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41,236 results for “Review of reviews”
Minecraft Systematic Review Dataset
<p>The document is a structured spreadsheet containing data from a systematic literature review on the use of Minecraft in education. It includes various sheets detailing study metadata, such as author information, publication year, journal impact, research methodologies, educational contexts (formal, non-formal), learning theories applied, and in-game mechanics used (e.g., building, exploration, programming). The <em>syntax</em> sheet outlines search strategies in academic databases like Web of Science and Scopus, while <em>fields</em> explains variable terms.</p>
An introductory review of thermal structure of subduction zones: II & III
<p>This repository contains the output from a global suite of subduction zone models used in: "An introductory review of the thermal structure of subduction zones: II—numerical approach and validation", Wilson & van Keken, PEPS, 2023 and "An introductory review of the thermal structure of subduction zones: III—Comparison between models and observations", van Keken & Wilson, PEPS, 2023.</p> <p><strong>Directories & Files</strong></p> <p>The subdirectories of `vankeken_wilson_peps_2023.zip` are:</p> <ul> <li> <p><span>`</span><span>D80</span><span>`</span><span>: a reproduction of the "D80" Syracuse et al., PEPI, 2010 suite of subduction zones with a "dynamic" slab (Stokes equations solved </span><span>in the slab portion of the domain as in van Keken & Wilson, PEPS, 2023 and </span><span>`</span><span>D80new</span><span>`</span><span> below) and performed using Sepran</span></p> </li> <li> <p>`D80new`: a new global suite based on Syracuse et al., PEPI, 2010 but using a lower mantle potential temperature and an error function boundary condition and performed using both Sepran and TerraFERMA. Input files provided for TerraFERMA.</p> </li> <li> <p>`benchmark`: a new subduction benchmark performed using both Sepran and TerraFERMA. Input files provided for TerraFERMA.</p> </li> <li>`Figure_III_4_models`: the results used in Figure 4, Part III.</li> </ul> <p>Files include:</p> <ul> <li>`Table2_Syracuse2010_updated.pdf` and `Table2_Syracuse2010_updated.xlsx`: updated parameter values for the Syracuse et al., PEPI, 2010 global suite.</li> <li>`Table3_part_Syracuse2010_updated.pdf` and `Table3_part_Syracuse2010_updated.xlsx`: updated parameter values for the Syracuse et al., PEPI, 2010 global suite.</li> <li>`Figure_comp_D80_WW09_all.pdf`: a comparison between the Sepran models presented in `D80` and `D80new` and those in Wada & Wang, G-cubed, 2009.</li> <li>`Figure_comp_D80new_TF-Sepran_all.pdf`: comparison between the TerraFERMA and Sepran models presented in `D80new`.</li> <li>`Figure_III_6_all_D80.pdf`: complete set of model results for Figure 6, Part III.</li> <li>`Subduction_parameters.pdf` and `Subduction_parameters.json`: a description of the parameters used in the global suite.</li> <li>`PEPS_part_II_preprint.pdf`: preprint of part II.</li> <li>`PEPS_part_III_preprint.pdf`: preprint of part III.</li> <li>`vankeken_wilson_peps_2023_TF_lowres_minimal.zip`: contains a set of low resolution vtu files from TerraFERMA with the final potential temperature output from steady state and time dependent simulations. Intended for fast downloading (compared to `vankeken_wilson_peps_2023.zip`) and comparison.</li> </ul> <p><strong>Running a simulation</strong></p> <p>Input files are provided for the TerraFERMA (the Transparent Finite Element Rapid Model Assembler) simulations in this repository (`TF` subdirectories). Running them requires a working installation of TerraFERMA. If one is not available then consider using the docker image provided for this paper at:</p> <p>https://github.com/users/cianwilson/packages/container/package/vankeken_wilson_peps_2023</p> <p>This docker image contains a complete installation of TerraFERMA and its dependencies, PETSc, FEniCS and SPuD, within an Ubuntu 20.04LTS OS. For a full description of TerraFERMA please refer to the webpage:</p> <p>http://terraferma.github.io</p> <p> </p>
Data Extracted for the Systematic Literature Review on Non-profit Open data Intermediaries and their effects on Open data Usability Barriers
<p>The dataset contains the data extracted from the literature for the Systematic Literature Review and is referenced or used in the extended abstract titled "How do Non-profit Open data Intermediaries enhance Open data Usability? A Systematic Literature Review", submitted to the 18th International Symposium on Open Collaboration (Companion), September 6–10, 2022, Madrid, Spain. <a href="https://doi.org/10.1145/3555051.3555061" target="_blank" rel="noopener">https://doi.org/10.1145/3555051.3555061</a> </p>
