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28 results for “Search Engine”
The language of sound search: Examining User Queries in Audio Search Engines (supplementary materials)
<h2>Overview</h2> <p>This dataset accompanies the <a href="https://dcase.community/documents/workshop2024/proceedings/DCASE2024Workshop_Weck_54.pdf" target="_blank" rel="noopener">paper</a> titled <strong>"The Language of Sound Search: Examining User Queries in Audio Search Engines."</strong> The study investigates user-generated textual queries within the context of sound search engines, which are commonly used for applications such as foley, sound effects, and general audio retrieval.</p> <p>The paper addresses the gap in current research regarding the real-world needs and behaviors of users when designing text-based audio retrieval systems. By analyzing search queries collected from two sources — a custom survey and Freesound query logs — the study provides insights into user behavior in sound search contexts. Our findings reveal that users tend to formulate longer and more detailed queries when not constrained by existing systems, and that both survey and <a href="https://freesound.org/">Freesound</a> queries are predominantly keyword-based.</p> <p>This dataset contains the raw data collected from the survey and annotations of Freesound query logs.</p> <h2>Files in This Dataset</h2> <p>The dataset includes the following files:</p> <ol> <li> <p><strong><code>participants.csv</code></strong><br>Contains data from the survey participants. Columns:</p> <ul> <li><code>id</code>: A unique identifier for each participant.</li> <li><code>fluency</code>: Self-reported English language proficiency.</li> <li><code>experience</code>: Whether the participant has used online sound libraries before.</li> <li><code>passed_instructions</code>: Boolean value indicating whether the participant advanced past the instructions page in the survey.</li> </ul> </li> <li> <p><strong><code>annotations.csv</code></strong><br>Contains annotations of the survey responses, detailing the participants' interaction with the sound search tasks. Columns:</p> <ul> <li><code>id</code>: A unique identifier for each annotation.</li> <li><code>participant_id</code>: Links to the participant’s ID in <code>participants.csv</code>.</li> <li><code>stimulus_id</code>: Identifier for the stimulus presented to the participant (audio, image, or text description).</li> <li><code>stimulus_type</code>: The type of stimulus (audio, image, text).</li> <li><code>audio_result_id</code>: Identifier for the hypothetical audio result presented during the search task.</li> <li><code>query1</code>: Initial search query submitted based on the stimulus.</li> <li><code>query2</code>: Refined search query after seeing the hypothetical search result.</li> <li><code>aspects1</code>: Aspects considered important when formulating the initial query.</li> <li><code>aspects2</code>: Aspects considered important when refining the query.</li> <li><code>result_relevance</code>: Participant's rating of the hypothetical search result's relevance.</li> <li><code>time</code>: Time taken to complete the search task.</li> </ul> </li> <li> <p><strong><code>freesound_queries_annotated.csv</code></strong><br>Contains annotated Freesound search queries. Columns:</p> <ul> <li><code>query</code>: Text of the search query submitted to Freesound.</li> <li><code>count</code>: The number of times the specific query was submitted.</li> <li><code>topic</code>: Annotated topic of the query, based on an ontology derived from AudioSet, with an additional category, <code>Other</code>, which includes non-English queries and NSFW-related content.</li> </ul> </li> <li> <p><strong><code>survey_stimuli_data.zip</code></strong><br>This ZIP file contains three CSV files corresponding to the three stimulus types used in the survey:</p> <ul> <li><strong>Audio stimuli</strong>: Categorized sound recordings presented to participants.</li> <li><strong>Image stimuli</strong>: Annotated images that prompted sound-related queries.</li> <li><strong>Text stimuli</strong>: Summarized descriptions of sounds provided to participants.</li> </ul> </li> </ol> <p>More details on the stimuli and the survey methodology can be found in the accompanying paper.</p> <h2><strong>Citation</strong></h2> <p>If you use this dataset in your research, please cite the corresponding paper:</p> <div> <pre>B. Weck and F. Font, ‘The Language of Sound Search: Examining User Queries in Audio Search Engines’, in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2024 Workshop (DCASE2024), Tokyo, Japan, Oct. 2024, pp. 181–185.</pre> <pre><code>@inproceedings{Weck2024, author = "Weck, Benno and Font, Frederic", title = "The Language of Sound Search: Examining User Queries in Audio Search Engines", booktitle = "Proceedings of the Detection and Classification of Acoustic Scenes and Events 2024 Workshop (DCASE2024)", address = "Tokyo, Japan", month = "October", year = "2024", pages = "181--185" }</code></pre> </div>
