Proceedings of the Conference on Research Data Infrastructure最新文献

{"title":"RSpace + iRODS A Scalable, Flexible and Versatile Solution That Facilitates Data and Metadata Interoperability and is Suitable for Deployment in Conjunction With a Wide Range of E-infrastructures and Research Commons","authors":"Rory Macneil, Terrell Russell","doi":"10.52825/cordi.v1i.305","DOIUrl":"https://doi.org/10.52825/cordi.v1i.305","url":null,"abstract":"Research infrastructures enabling scalable sharing of data are proliferating, at the institutional, project/consortium and national/international levels.  Many of these are domain-specific, but there also is a growing focus on Research Commons that enable general data sharing across domains.  Examples include the EUDAT Collaborative Data Infrastructure in Europe, the Gakunin RDM platform in Japan, and the Research Commons planned by Canada’s Digital Research Alliance (DRA).\u0000In some cases, such as Gakunin RDM, Research Commons are architected as an integrated set of complementary services, but in most existing and planned Research Commons a disparate set of unconnected services is offered.  This paper describes the integration between RSpace, an active content management digital research platform, and iRODS, a policy-driven data management platform, and explains how it is designed to serve as a flexible connecting component that ties together other services that make up a Research Commons.\u0000The paper discusses how, by tying together otherwise unconnected services, inclusion of RSpace + iRODS in Research Commons enables streamlined flows of data and metadata between services, enhancing FAIR principles.  This will be illustrated by considering inclusion of RSpace + iRODS in the two specific examples of Canada’s proposed Research Commons and the EUDAT Collaborative Data Infrastructure.  In both cases the interaction between RSpace, iRODS, and individual services provided  by the Commons will be discussed, and a comparison will be made between the two Commons and the benefits derived from the inclusion of RSpace + iRODS.","PeriodicalId":359879,"journal":{"name":"Proceedings of the Conference on Research Data Infrastructure","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124293995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RDM Services at the Luxembourg National Data Service","authors":"Wei Gu, C. Trefois, Pinar Alper, Danielle Welter, Y. Jarosz, Jacek Lebioda, Linda Ebermann, Regina Becker, V. Satagopam, Reinhard Schneider, Bert Verdonck","doi":"10.52825/cordi.v1i.247","DOIUrl":"https://doi.org/10.52825/cordi.v1i.247","url":null,"abstract":"The Luxembourg National Data Service (LNDS) was established on 28 July 2022 by the Luxembourg government and public research institutes. LNDS is a national organisation providing services for value creation from public sector data from different domains. LNDS’ primary mission is to offer technology and data services, know-how, capabilities, platform, and infrastructure to enable sharing and re-use of data for public and private data partners. LNDS aims to become a key component of Luxembourg's data innovation strategy by (1) supporting research and innovation through a high-quality service offering, (2) contributing to accelerating the development of Luxembourg towards a data economy, (3) enabling the participation of both public research and private actors in the complete value-creating chain of data, (4) enhancing data in the context of joint innovation among all actors, and (5) becoming a partner of choice for joint development of the next generation of data products.","PeriodicalId":359879,"journal":{"name":"Proceedings of the Conference on Research Data Infrastructure","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131488014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stamp - Standardized Data Management Plan for Educational Research An Approach to Improve Cross-Disciplinary Harmonization of Research Data Management","authors":"Sebastian Netscher, Alexia Meyermann, Julia Künstler-Sment, Lisa Pegelow","doi":"10.52825/cordi.v1i.372","DOIUrl":"https://doi.org/10.52825/cordi.v1i.372","url":null,"abstract":"While there is a strong tendency towards a harmonized, cross-disciplinary research data management (RDM) (Netscher et al., 2022), researchers require more guidelines and examples, tailored to their research discipline (Grootveld et al., 2018). Therefore, Science Europe (2018: 9) proposes developing so-called domain data protocols (DDP), “a ‘model DMP’ for a given domain or community”. Based on this concept, the project Domain Data Protocols for Educational Research[1] designed the Stamp - Standardized Data Management Plan for Educational Research (DDP-Bildung and German Network of Educational Research Data, 2023).\u0000Although the Stamp was designed to support researchers in educational research, we expect that RDM is rather a matter of the data processed, the methods employed, and the content of data, than of a particular research discipline or community, such as educational research. To discuss this expectation and the usability of the Stamp outside educational research, we organized various workshops with representatives from other research disciplines. In our talk at the CoRDI 2023, we will introduce the Stamp, recap findings of two of the workshops, introduce the next steps to examine the useability of the Stamp outside educational research, and draw some conclusions on how to adapt the Stamp to other disciplines and how it fosters a harmonized, cross-disciplinary RDM.