28 research outputs found
File Naming Convention Worksheet
This worksheet walks researchers through the process of creating a file naming convention for a group of files. This process includes: choosing metadata, encoding and ordering the metadata, adding version information, and properly formatting the file names. Two versions of the worksheet are available: a Caltech Library branded version and a generic editable version
Data Visualization 101
This workshop covers how to choose the right chart type for your data and how good design choices will make your chart easier to understand. The workshop focuses on visualization best practices, independent of any specific visualization software, and consists of lecture and hands on activities
The Problem with Dates: Applying ISO 8601 to Research Data Management
Dates appear regularly in research data and metadata but are a problematic data type to normalize due to a variety of potential formats. This suggests an opportunity for data librarians to assist with formatting dates, yet there are frequent examples of data librarians using diverse strategies for this purpose. Instead, data librarians should adopt the international date standard ISO 8601. This standard provides needed consistency in date formatting, allows for inclusion of several types of date-time information, and can sort dates chronologically. As regular advocates for standardization in research data, data librarians must adopt ISO 8601 and push for its use as a data management best practice
Data Management Practices in Academic Library Learning Analytics: A Critical Review
Introduction: Data handling in library learning analytics plays a pivotal role in protecting patron privacy, yet the landscape of data management by librarians is poorly understood. Methods: This critical review examines data-handling practices from 54 learning analytics studies in academic libraries and compares them against the NISO Consensus Principles on User’s Digital Privacy in Library, Publisher, and Software-Provider Systems and data management best practices. Results: A number of the published research projects demonstrate inadequate data protection practices including incomplete anonymization, prolonged data retention, collection of a broad scope of sensitive information, lack of informed consent, and sharing of patron-identified information. Discussion: As with researchers more generally, libraries should improve their data management practices. No studies aligned with the NISO Principles in all evaluated areas, but several studies provide specific exemplars of good practice. Conclusion: Libraries can better protect patron privacy by improving data management practices in learning analytics research
Project Close-Out Checklist for Research Data
The close-out checklist describes a range of activities for helping ensure that research data are properly managed at the end of a project or at researcher departure. Activities include: making stewardship decisions, preparing files for archiving, sharing data, and setting aside important files in a "FINAL" folder. Two versions of the checklist are available: a Caltech Library branded version and a generic editable version
File Naming Convention Worksheet
This worksheet walks researchers through the process of creating a file naming convention for a group of files. This process includes: choosing metadata, encoding and ordering the metadata, adding version information, and properly formatting the file names. Two versions of the worksheet are available: a Caltech Library branded version and a generic editable version
Data Management Practices in Academic Library Learning Analytics: A Critical Review
Introduction: Data handling in library learning analytics plays a pivotal role in protecting patron privacy, yet the landscape of data management by librarians is poorly understood. Methods: This critical review examines data-handling practices from 54 learning analytics studies in academic libraries and compares them against the NISO Consensus Principles on User’s Digital Privacy in Library, Publisher, and Software-Provider Systems and data management best practices. Results: A number of the published research projects demonstrate inadequate data protection practices including incomplete anonymization, prolonged data retention, collection of a broad scope of sensitive information, lack of informed consent, and sharing of patron-identified information. Discussion: As with researchers more generally, libraries should improve their data management practices. No studies aligned with the NISO Principles in all evaluated areas, but several studies provide specific exemplars of good practice. Conclusion: Libraries can better protect patron privacy by improving data management practices in learning analytics research
Data Management Plan (DMP) Checklist
The DMP checklist is based on the general NSF grant template and specifies content that should be in each section of the DMP. Two versions of the checklist are available: a Caltech Library branded version and a generic editable version. Always refer to the specific guidance for your grant proposal when writing a data management plan, as requirements differ
Project Close-Out Checklist for Research Data
The close-out checklist describes a range of activities for helping ensure that research data are properly managed at the end of a project or at researcher departure. Activities include: making stewardship decisions, preparing files for archiving, sharing data, and setting aside important files in a "FINAL" folder. Two versions of the checklist are available: a Caltech Library branded version and a generic editable version