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216 results for “systematic literature review”
Dataset for: A systematic literature review on user factors to support the sense of presence
<p>This dataset was created for a publication of Wiepke, Axel and Heinemann, Birte called "A systematic literature review on user factors to support the sense of presence". In this paper we used the PRISMA-method to collect Papers via Google Scholar on the third of April 2023 with the search term:<br>(framework OR model OR frameworks OR models OR processes OR ontologies) AND ((“personality traits” OR “personality variables” OR “personality factors”) AND “spatial presence”) AND (“virtual reality”) AND (learn OR edu\*)</p> <p>The results were pictured in "agreed" findings, where more than 50% of found studies supported a category of results and in "controversial", where there were significant findings, but less than 50% of the studies reported significance.</p> <p>This dataset contains:</p> <ul> <li>raw data for our literature review in .bib</li> <li>our main findings with categories in .csv</li> <li>a short Jupyter notebook script for one graphic in ipynb</li> <li>other graphics as .png</li> </ul>
Systematic Literature Review on Tourism Marketing in the Metaverse
<p>The aim of this research is to investigate tourist marketing within the embryonic context of the metaverse in order to comprehend the building blocks and the primary technologies employed in the sector. For this purpose, a systematic literature review is conducted. The references are extracted in January 2023. The data in this document correspond to the articles finally included in the systematic literature review after the article screening phase.</p> <p>Keywords: tourism marketing, metaverse, technologies, building blocks, SLR (Systematic Literature Review), PRISMA</p> <p> </p>
Automated Literature Screening for Systematic Reviews: Dataset for Evaluation Against Human Title and Abstract and Full-Text Screening Decisions
<p>This Zenodo entry contains the supplementary material associated with the manuscript titled <em>Automated Literature Screening for Systematic Reviews: A 5-Tier Prompting Approach Meeting Cochrane’s Sensitivity Requirement of Greater Than 0.99.</em> The paper will be presented at <a href="https://dbis.rwth-aachen.de/LLMs4MI2024/">LLMsMI 2024</a> in November 2024.</p> <p>A script is provided for replicating the executed experiments, along with a comprehensive evaluation file that reports all the experiment results. Provided data files represent an extension to the original datasets as provided by [1]. For associated systematic review manuscripts and eligibility criteria, please refer to [1] as well. </p> <p>[1] Guo, Eddie; Gupta, Mehul; Deng, Jiawen; Park, Ye-Jean; Paget, Mike; Naugler, Christopher (2023). "Automated Paper Screening for Clinical Reviews Using Large Language Models." <em>Mendeley Data</em>, V1, doi: 10.17632/np79tmhkh5.1. Accessed from: <a href="https://data.mendeley.com/datasets/np79tmhkh5/1" target="_new" rel="noopener">https://data.mendeley.com/datasets/np79tmhkh5/1</a>.</p>
Dataset for paper: A Systematic Literature Review and Recommendations for Ontology-based Support of Digital Forensics
<p>PLEASE, READ THE README.TXT FILE</p> <p>This document describes how to interpret the data and metadata files, and it is licensed under Creative Commons CC BY-NC-AS (https://creativecommons.org/licenses).</p> <p>The file "primary_studies_final_set-DATA.csv" is a CSV file format and contains the raw data extracted from our systematic literature review primary studies. Such data were extracted based on the research questions defined for our study.<br> The file "primary_studies_final_set-METADATA.csv" is a CSV file format and contains the following:<br> - the first row contains two pieces of information: the data type, which might be original or reused;<br> - the second row contains the reused data URL/DOI, which should inform the URL or DOI from which the data was reused, or n/a if the data is original;<br> - the third row contains the date of data generation in the format mm/dd/yyyy;<br> - the fourth row contains 11 elements describing each of the fields of the file "primary_studies_final_set-DATA.csv": the study ID, title, objective, six research questions, and an observation field; and<br> - the fifth row describes the data type of each field of the file "primary_studies_final_set-DATA.csv".<br> The .bib files contain the bibtex entry for the final set of studies.<br> The license.txt file describes the Creative Commons license for this material.</p> <p>We hope you have an excellent read!!</p> <p>Cheers!<br> Thiago, Edson, and Avelino</p>
Bots in Software Development: A Systematic Literature Review [Data Set]
<p>This repository contains al the artifacts of the research: Bots in Software Development: A Systematic Literature Review </p>
Bibliographic Dataset for the Systematic Literature Review on Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice
<p>This database contains all the bibliographic information found after applying the Search Strategy used for the Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice: Systematic Literature Review. The following electronic databases were searched:</p> <ul> <li>Scopus.</li> </ul> <p>A total of 907 records were found. The search was conducted on 16/02/2024.</p> <p>The information is presented in .ris, .bib, and .csv format.</p>