\u0000 \u0000[1]     The project DDP-Bildung was funded by the German Federal Ministry of Education and Research (grant number: 16QK01).","PeriodicalId":359879,"journal":{"name":"Proceedings of the Conference on Research Data Infrastructure","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133285308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NFDI4Health Local Data Hubs for Finding and Accessing Health Data Making Distributed Data Accessible Through a SEEK-Based Platform","authors":"F. Meineke, Martin Golebiewski, Xiaoming Hu, Toralf Kirsten, Matthias Löbe, Sebastian Klammt, Ulrich Sax, Wolfgang Müller","doi":"10.52825/cordi.v1i.375","DOIUrl":"https://doi.org/10.52825/cordi.v1i.375","url":null,"abstract":"To support federated data structuring and sharing for sensitive health data from clinical trial, epidemiological and public health studies in the context of the German National Research Data Infrastructure for Personal Health Data (NFDI4Health), we have developed Local Data Hubs (LDHs) based on the FAIRDOM-SEEK platform. Those LDHs connect to the German Central Health Study Hub (CSH) to make the health data searchable and findable. This decentralised approach supports researchers to make health studies with their data FAIR (Findable, Accessible, Interoperable and Reusable), and at the same time fully preserves data protection for sensitive data.","PeriodicalId":359879,"journal":{"name":"Proceedings of the Conference on Research Data Infrastructure","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127866571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality Assessment for Research Data Management in Research Projects","authors":"Max Leo Wawer, Johanna Wurst, Roland Lachmayer","doi":"10.52825/cordi.v1i.420","DOIUrl":"https://doi.org/10.52825/cordi.v1i.420","url":null,"abstract":"In the context of research data management (RDM), researchers are confronted with a multitude of new tasks and responsibilities. The totality of all tasks to ensure the re-use of data, long-term archiving, and access to data through data management planning, further data documentation, and provinces of data collection and analysis are described as research data management [1]. Often, the process of RDM is represented with data life cycle models, which include the basic phases of planning, data collection, analysis, archiving, access, and reuse [2].","PeriodicalId":359879,"journal":{"name":"Proceedings of the Conference on Research Data Infrastructure","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124123616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conda, Container and Bots How to Build and Maintain Tool Dependencies in Workflows and Training Materials","authors":"Paul Zierep, Sanjay Kumar Srikakulam, Sebastian Schaaf, Björn A. Grüning","doi":"10.52825/cordi.v1i.417","DOIUrl":"https://doi.org/10.52825/cordi.v1i.417","url":null,"abstract":"Abstract. The lifecycle of scientific tools comprises the creation of code releases, packages and containers which can be deployed into cloud platforms, such as the Galaxy Project, where they are run and integrated into workflows. The tools and workflows are further used to create training material that benefits a broad community. The need to organize and streamline this tool development lifecycle has led to a sophisticated development and deployment architecture.","PeriodicalId":359879,"journal":{"name":"Proceedings of the Conference on Research Data Infrastructure","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117042728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Documentation of Research Processes Using RDM","authors":"Lars Griem, Richard Thelen, Michael Selzer","doi":"10.52825/cordi.v1i.411","DOIUrl":"https://doi.org/10.52825/cordi.v1i.411","url":null,"abstract":"Published research results usually represent only a fraction of the data generated at a research institute. The unpublished data created in the process of producing the final result, however, often contain valuable information that can be reused. Through research data management, all these data should be stored centrally according to the FAIR principles (Findable, Accessible, Interoperable, Reusable). However, a significant part of knowledge is often not found in the data, but in the processes that led to their generation. It is therefore important to map these processes to archive and document this knowledge in a structured way. Procedures for documenting scientific processes already exist and are actively used at research institutes. However, these are often analogue or paper-based and hence do not meet the requirements for FAIR data management. At the Institute for Microstructure Technology of the KIT, such a paper-based procedure is used to document the production of microstructure components. During their manufacturing, it is essential to adhere to the correct process parameters in order to enable error-free production. Therefore, a so-called job ticket always accompanies the production of components. On this job ticket, the correct process sequence is listed and a detailed description of the respective process step is given. Depending on the component to be produced, a distinction is made between different types of job tickets according to internal conventions. On the one hand, there are so-called green job tickets, which describe a standardised process sequence, and on the other hand, blue job tickets, which are intended to document experimental manufacturing processes. The process sequence on the blue job tickets is initially empty and is filled in during the manufacturing process. Common to both types of job tickets is that they are stored in the institute's archive after completion of the component production. However, since the job tickets are paper-based, the corresponding archive of job tickets cannot be searched quickly and, given the sheer volume of