Integrity Of Biomedical Research Research Articles

Utilizing Blockchain Technology for Healthcare and Biomedical Research: A Review.

Blockchain is a decentralized, secure, and immutable public ledger that offers significant benefits over conventional centralized systems by preventing data breaches and cyber-attacks. It has a great potential to improve data security, privacy, and interoperability in healthcare and biomedical research. This review discusses the basic principles and the historical evolution of blockchain and evaluates the implications of blockchain for the existing healthcare infrastructure. It also highlights blockchain technology's advantages in electronic health records, supply chain management, clinical trials, and telemedicine. However, this technology faces several hurdles, including regulatory issues, technical complexity, and economic costs, which suggest a gradual adoption over time. In addition, the review emphasizes its ability to ensure data integrity, enhance collaboration, and protect intellectual property in biomedical research. This review shows that blockchain can enhance healthcare data management by providing secure, efficient, and patient-centric solutions. Furthermore, it also discusses the implications of blockchain for the future of healthcare and biomedical research and suggests that ongoing research and interdisciplinary approaches are essential for overcoming current barriers and realizing the full potential of this technology. Future research should focus on developing privacy-preserving hybrid data storage solutions that comply with international laws and regulations, thus enhancing the sustainability and scalability of this technology in healthcare.

Scientific integrity in biomedical research is a global problem

Science contributes to globalization by creating new knowledge and technologies that can be shared and applied across different regions and cultures. The Regional Cooperation for Health, Science and Technology (RECOOP HST) Association combines the scientific output of partner organizations at the local and regional levels and uses it at the global level to prevent and eliminate major public health problems. Since research integrity (RI) varies among participating research organizations from the U.S.A. to Ukraine, RECOOP HST recognizes that high-quality research and outcomes, as measured by published papers, require a common understanding of scientific integrity and bioethics. During the last 15 years, RECOOP HST has organized workshops to educate scientists about the most devastating forms of research dishonesty: fabrication, falsification or plagiarism, which destroy trust and respect among scientists. Different types of research misconduct require different methods of detection and investigation. Now, with the rapid development of artificial intelligence (AI), various plagiarism-checking software has appeared. However, detecting fabrication and falsification is not so easy. In addition, AI should not be used to replace human reviewers, as there is currently insufficient evidence to support AI application in peer review. Two main approaches that RECOOP HST has taken to prevent misconduct and promote RI are evidence-based education and mentoring of students. Mentoring should take the form of informal discussions with students about responsible conduct of research and serving as a role model. Key strategies for promoting integrity include the development of institutional policies and the monitoring of activities with appropriate auditing of data. Keywords: fabrication, falsification, plagiarism, scientific and research integrity

Early Detection of Parkinson Disease Throughbiomedical Speech and Voice Analysis

Parkinson's disease, widely recognized as a neurodegenerative condition characterized by subtle changes in voice, has spurred an investigation into voice analysis for diagnostic purposes. This study is dedicated to the early detection of Parkinson's disease through a comprehensive examination of biomedical speech attributes. Parameters such as fundamental frequency range, jitter, shimmer, noise-to-harmonics ratio, and features derived from nonlinear analysis are considered, alongside variables like status, indicating the presence of neurological disorders, and class for classification purposes. Together, these attributes provide a detailed representation of voice signals, offering valuable insights into both neurological and voice disorders for research purposes. The dataset exhibits promising potential for applications in medical diagnostics and voice analysis. In the pursuit of accurate disease detection, various machine learning methodologies are employed, including Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), Neural Networks (NN), and state- of-the-art Convolutional Neural Networks (CNNs). The incorporation of CNNs is pivotal, signifying a significantleap in accuracy of 100%for disease detection. The results showcase a model adept at discerning subtle changesassociated with Parkinson's disease, with SVM achieving 96%, Decision Tree demonstrating a perfect 100%, Neural Network attaining 98%, and Random Forest showcasing an accuracy of 99%. This innovative approach not only transforms early Parkinson's disease identification through voice analysis, setting a precision benchmark, but also underscores the transformative potential of cutting-edge technologies in healthcare practices. The study positions the model as a reliable diagnostic tool, capable of advancing medical diagnosticsthrough the seamless integration of biomedical research and machine learning, contributing to the broader fieldof neurodegenerative disease diagnostics.