Reproduction package for paper "How far are we from reproducible research on code smell detection? A systematic literature review"
<p>Checklist and data extracted from publications analyzed for "How far are we from reproducible research on code smell detection? A systematic literature review" paper, together with processing scripts and calculations of Cohen's Kappa.</p> <p>Paper that describes details of the data is available here: https://doi.org/10.1016/j.infsof.2021.106783</p>
Supplemental Material for a Systematic Literature Review on Benchmarks for Evaluating Debugging Approaches
<p>Bug benchmarks are used in development and evaluation of debugging approaches. Quantitative performance comparison of different debugging approaches is only possible when they have been evaluated on the same dataset or benchmark. However, benchmarks are often specialized towards usage for certain debugging approaches in their contained data, metrics, and artifacts. Such benchmarks can not be easily used on debugging approaches outside their scope as such approach may rely on specific data such as bug reports or code metrics not included in the dataset. Furthermore, benchmarks vary in their size w.r.t. the number of subject programs and the size of the individual subject programs. For these reasons, we have performed a systematic literature review where we have identified 73 benchmarks that can be used to evaluate debugging approaches.</p> <p>We compare the different benchmarks with respect to their size and the provided information such as bug reports, contained test cases, and other code metrics. Furthermore, we have investigated how well the benchmarks realize the <a href="https://www.go-fair.org/fair-principles/">FAIR guiding principles</a>. This comparison is intended to help researchers to quickly identify all suitable benchmarks for evaluating their specific debugging approaches. More information can be found in the publication:</p> <blockquote> <p>Thomas Hirsch and Birgit Hofer: "A Systematic Literature Review on Benchmarks for Evaluating Debugging Approaches", Journal of Systems and Software, in press, 2022.</p> </blockquote>
GERDAT011 Literature search for publication - Geriatric assessment in the management of older patients with cancer – a systematic review (update).xlsx
<p>Search data belonging to the publication Geriatric assessment in the management of older patients with cancer – a systematic review (update)</p>
Unpacking the concept of "educators' data literacy in Higher Education" - Systematic Review of the literature and Keyword Map
<p>As algorithmic decision-making and data collection become pervasive within higher education, how can educators make sense of the systems that shape life and learning in the 21st century? Through a systematic review of the literature, the paper investigates the gaps in the literature, which prevent the formulation of potential pathways and principles on which educators’ data literacy can - and should - be developed and fostered. The analysis of 137 papers through the methods of classification under relevant categories, and key words mapping, showed that there is little attention on HE teachers, and most approaches to educators’ data literacy address management and technical abilities for data processing, with less concern on critical, ethical and personal approaches to datafication in education.</p> <p>The present dataset shows the full list of articles analysed.</p> <p>The dataset, and ods file, is composed by the following sheets:</p> <ol> <li>Codebook</li> <li>List of articles extracted from SCOPUS</li> <li>List of articles extracted from WOS</li> <li>List of articles extracted from ERIC</li> <li>List of articles extracted from DOAJ</li> <li>Interrater Agreement</li> <li>PRISMA workflow</li> <li>Analysis - First Level (classification of 137 articles selected)</li> <li>Analysis - Second Level (List of articles relating faculty development)</li> <li>Supplementary tables (counting articles in relation to the categories of analysis).</li> </ol> <p>As for the Keywords' Map, a second file .csv displays the text over which basis was performed the keyword maps analysis. A .txt file shows notes relating the analysis procedures using the software VOS-Viewer <a href="http://www.vosviewer.com/">http://www.vosviewer.com/</a></p> <p> </p>
[Dataset] Software Process Line as an Approach to Support Software Process Reuse: a Systematic Literature Review
<p>Dataset of a Systematic Literature Review on Software Process Line as an Approach to Support Software Process Reuse</p> <p>Please, read README.txt file before go through dataset.</p>
A Systematic Literature Review of Machine Learning for Uncovering Software Faults and Failures
