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1,063 results for “Search”
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Data set for ``Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?''
<p>One of the black arts of data mining is learning the magic parameters that control the learners. In software analytics, at least for defect prediction, several methods, like grid search and differential evolution(DE), have been proposed to learn those parameters. They’ve been proved to be able to improve learner performance.</p> <p>We want to evaluate which method can find better parameters in terms of performance score and runtime. This paper compares grid search to differential evolution, which is an evolutionary algorithm that makes extensive use of stochastic jumps around the search space. We find that the seemingly complete approach of grid search does no better, and sometimes worse, than the stochastic search. Yet, when repeated 20 times to check for conclusion validity, DE was over 210 times faster (6.2 hours for DE vs 54 days for grid search when both tuning Random Forest over 17 test data sets with F-measure as optimization objective).</p> <p>These results are puzzling: why does a quick partial search be just as effective as a much slower, and much more, extensive search? To answer that question, we turned to the theoretical optimization literature. Bergstra and Bengio conjecture that grid search is not more effective than more randomized searchers if the underlying search space is inherently low dimensional. This is significant since recent results show that defect prediction exhibits very low intrinsic dimensionality– an observation that explains why a fast method like DE may work as well as a seemingly more thorough grid search. This suggests, as a future research direction, that it might be possible to peek at data sets before doing any optimization in order to match the optimization algorithm to the problem at hand.</p>
Reward draws the eye, uncertainty holds the eye: Associative learning modulates distracter interference in visual search.
<p>Eye tracking data and statistical analysis of:</p> <p>Koenig, S., Kadel, H., Uengoer, M., Schubö, A., & Lachnit, H. (2017). Reward draws the eye, uncertainty holds the eye: Associative learning modulates distracter interference in visual search. <em>Frontiers in Behavioral Neuroscience</em>. doi: 10.3389/fnbeh.2017.00128.</p> <p> Abstract: Stimuli in our sensory environment differ with respect to their physical salience, but moreover may acquire motivational salience by association with reward. If we repeatedly observed that reward is available in the context of a particular cue, but absent in the context of another cue, the former typically attracts more attention than the latter. However, we also may encounter cues uncorrelated with reward. A cue with 50% reward contingency may induce an average reward expectancy, but at the same time induces high reward uncertainty. In the current experiment we examined how both values, reward expectancy and uncertainty, affected overt attention. Two different colors were established as predictive cues for low reward and high reward respectively. A third color was followed by high reward on 50% of the trials and thus induced uncertainty. Colors then were introduced as distractors during search for a shape target and we examined the relative potential of the color distractors to capture and hold the first fixation. We observed that capture frequency corresponded to reward expectancy while capture duration corresponded to uncertainty. The results may suggest that within trial, reward expectancy is represented at an earlier time window than uncertainty.</p>
Part-by-part interface-based search and automatic reassembly of CAD models for database expansion and model reuse
<p>This dataset contains the generated cad assembly from three differents strategy from the article "Part-by-part interface-based search and automatic reassembly of CAD models for database expansion and model reuse".<br> In ReplacePart strategy, each assembly is available in .FCStd format which has the kinematic constraints in the A2+ workbench. A .STEP format of the assembly as well as a PNG screen of the assembly is also available. For the two other strategies, juste STEP files are availables.</p>
Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic
Open the record for dataset details and reuse information.
