scholarly journals Development of an informatics system for accelerating biomedical research.

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 1430 ◽  
Author(s):  
Vivek Navale ◽  
Michele Ji ◽  
Olga Vovk ◽  
Leonie Misquitta ◽  
Tsega Gebremichael ◽  
...  
Keyword(s):  
Biomedical Research ◽  
Science Research ◽  
Computing System ◽  
Study Data ◽  
Research Management ◽  
Data Repository ◽  
Biomedical Data ◽  
Unique Identifier ◽  
Data Repositories ◽  
Common Data Elements

The Biomedical Research Informatics Computing System (BRICS) was developed to support multiple disease-focused research programs. Seven service modules are integrated together to provide a collaborative and extensible web-based environment. The modules—Data Dictionary, Account Management, Query Tool, Protocol and Form Research Management System, Meta Study, Data Repository and Globally Unique Identifier —facilitate the management of research protocols, to submit, process, curate, access and store clinical, imaging, and derived genomics data within the associated data repositories. Multiple instances of BRICS are deployed to support various biomedical research communities focused on accelerating discoveries for rare diseases, Traumatic Brain Injury, Parkinson’s Disease, inherited eye diseases and symptom science research. No Personally Identifiable Information is stored within the data repositories. Digital Object Identifiers are associated with the research studies. Reusability of biomedical data is enhanced by Common Data Elements (CDEs) which enable systematic collection, analysis and sharing of data. The use of CDEs with a service-oriented informatics architecture enabled the development of disease-specific repositories that support hypothesis-based biomedical research.

scholarly journals Development of an informatics system for accelerating biomedical research.

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1430
Author(s):  
Vivek Navale ◽  
Michele Ji ◽  
Olga Vovk ◽  
Leonie Misquitta ◽  
Tsega Gebremichael ◽  
...  
Keyword(s):  
Biomedical Research ◽  
Service Oriented Architecture ◽  
Research Management ◽  
Data Element ◽  
Unique Identifier ◽  
Data Interoperability ◽  
Common Data Element ◽  
Data Repositories ◽  
Functional Capabilities ◽  
Informatics System

Biomedical translational research can benefit from informatics system that support the confidentiality, integrity and accessibility of data.  Such systems require functional capabilities for researchers to securely submit data to designated biomedical repositories. Reusability of data is enhanced by the availability functional capabilities that ensure confidentiality, integrity and access of data. A biomedical research system was developed by combining common data element methodology with a service-oriented architecture to support multiple disease focused research programs. Seven service modules are integrated together to provide a collaborative and extensible web-based environment. The modules - Data Dictionary, Account Management, Query Tool, Protocol and Form Research Management System, Meta Study, Repository Manager and globally unique identifier (GUID) facilitate the management of research protocols, submitting and curating data (clinical, imaging, and derived genomics) within the associated data repositories. No personally identifiable information is stored within the repositories. Data is made findable by use of digital object identifiers that are associated with the research studies. Reuse of data is possible by searching through volumes of aggregated research data across multiple studies. The application of common data element(s) methodology for development of content-based repositories leads to increase in data interoperability that can further hypothesis-based biomedical research.

scholarly journals Standardized Informatics Computing Platform for Advancing Biomedical Discovery Through Data Sharing

2018 ◽  
Author(s):  
Vivek Navale ◽  
Michelle Ji ◽  
Evan McCreedy ◽  
Tsega Gebremichael ◽  
Alison Garcia ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Biomedical Research ◽  
Computing System ◽  
Research Data ◽  
Research Needs ◽  
Unique Identifier ◽  
Common Data Elements ◽  
Informatics System ◽  
Data Elements ◽  
User Friendly

AbstractObjectiveThe goal is to develop a standardized informatics computing system that can support end-to-end research data lifecycle management for biomedical research applications.Materials and MethodsDesign and implementation of biomedical research informatics computing system (BRICS) is demonstrated. The system architecture is modular in design with several integrated tools: global unique identifier, validation, upload, download and query tools that support user friendly informatics system capability.ResultsBRICS instances were deployed to support research for improvements in diagnosis of traumatic brain injury, biomarker discovery for Parkinson’s Disease, the National Ophthalmic Disease Genotyping and Phenotyping network, the informatics core for the Center for Neuroscience and Regenerative Medicine, the Common Data Repository for Nursing Science, Global Rare Diseases Patient Registry, and National Institute of Neurological Disorders and Stroke Clinical Informatics system for trials and research.DiscussionData deidentification is conducted by using global unique identifier methodology. No personally identifiable information exists on the BRICS supported repositories. The Data Dictionary provides defined Common Data Elements and Unique Data Elements, specific to each of the BRICS instance that enables Query Tool to search through research data. All instances are supported by the Medical Imaging Processing, statistical analysis R, and Visualization software program.ConclusionThe BRICS core modules can be easily adapted for various biomedical research needs thereby reducing cost in developing new instances for additional biomedical research needs. It provides user friendly tools for researchers to query and aggregate genetic, phenotypic, clinical and medical imaging data. Data sets are findable, accessible and reusable for researchers to foster new research on various diseases.

