Stylized vector art: a network graph with one community circled.

Ethics and Governance

Start with the honest assessment: connectomics does not currently have a human-subjects problem. Its flagship human dataset is discarded surgical tissue that was going to be removed regardless, reviewed under the same frameworks that govern any human-tissue study. Nothing in this field asks a person to undergo a procedure for research, and nothing published so far identifies anyone. If you came here expecting a controversy, there isn’t one to report.

Two things are nonetheless live now, and they are practical rather than philosophical:

The rest is prospective, and worth discussing precisely because it is not yet urgent. Consent framings written for a cubic millimetre of discarded tissue (§1) were not written for whole-brain human volumes, for datasets that become machine-learning corpora, or for a regulatory environment now drafting rules about “neural data” (UNESCO adopted the first global standard in November 2025). De-identification (§2) is a non-issue at today’s volumes and an open question at tomorrow’s. Dual-use arguments (§4) are weak for a field with no pathogens and should be stated at their real strength, not inflated.

Treat the prospective sections as a seminar, not a compliance checklist. The right time to reason about them is before a project needs the answer.

This page holds the facts. The decisions you make with them — how to review a manuscript, how to write an authorship policy, how to raise a concern — are taught in Module 19. Use this page when you need to check what a licence says or where a dataset came from; use the module when you need to practise the judgement.

Where H01 came from

The site’s human reference volume has a specific and documentable provenance. Shapson-Coe et al. (2024) describe the sample as a “rapidly preserved, 170-µm-thick slab of human cortex from the anterior part of the middle temporal gyrus of a 45-year-old female”, of “just over 1 mm³”, which “was removed to gain access to an epileptic focus in the underlying hippocampus”. It was cut into 5,019 sections of mean thickness 33.9 nm, imaged by multibeam SEM at 4 × 4 nm, and yielded a dataset of about 1.4 petabytes containing roughly 57,000 cells and 150 million synapses.

The ethical structure follows from that description. The tissue is a surgical by-product: in the authors’ framing, human specimens become available “from neurosurgical interventions for neurological conditions in which pieces of the cortex are discarded because they obstruct access to a pathological site”. No tissue was removed for research that would not otherwise have been removed. That is the strongest position human connectomics can currently occupy, and it is why H01 — rather than any purpose-collected human sample — is the field’s reference volume.

What the paper does and does not say

Two things are worth knowing before you cite H01 in an ethics context.

The main article carries no consent or IRB statement. A full-text search of the published Science article for “consent”, “IRB”, “ethics” and “Institutional Review” returns nothing; the data-availability statement points at the release landing page, and the licence line covers the article, not the data. The consent and approval details live in the supplementary Materials and Methods. When you reuse human tissue data, find the ethics statement before you cite the dataset, and say where you found it. A citation to a landing page is not a citation to an ethics review.

The paper is explicit about the sample’s pathology. It states that human surgical samples “originate in individuals with pathologies of the nervous system such as epilepsy, tumors, or neurodegenerative diseases” and that in this case “we cannot exclude the possibility that long-term epilepsy, or its pharmacological treatment, had subtle effects on the nanometer-scale structure of the tissue”. This is simultaneously a scientific caveat and an ethical one: every claim of the form “the human brain does X, as shown by H01” is really a claim about tissue from one adult woman with drug-resistant epilepsy. Section 4 returns to why that matters beyond the lab.

The comparison that clarifies it

Source Oversight regime What consent covers
Human surgical tissue (H01) Institutional review of human-subjects research; tissue is discarded surgical material Research use of the specimen; whether “research use” was understood to include indefinite open redistribution and downstream machine-learning use is the live question, not a settled one
Vertebrate animal tissue (MICrONS, MouseConnects) Institutional animal care and use committee approval Protocol-level: species, numbers, procedures, endpoints
Invertebrate tissue (FlyWire, hemibrain) No animal-welfare committee requirement in the United States for Drosophila Not applicable

The asymmetry is the point. The dataset with the fewest formal gates — Drosophila — is the one the field proofreads most openly and credits most carefully. The dataset with the most gates is the one whose ethics statement is hardest to find.

2. De-identification: what an EM volume can and cannot reveal

Structural MRI has a well-known re-identification route: a head volume can be rendered as a face, which is why defacing is a standard preprocessing requirement. Nanoscale EM has no equivalent route. A 1 mm³ block of cortex imaged at 4 nm contains no face, no skull shape, no name, no date, and none of the direct identifiers a health-privacy framework enumerates. The image data carries no genotype: what is released is heavy-metal-stained, resin-embedded tissue rendered as grayscale voxels.