<p>This data set contains the results of an extensive, systematic literature review on the use of machine learning (ML) for uncovering software faults and failures. Covering the period of 2019 to 2022, this literature review identifies 874 relevant publications, classified into six distinct quality assurance tasks. Results show a compound annual growth rate (CAGR) of relevant publications of 38% over the last five years.</p> <p>This literature review particularly analyzed in how far these relevant papers leverage synergies between different quality assurance tasks. Results show that only 3% of all relevant papers leverage such synergies, indicating ample opportunities for future research. For example, a single type of quality assurance activity may not suffice to deliver the expected software quality. Ideally, one would use a suitable combination of different types of activities – such as combining dynamic testing with static code analysis. Also, leveraging the synergies between different quality assurance activities can increase the effectiveness of the individual activities. For example, having a good estimate of the fault density of a software component (e.g., using deep learning-driven fault prediction techniques) could help optimize and prioritize testing effort and budget.</p>
Literature Datasets for the publication "Systematic Review: Prevalence and Practices of Immunofluorescent Cell Image Processing"
<p>This dataset contains the CSV files returned from PubMed searches used to complete a Systematic Review of Image Processing Publication Practices for methods applied to immunofluorescent images of all CNS cells. <br> <br> The file names are organized "date_supplementarytablenumber" followed by the appropriate search terms. </p>
Dataset: A Systematic Literature Review on the topic of High-value datasets
<p>This dataset contains data collected during a study ("T<a href="http://https://arxiv.org/abs/2305.10234">owards High-Value Datasets determination for data-driven development: a systematic literature review</a>") conducted by Anastasija Nikiforova (University of Tartu), Nina Rizun, Magdalena Ciesielska (Gdańsk University of Technology), Charalampos Alexopoulos (University of the Aegean)<sup> </sup>and Andrea Miletič (University of Zagreb)<br> It being made public both to act as supplementary data for "Towards High-Value Datasets determination for data-driven development: a systematic literature review" paper (pre-print is available in Open Access here -><a href="http://https://arxiv.org/abs/2305.10234"> https://arxiv.org/abs/2305.10234</a>) and in order for other researchers to use these data in their own work.</p> <p><br> The protocol is intended for the Systematic Literature review on the topic of High-value Datasets with the aim to gather information on how the topic of High-value datasets (HVD) and their determination has been reflected in the literature over the years and what has been found by these studies to date, incl. the indicators used in them, involved stakeholders, data-related aspects, and frameworks. The data in this dataset were collected in the result of the SLR over Scopus, Web of Science, and Digital Government Research library (DGRL) in 2023.</p> <p> </p> <p>***Methodology***</p> <p>To understand how HVD determination has been reflected in the literature over the years and what has been found by these studies to date, all relevant literature covering this topic has been studied. To this end, the SLR was carried out to by searching digital libraries covered by Scopus, Web of Science (WoS), Digital Government Research library (DGRL).</p> <p>These databases were queried for keywords <em>("open data" OR "open government data") AND ("high-value data*" OR "high value data*"</em>), which were applied to the article title, keywords, and abstract to limit the number of papers to those, where these objects were primary research objects rather than mentioned in the body, e.g., as a future work. After deduplication, 11 articles were found unique and were further checked for relevance. As a result, a total of 9 articles were further examined. Each study was independently examined by at least two authors.</p> <p>To attain the objective of our study, we developed the protocol, where the information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information.</p> <p> </p> <p>***Test procedure***<br> Each study was independently examined by at least two authors, where after the in-depth examination of the full-text of the article, the structured protocol has been filled for each study.<br> The structure of the survey is available in the supplementary file available (see Protocol_HVD_SLR.odt, Protocol_HVD_SLR.docx)<br> The data collected for each study by two researchers were then synthesized in one final version by the third researcher.</p> <p>***Description of the data in this data set***</p> <p>Protocol_HVD_SLR provides the structure of the protocol<br> Spreadsheets #1 provides the filled protocol for relevant studies.<br> Spreadsheet#2 provides the list of results after the search over three indexing databases, i.e. before filtering out irrelevant studies</p> <p>The information on each selected study was collected in four categories:<br> (1) descriptive information,<br> (2) approach- and research design- related information,<br> (3) quality-related information,<br> (4) HVD determination-related information</p> <p>Descriptive information <br> 1) Article number - a study number, corresponding to the study number assigned in an Excel worksheet<br> 2) Complete reference - the complete source information to refer to the study<br> 3) Year of publication - the year in which the study was published<br> 4) Journal article / conference paper / book chapter - the type of the paper -{journal article, conference paper, book chapter}<br> 5) DOI / Website- a link to the website where the study can be found<br> 6) Number of citations - the number of citations of the article in Google Scholar, Scopus, Web of Science<br> 7) Availability in OA - availability of an article in the Open Access<br> 8) Keywords - keywords of the paper as indicated by the authors<br> 9) Relevance for this study - what is the relevance level of the article for this study? {high / medium / low}</p> <p>Approach- and research design-related information<br> 10) Objective / RQ - the research objective / aim, established research questions<br> 11) Research method (including unit of analysis) - the methods used to collect data, including the unit of analy-sis (country, organisation, specific unit that has been ana-lysed, e.g., the number of use-cases, scope of the SLR etc.)