Introduction

LaminDB is an open-source data framework to enable learning at scale in computational biology. It lets you track data transformations, validate & annotate datasets, and query a built-in database for biological metadata & data structures.

Why?

Reproducing analytical results or understanding how a dataset or model was created can be a pain. Leave alone training models on historical data, orthogonal assays, or datasets generated by other teams.

Biological datasets are typically managed with versioned storage systems (file systems, object storage, git, dvc), UI-focused community or SaaS platforms, structureless data lakes, rigid data warehouses (SQL, monolithic arrays), and data lakehouses for tabular data.

LaminDB goes further with a lakehouse that models biological datasets beyond tables with enough structure to enable queries and enough freedom to keep the pace of R&D high.

For data structures like DataFrame, AnnData, .zarr, .tiledbsoma, etc., LaminDB tracks the rich context that collaborative biological research requires and uses it to validate and index datasets to enable queries. In particular, you get

• data lineage: data sources and transformations; scientists and machine learning models

• domain knowledge and experimental metadata: the features and labels derived from domain entities

In this blog post, we discuss a breadth of data management problems of the field.

LaminDB specs

The Python & R packages lamindb & laminr share almost the same API (. → $).

Manage data & metadata with a unified API (“lakehouse”).

• Use a built-in SQLite/Postgres database to organize files, folders & arrays across any number of storage locations

• Query & search across data & metadata: filter, search

• Model entities via registries based on the Django ORM: SQLRecord

• Model files and folders as datasets & models: Artifact

• Slice large array stores: open → guide

• Cache & load artifacts: cache, load

• Manage features & labels: Feature, Schema, ULabel, Gene, Protein, CellType, CellLine, …

• Use array formats in memory & storage: DataFrame, AnnData, MuData, tiledbsoma, … backed by parquet, zarr, tiledb, HDF5, h5ad, DuckDB, …

• Create iterable & queryable collections of artifacts with data loaders: Collection

• Version artifacts, collections & transforms: IsVersioned

Track data lineage across notebooks, scripts, pipelines & UI.

• Track scripts & notebooks with a simple method call: track()

• Track functions with a decorator: tracked()

• A unified registry for all your notebooks, scripts & pipelines: Transform

• A unified registry for all data transformation runs: Run

• Manage execution reports, source code and Python environments for notebooks & scripts

• Integrate with workflow managers: redun, nextflow, snakemake

Manage registries for experimental metadata & in-house ontologies, import public ontologies.

• Use >20 public ontologies with module bionty: Gene, Protein, CellMarker, ExperimentalFactor, CellType, CellLine, Tissue, …

• Use a canonical wetlab database schema module wetlab

• Safeguards against typos & duplications

• Version ontologies and manage their life cycle

Validate, standardize & annotate.

• Validate & standardize metadata: validate, standardize.

• High-level curation flow including annotation: Curator

• Inspect validation failures: inspect

Organize and share data across a mesh of LaminDB instances.

• Create & connect to instances with the same ease as git repos: lamin init & lamin connect

• Zero-copy transfer data across instances

Integrate with analytics tools.

• Vitessce: save_vitessce_config

Zero lock-in, scalable, auditable.

• Zero lock-in: LaminDB runs on generic backends server-side and is not a client for “Lamin Cloud”

  • Flexible storage backends (local, S3, GCP, https, HF, R2, anything fsspec supports)

  • Two SQL backends for managing metadata: SQLite & Postgres

• Scalable: metadata registries support 100s of millions of entries, storage is as scalable as S3

• Plug-in custom schema modules & manage database schema migrations

• Auditable: data & metadata records are hashed, timestamped, and attributed to users (full audit log to come)

• Secure: embedded in your infrastructure

• Tested, typed, idempotent & ACID

LaminHub is a data collaboration hub built on LaminDB similar to how GitHub is built on git.

LaminHub specs

Explore at lamin.ai/explore.

Secure & intuitive access management.

While you stay in full control over storage & database permissions directly on AWS or GCP, LaminHub allows you and your users to additionally manage access similar to how you’d do it on GitHub, Google Drive, Microsoft Sharepoint, or Notion. See Manage access.

A UI to work with LaminDB instances.

See an overview of all datasets, models, code, and metadata in your instance.

See validated datasets in context of ontologies & experimental metadata.

Query & search.

See scripts, notebooks & pipelines with their inputs & outputs.

Track pipelines, notebooks & UI transforms in one place.

You can copy this summary.md into an LLM chat and let AI explain Lamin.

Quickstart

Py

Install the lamindb Python package:

pip install 'lamindb[jupyter,bionty]'  # support notebooks & biological ontologies

Either connect to a LaminDB instance for which you have write access:

lamin connect account/name

Or create a local instance:

lamin init --storage ./quickstart-data

Track a script or notebook run with source code, inputs, outputs, logs, and environment.

import lamindb as ln

ln.track()  # track the run of a script or notebook
open("sample.fasta", "w").write(">seq1\nACGT\n")
ln.Artifact("sample.fasta", key="sample.fasta").save()  # create a versioned artifact
ln.finish()  # finish the run, save source code & run report

R

For setup, install the laminr package and connect to a LaminDB instance.

install.packages("laminr", dependencies = TRUE)  # install the laminr package from CRAN
laminr::lamin_login()  # <-- you can skip this for local & self-hosted instances
laminr::lamin_connect("<account>/<instance>")  # <-- replace with your instance

I don’t have write access to an instance.

Here’s how to create a local instance.

laminr::lamin_init(storage = "./mydata", modules = c("bionty"))

In an R session, transfer an scRNA-seq dataset from the laminlabs/cellxgene instance, compute marker genes with Seurat, and save results.

ln <- laminr::import_module("lamindb")  # instantiate the central object of the API

# Access inputs -------------------------------------------

ln$track()  # track your run of a notebook or script
artifact <- ln$Artifact$using("laminlabs/cellxgene")$get("7dVluLROpalzEh8m")  # query the artifact https://lamin.ai/laminlabs/cellxgene/artifact/7dVluLROpalzEh8m
adata <- artifact$load()  # load the artifact into memory or sync to cache via filepath <- artifact$cache()

# Your transformation -------------------------------------

library(Seurat)  # find marker genes with Seurat
seurat_obj <- CreateSeuratObject(counts = as(Matrix::t(adata$X), "CsparseMatrix"), meta.data = adata$obs)
seurat_obj[["RNA"]] <- AddMetaData(GetAssay(seurat_obj), adata$var)
Idents(seurat_obj) <- "cell_type"
seurat_obj <- NormalizeData(seurat_obj)
markers <- FindAllMarkers(seurat_obj, features = Features(seurat_obj)[1:100])
seurat_path <- tempfile(fileext = ".rds")
saveRDS(seurat_obj, seurat_path)

# Save outputs --------------------------------------------

ln$Artifact(seurat_path, key = "my-datasets/my-seurat-object.rds")$save()  # save versioned output
ln$Artifact$from_df(markers, key = "my-datasets/my-markers.parquet")$save()  # save versioned output
ln$finish()  # finish the run, save source code & run report

If you did not use RStudio’s notebook mode, create an html export and then run the following.

laminr::lamin_save("my-analyis.Rmd")  # save source code and html report for a `.qmd` or `.Rmd` file

The script produced the following data lineage.

Py

artifact.view_lineage()

Hub

Explore data lineage for a more complicated example interactively here.

Track notebooks & scripts

LaminDB provides a framework to transform datasets into more useful representations: validated & queryable datasets, machine learning models, and analytical insights. The transformations can be notebooks, scripts, pipelines, or functions.

The metadata involved in this process are stored in a LaminDB instance, a database that manages datasets in storage. For the following walk through LaminDB’s core features, we’ll be working with a local instance.

Py

lamin init --storage ./lamin-intro --modules bionty

R

library(laminr)
lamin_init(storage = "./laminr-intro", modules = c("bionty"))

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!lamin init --storage ./lamin-intro --modules bionty

→ initialized lamindb: anonymous/lamin-intro

What else can I configure during setup?

1. You can pass a cloud storage location to --storage (S3, GCP, R2, HF, etc.)

   --storage s3://my-bucket
   

2. Instead of the default SQLite database pass a Postgres connection string to --db:

   --db postgresql://<user>:<pwd>@<hostname>:<port>/<dbname>
   

3. Instead of a default instance name derived from the storage location, provide a custom name:

   --name my-name
   

4. Mount additional schema modules:

   --modules bionty,wetlab,custom1
   

For more info, see Install & setup.

If you decide to connect your instance to the hub, you will see data & metadata in a UI.

Let’s now track the notebook that’s being run.

Py

import lamindb as ln

ln.track()  # track the current notebook or script

R

library(laminr)
ln <- import_module("lamindb")  # instantiate the central `ln` object of the API

ln$track()  # track a run of your notebook or script

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import lamindb as ln

ln.track()  # track the current notebook or script

→ connected lamindb: anonymous/lamin-intro

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

→ created Transform('ULOzzxBhFigf0000'), started new Run('tbBim7Ei...') at 2025-09-09 10:18:13 UTC

→ notebook imports: anndata==0.12.1 bionty==1.7a1 lamindb==1.11a1

• recommendation: to identify the notebook across renames, pass the uid: ln.track("ULOzzxBhFigf")

By calling track(), the notebook gets automatically linked as the source of all data that’s about to be saved! You can see all your transforms and their runs in the Transform and Run registries.

Py

ln.Transform.df()

R

ln$Transform$df()

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ln.Transform.df()

/tmp/ipykernel_3802/112129659.py:1: FutureWarning: Use to_dataframe instead of df, df will be removed in the future.
  ln.Transform.df()

	uid	key	description	type	source_code	hash	reference	reference_type	space_id	_template_id	version	is_latest	created_at	created_by_id	_aux	branch_id
id
1	ULOzzxBhFigf0000	introduction.ipynb	Introduction	notebook	None	None	None	None	1	None	None	True	2025-09-09 10:18:13.225000+00:00	1	None	1

Py

ln.Run.df()

R

ln$Run$df()

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ln.Run.df()

/tmp/ipykernel_3802/3151311965.py:1: FutureWarning: Use to_dataframe instead of df, df will be removed in the future.
  ln.Run.df()

	uid	name	started_at	finished_at	reference	reference_type	_is_consecutive	_status_code	space_id	transform_id	report_id	_logfile_id	environment_id	initiated_by_run_id	created_at	created_by_id	_aux	branch_id
id
1	tbBim7Ei5SsymWEG	None	2025-09-09 10:18:13.229552+00:00	None	None	None	None	-1	1	1	None	None	None	None	2025-09-09 10:18:13.230000+00:00	1	None	1

What happened under the hood?

1. The full run environment and imported package versions of current notebook were detected

2. Notebook metadata was detected and stored in a Transform record with a unique id

3. Run metadata was detected and stored in a Run record with a unique id

The Transform registry stores data transformations: scripts, notebooks, pipelines, functions.

