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1,063 results for “Search”
Search-and-Rescue From Drones With Computer Vision
<p>Unmanned aerial vehicles (UAVs), most commonly known as drones, are increasingly used as a technological support tool for search-and-rescue (SAR) operations (and post-disaster area explorations as well). UAVs equipped with high-resolution cameras and embedded, yet powerful GPUs, in fact, can provide an effective and efficient aid to emergency rescue operations, mainly because locating victims, which may be unconscious or injured, as much fast as possible, is crucial to improve their chance of survival. In particular, the use of drones that are able to automatically detect people in the scenes can increase detection rate, while reducing rescue time. In this repository, we provide a new dataset specifically conceived for SAR operations from drones with computer vision. As it is small-sized, the dataset is currently intended for testing and evaluation purposes only. The main aim of the repository is to encourage contributions on this intriguing topic. In particular, any contribution to make the dataset bigger is welcome.</p>
Data from: How do seabirds modify their search behaviour when encountering fishing boats?
Seabirds are well known to be attracted by fishing boats to forage on offal and baits. We used recently developed loggers that record accurate GPS position and detect the presence of boats through their radar emissions to examine how albatrosses use Area Restricted Search (ARS) and if so, have specific ARS behaviours, when attending boats. As much as 78.5% of locations with a radar detection (contact with boat) during a trip occurred within ARS: 36.8% of all large-scale ARS (n=212) and 14.7% of all small-scale ARS (n=1476) were associated with the presence of a boat. During small-scale ARS, birds spent more time and had greater sinuosity during boat-associated ARS compared with other ARS that we considered natural. For, small-scale ARS associated with boats, those performed over shelves were longer in duration, had greater sinuosity, and birds spent more time sitting on water compared with oceanic ARS associated with boats. We also found that the proportion of small-scale ARS tend to be more frequently nested in larger-scale ARS was higher for birds associated with boats and that ARS behaviour differed between oceanic (tuna fisheries) and shelf-edge (mainly Patagonian toothfish fisheries) habitats. We suggest that, in seabird species attracted by boats, a significant amount of ARS behaviours are associated with boats, and that it is important to be able to separate ARS behaviours associated to boats from natural searching behaviours. Our study suggest that studying ARS characteristics should help attribute specific behaviours associated to the presence of boats and understand associated risks between fisheries.
Enhancing Open Modification Searches via a Combined Approach Facilitated by Ursgal
<p>The identification of peptide sequences and their post-translational modifications (PTMs) is a crucial step in the analysis of bottom-up proteomics data. The recent development of open modification search (OMS) engines allows virtually all PTMs to be searched for. This not only increases the number of spectra that can be matched to peptides but also greatly advances the understanding of biological roles of PTMs through the identification, and thereby facilitated quantification, of peptidoforms (peptide sequences and their potential PTMs). While the benefits of combining results from multiple protein database search engines has been established previously, similar approaches for OMS results are missing so far. Here, we compare and combine results from three different OMS engines, demonstrating an increase in peptide spectrum matches of 8-18%. The unification of search results furthermore allows for the combined downstream processing of search results, including the mapping to potential PTMs. Finally, we test for the ability of OMS engines to identify glycosylated peptides. The implementation of these engines in the Python framework Ursgal facilitates the straightforward application of OMS with unified parameters and results files, thereby enabling yet unmatched high-throughput, large-scale data analysis.</p> <p>This dataset includes all relevant results files, databases, and scripts that correspond to the accompanying journal article. Specifically, the following files are deposited:</p> <ul> <li>Homo_sapiens_PXD004452_results.zip: result files from OMS and CS for the dataset PXD004452</li> <li>Homo_sapiens_PXD013715_results.zip: result files from OMS and CS for the dataset PXD013715</li> <li>Haloferax_volcanii_PXD021874_results.zip: result files from OMS and CS for the dataset PXD021874</li> <li>Escherichia_coli_PXD000498_results.zip: result files from OMS and CS for the dataset PXD000498</li> <li>databases.zip: target-decoy databases for <em>Homo sapiens</em>, <em>Escherichia coli </em>and <em>Haloferax volcanii</em> as well as a glycan database for <em>Homo sapiens</em></li> <li>scripts.zip: example scripts for all relevant steps of the analysis</li> <li>mzml_files.zip: mzML files for all included datasets</li> <li>ursgal.zip: current version of Ursgal (0.6.7) that has been used to generate the results (for most recent versions see https://github.com/ursgal/ursgal)</li> </ul>
Point, polygon, or marker? In search of the best geographic entity for mapping Cultural Ecosystem Services using the online PPGIS tool, "My Green Place."
