On February 28, 2019, the University of California (UC) announced that it would not renew its subscriptions to Elsevier journals. UC is a public research university in California, USA, with 10 campuses across the state.

Citation metrics are widely used and misused. We have created a publicly available database of top-cited scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator (c-score). Separate data are shown for career-long and, separately, for single recent year impact. Metrics with and without self-citations and ratio of citations to citing papers are given. Scientists are classified into 22 scientific fields and 174 sub-fields according to the standard Science-Metrix classification. Field- and subfield-specific percentiles are also provided for all scientists with at least 5 papers. Career-long data are updated to end-of-2022 and single recent year data pertain to citations received during calendar year 2022. The selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field. This version (6) is based on the October 1, 2023 snapshot from Scopus, updated to end of citation year 2022. This work uses Scopus data provided by Elsevier through ICSR Lab (https://www.elsevier.com/icsr/icsrlab). Calculations were performed using all Scopus author profiles as of October 1, 2023. If an author is not on the list it is simply because the composite indicator value was not high enough to appear on the list. It does not mean that the author does not do good work.

PLEASE ALSO NOTE THAT THE DATABASE HAS BEEN PUBLISHED IN AN ARCHIVAL FORM AND WILL NOT BE CHANGED. The published version reflects Scopus author profiles at the time of calculation. We thus advise authors to ensure that their Scopus profiles are accurate. REQUESTS FOR CORRECIONS OF THE SCOPUS DATA (INCLUDING CORRECTIONS IN AFFILIATIONS) SHOULD NOT BE SENT TO US. They should be sent directly to Scopus, preferably by use of the Scopus to ORCID feedback wizard (https://orcid.scopusfeedback.com/) so that the correct data can be used in any future annual updates of the citation indicator databases.

The c-score focuses on impact (citations) rather than productivity (number of publications) and it also incorporates information on co-authorship and author positions (single, first, last author). If you have additional questions, please read the 3 associated PLoS Biology papers that explain the development, validation and use of these metrics and databases. (https://doi.org/10.1371/journal.pbio.1002501, https://doi.org/10.1371/journal.pbio.3000384 and https://doi.org/10.1371/journal.pbio.3000918).

Finally, we alert users that all citation metrics have limitations and their use should be tempered and judicious. For more reading, we refer to the Leiden manifesto: https://www.nature.com/articles/520429a

Three tables and one figure of this paper. Table 1 is a summary of results of implementation of data sharing policies at Elsevier, 2017–2018. Over 2,200 journals were eligible for data sharing roll-out and their editors consulted for the advised policy to be instated. Table 2 shows deposition of data during manuscript submission to Mendeley Data Repository per subject category, 2017–2018. Table 3 is a roadmap to implement FAIR data support at Elsevier: high level overview of steps necessary to support FAIR data creation and sharing. Shaded cells (green to red) refl ect if implementation is in the future (red) or already been initiated (yellow), or otherwise are live (green). Note that the status of these implementations is subject to change as we are continuously revising our implementations with input from all stakeholders in the research community. Figure 1 shows the “data Maslow hierarchy” visualizing the components of data sharing.

Introduction

Folder structure

data

documentation

experiments

How to run the benchmarks?

Requirements

Scopus API usage

License

This dataset contains links to ThermoML files, which represent experimental thermophysical and thermochemical property data reported in the corresponding articles published by major journals in the field. These files are posted here through cooperation between the Thermodynamics Research Center (TRC) at the National Institute of Standards and Technology (NIST) and Elsevier. The ThermoML files corresponding to articles in the journals are available here with permission of the journal publishers.

