Building a Living Publication Workflow for Long-Term Social-Ecological Monitoring Data: The Taiwan LTSER Experience

What this was about

Jhu-Jyun Jhang presented a semi-automated 'living' publication workflow for Taiwan's Long-Term Social-Ecological Research (LTSER) network, which collects ecological, environmental and social data. Research teams fill a co-designed data template and upload to the depositar repository; a robot polls the depositar API daily, validates updated datasets against the template, emails contacts on failure, loads valid data into the LTSER portal, maps them to Darwin Core, syncs to the TaiBIF IPT and publishes to GBIF and TBIA. Four datasets have been published this way so far.

Why it matters. Long-term monitoring data are rarely republished after updates; an automated pipeline lowers the burden on researchers and keeps GBIF copies current.

In the room

• Taiwan LTSER is a site-based monitoring project combining ecological, environmental and social data to understand climate change and human impacts.
• Continuously growing datasets make one-time publication inadequate; repeated extraction and Darwin Core alignment is costly, so data risk never reaching biodiversity infrastructures.
• The workflow has three phases: research teams preparing data, an automated robot/script phase, and publication to GBIF and TBIA.
• An LTSER data template co-designed with each team specifies field name, definition, data type, controlled values, examples and requirement status, easing later Darwin Core mapping.
• depositar provides public data/metadata APIs, multiple formats and persistent ARK identifiers.
• A robot checks depositar daily at 3 a.m.; updated datasets are validated, invalid ones trigger an email to the team contact, valid ones go to the LTSER portal, are mapped to Darwin Core and synced to the TaiBIF IPT.
• Four datasets have been published through the workflow.

Notable moments