The Run registry stores executions of transforms. Many runs can be linked to the same transform if executed with different context (time, user, input data, etc.).

How do I track a pipeline instead of a notebook?

Leverage a pipeline integration, see: Pipelines – workflow managers. Or manually add code as seen below.

transform = ln.Transform(name="My pipeline")
transform.version = "1.2.0"  # tag the version
ln.track(transform)

Why should I care about tracking notebooks?

Because of interactivity & humans are in the loop, most mistakes happen when using notebooks.

track() makes notebooks & derived results reproducible & auditable, enabling to learn from mistakes.

This is important as much insight generated from biological data is driven by computational biologists interacting with it. An early blog post on this is here.

Is this compliant with OpenLineage?

Yes. What OpenLineage calls a “job”, LaminDB calls a “transform”. What OpenLineage calls a “run”, LaminDB calls a “run”.

Manage artifacts

The Artifact class manages datasets & models that are stored as files, folders, or arrays. Artifact is a registry to manage search, queries, validation & storage access.

You can register data objects (DataFrame, AnnData, …) and files or folders in local storage, AWS S3 (s3://), Google Cloud (gs://), Hugging Face (hf://), or any other file system supported by fsspec.

Create an artifact

Let’s first look at an exemplary dataframe.

Py

df = ln.core.datasets.small_dataset1(with_typo=True)
df

R

df <- ln$core$datasets$small_dataset1(otype = "DataFrame", with_typo = TRUE)
df

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df = ln.core.datasets.mini_immuno.get_dataset1(with_typo=True)
df

	ENSG00000153563	ENSG00000010610	ENSG00000170458	perturbation	sample_note	cell_type_by_expert	cell_type_by_model	assay_oid	concentration	treatment_time_h	donor	donor_ethnicity
sample1	1	3	5	DMSO	was ok	B cell	B cell	EFO:0008913	0.1%	24	D0001	[Chinese, Singaporean Chinese]
sample2	2	4	6	IFNJ	looks naah	CD8-positive, alpha-beta T cell	T cell	EFO:0008913	200 nM	24	D0002	[Chinese, Han Chinese]
sample3	3	5	7	DMSO	pretty! 🤩	CD8-positive, alpha-beta T cell	T cell	EFO:0008913	0.1%	6	None	[Chinese]

This is how you create an artifact from a dataframe.

Py

artifact = ln.Artifact.from_df(df, key="my_datasets/rnaseq1.parquet").save()  # create & save
artifact.describe()  # describe

R

artifact <- ln$Artifact$from_df(df, key = "my_datasets/rnaseq1.parquet")$save()  # create & save
artifact$describe()  # describe

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artifact = ln.Artifact.from_df(df, key="my_datasets/rnaseq1.parquet").save()
artifact.describe()

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

/tmp/ipykernel_3802/2069802094.py:1: FutureWarning: Use from_dataframe instead of from_df, from_df will be removed in the future.
  artifact = ln.Artifact.from_df(df, key="my_datasets/rnaseq1.parquet").save()

Artifact .parquet · DataFrame · dataset
└── General
    ├── key: my_datasets/rnaseq1.parquet
    ├── uid: zNHpyadOACQ3DIFl0000          hash: BQ2-j2-SP4x_xgvgT-AmeQ
    ├── size: 9.6 KB                       transform: introduction.ipynb
    ├── space: all                         branch: all
    ├── created_by: anonymous              created_at: 2025-09-09 10:18:13
    ├── n_observations: 3
    └── storage path: /home/runner/work/lamin-docs/lamin-docs/docs/lamin-intro/my_datasets/rnaseq1.parquet

Access artifacts

Get the artifact by key.

Py

artifact = ln.Artifact.get(key="my_datasets/rnaseq1.parquet")

R

artifact <- ln$Artifact$get(key = "my_datasets/rnaseq1.parquet")

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artifact = ln.Artifact.get(key="my_datasets/rnaseq1.parquet")

And this is how you load it back into memory.

Py

artifact.load()

R

artifact$load()

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artifact.load()

	ENSG00000153563	ENSG00000010610	ENSG00000170458	perturbation	sample_note	cell_type_by_expert	cell_type_by_model	assay_oid	concentration	treatment_time_h	donor	donor_ethnicity
sample1	1	3	5	DMSO	was ok	B cell	B cell	EFO:0008913	0.1%	24	D0001	[Chinese, Singaporean Chinese]
sample2	2	4	6	IFNJ	looks naah	CD8-positive, alpha-beta T cell	T cell	EFO:0008913	200 nM	24	D0002	[Chinese, Han Chinese]
sample3	3	5	7	DMSO	pretty! 🤩	CD8-positive, alpha-beta T cell	T cell	EFO:0008913	0.1%	6	None	[Chinese]

Typically your artifact is in a cloud storage location. To get a local file path to it, call cache().

Py

artifact.cache()

R

artifact$cache()

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artifact.cache()

PosixUPath('/home/runner/work/lamin-docs/lamin-docs/docs/lamin-intro/.lamindb/zNHpyadOACQ3DIFl0000.parquet')

If the data is large, you might not want to cache but stream it via open(). For more on this, see: Slice & stream arrays.

Trace data lineage

You can understand where an artifact comes from by looking at its Transform & Run records.

Py

artifact.transform

R

artifact$transform

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artifact.transform

Transform(uid='ULOzzxBhFigf0000', is_latest=True, key='introduction.ipynb', description='Introduction', type='notebook', branch_id=1, space_id=1, created_by_id=1, created_at=2025-09-09 10:18:13 UTC)

Py

artifact.run

R

artifact$run

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artifact.run

Run(uid='tbBim7Ei5SsymWEG', started_at=2025-09-09 10:18:13 UTC, branch_id=1, space_id=1, transform_id=1, created_by_id=1, created_at=2025-09-09 10:18:13 UTC)

Or visualize deeper data lineage with the view_lineage() method. Here we’re only one step deep.

Py

artifact.view_lineage()

R

artifact$view_lineage()

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artifact.view_lineage()

! calling anonymously, will miss private instances

Show me a more interesting example, please!

Py

Explore and load the notebook from here.

Hub

Explore data lineage interactively here.

Once you’re done, at the end of your notebook or script, call finish(). Here, we’re not yet done so we’re commenting it out.

Py

# ln.finish()  # mark run as finished, save execution report, source code & environment

R

# ln$finish()  # mark run as finished, save execution report & source code

If you did not use RStudio’s notebook mode, you have to render an HTML externally.

1. Render the notebook to HTML via one of:

   • In RStudio, click the “Knit” button

   • From the command line, run

     Rscript -e 'rmarkdown::render("introduction.Rmd")'
     

   • Use the rmarkdown package in R

     rmarkdown::render("introduction.Rmd")
     

2. Save it to your LaminDB instance via one of:

   • Using the lamin_save() function in R

     lamin_save("introduction.Rmd")
     

   • Using the lamin CLI

     lamin save introduction.Rmd
     

Here is how a notebook looks on the hub.

Explore.

To create a new version of a notebook or script, run lamin load on the terminal, e.g.,

$ lamin load https://lamin.ai/laminlabs/lamindata/transform/13VINnFk89PE0004
→ notebook is here: mcfarland_2020_preparation.ipynb

Note that data lineage also helps to understand what a dataset is being used for, not only where it comes from. Many datasets are being used over and over for different purposes and it’s often useful to understand how.

Annotate an artifact

You can annotate artifacts with features and labels. Features are measurement dimensions (e.g. "organism", "temperature") and labels are measured categories (e.g. "human", "mouse").

Let’s annotate an artifact with a ULabel, a built-in universal label ontology.

Py

# create a label
my_experiment = ln.ULabel(name="My experiment").save()

# annotate the artifact with a label
artifact.ulabels.add(my_experiment)

# describe the artifact
artifact.describe()

R

# create a label
my_experiment <- ln$ULabel(name = "My experiment")$save()

# annotate the artifact with a label
artifact$ulabels$add(my_experiment)

# describe the artifact
artifact$describe()

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# create & save a ulabel
my_experiment = ln.ULabel(name="My experiment").save()

# annotate the artifact with a ulabel
artifact.ulabels.add(my_experiment)

# describe the artifact
artifact.describe()

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

Artifact .parquet · DataFrame · dataset
├── General
│   ├── key: my_datasets/rnaseq1.parquet
│   ├── uid: zNHpyadOACQ3DIFl0000          hash: BQ2-j2-SP4x_xgvgT-AmeQ
│   ├── size: 9.6 KB                       transform: introduction.ipynb
│   ├── space: all                         branch: all
│   ├── created_by: anonymous              created_at: 2025-09-09 10:18:13
│   ├── n_observations: 3
│   └── storage path: /home/runner/work/lamin-docs/lamin-docs/docs/lamin-intro/my_datasets/rnaseq1.parquet
└── Labels
    └── .ulabels                        ULabel                             My experiment                           

This is how you query artifacts based on the annotation.

Py

ln.Artifact.filter(ulabels=my_experiment).df()

R

ln$Artifact$filter(ulabels = my_experiment)$df()

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ln.Artifact.filter(ulabels=my_experiment).df()

/tmp/ipykernel_3802/2958836106.py:1: FutureWarning: Use to_dataframe instead of df, df will be removed in the future.
  ln.Artifact.filter(ulabels=my_experiment).df()

	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	run_id	created_at	created_by_id	_aux	branch_id
id
1	zNHpyadOACQ3DIFl0000	my_datasets/rnaseq1.parquet	None	.parquet	dataset	DataFrame	9868	BQ2-j2-SP4x_xgvgT-AmeQ	None	3	md5	True	False	1	1	None	None	True	1	2025-09-09 10:18:13.841000+00:00	1	{'af': {'0': True}}	1

You can also annotate with labels from other registries, e.g., the biological ontologies in bionty.

Py

import bionty as bt

# create a cell type label from the source ontology
cell_type = bt.CellType.from_source(name="effector T cell").save()

# annotate the artifact with a cell type
artifact.cell_types.add(cell_type)

# describe the artifact
artifact.describe()

R

bt <- import_module("bionty")

# create a cell type label from the source ontology
cell_type <- bt$CellType$from_source(name = "effector T cell")$save()

# annotate the artifact with a cell type
artifact$cell_types$add(cell_type)

# describe the artifact
artifact$describe()

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import bionty as bt

# create a cell type label from the source ontology
cell_type = bt.CellType.from_source(name="effector T cell").save()

# annotate the artifact with a cell type
artifact.cell_types.add(cell_type)

# describe the artifact
artifact.describe()

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

Artifact .parquet · DataFrame · dataset
├── General
│   ├── key: my_datasets/rnaseq1.parquet
│   ├── uid: zNHpyadOACQ3DIFl0000          hash: BQ2-j2-SP4x_xgvgT-AmeQ
│   ├── size: 9.6 KB                       transform: introduction.ipynb
│   ├── space: all                         branch: all
│   ├── created_by: anonymous              created_at: 2025-09-09 10:18:13
│   ├── n_observations: 3
│   └── storage path: /home/runner/work/lamin-docs/lamin-docs/docs/lamin-intro/my_datasets/rnaseq1.parquet
└── Labels
    └── .cell_types                     bionty.CellType                    effector T cell                         
        .ulabels                        ULabel                             My experiment                           

This is how you query artifacts by cell type annotations.

