ro-crate-py is a Python library to create and consume Research Object Crates. It currently supports the RO-Crate 1.1 specification.

Installation

ro-crate-py requires Python 3.7 or later. The easiest way to install is via pip:

pip install rocrate

To install manually from this code base (e.g., to try the latest development revision):

git clone https://github.com/ResearchObject/ro-crate-py
cd ro-crate-py
pip install .

Usage

Creating an RO-Crate

In its simplest form, an RO-Crate is a directory tree with an ro-crate-metadata.json file at the top level that contains metadata about the other files and directories, represented by data entities. These metadata consist both of properties of the data entities themselves and of other, non-digital entities called contextual entities (representing, e.g., a person or an organization).

Suppose Alice and Bob worked on a research task together, which resulted in a manuscript written by both; additionally, Alice prepared a spreadsheet containing the experimental data, which Bob used to generate a diagram. Let's make an RO-Crate to package all this:

from rocrate.rocrate import ROCrate

crate = ROCrate()
paper = crate.add_file("exp/paper.pdf", properties={
    "name": "manuscript",
    "encodingFormat": "application/pdf"
})
table = crate.add_file("exp/results.csv", properties={
    "name": "experimental data",
    "encodingFormat": "text/csv"
})
diagram = crate.add_file("exp/diagram.svg", dest_path="images/figure.svg", properties={
    "name": "bar chart",
    "encodingFormat": "image/svg+xml"
})

We've started by adding the data entities. Now we need contextual entities to represent Alice and Bob:

from rocrate.model.person import Person

alice_id = "https://orcid.org/0000-0000-0000-0000"
bob_id = "https://orcid.org/0000-0000-0000-0001"
alice = crate.add(Person(crate, alice_id, properties={
    "name": "Alice Doe",
    "affiliation": "University of Flatland"
}))
bob = crate.add(Person(crate, bob_id, properties={
    "name": "Bob Doe",
    "affiliation": "University of Flatland"
}))

Next, we express authorship of the various files:

paper["author"] = [alice, bob]
table["author"] = alice
diagram["author"] = bob

Finally, we serialize the crate to disk:

crate.write("exp_crate")

Now the exp_crate directory should contain copies of the three files and an ro-crate-metadata.json file with a JSON-LD serialization of the entities and relationships we created, according to the RO-Crate profile. Note that we have chosen a different destination path for the diagram, while the other two files have been placed at the top level with their names unchanged (the default).

Some applications and services support RO-Crates stored as archives. To save the crate in zip format, use write_zip:

crate.write_zip("exp_crate.zip")

You can also add whole directories. A directory in RO-Crate is represented by the Dataset entity:

logs = crate.add_dataset("exp/logs")

Note that the above adds all files and directories contained in "exp/logs" recursively to the crate, but only the top-level "exp/logs" dataset itself is listed in the metadata file (there is no requirement to represent every file and folder in the JSON-LD). To also add files and directory recursively to the metadata, use add_tree (but note that it only works on local directory trees).

Appending elements to property values

What ro-crate-py entities actually store is their JSON representation:

paper.properties()

{
  "@id": "paper.pdf",
  "@type": "File",
  "name": "manuscript",
  "encodingFormat": "application/pdf",
  "author": [
    {"@id": "https://orcid.org/0000-0000-0000-0000"},
    {"@id": "https://orcid.org/0000-0000-0000-0001"},
  ]
}

When paper["author"] is accessed, a new list containing the alice and bob entities is generated on the fly. For this reason, calling append on paper["author"] won't actually modify the paper entity in any way. To add an author, use the append_to method instead:

donald = crate.add(Person(crate, "https://en.wikipedia.org/wiki/Donald_Duck"))
paper.append_to("author", donald)

Note that append_to also works if the property to be updated is missing or has only one value:

for n in "Mickey_Mouse", "Scrooge_McDuck":
    p = crate.add(Person(crate, f"https://en.wikipedia.org/wiki/{n}"))
    donald.append_to("follows", p)

Adding remote entities

Data entities can also be remote:

input_data = crate.add_file("http://example.org/exp_data.zip")

By default the file won't be downloaded, and will be referenced by its URI in the serialized crate:

{
  "@id": "http://example.org/exp_data.zip",
  "@type": "File"
},

If you add fetch_remote=True to the add_file call, however, the library (when crate.write is called) will try to download the file and include it in the output crate.

Another option that influences the behavior when dealing with remote entities is validate_url, also False by default: if it's set to True, when the crate is serialized, the library will try to open the URL to add / update metadata bits such as the content's length and format (but it won't try to download the file unless fetch_remote is also set).

Adding entities with an arbitrary type

An entity can be of any type listed in the RO-Crate context. However, only a few of them have a counterpart (e.g., File) in the library's class hierarchy (either because they are very common or because they are associated with specific functionality that can be conveniently embedded in the class implementation). In other cases, you can explicitly pass the type via the properties argument:

from rocrate.model.contextentity import ContextEntity

hackathon = crate.add(ContextEntity(crate, "#bh2021", properties={
    "@type": "Hackathon",
    "name": "Biohackathon 2021",
    "location": "Barcelona, Spain",
    "startDate": "2021-11-08",
    "endDate": "2021-11-12"
}))

Note that entities can have multiple types, e.g.:

    "@type" = ["File", "SoftwareSourceCode"]

Modifying the crate from JSON-LD dictionaries

The add_jsonld method allows to add a contextual entity directly from a JSON-LD dictionary containing at least the @id and @type keys:

crate.add_jsonld({
    "@id": "https://orcid.org/0000-0000-0000-0000",
    "@type": "Person",
    "name": "Alice Doe"
})