Data set of the article: Using Machine Learning for Web Page Classification in Search Engine Optimization
<p>Data of investigation published in the article: "Using Machine Learning for Web Page Classification in Search Engine Optimization"</p> <p>Abstract of the article:</p> <p>This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research. </p>
Checkbot API raw results from Libraries, Archives and Museums websites for evaluating a data-driven Search Engine Optimization methodology
<p>Results from Checkbot API to measure and collect 341 websites compatibility on multiple SEO variables (34 variables). Checkbot API indexes the website's code to find features capable of impacting SEO performance. Each website has been tested with the maximum number of links allowed to be crawled equally to 10.000 per test. In this way, we retrieved data about the overall websites performance including their sub-pages, and not only the main domain names. A scale from 0 (lowest rate) to 100 (highest rate) was adopted for each examined variable. This constitutes a useful managerial indicator of dealing with the quantification of websites performance while avoiding complex measurement systems that are difficult to be adopted by administrators. Websites tested were also categorized by the CMS type used. More information about the variables and the meaning of the results can be found at https://www.checkbot.io/ </p>
Phosphate Sensor Search Engine (P-SENSEE) Snapshot
<p>We demosntrate a snapshot of the Phosphate Sensor Search Engine (P-SENSEE) for EuroSensors 2024, which represents a small select fraction of the database collected by our team.</p>
The optimization of a jet turbojet engine by PSO and searching algorithms
<p>The turbojet engine operates on the ideal Brayton cycle (gas turbine) and consists of six main parts: diffusers, compressors, combustion chambers, turbines, afterburners and nozzles. Using computer code writing in MATLAB software environment, exergy analysis on all selected turbojet engine components, exergy analysis on J85-GE-21 turbojet engine for selective height of 10008000 meters above sea level at speeds of 200 m/s and temperatures of 10, 20 and 40 ° C have been provided and then, according to the system functions, the system is optimized based on the PSO method. For the purpose of optimization, variables of Mach number, efficiency of the compressor, turbine, nozzle and compressor pressure ratio are considered in the range of 0.6 to 1.4, 0.8 to 0.95, 0.8 to 0.95 and 7 to 10, respectively. The highest exergy efficiency of different parts of the engine at sea level with an inlet air velocity of 200 m/s corresponds to a diffuser with 73.1%. Then, the nozzle and combustion chamber are respectively 68.6% and 51.5%. The lowest exergy efficiency is related to compressor with 4%. After that, the afterburner is ranked second with 11.6%. Also, the values of entropy produced and the efficiency of the second law before optimization were 1176.99 and 479 w/k respectively and the same values after optimization were 1129 and 51.4 w/k respectively which is identified. After the optimization process, the amount of entropy produced is reduced and the efficiency of the second law of thermodynamics has increased.<br> </p>
Dataset for article 'How users' knowledge of advertisements influences their viewing and selection behaviour in search engines'
<p>This study examines how users’ understanding of search-based advertising influences search results viewing behaviour the PC and the smartphone. To investigate this, we used a mix of methods consisting of interview, eye-tracking experiment and questionnaire with n=100 subjects. We show that participants with a low level of knowledge on search advertising are more likely to click on ads than subjects with a high level of knowledge. Moreover, subjects with little knowledge show less willingness to scroll down to the organic results. Regarding the device, there are significant differences in viewing behaviour. These can be attributed to the influence of the direct visibility of search results on both devices tested. We recommend, depending on the device, that advertisers ensure their ads appear in the instantly visible area ("above the fold"). Future studies should investigate the motivations of searchers when clicking on ads.</p>
Epistemic Insight Search Engine