archived job tickets, represents an unmanageable collection of data. The existing system for process documentation is therefore to be implemented with the help of the research data infrastructure Kadi4Mat [1] in accordance with FAIR principles, thereby making the available process knowledge more accessible.","PeriodicalId":359879,"journal":{"name":"Proceedings of the Conference on Research Data Infrastructure","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123659427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The HMC Information Portal for Enhanced Metadata Collaboration in the Helmholtz FAIR Data Space","authors":"Lucas Kulla, Jens Bröder, Constanze Curdt, Markus Kubin, Helen Kollai, Christine Lemster, Marco Nolden, Kai Schmieder, Annika Strupp, K. Stucky, Emanuel Söding, Konstantin Pascal Walter, Arndt Witold","doi":"10.52825/cordi.v1i.383","DOIUrl":"https://doi.org/10.52825/cordi.v1i.383","url":null,"abstract":"The Helmholtz Metadata Collaboration (HMC) platform was launched in late 2019 to turn FAIR (Findable, Accessible, Interoperable, Reusable) research data into reality within the Helmholtz Association and beyond. The Information Portal was initiated to enable the structured cartography of metadata and FAIR landscape of Helmholtz, providing information for multi-level decision-making and creating a curated knowledge base for research data managers, scientists and other stakeholders. \u0000Developed through a top-down approach, 18 categories, and associated metadata schemas were defined and aligned by an HMC taskforce. Data curation followed, with resources collected from different domains based on the aligned metadata schema. The Information Portal is a web application for capturing FAIR data practices across all Helmholtz domains, offering a unified user interface for collecting and exploring  results. \u0000Built using state-of-the-art technologies, including Python and Docker, the Information Portal leverages  GitLab as a database. It offers a public / central  read-only version for stakeholders and a personal instance for curation - synchronized to a GitLab repository. Git-based systems offer advantages, such as raw data accessibility, flexible data curation, easy synchronization, and customizable repositories. \u0000The single-page web application is user-friendly and developed in multiple iterations for an intuitive and flexible interface. The Information Portal is crucial for creating a sustainable, distributed, semantically enriched Helmholtz data space, promoting seamless data sharing and reuse.","PeriodicalId":359879,"journal":{"name":"Proceedings of the Conference on Research Data Infrastructure","volume":"285 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132027269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Isn't a Number and a URL Enough? Why PIDs Matter and Technical Solutions Alone are not Sufficient","authors":"Stephanie Hagemann-Wilholt, Antonia C. Schrader, Andreas Czerniak","doi":"10.52825/cordi.v1i.291","DOIUrl":"https://doi.org/10.52825/cordi.v1i.291","url":null,"abstract":"Persistent identifiers (PIDs) are an integral part of research data management and can be found throughout the entire lifecycle of research data. However, their ability to function – to ensure persistence – depends on numerous factors: technical infrastructure, international standards and best practices and their dissemination, agreements on long-term governance of infrastructures, etc. Their applicability is diverse and requires adaptation to the resources and entities referenced by them. The paper describes two projects – PID4NFDI and PID Network Germany – that aim to address these challenges.","PeriodicalId":359879,"journal":{"name":"Proceedings of the Conference on Research Data Infrastructure","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132138797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Publication for Personalised Health Data A New Publication Standard Introduced by NFDI4Health","authors":"Juliane Fluck, Martin Golebiewski, Johannes Darms","doi":"10.52825/cordi.v1i.392","DOIUrl":"https://doi.org/10.52825/cordi.v1i.392","url":null,"abstract":"Health data collected in clinical trials and epidemiological as well as public health studies cannot be freely published, but are valuable datasets whose subsequent use is of high importance for health research. The National Research Data Infrastructure for Personal Health Data (NFDI4Health) aims to promote the publication of such health data without compromising privacy. Based on existing international standards, NFDI4Health has established a generic information model for the description and preservation of high-level metadata describing health-related studies, covering both clinical and epidemiological studies. As an infrastructure for publishing such preservation metadata as well as more detailed representation information of study data (e.g. questionaries and data dictionaries), NFDI4Health has developed the German Central Health Study Hub. Content is either harvested from existing distributed sources or entered directly via a user interface. This metadata makes health studies more discoverable, and researchers can use the published metadata to evaluate the content of data collections, learn about access conditions and how and where to request data access. The goal of NFDI4Health is to establish interoperable and internationally accepted standards and processes for the publication of health data sets to make health data FAIR.","PeriodicalId":359879,"journal":{"name":"Proceedings of the Conference on Research Data Infrastructure","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127980426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}