Challenges for assessing replicability in preclinical cancer biology

A registered report survey of open research practices in psychology departments in the UK and Ireland

Open research practices seek to enhance the transparency and reproducibility of research. While there is evidence of increased uptake in these practices, such as study preregistration and open data, facilitated by new infrastructure and policies, little research has assessed general uptake of such practices across psychology university researchers. The current study estimates psychologists' level of engagement in open research practices across universities in the United Kingdom and Ireland, while also assessing possible explanatory factors that may impact their engagement. Data were collected from 602 psychology researchers in the United Kingdom and Ireland on the extent to which they have implemented various practices (e.g., use of preprints, preregistration, open data, open materials). Here we present the summarized descriptive results, as well as considering differences between various categories of researcher (e.g., career stage, subdiscipline, methodology), and examining the relationship between researcher's practices and their self-reported capability, opportunity, and motivation (COM-B) to engage in open research practices. Results show that while there is considerable variability in engagement of open research practices, differences across career stage and subdiscipline of psychology are small by comparison. We observed consistent differences according to respondent's research methodology and based on the presence of institutional support for open research. COM-B dimensions were collectively significant predictors of engagement in open research, with automatic motivation emerging as a consistently strong predictor. We discuss these findings, outline some of the challenges experienced in this study, and offer suggestions and recommendations for future research. Estimating the prevalence of responsible research practices is important to assess sustained behaviour change in research reform, tailor educational training initiatives, and to understand potential factors that might impact engagement.

What do Australians affected by cancer think about oncology researchers sharing research data? A cross-sectional survey

AIM

Previous research has shown patients and the public in Australia generally support medical researchers in making de-identified research data available to other scientists. However, this research has focussed on certain types of data and recipients. We surveyed Australians affected by cancer to characterize their attitudes toward the sharing of research data with multiple third parties, including the public.

METHODS

A short, anonymous online survey of Australians with a previous diagnosis of cancer was advertised between October 27, 2022, and February 27, 2023. Quantitative responses were analyzed with descriptive statistics. Free-text responses were coded deductively and summarised using content analysis.

RESULTS

In total, 551 respondents contributed data to the survey. There was strong support for cancer researchers sharing non-human and de-identified human research data with clinicians (90% and 95%, respectively) and non-profit researchers (both 94%). However, fewer participants supported sharing data with for-profit researchers (both 64%) or publicly (both 61%). When asked if they would hypothetically consent to researchers at their treatment location using and sharing their de-identified data publicly, only half agreed. In contrast, after being shown a visual representation of the de-identified survey data, 80% of respondents supported sharing it publicly.

CONCLUSION

Australians affected by cancer support the sharing of research data, particularly with clinicians and non-profit researchers. Our results also imply that visualization of the data to be shared may enhance support for making it publicly available. These results should help alleviate any concerns about research participants' attitudes toward data sharing, as well as boost researchers' motivation for sharing.

The 30th Annual Prostate Cancer Foundation Scientific Retreat Report

BACKGROUND

The 30th Annual Prostate Cancer Foundation (PCF) Scientific Retreat was held at the Omni La Costa Resort in Carlsbad, CA, from October 26 to 28, 2023. A hybrid component was included for virtual attendees.

METHODS

The Annual PCF Scientific Retreat is a leading international scientific conference focused on pioneering, unpublished, and impactful studies across the spectrum of basic through clinical prostate cancer research, as well as research from related fields with significant potential for improving prostate cancer research and patient outcomes.

RESULTS

The 2023 PCF Retreat concentrated on key areas of research, including: (i) the biology of cancer stem cells and prostate cancer lineage plasticity; (ii) mechanisms of treatment resistance; (iii) emerging AI applications in diagnostic medicine; (iv) analytical and computational biology approaches in cancer research; (v) the role of nerves in prostate cancer; (vi) the biology of prostate cancer bone metastases; (vii) the contribution of ancestry and genomics to prostate cancer disparities; (viii) prostate cancer 3D genomics; (ix) progress in new targets and treatments for prostate cancer; (x) the biology and translational applications of tumor extracellular vesicles; (xi) updates from PCF TACTICAL Award teams; (xii) novel platforms for small molecule molecular glues and binding inhibitors; and (xiii) diversity, equity and inclusion strategies for advancing cancer care equity.

CONCLUSIONS

This meeting report summarizes the presentations and discussions from the 2023 PCF Scientific Retreat. We hope that sharing this information will deepen our understanding of current and emerging research and drive future advancements in prostate cancer patient care.

Memory effects of prior subculture may impact the quality of multiomic perturbation profiles

Mass spectrometry-based omics technologies are increasingly used in perturbation studies to map drug effects to biological pathways by identifying significant molecular events. Significance is influenced by fold change and variation of each molecular parameter, but also by multiple testing corrections. While the fold change is largely determined by the biological system, the variation is determined by experimental workflows. Here, it is shown that memory effects of prior subculture can influence the variation of perturbation profiles using the two colon carcinoma cell lines SW480 and HCT116. These memory effects are largely driven by differences in growth states that persist into the perturbation experiment. In SW480 cells, memory effects combined with moderate treatment effects amplify the variation in multiple omics levels, including eicosadomics, proteomics, and phosphoproteomics. With stronger treatment effects, the memory effect was less pronounced, as demonstrated in HCT116 cells. Subculture homogeneity was controlled by real-time monitoring of cell growth. Controlled homogeneous subculture resulted in a perturbation network of 321 causal conjectures based on combined proteomic and phosphoproteomic data, compared to only 58 causal conjectures without controlling subculture homogeneity in SW480 cells. Some cellular responses and regulatory events were identified that extend the mode of action of arsenic trioxide (ATO) only when accounting for these memory effects. Controlled prior subculture led to the finding of a synergistic combination treatment of ATO with the thioredoxin reductase 1 inhibitor auranofin, which may prove useful in the management of NRF2-mediated resistance mechanisms.