The EQIPD quality system – Assessment and certification procedures

The EQIPD Quality System was designed with the ultimate mission to provide a framework to ensure the quality and integrity of non-regulated preclinical biomedical research. For research quality to be sustained over time, it is crucial to have continuous improvement mechanisms that routinely monitor the research-related processes and enable solutions for identified issues. The present article is focused on these monitoring and assessment procedures that make the EQIPD Quality System a fully functional ‘system’ (as opposed to a mere collection of guidelines, work instructions and policies). In this context, a critical instrument are the internal and external assessments of the EQIPD Quality System performance described in detail. The assessment procedures emphasize the unique nature of the EQIPD Quality System being user-friendly, flexible and fit-for-purpose. By undergoing the (voluntary) external EQIPD assessment (leading to the EQIPD certification after all EQIPD core requirements have been implemented), a research unit: (i) secures confidence in the quality of data generated, (ii) ensures continuous improvement of research processes, and (iii) obtains an independent seal of quality communicating commitment to best research practices to the research community.

A Content Analysis of Self-Reported Financial Relationships in Biomedical Research

Introduction Financial conflicts of interest (fCOI) present well documented risks to the integrity of biomedical research. However, few studies differentiate among fCOI types in their analyses, and those that do tend to use preexisting taxonomies for fCOI identification. Research on fCOI would benefit from an empirically-derived taxonomy of self-reported fCOI and data on fCOI type and payor prevalence. Methods We conducted a content analysis of 6,165 individual self-reported relationships from COI statements distributed across 378 articles indexed with PubMed. Two coders used an iterative coding process to identify and classify individual fCOI types and payors. Inter-rater reliability was κ = 0.935 for fCOI type and κ = 0.884 for payor identification. Results Our analysis identified 21 fCOI types, 9 of which occurred at prevalences greater than 1%. These included research funding (24.8%), speaking fees (20.8%), consulting fees (18.8%), advisory relationships (11%), industry employment (7.6%), unspecified fees (4.8%), travel fees (3.2%), stock holdings (3.1%), and patent ownership (1%). Reported fCOI were held with 1,077 unique payors, 22 of which were present in more than 1% of financial relationships. The ten most common payors included Pfizer (4%), Novartis (3.9%), MSD (3.8%), Bristol Myers Squibb (3.2%), AstraZeneca (3.1%), GSK (3%), Boehringer Ingelheim (2.9%), Roche (2.8%), Eli LIlly (2.5%), and AbbVie (2.4%). Conclusions These results provide novel multi-domain prevalence data on self-reported fCOI and payors in biomedical research. As such, they have the potential to catalyze future research that can assess the differential effects of various types of fCOI. Specifically, the data suggest that comparative analyses of the effects of different fCOI types are needed and that special attention should be paid to the diversity of payor types for research relationships.

Design and Implementation of a Multipurpose Information System for Hematopoietic Stem-Cell Transplantation on the Basis of the Biomedical Research Integrated Domain Group Model.

An important obstacle to cancer research is that nearly all academic cancer centers maintain substantial collections of highly duplicative, poorly quality-assured, nonintercommunicating, difficult-to-access data repositories. It is inherently clear that this state of affairs increases costs and reduces quality and productivity of both research and nonresearch activities. We hypothesized that designing and implementing a multipurpose cancer information system on the basis of the Biomedical Research Integrated Domain (BRIDG) model developed by the National Cancer Institute and its collaborators might lessen the duplication of effort inherent in capturing, quality-assuring, and accessing data located in multiple single-purpose systems, and thereby increases productivity while reducing costs. We designed and implemented a core data structure on the basis of the BRIDG model and incorporated multiple entities, attributes, and functionalities to support the multipurpose functionality of the system. We used the resultant model as a foundation upon which to design and implement modules for importing preexisting data, capturing data prospectively, quality-assuring data, exporting data to analytic files, and analyzing the quality-assured data to support multiple functionalities simultaneously. To our knowledge, our system, which we refer to as the Cancer Informatics Data System, is the first multipurpose, BRIDG-harmonized cancer research information system implemented at an academic cancer center. We describe the BRIDG-harmonized system that simultaneously supports patient care, teaching, research, clinical decision making, administrative decision making, mandated volume-and-outcomes reporting, clinical quality assurance, data quality assurance, and many other functionalities. Implementation of a highly quality-assured, multipurpose cancer information system on the basis of the BRIDG model at an academic center is feasible and can increase access to accurate data to support research integrity and productivity as well as nonresearch activities.

