Big data from small animals: integrating multi-level environmental data into the Dog Aging Project

Environmental exposures can have large impacts on health outcomes. While many resources have been dedicated to understanding how humans are influenced by the environment, few efforts have been made to study the role of built and natural environmental features on animal health. The Dog Aging Project (DAP) is a longitudinal community science study of aging in companion dogs. Using a combination of owner-reported surveys and secondary sources linked through geocoded coordinates, DAP has captured home, yard and neighbourhood variables for over 40,000 dogs. The DAP environmental data set spans four domains: the physical and built environment; chemical environment and exposures; diet and exercise; and social environment and interactions. By combining biometric data, measures of cognitive function and behaviour, and medical records, DAP is attempting to use a big-data approach to transform the understanding of how the surrounding world affects the health of companion dogs. In this paper, the authors describe the data infrastructure developed to integrate and analyse multi-level environmental data that can be used to improve the understanding of canine co-morbidity and aging.

Well-being impact assessment of artificial intelligence - A search for causality and proposal for an open platform for well-being impact assessment of AI systems

In recent years, the well-being impact assessment approach has been applied in the area of Artificial Intelligence (AI). Existing well-being frameworks and tools provide a relevant starting point. Taking into account its multidimensional nature, well-being assessment is well suited to assess both the expected positive effects of the technology as well as unintended negative consequences. To-date the establishment of causal links mostly stems from intuitive causal models. Such approaches neglect the fact that to prove causal links between the operation of an AI system and observed effects is difficult due to the immense complexity of the socio-technical context. This article aims at providing a framework for ascertaining the attribution of effects of observed impact of AI on well-being. An elaborated approach to impact assessment potentially enabling causal inferences is demonstrated. Furthermore, a new Open Platform for Well-Being Impact Assessment of AI systems (OPIA) is introduced, which is based on a distributed community to build reproducible evidence through effective identification, refinement, iterative testing, and cross-validation of expected causal structures.

Publication bias impacts on effect size, statistical power, and magnitude (Type M) and sign (Type S) errors in ecology and evolutionary biology

Collaborative efforts to directly replicate empirical studies in the medical and social sciences have revealed alarmingly low rates of replicability, a phenomenon dubbed the 'replication crisis'. Poor replicability has spurred cultural changes targeted at improving reliability in these disciplines. Given the absence of equivalent replication projects in ecology and evolutionary biology, two inter-related indicators offer the opportunity to retrospectively assess replicability: publication bias and statistical power. This registered report assesses the prevalence and severity of small-study (i.e., smaller studies reporting larger effect sizes) and decline effects (i.e., effect sizes decreasing over time) across ecology and evolutionary biology using 87 meta-analyses comprising 4,250 primary studies and 17,638 effect sizes. Further, we estimate how publication bias might distort the estimation of effect sizes, statistical power, and errors in magnitude (Type M or exaggeration ratio) and sign (Type S). We show strong evidence for the pervasiveness of both small-study and decline effects in ecology and evolution. There was widespread prevalence of publication bias that resulted in meta-analytic means being over-estimated by (at least) 0.12 standard deviations. The prevalence of publication bias distorted confidence in meta-analytic results, with 66% of initially statistically significant meta-analytic means becoming non-significant after correcting for publication bias. Ecological and evolutionary studies consistently had low statistical power (15%) with a 4-fold exaggeration of effects on average (Type M error rates = 4.4). Notably, publication bias reduced power from 23% to 15% and increased type M error rates from 2.7 to 4.4 because it creates a non-random sample of effect size evidence. The sign errors of effect sizes (Type S error) increased from 5% to 8% because of publication bias. Our research provides clear evidence that many published ecological and evolutionary findings are inflated. Our results highlight the importance of designing high-power empirical studies (e.g., via collaborative team science), promoting and encouraging replication studies, testing and correcting for publication bias in meta-analyses, and adopting open and transparent research practices, such as (pre)registration, data- and code-sharing, and transparent reporting.

Current landscape and future directions of synthetic biology in South America

Synthetic biology (SynBio) is a rapidly advancing multidisciplinary field in which South American countries such as Chile, Argentina, and Brazil have made notable contributions and have established leadership positions in the region. In recent years, efforts have strengthened SynBio in the rest of the countries, and although progress is significant, growth has not matched that of the aforementioned countries. Initiatives such as iGEM and TECNOx have introduced students and researchers from various countries to the foundations of SynBio. Several factors have hindered progress in the field, including scarce funding from both public and private sources for synthetic biology projects, an underdeveloped biotech industry, and a lack of policies to promote bio-innovation. However, open science initiatives such as the DIY movement and OSHW have helped to alleviate some of these challenges. Similarly, the abundance of natural resources and biodiversity make South America an attractive location to invest in and develop SynBio projects.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software

BACKGROUND

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS).

