Data Management
Data Management - Related Videos
09:43
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Cited by 5 •
2019
Many researchers generate "medium-sized", low-velocity, and multi-dimensional data, which can be managed more efficiently with databases rather than spreadsheets. Here we provide a conceptual overview of databases including visualizing multi-dimensional data, linking tables in relational database structures, mapping semi-automated data pipelines, and using the database to elucidate data meaning.
10:23
A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management
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Cited by 5 •
2023
Here, we present a protocol to utilize machine-vision software to stabilize dynamic processes during TEM imaging, while concurrently indexing multiple streams of metadata to each image into a navigable timeline. We demonstrate how this platform enables automated calibration and mapping of the electron dose over the course of an experiment.
01:34
Early Intervention and Pain Management
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2026
All animal procedures described here must be conducted in accordance with institutional animal ethics guidelines and approved by IACUC. All procedures must follow the principles of the 3Rs—Replacement, Reduction, and Refinement—and must be performed by trained personnel. Monitoring Animals for Signs of Pain and Distress Recognizing early signs of pain is essential for ethical animal research. Animals are routinely monitored using behavioral, physical, and physiological parameters that reflect...
03:47
Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients
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2024
This protocol presents a scalable workflow for the use of point-of-care ultrasound in the management of hospitalized and outpatient heart failure patients.
08:09
Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management
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Cited by 1 •
2017
Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonates. Repeatable methods for field and laboratory measurements of these quantities and data analysis methods are described here.
FAQs
Consistent data formats give values a predictable structure, making them easier to combine, validate, and analyze. Metadata records information about variables and their handling, so collaborators can interpret fields consistently and trace how data were prepared. This reduces ambiguity during analysis and supports reuse across research tasks.
Quality checks help identify missing, duplicated, or inconsistent values before they influence statistical calculations. They provide a controlled way to compare records with expected formats or rules, allowing errors to be detected rather than silently carried forward. In a statistical workflow, early checking improves the reliability of cleaning and transformation decisions and helps distinguish problems in the data from patterns that may be scientifically meaningful.
Access controls limit who can view or modify data, while versioning preserves earlier states as changes occur. Together, these mechanisms support coordinated work without losing the ability to determine which dataset or revision produced a result. They also strengthen the auditable record, making later review of corrections, transformations, and analytical decisions more dependable.
In statistics, managed workflows connect data handling with analysis: sampling information can be retained, cleaning can address missing or duplicated values, and transformations can be tracked rather than applied invisibly. This connection helps analysts evaluate whether preparation choices could affect results, so conclusions remain tied to the evidence and its documented processing history.
Before analysis begins, a workflow should specify how data will be formatted, described, checked, accessed, versioned, stored, and maintained. Researchers can then apply quality checks, document corrections and transformations, preserve controlled revisions, and keep the resulting information available for authorized use. This sequence creates a consistent foundation for later statistical analysis and collaboration.
Well-managed data support reproducible statistical work because researchers can follow the information’s changes and understand how it reached an analytical state. An auditable record also helps collaborators review procedures, repeat analyses, and identify whether a conclusion reflects the underlying evidence or a preventable handling problem. The benefit is stronger confidence in interpretation, not merely better storage.