Frequently Asked Questions

The Ancient BioChain is a provenance layer for ancient DNA research. It records the events and relationships that connect the records already held in museum catalogues, laboratory systems, sequence archives, compiled datasets and publications, so that the route from an excavated specimen to a published finding can be traced, inspected and independently checked. It does not store or replace sequence data, archaeological records or curated datasets — it records how they relate to one another, how they changed, and on what basis each link is asserted.

It is running. The Ancient BioChain is a full working deployment of The BioChain, operating all five steps of the model on public ancient DNA research data. Access is available on request. Ancient pathogen work is the next phase and is described on this site as planned rather than running.

The Ancient BioChain is The BioChain running in the open. The BioChain is a shared provenance and integrity layer for evidence that moves between systems and organisations, generally deployed privately for a particular organisation or sector. The Ancient BioChain applies exactly the same five-step model and the same infrastructure to public ancient biological research data, where the chain and its gaps can be inspected by anyone.

Ancient DNA is an unusually demanding provenance problem, which makes it a good test. A single ancient individual can accumulate a research history spanning decades, institutions and technologies; the material is finite and often destroyed in sampling; and results are reinterpreted and corrected as methods improve. If a provenance layer works here, it will work in less demanding settings. Ancient DNA is the starting point, not the boundary of the project.

The Ancient BioChain is a research initiative of The BioChain, led by Ashley Morgan. It is published openly and developed in the open, and we are seeking partners among laboratories, data curators, museums, repositories and funders to test it.

No. The name refers to the chain of evidence, not to a distributed ledger. The Ancient BioChain records provenance as a versioned graph of entities, activities and agents following the W3C PROV standard. Records are never overwritten — corrections are recorded as new events that supersede earlier ones — and each record carries a content fingerprint so a later user can confirm it has not changed since it was cited. That gives tamper evidence without requiring a blockchain.

A provenance layer is an open, versioned record of the events and relationships that connect records held in different systems. In ancient DNA, it records that a museum accession, a laboratory sample code, a library identifier, an archive run accession, a publication label and a compiled-dataset identifier all refer to the same individual; that one identifier was later merged with another; which version of a record a given analysis used; and on what evidence each of those statements rests.

Large-scale ancient DNA research depends on reuse. A study of population history may draw on thousands of individuals from hundreds of earlier studies, compiled by other people. That is what makes the field powerful, but it means the researchers drawing conclusions are increasingly distant from the specimens, laboratories and decisions that produced the data. When researchers can no longer know the history of every observation personally, the infrastructure has to preserve it for them.

The field has begun to audit itself. In 2024 Anders Bergström examined the archived data from 42 ancient genomics studies published in Nature, Science and Cell between 2021 and 2023. Sample accessions did not match the paper’s description of its samples in 15 of the 42 studies; raw sequencing reads were archived in only 20; coordinates were provided in archive metadata in 3; and a radiocarbon laboratory code was provided in 1. The curators of AncientMetagenomeDir have reported errors in data accessions and metadata insufficient to link accessions to the samples reported in a study, and the Allen Ancient DNA Resource states plainly that its identifiers may change between data releases.

Identity — identifiers multiply across systems and change over time. Link — publications and archives disagree about which data belong to which individual. Raw evidence — unaligned reads and failed samples are not archived. Context — place, time and dating information stay in supplementary files rather than in archive metadata. Versions — corrections propagate forward to new releases but not to copies already made. None of these is primarily a failure to collect data; each is a failure to record a relationship.

No. The breaks described here are a consequence of how research infrastructure has grown up, not of carelessness by individual laboratories. Each existing resource is authoritative within its own domain and most are very well run. The gap lies between them: no existing system is responsible for recording that records held in different places refer to the same thing, or for tracking how those relationships change over time.

Yes, and it has been measured outside ancient DNA. A widely cited study by Vines and colleagues in 2014 found that the odds of research data remaining obtainable fell by 17 per cent for every year after publication, and the odds of successfully reaching the authors fell by 7 per cent a year. Context held only in supplementary files, or only by the authors, is not durable.

