Next-generation file formats (NGFF)

Final Community Group Report,

More details about this document
This version:
https://ngff.openmicroscopy.org/0.4/
Issue Tracking:
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GitHub
Editors:
Josh Moore (University of Dundee (UoD))
Sébastien Besson (University of Dundee (UoD))
Constantin Pape (European Molecular Biology Laboratory (EMBL))

Abstract

This document contains next-generation file format (NGFF) specifications for storing bioimaging data in the cloud. All specifications are submitted to the https://image.sc community for review.

Status of this document

This is the 0.4 release of this specification. Migration scripts will be provided between numbered versions. Data written with the latest version (an "editor’s draft") will not necessarily be supported.

1. Introduction

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.

1.1. 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 HDF5 from the HDF Group, 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 community, the Zarr [zarr] format was developed for easily storing collections of NumPy arrays. In the ImageJ 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 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) phase with the goal of having a first version early in 2021. As that process comes to an end, this document will be updated.

1.2. 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 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.

1.3. Document conventions

The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”, “SHOULD”, “SHOULD NOT”, “RECOMMENDED”, “MAY”, and “OPTIONAL” are to be interpreted as described in RFC 2119.

Some of the JSON examples in this document include commments. However, these are only for clarity purposes and comments MUST NOT be included in JSON objects.

2. On-disk (or in-cloud) layout

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 . OME-NGFF metadata MUST be stored as attributes in the corresponding Zarr groups.

2.1. Images

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 § 3.3 "multiscales" metadata for details. For this example we assume an image with 5 dimensions and axes called t,c,z,y,x.

.                             # 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

2.2. High-content screening

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

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.

.                             # 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

3. 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.

3.1. "axes" metadata

"axes" describes the dimensions of a physical coordinate space. It is a list of dictionaries, where each dictionary describes a dimension (axis) and:

If part of § 3.3 "multiscales" metadata, the length of "axes" MUST be equal to the number of dimensions of the arrays that contain the image data.

3.2. "coordinateTransformations" metadata

"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.

identity identity transformation, is the default transformation and is typically not explicitly defined
translation one of: "translation":List[float], "path":str 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. |
scale one of: "scale":List[float], "path":str 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. |
type fields description

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

3.3. "multiscales" metadata

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 § 3.1 "axes" metadata. 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".

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 in "datasets" 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 § 3.2 "coordinateTransformations" metadata. 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, the value MUST express the scaling factor between the current resolution and the first resolution for the given axis, defaulting to 1.0 if there is no downsampling along the axis. 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": [{
                        // the voxel size for the first scale level (0.5 micrometer)
                        "type": "scale",
                        "scale": [1.0, 1.0, 0.5, 0.5, 0.5]
                    }]
                },
                {
                    "path": "1",
                    "coordinateTransformations": [{
                        // the voxel size for the second scale level (downscaled by a factor of 2 -> 1 micrometer)
                        "type": "scale",
                        "scale": [1.0, 1.0, 1.0, 1.0, 1.0]
                    }]
                },
                {
                    "path": "2",
                    "coordinateTransformations": [{
                        // the voxel size for the third scale level (downscaled by a factor of 4 -> 2 micrometer)
                        "type": "scale",
                        "scale": [1.0, 1.0, 2.0, 2.0, 2.0]
                    }]
                }
            ],
            "coordinateTransformations": [{
                // the time unit (0.1 milliseconds), which is the same for each scale level
                "type": "scale",
                "scale": [0.1, 1.0, 1.0, 1.0, 1.0]
            }],
            "type": "gaussian",
            "metadata": {
                "description": "the fields in metadata depend on the downscaling implementation. Here, the parameters passed to the skimage function are given",
                "method": "skimage.transform.pyramid_gaussian",
                "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:

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"]]

3.4. "omero" metadata

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:

"id": 1,                              # ID in OMERO
"name": "example.tif",                # Name as shown in the UI
"version": "0.4",                     # 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.

3.5. "labels" metadata

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:

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

Unlisted groups MAY be labels.

