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10 results for “author analysis”
Impact of Software Engineering Research in Practice: A Patent and Author Survey Analysis
<p>Dataset of the research paper: <strong>Impact of Software Engineering Research in Practice: A Patent and Author Survey Analysis</strong></p> <p>Existing work on the practical impact of software engineering (SE) research examines industrial relevance rather than adoption of study results, hence the question of how results have been practically applied remains open. To answer this and investigate the outcomes of impactful research, we performed a quantitative and qualitative analysis of 4 354 SE patents citing 1 690 SE papers published in four leading SE venues between 1975–2017. Moreover, we conducted a survey on 475 authors of 593 top-cited and awarded publications, achieving 26% response rate. Overall, researchers have equipped practitioners with various tools, processes, and methods, and improved many existing products. SE practice values knowledge-seeking research and is impacted by diverse cross-disciplinary SE areas. Practitioner-oriented publication venues appear more impactful than researcher-oriented ones, while industry-related tracks in conferences could enhance their impact. Some research works did not reach a wide footprint due to limited funding resources or unfavorable cost-benefit trade-off of the proposed solutions. The need for higher SE research funding could be corroborated through a dedicated empirical study. In general, the assessment of impact is subject to its definition. Therefore, academia and industry could jointly agree on a formal description to set a common ground for subsequent research on the topic.</p> <p>The following data files are included.</p> <ul> <li><em>./fields</em>: <ul> <li><strong>engi-fields.csv</strong>: Publication and PhD dissertation counts of main engineering branches</li> <li><strong>engi-fields-queries.txt</strong>: Queries applied to Elsevier's Scopus and Open Access Theses and Dissertations databases to retrieve the publication and dissertation counts</li> </ul> </li> <li><em>./patents</em>: <ul> <li><strong>sample-se-references-verified.csv</strong>: Manual verification of a random sample of references by software engineering (SE) patents to SE papers</li> <li><strong>se-cpc.tsv</strong>: Manually-identified SE-related Cooperative Patent Classification (CPC) categories</li> <li><strong>se-references-in-patents.csv</strong>: SE references made by SE patents to SE papers</li> <li><em>./patents/litigation</em>: <ul> <li><strong>case-values.csv</strong>: Manually-retrieved litigation damages of citing SE patents</li> <li><strong>lit-per-paper.csv</strong>: Litigation cases of citing SE patents</li> </ul> </li> <li><em>./patents/maintenance</em>: <ul> <li><strong>maint-code-fee-mapping.csv</strong>: Mapping of patent maintenance fee codes to their fee values</li> <li><strong>maint-fees.csv</strong>: Fee values of maintenance fee codes</li> <li><strong>maint-per-paper.csv</strong>: Maintenance fee events of citing SE patents</li> </ul> </li> <li><em>./patents/reports</em>: <ul> <li><strong>lit-sum-per-paper.csv</strong>: Counts and total damages of litigation cases of patent-cited SE papers</li> <li><strong>maint-sum-per-paper.csv</strong>: Counts and total values of maintenance fee events of patent-cited SE papers</li> <li><strong>patent-ref-counts.csv</strong>: SE patent citation counts of patent-cited SE papers</li> </ul> </li> </ul> </li> <li><em>./survey</em>: <ul> <li><strong>emse-top.csv</strong>: Most-cited papers of the Empirical Software Engineering (EMSE) journal</li> <li><strong>icse-bp.csv</strong>: Distinguished papers of the International Conference of Software Engineering (ICSE)</li> <li><strong>icse-mip.csv</strong>: Most influential ICSE papers</li> <li><strong>icse-top.csv</strong>: Most-cited ICSE papers</li> <li><strong>survey-questionnaire-emse.pdf</strong>: The EMSE survey questionnaire</li> <li><strong>survey-questionnaire.pdf</strong>: The ICSE, TSE, and TOSEM survey questionnaire</li> <li><strong>survey-responses.csv</strong>: The anonymized survey responses</li> <li><strong>tosem-top.csv</strong>: Most-cited papers of the ACM Transactions on Software Engineering and Methodology (TOSEM)</li> <li><strong>tse-top.csv</strong>: