This educational article explores the convergence of open science practices and traditional, complementary, and integrative medicine (TCIM), shedding light on the potential benefits and challenges of open science for the development, dissemination, and implementation of evidence-based TCIM. We emphasize the transformative shift in medical science towards open and collaborative practices, highlighting the limited application of open science in TCIM research despite its growing acceptance among patients. We define open science practices and discuss those that are applicable to TCIM, including: study registration; reporting guidelines; data, code and material sharing; preprinting; publishing open access; and reproducibility/replication studies. We explore the benefits of open science in TCIM, spanning improved research quality, increased public trust, accelerated innovation, and enhanced evidence-based decision-making. We also acknowledge challenges such as data privacy concerns, limited resources, and resistance to cultural change. We propose strategies to overcome these challenges, including ethical guidelines, education programs, funding advocacy, interdisciplinary dialogue, and patient engagement. Looking to the future, we envision the maturation of open science in TCIM, the development of TCIM-specific guidelines for open science practices, advancements in data sharing platforms, the integration of open data and artificial intelligence in TCIM research, and changes in the context of policy and regulation. We foresee a future where open science in TCIM leads to a better evidence base, informed decision-making, interdisciplinary collaboration, and transformative impacts on healthcare and research methodologies, highlighting the promising synergy between open science and TCIM for holistic, evidence-based healthcare solutions.

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.

Antimicrobial resistance (AMR) is widely acknowledged as one of the most serious public health threats facing the world, yet the private sector finds it challenging to generate much-needed medicines. As an alternative discovery approach, a small array of diarylimidazoles was screened against the ESKAPE pathogens, and the results were made publicly available through the Open Source Antibiotics (OSA) consortium (https://github.com/opensourceantibiotics). Of the 18 compounds tested (at 32 μg/mL), 15 showed >90% growth inhibition activity against methicillin-resistant Staphylococcus aureus (MRSA) alone. In the subsequent hit-to-lead optimization of this chemotype, 147 new heterocyclic compounds containing the diarylimidazole and other core motifs were synthesized and tested against MRSA, and their structure-activity relationships were identified. While potent, these compounds have moderate to high intrinsic clearance and some associated toxicity. The best overall balance of parameters was found with OSA_975, a compound with good potency, good solubility, and reduced intrinsic clearance in rat hepatocytes. We have progressed toward the knowledge of the molecular target of these phenotypically active compounds, with proteomic techniques suggesting TGFBR1 is potentially involved in the mechanism of action. Further development of these compounds toward antimicrobial medicines is available to anyone under the licensing terms of the project.

UNASSIGNED: The Trail Making Test B (TMT-B) is indicative of cognitive flexibility and several other cognitive domains. Previous studies suggest that it might be associated with the risk of developing postoperative delirium, but evidence is limited and conflicting. We therefore aimed to replicate the association of preoperative TMT-B results with postoperative delirium.
UNASSIGNED: We included older adults (≥65 yr) scheduled for major surgery and without signs of dementia to participate in this binational two-centre longitudinal observational cohort study. Presurgical TMT-B scores were obtained. Delirium was assessed twice daily using validated instruments. Logistic regression was applied and the area under the receiver operating characteristic curve calculated to determine the predictive performance of TMT-B. We subsequently included covariates used in previous studies for consecutive sensitivity analyses. We further analysed the impact of outliers, missing or impaired data.
UNASSIGNED: Data from 841 patients were included and of those, 151 (18%) developed postoperative delirium. TMT-B scores were statistically significantly associated with the incidence of postoperative delirium {odds ratio per 10-s increment 1.06 (95% confidence interval [CI] 1.02-1.09), P=0.001}. The area under the receiver operating characteristic curve was 0.60 ([95% CI 0.55-0.64], P<0.001). The association persisted after removing 21 outliers (1.07 [95% CI 1.03-1.07], P<0.001). Impaired or missing TMT-B data (n=88) were also associated with postoperative delirium (odds ratio 2.74 [95% CI 1.71-4.35], P<0.001).
UNASSIGNED: The TMT-B was associated with postoperative delirium, but its predictive performance as a stand-alone test was low. The TMT-B alone is not suitable to predict delirium in a clinical setting.
UNASSIGNED: NCT02265263. (https://clinicaltrials.gov/ct2/show/results/NCT02265263).

A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in intrinsic brain function by mapping spontaneous brain activity, namely intrinsic functional network neuroscience (ifNN). However, the variability of methodologies applied across the ifNN studies-with respect to node definition, edge construction, and graph measurements-makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best ifNN practices by systematically comparing the measurement reliability of individual differences under different ifNN analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide ifNN studies: (1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions; (2) construct functional networks using spontaneous brain activity in multiple slow bands; and (3) optimize topological economy of networks at individual level; and (4) characterize information flow with specific metrics of integration and segregation. We built an interactive online resource of reliability assessments for future ifNN (https://ibraindata.com/research/ifNN).

