What is DataLad Catalog?
DataLad Catalog is a free and open source command line tool with a Python API that allows you to turn structured metadata into a user-friendly, browser-based data catalog.
DataLad is a distributed data management system that keeps track of your data, creates structure, ensures reproducibility, supports collaboration, and integrates with widely used data infrastructure.
DataLad MetaLad extends and equips DataLad with a command suite for metadata handling. This includes traversing a full data tree (of arbitrarily large size) to conduct metadata extraction (using extractors for various data types), metadata aggregation, and also reporting.
By combining the functionality of these three tools, you can:
manage the full lifecycle of your data (applying version control and capturing provenance records along the way)
add and extract detailed metadata records about every single item in a multi-level dataset, and
convert the metadata into a user-friendly browser application that increases the findability and accessibility of your data.
As a bonus, these processes can be applied in a decentralized and collaborative way.
Why use DataLad Catalog?
Working collaboratively with large and distributed datasets poses particular challenges for FAIR data access, browsing, and usage.
the administrative burden of keeping track of different versions of the data, who contributed what, where/how to gain access, and representing this information centrally and accessibly can be significant
data privacy regulations might restrict data from being shared or accessed across multi-national sites
costs of centrally maintained infrastructure for data hosting and web-portal type browsing could be prohibitive
These challenges impede the many possible gains obtainable from distributed data sharing and access. Decisions might even be made to forego FAIR principles in favour of saving time, effort and money, leading to the view that these efforts have seemingly contradicting outcomes.
DataLad Catalog helps counter this contradiction by focusing on interoperability with structured, linked, and machine-readable metadata.
Metadata about datasets, their file content, and their links to other datasets can be used to create abstract representations of datasets that are separate from the actual data content. This means that data content can be stored securely while metadata can be shared and operated on widely, thus improving decentralization and FAIRness.
By combining these features, DataLad Catalog can create a user-friendly catalog of your dataset and make it publicly available, complete with all additionally supplied metadata, while you maintain secured and permission-based access control over your actual file content. This catalog can itself be maintained and contributed to in a decentralized manner without compromising metadata integrity.
How does it work?
DataLad Catalog can receive commands to
create a new catalog,
remove metadata entries to/from an existing catalog,
serve an existing
catalog locally, and more. Metadata can be provided to DataLad Catalog from any
number of arbitrary metadata sources, as an aggregated set or as individual
items/objects. DataLad Catalog has a dedicated Catalog Schema (using the
JSON Schema vocabulary) against which incoming metadata items are validated.
This schema allows for standard metadata fields as one would expect for datasets
of any kind (such as
authors, and more), as well as fields that support identification, versioning,
dataset context and linkage, and file tree specification.
The process of generating a catalog, after metadata entry validation, involves:
aggregation of the provided metadata into the catalog filetree
generating the assets required to render the user interface in a browser
The output is a set of structured metadata files, as well as a Vue.js-based browser interface that understands how to render this metadata in the browser. What is left for the user is to host this content on their platform of choice and to serve it for the world to see.
For an example of the result, visit our demo catalog.
A detailed description of these steps can be found in the Pipeline Description