Background This bibliometric analysis examines the top 50 most-cited articles on COVID-19 complications, offering insights into the multifaceted impact of the virus. Since its emergence in Wuhan in December 2019, COVID-19 has evolved into a global health crisis, with over 770 million confirmed cases and 6.9 million deaths as of September 2023. Initially recognized as a respiratory illness causing pneumonia and ARDS, its diverse complications extend to cardiovascular, gastrointestinal, renal, hematological, neurological, endocrinological, ophthalmological, hepatobiliary, and dermatological systems. Methods Identifying the top 50 articles from a pool of 5940 in Scopus, the analysis spans November 2019 to July 2021, employing terms related to COVID-19 and complications. Rigorous review criteria excluded non-relevant studies, basic science research, and animal models. The authors independently reviewed articles, considering factors like title, citations, publication year, journal, impact factor, authors, study details, and patient demographics. Results The focus is primarily on 2020 publications (96%), with all articles being open-access. Leading journals include The Lancet, NEJM, and JAMA, with prominent contributions from Internal Medicine (46.9%) and Pulmonary Medicine (14.5%). China played a major role (34.9%), followed by France and Belgium. Clinical features were the primary study topic (68%), often utilizing retrospective designs (24%). Among 22,477 patients analyzed, 54.8% were male, with the most common age group being 26–65 years (63.2%). Complications affected 13.9% of patients, with a recovery rate of 57.8%. Conclusion Analyzing these top-cited articles offers clinicians and researchers a comprehensive, timely understanding of influential COVID-19 literature. This approach uncovers attributes contributing to high citations and provides authors with valuable insights for crafting impactful research. As a strategic tool, this analysis facilitates staying updated and making meaningful contributions to the dynamic field of COVID-19 research. Methods A bibliometric analysis of the most cited articles about COVID-19 complications was conducted in July 2021 using all journals indexed in Elsevier’s Scopus and Thomas Reuter’s Web of Science from November 1, 2019 to July 1, 2021. All journals were selected for inclusion regardless of country of origin, language, medical speciality, or electronic availability of articles or abstracts. The terms were combined as follows: (“COVID-19” OR “COVID19” OR “SARS-COV-2” OR “SARSCOV2” OR “SARS 2” OR “Novel coronavirus” OR “2019-nCov” OR “Coronavirus”) AND (“Complication” OR “Long Term Complication” OR “Post-Intensive Care Syndrome” OR “Venous Thromboembolism” OR “Acute Kidney Injury” OR “Acute Liver Injury” OR “Post COVID-19 Syndrome” OR “Acute Cardiac Injury” OR “Cardiac Arrest” OR “Stroke” OR “Embolism” OR “Septic Shock” OR “Disseminated Intravascular Coagulation” OR “Secondary Infection” OR “Blood Clots” OR “Cytokine Release Syndrome” OR “Paediatric Inflammatory Multisystem Syndrome” OR “Vaccine Induced Thrombosis with Thrombocytopenia Syndrome” OR “Aspergillosis” OR “Mucormycosis” OR “Autoimmune Thrombocytopenia Anaemia” OR “Immune Thrombocytopenia” OR “Subacute Thyroiditis” OR “Acute Respiratory Failure” OR “Acute Respiratory Distress Syndrome” OR “Pneumonia” OR “Subcutaneous Emphysema” OR “Pneumothorax” OR “Pneumomediastinum” OR “Encephalopathy” OR “Pancreatitis” OR “Chronic Fatigue” OR “Rhabdomyolysis” OR “Neurologic Complication” OR “Cardiovascular Complications” OR “Psychiatric Complication” OR “Respiratory Complication” OR “Cardiac Complication” OR “Vascular Complication” OR “Renal Complication” OR “Gastrointestinal Complication” OR “Haematological Complication” OR “Hepatobiliary Complication” OR “Musculoskeletal Complication” OR “Genitourinary Complication” OR “Otorhinolaryngology Complication” OR “Dermatological Complication” OR “Paediatric Complication” OR “Geriatric Complication” OR “Pregnancy Complication”) in the Title, Abstract or Keyword. A total of 5940 articles were accessed, of which the top 50 most cited articles about COVID-19 and Complications of COVID-19 were selected through Scopus. Each article was reviewed for its appropriateness for inclusion. The articles were independently reviewed by three researchers (JRP, MAM and TS) (Table 1). Differences in opinion with regard to article inclusion were resolved by consensus. The inclusion criteria specified articles that were focused on COVID-19 and Complications of COVID-19. Articles were excluded if they did not relate to COVID-19 and or complications of COVID-19, Basic Science Research and studies using animal models or phantoms. Review articles, Viewpoints, Guidelines, Perspectives and Meta-analysis were also excluded from the top 50 most-cited articles (Table 1). The top 50 most-cited articles were compiled in a single database and the relevant data was extracted. The database included: Article Title, Scopus Citations, Year of Publication, Journal, Journal Impact Factor, Authors, Number of Authors, Department Affiliation, Number of Institutions, Country of Origin, Study Topic, Study Design, Sample Size, Open Access, Non-Original Articles, Patient/Participants Age, Gender, Symptoms, Signs, Co-morbidities, Complications, Imaging Modalities Used and outcome.

