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670 results for “review study”
Replication Package for the Paper: "Code Smells Detection via Modern Code Review: A Study of the OpenStack and Qt Communities"
<p>This repository contains the data and results from the paper "Code Smells Detection via Modern Code Review: A Study of the OpenStack and Qt Communities" submitted to the ICPC 2021 special issue of the Empirical Software Engineering Journal, 2021.</p> <p> </p> <p>The replication package contains the following two folders:</p> <p> </p> <p><strong>1) data folder</strong></p> <p>The data folder contains the following four folders, which is organized by research questions (RQs).</p> <ul> <li>RQ1: The RQ1 folder contains the retrieved 1,539 code reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</li> <li>RQ2: The RQ2 folder contains the coded data for RQ2, called <em>Data Labeling & Encoding for RQ2.mx18</em>. It is the results of data labeling and encoding for RQ2, which was analyzed by the MAXQDA tool.</li> <li>RQ3 and RQ5: <ul> <li><em>Extracted data for RQ3.1.xlsx</em>: this file contains the extracted data (i.e., specific refactoring actions suggested by reviewers) for RQ3.1.</li> <li><em>Data Labeling & Encoding for RQ3 and RQ5.mx18</em>: this file contains the extracted data for RQ3 (excluding the specific refactoring actions in RQ3.1) and RQ5.</li> <li><em>Code change status for RQ5.xlsx</em>: this file contains the information of status of code changes where the developers disagreed with the reviewers and chose to ignore the identified code smells.</li> </ul> </li> <li>RQ4: The RQ4 folder contains the extracted data for RQ4, called <em>Extracted data for RQ4.xlsx</em>.</li> </ul> <p>Note: The mx18 files can be opened by MAXQDA 18 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download.</p> <p> </p> <p><strong>2) scripts folder</strong></p> <p>The scripts folder contains the Python scripts that were used to search for code smell terms and the list of code smell terms.</p> <ul> <li><em>keyword.txt</em> contains the keywords associated with code smells, such as "smell, duplication, and dead".</li> <li><em>get_changes.py</em> is used for getting code changes from OpenStack and Qt.</li> <li><em>get_comments.py</em> is used for getting review comments for each code change.</li> <li><em>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</li> <li><em>random_select.py</em> is used for randomly selecting review comments that do not contain any keyword.</li> <li><em>keywords_improve.py</em> is used for improving the keyword-based mining approach.</li> <li><em>tools.py</em> is used for supporting the process of keywords improving.</li> </ul>
MapOSR - A Mapping Review Dataset of Empirical Studies on Open Science
<p>Research that investigates respective researchers’ engagement in Open Science varies widely in the topics addressed, methods employed, and disciplines investigated, which makes it difficult to integrate and compare its results. To investigate current outcomes of Open Science research, and to get a better understanding on topicswell-researched and on research gaps we aim at providing an openly accessible overview of empirical studies that focus on different aspects of Open Science in different scientific disciplines, academic groups and geographical regions. The present data set of studies about Open Science practices was retrieved following a PRISM approach to compile a literature review. We include studies from the Scopus and Web of Science databases with keywords relating to Open Science between the years 2000 and 2020, as well as a snowball search for relevant articles. Studies that did not investigate any aspect of Open Science, or weren’t peer reviewed were excluded, resulting in a total of 695 remaining studies. The data set was collaboratively annotated to ensure intercoder reliability of the coded data.</p>
Reviewed Articles from the study "Running away to sea: a theoretical model of anthropogenic factors contribution for microplastics transportation from rivers to the ocean".
<p>Table containing the papers reviewed from the study "Running away to sea: a theoretical model of anthropogenic factors contribution for microplastics transportation from rivers to the ocean". </p>
Review of QTLs found in studies aimed at finding QTLs for bread wheat root traits (from 2005 to mid-2020)
<p>This list contains a number of articles that have been reviewed for QTLS for bread wheat root traits from 2005 to mid-2020 publication dates.</p>
Does mechanical loading restore ligament biomechanics after injury? A systematic review of studies using animal models
<p>This RevMan file accompanies our manuscript entitled "Does mechanical loading restore ligament biomechanics after injury? A systematic review of studies using animal models" published in <em>BMC Musculoskeletal Disorders</em>.</p> <p>The file hosts all outcomes (i.e., including tertiary outcomes) under "Data and analyses", as well as more detailed forest plots under "Figures". </p>
Figure 5 in Breeding biology review of White-backed Stilt Himantopus melanurus in Brazil and a case study in the largest restinga protected area (Aves, Charadriiformes, Recurvirostridae)
Figure 5. Footprints records of possible predators and cattle trampling near nests of White-backed Stilt Himantopus melanurus in Restinga de Jurubatiba National Park. (A) Footprints of domestic dogs and trampling of cattle in the Visgueiro lagoon (2018). (B) crab-eating fox (Cerdocyon thous) footprints, and (C) crab-eating raccoon (Procyon cancrivorus) footprints in adjacent area (2020). Photos: Lucas R.M. Porto.
