biosdk 0.1.4

BioSDK — Unified Neural Data Library. One open() for MEA, EEG, ecephys, Sleep, RNG. Evidence bundles included.

BioSDK — Unified Neural Data Library

v0.1.4 | 7 format adapters | 83 conformance tests | Evidence bundles

BioSDK is a unified library for biological neural data. One open() for any format — MEA, EEG, ecephys, Sleep, RNG — with integrity-verified evidence bundles. Think of it as pandas.read_* for neural data.

Why BioSDK

• One open(), 7 formats: HDF5 (MCS, Giroldini), NWB (DANDI/Allen), EDF (PhysioNet, OpenNeuro), XLSX (Tressoldi), CSV (GCP2), FinalSpark API.
• Same features across vendors: 6 standard features per channel (RMS, MAV, zero-crossings, variance, peak, skewness) — compare MEA from MCS to MEA from Giroldini in one pipeline.
• Evidence bundles: Every result is a SHA256-chained, HMAC-signed bundle — manifest + data + results + signatures in one folder. Reproducible, verifiable, tamper-evident. This is unique to BioSDK — no other neural data library provides this.
• Open Core: MIT (community) + Commercial (enterprise safety/closed-loop).

Licensing Model: Open Core

Tier	License	Includes	Price
Community	MIT	Format adapters, features, readout, evidence bundles, dashboard	Free
Enterprise	Commercial	Closed-loop safety controller, managed cloud, SLA, priority support	Contact

Why Open Core: The library must be maximally adopted — MIT ensures zero friction. Revenue comes from enterprise features that labs and companies need for production.

Install

# From real PyPI:
pip install biosdk

# From TestPyPI (pre-release):
pip install -i https://test.pypi.org/simple/ biosdk

Optional extras:

pip install "biosdk[dashboard]"   # Web dashboard
pip install "biosdk[mne]"         # EEG/EDF support via MNE
pip install "biosdk[all]"         # Everything

Quick Start

import biosdk

# Open ANY neural data — auto-detects format (HDF5, NWB, EDF, XLSX, CSV)
ds = biosdk.open("recording.h5")

# 6 standard features per channel: RMS, MAV, ZC, VAR, PEAK, SKEW
X = biosdk.features(ds, window_s=1.0)

# Classification readout (Random Forest, Logistic Regression, SVM)
result = biosdk.readout(X, y, classifier="rf")

# Integrity-verified evidence bundle (SHA256-chained, HMAC-signed)
bundle = biosdk.evidence_bundle(result, "my_results")

Format Adapters (7 total)

#	Adapter	Format	Vendor	Modality	Ch	Hz	Tests
1	mcs_mea2100	HDF5	MCS	MEA	17	500	8/8
2	dandi_nwb	NWB	DANDI/Allen	ecephys	5-96	100-30k	8/8
3	physionet_edf	EDF	PhysioNet	Sleep/EEG	7-21	100-200	8/8
4	gcp2_csv	CSV	GCP2	RNG	1	1/60	9/9
5	tressoldi_h3	XLSX	Tressoldi	EEG	14	128	9/9
6	finalspark_neuroplatform	API	FinalSpark	MEA wetware	8	30k	41/41*
R	giroldini_mea	HDF5	Giroldini	MEA	59	20k	ref

* FinalSpark: adapter skeleton built. Conformance tested on mocks (41/41). Awaiting API token for live validation.

Conformance total: 42 certified + 41 mock = 83/83 PASS

Validated Results

All numbers are current-snapshot values. Chance rates shown for context.

Dataset	Task	Accuracy	Chance	Improvement
OpenNeuro ds007558	Eyes open/closed (2-class)	83.7%	50%	1.7x
Sleep PSG (PhysioNet)	Sleep staging (5-class)	66.7%	20%	3.3x
Tressoldi H3 BBI	Stimulus vs rest (2-class)	64.2% per-pair	50%	1.3x
Giroldini MEA	4-class stimulus	52.4%	25%	2.1x
Cross-vendor MEA	Giroldini vs MCS	100% separable	50%	2.0x

Honest baseline: sklearn SVM achieves 50.7% on the same Giroldini MEA task. BioSDK's pipeline adds +1.7 percentage points. The value is in the unified API + evidence bundles, not in algorithmic edge over standard classifiers.

Cross-Modal Structure

NSI-1.0 features preserve modality identity — same modality clusters together, different modalities separate:

Modality cluster	Correlation	Interpretation
MEA (Giroldini-MCS-DANDI)	0.90-0.96	Same modality, different vendors
EEG (OpenNeuro-Sleep)	1.00	Same modality, same format (EDF)
Cross-modality (MEA vs EEG vs RNG)	~0.00	Different physical phenomena — correct behavior

What BioSDK Is NOT

• ❌ NOT a new file format (NWB, EDF, HDF5 already exist — BioSDK is a unified loader, not a replacement)
• ❌ NOT an operating system ("BiC OS" was an early working name — the project is a Python library)
• ❌ NOT a biological computer, GPU replacement, or energy platform
• ❌ NOT production-hardened for live stimulation (closed-loop safety gates are designed and simulator-tested; hardware validation pending FinalSpark token)

What's Next

#	Item	Status
1	FinalSpark live validation	Application submitted. Awaiting token.
2	Closed-loop on real hardware	Safety gates designed (7 Shannon limits), simulator-tested. Requires hardware.
3	Beta participants	Invite packet ready (beta/BETA_INVITE_PACKET_V93.md).
4	Cross-dataset classification	MCS structure proven (silhouette 0.559, 43.5x shuffled). Blocked: no labels.
5	Additional labeled datasets	CRCNS, 3Brain samples — requires registration/download.

Project Identity

Attribute	Value
Name	BioSDK (BioCompute Software Development Kit)
Positioning	Unified neural data library + evidence bundles
Package	biosdk v0.1.4
License	Open Core: MIT (community) + Commercial (enterprise)
Author	Vladislav Dobrovolskii ([e-mail])
PyPI	https://pypi.org/project/biosdk/
GitHub	https://github.com/Vladrus39/BioSDK

Key Documents

Document	Path
Master Status	MASTER_STATUS_V85.md
Session Handoff	HANDOFF_FOR_DEEPSEEK_2026_05_11.md
Machine Status	PROJECT_STATUS_V85.json
NSI-1.0 Spec	docs/standards/NSI_1_0_SPECIFICATION.md
Evidence Bundle Format	docs/BIOSDK_EVIDENCE_PACK_GUIDE_V512.md
Beta Invite Packet	beta/BETA_INVITE_PACKET_V93.md

License

• Community Edition (this repository): MIT License — adapters, features, readout, evidence bundles, dashboard
• Enterprise Edition: Commercial License — closed-loop safety controller, managed cloud, SLA

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BioSDK v0.1.4. Unified neural data library. Open Core. Evidence bundles. One open() for 7 formats.