Data and code availability statements in systematic reviews of interventions were often missing or inaccurate: a content analysis

Cancer researchers' experiences with and perceptions of research data sharing: Results of a cross-sectional survey

BACKGROUND

Despite wide recognition of the benefits of sharing research data, public availability rates have not increased substantially in oncology or medicine more broadly over the last decade.

METHODS

We surveyed 285 cancer researchers to determine their prior experience with sharing data and views on known drivers and inhibitors.

RESULTS

We found that 45% of respondents had shared some data from their most recent empirical publication, with respondents who typically studied non-human research participants, or routinely worked with human genomic data, more likely to share than those who did not. A third of respondents added that they had previously shared data privately, with 74% indicating that doing so had also led to authorship opportunities or future collaborations for them. Journal and funder policies were reported to be the biggest general drivers toward sharing, whereas commercial interests, agreements with industrial sponsors and institutional policies were the biggest prohibitors. We show that researchers' decisions about whether to share data are also likely to be influenced by participants' desires.

CONCLUSIONS

Our survey suggests that increased promotion and support by research institutions, alongside greater championing of data sharing by journals and funders, may motivate more researchers in oncology to share their data.

Completeness of Systematic Reviews in Nursing Literature Based on PRISMA Reporting Guidelines

Systematic reviews and meta-analyses provide the highest levels of evidence to support practice and research. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were established to ensure comprehensive and transparent reporting. Among the 70 reviews in our study, there was 100% adherence to 4 of the PRISMA items (review type in title, research objectives in introduction, inclusion/exclusion criteria and methods to synthesize results in methods section). We identified an improvement in adherence to the PRISMA guidelines when comparing early (done through 2020) and more recent reviews, suggesting that authors are increasingly adopting these guidelines.

Formalistic data and code availability policy in high-profile medical journals and pervasive policy-practice gaps in published articles: A meta-research study

BACKGROUND

Poor data and code (DAC) sharing undermines open science principles. This study evaluates the stringency of DAC availability policies in high-profile medical journals and identifies policy-practice gaps (PPG) in published articles.

METHODS

DAC availability policies of 931 Q1 medical journals (Clarivate JCR 2021) were evaluated, with PPGs quantified across 3,191 articles from The BMJ, JAMA, NEJM, and The Lancet.

RESULTS

Only 9.1% (85/931) of journals mandated DAC sharing and availability statements, with 70.6% of these lacking mechanisms to verify authenticity, and 61.2% allowing publication despite invalid sharing. Secondary analysis revealed a disproportionate distribution of policies across subspecialties, with 18.6% (11/59) of subspecialties having >20% journals with mandated policies. Journal impact factors exhibited positive correlations with the stringency of availability statement policies (ρ = 0.20, p < 0.001) but not with sharing policies (ρ = 0.01, p = 0.737). Among the 3,191 articles, PPGs were observed in over 90% of cases. Specifically, 33.7% lacked DAC availability statements, 23.3% refused sharing (58.4% of which without justification in public statements), and 13.5% declared public sharing, with 39.0% being unreachable. Finally, only 0.5% achieved full computational reproducibility.

CONCLUSIONS

Formalistic policies and prevalent PPGs undermine DAC transparency, necessitating a supportive publication ecosystem that empowers authors to uphold scientific responsibility and integrity.

Critical appraisal of methodological quality and completeness of reporting in Chinese social science systematic reviews with meta-analysis: A systematic review

Background

A systematic review is a type of literature review that uses rigorous methods to synthesize evidence from multiple studies on a specific topic. It is widely used in academia, including medical and social science research. Social science is an academic discipline that focuses on human behaviour and society. However, consensus regarding the standards and criteria for conducting and reporting systematic reviews in social science is lacking. Previous studies have found that the quality of systematic reviews in social science varies depending on the topic, database, and country.

Objectives

This study evaluates the completeness of reporting and methodological quality of intervention and non-intervention systematic reviews in social science in China. Additionally, we explore factors that may influence quality.

Search Methods

We searched three major Chinese electronic databases-CNKI, VIP, and Wangfang-for intervention and non-intervention reviews in social science published in Chinese journals from 1 January 2009 to 2 December 2022.

Selection Criteria

We included intervention and non-intervention reviews; however, we excluded overviews, qualitative syntheses, integrative reviews, rapid reviews, and evidence syntheses/summaries. We also excluded meta-analyses that used advanced methods (e.g., cross-sectional, cumulative, Bayesian, structural equation, or network meta-analyses) or that focused on instrument validation.

