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The presentation highlights data citation challenges in Social Sciences and the solution by KonsortSWD to enhance the findability and accessibility of dataset elements like survey variables. Often, researchers use a subset of variables contained in a dataset, making the common practice of assigning a persistent identifier (PID) to an entire dataset insufficient. Despite citations of reused data, current citation practices lack standards, provide inadequate metadata and documentation, or refer to inaccessible datasets. We introduce a registration service assigning PIDs to dataset elements, enabling reliable citation and reuse, developed in the framework of the NFDI consortium KonsortSWD. PIDs, on a more granular level, are central to the FAIR principles, advancing FAIR data management, credibility and reusability. The talk underscores the benefits of assigning PIDs to dataset elements. Being machine-actionable, PIDs aid in adhering to FAIR principles by increasing research traceability, enabling citation tracking, and promoting digital connections among researchers and research outputs. We present four use cases demonstrating how partners make data citation easier. The presentation concludes with recommendations for future functionalities, such as automated access to variables and visualizing their relationships as an open research knowledge graph. Our service expands the DOI registration agency for social and economic data, da|ra[1], utilizing the ePic[2] API, supported by a set of compliant metadata schema. The service was evaluated using the RDA FAIR Data Maturity Model[3] framework, showing high compliance levels. Participants will learn the benefits of assigning PIDs below the study level, enhancing citation transparency and Fairness of data. [1] https://www.da-ra.de/ [2] https://www.pidconsortium.net/?page_id=112 [3] doi: 10.15497/rda00050, The service is part of the KonsortSWD project deliverable, NFDI funding number 442494171.

In this report, we present our analysis of the FAIRness of GESIS as seen from the perspective of FAIRness assessment models and tools, such as the F-UJI tool, which was recently used in the European Research Data Landscape report[i]. In summary, GESIS has systematic weaknesses. Some are unavoidable, such as the restrictions given to us through our licensing policies, some lead us to consider improving our infrastructure, such as including .csv files when available, and some are technical in nature, meaning that we have the required metadata, but they are, however, not represented in a way that the tool recognizes. It is worth pointing out that while these weaknesses are unfortunate, they do not affect users negatively in any tangible way. All the information required for findability is available to the aggregators that are relevant to us and via the web interface the way it should be. However, the research data ecosystem is constantly changing, so providing more metadata in an easily machine-readable way will likely be relevant in the future. FAIR assessment tools are valuable in giving us feedback on what to improve. [i] European Commission, Directorate-General for Research and Innovation, European Research Data Landscape: final report, Publications Office of the European Union, 2022, https://data.europa.eu/doi/10.2777/3648

The session discusses current data citation hurdles in the Social Sciences and a technical solution provided by KonsortSWD which aims at making findability and accessibility on the level of dataset elements, such as survey variables, more efficient. Researchers often use just a subset of variables of a study. Thus, assigning a persistent identifier (PID) to a whole dataset, currently the common practice in the Social Sciences, is insufficient to unambiguously identify the dataset elements used. This is where KonsortSWD’s PID service comes in as it introduces PIDs on the level of dataset elements to boost reliable data citation and reuse, and finally advanced FAIR data management. The session addresses the advantages and benefits of assigning PIDs to dataset elements through use cases showing how researchers can effortlessly find and cite data. Data providers can also directly benefit from a more fine-grained PID approach, as it improves citation tracking and data visibility., KonsortSWD is funded by the German Research Foundation (DFG) within the framework of the NFDI – project number: 442494171.

The session discusses current data citation hurdles in the Social Sciences and a technical solution provided by KonsortSWD which aims at making findability and accessibility on the level of dataset elements, such as survey variables, more efficient. Researchers often use just a subset of variables of a study. Thus, assigning a persistent identifier (PID) to a whole dataset, currently the common practice in the Social Sciences, is insufficient to unambiguously identify the dataset elements used. This is where KonsortSWD’s PID service comes in as it introduces PIDs on the level of dataset elements to boost reliable data citation and reuse, and finally advanced FAIR data management. The session addresses the advantages and benefits of assigning PIDs to dataset elements through use cases showing how researchers can effortlessly find and cite data. Data providers can also directly benefit from a more fine-grained PID approach, as it improves citation tracking and data visibility., KonsortSWD is funded by the German Research Foundation (DFG) within the framework of the NFDI – project number: 442494171.

