Tag Association Protocol
Tag Association Protocol - Related Videos
10:10
The Implicit Association Test
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2023
Source: Julian Wills & Jay Van Bavel—New York University One of the core constructs in social psychology is the notion of an attitude toward an object or person. Traditionally, psychologists measured attitudes by simply asking people to self-report their beliefs, opinions, or feelings. This approach has limitations, however, when measuring socially sensitive attitudes, like racial prejudice, because people are often motivated to self-report unprejudiced, egalitarian beliefs (despite...
11:26
High-throughput Gene Tagging in Trypanosoma brucei
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Cited by 18 •
2016
Addition of a tag to a protein is a powerful way of gaining insight into its function. Here, we describe a protocol to endogenously tag hundreds of Trypanosoma brucei proteins in parallel such that genome scale tagging is achievable.
11:29
Investigation of Synaptic Tagging/Capture and Cross-capture using Acute Hippocampal Slices from Rodents
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Cited by 50 •
2015
This video article describes experimental procedures to study long-term plasticity and its associative processes such as synaptic tagging, capture and cross-tagging in the CA1 pyramidal neurons using acute hippocampal slices from rodents.
10:32
Orthogonal Protein Purification Facilitated by a Small Bispecific Affinity Tag
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Cited by 7 •
2012
A novel and highly efficient two-step affinity chromatography protocol has been developed and is described in detail. The method is based on a small purification tag with two inherent affinities and is applicable to a wide range of target proteins with different properties.
07:44
High-throughput Purification of Affinity-tagged Recombinant Proteins
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Cited by 1 •
2012
We describe a method for the affinity-tagged purification of recombinant proteins using liquid-handling robotics. This method is generally applicable to the small-scale purification of soluble His-tagged proteins in a high-throughput format.
FAQs
Standardized categories give researchers a shared vocabulary for describing observed events. By assigning consistent labels to comparable behaviors, the protocol reduces ambiguity that might otherwise arise when complex records are reviewed or compared. This consistency makes behavioral annotations easier to organize and supports more reliable analysis across observations, subjects, contexts, and time points.
A behavior tag becomes more informative when its surrounding conditions are recorded with it. Associating labels with particular subjects, contexts, or time points preserves the circumstances in which an event occurred. Researchers can then examine whether tagged behaviors correspond with differences among subjects, changes over time, or environmental and experimental conditions.
Once observations receive consistent tags, researchers can organize behavioral records into comparable groups and examine the occurrence or distribution of labeled events. This structure supports pattern recognition and quantitative analysis rather than relying only on narrative descriptions. The resulting comparisons can help reveal relationships between behaviors and the conditions under which they were observed.
Researchers should first define the behavior categories that the tags will represent and determine how those labels relate to observations or recorded events. They should also identify the relevant subjects, contexts, and time points to associate with each tag. Establishing these elements in advance creates a consistent framework for subsequent annotation and comparison.
During annotation, researchers review an observation or recorded event, select the standardized label that matches the defined behavior category, and associate it with relevant identifying information. That information may include the subject, context, or time point. Repeating this process across records produces an organized dataset suitable for comparing behavioral patterns across experiments.
This protocol is useful when behavioral studies generate complex observational records that must be compared systematically. It can help organize annotations across experiments, clarify ambiguous descriptions, and support searches for patterns in relation to environmental or experimental conditions. Researchers may therefore use it to connect individual behavioral events with broader trends in their study data.