The RDA Data Discovery Paradigms IG (https://www.rd-alliance.org/groups/data-discovery-paradigms-ig) aims to provide a forum where representatives from across the spectrum of stakeholders and roles pertaining to data search can discuss issues related to improving data discovery. The goal is to identify concrete deliverables such as a registry of data search engines, common test datasets, usage metrics, and a collection of data search use cases and competency questions.

In order to identify the key requirements evident across data discovery use-cases from various scientific fields and domains, the Use Cases Task Force (https://www.rd-alliance.org/group/data-discovery-paradigms-ig/wiki/use-cases-prototyping-tools-and-test-collections-task-force) was initiated. Direct outcome of this task force is this collection of use cases outlining what users might wish to search for data and what supports they would expect a data repository should provide.

Our dataset are transcripts and codebooks for a focus group study. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. EPA cannot release CBI, or data protected by copyright, patent, or otherwise subject to trade secret restrictions. Request for access to CBI data may be directed to the dataset owner by an authorized person by contacting the party listed. It can be accessed through the following means: Contact Katie Williams, williams.kathleen@epa.gov. Format: The data are transcripts and protected by IRB approvals. This dataset is associated with the following publication: Eisenhauer, E., K. Williams, K. Margeson, S. Paczuski, K. Mulvaney, and M.C. Hano. Advancing translational research in environmental science: The role and impact of social science. Environmental Science & Policy. Elsevier Science Ltd, New York, NY, USA, 120: 165-172, (2021).

This dataset contains links to ThermoML files, which represent experimental thermophysical and thermochemical property data reported in the corresponding articles published by major journals in the field. These files are posted here through cooperation between the Thermodynamics Research Center (TRC) at the National Institute of Standards and Technology (NIST) and Elsevier. The ThermoML files corresponding to articles in the journals are available here with permission of the journal publishers.

The United Nations Sustainable Development Goals (SDGs) challenge the global community to build a world where no one is left behind.

Since 2018, Elsevier has generated SDG search queries to help researchers and institutions track and demonstrate progress toward the SDG targets. In the past 5 years, these queries, along with the university’s own data and evidence supporting progress and contributions to the particular SDG outside of research-based metrics, are used for the THE Impact Rankings.

For 2023, the SDGs use the exact same search query and ML algorithm as the Elsevier 2022 SDG mappings, with only minor modifications to five SDGs, namely SDG 1, 4, 5, 7 and 14. In these cases, the queries were shortened by removing exclusion lists based on journal identifiers. These exclusion lists often contained thousands of items to filter out content in journals that were not core to the SDGs.

To replicate the effect of these journal exclusions, sets of keywords were used to closely mimic the effects the journal exclusions had on the SDG content, while greatly reducing the overall query size and complexity. By following this approach, we were able to limit the changes to the publications in each SDG by less than 2 percent for most SDGs, while reducing the query size by 50 percent or more.

