversity Information Facility (GBIF) is an international open data infrastructure, funded by governments since 2001. It is the biggest and the most influential network for biodiversity information service, which is joined by 58 countries and 38 organizations. By the end of 2019, 1554 data publishers mobilized 1.37 billion occurrence records to GBIF.org, mainly observation records and specimen records, providing massive biodiversity information services.

GBIF-CAS node used GBIF integrated publishing Toolkit (IPT) and published 14 datasets, including more than 1.6 million records. The data is integrated with existing data of China on GBIF effectively, allowing more people to know and better understand biodiversity in China. According to the GBIF website, there are 123 peer-reviewed SCI papers used the data published by GBIF-CAS node at the end of 2019.

GBIF-CAS node continues to cooperate with GBIF Secretariat and other nodes. On July 8-12, CAS node attended the BIFA Regional Engagement meeting and Capacity Enhancement workshop in Hanoi, Vietnam. The training content included Foundations, Planning, Data capture, Data cleaning and standardization, and Data publishing. On October 17-19, GBIF-CAS node attended the Global Nodes Training and 15th Global Nodes Meeting in Leiden, Netherland. There were four modules in the training- Strategic planning for nodes, Communications and stakeholder engagement, eveloping training for network, resourcing & collaborative project writing in the nodes community. During the Global Nodes Meeting, six sessions were set up, which were Common understanding of progress, Nodes stories, Parallel thematic groups, Data use cases, Parallel regional groups, and Conclusion. In the Data use cases session, GBIF-CAS node gave a talk on Status of GBIF Data Usage in China.

GBIF CAS node will continue to cooperate with other related platforms, introduce GBIF’s technology and datasets to the Chinese community, and publish Chinese datasets to the GBIF global network. This will greatly improve the current data gaps in China in GBIF network and also make research based on GBIF data in China more reliable.