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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.

The dataset is related to the paper "Explaining Inconsistencies in the Education Distributions of Ten Cross-National Surveys – The Role of Methodological Survey Characteristics" (Ortmanns, 2020). The paper compares the education distributions referring to the International Standard Classification of Education (ISCED variable) of nine cross-national surveys with the European Union Labour Force Survey, which is used as a benchmark. The paper identifies large inconsistencies in the education distribution across surveys and therefore, it examines how methodological survey characteristics may explain these inconsistencies. The paper refers to the Total Survey Error framework (TSE) and analyses survey characteristics relating to different kinds of errors. This dataset includes the education distribution and 15 survey characteristics of ten cross-national surveys.

Answers to cognitive probes implemented in (cross-national) web surveys ("web probing")
(1) Replication of closed-ended items on attitudes towards immigrants from the International Social Survey Program (ISSP), addition of a follow-up probe:
Immigrants increase crime rates.
Immigrants are generally good for Germany's economy.
Immigrants take jobs away from people who were born in Germany.
Immigrants improve German society by bringing in new ideas and cultures.
Probe after each item (split): Which type of immigrants were you thinking of when you answered the question?

The study and results are described in detail here:
Braun, Michael, Dorothée Behr, and Lars Kaczmirek. 2013. "Assessing cross-national equivalence of measures of xenophobia: evidence from probing in web surveys." International Journal of Public Opinion Research 25 (3): 383-395. doi: http://dx.doi.org/10.1093/ijpor/eds034.

(2) Replication of item on civil disobedience from the ISSP, addition of a follow-up probe:
How important is it that citizens may engage in acts of civil disobedience when they oppose government actions? (Not at all important 1 ... Very important 7)
Probe: What ideas do you associate with the phrase "civil disobedience"? Please give examples.

The study and results are described in detail here:
Behr, Dorothée, Michael Braun, Lars Kaczmirek, and Wolfgang Bandilla. 2014. "Item comparability in cross-national surveys: results from asking probing questions in cross-national web surveys about attitudes towards civil disobedience." Quality & Quantity 48 (1): 127-148. doi: http://dx.doi.org/10.1007/s11135-012-9754-8.

A newer version of this dataset is available at https://doi.org/10.7802/1.1954

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Social Network Science (SNS) is the field concerned with studying social systems in a relational way from the perspectives of the social and natural sciences. This data set consists of 25,760 biographical records retrieved from the Web of Science, ranging from 1916 to 2012. Each publication belongs to one of five subfields. To facilitate analyses of the social aspect of SNS, the names of 45,580 distinct authors are provided, linked to the papers in 68,227 publication-author relations. Author names have been disambiguated semi-automatically. To enable analyses of the cultural aspect of SNS, 23,026 distinct linguistic concepts are provided. These concepts resemble words or word combinations extracted from titles (for all publication years) and from abstracts and author keywords (only for publications published after 1990/1991). They are linked to the papers in 202,181 publication-concept relations.

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

Portrayals of history are never complete, and each description inherently exhibits a specific view-
point and emphasis. In this work, we automatically identified such differences by computing time-
lines and detecting temporal focal points of written history across languages on Wikipedia. In
particular, we studied articles related to the history of all UN member states and compared them in
30 language editions. We developed a computational approach that allows to identify focal points
quantitatively, and found that Wikipedia narratives about national histories (i) are skewed towards
more recent events (recency bias) and (ii) are distributed unevenly across the continents with sig-
nificant focus on the history of European countries (Eurocentric bias). Thus, our work explored
how colonial ties shape popular historiography on Wikipedia. We also established that national
historical timelines vary across language editions, although average interlingual consensus is rather
high. We hope that this work provides a starting point for a broader computational analysis of
written history on Wikipedia and elsewhere.

This dataset contains information about politicians from DBpedia, a crowd-sourced community effort to extract structured information from Wikipedia and make this information available on the Web. Some important information about people that is available on DBpedia are name, gender, nationality, occupation, birth date, death date, profession and for many politicians also the political party they belong to. This dataset is based on the English DBpedia dump from October 2015 and documents the temporal evolution of the hyperlink network that articles about politicians formed on Wikipedia between 2001 and 2016 every month. Wikipedia maintains revisions for each article to keep track of the changes over time. The first revision of each month was used to construct the hyperlink network between articles about politicians.

This codebook documents the harmonization of the Comparative Study of Electoral Systems Module 3 and the 2010 British Election Study internet data.