These shortened queries also have the added benefit of running faster in Scopus, allowing further analysis of the SDG data to be done more easily.

For each SDG, the full search query, along with further details about the top keyphrases, subfields, journals and keyphrases are available for download.

Description Data and figures for paper published in PLOS ONE:Larivière, V., Haustein, S. & Mongeon, P. (2015). The oligopoly of academic publishers in the digital era. PLoS ONE, 10(6), e0127502. doi:10.1371/journal.pone.0127502

Abstract. The consolidation of the scientific publishing industry has been the topic of much debate within and outside the scientific community, especially in relation to major publishers’ high profit margins. However, the share of scientific output published in the journals of these major publishers, as well as its evolution over time and across various disciplines, has not yet been analyzed. This paper provides such analysis, based on 45 million documents indexed in the Web of Science over the period 1973-2013. It shows that in both natural and medical sciences (NMS) and social sciences and humanities (SSH), Reed-Elsevier, Wiley-Blackwell, Springer, and Taylor & Francis increased their share of the published output, especially since the advent of the digital era (mid-1990s). Combined, the top five most prolific publishers account for more than 50% of all papers published in 2013. Disciplines of the social sciences have the highest level of concentration (70% of papers from the top five publishers), while the humanities have remained relatively independent (20% from top five publishers). NMS disciplines are in between, mainly because of the strength of their scientific societies, such as the ACS in chemistry or APS in physics. The paper also examines the migration of journals between small and big publishing houses and explores the effect of publisher change on citation impact. It concludes with a discussion on the economics of scholarly publishing.

Coliphage and adenovirus concentrations per liter. This dataset is associated with the following publication: Gassie, L., J. Englehardt, J. Wang, N. Brinkman, J. Garland, P. Gardinali, and T. Guo. Mineralizing urban net-zero water treatment: Phase II field results and design recommendations. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 105: 496-506, (2016).

Interview data from EPA researchers and partners involved with solutions-driven research pilots on nutrient management and wildland fire smoke. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Restricted access may be granted to authorized persons by contacting the party listed. Format: Confidential interview data contains identifiable information of interviewees, including identifiable experiences at or with EPA, position with EPA, and role within the projects studied. This dataset is associated with the following publication: Canfield, K.N., B. Hubbell, L. Rivers, B. Rodan, B. Hassett-Sipple, A. Rea, T. Gleason, A. Holder, C. Berg, C.D. Chatelain, S. Coefield, B. Schmidt, and B. McCaughey. Lessons learned and recommendations in conducting solutions-driven environmental and public health research. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 354(March 2024): 120270, (2024).

Description: Since their origin in the 17th century, publications in scientific journals have become the foundation of scholarly communication. Yet the publication process itself, duties and responsibilities of editors, and the preparation of manuscripts for submission have gone through many changes. The current drive towards study registration, sharing of protocols, manuscript pre-print, full transparency of reporting and the use of reporting guidelines, data sharing, and study replication, are seen as the future of scientific communication and methods of preventing scientific misconduct and undesirable research practices.

The goals of this project are:

1) Study the current state of publication ethics, research integrity- and transparency-related policies of scholarly Journals (by analysing instructions to authors from a representative sample of journals in the humanities, social, natural, and life sciences);

2) Study the trends and changes in publication ethics, research integrity- and transparency-related policies of scholarly Journals (by conducting a systematic review of all studies indexed in MEDLINE, Web of Science and Scopus that have analysed instructions to authors of journals);

3) Study editors’, authors’ and reviewer’ perceptions and attitudes towards topics related to transparent and responsible conduct of research (by conducting large scale surveys, focus group, web-chats and acceleration room sessions);

4) Make (evidence-based) recommendations of how publishers and journals may implement publication principles and foster the integrity and transparency of research (by summarizing the evidence of the first 3 steps of the project).

