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93 results for “peer review”
MiRoR15-P1-Tools used to assess the quality of peer review reports: a methodological systematic review
<p>Database, data extraction form, R codes and protocol related to: Superchi C, González JA, Solà I, Cobo E, Hren D, Boutron I. <em>Tools used to assess the quality of peer review reports: a methodological systematic review</em>. BMC Med Res Methodol. 2019;19(48):1–14. DOI: <a href="https://doi.org/10.1186/s12874-019-0688-x">https://doi.org/10.1186/s12874-019-0688-x</a></p> <p> </p>
MiRoR15-P2-Development of ARCADIA: a tool for assessing the quality of peer-review reports in biomedical research
<p>Survey questionnaire, anonymised survey data, and codebook related to: Superchi C, Hren D, Blanco D, Rius R, Recchioni A, Boutron I, González JA. Development of ARCADIA: a tool for assessing the quality of peer-review reports in biomedical research. BMJ Open 2020;0:e035604. doi:10.1136/bmjopen-2019-035604</p>
Datasets and codes for the peer review article "Human and natural impacts on the U.S. freshwater salinization and alkalinization: A machine learning approach"
<p>Ongoing salinization and alkalinization in U.S. rivers have been attributed to inputs of road salt and effects of human-accelerated weathering in previous studies. Salinization poses a severe threat to human and ecosystem health, while human derived alkalinization implies increasing uncertainty in the dynamics of terrestrial sequestration of atmospheric carbon dioxide. A mechanistic understanding of whether and how human activities accelerate weathering and contribute to the geochemical changes in U.S. rivers is lacking. To address this uncertainty, we compiled dissolved sodium (salinity proxy) and alkalinity values along with 32 watershed properties ranging from hydrology, climate, geomorphology, geology, soil chemistry, land use, and land cover for 226 river monitoring sites across the coterminous U.S. Using these data, we built two machine-learning models to predict monthly-aggregated sodium and alkalinity fluxes at these sites. The sodium-prediction model detected human activities (represented by population density and impervious surface area) as major contributors to the salinity of U.S. rivers. In contrast, the alkalinity-prediction model identified natural processes as predominantly contributing to variation in riverine alkalinity flux, including runoff, carbonate sediment or siliciclastic sediment, soil pH and soil moisture. Unlike prior studies, our analysis suggests that the alkalinization in U.S. rivers is largely governed by local climatic and hydrogeological conditions.</p>
Review of definitions of Open Peer Review in the scholarly literature 2016
<p>This data set contains:</p> <ul> <li>Full data files of a 2016 review of definitions of Open Peer Review in the scholarly literature in xls and csv formats</li> <li>Description of data collection methodology (txt)</li> <li>Readme file (txt)</li> </ul> <p>The term “open peer review” has neither a standardized definition nor an agreed schema of its features and implementations. Recognising the absence of a consensus view on what OPR is, OpenAIRE has undertaken a systematic review of definitions of “open peer review” or “open review”, to create a corpus of 122 definitions. These definitions have been systematically analysed to build a coherent typology of the many different innovations in peer review signified by the term and hence provide the precise technical definition currently lacking. This quantifiable data offers rich information on the range and extent of differing definitions over time and by broad subject area.</p> <p>Contact:</p> <p>Tony Ross-Hellauer: http://orcid.org/0000-0003-4470-7027 / ross-hellauer@sub.uni-goettingen.de</p>
OpenAIRE Open Peer Review Survey 2016
<p>This data set contains:</p> <p>- Full data files of a 2016 survey of attitudes to Open Peer Review in xls and csv formats<br> - Survey questions (pdf)<br> - Readme file (txt)</p> <p>Between 8 September and 7 October 2016, OpenAIRE held a survey designed to aid the development of appropriate OPR approaches by providing evidence about the attitudes of authors, editors and reviewers towards OPR, their reservations and needs, as well as to gauging current levels of experience and reservation with different types of OPR. A supplementary aim was to collect feedback on a provisional definition of OPR as created during another strand of work. The survey aimed to aid the development of appropriate OPR