Analysis materials for "Defininig a Knowledge Graph Development Process through a Systematic Review"
<p>This depository stores the analysis materials for the article "Defining a Knowledge Graph Development Process through a Systematic Review". It includes:</p> <ul> <li><strong>Analysis of KG development process - Articles.csv </strong>- a table of summary of the articles included in the systematic review.</li> <li><strong>Analysis of KG development process - Tasks by level (count).csv</strong> - a table of counting the frequency of the tasks in the knowledge graph development process.</li> <li><strong>Analysis of KG development process - Tasks by level count (synonyms) (1).csv </strong>- a table of counting the frequency of the tasks in the knowledge graph development process after its been adjusted to synonyms.</li> <li><strong>Knowledge graph development processes - </strong>a folder of process figures from the articles that have been included in the systematic review.</li> </ul>
Supplementary Table for Earth observation data-driven cropland soil monitoring: A review
<p>Table including 46 manuscripts written in English referring to topsoil monitoring related to Earth observation data-driven cropland soil monitoring: A review paper.</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>
Database Search Results for Resource Management in Converged Optical and MillimeterWave Radio Networks Review
<p><strong>Paper Selection Procedure</strong></p> <p>In order to conduct the survey titled "Resource Management in Converged Optical and MillimeterWave Radio Networks: A Review", the authors reviewed works published in the literature with a focus on those that cover most of the identified optimization requirements for converged optical fronthaul and mmWave wireless access networks. The research method is based on the research steps given in "The PRISMA 2020 statement"[1]. The selection procedure is also illustrated in "Database Search Flow Chart.png" figure.</p> <p>The first step was the selection of the papers. We completed this step by making database searches in the ACM, Elsevier (Science Direct), IEEE, IET, MDPI, Optical Society (OSA), Springer, Taylor & Francis, and Wiley online library databases with keywords ``resource allocation AND converged mmWave fiber wireless (FiWi)'', ``resource management AND converged mmWave fiber wireless (FiWi)'', and ``resource allocation AND converged fiber wireless (FiWi)''. The searches in all databases were completed in May 2021. The resulting collection was screened, to exclude non-scientific texts, book chapters, out of context papers, and survey papers. The remaining 189 papers found in our database search are provided in the excel file titled "FiWi Resource Allocation Database Search.xlsx".</p> <p>Among these papers, our selection criteria was created to present the works that are most relevant to the target network architecture, providing novel implementation solutions to the requirements of the optimization objective. The criteria selected for our eligibility step can be summarized as follows:</p> <ul> <li>The study provided a sound research approach and published after a scholarly review process;</li> <li>The study had a resource management optimization objective for mmWave networks;</li> <li>The study explained the system model and proposed a well-defined optimization algorithm;</li> <li>The effects of the algorithm on a performance metric was reported and the different aspects of the performance metric was analyzed with different evaluation criteria.</li> </ul> <p>This review is limited to the focus scope on converged optical and mmWave radio network solutions and by the databases taken into consideration. The prioritization of the works that address a well-defined optimization algorithm led to the omission of relevant papers. We did not include works that do not clearly define a resource management objective, i.e., a study that focuses on the the hardware implementation aspects of optical and mmWave radio networks with no resource management perspective. We manually excluded all studies that do not match these criteria with a simple scoring system, in which a point is deducted from an eligible paper for each missing criterion. The initial screening process and the data collection steps were carried out by the first author and the final inclusion decision was made by all the reviewers for the studies with the highest scores. After this screening process, we identified 37 papers that focused on at least one of the resource management objectives of throughput maximization, delay minimization, energy-efficiency, and virtualized resource allocation. The papers that have joint objectives are classified under their main optimization focus of that paper. The list of the selected papers are provided in "FiWi Resource Allocation Papers Selected for Review.xlsx" file. Our target in this review is to understand the recent optimization techniques used in resource allocation for converged optical fronthaul and radio mmWave access network implementations, therefore we focused our search to the works completed in the last five years (between 2016 and 2021), and approximately 95% of the selected papers fit under this category.