The residual risk is therefore contextual, not pictorial, and it lives in the metadata rather than the images:

Two consequences for practice. First, de-identification work in EM connectomics is metadata discipline, not image modification — there is nothing to blur. Second, the standard reference on responsible reuse of open human connectomics data (Betzel & Bhatt, 2021) was written about neuroimaging-scale datasets, and its treatment of privacy, consent, analytic flexibility and transparent reporting transfers only partly to nanoscale EM. The reporting and analytic-flexibility guidance transfers cleanly. The privacy guidance was designed for a modality with a face in it.

Governance is moving toward the general category rather than the modality. On 12 November 2025 UNESCO’s General Conference adopted the Recommendation on the Ethics of Neurotechnology, the first global standard in the area; it establishes a framework for “neural data”, holds that such data is uniquely sensitive and requires strict safeguards against misuse, and is non-binding on member states. Whether a volume EM dataset falls inside that definition of neural data is not settled, and this page does not claim it does.

3. Licences: what a reuser is actually obliged to do

This is the section people skip and then get wrong. The three flagship datasets are released under three different regimes, and in at least one case the paper and the data carry different licences.

Resource Licence What you must do Commercial use
MICrONS (microns-explorer.org) Creative Commons Attribution 4.0 International Retain creator identification and copyright notice, indicate modifications, and include the licence text or a link to it; impose no further restrictions downstream. Follow the site’s citation policy — the named publication is MICrONS Consortium et al. (2025), Nature 640: 435–47 Permitted
FlyWire public release (v783, an October 2023 snapshot) Creative Commons Attribution-NonCommercial 4.0 Attribute, and cite the papers named in FlyWire’s citation guide. Pre-publication data carries separate community-principles obligations Not permitted
FlyWire’s flagship paper (Dorkenwald et al., 2024, Nature) Creative Commons Attribution 4.0 Attribute Permitted
Hemibrain / neuPrint Creative Commons Attribution (version not confirmed from the primary source — check before redistributing) Attribute; cite Scheffer et al. (2020) Presumed permitted, unverified
H01 No licence statement was found on the release landing page; the Science article is under the AAAS journal licence, © the authors. Data availability points at the public Google Cloud bucket, open with no registration Cite Shapson-Coe et al. (2024) and check terms before redistributing Unclear — verify

The FlyWire row is the one that catches people. Reusing a figure from the Nature paper and reusing the connectome are governed by different instruments: the article is CC BY, the data is CC BY-NC. A commercial product built on FlyWire connectivity is a licence problem; a commercial product built on MICrONS connectivity is not.

Two more layers sit above the per-dataset licence.

Funder obligations bind the producer, not the reuser. The NIH Data Management and Sharing Policy (NOT-OD-21-013) took effect on 25 January 2023 and requires every NIH application that will generate scientific data to include a data management and sharing plan, and to comply with it. That is why CONNECTS-scale projects release data at all — but it grants a reuser nothing beyond what the dataset’s own licence grants.

Repository policies span a spectrum. Jwa & Poldrack (2022) surveyed data sharing policies across neuroimaging repositories and found that the level of access control and the restrictions placed on secondary use vary widely, from fully open repositories where data may be downloaded and reanalysed without constraint, through to controlled access requiring verified credentials and limiting what secondary analyses are permitted. The practical lesson is that “the data is public” is not a licence statement.

A decision table

You want to… Check
Put a published figure in your paper or slides The article licence, not the data licence
Redistribute a derived table (e.g. an edge list) The data licence plus its attribution requirements
Train a model on the imagery The data licence; a NonCommercial clause reaches the model if the model is commercial
Build a product or a paid service Whether any NC clause applies — FlyWire yes, MICrONS no
Publish a reanalysis The portal’s citation policy: cite the papers it names, not only the URL
Quote a number in a lecture Nothing — but pin the release version, per Provenance and versioning

4. Dual use, honestly

Connectomics has none of the hazards the classic dual-use frameworks were built for: there is no pathogen, no agent, no enhancement of transmissibility. Saying otherwise inflates the field’s risk profile and wastes the reader’s attention. Three concerns are real, and only one of them is currently governed.

