index.bs

<pre class='metadata'>
Title: Next-generation file formats (NGFF)
Shortname: ome-ngff
Level: 1
Status: LS-COMMIT
Status: w3c/ED
Group: ome
URL: https://ngff.openmicroscopy.org/latest/
Repository: https://github.com/ome/ngff
Issue Tracking: Forums https://forum.image.sc/tag/ome-ngff
Logo: http://www.openmicroscopy.org/img/logos/ome-logomark.svg
Local Boilerplate: header yes
Local Boilerplate: copyright yes
Boilerplate: style-darkmode off
Markup Shorthands: markdown yes
Editor: Josh Moore, Open Microscopy Environment (OME) https://www.openmicroscopy.org
Editor: Sébastien Besson, Open Microscopy Environment (OME) https://www.openmicroscopy.org
Editor: Constantin Pape, European Molecular Biology Laboratory (EMBL) https://www.embl.org/sites/heidelberg/
Abstract: This document contains next-generation file format (NGFF)
Abstract: specifications for storing bioimaging data in the cloud.
Abstract: All specifications are submitted to the https://image.sc community for review.
Status Text: The current released version of this specification is
Status Text: <a href="../0.3/index.html">0.3</a>. Migration scripts
Status Text: will be provided between numbered versions. Data written with these latest changes
Status Text: (an "editor's draft") will not necessarily be supported.
</pre>

Introduction {#intro}
=====================

Bioimaging science is at a crossroads. Currently, the drive to acquire more,
larger, preciser spatial measurements is unfortunately at odds with our ability
to structure and share those measurements with others. During a global pandemic
more than ever, we believe fervently that global, collaborative discovery as
opposed to the post-publication, "data-on-request" mode of operation is the
path forward. Bioimaging data should be shareable via open and commercial cloud
resources without the need to download entire datasets.

At the moment, that is not the norm. The plethora of data formats produced by
imaging systems are ill-suited to remote sharing. Individual scientists
typically lack the infrastructure they need to host these data themselves. When
they acquire images from elsewhere, time-consuming translations and data
cleaning are needed to interpret findings. Those same costs are multiplied when
gathering data into online repositories where curator time can be the limiting
factor before publication is possible. Without a common effort, each lab or
resource is left building the tools they need and maintaining that
infrastructure often without dedicated funding.

This document defines a specification for bioimaging data to make it possible
to enable the conversion of proprietary formats into a common, cloud-ready one.
Such next-generation file formats layout data so that individual portions, or
"chunks", of large data are reference-able eliminating the need to download
entire datasets.


Why "<dfn export="true"><abbr title="Next-generation file-format">NGFF</abbr></dfn>"? {#why-ngff}
-------------------------------------------------------------------------------------------------

A short description of what is needed for an imaging format is "a hierarchy
of n-dimensional (dense) arrays with metadata". This combination of features
is certainly provided by <dfn export="true"><abbr title="Hierarchical Data Format 5">HDF5</abbr></dfn>
from the <a href="https://www.hdfgroup.org">HDF Group</a>, which a number of
bioimaging formats do use. HDF5 and other larger binary structures, however,
are ill-suited for storage in the cloud where accessing individual chunks
of data by name rather than seeking through a large file is at the heart of
parallelization.

As a result, a number of formats have been developed more recently which provide
the basic data structure of an HDF5 file, but do so in a more cloud-friendly way.
In the [PyData](https://pydata.org/) community, the Zarr [[zarr]] format was developed
for easily storing collections of [NumPy](https://numpy.org/) arrays. In the
[ImageJ](https://imagej.net/) community, N5 [[n5]] was developed to work around
the limitations of HDF5 ("N5" was originally short for "Not-HDF5").
Both of these formats permit storing individual chunks of data either locally in
separate files or in cloud-based object stores as separate keys.

A [current effort](https://zarr-specs.readthedocs.io/en/core-protocol-v3.0-dev/protocol/core/v3.0.html)
is underway to unify the two similar specifications to provide a single binary
specification. The editor's draft will soon be entering a [request for comments (RFC)](https://github.com/zarr-developers/zarr-specs/issues/101) phase with the goal of having a first version early in 2021. As that
process comes to an end, this document will be updated.

