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1,063 results for “Search”
FREYA GraphQL API and Common DOI Search Webinar
<p>This webinar provided an overview of the recently launched DataCite's GraphQL API, which drives the PID Graph, the graph of scholarly resources described by persistent identifiers and the connections between them.<br> <br> Next, the webinar will provide an opportunity for attendees to contribute to the shape of a new service in development, the Common DOI Search, which will apply the new API to allow users to search for any type of DOI, regardless of where it was registered.</p> <p>Video timings</p> <ul> <li>00.00 Introduction</li> <li>04:30 GraphQL API Presentation and Q&A</li> <li>21:00 Common DOI Search Presentation, Q&A and Feedback</li> </ul>
Data: Search and Rescue with Airborne Optical Sectioning
<p>This dataset supports the finding of our study "Search and Rescue with Airborne Optical Sectioning".</p> <p>Abstract: We show that automated person detection under occlusion conditions can be significantly improved by combining multi-perspective images before classification. Here, we employed image integration by Airborne Optical Sectioning (AOS)---a synthetic aperture imaging technique that uses camera drones to capture unstructured thermal light fields---to achieve this with a recall of 93%. Finding lost or injured people in dense forests is not generally feasible with thermal recordings, but becomes practical with use of AOS integral images. Our findings lay the foundation for effective future search and rescue technologies that can be applied in combination with autonomous or manned aircraft.<br> </p>
Supplementary information to the article by van Beijnum et al. "Integrating phenotypic search and phosphoproteomic profiling of active kinases for optimization of drug mixtures for RCC treatment"
<p>Supplementary information to the article "Integrating phenotypic search and phosphoproteomic profiling of active kinases for optimization of drug mixtures for RCC treatment".</p> <p><strong>Judy R. van Beijnum<sup>1</sup>, Andrea Weiss<sup>2, 3</sup>, Robert H. Berndsen<sup>1,2 </sup>, Tse J. Wong<sup>1</sup>, Louise C. Reckman<sup>1</sup>, Sander R. Piersma<sup>4,5</sup>, Marloes Zoetemelk<sup>2,3</sup>, Richard de Haas<sup>1,4,5</sup>, Olivier Dormond<sup>6</sup>, Axel Bex<sup>7,8</sup>, Alexander A. Henneman<sup>4,5</sup>, Connie R. Jimenez<sup>4,5</sup>, Arjan W. Griffioen<sup>1</sup>, Patrycja Nowak-Sliwinska<sup>2,3,9</sup>*</strong></p> <p> </p> <p><sup>1</sup> Angiogenesis Laboratory, Department of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands;</p> <p><sup>2</sup> Molecular Pharmacology Group, School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland*;</p> <p><sup>3 </sup>Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland</p> <p><sup>4 </sup>Department of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands</p> <p><sup>5</sup> OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands</p> <p><sup>6</sup> Department of Visceral surgery, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland<sup> </sup> </p> <p><sup>7</sup> Royal Free London NHS Foundation Trust, Renal Cancer Centre, UCL Division of Surgical and Interventional Science, London, UK</p> <p><sup>8</sup> Netherlands Cancer Institute, Amsterdam, The Netherlands</p> <p><sup>9 </sup> Translational Research Centre in Oncohaematology, Geneva, Switzerland</p> <p> Correspondence: <a href="mailto:Patrycja.Nowak-Sliwinska@unige.ch">Patrycja.Nowak-Sliwinska@unige.ch</a></p>
Datasets for Effective Distributed Representations for Academic Expert Search
<p>Dataset for the "Effective Distributed Representations for Academic Expert Search" paper published at the <a href="https://ornlcda.github.io/SDProc/ ">SDP 2020 workshop</a> of the EMNLP conference.</p> <p>Two .zip archives are provided:</p> <p>1)<em><strong> papers_and_authors_source_csvs.zip : </strong></em>Contains <em>papers.csv </em>and <em>authors.csv</em>, which are CSV files with the source metadata for all the ~130k papers and ~68k authors used in the final version of the system.</p> <p>2) <em><strong>faiss_indexes.zip : </strong></em>Contains all the pre-populated FAISS indexes with all the embedding variations we used in our research.</p>
Supporting user preferences in search-based product line architecture design using Machine Learning
<p>The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line. PLA design requires intensive human effort as it involves several conflicting factors. In order to support this task, an interactive search-based approach, automated by a tool named OPLA-Tool, was proposed in a previous work. Through this tool the software architect evaluates the generated solutions during the optimization process. Considering that evaluating PLA is a complex task and search-based algorithms demand a high number of generations, the evaluation of all solutions in all generations cause human fatigue. In this work, we incorporated in OPLA-Tool a Machine Learning (ML) model to represent the architect in some moments during the optimization process aiming to decrease the architect's effort. Through the execution of a quanti-qualitative exploratory study it was possible to demonstrate the reduction of the fatigue problem and that the solutions produced at the end of the process, in most cases, met the architect’s needs.</p>
Dataset of 'Search for top-down and bottom-up drivers of latitudinal trends in insect herbivory in oak trees in Europe'