</p> <p><strong>Overview of the data collected from selected papers</strong></p> <p>In this section, we provide answers to the three following questions with the data collected from the eligible studies:</p> <ul> <li>Question 1: Which algorithms are used more often in performance optimization in converged mmWave networks?</li> <li>Question 2: Which performance metrics are determined to show that the optimization method achieves the objective?</li> <li>Question 3: Which criteria are used to evaluate the solution method?</li> </ul> <p>Regarding the first question, the figure titled "Distribution of Optimization Algorithms in Selected Papers" shows the distribution of the optimization algorithms used by the selected papers. The distribution of the main performance metrics according to the resource optimization objectives is given in Table 1 (Distribution of Evaluation Criteria) and the evaluation criteria to test the performances of the selected papers are grouped in Table 2 (Distribution of Main Performance Metrics Depending on Optimization Objectives), which shows how many times each criterion is used together with how many of the resource management objectives use these criterion.</p> <p><strong>References: </strong></p> <p>[1] Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.;Brennan, S.E.; Chou, R.; Glanville, J.; Grimshaw, J.M.; Hróbjartsson, A.; Lalu, M.M.; Li, T.; Loder, E.W.; Mayo-Wilson, E.;McDonald, S.; McGuinness, L.A.; Stewart, L.A.; Thomas, J.; Tricco, A.C.; Welch, V.A.; Whiting, P.; Moher, D. The PRISMA 2020statement: an updated guideline for reporting systematic reviews.Systematic Reviews2021,10. doi:10.1186/s13643-021-01626-4.</p>
Selected articles from the scoping review of biomarker discovery studies for the EU project on "Personalised Medicine Trials" (PERMIT)
<p>This dataset provides the data extracted for the scoping review of the literature on biomarker discovery studies for patient stratification using machine learning analysis of omics data, as part of the EU project on “Personalised Medicine Trials” (PERMIT). It covers the references for all articles selected as part of the scoping review, as well as information on the study type and methodology, the outcome measures, the validation type, and representative sentences extracted from each article on the main results and key findings of the corresponding biomarker study.</p>
Urinary biomarkers for the non-invasive diagnosis of endometriosis: a systematic literature review
<p>Raw dataset from a systematic literature review performed until August 2021 in which the research question was 'Are there urine biomarkers sensitive and specific enough for endometriosis detection?' and the Medical Subject Heading (MeSH) terms were (endometrios*) AND (urin*). </p>
Data from: Metacommunity theory review and its application in community assembly of soil animals
<p>We were interested in community assembly of soil animals and performed a literature study to find out what is known about soil metacommunities. We aimed to study keywords co-occurrence relationships of scientific journal articles in the field of "metacommunity" research from 1992-2017 as a whole. We also investigated the co-occurrence of the top 20 most frequent keywords in five 5-year time periods.</p> <p> </p> <p> </p> <p>In September 2017 we searched the Web of Science with ‘metacommunity’ as the only keyword, and found 1226 English papers published in international journals between January 1992 and September 2017. And we exported these papers from Web of Science as a plain text file (.txt) with the option ‘Full Record and Cited References’, which is archived here. The text file thus contains full records and cited references (in a concise format, so without the titles of the cited papers), and each field is prefaced by a two-character field tag. Afterwards we used the software ‘Citespace’ to create Table 1 and Figure 2 in Guo et al. (2018). We created that Figure 2 using the following settings in Citespace: we choose the “co-occurrence” function and “keyword” as node types, then created keywords co-occurrence relationships (Figure 2). Next, we created lists of the top 20 keywords in different periods, which can reveal the research hotspots (Table 1). The method for extracting the top 20 most frequent keywords was as follows: We use the Web of Science database to retrieve scientific papers from 1992 to 2017, taking into account the relationship between citation and publication time. We calculated the percentage of 200 most cited papers published in each of the 5-year periods. To do so we followed the following steps:</p> <p>Step1: we selected the 200 most cited papers from the entire datset (1226 in the 1992-2017 period).