Promoting Science and the Integrity of Biomedical Research at Universities: SNUCM as an Example

Promoting Science and the Integrity of Biomedical Research at Universities: SNUCM as an Example

A Medical Pedagogy Reform by Integration of Biomedical Research into the Clinical Medicine Program.

The objective of this study is to assess the effectiveness of integrating research-based biomedical sciences into a clinical medicine program. This reform aims to enable medical students to conduct both clinical and independent research work at an early stage and to consider human disease through a mechanistic and evidence-based perspective. We designed this innovative medical program using modules that are different from those used in traditional medical programs in both China and Western countries. Thus, in this new program, we incorporated biomedical sciences components including essential theoretical and practical elements, active learning, and research skills training in the first 3 years of a 5-year program. We also offered students opportunities for oral presentation, teamwork, and leadership training. We find that students are actively engaged in this program and are enthusiastic about medical research, academically competent, and confident at expression and presentation of their data. They demonstrate leadership and teamwork skills that are essential for contemporary medical practice and prepare them by developing these skills at this early stage as they embark on their medical career. We show that students who train through this reformed program perform well at various nationwide and province-wide academic contests and show increased competitiveness in applications onto post-graduate programs. Overall, we provide evidence that this new program is proving to be successful and is a worthwhile reform establishing a new paradigm for Chinese medical education. Furthermore, we suggest it is a reform that would be of interest to other countries whose medical education is not delivering the desired output of research- and evidence-based-driven doctors.

Perceptions and Attitudes about Research Integrity and Misconduct: a Survey among Young Biomedical Researchers in Italy

Research misconduct (RM) is an alarming concern worldwide, and especially in Italy, where there is no formal training of young researchers in responsible research practices. The main aim of this study was to map the perceptions and attitudes about RM in a sample of young researchers attending a one-week intensive course on methodology, ethics and integrity in biomedical research, held at the University of Insubria (Italy). To this end, we administered the Scientific Misconduct Questionnaire (SMQ-R) to all attendees at the beginning of the course. Thereafter, SMQ-R was re-administered at the end, to assess the impact of the course on the responsiveness of study participants, which is intended as the frequency of responses other than “don’t know”. Results show that respondents rate as high their own understanding about rules and procedures related to scientific misconduct (49.2% of respondents), as well as the effectiveness of their institution’s measures for reducing it (40%). Most of them (44.6%) perceive as low the chances of getting caught for RM. Some respondents believe that cases of misconduct occur in their workplace (20%–46.2%) and that the integrity of a research is not solely the responsibility of the principal investigator (73.8%). Among the main factors contributing to research misconduct, the need for publications, unclear definition of what constitutes misconduct and pressure for external funding do stand out. Respondents are concerned about the amount of misconduct and express a pressing need for training on research ethics. Remarkably, the responsiveness of participants tends to increase with course attendance. This finding may be useful to support education programmes devoted to research methodology, ethics and integrity among young researchers.