Py

ln.Artifact.filter(cell_types=cell_type).df()

R

ln$Artifact$filter(cell_types = cell_type)$df()

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ln.Artifact.filter(cell_types=cell_type).df()

/tmp/ipykernel_3802/717108961.py:1: FutureWarning: Use to_dataframe instead of df, df will be removed in the future.
  ln.Artifact.filter(cell_types=cell_type).df()

	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	run_id	created_at	created_by_id	_aux	branch_id
id
1	zNHpyadOACQ3DIFl0000	my_datasets/rnaseq1.parquet	None	.parquet	dataset	DataFrame	9868	BQ2-j2-SP4x_xgvgT-AmeQ	None	3	md5	True	False	1	1	None	None	True	1	2025-09-09 10:18:13.841000+00:00	1	{'af': {'0': True}}	1

If you want to annotate by non-categorical metadata or indicate the feature for a label, annotate via features.

Py

# define the "temperature" & "experiment" features
ln.Feature(name="temperature", dtype=float).save()
ln.Feature(name="experiment", dtype=ln.ULabel).save()

# annotate the artifact
artifact.features.add_values({"temperature": 21.6, "experiment": "My experiment"})

# describe the artifact
artifact.describe()

R

# define the "temperature" & "experiment" features
ln$Feature(name = "temperature", dtype = "float")$save()
ln$Feature(name = "experiment", dtype = ln$ULabel)$save()

# annotate the artifact
artifact$features$add_values(
  list(temperature = 21.6, experiment = "My experiment")
)

# describe the artifact
artifact$describe()

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# define the "temperature" & "experiment" features
ln.Feature(name="temperature", dtype=float).save()
ln.Feature(name="experiment", dtype=ln.ULabel).save()

# annotate the artifact
artifact.features.add_values({"temperature": 21.6, "experiment": "My experiment"})

# describe the artifact
artifact.describe()

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

Artifact .parquet · DataFrame · dataset
├── General
│   ├── key: my_datasets/rnaseq1.parquet
│   ├── uid: zNHpyadOACQ3DIFl0000          hash: BQ2-j2-SP4x_xgvgT-AmeQ
│   ├── size: 9.6 KB                       transform: introduction.ipynb
│   ├── space: all                         branch: all
│   ├── created_by: anonymous              created_at: 2025-09-09 10:18:13
│   ├── n_observations: 3
│   └── storage path: /home/runner/work/lamin-docs/lamin-docs/docs/lamin-intro/my_datasets/rnaseq1.parquet
├── Linked features
│   └── experiment                      cat[ULabel]                        My experiment                           
│       temperature                     float                              21.6                                    
└── Labels
    └── .cell_types                     bionty.CellType                    effector T cell                         
        .ulabels                        ULabel                             My experiment                           

This is how you query artifacts by features.

Py

ln.Artifact.filter(temperature=21.6).df()

R

ln$Artifact$filter(temperature = 21.6)$df()

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ln.Artifact.filter(temperature=21.6).df()

/tmp/ipykernel_3802/2424437695.py:1: FutureWarning: Use to_dataframe instead of df, df will be removed in the future.
  ln.Artifact.filter(temperature=21.6).df()

	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	run_id	created_at	created_by_id	_aux	branch_id
id
1	zNHpyadOACQ3DIFl0000	my_datasets/rnaseq1.parquet	None	.parquet	dataset	DataFrame	9868	BQ2-j2-SP4x_xgvgT-AmeQ	None	3	md5	True	False	1	1	None	None	True	1	2025-09-09 10:18:13.841000+00:00	1	{'af': {'0': True}}	1

Validate an artifact

Validated datasets are more re-usable by analysts and machine learning models. You can define what a valid artifact should look like by defining a schema.

In lamindb, validation also means annotation with the validated metadata which increases the findability of a dataset.

Can you give me examples for what findability and usability means?

1. Findability: Which datasets measured expression of cell marker CD14? Which characterized cell line K562? Which have a test & train split? Etc.

2. Usability: Are there typos in feature names? Are there typos in labels? Are types and units of features consistent? Etc.

Py

import bionty as bt  # <-- use bionty to access registries with imported public ontologies

# define a few more valid labels
ln.ULabel(name="DMSO").save()
ln.ULabel(name="IFNG").save()

# define a few more valid features
ln.Feature(name="perturbation", dtype=ln.ULabel).save()
ln.Feature(name="cell_type_by_model", dtype=bt.CellType).save()
ln.Feature(name="cell_type_by_expert", dtype=bt.CellType).save()
ln.Feature(name="assay_oid", dtype=bt.ExperimentalFactor.ontology_id).save()
ln.Feature(name="donor", dtype=str, nullable=True).save()
ln.Feature(name="concentration", dtype=str).save()
ln.Feature(name="treatment_time_h", dtype="num", coerce_dtype=True).save()

# define a schema that merely enforces a feature identifier type
schema = ln.Schema(itype=ln.Feature).save()

R

bt <- import_module("bionty")  # <-- use bionty to access registries with imported public ontologies

# define a few more valid labels
ln$ULabel(name = "DMSO")$save()
ln$ULabel(name = "IFNG")$save()

# define a few more valid features
ln$Feature(name = "perturbation", dtype = ln$ULabel)$save()
ln$Feature(name = "cell_type_by_model", dtype = bt$CellType)$save()
ln$Feature(name = "cell_type_by_expert", dtype = bt$CellType)$save()
ln$Feature(name = "assay_oid", dtype = bt$ExperimentalFactor$ontology_id)$save()
ln$Feature(name = "donor", dtype = "str", nullable = TRUE)$save()
ln$Feature(name = "concentration", dtype = "str")$save()
ln$Feature(name = "treatment_time_h", dtype = "num", coerce_dtype = TRUE)$save()

# define a schema that merely enforces a feature identifier type
schema <- ln$Schema(itype = ln$Feature)$save()

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import bionty as bt  # <-- use bionty to access registries with imported public ontologies

# define a few more valid labels
ln.ULabel(name="DMSO").save()
ln.ULabel(name="IFNG").save()

# define a few more valid features
ln.Feature(name="perturbation", dtype=ln.ULabel).save()
ln.Feature(name="cell_type_by_model", dtype=bt.CellType).save()
ln.Feature(name="cell_type_by_expert", dtype=bt.CellType).save()
ln.Feature(name="assay_oid", dtype=bt.ExperimentalFactor.ontology_id).save()
ln.Feature(name="donor", dtype=str, nullable=True).save()
ln.Feature(name="sample_note", dtype=str, nullable=True).save()
ln.Feature(name="concentration", dtype=str).save()
ln.Feature(name="treatment_time_h", dtype="num", coerce_dtype=True).save()

# define a schema that merely enforces a feature identifier type
schema = ln.Schema(itype=ln.Feature).save()

If you pass a schema object to the Artifact constructor, the artifact will be validated & annotated. Let’s try this.

Py

artifact = ln.Artifact.from_df(df, key="my_datasets/rnaseq1.parquet", schema=schema)

R

artifact <- ln$Artifact$from_df(df, key = "my_datasets/rnaseq1.parquet", schema = schema)

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try:
    artifact = ln.Artifact.from_df(df, key="my_datasets/rnaseq1.parquet", schema=schema)
except ln.errors.ValidationError as error:
    print(str(error))

→ returning existing artifact with same hash: Artifact(uid='zNHpyadOACQ3DIFl0000', is_latest=True, key='my_datasets/rnaseq1.parquet', suffix='.parquet', kind='dataset', otype='DataFrame', size=9868, hash='BQ2-j2-SP4x_xgvgT-AmeQ', n_observations=3, branch_id=1, space_id=1, storage_id=1, run_id=1, created_by_id=1, created_at=2025-09-09 10:18:13 UTC); to track this artifact as an input, use: ln.Artifact.get()

! 4 terms not validated in feature 'columns': 'ENSG00000153563', 'ENSG00000010610', 'ENSG00000170458', 'donor_ethnicity'
    → fix typos, remove non-existent values, or save terms via: curator.cat.add_new_from('columns')

! 1 term not validated in feature 'perturbation': 'IFNJ'
    → fix typos, remove non-existent values, or save terms via: curator.cat.add_new_from('perturbation')

/tmp/ipykernel_3802/3181188939.py:2: FutureWarning: Use from_dataframe instead of from_df, from_df will be removed in the future.
  artifact = ln.Artifact.from_df(df, key="my_datasets/rnaseq1.parquet", schema=schema)

1 term not validated in feature 'perturbation': 'IFNJ'
    → fix typos, remove non-existent values, or save terms via: curator.cat.add_new_from('perturbation')

Because there is a typo in the perturbation column, validation fails. Let’s fix it by making a new version.

Make a new version of an artifact

Py

# fix the "IFNJ" typo
df["perturbation"] = df["perturbation"].cat.rename_categories({"IFNJ": "IFNG"})

# create a new version
artifact = ln.Artifact.from_df(df, key="my_datasets/rnaseq1.parquet", schema=schema).save()

# see the annotations
artifact.describe()

# simplest way to check that artifact was validated
artifact.schema

# see all versions of the artifact
artifact.versions.df()

R

# fix the "IFNJ" typo
levels(df$perturbation) <- c("DMSO", "IFNG")
df["sample2", "perturbation"] <- "IFNG"

# create a new version
artifact <- ln$Artifact$from_df(df, key = "my_datasets/rnaseq1.parquet", schema = schema)$save()

# see the annotations
artifact$describe()

# simplest way to check that artifact was validated
artifact$schema

# see all versions of an artifact
artifact$versions$df()

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# fix the "IFNJ" typo
df["perturbation"] = df["perturbation"].cat.rename_categories({"IFNJ": "IFNG"})

# create a new version
artifact = ln.Artifact.from_df(
    df, key="my_datasets/rnaseq1.parquet", schema=schema
).save()

# see the annotations
artifact.describe()

# see all versions of the artifact
artifact.versions.df()

→ creating new artifact version for key='my_datasets/rnaseq1.parquet' (storage: '/home/runner/work/lamin-docs/lamin-docs/docs/lamin-intro')

! 4 terms not validated in feature 'columns': 'ENSG00000153563', 'ENSG00000010610', 'ENSG00000170458', 'donor_ethnicity'
    → fix typos, remove non-existent values, or save terms via: curator.cat.add_new_from('columns')

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

/tmp/ipykernel_3802/1308325521.py:5: FutureWarning: Use from_dataframe instead of from_df, from_df will be removed in the future.
  artifact = ln.Artifact.from_df(