Avoiding a reproducibility crisis in regulatory toxicology-on the fundamental role of ring trials

The ongoing transition from chemical hazard and risk assessment based on animal studies to assessment relying mostly on non-animal data, requires a multitude of novel experimental methods, and this means that guidance on the validation and standardisation of test methods intended for international applicability and acceptance, needs to be updated. These so-called new approach methodologies (NAMs) must be applicable to the chemical regulatory domain and provide reliable data which are relevant to hazard and risk assessment. Confidence in and use of NAMs will depend on their reliability and relevance, and both are thoroughly assessed by validation. Validation is, however, a time- and resource-demanding process. As updates on validation guidance are conducted, the valuable components must be kept: Reliable data are and will remain fundamental. In 2016, the scientific community was made aware of the general crisis in scientific reproducibility-validated methods must not fall into this. In this commentary, we emphasize the central importance of ring trials in the validation of experimental methods. Ring trials are sometimes considered to be a major hold-up with little value added to the validation. Here, we clarify that ring trials are indispensable to demonstrate the robustness and reproducibility of a new method. Further, that methods do fail in method transfer and ring trials due to different stumbling blocks, but these provide learnings to ensure the robustness of new methods. At the same time, we identify what it would take to perform ring trials more efficiently, and how ring trials fit into the much-needed update to the guidance on the validation of NAMs.

Verification of nucleotide sequence reagent identities in original publications in high impact factor cancer research journals

Human gene research studies that describe wrongly identified nucleotide sequence reagents have been mostly identified in journals of low to moderate impact factor, where unreliable findings could be considered to have limited influence on future research. This study examined whether papers describing wrongly identified nucleotide sequences are also published in high-impact-factor cancer research journals. We manually verified nucleotide sequence identities in original Molecular Cancer articles published in 2014, 2016, 2018, and 2020, including nucleotide sequence reagents that were claimed to target circRNAs. Using keywords identified in some 2018 and 2020 Molecular Cancer papers, we also verified nucleotide sequence identities in 2020 Oncogene papers that studied miRNA(s) and/or circRNA(s). Overall, 3.8% (251/6647) and 4.0% (47/1165) nucleotide sequences that were verified in Molecular Cancer and Oncogene papers, respectively, were found to be wrongly identified. Wrongly identified nucleotide sequences were distributed across 18% (91/500) original Molecular Cancer papers, including 38% (31/82) Molecular Cancer papers from 2020, and 40% (21/52) selected Oncogene papers from 2020. Original papers with wrongly identified nucleotide sequences were therefore unexpectedly frequent in two high-impact-factor cancer research journals, highlighting the risks of employing journal impact factors or citations as proxies for research quality.

A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research

Owing to their distinct physical and chemical properties, inorganic nanoparticles (NPs) have shown promising results in preclinical cancer therapy, but designing and engineering them for effective therapeutic purposes remains a challenge. Although a comprehensive database of inorganic NP research is not currently available, it is crucial for developing effective cancer therapies. In this context, machine learning (ML) has emerged as a transformative tool, but its adaptation to nanomedicine is hindered by inexistent or small datasets. Here we assembled a large database of inorganic NPs, comprising experimental datasets from 745 preclinical studies in cancer nanomedicine. Using descriptive statistics and explainable ML models we mined this database to gain knowledge of inorganic NP design patterns and inform future NP research for cancer treatment. Our analyses suggest that NP shape and therapy type are prominent features in determining in vivo efficacy, measured as a percentage of tumour reduction. Moreover, our database provides a large-scale open-access resource for discriminative ML that the broader nanotechnology community can utilize. Our work blueprints data mining for translational cancer research and offers evidence for standardizing NP reporting to accelerate and de-risk inorganic NP-based drug delivery, which may help to improve patient outcomes in clinical settings.

Transparency and Reproducibility in the Adolescent Brain Cognitive Development (ABCD) Study

Background

Transparency can build trust in the scientific process, but scientific findings can be undermined by poor and obscure data use and reporting practices. The purpose of this work is to report how data from the Adolescent Brain Cognitive Development (ABCD) Study has been used to date, and to provide practical recommendations on how to improve the transparency and reproducibility of findings.

Methods

Articles published from 2017 to 2023 that used ABCD Study data were reviewed using more than 30 data extraction items to gather information on data use practices. Total frequencies were reported for each extraction item, along with computation of a Level of Completeness (LOC) score that represented overall endorsement of extraction items. Univariate linear regression models were used to examine the correlation between LOC scores and individual extraction items. Post hoc analysis included examination of whether LOC scores were correlated with the logged 2-year journal impact factor.

Results

There were 549 full-length articles included in the main analysis. Analytic scripts were shared in 30% of full-length articles. The number of participants excluded due to missing data was reported in 60% of articles, and information on missing data for individual variables (e.g., household income) was provided in 38% of articles. A table describing the analytic sample was included in 83% of articles. A race and/or ethnicity variable was included in 78% of reviewed articles, while its inclusion was justified in only 41% of these articles. LOC scores were highly correlated with extraction items related to examination of missing data. A bottom 10% of LOC score was significantly correlated with a lower logged journal impact factor when compared to the top 10% of LOC scores (β=-0.77, 95% -1.02, -0.51; p-value < 0.0001).

Conclusion

These findings highlight opportunities for improvement in future papers using ABCD Study data to readily adapt analytic practices for better transparency and reproducibility efforts. A list of recommendations is provided to facilitate adherence in future research.

