scholarly journals CaosDB—Research Data Management for Complex, Changing, and Automated Research Workflows

Data ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 83 ◽  
Author(s):  
Timm Fitschen ◽  
Alexander Schlemmer ◽  
Daniel Hornung ◽  
Henrik tom Wörden ◽  
Ulrich Parlitz ◽  
...  

We present CaosDB, a Research Data Management System (RDMS) designed to ensure seamless integration of inhomogeneous data sources and repositories of legacy data in a FAIR way. Its primary purpose is the management of data from biomedical sciences, both from simulations and experiments during the complete research data lifecycle. An RDMS for this domain faces particular challenges: research data arise in huge amounts, from a wide variety of sources, and traverse a highly branched path of further processing. To be accepted by its users, an RDMS must be built around workflows of the scientists and practices and thus support changes in workflow and data structure. Nevertheless, it should encourage and support the development and observation of standards and furthermore facilitate the automation of data acquisition and processing with specialized software. The storage data model of an RDMS must reflect these complexities with appropriate semantics and ontologies while offering simple methods for finding, retrieving, and understanding relevant data. We show how CaosDB responds to these challenges and give an overview of its data model, the CaosDB Server and its easy-to-learn CaosDB Query Language. We briefly discuss the status of the implementation, how we currently use CaosDB, and how we plan to use and extend it.

2021 ◽  
Vol 9 ◽  
Author(s):  
Javad Chamanara ◽  
Jitendra Gaikwad ◽  
Roman Gerlach ◽  
Alsayed Algergawy ◽  
Andreas Ostrowski ◽  
...  

Obtaining fit-to-use data associated with diverse aspects of biodiversity, ecology and environment is challenging since often it is fragmented, sub-optimally managed and available in heterogeneous formats. Recently, with the universal acceptance of the FAIR data principles, the requirements and standards of data publications have changed substantially. Researchers are encouraged to manage the data as per the FAIR data principles and ensure that the raw data, metadata, processed data, software, codes and associated material are securely stored and the data be made available with the completion of the research. We have developed BEXIS2 as an open-source community-driven web-based research data management system to support research data management needs of mid to large-scale research projects with multiple sub-projects and up to several hundred researchers. BEXIS2 is a modular and extensible system providing a range of functions to realise the complete data lifecycle from data structure design to data collection, data discovery, dissemination, integration, quality assurance and research planning. It is an extensible and customisable system that allows for the development of new functions and customisation of its various components from database schemas to the user interface layout, elements and look and feel. During the development of BEXIS2, we aimed to incorporate key aspects of what is encoded in FAIR data principles. To investigate the extent to which BEXIS2 conforms to these principles, we conducted the self-assessment using the FAIR indicators, definitions and criteria provided in the FAIR Data Maturity Model. Even though the FAIR data maturity model is developed initially to judge the conformance of datasets, the self-assessment results indicated that BEXIS2 remarkably conforms and supports FAIR indicators. BEXIS2 strongly conforms to the indicators Findability and Accessibility. The indicator Interoperability is moderately supported as of now; however, for many of the lesssupported facets, we have concrete plans for improvement. Reusability (as defined by the FAIR data principles) is partially achieved. This paper also illustrates community deployment examples of the BEXIS2 instances as success stories to exemplify its capacity to meet the biodiversity and ecological data management needs of differently sized projects and serve as an organisational research data management system.


Author(s):  
Iryna Mozgova ◽  
Oliver Koepler ◽  
Angelina Kraft ◽  
Roland Lachmayer ◽  
Sören Auer

2020 ◽  
Vol 40 (03) ◽  
pp. 139-146 ◽  
Author(s):  
Anjana R Bunkar ◽  
Dhaval D. Bhatt

Research data management is a system that helps in archiving and retrieving of research data to reuse and preserving them for long term use. Many universities in developed countries have already started providing RDM services to their researchers and academicians. In India, it is still in the initial stage. The purpose of the present study is to investigate the perceptions of researchers and academicians of Parul University on research data management and research data sharing. It also explores the ways the researchers preserved their research data for future use. It also explores the ways the library can take initiatives to encourage and extend support to the researchers and academicians to the organisation, preservation, and sharing of research data. To investigate and study the problem 100 questionnaires were distributed. There are 88 responses we received out of 100. The study revealed that the majority of respondents were agreeing about the research data sharing and free accessibility of research data to browse and reuse. Researchers are very much interested and agreed in the library’s involvement in organizing and preservation of research data. Researchers and faculty members are more concerned about their intellectual property rights while sharing the data on the public domain.


2015 ◽  
Author(s):  
Karlheinz Pappenberger

See video of the presentation.On 17th July 2015 the Ministry of Science, Research and the Arts for Baden-Wuerttemberg, Germany, invited national experts to the presentation of the final report of the ‘bwFDM communities’ project. This 18 month project was launched at the beginning of 2014 to evaluate the needs of services and the support that libraries and IT service centres should offer researchers in the area of research data management. Full-time key project staff had been established at all 9 universities in the state of Baden-Wuerttemberg to conduct semi-structured personal interviews of all research groups working with research data (in a broad sense including all areas of science, social science and humanities) and to document them in the form of user stories. 627 interviews have been conducted and more than 2,500 user stories could be extracted, showing the wide range of needs and wishes articulated by researchers. On this basis issues of importance and requirements had be identified, categorised in 18 different groups and finalised into an analysis of the status quo and recommendations for concrete action plans. The results cover the areas ‘general requirements and policy framework’, ‘data collection and data sharing’, ‘technical framework and virtual research environments’, ‘preservation’,  ‘IT infrastructure and IT support’, ‘licencing’ and ‘Open Science’.The presentation will give an overview of the project results and will highlight the roles libraries and IT service centres are expected to play from the researcher´s point of view.As the final report to the Ministry contributes to a comprehensive research data management strategy for the State of Baden-Wuerttemberg, the presentation will also point out the status of the federal strategy in RDM.


2015 ◽  
Author(s):  
Barbara Sánchez Solís

In early 2015, the scientific and artistic-scientific personnel at all 21 universities and three non-university research institutions in Austria received the call to participate in an Austria-wide survey relating to research data. The survey was conducted within the project entitled e-Infrastructures Austria and facilitated the collection of the practical handling of digital data. The findings from this survey form the basis for a consecutive optimization of the infrastructures and services available in this field, in accordance with needs that have been expressed.Solid research data management is the foundation of cooperative and open research and thus of its comprehensibility and verifiability. The subject is equally relevant and current for researchers, funding bodies and senior posts of scientific institutions.The poster visualizes the main findings of this ambitious survey. For the first time, the status quo of domestic data management is displayed in a representative manner crossing all disciplines. This can be used by individual institutions to derive organizational, structural or strategic measures for the development of infrastructures. On the other hand, it allows a co-ordinated, Austria-wide approach for identifying common areas of responsibility and for establishing central services.


Author(s):  
Frank Oliver Glöckner ◽  
Michael Diepenbroek

Background: The NFDI process in Germany The digital revolution is fundamentally transforming research data and methods. Mastering this transformation poses major challenges for stakeholders in the domains of science and policy. The process of digitalisation creates immense opportunities, but it must be structured proactively. To this end, the establishment of effective governance mechanisms for research data management (RDM) is of fundamental importance and will be one key driver for successful research and innovation in the future. In 2016 the German Council for Information Infrastructures (RfII) recommended the establishment of a “Nationale Forschungsdateninfrastruktur” (National Research Data Infrastructure, or NFDI), which will serve as the backbone for research data management in Germany. The NFDI should be implemented as a dynamic national collaborative network that grows over time and is composed of various specialised nodes (consortia). The talk will provide a short overview of the status and objectives of the NFDI. It will commence with a description of the goals of the NFDI4BioDiversity consortium which was established for the targeted support of the biodiversity community with data management. The NFDI4BioDiversity Consortium: Biodiversity, Ecology & Environmental Data Biodiversity is more than just the diversity of living species. It includes genetic diversity, functional diversity, interactions and the diversity of whole ecosystems. Mankind continuous to dramatically impact the earth’s ecosystem: species dying-out genetic diversity as well as whole ecosystems are endangered or already lost. Next to the loss of charismatic species and conspicuous change in ecosystems, we are experiencing a quiet loss of common species which together has captured high level policy attention. This has impacts on vital ecosystem services that provide the foundation of human well-being. A general understanding of the status, trends and drivers of the biodiversity on earth is urgently needed to devise conservation responses. Besides the fact that data are often scattered across repositories or not accessible at all, the main challenge for integrative studies is the heterogeneity of measurements and observation types, combined with a substantial lack of documentation. This leads to inconsistencies and incompatibilities in data structures, interfaces and semantics and thus hinders the re-usability of data to answer scientifically and socially relevant questions. Synthesis as well as hypothesis generation will only proceed when data are compliant with the FAIR (Findable, Accessible, Interoperable and Re-usable) data principles. Over the last five years these key challenges have been addressed by the DFG funded German Federation for Biological Data (GFBio) project. GFBio encompasses technical, organizational, financial, and community aspects to raise awareness for research data management in biodiversity research and environmental sciences. To foster sustainability across this federated infrastructure the not-for-profit association “Gesellschaft für biologische Daten e.V. (GFBio e.V.)” has been set up in 2016 as an independent legal entity. NFDI4BioDiversity builds on the experience and established user community of GFBio and takes advantage of GFBio e.V. GFBio already comprises data centers for nucleotide and environmental data as well as the seven well-established data centers of Germany´s largest natural science research facilities, museums and world’s most diverse microbiological resource collection. The network is now extended to include the network of botanical gardens and the largest collections of crop plants and their wild relatives. All collections together host more than 75% of all museum objects (150 millions) in Germany and >80% of all described microbial species. They represent the biggest and internationally-relevant data repositories. NFDI4BioDiversity will extend its community engagement at the science-society-policy interface by including farm animal biology, crop sciences, biodiversity monitoring and citizen science, as well as systems biology encompassing world-leading tools and collections for FAIR data management. Partners of the German Network for Bioinformatics Infrastructure (de.NBI) provide large scale data analysis and storage capacities in the cloud, as well as extensive continuous training and education experiences. Dedicated personnel will be responsible for the mutual exchange of data and experiences with NFDI4Life-Umbrella,NFDI4Earth, NFDI4Chem, NFDI4Health and beyond. As digitalization and liberation of data proceeds, NFDI4BioDiversity will foster community standards, quality management and documentation as well as the harmonization and synthesis of heterogeneous data. It will pro-actively engage the user community to build a coordinated data management platform for all types of biodiversity data as a dedicated added value service for all users of NFDI.


Sign in / Sign up

Export Citation Format

Share Document