Developing a reusable infrastructure for machine learning on diverse earth observation data for sustainable agriculture and forestry

The integration of machine learning (ML) into environmental protection, particularly in sustainable agriculture and forestry, is increasingly vital given the spatio-temporal scale of the data and analysis. Earth observation data from Sentinel-1 (S1), Sentinel-2 (S2), weather, and LiDAR provide valuable insights, but applying ML algorithms to these diverse datasets presents challenges due to their differences in data structure and formats as well as spatial, spectral and temporal resolutions. This research develops a multi-purpose, extensible infrastructure using open-source technologies, implemented within the cloud platform CODE-DE at Julius Kühn-Institut (JKI), to streamline ML applications for geo-located earth observation data. The infrastructure supports diverse data types, including satellite, weather, and LiDAR records, and is adaptable to future ML models. It has been rigorously tested for detecting plant growth stages (BBCH), demonstrating its potential in agricultural analysis. Future work will extend this to detecting tree and shrub growth events. This research contributes to sustainable agriculture by advancing reusable ML solutions for environmental monitoring

There is no reliable evidence that providing authors with customized article templates including items from reporting guidelines improves completeness of reporting: the GoodReports randomized trial (GRReaT)

Background: Although medical journals endorse reporting guidelines, authors often struggle to find and use the right one for their study type and topic. The UK EQUATOR Centre developed the GoodReports website to direct authors to appropriate guidance. Pilot data suggested that authors did not improve their manuscripts when advised to use a particular reporting guideline by GoodReports.org at journal submission stage. User feedback suggested the checklist format of most reporting guidelines does not encourage use during manuscript writing. We tested whether providing customized reporting guidance within writing templates for use throughout the writing process resulted in clearer and more complete reporting than only giving advice on which reporting guideline to use. Design and methods: GRReaT was a two-group parallel 1:1 randomized trial with a target sample size of 206. Participants were lead authors at an early stage of writing up a health-related study. Eligible study designs were cohort, cross-sectional, or case-control study, randomized trial, and systematic review. After randomization, the intervention group received an article template including items from the appropriate reporting guideline and links to explanations and examples. The control group received a reporting guideline recommendation and general advice on reporting. Participants sent their completed manuscripts to the GRReaT team before submitting for publication, for completeness of each item in the title, methods, and results section of the corresponding reporting guideline. The primary outcome was reporting completeness against the corresponding reporting guideline. Participants were not blinded to allocation. Assessors were blind to group allocation. As a recruitment incentive, all participants received a feedback report identifying missing or inadequately reported items in these three sections. Results: Between 9 June 2021 and 30 June 2023, we randomized 130 participants, 65 to the intervention and 65 to the control group. We present findings from the assessment of reporting completeness for the 37 completed manuscripts we received, 18 in the intervention group and 19 in the control group. The mean (standard deviation) proportion of completely reported items from the title, methods, and results sections of the manuscripts (primary outcome) was 0.57 (0.18) in the intervention group and 0.50 (0.17) in the control group. The mean difference between the two groups was 0.069 (95% CI -0.046 to 0.184; p = 0.231). In the sensitivity analysis, when partially reported items were counted as completely reported, the mean (standard deviation) proportion of completely reported items was 0.75 (0.15) in the intervention group and 0.71 (0.11) in the control group. The mean difference between the two groups was 0.036 (95% CI -0.127 to 0.055; p = 0.423). Conclusion: As the dropout rate was higher than expected, we did not reach the recruitment target, and the difference between groups was not statistically significant. We therefore found no evidence that providing authors with customized article templates including items from reporting guidelines, increases reporting completeness. We discuss the challenges faced when conducting the trial and suggest how future research testing innovative ways of improving reporting could be designed to improve recruitment and reduce dropouts

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

Methods used to develop the SPIRIT 2024 and CONSORT 2024 Statements

Objectives: To describe, and explain the rationale for, the methods used and decisions made during development of the updated SPIRIT 2024 and CONSORT 2024 reporting guidelines. Methods: We developed SPIRIT 2024 and CONSORT 2024 together to facilitate harmonization of the two guidelines, and incorporated content from key extensions. We conducted a scoping review of comments suggesting changes to SPIRIT 2013 and CONSORT 2010, and compiled a list of other possible revisions based on existing SPIRIT and CONSORT extensions, other reporting guidelines, and personal communications. From this, we generated a list of potential modifications or additions to SPIRIT and CONSORT, which we presented to stakeholders for feedback in an international online Delphi survey. The Delphi survey results were discussed at an online expert consensus meeting attended by 30 invited international participants. We then drafted the updated SPIRIT and CONSORT checklists and revised them based on further feedback from meeting attendees. Results: We compiled 83 suggestions for revisions or additions to SPIRIT and/or CONSORT from the scoping review and 85 from other sources, from which we generated 33 potential changes to SPIRIT (n 5 5) or CONSORT (n 5 28). Of 463 participants invited to take part in the Delphi survey, 317 (68%) responded to Round 1, 303 (65%) to Round 2 and 290 (63%) to Round 3. Two additional potential checklist changes were added to the Delphi survey based on Round 1 comments. Overall, 14/35 (SPIRIT n 5 0; CONSORT n 5 14) proposed changes reached the predefined consensus threshold (80% agreement), and participants provided 3580 free-text comments. The consensus meeting participants agreed with implementing 11/14 of the proposed changes that reached consensus in the Delphi and supported implementing a further 4/21 changes (SPIRIT n 5 2; CONSORT n 5 2) that had not reached the Delphi threshold. They also recommended further changes to refine key concepts and for clarity. Conclusion: The forthcoming SPIRIT 2024 and CONSORT 2024 Statements will provide updated, harmonized guidance for reporting randomized controlled trial protocols and results, respectively. The simultaneous development of the SPIRIT and CONSORT checklists has been informed by current empirical evidence and extensive input from stakeholders. We hope that this report of the methods used will be helpful for developers of future reporting guidelines

