Andreas Witt’s research while affiliated with Leibniz Institute for the German Language and other places

Prior analyses and assessments of the impact of scientific research has mainly relied on analyzing its scope within academia and its influence within scholarly circles. However, by not considering the broader societal, economic, and policy implications of research projects, these studies overlook the ways in which scientific discoveries contribute to technological innovation, public health improvements, environmental sustainability, and other areas of real-world application. We expand upon this prior work by developing and validating a conceptual and computational solution to automatically identify and categorize the impact of scientific research within and especially beyond academia based on text data. We first empirically develop and evaluate an annotation schema to capture and classify the impact of research projects based on research reports from different scientific domains. We then annotate a large dataset of more than 45k sentences extracted from research reports for the developed impact categories. We examine the annotated dataset for patterns in the distribution of impact categories across different scientific domains, co-occurrences of impact categories, and signal words of impact. Using the annotated texts and the novel classification schema, we investigate the performance of large language models (LLMs) for automated impact classification. Our results show that fine-tuning the models on our annotated datasets statistically significantly outperforms zero- and fewshot prompting approaches. This indicates that state-of-the-art LLMs without fine-tuning may not work well for novel classification schemas such as our impact classification schema, and in turn highlights the importance of diligent manual annotations as empirical basis in the field of computational social science.

Language data are essential for any scientific endeavor. However, unlike numerical data, language data are often protected by copyright, as they easily meet the threshold of originality. The role of research infrastructures (such CLARIN, DARIAH, and Text+) is to bridge the gap between uses allowed by statutory exceptions and the requirements of Open Science. This is achieved on the one hand by sharing language data produced by research organisations with the widest possible circle of persons, and on the other by mutualizing efforts towards copyright clearance and appropriate licensing of datasets.

CLARIN is a European Research Infrastructure Consortium developing and providing a federated and interoperable platform to support scientists in the field of the Social Sciences and Humanities in carrying-out language-related research. This contribution provides an overview of the entire infrastructure with a particular focus on tool interoperability, ease of access to research data, tools and services, the importance of sharing knowledge within and across (national) communities, and community building. By taking into account FAIR principles from the very beginning, CLARIN succeeded in becoming a successful example of a research infrastructure that is actively used by its members. The benefits CLARIN members reap from their infrastructure secure a future for their common good that is both sustainable and attractive to partners beyond the original target groups.

CLARIN stands for “Common Language Resources and Technology Infrastructure”. In 2012 CLARIN ERIC was established as a legal entity with the mission to create and maintain a digital infrastructure to support the sharing, use, and sustainability of language data (in written, spoken, or multimodal form) available through repositories from all over Europe, in support of research in the humanities and social sciences and beyond. Since 2016 CLARIN has had the status of Landmark research infrastructure and currently it provides easy and sustainable access to digital language data and also offers advanced tools to discover, explore, exploit, annotate, analyse, or combine such datasets, wherever they are located. This is enabled through a networked federation of centres: language data repositories, service centres, and knowledge centres with single sign-on access for all members of the academic community in all participating countries. In addition, CLARIN offers open access facilities for other interested communities of use, both inside and outside of academia. Tools and data from different centres are interoperable, so that data collections can be combined and tools from different sources can be chained to perform operations at different levels of complexity. The strategic agenda adopted by CLARIN and the activities undertaken are rooted in a strong commitment to the Open Science paradigm and the FAIR data principles. This also enables CLARIN to express its added value for the European Research Area and to act as a key driver of innovation and contributor to the increasing number of industry programmes running on data-driven processes and the digitalization of society at large.

Text+ aims to develop a research data infrastructure for Humanities disciplines and beyond whose primary research focus is on language and text. Text+ will be flexible, scalable, and thus open for different discipline-specific requirements. By offering easy access to high quality research data, Text+ will support a maximum of methodological diversity, which in turn is a prerequisite for innovative and transdisciplinary research. Text+ focuses on Collections, Lexical Resources and Editions. These data domains have a long tradition of research and are linked to mature methodological paradigms that require distinctive but also cross-disciplinary practices of data generation, curation and management. The three types of research data are indispensable for a wide range of Humanities disciplines, including, but not limited to, Classical Philology, Linguistics, Literary Studies, Social and Cultural Anthropology, Non-European Cultures, Jewish Studies and Religious Studies, Philosophy, and language- and text-based research in the Social and Political Sciences. From the outset, 26 data centres will participate in Text+ that are technically sound and that are highly regarded in their fields of specialisation. They will provide data, tools, and services for the analysis and re-use of research data across a broad range of disciplines. By grouping data, tools, and services into thematic clusters, an optimal bundling is achieved. There are 34 institutions participating in Text+ that represent the communities addressed by Text+ as broadly as possible: research libraries, universities, Digital Humanities data centres as well as members of the Union of German Academies of Arts and Sciences and of the Leibniz Society. In addition, leading computing centres ensure robust and persistent operation of services for a distributed research data infrastructure. The high level of interest in Text+ is not only evidenced by the substantial in-kind contributions by the Text+ partner institutions, but is also documented by the more than 120 research-driven user stories and by the large number of letters of support from the communities of interest participating in Text+. At the heart of the governance structure are three scientific coordination committees for the data domains and one for the infrastructure. Their task is to continuously evaluate the portfolio of data, tools and services and to promote its further development according to the priorities of the participating disciplines in coordination with the infrastructure providers. The research data management strategy of Text+ is the core instrument for achieving the main objectives of Text+ in the NFDI context. It paves the way for the integration of data, tools and services into an infrastructure that meets relevant standards and implements the FAIR and CARE principles.

An ERIC (European Research Infrastructure Consortium) operates and provides research infrastructure offerings, usually within a disciplinary scope, for researchers regardless of their national or institutional background. Apart from direct funding, an ERIC is built upon the national contributions from partner countries, notably cash and in-kind contributions. This concept, the procedures, advantages and options for future enhancement are described in this paper from the German perspective focussing on the in-kinds. The paper is intended to support an informed discussion on the advancement of the in-kind contribution concept. The overall aim is to contribute to the efficiency and uptake of the ERIC's research infrastructure offerings. 2

Am 25./26. August 2020 wurde diskutiert, wie die Germanistik die Umstellung auf digitale Lehre in der Pandemie bewältigt hat und welche Schlüsse für das Fach kurz-, mittel- und langfristig daraus gezogen werden können.

Digital research infrastructures can be divided into four categories: large equipment, IT infrastructure, social infrastructure, and information infrastructure. Modern research institutions often employ both IT infrastructure and information infrastructure, such as databases or large-scale research data. In addition, information infrastructure depends to some extent on IT infrastructure. In this paper, we discuss the IT, information, and legal infrastructure issues that research institutions face.