Systematic overview of Freedom of Information Act requests to the Department of Health and Human Services from 2008 to 2017

BackgroundThe Freedom of Information Act (FOIA) provides access to unreleased government records that can be used to enhance the transparency and integrity of biomedical research. We characterized FOIA requests to Department of Health and Human Services (HHS) agencies, including request outcomes, processing times, backlogs, and costs.MethodsUsing HHS FOIA annual reports, we extracted data on the number of FOIA requests received and processed by HHS agencies between 2008 and 2017, as well as request outcomes. Processing times were reported in three time increments, < 1–20, 21–60, or 61+ days, and trends in backlog status were also described. Information about costs and fees collected were aggregated.ResultsBetween 2008 and 2017, 69.6% of 530,094 HHS FOIA requests were received by the Centers for Medicare and Medicaid Services (CMS), 18.9% by the Food and Drug Administration (FDA), and 11.6% by all other HHS agencies. During this period, CMS processed 374,728 requests, FDA 114,938, and other HHS agencies 61,890. CMS and FDA reduced backlogged requests by 9396 (89.7%) and 4289 (65.3%), respectively, leaving backlogs of 1081 and 2279 requests at the end of 2017. CMS fully or partially granted 60.3% of requests whereas FDA fully or partially granted 72.4%. Of all requests to CMS, 82.0% were considered simple and 18.0% complex; 82.2% of simple requests and 54.9% of complex requests were processed in 20 days, and 5.6% and 29.9% were processed in 61+ days. In contrast, 60.2% of requests to FDA were considered simple and 39.8% complex; 28.8% of simple requests and 9.0% of complex requests were processed in 20 days, and 58.3% and 81.5% were processed in 61+ days. The costs to HHS associated with FOIA requests totaled $446.4 million ($809 per processed request), increasing from $28.1 million ($423 per request) in 2008 to $53.3 million ($1544 per request) in 2017. In total, HHS collected $8.5 million in fees (1.9% of total costs).ConclusionsFOIA is frequently used to obtain information about HHS and its agencies. With growing costs, minimal fees collected, and lengthy processing times, HHS agencies’ FOIA programs might be made more efficient through greater proactive record disclosure.

Using an artificial neural network to map cancer common data elements to the biomedical research integrated domain group model in a semi-automated manner

BackgroundThe medical community uses a variety of data standards for both clinical and research reporting needs. ISO 11179 Common Data Elements (CDEs) represent one such standard that provides robust data point definitions. Another standard is the Biomedical Research Integrated Domain Group (BRIDG) model, which is a domain analysis model that provides a contextual framework for biomedical and clinical research data. Mapping the CDEs to the BRIDG model is important; in particular, it can facilitate mapping the CDEs to other standards. Unfortunately, manual mapping, which is the current method for creating the CDE mappings, is error-prone and time-consuming; this creates a significant barrier for researchers who utilize CDEs.MethodsIn this work, we developed a semi-automated algorithm to map CDEs to likely BRIDG classes. First, we extended and improved our previously developed artificial neural network (ANN) alignment algorithm. We then used a collection of 1284 CDEs with robust mappings to BRIDG classes as the gold standard to train and obtain the appropriate weights of six attributes in CDEs. Afterward, we calculated the similarity between a CDE and each BRIDG class. Finally, the algorithm produces a list of candidate BRIDG classes to which the CDE of interest may belong.ResultsFor CDEs semantically similar to those used in training, a match rate of over 90% was achieved. For those partially similar, a match rate of 80% was obtained and for those with drastically different semantics, a match rate of up to 70% was achieved.DiscussionOur semi-automated mapping process reduces the burden of domain experts. The weights are all significant in six attributes. Experimental results indicate that the availability of training data is more important than the semantic similarity of the testing data to the training data. We address the overfitting problem by selecting CDEs randomly and adjusting the ratio of training and verification samples.ConclusionsExperimental results on real-world use cases have proven the effectiveness and efficiency of our proposed methodology in mapping CDEs with BRIDG classes, both those CDEs seen before as well as new, unseen CDEs. In addition, it reduces the mapping burden and improves the mapping quality.

