List of Top Disciplines of University of Mannheim sorted by citations.

This chart compares annual peer-reviewed article output with citations to peer-reviewed articles published in the same year for University of Mannheim. These citations refer to peer-reviewed articles published in year X, not the total citations received in year X.

This replication package contains the experimental data and code from a study investigating how elements of a survey invitation message targeted to businesses influence their participation in a self-administered web survey. The experiment was conducted in collaboration with the German Business Panel (GBP) during its fifth survey wave, spanning from August 16, 2022, to November 25, 2022. A full factorial design was implemented, varying five key components of the email invitation. Unlike conventional experimental setups with static group assignments, the study employed adaptive randomization, wherein a Bayesian learning algorithm sequentially allocated more observations to invitation messages exhibiting higher survey starting rates. Over the 15-week experimental period, 738,598 invitation messages were distributed to business contacts, of which 176,000 were opened within one week. A total of 7,833 recipients initiated the survey, and 3,733 completed it. The dataset includes detailed records of message distribution, survey engagement metrics, and adaptive randomization adjustments, providing a comprehensive basis for analyzing the effectiveness of invitation design in business survey participation. Keywords: Adaptive Randomization, Reinforcement Learning, Nonresponse, Email Invitation, Web Survey, Firm Survey, Organizational Survey

📍 Looking for high-quality oil & gas industry data? ISTARI.AI offers tailored POI datasets to fit your exact business needs – whether you’re looking for all oil & gas exploration/refining operations, equipment manufacturers, Consultants, sub suppliers, service providers, or other specific type of location-based business.

📊 Our POI data includes: - Organizational structure & key personnel - Products, services & partnerships - Verified contact & domain info - Tech stack & business descriptions - Detailed geographic data (address, region, country)

We don’t offer one-size-fits-all datasets – instead, you tell us what you need.

This flexibility makes our data ideal for use cases in: - Location-based services & apps - Market analysis & competitive intelligence - Retail expansion & site planning - Ad targeting & geofencing - Lead generation & B2B outreach

All POI data is machine-generated, frequently updated, and sourced from publicly available web data, ensuring high freshness and consistency. With ISTARI.AI, you receive structured POI datasets ready for direct integration into your systems.

✅ Ensuring Data Quality - The webAI AI Agent was developed in close collaboration with academic experts to guarantee expert-level accuracy. - Developed together with researchers at the University of Mannheim - Validated in the award-winning academic study: "When is AI Adoption Contagious? Epidemic Effects and Relational Embeddedness in the Inter-Firm Diffusion of Artificial Intelligence" - Co-authored by scholars from University of Mannheim, University of Giessen, University of Hohenheim, and ETH Zurich

List of Top Authors of University of Mannheim sorted by PR articles.

Photogrammetry scan of inner courtyard of castle Mannheim (now the University of Mannheim). Processed using RealityCapture.

Source: Objaverse 1.0 / Sketchfab

The published syntax files (do- and inp-files) can be used to replicate the results reported in the cited article. To replicate the analyses, access the scientific use files of the full version of the CILS4EU-DE dataset (version 7.0.0): Wave 8, WaveCOVID, and Wave 9 can be requested from the GESIS Data Archive, Cologne, ZA6655 Data file Version 7.0.0, https://doi.org/10.4232/cils4eu-de.6655.7.0.0

The German Internet Panel (GIP) is a long-term study at the University of Mannheim. The GIP examines individual attitudes and preferences that are relevant in political and economic decision-making processes. To this end, more than 3,500 people throughout Germany have been regularly surveyed online every two months since 2012 on a wide range of topics. The GIP is based on a random sample of the general population in Germany between the ages of 16 and 75. The study started in 2012 and was supplemented by new participants in 2014 and 2018. The panel participants were recruited offline. The GIP questionnaires cover a variety of topics that deal with current events.

📍 Looking for high-quality mining industry data? ISTARI.AI offers tailored POI datasets to fit your exact business needs – whether you’re looking for all mining operations, equipment manufacturers, Consultants, sub suppliers, service providers, or other specific type of location-based business.

📊 Our POI data includes: - Organizational structure & key personnel - Products, services & partnerships - Verified contact & domain info - Tech stack & business descriptions - Detailed geographic data (address, region, country)

We don’t offer one-size-fits-all datasets – instead, you tell us what you need.

This flexibility makes our data ideal for use cases in: - Location-based services & apps - Market analysis & competitive intelligence - Retail expansion & site planning - Ad targeting & geofencing - Lead generation & B2B outreach

All POI data is machine-generated, frequently updated, and sourced from publicly available web data, ensuring high freshness and consistency. With ISTARI.AI, you receive structured POI datasets ready for direct integration into your systems.

✅ Ensuring Data Quality - The webAI AI Agent was developed in close collaboration with academic experts to guarantee expert-level accuracy. - Developed together with researchers at the University of Mannheim - Validated in the award-winning academic study: "When is AI Adoption Contagious? Epidemic Effects and Relational Embeddedness in the Inter-Firm Diffusion of Artificial Intelligence" - Co-authored by scholars from University of Mannheim, University of Giessen, University of Hohenheim, and ETH Zurich

Information about people and publications from the Research Group Data and Web Science at University of Mannheim

This dataset contains the vectors from computing RDF2vec embeddings from a uniformly weighted DBpedia 2016-04 graph.

The file has a group called "Vectors" which contains a dataset for each entity in the graph. The dataset name is the entity name and the dataset content is the embedded vector (length 200).

