Glossary#
This glossary provides definitions for key terms and concepts used in the Nominal platform.
Asset#
A physical or simulated device or system you track in Nominal, such as a vehicle, a component, a unit under test, or a test stand.
In Nominal, an asset is a metadata object that lets users and tools reference that system’s datasets, runs, and events.
Learn more: Data model core concepts: Asset.
Automatic test equipment (ATE)#
Another name for a test stand, used mostly in chip and board test: the equipment that runs tests on a unit under test.
See Test stand.
Channel#
A named signal: a series of measurements or computed values, such as voltage, pressure, or system state.
A channel belongs to one data source and has its own data type, unit, and description. Channel names are case-sensitive: Temperature and temperature are different channels. Each combination of tag values on it is stored as a separate series. Workbooks and checklists use a channel through variables.
Learn more: Data model core concepts: Channels.
Check#
A rule that evaluates time-series data against a condition, such as a threshold or a state change, and creates an event wherever the condition is met.
Learn more: 🔒︎Auto-flag events: Checks.
Checklist#
A versioned set of checks that you execute on runs or assets to evaluate test data during or after a test.
Learn more: 🔒︎Auto-flag events: Checklists.
Comparison workbook#
A workbook that compares aggregated metrics, such as each run’s mean or maximum, across many runs or assets.
It shows the results in a table or chart. To overlay the runs’ time series instead, use a run workbook.
Learn more: 🔒︎Comparison workbooks.
Connection (Nominal data source)#
A data source that links to an external time-series database, so Nominal can read data from outside systems.
Dataset#
A store in Nominal for time-series and log data, which grows as you upload or stream more into it.
Learn more: Data model core concepts: Time-series data.
Data scope#
The link between an asset or run and a data source, named by its ref name. It can filter the data source by tags and shift its timestamps.
A tag filter lets an asset see only part of a shared dataset, and an offset corrects a fixed clock difference between loggers.
Learn more: Data model core concepts: Data scopes and ref names.
Data source#
Anything an asset or run reads time-series data from: a dataset, a connection to an external database, or a video.
Each one attaches to an asset or run under a ref name.
Learn more: Data model core concepts: Data scopes and ref names.
Decimation#
Showing fewer points when there’s a lot of data. Zooming in shows full resolution for the range you choose.
Learn more: Decimate data.
Device under test (DUT)#
Another name for a unit under test (UUT): the device or system being tested.
End-of-line (EOL) test#
The final test sequence a unit goes through before it ships, run on a test stand at the end of the production line to confirm the finished unit meets its specifications.
Learn more: Using a single dataset for many identical assets.
Event#
A point or span in time that marks something meaningful, such as the start of a burn, a failure, or a rule violation.
Learn more: Data model core concepts: Events.
Extractor#
Your own parsing code, packaged as a Docker image, that Nominal runs during ingest to convert a file it can’t read natively into time-series data.
Also called a file extractor, or a containerized extractor because it runs as a Docker image.
Learn more: File extractors.
Formula#
An expression, such as a + b, that computes a new time series from existing numeric variables in a workbook or checklist.
Learn more: 🔒︎Transformations and formulas.
Group-by#
A workbook setting that splits a plotted variable into a separate series for each value of a tag.
For example, it can show one series per execution-id. In the app it’s Split by tags, in a variable’s Series settings. It changes how the data is shown, not how it’s stored.
Learn more: Filtering and splitting by tag in a workbook.
Hardware-in-the-Loop (HiL, HITL, HWIL) Testing#
Testing where real hardware, such as a unit under test’s controller, runs against simulated inputs from a test stand.
The HiL rig can be an asset of its own.
Learn more: The default data approach: Production tester monitoring.
Ingest#
Processing an uploaded file into a dataset: reading its rows, placing each point in time, and storing the values in channels.
Learn more: CSV upload.
Label#
A short piece of text for grouping, searching, and filtering assets, runs, data sources, and events, such as prototype or night-flight.
Learn more: Data model core concepts: Time-series data.
Log#
Time-series data in which each point is a timestamp and a text message.
Offset#
A time shift applied to data without changing its stored timestamps.
A data scope can carry one, to correct a fixed clock difference between loggers, and workbooks use offsets to align runs that started at different times. This isn’t the offset transform, which shifts a signal’s values.
Learn more: Data model core concepts: Data scopes and ref names.
Panel#
A block in a workbook where you plot, compare, or annotate time-series data, such as a chart, scatter plot, histogram, or map.
Learn more: 🔒︎Panels and tabs.
Plot#
A single time series drawn in a panel, which you can style with color, line style, or shading.
Learn more: 🔒︎Workbooks overview.
Production line#
The sequence of stations a product moves through as it’s built and tested. A production line can have many test stands, each running part of the test plan on every unit.
Learn more: Using a single dataset for many identical assets: Units run through multiple testers.
Profile#
A named set of connection settings for Nominal: a base URL, an API key, and a workspace RID.