Artifact .parquet · DataFrame · dataset
├── General
│   ├── key: my_datasets/rnaseq1.parquet
│   ├── uid: zNHpyadOACQ3DIFl0001          hash: aUPgpQYtBZ_zvZvl637r9g
│   ├── size: 9.6 KB                       transform: introduction.ipynb
│   ├── space: all                         branch: all
│   ├── created_by: anonymous              created_at: 2025-09-09 10:18:18
│   ├── n_observations: 3
│   └── storage path: /home/runner/work/lamin-docs/lamin-docs/docs/lamin-intro/my_datasets/rnaseq1.parquet
├── Dataset features
│   └── columns • 8                     [Feature]                                                                  
│       assay_oid                       cat[bionty.ExperimentalFactor.on…  single-cell RNA sequencing              
│       cell_type_by_expert             cat[bionty.CellType]               B cell, CD8-positive, alpha-beta T cell 
│       cell_type_by_model              cat[bionty.CellType]               B cell, T cell                          
│       perturbation                    cat[ULabel]                        DMSO, IFNG                              
│       donor                           str                                                                        
│       sample_note                     str                                                                        
│       concentration                   str                                                                        
│       treatment_time_h                num                                                                        
└── Labels
    └── .cell_types                     bionty.CellType                    T cell, B cell, CD8-positive, alpha-bet…
        .experimental_factors           bionty.ExperimentalFactor          single-cell RNA sequencing              
        .ulabels                        ULabel                             DMSO, IFNG                              

/tmp/ipykernel_3802/1308325521.py:13: FutureWarning: Use to_dataframe instead of df, df will be removed in the future.
  artifact.versions.df()

	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	run_id	created_at	created_by_id	_aux	branch_id
id
2	zNHpyadOACQ3DIFl0001	my_datasets/rnaseq1.parquet	None	.parquet	dataset	DataFrame	9868	aUPgpQYtBZ_zvZvl637r9g	None	3	md5	True	False	1	1	1.0	None	True	1	2025-09-09 10:18:18.303000+00:00	1	{'af': {'0': True}}	1
1	zNHpyadOACQ3DIFl0000	my_datasets/rnaseq1.parquet	None	.parquet	dataset	DataFrame	9868	BQ2-j2-SP4x_xgvgT-AmeQ	None	3	md5	True	False	1	1	NaN	None	False	1	2025-09-09 10:18:13.841000+00:00	1	{'af': {'0': True}}	1

The content of the dataset is now validated and the dataset is richly annotated and queryable by all entities that you defined.

Can I also create new versions without passing key?

That works, too, you can use revises:

artifact_v1 = ln.Artifact.from_df(df, description="Just a description").save()
# below revises artifact_v1
artifact_v2 = ln.Artifact.from_df(df_updated, revises=artifact_v1).save()

The good thing about passing revises: Artifact is that you don’t need to worry about coming up with naming conventions for paths.

The good thing about versioning based on key is that it’s how all data versioning tools are doing it.

Query & search registries

We’ve already seen a few queries. Let’s now walk through the topic systematically.

To get an overview over all artifacts in your instance, call df.

Py

ln.Artifact.df()

R

ln$Artifact$df()

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ln.Artifact.df()

/tmp/ipykernel_3802/3926378802.py:1: FutureWarning: Use to_dataframe instead of df, df will be removed in the future.
  ln.Artifact.df()

	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	run_id	created_at	created_by_id	_aux	branch_id
id
2	zNHpyadOACQ3DIFl0001	my_datasets/rnaseq1.parquet	None	.parquet	dataset	DataFrame	9868	aUPgpQYtBZ_zvZvl637r9g	None	3	md5	True	False	1	1	1.0	None	True	1	2025-09-09 10:18:18.303000+00:00	1	{'af': {'0': True}}	1
1	zNHpyadOACQ3DIFl0000	my_datasets/rnaseq1.parquet	None	.parquet	dataset	DataFrame	9868	BQ2-j2-SP4x_xgvgT-AmeQ	None	3	md5	True	False	1	1	NaN	None	False	1	2025-09-09 10:18:13.841000+00:00	1	{'af': {'0': True}}	1

The Artifact registry additionally supports seeing all feature annotations of an artifact.

Py

ln.Artifact.df(features=True)

R

ln$Artifact$df(features = TRUE)

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ln.Artifact.df(features=True)

→ queried for all categorical features with dtype ULabel or Record and non-categorical features: (7) ['temperature', 'experiment', 'perturbation', 'donor', 'sample_note', 'concentration', 'treatment_time_h']

/tmp/ipykernel_3802/4140663245.py:1: FutureWarning: Use to_dataframe instead of df, df will be removed in the future.
  ln.Artifact.df(features=True)

	uid	key	temperature	experiment	perturbation
id
2	zNHpyadOACQ3DIFl0001	my_datasets/rnaseq1.parquet	NaN	NaN	{DMSO, IFNG}
1	zNHpyadOACQ3DIFl0000	my_datasets/rnaseq1.parquet	21.6	My experiment	NaN

LaminDB’s central classes are registries that store records (SQLRecord objects). If you want to see the fields of a registry, look at the class or auto-complete.

Py

ln.Artifact

R

ln$Artifact

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ln.Artifact

Artifact
  Simple fields
    .uid: CharField
    .key: CharField
    .description: CharField
    .suffix: CharField
    .kind: CharField
    .otype: CharField
    .size: BigIntegerField
    .hash: CharField
    .n_files: BigIntegerField
    .n_observations: BigIntegerField
    .version: CharField
    .is_latest: BooleanField
    .created_at: DateTimeField
    .updated_at: DateTimeField
  Relational fields
    .branch: Branch
    .space: Space
    .storage: Storage
    .run: Run
    .schema: Schema
    .created_by: User
    .ulabels: ULabel
    .input_of_runs: Run
    .feature_sets: Schema
    .collections: Collection
    .linked_in_records: Record
    .records: Record
    .references: Reference
    .projects: Project
  Bionty fields
    .organisms: bionty.Organism
    .genes: bionty.Gene
    .proteins: bionty.Protein
    .cell_markers: bionty.CellMarker
    .tissues: bionty.Tissue
    .cell_types: bionty.CellType
    .diseases: bionty.Disease
    .cell_lines: bionty.CellLine
    .phenotypes: bionty.Phenotype
    .pathways: bionty.Pathway
    .experimental_factors: bionty.ExperimentalFactor
    .developmental_stages: bionty.DevelopmentalStage
    .ethnicities: bionty.Ethnicity

Each registry is a table in the relational schema of the underlying database. With view(), you can see the latest records in the database.

Py

ln.view()

R

ln$view()

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ln.view()

****************

* module: core *

****************

Artifact

	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	run_id	created_at	created_by_id	_aux	branch_id
id
2	zNHpyadOACQ3DIFl0001	my_datasets/rnaseq1.parquet	None	.parquet	dataset	DataFrame	9868	aUPgpQYtBZ_zvZvl637r9g	None	3	md5	True	False	1	1	1.0	None	True	1	2025-09-09 10:18:18.303000+00:00	1	{'af': {'0': True}}	1
1	zNHpyadOACQ3DIFl0000	my_datasets/rnaseq1.parquet	None	.parquet	dataset	DataFrame	9868	BQ2-j2-SP4x_xgvgT-AmeQ	None	3	md5	True	False	1	1	NaN	None	False	1	2025-09-09 10:18:13.841000+00:00	1	{'af': {'0': True}}	1

Feature

	uid	name	dtype	is_type	unit	description	array_rank	array_size	array_shape	proxy_dtype	synonyms	_expect_many	_curation	space_id	type_id	run_id	created_at	created_by_id	_aux	branch_id
id
10	BYTtP98yTLm8	treatment_time_h	num	None	None	None	0	0	None	None	None	None	None	1	None	1	2025-09-09 10:18:16.319000+00:00	1	{'af': {'0': None, '1': True, '2': True}}	1
9	wfgyt3TkQfdr	concentration	str	None	None	None	0	0	None	None	None	None	None	1	None	1	2025-09-09 10:18:16.315000+00:00	1	{'af': {'0': None, '1': True, '2': False}}	1
8	OTwADGld2GdW	sample_note	str	None	None	None	0	0	None	None	None	None	None	1	None	1	2025-09-09 10:18:16.312000+00:00	1	{'af': {'0': None, '1': True, '2': False}}	1
7	37dLCVr1VaHP	donor	str	None	None	None	0	0	None	None	None	None	None	1	None	1	2025-09-09 10:18:16.308000+00:00	1	{'af': {'0': None, '1': True, '2': False}}	1
6	fTRN8zhtGa5G	assay_oid	cat[bionty.ExperimentalFactor.ontology_id]	None	None	None	0	0	None	None	None	None	None	1	None	1	2025-09-09 10:18:16.305000+00:00	1	{'af': {'0': None, '1': True, '2': False}}	1
5	4cJBr82b58Bn	cell_type_by_expert	cat[bionty.CellType]	None	None	None	0	0	None	None	None	None	None	1	None	1	2025-09-09 10:18:16.301000+00:00	1	{'af': {'0': None, '1': True, '2': False}}	1
4	FqjhDlbHEsb6	cell_type_by_model	cat[bionty.CellType]	None	None	None	0	0	None	None	None	None	None	1	None	1	2025-09-09 10:18:16.297000+00:00	1	{'af': {'0': None, '1': True, '2': False}}	1

FeatureValue

	value	hash	space_id	feature_id	run_id	created_at	created_by_id	_aux	branch_id
id
1	21.6	XftFE5byhwPHY-11WjfNAw	1	1	1	2025-09-09 10:18:16.219000+00:00	1	None	1

Run

	uid	name	started_at	finished_at	reference	reference_type	_is_consecutive	_status_code	space_id	transform_id	report_id	_logfile_id	environment_id	initiated_by_run_id	created_at	created_by_id	_aux	branch_id
id
1	tbBim7Ei5SsymWEG	None	2025-09-09 10:18:13.229552+00:00	None	None	None	None	-1	1	1	None	None	None	None	2025-09-09 10:18:13.230000+00:00	1	None	1

Schema

	uid	name	description	n	is_type	itype	otype	dtype	hash	minimal_set	ordered_set	maximal_set	_curation	slot	space_id	type_id	validated_by_id	composite_id	run_id	created_at	created_by_id	_aux	branch_id
id
1	0000000000000000	None	None	-1	False	Feature	None	None	kMi7B_N88uu-YnbTLDU-DA	True	False	False	None	None	1	None	None	None	1	2025-09-09 10:18:16.321000+00:00	1	{'af': {'2': True}}	1
2	ApT9JjvXYT25LIgl	None	None	8	False	Feature	None	None	8V60HZGi0SQY2vj48ASPZA	True	False	False	None	None	1	None	None	None	1	2025-09-09 10:18:18.321000+00:00	1	{'af': {'2': False}}	1