Technical pitfalls when collecting, cryopreserving, thawing, and stimulating human T-cells

The collection, cryopreservation, thawing, and culture of peripheral blood mononuclear cells (PBMCs) can profoundly influence T cell viability and immunogenicity. Gold-standard PBMC processing protocols have been developed by the Office of HIV/AIDS Network Coordination (HANC); however, these protocols are not universally observed. Herein, we have explored the current literature assessing how technical variation during PBMC processing can influence cellular viability and T cell immunogenicity, noting inconsistent findings between many of these studies. Amid the mounting concerns over scientific replicability, there is growing acknowledgement that improved methodological rigour and transparent reporting is required to facilitate independent reproducibility. This review highlights that in human T cell studies, this entails adopting stringent standardised operating procedures (SOPs) for PBMC processing. We specifically propose the use of HANC's Cross-Network PBMC Processing SOP, when collecting and cryopreserving PBMCs, and the HANC member network International Maternal Pediatric Adolescent AIDS Clinical Trials (IMPAACT) PBMC Thawing SOP when thawing PBMCs. These stringent and detailed protocols include comprehensive reporting procedures to document unavoidable technical variations, such as delayed processing times. Additionally, we make further standardisation and reporting recommendations to minimise and document variability during this critical experimental period. This review provides a detailed overview of the challenges inherent to a procedure often considered routine, highlighting the importance of carefully considering each aspect of SOPs for PBMC collection, cryopreservation, thawing, and culture to ensure accurate interpretation and comparison between studies.

Twelve years after the ARRIVE guidelines: Animal research has not yet arrived at high standards

The reproducibility crisis across animal studies jeopardizes the credibility of the main findings derived from animal research, even though these findings are critical for informing human studies. To clarify and improve transparency among animal studies, the ARRIVE reporting guidelines were first announced in 2010 and upgraded to version 2.0 in 2020. However, compliance with and awareness of those reporting guidelines has remained suboptimal. Journal editors should encourage the authors to adhere to those guidelines. Authors, editors, referees, and reviewers should be aware of the ARRIVE guideline 2.0 when assessing and evaluating the methodology and findings of animal studies. However, we should also question whether reporting guidelines alone can change a research culture and improve the reproducibility of animal investigations. Reported research may not reflect actual research. Large segments of animal research efforts are wasted because of poor design choices and because of non-publication rather than suboptimal reporting. Better training of the scientific workforce, interventions at improving animal research at the design stage, registration practices, and alignment of the reward system with the publication of rigorous animal research may achieve more than reporting guidelines alone.

Reproductive system, temperature, and genetic background effects in experimentally evolving populations of Caenorhabditis elegans

Experimental evolution (EE) is a powerful research framework for gaining insights into many biological questions, including the evolution of reproductive systems. We designed a long-term and highly replicated EE project using the nematode C. elegans, with the main aim of investigating the impact of reproductive system on adaptation and diversification under environmental challenge. From the laboratory-adapted strain N2, we derived isogenic lines and introgressed the fog-2(q71) mutation, which changes the reproductive system from nearly exclusive selfing to obligatory outcrossing, independently into 3 of them. This way, we obtained 3 pairs of isogenic ancestral populations differing in reproductive system; from these, we derived replicate EE populations and let them evolve in either novel (increased temperature) or control conditions for over 100 generations. Subsequently, fitness of both EE and ancestral populations was assayed under the increased temperature conditions. Importantly, each population was assayed in 2-4 independent blocks, allowing us to gain insight into the reproducibility of fitness scores. We expected to find upward fitness divergence, compared to ancestors, in populations which had evolved in this treatment, particularly in the outcrossing ones due to the benefits of genetic shuffling. However, our data did not support these predictions. The first major finding was very strong effect of replicate block on populations' fitness scores. This indicates that despite standardization procedures, some important environmental effects were varying among blocks, and possibly compounded by epigenetic inheritance. Our second key finding was that patterns of EE populations' divergence from ancestors differed among the ancestral isolines, suggesting that research conclusions derived for any particular genetic background should never be generalized without sampling a wider set of backgrounds. Overall, our results support the calls to pay more attention to biological variability when designing studies and interpreting their results, and to avoid over-generalizations of outcomes obtained for specific genetic and/or environmental conditions.

Why do probabilistic clinical models fail to transport between sites

The rising popularity of artificial intelligence in healthcare is highlighting the problem that a computational model achieving super-human clinical performance at its training sites may perform substantially worse at new sites. In this perspective, we argue that we should typically expect this failure to transport, and we present common sources for it, divided into those under the control of the experimenter and those inherent to the clinical data-generating process. Of the inherent sources we look a little deeper into site-specific clinical practices that can affect the data distribution, and propose a potential solution intended to isolate the imprint of those practices on the data from the patterns of disease cause and effect that are the usual target of probabilistic clinical models.

A guide for social science journal editors on easing into open science

Journal editors have a large amount of power to advance open science in their respective fields by incentivising and mandating open policies and practices at their journals. The Data PASS Journal Editors Discussion Interface (JEDI, an online community for social science journal editors: www.dpjedi.org ) has collated several resources on embedding open science in journal editing ( www.dpjedi.org/resources ). However, it can be overwhelming as an editor new to open science practices to know where to start. For this reason, we created a guide for journal editors on how to get started with open science. The guide outlines steps that editors can take to implement open policies and practices within their journal, and goes through the what, why, how, and worries of each policy and practice. This manuscript introduces and summarizes the guide (full guide: https://doi.org/10.31219/osf.io/hstcx ).

Sensor extended imaging workflow for creating fit for purpose models in basic and applied cell biology

While various engineering disciplines spent years on developing methods and workflows to increase their R&D efficiency, the field of cell biology has seen limited evolution in the fundamental approaches to interact with living cells. Perturbations are mostly of chemical nature, and physiologically relevant contexts and stimuli are left with limited attention, resulting in a solution space constrained within the boundaries of presently manageable perturbations. To predict in the laboratory how a drug will work in a human patient, cell biology must have a closer look at life and strive to mimic the human being in all his complexity. By implementing an iterative process from perturbation to measurement and vice versa, the authors suggest using a sensor-extended imaging workflow to implement product development practices to cell biology, opening a physiologically relevant solution space for the development of truly translational and predictive fit for purpose in vitro cell models.