3.6. "image-label" metadata

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 image-label dictionary SHOULD contain a colors key whose value MUST be a list of JSON objects describing the unique label values. Each color object MUST contain the label-value key whose value MUST be an integer specifying the pixel value for that label. It MAY contain an rgba key whose value MUST be an array of four integers between 0 and 255 [uint8, uint8, uint8, uint8] specifying the label color as RGBA. All the values under the label-value key 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 image-label dictionary MAY contain a properties key whose value MUST be a list of JSON objects which also describes the unique label values. Each property object MUST contain the label-value key whose value MUST be an integer specifying 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 image-label dictionary MAY contain a source key whose value MUST be a JSON object containing information on the image the label is associated with. If included, it MAY include a key image whose value MUST be a string specifying the relative path to a Zarr image group. The default value is "../../" since most labels are stored under a subgroup named "labels/" (see above).

The image-label dictionary SHOULD contain a version key whose value MUST be a string specifying the version of the image-label specification.

{
  "image-label": {
    "version": "0.4",
    "colors": [
      {
        "label-value": 1,
        "rgba": [255, 255, 255, 255]
      },
      {
        "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": "../../"
    }
  }
}

3.7. "plate" metadata

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

The plate dictionary MAY contain an acquisitions key whose value MUST be a list of JSON objects defining the acquisitions for a given plate to which wells can refer to. Each acquisition object MUST contain an id key whose value MUST be an unique integer identifier greater than or equal to 0 within the context of the plate to which fields of view can refer to (see #well-md). Each acquisition object SHOULD contain a name key whose value MUST be a string identifying the name of the acquisition. Each acquisition object SHOULD contain a maximumfieldcount key whose value MUST be a positive integer indicating the maximum number of fields of view for the acquisition. Each acquisition object MAY contain a description key whose value MUST be a string specifying a description for the acquisition. Each acquisition object MAY contain a starttime and/or endtime key whose values MUST be integer epoch timestamps specifying the start and/or end timestamp of the acquisition.

The plate dictionary MUST contain a columns key whose value MUST be 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 column object MUST contain a name key whose value is a string 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).

The plate dictionary SHOULD contain a field_count key whose value MUST be a positive integer defining the maximum number of fields per view across all wells.

The plate dictionary SHOULD contain a name key whose value MUST be a string defining the name of the plate.

The plate dictionary MUST contain a rows key whose value MUST be 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 whose value MUST be a string defining 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).

The plate dictionary SHOULD contain a version key whose value MUST be a string specifying the version of the plate specificaton.

The plate dictionary MUST contain a wells key whose value MUST be a list of JSON objects defining the wells of the plate. Each well object MUST contain a path key whose value MUST be a string specifying 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 whose value MUST be an integer identifying the index into the rows list and a columnIndex key whose value MUST be an integer 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.

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.

{
    "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": "0.4",
        "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.

{
    "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": "0.4",
        "wells": [
            {
                "path": "C/5",
                "rowIndex": 2,
                "columnIndex": 4
            },
            {
                "path": "D/7",
                "rowIndex": 3,
                "columnIndex": 6
            }
        ]
    }
}

3.8. "well" metadata

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.

The well dictionary MUST contain an images key whose value MUST be a list of JSON objects specifying all fields of views for a given well. Each image object MUST contain a path key whose value MUST be a string specifying the path to the field of view. The path MUST contain only alphanumeric characters, MUST be case-sensitive, and MUST NOT be a duplicate of any other path in the images list. If multiple acquisitions were performed in the plate, it MUST contain an acquisition key whose value MUST be an integer identifying the acquisition which MUST match one of the acquisition JSON objects defined in the plate metadata (see #plate-md).

The well dictionary SHOULD contain a version key whose value MUST be a string specifying the version of the well specification.

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.

{
    "well": {
        "images": [
            {
                "acquisition": 1,
                "path": "0"
            },
            {
                "acquisition": 1,
                "path": "1"
            },
            {
                "acquisition": 2,
                "path": "2"
            },
            {
                "acquisition": 2,
                "path": "3"
            }
        ],
        "version": "0.4"
    }
}

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.