<p>Excel files include the raw database and the processed data that led to the quadrat analyses. The "Matrix_raw data" file includes the raw data as downloaded from the server. This data was cleaned and organized for its posterior use. "Quadrat analyzes "file includes all the quadrat analyses resulting in each research question in the paper except question four. Question 4 can be seen in the file "Water analysis_Blaarmeersen." All excel files come with a "CODE" tab that describes each of the codes used, their meaning, and ways that were calculated where necessary. Two zip files include all the GIS files. The first one includes the GIS files from which "Matrix_raw data" was built from. The second folder includes the resulting maps from the quadrat analyses. In order to visualize them as in the paper, configure the symbology tab at the GIS software in quantile and the categories number, as shown in the paper.</p> <p>The production of the files in the "GIS_Processed data" folder was done via a repetitive line of commands in ArcGIS pro. The same process was followed for each one of the quadrat analysis described in the paper. Refer to "<a href="https://zenodo.org/api/files/72403e0b-78a9-4bbf-8dca-ed9b8702e3ae/Reproduction%20commands%20and%20parameters.pdf">Reproduction commands and parameters.pdf</a>" for further information.</p>
Dataset for: Searching for Imaging Biomarkers of Psychotic Dysconnectivity
<p>This dataset contains features used in analyses for the following manuscript:</p> <p>Rodrigue, AL, et al. (2021). Searching for Imaging Biomarkers of Psychotic Dysconnectivity. <em>Biological Psychiatry: Cognitive Neuroscience and Neuroimaging,</em> in press.</p> <p>Contents:</p> <p><br> - 4 Demographic csv files. Each dataset has a csv for covariates of interest- Age, Sex, and Site (BSNIP1 only)<br> site coding: 1=Hartford,CT, 2=Baltimore,MD<br> DIAG coding: SZ=Schizophrenia, SAD=Schizoaffective Disorder, BPP=Bipolar Disorder I with Psychosis, MDD=Major Depressive Disorder with Psychosis,OTH=Other Psychotic Disorder</p> <p>- 16 feature csv files. Each dataset has a .csv for raw and residualized DTI and rsfMRI features<br> ISMMS_DTI_raw_Features.csv<br> Olin_DTI_raw_Features.csv<br> BSNIP1_DTI_raw_Features.csv<br> BSNIP2_DTI_raw_Features.csv<br> ISMMS_rsfMRI_raw_Features.csv<br> Olin_rsfMRI_raw_Features.csv<br> BSNIP1_rsfMRI_raw_Features.csv<br> BSNIP2_rsfMRI_raw_Features.csv</p> <p>Residualized (age, sex, site (BNIP1 only))<br> ISMMS_DTI_res_Features.csv<br> Olin_DTI_res_Features.csv<br> BSNIP1_DTI_res_Features.csv<br> BSNIP2_DTI_res_Features.csv<br> ISMMS_rsfMRI_res_Features.csv<br> Olin_rsfMRI_res_Features.csv<br> BSNIP1_rsfMRI_res_Features.csv<br> BSNIP2_rsfMRI_res_Features.csv</p>
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>
Summon Topic Explorer Results by Search Query
<p>8,000 Summon Topic Explorer results from actual user search queries at GVSU using Summon 2.0 with the Topic Explorer fed from Wikipedia and Gale Virtual Reference Library.</p> <p>topics.xlsx is a collection of the 8,000 analyzed search queries with Topic Explorer results.</p> <p>All_Problematic_Searches.xlsx is a sheet of 561 incorrect or biased Topic Explorer results.</p> <p>Results_with_Bias.xlsx contains 54 biased Topic Explorer results.</p>
Understanding in vivo Models of Depression: A Systematic Review - Records of Full Search
<p>This Zenodo record outlines the full list of journal articles retrieved from the search string as well as a a subset of articles that have been screened by two independent human reviews and reconciled by a third independent screener. </p> <p> </p> <p>We carried out a search of 2 online databases for studies reporting animal models of depression. This search, carried out in May 2016, identified 70,365 unique publications (File: Depression-Dataset-SLIM-AllRecords.txt )</p> <p> </p> <p>Two independent investigators have, to date, screened 5749 of these publications for inclusion or exclusion and these publications form the dataset for this study (File: Updated-training-data.txt ). </p> <p>Several text-mining approaches will be