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 Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

250
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
250
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

35
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
35
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

19
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
19
Statistical Package for the Social Sciences (SPSS)01:22

Statistical Package for the Social Sciences (SPSS)

214
The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...
214
5-Number Summary01:04

5-Number Summary

4.1K
In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
4.1K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

66
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
66

You might also read

Related Articles

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

Sort by
Same author

Compounding hazards increase flood economic losses across Europe.

Nature communications·2026
Same author

From rows to yields: how foundation models for tabular data simplify crop yield prediction.

Scientific reports·2026
Same author

Disaster Storylines and Knowledge Graphs from Global News with Large Language Models and Retrieval-Augmented Generation.

Scientific data·2026
Same author

Causal discovery reveals complex patterns of drought-induced displacement.

iScience·2024
Same author

Evidence library of meta-analytical literature assessing the sustainability of agriculture - a dataset.

Scientific data·2024
Same author

The Multi-temporal and Multi-dimensional Global Urban Centre Database to Delineate and Analyse World Cities.

Scientific data·2024

Related Experiment Video

Updated: May 16, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K

A monthly sub-national Harmonized Food Insecurity Dataset for comprehensive analysis and predictive modeling.

Melissande Machefer1, Michele Ronco2, Anne-Claire Thomas3

  • 1European Commission, Joint Research Centre (JRC), Ispra, 21027, Italy. melissande.machefer@ec.europa.eu.

Scientific Data
|May 5, 2025
PubMed
Summary

The Harmonized Food Insecurity Dataset (HFID) unifies global food security data for better crisis anticipation. This open-source resource aids experts and agencies in analyzing and preventing food crises worldwide.

More Related Videos

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
04:46

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Published on: September 18, 2018

7.2K
Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

688

Related Experiment Videos

Last Updated: May 16, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
04:46

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Published on: September 18, 2018

7.2K
Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

688

Area of Science:

  • Global food security analysis
  • Humanitarian data science
  • Predictive modeling for food crises

Background:

  • Food insecurity measurement is complex and lacks comprehensive global data.
  • Timely and accurate data are crucial for anticipating, monitoring, and mitigating food crises.
  • Existing data sources are fragmented, hindering unified analysis.

Purpose of the Study:

  • Introduce the Harmonized Food Insecurity Dataset (HFID) as an open-source resource.
  • Consolidate key global food insecurity indicators into a unified dataset.
  • Enhance the analysis and prediction of global food insecurity.

Main Methods:

  • Consolidated data from Integrated Food Security Phase Classification (IPC)/Cadre Harmonisé (CH), Famine Early Warning Systems Network (FEWS NET), World Food Program's (WFP) Food Consumption Score (FCS), and reduced Coping Strategy Index (rCSI).
  • Utilized a common reference system for administrative units for spatial consistency.
  • Ensured monthly updates for comprehensive temporal coverage.

Main Results:

  • The HFID provides a unified, open-source resource for analyzing food insecurity.
  • The dataset offers comprehensive spatial and temporal coverage where data permits.
  • Highlights global data disparities and gaps in food insecurity monitoring.

Conclusions:

  • The HFID is a vital tool for food insecurity experts and humanitarian agencies.
  • Facilitates a unified approach to analyzing food insecurity conditions globally.
  • Empowers the scientific community to develop predictive models for enhanced food crisis prevention.