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The aim of the study was to compile a conceptual model of a Virtual Research Environment (VRE) that indicates the relationship between Research Data Management (RDM) and VREs. The outcome of this study was that VREs are ideal platforms for the management of research data. In the first part of the study, a literature review was conducted by focusing on four themes: VREs and other concepts related to VREs; VRE components and tools; RDM; and the relationship between VREs and RDM. The first theme included a discussion of definitions of concepts, approaches to VREs, their development, aims, characteristics, similarities and differences of concepts, an overview of the e-Research approaches followed in this study, as well as an overview of concepts used in this study. The second theme consisted of an overview of developments of VREs in four countries (United Kingdom, USA, The Netherlands, and Germany), an indication of the differences and similarities of these programmes, and a discussion on the concept of research lifecycles, as well as VRE components. These components were then matched with possible tools, as well as to research lifecycle stages, which led to the development of a first conceptual VRE framework. The third theme included an overview of the definitions of the concepts ‘data’ and ‘research data’, as well as RDM and related concepts, an investigation of international developments with regards to RDM, an overview of the differences and similarities of approaches followed internationally, and a discussion of RDM developments in South Africa. This was followed by a discussion of the concept ‘research data lifecycles’, their various stages, corresponding processes and the roles various stakeholders can play in each stage. The fourth theme consisted of a discussion of the relationship between research lifecycles and research data lifecycles, a discussion on the role of RDM as a component within a VRE, the management of research data by means of a VRE, as well as the presentation of a possible conceptual model for the management of research data by means of a VRE. This literature review was conducted as a background and basis for this study. In the second part of the study, the research methodology was outlined. The chosen methodology entailed a non-empirical part consisting of a literature study, and an empirical part consisting of two case studies from a South African University. The two case studies were specifically chosen because each used different methods in conducting research. The one case study used natural science oriented data and laboratory/experimental methods, and the other, human orientated data and survey instruments. The proposed conceptual model derived from the literature study was assessed through these case studies and feedback received was used to modify and/or enhance the conceptual model. The contribution of this study lies primarily in the presentation of a conceptual VRE model with distinct component layers and generic components, which can be used as technological and collaborative frameworks for the successful management of research data. / Thesis (DPhil)--University of Pretoria, 2018. / National Research Foundation / Information Science / DPhil / Unrestricted

Mit dem Ziel, das Publizieren von Artikeln, Forschungsdaten und wissenschaftlicher Software gemäß den FAIR-Prinzipien zu unterstützen, wurde am HZDR ein integriertes Publikationsmanagement aufgebaut. Insbesondere Daten- und Softwarepublikationen erfordern die Entwicklung bedarfsgerechter organisatorischer und technischer Strukturen ergänzend zu bereits sehr gut funktionierenden Services im Publikationsmanagement. In der Zusammenarbeit mit Wissenschaftlern des HZDR und internationalen Partnern in ausgewählten Projekten wurde der Bedarf an Unterstützung im Forschungsdatenmanagement analysiert. Darauf aufbauend wurde schrittweise ein integriertes System von Infrastrukturen und Services entwickelt und bereitgestellt. In einer seit Mai 2018 gültigen Data Policy wurden die Rahmenbedingungen und Regelungen sowohl für wissenschaftliche Mitarbeiter als auch für externe Messgäste definiert. Im Vortrag wird auf die Erfahrungen im integrierten Publikationsmanagement für Artikel, Forschungsdaten und Forschungssoftware eingegangen und daraus resultierend werden die nächsten Aufgaben und Ziele entwickelt.

In diesem 'Snack' stellen wir SaxFDM - die Sächsische Landesinitiative für Forschungsdatenmanagement - und deren Serviceangebote vor.

Die im Rahmen von Forschungsprojekten gewonnenen Forschungsdaten sind eine wesentliche Grundlage der wissenschaftlichen Arbeit. In nahezu allen Fachdisziplinen gewinnen sie immer mehr an Bedeutung. Die Nachvollziehbarkeit und die Qualität wissenschaftlicher Forschung wird durch die Dokumentation, die langfristige Sicherung und Bereitstellung der Forschungsdaten gefördert. Außerdem stellt die Publikation und die langfristige Sicherung von Forschungsdaten bei der DFG, EU und beim BMBF in vielen Fällen eine Voraussetzung für die Förderung von Forschungsvorhaben dar. Was genau sind Forschungsdaten? Was versteht man unter dem FAIR-Prinzip? Offene Forschungsdaten: Welche Vorteile bringen sie für die Forschenden? Wo kann man Forschungsdaten archivieren und veröffentlichen? Welche Services für Forschende der TU Bergakademie Freiberg zum Thema Forschungsdaten bieten die UB Freiberg und die Kontaktstelle Forschungsdaten der SLUB/ZiH Dresden? Diese und weitere Fragen beantworten Manuela Queitsch, Koordinatorin für Forschungsdaten an der SLUB Dresden und Teammitglied an der Kontaktstelle Forschungsdaten in Dresden und Dr. Dana Kuhnert, Fachreferentin für Wirtschafts- und Rechtswissenschaften der UB Bergakademie Freiberg.

