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47 results for “browsers”
EvoRef: EvoBib Reference Browser
<p>This is the dump of the data underlying <a href="http://calc.digling.org/evoref/">EvoRef</a>, a collection of quotes from different bibliographic references, which is in part connected to the EvoBib bibliography.</p>
[Dataset] FP-Redemption: Measuring Browser Fingerprinting Adoption for the Sake of Web Security
<p>Full dataset for the paper "FP-Redemption: Measuring Browser Fingerprinting Adoption for the Sake of Web Security"</p> <p>5 files are provided:</p> <ul> <li>dataset.csv. The raw elements collected when browsing the web. Each entry corresponds to one attribute being accessed with one parameter combination by one script on one webpage. A single attribute with the same parameters can be accessed several times. It is represented with the key <em>nbTimes</em></li> <li>domainTags.csv: For each website, it provides its category and country tag.</li> <li>webpageTags.csv: For each webpage, it provides its type.</li> <li>fingerprinters.zip/<filenumber>.js: Our fingerprinters. Out of the 199 we requested, 7 are missing, leading in 192 js files.</li> <li>mapping.csv. 3 columns CSV file: <ul> <li>The first one lists the 199 fingerprinters detected by our algorithm.</li> <li>The second one gives the <filenumber> used to link a fingerprinter and its file in the directory.</li> <li>The third one gives the groups the fingerprinters belongs to. By default, each fingerprinter belongs to his own group. However, several fingerprinters are belonging to the same group as we evaluate there were duplicates. Thus, the number of distinct groups corresponds to the distinct fingerprinters we measured in our dataset: 169.</li> </ul> </li> </ul>
Web browser useragent and activity tracking data
<p>600 000 000 web traffic records normalized into MySQL tables using TokuDB storage, complete with original web server response codes. Suitable for browser data and trend analysis as well as AI training of exploit and bot detection algorithms. The data had been collected from multiple Apache 2.x web servers across 8000+ domain names with special care for GDPR compliance.</p> <p> </p>
Data from: Tree growth-forms reveal dominant browsers shaping the vegetation
<p>This data repository belongs to the publication "Tree growth-forms reveal dominant browsers shaping the vegetation" by Churski et al. in Functional Ecology</p> <p>Authors Marcin Churski<sup>1</sup>, Dries P.J. Kuijper<sup>1</sup>, Katharina Semmelmayer<sup>1</sup>, William J. Bond<sup>2</sup>, Joris P.G.M. Cromsigt<sup>3</sup><sup>,</sup><sup>4</sup>, Yan Wang<sup>5</sup> & Tristan Charles-Dominique<sup>6</sup><sup>,</sup><sup>7</sup></p> <p>Author for correspondence: Marcin Churski email: <a href="mailto:mchurski@ibs.bialowieza.pl">mchurski@ibs.bialowieza.pl</a></p> <p><sup>1</sup>Mammal Research Institute Polish Academy of Sciences, ul. Stoczek 1, 17-230 Białowieża; <sup>2</sup>University of Cape Town, HW Pearson Building, University Ave N, Rondebosch, Cape Town, 7701; <sup>3</sup>SLU, Department of Wildlife, Fish and Environmental Studies, 901 83 Umeå, Sweden; <sup>4</sup>Centre for African Conservation Ecology, Department of Zoology, Nelson Mandela University, PO Box 77000, Gqeberha, 6031, South Africa; <sup>5</sup>Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland; <sup>6</sup>AMAP, University of Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France; <sup>7</sup>CNRS UMR7618; Sorbonne University; Institute of Ecology and Environmental Sciences Paris; 4, place Jussieu 75005 PARIS</p> <div> <h4>Summary</h4> <a href="https://github.com/mripasteam/transformers#summary"></a></div> <ul> <li>Plants adopt particular growth-forms when they are exposed to extreme environmental conditions. In this study, we describe a unique woody plant growth-form induced by large mammalian herbivores and discuss that this growth-form could have evolved as a strategy for escaping the browser zone in herbivore driven ecosystems.</li> <li>We analysed responses of key architectural and morphological attributes (branching and thorn density, tree dimensions, presence of flowers and fruits) of three Eurasian spiny tree species (Malus sylvestris, Prunus cerasifera, Pyrus pyraster) to different levels of browsing by large herbivores in the temperate Białowieża Forest, Poland.