Novartis Institutes for Biomedical Research
Ontology highlight
ABSTRACT:
PROVIDER: EGAO00000001085 | EGA |
REPOSITORIES: EGA
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Project description:Organisation EGAO00000000730
| EGAO00000000730 | EGA
Project description:ObjectivesTo understand the publishing priorities, especially in relation to open access, of 10 UK biomedical research funders.DesignSemistructured interviews.Setting10 UK biomedical research funders.Participants12 employees with responsibility for research management at 10 UK biomedical research funders; a purposive sample to represent a range of backgrounds and organisation types.ConclusionsPublicly funded and large biomedical research funders are committed to open access publishing and are pleased with recent developments which have stimulated growth in this area. Smaller charitable funders are supportive of the aims of open access, but are concerned about the practical implications for their budgets and their funded researchers. Across the board, biomedical research funders are turning their attention to other priorities for sharing research outputs, including data, protocols and negative results. Further work is required to understand how smaller funders, including charitable funders, can support open access.
| S-EPMC3808764 | biostudies-literature
Project description:Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper's eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community's early reaction to a paper.
| S-EPMC6786525 | biostudies-literature
Project description:We found tremendous inequality across gene and protein annotation resources. We observed that this bias leads biomedical researchers to focus on richly annotated genes instead of those with the strongest molecular data. We advocate that researchers reduce these biases by pursuing data-driven hypotheses.
| S-EPMC5778030 | biostudies-literature
Project description:PurposeThe analysis of existing institutional research proposal databases can provide novel insights into science funding parity. The purpose of this study was to analyze the relationship between race/ethnicity and extramural research proposal and award rates across a medical school faculty and to determine whether there was evidence that researchers changed their submission strategies because of differential inequities across submission categories.MethodThe authors performed an analysis of 14,263 biomedical research proposals with proposed start dates between 2010-2022 from the University of Michigan Medical School, measuring the proposal submission and award rates for each racial/ethnic group across 4 possible submission categories (R01 & Equivalent programs, other federal, industry, and non-profit).ResultsResearchers from each self-identified racial/ethnic group (Asian, Black/African American, Hispanic/Latino) pursued a different proposal submission strategy than the majority group (White). The authors found that Black/African American researchers experienced negative award rate differentials across all submission categories, which resulted in the lowest R01 & Equivalent and Other Federal submission rates of any racial/ethnic group and the highest submission rate to non-profit sources. The authors did not find support for the hypothesis that researchers changed submission strategies in response to award rate inequalities across submission categories.ConclusionsBiomedical researchers from different racial/ethnic groups follow markedly different proposal submission strategies within the University of Michigan Medical School. There is also a clear relationship between race/ethnicity and rates of proposal award. Black/African American and Asian researchers appear disadvantaged across all submission categories relative to White researchers. This study can be easily replicated by other academic research institutions, revealing opportunities for positive intervention.
| S-EPMC9249172 | biostudies-literature
Project description:Genomics and molecular imaging, along with clinical and translational research have transformed biomedical science into a data-intensive scientific endeavor. For researchers to benefit from Big Data sets, developing long-term biomedical digital data preservation strategy is very important. In this opinion article, we discuss specific actions that researchers and institutions can take to make research data a continued resource even after research projects have reached the end of their lifecycle. The actions involve utilizing an Open Archival Information System model comprised of six functional entities: Ingest, Access, Data Management, Archival Storage, Administration and Preservation Planning. We believe that involvement of data stewards early in the digital data life-cycle management process can significantly contribute towards long term preservation of biomedical data. Developing data collection strategies consistent with institutional policies, and encouraging the use of common data elements in clinical research, patient registries and other human subject research can be advantageous for data sharing and integration purposes. Specifically, data stewards at the onset of research program should engage with established repositories and curators to develop data sustainability plans for research data. Placing equal importance on the requirements for initial activities (e.g., collection, processing, storage) with subsequent activities (data analysis, sharing) can improve data quality, provide traceability and support reproducibility. Preparing and tracking data provenance, using common data elements and biomedical ontologies are important for standardizing the data description, making the interpretation and reuse of data easier. The Big Data biomedical community requires scalable platform that can support the diversity and complexity of data ingest modes (e.g. machine, software or human entry modes). Secure virtual workspaces to integrate and manipulate data, with shared software programs (e.g., bioinformatics tools), can facilitate the FAIR (Findable, Accessible, Interoperable and Reusable) use of data for near- and long-term research needs.