Storage

	uid	root	description	type	region	instance_uid	space_id	run_id	created_at	created_by_id	_aux	branch_id
id
1	GeBKuLxWMjpM	/home/runner/work/lamin-docs/lamin-docs/docs/l...	None	local	None	3MepSh2Col3I	1	None	2025-09-09 10:18:10.022000+00:00	1	None	1

Transform

	uid	key	description	type	source_code	hash	reference	reference_type	space_id	_template_id	version	is_latest	created_at	created_by_id	_aux	branch_id
id
1	ULOzzxBhFigf0000	introduction.ipynb	Introduction	notebook	None	None	None	None	1	None	None	True	2025-09-09 10:18:13.225000+00:00	1	None	1

ULabel

	uid	name	is_type	description	reference	reference_type	space_id	type_id	run_id	created_at	created_by_id	_aux	branch_id
id
3	aBLZRx9m	IFNG	False	None	None	None	1	None	1	2025-09-09 10:18:16.290000+00:00	1	None	1
2	jPHrFT9D	DMSO	False	None	None	None	1	None	1	2025-09-09 10:18:16.286000+00:00	1	None	1
1	nMoquB9r	My experiment	False	None	None	None	1	None	1	2025-09-09 10:18:15.013000+00:00	1	None	1

******************

* module: bionty *

******************

CellType

	uid	name	ontology_id	abbr	synonyms	description	space_id	source_id	run_id	created_at	created_by_id	_aux	branch_id
id
16	6By01L04	alpha-beta T cell	CL:0000789	None	alpha-beta T-cell|alpha-beta T-lymphocyte|alph...	A T Cell That Expresses An Alpha-Beta T Cell R...	1	16	1	2025-09-09 10:18:17.387000+00:00	1	None	1
17	4BEwsp1Q	mature alpha-beta T cell	CL:0000791	None	mature alpha-beta T-cell|mature alpha-beta T l...	A Alpha-Beta T Cell That Has A Mature Phenotype.	1	16	1	2025-09-09 10:18:17.387000+00:00	1	None	1
15	6IC9NGJE	CD8-positive, alpha-beta T cell	CL:0000625	None	CD8-positive, alpha-beta T-lymphocyte|CD8-posi...	A T Cell Expressing An Alpha-Beta T Cell Recep...	1	16	1	2025-09-09 10:18:17.135000+00:00	1	None	1
14	7GpphKmr	lymphocyte of B lineage	CL:0000945	None	None	A Lymphocyte Of B Lineage With The Commitment ...	1	16	1	2025-09-09 10:18:16.863000+00:00	1	None	1
13	ryEtgi1y	B cell	CL:0000236	None	B-cell|B-lymphocyte|B lymphocyte	A Lymphocyte Of B Lineage That Is Capable Of B...	1	16	1	2025-09-09 10:18:16.629000+00:00	1	None	1
3	4bKGljt0	cell	CL:0000000	None	None	A Material Entity Of Anatomical Origin (Part O...	1	16	1	2025-09-09 10:18:16.142000+00:00	1	None	1
4	22LvKd01	T cell	CL:0000084	None	T lymphocyte|T-lymphocyte|T-cell	A Type Of Lymphocyte Whose Defining Characteri...	1	16	1	2025-09-09 10:18:16.142000+00:00	1	None	1

ExperimentalFactor

	uid	name	ontology_id	abbr	synonyms	description	molecule	instrument	measurement	space_id	source_id	run_id	created_at	created_by_id	_aux	branch_id
id
2	789nVHwo	RNA assay	EFO:0001457	None	None	An Assay With Input Rna	RNA assay	None	None	1	21	1	2025-09-09 10:18:18.220000+00:00	1	None	1
3	1wLRxESw	assay by molecule	EFO:0002772	None	None	None	None	None	None	1	21	1	2025-09-09 10:18:18.220000+00:00	1	None	1
4	6oIjaW4X	assay by instrument	EFO:0002773	None	None	None	None	None	None	1	21	1	2025-09-09 10:18:18.220000+00:00	1	None	1
5	6dI7vyK2	assay by sequencer	EFO:0003740	None	sequencing assay	An Assay That Exploits A Sequencer As The Inst...	None	assay by sequencer	None	1	21	1	2025-09-09 10:18:18.220000+00:00	1	None	1
6	2zGOHoUs	single cell sequencing	EFO:0007832	None	None	Single Cell Sequencing Examines The Sequence I...	None	single cell sequencing	None	1	21	1	2025-09-09 10:18:18.220000+00:00	1	None	1
1	4WYv9kl0	single-cell RNA sequencing	EFO:0008913	None	single-cell RNA-seq|scRNA-seq|single cell RNA ...	A Protocol That Provides The Expression Profil...	RNA assay	single cell sequencing	None	1	21	1	2025-09-09 10:18:17.951000+00:00	1	None	1

Source

	uid	entity	organism	name	in_db	currently_used	description	url	md5	source_website	space_id	dataframe_artifact_id	version	run_id	created_at	created_by_id	_aux	branch_id
id
16	3T9KZcjQ	bionty.CellType	all	cl	True	True	Cell Ontology	http://purl.obolibrary.org/obo/cl/releases/202...	None	https://obophenotype.github.io/cell-ontology	1	None	2025-04-10	None	2025-09-09 10:18:10.068000+00:00	1	None	1
21	2a1HvjdB	bionty.ExperimentalFactor	all	efo	False	True	The Experimental Factor Ontology	http://www.ebi.ac.uk/efo/releases/v3.70.0/efo.owl	None	https://bioportal.bioontology.org/ontologies/EFO	1	None	3.70.0	None	2025-09-09 10:18:10.068000+00:00	1	None	1
1	33TUF039	bionty.Organism	vertebrates	ensembl	False	True	Ensembl	https://ftp.ensembl.org/pub/release-112/specie...	None	https://www.ensembl.org	1	None	release-112	None	2025-09-09 10:18:10.068000+00:00	1	None	1
2	6bbVUTCS	bionty.Organism	bacteria	ensembl	False	True	Ensembl	https://ftp.ensemblgenomes.ebi.ac.uk/pub/bacte...	None	https://www.ensembl.org	1	None	release-57	None	2025-09-09 10:18:10.068000+00:00	1	None	1
3	6s9nV6xh	bionty.Organism	fungi	ensembl	False	True	Ensembl	https://ftp.ensemblgenomes.ebi.ac.uk/pub/fungi...	None	https://www.ensembl.org	1	None	release-57	None	2025-09-09 10:18:10.068000+00:00	1	None	1
4	2PmTrc8x	bionty.Organism	metazoa	ensembl	False	True	Ensembl	https://ftp.ensemblgenomes.ebi.ac.uk/pub/metaz...	None	https://www.ensembl.org	1	None	release-57	None	2025-09-09 10:18:10.068000+00:00	1	None	1
5	7GPHh16S	bionty.Organism	plants	ensembl	False	True	Ensembl	https://ftp.ensemblgenomes.ebi.ac.uk/pub/plant...	None	https://www.ensembl.org	1	None	release-57	None	2025-09-09 10:18:10.068000+00:00	1	None	1

Which registries have I already learned about? 🤔

• Artifact: datasets & models stored as files, folders, or arrays

• Transform: transforms of artifacts

• Run: runs of transforms

• User: users

• Storage: local or cloud storage locations

Every registry supports arbitrary relational queries using the class methods get and filter. The syntax for it is Django’s query syntax.

Here are some simple query examples.

Py

# get a single record (here the current notebook)
transform = ln.Transform.get(key="introduction.ipynb")

# get a set of records by filtering for a directory (LaminDB treats directories like AWS S3, as the prefix of the storage key)
ln.Artifact.filter(key__startswith="my_datasets/").df()

# query all artifacts ingested from a transform
artifacts = ln.Artifact.filter(transform=transform).all()

# query all artifacts ingested from a notebook with "intro" in the title
artifacts = ln.Artifact.filter(
    transform__description__icontains="intro",
).all()

R

# get a single record (here the current notebook)
transform <- ln$Transform$get(key = "introduction.Rmd")

# get a set of records by filtering for a directory (LaminDB treats directories like AWS S3, as the prefix of the storage key)
ln$Artifact$filter(key__startswith = "my_datasets/")$df()

# query all artifacts ingested from a transform
artifacts <- ln$Artifact$filter(transform = transform)$all()

# query all artifacts ingested from a notebook with "intro" in the title
artifacts <- ln$Artifact$filter(
  transform__description__icontains = "intro",
)$all()

Show code cell content Hide code cell content

# get a single record (here the current notebook)
transform = ln.Transform.get(key="introduction.ipynb")

# get a set of records by filtering for a directory (LaminDB treats directories like AWS S3, as the prefix of the storage key)
ln.Artifact.filter(key__startswith="my_datasets/").df()

# query all artifacts ingested from a transform
artifacts = ln.Artifact.filter(transform=transform).all()

# query all artifacts ingested from a notebook with "intro" in the title
artifacts = ln.Artifact.filter(
    transform__description__icontains="intro",
).all()

/tmp/ipykernel_3802/3538396199.py:5: FutureWarning: Use to_dataframe instead of df, df will be removed in the future.
  ln.Artifact.filter(key__startswith="my_datasets/").df()

What does a double underscore mean?

For any field, the double underscore defines a comparator, e.g.,

• name__icontains="Martha": name contains "Martha" when ignoring case

• name__startswith="Martha": name starts with "Martha

• name__in=["Martha", "John"]: name is "John" or "Martha"

For more info, see: Query & search registries.

Can I chain filters and searches?

Yes: ln.Artifact.filter(suffix=".jpg").search("my image")

The class methods search and lookup help with approximate matches.

Py

# search artifacts
ln.Artifact.search("iris").df().head()

# search transforms
ln.Transform.search("intro").df()

# look up records with auto-complete
ulabels = ln.ULabel.lookup()

R

# search artifacts
ln$Artifact$search("iris")$df()

# search transforms
ln$Transform$search("intro")$df()

# look up records with auto-complete
ulabels <- ln$ULabel$lookup()

Show me a screenshot

For more info, see: Query & search registries.

Manage files & folders

Let’s look at a folder in the cloud that contains 3 sub-folders storing images & metadata of Iris flowers, generated in 3 subsequent studies.