Team Members

Mario Malički Department of General Practice, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands

IJsbrand Jan Aalbersberg Elsevier, Amsterdam, The Netherlands

Lex Bouter Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands Department of Philosophy, Faculty of Humanities, Vrije Universiteit, Amsterdam, The Netherlands

Gerben ter Riet Department of General Practice, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands

Project collaborators: Ana Jerončić Department of Research in Biomedicine and Health, University of Split School of Medicine, Split, Croatia

Adrian Mulligan Elsevier, Amsterdam, The Netherlands

Funding This project is funded by Elsevier.

Dataset of the research paper: Machine Learning for Software Engineering: A Tertiary Study

Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009–2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions including: conducting further empirical validation and industrial studies on ML; reconsidering deficient SE methods; documenting and automating data collection and pipeline processes; reexamining how industrial practitioners distribute their proprietary data; and implementing incremental ML approaches.

The following data and source files are included.

review-protocol.md: The protocol employed in this tertiary study

data/

dl-search/

input/

acm_comput_surveys_overviews.bib: Surveys of ACM Computing Surveys journal

acm_comput_surveys_overviews_titles.txt: Titles of surveys

acm_comput_ml_surveys.bib: Machine learning (ML)-related surveys of ACM Computing Surveys journal

acm_comput_ml_surveys_titles.txt: Titles of ML-related surveys

dl_search_queries.txt: Search queries applied to IEEE Xplore, ACM Digital Library, and Elsevier Scopus

ml_keywords.txt: ML-related keywords extracted from ML-related survey titles and used in the search queries

se_keywords.txt: Software Engineering (SE)-related keywords derived from the 15 SWEBOK Knowledge Areas (KAs—except for Computing Foundations, Mathematical Foundations, and Engineering Foundations) and used in the search queries

secondary_studies_keywords.txt: Survey-related keywords composed of the 15 keywords introduced in the tertiary study on SLRs in SE by Kitchenham et al. (2010), and the survey titles, and used in the search queries

output/

acm/

acm{1–9}.bib: Search results from ACM Digital Library

ieee.csv: Search results from IEEE Xplore

scopus_analyze_year.csv: Yearly distribution of ML and SE documents extracted from Scopus's Analyze search results page

scopus.csv: Search results from Scopus

study-selection/

backward_snowballing.csv: Additional secondary studies found through the backward snowballing process

backward_snowballing_references.csv: References of quality-accepted secondary studies

cohen_kappa_agreement.csv: Inter-rater reliability of reviewers in study selection

dl_search_results.csv: Aggregated search results of all three digital libraries

forward_snowballing_reviewer_{1,2}.csv: Divided forward snowballing citations of quality-accepted studies assessed by reviewer 1 and 2, correspondingly, based on IC/EC

study_selection_reviewer_{1,2}.csv: Divided search results assessed by reviewer 1 and 2, correspondingly, based on IC/EC

quality-assessment/

dare_assessment.csv: Quality assessment (QA) of selected secondary studies based on the Database of Abstracts of Reviews of Effects (DARE) criteria by York University, Centre for Reviews and Dissemination

quality_accepted_studies.csv: Details of quality-accepted studies

studies_for_review.bib: Bibliography details and QA scores of selected secondary studies

data-extraction/

further_research.csv: Recommendations for further research of quality-accepted studies

further_research_general.csv: The complete list of associated studies for each general recommendation

knowledge_areas.csv: Classification of quality-accepted studies using the SWEBOK KAs and subareas

ml_techniques.csv: Classification of the quality-accepted studies based on a four-axis ML classification scheme, along with extracted ML techniques employed in the studies

primary_studies.csv: Details of reviewed primary studies by the quality-accepted secondary

research_methods.csv: Citations of the research methods employed by the quality-accepted studies

research_types_methods.csv: Research types and methods employed by the quality-accepted studies

src/

data-analysis.ipynb: Analysis of data extraction results (data preprocessing, top authors and institutions, study types, yearly distribution of publishers, QA scores, and SWEBOK KAs) and creation of all figures included in the study

scopus-year-analysis.ipynb: Yearly distribution of ML and SE publications retrieved from Elsevier Scopus

study-selection-preprocessing.ipynb: Processing of digital library search results to conduct the inter-rater reliability estimation and study selection process