developed for this depression literature search using the results from manual screening to train the machine, where machine-learning software set rules to automatically define each publication as included or excluded without the need for human screening. In this project, we seek to identify the best performing machine learning algorithm for this depression literature search. Performance is measured on sensitivity, specificity, and precision. </p> <p> </p> <p>Column Names in Datasets: </p> <p>Depression-Dataset-SLIM-AllRecords.txt - ID, Author, Year, Title, Journal, Volume, Issue, Pages, Abstract, URL, SetNumber</p> <p>Depression-Dataset-SLIM-DevelopmentTrainingSet.txt - ID, Author, Year, Title, Journal, Volume, Issue, Pages, Abstract, URL, Incl(1)/Excl(0)</p> <p>Updated-training-data.txt - ID, Author, Year, Title, Journal, Volume, Issue, Pages, Abstract, URL, Incl(1)/Excl(0)</p> <p> </p>
Data sets and R codes for "It's about her: male within-season movements are related to mate searching in a songbird"
<p><strong>Abstract</strong></p><p>In species with resource-defense mating systems (such as most temperate-breeding songbirds), male dispersal is often considered to be limited in both frequency and spatial extent. When dispersal occurs within a breeding season, the favored explanation is ecological resource tracking. In contrast, movements of male birds associated with temporary emigration, such as polyterritoriality (i.e., defense of an additional location after attracting a female in the initial territory), are usually attributed to mate searching. We suggest that male dispersal and polyterritoriality are functionally related, and that mate searching may be a unifying hypothesis for predicting the within-season movements of male songbirds. Here, we test three key predictions derived from this hypothesis in Wood Warblers <i>Phylloscopus sibilatrix</i>. We collected data on the spatial behavior of 107 males between 2017 and 2019, and related male movements to a new territory (both in a dispersal and polyterritorial context) to mating potential in the current territory. Most males dispersed from their territories within days or weeks after failing to attract a female, despite occupying territories in apparently suitable habitat. Probability of polyterritoriality by paired males increased after the peak fertile period of their mate. Males never dispersed following nest predation if the female remained to renest. Thus, our data are consistent with the hypothesis that both movement types are functionally related to mate searching.</p>
Dynamic 1D search and processive nucleosome translocations by RSC and ISW2 chromatin remodelers
<p>Eukaryotic gene expression is linked to chromatin structure and nucleosome positioning by ATP-dependent chromatin remodelers that establish and maintain nucleosome-depleted regions (NDRs) near transcription start-sites. Conserved yeast RSC and ISW2 remodelers exert antagonistic effects on nucleosomes flanking NDRs, but the temporal dynamics of remodeler search, engagement and directional nucleosome mobilization for promoter accessibility are unknown. Using optical tweezers and 2-color single-particle imaging, we investigated the Brownian diffusion of RSC and ISW2 on free DNA and sparse nucleosome arrays. RSC and ISW2 rapidly scan DNA by one-dimensional hopping and sliding respectively, with dynamic collisions between remodelers followed by recoil or apparent co-diffusion. Static nucleosomes block remodeler diffusion resulting in remodeler recoil or sequestration. Remarkably, both RSC and ISW2 use ATP hydrolysis to translocate mono-nucleosomes processively at ~30 bp/sec for surprising distances on extended linear DNA. Processivity and opposing push-pull directionalities of nucleosome translocation shown by RSC and ISW2 shape the distinctive landscape of promoter chromatin.</p>
Data for "A learned score function improves the power of mass spectrometry database search"
