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Resources for Further Exploration

Reading and Tutorials​

Data Use Agreements (DUAs)​

A Data Use Agreement (DUA) is a legal contract that outlines the terms and conditions for accessing, using, and sharing data. DUAs are particularly important when dealing with sensitive or confidential data, such as health data, as they establish clear guidelines for data usage, privacy protection, and compliance with relevant regulations. DUAs help ensure that data is used responsibly and ethically, safeguarding both the data providers' interests and the privacy of individuals whose data is being used.

Key Components of a Data Use Agreement​

  1. Data Description: Clearly describe the type of data being shared, including its nature, scope, and format. Specify whether the data is identifiable or de-identified.

  2. Purpose of Use: Explain the purpose for which the data will be used. This could include research, analysis, or other specific objectives.

  3. Data Recipients: Identify who will have access to the data. This could be individuals, researchers, organizations, or institutions. Specify their roles and responsibilities.

  4. Data Security and Protection: Describe the measures that will be taken to protect the data's security, confidentiality, and integrity. Address issues such as encryption, access controls, and data storage.

  5. Data Handling and Access: Outline how the data will be handled, accessed, and used. Specify any restrictions on copying, sharing, or disseminating the data.

  6. Data Retention and Destruction: Define the timeline for retaining the data and the procedures for its secure destruction once the agreed-upon purpose is fulfilled.

  7. Legal Compliance: Ensure that the data usage complies with relevant laws and regulations, such as HIPAA, GDPR, and other privacy laws.

  8. Ethical Considerations: Address ethical concerns related to data usage, including obtaining informed consent, protecting individual privacy, and avoiding harm.

  9. Liabilities and Indemnification: Clarify the responsibilities and liabilities of both the data provider and the data recipients. Include provisions for indemnification in case of data breaches or misuse.

  10. Dispute Resolution: Establish procedures for resolving disputes that may arise during the data usage period.

  11. Termination: Outline the conditions under which the agreement can be terminated by either party.

Importance of Consultation​

Creating an effective DUA requires collaboration between data providers, legal experts, researchers, and ethics committees. Different organizations may have specific templates or guidelines for DUAs, and it's important to tailor the agreement to the context of the data sharing arrangement. Consulting with an Institutional Review Board (IRB) or ethics committee can help ensure that the DUA aligns with ethical standards and legal requirements.

Case Study: NIH Data Sharing Agreements​

The National Institutes of Health (NIH) provides a variety of data sharing agreements for research data. The NIH Data Sharing Agreement Template covers important aspects such as data ownership, intellectual property, publication rights, and data usage. Researchers seeking to share data collected from NIH-funded projects are required to adhere to these agreements to ensure responsible and ethical data sharing.

Remember that DUAs are legally binding agreements, and careful consideration of their terms is essential to protect the interests of all parties involved and to maintain data integrity and privacy.

Please visit the official NIH Institute of Center Data Sharing Policies for examples.

Python Libraries / Packages​

What are they​

Python libraries or packages are collections of functions and methods that allow you to perform specific tasks without having to write the code from scratch. These libraries are designed to be reusable, efficient, and easy to use, making them essential tools for data analysis, machine learning, web development, and other applications.

Pypi​

The Python Package Index (PyPI) is a repository of software packages for the Python programming language. You can browse, search, and install Python packages from PyPI using tools like pip, the Python package manager. PyPI hosts thousands of open-source libraries and packages that you can use to enhance your Python projects.

  1. Pandas: A powerful data manipulation library that provides data structures and functions for working with structured data.
  2. NumPy: A fundamental library for numerical computing that provides support for arrays, matrices, and mathematical operations.
  3. Seaborn: A data visualization library based on Matplotlib that provides high-level functions for creating attractive and informative plots.
  4. Scikit-learn: A machine learning library that provides tools for building predictive models and performing data analysis.
  5. Requests: A library for making HTTP requests and interacting with web APIs.
  6. Beautiful Soup: A library for parsing HTML and XML documents, useful for web scraping and data extraction.
  7. Flask: A lightweight web framework for building web applications and APIs.
  8. Sqlalchemy: A SQL toolkit and Object-Relational Mapping (ORM) library for interacting with databases.
  9. TensorFlow: An open-source machine learning library developed by Google for building and training deep learning models.
  10. PyTorch: A machine learning library developed by Facebook for building neural networks and deep learning models.

Note: This list is not exhaustive, and there are many other Python libraries available for various purposes. You can explore the Python Package Index (PyPI) to discover more libraries and packages that suit your needs.

Other resources / links​

Below are some additional packages (links) that can be reviewed that contain list of medical / healthcare related python tooling:

Page last updated: Aug 6, 2024