AIM OF REVIEW

This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

Can machine learning accelerate soft material parameter identification from complex mechanical test data?

Identifying the constitutive parameters of soft materials often requires heterogeneous mechanical test modes, such as simple shear. In turn, interpreting the resulting complex deformations necessitates the use of inverse strategies that iteratively call forward finite element solutions. In the past, we have found that the cost of repeatedly solving non-trivial boundary value problems can be prohibitively expensive. In this current work, we leverage our prior experimentally derived mechanical test data to explore an alternative approach. Specifically, we investigate whether a machine learning-based approach can accelerate the process of identifying material parameters based on our mechanical test data. Toward this end, we pursue two different strategies. In the first strategy, we replace the forward finite element simulations within an iterative optimization framework with a machine learning-based metamodel. Here, we explore both Gaussian process regression and neural network metamodels. In the second strategy, we forgo the iterative optimization framework and use a stand alone neural network to predict the entire material parameter set directly from experimental results. We first evaluate both approaches with simple shear experiments on blood clot, an isotropic, homogeneous material. Next, we evaluate both approaches against simple shear and uniaxial loading experiments on right ventricular myocardium, an anisotropic, heterogeneous material. We find that replacing the forward finite element simulations with metamodels significantly accelerates the parameter identification process with excellent results in the case of blood clot, and with satisfying results in the case of right ventricular myocardium. On the other hand, we find that replacing the entire optimization framework with a neural network yielded unsatisfying results, especially for right ventricular myocardium. Overall, the importance of our work stems from providing a baseline example showing how machine learning can accelerate the process of material parameter identification for soft materials from complex mechanical data, and from providing an open access experimental and simulation dataset that may serve as a benchmark dataset for others interested in applying machine learning techniques to soft tissue biomechanics.

Scientific progress: By-whom or for-whom?

When science makes cognitive progress, who or what is it that improves in the requisite way? According to a widespread and unchallenged assumption, it is the cognitive attitudes of scientists themselves, i.e. the agents by whom scientific progress is made, that improve during progressive episodes. This paper argues against this assumption and explores a different approach. Scientific progress should be defined in terms of potential improvements to the cognitive attitudes of those for whom progress is made, i.e. the receivers rather than the producers of scientific information. This includes not only scientists themselves, but also various other individuals who utilize scientific information in different ways for the benefit of society as a whole.

Do biodiversity monitoring citizen science surveys meet the core principles of open science practices?

Citizen science (CS), as an enabler of open science (OS) practices, is a low-cost and accessible method for data collection in biodiversity monitoring, which can empower and educate the public both on scientific research priorities and on environmental change. Where OS increases research transparency and scientific democratisation; if properly implemented, CS should do the same. Here, we present the findings of a systematic review exploring "openness" of CS in biodiversity monitoring. CS projects were scored between - 1 (closed) and 1 (open) on their adherence to defined OS principles: accessible data, code, software, publication, data management plans, and preregistrations. Openness scores per principle were compared to see where OS is more frequently utilised across the research process. The relationship between interest in CS and openness within the practice was also tested. Overall, CS projects had an average open score of 0.14. There was a significant difference in open scores between OS principles (p =  < 0.0001), where "open data" was the most adhered to practice compared to the lowest scores found in relation to preregistrations. The apparent level of interest in CS was not shown to correspond to a significant increase in openness within CS (p = 0.8464). These results reveal CS is not generally "open" despite being an OS approach, with implications for how the public can interact with the research that they play an active role in contributing to. The development of systematic recommendations on where and how OS can be implemented across the research process in citizen science projects is encouraged.

Genetic and Environmental Variation in Continuous Phenotypes in the ABCD Study®

Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9-10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background.

Consent Codes: Maintaining Consent in an Ever-expanding Open Science Ecosystem

We previously proposed a structure for recording consent-based data use 'categories' and 'requirements' - Consent Codes - with a view to supporting maximum use and integration of genomic research datasets, and reducing uncertainty about permissible re-use of shared data. Here we discuss clarifications and subsequent updates to the Consent Codes (v4) based on new areas of application (e.g., the neurosciences, biobanking, H3Africa), policy developments (e.g., return of research results), and further practical considerations, including developments in automated approaches to consent management.