In order to gain an overview of the current state of the discussion on PIDs and for the identification of use cases for the initiation phase of a PID service within the NFDI basic services, the working group Persistent Identifier of the Section Common Infrastructures of the NFDI hosted an online workshop in January 2023. In the course of the workshop, members of nine different NFDI consortia presented the current application of PIDs in their consortia.

The persistent identifiers (PIDs) assigned to the level of attributes, i.e., a variable in Social Science research data, can unambiguously cite and direct retrieve data. Variables change through the studies' timeline and promote cross-linking through the survey waves, studies, and other entities such as questions and concepts among questionnaires. Given the importance of variables and their ties, the associated metadata has to register such relations and allow machine actionable features through PIDs and controlled vocabulary terms. The KonsortSWD "Measure TA.5-M.1 Enhancing PID services as a base for a FAIR data infrastructure" outputs a PID registration widening of the da|ra service to assign PIDs. The basic metadata schema was extended to meet the increasing demand for interoperability, data mappings, and knowledge graph. This solution comprises a metadata schema for the persistent variables identification and cross-linking relations., KonsortSWD is funded by the German Research Foundation (DFG) within the framework of the NFDI – project number: 442494171.

This report extends the use case descriptions and provides recommendations for assigning Persistent Identifiers (PIDs) below the study level, i.e., here, as a first approach, a variable within a dataset. The recommendations rely on use case partners' data types and services. However, it would also benefit other institutions, such as data repositories that hold tabular data and want to take advantage of uniquely identifying their data at a lower granularity level. The report describes seven use cases from four consortium partners from KonsortSWD. The target elements to get a PID are variables within large and small datasets, PIDs for information bundles (group of variables), and qualitative data, such as observation recording, interviews and transcriptions. PIDs for variables used by harmonization tools are also included. Institutions will be responsible for documenting the variable or other attribute they want to get a PID for, providing the landing page URL and metadata records to da|ra to get a PID. To this end, the da|ra PID registration service was enlarged under the KonsortSWD Measure TA.5-M.1. The da|ra will then register the corresponding metadata and get the PID on behalf of the institution, returning the PID to the institution, which will store the data with the corresponding PIDs., KonsortSWD is funded by the German Research Foundation (DFG) within the framework of the NFDI – project number: 442494171.

NFDI4 PID Working Group promoted a workshop on PIDs within the NFDI community. This presentation demonstrates the service's use cases. Survey variables (KonsortSWD TA5.M1 Enhancing PID services as a base for a FAIR data infrastructure) Context: Social sciences, PIDs for survey variables into the tabular format as a first approach to different data formats (video and audio segments, transcripts, files in datasets) connected in future. Using ePIC API as a widening of da|ra registration service. Several services use PIDs, such as RDCs which use da|ra registration service. Rich metadata schema, which allows related entities and knowledge graph visualization. PID service report and demo available (see slides) Zenodo Community: https://zenodo.org/communities/konsortswd-ta5-m1, The service is part of the KonsortSWD project deliverable, NFDI funding number 442494171

In many scientific disciplines, Persistent Identifiers (PIDs) are commonly available only at the study or dataset level but not at the level of the inline data objects that are usually used by researchers, making it difficult to track and cite them. This paper focuses on registering PIDs also for finer-grained elements of a dataset representing the primary entities of research, such as survey variables in the Social Sciences, beyond the traditional approach of assigning PIDs to entire datasets. The paper highlights the benefits of this approach for researchers and research data centers and discusses four use cases from the consortium KonsortSWD of the German National Research Data Infrastructure (NFDI), addressing how project partners adopt the idea of having PIDs for dataset elements below study level according to their needs, data types and available services. The use cases address requirements from the perspective of empirical Social Sciences, targeting different types of dataset elements that should have a PID, such as individual survey variables, information bundles (variable groups), and qualitative data contained in observation recordings, interviews, and transcriptions. The paper concludes with functionalities that require persistent identifiers to be implemented, such as automated access mechanisms to access elements in the dataset directly., The service is part of KonsortSWD project deliverable, NFDI funding number 442494171.