<p>Prototype search engine linked to GenAI that encourages users to explore how different disciplines approach a question</p>
Unified MOOCs Semantic Search Engine
<p>Full dataset of Unified MOOCs</p>
A Novel Algorithm for Estimating Web Page Ranking in Search Engine Results Pages
<p><em><strong>Abstract:</strong> </em>Search engine optimization (SEO) can make a big improvement in the traffic to a web page. Because search engines keep their main rules of ranking undeclared, it’s important to develop models that can estimate the ranking of a web page in the search engine to be able to optimize web pages to rank higher in the search engine. The available research methodologies used machine learning algorithms to provide solutions for this target with the help of generated datasets by scraping the search engine results pages (SERP) and crawling web pages. Their proposed models suffered from the inability to be updated dynamically if the search engine updated its ranking algorithm, and their input data did not include the diversity of web pages and languages. This research will propose a novel original rank estimation algorithm that’s able to overcome other research challenges, with a set of comparative experiments and complexity analysis. Results will show that the proposed algorithm could achieve higher values of accuracy, precision, and recall.</p> <p><strong><em>Dataset: </em></strong></p> <p>For research purpose, the dataset will play two roles, first, it will act the role of search engine result pages (SERP), and second, it will be used to test algorithms and calculate performance measurements. Dataset is consisting of 9930 web pages, aimed to identify search results pages, focusing on the top 3 pages of SERP, with 31 extracted attributes that's related to search engine optimization (SEO). The distribution of examples between class labels was balanced, with changes due to scraping operation issues, but not significantly different, with fractions of 39.9%, 34.6%, and 25.5% for the class labels page1, page2, and page 3. Feature names are: 'Title 1 Length', 'Title 2 Length', 'Meta Description 1 Length', 'Meta Description 2 Length', 'Meta Keywords 1 Length', 'H1-1 Length', 'H1-2 Length', 'H2-1 Length', 'H2-2 Length', 'Size (bytes)', 'Word Count', 'Text Ratio', 'Inlinks', 'Unique Inlinks', 'Unique JS Inlinks', '% of Total', 'Outlinks', 'Unique Outlinks', 'Unique JS Outlinks', 'External Outlinks', 'Unique External Outlinks', 'Unique External JS Outlinks', 'Response Time', 'Status Code', 'Keyword in MetaDescription1', 'Keyword in Title1', 'Keyword in MetaKeywords1', 'Keyword in URL', 'Has LastModified', 'Keyword in Headers', and 'Keyword in Emphasized Text'.</p> <p>The process of dataset generation involved scraping the search engine, extracting URLs for selected keywords, focusing on feature extraction, cleaning and preprocessing, and generating new attributes related to keywords in web pages. It involved also removing missing values, duplicates, and data type conversions to obtain a comprehensive dataset.<br> Keyword selection involves selecting keywords from various categories and considering diversity, including high and low traffic, long-term and short-term keywords, and generic and branded keywords. Apify online tool was used for search engine scraping with default language and US country, resulting in 388 selected keywords with 30 results per keyword. Dataset included extracted SEO features from 9991 web pages using screamingFrog desktop software and Rapidminer desktop software, determining page SEO-friendliness and comparing it to SERP rankings. Dataset cleaning involved removing redundant attributes, removing paid SERP results, replacing missing values, and converting data types. Rapidminer was used for data cleaning and preprocessing, generating new attributes related to keyword usage in web pages.<br> </p>
Experimental Data for "What Makes a Top-Performing Precision Medicine Search Engine? Tracing Main System Features in a Systematic Way" at SIGIR2020