An analytical framework-based pedagogical method for scholarly community coaching: A proof of concept

Working in academia is challenging, even more so for those with limited resources and opportunities. Researchers around the world do not have equal working conditions. The paper presents the structure, operation method, and conceptual framework of the SM3D Portal's community coaching method, which is built to help Early Career Researchers (ECRs) and researchers in low-resource settings overcome the obstacle of inequality and start their career progress. The community coaching method is envisioned by three science philosophies (cost-effectiveness, transparency spirit, and proactive attitude) and established and operated based on the Serendipity-Mindsponge-3D knowledge (SM3D) management framework (i.e., mindsponge thinking and Bayesian Mindsponge Framework analytics serve as the coaching program's foundational theory and analytical tools). The coaching method also embraces Open Science's values for lowering the cost of doing science and encouraging the trainees to be transparent, which is expected to facilitate the self-correcting mechanism of science through open data, open review, and open dialogue. Throughout the training process, members are central beneficiaries by gaining research knowledge and skills, acquiring publication as the training's product, and shifting their mindsets from "I can't do it" to "I can do it," and at the same time transforming a mentee to be ready for a future mentor's role. The coaching method is thus one of the members, for the member, by the members.•The paper provides the structure, operation method, and conceptual framework of the SM3D Portal's community coaching method, which is built to help Early Career Researchers (ECRs) and researchers in low-resource settings overcome the obstacle of inequality and start their career progress.•The paper presents three major science philosophies envisioning the establishment and operation of scholarly community coaching.•The paper employs the mindsponge theory and BMF analytics to construct a conceptual framework explaining how an environment is created to help shift members' mindsets from "I can't do it" to "I can do it."

A practical guide for studying human behavior in the lab

In the last few decades, the field of neuroscience has witnessed major technological advances that have allowed researchers to measure and control neural activity with great detail. Yet, behavioral experiments in humans remain an essential approach to investigate the mysteries of the mind. Their relatively modest technological and economic requisites make behavioral research an attractive and accessible experimental avenue for neuroscientists with very diverse backgrounds. However, like any experimental enterprise, it has its own inherent challenges that may pose practical hurdles, especially to less experienced behavioral researchers. Here, we aim at providing a practical guide for a steady walk through the workflow of a typical behavioral experiment with human subjects. This primer concerns the design of an experimental protocol, research ethics, and subject care, as well as best practices for data collection, analysis, and sharing. The goal is to provide clear instructions for both beginners and experienced researchers from diverse backgrounds in planning behavioral experiments.

Notes on fiber length measurements: A case study in the underbelly of open source neuroscience

Being on the bleeding edge of research requires the use of new and regularly updated software. The result is the occasional and inevitable occurrence of bugs. In the following work we present a case study where a feature request introduced a bug in a neuroimaging software package, which had consequences for the quality of results in a published article. We discuss the process of diagnosis, rectification and analysis replication.

Impact of obesity and diet on brain structure and function: a gut-brain-body crosstalk

Most societies witness an ever increasing prevalence of both obesity and dementia, a scenario related to often underestimated individual and public health burden. Overnutrition and weight gain have been linked with abnormal functionality of homoeostasis brain networks and changes in higher cognitive functions such as reward evaluation, executive functions and learning and memory. In parallel, evidence has accumulated that modifiable factors such as obesity and diet impact the gut-brain axis and modulate brain health and cognition through various pathways. Using neuroimaging data from epidemiological studies and randomised clinical trials, we aim to shed light on the underlying mechanisms and to determine both determinants and consequences of obesity and diet at the level of human brain structure and function. We analysed multimodal 3T MRI of about 2600 randomly selected adults (47 % female, 18-80 years of age, BMI 18-47 kg/m²) of the LIFE-Adult study, a deeply phenotyped population-based cohort. In addition, brain MRI data of controlled intervention studies on weight loss and healthy diets acquired in lean, overweight and obese participants may help to understand the role of the gut-brain axis in food craving and cognitive ageing. We find that higher BMI and visceral fat accumulation correlate with accelerated brain age, microstructure of the hypothalamus, lower thickness and connectivity in default mode- and reward-related areas, as well as with subtle grey matter atrophy and white matter lesion load in non-demented individuals. Mediation analyses indicated that higher visceral fat affects brain tissue through systemic low-grade inflammation, and that obesity-related regional changes translate into cognitive disadvantages. Considering longitudinal studies, some, but not all data indicate beneficial effects of weight loss and healthy diets such as plant-based nutrients and dietary patterns on brain ageing and cognition. Confounding effects of concurrent changes in other lifestyle factors or false positives might help to explain these findings. Therefore a more holistic intervention approach, along with open science tools such as data and code sharing, in-depth pre-registration and pooling of data could help to overcome these limitations. In addition, as higher BMI relates to increased head micro-movements during MRI, and as head motion in turn systematically induces image artefacts, future studies need to rigorously control for head motion during MRI to enable valid neuroimaging results. In sum, our results support the view that overweight and obesity are intertwined with markers of brain health in the general population, and that weight loss and plant-based diets may help to promote brain plasticity. Meta-analyses and longitudinal cohort studies are underway to further differentiate causation from correlation in obesity- and nutrition-brain research.