This data set contains individual search sessions from the transaction log of the academic search engine sowiport (www.sowiport.de). The data was collected over a period of one year (between 2nd April 2014 and 2nd April 2015). The web server log files and specific javascript-based logging techniques were used to capture the usage behaviour within the system. All activities are mapped to a list of 58 actions. This list covers all types of activities and pages that can be carried out/visited within the system (e.g. typing a query, visiting a document, selecting a facet, etc.). For each action, a session id, the date stamp and additional information (e.g. queries, document ids, and result lists) are stored. The session id is assigned via browser cookie and allows tracking user behaviour over multiple searches. Based on the session id and date stamp, the step in which an action is conducted and the length of the action is included in the data set as well. The data set contains 558,008 individual search sessions and a total of 7,982,427 logs entries. The average number of actions per search session is 7.


This work was funded by Deutsche Forschungsgemeinschaft (DFG), grant no. MA 3964/5-1; the AMUR project at GESIS.

Lists of Twitter acccounts of U.S. political elites from government, news media and political parties. Metadata on number of followers (as of December 2015), date of account registration and Twitter ID.
List of news media actors contains major newspapers and national TV stations as well as politically influential media persons and individual journalists. List of politicians contains all members of U.S. congress, governors, presidential candidates and main accounts of the Republican and Democratic parties. Metadata on politicians contains political office and party affiliation.
List of government actors contains federal agencies, secretaries, the President and affiliated accounts.

This data set contains a random sample of 1,800 user queries taken from the transaction log of the academic search engine sowiport (www.sowiport.de). The queries (mainly German query terms) were extracted from a larger set of randomly chosen user sessions which have been recorded in sowiport between September 1st 2014 and March 1st 2015. To reduce noise in the data set, we selected sessions in which at least two different searches were conducted and at least one document has been clicked on. In addition, we excluded all searches for numbers (mostly ISSN numbers). The selected queries were sorted randomly and manually assessed by a domain expert. The randomness was introduced to reduce potential biases in the assessment. When assessing multiple queries from one session, the previous queries might influence the decision for following queries as they are then evaluated within a context. The 1,800 user queries were categorized into the 29 facets of the subject categories used in the Thesaurus Social Sciences (TheSoz, see http://lod.gesis.org/thesoz/en.html and http://sowiport.gesis.org/thesaurus?view=systematic&lang=en). It was allowed to apply multiple facets, as some queries cover multiple topics.


This work was funded by Deutsche Forschungsgemeinschaft (DFG), grant no. MA 3964/5-1;the AMUR project at GESIS.

Rapid Miner Process files and XML test set including the predicted labels for the Linked Data Mining Challenge 2015.

Replication code for presentation at the EGU24, using ISSP 2020 data: see https://meetingorganizer.copernicus.org/EGU24/EGU24-3191.html

Attitudes on climate change and the protection of the environment have been found to relate in different ways to the current economic and social situation of the respondents. This presentation will describe people's attitudes by analyzing surveys on the topic of climate change and the protection of the environment, including the recent International Social Survey Programme (ISSP) and the Swiss Environmental Panel Study (due to time restrictions, the presentation only shows ISSP data results). A closer look will be taken at the economic opinions and willingness to pay higher prices or taxes and their relationship to climate change attitudes. In addition, respondent's trust in people and different institutions will be analyzed. A structural equation analysis is performed to highlight the relations between those concepts. The results will show that support for a better economy and private enterprises are related to lower environmental and climate change concerns, support for paying higher prices or taxes is related to more environmental concerns and higher trust in people and institutions is related to deeper environmental concerns. After that, several demographic characteristics will be used to show if the results are stable when controlling for these. Demographic variables used are age, gender, education level, employment status, income, and political left-right placement. It can be shown that the factors of economic opinions, willingness to pay, and trust in people and institutions all relate to the environmental and climate change attitudes.

This Python script can be used to rehydrate Tweets from Tweet IDs. It uses the Twython library to access the Twitter API.
The script was built to support rehydration for the project "Geotagged Twitter posts from the United States: A tweet collection to investigate representativeness" by Jürgen Pfeffer and Fred Morstatter. Argument is a zip file with text files in it with tweed ids (one per line).
Output is a text file with number of found tweets per county.

Election studies are an important data pillar in political and social science, as most political research investigations involve secondary use of existing datasets. Researchers depend on high-quality data because data quality determines the accuracy of the conclusions drawn from statistical analyses. We outline data reuse quality criteria pertaining to data accessibility, metadata provision, and data documentation using the FAIR Principles of research data management as a framework. We then investigate the extent to which a selection of election studies fulfils these criteria using studies from Western democracies. Our results reveal that although most election studies are easily accessible and well documented and that the overall level of data processing is satisfactory, some important deficits remain. Further analyses of technical documentation indicate that while a majority of election studies provide the necessary documents, there is still room for improvement.