OME-NGFF {#ome-ngff}
--------------------

The conventions and specifications defined in this document are designed to
enable next-generation file formats to represent the same bioimaging data
that can be represented in \[OME-TIFF](http://www.openmicroscopy.org/ome-files/)
and beyond. However, the conventions will also be usable by HDF5 and other sufficiently advanced
binary containers. Eventually, we hope, the moniker "next-generation" will no longer be
applicable, and this will simply be the most efficient, common, and useful representation
of bioimaging data, whether during acquisition or sharing in the cloud.

Note: The following text makes use of OME-Zarr [[ome-zarr-py]], the current prototype implementation,
for all examples.

On-disk (or in-cloud) layout {#on-disk}
=======================================

An overview of the layout of an OME-Zarr fileset should make
understanding the following metadata sections easier. The hierarchy
is represented here as it would appear locally but could equally
be stored on a web server to be accessed via HTTP or in object storage
like S3 or GCS.

OME-Zarr is an implementation of the OME-NGFF specification using the Zarr
format. Arrays MUST be defined and stored in a hierarchical organization as
defined by the
[version 2 of the Zarr specification ](https://zarr.readthedocs.io/en/stable/spec/v2.html).
OME-NGFF metadata MUST be stored as attributes in the corresponding Zarr
groups.

Images {#image-layout}
----------------------

The following layout describes the expected Zarr hierarchy for images with
multiple levels of resolutions and optionally associated labels.
Note that the number of dimensions is variable between 2 and 5 and that axis names are arbitrary, see [[#multiscale-md]] for details.
For this example we assume an image with 5 dimensions and axes called `t,c,z,y,x`.

<pre>
.                             # Root folder, potentially in S3,
│                             # with a flat list of images by image ID.
│
├── 123.zarr                  # One image (id=123) converted to Zarr.
│
└── 456.zarr                  # Another image (id=456) converted to Zarr.
    │
    ├── .zgroup               # Each image is a Zarr group, or a folder, of other groups and arrays.
    ├── .zattrs               # Group level attributes are stored in the .zattrs file and include
    │                         # "multiscales" and "omero" (see below). In addition, the group level attributes
    │                         # must also contain "_ARRAY_DIMENSIONS" if this group directly contains multi-scale arrays.
    │
    ├── 0                     # Each multiscale level is stored as a separate Zarr array,
    │   ...                   # which is a folder containing chunk files which compose the array.
    ├── n                     # The name of the array is arbitrary with the ordering defined by
    │   │                     # by the "multiscales" metadata, but is often a sequence starting at 0.
    │   │
    │   ├── .zarray           # All image arrays must be up to 5-dimensional
    │   │                     # with the axis of type time before type channel, before spatial axes.
    │   │
    │   └─ t                  # Chunks are stored with the nested directory layout.
    │      └─ c               # All but the last chunk element are stored as directories.
    │         └─ z            # The terminal chunk is a file. Together the directory and file names
    │            └─ y         # provide the "chunk coordinate" (t, c, z, y, x), where the maximum coordinate
    │               └─ x      # will be `dimension_size / chunk_size`.
    │
    └── labels
        │
        ├── .zgroup           # The labels group is a container which holds a list of labels to make the objects easily discoverable
        │
        ├── .zattrs           # All labels will be listed in `.zattrs` e.g. `{ "labels": [ "original/0" ] }`
        │                     # Each dimension of the label `(t, c, z, y, x)` should be either the same as the
        │                     # corresponding dimension of the image, or `1` if that dimension of the label
        │                     # is irrelevant.
        │
        └── original          # Intermediate folders are permitted but not necessary and currently contain no extra metadata.
            │
            └── 0             # Multiscale, labeled image. The name is unimportant but is registered in the "labels" group above.
                ├── .zgroup   # Zarr Group which is both a multiscaled image as well as a labeled image.
                ├── .zattrs   # Metadata of the related image and as well as display information under the "image-label" key.
                │
                ├── 0         # Each multiscale level is stored as a separate Zarr array, as above, but only integer values
                │   ...       # are supported.
                └── n
</pre>



High-content screening {#hcs-layout}
------------------------------------

The following specification defines the hierarchy for a high-content screening
dataset. Three groups MUST be defined above the images:

-   the group above the images defines the well and MUST implement the
    [well specification](#well-md). All images contained in a well are fields
    of view of the same well
-   the group above the well defines a row of wells
-   the group above the well row defines an entire plate i.e. a two-dimensional
    collection of wells organized in rows and columns. It MUST implement the
    [plate specification](#plate-md)

A well row group SHOULD NOT be present if there are no images in the well row.
A well group SHOULD NOT be present if there are no images in the well.


