Structured Data
Structured Data - Related Videos
10:10
Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
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Cited by 8 •
2020
Neutron protein crystallography is a structural technique that permits the localization of hydrogen atoms, thereby providing important mechanistic details of protein function. We present here the workflow for mounting a protein crystal, neutron diffraction data collection, structure refinement and analysis of the neutron scattering length density maps.
11:55
Cryo-Structured Illumination Microscopic Data Collection from Cryogenically Preserved Cells
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Cited by 18 •
2021
This protocol demonstrates how to image biological cryo-preserved samples using cryo-structured illumination microscopy. We demonstrate the methodology by imaging the cytoskeleton of U2OS cells.
09:09
Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data
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Cited by 17 •
2015
To investigate flow velocities and directionality of filamentous-actin at the T cell immunological synapse, live-cell super-resolution imaging is combined with total internal reflection fluorescence and quantified with spatio-temporal image correlation spectroscopy.
12:03
Dynamics of Structures
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2023
Source: Roberto Leon, Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA It is rare nowadays that a whole year goes by without a major earthquake event wreaking havoc somewhere around the world. In some cases, like the 2005 Banda Ache earthquake in Indonesia, the damage involved large geographic areas and casualties in the six figures. In general, the number and intensity of earthquakes is not increasing, however, the vulnerability of the built environment is...
01:30
Data Interpretation
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2026
Research papers often present findings in the form of numerical tables, statistical outputs, and graphical representations. However, numerical results alone do not convey meaning unless researchers examine and place them in context. Data interpretation is the systematic process through which researchers transform raw information into clear, meaningful insights. It begins only after researchers have collected data through experiments, surveys, or observational studies. In their unprocessed form,...
FAQs
Predefined fields constrain each data element to an expected format, while relationships connect related elements such as diagnoses, medications, laboratory results, and patient demographics. That structure lets software check whether records fit the intended organization, retrieve selected elements through queries, and combine comparable records for analysis. The result is more consistent processing across clinical and research workflows.
Validation acts as a control point before medical records are queried, exchanged, or analyzed. Software can use the defined structure to identify information that does not match expected fields or relationships, helping teams detect quality problems earlier. This matters because downstream aggregation and statistical analysis depend on records being organized consistently rather than merely present in a database.
Schema consistency makes information from different records or institutions easier to interpret together. When the same types of medical elements use compatible formats and relationships, systems can exchange records with less ambiguity and aggregate them for broader analyses. In medicine, this supports interoperability across electronic health records, clinical registries, and research workflows without relying solely on free-form text.
Structured data does not remove the need for oversight; it gives teams defined elements that can be monitored. Data quality checks can focus on whether fields and relationships remain consistent, while governance processes address how records are managed and protected. Keeping these concerns visible is important when medical information supports clinical systems, population-health studies, or research.
A practical workflow begins by deciding which medical elements a project needs and assigning them defined fields and relationships. Teams can then organize records, apply validation, and query the resulting dataset before using it for exchange, aggregation, or analysis. Reviewing quality, privacy, and governance requirements alongside these steps helps keep the dataset suitable for its intended clinical or research use.
Electronic health records can use structured elements to support consistent storage and retrieval of diagnoses, medications, laboratory results, and demographics. Clinical registries apply the same organizational logic across patient records, while population-health studies use comparable elements for aggregation. Research workflows benefit because the resulting information can be queried and analyzed in a more standardized form.
Aggregation becomes more meaningful when records share compatible fields and relationships. Medical teams can combine information across patients or institutions, then use the resulting dataset for statistical analysis or population-health studies. The value lies not simply in having more records, but in making their elements comparable enough to support consistent queries, summaries, and interpretation.