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As one of the fundamental concepts underpinning the FAIR (Findability, Accessibility, Interoperability, and Reusability) guiding principles, data provenance entails keeping track of each version for a given dataset from its original to its latest version. However, standard terms to determine and include versioning information in the metadata of a given dataset are still ambiguous and do not explicitly define how to assess the overlap of information between items along a versioning stream. In this work, we propose a novel approach for automatic versioning of time series datasets, based on the use of parameters from two dimensionality reduction approaches, namely Principal Component Analysis and Autoencoders. That is to say, we systematically detect and measure similarities (information distances)in datasets via dimensionality reduction, encode them as different versions, and then automatically generate provenance metadata via a FAIRversioning service using the W3C DCAT 3.0 nomenclature. We illustrate this approach with two time series datasets and demonstrate how the proposed parameters effectively assess the similarity between different data versions. Our results have shown that the proposed version similarity metrics are robust (\(s^{(0,1)} = 1\)) to the alteration of up to 60% of cells, the removal of up to 60% of rows, and the log-scale transformation of variables. In contrast, row-wise transformations (e.g. converting absolute values to a percentage of a second variable) yield minimal similarity values (\(s^{(0,1)} < 0.75\)). Our code and datasets are openly available to enable reproducibility.&nbsp

Protocol for an observational study of the data availability statements accompanying preprints posted on the medRxiv repository between 25th June 2019 and 1st May 2020.

BackgroundEvidence on the impact of the pandemic on healthcare presentations for self-harm has accumulated rapidly. However, existing reviews do not include studies published beyond 2020.AimsTo systematically review evidence on presentations to health services following self-harm during the COVID-19 pandemic.MethodA comprehensive search of databases (WHO COVID-19 database; Medline; medRxiv; Scopus; PsyRxiv; SocArXiv; bioRxiv; COVID-19 Open Research Dataset, PubMed) was conducted. Studies published from 1 January 2020 to 7 September 2021 were included. Study quality was assessed with a critical appraisal tool.ResultsFifty-one studies were included: 57% (29/51) were rated as ‘low’ quality, 31% (16/51) as ‘moderate’ and 12% (6/51) as ‘high-moderate’. Most evidence (84%, 43/51) was from high-income countries. A total of 47% (24/51) of studies reported reductions in presentation frequency, including all six rated as high-moderate quality, which reported reductions of 17–56%. Settings treating higher lethality self-harm were overrepresented among studies reporting increased demand. Two of the three higher-quality studies including study observation months from 2021 reported reductions in self-harm presentations. Evidence from 2021 suggests increased numbers of presentations among adolescents, particularly girls.ConclusionsSustained reductions in numbers of self-harm presentations were seen into the first half of 2021, although this evidence is based on a relatively small number of higher-quality studies. Evidence from low- and middle-income countries is lacking. Increased numbers of presentations among adolescents, particularly girls, into 2021 is concerning. Findings may reflect changes in thresholds for help-seeking, use of alternative sources of support and variable effects of the pandemic across groups.

The “Marketplace for technology transfer of R&I data, software and results (SMARDY)” project aims to develop a data marketplace where academia, industry, and government, can exchange curated datasets, technology, and tools to foster economic and social development. From a business perspective, such a marketplace puts together data producers and data consumers to foster the delivery of cross-cutting solutions based on an open innovation model. To do so, SMARDY proposes a blockchain-based system to build a decentralised data sharing platform. Key aspects of this project involve providing data traceability, regulating data access, guaranteeing intellectual property rights, and checking permissions for further usage of the data. This is also extended to the software developed in relation to certain databases and studies, pursuing the reproducibility of the results and enabling to truly follow the logic and rationale behind scientific publications based on data analysis.

Supplemental material, sj-docx-4-cpa-10.1177_07067437221094552 for SARS-CoV-2 Infection and the Risk of Suicidal and Self-Harm Thoughts and Behaviour: A Systematic Review by Mark Sinyor, Rabia Zaheer, Roger T. Webb, Duleeka Knipe, Emily Eyles, Julian P.T. Higgins, Luke McGuinness, Lena Schmidt, Catherine Macleod-Hall, Dana Dekel, David Gunnell and Ann John in The Canadian Journal of Psychiatry

Supplemental material, sj-docx-3-cpa-10.1177_07067437221094552 for SARS-CoV-2 Infection and the Risk of Suicidal and Self-Harm Thoughts and Behaviour: A Systematic Review by Mark Sinyor, Rabia Zaheer, Roger T. Webb, Duleeka Knipe, Emily Eyles, Julian P.T. Higgins, Luke McGuinness, Lena Schmidt, Catherine Macleod-Hall, Dana Dekel, David Gunnell and Ann John in The Canadian Journal of Psychiatry

Supplemental material, sj-pdf-1-cpa-10.1177_07067437221094552 for SARS-CoV-2 Infection and the Risk of Suicidal and Self-Harm Thoughts and Behaviour: A Systematic Review by Mark Sinyor, Rabia Zaheer, Roger T. Webb, Duleeka Knipe, Emily Eyles, Julian P.T. Higgins, Luke McGuinness, Lena Schmidt, Catherine Macleod-Hall, Dana Dekel, David Gunnell and Ann John in The Canadian Journal of Psychiatry

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

Supplemental material, sj-docx-2-cpa-10.1177_07067437221094552 for SARS-CoV-2 Infection and the Risk of Suicidal and Self-Harm Thoughts and Behaviour: A Systematic Review by Mark Sinyor, Rabia Zaheer, Roger T. Webb, Duleeka Knipe, Emily Eyles, Julian P.T. Higgins, Luke McGuinness, Lena Schmidt, Catherine Macleod-Hall, Dana Dekel, David Gunnell and Ann John in The Canadian Journal of Psychiatry

This workshop will provide walkthroughs, examples and advice on how to use GitHub to support your work in R, whether developing packages or managing projects.

