# Slides - IWBDA2020 Lab Automation - Problem Area 1.1 - DRAFT FOR EDITING

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[IWBDA2020 Lab Automation 1.1: Testify](https://hackmd.io/G6Y-yHLLSnaFaRgHaz-A0Q)

## Testify

## Introducing Testify

- Testify is the human-first system for laboratory automation protocols.
- Today, researchers around the world suffer from a disconnect.
- There is an expressive gap between lab protocols-as-prose and protocols-as-code.
- Doing the same work on different lab devices often requires recoding the same instructions.
- Testify bridges that gap.
- It enables researchers to write automation protocols in plain language.

## Scope

## Proof of Concept

## Deliverables

- A research taxonomy derived from scientific literature.
- A domain-specific language intended for natural-language driven specification of lab automation procedures.
- An abstraction layer for writing device drivers that map manufacturers' device APIs with the DSL.

## Goals

Testify's key goals are two-fold.
1. Improve lab automation portability and interoperability *today*.
- Testify aims to enable semantic, platform-agnostic coding of lab automation.
- This opens the door to protocols with code as a first-class citizen.
2. Lock in these gains for *tomorrow*.
- Research playbooks written now should be run tomorrow or in 1000 years.
- Open access to scientific knowledge relies on *fixity*, the property of sameness over time.
- Lab protocols published today often become unreproducible within years.
- We can futureproof our work by humanizing our description of it.

### Heilmeier Catechism

- What are you trying to do? Articulate your objectives using absolutely no jargon.
- How is it done today, and what are the limits of current practice?
- What is new in your approach and why do you think it will be successful?
- Who cares? If you are successful, what difference will it make?
- What are the risks?
- How much will it cost?
- How long will it take?
- What are the mid-term and final “exams” to check for success?

### Misc

- Name
- Testify
- Stack
- NLP Data Stores
- Parquet
- Avro
- Architecture and Update Model
- Research Taxonomy derived from NLP methods on scientific literature
- We are essentially positioning the specification of machine operations from the perspective of machine operators, framed in terms of
- research activities,
- research objects,
- research techniques, and
- incidental mechanical facts of those research objects responsible for implementing instruction sets.
- This amounts to specifying a controlled vocabulary plus a set of operators, forming a grammar.
- We need to demonstrate this attains sufficiency to operate as an intermediate language between prose protocols and machine code.
- This is doable:
- device drivers update to support ontology<->API mappings with new hardware or firmware support for subsets of the ontology
- if device capabilities do not yet exist in ontology, previous collective buy-in to open system incentivizes device manufacturer to add these capabilities to ontology

### Technical