scholarly journals DataMed: Finding useful data across multiple biomedical data repositories

2016 ◽  
Author(s):  
L Ohno-Machado ◽  
SA Sansone ◽  
G Alter ◽  
I Fore ◽  
J Grethe ◽  
...  
Keyword(s):  
Big Data ◽  
Biomedical Research ◽  
Scientific Literature ◽  
Service Providers ◽  
Research Community ◽  
Biomedical Data ◽  
Data Repositories ◽  
Data Intensive ◽  
Fair Principles ◽  
Community Of Researchers

AbstractThe value of broadening searches for data across multiple repositories has been identified by the biomedical research community. As part of the NIH Big Data to Knowledge initiative, we work with an international community of researchers, service providers and knowledge experts to develop and test a data index and search engine, which are based on metadata extracted from various datasets in a range of repositories. DataMed is designed to be, for data, what PubMed has been for the scientific literature. DataMed supports Findability and Accessibility of datasets. These characteristics - along with Interoperability and Reusability - compose the four FAIR principles to facilitate knowledge discovery in today’s big data-intensive science landscape.

scholarly journals Estimating the scale of biomedical data generation using text mining

2017 ◽  
Author(s):  
Gabriel Rosenfeld ◽  
Dawei Lin
Keyword(s):  
Text Mining ◽  
Biomedical Research ◽  
Word Embedding ◽  
Research Articles ◽  
Free Text ◽  
Similar Amount ◽  
Biomedical Data ◽  
Data Types ◽  
Data Repositories ◽  
The Impact

AbstractWhile the impact of biomedical research has traditionally been measured using bibliographic metrics such as citation or journal impact factor, the data itself is an output which can be directly measured to provide additional context about a publication’s impact. Data are a resource that can be repurposed and reused providing dividends on the original investment used to support the primary work. Moreover, it is the cornerstone upon which a tested hypothesis is rejected or accepted and specific scientific conclusions are reached. Understanding how and where it is being produced enhances the transparency and reproducibility of the biomedical research enterprise. Most biomedical data are not directly deposited in data repositories and are instead found in the publication within figures or attachments making it hard to measure. We attempted to address this challenge by using recent advances in word embedding to identify the technical and methodological features of terms used in the free text of articles’ methods sections. We created term usage signatures for five types of biomedical research data, which were used in univariate clustering to correctly identify a large fraction of positive control articles and a set of manually annotated articles where generation of data types could be validated. The approach was then used to estimate the fraction of PLOS articles generating each biomedical data type over time. Out of all PLOS articles analyzed (n = 129,918), ~7%, 19%, 12%, 18%, and 6% generated flow cytometry, immunoassay, genomic microarray, microscopy, and high-throughput sequencing data. The estimate portends a vast amount of biomedical data being produced: in 2016, if other publishers generated a similar amount of data then roughly 40,000 NIH-funded research articles would produce ~56,000 datasets consisting of the five data types we analyzed.One Sentence SummaryApplication of a word-embedding model trained on the methods sections of research articles allows for estimation of the production of diverse biomedical data types using text mining.

scholarly journals Chances and challenges of a long-term data repository in multiple sclerosis: 20th birthday of the German MS registry

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lisa-Marie Ohle ◽  
David Ellenberger ◽  
Peter Flachenecker ◽  
Tim Friede ◽  
Judith Haas ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Sociodemographic Factors ◽  
Healthcare Research ◽  
Structure Design ◽  
Data Repository ◽  
Research Projects ◽  
Real World Data ◽  
Data Repositories ◽  
Term Data

AbstractIn 2001, the German Multiple Sclerosis Society, facing lack of data, founded the German MS Registry (GMSR) as a long-term data repository for MS healthcare research. By the establishment of a network of participating neurological centres of different healthcare sectors across Germany, GMSR provides observational real-world data on long-term disease progression, sociodemographic factors, treatment and the healthcare status of people with MS. This paper aims to illustrate the framework of the GMSR. Structure, design and data quality processes as well as collaborations of the GMSR are presented. The registry’s dataset, status and results are discussed. As of 08 January 2021, 187 centres from different healthcare sectors participate in the GMSR. Following its infrastructure and dataset specification upgrades in 2014, more than 196,000 visits have been recorded relating to more than 33,000 persons with MS (PwMS). The GMSR enables monitoring of PwMS in Germany, supports scientific research projects, and collaborates with national and international MS data repositories and initiatives. With its recent pharmacovigilance extension, it aligns with EMA recommendations and helps to ensure early detection of therapy-related safety signals.

scholarly journals Finding useful data across multiple biomedical data repositories using DataMed

2017 ◽  
Vol 49 (6) ◽  
pp. 816-819 ◽  
Author(s):  
Lucila Ohno-Machado ◽  
Susanna-Assunta Sansone ◽  
George Alter ◽  
Ian Fore ◽  
Jeffrey Grethe ◽  
...  
Keyword(s):  
Biomedical Data ◽  
Data Repositories