SeaPaCS_In search of Plastic
<p>Supported by the public engagement activities funds by the <a href="https://crowdusg.net/diggeoesomas/">DIGGEO@ESOMAS</a>, Department ESOMAS – University of Turin and the SeaPaCS project (funded by I<a href="https://impetus4cs.eu/">MPETUS project</a> – funded by the European Union), the video tells a compelling story about the <a href="https://crowdusg.net/2023/04/03/exploration-of-the-plastisphere-in-the-chthulucene-participatory-science-for-microplastic-monitoring-on-a-sailboat-at-lni-in-anzio/">explorations of the plastisphere </a>and <a href="https://crowdusg.net/2023/05/23/photo-exhibition-on-new-hybrid-ecologies-of-the-mediterranean-sea/">its visual documentation</a>.</p>
Search strategies for screening for abdominal aortic aneurysm (AAA) in men: a health technology assessment
<p>The dataset includes the complete, reproducible search strategies for all literature databases searched during this project. The search strategies address the following research question: </p><p>What is the clinical effectiveness and safety of population-based ultrasound screening for AAA in men compared with no systematic screening?</p>
USPTO Dataset for: Fast Chemical Reaction Condition Suggestion via Rule-Based Classification and Similarity Search
<p>USPTO database that is analyzed with Rxn-INSIGHT (<a href="https://github.com/mrodobbe/Rxn-INSIGHT">https://github.com/mrodobbe/Rxn-INSIGHT</a>).</p><p>This gzip file contains a very large Pandas DataFrame that can be loaded via pd.read_parquet('uspto_rxn_insight.gzip'). Because of the large size of the data, PyArrow version 13.0 must be used. </p><p>To use parquet in Pandas, install PyArrow and fastparquet using pip:</p><p>pip install pyarrow==13.0<br>pip install fastparquet</p>
WW1 search light position, Espoo, Finland
First World War trench search light position in base XXVIII:5 in [Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) land front defense line in Vallikallio, Espoo, Finland. The base was built in 1915 - 1918. The whole base contains: * 110 meters of trench * 2 observation positions * 2 search light positions * 6 shelter rooms More info: * [Location in Google Maps](https://goo.gl/maps/aY5EdsrJFiSWNQmy6) * [Wikipedia: Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) * [John Lagerstedt , Markku Saari: Krepost Sveaborg](http://www.novision.fi/viapori/eavaus.htm) * [Finnish Heritage Agency site info](https://www.kyppi.fi/to.aspx?id=112.1000007748) (only in finnish) Source: Objaverse 1.0 / Sketchfab
Ceres searching for Proserpina
Ceres searching for Proserpina, 1780 Johan Tobias Sergel Drag and Drop and you are good to go. 4k Textures. Check my profile for free models https://sketchfab.com/re1monsen If you enjoy my work please consider supporting me I have many affordable models in the shop. Smash that follow! Feel free to contact me. I'd love yo hear from you. Thanks! Source: Objaverse 1.0 / Sketchfab
The known pulsars detected in the GP survey in only beamformed searches.
<p><span>The known pulsars detected in the GP survey in only beamformed </span><span>searches. It shows the names and parameters of the pulsars. Detailed analysis of </span><span>these pulsars can be found in Xue et al. (2017) and Bhat et al. (2023b). As this </span><span>work is mainly focused on the imaging aspect of pulsar searching, these pulsars </span><span>are not included as part of the analysis done for this work.</span></p>
The data for "Search for gravitational-lensing signatures in the full third observing run of the LIGO–Virgo network"
<p>This material is part of several data products associated with the publication "Search for gravitational-lensing signatures in the full third observing run of the LIGO–Virgo network" from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://arxiv.org/abs/2304.08393">arXiv</a>) and the related material linked from this page.</p><p><strong>Data release</strong></p><p>This data release contains plotting scripts, jupyter notebooks and datasets for the figures / tables in the aforementioned paper. Each script/notebook have detailed instructions and comments.</p><p><strong>How to download all files from this page</strong></p><p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p><p>pip install zenodo_get zenodo-get RECORD_ID_OR_DOI</p><p>where the record ID for the most recent version of this page is v5 and IDs for other versions can be found in the Versions section at the side of this page.</p>
NSRC-Search: Efficient searching for similar protein sequences of non-standard amino acid composition
<p>This research was funded by the National Science Centre in Poland (grant number 2021/41/N/ST6/01919)</p>
Project Section: Searching for Mutations Leading to Isoniazid Resistance in TB (HackBio)