Py

# we use anon=True here in case no aws credentials are configured
ln.UPath("s3://lamindata/iris_studies", anon=True).view_tree()

R

# we use anon=True here in case no aws credentials are configured
ln$UPath("s3://lamindata/iris_studies", anon = TRUE)$view_tree()

Show code cell content Hide code cell content

# we use anon=True here in case no aws credentials are configured
ln.UPath("s3://lamindata/iris_studies", anon=True).view_tree()

3 sub-directories & 151 files with suffixes '.jpg', '.csv'
s3://lamindata/iris_studies
├── study0_raw_images/
│   ├── iris-0337d20a3b7273aa0ddaa7d6afb57a37a759b060e4401871db3cefaa6adc068d.jpg
│   ├── iris-0797945218a97d6e5251b4758a2ba1b418cbd52ce4ef46a3239e4b939bd9807b.jpg
│   ├── iris-0f133861ea3fe1b68f9f1b59ebd9116ff963ee7104a0c4200218a33903f82444.jpg
│   ├── iris-0fec175448a23db03c1987527f7e9bb74c18cffa76ef003f962c62603b1cbb87.jpg
│   ├── iris-125b6645e086cd60131764a6bed12650e0f7f2091c8bbb72555c103196c01881.jpg
│   ├── iris-13dfaff08727abea3da8cfd8d097fe1404e76417fefe27ff71900a89954e145a.jpg
│   ├── iris-1566f7f5421eaf423a82b3c1cd1328f2a685c5ef87d8d8e710f098635d86d3d0.jpg
│   ├── iris-1804702f49c2c385f8b30913569aebc6dce3da52ec02c2c638a2b0806f16014e.jpg
│   ├── iris-318d451a8c95551aecfde6b55520f302966db0a26a84770427300780b35aa05a.jpg
│   ├── iris-3dec97fe46d33e194520ca70740e4c2e11b0ffbffbd0aec0d06afdc167ddf775.jpg
│   ├── iris-3eed72bc2511f619190ce79d24a0436fef7fcf424e25523cb849642d14ac7bcf.jpg
│   ├── iris-430fa45aad0edfeb5b7138ff208fdeaa801b9830a9eb68f378242465b727289a.jpg
│   ├── iris-4cc15cd54152928861ecbdc8df34895ed463403efb1571dac78e3223b70ef569.jpg
│   ├── iris-4febb88ef811b5ca6077d17ef8ae5dbc598d3f869c52af7c14891def774d73fa.jpg
│   ├── iris-590e7f5b8f4de94e4b82760919abd9684ec909d9f65691bed8e8f850010ac775.jpg
│   ├── iris-5a313749aa61e9927389affdf88dccdf21d97d8a5f6aa2bd246ca4bc926903ba.jpg
│   ├── iris-5b3106db389d61f4277f43de4953e660ff858d8ab58a048b3d8bf8d10f556389.jpg
│   ├── iris-5f4e8fffde2404cc30be275999fddeec64f8a711ab73f7fa4eb7667c8475c57b.jpg
│   ├── iris-68d83ad09262afb25337ccc1d0f3a6d36f118910f36451ce8a6600c77a8aa5bd.jpg
│   ├── iris-70069edd7ab0b829b84bb6d4465b2ca4038e129bb19d0d3f2ba671adc03398cc.jpg
│   ├── iris-7038aef1137814473a91f19a63ac7a55a709c6497e30efc79ca57cfaa688f705.jpg
│   ├── iris-74d1acf18cfacd0a728c180ec8e1c7b4f43aff72584b05ac6b7c59f5572bd4d4.jpg
│   ├── iris-7c3b5c5518313fc6ff2c27fcbc1527065cbb42004d75d656671601fa485e5838.jpg
│   ├── iris-7cf1ebf02b2cc31539ed09ab89530fec6f31144a0d5248a50e7c14f64d24fe6e.jpg
│   ├── iris-7dcc69fa294fe04767706c6f455ea6b31d33db647b08aab44b3cd9022e2f2249.jpg
│   ├── iris-801b7efb867255e85137bc1e1b06fd6cbab70d20cab5b5046733392ecb5b3150.jpg
│   ├── iris-8305dd2a080e7fe941ea36f3b3ec0aa1a195ad5d957831cf4088edccea9465e2.jpg
│   ├── iris-83f433381b755101b9fc9fbc9743e35fbb8a1a10911c48f53b11e965a1cbf101.jpg
│   ├── iris-874121a450fa8a420bdc79cc7808fd28c5ea98758a4b50337a12a009fa556139.jpg
│   ├── iris-8c216e1acff39be76d6133e1f549d138bf63359fa0da01417e681842210ea262.jpg
│   ├── iris-92c4268516ace906ad1ac44592016e36d47a8c72a51cacca8597ba9e18a8278b.jpg
│   ├── iris-95d7ec04b8158f0873fa4aab7b0a5ec616553f3f9ddd6623c110e3bc8298248f.jpg
│   ├── iris-9ce2d8c4f1eae5911fcbd2883137ba5542c87cc2fe85b0a3fbec2c45293c903e.jpg
│   ├── iris-9ee27633bb041ef1b677e03e7a86df708f63f0595512972403dcf5188a3f48f5.jpg
│   ├── iris-9fb8d691550315506ae08233406e8f1a4afed411ea0b0ac37e4b9cdb9c42e1ec.jpg
│   ├── iris-9ffe51c2abd973d25a299647fa9ccaf6aa9c8eecf37840d7486a061438cf5771.jpg
│   ├── iris-a2be5db78e5b603a5297d9a7eec4e7f14ef2cba0c9d072dc0a59a4db3ab5bb13.jpg
│   ├── iris-ad7da5f15e2848ca269f28cd1dc094f6f685de2275ceaebb8e79d2199b98f584.jpg
│   ├── iris-bc515e63b5a4af49db8c802c58c83db69075debf28c792990d55a10e881944d9.jpg
│   ├── iris-bd8d83096126eaa10c44d48dbad4b36aeb9f605f1a0f6ca929d3d0d492dafeb6.jpg
│   ├── iris-bdae8314e4385d8e2322abd8e63a82758a9063c77514f49fc252e651cbd79f82.jpg
│   ├── iris-c175cd02ac392ecead95d17049f5af1dcbe37851c3e42d73e6bb813d588ea70b.jpg
│   ├── iris-c31e6056c94b5cb618436fbaac9eaff73403fa1b87a72db2c363d172a4db1820.jpg
│   ├── iris-ca40bc5839ee2f9f5dcac621235a1db2f533f40f96a35e1282f907b40afa457d.jpg
│   ├── iris-ddb685c56cfb9c8496bcba0d57710e1526fff7d499536b3942d0ab375fa1c4a6.jpg
│   ├── iris-e437a7c7ad2bbac87fef3666b40c4de1251b9c5f595183eda90a8d9b1ef5b188.jpg
│   ├── iris-e7e0774289e2153cc733ff62768c40f34ac9b7b42e23c1abc2739f275e71a754.jpg
│   ├── iris-e9da6dd69b7b07f80f6a813e2222eae8c8f7c3aeaa6bcc02b25ea7d763bcf022.jpg
│   ├── iris-eb01666d4591b2e03abecef5a7ded79c6d4ecb6d1922382c990ad95210d55795.jpg
│   ├── iris-f6e4890dee087bd52e2c58ea4c6c2652da81809603ea3af561f11f8c2775c5f3.jpg
│   └── meta.csv
├── study1_raw_images/
│   ├── iris-0879d3f5b337fe512da1c7bf1d2bfd7616d744d3eef7fa532455a879d5cc4ba0.jpg
│   ├── iris-0b486eebacd93e114a6ec24264e035684cebe7d2074eb71eb1a71dd70bf61e8f.jpg
│   ├── iris-0ff5ba898a0ec179a25ca217af45374fdd06d606bb85fc29294291facad1776a.jpg
│   ├── iris-1175239c07a943d89a6335fb4b99a9fb5aabb2137c4d96102f10b25260ae523f.jpg
│   ├── iris-1289c57b571e8e98e4feb3e18a890130adc145b971b7e208a6ce5bad945b4a5a.jpg
│   ├── iris-12adb3a8516399e27ff1a9d20d28dca4674836ed00c7c0ae268afce2c30c4451.jpg
│   ├── iris-17ac8f7b5734443090f35bdc531bfe05b0235b5d164afb5c95f9d35f13655cf3.jpg
│   ├── iris-2118d3f235a574afd48a1f345bc2937dad6e7660648516c8029f4e76993ea74d.jpg
│   ├── iris-213cd179db580f8e633087dcda0969fd175d18d4f325cb5b4c5f394bbba0c1e0.jpg
│   ├── iris-21a1255e058722de1abe928e5bbe1c77bda31824c406c53f19530a3ca40be218.jpg
│   ├── iris-249370d38cc29bc2a4038e528f9c484c186fe46a126e4b6c76607860679c0453.jpg
│   ├── iris-2ac575a689662b7045c25e2554df5f985a3c6c0fd5236fabef8de9c78815330c.jpg
│   ├── iris-2c5b373c2a5fd214092eb578c75eb5dc84334e5f11a02f4fa23d5d316b18f770.jpg
│   ├── iris-2ecaad6dfe3d9b84a756bc2303a975a732718b954a6f54eae85f681ea3189b13.jpg
│   ├── iris-32827aec52e0f3fa131fa85f2092fc6fa02b1b80642740b59d029cef920c26b3.jpg
│   ├── iris-336fc3472b6465826f7cd87d5cef8f78d43cf2772ebe058ce71e1c5bad74c0e1.jpg
│   ├── iris-432026d8501abcd495bd98937a82213da97fca410af1c46889eabbcf2fd1b589.jpg
│   ├── iris-49a9158e46e788a39eeaefe82b19504d58dde167f540df6bc9492c3916d5f7ca.jpg
│   ├── iris-4b47f927405d90caa15cbf17b0442390fc71a2ca6fb8d07138e8de17d739e9a4.jpg
│   ├── iris-5691cad06fe37f743025c097fa9c4cec85e20ca3b0efff29175e60434e212421.jpg
│   ├── iris-5c38dba6f6c27064eb3920a5758e8f86c26fec662cc1ac4b5208d5f30d1e3ead.jpg
│   ├── iris-5da184e8620ebf0feef4d5ffe4346e6c44b2fb60cecc0320bd7726a1844b14cd.jpg
│   ├── iris-66eee9ff0bfa521905f733b2a0c6c5acad7b8f1a30d280ed4a17f54fe1822a7e.jpg
│   ├── iris-6815050b6117cf2e1fd60b1c33bfbb94837b8e173ff869f625757da4a04965c9.jpg
│   ├── iris-793fe85ddd6a97e9c9f184ed20d1d216e48bf85aa71633eff6d27073e0825d54.jpg
│   ├── iris-850229e6293a741277eb5efaa64d03c812f007c5d0f470992a8d4cfdb902230c.jpg
│   ├── iris-86d782d20ef7a60e905e367050b0413ca566acc672bc92add0bb0304faa54cfc.jpg
│   ├── iris-875a96790adc5672e044cf9da9d2edb397627884dfe91c488ab3fb65f65c80ff.jpg
│   ├── iris-96f06136df7a415550b90e443771d0b5b0cd990b503b64cc4987f5cb6797fa9b.jpg
│   ├── iris-9a889c96a37e8927f20773783a084f31897f075353d34a304c85e53be480e72a.jpg
│   ├── iris-9e3208f4f9fedc9598ddf26f77925a1e8df9d7865a4d6e5b4f74075d558d6a5e.jpg
│   ├── iris-a7e13b6f2d7f796768d898f5f66dceefdbd566dd4406eea9f266fc16dd68a6f2.jpg
│   ├── iris-b026efb61a9e3876749536afe183d2ace078e5e29615b07ac8792ab55ba90ebc.jpg
│   ├── iris-b3c086333cb5ccb7bb66a163cf4bf449dc0f28df27d6580a35832f32fd67bfc9.jpg
│   ├── iris-b795e034b6ea08d3cd9acaa434c67aca9d17016991e8dd7d6fd19ae8f6120b77.jpg
│   ├── iris-bb4a7ad4c844987bc9dc9dfad2b363698811efe3615512997a13cd191c23febc.jpg
│   ├── iris-bd60a6ed0369df4bea1934ef52277c32757838123456a595c0f2484959553a36.jpg
│   ├── iris-c15d6019ebe17d7446ced589ef5ef7a70474d35a8b072e0edfcec850b0a106db.jpg
│   ├── iris-c45295e76c6289504921412293d5ddbe4610bb6e3b593ea9ec90958e74b73ed2.jpg
│   ├── iris-c50d481f9fa3666c2c3808806c7c2945623f9d9a6a1d93a17133c4cb1560c41c.jpg
│   ├── iris-df4206653f1ec9909434323c05bb15ded18e72587e335f8905536c34a4be3d45.jpg
│   ├── iris-e45d869cb9d443b39d59e35c2f47870f5a2a335fce53f0c8a5bc615b9c53c429.jpg
│   ├── iris-e76fa5406e02a312c102f16eb5d27c7e0de37b35f801e1ed4c28bd4caf133e7a.jpg
│   ├── iris-e8d3fd862aae1c005bcc80a73fd34b9e683634933563e7538b520f26fd315478.jpg
│   ├── iris-ea578f650069a67e5e660bb22b46c23e0a182cbfb59cdf5448cf20ce858131b6.jpg
│   ├── iris-eba0c546e9b7b3d92f0b7eb98b2914810912990789479838807993d13787a2d9.jpg
│   ├── iris-f22d4b9605e62db13072246ff6925b9cf0240461f9dfc948d154b983db4243b9.jpg
│   ├── iris-fac5f8c23d8c50658db0f4e4a074c2f7771917eb52cbdf6eda50c12889510cf4.jpg
│   └── meta.csv
└── study2_raw_images/
    ├── iris-01cdd55ca6402713465841abddcce79a2e906e12edf95afb77c16bde4b4907dc.jpg
    ├── iris-02868b71ddd9b33ab795ac41609ea7b20a6e94f2543fad5d7fa11241d61feacf.jpg
    ├── iris-0415d2f3295db04bebc93249b685f7d7af7873faa911cd270ecd8363bd322ed5.jpg
    ├── iris-0c826b6f4648edf507e0cafdab53712bb6fd1f04dab453cee8db774a728dd640.jpg
    ├── iris-10fb9f154ead3c56ba0ab2c1ab609521c963f2326a648f82c9d7cabd178fc425.jpg
    ├── iris-14cbed88b0d2a929477bdf1299724f22d782e90f29ce55531f4a3d8608f7d926.jpg
    ├── iris-186fe29e32ee1405ddbdd36236dd7691a3c45ba78cc4c0bf11489fa09fbb1b65.jpg
    ├── iris-1b0b5aabd59e4c6ed1ceb54e57534d76f2f3f97e0a81800ff7ed901c35a424ab.jpg
    ├── iris-1d35672eb95f5b1cf14c2977eb025c246f83cdacd056115fdc93e946b56b610c.jpg
    ├── iris-1f941001f508ff1bd492457a90da64e52c461bfd64587a3cf7c6bf1bcb35adab.jpg
    ├── iris-2a09038b87009ecee5e5b4cd4cef068653809cc1e08984f193fad00f1c0df972.jpg
    ├── iris-308389e34b6d9a61828b339916aed7af295fdb1c7577c23fb37252937619e7e4.jpg
    ├── iris-30e4e56b1f170ff4863b178a0a43ea7a64fdd06c1f89a775ec4dbf5fec71e15c.jpg
    ├── iris-332953f4d6a355ca189e2508164b24360fc69f83304e7384ca2203ddcb7c73b5.jpg
    ├── iris-338fc323ed045a908fb1e8ff991255e1b8e01c967e36b054cb65edddf97b3bb0.jpg
    ├── iris-34a7cc16d26ba0883574e7a1c913ad50cf630e56ec08ee1113bf3584f4e40230.jpg
    ├── iris-360196ba36654c0d9070f95265a8a90bc224311eb34d1ab0cf851d8407d7c28e.jpg
    ├── iris-36132c6df6b47bda180b1daaafc7ac8a32fd7f9af83a92569da41429da49ea5b.jpg
    ├── iris-36f2b9282342292b67f38a55a62b0c66fa4e5bb58587f7fec90d1e93ea8c407a.jpg
    ├── iris-37ad07fd7b39bc377fa6e9cafdb6e0c57fb77df2c264fe631705a8436c0c2513.jpg
    ├── iris-3ba1625bb78e4b69b114bdafcdab64104b211d8ebadca89409e9e7ead6a0557c.jpg
    ├── iris-4c5d9a33327db025d9c391aeb182cbe20cfab4d4eb4ac951cc5cd15e132145d8.jpg
    ├── iris-522f3eb1807d015f99e66e73b19775800712890f2c7f5b777409a451fa47d532.jpg
    ├── iris-589fa96b9a3c2654cf08d05d3bebf4ab7bc23592d7d5a95218f9ff87612992fa.jpg
    ├── iris-61b71f1de04a03ce719094b65179b06e3cd80afa01622b30cda8c3e41de6bfaa.jpg
    ├── iris-62ef719cd70780088a4c140afae2a96c6ca9c22b72b078e3b9d25678d00b88a5.jpg
    ├── iris-819130af42335d4bb75bebb0d2ee2e353a89a3d518a1d2ce69842859c5668c5a.jpg
    ├── iris-8669e4937a2003054408afd228d99cb737e9db5088f42d292267c43a3889001a.jpg
    ├── iris-86c76e0f331bc62192c392cf7c3ea710d2272a8cc9928d2566a5fc4559e5dce4.jpg
    ├── iris-8a8bc54332a42bb35ee131d7b64e9375b4ac890632eb09e193835b838172d797.jpg
    ├── iris-8e9439ec7231fa3b9bc9f62a67af4e180466b32a72316600431b1ec93e63b296.jpg
    ├── iris-90b7d491b9a39bb5c8bb7649cce90ab7f483c2759fb55fda2d9067ac9eec7e39.jpg
    ├── iris-9dededf184993455c411a0ed81d6c3c55af7c610ccb55c6ae34dfac2f8bde978.jpg
    ├── iris-9e6ce91679c9aaceb3e9c930f11e788aacbfa8341a2a5737583c14a4d6666f3d.jpg
    ├── iris-a0e65269f7dc7801ac1ad8bd0c5aa547a70c7655447e921d1d4d153a9d23815e.jpg
    ├── iris-a445b0720254984275097c83afbdb1fe896cb010b5c662a6532ed0601ea24d7c.jpg
    ├── iris-a6b85bf1f3d18bbb6470440592834c2c7f081b490836392cf5f01636ee7cf658.jpg
    ├── iris-b005c82b844de575f0b972b9a1797b2b1fbe98c067c484a51006afc4f549ada4.jpg
    ├── iris-bfcf79b3b527eb64b78f9a068a1000042336e532f0f44e68f818dd13ab492a76.jpg
    ├── iris-c156236fb6e888764485e796f1f972bbc7ad960fe6330a7ce9182922046439c4.jpg
    ├── iris-d99d5fd2de5be1419cbd569570dbb6c9a6c8ec4f0a1ff5b55dc2607f6ecdca8f.jpg
    ├── iris-d9aae37a8fa6afdef2af170c266a597925eea935f4d070e979d565713ea62642.jpg
    ├── iris-dbc87fcecade2c070baaf99caf03f4f0f6e3aa977e34972383cb94d0efe8a95d.jpg
    ├── iris-e3d1a560d25cf573d2cbbf2fe6cd231819e998109a5cf1788d59fbb9859b3be2.jpg
    ├── iris-ec288bdad71388f907457db2476f12a5cb43c28cfa28d2a2077398a42b948a35.jpg
    ├── iris-ed5b4e072d43bc53a00a4a7f4d0f5d7c0cbd6a006e9c2d463128cedc956cb3de.jpg
    ├── iris-f3018a9440d17c265062d1c61475127f9952b6fe951d38fd7700402d706c0b01.jpg
    ├── iris-f47c5963cdbaa3238ba2d446848e8449c6af83e663f0a9216cf0baba8429b36f.jpg
    ├── iris-fa4b6d7e3617216104b1405cda21bf234840cd84a2c1966034caa63def2f64f0.jpg
    ├── iris-fc4b0cc65387ff78471659d14a78f0309a76f4c3ec641b871e40b40424255097.jpg
    └── meta.csv

