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The open science movement is gaining strength and momentum worldwide, signalling a fundamental shift in how scientific research is made accessible and reusable. In order to fulfill the promises of open science, reliable and sustainable research data infrastructures must be developed. While the FAIR data principles provide a promising conceptual basis for developing such data infrastructures, they do not provide technological guidance on how to do so. Computer science is uniquely situated to fill this gap by researching and developing tools and technical specifications which can help to realize the creation of FAIR data infrastructures. To this end, this Dagstuhl Perspectives Workshop brought together computer scientists and digital infrastructure experts from across disciplinary domains to discuss key challenges and technical solutions to implementing and promoting the establishment of FAIR-compliant infrastructures for research data. This manifesto reports the findings from the workshop and provides recommendations along two lines: (1) how computer science can contribute to implementing FAIR data infrastructures and (2) how to make computer science research itself more FAIR.

The open science movement is gaining strength and momentum worldwide, signalling a fundamental shift in how scientific research is made accessible and reusable. In order to fulfill the promises of open science, reliable and sustainable research data infrastructures must be developed. While the FAIR data principles provide a promising conceptual basis for developing such data infrastructures, they do not provide technological guidance on how to do so. Computer science is uniquely situated to fill this gap by researching and developing tools and technical specifications which can help to realize the creation of FAIR data infrastructures. To this end, this Dagstuhl Perspectives Workshop brought together computer scientists and digital infrastructure experts from across disciplinary domains to discuss key challenges and technical solutions to implementing and promoting the establishment of FAIR-compliant infrastructures for research data. This manifesto reports the findings from the workshop and provides recommendations along two lines: (1) how computer science can contribute to implementing FAIR data infrastructures and (2) how to make computer science research itself more FAIR.

This report documents the programme and the outcomes of Dagstuhl Perspectives Workshop 18472 "Implementing FAIR Data Infrastructures". The workshop aimed at bringing together computer scientists with digital infrastructure experts from different domains to discuss open issues implementing and adopting the FAIR principles in research data infrastructures and to shape the role that the field of computer science has to play.

This report documents the programme and the outcomes of Dagstuhl Perspectives Workshop 18472 "Implementing FAIR Data Infrastructures". The workshop aimed at bringing together computer scientists with digital infrastructure experts from different domains to discuss open issues implementing and adopting the FAIR principles in research data infrastructures and to shape the role that the field of computer science has to play.

In the social sciences, researchers search for information on the Web, but this is most often distributed on different websites, search portals, digital libraries, data archives, and databases. In this work, we present an integrated search system for social science information that allows finding information around research data in a single digital library. Users can search for research data sets, publications, survey variables, questions from questionnaires, survey instruments, and tools. Information items are linked to each other so that users can see, for example, which publications contain data citations to research data. The integration and linking of different kinds of information increase their visibility so that it is easier for researchers to find information for re-use. In a log-based usage study, we found that users search across different information types, that search sessions contain a high rate of positive signals and that link information is often explored.

In this paper, we describe our effort to create a new corpus for the evaluation of detecting and linking so-called survey variables in social science publications (e.g., "Do you believe in Heaven?"). The task is to recognize survey variable mentions in a given text, disambiguate them, and link them to the corresponding variable within a knowledge base. Since there are generally hundreds of candidates to link to and due to the wide variety of forms they can take, this is a challenging task within NLP. The contribution of our work is the first gold standard corpus for the variable detection and linking task. We describe the annotation guidelines and the annotation process. The produced corpus is multilingual - German and English - and includes manually curated word and phrase alignments. Moreover, it includes text samples that could not be assigned to any variables, denoted as negative examples. Based on the new dataset, we conduct an evaluation of several state-of-the-art text classification and textual similarity methods. The annotated corpus is made available along with an open-source baseline system for variable mention identification and linking.

Research in Social Science is usually based on survey data where individual research questions relate to observable concepts (variables). However, due to a lack of standards for data citations a reliable identification of the variables used is often difficult. In this paper, we present a work-in-progress study that seeks to provide a solution to the variable detection task based on supervised machine learning algorithms, using a linguistic analysis pipeline to extract a rich feature set, including terminological concepts and similarity metric scores. Further, we present preliminary results on a small dataset that has been specifically designed for this task, yielding modest improvements over the baseline.