Assessment of research methodology concepts through critical appraisal among undergraduate medical students and their perception: A single center study

Introduction: Evidence based medicine is an interplay of individual clinical acumen and best available evidence through scientific systematic research. With the advent of modern medicine in the last two centuries, medical education has seen and continues to experience its revolutionary effects. An important aspect in this regard is the integration of biomedical research.&#x0D; Objectives: To assess the research skills among undergraduate medical students exposed to the teaching of research longitudinally through all the five academic years.&#x0D; Methodology: A mixed method study was conducted in which third year MBBS students were assessed in a group through power point presentation regarding different concepts of research while critically appraising a research article. Quantitative and qualitative data were collected. Descriptive statistics were calculated, as well as thematic analysis was done.&#x0D; Results: From a total of 102 participants, 58 were male and 42 were female. Majority of the participants (81.4%) achieved an average or a good score and only 18.6% achieved a poor score. Six themes were generated. General enthusiasm about the activity was noted among participants who considered it beneficial. However, only a few were in the favor of incorporating this activity as a mandatory component in undergraduate studies.&#x0D; Conclusion: Incorporating research in a mandatory longitudinal theme component as part of undergraduate medical studies seems a potential method of laying the foundation for future physician scientists.

Perceptions of research integrity and the Chinese situation: In-depth interviews with Chinese biomedical researchers in Europe

ABSTRACTResearch misconduct has been a threat to Chinese biomedical research. Despite many publications dealing with research integrity in China, little empirical data is available concerning Chinese biomedical researchers’ perceptions of research integrity and misconduct. To learn more about this issue, we interviewed Chinese biomedical researchers in Europe to investigate their perceptions of this issue. Semi-structured interviews were conducted with 25 participants until data saturation was reached. The findings indicate that certain aspects of research integrity need elaboration among Chinese biomedical researchers. Participants had a vague understanding of general concepts related to research integrity. Data fabrication, data falsification and plagiarism were perceived as the most severe deviance. Inappropriate authorship (especially gift authorship) and ghost writing were regarded as the most prevalent types of research misconduct in Chinese biomedical research. The harms of certain practices, such as inappropriate authorship, salami publication and multiple submission, were not well recognized. Attitudes toward research misconduct were divided. The current scientific evaluation system, pressures of promotion, motives for fame and other factors were perceived as the main reasons for research misconduct. Participants suggested various measures in addition to existing safeguards to improve research integrity in Chinese biomedical research.

How do Chinese universities address research integrity and misconduct? A review of university documents.

Scientific researchers are expected to follow the professional norms in their own domain. With a growing number of scientific publications retracted and research misconduct cases revealed in recent years, Chinese biomedical research integrity is questioned. As institutions educating and training future researchers, universities and the guidance they provide are important for the research quality and integrity of the country. Therefore, through a review of the guidance and policy documents on research integrity in Chinese universities, this work aims to investigate how the professional norms are specified in these documents. After a stratified sampling, 53 universities were selected. Their guidance and policy documents on research integrity were collected via a web search of their official websites. The search was confirmed by these universities. Then the content of all the collected documents were analyzed using inductive content analysis. 118 active university documents were collected and analyzed. Most of the Chinese universities we investigated had their own guidance or policy on research integrity. They listed principles or examples of desired and undesired academic practices, investigation procedures and punishments of academic misconduct, and put forward measures to promote research integrity. Differences on specific practices and principles were observed between university groups and with European university documents. Despite the discrepancy they have, all these documents were designed to promote research integrity and cultivate a good research environment in Chinese biomedical domain. Nevertheless, there is still room for improvement, for example, through more consultation of international guidance.

Design and Implementation of a Multipurpose Hematopoietic Stem Cell Information System Based on the Biomedical Research Integrated Domain Model