<p><strong>Introduction</strong></p> <p><span>Tuberculosis (TB) remains a global health concern, and the emergence of drug-resistant strains poses a significant challenge to effective treatment. Isoniazid is a key first-line drug used in the treatment of TB, and resistance to this drug can compromise the success of therapy. </span></p> <p><span>This project aims to identify and characterise genetic mutations associated with isoniazid resistance in </span><em>Mycobacterium tuberculosis</em><span>, the causative agent of TB. </span></p> <p><strong>Datasets</strong><span>: Here</span></p> <p><strong>Task:</strong></p> <ul> <li><span>Given the datasets above, perform the complete variant calling and annotation pipeline to specifically identify the following known mutations in </span><em>Mycobacterium tuberculosis</em><span> (against Isoniazid). </span></li> <li><span>Provide pretty IGV visualisation of mutations in </span><em>katG</em><span> sequences (Position: 2156111 - 2153889) across all the samples. N/B: </span><em>katG</em><span> mutations are prevalent in isoniazid resistance.</span></li> <li><span>Also, explore associated biological pathways to understand the molecular mechanisms of isoniazid resistance.</span></li> </ul> <p><strong><span>Expected Output:</span></strong></p> <p><span>Kindly submit a comprehensive project report, including methods, results, and interpretations.</span></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>
Dataset for "Deep Learning to Improve the Sensitivity of Di-Higgs Searches in the 4b Channel"
<p>Dataset for the paper "Deep Learning to Improve the Sensitivity of Di-Higgs Searches in the 4b Channel".</p>
Figure 6 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities
Figure 6. Right scapula of the specimen MUSM 2542, Messapicetus gregarius, in dorsal view.
Searching to improve connectivity in fragmented landscapes: Road verges as complementary habitats
<p><strong>Abstract</strong></p> <p><strong>Aims: </strong>Functional connectivity is crucial for conserving biodiversity and ecosystem services in fragmented landscapes. Linear landscape elements play an important role in providing refuge for plants and pollinators, as well as serving as biological corridors.<strong> </strong>The aim of this study was to assess the importance of road verges in maintaining the ecological connectivity of plant species assemblages in the Tandilia mountain system.</p> <p><strong>Location: </strong>South America. Southern Pampa region of Argentina. Tandilia mountain system.</p> <p><strong>Methods:</strong> Using graph theory, landscape connectivity was quantified both at landscape scale and at the patch scale using the probability connectivity index (PC<sub>num</sub>), taking into account the dispersal ability of observed species. The contribution of each landscape element to habitat availability and connectivity was evaluated using different fractions of the PC<sub>num</sub> metric. Likewise, the influence of connectivity variables in explaining species assemblages grouped by functional categories was investigated using canonical ordination techniques.</p> <p><strong>Results: </strong>The inclusion of road verges led to an 81% increase in overall connectivity at a threshold distance of 500 m. Connectivity significantly explained the assemblages of entomophilous plant species for all dispersal distances. Annual species assemblage variation was associated with intra-patch connectivity. Variation in perennial and shrub species assemblages was explained by intra-patch and inter-patch connectivity.</p> <p><strong>Conclusions: </strong>Preservation and restoration of these linear landscape elements play a pivotal role in transitioning towards more sustainable agroecosystems. This ecological and practical information may help prioritize plant species and the sites used to restore an essential but neglected ecosystem.</p> <p>This dataset includes: </p> <ol> <li>GPS coordinates for sampling sites.</li> <li>Data matrix includes sites x presence-absence of entomophilous plant species for the 54 sampling sites, 20 sierras and 34 rural road verges. The sierras are listed by name, while the road verges are identified by numbers.</li> </ol>
Search Strategies for the Cost Effectiveness of Colorectal Cancer Screening
<p><span>The dataset includes the complete, reproducible search strategies for all bibliographic databases searched during this project. The search strategies were designed to answer the following research question:</span></p> <p><span>For the Irish population at average risk of colorectal cancer, is biennial FIT-based colorectal cancer screening at a FIT threshold of 45 ug/g, in persons aged from age 50 to 74 years, cost effective compared to screening in persons aged 55 to 74 years</span></p>
Search strategies for a rapid review for economic modelling studies on RSV immunisation
<p>The dataset includes the complete, reproducible search strategies for all bibliographic databases searched during this project. The search strategies were designed to answer the following research question: </p> <p>What approaches are taken to modelling the expected costs and benefits of RSV immunisation in children and/or adults in high-income countries (as defined by the OECD)?</p> <p> </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.