The usefulness evaluation model proposed by Cole et al. in 2009 [2] focuses on the evaluation of interactive IR systems by their support towards the user's overall goal, sub goals and tasks. This is a more human focus of the IR evaluation process than with classical TREC-oriented studies and gives a more holistic view on the IR evaluation process. However, yet there is no formal framework how the usefulness model can be operationalized. Additionally, a lot of information needed for the operationalization is only available in explicit user studies where for example the overall goal and the tasks are prompted from the users or are predefined. Measuring the usefulness of IR systems outside the laboratory is a challenging task as most often only log data of user interaction is available. But, an operationalization of the usefulness model based on interaction data could be applied to diverse systems and evaluation results would be comparable. In this article we discuss the challenges for measuring the usefulness of IIR systems with log-based approaches., Comment: Proceedings of 1st International Workshop on System And User Centered Evaluation Approaches in Interactive Information Retrieval at CHIIR 2016

In Interactive Information Retrieval (IIR) different services such as search term suggestion can support users in their search process. The applicability and performance of such services is either measured with different user-centered studies (like usability tests or laboratory experiments) or, in the context of IR, with their contribution to measures like precision and recall. However, each evaluation methodology has its certain disadvantages. For example, user-centered experiments are often costly and small-scaled; IR experiments rely on relevance assessments and measure only relevance of documents. In this work we operationalize the usefulness model of Cole et al. (2009) on the level of system support to measure not only the local effect of an IR service, but the impact it has on the whole search process. We therefore use a log-based evaluation approach which models user interactions within sessions with positive signals and apply it for the case of a search term suggestion service. We found that the usage of the service significantly often implicates the occurrence of positive signals during the following session steps., Comment: In CHIIR '16 Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval , 153-162

"Die Sonderfördermaßnahme CARMEN ('Content Analysis, Retrieval and Metadata: Effective Networking') zielte im Rahmen des vom BMB+F geförderten Programms GLOBAL INFO unter anderem auf eine (semantische) Verbesserung der Ausweitung von Recherchen in Fachdatenbanken zu Internet-Recherchen und umgekehrt ab. Die dafür gewählten Konzepte zur Behandlung semantischer Heterogenität wurden entwickelt, implementiert und evaluiert. Der vorliegende Bericht beschreibt die Arbeiten und Ergebnisse der Projektpartner dieses Arbeitspakets (Fachbereich Mathematik/Informatik der Universität Osnabrück sowie das Informationszentrum Sozialwissenschaften Bonn)." (Autorenreferat)

"Nowadays, users of information services are faced with highly decentralised, heterogeneous document sources with different content analysis. Semantic heterogeneity occurs e.g. when resources using different systems for content description are searched using a single query system. This report describes several approaches of handling semantic heterogeneity used in projects of the German Social Science Information Centre." (author's abstract)

"Der Arbeitsbericht informiert über Entwicklungen am IZ, die darauf abzielen, Wissen über das Interaktionsgeschehen in wissenschaftlichen Communities und den sozialen Status ihrer Akteure für das Retrieval auszunutzen. Grundlage hierfür sind soziale Netzwerke, die sich durch Kooperation der wissenschaftlichen Akteure konstituieren und in den Dokumenten der Datenbasis z.B. als Koautorbeziehungen repräsentiert sind (Autorennetzwerke). Die in dem Bericht beschriebenen Studien zur Small-World-Topologie von Autorennetzwerken zeigen, dass diese Netzwerke ein erhebliches Potential für Informationssysteme haben. Kernansatz der beschriebenen Retrievalmodelle ist die Suche nach Experten und das Ranking von Dokumenten auf der Basis der Zentralität von Autoren in Autorennetzwerken." (Autorenreferat)