This panel discussion covers why evidence synthesis capacity development is vital for rigorous synthesis and evidence-informed decision-making. The panellists discuss the beneficial role that systems like R can play in increasing awareness of and use of robust methods, and possible challenges with Open platforms for training future generations of evidence synthesists. Panellists: Marc Lajeunesse, Wolfgang Viechtbauer and Harriet Downey The ESMARConf Organising Team then close the event

Presenter: Neal Haddaway Title: Producing interactive flow diagrams for systematic reviews using the PRISMA2020 package Presenter: Charles Gray Title: nmareporting:: a computational waystation for toolchains in network meta-analysis reporting The speakers take part in a panel discussion Facilitator: Martin Westgate

{robvis} is an established R package which allows users to create publication-quality risk-of-bias plots. Recently, it has expanded its scope to allow for new functionality, focusing in particular on better integration with the {metafor} package for meta-analysis. Following a public discussion of the proposed functionality run through GitHub and Twitter, in addition to consultation with the maintainer of the {metafor} package, two new functions were added to {robvis}. The first creates paired risk-of-bias plots, where a ""traffic light""-style risk-of-bias plot is appended onto a standard forest plot so that the risk of bias for each result in the meta-analysis is readily available to the reader. This function builds on the output of the {metafor} forest plot function to create these graphs. In addition, as users frequently wish to stratify their analysis by the level of bias in each included study, a new function which automatically subsets the data by this variable and presents a subgroup meta-analysis for each subgroup has also been developed. This presentation will use the {robvis} package as a case study to demonstrate how collaboration between packages in the evidence synthesis landscape can result in new functionality and increased ease-of-use for end users. It will also highlight the benefit of thinking of the evidence synthesis workflow as a whole when developing and expanding new functionality, rather than seeing packages as standalone silos.

Presenter: James E. Pustejovsky Title: Synthesis of dependent effect sizes: Versatile models through clubSandwich and metafor Presenter: Maya Mathur Title: R package PublicationBias: Sensitivity analysis for publication bias Presenter: Philip Martin Title: Dynamic meta-analysis: Providing evidence to guide local decisions using a global evidence base Presenter: Wolfgang Viechtbauer Title: Selection models for publication bias in meta-analysis The speakers are be joined by Julia Koricheva for a panel discussion Facilitator: Matt Grainger

Livestream of ESMARConf2021 Session 1A - Automation (text analysis) Presenter: Arindam Basu Title: Using computational text analysis to filter titles and abstracts of initial search for meta-analysis: case of Quanteda and tidytext Presenter: Martin Westgate Title: An introduction to revtools - R and Shiny App for article screening and topic modelling Presenter: Max Callaghan Title: Introduction to the concept of robust stopping criteria for using machine learning classifiers for inclusion decisions in evidence syntheses Presenter: Richard Cornford Title: Automated identification of articles for ecological datasets The speakers take part in a panel discussion Facilitator: Neal Haddaway

Presenter: Clareece Nevill Title: Pilot for an Interactive Living Network Meta-Analysis web application Presenter: Suzanne Freeman Title: MetaDTA: An interactive web-based tool for meta-analysis of diagnostic test accuracy studies Presenter: W. Kyle Hamilton Title: Jamovi as a Platform for Running a Meta-Analysis The speakers take part in a panel discussion Facilitator: Ciara Keenan

Welcome to #ESMARConf2021! Discover more about the conference: https://bit.ly/ESMARConf Watch the live stream or individual presentations: https://bit.ly/ESMARConfYouTube Join in, by registering and engaging via the Slack Workspace for Q&A, workshops and more: https://forms.gle/LNWrPND8zwcBv1pQ8 Vote for your favourite presentation: https://bit.ly/ESMARConfVote Thanks and we hope you enjoy the conference! The ESMARConf2021 Organising Team Matthew Grainger Neal Haddaway Emily Hennessy Ciara Keenan Luke McGuiness Martin Westgate

Additional file 1. PRISMA 2020 checklist.

The ESMARConf organising team open the event and welcome participants.

Presenter: Sarah Young Title: What can networks tell us in an evidence and gap map context? Vegetated strips in agricultural fields as a case study Presenters: Tim Alamenciak and Dorian Pomezanski Title: Analyzing Canadian ecological restoration literature with bibliometric analysis and a systematic map Presenter: Loretta Gasparini Title: Introducing metalabR: A package to facilitate living meta-analyses and dynamic meta-analytic visualizations The speakers are joined by Shinichi Nakagawa for a panel discussion Facilitator: Neal Haddaway

Presenter: Guido Schwarzer Title: Network meta-analysis with netmeta - Present and Future Presenter: David Phillippo Title: multinma: An R package for Bayesian network meta-analysis of individual and aggregate data Presenter: Thodoros Papakonstantinou Title: How to estimate the contribution of each study in network meta-analysis Presenter: Hugo Pedder Title: MBNMAdose: An R package for incorporating dose-response information into Network Meta-Analysis Presenter: Michael Seo Title: bnma: Bayesian Network Meta-Analysis using 'JAGS' The speakers take part in a panel discussion Facilitator: Emily Hennessy