<div> <h1>DATA for "A learned score function improves the power of mass spectrometry database search"</h1> <br> <div>These data files are associated with the following publication:</div> <br> <div> <ul> <li>Varun Ananth, Justin Sanders, Melih Yilmaz, Sewoong Oh and William Stafford Noble. "<a title="biorXiv Preprint Link" href="https://www.biorxiv.org/content/10.1101/2024.01.26.577425v2" target="_blank" rel="noopener">A learned score function improves the power of mass spectrometry database search</a>". Bioinformatics (Proceedings of the ISMB). 2024.</li> </ul> </div> <br> <div>For the benchmarking data, we used a dataset that is publicly available on ProteomeXchange (PXD028735). The paper that introduced this dataset is:</div> <br> <div> <ul> <li>Van Puyvelde, B., Daled, S., Willems, S., Gabriels, R., Gonzalez de Peredo, A., Chaoui, K., Mouton-Barbosa, E., Bouyssié, D., Boonen, K., Hughes, C. J., Gethings, L. A., Perez-Riverol, Y., Bloomfield, N., Tate, S., Schiltz, O., Martens, L., Deforce, D., & Dhaenens, M. (2022). A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics. In Scientific Data (Vol. 9, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41597-022-01216-6</li> </ul> </div> <br> <div>More specifically, the following `.raw` files were downloaded:</div> <br> <ul> <li><code>LFQ_Orbitrap_DDA_Ecoli_01.raw</code></li> <li><code>LFQ_Orbitrap_DDA_Human_01.raw</code></li> <li><code>LFQ_Orbitrap_DDA_Yeast_01.raw</code></li> </ul> <br> <div>Those files can be accessed via FTP <a title="Link to ProteomeXchange: PXD028735" href="https://ftp.pride.ebi.ac.uk/pride/data/archive/2022/02/PXD028735/" target="_blank" rel="noopener">here</a>.</div> <br> <div>We upload here the annotated <code>.mgf</code> files created from these <code>.raw</code> files, as described in our paper.</div> <br> <div>The human, yeast, and E. coli .fasta files used in all database searches were downloaded from UniProt on 11/6/23, 4:30 PM.</div> <br> <div> <ul> <li>Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., Cukura, A., Denny, P., Dogan, T., Ebenezer, T., Fan, J., Garmiri, P., da Costa Gonzales, L. J., Hatton-Ellis, E., Hussein, A., … Zhang, J. (2022). UniProt: the Universal Protein Knowledgebase in 2023. In Nucleic Acids Research (Vol. 51, Issue D1, pp. D523–D531). Oxford University Press (OUP). https://doi.org/10.1093/nar/gkac1052</li> </ul> </div> <br> <div>We include these files here, with only minor modifications to replace `U` amino acids with `X` so that all amino acids fall into Casanovo-DB's vocabulary.</div> </div>
[NGC3079 / UGC5101] SAUNAS: I. Searching for low surface brightness X-ray emission with Chandra/ACIS
<p>The contained FITS files represent the processed Chandra/ACIS X-ray surface brightness maps of the NGC3079 and UGC5101 galaxies, observed with Chandra/ACIS and analyzed with the SAUNAS pipeline as described in Borlaff et al. 2024 (in revision). All the images have the photometric calibrations (in units of photons cm-2 s-1 pixel-1) and have been astrometrically aligned. </p> <div>Each file contains four FITS extensions as detailed below: </div> <div>----</div> <div>EXTENSION NAME TYPE SIZE DETAILS </div> <div>----</div> <div>0 INFO no-data 0 BLANK EXTENSION. <br>1 SB_FLUX float64 512x512 X-RAY SURFACE BRIGHTNESS MAP. [photons cm-2 s-1 pixel-1] <br>2 STD_SB_FLUX float64 512x512 X-RAY SURFACE BRIGHTNESS NOISE MAP [photons cm-2 s-1 pixel-1]<br>3 SNR float64 512x512 SIGNAL-TO-NOISE RATIO [ - ]</div> <div>-----------</div> <div> </div> <div>Use the SNR extension (extension #3) to determine if your source of interest in the SB_FLUX map (extension #1) is statistically significant over the background limit.</div> <div> </div> <div> </div> <div> </div> <div> </div> <div> </div>
Figure 12 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities
Figure 12. Phylomorphospace of the principal components 1 and 2 for the forelimb (a) and its correlation circle (b). The dotted circle delimits the Ziphiidae family. The branches represent the phylogenetic relationships between the different species. The abbreviations are the same as in Fig. 11 except for the following: Dele: Delphinapterus leucas; Doat: Dorudon atrox; Mebo:Mesoplodon bowdoini; Oror: Orcinus orca; Stco: Stenella commersonii.