Region of Attainable Redaction, an extension of Ellipse of Insignificance analysis for gauging impacts of data redaction in dichotomous outcome trials

In biomedical science, it is a reality that many published results do not withstand deeper investigation, and there is growing concern over a replicability crisis in science. Recently, Ellipse of Insignificance (EOI) analysis was introduced as a tool to allow researchers to gauge the robustness of reported results in dichotomous outcome design trials, giving precise deterministic values for the degree of miscoding between events and non-events tolerable simultaneously in both control and experimental arms (Grimes, 2022). While this is useful for situations where potential miscoding might transpire, it does not account for situations where apparently significant findings might result from accidental or deliberate data redaction in either the control or experimental arms of an experiment, or from missing data or systematic redaction. To address these scenarios, we introduce Region of Attainable Redaction (ROAR), a tool that extends EOI analysis to account for situations of potential data redaction. This produces a bounded cubic curve rather than an ellipse, and we outline how this can be used to identify potential redaction through an approach analogous to EOI. Applications are illustrated, and source code, including a web-based implementation that performs EOI and ROAR analysis in tandem for dichotomous outcome trials is provided.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Do organisms need an impact factor? Citations of key biological resources including model organisms reveal usage patterns and impact

Research resources like transgenic animals and antibodies are the workhorses of biomedicine, enabling investigators to relatively easily study specific disease conditions. As key biological resources, transgenic animals and antibodies are often validated, maintained, and distributed from university based stock centers. As these centers heavily rely largely on grant funding, it is critical that they are cited by investigators so that usage can be tracked. However, unlike systems for tracking the impact of papers, the conventions and systems for tracking key resource usage and impact lag behind. Previous studies have shown that about 50% of the resources are not findable, making the studies they are supporting irreproducible, but also makes tracking resources difficult. The RRID project is filling this gap by working with journals and resource providers to improve citation practices and to track the usage of these key resources. Here, we reviewed 10 years of citation practices for five university based stock centers, characterizing each reference into two broad categories: findable (authors could use the RRID, stock number, or full name) and not findable (authors could use a nickname or a common name that is not unique to the resource). The data revealed that when stock centers asked their communities to cite resources by RRID, in addition to helping stock centers more easily track resource usage by increasing the number of RRID papers, authors shifted from citing resources predominantly by nickname (~50% of the time) to citing them by one of the findable categories (~85%) in a matter of several years. In the case of one stock center, the MMRRC, the improvement in findability is also associated with improvements in the adherence to NIH rigor criteria, as determined by a significant increase in the Rigor and Transparency Index for studies using MMRRC mice. From this data, it was not possible to determine whether outreach to authors or changes to stock center websites drove better citation practices, but findability of research resources and rigor adherence was improved.

Adapting Open Science and Pre-registration to Longitudinal Research

Open science practices, such as pre-registration and data sharing, increase transparency and may improve the replicability of developmental science. However, developmental science has lagged behind other fields in implementing open science practices. This lag may arise from unique challenges and considerations of longitudinal research. In this paper, preliminary guidelines are provided for adapting open science practices to longitudinal research to facilitate researchers' use of these practices. The guidelines propose a serial and modular approach to registration that includes an initial pre-registration of the methods and focal hypotheses of the longitudinal study, along with subsequent pre- or co-registered questions, hypotheses, and analysis plans associated with specific papers. Researchers are encouraged to share their research materials and relevant data with associated papers, and to report sufficient information for replicability. In addition, there should be careful consideration about requirements regarding the timing of data sharing, to avoid disincentivizing longitudinal research.

Is biomedical research self-correcting? Modelling insights on the persistence of spurious science

The reality that volumes of published biomedical research are not reproducible is an increasingly recognized problem. Spurious results reduce trustworthiness of reported science, increasing research waste. While science should be self-correcting from a philosophical perspective, that in insolation yields no information on efforts required to nullify suspect findings or factors shaping how quickly science may be corrected. There is also a paucity of information on how perverse incentives in the publishing ecosystem favouring novel positive findings over null results shape the ability of published science to self-correct. Knowledge of factors shaping self-correction of science remain obscure, limiting our ability to mitigate harms. This modelling study introduces a simple model to capture dynamics of the publication ecosystem, exploring factors influencing research waste, trustworthiness, corrective effort and time to correction. Results from this work indicate that research waste and corrective effort are highly dependent on field-specific false positive rates and time delays to corrective results to spurious findings are propagated. The model also suggests conditions under which biomedical science is self-correcting and those under which publication of correctives alone cannot stem propagation of untrustworthy results. Finally, this work models a variety of potential mitigation strategies, including researcher- and publisher-driven interventions.

Toward a common standard for data and specimen provenance in life sciences

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

Development of a Comprehensive Food Data Citation Standard: A Surprising Gap in the Nutrition Research Literature

Currently, there is no standard for the citation of food composition data. This leads to the questions: how are food and nutrient data cited in research papers, and are they presented in a way that allows studies to be reproduced? To answer these questions, we performed a review of the literature and quantified the accuracy and completeness of data citations from publications (January to December 2020) in the top 5 nutrition journals as ranked by the Scimago Journal Rankings. We then performed a review of citation guidelines currently in place in other disciplines. Similar to the requirement of completing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist for systematic reviews, we have developed a comprehensive data citation checklist, the Comprehensive Food Data Citation (CFDC) checklist. The CFDC checklist was developed through a benchmarking assessment against established data citation standards. Its purpose is to establish a standardized, best-practice approach for reporting food composition data. The CFDC checklist has been designed to cater to both publishers and authors, ensuring consistency and accuracy in food composition data reporting. The CFDC checklist is also available as an interactive citation generator to facilitate the adoption of consistent and comprehensive citation of food composition data and is available at https://www.nutrientinstitute.org/cfdc. Despite general agreement that accurate data citation is paramount, this is the first citation standard specifically developed to capture food composition data. Because food composition data are the foundation of nutrition research, our proposed guidelines aim to provide the field with a much-needed foundation for acknowledging and sharing data in a way that fosters reproducibility.