approaches by providing evidence about the attitudes of authors, editors and reviewers towards OPR, their reservations and needs, as well as to gauge current levels of experience and reservations with different types of OPR. The survey was conducted via an openly accessible online questionnaire (using the scientific survey platform SoSci, www.soscisurvey.de). It received a total of 3062 complete responses (a further 635 responses were discarded as incomplete). The survey was open to all wishing to take part and distributed via social media, scholarly communications mailing lists, publisher newsletters and, in one case, a publisher internal mailing list (Copernicus Publications). </p> <p>Acknowledgement: This work is funded by the European Commission H2020 project OpenAIRE2020 (Grant agreement: 643410, Call: H2020-EINFRA-2014-1)</p> <p>Contact: Dr Tony Ross-Hellauer, University of Göttingen, State and University Library, ross-hellauer@sub.uni-goettingen.de<br> </p>
OpenUP survey on researchers' current perceptions and practices in peer review, impact measurement and dissemination of research results
<p>OpenUP project (http://openup-h2020.eu/) conducted a survey to capture current perceptions and practices in peer review, dissemination of research results and impact measurement among European researchers. The survey was coducted between 20 January and 23 February 2017. It consisted of four sections. The first section asked a series of questions on the respondents’ scientific discipline, career stage, gender and other characteristics. The following sections asked a series of questions on peer review practices, dissemination of research results and impact measurement/use of altmetrics. The questionnaire was collaboratively prepared by the OpenUP consortium. </p> <p>The survey was implemented via surveygizmo tool (https://www.surveygizmo.com/). Invitations to participate were sent to a random sample of researchers from arXiv, Pubmed and RePEc. The OpenUP team mined researchers’ contact details from these platforms. The OpenUP project team made efforts to further boost the repondent sample for certain underrepresented areas through the DARIAH website, THESIS network, EURODOC, AIMS portal, the Parthenos community and other channels. The survey targeted researchers from the EU-28, Switzerland and Norway. The goal was to get around 1,000 responses. In total, there were 976 completed response and completion rate was 72.4%. </p> <p>The attached documents include the questionnaire and the dataset. In the dataset (cvs file) the top row contains numbered questions that correspond to the numberring in the questionnaire (word file). The data was exported as an excel file, anonymised by creating respondent IDs and IP data were deleted. The file was then converted to CSV.</p> <p> </p>
Circularity3 DDOMP - Project tracksheets: scholarly publications, non-peer-reviewed digital outputs, dataset log, and software log.
<p>Project tracking sheets for the Circularity3 project. </p> <p>The following tracking sheets are provided:</p> <ol> <li>Scholarly Publications </li> <li>Non-peer-reviewed digital outputs</li> <li>Dataset log </li> <li>Software log </li> </ol> <p>For further specifications and explanations of the tracking sheets refer to the Circularity3 DDOMP here: https://doi.org/10.5281/zenodo.11047951</p> <p> </p> <p>This tracking sheets reference widely the PARSEC research teams tracking sheets, to whom we are very grateful for their transparent and insightful documentation.</p> <p>Stall, Shelley, Specht, Alison, Corrêa, Pedro Luiz Pizzigatti, David, Romain, Edmunds, Rorie, Mabile, Laurence, Machicao, Jeaneth, Miyairi, Nobuko, Murayama, Yasuhiro, O'Brien, Margaret, Wyborn, Lesley, & Vellenich, Danton Ferreira. (2023). PARSEC Data and Digital Output Management Plan and Workbook. Zenodo. <a href="https://doi.org/10.5281/zenodo.3891426">https://doi.org/10.5281/zenodo.3891426</a></p>
2023 Utrecht University Open Access Monitor (peer reviewed journal articles)
<p>Results of the OA monitor of Utrecht University (UU) and University Medical Center Utrecht(UMCU) for the year 2023. It lists the open access availability of all peer reviewed journal articles registered in the CRIS (Pure) of Utrecht University and/or University Medical Center Utrecht. </p>
Data Potential Bias in Peer Review of Grant Applications at the Swiss National Science Foundation
<p>Potential biases in the peer review of grant applications at the Swiss National Science Foundation.</p> <p> </p>
