DIMS Dashboard — reference study

The dashboard everything else is measured against: one recorded session with its signals, its annotations, and every analysis DIMS ships. If you want to see what a DIMS dashboard is before building one, start here.

Run it

python serve.py            # then open http://localhost:8000
python serve.py 8080       # if that port is taken

Nothing to install. The dashboard is plain files and serve.py uses only the Python standard library. Everything it needs is in this repository, video included.

What you get

One session, 3120, with four signals: bodysync and three neuralsync channels. Pick a point on the timeline and every tab narrows to a window around it — the window is defaultWindowSize seconds, 5 here.

tab	what it draws
Time series	all four signals on a shared time axis, beside the video
RQA	a recurrence plot per signal — when the signal returns to a state it held before — with recurrence rate, determinism and laminarity tracked over time
Cross-RQA	the same between bodysync and neuralsync. Structure off the main diagonal is a lagged relationship, which is the point of the analysis
Cross-Wavelet	coherence between the two by time and timescale, with a 95% chance level from a Monte Carlo null. Coherence has no meaningful zero, so the tab reports the share of cells that beat chance rather than the raw value alone
ELAN	annotation tiers aligned to the video

The data

file	what it is
assets/videos/3120.mp4	the recording
assets/timeseries/3120_{signal}.csv	one signal per file, columns Time and a value
assets/transcripts/3120_transcript.json	the diarised transcript
assets/elan/3120.eaf	the annotations
assets/{rqa,crqa,crosswavelet}/	analysis output — regenerable, see below

The time series are the study's raw inputs: they came from the original DIMS work cited below and are not derived from anything else in this repository. The analysis directories are, and can be rebuilt from the CSVs at any time.

Each analysis writes two files: a rounded, reduced .json for the browser to draw, and a full-resolution .npz beside it. Analyse from the .npz — the JSON is a picture.

Rebuild the analyses

pip install -e /path/to/dims     # the analyses; not on PyPI yet
python build_assets.py --check   # what would run
python build_assets.py           # run it

Or run the analyses directly: dims-analysis run --config config.json.

Layout

config.json          which signals exist and which analyses run
index.html           the page; loads the vendored core
serve.py             local server (stdlib only)
build_assets.py      rebuild the analyses
vendor/              pinned copy of the shared core — never edit
assets/              data in, analysis out

vendor/ is a pinned copy of dims, verified against its release in CI. A fix belongs upstream and arrives here as a version bump (dims-case sync), never as an edit here.

History

This repository was previously dims-network/DIMS_Dashboard, now archived. Its full history is preserved here.

Citation

Miao, G. Q., Trujillo, J., Bulls, L. S., Thornton, M. A., Dale, R., & Pouw, W. (2025). DIMS Dashboard for Exploring Dynamic Interactions and Multimodal Signals. CogSci 2025.