Data Literacy Series Search

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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Data visualizations are powerful tools to convey data graphically. Below, we categorize some of the commonly used types for varying communication purposes according to their primary functions.

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The new NIH's Data Management and Sharing (DM&S) Policy goes into effect for grants submitted on or after January 25, 2023.

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While working with large data files, you may need to reduce their size to make them more convenient to store, transmit, and download. Let's understand the bit-rate reduction process and its types.

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To celebrate the scariest month of the year, we have compiled six eerie short data tales from the TKFDM Network.

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Are you working on a research project or seeking to improve your data skills? The UCSB Library departments Research Data Services (RDS) and the Data, Research, Exploration, Access, and Methods (DREAM) Lab have many resources available to you.

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