Existing entities can be updated from JSON-LD dictionaries via update_jsonld:

crate.update_jsonld({
    "@id": "https://orcid.org/0000-0000-0000-0000",
    "name": "Alice K. Doe"
})

There is also an add_or_update_jsonld method that adds the entity if it's not already in the crate and updates it if it already exists (note that, when updating, the @type key is ignored). This allows to "patch" an RO-Crate from a JSON-LD file. For instance, suppose you have the following patch.json file:

{
    "@graph": [
        {
            "@id": "./",
            "author": {"@id": "https://orcid.org/0000-0000-0000-0001"}
        },
        {
            "@id": "https://orcid.org/0000-0000-0000-0001",
            "@type": "Person",
            "name": "Bob Doe"
        }
    ]
}

Then the following sets Bob as the author of the crate according to the above file:

crate = ROCrate("temp-crate")
with open("patch.json") as f:
    json_data = json.load(f)
for d in json_data.get("@graph", []):
    crate.add_or_update_jsonld(d)

Consuming an RO-Crate

An existing RO-Crate package can be loaded from a directory or zip file:

crate = ROCrate('exp_crate')  # or ROCrate('exp_crate.zip')
for e in crate.get_entities():
    print(e.id, e.type)

ro-crate-metadata.json CreativeWork
./ Dataset
paper.pdf File
results.csv File
images/figure.svg File
https://orcid.org/0000-0000-0000-0000 Person
https://orcid.org/0000-0000-0000-0001 Person

The first two entities shown in the output are the metadata file descriptor and the root data entity, respectively. These are special entities managed by the ROCrate object, and are always present. The other entities are the ones we added in the section on RO-Crate creation. You can access data entities with crate.data_entities and contextual entities with crate.contextual_entities. For instance:

for e in crate.data_entities:
    author = e.get("author")
    if not author:
        continue
    elif isinstance(author, list):
        print(e.id, [p["name"] for p in author])
    else:
        print(e.id, repr(author["name"]))

paper.pdf ['Alice Doe', 'Bob Doe']
results.csv 'Alice Doe'
images/figure.svg 'Bob Doe'

You can fetch an entity by its @id as follows:

article = crate.dereference("paper.pdf")

Command Line Interface

ro-crate-py includes a hierarchical command line interface: the rocrate tool. rocrate is the top-level command, while specific functionalities are provided via sub-commands. Currently, the tool allows to initialize a directory tree as an RO-Crate (rocrate init) and to modify the metadata of an existing RO-Crate (rocrate add).

$ rocrate --help
Usage: rocrate [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  add
  init
  write-zip

Crate initialization

The rocrate init command explores a directory tree and generates an RO-Crate metadata file (ro-crate-metadata.json) listing all files and directories as File and Dataset entities, respectively.

$ rocrate init --help
Usage: rocrate init [OPTIONS]

Options:
  --gen-preview
  -e, --exclude CSV
  -c, --crate-dir PATH
  --help                Show this message and exit.

The command acts on the current directory, unless the -c option is specified. The metadata file is added (overwritten if present) to the directory at the top level, turning it into an RO-Crate.

Adding items to the crate

The rocrate add command allows to add workflows and other entity types (currently testing-related metadata) to an RO-Crate:

$ rocrate add --help
Usage: rocrate add [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  test-definition
  test-instance
  test-suite
  workflow

Note that data entities (e.g., workflows) must already be present in the directory tree: the effect of the command is to register them in the metadata file.

Example

# From the ro-crate-py repository root
cd test/test-data/ro-crate-galaxy-sortchangecase

This directory is already an RO-Crate. Delete the metadata file to get a plain directory tree:

rm ro-crate-metadata.json

Now the directory tree contains several files and directories, including a Galaxy workflow and a Planemo test file, but it's not an RO-Crate since there is no metadata file. Initialize the crate:

rocrate init

This creates an ro-crate-metadata.json file that lists files and directories rooted at the current directory. Note that the Galaxy workflow is listed as a plain File:

{
  "@id": "sort-and-change-case.ga",
  "@type": "File"
}

To register the workflow as a ComputationalWorkflow:

rocrate add workflow -l galaxy sort-and-change-case.ga

Now the workflow has a type of ["File", "SoftwareSourceCode", "ComputationalWorkflow"] and points to a ComputerLanguage entity that represents the Galaxy workflow language. Also, the workflow is listed as the crate's mainEntity (see the Workflow RO-Crate profile).

To add workflow testing metadata to the crate:

rocrate add test-suite -i test1
rocrate add test-instance test1 http://example.com -r jobs -i test1_1
rocrate add test-definition test1 test/test1/sort-and-change-case-test.yml -e planemo -v '>=0.70'

License

• Copyright 2019-2023 The University of Manchester, UK
• Copyright 2020-2023 Vlaams Instituut voor Biotechnologie (VIB), BE
• Copyright 2020-2023 Barcelona Supercomputing Center (BSC), ES
• Copyright 2020-2023 Center for Advanced Studies, Research and Development in Sardinia (CRS4), IT
• Copyright 2022-2023 École Polytechnique Fédérale de Lausanne, CH

Licensed under the Apache License, version 2.0 https://www.apache.org/licenses/LICENSE-2.0, see the file LICENSE.txt for details.

Cite as

The above DOI corresponds to the latest versioned release as published to Zenodo, where you will find all earlier releases. To cite ro-crate-py independent of version, use https://doi.org/10.5281/zenodo.3956493, which will always redirect to the latest release.

You may also be interested in the paper Packaging research artefacts with RO-Crate.