You could certainly define similar schemas for XML or use vanilla JSON schemas or whatever. From my perspective, one point in favor of ATProto is about the infrastructure it provides for discovery + sharing: defining lexicons (rather than another schema) supports hooking into that infrastructure.
Layers (v0.5.0) now has a pub.layers.changelog lexicon for targeted semantic versioning of all data and metadata components in the pub.layers namespace.
docs.layers.pub/lexicons/cha...
We've also documented plans for developing an AppView.
docs.layers.pub/appview/
Discussion welcome.
Added a page on @sealight.xyz's seams.so: docs.layers.pub/integration/....
Had to root around a bit, since tangled.org/sealight.xyz... shows as empty, so if there is anything incorrect on that page, happy to update it.
Layers (v0.4.0) now has a full set of XRPC queries with domain-specific filter parameters for every record type, and a major simplification to the way segmentation works.
Github: github.com/layers-pub/l...
Docs: docs.layers.pub
We’re happy to add additional integration plans as well!
Yup! We’ve got integration plans for @margin.at and @semble.so among others in the ecosystem! docs.layers.pub/integration/
This is very much a draft proposal. We'd genuinely appreciate feedback from anyone who works with annotation data, psycholinguistic experiments, language modeling, NLU, or digital humanities. All input is welcome.
github.com/layers-pub/layers/discussions
The ultimate goal is to build a system that integrates fully with the broader @atproto.science ecosystem.
The plan is for community governance to follow @chive.pub's approach. All the enums (annotation kinds, tagsets, categories, formalisms) are community-expandable. Anyone can mint new ones by publishing ontology records in their own PDS without any schema changes on our end.
Layers is a sister project to @chive.pub. The idea is bidirectional linking between annotation resources and eprints: datasets link to the papers that describe them, and papers link back to their data. This works with eprints on Chive and other repositories.
For knowledge representation, there's a typed property graph (pub.layers.graph) that works similarly to how @chive.pub does its knowledge graph with pub.chive.graph.*. Annotations ground to Wikidata, FrameNet, WordNet, PropBank, and so on through knowledgeRef links.
Layers also covers offline judgment experiments with four orthogonal design dimensions (what you measure, the response instrument, how stimuli are presented, and what captures the data) and annotation project design metadata for things like annotator redundancy, adjudication, and quality criteria.
Beyond annotation, Layers covers psycholinguistic and neurolinguistic signals: eye-tracking fixations, EEG/ERP waveforms, MEG sensor data, fMRI activations. These use the same anchoring and annotation primitives as text, just with temporal and spatial selectors.
For computational linguistics and NLP, the goal is a common interchange format you can import training and evaluation data through. The docs cover how Layers subsumes a bunch of data models (CoNLL, brat, ELAN, TEI, AMR, LAF/GrAF, and others), and the plan for the appview is adapters from each.
If you do fieldwork or language documentation: the same schema that handles treebank annotations also handles interlinear glossed text, ELAN-style time-aligned tiers, and multimodal alignment across audio, video, and images. All linked to the same expressions and living in your own PDS.
The format doesn't commit to any particular tagset or theoretical framework. Whether you work in UD, HPSG, LFG, or Minimalism, your labels and categories are data values within the schema, not baked into it. The same primitives represent your annotations regardless of formalism.
The core idea is a small set of shared primitives (anchors, selectors, feature maps) that all data types compose from. Expressions are recursive: a document contains paragraphs, which contain sentences, which contain words, which contain morphemes. Annotations attach to any level of that hierarchy.
On top of that, most annotation data lives in isolated repositories on centralized services, and there's no standard way to link annotations to the experiments that produced them or other signals (e.g. neural recordings) they were collected alongside. Layers tries to address both of these problems.
The motivation is pretty simple. There are tons of linguistic annotation formats, and they don't really talk to each other. If you want to use annotations from one tool in another, you end up writing conversion scripts that inevitably break on edge cases.
Layers is a set of ATProto lexicons for representing and interlinking linguistic data across modalities. We're hoping to get feedback on a draft proposal for the lexicons.
docs.layers.pub
github.com/layers-pub/layers