The NFDI KonsortSWD Measure TA.5-M.1 "Enhancing PID services as a base for a FAIR data infrastructure" aims to assign persistent identifiers on attributes, such as specific variables, instead of only on a dataset and study level. At this presentation, we demonstrate how the service is flexible and allows other data formats, including new attribute levels at the further stages, such as survey questions, audio and video data fragments, a selected part of an image, or any digital object elements that register data throughout the various knowledge subject fields. In this sense, further applications of the PID service at other attributes than variables require metadata fields to be foreseen accordingly. Besides the PID registration service for variables available through da|ra (the DOI registration agency for research data in Germany), all code produced within this measure will be made available as open-source software., The service is part of the KonsortSWD project deliverable (Persistent Identifiers for Variables - KonsortSWD Task Area 5: Measure 1), NFDI funding number 442494171. PID Service report https://doi.org/10.5281/zenodo.6397367.

The webinar updated and extended webinar from 2021 on users’ experiences with FAIR evaluation tools and services. Panelists presented their user stories with FAIRness evaluation tests, lessons learned, and recommendations to other Service Providers. For example, recommendations have been offered on steps to take to improve FAIRness evaluation tests scores. New topics were also featured, including: DATICE: The Icelandic Social Science Data Service and an application of the RDA FAIR Data Maturity Model to PIDs at GESIS.

This session introduces the output of the NFDI-funded consortium KonsortSWD, Measure TA.5-M.1 "Enhancing PID services as a base for a FAIR data infrastructure," a service to assign PIDs on the level of the inline data objects, such as survey variables. This technical solution aims to make data findability and accessibility on the lower granularity level of studies more efficient. Particularly in the Social Sciences, PIDs are commonly available on the study level, which is insufficient to unambiguously identify the information used and ensure an accurate data citation. By assigning PIDs to the fine-grained level of attributes, individual elements can be referenced and retrieved with the required metadata. Referencing research data and their inherited entities by Persistent Identifiers (PIDs) supports FAIR data usage. Textual data citations without a unique identifier are non-standard practices that lead to consequences such as making it inefficient to identify critical variables and reuse them. From the technical perspective, it also hinders automated access to variables, making harmonisation very expensive since it is costly and time-consuming. The PIDs for variables registration service simplify FAIR data management and benefit both researchers and research data repositories (RDCs), fostering credibility results and ensuring the sustainable reusability of data. RDCs directly benefit from PIDs regarding data governance activities and the services they provide. PIDs as data identifiers have far advantages in terms of machine-actionable features: enabling citation tracking and aggregating, scientific production combination; empowering authority; promoting digital connections among researchers, organisations, and research outputs. Explicit relations between those elements are possible and favour the formation of a network into a knowledge graph representation. Since PIDs are machine-actionable, they are the technical bridges to the FAIR principles that can increase the traceability of research results in the Social and Economic Sciences., KonsortSWD is funded by the German Research Foundation (DFG) within the framework of the NFDI – project number: 442494171.

Introduction: Assigning a PID to a whole dataset, as common practice within research data management, is not enough to unambiguously identify the piece of information used and ensure the data citation properly and, consequently, promote the accreditation of research results. Particularly in the Social Sciences, PIDs are only available at the study level but not at the level of the inline data objects, such as survey variables. Since citing research data is the backbone of proper data reuse, our approach proposes an infrastructure to reference specific attributes within data sets, assigning PIDs to the fine-grained granularity level of attributes. By assigning PIDs to these attributes, individual elements of the data files can be referenced and retrieved with the required metadata for machine-actionable and human access. Methodology: Our approach to maturity level assessment relied on the RDA recommendation FAIR Data Maturity Model, an output of the FAIR Data Maturity Model WG. The solution was evaluated under the core criteria proposed by the cited framework to implement a level of the FAIR data principles. We assessed the service under the FAIR Data Maturity Model (RDA Working Group on FAIR Data Maturity Model, 2020, see DOI: 10.15497/rda00050), applying the stricter evaluation method on each indicator, assessing them by passing or failing binary answers. This approach was selected because the PID registration service is a widening solution to an established service through da|ra (da-ra.de). Results: The results demonstrate outstanding achievements at levels 1 and 2, marking 100% on the assessment measure. The service achieves 88% compliance at level 3 and 89% at level 4. At level 5, the results show 80% of passed indicators. Our service meets all indicators classified as essential. The indicator classes which do not meet the measures were four from the important and useful classified categories. However, it is essential to highlight the failed indicators concerned with automatic features, including references and/or qualified references to other data, and data is accessed automatically (i.e., by a computer program). Future work: We intend to address automatic features such as getting data automatically from a given dataset. Due to the high relevance of the service for implementing FAIR, we aim to provide reusable and generalised components as a blueprint for other projects., The service is part of the KonsortSWD project deliverable (Persistent Identifiers for Variables - KonsortSWD Task Area 5: Measure 1), NFDI funding number 442494171. PID Service report https://doi.org/10.5281/zenodo.6397367.