Governed: neural-data misuse. The UNESCO Recommendation (§2) situates neurotechnology within a human-rights framework — dignity, freedom of thought, mental privacy, autonomy — and calls on states to prevent applications that facilitate coercive control, unlawful surveillance, or manipulation. It is not binding, and it primarily targets devices that read or write brain activity in living people, not post-mortem or surgical structural datasets.

Arguable, undocumented: method transfer. The segmentation, tracking and instance-association methods developed for connectomics are general computer vision, and general computer vision has surveillance applications. This is an argument from capability, not from any documented case; it is recorded here as an argument and not as a finding.

Actual, and the one a student is likely to commit: over-claiming from a single sample. “H01 shows that the human brain does X” is a sentence about tissue from one person with epilepsy (§1). Extended into difference or disease narratives, a claim of that shape is a scientific integrity failure with social consequences well outside the lab. The mitigation is not a policy: it is writing the sample description into the claim, every time, and the review practice in Module 19 is where that gets drilled.

5. Credit for proofreading labour

The scale

Dorkenwald et al. (2024) estimate that FlyWire’s brain reconstruction took around 33 person-years of manual proofreading, distributed across consortium labs, centralised teams at Princeton and Cambridge, and citizen scientists worldwide; community members shared 133,700 annotations. The product was 139,255 proofread neurons and 54.5 million synapses.

That is the only published effort figure of its kind this page could source. MICrONS and H01 do not publish an equivalent person-year number. The absence is itself a finding: the largest single labour input to a connectome is routinely unquantified in the paper that reports the connectome.

Four credit models actually in use

Model Example What a contributor can put on a CV Failure mode
Consortium co-authorship “The FlyWire Consortium” as a co-author of Dorkenwald et al. (2024) Membership of a named group; the individual is not indexed by name in bibliographic databases Hard to claim in a job application or a tenure case; invisible to citation metrics
Collective acknowledgement in the author line Kim et al. (2014), Nature — the author list ends “and the EyeWirers”, with the individual EyeWirers who reconstructed the cells listed in supplementary information A verifiable named appearance, but in supplementary material Not authorship; disappears from every automated record
Per-contribution platform attribution FlyWire Codex shows per-cell credits and a labelling leaderboard A durable, checkable link to specific work Not a publication; depends on the platform continuing to exist
Named individual authorship with a contributions statement Shapson-Coe et al. (2024) names individuals for “proofreading of neurons”, “production of ground truth for synapse prediction and excitatory versus inhibitory classification”, and each other task Full authorship plus a specific, quotable role Only workable when the contributor count is small

What to do about it

Three things this page can state without hedging.

  1. CRediT has no term for proofreading. The nearest contributor-role terms are Data curation and Investigation, and neither describes segment-level error correction. If your project uses CRediT, write down which term you are mapping proofreading onto, before results exist.
  2. The threshold has to be written before the work. “How much proofreading earns authorship?” answered after a paper is drafted is answered under pressure. The four models above are the menu; pick one and publish the rule.
  3. Effort is measurable, so measure it. FlyWire’s 33 person-years exists because someone counted. Edit histories in CAVE-backed datasets make per-contributor effort computable. A project that does not report it has chosen not to.

Self-check

  1. You want to build a paid teaching product around a connectome. Which of MICrONS and FlyWire can you use, and why?
  2. Why is “we removed the patient’s name” not the whole of de-identification for H01 — and what is the residual risk?
  3. A colleague says an undergraduate who proofread 800 segments should be “acknowledged, not an author”. Which credit model is that, and what does the contributor lose?

Answers.

  1. MICrONS: it is CC BY 4.0, which permits commercial use provided you attribute, mark modifications and pass the licence on. FlyWire’s data release is CC BY-NC 4.0, so a paid product is outside the licence — even though FlyWire’s Nature paper is CC BY and its figures are reusable.
  2. Because the volume never contained a name, or a face, or any direct identifier — there is nothing in the image to remove. The residual risk is contextual: the clinical description (age, sex, procedure, site, approximate date) picks out a small population, and it lives in metadata and prose, not in voxels.
  3. Collective acknowledgement — the Eyewire model. The contributor loses authorship, indexing under their own name in bibliographic databases, and any citation credit; they gain a named appearance that most automated records will never see. That may be the right call; it should be the stated call, made before the work, not after.

What this page does not cover

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References