"Kommerzielle Textretrievalsysteme basieren heute im wesentlichen auf intellektuell und/oder automatisch ermittelten Deskriptoren, die mit oder ohne zusätzliche Thesaurusrelationen mit Hilfe der Booleschen Algebra recherchiert werden. Dem stehen die quantitativ-statistischen Verfahren gegenüber, die zu nach Relevanz geordneten Ergebnislisten führen (ranking). Sie setzten sich in den letzten Jahren verstärkt auch im kommerziellen Bereich durch. Im vorliegenden Arbeitsbericht wird der Frage nach dem adäquatesten Inhaltserschließungskonzept für ein Informationssystem nachgegangen. Es werden die Vor- und Nachteile der intellektuellen Inhaltserschließung und der automatischen Indexierung im Kontext von Booleschen und quantitativ-statistischen Recherchesystemen ganz generell diskutiert. Desweiteren erfolgt eine Evaluierung der informationstechnologischen Grundlagen für ein modernes Informationssystem." (Autorenreferat)

The ability to browse an information space in a structured way by exploiting similarities and dissimilarities between information objects is crucial for knowledge discovery. Knowledge maps use visualizations to gain insights into the structure of large-scale information spaces, but are still far away from being applicable for searching. The paper proposes a use case for enhancing search term recommendations by heat map visualizations of co-word relationships taken from indexing vocabulary. By contrasting areas of different “heat” the user is enabled to indicate mainstream areas of the field in question more easily. (author's abstract)

This paper is about an information retrieval evaluation on three different retrieval-supporting services. All three services were designed to compensate typical problems that arise in metadata- driven Digital Libraries, which are not adequately handled by a simple tf-idf based retrieval. The services are: (1) a co-word analysis based query expansion mechanism and re-ranking via (2) Bradfordizing and (3) author centrality. The services are evaluated with relevance assessments conducted by 73 information science students. Since the students are neither information professionals nor domain experts the question of inter-rater agreement is taken into consideration. Two important implications emerge: (1) the inter- rater agreement rates were mainly fair to moderate and (2) after a data-cleaning step which erased the assessments with poor agreement rates the evaluation data shows that the three retrieval services returned disjoint but still relevant result sets. (author's abstract)

This paper is about a better understanding on the structure and dynamics of science and the usage of these insights for compensating the typical problems that arises in metadata-driven Digital Libraries. Three science model driven retrieval services are presented: co-word analysis based query expansion, re-ranking via Bradfordizing and author centrality. The services are evaluated with relevance assessments from which two important implications emerge: (1) precision values of the retrieval service are the same or better than the tf-idf retrieval baseline and (2) each service retrieved a disjoint set of documents. The different services each favor quite other - but still relevant - documents than pure term-frequency based rankings. The proposed models and derived retrieval services therefore open up new viewpoints on the scientific knowledge space and provide an alternative framework to structure scholarly information systems. (author's abstract)

In recent years Linked Open Data (LOD) has matured and gained acceptance across various communities and domains. Large potential of Linked Data technologies is seen for an application in scientific disciplines. In this article, we present use cases and applications for an application of Linked Data in the social sciences. They focus on (a) interlinking domain-specific information, and (b) linking social science data to external LOD sources (e.g. authority data) from other domains. However, several technical and research challenges arise, when applying Linked Data technologies to a scientific domain with its specific data, information needs and use cases. We discuss these challenges and show how they can be addressed. (author's abstract)

The dramatic increase in information on the World Wide Web makes it more difficult for web users using web search engines to effectively satisfy their information needs The users' lack of knowledge regarding the searched topics creates a complicated problem when formulating an effective query. Query expansion can play an essential role in overcoming such a deficit. However, because they lack sufficient knowledge about the searched topics, users sometimes find it difficult to evaluate the relatedness of the system's automatically expanded terms. This problem arises mostly in domain-specific areas, e.g Social Science. In this paper, we expand the queries based on a structured, open knowledge resource on the Web (Wikipedia). We link the entities from a domain-specific corpus (qualitative journals in social science) to Wikipedia entities. With Wikipedia serving as background knowledge, we help users with their selections by providing the most likely related terms. Furthermore, users are provided with contextual information that describes each expanded term in order to give users a clearer idea about the meaning of each expanded term. By utilizing 10 test queries chosen by experts who also evaluated the results, we compared the results of using Wikipedia with the results achieved by using a qualitative journal (author's abstract)