Data Collection and Analysis

We extracted data using a coding form with publication information and study content characteristics. This study conducted pilot extraction and quality assessment with four authors and formal extraction and assessment with two groups of four authors each. PRISMA2020 and MOOSE were used to evaluate the reporting completeness of intervention and non-intervention reviews. AMSTAR-2 and DART tools were adopted to assess their methodological quality. We described the characteristics of the included reviews with frequencies and percentages. We used SPSS (version 26.0) to conduct a linear regression analysis and ANOVA to explore the factors that may influence both completeness of reporting and methodological quality.

Main Results

We included 1176 systematic reviews with meta-analyses published in Chinese journals between 2009 and 2022. The top three fields of publication were psychology (417, 35.5%), education (388, 33.0%), and management science (264, 22.4%). Four hundred and thirty-two intervention reviews were included. The overall completeness of reporting in PRISMA and compliance rate of the methodological process in AMSTAT-2 were 49.9% and 45.5%, respectively. Intervention reviews published in Chinese Social Science Citation Index (CSSCI) journals had lower reporting completeness than those published in non-CSSCI journals (46.7% vs. 51.1%), similar to methodological quality (39.6% vs. 47.9%). A few reviews reported the details on registration (0.2%), rationality of study selection criteria (1.6%), sources of funding for primary studies (0.2%), reporting bias assessment (2.8%), certainty of evidence assessment (1.2%), and sensitivity analysis (107, 24.8%). Seven hundred and forty-four non-intervention reviews were included. The overall completeness of reporting in MOOSE and compliance rate of the methodological process in DART were 51.8% and 50.5%, respectively. Non-intervention reviews published in CSSCI journals had higher reporting completeness than those published in non-CSSCI journals (53.3% vs. 50.3%); however, there was no difference in methodological quality (51.0% vs. 50.0%). Most reviews did not report the process and results of selection (80.8%), and 58.9% of reviews did not describe the process of data extraction; only 9.5% assessed the quality of included studies; while none of the reviews examined bias by confounding, outcome reporting bias, and loss to follow-up. An improving trend over time was observed for both intervention and non-intervention reviews in completeness of reporting and methodological quality (PRISMA: β = 0.24, p < 0.01; AMSTAR-2: β = 0.17, p < 0.01; MOOSE: β = 0.34, p < 0.01; DART: β = 0.30, p < 0.01). The number of authors and financial support also have a positive effect on quality.

Authors' Conclusions

Completeness of reporting and methodological quality were low in both intervention and non-intervention reviews in Chinese social sciences, especially regarding registration, protocol, risk of bias assessment, and data and code sharing. The sources of literature, number of authors, publication year, and funding source declarations were identified as factors that may influence the quality of reviews. More rigorous standards and guidelines for conducting and reporting reviews are required in social science research as well as more support and incentives for reviewers to adhere to them.

Reliability and reproducibility of systematic reviews informing the 2020-2025 Dietary Guidelines for Americans: a pilot study

BACKGROUND

Although high-quality nutrition systematic reviews (SRs) are important for clinical decision making, there remains debate on their methodological quality and reporting transparency.

OBJECTIVES

The objective of this study was to assess the reliability and reproducibility of a sample of SRs produced by the Nutrition Evidence Systematic Review (NESR) team to inform the 2020-2025 Dietary Guidelines for Americans (DGAs).

METHODS

We evaluated a sample of 8 SRs from the DGA dietary patterns subcommittee for methodological quality using the Assessment of Multiple Systematic Reviews 2 (AMSTAR 2) tool and for reporting transparency using the PRISMA 2020 and PRISMA literature search extension (PRISMA-S) checklists. We assessed the quality and reproducibility of the original search strategy of one selected SR using the Peer Review of Electronic Search Strategies checklist. The reporting transparency of the SR's narrative data synthesis was assessed using the Synthesis Without Meta-Analysis (SWiM) checklist. Interpretation bias was evaluated using existing spin bias classifications in systematic reviews.