<br> 12) Contributions - the contributions of the study<br> 13) Method - whether the study uses a qualitative, quantitative, or mixed methods approach?<br> 14) Availability of the underlying research data- whether there is a reference to the publicly available underly-ing research data e.g., transcriptions of interviews, collected data, or explanation why these data are not shared?<br> 15) Period under investigation - period (or moment) in which the study was conducted<br> 16) Use of theory / theoretical concepts / approaches - does the study mention any theory / theoretical concepts / approaches? If any theory is mentioned, how is theory used in the study?</p> <p>Quality- and relevance- related information <br> 17) Quality concerns - whether there are any quality concerns (e.g., limited infor-mation about the research methods used)?<br> 18) Primary research object - is the HVD a primary research object in the study? (primary - the paper is focused around the HVD determination, sec-ondary - mentioned but not studied (e.g., as part of discus-sion, future work etc.))</p> <p>HVD determination-related information <br> 19) HVD definition and type of value - how is the HVD defined in the article and / or any other equivalent term?<br> 20) HVD indicators - what are the indicators to identify HVD? How were they identified? (components & relationships, “input -> output")<br> 21) A framework for HVD determination - is there a framework presented for HVD identification? What components does it consist of and what are the rela-tionships between these components? (detailed description)<br> 22) Stakeholders and their roles - what stakeholders or actors does HVD determination in-volve? What are their roles?<br> 23) Data - what data do HVD cover?<br> 24) Level (if relevant) - what is the level of the HVD determination covered in the article? (e.g., city, regional, national, international)</p> <p><br> ***Format of the file***<br> .xls, .csv (for the first spreadsheet only), .odt, .docx</p> <p>***Licenses or restrictions***<br> CC-BY</p> <p> </p> <p>For more info, see README.txt</p>
Cognition in Social Engineering Empirical Research: a Systematic Literature Review
<p># Description of contents</p> <p>This repository contains the codebook, dataset and analysis scripts in R used for the following publication: Pavlo Burda, Luca Allodi, Nicola Zannone. "Cognition in Social Engineering Empirical Research: a Systematic Literature Review". ACM Transactions on Computer-Human Interaction (TOCHI).<br> The repository consists of the following files:</p> <p>## dataset_and_codebook.xlsx contains the dataset, the codebook and a detailed description of contents.</p> <p>## scripts/ contains the R scripts used for the analysis</p> <p>## readme.txt contains this readme</p> <p><br> # Dataset and codebook</p> <p>The dataset_and_codebook.xlsx contains the following sheets:</p> <p>## Codebook<br> Contains a detailed description of the dataset (tables, columns, fields, etc.).<br> The codebook describes the concepts and variables that are present in the dataset. This includes explanations on meaning, numerical values, classification schemes and labels.</p> <p>## Hypotheses table <br> Contains the hypotheses for all analyzed papers and is used in the results of the paper. <br> Each row is a hypothesis of an included paper, cells contain one or more values (e.g., value1,value2,...) with or without sub values (e.g., value1,value2(sub-value1, ...)). Any content that is in square brackets [] is ignored in the analysis. Empty cells mean that there is no applicable value for that column.</p> <p>## Papers table<br> Contains the analyzed papers and is used in the results of the paper. It is also used in the overview table (Table 6) in Appendix C.<br> Each row is an included paper, cells contain up to two values (e.g., value1, value2) or the word 'multiple' in case of more than two values. Empty cells mean that there is no applicable value for that column.</p> <p>## Values table <br> Contains the description of cell values of 'Hypotheses' and 'Papers' and sub-values (specific variables in a study) that belong to a value. <br> It has a hierarchical structure from left to right where each field on the right column falls under the first non-empty field on the immediate left-top.</p> <p><br> # Reproducing results with scripts/ (generate figures)<br> To run the R scripts included in the 'scripts' directory it is sufficient to follow the instructions in and run the 'RUN_ALL_SCRIPTS.R' in 'scripts' directory.<br> The scripts use the TSV (Tab Separated Values) format of the dataset, which is the exact copy of the 'Papers' and 'Hypotheses' tables in the 'dataset_and_codebook.xlsx' file.<br> The resulting figures are stored in the 'scripts/results' directory.</p>