Metformin's impact on statin-associated muscle symptoms: An analysis of ACCORD study data and research materials from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center

2018 ◽  
Vol 20 (8) ◽  
pp. 1994-1999 ◽  
Author(s):  
Nicholas W. Carris ◽  
Athanasios Tsalatsanis ◽  
Srinivas M. Tipparaju ◽  
Feng Cheng ◽  
Ronald R. Magness ◽  
...  
Keyword(s):  
Study Data ◽  
Data Repository ◽  
Accord Study

scholarly journals GAMS – An infrastructure for the long-term preservation and publication of research data from the Humanities

2018 ◽  
Vol 71 (1) ◽  
pp. 207-216 ◽  
Author(s):  
Johannes Hubert Stigler ◽  
Elisabeth Steiner
Keyword(s):  
System Architecture ◽  
Academic Research ◽  
Research Data ◽  
Data Repository ◽  
Data Repositories ◽  
Domain Specific ◽  
Preservation Policy ◽  
Long Term Preservation ◽  
Data Centres

Research data repositories and data centres are becoming more and more important as infrastructures in academic research. The article introduces the Humanities’ research data repository GAMS, starting with the system architecture to preservation policy and content policy. Challenges of data centres and repositories and the general and domain-specific approaches and solutions are outlined. Special emphasis lies on the sustainability and long-term perspective of such infrastructures, not only on the technical but above all on the organisational and financial level.

scholarly journals Improving the Usability of Organizational Data Systems

2021 ◽  
Vol 16 (1) ◽  
pp. 21
Author(s):  
Chung-Yi Hou ◽  
Matthew S. Mayernik
Keyword(s):  
User Interfaces ◽  
User Study ◽  
Data Repository ◽  
Data Systems ◽  
User Interactions ◽  
Data Repositories ◽  
Web Interfaces ◽  
Design And Implementation ◽  
Usability Assessment ◽  
Assessment Techniques

For research data repositories, web interfaces are usually the primary, if not the only, method that data users have to interact with repository systems. Data users often search, discover, understand, access, and sometimes use data directly through repository web interfaces. Given that sub-par user interfaces can reduce the ability of users to locate, obtain, and use data, it is important to consider how repositories’ web interfaces can be evaluated and improved in order to ensure useful and successful user interactions. This paper discusses how usability assessment techniques are being applied to improve the functioning of data repository interfaces at the National Center for Atmospheric Research (NCAR). At NCAR, a new suite of data system tools is being developed and collectively called the NCAR Digital Asset Services Hub (DASH). Usability evaluation techniques have been used throughout the NCAR DASH design and implementation cycles in order to ensure that the systems work well together for the intended user base. By applying user study, paper prototype, competitive analysis, journey mapping, and heuristic evaluation, the NCAR DASH Search and Repository experiences provide examples for how data systems can benefit from usability principles and techniques. Integrating usability principles and techniques into repository system design and implementation workflows helps to optimize the systems’ overall user experience.

scholarly journals Connecting Data Publication to the Research Workflow: A Preliminary Analysis

2017 ◽  
Vol 12 (1) ◽  
pp. 88-105 ◽  
Author(s):  
Sünje Dallmeier-Tiessen ◽  
Varsha Khodiyar ◽  
Fiona Murphy ◽  
Amy Nurnberger ◽  
Lisa Raymond ◽  
...  
Keyword(s):  
Working Group ◽  
Research Data ◽  
Data Publishing ◽  
Data Repository ◽  
Research Activity ◽  
Data Repositories ◽  
Data Publication ◽  
Loosely Coupled ◽  
Definition Of ◽  
Data Documentation

The data curation community has long encouraged researchers to document collected research data during active stages of the research workflow, to provide robust metadata earlier, and support research data publication and preservation. Data documentation with robust metadata is one of a number of steps in effective data publication. Data publication is the process of making digital research objects ‘FAIR’, i.e. findable, accessible, interoperable, and reusable; attributes increasingly expected by research communities, funders and society. Research data publishing workflows are the means to that end. Currently, however, much published research data remains inconsistently and inadequately documented by researchers. Documentation of data closer in time to data collection would help mitigate the high cost that repositories associate with the ingest process. More effective data publication and sharing should in principle result from early interactions between researchers and their selected data repository. This paper describes a short study undertaken by members of the Research Data Alliance (RDA) and World Data System (WDS) working group on Publishing Data Workflows. We present a collection of recent examples of data publication workflows that connect data repositories and publishing platforms with research activity ‘upstream’ of the ingest process. We re-articulate previous recommendations of the working group, to account for the varied upstream service components and platforms that support the flow of contextual and provenance information downstream. These workflows should be open and loosely coupled to support interoperability, including with preservation and publication environments. Our recommendations aim to stimulate further work on researchers’ views of data publishing and the extent to which available services and infrastructure facilitate the publication of FAIR data. We also aim to stimulate further dialogue about, and definition of, the roles and responsibilities of research data services and platform providers for the ‘FAIRness’ of research data publication workflows themselves.

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