RESULTS

The AMSTAR 2 assessment identified critical methodological weaknesses, and all included SRs were judged to be of critically low quality. Overall, 74% of the PRISMA 2020 checklist items and 63% of the PRISMA-S checklist items were satisfactorily fulfilled. We identified several errors and inconsistencies in the search strategy and could not reproduce searches within a 10% margin of the original results. The SWiM assessment identified concerns regarding the reporting transparency of the narrative data synthesis, but the spin bias assessment revealed no evidence of interpretation bias.

CONCLUSIONS

Several methodological quality and reporting concerns were identified, which could lead to reliability and reproducibility issues should a full reproduction attempt be made. However, additional research is needed to confirm the impact of these findings on conclusions statements and their generalizability across the NESR team SRs. This study was registered in the Open Science Framework (https://osf.io/ns6a9/).

Raw data were not disclosed in 95% of PubMed-indexed heart failure meta-analyses in 2021: A systematic analysis of transparency

BACKGROUND

The rising concern of irreproducible and non-transparent studies poses a significant challenge in modern medical literature. The impact of this issue on cardiology, particularly in the subfield of heart failure, remains poorly understood. To address this knowledge gap, we assessed the quality of evidence presented in recent heart failure meta-analyses by exploring several crucial transparency indicators.

METHODS

We conducted a cross-sectional study and searched PubMed for meta - analyses themed around heart failure. We included the 100 most recent publications from 2021 and investigated the presence of several indices that are associated with transparency and reproducibility.

RESULTS

The vast majority of the papers did not include their raw data (95/100, 95%) nor their analytic code (99/100, 99%). Less than half (42/100, 42%) preregistered their protocol, while only 65/100 (65%) adhered to a reporting guidelines method. Bias calculation for the respective studies included in each meta - analysis was present in 83/100 (83%) papers and publication bias was measured in approximately half (56/100, 56%).

CONCLUSIONS

Our study indicates that meta-analyses in the field of heart failure present important information of transparency infrequently. Therefore, reproduction and validation of their findings seems to be practically impossible.

A study of search strategy availability statements and sharing practices for systematic reviews: Ask and you might receive

The literature search underpins data collection for all systematic reviews (SRs). The SR reporting guideline PRISMA, and its extensions, aim to facilitate research transparency and reproducibility, and ultimately improve the quality of research, by instructing authors to provide specific research materials and data upon publication of the manuscript. Search strategies are one item of data that are explicitly included in PRISMA and the critical appraisal tool AMSTAR2. Yet some authors use search availability statements implying that the search strategies are available upon request instead of providing strategies up front. We sought out reviews with search availability statements, characterized them, and requested the search strategies from authors via email. Over half of the included reviews cited PRISMA but less than a third included any search strategies. After requesting the strategies via email as instructed, we received replies from 46% of authors, and eventually received at least one search strategy from 36% of authors. Requesting search strategies via email has a low chance of success. Ask and you might receive-but you probably will not. SRs that do not make search strategies available are low quality at best according to AMSTAR2; Journal editors can and should enforce the requirement for authors to include their search strategies alongside their SR manuscripts.

The reporting completeness and transparency of systematic reviews of prognostic prediction models for COVID-19 was poor: a methodological overview of systematic reviews

OBJECTIVES

To conduct a methodological overview of reviews to evaluate the reporting completeness and transparency of systematic reviews (SRs) of prognostic prediction models (PPMs) for COVID-19.

STUDY DESIGN AND SETTING

MEDLINE, Scopus, Cochrane Database of Systematic Reviews, and Epistemonikos (epistemonikos.org) were searched for SRs of PPMs for COVID-19 until December 31, 2022. The risk of bias in systematic reviews tool was used to assess the risk of bias. The protocol for this overview was uploaded in the Open Science Framework (https://osf.io/7y94c).

RESULTS

Ten SRs were retrieved; none of them synthesized the results in a meta-analysis. For most of the studies, there was absence of a predefined protocol and missing information on study selection, data collection process, and reporting of primary studies and models included, while only one SR had its data publicly available. In addition, for the majority of the SRs, the overall risk of bias was judged as being high. The overall corrected covered area was 6.3% showing a small amount of overlapping among the SRs.

CONCLUSION

The reporting completeness and transparency of SRs of PPMs for COVID-19 was poor. Guidance is urgently required, with increased awareness and education of minimum reporting standards and quality criteria. Specific focus is needed in predefined protocol, information on study selection and data collection process, and in the reporting of findings to improve the quality of SRs of PPMs for COVID-19.