Generative artificial intelligence (AI), including large language models (LLMs), is poised to transform scientific research, enabling researchers to elevate their research productivity. This article presents a how-to guide for employing LLMs in academic settings, focusing on their unique strengths, constraints and implications through the lens of philosophy of science and epistemology. Using ChatGPT as a case study, I identify and elaborate on three attributes contributing to its effectiveness-intelligence, versatility and collaboration-accompanied by tips on crafting effective prompts, practical use cases and a living resource online (https://osf.io/8vpwu/). Next, I evaluate the limitations of generative AI and its implications for ethical use, equality and education. Regarding ethical and responsible use, I argue from technical and epistemic standpoints that there is no need to restrict the scope or nature of AI assistance, provided that its use is transparently disclosed. A pressing challenge, however, lies in detecting fake research, which can be mitigated by embracing open science practices, such as transparent peer review and sharing data, code and materials. Addressing equality, I contend that while generative AI may promote equality for some, it may simultaneously exacerbate disparities for others-an issue with potentially significant yet unclear ramifications as it unfolds. Lastly, I consider the implications for education, advocating for active engagement with LLMs and cultivating students\' critical thinking and analytical skills. The how-to guide seeks to empower researchers with the knowledge and resources necessary to effectively harness generative AI while navigating the complex ethical dilemmas intrinsic to its application.

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For many years, the journal evaluation system has been centered on impact indicators, resulting in evaluation results that do not reflect the academic innovation of journals. To solve this issue, this study attempts to construct the Journal Disruption Index (JDI) from the perspective of measuring the disruption of each journal article. In the actual study, we measured the disruption of articles of 22 selected virology journals based on the OpenCitations Index of Crossref open DOI-to-DOI citations (COCI) first. Then we calculated the JDI of 22 virology journals based on the absolute disruption index (DZ) of the articles. Finally, we conducted an empirical study on the differences and correlations between the impact indicators and disruption indicators as well as the evaluation effect of the disruption index. The results of the study show: (1) There are large differences in the ranking of journals based on disruption indicators and impact indicators. Among the 22 journals, 12 are ranked higher by JDI than Cumulative Impact Factor for 5 years (CIF5), the Journal Index for PR6 (JIPR6) and average Percentile in Subject Area (aPSA). The ranking difference of 17 journals between the two kinds of indicators is greater than or equal to 5. (2) There is a medium correlation between disruption indicators and impact indicators at the level of journals and papers. JDI is moderately correlated with CIF5, JIPR6 and aPSA, with correlation coefficients of 0.486, 0.471 and - 0.448, respectively. DZ was also moderately correlated with Cumulative Citation (CC), Percentile Ranking with 6 Classifications (PR6) and Percentile in Subject Area (PSA) with correlation coefficients of 0.593, 0.575 and - 0.593, respectively. (3) Compared with traditional impact indicators, the results of journal disruption evaluation are more consistent with the evaluation results of experts\' peer review. JDI reflects the innovation level of journals to a certain extent, which is helpful to promote the evaluation of innovation in sci-tech journals.

In recent years, the scientific community has called for improvements in the credibility, robustness and reproducibility of research, characterized by increased interest and promotion of open and transparent research practices. While progress has been positive, there is a lack of consideration about how this approach can be embedded into undergraduate and postgraduate research training. Specifically, a critical overview of the literature which investigates how integrating open and reproducible science may influence student outcomes is needed. In this paper, we provide the first critical review of literature surrounding the integration of open and reproducible scholarship into teaching and learning and its associated outcomes in students. Our review highlighted how embedding open and reproducible scholarship appears to be associated with (i) students\' scientific literacies (i.e. students\' understanding of open research, consumption of science and the development of transferable skills); (ii) student engagement (i.e. motivation and engagement with learning, collaboration and engagement in open research) and (iii) students\' attitudes towards science (i.e. trust in science and confidence in research findings). However, our review also identified a need for more robust and rigorous methods within pedagogical research, including more interventional and experimental evaluations of teaching practice. We discuss implications for teaching and learning scholarship.

Data sharing is an important part of open science (OS), and more and more institutions and journals have been enforcing open data (OD) policies. OD is advocated to help increase academic influences and promote scientific discovery and development, but such a proposition has not been elaborated on well. This study explores the nuanced effects of the OD policies on the citation pattern of articles by using the case of Chinese economics journals. China Industrial Economics (CIE) is the first and only Chinese social science journal so far to adopt a compulsory OD policy, requiring all published articles to share original data and processing codes. We use the article-level data and difference-in-differences (DID) approach to compare the citation performance of articles published in CIE and 36 comparable journals. Firstly, we find that the OD policy quickly increased the number of citations, and each article on average received 0.25, 1.19, 0.86, and 0.44 more citations in the first four years after publication respectively. Furthermore, we also found that the citation benefit of the OD policy rapidly decreased over time, and even became negative in the fifth year after publication. In conclusion, this changing citation pattern suggests that an OD policy can be double edged sword, which can quickly increase citation performance but simultaneously accelerate the aging of articles.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s11192-023-04684-8.