Data archive for the peer-reviewed journal article "Links between atmospheric aerosols and sea state in the Arctic Ocean"
<p>This dataset accompanies the peer-reviewed journal article titled "Links between atmospheric aerosols and sea state in the Arctic Ocean" which was accepted for publication in the Journal of Atmospheric Environment in September 2024, https://doi.org/10.1016/j.atmosenv.2024.120844. </p> <p>This dataset contains information on sea surface properties, meteorology, and aerosol data from measurements conducted during the Arctic Century Expedition which was carried out in August and September of 2021 in the Russian Arctic region. The dataset contains the following information:</p> <p><br>1) aerosol_size_distributions.csv: The hourly averaged time-series of aerosol size distribution measurements from an aerodynamic particle sizer. Further information for this data file is provided in Meta_data_for_aerosol_size_distributions.txt.</p> <p><br>2) aerosol_composition_and_volume.csv: Time series of mass concentrations of Na+Mg (SSA proxy) and Al+Si+Ca (dust proxy) in aerosol particles collected on filters. The time-series also contains aerosol volume concentration information for the coarse and fine aerosol categories, i.e., samples with count median diameters larger than 0.99 µm and smaller than 0.99 µm, respectively. Further information for this data file is provided in Meta_data_for_aerosol_composition_and_volume.txt. </p> <p><br>3) sea_surface_elevation_time_series.pkl: a pickle file containing the sea surface elevation time-series. The sea surface elevation data was extracted from 3D-reconstructed sea surface data. The 3D reconstruction of the sea surface was achieved by processing stereoscopic images of the sea surface using the Waves Acquisition Stereo System (WASS) software (Bergamasco et al., 2017). Further information for this data file is provided in Metadata_for_sea_surface_elevation_time_series.txt.</p> <p><br>4) aerosol_meteo_wave_merged_data.csv: This file contains the time-series of merged hourly averages of aerosol number concentrations, meteorological data, environmental data, and sea surface properties. The dataset also contains the average coordinate of the research vessel and its distance to land masses throughout the expedition. The meteorological data were measured during the expedition and the original unmerged data are available in Thurnherr et al. (2024). Other environmental data, such as sea surface temperature, are obtained from the fifth generation ECMWF reanalysis for the global climate and weather (ERA5, Hersbach et al., 2023), and sea ice concentration was obtained from AMSR-2 daily satellite measurements (Copernicus Climate Change Service (C3S), 2020). Sea surface properties are extracted from time series of sea surface elevation. Further information for this data file is provided in Metadata_for_aerosol_meteo_wave_merged_data.txt.</p>
Metrics and peer review agreement at the institutional level - Data
<p>This data is released to accompany the paper:</p> <p>Traag, VA, Malgarini, M and Sarlo, S (2020) Metrics and peer review agreement at the institutional level.</p>
Factors affecting altmetrics attention to scholarly publication in peer-reviewed journals published in Iran and Turkey
<p>The goal of this study was to trace the altmetric measures of peer-reviewed journals in two non-English speaking countries na,ely Iran and Turkey, in order to understand their correlation with some website structure and design determinants, as well as the subject and the full-text language of the journals.</p> <p> </p>
Data archive for the peer-reviewed journal article "Variability in the mass absorption cross-section of black carbon (BC) aerosols is driven by BC internal mixing state at a central European background site (Melpitz, Germany) in winter""
<p>Data archive for figures accompanying the peer-reviewed journal article "Variability in the mass absorption cross-section of black carbon (BC) aerosols is driven by BC internal mixing state at a central European background site (Melpitz, Germany) in winter". In 2020 this article was accepted for publication in the journal <em>Atmospheric Chemistry and Physics</em>. Data are uploaded in the form of Igor Pro experiment files (.pxp).</p>
Argument Mining Driven Analysis of Peer-Reviews Dataset