Connect, structure, link, authenticate and verify. Connect brings in the systems that already hold the evidence. Structure records the identifiers, events, relationships and selected evidence the chain depends on. Link joins those records across systems while preserving origin, version and history. Authenticate establishes which organisation, person or system made an assertion and whether it was authorised to do so. Verify allows the provenance, signature, version and integrity of the evidence to be checked, including whether anything has changed since. The Ancient BioChain runs all five today.

The model follows the W3C PROV data model, a web standard for provenance. PROV describes the world in terms of entities (things such as specimens, samples, libraries, files, dataset records and publications), activities (events such as sampling, sequencing, processing or curation, which use some entities and generate others) and agents (the people, laboratories, institutions and software responsible for those activities). Using PROV means records can be exchanged with other provenance-aware systems and queried with standard tools.

It stores the relationships, not the science. The Ancient BioChain records identifiers, events, links between records, version history and the evidence basis for each link. Sequence data stays in the sequence archives, genotypes stay in the compiled datasets, specimens stay in their collections and papers stay with their publishers. Hosted storage is available as an option for organisations that want it, with backup and recovery, but the layer is designed to connect existing repositories rather than replace them.

Every relationship in the graph records how it is known, in one of five classes. Asserted — stated explicitly by the original data producer, for example in archive metadata. Documented — stated in a publication or supplementary table but not in structured metadata. Curated — established by a curator from multiple sources, with the reasoning recorded. Inferred — matched automatically, for example by identifier similarity or sequence comparison, with the method recorded. Disputed — conflicting sources exist and both are recorded. This makes gaps visible rather than hiding them, and users can filter on the distinction.

Nothing is overwritten. When a date is recalibrated, two individuals are merged, a sample is reassigned or a contamination flag is added, the layer records a new event linking the new state to the old one, with the reason and the source. Analyses record which version they used. It then becomes possible to ask which published results depend on a version that has since been superseded, and to notify their authors.

A content fingerprint is a cryptographic hash of a record — a short value computed from the record’s contents that changes if any part of those contents changes. Every record in The Ancient BioChain carries one, so anyone who cited a record can later confirm that what they cited is still what they are looking at, without having to trust the system that holds it.

Existing identifiers remain authoritative. An ENA or SRA accession stays an ENA or SRA accession, a DOI stays a DOI, and a museum number stays meaningful in its museum. The Ancient BioChain creates its own identifiers only for the events and links it records. Agents are identified with existing persistent identifiers wherever possible — ORCID for people and ROR for institutions.

Several kinds. Trace: show everything that happened between a specimen and a result. Resolve: which identifiers, across all systems, refer to this individual? Audit: which individuals in this dataset lack archived raw reads, a radiocarbon laboratory code or a sampling permission? Assess: for this analysis, what share of the evidence chain is asserted, documented, curated or inferred? Propagate: which published analyses used a version of an individual that has since been merged, re-dated or flagged as contaminated? Credit: which laboratories, museums and curators contributed to the evidence behind a given result?

No, and it is explicitly designed not to. Sequence archives such as ENA and NCBI SRA preserve reads and alignments. Compiled datasets such as the Allen Ancient DNA Resource curate genotypes and metadata. Community indexes such as AncientMetagenomeDir standardise and validate metadata for published samples. Packaging frameworks such as Poseidon bundle genotype data with archaeological and laboratory context. Each is authoritative in its own domain. The Ancient BioChain records the relationships between them, which none of them is responsible for holding.

No. The Ancient BioChain connects to the systems you already run and records provenance alongside them. The first phase is designed to work from public information alone, reconstructing evidence chains from compiled datasets, sequence archives and publications without asking anyone to change anything. Later phases offer tools for recording provenance at the point of deposit, for producers who want to take part directly.

The FAIR principles — findable, accessible, interoperable, reusable — describe what good data management should achieve. The Ancient BioChain is designed to support them by adding the relationships that FAIR metadata alone does not capture: which record came from which, what changed between versions, and on what basis each link is asserted. Good FAIR metadata makes a record usable; provenance makes the chain behind it inspectable.