</p> <p>Step 2: we calculated the percentage of those 200 papers published each 5-year period.</p> <p>Step 3: we also calculated an correction coefficient by taking, for each 5-year period, the number of top-200 most cited papers published in that period, and dividing that number by the total number of papers (out of the 1226 selected papers) published in that period.</p> <p>Step 4 Last, we calculate the real keyword frequency by multiplying the keyword frequency in specific 5-year periods with the correction coefficient.</p>
Dataset underpinning "Understanding health behaviours in context: A systematic review and meta-analysis of Ecological Momentary Assessment studies of five key health behaviours"
<p>This is the dataset underpinning the article "Understanding health behaviours in context: A systematic review and meta-analysis of Ecological Momentary Assessment studies of five key health behaviours": https://pubmed.ncbi.nlm.nih.gov/35975950/</p>
Fig. 5 in The Smicronychini of southern Africa (Coleoptera, Curculionidae): Review of the tribe and description of 12 new species
Fig. 5 (opposite page). Penis of Smicronyx from southern Africa, in dorsal (left) and lateral (right) view. A. Sharpia madibai sp. nov., ♂, holotype (SAMC). B. Afrosmicronyx cycnii sp. nov., ♂, holotype (SAMC). C. Afrosmicronyx louwi sp. nov., ♂, holotype (SAMC). D. Afrosmicronyx marshalli sp. nov., ♂, holotype (SANC). E. Afrosmicronyx nebulosipennis sp. nov., ♂, holotype (BMNH). F. Smicronyx similis sp. nov., ♂, holotype (SANC). G. Smicronyx pseudocoecus sp. nov., ♂, holotype (SAMC). H. Smicronyx paucisquamis sp. nov., ♂, holotype (SAMC). I. Smicronyx fallax (Gyllenhal, 1836), ♂, neotype (NHRS). J. Smicronyx australis sp. nov., ♂, holotype (SAMC). K. Smicronyx pauperculus Wollaston, 1864, ♂, specimen from Tanzania. L. Smicronyx san sp. nov., ♂, holotype (SAMC), bred from Chironia baccifera L. M. Smicronyx drakensbergensis sp. nov., ♂, holotype (TMSA). N. Smicronyx zonatus Haran, 2018, ♂, paratype (CBGP), specimen from the Western Cape Province of the Republic of South Africa. O. Smicronyx lutulentus Dietz, 1894, ♂ (CBGP). P. Smicronyx namibicus Haran, 2018, ♂, holotype (MNHN), specimen from Tanzania. Scale bars: 100 μm.
Fig. 3 in The Smicronychini of southern Africa (Coleoptera, Curculionidae): Review of the tribe and description of 12 new species
Fig. 3. Head and prothorax in lateral view of species of Smicronyx from southern Africa (Part 1). A. Sharpia madibai sp. nov., ♂, holotype (SAMC). B. Afrosmicronyx cycnii sp. nov., ♂, holotype (SAMC). C. Afrosmicronyx louwi sp. nov., ♂, holotype (SAMC). D. Afrosmicronyx marshalli sp. nov., ♂, holotype (SANC). E. Afrosmicronyx nebulosipennis, sp. nov., ♂, holotype (BMNH). F. Smicronyx gracilipes sp. nov., ♀, holotype (SAMC). G. Smicronyx similis sp. nov., ♂, holotype (SANC). H. Smicronyx pseudocoecus sp. nov., ♂, holotype (SAMC). Scale bars: 0.5 mm.
Fig. 6 in The Smicronychini of southern Africa (Coleoptera, Curculionidae): Review of the tribe and description of 12 new species
Fig. 6. Habitus in natura, host plants and habitats of Smicronyx of southern Africa. A. S. fallax (Gyllenhal, 1863) on Cuscuta campestris Yunck, 1932. B. Cuscuta campestris. C. S. pseudocoecus sp. nov. on Cuscuta sp. D. Cuscuta nitida E. Mey ex Choisy, host of S. pseudocoecus sp. nov. and S. australis sp. nov. E. S. san sp. nov. on Chironia baccifera L. F. Sebaea Sol. ex R.Br. sp., host of S. san sp. nov. G. Orphium frutescens L. (E. Mey), host of S. san sp. nov. and surrounding fynbos vegetation. H. Chironia baccifera, host of S. san sp. nov. I. S. zonatus Haran, 2018, in copula. J. Orobanchaceae Vent., host of S. zonatus growing in a marshy environment at the base of Cyperaceae Juss.
Fig. 4 in The Smicronychini of southern Africa (Coleoptera, Curculionidae): Review of the tribe and description of 12 new species
Fig. 4. Head and prothorax in lateral view of species of Smicronyx from southern Africa (Part 2). A. Smicronyx paucisquamis sp. nov., ♂, holotype (SAMC). B. Smicronyx fallax (Gyllenhal, 1836), ♂, neotype (NHRS). C. Smicronyx australis sp. nov., ♂, holotype (SAMC). D. Smicronyx san sp. nov., ♂, bred from Chironia baccifera L., holotype (SAMC). E. Smicronyx drakensbergensis sp. nov., ♂, holotype (TMSA). F. Smicronyx lutulentus Dietz, 1894, ♂ (CBGP). Scale bars: 0.5 mm.
