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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

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.

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 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.

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.

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.

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.