Language inclusion in ecological systematic reviews and maps: Barriers and perspectives.

Systematic reviews and maps are considered a reliable form of research evidence, but often neglect non-English-language literature, which can be a source of important evidence. To understand the barriers that might limit authors' ability or intent to find and include non-English-language literature, we assessed factors that may predict the inclusion of non-English-language literature in ecological systematic reviews and maps, as well as the review authors' perspectives. We assessed systematic reviews and maps published in Environmental Evidence (n = 72). We also surveyed authors from each paper (n = 32 responses), gathering information on the barriers to the inclusion of non-English language literature. 44% of the reviewed papers (32/72) excluded non-English literature from their searches and inclusions. Commonly cited reasons included constraints related to resources and time. Regression analysis revealed that reviews with larger author teams, authors from diverse countries, especially those with non-English primary languages, and teams with multilingual capabilities searched in a significantly greater number of non-English languages. Our survey exposed limited language diversity within the review teams and inadequate funding as the principal barriers to incorporating non-English language literature. To improve language inclusion and reduce bias in systematic reviews and maps, our study suggests increasing language diversity within review teams. Combining machine translation with language skills can alleviate the financial and resource burdens of translation. Funding applications could also include translation costs. Additionally, establishing language exchange systems would enable access to information in more languages. Further studies investigating language inclusion in other journals would strengthen these conclusions.

Systematic reviews: A glossary for public health.

Literature reviews are conducted for a range of purposes, from providing an overview or primer of a novel topic, to providing a comprehensive, precise, and accurate estimate of an effect estimate. There is much confusion over nomenclature related to literature reviews, with the term 'systematic review' often used to mean any review based on some form of explicit methodology. However, guidance and minimum standards exist for these kinds of robust reviews that are intended to support evidence-informed decision-making, and reviewers must carefully ensure their syntheses are conducted and reported to a high standard if this is their objective. The diversity of names given to reviews is reflected in the diversity of methods used for these evidence syntheses: the result is a general confusion about what is important to ensure a review is fit-for-purpose, and many reviews are labelled as 'systematic reviews' when they do not follow standardised or replicable approaches. Here, we provide a glossary or typology that aims to highlight the importance of the reviewers' objectives in choosing and naming their review method. We focus on reviews in public health and provide guidance on selecting an objective, methodological guidance to follow, justifying and reporting the methods chosen, and attempting to ensure consistent and clear nomenclature. We hope this will help review authors, editors, peer-reviewers, and readers understand, interpret, and critique a review depending on its intended use.

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.

Evidence of the impacts of metal mining and the effectiveness of mining mitigation measures on social-ecological systems in Arctic and boreal regions: a systematic map.