Reporting quality and risk of bias of systematic reviews of ultra-processed foods: a methodological study

A dramatic shift in the global food system is occurring with the rapid growth of ultra-processed foods (UPFs) consumption, which poses potentially serious health risks. Systematic review (SR) method has been used to summarise the association between UPF consumption and multiple health outcomes; however, a suboptimal-quality SR may mislead the decision-making in clinical practices and health policies. Therefore, a methodological review was conducted to identify the areas that can be improved regarding the risk of bias and reporting quality of relevant SRs. Systematic searches to collect SRs with meta-analyses of UPFs were performed using four databases from their inception to April 14, 2023. The risk of bias and reporting quality were evaluated using ROBIS and PRISMA 2020, respectively. The key characteristics of the included SRs were summarised descriptively. Excel 2019 and R 4.2.3 were used to analyse the data and draw graphs. Finally, 16 relevant SRs written in English and published between 2020 and 2023 in 12 academic journals were included. Only one SR was rated as low risk of bias, and the others were rated as higher risk of bias mainly because the risk of bias in the original studies was not explicitly addressed when synthesising the evidence. The reporting was required to be advanced significantly, involving amendments of registration and protocol, data and analytic code statement, and lists of excluded studies with justifications. The reviews' results could improve the quality, strengthen future relevant SRs' robustness, and further underpin the evidence base for supporting clinical decisions and health policies.

Systematic review search strategies are poorly reported and not reproducible: a cross-sectional metaresearch study

OBJECTIVES

To determine the reproducibility of biomedical systematic review search strategies.

STUDY DESIGN AND SETTING

A cross-sectional reproducibility study was conducted on a random sample of 100 systematic reviews indexed in MEDLINE in November 2021. The primary outcome measure is the percentage of systematic reviews for which all database searches can be reproduced, operationalized as fulfilling six key Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension (PRISMA-S) reporting guideline items and having all database searches reproduced within 10% of the number of original results. Key reporting guideline items included database name, multi-database searching, full search strategies, limits and restrictions, date(s) of searches, and total records.

RESULTS

The 100 systematic review articles contained 453 database searches. Only 22 (4.9%) database searches reported all six PRISMA-S items. Forty-seven (10.4%) database searches could be reproduced within 10% of the number of results from the original search; six searches differed by more than 1,000% between the originally reported number of results and the reproduction. Only one systematic review article provided the necessary search details to be fully reproducible.

CONCLUSION

Systematic review search reporting is poor. To correct this will require a multifaceted response from authors, peer reviewers, journal editors, and database providers.

Open science practices need substantial improvement in prognostic model studies in oncology using machine learning

OBJECTIVE

To describe the frequency of open science practices in a contemporary sample of studies developing prognostic models using machine learning methods in the field of oncology.

STUDY DESIGN AND SETTING

We conducted a systematic review, searching the MEDLINE database between December 1, 2022, and December 31, 2022, for studies developing a multivariable prognostic model using machine learning methods (as defined by the authors) in oncology. Two authors independently screened records and extracted open science practices.

RESULTS

We identified 46 publications describing the development of a multivariable prognostic model. The adoption of open science principles was poor. Only one study reported availability of a study protocol, and only one study was registered. Funding statements and conflicts of interest statements were common. Thirty-five studies (76%) provided data sharing statements, with 21 (46%) indicating data were available on request to the authors and seven declaring data sharing was not applicable. Two studies (4%) shared data. Only 12 studies (26%) provided code sharing statements, including 2 (4%) that indicated the code was available on request to the authors. Only 11 studies (24%) provided sufficient information to allow their model to be used in practice. The use of reporting guidelines was rare: eight studies (18%) mentioning using a reporting guideline, with 4 (10%) using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis statement, 1 (2%) using Minimum Information About Clinical Artificial Intelligence Modeling and Consolidated Standards Of Reporting Trials-Artificial Intelligence, 1 (2%) using Strengthening The Reporting Of Observational Studies In Epidemiology, 1 (2%) using Standards for Reporting Diagnostic Accuracy Studies, and 1 (2%) using Transparent Reporting of Evaluations with Nonrandomized Designs.

CONCLUSION

The adoption of open science principles in oncology studies developing prognostic models using machine learning methods is poor. Guidance and an increased awareness of benefits and best practices of open science are needed for prediction research in oncology.

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.

