How to contribute

If you maintain a publicly available Google BigQuery project with open scholarly data, we’d love to include it in this collection. You can use the template below to share your project description, and after you submit a pull request, we’ll integrate the metadata and update it regularly.

Prerequisites

This project aims to make it easier to discover trusted, quality-assured big and open data warehouses for bibliometrics and data analytics on Google BigQuery. By bringing these collections together, we hope to improve the analyses, applications, and research built on them.

To participate, you’ll need to:

  • Provide large, open datasets via Google BigQuery in a user-friendly format
  • Use a permissive open license for all datasets
  • Cover storage costs (users are responsible for their own query costs)
  • Make your data preprocessing code publicly available
  • Preserve historical dataset versions to ensure reproducibility (you can store these anywhere, not just BigQuery)
  • Provide training materials like getting started guides, use cases, integration instructions, and contact information for support

Sharing

To share your collection, file an issue using this template. You’ll need to provide:

  • The ID of your BigQuery project
  • An acronym for your collection
  • A brief description
  • The maintainer name(s)

We’ll use this information to create a dedicated webpage for your collection (though you’re welcome to do this yourself):

  • Copy _template.qmd to a new folder under collections/ and save it as index.qmd. Use your collection’s acronym as the folder name.
  • Update the title and project ID in the YAML header (the bq_project parameter).
---
title: "Open Scholarly Data @ SUB Göttingen"
format:
  html:
    page-layout: full
toc-title: Datasets
params:
  bq_project: "subugoe-collaborative"
---
  • Add your description below the YAML header.
  • Create a Pull Request on GitHub.

Local testing

Before submitting your pull request, you can verify that the site builds correctly on your machine. You need Quarto and R installed.

Install the R dependencies listed in the DESCRIPTION file using pak:

# install.packages("pak")
pak::local_install_deps()

Then, render just your new collection page to check for errors:

quarto render collections/<your-collection>/index.qmd

Or render the full site:

quarto render

You can also start a live-reloading preview server to inspect your changes in the browser:

quarto preview

If a dataset or table in your collection isn’t publicly available, the build will fail.

Documentation

We aim to reuse as much information as possible from Google Cloud BigQuery using the REST API. To help others discover and use your data, you should provide:

  • Descriptions for projects, datasets, and tables that speak to first-time users. Focus on the essentials—the data source, version, and license—rather than overly technical details. You can link to a GitHub repository with your data processing code if that’s helpful.
  • Schemas for tables. Describe each field thoroughly; this makes it much easier for others to understand the datasets.

GitHub Actions automatically fetches metadata like update times, row counts, size, and regions during the daily website builds.

Contact

In case of questions or issues, feel free to create an issue: https://github.com/orion-dbs-community/website/issues

Repo-Maintainer: Najko Jahn