Let’s create an artifact for the first sub-folder.

Py

artifact = ln.Artifact("s3://lamindata/iris_studies/study0_raw_images").save()
artifact

R

artifact <- ln$Artifact("s3://lamindata/iris_studies/study0_raw_images")$save()
artifact

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artifact = ln.Artifact("s3://lamindata/iris_studies/study0_raw_images").save()
artifact

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

→ referenced read-only storage location at s3://lamindata, is managed by instance with uid 4XIuR0tvaiXM

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

Artifact(uid='R8OJQ0R6bhiOvWzL0000', is_latest=True, key='iris_studies/study0_raw_images', suffix='', size=658465, hash='IVKGMfNwi8zKvnpaD_gG7w', n_files=51, branch_id=1, space_id=1, storage_id=2, run_id=1, created_by_id=1, created_at=2025-09-09 10:18:22 UTC)

As you see from path, the folder was merely registered in its present storage location without copying it.

Py

artifact.path

R

artifact$path

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artifact.path

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

S3QueryPath('s3://lamindata/iris_studies/study0_raw_images')

LaminDB keeps track of all your storage locations.

Py

ln.Storage.df()

R

ln$Storage$df()

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ln.Storage.df()

/tmp/ipykernel_3802/1759264747.py:1: FutureWarning: Use to_dataframe instead of df, df will be removed in the future.
  ln.Storage.df()

	uid	root	description	type	region	instance_uid	space_id	run_id	created_at	created_by_id	_aux	branch_id
id
2	YmV3ZoHvAAAA	s3://lamindata	None	s3	us-east-1	4XIuR0tvaiXM	1	1.0	2025-09-09 10:18:21.980000+00:00	1	None	1
1	GeBKuLxWMjpM	/home/runner/work/lamin-docs/lamin-docs/docs/l...	None	local	None	3MepSh2Col3I	1	NaN	2025-09-09 10:18:10.022000+00:00	1	None	1

How do I update or delete an artifact?

artifact.description = "My new description"  # change description
artifact.save()  # save the change to the database
artifact.delete()  # move to trash
artifact.delete(permanent=True)  # permanently delete

How do I create an artifact for a local file or folder?