| S-EPMC6144948 | biostudies-other
Project description:Female mammals have long been neglected in biomedical research. The NIH mandated enrollment of women in human clinical trials in 1993, but no similar initiatives exist to foster research on female animals. We reviewed sex bias in research on mammals in 10 biological fields for 2009 and their historical precedents. Male bias was evident in 8 disciplines and most prominent in neuroscience, with single-sex studies of male animals outnumbering those of females 5.5 to 1. In the past half-century, male bias in non-human studies has increased while declining in human studies. Studies of both sexes frequently fail to analyze results by sex. Underrepresentation of females in animal models of disease is also commonplace, and our understanding of female biology is compromised by these deficiencies. The majority of articles in several journals are conducted on rats and mice to the exclusion of other useful animal models. The belief that non-human female mammals are intrinsically more variable than males and too troublesome for routine inclusion in research protocols is without foundation. We recommend that when only one sex is studied, this should be indicated in article titles, and that funding agencies favor proposals that investigate both sexes and analyze data by sex.
| S-EPMC3008499 | biostudies-literature
Project description:BackgroundData provenance refers to the origin, processing, and movement of data. Reliable and precise knowledge about data provenance has great potential to improve reproducibility as well as quality in biomedical research and, therefore, to foster good scientific practice. However, despite the increasing interest on data provenance technologies in the literature and their implementation in other disciplines, these technologies have not yet been widely adopted in biomedical research.ObjectiveThe aim of this scoping review was to provide a structured overview of the body of knowledge on provenance methods in biomedical research by systematizing articles covering data provenance technologies developed for or used in this application area; describing and comparing the functionalities as well as the design of the provenance technologies used; and identifying gaps in the literature, which could provide opportunities for future research on technologies that could receive more widespread adoption.MethodsFollowing a methodological framework for scoping studies and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, articles were identified by searching the PubMed, IEEE Xplore, and Web of Science databases and subsequently screened for eligibility. We included original articles covering software-based provenance management for scientific research published between 2010 and 2021. A set of data items was defined along the following five axes: publication metadata, application scope, provenance aspects covered, data representation, and functionalities. The data items were extracted from the articles, stored in a charting spreadsheet, and summarized in tables and figures.ResultsWe identified 44 original articles published between 2010 and 2021. We found that the solutions described were heterogeneous along all axes. We also identified relationships among motivations for the use of provenance information, feature sets (capture, storage, retrieval, visualization, and analysis), and implementation details such as the data models and technologies used. The important gap that we identified is that only a few publications address the analysis of provenance data or use established provenance standards, such as PROV.ConclusionsThe heterogeneity of provenance methods, models, and implementations found in the literature points to the lack of a unified understanding of provenance concepts for biomedical data. Providing a common framework, a biomedical reference, and benchmarking data sets could foster the development of more comprehensive provenance solutions.
| S-EPMC10132013 | biostudies-literature
Project description:Authors endure considerable hardship carrying out biomedical research, from generating ideas to completing their manuscripts and submitting their findings and data (as is increasingly required) to a journal. When researchers submit to journals, they entrust their findings and ideas to editors and peer reviewers who are expected to respect the confidentiality of peer review. Inherent trust in peer review is built on the ethical conduct of authors, editors and reviewers, and on the respect of this confidentiality. If such confidentiality is breached by unethical reviewers who might steal or plagiarize the authors' ideas, researchers will lose trust in peer review and may resist submitting their findings to that journal. Science loses as a result, scientific and medical advances slow down, knowledge may become scarce, and it is unlikely that increasing bias in the literature will be detected or eliminated. In such a climate, society will ultimately be deprived from scientific and medical advances. Despite a rise in documented cases of abused peer review, there is still a relative lack of qualitative and quantitative studies on reviewer-related misconduct, most likely because evidence is difficult to come by. Our paper presents an assessment of editors' and reviewers' responsibilities in preserving the confidentiality of manuscripts during the peer review process, in response to a 2016 case of intellectual property theft by a reviewer. Our main objectives are to propose additional measures that would offer protection of authors' intellectual ideas from predatory reviewers, and increase researchers' awareness of the responsible reviewing of journal articles and reporting of biomedical research.
| S-EPMC6310661 | biostudies-literature
Project description:Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016-2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten articles organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability. Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses. In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community.
| S-EPMC11232620 | biostudies-literature