<pre>
.                             # Root folder, potentially in S3,
│
└── 5966.zarr                 # One plate (id=5966) converted to Zarr
    ├── .zgroup
    ├── .zattrs               # Implements "plate" specification
    ├── A                     # First row of the plate
    │   ├── .zgroup
    │   │
    │   ├── 1                 # First column of row A
    │   │   ├── .zgroup
    │   │   ├── .zattrs       # Implements "well" specification
    │   │   │
    │   │   ├── 0             # First field of view of well A1
    │   │   │   │
    │   │   │   ├── .zgroup
    │   │   │   ├── .zattrs   # Implements "multiscales", "omero"
    │   │   │   ├── 0
    │   │   │   │   ...       # Resolution levels
    │   │   │   ├── n
    │   │   │   └── labels    # Labels (optional)
    │   │   ├── ...           # Fields of view
    │   │   └── m
    │   ├── ...               # Columns
    │   └── 12
    ├── ...                   # Rows
    └── H
</pre>

Metadata {#metadata}
====================

The various `.zattrs` files throughout the above array hierarchy may contain metadata
keys as specified below for discovering certain types of data, especially images.

"axes" metadata {#axes-md}
--------------------------

"axes" describes the dimensions of a physical coordinate space. It is a list of dictionaries, where each dictionary describes a dimension (axis) and:
- MUST contain the field "name" that gives the name for this dimension. The values MUST be unique across all "name" fields.
- SHOULD contain the field "type". It SHOULD be one of "space", "time" or "channel", but MAY take other values for custom axis types that are not part of this specification yet.
- SHOULD contain the field "unit" to specify the physical unit of this dimension. The value SHOULD be one of the following strings, which are valid units according to UDUNITS-2.
    - Units for "space" axes: 'angstrom', 'attometer', 'centimeter', 'decimeter', 'exameter', 'femtometer', 'foot', 'gigameter', 'hectometer', 'inch', 'kilometer', 'megameter', 'meter', 'micrometer', 'mile', 'millimeter', 'nanometer', 'parsec', 'petameter', 'picometer', 'terameter', 'yard', 'yoctometer', 'yottameter', 'zeptometer', 'zettameter'
    - Units for "time" axes: 'attosecond', 'centisecond', 'day', 'decisecond', 'exasecond', 'femtosecond', 'gigasecond', 'hectosecond', 'hour', 'kilosecond', 'megasecond', 'microsecond', 'millisecond', 'minute', 'nanosecond', 'petasecond', 'picosecond', 'second', 'terasecond', 'yoctosecond', 'yottasecond', 'zeptosecond', 'zettasecond'

If part of [[#multiscale-md]], the length of "axes" MUST be equal to the number of dimensions of the arrays that contain the image data.


"coordinateTransformations" metadata {#trafo-md}
-------------------------------------

"coordinateTransformations" describe a series of transformations that map between two coordinate spaces (defined by "axes").
For example, to map a discrete data space of an array to the corresponding physical space.
It is a list of dictionaries. Each entry describes a single transformation and MUST contain the field "type".
The value of "type" MUST be one of the elements of the `type` column in the table below.
Additional fields for the entry depend on "type" and are defined by the column `fields`.

<table>
  <tr><th>`identity`    <td>                                                   <td>identity transformation, is the default transformation and is typically not explicitly defined
  <tr><th>`translation` <td> one of: `"translation":List[float]`, `"path":str` <td>translation vector, stored either as a list of floats (`"translation"`) or as binary data at a location in this container (`path`). The length of vector defines number of dimensions. |
  <tr><th>`scale`       <td> one of: `"scale":List[float]`, `"path":str`       <td>scale vector, stored either as a list of floats (`scale`) or as binary data at a location in this container (`path`). The length of vector defines number of dimensions. |
 <thead>
   <tr><th>type<th>fields<th>description
</table>

The transformations in the list are applied sequentially and in order.


"multiscales" metadata {#multiscale-md}
---------------------------------------

Metadata about an image can be found under the "multiscales" key in the group-level metadata. Here, image refers to 2 to 5 dimensional data representing image or volumetric data with optional time or channel axes. It is stored in a multiple resolution representation.

"multiscales" contains a list of dictionaries where each entry describes a multiscale image.