Up Front and Open? Shrouded in Secrecy? Or Somewhere in Between? A Meta-Research Systematic Review of Open Science Practices in Sport Medicine Research

OBJECTIVE: To investigate open science practices in research published in the top 5 sports medicine journals from May 1, 2022, and October 1, 2022. DESIGN: A meta-research systematic review. LITERATURE SEARCH: Open science practices were searched in MEDLINE. STUDY SELECTION CRITERIA: We included original scientific research published in one of the identified top 5 sports medicine journals in 2022 as ranked by Clarivate: (1) British Journal of Sports Medicine, (2) Journal of Sport and Health Science, (3) American Journal of Sports Medicine, (4) Medicine and Science in Sports and Exercise, and (5) Sports Medicine-Open. Studies were excluded if they were systematic reviews, qualitative research, gray literature, or animal or cadaver models. DATA SYNTHESIS: Open science practices were extracted in accordance with the Transparency and Openness Promotion guidelines and patient and public involvement. RESULTS: Two hundred forty-three studies were included. The median number of open science practices in each study was 2, out of a maximum of 12 (range: 0-8; interquartile range: 2). Two hundred thirty-four studies (96%, 95% confidence interval [CI]: 94%-99%) provided an author conflict-of-interest statement and 163 (67%, 95% CI: 62%-73%) reported funding. Twenty-one studies (9%, 95% CI: 5%-12%) provided open-access data. Fifty-four studies (22%, 95% CI: 17%-27%) included a data availability statement and 3 (1%, 95% CI: 0%-3%) made code available. Seventy-six studies (32%, 95% CI: 25%-37%) had transparent materials and 30 (12%, 95% CI: 8%-16%) used a reporting guideline. Twenty-eight studies (12%, 95% CI: 8%-16%) were preregistered. Six studies (3%, 95% CI: 1%-4%) published a protocol. Four studies (2%, 95% CI: 0%-3%) reported an analysis plan a priori. Seven studies (3%, 95% CI: 1%-5%) reported patient and public involvement. CONCLUSION: Open science practices in the sports medicine field are extremely limited. The least followed practices were sharing code, data, and analysis plans. J Orthop Sports Phys Ther 2023;53(12):1-13. Epub 20 October 2023. doi:10.2519/jospt.2023.12016.

Eleven strategies for making reproducible research and open science training the norm at research institutions

Reproducible research and open science practices have the potential to accelerate scientific progress by allowing others to reuse research outputs, and by promoting rigorous research that is more likely to yield trustworthy results. However, these practices are uncommon in many fields, so there is a clear need for training that helps and encourages researchers to integrate reproducible research and open science practices into their daily work. Here, we outline eleven strategies for making training in these practices the norm at research institutions. The strategies, which emerged from a virtual brainstorming event organized in collaboration with the German Reproducibility Network, are concentrated in three areas: (i) adapting research assessment criteria and program requirements; (ii) training; (iii) building communities. We provide a brief overview of each strategy, offer tips for implementation, and provide links to resources. We also highlight the importance of allocating resources and monitoring impact. Our goal is to encourage researchers - in their roles as scientists, supervisors, mentors, instructors, and members of curriculum, hiring or evaluation committees - to think creatively about the many ways they can promote reproducible research and open science practices in their institutions.

Implementing code review in the scientific workflow: Insights from ecology and evolutionary biology

Code review increases reliability and improves reproducibility of research. As such, code review is an inevitable step in software development and is common in fields such as computer science. However, despite its importance, code review is noticeably lacking in ecology and evolutionary biology. This is problematic as it facilitates the propagation of coding errors and a reduction in reproducibility and reliability of published results. To address this, we provide a detailed commentary on how to effectively review code, how to set up your project to enable this form of review and detail its possible implementation at several stages throughout the research process. This guide serves as a primer for code review, and adoption of the principles and advice here will go a long way in promoting more open, reliable, and transparent ecology and evolutionary biology.

A multi-laboratory preclinical trial in rodents to assess treatment candidates for acute ischemic stroke

Human diseases may be modeled in animals to allow preclinical assessment of putative new clinical interventions. Recent, highly publicized failures of large clinical trials called into question the rigor, design, and value of preclinical assessment. We established the Stroke Preclinical Assessment Network (SPAN) to design and implement a randomized, controlled, blinded, multi-laboratory trial for the rigorous assessment of candidate stroke treatments combined with intravascular thrombectomy. Efficacy and futility boundaries in a multi-arm multi-stage statistical design aimed to exclude from further study highly effective or futile interventions after each of four sequential stages. Six independent research laboratories performed a standard focal cerebral ischemic insult in five animal models that included equal numbers of males and females: young mice, young rats, aging mice, mice with diet-induced obesity, and spontaneously hypertensive rats. The laboratories adhered to a common protocol and efficiently enrolled 2615 animals with full data completion and comprehensive animal tracking. SPAN successfully implemented treatment masking, randomization, prerandomization inclusion and exclusion criteria, and blinded assessment of outcomes. The SPAN design and infrastructure provide an effective approach that could be used in similar preclinical, multi-laboratory studies in other disease areas and should help improve reproducibility in translational science.

Evolution of fertilization ability in obligatorily outcrossing populations of Caenorhabditis elegans

In species reproducing by selfing, the traits connected with outcrossing typically undergo degeneration, a phenomenon called selfing syndrome. In Caenorhabditis elegans nematodes, selfing syndrome affects many traits involved in mating, rendering cross-fertilization highly inefficient. In this study, we investigated the evolution of cross-fertilization efficiency in populations genetically modified to reproduce by obligatory outcrossing. Following the genetic modification, replicate obligatorily outcrossing were maintained for over 100 generations, at either optimal (20 °C) or elevated (24 °C) temperatures, as a part of a broader experimental evolution program. Subsequently, fertilization rates were assayed in the evolving populations, as well as their ancestors who had the obligatory outcrossing introduced but did not go through experimental evolution. Fertilization effectivity was measured by tracking the fractions of fertilized females in age-synchronized populations, through 8 h since reaching adulthood. In order to check the robustness of our measurements, each evolving population was assayed in two or three independent replicate blocks. Indeed, we found high levels of among-block variability in the fertilization trajectories, and in the estimates of divergence between evolving populations and their ancestors. We also identified five populations which appear to have evolved increased fertilization efficiency, relative to their ancestors. However, due to the abovementioned high variability, this set of populations should be treated as candidate, with further replications needed to either confirm or disprove their divergence from ancestors. Furthermore, we also discuss additional observations we have made concerning fertilization trajectories.

