Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Maximum Size of Aggregate01:12

Maximum Size of Aggregate

520
The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
520
Data Reporting and Recording01:24

Data Reporting and Recording

5.3K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.3K
Data: Types and Distribution01:19

Data: Types and Distribution

1.5K
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
1.5K
Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

822
SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
822
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.4K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.4K
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

6.1K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
6.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Crystallographic and EPR-based characterisation of Cu<sup>2+</sup>-binding to serum albumin: ATCUN coordination and additional sites.

Inorganic chemistry frontiers·2026
Same author

How to mitigate the caveat emptor burden of human and machine users of the Protein Data Bank.

Acta crystallographica. Section D, Structural biology·2026
Same author

Enhancing structural insights for advanced drug discovery by mitigating protein crystal damage.

Expert opinion on drug discovery·2025
Same author

Evolutionary Adaptation of Prephenate Dehydrogenases: A regulatory ACT domain acquisition in ecological niche specialization.

bioRxiv : the preprint server for biology·2025
Same author

PinMyMetal: a hybrid learning system to accurately model transition metal binding sites in macromolecules.

Nature communications·2025
Same author

Duplicate entries in the Protein Data Bank: how to detect and handle them.

Acta crystallographica. Section D, Structural biology·2025

Related Experiment Video

Updated: Jan 13, 2026

A User-friendly and Powerful R Analysis of Large-scale Datasets
10:56

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

332

Sharing Big Data.

Marek Grabowski1, Wladek Minor1

  • 1Department of Molecular Physiology and Biological Physics, University of Virginia , Charlottesville, VA 22903, USA.

Iucrj
|March 3, 2017
PubMed
Summary
This summary is machine-generated.

Handling Macromolecular Big Data presents significant challenges. This discussion covers emerging initiatives designed to address these complex issues in data science.

Keywords:
Big Datadata sharingmetadataopen sciencereproducibility

More Related Videos

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.7K
Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
13:01

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

Published on: April 10, 2016

34.7K

Related Experiment Videos

Last Updated: Jan 13, 2026

A User-friendly and Powerful R Analysis of Large-scale Datasets
10:56

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

332
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.7K
Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
13:01

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

Published on: April 10, 2016

34.7K

Area of Science:

  • Biochemistry and Molecular Biology
  • Bioinformatics and Computational Biology

Background:

  • The exponential growth of macromolecular data, including genomics, proteomics, and structural biology, presents unprecedented challenges.
  • Effective management and analysis of this big data are crucial for scientific advancement.

Purpose of the Study:

  • To identify and discuss the key challenges associated with Macromolecular Big Data.
  • To review current and emerging initiatives aimed at overcoming these data-related issues.

Main Methods:

  • Literature review of current big data challenges in macromolecular sciences.
  • Analysis of ongoing initiatives and proposed solutions for data management and analysis.

Main Results:

  • Several key challenges were identified, including data storage, integration, analysis, and interpretation.
  • A range of innovative initiatives are emerging to tackle these challenges, leveraging advanced computational techniques and collaborative platforms.

Conclusions:

  • Addressing the challenges of Macromolecular Big Data requires a multi-faceted approach involving technological innovation and collaborative efforts.
  • The discussed initiatives offer promising pathways to unlock the full potential of big data in advancing macromolecular science.