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

Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

311
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...
311
Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

806
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
806
Manipulation and Analysis01:21

Manipulation and Analysis

56
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
56
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

759
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...
759
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

62
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
62
Central Tendency: Analysis01:10

Central Tendency: Analysis

199
Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...
199

You might also read

Related Articles

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

Sort by
Same author

Efficacy and safety of the tree sublingual immunotherapy tablet in the subpopulation of Canadian children with allergic rhinitis and/or conjunctivitis: phase III trial results.

Allergy, asthma, and clinical immunology : official journal of the Canadian Society of Allergy and Clinical Immunology·2026
Same author

The Somnolink-Hub: A Central Infrastructure That Unites Sleep Data at Point of Care.

Studies in health technology and informatics·2026
Same author

Stress-induced OMA1-mediated cleavage of AIFM1 suppresses cell growth by controlling mitochondrial OXPHOS activity.

The EMBO journal·2026
Same author

Stress adaptation of mitochondrial protein import by OMA1-mediated degradation of DNAJC15.

Nature structural & molecular biology·2026
Same author

Mutant CHCHD10 disrupts cytochrome c oxidation and activates mitochondrial retrograde signaling.

EMBO molecular medicine·2025
Same author

SQ House Dust Mite Sublingual Immunotherapy Tablet in Children With Allergic Asthma: A Randomised Phase III Trial.

Allergy·2025

Related Experiment Video

Updated: Aug 29, 2025

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

6.3K

Toward data lakes as central building blocks for data management and analysis.

Philipp Wieder1, Hendrik Nolte1

  • 1Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG), Göttingen, Germany.

Frontiers in Big Data
|September 8, 2022
PubMed
Summary

Data lakes store raw data for research, offering flexibility over data warehouses. Challenges in modeling, querying, and compute integration require further research for effective use in scientific data management.

Keywords:
FAIRbig datadata analyticsdata lakeprovenanceresearch data management

More Related Videos

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.2K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.8K

Related Experiment Videos

Last Updated: Aug 29, 2025

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

6.3K
Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.2K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.8K

Area of Science:

  • Computer Science
  • Data Science
  • Information Management

Background:

  • Data lakes are increasingly vital for industrial data analysis and research, serving as central repositories for raw data.
  • Unlike data warehouses (schema-on-write), data lakes (schema-on-read) retain native data formats, enhancing reusability and flexibility for diverse use cases.
  • Storing large volumes of raw data presents significant challenges in data modeling, indexing, querying, and scalable compute integration.

Purpose of the Study:

  • To provide a comprehensive overview of data lake developments over the past decade.
  • To analyze influential papers for their applicability to data lakes in research institutions.
  • To identify open challenges and future research directions for data lakes in scientific data management.

Main Methods:

  • Systematic review and analysis of influential papers from the last decade.
  • Evaluation of contributions in data lake architectures, metadata models, data provenance, workflow support, and FAIR principles.
  • Mapping identified capabilities against the requirements of common research personae.

Main Results:

  • Identified key advancements in data lake architectures and supporting technologies.
  • Highlighted the importance of metadata, provenance, and workflow support for research data management.
  • Revealed gaps in current data lake solutions concerning specific research needs and FAIR principles.

Conclusions:

  • Data lakes offer significant potential for research data management but face challenges in practical implementation.
  • Further research is needed in areas like advanced data modeling, efficient querying, and scalable compute integration for research data lakes.
  • Addressing these challenges is crucial for realizing the full potential of data lakes as central building blocks in scientific research.