Am 28.11.2023 hat der Senat der Technischen Universität Bergakademie Freiberg eine institutionelle Forschungsdaten-Policy verabschiedet, die allen Wissenschaftlerinnen und Wissenschaftlern der Hochschule eine wichtige Orientierungshilfe für den Umgang mit Forschungsdaten bietet.

Presented at the University of Arizona 2016 IT Summit / The past several years has seen an increasing emphasis on providing access to the results of research, both publications and data. The majority of federal grant funding agencies require that researchers include a data management plan as part of their grant proposal. In response, the University of Arizona Libraries, in collaboration with the Office of Research and Discovery and the University Information Technology Services, has been providing data management services and resources to the campus for the past several years. In 2014, we conducted a research data management survey to find out how UA researchers manage their research data, determine the demand for existing services and identify new services that UA researchers need. In the fall of 2015, the Data Management and Data Publication and Curation (DMDC) Pilot was started to determine what specific services and tools, including training and support and the needed technology infrastructure, researchers need to effectively and efficiently manage and curate their research data. This presentation will present what data management services we currently are offering, discuss findings from the 2014 survey, and present initial results from the DMDC pilot.

Philosophiae Doctor - PhD / The emergence and subsequent uptake of Information and Communication Technologies has transformed the research processes in universities and research institutions across the globe. One indelible impact of Information and Communication Technologies on the research process is the increased generation of research data in digital format. This study investigated how research data has been generated, organised, shared, stored, preserved, accessed and re-used in Malawian public universities with a view to proposing a framework for research data management in universities in Malawi. The objectives of the study were: to determine research data creation, sharing and re-use practices in public universities in Malawi; to investigate research data preservation practices in public universities in Malawi; to investigate the competencies that librarians and researchers need to effectively manage research data; and to find out the challenges that affect the management of research data in public universities in Malawi. Apart from being guided by the Community Capability Model Framework (Lyon, Ball, Duke & Day, 2011) and Data Curation Centre Lifecycle Model (Higgins, 2008), the study was inspired by the pragmatic school of thought which is the basis for a mixed methods research enabling the collection of quantitative and qualitative data from two purposively selected universities. A census was used to identify researchers and librarians while purposive sampling was used to identify directors of research. Questionnaires were used to collect mostly quantitative and some qualitative data from 36 librarians and 187 researchers while interviews were conducted with directors of research. The Statistical Package for the Social Sciences was used to analyse the quantitative data by producing percentages, means, independent samples ttest and one-way analysis of variance. Thematic analysis was used to analyse the qualitative data.

by Tam Chi-Fai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 87-88). / abstract --- p.I / table of content --- p.II / list of figures --- p.V / acknowledgments --- p.VII / Chapter chapter 1 --- introduction --- p.1 / Chapter 1.1 --- Two Stage Operations Planning --- p.1 / Chapter 1.2 --- Iterative Activities in the Two Stage Planning Approach --- p.3 / Chapter 1.3 --- Expert Decision Support System for Two Stage Planning --- p.4 / Chapter 1.4 --- Scope of the Study --- p.5 / Chapter 1.5 --- Organization of the Thesis --- p.6 / Chapter chapter 2 --- literature review --- p.7 / Chapter 2.1 --- Network Design for Air Express Service --- p.7 / Chapter 2.2 --- Integrative Use of Optimization and Simulation Model --- p.8 / Chapter 2.3 --- Expert System & Decision Support System --- p.11 / Chapter 2.3.1 --- Expert System --- p.11 / Chapter 2.3.2 --- Decision Support System --- p.13 / Chapter 2.3.3 --- ES / DSS Integration --- p.14 / Chapter chapter 3 --- research methodology --- p.19 / Chapter 3.1 --- Review on DSS / ES Integration --- p.19 / Chapter 3.2 --- System Design --- p.20 / Chapter 3.3 --- Prototyping --- p.22 / Chapter 3.4 --- Analysis and Evaluation --- p.23 / Chapter chapter 4 --- system architecture and knowledge modeling --- p.24 / Chapter 4.1 --- Architecture Overview --- p.24 / Chapter 4.1.1 --- System Architecture and Interactions --- p.26 / Chapter 4.1.2 --- Decision Support System --- p.27 / Chapter 4.1.3 --- Expert System --- p.32 / Chapter 4.2 --- System Operations --- p.35 / Chapter 4.2.1 --- Operations Flow --- p.35 / Chapter chapter 5 --- case study and prototyping --- p.38 / Chapter 5.1 --- Case Background --- p.38 / Chapter 5.1.1 --- The Service Network --- p.38 / Chapter 5.1.2 --- Objectives of the Project --- p.40 / Chapter 5.1.3 --- Network Design Methodology --- p.41 / Chapter 5.2 --- Iterative Network Planning --- p.49 / Chapter 5.2.1 --- Multi-period Network Planning Feedback --- p.50 / Chapter 5.2.2 --- Feedback in Validation and Evaluation --- p.51 / Chapter 5.3 --- The System Prototype --- p.57 / Chapter 5.3.1 --- Data Management and Model Manipulation --- p.57 / Chapter 5.3.2 --- Intelligent Guidance for the Iterations --- p.65 / Chapter chapter 6 --- evaluation and analysis --- p.75 / Chapter 6.1 --- Test Scenario for Network Planning --- p.75 / Chapter 6.1.1 --- Consultation Process --- p.75 / Chapter 6.1.2 --- Consultation Results --- p.78 / Chapter 6.2 --- Effectiveness of EDSS in Network Planning --- p.81 / Chapter 6.3 --- Generalized Advancement and Limitation --- p.82 / Chapter chapter 7 --- conclusion --- p.85 / bibliography --- p.87 / appendices --- p.89

A ciência moderna, apoiada pela e-science, tem enfrentado desafios de lidar com o grande volume e variedade de dados, gerados principalmente pelos avanços tecnológicos nos processos de coleta e processamento dos dados científicos. Como consequência, houve também um aumento na complexidade dos processos de análise e experimentação. Estes processos atualmente envolvem múltiplas fontes de dados e diversas atividades realizadas por grupos de pesquisadores geograficamente distribuídos, que devem ser compreendidas, reutilizadas e reproduzíveis. No entanto, as iniciativas da comunidade científica que buscam disponibilizar ferramentas e conscientizar os pesquisadores a compartilharem seus dados e códigos-fonte, juntamente com as publicações científicas, são, em muitos casos, insuficientes para garantir a reprodutibilidade e o reuso das contribuições científicas. Esta pesquisa objetiva definir uma estratégia computacional para o apoio ao reuso e a reprodutibilidade dos dados científicos, por meio da gestão da proveniência dos dados durante o seu ciclo de vida. A estratégia proposta nesta pesquisa é apoiada em dois componentes principais, um perfil de aplicação, que define um modelo padronizado para a descrição da proveniência dos dados, e uma arquitetura computacional para a gestão dos metadados de proveniência, que permite a descrição, armazenamento e compartilhamento destes metadados em ambientes distribuídos e heterogêneos. Foi desenvolvido um protótipo funcional para a realização de dois estudos de caso que consideraram a gestão dos metadados de proveniência de experimentos de modelagem de distribuição de espécies. Estes estudos de caso possibilitaram a validação da estratégia computacional proposta na pesquisa, demonstrando o seu potencial no apoio à gestão de dados científicos. / Modern science, supported by e-science, has faced challenges in dealing with the large volume and variety of data generated primarily by technological advances in the processes of collecting and processing scientific data. Therefore, there was also an increase in the complexity of the analysis and experimentation processes. These processes currently involve multiple data sources and numerous activities performed by geographically distributed research groups, which must be understood, reused and reproducible. However, initiatives by the scientific community with the goal of developing tools and sensitize researchers to share their data and source codes related to their findings, along with scientific publications, are often insufficient to ensure the reproducibility and reuse of scientific results. This research aims to define a computational strategy to support the reuse and reproducibility of scientific data through data provenance management during its entire life cycle. Two principal components support our strategy in this research, an application profile that defines a standardized model for the description of provenance metadata, and a computational architecture for the management of the provenance metadata that enables the description, storage and sharing of these metadata in distributed and heterogeneous environments. We developed a functional prototype for the accomplishment of two case studies that considered the management of provenance metadata during the experiments of species distribution modeling. These case studies enabled the validation of the computational strategy proposed in the research, demonstrating the potential of this strategy in supporting the management of scientific data.