Background: Mining can directly and indirectly affect social and environmental systems in a range of positive and negative ways, and may result in societal benefits, but may also cause conflicts, not least in relation to land use. Mining always affects the environment, whilst remediation and mitigation efforts may effectively ameliorate some negative environmental impacts. Social and environmental systems in Arctic and boreal regions are particularly sensitive to impacts from development for numerous reasons, not least of which are the reliance of Indigenous peoples on subsistence livelihoods and long recovery times of fragile ecosystems. With growing metal demand, mining in the Arctic is expected to increase, demanding a better understand its social and environmental impacts. We report here the results of a systematic mapping of research evidence of the impacts of metal mining in Arctic and boreal regions. Methods: We searched multiple bibliographic databases and organisational websites for relevant research using tested search strategies. We also collected evidence from stakeholders and rightsholders identified in the wider 3MK project (Mapping the impacts of Mining using Multiple Knowledges, https://osf.io/cvh3u). We screened articles at three stages (title, abstract, and full text) according to a predetermined set of inclusion criteria, with consistency checks between reviewers at each level. We extracted data relating to causal linkages between actions or impacts and measured outcomes, along with descriptive information about the articles and studies. We have produced an interactive database along with interactive visualisations, and identify knowledge gaps and clusters using heat maps. Review findings: Searches identified over 32,000 potentially relevant records, which resulted in a total of 585 articles being retained in the systematic map. This corresponded to 902 lines of data on impact or mitigation pathways. The evidence was relatively evenly spread across topics, but there was a bias towards research in Canada (35% of the evidence base). Research was focused on copper (23%), gold (18%), and zinc (16%) extraction as the top three minerals, and open pit mines were most commonly studied (33%). Research most commonly focused on operation stages, followed by abandonment and post-closure, with little evidence on early stages (prospecting, exploration, construction; 2%), expansion (0.2%), or decommissioning/closure (0.3%). Mitigation measures were not frequently studied (18% articles), with groundwater mitigation most frequently investigated (54% of mitigations), followed by soil quality (12%) and flora species groups (10%). Control-impact study designs were most common (68%) with reference sites as the most frequently used comparator (43%). Only 7 articles investigated social and environmental outcomes together. the most commonly reported system was biodiversity (39%), followed by water (34%), societies (20%), and soil/geology (6%), with air the least common (1%). Conclusions: The evidence found highlights a suite of potential knowledge gaps, namely: on early stages prior to operation; effectiveness of mitigation measures; stronger causal inference study designs; migration and demography; cumulative impacts; and impacts on local and Indigenous communities. We also tentatively suggest subtopics where the number of studies could allow systematic reviews: operation, post-closure, and abandonment stages; individual faunal species, surface water quality, water sediment quality; and, groundwater mitigation measure effectiveness. Supplementary Information: The online version contains supplementary material available at 10.1186/s13750-022-00282-y.

The evidence synthesis and meta-analysis in R conference (ESMARConf): levelling the playing field of conference accessibility and equitability.

Rigorous evidence is vital in all disciplines to ensure efficient, appropriate, and fit-for-purpose decision-making with minimised risk of unintended harm. To date, however, disciplines have been slow to share evidence synthesis frameworks, best practices, and tools amongst one another. Recent progress in collaborative digital and programmatic frameworks, such as the free and Open Source software R, have significantly expanded the opportunities for development of free-to-use, incrementally improvable, community driven tools to support evidence synthesis (e.g. EviAtlas, robvis, PRISMA2020 flow diagrams and metadat). Despite this, evidence synthesis (and meta-analysis) practitioners and methodologists who make use of R remain relatively disconnected from one another. Here, we report on a new virtual conference for evidence synthesis and meta-analysis in the R programming environment (ESMARConf) that aims to connect these communities. By designing an entirely free and online conference from scratch, we have been able to focus efforts on maximising accessibility and equity-making these core missions for our new community of practice. As a community of practice, ESMARConf builds on the success and groundwork of the broader R community and systematic review coordinating bodies (e.g. Cochrane), but fills an important niche. ESMARConf aims to maximise accessibility and equity of participants across regions, contexts, and social backgrounds, forging a level playing field in a digital, connected, and online future of evidence synthesis. We believe that everyone should have the same access to participation and involvement, and we believe ESMARConf provides a vital opportunity to push for equitability across disciplines, regions, and personal situations.

Citationchaser: A tool for transparent and efficient forward and backward citation chasing in systematic searching.

Systematic searching aims to find all possibly relevant research from multiple sources, the basis for an unbiased and comprehensive evidence base. Along with bibliographic databases, systematic reviewers use a variety of additional methods to minimise procedural bias. Citation chasing exploits connections between research articles to identify relevant records for a review by making use of explicit mentions of one article within another. Citation chasing is a popular supplementary search method because it helps to build on the work of primary research and review authors. It does so by identifying potentially relevant studies that might otherwise not be retrieved by other search methods; for example, because they did not use the review authors' search terms in the specified combinations in their titles, abstracts, or keywords. Here, we briefly provide an overview of citation chasing as a method for systematic reviews. Furthermore, given the challenges and high resource requirements associated with citation chasing, the limited application of citation chasing in otherwise rigorous systematic reviews, and the potential benefit of identifying terminologically disconnected but semantically linked research studies, we have developed and describe a free and open source tool that allows for rapid forward and backward citation chasing. We introduce citationchaser, an R package and Shiny app for conducting forward and backward citation chasing from a starting set of articles. We describe the sources of data, the backend code functionality, and the user interface provided in the Shiny app.