Reporting guidelines used varying methodology to develop recommendations

Background and Objectives We investigated the developing methods of reporting guidelines in the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network's database. Methods In October 2018, we screened all records and excluded those not describing reporting guidelines from further investigation. Twelve researchers performed duplicate data extraction on bibliometrics, scope, development methods, presentation, and dissemination of all publications. Descriptive statistics were used to summarize the findings. Results Of the 405 screened records, 262 described a reporting guidelines development. The number of reporting guidelines increased over the past 3 decades, from 5 in the 1990s and 63 in the 2000s to 157 in the 2010s. Development groups included 2–151 people. Literature appraisal was performed during the development of 56% of the reporting guidelines; 33% used surveys to gather external opinion on items to report; and 42% piloted or sought external feedback on their recommendations. Examples of good reporting for all reporting items were presented in 30% of the reporting guidelines. Eighteen percent of the reviewed publications included some level of spin. Conclusion Reporting guidelines have been developed with varying methodology. Reporting guideline developers should use existing guidance and take an evidence-based approach, rather than base their recommendations on expert opinion of limited groups of individuals

Novel Methods for Analysing Bacterial Tracks Reveal Persistence in Rhodobacter sphaeroides

Tracking bacteria using video microscopy is a powerful experimental approach to probe their motile behaviour. The trajectories obtained contain much information relating to the complex patterns of bacterial motility. However, methods for the quantitative analysis of such data are limited. Most swimming bacteria move in approximately straight lines, interspersed with random reorientation phases. It is therefore necessary to segment observed tracks into swimming and reorientation phases to extract useful statistics. We present novel robust analysis tools to discern these two phases in tracks. Our methods comprise a simple and effective protocol for removing spurious tracks from tracking datasets, followed by analysis based on a two-state hidden Markov model, taking advantage of the availability of mutant strains that exhibit swimming-only or reorientating-only motion to generate an empirical prior distribution. Using simulated tracks with varying levels of added noise, we validate our methods and compare them with an existing heuristic method. To our knowledge this is the first example of a systematic assessment of analysis methods in this field. The new methods are substantially more robust to noise and introduce less systematic bias than the heuristic method. We apply our methods to tracks obtained from the bacterial species Rhodobacter sphaeroides and Escherichia coli. Our results demonstrate that R. sphaeroides exhibits persistence over the course of a tumbling event, which is a novel result with important implications in the study of this and similar species

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Completeness of Reporting in Diet- and Nutrition-Related Randomized Controlled Trials and Systematic Reviews With Meta-Analysis:Protocol for 2 Independent Meta-Research Studies

Background: Journal articles describing randomized controlled trials (RCTs) and systematic reviews with meta-analysis of RCTs are not optimally reported and often miss crucial details. This poor reporting makes assessing these studies’ risk of bias or reproducing their results difficult. However, the reporting quality of diet- and nutrition-related RCTs and meta-analyses has not been explored. Objective: We aimed to assess the reporting completeness and identify the main reporting limitations of diet- and nutrition-related RCTs and meta-analyses of RCTs, estimate the frequency of reproducible research practices among these RCTs, and estimate the frequency of distorted presentation or spin among these meta-analyses. Methods: Two independent meta-research studies will be conducted using articles published in PubMed-indexed journals. The first will include a sample of diet- and nutrition-related RCTs; the second will include a sample of systematic reviews with meta-analysis of diet- and nutrition-related RCTs. A validated search strategy will be used to identify RCTs of nutritional interventions and an adapted strategy to identify meta-analyses in PubMed. We will search for RCTs and meta-analyses indexed in 1 calendar year and randomly select 100 RCTs (June 2021 to June 2022) and 100 meta-analyses (July 2021 to July 2022). Two reviewers will independently screen the titles and abstracts of records yielded by the searches, then read the full texts to confirm their eligibility. The general features of these published RCTs and meta-analyses will be extracted into a research electronic data capture database (REDCap; Vanderbilt University). The completeness of reporting of each RCT will be assessed using the items in the CONSORT (Consolidated Standards of Reporting Trials), its extensions, and the TIDieR (Template for Intervention Description and Replication) statements. Information about practices that promote research transparency and reproducibility, such as the publication of protocols and statistical analysis plans will be collected. There will be an assessment of the completeness of reporting of each meta-analysis using the items in the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) statement and collection of information about spin in the abstracts and full-texts. The results will be presented as descriptive statistics in diagrams or tables. These 2 meta-research studies are registered in the Open Science Framework. Results: The literature search for the first meta-research retrieved 20,030 records and 2182 were potentially eligible. The literature search for the second meta-research retrieved 10,918 records and 850 were potentially eligible. Among them, random samples of 100 RCTs and 100 meta-analyses were selected for data extraction. Data extraction is currently in progress, and completion is expected by the beginning of 2023. Conclusions: Our meta-research studies will summarize the main limitation on reporting completeness of nutrition- or diet-related RCTs and meta-analyses and provide comprehensive information regarding the particularities in the reporting of intervention studies in the nutrition field

