Sites:WWW/Projects/ZiF/description - Revision history

Bootsa: add link to the EcoWeaver & TReK website

2026-02-05T13:25:52Z

add link to the EcoWeaver & TReK website

← Older revision		Revision as of 15:25, 5 February 2026
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	Recent advances in data science and AI technology may offer novel ways of dealing with complexity in ecology and may allow the development of knowledge synthesis tools that can manage context dependence. Especially promising seems the idea to bring together advanced AI-based technologies with conceptual causal models, because this may allow moving beyond pure pattern recognition towards causal inference. In an interdisciplinary setting including ecologists, data scientists, computational linguists and philosophers, the Resident Group at the Center for Interdisciplinary Research (ZiF) in Bielefeld titled “Mapping Evidence to Theory in Ecology: Addressing the Challenges of Generalization and Causality” explores ways for combining ecological theory represented in the form of causal network graphs with evidence found in scientific papers. The vision is that complex, multifactorial hypotheses about ecological mechanisms would become the basis of a digital atlas of knowledge, and in this atlas the available empirical evidence would be mapped on these hypotheses to allow for case-specific explanations and predictions.		Recent advances in data science and AI technology may offer novel ways of dealing with complexity in ecology and may allow the development of knowledge synthesis tools that can manage context dependence. Especially promising seems the idea to bring together advanced AI-based technologies with conceptual causal models, because this may allow moving beyond pure pattern recognition towards causal inference. In an interdisciplinary setting including ecologists, data scientists, computational linguists and philosophers, the Resident Group at the Center for Interdisciplinary Research (ZiF) in Bielefeld titled “Mapping Evidence to Theory in Ecology: Addressing the Challenges of Generalization and Causality” explores ways for combining ecological theory represented in the form of causal network graphs with evidence found in scientific papers. The vision is that complex, multifactorial hypotheses about ecological mechanisms would become the basis of a digital atlas of knowledge, and in this atlas the available empirical evidence would be mapped on these hypotheses to allow for case-specific explanations and predictions.

			This has led to the EcoWeaver & TReK collaboration. To find out more, visit their [https://ecoweaver.hi-knowledge.org/ website].

Bootsa: Bootsa moved page Sites:Projects-ZiF to Sites:WWW/Projects/ZiF/description without leaving a redirect: Reorganise

2024-11-20T10:56:43Z

Bootsa moved page Sites:Projects-ZiF to Sites:WWW/Projects/ZiF/description without leaving a redirect: Reorganise

← Older revision	Revision as of 12:56, 20 November 2024
(No difference)

Bootsa: Focus => Resident

2024-10-27T08:05:21Z

Focus => Resident

← Older revision		Revision as of 10:05, 27 October 2024
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	Recent advances in data science and AI technology may offer novel ways of dealing with complexity in ecology and may allow the development of knowledge synthesis tools that can manage context dependence. Especially promising seems the idea to bring together advanced AI-based technologies with conceptual causal models, because this may allow moving beyond pure pattern recognition towards causal inference. In an interdisciplinary setting including ecologists, data scientists, computational linguists and philosophers, the ~~Focus~~ Group at the Center for Interdisciplinary Research (ZiF) in Bielefeld titled “Mapping Evidence to Theory in Ecology: Addressing the Challenges of Generalization and Causality” explores ways for combining ecological theory represented in the form of causal network graphs with evidence found in scientific papers. The vision is that complex, multifactorial hypotheses about ecological mechanisms would become the basis of a digital atlas of knowledge, and in this atlas the available empirical evidence would be mapped on these hypotheses to allow for case-specific explanations and predictions.		Recent advances in data science and AI technology may offer novel ways of dealing with complexity in ecology and may allow the development of knowledge synthesis tools that can manage context dependence. Especially promising seems the idea to bring together advanced AI-based technologies with conceptual causal models, because this may allow moving beyond pure pattern recognition towards causal inference. In an interdisciplinary setting including ecologists, data scientists, computational linguists and philosophers, the Resident Group at the Center for Interdisciplinary Research (ZiF) in Bielefeld titled “Mapping Evidence to Theory in Ecology: Addressing the Challenges of Generalization and Causality” explores ways for combining ecological theory represented in the form of causal network graphs with evidence found in scientific papers. The vision is that complex, multifactorial hypotheses about ecological mechanisms would become the basis of a digital atlas of knowledge, and in this atlas the available empirical evidence would be mapped on these hypotheses to allow for case-specific explanations and predictions.

Bootsa: Import from Website Content Document

2024-10-18T09:57:42Z

Import from Website Content Document

New page

Recent advances in data science and AI technology may offer novel ways of dealing with complexity in ecology and may allow the development of knowledge synthesis tools that can manage context dependence. Especially promising seems the idea to bring together advanced AI-based technologies with conceptual causal models, because this may allow moving beyond pure pattern recognition towards causal inference. In an interdisciplinary setting including ecologists, data scientists, computational linguists and philosophers, the Focus Group at the Center for Interdisciplinary Research (ZiF) in Bielefeld titled “Mapping Evidence to Theory in Ecology: Addressing the Challenges of Generalization and Causality” explores ways for combining ecological theory represented in the form of causal network graphs with evidence found in scientific papers. The vision is that complex, multifactorial hypotheses about ecological mechanisms would become the basis of a digital atlas of knowledge, and in this atlas the available empirical evidence would be mapped on these hypotheses to allow for case-specific explanations and predictions.