Each "multiscales" dictionary MUST contain the field "axes", see [[#axes-md]].
The length of "axes" must be between 2 and 5 and MUST be equal to the dimensionality of the zarr arrays storing the image data (see "datasets:path").
The "axes" MUST contain 2 or 3 entries of "type:space" and MAY contain one additional entry of "type:time" and MAY contain one additional entry of "type:channel" or a null / custom type.
The order of the entries MUST correspond to the order of dimensions of the zarr arrays. In addition, the entries MUST be ordered by "type" where the "time" axis must come first (if present), followed by the  "channel" or custom axis (if present) and the axes of type "space".
If there are three spatial axes where two correspond to the image plane ("yx") and images are stacked along the other (anisotropic) axis ("z"), the spatial axes SHOULD be ordered as "zyx".
The values of the "name" fields must be given as a list in the field "_ARRAY_DIMENSIONS" in the attributes (.zattr) of the zarr arrays.
This ensures compatibility with the [xarray zarr encoding](http://xarray.pydata.org/en/stable/internals/zarr-encoding-spec.html#zarr-encoding).
E.g. for "axes: [{"name": "z"}, {"name": "y"}, {"name": x}]", the zarr arrays must contain "{"_ARRAY_DIMENSIONS": ["z", "y", "x"]}" in their attributes.

Each "multiscales" dictionary MUST contain the field "datasets", which is a list of dictionaries describing the arrays storing the individual resolution levels.
Each dictionary in "datasets" MUST contain the field "path", whose value contains the path to the array for this resolution relative
to the current zarr group. The "path"s MUST be ordered from largest (i.e. highest resolution) to smallest.

Each "datasets" dictionary MUST have the same number of dimensions and MUST NOT have more than 5 dimensions. The number of dimensions and order MUST correspond to number and order of "axes".
Each dictionary MUST contain the field "coordinateTransformations", which contains a list of transformations that map the data coordinates to the physical coordinates (as specified by "axes") for this resolution level.
The transformations are defined according to [[#trafo-md]]. The transformation MUST only be of type `translation` or `scale`.
They MUST contain exactly one `scale` transformation that specifies the pixel size in physical units or time duration. If scaling information is not available or applicable for one of the axes set it to 1.
It MAY contain exactly one `translation` that specifies the offset from the origin in physical units. If `translation` is given it MUST be listed after `scale` to ensure that it is given in physical coordinates.
The length of the `scale` and `translation` array MUST be the same as the length of "axes".
The requirements (only `scale` and `translation`, restrictions on order) are in place to provide a simple mapping from data coordinates to physical coordinates while being compatible with the general transformation spec.

Each "multiscales" dictionary MAY contain the field "coordinateTransformations", describing transformations that are applied to all resolution levels in the same manner.
The transformations MUST follow the same rules about allowed types, order, etc. as in "datasets:coordinateTransformations" and are applied after them.
They can for example be used to specify the `scale` for a dimension that is the same for all resolutions.

Each "multiscales" dictionary SHOULD contain the field "name". It SHOULD contain the field "version", which indicates the version of the multiscale metadata of this image (current version is 0.4).

Each "multiscales" dictionary SHOULD contain the field "type", which gives the type of downscaling method used to generate the multiscale image pyramid.
It SHOULD contain the field "metadata", which contains a dictionary with additional information about the downscaling method.

```
{
    "multiscales": [
        {
            "version": "0.4",
            "name": "example",
            "axes": [
                {"name": "t", "type": "time", "unit": "millisecond"},
                {"name": "c", "type": "channel"},
                {"name": "z", "type": "space", "unit": "micrometer"},
                {"name": "y", "type": "space", "unit": "micrometer"},
                {"name": "x", "type": "space", "unit": "micrometer"}
            ],
            "datasets": [
                {
                    "path": "0",
                    "coordinateTransformations": [{"type": "scale", "scale": [1.0, 1.0, 0.5, 0.5, 0.5]}]  # the voxel size for the first scale level (0.5 micrometer)
                }
                {
                    "path": "1",
                    "coordinateTransformations": [{"type": "scale", "scale": [1.0, 1.0, 1.0, 1.0, 1.0]}]  # the voxel size for the second scale level (downscaled by a factor of 2 -> 1 micrometer)
                },
                {
                    "path": "2",
                    "coordinateTransformations": [{"type": "scale", "scale": [1.0, 1.0, 2.0, 2.0, 2.0]}]  # the voxel size for the second scale level (downscaled by a factor of 4 -> 2 micrometer)
                }
            ],
            "coordinateTransformations": [{"type": "scale", "scale": [0.1, 1.0, 1.0, 1.0, 1.0]],  # the time unit (0.1 milliseconds), which is the same for each scale level
            "type": "gaussian",
            "metadata": {                                       # the fields in metadata depend on the downscaling implementation
                "method": "skimage.transform.pyramid_gaussian", # here, the paramters passed to the skimage function are given
                "version": "0.16.1",
                "args": "[true]",
                "kwargs": {"multichannel": true}
            }
        }
    ]
}
```