Completeness of reporting in diet- and nutrition-related randomized controlled trials and systematic reviews with meta-analysis: protocol for 2 independent meta-research studies

Background: Journal articles describing randomized controlled trials (RCTs) and systematic reviews with meta-analysis of RCTs are not optimally reported and often miss crucial details. This poor reporting makes assessing these studies’ risk of bias or reproducing their results difficult. However, the reporting quality of diet- and nutrition-related RCTs and meta-analyses has not been explored. Objective: We aimed to assess the reporting completeness and identify the main reporting limitations of diet- and nutrition-related RCTs and meta-analyses of RCTs, estimate the frequency of reproducible research practices among these RCTs, and estimate the frequency of distorted presentation or spin among these meta-analyses. Methods: Two independent meta-research studies will be conducted using articles published in PubMed-indexed journals. The first will include a sample of diet- and nutrition-related RCTs; the second will include a sample of systematic reviews with meta-analysis of diet- and nutrition-related RCTs. A validated search strategy will be used to identify RCTs of nutritional interventions and an adapted strategy to identify meta-analyses in PubMed. We will search for RCTs and meta-analyses indexed in 1 calendar year and randomly select 100 RCTs (June 2021 to June 2022) and 100 meta-analyses (July 2021 to July 2022). Two reviewers will independently screen the titles and abstracts of records yielded by the searches, then read the full texts to confirm their eligibility. The general features of these published RCTs and meta-analyses will be extracted into a research electronic data capture database (REDCap; Vanderbilt University). The completeness of reporting of each RCT will be assessed using the items in the CONSORT (Consolidated Standards of Reporting Trials), its extensions, and the TIDieR (Template for Intervention Description and Replication) statements. Information about practices that promote research transparency and reproducibility, such as the publication of protocols and statistical analysis plans will be collected. There will be an assessment of the completeness of reporting of each meta-analysis using the items in the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) statement and collection of information about spin in the abstracts and full-texts. The results will be presented as descriptive statistics in diagrams or tables. These 2 meta-research studies are registered in the Open Science Framework. Results: The literature search for the first meta-research retrieved 20,030 records and 2182 were potentially eligible. The literature search for the second meta-research retrieved 10,918 records and 850 were potentially eligible. Among them, random samples of 100 RCTs and 100 meta-analyses were selected for data extraction. Data extraction is currently in progress, and completion is expected by the beginning of 2023. Conclusions: Our meta-research studies will summarize the main limitation on reporting completeness of nutrition- or diet-related RCTs and meta-analyses and provide comprehensive information regarding the particularities in the reporting of intervention studies in the nutrition field. International Registered Report Identifier (IRRID): DERR1-10.2196/4353

Protocol for a meta-research study of protocols for diet or nutrition-related trials published in indexed journals:general aspects of study design, rationale and reporting limitations

INTRODUCTION: The Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) reporting guideline establishes a minimum set of items to be reported in any randomised controlled trial (RCT) protocol. The Template for Intervention Description and Replication (TIDieR) reporting guideline was developed to improve the reporting of interventions in RCT protocols and results papers. Reporting completeness in protocols of diet or nutrition-related RCTs has not been systematically investigated. We aim to identify published protocols of diet or nutrition-related RCTs, assess their reporting completeness and identify the main reporting limitations remaining in this field. METHODS AND ANALYSIS: We will conduct a meta-research study of RCT protocols published in journals indexed in at least one of six selected databases between 2012 and 2022. We have run a search in PubMed, Embase, CINAHL, Web of Science, PsycINFO and Global Health using a search strategy designed to identify protocols of diet or nutrition-related RCTs. Two reviewers will independently screen the titles and abstracts of records yielded by the search in Rayyan. The full texts will then be read to confirm protocol eligibility. We will collect general study features (publication information, types of participants, interventions, comparators, outcomes and study design) of all eligible published protocols in this contemporary sample. We will assess reporting completeness in a randomly selected sample of them and identify their main reporting limitations. We will compare this subsample with the items in the SPIRIT and TIDieR statements. For all data collection, we will use data extraction forms in REDCap. This protocol is registered on the Open Science Framework (DOI: 10.17605/OSF.IO/YWEVS). ETHICS AND DISSEMINATION: This study will undertake a secondary analysis of published data and does not require ethical approval. The results will be disseminated through journals and conferences targeting stakeholders involved in nutrition research.</p