FAIR Data Principles & Biodiversity Standards

Why and how to publish FAIR data

Elizabeth Lawrence, Saara Suominen

2026-07-21

What makes data FAIR?

• Descriptive metadata with stable links (e.g. DOI)
• Clear instructions on how to access
  • Not just “available upon request”
• Easily combined with other datasets & machine readable
• Metadata enables replication
  • Data is contextualized

This is the “why” before we get into the “how” (Darwin Core, EOVs, sensitive data). Starting with a quick overview on what actually makes data FAIR. To summarize each aspect – Findable means that there is descriptive metadata with stable links and identifiers e.g. DOI that enable finding our data Accessible means that there are clear instructions on how to access our data, going beyond ‘available upon request’ statements Interoperable means that our data can be easily combined with other datasets and that our data & metadata are machine readable. This often means adhering to data standards and using controlled vocabulary whenever possible Reusable means that the metadata is documented well enough to enable others to replicate what we’ve done and there is enough context so that users know what situations our data can be reused in (e.g looking back on data collected 20 yrs ago, code)

What can FAIR data do?

Invasive species decision tool & alert system portal.pacman.obis.org

Before I talk more about data standards, let’s look at some examples that demonstrate the power of FAIR data This first example is work that was done through the PacMAN project (Pacific islands Marine bioinvasions Alert Network), where our team developed a decision tool & alert system for detecting invasive species. The tool uses data from OBIS AND GBIF, including dna derived detections and other types of observations. When a pest species is detected, metadata about the detection is used to determine the detection confidence and confirm its status, if it’s a real detection. This allows informed decisions to be made on how to deal with the bioinvasion This last screenshot shows the tool and where you can see the confidence and status of detections

How to manage and make data FAIR?

How do we make data FAIR - OBIS + DwC standard!

What is OBIS?

Ocean Biodiversity Information System

• Global open-access data system for marine life
  
• UNESCO Intergovernmental Oceanographic Commission
  
• > 199 M occurrence records from >7,000 datasets
  
• Data flows to GBIF — easy to publish once, reach both

Free to use. Free to contribute.

🌐 obis.org
📖 manual.obis.org

OBIS is endorsed by UNESCO’s IOC. Data published there can flow to GBIF as well, giving you double visibility with a single submission. Emphasize that OBIS is not just a repository but actively feeds into global biodiversity assessments, climate analyses, and conservation planning.

A Mature Global Community

OBIS is the world’s largest open-access repository
for marine biodiversity data.

36 nodes
across 99 countries
representing 1,000+ institutions
and 6,000+ scientists.

OBIS sits within UNESCO’s IOC (IODE, est. 1961), working alongside ODIS for interoperability and OTGA for training. Strong partnerships include GBIF, TDWG, EMODnet, GOOS, and DataONE.

Current stats: 136M species observations, 12M records from ABNJ, 28M DNA sequences (Jan 2026).

This data supports multiple agreements: Paris Agreement, 30x30, GBF (Targets 3, 6, 8, 20, 21), SDG14, and now BBNJ.

Recognition: CBD COP16 recognized OBIS, GOOS, and ODIS as ocean components of the Global Biodiversity Observing System.

OBIS is listed in KMGBF Targets 20 and 21.

OBIS data supports: IPBES Global Assessment, UN World Ocean Assessments, UNESCO IOC Global HAB reports, State of the Ocean reports, TWAP Open Ocean component (UNEP), and CBD EBSA.

OBIS & FAIR Data

	What OBIS gives you
Findable	Your dataset joins a global, searchable index - with a DOI
Accessible	Standard download/API access, no one-off email requests
Interoperable	Darwin Core structure - readable by people and machines
Reusable	Documented metadata + a clear reuse license (e.g. CC-BY)

A real example

A marine invasive species dataset from Venezuela, published July 2022, was included in 1,873 separate download requests within 3 months — with zero extra effort from the data provider!

…it’s the reason people don’t have to keep emailing you for your spreadsheet.

Do you use a standard to format your data?

¿Utiliza algún estándar para formatear los datos?

What is Darwin Core?

• Stable, shared vocabulary for biodiversity data
  • Standardized field names, identifiers, & definitions
• Maintained by TDWG, used by OBIS, GBIF, and the wider biodiversity community
• Not a database itself - a set of agreed-upon terms (e.g. scientificName, eventDate, decimalLatitude)

Think of it as everyone agreeing to call the same column the same thing - so your data and someone else’s can sit in the same table!

