Data Literacy Series Search

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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Is your project R-based? The renv package helps you set up R projects and manage dependencies to keep your environment consistent and reproducible.

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Everybody has a "dependency hell" horror story to tell. In the spookiest month of the year, we describe the leading causes of this problem and how it impacts scientific reproducibility.

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APIs or Application Programming Interfaces have become increasingly popular in academic research. They simplify data access, streamline data collection and analysis processes, enable real-time updates, support collaboration, provide access to specialized tools, and more.

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Predatory publishers disguise themselves as credible open access (OA) publishers. They employ deceitful tactics and operate profit-driven schemes that can harm academics' reputations, undermining their chances of disseminating authentic research through established and credible publishing models. Here are tips for avoiding predatory publishers to help you safeguard your work and maintain scholarly honesty while publishing open access.

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Whether you have collected your own data or will be reusing existing datasets, you probably need to clean them up before you move forward with data analysis. This process includes fixing or removing incorrect, corrupted, unformatted, duplicate, or incomplete data. While the cleaning-up process may look different depending on the dataset you have at hand, this handout covers some essential tips to complete this task more efficiently while making your data more consistent, accurate, and high quality.

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The Dublin Core Metadata Initiative (DCMI) was named after its inaugural meeting in 1995 in Dublin, Ohio. The organization maintains the DCMI Metadata Element Set, one of the most straightforward and widely adopted metadata schema. Initially intended for web resources, Dublin Core (DC) has proven its versatility in describing various physical and digital objects, including datasets.

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The Data Documentation Initiative (DDI) is an international and open suite of standards expressed in XML for describing the research data produced primarily by surveys and other observational methods in the social, behavioral, and economic (SBE) sciences, health sciences, and official statistics. DDI documents and manages different stages in the research data lifecycle to facilitate the understanding, interpretation, and use of data by people, software, and computer networks.

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EML is a community-maintained machine-readable metadata schema that provides a comprehensive vocabulary and XML markup syntax for documenting research data and related outputs. It is widely used in the Earth and Environmental Sciences and sibling disciplines to meet researchers' needs for sharing, preserving, discovering, and reusing data.

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High-quality metadata ensures accuracy and interoperability across systems, and the adoption of metadata standards or schemas facilitates data sharing and collaboration, making data understandable and reusable.

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