Most-cited papers of the IEEE Transactions on Software Engineering (TSE)</li> <li><em>./survey/manual-coding</em>: <ul> <li><strong>feedback.txt</strong>: Manual coding of survey feedback</li> <li><strong>practical-impact.csv</strong>: Manual coding of responses about practical impact of work</li> <li><strong>practical-impact-lack.csv</strong>: Manual coding of responses about lack of practical impact</li> <li><strong>research-methods.csv</strong>: Manual coding of additional research methods of surveyed papers</li> <li><strong>state-of-practice.csv</strong>: Manual coding of responses about changes in state of practice</li> </ul> </li> </ul> </li> <li><em>./venues</em>: <ul> <li><strong>se-venues.csv</strong>: Top SE venues according to Google Scholar Metrics</li> <li><strong>se-venues-impact.csv</strong>: SE patent citations and patent-based impact factors of SE venues</li> <li><strong>se-venues-scopus-queries.txt</strong>: Queries applied to Scopus to retrieve the publication counts of the SE venues</li> </ul> </li> </ul>
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
manuscript (atmosphere-3145955) titled: The Black Sea Upwelling System: Analysis on the Western Shallow Waters Authored by: Maria Emanuela Mihailov was accepted in Atmosphere (ISSN 2073-4433) on 15 August 2024
<p>Datasets represents the modelling results for:</p> <p>- Coastal Upwelling Transport Index (CUTI) of the National Oceanic and Administrative Administration’s Environmental Research Division (NOAA-ERD) was used to derive the time series of the coastal upwelling index in four locations on the north-western Black Sea coast. The coastal upwelling index time series was calculated using monthly average wind fields from the European Centre for Medium-Range Weather Forecasts (ECMWFs) reanalysis and MATLAB software to compute the CUTI </p> <p>- The upwelling index (UI) is computed using the CUTI Formula (<span>Bakun Index </span>), defined as CUTI (m3·s−1·100 m−1), representing the volume transport per distance unit of an alongshore section. The sign of Ekman transport is changed to define positive or negative values of UI as a response to upwelling or downwelling favourable winds.<br>To compute the BEUTI, Copernicus Marine Service [1] data are used for dedicated locations.</p> <p>[1] <span>Grégoire,<em> </em>M.;<em> </em>Vandenbulcke,<em> </em>L.;<em> </em>Capet,<em> </em>A.<em> </em>Black<em> </em>Sea<em> </em>Biogeochemical<em> </em>Reanalysis<em> </em>(CMEMS<em> </em>BS-Biogeochemistry)<em> </em>(Version<em> </em>1)<em> </em>set.<em> </em>Copernicus<em> </em>Monitoring<em> </em>Environment<em> </em>Marine<em> </em>Service<em> </em>(CMEMS).<em> </em>2020.<em> </em>Available<em> </em>online:<em> </em>https://marine.copernicus.eu/<em> </em>(accessed<em> </em>on<em> </em>10<em> </em>November<em> </em>2023).</span></p>
Comparative analysis of health authorities spokesperson and health influencer during the COVID-19 pandemic: A case in Indonesia
<p><span><strong>Background</strong>: </span><span>Concerns over an infodemic following a surge in health misinformation circulating on social media sets out the government's priority for Indonesia. Given the urgent work on the coronavirus disease 2019 (COVID-19) response, the government collaborated with health-related spokespersons and influencers with a medical background by starting a COVID-19 public education campaign on social media. A collaborative initiative involved health spokespersons from government and non-government to clarify misinformation about COVID-19.</span></p> <p><span><strong>Methods</strong>: </span><span>The primary purpose of this research is to compare government and non-government spokespersons by examining their role in educating about the COVID-19 vaccine and health services. This study employed comparative factor analysis and non-participatory observation toward the media activity of spokespersons in Indonesia. Using a questionnaire, this study examines the dimensions of public campaigns, risk communication, health and emergency, leadership, and communication from Indonesian spokespersons. The data collection was conducted in two stages. The first stage was a pilot study that collected data from 102 respondents, the second stage collected data from 276 respondents.</span></p> <p><span><strong>Results</strong>: </span><span>Findings show that utilizing the spokesperson is important due to its capabilities of reaching diverse audiences, and improving public engagement, trustworthiness, and credibility.</span></p> <p><span><strong>Conclusions</strong>: </span><span>With the combination of health authorities spokespersons and health influencers in Indonesia, this study provides valuable insights for communication management in developing and supporting the role of health authorities from the government, non-government as well as medical sectors.</span></p>