</li> <li>Under high browsing pressure, studied trees displayed two distinct forms of the crown: a bottom sterile part developing into a densely branched structure with high density of thorns (‘cage-form’), and an upper reproductive part that escaped from herbivore control (‘escaped-form’). The size of cage-form influenced the feeding behaviour of red deer (Cervus elaphus) by increasing the time deer spend foraging and increasing the bite rate. The height at which cages started to escape and their diameter matched with foraging reach of red deer.</li> <li>Synthesis. We argue that the frequency and cage dimensions of this woody growth-form in the landscape could inform on the type and intensity of recent herbivory. Moreover, its distinctive inducibility suggests that this growth-form did not emerge recently under anthropogenic pressure but could be the legacy of ancient herbivory effects. Observational evidence suggests that this growth-form emerged in several herbivore-driven systems around the globe and may be used to identify the dominant herbivores that control vegetation structure in these ecosystems.</li> </ul> <p>Data</p> <p>The table 'cage_traits.csv' contains data on morphological traits measured on individual trees and was used to describe the key architectural attributes defining the cage and escape forms (trapped branches vs. escaped branches), test if the cage form is induced by mammalian herbivores or not and if the dimensions of the cage form could inform on which animal induced them.</p> <div> <pre><code>Column headers description: N_Protocol: Tree ID Status: if the tree individual grow taller than animal reach (escaped or trapped) Species: tree species Branch_N: observed branch ID Length: branch length (cm) N_Thorns: number of thorns N_Twigs: number of twigs Longest_thorn: the length of longest thorn on the branch (mm) N_browsed_Twigs: Number of browsed twigs (1) vs non browsed (0) twigs on a branch Branch_esc: branch position, "escape" indicates the branch growing on the escaped part of a tree FlowerOrFruit: flower or fruit number found on the branch BDI: branch density index. It is calculated by twigs number divided by branch length Thorn_density:Thorn number divided by branch length BrowRate: observed number of browsed_Twigs divided by branch length Bite: 1 indicates the branch was browsed, 0 indicated the branch was not browsed. browsing_environment: if the tree is exposed to high browsing environment or not. </code></pre> <div> </div> </div> <p>The table 'foraging_time_barplot.csv' contains camera trap data on total foraging time of all the animal on all the tree species and was used to answer the question how the presence of cage form affect herbivore foraging behaviour. This data set was specifically used to produce the bar plot in Figure 5C.</p> <div> <pre><code>Column headers description: N_Protocol : Tree ID animal_species : observed animal species Species: tree species Foraging_time: observed total foraging time. </code></pre> <div> </div> </div> <p>The table 'foraging_time.csv' contains camera trap data on total foraging time of all the animal on all the tree species and was used to answer the question how the presence of cage form affect herbivore foraging behaviour.</p> <div> <pre><code>Column headers description: N_Protocol: Tree ID Total_foraging_time: total record foraging time per tree individual Total_bite_rate : bite rate per tree individual Bite_rate : bite rate per tree branch BDI: branch density index. Foraging_time_av: record foraging time per tree branch Surface: the surface of the crown </code></pre> <div> </div> </div> <p>The table 'escape_height.csv' contains data on individual tree heights in relation to their status (escaped vs trapped). This data set was used to test if the dimensions of the cage form could inform on which animal induced them.</p> <div> <pre><code>Column headers description: N_Protocol: Tree ID Status: if the tree individual grow taller than animal reach Species: Tree species Height: Tree height</code></pre> </div>
Dataset for the Galaxy Training Network (GTN) Tutorial "Viewing Cancer Alignments in a Genome Browser"
<p>Datasets for the Galaxy Training Network (GTN) Tutorial "Viewing Cancer Alignments in a Genome Browser"</p>
Opportunities and Limitations of Running Python Code in the Web Browser
