Hello everyone,
I’ve been working around data systems and recently started exploring how large-scale platforms like GBIF manage structured biodiversity data, particularly through standards such as Darwin Core and API-based integration.
This made me curious about how similar challenges are handled in other complex domains, especially healthcare information systems where structured data, interoperability, and privacy are also critical.
In healthcare (for example, clinical systems managing surgical workflows), information is highly sensitive and often fragmented across different tools, hospitals, and software systems. This feels somewhat similar to biodiversity data ecosystems, where multiple sources, institutions, and formats need to be unified for meaningful analysis.
I’m interested in understanding:
- How does GBIF ensure consistency and interoperability across such diverse datasets?
- What lessons from biodiversity data standards (like Darwin Core) could be applied to healthcare data systems?
- Are there common architectural patterns between ecological data platforms and medical data platforms when it comes to APIs and large-scale data integration?
Even though these domains are very different in subject matter, the underlying data challenges standardization, integration, and accessibility feel surprisingly similar.
Would love to hear thoughts from people working on data publishing, APIs, or large-scale scientific databases.
@merryla, I was hoping there might be more responses to your query before I commented, but GBIF staff and others might be thinking that your questions are too big to answer! I’d like to contribute to answers by pointing out two important differences between what GBIF does and what some other data processing structures and warehouses do.
To begin with (your question 1), GBIF and other biodiversity data aggregators do not “ensure consistency and interoperability across such diverse datasets”. Darwin Core is a well-thought-out set of standards, but unfortunately many data contributors do not apply the standards correctly or in recommended formats. Aggregators only hope that dataset contributors will adhere to the standards and supply required data items, but they aggregate datasets regardless.
It’s therefore entirely up to end users of GBIF-mediated data to decide whether a biodiversity data record is good enough to use. To that end GBIF flags selected data issues when processing records and adds the flags to GBIF-processed datasets. Data contributors are not required to fix even those flagged issues, and only rarely do so.
Second, there is no incentive for biodiversity data contributors or data aggregators to correct errors or improve data re-usability. There are at least two such incentives in medical data processing. The first is that people’s lives can depend on data availability and correctness, and medical data administrators and contributors are not heartless monsters who don’t care what’s in their databases. The second incentive is financial, because there are degrees of legal liability involved in providing and managing medical data.
Legal and financial incentives are also behind business data management. Corporates expect adherence to standards, and if businesses don’t comply they lose both money and reputation.
Unfortunately these incentives are missing in biodiversity data supply and aggregation.