If only one multiscale is provided, use it. Otherwise, the user can choose by
name, using the first multiscale as a fallback:

```python
datasets = []
for named in multiscales:
    if named["name"] == "3D":
        datasets = [x["path"] for x in named["datasets"]]
        break
if not datasets:
    # Use the first by default. Or perhaps choose based on chunk size.
    datasets = [x["path"] for x in multiscales[0]["datasets"]]
```

"omero" metadata {#omero-md}
----------------------------

Information specific to the channels of an image and how to render it
can be found under the "omero" key in the group-level metadata:

```json
"id": 1,                              # ID in OMERO
"name": "example.tif",                # Name as shown in the UI
"version": "0.3",                     # Current version
"channels": [                         # Array matching the c dimension size
    {
        "active": true,
        "coefficient": 1,
        "color": "0000FF",
        "family": "linear",
        "inverted": false,
        "label": "LaminB1",
        "window": {
            "end": 1500,
            "max": 65535,
            "min": 0,
            "start": 0
        }
    }
],
"rdefs": {
    "defaultT": 0,                    # First timepoint to show the user
    "defaultZ": 118,                  # First Z section to show the user
    "model": "color"                  # "color" or "greyscale"
}
```

See https://docs.openmicroscopy.org/omero/5.6.1/developers/Web/WebGateway.html#imgdata
for more information.

"labels" metadata {#labels-md}
------------------------------

The special group "labels" found under an image Zarr contains the key `labels` containing
the paths to label objects which can be found underneath the group:

```json
{
  "labels": [
    "orphaned/0"
  ]
}
```

Unlisted groups MAY be labels.

"image-label" metadata {#label-md}
----------------------------------

Groups containing the `image-label` dictionary represent an image segmentation
in which each unique pixel value represents a separate segmented object.
`image-label` groups MUST also contain `multiscales` metadata and the two
"datasets" series MUST have the same number of entries.

The `colors` key defines a list of JSON objects describing the unique label
values. Each entry in the list MUST contain the key "label-value" with the
pixel value for that label. Additionally, the "rgba" key MAY be present, the
value for which is an RGBA unsigned-int 4-tuple: `[uint8, uint8, uint8, uint8]`
All `label-value`s must be unique. Clients who choose to not throw an error
should ignore all except the _last_ entry.

Some implementations may represent overlapping labels by using a specially assigned
value, for example the highest integer available in the pixel range.

The `properties` key defines a list of JSON objects which also describes the unique
label values. Each entry in the list MUST contain the key "label-value" with the
pixel value for that label. Additionally, an arbitrary number of key-value pairs
MAY be present for each label value denoting associated metadata. Not all label
values must share the same key-value pairs within the properties list.

The `source` key is an optional dictionary which contains information on the
image the label is associated with. If included it MAY include a key `image`
whose value is the relative path to a Zarr image group. The default value is
"../../" since most labels are stored under a subgroup named "labels/" (see
above).


```json
"image-label":
  {
    "version": "0.3",
    "colors": [
      {
        "label-value": 1,
        "rgba": [255, 255, 255, 0]
      },
      {
        "label-value": 4,
        "rgba": [0, 255, 255, 128]
      },
      ...
      ],
    "properties": [
      {
        "label-value": 1,
        "area (pixels)": 1200,
        "class": "foo"

      },
      {
        "label-value": 4,
        "area (pixels)": 1650
      },
      ...
      ]
  },
  "source": {
    "image": "../../"
  }
]
```

"plate" metadata {#plate-md}
----------------------------

For high-content screening datasets, the plate layout can be found under the
custom attributes of the plate group under the `plate` key.