Publication bias, statistical power and reporting practices in the Journal of Sports Sciences: potential barriers to replicability

Two factors that decrease the replicability of studies in the scientific literature are publication bias and studies with underpowered desgins. One way to ensure that studies have adequate statistical power to detect the effect size of interest is by conducting a-priori power analyses. Yet, a previous editorial published in the Journal of Sports Sciences reported a median sample size of 19 and the scarce usage of a-priori power analyses. We meta-analysed 89 studies from the same journal to assess the presence and extent of publication bias, as well as the average statistical power, by conducting a z-curve analysis. In a larger sample of 174 studies, we also examined a) the usage, reporting practices and reproducibility of a-priori power analyses; and b) the prevalence of reporting practices of t-statistic or F-ratio, degrees of freedom, exact p-values, effect sizes and confidence intervals. Our results indicate that there was some indication of publication bias and the average observed power was low (53% for significant and non-significant findings and 61% for only significant findings). Finally, the usage and reporting practices of a-priori power analyses as well as statistical results including test statistics, effect sizes and confidence intervals were suboptimal.

Research Into Evidence-Based Psychological Interventions Needs a Stronger Focus on Replicability

Background

It is a precondition for evidence-based practice that research is replicable in a wide variety of clinical settings. Current standards for identifying evidence-based psychological interventions and making recommendations for clinical practice in clinical guidelines include criteria that are relevant for replicability, but a better understanding as well refined definitions of replicability are needed enabling empirical research on this topic. Recent advances on this issue were made in the wider field of psychology and in other disciplines, which offers the opportunity to define and potentially increase replicability also in research on psychological interventions.

Method

This article proposes a research strategy for assessing, understanding, and improving replicability in research on psychological interventions.

Results/Conclusion

First, we establish a replication taxonomy ranging from direct to conceptual replication adapted to the field of research on clinical interventions, propose study characteristics that increase the trustworthiness of results, and define statistical criteria for successful replication with respect to the quantitative outcomes of the original and replication studies. Second, we propose how to establish such standards for future research, i.e., in order to design future replication studies for psychological interventions as well as to apply them when investigating which factors are causing the (non-)replicability of findings in the current literature.

Neuroscience Needs Network Science

The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity. Here, we discuss emerging frontiers for network neuroscience in the brain atlas era, addressing the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease. We underscore the importance of fostering interdisciplinary opportunities through workshops, conferences, and funding initiatives, such as supporting students and postdoctoral fellows with interests in both disciplines. By bringing together the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way toward a deeper understanding of the brain and its functions, as well as offering new challenges for network science.

Reproducibility in Neuroimaging Analysis: Challenges and Solutions

Recent years have marked a renaissance in efforts to increase research reproducibility in psychology, neuroscience, and related fields. Reproducibility is the cornerstone of a solid foundation of fundamental research-one that will support new theories built on valid findings and technological innovation that works. The increased focus on reproducibility has made the barriers to it increasingly apparent, along with the development of new tools and practices to overcome these barriers. Here, we review challenges, solutions, and emerging best practices with a particular emphasis on neuroimaging studies. We distinguish 3 main types of reproducibility, discussing each in turn. Analytical reproducibility is the ability to reproduce findings using the same data and methods. Replicability is the ability to find an effect in new datasets, using the same or similar methods. Finally, robustness to analytical variability refers to the ability to identify a finding consistently across variation in methods. The incorporation of these tools and practices will result in more reproducible, replicable, and robust psychological and brain research and a stronger scientific foundation across fields of inquiry.

Transparency of COVID-19-related research: A meta-research study

BACKGROUND

We aimed to assess the adherence to five transparency practices (data availability, code availability, protocol registration and conflicts of interest (COI), and funding disclosures) from open access Coronavirus disease 2019 (COVID-19) related articles.

METHODS

We searched and exported all open access COVID-19-related articles from PubMed-indexed journals in the Europe PubMed Central database published from January 2020 to June 9, 2022. With a validated and automated tool, we detected transparent practices of three paper types: research articles, randomized controlled trials (RCTs), and reviews. Basic journal- and article-related information were retrieved from the database. We used R for the descriptive analyses.

RESULTS

The total number of articles was 258,678, of which we were able to retrieve full texts of 186,157 (72%) articles from the database Over half of the papers (55.7%, n = 103,732) were research articles, 10.9% (n = 20,229) were review articles, and less than one percent (n = 1,202) were RCTs. Approximately nine-tenths of articles (in all three paper types) had a statement to disclose COI. Funding disclosure (83.9%, confidence interval (CI): 81.7-85.8 95%) and protocol registration (53.5%, 95% CI: 50.7-56.3) were more frequent in RCTs than in reviews or research articles. Reviews shared data (2.5%, 95% CI: 2.3-2.8) and code (0.4%, 95% CI: 0.4-0.5) less frequently than RCTs or research articles. Articles published in 2022 had the highest adherence to all five transparency practices. Most of the reviews (62%) and research articles (58%) adhered to two transparency practices, whereas almost half of the RCTs (47%) adhered to three practices. There were journal- and publisher-related differences in all five practices, and articles that did not adhere to transparency practices were more likely published in lowest impact journals and were less likely cited.

CONCLUSION

While most articles were freely available and had a COI disclosure, adherence to other transparent practices was far from acceptable. A much stronger commitment to open science practices, particularly to protocol registration, data and code sharing, is needed from all stakeholders.

