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Handreichung Urheberrecht und Datenschutz
2023, Blumtritt, Ute, Euler, Ellen, Fadeevy, Yuliya, Pohle, Jörg, Rack, Fabian, Wrzesinski, Marcel
Die vorliegende Handreichung adressiert wissenschaftsgeleitete Zeitschriften sowie herausgebende Einrichtungen. Sie sollen in die Lage versetzt werden, erste urheberrechtliche wie datenschutzrechtliche Fragen zu beantworten und dabei Qualitätsstandards einzuhalten. Dieser Text ersetzt keine Rechtsberatung, sondern bietet grundsätzliche Informationen, gibt Empfehlungen zum Weiterlesen für klassische Fragestellungen und verweist auf gelungene Beispiele im weiteren Feld des wissenschaftsgeleiteten Publizierens.
Diving into Knowledge Graphs for Patents: Open Challenges and Benefits
2023, Dessi, Danilo, Dessi, Rima, Alam, Mehwish, Trojahn, Cassia, Hertling, Sven, Pesquita, Catia, Aebeloe, Christian, Aras, Hidir, Azzam, Amr, Cano, Juan, Domingue, John, Gottschalk, Simon, Hartig, Olaf, Hose, Katja, Kirrane, Sabrina, Lisena, Pasquale, Osborne, Francesco, Rohde, Philipp, Steels, Luc, Taelman, Ruben, Third, Aisling, Tiddi, Ilaria, Türker, Rima
Textual documents are the means of sharing information and preserving knowledge for a large variety of domains. The patent domain is also using such a paradigm which is becoming difficult to maintain and is limiting the potentialities of using advanced AI systems for domain analysis. To overcome this issue, it is more and more frequent to find approaches to transform textual representations into Knowledge Graphs (KGs). In this position paper, we discuss KGs within the patent domain, present its challenges, and envision the benefits of such technologies for this domain. In addition, this paper provides insights of such KGs by reproducing an existing pipeline to create KGs and applying it to patents in the computer science domain.
NFDI4Chem - A Research Data Network for International Chemistry
2023, Steinbeck, Christoph, Koepler, Oliver, Herres-Pawlis, Sonja, Bach, Felix, Jung, Nicole, Razum, Matthias, Liermann, Johannes C., Neumann, Steffen
Research data provide evidence for the validation of scientific hypotheses in most areas of science. Open access to them is the basis for true peer review of scientific results and publications. Hence, research data are at the heart of the scientific method as a whole. The value of openly sharing research data has by now been recognized by scientists, funders and politicians. Today, new research results are increasingly obtained by drawing on existing data. Many organisations such as the Research Data Alliance (RDA), the goFAIR initiative, and not least IUPAC are supporting and promoting the collection and curation of research data. One of the remaining challenges is to find matching data sets, to understand them and to reuse them for your own purpose. As a consequence, we urgently need better research data management.
The integration of OEIS links in zbMATH Open
2023, Ehsani, Dariush, Petrera, Matteo, Teschke, Olaf
The transition towards an Open Data Platform enabled zbMATH Open to build a network of open resources. Important components in the evolving information system are mathematical research data, which are of quite heterogeneous nature. For their interlinking, zbMATH Open provides Application Programming Interface (API) solutions to offer mathematical research data to the community. Among other APIs recently implemented at zbMATH Open, the so-called Links API is aimed to document interconnections between our database and external platforms which display mathematical literature indexed at zbMATH Open. The Digital Library of Mathematical Functions (DLMF) has been our first partner and their data have been integrated in our database in 2021. Recently we interlinked with the second platform, the Online Encyclopedia of Integer Sequences (OEIS), a renowned digital database of number sequences that cites a lot of mathematical literature, especially from number theory and graph theory. The purpose of this short contribution is to announce and discuss the links to OEIS data in zbMATH Open.
A Data Model for Linked Stage Graph and the Historical Performing Arts Domain
2023, Tietz, Tabea, Bruns, Oleksandra, Sack, Harald, Bikakis, Antonis, Ferrario, Roberta, Jean, Stéphane, Markhoff, Béatrice, Mosca, Alessandro, Nicolosi Asmundo, Marianna
The performing arts are complex, dynamic and embedded into societal and political systems. Providing means to research historical performing arts data is therefore crucial for understanding our history and culture. However, currently no commonly accepted ontology for historical performing arts data exists. On the example of the Linked Stage Graph, this position paper presents the ongoing process of creating an application-driven and efficient data model by leveraging and building upon existing standards and ontologies like CIDOC-CRM, FRBR, and FRBRoo.
TRANSRAZ Data Model: Towards a Geosocial Representation of Historical Cities
2023, Bruns, Oleksandra, Tietz, Tabea, Göller, Sandra, Sack, Harald, Acosta, M., Peroni, S., Vahdati, S., Gentile, A.-L., Pellegrini, T., Kalo, J.-C.