<dl>
  <dt><strong>acquisitions</strong></dt>
  <dd>An optional list of JSON objects defining the acquisitions for a given
      plate. Each acquisition object MUST contain an `id` key providing an
      unique identifier within the context of the plate to which fields of
      view can refer to. It SHOULD contain  a `name` key identifying the name
      of the acquisition. It SHOULD contain a `maximumfieldcount` key
      indicating the maximum number of fields of view for the acquisition. It
      MAY contain a `description` key providing a description for the
      acquisition. It MAY contain a `startime` and/or `endtime` key specifying
      the start and/or end timestamp of the acquisition using an epoch
      string.</dd>
  <dt><strong>columns</strong></dt>
  <dd>A list of JSON objects defining the columns of the plate. Each column
      object defines the properties of the column at the index of the object
      in the list. Each column in the physical plate MUST be defined, even
      if no wells in the column are defined. Each defined column MUST contain
      a `name` key specifying the column name. The `name` MUST contain only
      alphanumeric characters, MUST be case-sensitive, and MUST NOT be a
      duplicate of any other `name` in the `columns` list. Care SHOULD be
      taken to avoid collisions on case-insensitive filesystems
      (e.g. avoid using both `Aa` and `aA`).</dd>
  <dt><strong>field_count</strong></dt>
  <dd>An integer defining the maximum number of fields per view across all
      wells.</dd>
  <dt><strong>name</strong></dt>
  <dd>A string defining the name of the plate.</dd>
  <dt><strong>rows</strong></dt>
  <dd>A list of JSON objects defining the rows of the plate. Each row object
      defines the properties of the row at the index of the object in the
      list. Each row in the physical plate MUST be defined, even if no wells
      in the row are defined. Each defined row MUST contain a `name` key
      specifying the row name. The `name` MUST contain only alphanumeric
      characters, MUST be case-sensitive, and MUST NOT be a duplicate
      of any other `name` in the `rows` list. Care SHOULD be taken to avoid
      collisions on case-insensitive filesystems (e.g. avoid using both `Aa`
      and `aA`).</dd>
  <dt><strong>version</strong></dt>
  <dd>A string defining the version of the specification.</dd>
  <dt><strong>wells</strong></dt>
  <dd>A list of JSON objects defining the wells of the plate. Each well object
      MUST contain a `path` key identifying the path to the well subgroup.
      The `path` MUST consist of a `name` in the `rows` list, a file separator (`/`),
      and a `name` from the `columns` list, in that order. The `path` MUST NOT contain
      additional leading or trailing directories.
      Each well object MUST contain both a `rowIndex` key identifying the index into
      the `rows` list and a `columnIndex` key indentifying the index into
      the `columns` list. `rowIndex` and `columnIndex` MUST be 0-based.
      The `rowIndex`, `columnIndex`, and `path` MUST all refer to the same
      row/column pair.</dd>
</dl>

For example the following JSON object defines a plate with two acquisitions and
6 wells (2 rows and 3 columns), containing up to 2 fields of view per acquisition.

```json
"plate": {
    "acquisitions": [
        {
            "id": 1,
            "maximumfieldcount": 2,
            "name": "Meas_01(2012-07-31_10-41-12)",
            "starttime": 1343731272000
        },
        {
            "id": 2,
            "maximumfieldcount": 2,
            "name": "Meas_02(201207-31_11-56-41)",
            "starttime": 1343735801000
        }
    ],
    "columns": [
        {
            "name": "1"
        },
        {
            "name": "2"
        },
        {
            "name": "3"
        }
    ],
    "field_count": 4,
    "name": "test",
    "rows": [
        {
            "name": "A"
        },
        {
            "name": "B"
        }
    ],
    "version": "0.3",
    "wells": [
        {
            "path": "A/1",
            "rowIndex": 0,
            "columnIndex": 0
        },
        {
            "path": "A/2"
            "rowIndex": 0,
            "columnIndex": 1
        },
        {
            "path": "A/3"
            "rowIndex": 0,
            "columnIndex": 2
        },
        {
            "path": "B/1"
            "rowIndex": 1,
            "columnIndex": 0
        },
        {
            "path": "B/2"
            "rowIndex": 1,
            "columnIndex": 1
        },
        {
            "path": "B/3"
            "rowIndex": 1,
            "columnIndex": 2
        }
    ]
  }
```