Your config file can hold several profiles, each under its own name, such as default. Code picks one by name, as in Python’s NominalClient.from_profile("default").
Learn more: API keys setup.
Property#
A key-value pair, of type Text or Number, that describes an asset, run, or data source, such as an identifier, context, or a quantity.
Numeric properties sort as numbers on index tables and plot as constants in workbooks.
Learn more: Data model core concepts: Time-series data.
Range#
A start and end time that marks a period of interest, from time-series output, manual notes, or checklist results.
Learn more: 🔒︎Workbooks overview: Time range controls.
Ref name#
The name an asset or run gives each data source attached to it, such as default. Workbooks and checklists find channels by ref name and channel name.
Using the same ref names on every asset and run lets one workbook or checklist work on all of them. Different ref names tell overlapping sources apart, such as primary and backup.
For legacy reasons, some documentation refers to the ref name as a “data scope name” or “refname”, but these all represent the same concept.
Learn more: Data model core concepts: Data scopes and ref names.
RID#
Short for resource identifier: a unique ID for any item in Nominal.
Copy a RID whole from the Nominal app, such as from a dataset’s or asset’s Copy RID button. Its parts name the service, instance, type and item, so a dataset’s starts with ri.catalog and an asset’s with ri.scout. Examples on this site write a RID as a placeholder like <ri.scout.instance.asset.identifier>.
Run#
A named, time-bounded period of interest on one or more assets, such as a test, a flight, or a race session.
A run has its own labels and properties. A workbook opened on a run shows just that period’s data, and checklists can be executed on it.
Learn more: Data model core concepts: Runs.
Series (or Time series)#
A sequence of values over time that shows how a signal changes. It can be numeric, categorical, log-based, or range-based.
Software-in-the-Loop (SiL) testing#
Testing where the software under test runs against a simulation instead of real hardware.
Organize SiL results with the simulations approach, like other simulation output.
Learn more: The simulations approach.
Stream#
To send points to Nominal as they’re produced, instead of uploading a finished file.
Streamed points land in a dataset and can be watched live in a workbook.
Learn more: Quick visualization.
Streaming connection#
A network link that sends live data to Nominal as it’s produced. The data sent over it is a stream.
Tag#
A key-value pair on your data points, such as motor_id: 1, that lets you filter a subset of points from a single dataset.
Learn more: Data model core concepts: Channels.
Template#
A workbook saved without a run or asset, so the same analysis can be applied to each new run or asset.
It keeps the workbook’s layout, panels, variables, and formulas. Also called a workbook template.
Learn more: Using workbook templates.
Test socket#
The removable fixture on a test stand that holds a unit under test and makes temporary electrical contact with it, so units can be swapped quickly between tests.
A test stand can have several sockets, each testing its own unit at the same time.
Test stand#
The equipment that runs tests on a unit under test: instruments, power supplies, fixtures, and the software that drives them.
Also called a test station or tester. It can be an asset of its own, for example to monitor the tester over time.
Learn more: Using a single dataset for many identical assets: Production tester monitoring.
Test station#
Another name for a test stand: the equipment that runs tests on a unit under test.
See Test stand.
Tester#
Another name for a test stand: the equipment that runs tests on a unit under test.
See Test stand.
Time window#
The time range currently shown in a workbook. It controls what data Nominal loads and updates when you zoom or pan.
Learn more: 🔒︎Time range controls.
Transform#
An operation that builds a new time series from an existing one, such as a rate of change or a unit conversion, without changing the original data.
Learn more: 🔒︎Transformations and formulas.
Upload#
To send a finished file to Nominal, from the app or a client library, so it can be ingested into a dataset.
To send points as they’re produced, stream them instead.
Learn more: CSV upload.
Unit under test (UUT)#
The device or system being tested, also called a device under test (DUT).
Each UUT can be an asset of its own.
Learn more: The default data approach.
Variable#
A reference to a channel, with any tag filters, splits, and transforms applied, or a formula built from other variables. Workbooks plot variables, and checklists check them.
A variable isn’t the channel itself. It refers to a channel by ref name and channel name, such as default.voltage, not by dataset, so the same workbook or checklist works on any asset or run that uses those names. Each variable has a variable name the workbook assigns, such as aa, which formulas use, and a display name shown on charts, which starts as the channel’s name. A variable’s unit comes from its channel and transforms. To change the stored unit, change the channel’s unit.
Learn more: 🔒︎Variable types.
Variable name#
A variable’s unique identifier in a workbook, such as aa, which formulas use to refer to it.
The workbook assigns it. It’s separate from the variable’s display name, which appears on charts and starts as the channel’s name.
Learn more: How analysis works in Nominal.
Violation#
An event type marking failures, out-of-bounds values, or unmet conditions.
Workbook#
Where you analyze time-series data from assets and runs, in tabs and panels, after a test or live.
Learn more: Workbooks.
Workspace#
A shared space where your team stores and analyzes test data, with its own assets, data sources, runs, workbooks, and users.
Learn more: 🔒︎Workspaces.