Prevalence and predictors of data and code sharing in the medical and health sciences: systematic review with meta-analysis of individual participant data

OBJECTIVES

To synthesise research investigating data and code sharing in medicine and health to establish an accurate representation of the prevalence of sharing, how this frequency has changed over time, and what factors influence availability.

DESIGN

Systematic review with meta-analysis of individual participant data.

DATA SOURCES

Ovid Medline, Ovid Embase, and the preprint servers medRxiv, bioRxiv, and MetaArXiv were searched from inception to 1 July 2021. Forward citation searches were also performed on 30 August 2022.

REVIEW METHODS

Meta-research studies that investigated data or code sharing across a sample of scientific articles presenting original medical and health research were identified. Two authors screened records, assessed the risk of bias, and extracted summary data from study reports when individual participant data could not be retrieved. Key outcomes of interest were the prevalence of statements that declared that data or code were publicly or privately available (declared availability) and the success rates of retrieving these products (actual availability). The associations between data and code availability and several factors (eg, journal policy, type of data, trial design, and human participants) were also examined. A two stage approach to meta-analysis of individual participant data was performed, with proportions and risk ratios pooled with the Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis.

RESULTS

The review included 105 meta-research studies examining 2 121 580 articles across 31 specialties. Eligible studies examined a median of 195 primary articles (interquartile range 113-475), with a median publication year of 2015 (interquartile range 2012-2018). Only eight studies (8%) were classified as having a low risk of bias. Meta-analyses showed a prevalence of declared and actual public data availability of 8% (95% confidence interval 5% to 11%) and 2% (1% to 3%), respectively, between 2016 and 2021. For public code sharing, both the prevalence of declared and actual availability were estimated to be <0.5% since 2016. Meta-regressions indicated that only declared public data sharing prevalence estimates have increased over time. Compliance with mandatory data sharing policies ranged from 0% to 100% across journals and varied by type of data. In contrast, success in privately obtaining data and code from authors historically ranged between 0% and 37% and 0% and 23%, respectively.

CONCLUSIONS

The review found that public code sharing was persistently low across medical research. Declarations of data sharing were also low, increasing over time, but did not always correspond to actual sharing of data. The effectiveness of mandatory data sharing policies varied substantially by journal and type of data, a finding that might be informative for policy makers when designing policies and allocating resources to audit compliance.

SYSTEMATIC REVIEW REGISTRATION

Open Science Framework doi:10.17605/OSF.IO/7SX8U.

A Connected World: System-Level Support Through Biosensors

The goal of protecting the health of future generations is a blueprint for future biosensor design. Systems-level decision support requires that biosensors provide meaningful service to society. In this review, we summarize recent developments in cyber physical systems and biosensors connected with decision support. We identify key processes and practices that may guide the establishment of connections between user needs and biosensor engineering using an informatics approach. We call for data science and decision science to be formally connected with sensor science for understanding system complexity and realizing the ambition of biosensors-as-a-service. This review calls for a focus on quality of service early in the design process as a means to improve the meaningful value of a given biosensor. We close by noting that technology development, including biosensors and decision support systems, is a cautionary tale. The economics of scale govern the success, or failure, of any biosensor system.

Systematic literature review reveals suboptimal use of chemical probes in cell-based biomedical research

Chemical probes have reached a prominent role in biomedical research, but their impact is governed by experimental design. To gain insight into the use of chemical probes, we conducted a systematic review of 662 publications, understood here as primary research articles, employing eight different chemical probes in cell-based research. We summarised (i) concentration(s) at which chemical probes were used in cell-based assays, (ii) inclusion of structurally matched target-inactive control compounds and (iii) orthogonal chemical probes. Here, we show that only 4% of analysed eligible publications used chemical probes within the recommended concentration range and included inactive compounds as well as orthogonal chemical probes. These findings indicate that the best practice with chemical probes is yet to be implemented in biomedical research. To achieve this, we propose 'the rule of two': At least two chemical probes (either orthogonal target-engaging probes, and/or a pair of a chemical probe and matched target-inactive compound) to be employed at recommended concentrations in every study.

Predicting and reasoning about replicability using structured groups

This paper explores judgements about the replicability of social and behavioural sciences research and what drives those judgements. Using a mixed methods approach, it draws on qualitative and quantitative data elicited from groups using a structured approach called the IDEA protocol ('investigate', 'discuss', 'estimate' and 'aggregate'). Five groups of five people with relevant domain expertise evaluated 25 research claims that were subject to at least one replication study. Participants assessed the probability that each of the 25 research claims would replicate (i.e. that a replication study would find a statistically significant result in the same direction as the original study) and described the reasoning behind those judgements. We quantitatively analysed possible correlates of predictive accuracy, including self-rated expertise and updating of judgements after feedback and discussion. We qualitatively analysed the reasoning data to explore the cues, heuristics and patterns of reasoning used by participants. Participants achieved 84% classification accuracy in predicting replicability. Those who engaged in a greater breadth of reasoning provided more accurate replicability judgements. Some reasons were more commonly invoked by more accurate participants, such as 'effect size' and 'reputation' (e.g. of the field of research). There was also some evidence of a relationship between statistical literacy and accuracy.

Neuroscience needs Network Science

The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions.

A Biomolecular Toolbox for Precision Nanomotors

The application of nanomotors for cancer diagnosis and therapy is a new and exciting area of research, which when combined with precision nanomedicine, promises to solve many of the issues encountered by previous development of passive nanoparticles. The goal of this article is to introduce nanomotor and nanomedicine researchers to the deep pool of knowledge available regarding cancer cell biology and biochemistry, as well as provide a greater appreciation of the complexity of cell membrane compositions, extracellular surfaces, and their functional consequences. A short description of the nanomotor state-of-art for cancer therapy and diagnosis is first provided, as well as recommendations for future directions of the field. Then, a biomolecular targeting toolbox has been collated for researchers looking to apply their nanomaterial of choice to a biological setting, as well as providing a glimpse into currently available clinical therapies and technologies. This toolbox contains an overview of different classes of targeting molecules available for high affinity and specific targeting and cell surface targets to aid researchers in the selection of a clinical disease model and targeting methodology. It is hoped that this review will provide biological context, inspiration, and direction to future nanomotor and nanomedicine research.