The following JSON object defines a sparse plate with one acquisition and
2 wells in a 96 well plate, containing one field of view per acquisition.

```json
"plate": {
    "acquisitions": [
        {
            "id": 1,
            "maximumfieldcount": 1,
            "name": "single acquisition",
            "starttime": 1343731272000
        },
    ],
    "columns": [
        {
            "name": "1"
        },
        {
            "name": "2"
        },
        {
            "name": "3"
        },
        {
            "name": "4"
        },
        {
            "name": "5"
        },
        {
            "name": "6"
        },
        {
            "name": "7"
        },
        {
            "name": "8"
        },
        {
            "name": "9"
        },
        {
            "name": "10"
        },
        {
            "name": "11"
        },
        {
            "name": "12"
        }
    ],
    "field_count": 1,
    "name": "sparse test",
    "rows": [
        {
            "name": "A"
        },
        {
            "name": "B"
        },
        {
            "name": "C"
        },
        {
            "name": "D"
        },
        {
            "name": "E"
        },
        {
            "name": "F"
        },
        {
            "name": "G"
        },
        {
            "name": "H"
        }
    ],
    "version": "0.1",
    "wells": [
        {
            "path": "C/5"
            "rowIndex": 2,
            "columnIndex": 4
        },
        {
            "path": "D/7"
            "rowIndex": 3,
            "columnIndex": 6
        }
    ]
  }
```

"well" metadata {#well-md}
--------------------------

For high-content screening datasets, the metadata about all fields of views
under a given well can be found under the "well" key in the attributes of the
well group.

<dl>
  <dt><strong>images</strong></dt>
  <dd>A list of JSON objects defining the fields of views for a given well.
      Each object MUST contain a `path` key identifying the path to the
      field of view. If multiple acquisitions were performed in the plate, it
      MUST contain an `acquisition` key identifying the id of the
      acquisition which must match one of acquisition JSON objects defined in
      the plate metadata.</dd>
  <dt><strong>version</strong></dt>
  <dd>A string defining the version of the specification.</dd>
</dl>

For example the following JSON object defines a well with four fields of
view. The first two fields of view were part of the first acquisition while
the last two fields of view were part of the second acquisition.

```json
"well": {
    "images": [
        {
            "acquisition": 1,
            "path": "0"
        },
        {
            "acquisition": 1,
            "path": "1"
        },
        {
            "acquisition": 2,
            "path": "2"
        },
        {
            "acquisition": 2,
            "path": "3"
        }
    ],
    "version": "0.3"
  }
```

The following JSON object defines a well with two fields of view in a plate with
four acquisitions. The first field is part of the first acquisition, and the second
field is part of the last acquisition.

```json
"well": {
    "images": [
        {
            "acquisition": 0,
            "path": "0"
        },
        {
            "acquisition": 3,
            "path": "1"
        }
    ],
    "version": "0.1"
}
```

Specification naming style {#naming-style}
==========================================

Multi-word keys in this specification should use the `camelCase` style.
NB: some parts of the specification don't obey this convention as they
were added before this was adopted, but they should be updated in due course.

Implementations {#implementations}
==================================

Projects which support reading and/or writing OME-NGFF data include:

<dl>

  <dt><strong>[bigdataviewer-ome-zarr](https://github.com/mobie/bigdataviewer-ome-zarr)</strong></dt>
  <dd>Fiji-plugin for reading OME-Zarr.</dd>

  <dt><strong>[bioformats2raw](https://github.com/glencoesoftware/bioformats2raw)</strong></dt>
  <dd>A performant, Bio-Formats image file format converter.</dd>

  <dt><strong>[omero-ms-zarr](https://github.com/ome/omero-ms-zarr)</strong></dt>
  <dd>A microservice for OMERO.server that converts images stored in OMERO to OME-Zarr files on the fly, served via a web API.</dd>

  <dt><strong>[idr-zarr-tools](https://github.com/IDR/idr-zarr-tools)</strong></dt>
  <dd>A full workflow demonstrating the conversion of IDR images to OME-Zarr images on S3.</dd>

  <dt><strong>[OMERO CLI Zarr plugin](https://github.com/ome/omero-cli-zarr)</strong></dt>
  <dd>An OMERO CLI plugin that converts images stored in OMERO.server into a local Zarr file.</dd>

  <dt><strong>[ome-zarr-py](https://github.com/ome/ome-zarr-py)</strong></dt>
  <dd>A napari plugin for reading ome-zarr files.</dd>

  <dt><strong>[vizarr](https://github.com/hms-dbmi/vizarr/)</strong></dt>
  <dd>A minimal, purely client-side program for viewing Zarr-based images with Viv & ImJoy.</dd>

</dl>

<img src="https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/images/zarr-ome-diagram.png"
    alt="Diagram of related projects"/>

All implementations prevent an equivalent representation of a dataset which can be downloaded or uploaded freely. An interactive
version of this diagram is available from the [OME2020 Workshop](https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/).
Mouseover the blackboxes representing the implementations above to get a quick tip on how to use them.