Assigning a PID to a whole dataset, as common practice within research data management, is not enough to unambiguously identify the piece of information used and ensure the data citation properly and, consequently, promote the accreditation of research results. There is an increasing research data availability within data repositories, which leads to data visibility and intensifies re-use and reproducibility approaches. Data per se has various levels of granularity. For the case of Social Sciences, for instance, the variable level is the construct that provides evidence for the research results and allows future inferences and analyses. The variable level in the Social Sciences research data is a unit of quantitative data, commonly obtained through survey questionnaires or experiments and represented in a tabular datasets format. In the sense of re-use, researchers are much more interested in the concept of those variables. When re-used, variables are currently cited "in the text" without a unique identifier; usually, only the study or parts of the questions are cited. These non-standard practices lead to consequences such as making it inefficient for the service provider to identify critical variables and for the researcher to re-use variables. It also hinders automated access to variables, making harmonization very expensive since it is a costly and time-consuming task. To solve this problem, we propose assigning a PID to the variable level to cite it unambiguously. A Persistent Identifier (PID) is a persistent, unique, and globally resolvable identifier based on an openly specified PID Scheme (EOSC, 2020, see doi: 10.2777/926037). Persistent Identifiers (PIDs) have been the assignment for data identification whatever the standard is (DOI, Handle, URN, ARK). A given study that re-used data (variables) relies on the variable's analysis to provide results and recommendations, make inferences, and produce outcomes through secondary data analysis practices. The approach means identifying the variables must assure a precise, unique, traceable, unambiguous, long-lasting, and undouble standard, consequently qualifying for an accurate data citation using a PID. PIDs as data identifiers have far advantages in terms of machine-actionable features: enable citation tracking and aggregating; scientific production combination; empowering authoritative; promote digital connections among researchers, organizations, and research outputs. Our proposal addresses providing the complete citation possible, even though the granularity level requires adding a PID to a smaller unit within the dataset. Assigning PIDs to the variables will make research data easier to find and cite, improving data findability and accessibility. The more unambiguously researched data identification becomes when citing data at a most discrete level, such as a variable. A more detailed citation method can build trust in data, provide provenance, foster reusability, and favor reproducibility. The functionality of registering variable's service assigns a PID with Handle standard, using a PID as a third-party registration process service (ePIC API see http://www.pidconsortium.net/). The service also enables "bulk registration," which means the registration of many variables at once. It provides a landing page and requires a minimal metadata schema such as Study DOI, variable name, variable label, landing page, resource type, title, creators, publisher, publication date, and availability. We advocate that more specific data citation practices can perform to a higher maturity level, in the best sense of the FAIR Principles. We assessed the service under the FAIR Data Maturity Model (RDA Working Group on FAIR Data Maturity Model, 2020, see doi: 10.15497/rda00050), applying the stricter evaluation method on each indicator, assessing them by passing or failing binary answers. The results demonstrate outstanding achievements at levels 1 and 2, marking 100% on the assessment measure. The service achieves 88% compliance at level 3 and 89% at level 4. At level 5, the results show 80% of passed indicators. Our service meets all indicators classified as essential. The indicator classes which do not meet the measures were four from the important and useful classified categories. However, it is essential to highlight the failed indicators concerned with automatic features, including references and/or qualified references to other data, and data is accessed automatically (i.e., by a computer program). We intend to address these in our future work. Given the high relevance of the service for implementing FAIR, we aim to provide reusable and generalized components as a blueprint for other projects., Funded by Deutsche Forschungsgemeinschaft as part of NFDI under project number 442494171.