Figure 4 in Breeding biology review of White-backed Stilt Himantopus melanurus in Brazil and a case study in the largest restinga protected area (Aves, Charadriiformes, Recurvirostridae)
Figure 4. Predated/degraded eggs of White-backed Stilt Himantopus melanurus in Restinga de Jurubatiba National Park in October 2018. A, B and C: Colony 1 (Visgueiro), D: Colony 2 (Maria Menina). Photos: Lucas R.M. Porto.
Figure 3 in Breeding biology review of White-backed Stilt Himantopus melanurus in Brazil and a case study in the largest restinga protected area (Aves, Charadriiformes, Recurvirostridae)
Figure 3. Frequency of occurrence of the materials used to build the nests of the White-backed Stilt Himantopus melanurus in the Restinga de Jurubatiba National Park and adjacent area.
Figure 2 in Breeding biology review of White-backed Stilt Himantopus melanurus in Brazil and a case study in the largest restinga protected area (Aves, Charadriiformes, Recurvirostridae)
Figure 2. Nests of White-backed Stilt Himantopus melanurus monitored in Restinga de Jurubatiba National Park and adjacent area. A = Nest built with saltmarsh plant Sesuvium portulacastrum L. and suspended over cattle feces; B = Nest with dry saltmarsh plant and mud fragments; C = Nest with shells, saltmarsh plant and mud; D = Nest with mud and dry saltmarsh plant fragments. Photos: Lucas R.M. Porto.
Figure 6 in Breeding biology review of White-backed Stilt Himantopus melanurus in Brazil and a case study in the largest restinga protected area (Aves, Charadriiformes, Recurvirostridae)
Figure 6. Successful nesting records of the White-backed Stilt Himantopus melanurus in Restinga de Jurubatiba National Park and adjacent area. (A) White-backed Stilt chicks found in Visgueiro lagoon (September 2018); (B) Hatchling and eggs in the Maria Menina Lagoon (October 2018); (C and D) Chicks in the nests in Ubatuba lagoon (September 2019). Photos: Lucas R.M. Porto.
Figure 1 in Breeding biology review of White-backed Stilt Himantopus melanurus in Brazil and a case study in the largest restinga protected area (Aves, Charadriiformes, Recurvirostridae)
Figure 1. Breeding records of White-backed Stilt Himantopus melanurus in Brazil (WikiAves – blue, eBird – orange and literature – yellow, Table 2) and this study area (red) with colonies identified in the Restinga de Jurubatiba National Park and adjacent area, in 2018, 2019 and 2020, in the northern coast of Rio de Janeiro state. *Colonies: 1 = Visgueiro/2018; 2 = Maria Menina/2018; 3 = Robalo/2018; 4 = Ubatuba/2019; 5 = Visgueiro/2020; 6 = Adjacent Area/2020.
Fig. 5 in A review of Sciurus Group studies on the red squirrel (Sciurus vulgaris): presence, population density and colour phases in Lombardy (Italy)
Fig. 5 - Orientation of the red squirrel dreys per study area.
Fig. 3 in A review of Sciurus Group studies on the red squirrel (Sciurus vulgaris): presence, population density and colour phases in Lombardy (Italy)
Fig. 3 - Study areas in Lombardy. Box: geographic position of Lombardy (black) in Italy.
The efficacy of hemoglobin spray in wound management: a systematic review and network meta-analysis of comparative studies
<p>Dataset</p>
Can Machine Learning Support the Selection of Studies for Systematic Literature Review Updates?