Many hematopoietic stem cell programs capture transplant-related data into multiple data repositories simultaneously, including electronic medical record systems, repositories to support specific research projects, repositories to support specific quality management initiatives, repositories to track and guide the pre-transplant evaluation and workup, repositories to guide stem cell collection and harvest, repositories to support reporting to internal and external regulatory entities, and others. This state of affairs increases costs and decreases productivity as a result of marked duplication of data-capturing effort and poor quality, accessibility, and interoperability of captured data. An important barrier to addressing this issue is the fact that most data repositories in academic medical centers are based on different underlying data models, which constitutes a so-called chasm of semantic with regard to sharing and/or combining data residing in two or more data repositories, both within and between institutions. To address this issue, NCI, FDA, ISO, HL7, and C-DISC have developed a Biomedical Research Integrated Domain Group (BRIDG) Model. The purpose of the BRIDG model is to bridge the multiple chasms of semantic despair that exist between data repositories maintained by basic researchers, translational researchers, clinical researchers, pharmaceutical companies, and government regulators. To our knowledge, we are the first academic cancer center in the world to design and implement a cancer research system based on the NCI-BRIDG model. Important aspects of our implementation derived from the BRIDG model include extensive utilization of the NCI/BRIDG standard data elements, and resolution of the many-to-many relationship between patients and treatment protocols with a patient treatment course instance table. The latter feature provides powerful support for quality assurance of both clinical documentation and patient management by enabling our software to continually compare what is supposed to be happening to each patient, based on the treatment protocol or protocols on which the patient is registered, against what the clinical documentation indicates is happening to each patient. In addition to enhancing data sharing between researchers, we have also begun to use the system to support clinical decision-making, administrative decision-making, and clinical quality assurance.

Registration of published randomized trials: a systematic review and meta-analysis

BackgroundProspective trial registration is a powerful tool to prevent reporting bias. We aimed to determine the extent to which published randomized controlled trials (RCTs) were registered and registered prospectively.MethodsWe searched MEDLINE and EMBASE from January 2005 to October 2017; we also screened all articles cited by or citing included and excluded studies, and the reference lists of related reviews. We included studies that examined published RCTs and evaluated their registration status, regardless of medical specialty or language. We excluded studies that assessed RCT registration status only through mention of registration in the published RCT, without searching registries or contacting the trial investigators. Two independent reviewers blinded to the other’s work performed the selection. Following PRISMA guidelines, two investigators independently extracted data, with discrepancies resolved by consensus. We calculated pooled proportions and 95% confidence intervals using random-effects models.ResultsWe analyzed 40 studies examining 8773 RCTs across a wide range of clinical specialties. The pooled proportion of registered RCTs was 53% (95% confidence interval 44% to 58%), with considerable between-study heterogeneity. A subset of 24 studies reported data on prospective registration across 5529 RCTs. The pooled proportion of prospectively registered RCTs was 20% (95% confidence interval 15% to 25%). Subgroup analyses showed that registration was higher for industry-supported and larger RCTs. A meta-regression analysis across 19 studies (5144 RCTs) showed that the proportion of registered trials significantly increased over time, with a mean proportion increase of 27%, from 25 to 52%, between 2005 and 2015.ConclusionsThe prevalence of trial registration has increased over time, but only one in five published RCTs is prospectively registered, undermining the validity and integrity of biomedical research.

Integrity in Biomedical Research: A Systematic Review of Studies in China

Recent empirical evidence has demonstrated that research misconduct occurs to a substantial degree in biomedical research. It has been suggested that scientific integrity is also of concern in China, but this seems to be based largely on anecdotal evidence. We, therefore, sought to explore the Chinese situation, by making a systematic review of published empirical studies on biomedical research integrity in China. One of our purposes was also to summarize the existing body of research published in Chinese. We searched the China National Knowledge Infrastructure, Wanfang Data, PubMed and Web of Science for potentially relevant studies, and included studies meeting our inclusion criteria, i.e. mainly those presenting empirically obtained data about the practice of research in China. All the data was extracted and synthesized using an inductive approach. Twenty-one studies were included for review. Two studies used qualitative methods (interviews) and nineteen studies used quantitative methods (questionnaires). Studies involved mainly medical postgraduates and nurses and they investigated awareness, attitudes, perceptions and experiences of research integrity and misconduct. Most of the participants in these 21 studies reported that research integrity is of great importance and that they obey academic norms during their research. Nevertheless, the occurrence of research misbehaviors, such as fabrication, falsification, plagiarism, improper authorship and duplicate submission was also reported. Strengthening research integrity training, developing the governance system and improving the scientific evaluation system were areas of particular attention in several studies. Our review demonstrates that a substantial number of articles have been devoted to research integrity in China, but only a few studies provide empirical evidence. With more safeguard measures of research integrity being taken in China, it would be crucial to conduct more research to explore researchers' in-depth perceptions and evaluate the changes.

