Suchergebnisse

A newer version of this dataset is available at https://doi.org/10.7802/1.1954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

Systematic research reviews have become essential in all empirical sciences. However, the validity of research syntheses is threatened if the preparation, submission or publication of research findings depends on the statistical significance of these findings. The present study investigates publication bias in three top-tier journals in the German social sciences, utilizing the caliper test. For the period between 2001 and 2010, we have collected 156 articles that appeared in the Kölner Zeitschrift für Soziologie und Sozialpsychologie (KZfSS), the Zeitschrift für Soziologie (ZfS) and the Politische Vierteljahresschrift (PVS). In all three journals, we found empirical evidence for the existence of a publication bias at the 10% level. We also investigated possible causes linked to this bias, including single versus multiple authorship as well as academic degree. We find only weak support for the relationships between individual author characteristics and publication bias.

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 in, or after, 1990). They are linked to the papers in 201,608 publication-concept relations.

The B-STA-R dataset provides information on more than 1138 bilateral agreements on cooperation in matters of science and technology, mostly concluded by countries of the G20 and OECD in the period between 1937 and 2020. These science and technology agreements (STA) are intergovernmental treaties in which the participating countries establish a framework for bilateral cooperation in science and technology (S&T). The files associated with the dataset contain information on the concluding countries, dates of signing, and entry into force. Moreover, links to the full texts of the agreements are provided where possible. B-STA-R is meant to inform studies from the social sciences and humanities that deal with the topic of international science policy, science diplomacy, innovation studies, foreign policy analysis, history of S&T, globalization and diffusion studies, etc.

The LATINNO Dataset comprises data on 3700 democratic innovations evolved in 18 countries in Latin America. The data reflects the context, the institutional design, and the impact of democratic innovations from 1990 to 2020.

This dataset comprises initiatives from civil society organizations to respond to the COVID-19 pandemic in 18 countries in Latin America. This dataset complements the "LATINNO Dataset on Democratic Innovations in Latin America" and the "Collective Intelligence Initiatives against COVID-19 in Latin America Dataset", which only include cases that fulfill three criteria: direct citizen participation, design able to impact on policy cycle, and aim to enhance democracy. Case descriptions are provided only in Spanish and Portuguese.

This dataset comprises a selection of democratic innovations from the "LATINNO Dataset on Democratic Innovations in Latin America" which rely on collective intelligence to respond to problems resulting from the COVID-19 pandemic in 18 countries of Latin America. It complements the "Civil Society Responses to COVID-19 in Latin America Dataset" in which it comprises also governmental responses.

Young Germany during COVID-19 is a youth study, which focuses on young people aged 16 to 26 years living in Germany. The study has been conducted in September 2020 and is part of a larger series called Young Europe – The Youth Study of TUI Foundation which aims to shed light on the lifeworld, identity, and attitudes of young Europeans. In the September 2020 wave, 1.011 young adults (age 16-26) participated via an online survey provided by the opinion research institute YouGov. Further, an additional sample of 2.025 older adults has answered specific questions to create comparability. The survey's main focus is the situation of young adults in Germany during the Coronavirus pandemic, especially their compliance with the restrictions and measurements taken by the government and the youth's individual motivations to comply. The survey asks which measurements are particularly difficult and where do young people see the potential for societal conflict. These specific questions regarding the pandemic have then been supplemented by questions regarding the political representation of the youth, opinions on European integration and EU issues, as well as on the EU's joint debt borrowing (Next Generation EU).

The project assesses the use of Big Data in teaching in the social sciences.

The dataset contains information on the number of new house buildings, reconstructions and retail sales (Einzelhandelsumsatz) on the national level for the years 1952 to 1989. For the years 1955 to 1989 the data also contains information on the canton (Bezirk) level. The information is based on the Statistical Yearbooks of the German Democratic Republic.

The increasing density and entanglement of international law and institutions leads to a growing potential for collisions between norms and rules emanating from different international institutions. It is an open question, however, when actors actually create manifest conflicts about overlapping norms and rules and how – and with what consequences – such conflicts are handled. We therefore utilize the concept of "interface conflicts" (ICs) in which two or more actors express positional differences over the scope or prevalence of different international norms. Building on the findings of the DFG research group OSAIC, we introduce the Interface Conflicts 1.0 dataset, which assembles information on 78 ICs. The dataset provides information on the actors and norms at stake in ICs and focuses specifically on their subsequent handling. It distinguishes co-operative from non-cooperative conflict management and codes the institutional as well as distributional outcomes of all management efforts.

Die Studie ist Teil einer international vergleichenden Studie (vgl. ZA-Studien-Nr. 3430)

The advent of deepfakes – the manipulation of audio records, images and videos based on deep learning techniques – has important implications for science and society. Current studies focus primarily on the detection and dangers of deepfakes. In contrast, less attention is paid to the potential of this technology for substantive research – particularly as an approach for controlled experimental manipulations in the social sciences. In this paper, we aim to fill this research gap and argue that deepfakes can be a valuable tool for conducting social science experiments. To demonstrate some of the potentials and pitfalls of deepfakes, we conducted a pilot study on the effects of physical attractiveness on student evaluations of teachers. To this end, we created a deepfake video varying the physical attractiveness of the instructor as compared to the original video and asked students to rate the presentation and instructor. First, our results show that social scientists without special knowledge in computational science can successfully create a credible deepfake within reasonable time. Student ratings of the quality of the two videos were comparable and students did not detect the deepfake. Second, we use deepfakes to examine a substantive research question: whether there are differences in the ratings of a physically more and a physically less attractive instructor. Our suggestive evidence points towards a beauty penalty. Thus, our study supports the idea that deepfakes can be used to introduce systematic variations into experiments while offering a high degree of experimental control. Finally, we discuss the feasibility of deepfakes as an experimental manipulation and the ethical challenges of using deepfakes in experiments. This is the provision of the data and code.

Keywords: deepfakes, face swap, deep learning, experiment, physical attractiveness, student evaluations of teachers