<p>Artifacts for "Can Machine Learning Support the Selection of Studies for Systematic Literature Review Updates?".</p> <p>File used to answer RQ1:</p> <ul> <li>RQ1-RF-predictions.csv</li> <li>RQ1-RQ3-best-configuration-RF.csv</li> </ul> <p>File used to answer RQ2:</p> <ul> <li>RQ2-SVM-predictions.csv</li> <li>RQ2-best-configuration-SVM.csv</li> </ul> <p>File used to answer RQ3:</p> <ul> <li>RQ3-RF-normalized-predictions.csv</li> <li>RQ1-RQ3-best-configuration-RF.csv</li> </ul> <p> </p> <p>The file assessment-team-votes.csv contains the title of each study, a bolean indicating if it was included or not and the individual marks of each reviewer before applying the agreement criteria.</p> <p> </p> <p>The .bib files used in our experiment are available at:</p> <ul> <li> <div>Our testing set: 'Testing set - Excluded.bib' (513 studies) and 'Testing set - Included.bib' (38 studies). All of the 551 studies we used, were obtained from the actual SLR Update</div> </li> <li>Our training set: 'Training set - Excluded.bib' (83 studies - obtained by performing the backward snowballing using the Original SLR) and 'Training set - Included.bib' (45 studies - all studies that were included in the Original SLR).</li> </ul> <p>All of our code is available in the .zip file. Besides our pipeline, there's also some jupyter notebooks in code/analysis showing illustrating how we answered each of our questions.</p>
Dataset (46 analyzed core studies) of the publication "Conservation perspectives of small-scale private forest owners in Europe: A systematic review"
<p>The dataset contains the data collected to conduct the review "Conservation perspectives of small-scale private forest owners in Europe: A systematic review".</p>
Epidemiological geography at work. An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year (DATASET)
<p><strong>Literature review dataset</strong></p> <p>This table lists the surveyed papers concerning the application of spatial analysis, GIS (Geographic Information Systems) as well as general geographic approaches and geostatistics, to the assessment of CoViD-19 dynamics. The period of survey is from January 1<sup>st</sup>, 2020 to December 15<sup>th</sup>, 2020. The first column lists the reference. The second lists the date of publication (preferably, the date of online publication). The third column lists the Country or the Countries and/or the subnational entities investigated. The fourth column lists the epidemiological data utilized in each paper. The fifth column lists other types of data utilized for the analysis. The sixth column lists the more traditionally statistically-based methods, if utilized. The seventh column lists the geo-statistical, GIS or geographic methods, if utilized. The eight column sums up the findings of each paper. The papers are also classified within seven thematic categories. The full references are available at the end of the table in alphabetical order.</p> <p>This table was the basis for the realization of a comprehensive geographic literature review. It aims to be a useful tool to ease the "due-diligence" activity of all the researchers interested in the spatial analysis of the pandemic.</p> <p>The reference to cite the related paper is the following:</p> <p><strong>Pranzo, A.M.R., Dai Prà, E. & Besana, A. Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year. GeoJournal (2022). https://doi.org/10.1007/s10708-022-10601-y</strong></p> <p>To read the manuscript please follow this link: <strong>https://doi.org/10.1007/s10708-022-10601-y</strong></p> <p> </p>
Dataset related to article "Systematic review and meta-analysis of preclinical studies testing mesenchymal stromal cells for traumatic brain injury"
<p>Mesenchymal stromal cells (MSCs) are widely used in preclinical models of traumatic brain injury (TBI). Results are promising in terms of neurological improvement but are hampered by wide variability in treatment responses. We made a systematic review and meta-analysis: 1) to assess the quality of evidence for MSC treatment in TBI rodent models; 2) to determine the effect size of MSCs on sensorimotor function, cognitive function and anatomical damage; 3) to identify MSC-related and protocol-related variables associated with greater efficacy; 4) to understand whether MSC manipulations boost therapeutic efficacy.</p> <p>The meta-analysis included 80 studies. After TBI, MSCs improved sensorimotor and cognitive deficits, and reduced anatomical damage. Stratified meta-analysis on sensorimotor outcome showed similar efficacy for different MSC sources and for syngeneic or xenogenic transplants. Efficacy was greater when MSCs were delivered in the first week post-injury, and when implanted directly into the lesion cavity. The greatest effect size was for cells embedded in matrices or for MSC-derivatives.</p> <p>MSC therapy is effective in preclinical TBI models, improving sensorimotor, cognitive and anatomical outcomes, with large effect sizes. These findings support clinical studies in TBI.</p> <p>The present dataset reports extrapolated data used for the meta-analysis</p>
Data for Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study
<p>Data for Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study</p>
Replication Package for the Paper: "Code Reviewer Recommendation for Architecture Violations: An Exploratory Study"
<p>This is the replication package for the paper: "Code Reviewer Recommendation for Architecture Violations: An Exploratory Study".</p> <p><strong>1) scripts.zip </strong>includes the Python scripts used to run the experiments in this work. Experimental details (e.g., parameters) are described in the Python files. Choose the relevant experimental settings and run "Experiment.py" to start the experiments.</p> <p><strong>2) dataset.xlsx </strong>is the dataset used in the experiments on code reviewer recommendation, which includes the code review comments (from the four OSS projects) related to architecture violations and the file paths of code changes.</p>
ScienceDex guides
Understand access before you commit
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.