<p>Python is a popular programming language that is widely used for a variety of applications such as web development, data analysis, scientific computing, and education. This versatility makes it a popular choice for developers and educators who need a language that can be used for a wide variety of tasks. While Python is typically run on the server side or on the desktop, there is a growing interest in running Python code directly on the client side, i.e., in the browser.</p> <p>There are several possibilities available for running Python code in the browser, among others,</p> <ul> <li>transpiling it into JavaScript (cf. e.g., Transcrypt),</li> <li>running it by making use of an interpreter implemented in JavaScript (cf. e.g., Brython or Skulpt),</li> <li>or executed it by leveraging a Python interpreter compiled to WebAssembly (cf. e.g., Pyodide or CoWasm), an open standard defined by the World Wide Web Consortium specifying a bytecode for running programs in browsers.</li> </ul> <p>Since the choice of the right tool for a given application depends on the specific requirements and constraints, the aim of this bachelor thesis is to provide an overview of the state of the art in running Python code in the browser. This is done by presenting a variety of different tools and environments available, their capabilities and limitations, as well as some application examples and sample code. Furthermore, similar to Kiyokawa’s & Jin’s (2022) work on “A Front-End Framework Selection Assistance System [...]”, the objective of this bachelor’s thesis is to develop criteria to help developers and educators in the selection process.</p> <p> </p> <p>Link to Respository: <a href="https://git.uibk.ac.at/csav4362/running-python-in-web-browser">https://git.uibk.ac.at/csav4362/running-python-in-web-browser</a></p>
UCSC Xena Data Browser Copy Number Data - Genes (Thresholded)
<p>Downloaded by Gregory Way from the UCSC Xena data browser on 14 July 2017</p> <p>https://xenabrowser.net/datapages/?dataset=TCGA.PANCAN.sampleMap/Gistic2_CopyNumber_Gistic2_all_thresholded.by_genes&host=https://tcga.xenahubs.net</p>
Fire, grazers and browsers interact with grass competition to determine tree establishment in an African savanna
<p>In savanna ecosystems, fire and herbivory alter the competitive relationship between trees and grasses. Mechanistically, grazing herbivores favor trees by removing grass, which reduces tree-grass competition and limits fire. Conversely, browsing herbivores consume trees and limit their recovery from fire. Herbivore feeding decisions are in turn shaped by risk-resource trade-offs that potentially determine the spatial patterns of herbivory. Identifying the dominant mechanistic pathways by which fire and herbivores control tree cover remains challenging, but is essential for understanding savanna dynamics. We used an experiment in the Serengeti ecosystem and a simple simulation driven by experimental results to address two main aims: (1) determine the importance of direct and indirect effects of grass, fire and herbivory on seedling establishment; and (2) establish whether predators determine the spatial pattern of successful seedling establishment via effects on mesoherbivore distribution. We transplanted tree seedlings into plots with a factorial combination of grass and herbivores (present/absent) across a lion kill-risk gradient in the Serengeti, burning half of the plots near the end of the experiment. Ungrazed grass limited tree seedling survival directly via competition, indirectly via fire, and by slowing seedling growth, which drove higher seedling mortality during fires. These effects restricted seedling establishment to below 18% and, in conjunction with browsing, resulted in seedling establishment dropping below 5%. In the absence of browsing and fire, grazing drove a 7.5-fold increase in seedling establishment. Lion predation risk had no observable impact on herbivore effects on seedling establishment. The severe negative effects of grass on seedling mortality suggests that regional patterns of tree cover and fire may overestimate the role of fire in limiting tree cover, with regular fires representing a proxy for the competitive effects of grass.</p>
Artifacts for "Did I Vet You Before? Assessing the Chrome Web Store Vetting Process through Browser Extension Similarity"
<p>This record contains the artifacts for the aforementioned paper.</p> <p>All artifacts are provided in <a title="https://parquet.apache.org/" href="https://parquet.apache.org/">Apache Parquet</a> format. We recommend using Python's <a title="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_parquet.html" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_parquet.html">pandas library</a> to open these files.</p> <p>The following is a summary of the provided artifacts:</p> <ul> <li><code>ground-truth.parquet</code>: Contains pairs of extensions used as ground truth for evaluating the pipeline employed in our research.</li> <li><code>infringing-extensions.parquet</code>: Metadata of vetted extensions labeled by Google and infringing extensions found by our pipeline. Includes the cluster assigned by HDBSCAN and the UMAP 2D coordinates used for visualizations.</li> <li><code>embeddings.parquet</code>: Vector embeddings of infringing extensions generated by our pipeline.</li> </ul>