Preserving historical city architectures and making them (publicly) available has emerged as an important field of the cultural heritage and digital humanities research domain. In this context, the TRANSRAZ project is creating an interactive 3D environment of the historical city of Nuremberg which spans over different periods of time. Next to the exploration of the city’s historical architecture, TRANSRAZ is also integrating information about its inhabitants, organizations, and important events, which are extracted from historical documents semi-automatically. Knowledge Graphs have proven useful and valuable to integrate and enrich these heterogeneous data. However, this task also comes with versatile data modeling challenges. This paper contributes the TRANSRAZ data model, which integrates agents, architectural objects, events, and historical documents into the 3D research environment by means of ontologies. Goal is to explore Nuremberg’s multifaceted past in different time layers in the context of its architectural, social, economical, and cultural developments.
Verantwortungsbewusster Umgang mit IT-Sicherheitslücken : Problemlagen und Optimierungsoptionen für ein effizientes Zusammenwirken zwischen IT-Sicherheitsforschung und IT-Verantwortlichen
2023, Wagner, Manuela, Vettermann, Oliver, Arzt, Steven, Brodowski, Dominik, Dickmann, Roman, Golla, Sebastian, Goerke, Niklas, Kreutzer, Michael, Leicht, Maximilian, Obermaier, Johannes, Schink, Marc, Schreiber, Linda, Sorge, Christoph
IT-Sicherheitslücken in Hard- und Software betreffen private, unternehmerische und auch staatliche Systeme. Sobald eine Ausnutzung der Lücken technisch möglich ist, stellen sie eine Bedrohung für die IT-Sicherheit aller Beteiligten dar. Konkret betroffen sind Bürger:innen und Unternehmen als Nutzende, Hersteller von Soft- und Hardware sowie staatliche (kritische) IT-Infrastruktur. Es ist daher im gesamtgesellschaftlichen Interesse, die Zahl der ausnutzbaren Sicherheitslücken so gering wie möglich zu halten. Dieses Whitepaper führt in die rechtlichen und praktischen Probleme der IT-Sicherheitsforschung ein. Zugleich zeigt es vor allem rechtliche Auswege auf, die perspektivisch zu einer rechtssicheren IT-Sicherheitsforschung führen.
Exploring the Impact of Negative Sampling on Patent Citation Recommendation
2023, Dessi, Rima, Aras, Hidir, Alam, Mehwish
Due to the increasing number of patents being published every day, patent citation recommendations have become one of the challenging tasks. Since patent citations may lead to legal and economic consequences, patent recommendations are even more challenging as compared to scientific article citations. One of the crucial components of the patent citation algorithm is negative sampling which is also a part of many other tasks such as text classification, knowledge graph completion, etc. This paper, particularly focuses on proposing a transformer-based ranking model for patent recommendations. It further experimentally compares the performance of patent recommendations based on various state-of-the-art negative sampling approaches to measure and compare the effectiveness of these approaches to aid future developments. These experiments are performed on a newly collected dataset of US patents from Google patents.
Incentive Mechanisms in Peer-to-Peer Networks — A Systematic Literature Review
2023, Ihle, Cornelius, Trautwein, Dennis, Schubotz, Moritz, Meuschke, Norman, Gipp, Bela
Centralized networks inevitably exhibit single points of failure that malicious actors regularly target. Decentralized networks are more resilient if numerous participants contribute to the network’s functionality. Most decentralized networks employ incentive mechanisms to coordinate the participation and cooperation of peers and thereby ensure the functionality and security of the network. This article systematically reviews incentive mechanisms for decentralized networks and networked systems by covering 165 prior literature reviews and 178 primary research papers published between 1993 and October 2022. Of the considered sources, we analyze 11 literature reviews and 105 primary research papers in detail by categorizing and comparing the distinctive properties of the presented incentive mechanisms. The reviewed incentive mechanisms establish fairness and reward participation and cooperative behavior. We review work that substitutes central authority through independent and subjective mechanisms run in isolation at each participating peer and work that applies multiparty computation. We use monetary, reputation, and service rewards as categories to differentiate the implementations and evaluate each incentive mechanism’s data management, attack resistance, and contribution model. Further, we highlight research gaps and deficiencies in reproducibility and comparability. Finally, we summarize our assessments and provide recommendations to apply incentive mechanisms to decentralized networks that share computational resources.
Data Steward Service Center (DSSC): FAIRagro RDM-Expertise Hub
2023, Svoboda, Nikolai, Vedder, Lucia, Böhm, Franziska, Möller, Markus, Rey-Mazón, Elena, Schmidt, Marcus, Lindstädt, Birte, Stahl, Ulrike
The Data Steward Service Center (DSSC) is the central institution within FAIRagro to develop data management tools based on the needs of the scientific community. The DSSC organizes the continuous exchange of RDM knowledge and experience with other institutions, channels user requests from the community, and transfers knowledge from the FAIRagro task areas to the FAIRagro data stewards. FAIRagro data stewards are experts in the field of RDM for agrosystems research supervising and will train data curators in our community. Data stewards have core competencies in research data management (e.g., cross-scale from genes, phenomics, management to region; sensitive data, remote sensing, time series, plant, soil and related FAIRagro data). Knowledge and expertise is pooled to provide the full range of expertise to the community in one place to foster the coalescence of the community. The DSSC is headed by a coordinator and will house five data stewards, who are active in the community e.g. train data curators, give legal support. In the course of the project, further institutional or project data stewards will be integrated and the pool of experts will be further expanded. The network to the other NFDI consortia is continuously growing.