🤝

For folks who’ve never touched DwC: it’s just a controlled list of column names and what they mean, nothing more mystical than that. For the DwC veterans in the room, this is review — we’re about to build on this with how those terms get organized into tables (next slide), which matters a lot once we get into DNA data this afternoon/tomorrow.

Flat vs. relational data tables

Flat file

One giant table - everything repeated on every row

date	site	lat	lon	species	count
2024-01	A	11	-74	fish1	3
2024-01	A	11	-74	fish2	1
2024-01	A	11	-74	fish3	7

Relational

Separate, linked tables — shared info stored once

site.csv:      site, date, lat, lon
species.csv:   site, species, count

🔗 Linked by a shared identifier (an ID column)

This is the concept underlying Darwin Core Archives: a “core” table (e.g. Event) and one or more “extension” tables (e.g. Occurrence, DNA extension) linked by eventID/occurrenceID. It reduces repetition and errors — fix the date once in the Event table instead of on every one of a thousand species rows. Keep this fast for people who already know relational databases; it’s here mainly to set up tomorrow’s DNA data session where we’ll have Event → Occurrence → DNA extension as three linked tables.

Core & Extension tables

• Every Darwin Core Archive has one core table: either Event or Occurrence
• Plus zero, one, or more extension tables (eMoF, DNA extension…)
• Extensions link back to the core via an ID

This is the exact structure we’ll use for DNA-derived data later — so if this clicks now, the DNA session will feel familiar rather than brand new. This “star schema” (extensions only link to the core, not to each other) is a real constraint of the Darwin Core Archive format, which is why OBIS built eMoF as a workaround — but that’s a detail for later, not needed here.

A note on sensitive data

Common cases:

• Endangered or commonly poached species locations
• Species/locations with economic sensitivity

Options → you don’t have to choose between “share everything” or “share nothing”:

• Generalize location - provide a region or polygon (footprintWKT) instead of an exact point
• Delay publication - prepare to publish but set an embargo for later
• Generalize taxonomic name - provide a higher taxon name

Note: Generalizations should be documented alongside your data! See OBIS Manual & GBIF Guide for more details

Sensitivity is not a reason to skip publishing. There’s a spectrum of options short of full open release. GBIF also has a good best-practices doc on generalizing sensitive species data if anyone wants to go deeper (Chapman 2020, GBIF Secretariat). There are tools for generalizing locations (OBIS Map Tool, Marine Regions Gazetteer) in the OBIS manual + GBIF guide

Essential Ocean Variables (EOVs)

goosocean.org/eov

• Defined by GOOS (Global Ocean Observing System, IOC-UNESCO)
  • Minimum set of variables needed to track ocean state and change
• 36 EOVs → physical, biogeochemical, biological & ecosystem
• Specification sheets to guide data collection

Why this matters

Well-structured data can directly feed EOV assessments used for global reporting and policy (e.g. UN Ocean Decade, conservation targets)

GOOS Biology & Ecosystems (BioEco) Panel defines the biological/ecosystem EOVs. OBIS is a key data source these assessments draw on. You don’t need to memorize the EOV list — the point is that standardized, well-formatted data (what the rest of today covers) is what makes this pipeline work at all. If useful, mention the EOV Metadata App (eovmetadata.obis.org) as a lightweight related tool, but it’s a separate system from the IPT we’ll cover this afternoon.

Why standardize data?

• Enables data to be found, combined, and reused across research groups, institutions, and countries
• Avoids duplicated effort — no need to re-collect or re-process data that already exists elsewhere
• Supports large-scale synthesis (e.g. global biodiversity assessments, ocean health indicators) that no single dataset could achieve alone
• Keeps data accessible and usable long after a project ends or funding runs out
• Makes data machine-readable, so pipelines and tools can integrate it automatically instead of manual reformatting
• Provides context via metadata so others can correctly interpret and trust the data
• Maximizes the return on investment of data collection, which is often costly (fieldwork, sequencing, expert time)

To summarzize… standardization isn’t just for its own sake — it’s what lets individual datasets add up to something bigger than the sum of their parts, and what protects the investment already made in collecting the data.