<p>This deposit contains data used for the experiments reported in the paper "<a href="https://doi.org/10.1145/3397271.3401048">What Makes a Top-Performing Precision Medicine Search Engine? Tracing Main System Features in a Systematic Way</a>", most notably the ElasticSearch 5.4 indices used for the reported experiments.</p> <p>To load the indices into an ElasticSearch cluster of your own, use the restore function described in the <a href="https://www.elastic.co/guide/en/elasticsearch/reference/5.4/modules-snapshots.html">ElasticSearch documentation</a>.</p> <p>The names of the index snapshots contained here are</p> <ul> <li>ct1718 for the indexed ClinicalTrials data used in the TREC-PM challenges in <a href="http://www.trec-cds.org/2017.html">2017</a> and <a href="http://www.trec-cds.org/2018.html">2018</a>.</li> <li>ct19 for the indexed ClinicalTrials data used in the TREC-PM challenge in <a href="http://www.trec-cds.org/2019.html">2019</a>.</li> <li>ba1718 for the indexed PubMed data used in the TREC-PM challenges in <a href="http://www.trec-cds.org/2017.html">2017</a> and <a href="http://www.trec-cds.org/2018.html">2018</a>.</li> <li>ba19 for the indexed PubMed data used in the TREC-PM challenge in <a href="http://www.trec-cds.org/2019.html">2019</a>.</li> </ul> <p>The other file contains the original output that <a href="https://www.automl.org/automated-algorithm-design/algorithm-configuration/smac/">SMAC</a> wrote to disc during the parameter optimization process. There are directories for the biomedical abstracts (BA) and clinical trials (ct) and for each respective 10 fold cross validation split. Those file contain the exact parameter configurations and their evalation score (the infNDCG metric was used) in live-runXX.json files.</p> <p>The code to these files is located in <a href="https://zenodo.org/record/3856403">this Zenodo deposit</a>.</p>
SoSEN-KG: Knowledge Graph Dump v0 for SoSEn: Software Search Engine.
<p>SoSEn is a semantic search engine for scientific software. We index scientific software in a knowledge graph, which is used for search and understanding of the software. The graph `graph.ttl` contains information about the software, and `keywords.ttl` contains keyword information about the software. `graph.ttl` can be used alone, or it can be combined with `keywords.ttl` to facilitate tf-idf based keyword search.</p>
Investigating Transformers as Context-aware Word Search Engines - Data and Code
<p>This is the review version, please use the published record: </p> <p> https://zenodo.org/record/6425595</p> <p> </p> <p>Data and code for the paper 'Investigating Transformers as Context-aware Word Search Engines' as submitted for review at ACL21. </p> <p> </p>
Screencast of the Lokahi2 Prototype: Search Engine with Interactive Knowledge Network Browser Extracted from Text
<p>This video shows a recording of the prototype system Lokahi2. It supports concept surfing for interacting with the search engine, automated document tagging, exploring tags, generating tags for text, and changing depth and dimension of the graph.</p>
Linkage of hospital records and death certificates by a search engine and machine learning: training and test set data
<p>INTRODUCTION: Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals. The objective of this study was to link French death certificates to the Bordeaux University Hospital records to complete the vital status information.</p> <p>MATERIALS AND METHODS: Our linkage strategy was composed of a search engine to reduce the number of comparisons and machine-learning algorithms. The overall pipeline was evaluated by assembling a file containing 3,565 in-hospital deaths and 15,000 alive persons.</p> <p>RESULTS: The recall and precision of our linkage strategy were 97.5% and 99.97% for the upper threshold and 99.4% and 98.9% for the lower threshold, respectively.</p> <p>CONCLUSION: In this article, we demonstrated the feasibility of accurately linking hospital records with death certificates using a search engine and machine learning.</p>
[Research Data] Mining Relevant Solutions for Programming Tasks from Search Engine Results
<p>[Abstract]</p> <p>Software development is a knowledge-intensive activity. Official documentation for developers may not be sufficient for all developer needs. Searching for information on the Internet is a usual practice, but finding really useful information may be challenging, because the best solutions are not always among the first ranked pages. So, developers have to read and discard irrelevant pages, that is, pages that do not have code examples or that have content with little focus on the desired solution. This work aims at proposing an approach to mine relevant solutions for programming tasks from search engine results that remove irrelevant pages. The approach works as follows: a query related to the programming task is prepared, and given as an input to a search engine. The returned pages pass through an automatic filter to select relevant pages. We evaluated the top-20 pages returned by the Google search engine, for 10 different queries, and observed that only 31\% of the evaluated pages are relevant to developers. Then, we proposed and evaluated three different approaches to mine the relevant pages returned by the search engine. Google’s search engine has been used as a baseline, and our results have shown that Google’s search engine returns a reasonable number of irrelevant pages for developers, and we could find an effective approach to remove irrelevant pages, suggesting that developers could benefit from a customized web search filter for development content.