Referencing research data and there inherit detailed entities supports FAIR usage. In measure 5.1 we enhance the state of the art of citing research data, by developing an infrastructure to reference detailed attributes, here initially variables, within such data. By assigning PIDs to these attributes, individual elements of the data files can be referenced and retrieved with the required metadata for machine-actionable and human access. The PIDs will not only enable citeability within scientific papers but also give access for processing the contained data itself, e.g., within script languages like R or phyton. This report provides detailed use cases, the necessary metadata schema, and the envisioned architecture for a general, maintainable, and scalable infrastructure that enables the registration of PIDs for small-scale attributes in Social Science research data. Besides the main functionality, we emphasize reusable and generalized components as a blueprint for other projects. All developed components will be published as open-source software., Funded by Deutsche Forschungsgemeinschaft as part of NFDI under project number 442494171., {"references":["Abad, Ricardo G. (2001). Religion in the Philippines. Philippine Studies, v. 49, n. 3, p. 337–367.","Bensmann, F., Papenmeier, A., Kern, D., Zapilko, B., & Dietze, S. (2020). Semantic Annotation, Representation and Linking of Survey Data. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12378 LNCS, 53–69. https://doi.org/10.1007/978-3-030-59833-4_4.","Clobert, M., Saroglou, V., Hwang, K. K., & Soong, W. L. (2014). East Asian Religious Tolerance—A Myth or a Reality? Empirical Investigations of Religious Prejudice in East Asian Societies. Journal of Cross-Cultural Psychology, 45(10), 1515–1533. https://doi.org/10.1177/0022022114546641.","Data Citation Synthesis Group. (2014). Joint Declaration of Data Citation Principles. 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A Persistent Identifier (PID) policy for the European Open Science Cloud: Report from the European Open Science Cloud FAIR and Architecture Working Groups. EOSC Executive Board (ed.). October, 2020. 20p. doi: 10.2777/926037.","EVS (2020). European Values Study 2017: Integrated Dataset (EVS 2017). GESIS Data Archive, Cologne. ZA7500 Data file Version 4.0.0, https://doi.org/10.4232/1.13560.","Hausstein, Brigitte, Borschewski, Kerrin, Jerlehag, Birger, van Horik, René, & van der Vaart, Lilian. (2017). CESSDA ERIC Persistent Identifier Policy (1.0). Zenodo. https://doi.org/10.5281/zenodo.3611317.","Merriam-Webster. (n.d.). Variable. In Merriam-Webster.com dictionary. Retrieved November 15, 2021. https://www.merriam-webster.com/dictionary/variable.","Minarik, P. (2014). Employment, wages, and religious revivals in postcommunist countries. Journal for the Scientific Study of Religion, 53(2), 296–315. https://doi.org/10.1111/jssr.12113.","Nicolas Larrousse; Edward J. Gray. (2021). Recommendations for FAIR Data Citation in the Social Sciences and Humanities. Zenodo. https://doi.org/10.5281/zenodo.5361718.","Organisation for Economic Co-operation and Development (OECD). (2007). Glossary of statistical terms. p. 836. https://ec.europa.eu/eurostat/ramon/coded_files/OECD_glossary_stat_terms.pdf.","Persistent Identifiers for eResearch. (2020). ePIC Quality of Service and Policies. http://www.pidconsortium.net/?page_id=904.","Task Group on Data Citation Standards and Practices, C.-I., (2013). Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data. Data Science Journal, 12, pp. CIDCR1–CIDCR7. DOI: http://doi.org/10.2481/dsj.OSOM13-043.","The International Statistical Institute. (2003). The Oxford Dictionary of Statistical Terms. Yadolah Dodge [ed]. Oxford University Press, 2003.","Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18.","World Wide Web Consortium (W3C). (2022). DDI-RDF Discovery Vocabulary: A vocabulary for publishing metadata about data sets (research and survey data) into the Web of Linked Data. Variable and Variable Definition. https://rdf-vocabulary.ddialliance.org/discovery.html#variable-and-variable-definition."]}