Note: If you would like to see your project listed, please open an issue or PR on the [ome/ngff](https://github.com/ome/ngff) repository.

Citing {#citing}
================

[Next-generation file format (NGFF) specifications for storing bioimaging data in the cloud.](https://ngff.openmicroscopy.org/0.3)
J. Moore, *et al*. Editors. Open Microscopy Environment Consortium, 24 August 2021.
This edition of the specification is [https://ngff.openmicroscopy.org/0.3/](https://ngff.openmicroscopy.org/0.3/]).
The latest edition is available at [https://ngff.openmicroscopy.org/latest/](https://ngff.openmicroscopy.org/latest/).
[(doi:10.5281/zenodo.4282107)](https://doi.org/10.5281/zenodo.4282107)

Version History {#history}
==========================

<table>
  <thead>
    <tr>
      <td>Revision</td>
      <td>Date</td>
      <td>Description</td>
    </tr>
  </thead>
  <tr>
    <td>0.3.0</td>
    <td>2021-08-24</td>
    <td>Add axes field to multiscale metadata     </td>
  </tr>
  <tr>
    <td>0.2.0</td>
    <td>2021-03-29</td>
    <td>Change chunk dimension separator to "/"   </td>
  </tr>
  <tr>
    <td>0.1.4</td>
    <td>2020-11-26</td>
    <td>Add HCS specification                      </td>
  </tr>
  <tr>
    <td>0.1.3</td>
    <td>2020-09-14</td>
    <td>Add labels specification                   </td>
  </tr>
  <tr>
    <td>0.1.2     </td>
    <td>2020-05-07</td>
    <td>Add description of "omero" metadata        </td>
  </tr>
  <tr>
    <td>0.1.1     </td>
    <td>2020-05-06</td>
    <td>Add info on the ordering of resolutions    </td>
  </tr>
  <tr>
    <td>0.1.0     </td>
    <td>2020-04-20</td>
    <td>First version for internal demo            </td>
  </tr>
</table>


<pre class="biblio">
{
  "blogNov2020": {
    "href": "https://blog.openmicroscopy.org/file-formats/community/2020/11/04/zarr-data/",
    "title": "Public OME-Zarr data (Nov. 2020)",
    "authors": [
      "OME Team"
    ],
    "status": "Informational",
    "publisher": "OME",
    "id": "blogNov2020",
    "date": "04 November 2020"
  },
  "imagesc26952": {
    "href": "https://forum.image.sc/t/ome-s-position-regarding-file-formats/26952",
    "title": "OME’s position regarding file formats",
    "authors": [
      "OME Team"
    ],
    "status": "Informational",
    "publisher": "OME",
    "id": "imagesc26952",
    "date": "19 June 2020"
  },
  "n5": {
    "id": "n5",
    "href": "https://github.com/saalfeldlab/n5/issues/62",
    "title": "N5---a scalable Java API for hierarchies of chunked n-dimensional tensors and structured meta-data",
    "status": "Informational",
    "authors": [
      "John A. Bogovic",
      "Igor Pisarev",
      "Philipp Hanslovsky",
      "Neil Thistlethwaite",
      "Stephan Saalfeld"
    ],
    "date": "2020"
  },
  "ome-zarr-py": {
    "id": "ome-zarr-py",
    "href": "https://doi.org/10.5281/zenodo.4113931",
    "title": "ome-zarr-py: Experimental implementation of next-generation file format (NGFF) specifications for storing bioimaging data in the cloud.",
    "status": "Informational",
    "publisher": "Zenodo",
    "authors": [
      "OME",
      "et al"
    ],
    "date": "06 October 2020"
  },
  "zarr": {
    "id": "zarr",
    "href": "https://doi.org/10.5281/zenodo.4069231",
    "title": "Zarr: An implementation of chunked, compressed, N-dimensional arrays for Python.",
    "status": "Informational",
    "publisher": "Zenodo",
    "authors": [
      "Alistair Miles",
      "et al"
    ],
    "date": "06 October 2020"
  }
}
</pre>