A Time and Cost Effective Data Management System for Clinical Research Data to Support Clinical Monitoring Guidelines

1980 ◽  
Vol 14 (1) ◽  
pp. 10-14
Author(s):  
Madeline C. van Hoose ◽  
Floyd E. Leaders
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.


2017 ◽  
Vol 2 (Suppl 2) ◽  
pp. A19.1-A19
Author(s):  
Amélie Julé ◽  
Hazel Ashurst ◽  
Laura Merson ◽  
Piero Olliaro ◽  
Vicki Marsh ◽  
...  

2001 ◽  
Vol 22 (6) ◽  
pp. S135-S155 ◽  
Author(s):  
Robert M Curley ◽  
Richard L Evans ◽  
James Kaylor ◽  
Rosanne M Pogash ◽  
Vernon M Chinchilli

Author(s):  
Mary Banach ◽  
Kaye H Fendt ◽  
Johann Proeve ◽  
Dale Plummer ◽  
Samina Qureshi ◽  
...  

With the focus of the COVID-19 pandemic, we wanted to reach all stakeholders representing communities concerned with good clinical data management practices. We wanted to represent not only data managers but bio-statisticians, clinical monitors, data scientists, informaticians, and all those who collect, organize, analyze, and report on clinical research data. In our paper we will discuss the history of clinical data management in the US and its evolution from the early days of FDA guidance. We will explore the role of biomedical research focusing on the similarities and differences in industry and academia clinical research data management and what we can learn from each other. We will talk about our goals for recruitment and training for the CDM community and what we propose for increasing the knowledge and understanding of good clinical data practice to all – particularly our front-line data collectors i.e., nurses, medical assistants, patients, other data collectors. Finally, we will explore the challenges and opportunities to see CDM as the hub for good clinical data research practices in all of our communities.We will also discuss our survey on how the COVID-19 pandemic has affected the work of CDM in clinical research.


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

2021 ◽  
pp. 532-543
Author(s):  
Matthias Ganzinger ◽  
Enrico Glaab ◽  
Jules Kerssemakers ◽  
Sven Nahnsen ◽  
Ulrich Sax ◽  
...  

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