Comparative analysis of health authorities spokesperson and health influencer during the COVID-19 pandemic: A case in Indonesia
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Datasets and analysis for "Geographical trends in academic conferences: an analysis on authors' affiliations"
<p>Datasets and analysis for "Geographical trends in academic conferences: an analysis on authors' affiliations"</p>
PAN23 Multi-Author Writing Style Analysis
<p>This is the dataset for the shared task on <a href="https://pan.webis.de/clef23/pan23-web/style-change-detection.html">Multi-Author Writing Style Analysis PAN@CLEF2023</a>. Please consult the task's page for further details on the format, the dataset's creation, and links to baselines and utility code.</p> <p><strong>Task: </strong>We ask participants to solve the following intrinsic style change detection task: <strong>for a given text, find all positions of writing style change on the paragraph-level</strong> (i.e., for each pair of consecutive paragraphs, assess whether there was a style change). The simultaneous change of authorship and topic will be carefully controlled and we will provide participants with datasets of three difficulty levels:</p> <ol> <li><strong>Easy:</strong> The paragraphs of a document cover a variety of topics, allowing approaches to make use of topic information to detect authorship changes.</li> <li><strong>Medium:</strong> The topical variety in a document is small (though still present) forcing the approaches to focus more on style to effectively solve the detection task.</li> <li><strong>Hard:</strong> All paragraphs in a document are on the same topic.</li> </ol> <p>All documents are provided in English and may contain an arbitrary number of style changes. However, style changes may only occur between paragraphs (i.e., a single paragraph is always authored by a single author and contains no style changes).</p> <p><strong>Data: </strong>To develop and then test your algorithms, three datasets including ground truth information are provided (<em>dataset1</em> for the easy task, <em>dataset2</em> for the medium task, and <em>dataset3</em> for the hard task).</p> <p>Each dataset is split into three parts:</p> <ol> <li><em>training set:</em> Contains 70% of the whole dataset and includes ground truth data. Use this set to develop and train your models.</li> <li><em>validation set:</em> Contains 15% of the whole dataset and includes ground truth data. Use this set to evaluate and optimize your models.</li> <li><em>test set:</em> Contains 15% of the whole dataset, no ground truth data is given. This set is used for evaluation.</li> </ol> <p>You are free to use additional external data for training your models. However, we ask you to make the additional data utilized freely available under a suitable license.</p> <p><strong>Versioning:</strong> </p> <ul> <li>1.0: initial upload</li> </ul>
PAN18 Multi-Author Analysis: Style-Change-Detection
<p>Dataset for binary style change detection.</p> <p>More information about the task: <a href="https://pan.webis.de/clef18/pan18-web/style-change-detection.html">Link</a></p>
PAN17 Multi-Author Analysis: Style-Change-Detection
<p>All documents are provided in English and may contain zero up to arbitrarily many switches (style breaches). Thereby switches of authorships may only occur at the end of sentences, i.e., not within.</p> <p>More information: <a href="https://pan.webis.de/clef17/pan17-web/style-change-detection.html">Link</a></p>
ARRIVE guidelines author checklist of "Structural and functional analysis reveals the catalytic mechanism and substrate binding mode of the broad-spectrum endolysin Ply2741"
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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.