The Effects of Perspective Taking Implementing Intentions on Employee Evaluations and Hostile Sexism

The current research examined whether gender bias in the workplace could be reduced through perspective taking implementation intentions, which are if–then statements that specify how to accomplish goals (Gollwitzer, 1999). Amazon MTurk participants (N = 180, 53% male) learned they would complete a two-step performance review for a consulting company. Prior to receiving a male or female employee’s record, all participants were given a goal strategy to be fair in their review, with half also receiving an if–then strategy that encouraged perspective taking. Participants rated the employee on three work related dimensions (skillset, performance, and traits), provided an overall promotion recommendation, and completed the Ambivalent Sexism Inventory (Glick & Fiske, 1996). Although we did not find evidence of gender bias on the work measures, we found that the implementation intention strategy resulted in more positive employee evaluations overall and less hostile sexism than a simple goal strategy. We discuss the potential organizational benefits of employing perspective taking implementation intentions

Collaborative development of the Arrowsmith two node search interface designed for laboratory investigators.

Arrowsmith is a unique computer-assisted strategy designed to assist investigators in detecting biologically-relevant connections between two disparate sets of articles in Medline. This paper describes how an inter-institutional consortium of neuroscientists used the UIC Arrowsmith web interface http://arrowsmith.psych.uic.edu in their daily work and guided the development, refinement and expansion of the system into a suite of tools intended for use by the wider scientific community

A Guide to the Brain Initiative Cell Census Network Data Ecosystem

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain

Collaborative development of the Arrowsmith two node search interface designed for laboratory investigators

Arrowsmith is a unique computer-assisted strategy designed to assist investigators in detecting biologically-relevant connections between two disparate sets of articles in Medline. This paper describes how an inter-institutional consortium of neuroscientists used the UIC Arrowsmith web interface 'http://arrowsmith.psych.uic.edu' in their daily work and guided the development, refinement and expansion of the system into a suite of tools intended for use by the wider scientific community

Journal of Biomedical Discovery and Collaboration

Collaborative development of the Arrowsmith two node search interface designed for laboratory investigator

A guide to the BRAIN Initiative Cell Census Network data ecosystem

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain