scholarly journals Implementation of an open adoption research data management system for clinical studies

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Jan Müller ◽  
Kirsten Ingmar Heiss ◽  
Renate Oberhoffer
Keyword(s):  
Data Management ◽  
Clinical Studies ◽  
Management System ◽  
Research Data ◽  
Data Management System ◽  
Open Adoption ◽  
Research Data Management ◽  
Adoption Research

scholarly journals BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data

2021 ◽  
Vol 9 ◽  
Author(s):  
Javad Chamanara ◽  
Jitendra Gaikwad ◽  
Roman Gerlach ◽  
Alsayed Algergawy ◽  
Andreas Ostrowski ◽  
...  
Keyword(s):  
Data Management ◽  
Management System ◽  
Research Data ◽  
Environmental Data ◽  
The Self ◽  
Data Management System ◽  
Maturity Model ◽  
Ecological Data ◽  
Self Assessment ◽  
Research Data Management

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.

scholarly journals Research Data Management System for a large Collaborative Project

2020 ◽  
Author(s):  
Iryna Mozgova ◽  
Oliver Koepler ◽  
Angelina Kraft ◽  
Roland Lachmayer ◽  
Sören Auer
Keyword(s):  
Data Management ◽  
Management System ◽  
Research Data ◽  
Data Management System ◽  
Collaborative Project ◽  
Research Data Management

scholarly journals Progress with implementing the new research data management system at HartRAO

2018 ◽  
Vol 186 ◽  
pp. 12002
Author(s):  
Glenda Coetzer ◽  
Roelf Botha ◽  
Lorette Jacobs
Keyword(s):  
Data Management ◽  
Radio Astronomy ◽  
Management System ◽  
Research Data ◽  
Data Management System ◽  
Research Data Management ◽  
Research Activities ◽  
Fundamental Astronomy ◽  
New Research ◽  
Service Standards

The Hartebeesthoek Radio Astronomy Observatory (HartRAO) participates in global radio astronomy and fundamental astronomy (space geodesy) research activities. Data and data products produced by HartRAO’s expanding range of on-site and off-site instrumentation must be archived and stored at HartRAO and made accessible to the scientific community. The data management and storage systems currently being used for managing fundamental astronomy data are not capable of handling the large volumes of data and have become obsolete. This necessitated the design and implementation of a next-generation Geodetic Research Data Management System (GRDMS), which complies with internationally accepted data service standards. We present the top-level conceptual model of the GRDMS and progress to date with developments of various sub-systems, data structuring and organisation within the sub-systems.

Collaboration Between Researchers and Academic Library

2018 ◽  
pp. 1294-1311
Author(s):  
Liisi Lembinen
Keyword(s):  
Data Management ◽  
Research Universities ◽  
Management System ◽  
Academic Library ◽  
Research Data ◽  
Data Management System ◽  
Development Work ◽  
Funding Agency ◽  
Research Data Management ◽  
Management Services

Even though many academic libraries are offering various types of data management services (RDM) and infrastructure, many universities struggle with convincing researchers to publish their data. Collaboration in data management services between libraries, researchers, universities and government is the only way to make research data available and accessible. The purpose of this paper is to demonstrate how initiative from researchers led to wider collaboration between researchers and an academic library and resulted in the development of data management services in the library as well as a country-wide research data management consortium in Estonia. The country-wide research data management system was a requirement of the funding agency which put the library into a new position to initiate and lead work towards research universities' consortium. The development work of RDM services has tremendously raised the library's value as a partner rather than just a service provider in the eyes of researchers.

Collaboration between Researchers and Academic Library

2016 ◽  
pp. 220-244
Author(s):  
Liisi Lembinen
Keyword(s):  
Data Management ◽  
Research Universities ◽  
Management System ◽  
Academic Library ◽  
Research Data ◽  
Data Management System ◽  
Development Work ◽  
Funding Agency ◽  
Research Data Management ◽  
Management Services

Even though many academic libraries are offering various types of data management services (RDM) and infrastructure, many universities struggle with convincing researchers to publish their data. Collaboration in data management services between libraries, researchers, universities and government is the only way to make research data available and accessible. The purpose of this paper is to demonstrate how initiative from researchers led to wider collaboration between researchers and an academic library and resulted in the development of data management services in the library as well as a country-wide research data management consortium in Estonia. The country-wide research data management system was a requirement of the funding agency which put the library into a new position to initiate and lead work towards research universities' consortium. The development work of RDM services has tremendously raised the library's value as a partner rather than just a service provider in the eyes of researchers.

scholarly journals Scientific Research Data Management for Soil-Vegetation-Atmosphere Data – The TR32DB

2012 ◽  
Vol 7 (2) ◽  
pp. 68-80 ◽  
Author(s):  
Constanze Curdt ◽  
Dirk Hoffmeister ◽  
Guido Waldhoff ◽  
Christian Jekel ◽  
Georg Bareth
Keyword(s):  
Data Management ◽  
Interdisciplinary Research ◽  
Management System ◽  
Scientific Research ◽  
Research Data ◽  
Data Management System ◽  
Research Data Management ◽  
Interdisciplinary Research Project ◽  
Practical Experiences

The implementation of a scientific research data management system is an important task within long-term, interdisciplinary research projects. Besides sustainable storage of data, including accurate descriptions with metadata, easy and secure exchange and provision of data is necessary, as well as backup and visualisation. The design of such a system poses challenges and problems that need to be solved.This paper describes the practical experiences gained by the implementation of a scientific research data management system, established in a large, interdisciplinary research project with focus on Soil-Vegetation-Atmosphere Data.

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 ◽  
...  
Keyword(s):  
Data Management ◽  
Data Model ◽  
Query Language ◽  
Research Data ◽  
Data Management System ◽  
Biomedical Sciences ◽  
Seamless Integration ◽  
Research Data Management ◽  
The Status ◽  
Data Acquisition And Processing

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.

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