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

OBJECTIVES: To estimate the frequency of data and code availability statements in a random sample of systematic reviews with meta-analysis of aggregate data, summarize the content of the statements and investigate how often data and code files were shared. METHODS: We searched for systematic reviews with meta-analysis of aggregate data on the effects of a health, social, behavioral, or educational intervention that were indexed in PubMed, Education Collection via ProQuest, Scopus via Elsevier, or Social Sciences Citation Index and Science Citation Index Expanded via Web of Science during a 4-week period (between November 2, and December 2, 2020). Records were randomly sorted and screened independently by two authors until our target sample of 300 systematic reviews was reached. Two authors independently recorded whether a data or code availability statement (or both) appeared in each review and coded the content of the statements using an inductive approach. RESULTS: Of the 300 included systematic reviews with meta-analysis, 86 (29%) had a data availability statement, and seven (2%) had both a data and code availability statement. In 12/93 (13%) data availability statements, authors stated that data files were available for download from the journal website or a data repository, which we verified as being true. While 39/93 (42%) authors stated data were available upon request, 37/93 (40%) implied that sharing of data files was not necessary or applicable to them, most often because "all data appear in the article" or "no datasets were generated or analyzed". DISCUSSION: Data and code availability statements appear infrequently in systematic review manuscripts. Authors who do provide a data availability statement often incorrectly imply that data sharing is not applicable to systematic reviews. Our results suggest the need for various interventions to increase data and code sharing by systematic reviewers.

PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis.

Background: Reporting standards, such as PRISMA aim to ensure that the methods and results of systematic reviews are described in sufficient detail to allow full transparency. Flow diagrams in evidence syntheses allow the reader to rapidly understand the core procedures used in a review and examine the attrition of irrelevant records throughout the review process. Recent research suggests that use of flow diagrams in systematic reviews is poor and of low quality and called for standardised templates to facilitate better reporting in flow diagrams. The increasing options for interactivity provided by the Internet gives us an opportunity to support easy-to-use evidence synthesis tools, and here we report on the development of a tool for the production of PRISMA 2020-compliant systematic review flow diagrams. Methods and Findings: We developed a free-to-use, Open Source R package and web-based Shiny app to allow users to design PRISMA flow diagrams for their own systematic reviews. Our tool allows users to produce standardised visualisations that transparently document the methods and results of a systematic review process in a variety of formats. In addition, we provide the opportunity to produce interactive, web-based flow diagrams (exported as HTML files), that allow readers to click on boxes of the diagram and navigate to further details on methods, results or data files. We provide an interactive example here; https://prisma-flowdiagram.github.io/. Conclusions: We have developed a user-friendly tool for producing PRISMA 2020-compliant flow diagrams for users with coding experience and, importantly, for users without prior experience in coding by making use of Shiny (https://estech.shinyapps.io/prisma_flowdiagram/). This free-to-use tool will make it easier to produce clear and PRISMA 2020-compliant systematic review flow diagrams. Significantly, users can also produce interactive flow diagrams for the first time, allowing readers of their reviews to smoothly and swiftly explore and navigate to further details of the methods and results of a review. We believe this tool will increase use of PRISMA flow diagrams, improve the compliance and quality of flow diagrams, and facilitate strong science communication of the methods and results of systematic reviews by making use of interactivity. We encourage the systematic review community to make use of the tool, and provide feedback to streamline and improve their usability and efficiency.

Ensuring Prevention Science Research is Synthesis-Ready for Immediate and Lasting Scientific Impact.