Bibliography of the Systematic Literature Review of the IPBES Global Assessment, Chapter 4
<p>Bibliographies from the systematic literature review in Chapter 4 of the IPBES Global Assessment</p> <p>DOI: 10.5281/zenodo.3553579</p>
Domain-Specific Language domain analysis and evaluation: a systematic literature review
<p>In order to successfully implement Domain-Specific Languages (DSLs), it is needed to systematically define and to support its development process; namely its Evaluation and the Domain Analysis phase. For that purpose, the studies were systematically selected from the most relevant venues that focus on the implementation of DSLs, in order to get insight if and how these development phases were performed. The special focus was given to the human-machine DSLs (excluding the machine-machine languages), the involvement of its end-users in the development process and the evaluation of DSLs usability, i.e. quality in use of DSLs. </p> <p>Preliminary results give us a notion that there is increased the state of practice of performing the evaluation of the DSLs, mostly including usability concerns, at least after its implementation. Generally, the quality of the reviewed studies was high. On another hand, rarely the assessments are done during domain analysis, which in general is not reporting inclusion of end-users or consideration of different use-cases, although the majority of studies refer to target non-programmers and to contribute easy in use. </p> <p>We did collect the valuable body of primary studies that are giving us answers to our research questions, however, to raise the credibility of the conclusions we should extend the analysis to other venues. Also, as we get insights into the categorization of practices we could specify more concretely answers that would give us means to perform more detailed meta-analysis. </p>
Dataset of Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: A Systematic Literature Review
<p>Data set for the paper entitled “<strong>Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: a Systematic Literature Review</strong>”</p> <p>In this repo, we have some pictures and Excel files.</p> <ul> <li>Pictures are screenshots from the Parsifal tool (https://parsif.al/) which we use for performing the SLR.</li> <li>Excel files are as follows:</li> </ul> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 21.8789%;"><col style="width: 78.1211%;"></colgroup> <tbody> <tr> <td><strong>Excel’s file name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Keyword_analysis </td> <td>In this file, you can see the evolution of our keyword selection.</td> </tr> <tr> <td>Articles_InclusionExclusion_QA </td> <td>In this file, you can find all found papers until Feb. 27, 2025. In the last column of this excel file, we can see the status of each paper, if it has been included, or excluded by authors. For the included paper (their status is “Accepted”) you can see their quality score in the last column.</td> </tr> <tr> <td>Extracted_data </td> <td>In this file, we logged the result of data extraction from qualified paper. In the first sheet “Articles”, you can see a list of the read papers with corresponding data. Other sheets in this Excel file are driven from the “Article” sheet for data visualization. So, they are not important.</td> </tr> </tbody> </table> <p> <br>If you have any questions, you can read the corresponding paper and contact the authors.</p>
(Open) Data literacy: Dataset for a systematic review of the literature
<p>This dataset presents data used for a systematic review of the literature.</p><p>We conducted a comprehensive literature review, in which we identified the following: a) the role of data literacy as one of several barriers to use; and b) open data-related activities that foster informal learning by assisting in the development of critical data literacy as a surrogate for citizens' continued engagement with open data. Following the screening and selection of 66 articles through the use of keyword mapping, the articles were coded and subjected to quantitative analysis. On the one hand, our findings demonstrate that inadequate data literacy hinders the utilisation of open data. Conversely, it seems that open data initiatives create pertinent prospects for fostering technical data literacy among the general public, enabling them to comprehend and engage with decision-making processes that are informed by data. However, critical data literacy as a primary catalyst for the strategic and transformative utilisation of open government data receives scant attention. In conclusion, this research has the potential to provide a foundation for interventions that promote open data literacy and lifelong learning.</p>
Dataset for article "Performance and Efficiency Evaluation of Technology-Based Business Incubators: A Systematic Literature Review"
<p>Dataset for article entitled "<strong>Performance and Efficiency Evaluation of Technology-Based Business Incubators: A Systematic Literature Review"</strong></p>
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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)
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DANDI Archive for NWB datasets
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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.