This dataset contains one of the main outputs of a study of internal migration among researchers in Mexico inferred from the affiliation addresses of Scopus publications from 1996-2018. Scopus data is owned and maintained by Elsevier.This dataset is provided under a CC BY-NC-SA Creative Commons v 4.0 license (Attribution-NonCommercial-ShareAlike). This means that other individuals may remix, tweak, and build upon these data non-commercially, as long as they provide citations to this data repository (10.6084/m9.figshare.12619016) and the reference articles listed below, and license the new creations under the identical terms. For more details about the study, please refer toMiranda-González, Andrea, Samin Aref, Tom Theile, and Emilio Zagheni. "Scholarly migration within Mexico: Analyzing internal migration among researchers using Scopus longitudinal bibliometric data." EPJ Data Science (2020). https://doi.org/10.1140/epjds/s13688-020-00252-9The dataset is provided in a comma-separated values file (.csv file) and each row represents one movement of one researcher-active scholar from a state (source) to another state (target) in Mexico in a specific year (move_year). The data can be used to produce internal migration flows for the states or possibly other migration estimates. It can also be used as an edge-list for creating a network model of migration events between states (states being the nodes of the network and each movement being represented as a directed edge from source to target).A zip file of annual networks (directed and weighted) in gml format is also provided.

BackgroundPoor employee mental health and wellbeing are highly prevalent and costly. Time-related factors such as work intensification and perceptions of time poverty or pressure pose risks to employee health and wellbeing. While reviews suggest that there are positive associations between time management behavior and wellbeing, there is limited rigorous and systematic research examining the effectiveness of time management interventions on wellbeing in the workplace. A thorough review is needed to synthesize time management interventions and their effectiveness to promote employee mental health and wellbeing.MethodA systematic search will be conducted using the following databases: PsychINFO via OVID (1806-Present), Web of Science, Scopus via Elsevier (1976-Present), Academic Search Complete (EBSCO), Cochrane Library via Wiley (1992-Present), and MEDLINE via OVID (1946-Present). The review will include experimental and quasi-experimental studies that evaluate the effects of time management interventions on wellbeing outcomes on healthy adults in a workplace context. Only studies in English will be included. Two authors will independently perform the literature search, record screening, data extraction, and quality assessment of each study included in the systematic review and meta-analysis. Data will be critically appraised using the Cochrane risk-of-bias tools. Depending on the data, a meta-analysis or a narrative synthesis will be conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed in the development of this protocol. The protocol has been registered in PROSPERO (CRD4202125715).DiscussionThis review will provide systematic evidence on the effects of time management interventions on wellbeing outcomes in the workplace. It will contribute to our understanding of how time management approaches may help to address growing concerns for employee mental health and wellbeing.

