Establishing an Online Access Panel for Interactive Information Retrieval Research

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We propose an online access panel to support the evaluation process of Interactive Information Retrieval (IIR) systems. By maintaining an online access panel with users of IIR systems we assume that the recurring effort to recruit participants for web-based as well as for lab studies can be minimized. We target on using the online access panel not only for our own development processes but to open it for other interested researchers in the field of IIR. In this paper we present the concept of the online access panel as well as first implementation details.

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In this paper we describe a case study where researchers in the social sciences (n=19) assess topical relevance for controlled search terms, journal names and author names which have been compiled automatically by bibliometric-enhanced information retrieval (IR) services. We call these bibliometric-enhanced IR services Search Term Recommender (STR), Journal Name Recommender (JNR) and Author Name Recommender (ANR) in this paper. The researchers in our study (practitioners, PhD students and postdocs) were asked to assess the top n pre-processed recommendations from each recommender for specific research topics which have been named by them in an interview before the experiment. Our results show clearly that the presented search term, journal name and author name recommendations are highly relevant to the researchers’ topic and can easily be integrated for search in Digital Libraries. The average precision for top ranked recommendations is 0.75 for author names, 0.74 for search terms and 0.73 for journal names. The relevance distribution differs largely across topics and researcher types. Practitioners seem to favor author name recommendations while postdocs have rated author name recommendations the lowest. In the experiment the small postdoc group (n=3) favor journal name recommendations.

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Our system will initially provide readers, authors and reviewers with an alternative, thus having the potential to gain wider acceptance and gradually replace the old, incoherent publication process of our journals and of others in related fields. It will make journals more “open” (in terms of reusability) that are open access already, and it has the potential to serve as an incentive for turning “closed” journals into open access ones. OSCOSS is funded by the DFG in the Open Access Transformation programme.

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As more and more people use social media to communicate their view and perception of elections, researchers have increasingly been collecting and analyzing data from social media platforms. Our research focuses on social media communication related to the 2013 election of the German parlia-ment [translation: Bundestagswahl 2013]. We constructed several social media datasets using data from Facebook and Twitter. First, we identified the most relevant candidates (n=2,346) and checked whether they maintained social media accounts. The Facebook data was collected in November 2013 for the period of January 2009 to October 2013. On Facebook we identified 1,408 Facebook walls containing approximately 469,000 posts. Twitter data was collected between June and December 2013 finishing with the constitution of the government. On Twitter we identified 1,009 candidates and 76 other agents, for example, journalists. We estimated the number of relevant tweets to exceed eight million for the period from July 27 to September 27 alone. In this document we summarize past research in the literature, discuss possibilities for research with our data set, explain the data collection procedures, and provide a description of the data and a discussion of issues for archiving and dissemination of social media data.

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With the increasing size of digital libraries (DLs) it has become a challenge to identify author names correctly and assign publications to them. The situation becomes more critical when different persons share the same name (homonym problem) or when the names of authors are presented in several different ways (synonym problem). This paper focuses on homonym names in the computer science bibliography DBLP. The goal of this study is to implement and evaluate a method which uses co-authorship networks in order to disambiguate homonym names, especially common names. The results show that the implemented method has a good performance and can be used for author name disambiguation of sparse bibliographic records.

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The objective of the OSCOSS research project on "Opening Scholarly Communication in the Social Sciences" is to build a coherent collaboration environment that facilitates scholarly communication workflows of social scientists in the roles of authors, reviewers, editors and readers. This paper presents the implementation of the core of this environment: the integration of the Fidus Writer academic word processor with the Open Journal Systems (OJS) submission and review management system.

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Data Science is described as the process of knowledge extraction from large data sets by means of scientific methods. The discipline draws heavily from techniques and theories from many fields, which are jointly used to furthermore develop information retrieval on structured or unstructured very large datasets. While the term Data Science was already coined in 1960, the current perception of this field places is still in the first section of the hype cycle according to Gartner, being well en route from the technology trigger stage to the peak of inflated expectations. In our view the future development of Data Science could benefit from the analysis of experiences from related evolutionary processes. One predecessor is the area of Geographic Information Systems (GIS). The intrinsic scope of GIS is the integration and storage of spatial information from often heterogeneous sources, data analysis, sharing of reconstructed or aggregated results in visual form or via data transfer. GIS is successfully applied to process and analyse spatially referenced content in a wide and still expanding range of science areas, spanning from human and social sciences like archeology, politics and architecture to environmental and geoscientific applications, even including planetology. This paper presents proven patterns for innovation and organisation derived from the evolution of GIS, which can be ported to Data Science. Within the GIS landscape, three strategic interacting tiers can be denoted: i) Standardisation, ii) applications based on closed-source software, without the option of access to and analysis of the implemented algorithms, and iii) Free and Open Source Software (FOSS) based on freely accessible program code enabling analysis, education and ,improvement by everyone. This paper focuses on patterns gained from the synthesis of three decades of FOSS development. We identified best-practices which evolved from long term FOSS projects, describe the role of community-driven global umbrella organisations such as OSGeo, as well as the standardization of innovative services. The main driver is the acknowledgement of a meritocratic attitude. These patterns follow evolutionary processes of establishing and maintaining a web-based democratic culture spawning new kinds of communication and projects. This culture transcends the established compartmentation and stratification of science by creating mutual benefits for the participants, irrespective of their respective research interest and standing. Adopting these best practices will enable

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