The Graph Learning Indexer (GLI) is a collaborative project that aims to include contributors from the broader graph learning community with a wide range of identities and backgrounds. The diversity of participants will be an important asset to the project for its goal towards developing and curating a representative set of graph learning benchmarks that reflect the diverse real-world applications well. Consequently, GLI adheres to this Code of Conduct based on the Contributor Covenant Code of Conduct and Geoblacklight Code of Conduct as guiding principles for our work together.
We as members, contributors, and maintainers of the GLI project pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.
Examples of behavior that contributes to a positive environment for our community include:
- Demonstrating empathy and kindness toward other people
- Being respectful of differing opinions, viewpoints, and experiences
- Giving and gracefully accepting constructive feedback
- Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience
- Focusing on what is best not just for us as individuals, but for the overall community
Examples of unacceptable behavior include:
- The use of sexualized language or imagery, and sexual attention or advances of any kind
- Trolling, insulting or derogatory comments, and personal or political attacks
- Public or private harassment
- Publishing others' private information, such as a physical or email address, without their explicit permission
- Other conduct which could reasonably be considered inappropriate in a professional setting
Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the project team at [email protected]. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident.
Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.
We ask the dataset contributors to adhere to a high ethics standard for dataset contribution, especially for data collection. Project maintainers may deprecate datasets on ethical grounds.
Examples of ethical considerations to keep in mind include (but not limited to):
- Conduct an ethical review process (e.g., by an Institutional Review Board) when human subjects are involved.
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- When the data involves sensitive information about human subjects, ensure the data does not involve Personally Identifiable Information (PII).
- Think about how the data collection process may inject (potentially harmful) biases into the dataset.
- Avoid toxic content unless the dataset is purposed for goals such as content detoxification, in which cases an explicit warning should be marked in the documentation of the dataset.
- Consider what are the potential unethical exploitation of the collected data.
We refer the dataset contributors to the following resources for more ethical considerations in data collection.
- Bender, Emily M., and Batya Friedman. "Data statements for natural language processing: Toward mitigating system bias and enabling better science." Transactions of the Association for Computational Linguistics 6 (2018): 587-604.
- Gebru, Timnit, et al. "Datasheets for datasets." Communications of the ACM 64.12 (2021): 86-92.
- Cote, Catherine. 5 PRINCIPLES OF DATA ETHICS FOR BUSINESS. Harvard Business School Online's Business Insights Blog (2021).
Ethical concerns regarding the datasets hosted in the GLI repository can be raised as issues on GitHub or sent to the project team at [email protected]. Project maintainers will review and investigate the reported concerns and will deprecate any dataset with ethical issues. An ethical note explaining the specific issues will be first incorporated into the metadata of the deprecated dataset, and then the dataset will be removed from the main branch to discourage further use of the dataset. The deprecation of the Boston Housing dataset in Scikit-Learn is an example of this process.
This Code of Conduct is adapted from the Contributor Covenant, version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html, and Geoblacklight Code of Conduct.