Biomedical Research Data Cloud Services with Duckling Collaboration LiBrary (CLB)

Author(s):  
Kejun Dong ◽  
Ji Li ◽  
Kai Nan ◽  
Wilfred W. Li
2021 ◽  
Author(s):  
Anita Bandrowski ◽  
Jeffrey S. Grethe ◽  
Anna Pilko ◽  
Tom Gillespie ◽  
Gabi Pine ◽  
...  

AbstractThe NIH Common Fund’s Stimulating Peripheral Activity to Relieve Conditions (SPARC) initiative is a large-scale program that seeks to accelerate the development of therapeutic devices that modulate electrical activity in nerves to improve organ function. Integral to the SPARC program are the rich anatomical and functional datasets produced by investigators across the SPARC consortium that provide key details about organ-specific circuitry, including structural and functional connectivity, mapping of cell types and molecular profiling. These datasets are provided to the research community through an open data platform, the SPARC Portal. To ensure SPARC datasets are Findable, Accessible, Interoperable and Reusable (FAIR), they are all submitted to the SPARC portal following a standard scheme established by the SPARC Curation Team, called the SPARC Data Structure (SDS). Inspired by the Brain Imaging Data Structure (BIDS), the SDS has been designed to capture the large variety of data generated by SPARC investigators who are coming from all fields of biomedical research. Here we present the rationale and design of the SDS, including a description of the SPARC curation process and the automated tools for complying with the SDS, including the SDS validator and Software to Organize Data Automatically (SODA) for SPARC. The objective is to provide detailed guidelines for anyone desiring to comply with the SDS. Since the SDS are suitable for any type of biomedical research data, it can be adopted by any group desiring to follow the FAIR data principles for managing their data, even outside of the SPARC consortium. Finally, this manuscript provides a foundational framework that can be used by any organization desiring to either adapt the SDS to suit the specific needs of their data or simply desiring to design their own FAIR data sharing scheme from scratch.


2021 ◽  
Author(s):  
Craig Barnes ◽  
Binam Bajracharya ◽  
Matthew Cannalte ◽  
Zakir Gowani ◽  
Will Haley ◽  
...  

Objective. The objective was to develop and operate a cloud-based federated system for managing, analyzing and sharing patient data for research purposes, while allowing each resource sharing patient data to operate their component based upon their own governance rules. The federated system is called the Biomedical Research Hub (BRH). Methods. The BRH is a cloud-based federated system built over a core set of software services called framework services. BRH framework services include authentication and authorization, services for generating and assessing FAIR data, and services for importing and exporting bulk clinical data. The BRH includes data resources providing data operated by different entities and workspaces that can access and analyze data from one or more of the data resources in the BRH. Results. The BRH contains multiple data commons that in aggregate provide access to over 6 PB of research data from over 400,000 research participants. Discussion and conclusion. With the growing acceptance of using public cloud computing platforms for biomedical research, and the growing use of opaque persistent digital identifiers for datasets, data objects, and other entities, there is now a foundation for systems that federate data from multiple independently operated data resources that expose FAIR APIs, each using a separate data model. Applications can be built that access data from one or more of the data resources.


2020 ◽  
pp. medethics-2020-106443
Author(s):  
Vasiliki Nataly Rahimzadeh

In their recent article, Porsdam Mann et al propose to share biomedical research data more widely, securely and efficiently using blockchain technologies.1 They present compelling arguments for how the blockchain presents both a technological innovation, and a deontologically grounded policy innovation to traditional research consent. Their proposal can be read in conversation with a rich body of evidence to suggest current consent processes are problematic on at least one of tripartite bases in biomedical research: that it be fully informed. This response attempts to further the author’s discussion of social justice discourse in, and of their proposed prosent model to enhance engagement among under-represented and vulnerable populations in research, specifically. Motivating this response is the view that advancing technological capabilities is no doubt necessary, but on its own insufficient to reinvigorate distributive, procedural and social justice as guiding principles for con/prosent processes. I offer three pros and cons to consider in effort to deepen the model’s commitments to social justice to historically marginalised groups in the biomedical research enterprise.


2014 ◽  
Vol 05 (04) ◽  
pp. 916-929 ◽  
Author(s):  
I. Mare ◽  
S. Hazelhurst ◽  
B. Kramer ◽  
M. Klipin

Summary Background: Clinical and research data are essential for patient care, research and healthcare system planning. REDCapTM is a web-based tool for research data curatorship developed at Vanderbilt University in Nashville, USA. The Faculty of Health Sciences at the University of the Witwatersrand, Johannesburg South Africa identified the need for a cost effective data management instrument. REDCap was installed as per the user agreement with Vanderbilt University in August 2012. Objectives: In order to assist other institutions that may lack the in-house Information Technology capacity, this paper describes the installation and support of REDCap and incorporates an analysis of user uptake over the first year of use. Methods: We reviewed the staffing requirements, costs of installation, process of installation and necessary infrastructure and end-user requests following the introduction of REDCap at Wits. The University Legal Office and Human Research Ethics Committee were consulted regarding the REDCap end-user agreement. Bi-monthly user meetings resulted in a training workshop in August 2013. We compared our REDCap software user numbers and records before and after the first training workshop. Results: Human resources were recruited from existing staff. Installation costs were limited to servers and security certificates. The total costs to provide a functional REDCap platform was less than $9000. Eighty-one (81) users were registered in the first year. After the first training workshop the user numbers increased by 59 in one month and the total number of active users to 140 by the end of August 2013. Custom software applications for REDCap were created by collaboration between clinicians and software developers. Conclusion: REDCap was installed and maintained at limited cost. A small number of people with defined skills can support multiple REDCap users in two to four hours a week. End user training increased in the number of users, number of projects created and the number of projects moved to production. Citation: Klipin M, Mare I, Hazelhurst S, Kramer B. The process of installing REDCap, a web based database supporting biomedical research – the first year. Appl Clin Inf 2014; 5: 916–929http://dx.doi.org/10.4338/ACI-2014-06-CR-0054


2021 ◽  
Author(s):  
Kai Fay ◽  
Julie Goldman

The Harvard Medical School Countway Library’s Massive Open Online Course (MOOC) Best Practices for Biomedical Research Data Management launched on Canvas in January 2018. This report analyzes student reported data and course generated analytics from January 2018, through July 8, 2020, for the course Best Practices for Biomedical Research Data Management. By comparing the findings from the enrollment period through March 8, 2020 (pre-pandemic) to the period through July 8, 2020 (during-pandemic), the main goal is to investigate potential shifts due to the COVID-19 pandemic.


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