Establishment of an International Evidence Sharing Network Through Common Data Model for Cardiovascular Research

A retrospective observational study is one of the most widely used research methods in medicine. However, evidence postulated from a single data source likely contains biases such as selection bias, information bias, and confounding bias. Acquiring enough data from multiple institutions is one of the most effective methods to overcome the limitations. However, acquiring data from multiple institutions from many countries requires enormous effort because of financial, technical, ethical, and legal issues as well as standardization of data structure and semantics. The Observational Health Data Sciences and Informatics (OHDSI) research network standardized 928 million unique records or 12% of the world's population into a common structure and meaning and established a research network of 453 data partners from 41 countries around the world. OHDSI is a distributed research network wherein researchers do not own or directly share data but only analyzed results. However, sharing evidence without sharing data is difficult to understand. In this review, we will look at the basic principles of OHDSI, common data model, distributed research networks, and some representative studies in the cardiovascular field using the network. This paper also briefly introduces a Korean distributed research network named FeederNet.

Special Issue Introduction: The GLES Open Science Challenge 2021: A Pilot Project on the Applicability of Registered Reports in Quantitative Political Science

The GLES Open Science Challenge 2021 was a pioneering initiative in quantitative political science. Aimed at increasing the adoption of replicable and transparent research practices, it led to this special issue. The project combined the rigor of registered reports-a new publication format in which studies are evaluated prior to data collection/access and analysis-with quantitative political science research in the context of the 2021 German federal election. This special issue, which features the registered reports that resulted from the project, shows that transparent research following open science principles benefits our discipline and substantially contributes to quantitative political science. In this introduction to the special issue, we first elaborate on why more transparent research practices are necessary to guarantee the cumulative progress of scientific knowledge. We then show how registered reports can contribute to increasing the transparency of scientific practices. Next, we discuss the application of open science practices in quantitative political science to date. And finally, we present the process and schedule of the GLES Open Science Challenge and give an overview of the contributions included in this special issue.

Establishing open science research priorities in health psychology: a research prioritisation Delphi exercise

OBJECTIVE

Research on Open Science practices in Health Psychology is lacking. This meta-research study aimed to identify research question priorities and obtain consensus on the Top 5 prioritised research questions for Open Science in Health Psychology.

METHODS AND MEASURES

An international Delphi consensus study was conducted. Twenty-three experts in Open Science and Health Psychology within the European Health Psychology Society (EHPS) suggested research question priorities to create a 'long-list' of items (Phase 1). Forty-three EHPS members rated the importance of these items, ranked their top five and suggested their own additional items (Phase 2). Twenty-four EHPS members received feedback on Phase 2 responses and then re-rated and re-ranked their top five research questions (Phase 3).

RESULTS

The top five ranked research question priorities were: 1. 'To what extent are Open Science behaviours currently practised in Health Psychology?', 2. 'How can we maximise the usefulness of Open Data and Open Code resources?', 3. 'How can Open Data be increased within Health Psychology?', 4. 'What interventions are effective for increasing the adoption of Open Science in Health Psychology?' and 5. 'How can we increase free Open Access publishing in Health Psychology?'.

CONCLUSION

Funding and resources should prioritise the research questions identified here.

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background

The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.

Results

The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101).

Conclusions

The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12302-022-00680-6.

Integrating open science education into an undergraduate health professional research program

Objective

Open science (OS) is a global movement focused on improving research equity, reproducibility, and transparency of research outputs in publicly funded research. While OS education in academia is becoming more common, examples of health sciences librarians providing OS training are not. This paper describes how a librarian collaborated with teaching faculty and a research program coordinator to integrate an OS curriculum into an undergraduate professional practice course and assess students' perceptions of OS after participating.

