Data Literacy Series Search

GitHub repositories serve as vital platforms in today’s computational research, allowing scientists to store and share their project code. Although these repositories weren't specifically designed for data storage and preservation, their collaborative features and version control capabilities significantly aid project workflows. To boost their long-term value, we recommend the following best practices.

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Storing data in tabular form is a common practice for managing information. Explore essential tips for organizing spreadsheets to ensure your data is easily accessible and usable by both people and machines.

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Wondering what to share at the end of your qualitative data project? Effective sharing goes beyond archiving; it must preserve the contextual value of the data for future use. Here’s a checklist of critical data files and supporting documents, along with file organization tips and repository options. 

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Qualitative coding is the systematic process of categorizing and segmenting textual or visual content into meaningful analytical units to identify patterns and themes within the data. This handout compares deductive and inductive approaches to qualitative coding and highlights campus-licensed and supported tools to aid in this process.

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De-identifying text with human participant data is challenging due to its unstructured and sensitive nature, requiring careful measures for safe and ethical sharing. Here are eight essential steps to minimize re-identification risks while preserving reusability.

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The informed consent process is a central component of the ethical conduct of research with human subjects overseen by an Institutional Review Board (IRB). Crafting compliant consent forms with permissive language broadens the shareability and usability of research data. Incorporating considerations for data sharing and potential reuse into the informed consent process is crucial to preempt future complications and adverse outcomes, all while meeting funders' and publishers' increasing data sharing requirements.

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Qualitative data is inherently nuanced and deeply contextual. While it offers rich interpretive possibilities, its analysis and presentation often demand considerable resources. Moreover, due to its sensitivity, managing qualitative research data obtained from human subjects—such as interviews and observations— poses distinct ethical challenges. This handout delineates key considerations, practical tips, and available resources for effectively handling this data type across its lifecycle.

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Many scientific journals require authors to provide a data availability statement (DAS) to adhere to openness and transparency principles. The DAS specifies how and where the underlying data used in a study can be accessed and the terms under which they can be obtained. We cover best practicves for writing such statements in compliance with policy requirements.

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Binder is an open-source and free service for researchers to create a shareable project version that can be viewed and interacted with within a reproducible computational environment that runs in the cloud via a web browser.

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Venv operates independently, ensuring alterations to installed dependencies within one environment remain isolated from others and system-wide libraries. This isolation allows the creation of multiple virtual environments, each hosting its own Python versions and varying sets of libraries.

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