Web 2.0 platforms have become a ubiquitous way of information exchange, but are seldom integrated with the Web of Data. To overcome this situation we propose the usage of SKOS thesauri acting as back-of-the-book index providing domain-specific axes transcending applications. We illustrate this concept with a use-case in the social sciences domain but applications in other domains are possible. (author's abstract)

The ability to browse an information space in a structured way by exploiting similarities and dissimilarities between information objects is crucial for knowledge discovery. Knowledge maps use visualizations to gain insights into the structure of large-scale information spaces, but are still far away from being applicable for searching. The paper proposes a use case for enhancing search term recommendations by heat map visualizations of co-word relationships taken from indexing vocabulary. By contrasting areas of different “heat” the user is enabled to indicate mainstream areas of the field in question more easily. (author's abstract)

Dieser Beitrag führt in die wichtigsten Zentralitätskonzepte ein - ohne Anspruch auf Vollständigkeit, aber in dem Bemühen, einen Katalog grundlegender, in der Netzwerkforschung gebräuchlicher Zentralitäts- und Prestigemaße zu liefern. Dabei wurde der Schwerpunkt auf die Darstellung der Konzepte gelegt. Kapitel 4 diskutiert die Maße im Hinblick auf ihre Interpretier- und Anwendbarkeit sowie einige Desiderate der Zentralitätsforschung. (Autorenreferat)

Information systems usually show as a particular point of failure the vagueness between user search terms and the knowledge orders of the information space in question. Some kind of guided searching therefore becomes more and more important in order to more precisely discover information without knowing the right search terms. Knowledge maps are promising tools for visualizing the structure of large-scale information spaces. However, there is no continuous knowledge exchange between the “map makers” on the one hand and the Information Retrieval (IR) specialists on the other hand. Thus, knowledge maps are still far away from being applicable for searching a digital library, due to a lack of models, explorations and user studies that properly combine insights of the two strands. The workshop aims at bringing together these two communities: experts in IR reflecting on visual enhanced search interfaces and experts in knowledge mapping reflecting on interactive visualizations of the content of a digital library collection. The focus of the workshop is to discuss the potential of interactive knowledge maps for information exploration.

This workshop brings together experts of communities which often have been perceived as different once: bibliometrics / scientometrics / informetrics on the one side and information retrieval on the other. Our motivation as organizers of the workshop started from the observation that main discourses in both fields are different, that communities are only partly overlapping and from the belief that a knowledge transfer would be profitable for both sides. Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. On the other side, more and more information professionals, working in libraries and archives are confronted with applying bibliometric techniques in their services. This way knowledge exchange becomes more urgent. The first workshop set the research agenda, by introducing in each other methods, reporting about current research problems and brainstorming about common interests. This follow-up workshop continues the overall communication, but also puts one problem into the focus. In particular, we will explore how statistical modelling of scholarship can improve retrieval services for specific communities, as well as for large, cross-domain collections like Mendeley or ResearchGate. This second BIR workshop continues to raise awareness of the missing link between Information Retrieval (IR) and bibliometrics and contributes to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the scholarly search engine interface.

The general science portal "vascoda" merges structured, high-quality information collections from more than 40 providers on the basis of search engine technology (FAST) and a concept which treats semantic heterogeneity between different controlled vocabularies. First experiences with the portal show some weaknesses of this approach which come out in most metadata-driven Digital Libraries (DLs) or subject specific portals. The purpose of the paper is to propose models to reduce the semantic complexity in heterogeneous DLs. The aim is to introduce value-added services (treatment of term vagueness and document re-ranking) that gain a certain quality in DLs if they are combined with heterogeneity components established in the project “Competence Center Modeling and Treatment of Semantic Heterogeneity”. Two methods, which are derived from scientometrics and network analysis, will be implemented with the objective to re-rank result sets by the following structural properties: the ranking of the results by core journals (so-called Bradfordizing) and ranking by centrality of authors in co-authorship networks. The methods, which will be implemented, focus on the query and on the result side of a search and are designed to positively influence each other. Conceptually, they will improve the search quality and guarantee that the most relevant documents in result sets will be ranked higher. The central impact of the paper focuses on the integration of three structural value-adding methods, which aim at reducing the semantic complexity represented in distributed DLs at several stages in the information retrieval process: query construction, search and ranking and re-ranking. (author's abstract)

