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

Correlation and Causation01:27

Correlation and Causation

40.2K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
40.2K
Causality in Epidemiology01:21

Causality in Epidemiology

1.1K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.1K
Correlation and Regression00:53

Correlation and Regression

2.6K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
2.6K
Regression Analysis01:11

Regression Analysis

6.6K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.6K
Correlation01:09

Correlation

13.1K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
13.1K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

10.6K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
10.6K

You might also read

Related Articles

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

Sort by
Same author

Indigenous Peoples and local communities as agents of transformative change for sustainability.

Communications earth & environment·2026
Same author

Quantifying tourism booms and the increasing footprint in the Arctic with social media data.

PloS one·2020
Same author

Local land use associated with socio-economic development in six arctic regions.

Ambio·2018
Same author

Soil organic carbon depletion and degradation in surface soil after long-term non-growing season warming in High Arctic Svalbard.

The Science of the total environment·2018
Same author

Community-based management: under what conditions do Sámi pastoralists manage pastures sustainably?

PloS one·2012
Same author

Incentives and regulations to reconcile conservation and development: thirty years of governance of the Sami pastoral ecosystem in Finnmark, Norway.

Journal of environmental management·2011

Related Experiment Video

Updated: Oct 26, 2025

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

8.9K

causalizeR: a text mining algorithm to identify causal relationships in scientific literature.

Francisco J Ancin-Murguzur1, Vera H Hausner1

  • 1The Arctic Sustainability Lab, UiT the Arctic University of Norway, Tromsø, Norway.

Peerj
|July 29, 2021
PubMed
Summary

This study introduces causalizeR, a text-processing algorithm that extracts causal relationships from scientific literature. This tool aids in understanding complex ecosystem changes and predicting potential abrupt shifts.

Keywords:
Big dataEvidence synthesisLiterature reviewNatural language processingScenarios

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

784
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.9K

Related Experiment Videos

Last Updated: Oct 26, 2025

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

8.9K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

784
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.9K

Area of Science:

  • Ecology
  • Computational Biology
  • Environmental Science

Background:

  • Ecosystems worldwide are undergoing rapid changes due to complex interactions between abiotic and biotic drivers.
  • Predicting cascading effects and abrupt ecosystem shifts is crucial for policymaking but challenging to quantify from isolated data.
  • Synthesizing evidence from unstructured scientific texts remains a significant hurdle in ecological research.

Purpose of the Study:

  • To introduce causalizeR, a novel text-processing algorithm designed to extract causal relationships from scientific literature.
  • To provide a structured method for synthesizing evidence from unstructured texts, aiding in the understanding of complex ecological interactions.
  • To facilitate the estimation of direct and indirect effects of multiple drivers at a network level for hypothesis generation and testing.

Main Methods:

  • Developed causalizeR, a text-processing algorithm utilizing grammatical rules to identify causal links.
  • Algorithm extracts causal relations by analyzing the relative positions of nouns to keywords of interest.
  • The extracted causal database is designed for integration with network analysis tools.

Main Results:

  • Successfully demonstrated the extraction of causal relationships from scientific literature concerning the tundra ecosystem.
  • The algorithm structures unstructured text data into a usable database of cause-and-effect relationships.
  • Enabled the estimation of direct and indirect effects of multiple drivers within an ecological network.

Conclusions:

  • causalizeR offers a scalable approach to synthesize evidence from scientific literature, enhancing our ability to predict ecosystem dynamics.
  • The tool facilitates a network-level understanding of driver impacts, supporting informed hypothesis creation and testing.
  • This method aids in quantifying complex interactions, providing valuable insights for ecological research and policy decisions.