When seeking to inform and improve prevention efforts and policy, it is important to be able to robustly synthesize all available evidence. But evidence sources are often large and heterogeneous, so understanding what works, for whom, and in what contexts can only be achieved through a systematic and comprehensive synthesis of evidence. Many barriers impede comprehensive evidence synthesis, which leads to uncertainty about the generalizability of intervention effectiveness, including inaccurate titles/abstracts/keywords terminology (hampering literature search efforts), ambiguous reporting of study methods (resulting in inaccurate assessments of study rigor), and poorly reported participant characteristics, outcomes, and key variables (obstructing the calculation of an overall effect or the examination of effect modifiers). To address these issues and improve the reach of primary studies through their inclusion in evidence syntheses, we provide a set of practical guidelines to help prevention scientists prepare synthesis-ready research. We use a recent mindfulness trial as an empirical example to ground the discussion and demonstrate ways to ensure the following: (1) primary studies are discoverable; (2) the types of data needed for synthesis are present; and (3) these data are readily synthesizable. We highlight several tools and practices that can aid authors in these efforts, such as using a data-driven approach for crafting titles, abstracts, and keywords or by creating a repository for each project to host all study-related data files. We also provide step-by-step guidance and software suggestions for standardizing data design and public archiving to facilitate synthesis-ready research.

A suggested data structure for transparent and repeatable reporting of bibliographic searching.

Academic searching is integral to research activities: (1) searching to retrieve specific information, (2) to expand our knowledge iteratively, (3) and to collate a representative and unbiased selection of the literature. Rigorous searching methods are vital for reliable, repeatable and unbiased searches needed for these second and third forms of searches (exploratory and systematic searching, respectively) that form a core part of evidence syntheses. Despite the broad awareness of the importance of transparency in reporting search activities in evidence syntheses, the importance of searching has been highlighted only recently and has been the explicit focus of reporting guidance (PRISMA-S). Ensuring bibliographic searches are reported in a way that is transparent enough to allow for full repeatability or evaluation is challenging for a number of reasons. Here, we detail these reasons and provide for the first time a standardised data structure for transparent and comprehensive reporting of search histories. This data structure was produced by a group of international experts in informatics and library sciences. We explain how the data structure was produced and describe its components in detail. We also demonstrate its practical applicability in tools designed to support literature review authors and explain how it can help to improve interoperability across tools used to manage literature reviews. We call on the research community and developers of reference and review management tools to embrace the data structure to facilitate adequate reporting of academic searching in an effort to raise the standard of evidence syntheses globally.

Systematic mapping of global research on climate and health: a machine learning review.

BACKGROUND: The global literature on the links between climate change and human health is large, increasing exponentially, and it is no longer feasible to collate and synthesise using traditional systematic evidence mapping approaches. We aimed to use machine learning methods to systematically synthesise an evidence base on climate change and human health. METHODS: We used supervised machine learning and other natural language processing methods (topic modelling and geoparsing) to systematically identify and map the scientific literature on climate change and health published between Jan 1, 2013, and April 9, 2020. Only literature indexed in English were included. We searched Web of Science Core Collection, Scopus, and PubMed using title, abstract, and keywords only. We searched for papers including both a health component and an explicit mention of either climate change, climate variability, or climate change-relevant weather phenomena. We classified relevant publications according to the fields of climate research, climate drivers, health impact, date, and geography. We used supervised and unsupervised machine learning to identify and classify relevant articles in the field of climate and health, with outputs including evidence heat maps, geographical maps, and narrative synthesis of trends in climate health-related publications. We included empirical literature of any study design that reported on health pathways associated with climate impacts, mitigation, or adaptation. FINDINGS: We predict that there are 15 963 studies in the field of climate and health published between 2013 and 2019. Climate health literature is dominated by impact studies, with mitigation and adaptation responses and their co-benefits and co-risks remaining niche topics. Air quality and heat stress are the most frequently studied exposures, with all-cause mortality and infectious disease incidence being the most frequently studied health outcomes. Seasonality, extreme weather events, heat, and weather variability are the most frequently studied climate-related hazards. We found major gaps in evidence on climate health research for mental health, undernutrition, and maternal and child health. Geographically, the evidence base is dominated by studies from high-income countries and China, with scant evidence from low-income counties, which often suffer most from the health consequences of climate change. INTERPRETATION: Our findings show the importance and feasibility of using automated machine learning to comprehensively map the science on climate change and human health in the age of big literature. These can provide key inputs into global climate and health assessments. The scant evidence on climate change response options is concerning and could significantly hamper the design of evidence-based pathways to reduce the effects on health of climate change. In the post-2015 Paris Agreement era of climate solutions, we believe much more attention should be given to climate adaptation and mitigation options and their effects on human health. FUNDING: Foreign, Commonwealth & Development Office.