<p>Argument Mining in Scientific Reviews (AMSR)</p> <p>We release a new dataset of peer-reviews from different computer science conferences with annotated arguments, called AMSR (<strong>A</strong>rgument <strong>M</strong>ining in <strong>S</strong>cientific <strong>R</strong>eviews).<br> <br> The dataset has been crawled by the OpenReview platform (https://openreview.net/) and the OpenReviewCrawler (https://openreview-py.readthedocs.io/en/latest/getting data.html)<br> <br> From 12,135 collected papers and reviews, we sample 77 for the annotation.<br> We use a simple argumentation scheme, which distinguishes between non-arguments, supporting arguments, and attacking arguments, which we denote as NON/PRO/CON accordingly.</p>
Proportion of Open Access Papers Published in Peer-Reviewed Journals in Austria 2008-2013
<p>In 2014, Archambault et al. published a report which provided data about Open Access publishing for the years 2008-2013 for all countries of the European Research Area (ERA) as well as for Brazil, Canada, Japan and the USA. They differentiated not only by disciplines but also by three OA categories: Golden, Green and a residual category. For the dataset, the data for Austria and the whole area examined has been extracted and modified. The results show above-average rates for Austria in almost all disciplines.</p>
Scientific names used for the South American sea lion in peer-review papers (2010-2020)
<p>The valid specific name of the South American sea lion was controversial for many years since <em>Otaria flavescens</em> (Shaw, 1800) and <em>Otaria byronia</em> (de Blainville, 1820) were the disputing designations. The present database summarizes the use of both names in peer-reviewed papers obtain in Google Scholar for the period 2010-2020.</p>
Data archive for the peer-reviewed journal article "Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data"
<p>Data archive accompanying the peer-reviewed journal article "Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data". This article was accepted for publication in the journal <em>Atmospheric Measurement Techniques</em> in 2022. The original contributions presented in the study are included in the article and its supplementary information. The GRASP-OPEN model was used to perform forward calculations: this model is publicly available on the official GRASP website (https://www.grasp-open.com/; last access: 14 September, 2022). The specific GRASP-OPEN model outputs that were used for the study are contained in this data archive. </p>
Guidelines for Open Peer Review Implementation
<p>Background data and documents for submitted article "<strong>Guidelines for open peer review implementation</strong>", created as a result of a workshop organised by the OpenUP project 27th March 2018 in London, UK.</p> <p>Article abstract: Open peer review (OPR) is moving into the mainstream, but it is often poorly understood and surveys of researcher attitudes show important barriers to implementation. As more journals move to implement and experiment with the myriad of innovations covered by this term, there is a clear need for best practice guidelines to guide implementation. This brief article aims to address this knowledge gap, reporting work based on an interactive stakeholder workshop to create best-practice guidelines for editors and journals who wish to transition to OPR. Although the advice is aimed mainly at editors and publishers of scientific journals, since this is the area in which OPR is at its most mature, many of the principles may also be applicable for the implementation of OPR in other areas (e.g., books, conference submissions).</p> <p>Documents contained here:</p> <p>- Pre-workshop informal data-gathering</p> <p>- Workshop agenda and attendee list</p> <p>- Minutes from the Workshop session on implementation guidelines</p> <p>- Slides used to introduce the theme and begin discussion</p> <p> </p>
DeepThought DPR: Distributed peer review enhanced with natural language processing and machine learning - Dataset I
<p>This is the anonymized dataset obtained from the DPR Experiment run at ESO in Fall 2018. If this dataset is used both this DOI as well as the main paper need to be cited. </p>
Usage and Impact Vocabularies Peer Review Dataset
<p>This dataset reflects responses received via the Qualtrics data collection instrument provided to usage and impact vocabulary stakeholders to solicit peer review on a draft version of the glossary and crosswalk spreadsheet developed for the "EAGER: Secure Research Impact Metric Data Exchange: Data Supply Chain" project funded by The National Science Foundation (Award # 2335827).</p>
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OpenNeuro
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