Challenges and considerations for reproducibility of STARR-seq assays

High-throughput methods such as RNA-seq, ChIP-seq, and ATAC-seq have well-established guidelines, commercial kits, and analysis pipelines that enable consistency and wider adoption for understanding genome function and regulation. STARR-seq, a popular assay for directly quantifying the activities of thousands of enhancer sequences simultaneously, has seen limited standardization across studies. The assay is long, with more than 250 steps, and frequent customization of the protocol and variations in bioinformatics methods raise concerns for reproducibility of STARR-seq studies. Here, we assess each step of the protocol and analysis pipelines from published sources and in-house assays, and identify critical steps and quality control (QC) checkpoints necessary for reproducibility of the assay. We also provide guidelines for experimental design, protocol scaling, customization, and analysis pipelines for better adoption of the assay. These resources will allow better optimization of STARR-seq for specific research needs, enable comparisons and integration across studies, and improve the reproducibility of results.

A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias

We assess inferential quality in the field of differential expression profiling by high-throughput sequencing (HT-seq) based on analysis of datasets submitted from 2008 to 2020 to the NCBI GEO data repository. We take advantage of the parallel differential expression testing over thousands of genes, whereby each experiment leads to a large set of p-values, the distribution of which can indicate the validity of assumptions behind the test. From a well-behaved p-value set π0, the fraction of genes that are not differentially expressed can be estimated. We found that only 25% of experiments resulted in theoretically expected p-value histogram shapes, although there is a marked improvement over time. Uniform p-value histogram shapes, indicative of <100 actual effects, were extremely few. Furthermore, although many HT-seq workflows assume that most genes are not differentially expressed, 37% of experiments have π0-s of less than 0.5, as if most genes changed their expression level. Most HT-seq experiments have very small sample sizes and are expected to be underpowered. Nevertheless, the estimated π0-s do not have the expected association with N, suggesting widespread problems of experiments with controlling false discovery rate (FDR). Both the fractions of different p-value histogram types and the π0 values are strongly associated with the differential expression analysis program used by the original authors. While we could double the proportion of theoretically expected p-value distributions by removing low-count features from the analysis, this treatment did not remove the association with the analysis program. Taken together, our results indicate widespread bias in the differential expression profiling field and the unreliability of statistical methods used to analyze HT-seq data.

Research on scalable graphene faces a reproducibility gap

More than a decade after the first demonstration of large-scale graphene synthesis by chemical vapor deposition, the commercialization of graphene products is limited not only by price, but also by consistency, reproducibility, and predictability. Here, the author discusses the reproducibility issues in the field and proposes possible solutions to improve the reliability of published results.

Developing a standardized but extendable framework to increase the findability of infectious disease datasets

Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.

From the Catastrophic Objective Irreproducibility of Cancer Research and Unavoidable Failures of Molecular Targeted Therapies to the Sparkling Hope of Supramolecular Targeted Strategies

The unprecedented non-reproducibility of the results published in the field of cancer research has recently come under the spotlight. In this short review, we try to highlight some general principles in the organization and evolution of cancerous tumors, which objectively lead to their enormous variability and, consequently, the irreproducibility of the results of their investigation. This heterogeneity is also extremely unfavorable for the effective use of molecularly targeted medicine. Against the seemingly comprehensive background of this heterogeneity, we single out two supramolecular characteristics common to all tumors: the clustered nature of tumor interactions with their microenvironment and the formation of biomolecular condensates with tumor-specific distinctive features. We suggest that these features can form the basis of strategies for tumor-specific supramolecular targeted therapies.

Research transparency in dental research: A programmatic analysis

We assessed adherence to five transparency practices-data sharing, code sharing, conflict of interest disclosure, funding disclosure, and protocol registration-in articles in dental journals. We searched and exported the full text of all research articles from PubMed-indexed dental journals available in the Europe PubMed Central database until the end of 2021. We programmatically assessed their adherence to the five transparency practices using a validated and automated tool. Journal- and article-related information was retrieved from ScimagoJR and Journal Citation Reports. Of all 329,784 articles published in PubMed-indexed dental journals, 10,659 (3.2%) were available to download. Of those, 77% included a conflict of interest disclosure, and 62% included a funding disclosure. Seven percent of the articles had a registered protocol. Data sharing (2.0%) and code sharing (0.1%) were rarer. Sixteen percent of articles did not adhere to any of the five transparency practices, 29% adhered to one, 48% adhered to two, 7.0% adhered to three, 0.3% adhered to four, and no article adhered to all five practices. Adherence to transparency practices increased over time; however, data and code sharing especially remained rare. Coordinated efforts involving all stakeholders are needed to change current transparency practices in dental research.

Automated cell segmentation for reproducibility in bioimage analysis

Live-cell imaging is extremely common in synthetic biology research, but its ability to be applied reproducibly across laboratories can be hindered by a lack of standardized image analysis. Here, we introduce a novel cell segmentation method developed as part of a broader Independent Verification & Validation (IV&V) program aimed at characterizing engineered Dictyostelium cells. Standardizing image analysis was found to be highly challenging: the amount of human judgment required for parameter optimization, algorithm tweaking, training and data pre-processing steps forms serious challenges for reproducibility. To bring automation and help remove bias from live-cell image analysis, we developed a self-supervised learning (SSL) method that recursively trains itself directly from motion in live-cell microscopy images without any end-user input, thus providing objective cell segmentation. Here, we highlight this SSL method applied to characterizing the engineered Dictyostelium cells of the original IV&V program. This approach is highly generalizable, accepting images from any cell type or optical modality without the need for manual training or parameter optimization. This method represents an important step toward automated bioimage analysis software and reflects broader efforts to design accessible measurement technologies to enhance reproducibility in synthetic biology research.