An analysis of reporting practices in the top 100 cited health and medicine-related bibliometric studies from 2019 to 2021 based on a proposed guidelines

Bibliometric analysis has gained popularity as a quantitative research methodology to evaluate scholarly productivity and identify trends within specific research areas. However, there are currently no established reporting guidelines for bibliometric studies. The present study aimed to investigate the reporting practices of bibliometric research related to health and medicine based on a guidelines "Preferred Reporting Items for Bibliometric Analysis (PRIBA)" proposed in this study. The Science Citation Index, Expanded of the Web of Science was used to identify the top 100 articles with the highest normalized citation counts per year. The search was conducted on April 9, 2022, using the search topic "bibliometric" and including publications from 2019 to 2021. The results substantiated the need for a standardized reporting guideline for bibliometric research. Specifically, among the 25 proposed items in the PRIBA, only five were consistently reported across all articles examined. Further, 11 items were reported by at least 80% of the articles, while nine items were reported by less than 80% of the articles. In conclusion, our findings suggest that the reporting practices of bibliometric studies in the field of health and medicine are in need of improvement. Future research should be conducted to refine the PRIBA guidelines.

Changing patterns in reporting and sharing of review data in systematic reviews with meta-analysis of the effects of interventions: cross sectional meta-research study

OBJECTIVES

To examine changes in completeness of reporting and frequency of sharing data, analytical code, and other review materials in systematic reviews over time; and factors associated with these changes.

DESIGN

Cross sectional meta-research study.

POPULATION

Random sample of 300 systematic reviews with meta-analysis of aggregate data on the effects of a health, social, behavioural, or educational intervention. Reviews were indexed in PubMed, Science Citation Index, Social Sciences Citation Index, Scopus, and Education Collection in November 2020.

MAIN OUTCOME MEASURES

The extent of complete reporting and the frequency of sharing review materials in the systematic reviews indexed in 2020 were compared with 110 systematic reviews indexed in February 2014. Associations between completeness of reporting and various factors (eg, self-reported use of reporting guidelines, journal policies on data sharing) were examined by calculating risk ratios and 95% confidence intervals.

RESULTS

Several items were reported suboptimally among 300 systematic reviews from 2020, such as a registration record for the review (n=113; 38%), a full search strategy for at least one database (n=214; 71%), methods used to assess risk of bias (n=185; 62%), methods used to prepare data for meta-analysis (n=101; 34%), and source of funding for the review (n=215; 72%). Only a few items not already reported at a high frequency in 2014 were reported more frequently in 2020. No evidence indicated that reviews using a reporting guideline were more completely reported than reviews not using a guideline. Reviews published in 2020 in journals that mandated either data sharing or inclusion of data availability statements were more likely to share their review materials (eg, data, code files) than reviews in journals without such mandates (16/87 (18%) v 4/213 (2%)).

CONCLUSION

Incomplete reporting of several recommended items for systematic reviews persists, even in reviews that claim to have followed a reporting guideline. Journal policies on data sharing might encourage sharing of review materials.

How often do cancer researchers make their data and code available and what factors are associated with sharing?

BACKGROUND

Various stakeholders are calling for increased availability of data and code from cancer research. However, it is unclear how commonly these products are shared, and what factors are associated with sharing. Our objective was to evaluate how frequently oncology researchers make data and code available and explore factors associated with sharing.

METHODS

A cross-sectional analysis of a random sample of 306 cancer-related articles indexed in PubMed in 2019 which studied research subjects with a cancer diagnosis was performed. All articles were independently screened for eligibility by two authors. Outcomes of interest included the prevalence of affirmative sharing declarations and the rate with which declarations connected to data complying with key FAIR principles (e.g. posted to a recognised repository, assigned an identifier, data license outlined, non-proprietary formatting). We also investigated associations between sharing rates and several journal characteristics (e.g. sharing policies, publication models), study characteristics (e.g. cancer rarity, study design), open science practices (e.g. pre-registration, pre-printing) and subsequent citation rates between 2020 and 2021.

RESULTS

One in five studies declared data were publicly available (59/306, 19%, 95% CI: 15-24%). However, when data availability was investigated this percentage dropped to 16% (49/306, 95% CI: 12-20%), and then to less than 1% (1/306, 95% CI: 0-2%) when data were checked for compliance with key FAIR principles. While only 4% of articles that used inferential statistics reported code to be available (10/274, 95% CI: 2-6%), the odds of reporting code to be available were 5.6 times higher for researchers who shared data. Compliance with mandatory data and code sharing policies was observed in 48% (14/29) and 0% (0/6) of articles, respectively. However, 88% of articles (45/51) included data availability statements when required. Policies that encouraged data sharing did not appear to be any more effective than not having a policy at all. The only factors associated with higher rates of data sharing were studying rare cancers and using publicly available data to complement original research.