Figure 11 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities
Figure 11. Phylomorphospace of the principal components 1 and 2 for the hamular fossa of the pterygoid sinus (a) and its correlation circle (b). The dotted circle delimits the Ziphiidae family. The branches represent the phylogenetic relationships between the different species. Abbreviations: Ceco: Cephalorhynchus commersonii; Glme: Globicephala melas; Grgr: Grampus griseus; Hyam: Hyperoodon ampullatus; Laac: Lagenorhynchus acutus; Mebi: Mesoplodon bidens; Megr: Mesoplodon grayi; Megre: Messapicetus gregarius; Mepe: Mesoplodon peruvianus; Momo: Monodon monoceros; Orbr:Orcaella brevirostris; Phma: Physeter macrocephalus; Pobl: Pontoporia blainvillei; Plga: Platanista gangetica; Pscr: Pseudorca crassidens; Saob:Sagmatias obscurus; Sofl: Sotalia fluviatilis; Stat: Stenella attenuata; Stcl: Stenella clymene; Stfr: Stenella frontalis; Tutr: Tursiops truncatus; Zica: Ziphius cavirostris.
Figure 7 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities
Figure 7. Humeri and left radius of MUSM 2542, Messapicetus gregarius. Right humerus in medial (a), lateral (b), ulnar (c), and radial view (d); left humerus in medial (e), lateral (f), ulnar (g), radial (h), and posterior view (i); left radius in lateral (j), medial (k), and ulnar view (l).
Figure 5 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities
Figure 5. Ribs of the specimen MUSM 2548, Messapicetus gregarius, in anterior view. Pair 1 (a) and (b); pair 2 (c) and (d); pair 3 (e) and (f); pair 4 (g) and (h); (i), (j), (k), and (l) cannot be precisely positioned.
Figure 8 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities
Figure 8. Comparison of muscular insertions along the atlas and axis in Messapicetus gregarius MUSM 2548 in ventral view (a) and posterior view (b); in Ninoziphius platyrostris MNHN SAS 941 in ventral view (c) and posterior view (d); in Mesoplodon densirostris USNM 593522 in ventral view (e) and posterior view (f); in Berardius sp. MNHN 1885-278.
Figure 3 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities
Figure 3. Thoracic and post-thoracic vertebrae of the specimen MUSM 2548, Messapicetus gregarius. Thoracic vertebra A in anterior (a), posterior (b), and dorsal view (c); thoracic B in posterior (d), left lateral (e), and ventral view (f); thoracic C in posterior (g), lateral (h), and ventral view (i); thoracic–post-thoracic D in posterior (j) and lateral view (k); thoracic–post-thoracic E in lateral view (l).
Figure 10 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities
Figure 10. Comparison of the muscle origins and insertions of the scapula and humerus in lateral view in Tursiops truncatus (SNM CN2x) (a); a reconstruction of the scapula of Messapicetus gregarius (MUSM 2548) (b); Mesoplodon bidens (SNM CN4x) (c); Physeter macrocephalus (SNM CN1x) (d); Inia geoffrensis (NRS A608415) (e); Pontoporia blainvillei (SNM CN1x) (f). Insertion of the M. infraspinatus could not be assessed in M. gregarius. Scale = 50 mm. Dotted lines correspond to the reconstructed parts.
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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)
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.