Padding Ain't Enough: Assessing the Privacy Guarantees of Encrypted DNS – Subpage-Agnostic Domain Classification Tor Browser
<p>This dataset contains the second part of the "Subpage-Agnostic Domain Classification" section of our FOCI 2020 paper "Padding Ain’t Enough: Assessing the Privacy Guarantees of Encrypted DNS".</p> <p><a href="https://www.usenix.org/conference/foci20/presentation/bushart">https://www.usenix.org/conference/foci20/presentation/bushart</a></p> <p>You can find the source code for this project on GitHub: <a href="https://github.com/jonasbb/padding-aint-enough">https://github.com/jonasbb/padding-aint-enough</a></p> <p>When using this software or our dataset, please cite our FOCI 20 paper.</p> <pre>@inproceedings {PaddingAintEnough, author = {Jonas Bushart and Christian Rossow}, booktitle = {10th {USENIX} Workshop on Free and Open Communications on the Internet ({FOCI} 20)}, month = aug, publisher = {{USENIX} Association}, title = {Padding Ain{\textquoteright}t Enough: Assessing the Privacy Guarantees of Encrypted {DNS}}, year = {2020}, }</pre>
Testfile for support of Arabic script in web browsers
<p>A very basic HTML file for testing the extent of browser support for the `@lang` attribute in HTML5 with Arabic examples. </p>
Persistence of Glaucoma Patients With Web-Browser-Based Visual Field Test
ClinicalTrials.gov study NCT05690152. IPD Sharing: NO. Countries: 1. Publications: 4.
Fire, grazers and browsers interact with grass competition to determine tree establishment in an African savanna
Open the record for dataset details and reuse information.
Example modbed files can be used for visualization on the WashU Epigenome Browser
<p>Example modbed files can be used for visualization on the <a href="https://epigenomegateway.wustl.edu/browser/">WashU Epigenome Browser</a>. Please check README for more details.</p><p>Please contact Daofeng Li (dli23@wustl.edu) if you have any questions.</p><h3>Description of the files:</h3><p>HG00621.remora.modbed.gz: Genome wide ONT remora data</p><p>remora-test-chr11.modbed.gz: ONT remora data only on chr11</p><p>HG00621.hifi.cpg.modbed.gz: Genome wide PacBio Hifi data</p><p>hifi-test-chr11.cpg.modbed.gz: PacBio Hifi data only on chr11 at CpG mode</p><p>hifi-test.modbed-hbg.gz: PacBio Hifi data only on chr11:5162720-5356331</p><p>GSM4411218_tracks_m6A_DS75167.dm6.modbed.gz: Fruit fly Fiber-seq data</p><h3>Note:</h3><p>Please note that you would need <a href="https://www.htslib.org/doc/tabix.html">Tabix</a> to index the .gz files.</p>
Example datasets for ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images
<p>This is an example dataset.</p><p>It accompanies the manuscript titled "ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images".</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>
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Figure 21 in The axial biomechanics of Trigonosaurus pricei (Neosauropoda: Titanosauria) and the importance of the cervical-dorsal region to sauropod high-browser feeding strategy
Figure 21. Reconstruction of what the sauropod Trigonosaurus pricei looked like in life, with an elevated neck and a sigmoidal tail above the horizontal line.
Figure 20. A in The axial biomechanics of Trigonosaurus pricei (Neosauropoda: Titanosauria) and the importance of the cervical-dorsal region to sauropod high-browser feeding strategy
Figure 20. A, reconstruction of the general body plan of Trigonosaurus associated with MCT 1719-R as a paratype. B, reconstruction of the general body plan of Trigonosaurus considering only the MCT 1488-R holotype. A standard tail was used.
Figure 19. A, C, E in The axial biomechanics of Trigonosaurus pricei (Neosauropoda: Titanosauria) and the importance of the cervical-dorsal region to sauropod high-browser feeding strategy
Figure 19. A, C, E, Apatosaurus, Diplodocus, and Barosaurus, respectively, with horizontalized neck [according to Stevens and Parrish 2005a (A, C) and Lovelace, 2007 (E), in light grey]. B, D, F, Apatosaurus, Diplodocus, and Barosaurus, respectively, with a more upward-facing neck, in dark grey (according to the authors). In F, the Cv13 vertebra is a simple line art representation. No scale.
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.