Fig. 1 in The Smicronychini of southern Africa (Coleoptera, Curculionidae): Review of the tribe and description of 12 new species
Fig. 1. Habitus of species of Smicronychini from southern Africa (Part 1). A. Sharpia madibai sp. nov., ♂, holotype (SAMC). B. Afrosmicronyx cycnii sp. nov., ♂, holotype (SAMC). C. Afrosmicronyx louwi sp. nov., ♂, holotype (SAMC). D. Afrosmicronyx marshalli sp. nov., ♂, holotype (SANC). E. Afrosmicronyx nebulosipennis sp. nov., ♂, holotype (BMNH). F. Smicronyx gracilipes sp. nov., ♀, holotype (SAMC). G. Smicronyx similis sp. nov., ♂, holotype (SANC). H. Smicronyx pseudocoecus sp. nov., ♂, holotype (SAMC). I. Smicronyx paucisquamis sp. nov., ♂, holotype (SAMC). J. Smicronyx fallax (Gyllenhal, 1836), ♂, neotype (NHRS). K. Smicronyx australis sp. nov., ♂, holotype (SAMC). L. Smicronyx pauperculus Wollaston, 1864, ♂, specimen from Tanzania. Scale bars: 1 mm.
Outdoor air pollution impacts chronic obstructive pulmonary disease deaths in South Asia and China: a systematic review and meta-analysis
<p><strong>Background: </strong>Chronic obstructive pulmonary disease (COPD) is among leading causes of death globally. Exposure to outdoor pollution is an important cause for increased mortality and morbidity. This study presents a systemic review regarding the impact of outdoor pollution on COPD mortality in South Asia and China.</p> <p><strong>Methods: </strong>A systematic search was conducted from 1990 to June 30<sup>th</sup> 2020 in English electronic databases: PubMed, Google Scholar and CDSR (Cochrane Database of Systematic Reviews) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The following terms were used: Chronic Obstructive Pulmonary disease OR COPD OR Chronic Bronchitis OR Emphysema OR COPD Deaths OR Chronic Obstructive Lung Disease OR Airflow Obstruction OR Chronic Airflow Obstruction OR Airflow Obstruction, Chronic OR Bronchitis, Chronic AND Mortality OR Death OR Deceased AND Outdoor pollution, ambient pollution was conducted.</p> <p><strong>Results:</strong> Out of 1899 papers screened only 17 were found eligible to be included. Subjects with COPD exposed to higher levels of outdoor air pollution had a 49% higher risk of death as compared to COPD subjects exposed to lower levels of outdoor air pollution. When taking common air pollutants individually into consideration, PM10 had an odds ratio (OR) of 1.99 respectively at CI 95%, whereas SO2 had OR of 1.8 at 95% CI, and NO2 had an OR of 1.23 OR at 95% CI. These values suggest that there is an effect of outdoor pollution on COPD but not to a significant level.</p> <p><strong>Conclusion: </strong>Despite heterogeneity across selected studies, individuals exposed to outdoor pollutants were found to be at risk of COPD mortality. Though it appears to have risk, COPD mortality was not significantly associated with outdoor pollutants. Controlling air pollution can substantially decrease the risk of COPD in South Asia and China. Further researches including more prospective and longitudinal studies are urgently needed in COPD sub-groups.</p>
Figs 12–15 in A Review Of Taiwanese Paramisolampidius (Coleoptera, Tenebrionidae: Cnodalonini)
Figs 12–15. Aedeagus of Paramisolampidius species, dorsal view: 12 = P. csorbai sp. n., 13 = P. formosanus MASUMOTO, 1981, 14 = P. shirozui (M. T. CHÛJÔ, 1967), 15 = P. tenghsiensis MASUMOTO, 1984
Figs 7–11 in A Review Of Taiwanese Paramisolampidius (Coleoptera, Tenebrionidae: Cnodalonini)
Figs 7–11. Head of Paramisolampidius species: 7 = P. csorbai sp. n., 8 = P. formosanus MASU- MOTO, 1981, 9 = P. kinugasai MASUMOTO, 1984, 10 = P. shirozui (M. T. CHÛJÔ, 1967), 11 = P. tenghsiensis MASUMOTO, 1984
Figs 2–6 in A Review Of Taiwanese Paramisolampidius (Coleoptera, Tenebrionidae: Cnodalonini)
Figs 2–6. Habitus of Paramisolampidius species: 2 = P. csorbai sp. n., 3 = P. formosanus MASUMOTO, 1981, 4 = P. kinugasai MASUMOTO, 1984, 5 = P. shirozui (M. T. CHÛJÔ, 1967), 6 = P. tenghsiensis MASUMOTO, 1984
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.