cldfbench

Tooling to create CLDF datasets from existing data

Overview

This package provides tools to curate cross-linguistic data, with the goal of packaging it as CLDF dataset.

In particular, it supports a workflow where

• "raw" source data is downloaded to a raw subdirectory,
• and subsequently converted to a CLDF dataset in a cldf subdirectory, with the help of
  • configuration data in a etc directory
  • custom Python code (a subclass of cldfbench.Dataset which implements the workflow actions)

This workflow is supported via

• a commandline interface cldfbench which calls the workflow actions via subcommands,
• a cldfbench.Dataset base class, which must be overwritten in a custom module to hook custom code into the workflow.

Creating a skeleton for a new dataset directory

A directory containing stub entries for a dataset can be created running

cldfbench new cldfbench OUTDIR

This will create the following layout (where <ID> stands for the chosen dataset ID):

<ID>/
├── cldf               # A stub directory for the CLDF data
│   └── README.md
├── cldfbench_<ID>.py  # The python module, providing the Dataset subclass
├── etc                # A stub directory for the configuration data
│   └── README.md
├── metadata.json      # The metadata provided to the subcommand serialized as JSON
├── raw                # A stub directory for the raw data
│   └── README.md
├── setup.cfg          # Python setup config, providing defaults for test integration
├── setup.py           # Python setup file, making the dataset "installable" 
├── test.py            # The python code to run for dataset validation
└── .travis.yml        # Integrate the validation with Travis-CI

Dataset discovery

TODO: by entry point, by module

Implementing CLDF creation

cldfbench provides tools to make CLDF creation simple. Still, each dataset is different, and so each dataset will have to provide its own custom code to do so. This custom code goes into the cmd_makecldf method of the Dataset subclass in the dataset's python module.

Typically, this code will make use of one or more

• cldfbench.CLDFWriter instances, which can be obtained by calling Dataset.cldf_writer, passing in a
• cldfbench.CLDFSpec instance, which describes what kind of CLDF to create.

cldfbench supports several scenarios of CLDF creation:

• The typical use case is turning raw data into a single CLDF dataset. This would require instantiating one CLDFWriter writer in the cmd_makecldf method, and the defaults of CLDFSpec will probably be ok.
• But it is also possible to create multiple CLDF datasets:
  • For a dataset containing both, lexical and typological data, it may be appropriate to create a Ẁordlist and a StructureDataset. To do so, one would have to call cldf_writer twice, passing in an approriate CLDFSpec. Note that if both CLDF datasets are created in the same directory, they can share the LanguageTable - but would have to specify distinct file names for the ParameterTable, passing distinct values to CLDFSpec.data_fnames
  • When creating multiple datasets of the same CLDF module, e.g. to split a large dataset into smaller chunks, care must be taken to also disambiguate the name of the metadata file, passing distinct values to CLDFSpec.metadata_fname.

When creating CLDF, it is also often useful to have standard reference catalogs accessible, in particular Glottolog. See the section on Catalogs for a description of how this is supported by cldfbench.

Commands

TODO: custom commands

Catalogs

TODO: Catalog objects, Catalogs in cli,

Curating a dataset on GitHub

One of the design goals of CLDF was to specify a data format that plays well with version control. Thus, it's natural - and actually recommended - to curate a CLDF dataset in a version controled repository. The most popular way to do this in a collaborative fashion is by using a git repository hosted on GitHub.

The directory layout supported by cldfbench caters to this use case in several ways:

• Each directory contains a file README.md, which will be rendered as human readable description when browsing the repository at GitHub.
• The file .travis.yml contains the configuration for hooking up a repository with Travis CI, to provide continuous consistency checking of the data.

Archiving a dataset with Zenodo

Curating a dataset on GitHub also provides a simple way to archiving and publishing released versions of the data. You can hook up your repository with Zenodo (following this guide). Then, Zenodo will pick up any released package, assign a DOI to it, archive it and make it accessible in the long-term.

Some notes:

• Hook-up with Zenodo requires the repository to be public (not private).
• You should consider using an institutional account on GitHub and Zenodo to associate the repository with. Currently, only the user account registering a repository on Zenodo can change any metadata of releases lateron.
• Once released and archived with Zenodo, it's a good idea to add the DOI assigned by Zenodo to the release description on GitHub.
• To make sure a release is picked up by Zenodo, the version number must start with a letter, e.g. "v1.0" - not "1.0".

Thus, with a setup as described here, you can make sure you create FAIR data.