BRIDG: a domain information model for translational and clinical protocol-driven research.

It is critical to integrate and analyze data from biological, translational, and clinical studies with data from health systems; however, electronic artifacts are stored in thousands of disparate systems that are often unable to readily exchange data. To facilitate meaningful data exchange, a model that presents a common understanding of biomedical research concepts and their relationships with health care semantics is required. The Biomedical Research Integrated Domain Group (BRIDG) domain information model fulfills this need. Software systems created from BRIDG have shared meaning "baked in," enabling interoperability among disparate systems. For nearly 10 years, the Clinical Data Standards Interchange Consortium, the National Cancer Institute, the US Food and Drug Administration, and Health Level 7 International have been key stakeholders in developing BRIDG. BRIDG is an open-source Unified Modeling Language-class model developed through use cases and harmonization with other models. With its 4+ releases, BRIDG includes clinical and now translational research concepts in its Common, Protocol Representation, Study Conduct, Adverse Events, Regulatory, Statistical Analysis, Experiment, Biospecimen, and Molecular Biology subdomains. The model is a Clinical Data Standards Interchange Consortium, Health Level 7 International, and International Standards Organization standard that has been utilized in national and international standards-based software development projects. It will continue to mature and evolve in the areas of clinical imaging, pathology, ontology, and vocabulary support. BRIDG 4.1.1 and prior releases are freely available at https://bridgmodel.nci.nih.gov .

A metadata schema for data objects in clinical research.

BackgroundA large number of stakeholders have accepted the need for greater transparency in clinical research and, in the context of various initiatives and systems, have developed a diverse and expanding number of repositories for storing the data and documents created by clinical studies (collectively known as data objects). To make the best use of such resources, we assert that it is also necessary for stakeholders to agree and deploy a simple, consistent metadata scheme.MethodsThe relevant data objects and their likely storage are described, and the requirements for metadata to support data sharing in clinical research are identified. Issues concerning persistent identifiers, for both studies and data objects, are explored.ResultsA scheme is proposed that is based on the DataCite standard, with extensions to cover the needs of clinical researchers, specifically to provide (a) study identification data, including links to clinical trial registries; (b) data object characteristics and identifiers; and (c) data covering location, ownership and access to the data object. The components of the metadata scheme are described.ConclusionsThe metadata schema is proposed as a natural extension of a widely agreed standard to fill a gap not tackled by other standards related to clinical research (e.g., Clinical Data Interchange Standards Consortium, Biomedical Research Integrated Domain Group). The proposal could be integrated with, but is not dependent on, other moves to better structure data in clinical research.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-016-1686-5) contains supplementary material, which is available to authorized users.

Using Semantic Web technologies for the generation of domain-specific templates to support clinical study metadata standards.

BackgroundThe Biomedical Research Integrated Domain Group (BRIDG) model is a formal domain analysis model for protocol-driven biomedical research, and serves as a semantic foundation for application and message development in the standards developing organizations (SDOs). The increasing sophistication and complexity of the BRIDG model requires new approaches to the management and utilization of the underlying semantics to harmonize domain-specific standards. The objective of this study is to develop and evaluate a Semantic Web-based approach that integrates the BRIDG model with ISO 21090 data types to generate domain-specific templates to support clinical study metadata standards development.MethodsWe developed a template generation and visualization system based on an open source Resource Description Framework (RDF) store backend, a SmartGWT-based web user interface, and a “mind map” based tool for the visualization of generated domain-specific templates. We also developed a RESTful Web Service informed by the Clinical Information Modeling Initiative (CIMI) reference model for access to the generated domain-specific templates.ResultsA preliminary usability study is performed and all reviewers (n = 3) had very positive responses for the evaluation questions in terms of the usability and the capability of meeting the system requirements (with the average score of 4.6).ConclusionsSemantic Web technologies provide a scalable infrastructure and have great potential to enable computable semantic interoperability of models in the intersection of health care and clinical research.