scholarly journals Assessment of gender divide in scientific communities

2021 ◽  
Vol 126 (5) ◽  
pp. 3807-3840
Author(s):  
Antonio De Nicola ◽  
Gregorio D’Agostino
Keyword(s):  
Gender Differences ◽  
Social Networks ◽  
Data Analytics ◽  
Complexity Science ◽  
Science Methods ◽  
Social Phenomenon ◽  
Dimensional Approach ◽  
Scientific Communities ◽  
Scientific Disciplines ◽  
Using Data

AbstractIncreasing evidence of women’s under-representation in some scientific disciplines is prompting researchers to expand our understanding of this social phenomenon. Moreover, any countermeasures proposed to eliminate this under-representation should be tailored to the actual reasons for this different participation. Here, we take a multi-dimensional approach to assessing gender differences in science by representing scientific communities as social networks, and using data analytics, complexity science methods, and semantic methods to measure gender differences in the context, the attitude and the success of scientists. We apply this approach to four scientific communities in the two fields of computer science and information systems using the network of authors at four different conferences. For each discipline, one conference is based in Italy and attracts mostly Italians, while one conference is international in both location and participants. The present paper provides evidence against common narratives that women’s under-representation is due to women’s limited skills and/or less social centrality.

scholarly journals Social Networks, Support, and Depressive Symptoms: Gender Differences among Clergy

2019 ◽  
Vol 5 ◽  
pp. 237802311987382
Author(s):  
Jennifer Lutz ◽  
David E. Eagle
Keyword(s):  
Gender Differences ◽  
Social Networks ◽  
Depressive Symptoms ◽  
Negative Relationship ◽  
Positive Association ◽  
Psychological Research ◽  
Significant Positive Association ◽  
Socially Isolated ◽  
Using Data ◽  
Perceived Social Isolation

This study extends social-psychological research on social networks and mental health by examining cross-gender differences in social integration and depression among United Methodist clergy in North Carolina. Using data from the fifth wave of the Clergy Health Initiative panel survey, we used cross-group models to examine the association of depressive symptoms and network in-degree, out-degree, and perceived social isolation among men (N = 1,145) and women (N = 535) clergy. The analysis reveals gendered differences in this association. Specifically, out-degree bore a significant negative relationship with depressive symptoms for men but not women. Feeling socially isolated had a significant positive association with depression in both men and women.

scholarly journals The extent and drivers of gender imbalance in neuroscience reference lists

2020 ◽  
Author(s):  
Jordan D. Dworkin ◽  
Kristin A. Linn ◽  
Erin G. Teich ◽  
Perry Zurn ◽  
Russell T. Shinohara ◽  
...  
Keyword(s):  
Social Networks ◽  
Gender Bias ◽  
Career Advancement ◽  
Reference Lists ◽  
Gender Imbalance ◽  
Downstream Effects ◽  
Scientific Disciplines ◽  
Conference Participation ◽  
Citation Practices ◽  
Using Data

Like many scientific disciplines, neuroscience has increasingly attempted to confront pervasive gender imbalances within the field. While much of the conversation has centered around publishing and conference participation, recent research in other fields has called attention to the prevalence of gender bias in citation practices. Because of the downstream effects that citations can have on visibility and career advancement, understanding and eliminating gender bias in citation practices is vital for addressing inequity in a scientific community. In this study, we sought to determine whether there is evidence of gender bias in the citation practices of neuroscientists. Using data from five top neuroscience journals, we find that reference lists tend to include more papers with men as first and last author than would be expected if gender were not a factor in referencing. Importantly, we show that this overcitation of men and undercitation of women is driven largely by the citation practices of men, and is increasing over time as the field becomes more diverse. We develop a co-authorship network to assess homophily in researchers’ social networks, and we find that men tend to overcite men even when their social networks are representative. We discuss possible mechanisms and consider how individual researchers might address these findings in their own practices.

Energy Prediction Using Data Analytics in Smart Grid

2018 ◽  
Vol 06 (06) ◽  
pp. 110-115
Author(s):  
Panchami Anil ◽  
Anas P V ◽  
Naseef Kuruvakkottil ◽  
Anusha K V ◽  
Balagopal N
Keyword(s):  
Smart Grid ◽  
Data Analytics ◽  
Energy Prediction ◽  
Using Data

Quality Management Using Data Analytics: An Application to Pharmaceutical Regulation

2015 ◽  
Author(s):  
Vishal Ahuja ◽  
John R. Birge ◽  
Chad Syverson ◽  
Elbert S. Huang ◽  
Min-Woong Sohn
Keyword(s):  
Quality Management ◽  
Data Analytics ◽  
Pharmaceutical Regulation ◽  
Using Data

The True Believers

2018 ◽  
Author(s):  
Barbara J. Risman
Keyword(s):  
Gender Differences ◽  
Social Networks ◽  
Social Life ◽  
Gender Ideology ◽  
Macro Level ◽  
Individual Level ◽  
Gender Differentiation ◽  
The World ◽  
The Individual ◽  
Gendered Expectations

This is the first data chapter. In this chapter, respondents who are described as true believers in the gender structure, and essentialist gender differences are introduced and their interviews analyzed. They are true believers because, at the macro level, they believe in a gender ideology where women and men should be different and accept rules and requirements that enforce gender differentiation and even sex segregation in social life. In addition, at the interactional level, these Millennials report having been shaped by their parent’s traditional expectations and they similarly feel justified to impose gendered expectations on those in their own social networks. At the individual level, they have internalized masculinity or femininity, and embody it in how they present themselves to the world. They try hard to “do gender” traditionally.

Organizing and Managing Data Analytics Teams for Procurement

2020 ◽  
Author(s):  
Benjamin Shao ◽  
Robert D. St. Louis
Keyword(s):  
Best Practices ◽  
Business Strategy ◽  
Data Analytics ◽  
Team Members ◽  
Procurement Process ◽  
Using Data ◽  
And Training ◽  
Different Levels ◽  
The Way

Many companies are forming data analytics teams to put data to work. To enhance procurement practices, chief procurement officers (CPOs) must work effectively with data analytics teams, from hiring and training to managing and utilizing team members. This chapter presents the findings of a study on how CPOs use data analytics teams to support the procurement process. Surveys and interviews indicate companies are exhibiting different levels of maturity in using data analytics, but both the goal of CPOs (i.e., improving performance to support the business strategy) and the way to interact with data analytics teams for achieving that goal are common across companies. However, as data become more reliably available and technologies become more intelligently embedded, the best practices of organizing and managing data analytics teams for procurement will need to be constantly updated.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

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