A monthly series of infographics on research data
Published by the Library's Research Data Services (RDS) department, the Data Literacy Series (DLS) are visually-compelling one-page handouts that break down complex and important data-related topics. DLS complement the RDS's instructional efforts and mission to promote data education and research data management practices. The handouts are distributed under a CC BY-NC-SA 4.0 license. If you’d like to suggest a topic for an upcoming issue, please submit your feedback to rds@library.ucsb.edu.
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Virtual Environments in Python with Venv
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.
PDF - ALTTAGS: Code Documentation, Dependency Management, Reproducibility, Python programming
DATE: 12-2023
Reproducible Environments with RENV
Is your project R-based? The renv package helps you set up R projects and manage dependencies to keep your environment consistent and reproducible.
PDF - ALT
TAGS: Reproducibility, Dependency Management, Code Documentation, R Programming
DATE: 11-2023
Taming the Dependency Hell
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.
PDF - ALTTAGS: Reproducibility, Dependency Management, Code Documentation
DATE: 10-2023
The ABCs of Web APIs
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.
PDF - ALTTAGS: API, Data Access
DATE: 09-2023
Watch Out for Predatory Publishers
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.
PDF - ALTTAGS: Open Access, Open Science, Scholarly Communication
DATE: 08-2023
Roll up your Sleeves for some Data Cleaning
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.
PDF - ALTTAGS: Data Cleaning, Data Preparation, Tidy Data
DATE: 07-2023
Dublin Core
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.
PDF - ALTTAGS: Data Documentation, Metadata, Metadata Standards, Interoperability, DCMI
DATE: 06-2023
Data Documentation Initiative
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.
PDF - ALTTAGS: Data Documentation, Metadata, Metadata Standards
DATE: 05-2023
Ecological Metadata Language
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.
PDF - ALTTAGS: Data Documentation, Metadata, Metadata Standards, Interoperability, EML
DATE: 03-2023
The Role of Metadata Standards
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.
PDF - ALT