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Attribution in science is crucial for recognising the contributions of others and ensuring transparency in research. Proper citation not only credits the original creators but also strengthens the credibility of new findings by linking them to previous work. Using persistent identifiers, like ORCIDs, helps uniquely identify contributors, ensuring their work is properly acknowledged across all forms of scholarly output, including publications, datasets, and code. This fosters collaboration, increases reproducibility, and ensures that all contributions, whether in data, code, or methodology, receive due recognition.
Licenses:
When producing scientific results, such as datasets or code, it is essential to label them clearly and visibly with the appropriate licenses for reuse, such as the Creative Commons CC-BY licenses. This transparency ensures that others understand the terms under which they can use, modify, or share the work. For users, checking the licenses before reusing scientific results is critical, as it helps them comply with the legal and ethical requirements of the original creator, ensuring proper attribution and avoiding misuse. Clear licensing promotes openness while safeguarding the rights of contributors.
When reusing data and code, it is crucial to assess whether they are fit for the intended purpose, especially in sensitive areas like health research. In health contexts, data is often not representative of the broader population, which can lead to biased or inaccurate conclusions if used without proper scrutiny.
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Attribution and citation:
Attribution in science is crucial for recognising the contributions of others and ensuring transparency in research. Proper citation not only credits the original creators but also strengthens the credibility of new findings by linking them to previous work. Using persistent identifiers, like ORCIDs, helps uniquely identify contributors, ensuring their work is properly acknowledged across all forms of scholarly output, including publications, datasets, and code. This fosters collaboration, increases reproducibility, and ensures that all contributions, whether in data, code, or methodology, receive due recognition.
Licenses:
When producing scientific results, such as datasets or code, it is essential to label them clearly and visibly with the appropriate licenses for reuse, such as the Creative Commons CC-BY licenses. This transparency ensures that others understand the terms under which they can use, modify, or share the work. For users, checking the licenses before reusing scientific results is critical, as it helps them comply with the legal and ethical requirements of the original creator, ensuring proper attribution and avoiding misuse. Clear licensing promotes openness while safeguarding the rights of contributors.
When reusing data and code, it is crucial to assess whether they are fit for the intended purpose, especially in sensitive areas like health research. In health contexts, data is often not representative of the broader population, which can lead to biased or inaccurate conclusions if used without proper scrutiny.
Resources
If there are resources that could be utilised for writing the new page, please list them below:
Context
If this request is coming from a particular project, domain, or use-case please list them below:
The text was updated successfully, but these errors were encountered: