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Electronic Dance Music (EDM) is a music style as well as a global creative industry. Performing live and releasing music are the main practices in this field. This dataset tracks the careers of about 4000 artists in EDM from 2001 to 2015. It is based on a collection of artist gigographies (who performed where and when) and discographies (who released what and when) taken and combined from three online platforms (Resident Advisor, Discogs, and Juno Download). Each career point is represented by year, career stage, cohort, the number of gigs, number of performances, and 16 scores quantifying four types of positions (global closure, local closure, global openness, local openness) in four analytical networks (artist co-gig, artist co-venue, artist co-style, and artist co-label). In addition, the artist's success at each career point based on travel distances is given. Details on the variables and its construction can be found in the publication referenced below.

This dataset describes the temporal evolution of collaborations in Computer Science based on papers that have been published between 1970 to 2016. It contains several data files in CSV format which embody information such as the author of publications for each year, its citations and the papers it is cited by. It also contains the inferred gender information of all authors. The publication and citation details are taken from DBLP and Aminer datasets respectively. This dataset along with the gender information is obtained from the methodology as specified in the 'Data' section of following paper https://arxiv.org/pdf/1704.05801.pdf

Scientific collaborations shape ideas as well as innovations and are both the substrate for, and the outcome of, academic careers. Recent studies show that gender inequality is still present in many scientific practices ranging from hiring to peer-review processes and grant applications. In this work, we investigate gender-specific differences in collaboration patterns of more than one million computer scientists over the course of 47 years. We explore how these patterns change over years and career ages and how they impact scientific success. Our results highlight that successful male and female scientists reveal the same collaboration patterns: compared to scientists in the same career age, they tend to collaborate with more colleagues than other scientists, seek innovations as brokers and establish longer-lasting and more repetitive collaborations. However, women are on average less likely to adopt the collaboration patterns that are related with success, more likely to embed into ego networks devoid of structural holes, and they exhibit stronger gender homophily as well as a consistently higher dropout rate than men in all career ages.

Wikipedia is a community-created encyclopedia that contains information about notable people from different countries, epochs and disciplines and aims to document the world's knowledge from a neutral point of view. However, the narrow diversity of the Wikipedia editor community has the potential to introduce systemic biases such as gender biases into the content of Wikipedia. In this paper we aim to tackle a sub problem of this larger challenge by presenting and applying a computational method for assessing gender bias on Wikipedia along multiple dimensions. We find that while women on Wikipedia are covered and featured well in many Wikipedia language editions, the way women are portrayed starkly differs from the way men are portrayed. We hope our work contributes to increasing awareness about gender biases online, and in particular to raising attention to the different levels in which gender biases can manifest themselves on the web.