Methods

A librarian developed an OS-specific curriculum for an undergraduate professional practice course in Nutrition. This course is part of the First Year Research Experience (FYRE) program, which is integrated into 13-week undergraduate courses to introduce students to core elements of the research process in their first year of study by carrying out a research project. The OS curriculum included an Introduction to OS class, a requirement that students share their research outputs in the Open Science Framework, and an assignment asking students to reflect on their experience learning about and practicing OS. Twenty-one of 30 students consented to having their reflection assignment undergo thematic analysis.

Results

Students indicated transparency, accountability, accessibility to research outputs, and increased efficiency as positive attributes of OS. The time commitment, fear of being scooped, and concerns over having research be misinterpreted were considered negative attributes. 90% (n=19) of students indicated that they intend to practice OS in the future.

Conclusion

Based on strong engagement from the students, we believe that this OS curriculum could be adapted to other undergraduate or graduate student contexts where a research project is required.

Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging

Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the brain precede a cognitive decline in healthy and pathological aging, our study predicts future decline as a continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Nonbrain data (demographics, clinical, and neuropsychological scores), structural MRI, and functional connectivity data from OASIS-3 (N = 662; age = 46-96 years) were entered into cross-validated multitarget random forest models to predict future cognitive decline (measured by CDR and MMSE), on average 5.8 years into the future. The analysis was preregistered, and all analysis code is publicly available. Combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42). Cognitive performance, daily functioning, and subcortical volume drove the performance of our model. Including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective individualized cognitive, pharmacological, and behavioral interventions.

The transparency and reproducibility of systematic reviews in forensic science

Systematic reviews are indispensable tools for both reliably informing decision-makers about the state of the field and for identifying areas that need further study. Their value, however, depends on their transparency and reproducibility. Readers should be able to determine what was searched for and when, where the authors searched, and whether that search was predetermined or evolved based on what was found. In this article, we measured the transparency and reproducibility of systematic reviews in forensic science, a field where courts, policymakers, and legislators count on systematic reviews to make informed decisions. In a sample of 100 systematic reviews published between 2018 and 2021, we found that completeness of reporting varied markedly. For instance, 50 % of reviews claimed to follow a reporting guideline and such statements were only modestly related to compliance with that reporting guideline. As to specific reporting items, 82 % reported all of the databases searched, 22 % reported the review's full Boolean search logic, and just 7 % reported the review was registered. Among meta-analyses (n = 23), only one stated data was available and none stated the analytic code was available. After considering the results, we end with recommendations for improved regulation of reporting practices, especially among journals. Our results may serve as a useful benchmark as the field evolves.

Promoting trust in research and researchers: How open science and research integrity are intertwined

Proponents of open science often refer to issues pertaining to research integrity and vice versa. In this commentary, we argue that concepts such as responsible research practices, transparency, and open science are connected to one another, but that they each have a different focus. We argue that responsible research practices focus more on the rigorous conduct of research, transparency focuses predominantly on the complete reporting of research, and open science's core focus is mostly about dissemination of research. Doing justice to these concepts requires action from researchers and research institutions to make research with integrity possible, easy, normative, and rewarding. For each of these levels from the Center for Open Science pyramid of behaviour change, we provide suggestions on what researchers and research institutions can do to promote a culture of research integrity. We close with a brief reflection on initiatives by other research communities and stakeholders and make a call to those working in the fields of research integrity and open science to pay closer attention to one other's work.

Open and reproducible neuroimaging: From study inception to publication

Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.

Appraising reconsolidation theory and its empirical validation

Re-exposure to elements of prior experiences can create opportunities for inducing amnesia for those events. The dominant theoretical framework posits that such re-exposure can result in memory destabilization, making the memory representation temporarily sensitive to disruption while it awaits reconsolidation. If true, such a mechanism that allows for memories to be permanently changed could have important implications for the treatment of several forms of psychopathology. However, there have been contradictory findings and elusive occurrences of replication failures within the "reconsolidation" field. Considering its potential relevance for clinical applications, the fact that this "hot" research area is being dominated by a single mechanistic theory, and the presence of unexplainable contradictory findings, we believe that it is both useful and timely to critically evaluate the reconsolidation framework. We discuss potential issues that may arise from how reconsolidation interference has typically been deducted from behavioral observations, and provide a principled assessment of reconsolidation theory that illustrates that the theory and its proposed boundary conditions are vaguely defined, which has made it close to impossible to refute reconsolidation theory. We advocate for caution, encouraging researchers not to blindly assume that a reconsolidation process must underlie their findings, and pointing out the risks of doing so. Finally, we suggest concrete theoretical and methodological advances that can promote a fruitful translation of reminder-dependent amnesia into clinical treatment.