It is the information we propose should exist for every published ancient genome, organised by the five breaks: a mapping of all identifiers for each individual; one archive sample accession per sample, with read-group names matching; all sequencing reads archived, including unaligned reads and data from screened and failed samples; site name and coordinates, or a protected reference to them, plus the radiocarbon laboratory code, uncalibrated age, calibration curve and software; and reference genome and pipeline versions, holding institution, sampling permission and a record of later corrections linked to the original. It is deliberately modest — almost all of it already exists somewhere in a typical study.

Ancient DNA research concerns people, and the field increasingly recognises obligations to the communities connected to them. In 2021 an international group of researchers set out five globally applicable ethics guidelines for DNA research on human remains, covering regulatory compliance, detailed planning, minimising damage to remains, data availability after publication, and engagement with stakeholders. The CARE Principles for Indigenous Data Governance — collective benefit, authority to control, responsibility and ethics — address who governs data about Indigenous peoples. A provenance layer supports these obligations directly: recording sampling permissions, consultation and data-use conditions as part of the chain makes them visible to every later user, rather than confining them to the original study.

No. Precise burial locations can put sites at risk of looting or disturbance, and some communities may not wish certain information to be public. Location and other sensitive fields can be recorded as protected references, accessible only under conditions set by the responsible institution or community, while the rest of the chain remains open.

Yes. Provenance records are openly licensed and exportable in standard formats, and the model and software are being developed openly so that the community can inspect, adopt and extend them. The white paper is published as a draft for comment, and both it and the Ancient DNA Provenance Checklist can be read in full on this site without a form. The PDF editions ask for an email address so that we can tell you when a resource is revised; the files themselves are free to use and adapt with attribution.

The Ancient BioChain is a research initiative and its published resources — the white paper, the provenance checklist and the Origins articles — are free. Access to the running chain is available on request. For organisations wanting their own deployment, The BioChain offers several commercial models; because every deployment differs in scope, systems and data, pricing is discussed case by case rather than published.

Request access through this site. We are particularly interested in hearing from laboratories willing to confirm or correct reconstructed chains for their own published data, curators of compiled datasets and community indexes, museums and collections interested in linking sampling events to derived data, and funders or partners for the pilot.

A provenance layer is infrastructure and needs long-term stewardship. We expect its governance to involve the communities that produce and curate ancient DNA data, and its funding to combine research grants in the early phases with institutional partnerships and services for data producers later. It sits within the wider architecture of The BioChain, which applies the same principle — connecting evidence rather than replacing the systems that hold it — to modern biological data.

The first phase tests how much of the evidence chain behind published ancient genomes can be reconstructed from public information alone, and where it breaks. It takes a defined set of published individuals spanning a range of publication dates, laboratories and data types, extracts identifiers and metadata from compiled datasets, sequence archives and publications, builds the provenance graph with each link classified by confidence, and records every break by type together with the effort needed to resolve it.

By measurement rather than assertion. The pilot reports the share of individuals with a complete chain from specimen to archived raw reads; the distribution of link confidence classes; the frequency of each break type by publication year and data type; the time and effort needed to resolve a break manually; and the accuracy of automated inference against links confirmed by the original producers.

Three things, and we say so in the white paper. If the pilot found that most evidence chains can in fact be reconstructed reliably and cheaply from existing sources; if the breaks identified proved to have little effect on scientific conclusions; or if community resources adopted cross-system provenance recording on their own, making a separate layer unnecessary. Each of those would be a good outcome for the field, and the pilot is designed to find out.

A provenance layer cannot recover information that was never recorded. Where a laboratory did not keep the relationship between a library and a sample, the layer can only record that the link is inferred or missing. It also depends on participation: reconstruction from public data can show where chains break, but closing those gaps requires data producers and curators to record relationships at the source.

Ancient pathogen research is the next phase. Pathogen work introduces an especially demanding provenance problem, because authentication, contamination assessment, reference selection, computational transformations and epidemiological interpretation can each affect the evidential meaning of a result — and a pathogen genome may be recovered from reads originally sequenced for human population analysis, then combined with modern isolates. Beyond that, the same logic connects ancient DNA to modern genomic surveillance: in both, the question is whether we can show where the evidence came from.