Source path is local:

ln.Artifact("./my_data.fcs", key="my_data.fcs")
ln.Artifact("./my_images/", key="my_images")

Upon artifact.save(), the source path will be copied or uploaded into your instance’s current storage, visible & changeable via ln.settings.storage.

If the source path is remote or already in a registered storage location (one that’s registered in ln.Storage), artifact.save() will not trigger a copy or upload but register the existing path.

ln.Artifact("s3://my-bucket/my_data.fcs")  # key is auto-populated from S3, you can optionally pass a description
ln.Artifact("s3://my-bucket/my_images/")  # key is auto-populated from S3, you can optionally pass a description

You can use any storage location supported by `fsspec`.

Which fields are populated when creating an artifact record?

Basic fields:

• uid: universal ID

• key: a (virtual) relative path of the artifact in storage

• description: an optional string description

• storage: the storage location (the root, say, an S3 bucket or a local directory)

• suffix: an optional file/path suffix

• size: the artifact size in bytes

• hash: a hash useful to check for integrity and collisions (is this artifact already stored?)

• hash_type: the type of the hash

• created_at: time of creation

• updated_at: time of last update

Provenance-related fields:

• created_by: the User who created the artifact

• run: the Run of the Transform that created the artifact

For a full reference, see Artifact.

What exactly happens during save?

In the database: An artifact record is inserted into the Artifact registry. If the artifact record exists already, it’s returned.

In storage:

• If the default storage is in the cloud, .save() triggers an upload for a local artifact.

• If the artifact is already in a registered storage location, only the metadata of the record is saved to the artifact registry.

How does LaminDB compare to a AWS S3?

LaminDB provides a database on top of AWS S3 (or GCP storage, file systems, etc.).

Similar to organizing files with paths, you can organize artifacts using the key parameter of Artifact.

However, you’ll see that you can more conveniently query data by entities you care about: people, code, experiments, genes, proteins, cell types, etc.

Are artifacts aware of array-like data?

Yes.

You can make artifacts from paths referencing array-like objects:

ln.Artifact("./my_anndata.h5ad", key="my_anndata.h5ad")
ln.Artifact("./my_zarr_array/", key="my_zarr_array")

Or from in-memory objects:

ln.Artifact.from_df(df, key="my_dataframe.parquet")
ln.Artifact.from_anndata(adata, key="my_anndata.h5ad")

You can open large artifacts for slicing from the cloud or load small artifacts directly into memory via:

artifact.open()

Manage biological registries

Every bionty registry is based on configurable public ontologies (>20 of them) that are automatically leveraged during validation & annotation. Sometimes you want to access the public ontology directly.

import bionty as bt

cell_type_ontology = bt.CellType.public()
cell_type_ontology

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PublicOntology
Entity: CellType
Organism: all
Source: cl, 2025-04-10
#terms: 3136

The returned object can be searched like you can search a registry.

cell_type_ontology.search("gamma-delta T cell").head(2)

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	name	definition	synonyms	parents
ontology_id
CL:0000798	gamma-delta T cell	A T Cell That Expresses A Gamma-Delta T Cell R...	gamma-delta T lymphocyte|gamma-delta T-lymphoc...	[CL:0000084]
CL:4033072	cycling gamma-delta T cell	A(N) Gamma-Delta T Cell That Is Cycling.	proliferating gamma-delta T cell	[CL:4033069, CL:0000798]

Because you can’t update an external public ontology, you update the content of the corresponding registry. Here, you create a new cell type.

# create an ontology-coupled cell type record and save it
neuron = bt.CellType.from_source(name="neuron").save()

# create a record to track a new cell state
new_cell_state = bt.CellType(
    name="my neuron cell state", description="explains X"
).save()

# express that it's a neuron state
new_cell_state.parents.add(neuron)

# view ontological hierarchy
new_cell_state.view_parents(distance=2)

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Manage AnnData objects

LaminDB supports a growing number of data structures: DataFrame, AnnData, MuData, SpatialData, and Tiledbsoma with their corresponding representations in storage.

Let’s go through the example of the quickstart, but store the dataset in an AnnData this time.

# define var schema
var_schema = ln.Schema(itype=bt.Gene.ensembl_gene_id, dtype=int).save()

# define composite schema
anndata_schema = ln.Schema(
    otype="AnnData", slots={"obs": schema, "var.T": var_schema}
).save()

Validate & annotate an AnnData.

import anndata as ad

# store the dataset as an AnnData object to distinguish data from metadata
adata = ad.AnnData(df.iloc[:, :3], obs=df.iloc[:, 3:-1])

# save curated artifact
artifact = ln.Artifact.from_anndata(
    adata, key="my_datasets/my_rnaseq1.h5ad", schema=anndata_schema
).save()
artifact.describe()

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→ returning existing schema with same hash: Schema(uid='ApT9JjvXYT25LIgl', n=8, is_type=False, itype='Feature', hash='8V60HZGi0SQY2vj48ASPZA', minimal_set=True, ordered_set=False, maximal_set=False, branch_id=1, space_id=1, created_by_id=1, run_id=1, created_at=2025-09-09 10:18:18 UTC)

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

Artifact .h5ad · AnnData · dataset
├── General
│   ├── key: my_datasets/my_rnaseq1.h5ad
│   ├── uid: kH95lc8XzjG9xVmm0000          hash: TWUlDi-V-Try1uCxWJ65nQ
│   ├── size: 29.5 KB                      transform: introduction.ipynb
│   ├── space: all                         branch: all
│   ├── created_by: anonymous              created_at: 2025-09-09 10:18:26
│   ├── n_observations: 3
│   └── storage path: /home/runner/work/lamin-docs/lamin-docs/docs/lamin-intro/my_datasets/my_rnaseq1.h5ad
├── Dataset features
│   ├── obs • 8                         [Feature]                                                                  
│   │   assay_oid                       cat[bionty.ExperimentalFactor.on…  single-cell RNA sequencing              
│   │   cell_type_by_expert             cat[bionty.CellType]               B cell, CD8-positive, alpha-beta T cell 
│   │   cell_type_by_model              cat[bionty.CellType]               B cell, T cell                          
│   │   perturbation                    cat[ULabel]                        DMSO, IFNG                              
│   │   donor                           str                                                                        
│   │   sample_note                     str                                                                        
│   │   concentration                   str                                                                        
│   │   treatment_time_h                num                                                                        
│   └── var.T • 3                       [bionty.Gene.ensembl_gene_id]                                              
│       CD8A                            num                                                                        
│       CD4                             num                                                                        
│       CD14                            num                                                                        
└── Labels
    └── .cell_types                     bionty.CellType                    T cell, B cell, CD8-positive, alpha-bet…
        .experimental_factors           bionty.ExperimentalFactor          single-cell RNA sequencing              
        .ulabels                        ULabel                             DMSO, IFNG                              

Because AnnData separates the high-dimensional count matrix that’s typically indexed with Ensembl gene ids from the metadata, we’re now working with two types of feature sets (bt.Gene for the counts and ln.Feature for the metadata). These correspond to the obs and the var schema in the anndata_schema.

If you want to find a dataset by whether it measured CD8A, you can do so as as follows.

# query for all feature sets that contain CD8A
feature_sets = ln.Schema.filter(genes__symbol="CD8A").all()

# query for all artifacts linked to these feature sets
ln.Artifact.filter(feature_sets__in=feature_sets).df()

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/tmp/ipykernel_3802/677433425.py:5: FutureWarning: Use to_dataframe instead of df, df will be removed in the future.
  ln.Artifact.filter(feature_sets__in=feature_sets).df()

	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	run_id	created_at	created_by_id	_aux	branch_id
id
4	kH95lc8XzjG9xVmm0000	my_datasets/my_rnaseq1.h5ad	None	.h5ad	dataset	AnnData	30232	TWUlDi-V-Try1uCxWJ65nQ	None	3	md5	True	False	1	1	4	None	True	1	2025-09-09 10:18:26.292000+00:00	1	{'af': {'0': True}}	1

Scale learning

How do you integrate new datasets with your existing datasets? Leverage Collection.

# a new dataset
df2 = ln.core.datasets.small_dataset2(otype="DataFrame")
adata = ad.AnnData(df2.iloc[:, :3], obs=df2.iloc[:, 3:-1])
artifact2 = ln.Artifact.from_anndata(
    adata, key="my_datasets/my_rnaseq2.h5ad", schema=anndata_schema
).save()

Create a collection using Collection.

collection = ln.Collection([artifact, artifact2], key="my-RNA-seq-collection").save()
collection.describe()
collection.view_lineage()

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! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

! creating spaces manually on sqlite databases is possible for demo purposes, but does *not* affect access permissions

Collection 
└── General
    ├── key: my-RNA-seq-collection
    ├── uid: KQdwwa8dzxMv3qmp0000                    transform: introduction.ipynb
    ├── space: all                                   branch: main
    └── created_by: anonymous                        created_at: 2025-09-09 10:18:28

# if it's small enough, you can load the entire collection into memory as if it was one
collection.load()

# typically, it's too big, hence, open it for streaming (if the backend allows it)
# collection.open()

# or iterate over its artifacts
collection.artifacts.all()

# or look at a DataFrame listing the artifacts
collection.artifacts.df()

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/tmp/ipykernel_3802/3554135650.py:11: FutureWarning: Use to_dataframe instead of df, df will be removed in the future.
  collection.artifacts.df()

	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	run_id	created_at	created_by_id	_aux	branch_id
id
4	kH95lc8XzjG9xVmm0000	my_datasets/my_rnaseq1.h5ad	None	.h5ad	dataset	AnnData	30232	TWUlDi-V-Try1uCxWJ65nQ	None	3	md5	True	False	1	1	4	None	True	1	2025-09-09 10:18:26.292000+00:00	1	{'af': {'0': True}}	1
5	uP0mtfRn4Lsjnw5o0000	my_datasets/my_rnaseq2.h5ad	None	.h5ad	dataset	AnnData	23712	evwa9JaetcfvvMASeqqDVA	None	3	md5	True	False	1	1	4	None	True	1	2025-09-09 10:18:27.986000+00:00	1	{'af': {'0': True}}	1

Directly train models on collections of AnnData.

# to train models, batch iterate through the collection as if it was one array
from torch.utils.data import DataLoader, WeightedRandomSampler
dataset = collection.mapped(obs_keys=["cell_medium"])
sampler = WeightedRandomSampler(
    weights=dataset.get_label_weights("cell_medium"), num_samples=len(dataset)
)
data_loader = DataLoader(dataset, batch_size=2, sampler=sampler)
for batch in data_loader:
    pass

Read this blog post for more on training models on distributed datasets.