The parameter settings for the embedding are as specified in the paper:

Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279

The German General Personas is a dataset based on the survey data from the German General Social Survey (GGSS/ALLBUS) wave of 2023. The data converted the tabular survey data into a natural language format that is compatible with the use in Large Language Model (LLM) prompts. We designed a process for the systematic selection of survey variables featured in different versions of the GGP. Since the idea of the GGP is to be a general-purpose persona prompt collection, this persona attribute selection process aims at identifying the subsets of sociodemographic and additional attributes that are most important for explaining variation in human responses across a range of different topics.
The GGP data comprises a total of 20 versions, differing in format (key-value pairs and full-text versions) and level of included information (ten levels, ranging from only core socio-demographic information to all available survey variables). Each version includes all 5,246 participants surveyed in the GGSS/ALLBUS 2023 as persona descriptions, featuring varying numbers of the variables available in the GGSS/ALLBUScompact 2023 (ZA8831). The first version of the persona description format is a JSON-like key-value structure, where the questions or statements in the ALLBUScompact are the keys and their response options in text format are the corresponding values. To ensure that the response options (the variable labels are usually stored as integers in the tabular survey data) are interpretable for LLMs, we converted them back into the original text labels found in the survey codebook and interview question documentation. In addition to the fully structured key-value persona descriptions, we also provide more descriptive natural-language versions of the same personas. Each of these persona descriptions is created by prompting a proprietary, state-of-the-art LLM (Gemini-2.5-flash-lite) to turn the key-value persona description into a short textual description without adding, altering or omitting any of the provided information.

This dataset contains the vectors from computing RDF2vec embeddings from a object frequency weighted DBpedia 2016-04 graph.

For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.

The parameter settings for the embedding are as specified in the paper:

Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279

The SPARQL Query Language for RDF and the SPARQL Protocol for RDF are implemented by a growing number of storage systems and are used within enterprise and open web settings. As SPARQL is taken up by the community there is a growing need for benchmarks to compare the performance of storage systems that expose SPARQL endpoints via the SPARQL protocol. Such systems include native RDF stores, Named Graph stores, systems that map relational databases into RDF, and SPARQL wrappers around other kinds of data sources. The Berlin SPARQL Benchmark (BSBM) defines a suite of benchmarks for comparing the performance of these systems across architectures. The benchmark is built around an e-commerce use case in which a set of products is offered by different vendors and consumers have posted reviews about products. The benchmark query mix illustrates the search and navigation pattern of a consumer looking for a product. More information about BSBM can be found in: The project website at University of Mannheim: http://wbsg.informatik.uni-mannheim.de/bizer/berlinsparqlbenchmark/ Christian Bizer, Andreas Schultz. The Berlin SPARQL Benchmark. In Int. J. Semantic Web Inf. Syst. 5(2): 1-24 (2009). DOI: 10.4018/jswis.2009040101

📍 Looking for high-quality global data on tourism industry? ISTARI.AI provides comprehensive, ready-to-use datasets covering hotels, tourist agencies, travel agents, travel magazine, bars, and restaurants worldwide – including location, contact, and detailed business information.

📊 Our Tourism data includes: - Organizational structure & key personnel - Products, services & partnerships - Verified contact & domain information - Technology stack & business descriptions - Detailed geographic data (address, region, country)

Our datasets are ideal for: - Location-based services & apps - Market analysis & competitive intelligence - Retail expansion & site planning - Ad targeting & geofencing - Lead generation & marketing outreach

All data is machine-generated, frequently updated, and sourced from publicly available web data, ensuring high freshness and consistency.

✅ Ensuring Data Quality - Developed in close collaboration with academic experts to guarantee expert-level accuracy - Created together with researchers at the University of Mannheim - Validated in the award-winning academic study: "When is AI Adoption Contagious? Epidemic Effects and Relational Embeddedness in the Inter-Firm Diffusion of Artificial Intelligence" - Co-authored by scholars from the University of Mannheim, University of Giessen, University of Hohenheim, and ETH Zurich

With ISTARI.AI, you get structured, high-quality tourism datasets from across the globe – ready for direct integration into your systems.

The Immigration Policies in Comparison (IMPIC) project provides a set of sophisticated quantitative indices to measure immigration policies in most OECD countries and for the time period 1980-2018. For more information see the project webpage: http://www.impic-project.eu/ . An earlier version has been prepublished there.

This dataset comprises event logs (XES = Extensible Event Stream) regarding the activities of daily living performed by several individuals. The event logs were derived from sensor data which was collected in different scenarios and represent activities of daily living performed by several individuals. These include e.g., sleeping, meal preparation, and washing. The event logs show the different behavior of people in their own homes but also common patterns. The attached event logs were created with Fluxicon Disco ({http://fluxicon.com/disco/}).

This dataset contains the vectors from computing RDF2vec embeddings from a Page Rank weighted DBpedia 2016-04 graph.

For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.

The parameter settings for the embedding are as specified in the paper:

Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279

!!! This is the EPO/European version of the patccat classifier of patent claims. !!!

Note: We use the same approach that we use for USPTO patents. For a detailed description, see https://doi.org/10.5281/zenodo.6395307.

Data version: 3.4.0

Authors: Bernhard Ganglmair (University of Mannheim, Department of Economics, and ZEW Mannheim) W. Keith Robinson (Wake Forest University, School of Law) Michael Seeligson (Southern Methodist University, Cox School of Business)

Please cite the following paper when using the data in your own work:

Ganglmair, Bernhard, W. Keith Robinson, and Michael Seeligson (2022): "The Rise of Process Claims: Evidence from a Century of U.S. Patents," unpublished manuscript available at https://papers.ssrn.com/abstract=4069994.

This dataset contains the vectors from computing RDF2vec embeddings from a Page Rank split weighted DBpedia 2016-04 graph.

For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.

The parameter settings for the embedding are as specified in the paper:

Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279