{
    "well": {
        "images": [
            {
                "acquisition": 0,
                "path": "0"
            },
            {
                "acquisition": 3,
                "path": "1"
            }
        ],
        "version": "0.4"
    }
}

4. Specification 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.

5. Implementations

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

bigdataviewer-ome-zarr
Fiji-plugin for reading OME-Zarr.
bioformats2raw
A performant, Bio-Formats image file format converter.
omero-ms-zarr
A microservice for OMERO.server that converts images stored in OMERO to OME-Zarr files on the fly, served via a web API.
idr-zarr-tools
A full workflow demonstrating the conversion of IDR images to OME-Zarr images on S3.
OMERO CLI Zarr plugin
An OMERO CLI plugin that converts images stored in OMERO.server into a local Zarr file.
ome-zarr-py
A napari plugin for reading ome-zarr files.
vizarr
A minimal, purely client-side program for viewing Zarr-based images with Viv & ImJoy.

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. 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 repository.

6. Citing

Next-generation file format (NGFF) specifications for storing bioimaging data in the cloud. J. Moore, et al. Editors. Open Microscopy Environment Consortium, 8 February 2022. This edition of the specification is https://ngff.openmicroscopy.org/0.4/. The latest edition is available at https://ngff.openmicroscopy.org/latest/. (doi:10.5281/zenodo.4282107)

7. Version History

Revision Date Description
0.4.0 2022-02-08 multiscales: add axes type, units and coordinateTransformations
0.4.0 2022-02-08 plate: add rowIndex/columnIndex
0.3.0 2021-08-24 Add axes field to multiscale metadata
0.2.0 2021-03-29 Change chunk dimension separator to "/"
0.1.4 2020-11-26 Add HCS specification
0.1.3 2020-09-14 Add labels specification
0.1.2 2020-05-07 Add description of "omero" metadata
0.1.1 2020-05-06 Add info on the ordering of resolutions
0.1.0 2020-04-20 First version for internal demo

Conformance

Document conventions

Conformance requirements are expressed with a combination of descriptive assertions and RFC 2119 terminology. The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”, “SHOULD”, “SHOULD NOT”, “RECOMMENDED”, “MAY”, and “OPTIONAL” in the normative parts of this document are to be interpreted as described in RFC 2119. However, for readability, these words do not appear in all uppercase letters in this specification.

All of the text of this specification is normative except sections explicitly marked as non-normative, examples, and notes. [RFC2119]

Examples in this specification are introduced with the words “for example” or are set apart from the normative text with class="example", like this:

This is an example of an informative example.

Informative notes begin with the word “Note” and are set apart from the normative text with class="note", like this:

Note, this is an informative note.

Conformant Algorithms

Requirements phrased in the imperative as part of algorithms (such as "strip any leading space characters" or "return false and abort these steps") are to be interpreted with the meaning of the key word ("must", "should", "may", etc) used in introducing the algorithm.

Conformance requirements phrased as algorithms or specific steps can be implemented in any manner, so long as the end result is equivalent. In particular, the algorithms defined in this specification are intended to be easy to understand and are not intended to be performant. Implementers are encouraged to optimize.

Index

Terms defined by this specification

References

Normative References

[RFC2119]
S. Bradner. Key words for use in RFCs to Indicate Requirement Levels. March 1997. Best Current Practice. URL: https://datatracker.ietf.org/doc/html/rfc2119

Informative References

[N5]
John A. Bogovic; et al. N5---a scalable Java API for hierarchies of chunked n-dimensional tensors and structured meta-data. 2020. Informational. URL: https://github.com/saalfeldlab/n5/issues/62
[OME-ZARR-PY]
OME; et al. ome-zarr-py: Experimental implementation of next-generation file format (NGFF) specifications for storing bioimaging data in the cloud.. 06 October 2020. Informational. URL: https://doi.org/10.5281/zenodo.4113931
[ZARR]
Alistair Miles; et al. Zarr: An implementation of chunked, compressed, N-dimensional arrays for Python.. 06 October 2020. Informational. URL: https://doi.org/10.5281/zenodo.4069231