Information systems usually show as a particular point of failure the vagueness between user search terms and the knowledge orders of the information space in question. Some kind of guided searching therefore becomes more and more important in order to more precisely discover information without knowing the right search terms. Knowledge maps are promising tools for visualizing the structure of large-scale information spaces. However, there is no continuous knowledge exchange between the “map makers” on the one hand and the Information Retrieval (IR) specialists on the other hand. Thus, knowledge maps are still far away from being applicable for searching a digital library, due to a lack of models, explorations and user studies that properly combine insights of the two strands. The workshop aims at bringing together these two communities: experts in IR reflecting on visual enhanced search interfaces and experts in knowledge mapping reflecting on interactive visualizations of the content of a digital library collection. The focus of the workshop is to discuss the potential of interactive knowledge maps for information exploration.

This workshop brings together experts of communities which often have been perceived as different once: bibliometrics / scientometrics / informetrics on the one side and information retrieval on the other. Our motivation as organizers of the workshop started from the observation that main discourses in both fields are different, that communities are only partly overlapping and from the belief that a knowledge transfer would be profitable for both sides. Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. On the other side, more and more information professionals, working in libraries and archives are confronted with applying bibliometric techniques in their services. This way knowledge exchange becomes more urgent. The first workshop set the research agenda, by introducing in each other methods, reporting about current research problems and brainstorming about common interests. This follow-up workshop continues the overall communication, but also puts one problem into the focus. In particular, we will explore how statistical modelling of scholarship can improve retrieval services for specific communities, as well as for large, cross-domain collections like Mendeley or ResearchGate. This second BIR workshop continues to raise awareness of the missing link between Information Retrieval (IR) and bibliometrics and contributes to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the scholarly search engine interface.

The ability to browse an information space in a structured way by exploiting similarities and dissimilarities between information objects is crucial for knowledge discovery. Knowledge maps use visualizations to gain insights into the structure of large-scale information spaces, but are still far away from being applicable for searching. The paper proposes a use case for enhancing search term recommendations by heat map visualizations of co-word relation-ships taken from indexing vocabulary. By contrasting areas of different "heat" the user is enabled to indicate mainstream areas of the field in question more easily., Comment: URL workshop proceedings: http://ceur-ws.org/Vol-1311

Knowledge maps are promising tools for visualizing the structure of large-scale information spaces, but still far away from being applicable for searching. The first international workshop on "Knowledge Maps and Information Retrieval (KMIR)", held as part of the International Conference on Digital Libraries 2014 in London, aimed at bringing together experts in Information Retrieval (IR) and knowledge mapping in order to discuss the potential of interactive knowledge maps for information seeking purposes., Comment: URL workshop proceedings: http://ceur-ws.org/Vol-1311

This workshop brings together experts of communities which often have been perceived as different once: bibliometrics / scientometrics / informetrics on the one side and information retrieval on the other. Our motivation as organizers of the workshop started from the observation that main discourses in both fields are different, that communities are only partly overlapping and from the belief that a knowledge transfer would be profitable for both sides. Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. On the other side, more and more information professionals, working in libraries and archives are confronted with applying bibliometric techniques in their services. This way knowledge exchange becomes more urgent. The first workshop set the research agenda, by introducing in each other methods, reporting about current research problems and brainstorming about common interests. This follow-up workshop continues the overall communication, but also puts one problem into the focus. In particular, we will explore how statistical modelling of scholarship can improve retrieval services for specific communities, as well as for large, cross-domain collections like Mendeley or ResearchGate. This second BIR workshop continues to raise awareness of the missing link between Information Retrieval (IR) and bibliometrics and contributes to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the scholarly search engine interface., Comment: 4 pages, 37th European Conference on Information Retrieval, BIR workshop

This paper reports on a survey of 26 social scientists and computer scientists. Through the vignette technique, resource use in situations in which scholars have a lot and very little time were explored. Findings suggest academic discipline and time may play a role in resource use. Cette présentation rend compte d’une enquête auprès de 26 spécialistes en sciences sociales et en informatique. À l’aide de la technique de la vignette, nous avons exploré l’utilisation des ressources dans les situations où les savants ont beaucoup et à l’inverse très peu de temps. Les résultats suggèrent que les disciplines scientifiques et le temps peuvent jouer un rôle dans l’utilisation des ressources.

