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datonic / hub

🏛️ Open Data, together
https://publish.obsidian.md/davidgasquez/Open+Data
MIT License
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ChatGPT Answer the hard questions #2

Closed davidgasquez closed 1 year ago

davidgasquez commented 1 year ago

This is a experiment.

davidgasquez commented 1 year ago

What is the best way to publish open data online nowadays? How can I guarantee data is easy to access?

There are various ways to publish open data online nowadays. Some of the best practices for publishing open data that ensures easy access to your data are:

  1. Use a reliable open data platform: You can choose a popular and reliable open data platform such as CKAN, Socrata, or Data.gov. These platforms provide tools to help you organize and publish data in a way that makes it easy to find and access.
  2. Make your data machine-readable: The data you want to publish should be machine-readable, which means it can be easily read by computers. This can be achieved by using standard and open data formats such as CSV, XML, JSON or RDF.
  3. Provide clear and detailed metadata: Metadata is essential to facilitate search and discovery of your data. Use descriptive and accurate metadata to provide information about the source, structure, and contents of your data.
  4. Ensure data quality and accuracy: Check your data for accuracy and completeness before publishing. Having high-quality data will increase the chances of your data being used and reused.
  5. Provide APIs and tools for developers: Building APIs and tools for developers to access your data can make it easy to integrate your data into other applications and systems.
  6. Engage with the community: Reach out to the community of users who might be interested in your data. This could include researchers, journalists, or developers. Engage with them to understand their needs and how you can make your data more useful to them.

By following these best practices, you can ensure that your open data is easily accessible and reusable by anyone who wants to use it.

davidgasquez commented 1 year ago

Based on the research results, we propose a grant to develop a standard for managing open source datasets. Open science and open data are becoming increasingly popular and required by grant funding agencies and journal publishers [1]. However, managing open source datasets can be challenging, especially in collaborative, multidisciplinary, and multi-institutional science projects. One of the central components of open data management is documentation of complete and accurate metadata, workflow, and source code in addition to access to raw data and data products to uphold FAIR (Findable, Accessible, Interoperable, Reusable) principles [1].

Although best practice in data/metadata management is to use established internationally accepted metadata schemata, many of these standards are discipline-specific, making it difficult to catalog multidisciplinary data and data products in a way that is easily findable and accessible [1]. Therefore, we propose developing a standard for managing open source datasets that is discipline-agnostic and can be used across different fields. This standard will enable researchers to document complete and accurate metadata, workflow, and source code, and provide access to raw data and data products to uphold FAIR principles.

The standard will also facilitate data findability and accessibility, making it easier for researchers to find and link multidisciplinary datasets. This will increase data sharing and collaboration, leading to scientific innovation