CONCLUSIONS

Data and code sharing in oncology occurs infrequently, and at a lower rate than would be expected given the prevalence of mandatory sharing policies. There is also a large gap between those declaring data to be available, and those archiving data in a way that facilitates its reuse. We encourage journals to actively check compliance with sharing policies, and researchers consult community-accepted guidelines when archiving the products of their research.

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.

Data sharing practices across knowledge domains: A dynamic examination of data availability statements in PLOS ONE publications

As the importance of research data gradually grows in sciences, data sharing has come to be encouraged and even mandated by journals and funders in recent years. Following this trend, the data availability statement has been increasingly embraced by academic communities as a means of sharing research data as part of research articles. This article presents a quantitative study of which mechanisms and repositories are used to share research data in PLOS ONE articles. We offer a dynamic examination of this topic from the disciplinary and temporal perspectives based on all statements in English-language research articles published between 2014 and 2020 in the journal. We find a slow yet steady growth in the use of data repositories to share data over time, as opposed to sharing data in the article and/or supplementary materials; this indicates improved compliance with the journal’s data sharing policies. We also find that multidisciplinary data repositories have been increasingly used over time, whereas some disciplinary repositories show a decreasing trend. Our findings can help academic publishers and funders to improve their data sharing policies and serve as an important baseline dataset for future studies on data sharing activities.

Dental Research Data Availability and Quality According to the FAIR Principles

According to the FAIR principles, data produced by scientific research should be findable, accessible, interoperable, and reusable-for instance, to be used in machine learning algorithms. However, to date, there is no estimate of the quantity or quality of dental research data evaluated via the FAIR principles. We aimed to determine the availability of open data in dental research and to assess compliance with the FAIR principles (or FAIRness) of shared dental research data. We downloaded all available articles published in PubMed-indexed dental journals from 2016 to 2021 as open access from Europe PubMed Central. In addition, we took a random sample of 500 dental articles that were not open access through Europe PubMed Central. We assessed data sharing in the articles and compliance of shared data to the FAIR principles programmatically. Results showed that of 7,509 investigated articles, 112 (1.5%) shared data. The average (SD) level of compliance with the FAIR metrics was 32.6% (31.9%). The average for each metric was as follows: findability, 3.4 (2.7) of 7; accessibility, 1.0 (1.0) of 3; interoperability, 1.1 (1.2) of 4; and reusability, 2.4 (2.6) of 10. No considerable changes in data sharing or quality of shared data occurred over the years. Our findings indicated that dental researchers rarely shared data, and when they did share, the FAIR quality was suboptimal. Machine learning algorithms could understand 1% of available dental research data. These undermine the reproducibility of dental research and hinder gaining the knowledge that can be gleaned from machine learning algorithms and applications.

Bioactive Compounds and Their Derivatives: An Insight into Prospective Phytotherapeutic Approach against Alzheimer's Disease

Alzheimer's disease (AD) is a common neurodegenerative brain disorder that causes cellular response alterations, such as impaired cholinergic mechanism, amyloid-beta (Aβ) AD aggregation, neuroinflammation, and several other pathways. AD is still the most prevalent form of dementia and affects many individuals across the globe. The exact cause of the disorder is obscure. There are yet no effective medications for halting, preventing, or curing AD's progress. Plenty of natural products are isolated from several sources and analyzed in preclinical and clinical settings for neuroprotective effects in preventing and treating AD. In addition, natural products and their derivatives have been promising in treating and preventing AD. Natural bioactive compounds play an active modulatory role in the pathological molecular mechanisms of AD development. This review focuses on natural products from plant sources and their derivatives that have demonstrated neuroprotective activities and maybe promising to treat and prevent AD. In addition, this article summarizes the literature pertaining to natural products as agents in the treatment of AD. Rapid metabolism, nonspecific targeting, low solubility, lack of BBB permeability, and limited bioavailability are shortcomings of most bioactive molecules in treating AD. We can use nanotechnology and nanocarriers based on different types of approaches.