This first "Bibliometric-enhanced Information Retrieval" (BIR 2014) workshop aims to engage with the IR community about possible links to bibliometrics and scholarly communication. Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this workshop we will explore how statistical modelling of scholarship, such as Bradfordizing or network analysis of co-authorship network, can improve retrieval services for specific communities, as well as for large, cross-domain collections. This workshop aims to raise awareness of the missing link between information retrieval (IR) and bibliometrics / scientometrics and to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the digital library interface. Our interests include information retrieval, information seeking, science modelling, network analysis, and digital libraries. The goal is to apply insights from bibliometrics, scientometrics, and informetrics to concrete practical problems of information retrieval and browsing.

Knowledge maps are promising tools for visualizing the structure of large - sc ale information spaces, but still far away from being applicable for searching. The first international workshop on ``Knowledge Maps and Information Retrieval (KMIR)'', held as part of the International Conference on Digital Libraries 2014 in London, aimed a t bringing together experts in Information Retrieval (IR) and knowledge mapping in order to discuss the potential of interactive knowledge maps for information seeking purposes.

Information systems usually show as a particular point of failure the vagueness between user search terms and the knowledge orders of the information space in question. Some kind of guided searching therefore becomes more and more important in order to precisely discover information without knowing the right search terms. Knowledge maps of digital library collections are promising navigation tools through knowledge spaces but still far away from being applicable for searching digital libraries. However, there is no continuous knowledge exchange between the "map makers" on the one hand and the Information Retrieval (IR) specialists on the other hand. Thus, there is also a lack of models that properly combine insights of the two strands. The proposed workshop aims at bringing together these two communities: experts in IR reflecting on visual enhanced search interfaces and experts in knowledge mapping reflecting on visualizations of the content of a collection that might also present a context for a search term in a visual manner. The intention of the workshop is to raise awareness of the potential of interactive knowledge maps for information seeking purposes and to create a common ground for experiments aiming at the incorporation of knowledge maps into IR models at the level of the user interface.

This first "Bibliometric-enhanced Information Retrieval" (BIR 2014) workshop aims to engage with the IR community about possible links to bibliometrics and scholarly communication. Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this workshop we will explore how statistical modelling of scholarship, such as Bradfordizing or network analysis of co-authorship network, can improve retrieval services for specific communities, as well as for large, cross-domain collections. This workshop aims to raise awareness of the missing link between information retrieval (IR) and bibliometrics / scientometrics and to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the digital library interface. Our interests include information retrieval, information seeking, science modelling, network analysis, and digital libraries. The goal is to apply insights from bibliometrics, scientometrics, and informetrics to concrete practical problems of information retrieval and browsing.

Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this workshop we will explore how statistical modelling of scholarship, such as Bradfordizing or network analysis of coauthorship network, can improve re-trieval services for specific communities, as well as for large, cross-domain col-lections. This workshop aims to raise awareness of the missing link between in-formation retrieval (IR) and bibliometrics/scientometrics and to create a com-mon ground for the incorporation of bibliometric-enhanced services into re-trieval at the digital library interface.

This first "Bibliometric-enhanced Information Retrieval" (BIR 2014) workshop aims to engage with the IR community about possible links to bibliometrics and scholarly communication. Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this workshop we will explore how statistical modelling of scholarship, such as Bradfordizing or network analysis of co-authorship network, can improve retrieval services for specific communities, as well as for large, cross-domain collections. This workshop aims to raise awareness of the missing link between information retrieval (IR) and bibliometrics / scientometrics and to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the digital library interface. Our interests include information retrieval, information seeking, science modelling, network analysis, and digital libraries. The goal is to apply insights from bibliometrics, scientometrics, and informetrics to concrete practical problems of information retrieval and browsing.

Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this workshop we will explore how statistical modelling of scholarship, such as Bradfordizing or network analysis of coauthorship network, can improve re-trieval services for specific communities, as well as for large, cross-domain col-lections. This workshop aims to raise awareness of the missing link between in-formation retrieval (IR) and bibliometrics/scientometrics and to create a com-mon ground for the incorporation of bibliometric-enhanced services into re-trieval at the digital library interface.

Information systems usually show as a particular point of failure the vagueness between user search terms and the knowledge orders of the information space in question. Some kind of guided searching therefore becomes more and more important in order to precisely discover information without knowing the right search terms. Knowledge maps of digital library collections are promising navigation tools through knowledge spaces but still far away from being applicable for searching digital libraries. However, there is no continuous knowledge exchange between the "map makers" on the one hand and the Information Retrieval (IR) specialists on the other hand. Thus, there is also a lack of models that properly combine insights of the two strands. The proposed workshop aims at bringing together these two communities: experts in IR reflecting on visual enhanced search interfaces and experts in knowledge mapping reflecting on visualizations of the content of a collection that might also present a context for a search term in a visual manner. The intention of the workshop is to raise awareness of the potential of interactive knowledge maps for information seeking purposes and to create a common ground for experiments aiming at the incorporation of knowledge maps into IR models at the level of the user interface.

This first "Bibliometric-enhanced Information Retrieval" (BIR 2014) workshop aims to engage with the IR community about possible links to bibliometrics and scholarly communication. Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this workshop we will explore how statistical modelling of scholarship, such as Bradfordizing or network analysis of co-authorship network, can improve retrieval services for specific communities, as well as for large, cross-domain collections. This workshop aims to raise awareness of the missing link between information retrieval (IR) and bibliometrics / scientometrics and to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the digital library interface. Our interests include information retrieval, information seeking, science modelling, network analysis, and digital libraries. The goal is to apply insights from bibliometrics, scientometrics, and informetrics to concrete practical problems of information retrieval and browsing.

Knowledge maps are promising tools for visualizing the structure of large - sc ale information spaces, but still far away from being applicable for searching. The first international workshop on ``Knowledge Maps and Information Retrieval (KMIR)'', held as part of the International Conference on Digital Libraries 2014 in London, aimed a t bringing together experts in Information Retrieval (IR) and knowledge mapping in order to discuss the potential of interactive knowledge maps for information seeking purposes.

The paper presents a web prototype that visualises different characteristics of research projects in the heterogeneous domain of educational research. The concept of the application derives from the project "Monitoring Educational Research" (MoBi) that aims at identifying and implementing indicators that adequately describe structural properties and dynamics of the research field. The prototype enables users to visualise data regarding different indicators, e.g. "research activity", "funding", "qualification project", "disciplinary area". Since the application is based on Semantic MediaWikitechnology it furthermore provides an easily accessible opportunity to collaboratively work on a database of research projects. Users can jointly and in a semantically controlled way enter metadata on research projects which are the basis for the computation and visualisation of indicators., Comment: 8 pages, 10 figures, Libraries in the digital age (LIDA) 2014 conference

This first "Bibliometric-enhanced Information Retrieval" (BIR 2014) workshop aims to engage with the IR community about possible links to bibliometrics and scholarly communication. Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this workshop we will explore how statistical modelling of scholarship, such as Bradfordizing or network analysis of co-authorship network, can improve retrieval services for specific communities, as well as for large, cross-domain collections. This workshop aims to raise awareness of the missing link between information retrieval (IR) and bibliometrics / scientometrics and to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the digital library interface. Our interests include information retrieval, information seeking, science modelling, network analysis, and digital libraries. The goal is to apply insights from bibliometrics, scientometrics, and informetrics to concrete practical problems of information retrieval and browsing., Comment: 4 pages, Bibliometric-enhanced Information Retrieval Workshop at ECIR 2014, Amsterdam, NL

We propose an online access panel to support the evaluation process of Interactive Information Retrieval (IIR) systems - called IIRpanel. 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 IIRpanel as well as first implementation details., Comment: 2 pages, 1 figure, 2014 IEEE/ACM Joint Conference on Digital Libraries (JCDL), London, 8th-12th September 2014