Researcher perspectives on challenges and opportunities in conservation physiology revealed from an online survey.

Conservation physiology represents a recently emerging arm of conservation science that applies physiological tools and techniques to understand and solve conservation issues. While a multi-disciplinary toolbox can only help to address the global biodiversity crisis, any field can face challenges while becoming established, particularly highly applied disciplines that require multi-stakeholder involvement. Gaining first-hand knowledge of the challenges that conservation physiologists are facing can help characterize the current state of the field and build a better foundation for determining how it can grow. Through an online survey of 468 scientists working at the intersection of physiology and conservation, we aimed to identify characteristics of those engaging in conservation physiology research (e.g. demographics, primary taxa of study), gauge conservation physiology's role in contributing to on-the-ground conservation action, identify the perceived barriers to achieving success and determine how difficult any identified barriers are to overcome. Despite all participants having experience combining physiology and conservation, only one-third considered themselves to be 'conservation physiologists'. Moreover, there was a general perception that conservation physiology does not yet regularly lead to tangible conservation success. Respondents identified the recent conceptualization of the field and the broader issue of adequately translating science into management action as the primary reasons for these deficits. Other significant barriers that respondents have faced when integrating physiology and conservation science included a lack of funding, logistical constraints (e.g. sample sizes, obtaining permits) and a lack of physiological baseline data (i.e. reference ranges of a physiological metric's 'normal' or pre-environmental change levels). We identified 12 actions based on suggestions of survey participants that we anticipate will help deconstruct the barriers and continue to develop a narrative of physiology that is relevant to conservation science, policy and practice.

The REPRISE project: protocol for an evaluation of REProducibility and Replicability In Syntheses of Evidence.

BACKGROUND: Investigations of transparency, reproducibility and replicability in science have been directed largely at individual studies. It is just as critical to explore these issues in syntheses of studies, such as systematic reviews, given their influence on decision-making and future research. We aim to explore various aspects relating to the transparency, reproducibility and replicability of several components of systematic reviews with meta-analysis of the effects of health, social, behavioural and educational interventions. METHODS: The REPRISE (REProducibility and Replicability In Syntheses of Evidence) project consists of four studies. We will evaluate the completeness of reporting and sharing of review data, analytic code and other materials in a random sample of 300 systematic reviews of interventions published in 2020 (Study 1). We will survey authors of systematic reviews to explore their views on sharing review data, analytic code and other materials and their understanding of and opinions about replication of systematic reviews (Study 2). We will then evaluate the extent of variation in results when we (a) independently reproduce meta-analyses using the same computational steps and analytic code (if available) as used in the original review (Study 3), and (b) crowdsource teams of systematic reviewers to independently replicate a subset of methods (searches for studies, selection of studies for inclusion, collection of outcome data, and synthesis of results) in a sample of the original reviews; 30 reviews will be replicated by 1 team each and 2 reviews will be replicated by 15 teams (Study 4). DISCUSSION: The REPRISE project takes a systematic approach to determine how reliable systematic reviews of interventions are. We anticipate that results of the REPRISE project will inform strategies to improve the conduct and reporting of future systematic reviews.

Mapping global research on climate and health using machine learning (a systematic evidence map).

