An open framework, born in Brazil, for the complete lifecycle of biological data — from production in the lab and in the field to analysis, visualization and knowledge.
People who work with biological data live between bioinformatics, biostatistics, curation and data engineering. There was no single word for the whole cycle. So we built one, piece by piece, the way languages always have.
Organisms, cells, genomes, ecosystems, crops, herds, patients.
+That which is given: the measurement, the reading, the record. With a D, the way we say it in Brazil.
+The suffix of fields of knowledge: genetic, electronic, informatic.
The obvious translation would swap the D for the T in data. We don't. Biodadica was invented in Brazil, and its name carries that origin into every language.
Built from dado — the everyday Brazilian Portuguese word for “data”.
Keeps the D. Not biodatic — biodadic.
Latin datum literally means “that which is given”. In Portuguese that is dado — with a D. The Brazilian word is the most faithful translation of the Latin root.
The D is a signature. Anyone who finds Biodadic in a paper, anywhere in the world, will know the concept has Brazilian roots.
Biodata already means “biographical data” in English. Biodadic is new, short and easy to search — clean ground for a new field.
Just as the world says bossa nova and capoeira, the world says Biodadic — a Brazilian word, Biodadica, traveling abroad. official spelling convention · PT: Biodadica · EN/ES: Biodadic
A sales figure doesn't change once it's recorded. A blood sample does. Biological data is a snapshot of something alive, and living things change, vary, err, evolve and belong to someone. That is why this data needs care that no other data field has to provide all at once.
Sequencers don't read DNA, they estimate it. Every sequenced base comes with an error probability on the Phred scale: Q20 means 1 error in 100 and Q30 means 1 in 1,000. Mass spectrometers, sensors and automated microscopes work the same way. Biological data without its confidence is only half the data.
Q30 → 99.9% accuracy per baseOnce removed from the organism, the material stays alive for a while: cells consume nutrients, RNA degrades and proteins change. A sample processed 2 hours after collection is no longer the one it was 10 minutes after. The collection time, the processing time and the interval between them are all part of the data.
collected 08:14 · processed 10:41 · Δ 2h27 ⚠Circadian rhythm, fasting, season, developmental stage and reproductive cycle all change the results. The same individual gives different numbers at different moments. Without that context, a comparison may be measuring nothing but coincidence.
cortisol: high in the morning, low at nightCapturing an animal raises its stress, a biopsy triggers inflammation, and freezing and thawing break cells. The act of collecting changes the biology itself, so how it was done must be recorded as a condition of the data.
temperature · preservative · freeze–thaw cyclesThere is variation between individuals, between cells in the same tissue and between repeats of an experiment. You must tell biological replicates from technical ones, and real effects from batch effects. An average without its variation does not describe life.
biological replicate ≠ technical replicateTissue is finite and perishable. Patients recover, crops are harvested, species can vanish. Often there is no second measurement, and the data is the only record left of that organism at that moment.
unique, unrepeatable materialReference genomes are updated (GRCh37 → GRCh38), species get new names and genes are renamed. Even Excel has forced changes: genes such as SEPT1 and MARCH1 kept turning into dates and were renamed SEPTIN1 and MARCHF1. All biological data must state which version of knowledge it was interpreted against.
SEPT1 → 1-Sep → SEPTIN1A genome identifies a person and reveals information about their whole family, and perfect anonymization doesn't exist. Brazil's LGPD and Europe's GDPR classify genetic data as sensitive personal data. Consent, purpose and access rules are part of the data, not paperwork beside it.
LGPD art. 5 II · GDPR art. 9In Brazil, access to genetic heritage and associated traditional knowledge is regulated by Law 13,123/2015 and must be registered in SisGen. Worldwide, it is governed by the Nagoya Protocol. Where a sample comes from has legal consequences and determines how benefits are shared.
Law 13,123/2015 · SisGen · NagoyaBiological data spans scales (molecules, cells, organs, individuals, populations, biomes) and formats (sequences, images, time series, occurrence records). Integrating all of it takes shared ontologies and identifiers, not just tables.
genome · image · sensor · occurrenceData travels from organism → sample → aliquot → library → run → file → analysis. Any step can introduce error or contamination. Without end-to-end traceability, results cannot be reproduced.
who · when · where · howHerbarium specimens collected more than 100 years ago now reveal the effects of climate change and preserve ancient DNA. Well-documented biological data can answer questions nobody has asked yet, as long as it is findable, accessible, interoperable and reusable (FAIR).
FAIR principlesIn Biodadic, a measured value never travels alone. It always carries its uncertainty, its time, its context, its origin and its permissions, from the first record to the last reuse.
{
"value": "ATGGCGTACCTGA…",
"uncertainty": "Q32 · 99.94% per base",
"collected": "2026-11-12T08:14-03:00",
"processed": "2026-11-12T10:41-03:00",
"interval": "2h27min", // ⚠ over 2h
"condition": "4 °C · EDTA · 0 thaws",
"organism": "Homo sapiens · NCBITaxon:9606",
"reference": "GRCh38.p14",
"provenance": "sample BD-0192 → lib L07 → run R2311",
"permission": "consent 2026-118 · sensitive data"
}
Ordinary data describes things. Biological data describes something that was alive, and a moment later had already changed.
Biodadic organizes everything that happens to a piece of biological data, from the moment it is measured until it becomes knowledge.
Sampling, sequencing, sensors, protocols and source metadata.
Validation, traceability, error detection and reproducibility.
Pipelines, storage, open formats and integration.
Ontologies, annotation, curation and links to other databases.
Statistics, bioinformatics, models and artificial intelligence.
Dashboards, charts and communication of the knowledge produced.
Biodadic is the discipline devoted to the complete lifecycle of biological data — production, quality control, engineering, enrichment, analysis and visualization — and to turning it into knowledge that is reliable, open and reusable.
Biodadic will grow as an open ecosystem with three fronts, each with its own purpose.
The public reference for the field: what it is, how it's done, which vocabulary to use.
Where biological data is stored, standardized and transformed.
The place for people: ask questions, share experience and learn.
This page is the first step. This is where the Biodadic biological data platform will be built.
The name, the definition and the invitation. Bringing the first people together.
The framework published with a DOI: scope, taxonomy and use cases.
An open vocabulary and Biodadic glossary in English and Portuguese.
The first biological data tools, the forum and the school.
If you produce, curate, transform or analyze data from living things, Biodadic is yours. Help us define the vocabulary, the tools and the community.