The General Engineering Research Efficiency Platform market is poised for significant expansion, projected to reach approximately $10,420 million in value. This robust growth is fueled by an impressive Compound Annual Growth Rate (CAGR) of 8.9% over the forecast period of 2025-2033. The primary drivers of this upward trajectory include the increasing complexity of engineering projects, the growing demand for sophisticated simulation and analysis tools, and the continuous push for faster innovation cycles across various industries. The platform's ability to streamline research workflows, enhance collaboration among engineers, and provide advanced data analytics makes it an indispensable asset for organizations aiming to maintain a competitive edge. Moreover, the ongoing digital transformation initiatives within the engineering sector are further accelerating the adoption of these efficiency platforms. The market is segmented across enterprise sizes, with Large Enterprises representing a significant portion of current adoption, driven by their extensive R&D budgets and complex project needs. However, Medium and Small Enterprises are increasingly recognizing the value proposition of these platforms, leading to their faster growth rates. Cloud-based solutions are dominating the market due to their scalability, flexibility, and cost-effectiveness, although on-premises solutions continue to hold a share, particularly for organizations with stringent data security requirements. Key players like MathWorks, Microsoft, IBM, Autodesk, and Siemens are at the forefront of innovation, offering comprehensive suites of tools and services that cater to a diverse range of engineering disciplines. Geographic expansion is also a notable trend, with North America and Europe currently leading, while the Asia Pacific region is expected to witness substantial growth due to its burgeoning manufacturing and technology sectors. This comprehensive report delves into the evolving landscape of the General Engineering Research Efficiency Platform (GEREP). Spanning a crucial Study Period from 2019 to 2033, with a Base Year of 2025 and a Forecast Period extending from 2025 to 2033, this analysis provides granular insights into market dynamics, strategic players, and future trajectories. We have meticulously analyzed the Historical Period of 2019-2024 to establish a robust foundation for our projections. The report aims to equip stakeholders with actionable intelligence to navigate and capitalize on the burgeoning opportunities within this vital technological domain.

EPA Draft Method C QPCR cycle threshold (Ct) measurements of standardized reference materials as described in D-EMMD-MEB-025-QAPP-01 and Journal article. This dataset is associated with the following publication: Sivaganesan, M., T. Aw, S. Briggs, E. Dreelin, A. Aslan, S. Dorevitch, A. Shrestha, N. Isaacs, J. Kinzelman, G. Kleinheinz, R. Noble, R. Rediske, B. Scull, S. Rosenberg, B. Weberman, T. Sivy, B. Southwell, S. Siefring, K. Oshima, and R. Haugland. Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 156: 456-464, (2019).

Experiments with faster dissemination of research began in the 1960s, and in the 1990s first preprint servers emerged and became widely used in Physical Sciences and Economics. Since 2010, more than 30 new preprint servers have emerged and the number of deposited preprints has grown exponentially, with numerous journals now supporting posting of preprints and accepting preprints as submissions for journal peer review and publication. Research on preprints is, however, still scarce.

The goals of this project are: 1) Study preprint policies, submission requirements and addressing of transparency in reporting and research integrity topics of all know preprint servers that allow deposit of preprints to researchers regardless of their institutional affiliation or funding.
2) Study comments deposited on preprint servers’ platforms and social media and their relation to peer review and information exchange. 3) Study differences between preprint version(s) and version of record. 4) Living review of manuscript changes

Team Members (by first name alphabetical order):

Ana Jerončić,1 Gerben ter Riet,2,3 IJsbrand Jan Aalbersberg,4 John P.A. Ioannidis,5-9 Joseph Costello,10 Juan Pablo Alperin,11,12 Lauren A. Maggio,10 Lex Bouter,13,14 Mario Malički,5 Steve Goodman5-7

1 Department of Research in Biomedicine and Health, University of Split School of Medicine, Split, Croatia 2 Urban Vitality Centre of Expertise, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands 3 Amsterdam UMC, University of Amsterdam, Department of Cardiology, Amsterdam, The Netherlands 4 Elsevier, Amsterdam, The Netherlands 5 Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA 6 Department of Medicine, Stanford University School of Medicine, Stanford, California, USA 7 Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA 8 Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA 9 Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, California, USA 10 Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA 11 Scholarly Communications Lab, Simon Fraser University, Vancouver, British Columbia, Canada 12 School of Publishing, Simon Fraser University, Vancouver, British Columbia, Canada 13 Department of Philosophy, Faculty of Humanities, Vrije Universiteit, Amsterdam, The Netherlands 14 Amsterdam UMC, Vrije Universiteit, Department of Epidemiology and Statistics, Amsterdam, The Netherlands