Climate change is already affecting health in populations around the world, threatening to undermine the past 50 years of global gains in public health. Health is not only affected by climate change via many causal pathways, but also by the emissions that drive climate change and their co-pollutants. Yet there has been relatively limited synthesis of key insights and trends at a global scale across fragmented disciplines. Compounding this, an exponentially increasing literature means that conventional evidence synthesis methods are no longer sufficient or feasible. Here, we outline a protocol using machine learning approaches to systematically synthesize global evidence on the relationship between climate change, climate variability, and weather (CCVW) and human health. We will use supervised machine learning to screen over 300,000 scientific articles, combining terms related to CCVW and human health. Our inclusion criteria comprise articles published between 2013 and 2020 that focus on empirical assessment of: CCVW impacts on human health or health-related outcomes or health systems; relate to the health impacts of mitigation strategies; or focus on adaptation strategies to the health impacts of climate change. We will use supervised machine learning (topic modeling) to categorize included articles as relevant to impacts, mitigation, and/or adaptation, and extract geographical location of studies. Unsupervised machine learning using topic modeling will be used to identify and map key topics in the literature on climate and health, with outputs including evidence heat maps, geographic maps, and narrative synthesis of trends in climate-health publishing. To our knowledge, this will represent the first comprehensive, semi-automated, systematic evidence synthesis of the scientific literature on climate and health.

Methods and guidance on conducting, reporting, publishing and appraising living systematic reviews: a scoping review protocol.

Background: The living systematic review (LSR) approach is based on an ongoing surveillance of the literature and continual updating. A few guidance documents address the conduct, reporting, publishing and appraisal of systematic reviews (SRs), but the methodology described is either not up-to date or not suitable for LSRs and misses additional LSR-specific considerations. The objective of this scoping review is to systematically collate methodological literature and guidance on how to conduct, report, publish and appraise the quality of LSRs. The scoping review will allow the mapping of the existing evidence on the topic to support LSRs authors seeking guidance and identify related gaps.  Methods: To achieve our objectives, we will conduct a scoping review to survey and evaluate existing evidence, using the standard scoping review methodology. We will search MEDLINE, EMBASE, and Cochrane using the OVID interface. The search strategy was developed by a researcher experienced in developing literature search strategies with the help of an information specialist. As for searching grey literature, we will seek existing guidelines and handbooks on LSRs from organizations that conduct evidence syntheses using the Lens.org website. Two review authors will extract and catalogue the study data on LSR methodological aspects into a standardized and pilot-tested data extraction form. The main categories will reflect proposed methods for (i) conducting LSRs, (ii) reporting of LSRs, (iii) publishing and (iv) appraising the quality of LSRs. Data synthesis and conclusion: By collecting these data from methodological surveys and papers, as well as existing guidance documents and handbooks on LSRs, we might identify specific issues and components lacking within current LSR methodology. Thus, the systematically obtained findings of the scoping review could be used as basis for the revision of existing methods tools on LSR, for instance a PRISMA statement extension for LSRs.

Tailored PRISMA 2020 flow diagrams for living systematic reviews: a methodological survey and a proposal.

Background: While the PRISMA flow diagram is widely used for reporting standard systematic reviews (SRs), it was not designed for capturing the results of continual searches for studies in living systematic reviews (LSRs). The objectives of this study are (1) to assess how published LSRs report on the flow of studies through the different phases of the review for the different updates; (2) to propose an approach to reporting on that flow. Methods: For objective 1, we identified all LSRs published up to July 2020. We abstracted information regarding their general characteristics and how they reported on search results. For objective 2, we based our proposal for tailored PRISMA approaches on the findings from objective 1, as well as on our experience with conducting Cochrane LSRs. Results: We identified 108 living publications relating to 32 LSRs. Of the 108 publications, 7% were protocols, 24% were base versions (i.e., the first version), 62% were partial updates (i.e., does not include all typical sections of an SR), and 7% were full updates (i.e., includes all typical sections of an SR). We identified six ways to reporting the study flow: base separately, each update separately (38%); numbers not reported (32%); latest update separately, all previous versions combined (20%); base separately, all updates combined (7%); latest update version only (3%);  all versions combined (0%). We propose recording in detail the results of the searches to keep track of all identified records. For structuring the flow diagram, we propose using one of four approaches. Conclusion: We identified six ways for reporting the study flowthrough the different phases of the review for the different update versions. We propose to document in detail the study flow for the different search updates and select one of our four tailored PRISMA diagram approaches to present that study flow.