</p> <p>[Contents of Research Data.rar file]</p> <p>The Research Data.rar file has a folder called Research Data that contains 3 folders internally, with the names: “01 – Source Code”, “02 - Data” and “03 – Preprocessing rules”. The folder “01 – Source Code” contains the JAVA source code of the implementations of the proposed approaches. The folder “02 - Data” contains the data of the evaluations carried out in the work, which are in the folders “01 - Evaluation results of pages returned by Google” and “02 - Results of approaches comparisons”. The folder “01 - Evaluation results of pages returned by Google” has the evaluations carried out on the first 20 pages returned by Google, following the criteria defined in the work, for the 10 queries considered in the evaluation. The folder “02 - Results of approaches comparisons” contains the results of the evaluation of the proposed approaches, for the 10 queries considered in the evaluation. In this evaluation, the number of pages given as input for the approaches was increased from 3 to 20 pages, for each number of pages a folder was generated with the results. In addition to the results of the Precision, Recall and F-Measure metrics that are in the file named Results Approaches.txt, other files were generated for analysis. For example, the Instances_without_outliers.txt file shows which pages were filtered out after applying the outlier page removal filter. The Selected Pages Approach 4.txt file, on the other hand, shows which pages were filtered after applying the filters of the GORCUO approach. The folder “03 - Preprocessing rules” has a file called Rules.java. In this file, there is the commented JAVA source code, from the implementation of the rules created in the pre-processing stage of the proposed approach.</p>
User Evaluation and Metrics Analysis of a Prototype Web-based Federated Search Engine for Art and Cultural Heritage
<p>This dataset includes the quantitative data of the usage during the evaluation phase of a prototype web-based federated search engine for art and cultural heritage related content. The metrics which resulted in the dataset were in the form of a timeline of actions taken from a user (evaluator) in the course of a single session of interaction with the platform. A total of 20 different metrics were being monitored regarding the usage of the search engine, including submitting a query, a voice query, preforming a visual search, viewing a result, viewing a visual search result, updating an avatar, editing a user profile or changing user preferences, bookmarking and removing bookmarks of results and visual search results, using text to speech of all the various elements, opening the source view of a result and clicking a concept tag. All metrics included the timestamp of the event taking place and the value of the related event (e.g. the term of a search query).</p>
Driving Traffic to Institutional Repositories: How Search Engine Optimization can Increase the Number of Downloads from IR
<p>The success of institutional repositories (IR) is measured in large part by the number of file downloads they sustain. Most traffic to IR is referred by Internet search engines, but referrals are hindered when IR are not properly optimized for search engine harvesting and indexing, leading to low visitation and downloads. This presentation will discuss search engine optimization techniques, especially for Google Scholar, which can be responsible for the majority of referrals that result in IR file downloads. The presentation will also introduce a new web service called RAMP (Repository Analytics & Metrics Portal) that accurately counts file downloads from IR and requires no installation or training.</p>
Automated Support for Searching and Selecting Evidence in Software Engineering: A Cross-domain Systematic Mapping
<p>Dataset -- Brief summary of the automated approaches for searching and selecting studies for secondary studies in software engineering </p>
Automated Support for Searching and SelectingEvidence in Software Engineering: A Cross-domainSystematic Mapping
<p>Data from the studies that adrress search and selection approaches. </p>
Dataset for paper "The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software Engineering"
<p>This contains the dataset matched with the code on the GitHub page: https://github.com/ideas-labo/pareto-vs-weight-for-sbse.</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.