The Need for Novel Informatics Tools for Integrating and Planning Research in Molecular and Cellular Cognition

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Title: The Need for Novel Informatics Tools for Integrating and Planning Research in Molecular and Cellular Cognition
Language: English
Authors: Silva, Alcino J., Müller, Klaus-Robert
Source: Learning & Memory. Sep 2015 22(9):494-498.
Availability: Cold Spring Harbor Laboratory Press. 500 Sunnyside Boulevard, Woodbury, NY 11797-2924. Tel: 800-843-4388; Tel: 516-367-8800; Fax: 516-422-4097; e-mail: cshpres@cshl.edu; Web site: http://www.learnmem.org/
Peer Reviewed: Y
Page Count: 5
Publication Date: 2015
Document Type: Journal Articles
Reports - Descriptive
Descriptors: Molecular Biology, Molecular Structure, Neurosciences, Neurology, Physiology, Cognitive Processes, Biological Sciences, Cytology, Interdisciplinary Approach, Neurological Organization, Research Methodology
DOI: 10.1101/lm.029355.112
ISSN: 1072-0502
Abstract: The sheer volume and complexity of publications in the biological sciences are straining traditional approaches to research planning. Nowhere is this problem more serious than in molecular and cellular cognition, since in this neuroscience field, researchers routinely use approaches and information from a variety of areas in neuroscience and other biology fields. Additionally, the multilevel integration process characteristic of this field involves the establishment of experimental connections between molecular, electrophysiological, behavioral, and even cognitive data. This multidisciplinary integration process requires strategies and approaches that originate in several different fields, which greatly increases the complexity and demands of this process. Although causal assertions, where phenomenon A is thought to contribute or relate to B, are at the center of this integration process and key to research in biology, there are currently no tools to help scientists keep track of the increasingly more complex network of causal connections they use when making research decisions. Here, we propose the development of semiautomated graphical and interactive tools to help neuroscientists and other biologists, including those working in molecular and cellular cognition, to track, map, and weight causal evidence in research papers. There is a great need for a concerted effort by biologists, computer scientists, and funding institutions to develop maps of causal information that would aid in integration of research findings and in experiment planning.
Abstractor: As Provided
Entry Date: 2015
Accession Number: EJ1072254
Database: ERIC
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  Data: Cold Spring Harbor Laboratory Press. 500 Sunnyside Boulevard, Woodbury, NY 11797-2924. Tel: 800-843-4388; Tel: 516-367-8800; Fax: 516-422-4097; e-mail: cshpres@cshl.edu; Web site: http://www.learnmem.org/
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  Data: The sheer volume and complexity of publications in the biological sciences are straining traditional approaches to research planning. Nowhere is this problem more serious than in molecular and cellular cognition, since in this neuroscience field, researchers routinely use approaches and information from a variety of areas in neuroscience and other biology fields. Additionally, the multilevel integration process characteristic of this field involves the establishment of experimental connections between molecular, electrophysiological, behavioral, and even cognitive data. This multidisciplinary integration process requires strategies and approaches that originate in several different fields, which greatly increases the complexity and demands of this process. Although causal assertions, where phenomenon A is thought to contribute or relate to B, are at the center of this integration process and key to research in biology, there are currently no tools to help scientists keep track of the increasingly more complex network of causal connections they use when making research decisions. Here, we propose the development of semiautomated graphical and interactive tools to help neuroscientists and other biologists, including those working in molecular and cellular cognition, to track, map, and weight causal evidence in research papers. There is a great need for a concerted effort by biologists, computer scientists, and funding institutions to develop maps of causal information that would aid in integration of research findings and in experiment planning.
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