What every researcher should know about searching - clarified concepts, search advice, and an agenda to improve finding in academia.

We researchers have taken searching for information for granted for far too long. The COVID-19 pandemic shows us the boundaries of academic searching capabilities, both in terms of our know-how and of the systems we have. With hundreds of studies published daily on COVID-19, for example, we struggle to find, stay up-to-date, and synthesize information-all hampering evidence-informed decision making. This COVID-19 information crisis is indicative of the broader problem of information overloaded academic research. To improve our finding capabilities, we urgently need to improve how we search and the systems we use. We respond to Klopfenstein and Dampier (Res Syn Meth. 2020) who commented on our 2020 paper and proposed a way of improving PubMed's and Google Scholar's search functionalities. Our response puts their commentary in a larger frame and suggests how we can improve academic searching altogether. We urge that researchers need to understand that search skills require dedicated education and training. Better and more efficient searching requires an initial understanding of the different goals that define the way searching needs to be conducted. We explain the main types of searching that we academics routinely engage in; distinguishing lookup, exploratory, and systematic searching. These three types must be conducted using different search methods (heuristics) and using search systems with specific capabilities. To improve academic searching, we introduce the "Search Triangle" model emphasizing the importance of matching goals, heuristics, and systems. Further, we suggest an urgently needed agenda toward search literacy as the norm in academic research and fit-for-purpose search systems.

Ten principles for generating accessible and useable COVID-19 environmental science and a fit-for-purpose evidence base.

1. The 'anthropause', a period of unusually reduced human activity and mobility due to COVID-19 restrictions, has serendipitously opened up unique opportunities for research on how human activities impact the environment. 2. In the field of health, COVID-19 research has led to concerns about the quality of research papers and the underlying research and publication processes due to accelerated peer review and publication schedules, increases in pre-prints and retractions. 3. In the field of environmental science, framing the pandemic and associated global lockdowns as an unplanned global human confinement experiment with urgency should raise the same concerns about the rigorousness and integrity of the scientific process. Furthermore, the recognition of an 'infodemic', an unprecedented explosion of research, risks research waste and duplication of effort, although how information is used is as important as the quality of evidence. This highlights the need for an evidence base that is easy to find and use - that is discoverable, curated, synthesizable, synthesized. 4. We put forward a list of 10 key principles to support the establishment of a reproducible, replicable, robust, rigorous, timely and synthesizable COVID-19 environmental evidence base that avoids research waste and is resilient to the pressures to publish urgently. These principles focus on engaging relevant actors (e.g. local communities, rightsholders) in research design and production, statistical power, collaborations, evidence synthesis, research registries and protocols, open science and transparency, data hygiene (cleanliness) and integrity, peer review transparency, standardized keywords and controlled vocabularies.

Creating and curating a community of practice: introducing the evidence synthesis Hackathon and a special series in evidence synthesis technology.

Evidence synthesis is a vital part of evidence-informed decision-making, but high growth in the volume of research evidence over recent decades has made efficient evidence synthesis increasingly challenging. As the appreciation and need for timely and rigorous evidence synthesis continue to grow, so too will the need for tools and frameworks to conduct reviews of expanding evidence bases in an efficient and time-sensitive manner. Efforts to future-proof evidence synthesis through the development of new evidence synthesis technology (ESTech) have so far been isolated across interested individuals or groups, with no concerted effort to collaborate or build communities of practice in technology production. We established the evidence synthesis Hackathon to stimulate collaboration and the production of Free and Open Source Software and frameworks to support evidence synthesis. Here, we introduce a special series of papers on ESTech, and invite the readers of environmental evidence to submit manuscripts introducing and validating novel tools and frameworks. We hope this collection will help to consolidate ESTech development efforts and we encourage readers to join the ESTech revolution. In order to future-proof evidence synthesis against the evidence avalanche, we must support community enthusiasm for ESTech, reduce redundancy in tool design, collaborate and share capacity in tool production, and reduce inequalities in software accessibility.