FAIR Digital Objects (FDOs) are especially needed when it comes to linking together heterogeneous data from different communities in order to advance cross-disciplinary research. However, it is still neither clear what the minimum set of attributes making a FDO is nor do we have a model for representing these attributes in a semantically unambiguous and machine-actionable way. The session will discuss requirements to be taken into account for future discussions on a semantic model for FDOs., GO FAIR Implementation Network, Go Inter, FDO Forum

The social, behavioural, educational, and economic sciences offer indispensable perspectives and responses to the grand societal challenges the world is facing today. In addressing these challenges, the disciplines represented in KonsortSWD work with a range of different data types. These data are typically sensitive due to legal or ethical constraints and they are frequently generated for non-scientific purposes. KonsortSWD aims to support the research data management (RDM) needs of its communities, which increasingly collaborate in multi- and interdisciplinary research projects. The (co-)applicant institutions bring their long-standing experience in operating community-driven research data infrastructures to the NFDI. Our mission is to develop – strengthen, widen, deepen – a research data infrastructure for the study of human society. This effort is to be primarily user-driven and addresses the needs of our research communities. The core of KonsortSWD’s RDM strategy is to provide researchers and research data centres (RDCs) with the tools and services they need for managing and sharing (new) sensitive and non-sensitive data in compliance with the FAIR principles. This includes supporting sustainable RDM in all phases of the research data lifecycle and securing data accessibility with an awareness of ethical and legal aspects. KonsortSWD will increase efficiency in RDM processes by coordinating · data producers with technical expertise to simplify sustainable RDM throughout the data lifecycle, · a growing infrastructure of currently 34 accredited RDCs with 3,940 datasets, 46,000 data users, and a staff of 285 full time-equivalents (in 2018), · the German Data Forum (RatSWD), where data users and producers jointly facilitate access to research data, · a forum for ethical reflection to support researchers and their institutions and to discuss and disseminate standards for data-driven, high quality research on human subjects up to entire societies. KonsortSWD combines a multi-stakeholder approach with transparent governance: a Steering Committee is in charge of operations, an Advisory Board with elected community representatives provides strategic feedback, and a Consortial Conference helps to disseminate KonsortSWD’s services and to collect feedback. The Task Areas (TAs) focus on: (1) Community Participation. (2) Data Access, (3) Data Production, (4) Research (Data) Ethics, and (5) Technical Solutions.

Das Paper stellt Modelle vor, die Wissen über das Interaktionsgeschehen in wissenschaftlichen Communities und den sozialen Status ihrer Akteure für das Retrieval ausnutzen. Grundlage hierfür sind soziale Netzwerke, die sich durch Kooperation der wissenschaftlichen Akteure konstituieren (Autorennetzwerke). Die beschriebenen Studien zur Topologie von Autorennetzwerken zeigen, dass diese Netzwerke ein erhebliches Potential für Informationssysteme haben. Das Paper diskutiert Szenarios, die beschreiben, wie Autorennetzwerke und hier insbesondere das Konzept der Akteurszentralität für die Informationssuche in Datenbanken sinnvoll genutzt werden können.

The paper describes a research project at GESIS - Leibniz Institute for the Social Sciences. The project implements and tests value-added services for information retrieval (IR). a) Search Term Recommenders, b) Reranking services (Bradfordizing and author centrality).

Purpose - 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 (DL) 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”. Design/methodology/approach - First, semantic heterogeneity components translate automatically between different indexing languages. This approach will have an impact on search in a scenario when the searcher uses controlled vocabularies which are cross-linked with cross-concordances. However, users usually formulate query terms freely without any vocabulary support. Empirical observations show that freely formulated user terms and terms from controlled vocabularies are often not the same or match just by coincidence. Therefore, a value-added service will be developed which rephrases the natural language searcher terms into suggestions from the controlled vocabulary, the Search Term Recommender (STR). Second, the result sets of transformed or expanded queries in distributed collections are often very large and tests show that the conventional web-based ranking methods are not appropriate for presenting heterogeneous metadata records as suitable result sets to the user. Therefore, 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. Findings - 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. Originality/value - 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. Paper type - Research paper

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., Proceedings of 1st International Workshop on System And User Centered Evaluation Approaches in Interactive Information Retrieval at CHIIR 2016

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.

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)