<p>This file correspond to the dataset that has being used in the article ‘Search for top-down and bottom-up drivers of latitudinal trends in insect herbivory in oak trees in Europe’ by Elena Valdés-Correcher et al. in Global Ecology and Biogeography.</p>
Artifacts for Incremental Search for Conflict and Unit Instances of Quantified Formulas with E-Matching
<p>This zip file contains the artifacts for the paper:</p> <p>J. Hoenicke and T. Schindler, <em>Incremental Search for Conflict and Unit Instances of Quantified Formulas with E-Matching, </em>VMCAI 2021, Springer</p> <p>The artifact is tested to work in the VMCAI 2021 virtual machine: <a href="https://doi.org/10.5281/zenodo.4017292">https://doi.org/10.5281/zenodo.4017292</a>.</p>
Data: Autonomous Drones for Search and Rescue in Forests
<p>Supplementary Dataset for the article Autonomous Drones for Search and Rescue in Forests.</p> <p><strong>Abstract:</strong></p> <p>Drones will play an essential role in human-machine teaming in future search and rescue (SAR) missions. We present a first prototype that finds people fully autonomously in densely occluded forests. In the course of 17 field experiments conducted over various forest types and under different flying conditions, our drone found 38 out of 42 hidden persons; average precision was 86% for predefined flight paths, while adaptive path planning (where potential findings are double-checked) increased confidence by 15%. Image processing, classification, and dynamic flight-path adaptation are computed on-board in real time and while flying. Our finding that deep-learning-based person classification is unaffected by sparse and error-prone sampling within one-dimensional synthetic apertures allows flights to be shortened and reduces recording requirements to one tenth of the number of images needed for sampling using two-dimensional synthetic apertures. The goal of our adaptive path planning is to find people as reliably and quickly as possible, which is essential in time-critical applications, such as SAR. Our drone enables SAR operations in remote areas without stable network coverage, as it transmits to the rescue team only classification results that indicate detections and can thus operate with intermittent minimal-bandwidth connections (e.g., by satellite). Once received, these results can be visually enhanced for interpretation on remote mobile devices.</p>
Dataset for the paper "Googling for Software Development: What Developers Search For and What They Find?", MSR 2021.
<p>This is the dataset for the paper "Googling for Software Development: What Developers Search For and What They Find?" submitted to the Mining Software Repositories Conference (MSR), 2021.</p> <p>This dataset has three data:</p> <ul> <li><strong>Search queries</strong>: contains the search queries to compute RQ1, RQ2, RQ3, and RQ4.</li> <li><strong>Search results RQ5</strong>: contains the search results to compute RQ5 (files starting with "search-results-rq5").</li> <li><strong>Search results RQ6</strong>: contains the search results to compute RQ6 (files starting with "search-results-rq6").</li> </ul> <p>The dataset "Search results RQ6" has 8 columns: </p> <ol> <li>same_top10: whether the top 10 links are exactly the same (0 or 1)</li> <li>same_top1: whether the top 1 links are exactly the same (0 or 1)</li> <li>inter_ratio_top5: the intersection of links in the top 5</li> <li>inter_ratio_top10: the intersection of links in the top 10</li> <li>original_query: the original queries</li> <li>original_search_resuls: the top 10 links returned for the original queries</li> <li>modified_query: the modified queries</li> <li>modified_search_resuls: the top 10 links returned for the modified queries</li> </ol> <p>Example (word swap, context)</p> <ul> <li>Original query: "java string replaceall case insensitive"</li> <li>Modified query: "string replaceall case insensitive java"</li> <li>Single row example: "0","1.0","0.8","0.9","java string replaceall case insensitive","['https://stackoverflow.com/questions/5054995/how-to-replace-case-insensitive-literal-substrings-in-java', ...]","string replaceall case insensitive java","['https://stackoverflow.com/questions/5054995/how-to-replace-case-insensitive-literal-substrings-in-java', ...]"</li> </ul>
Space invaders: searching for invasive Smallmouth Bass (Micropterus dolomieu) in a renowned Atlantic Salmon (Salmo salar) river
<p>Humans have the ability to permanently alter aquatic ecosystems and the introduction of species is often the most serious alteration. Non-native Smallmouth Bass (<i>Micropterus dolomieu</i>) were identified in Miramichi Lake <i>c</i>. 2008, which is a headwater tributary to the Southwest Miramichi River, a renowned Atlantic Salmon (<i>Salmo salar</i>) river whose salmon population is dwindling. A containment programme managed by the Department of Fisheries and Oceans, Canada (DFO) was implemented in 2009 to confine Smallmouth Bass (SMB) to the lake. We utilized environmental DNA (eDNA) as a detection tool to establish the potential escape of SMB into the Southwest Miramichi River. We sampled at 26 unique sites within Miramichi Lake, the outlet of Miramichi Lake (Lake Brook), which flows into the main stem Southwest Miramichi River, and the main stem Southwest Miramichi River between August and October 2017. We observed n=6 positive detections located in the lake, Lake Brook, and the main stem Southwest Miramichi downstream of the lake. No detections were observed upstream of the confluence of Lake Brook and the main stem Southwest Miramichi. The spatial pattern of positive eDNA detections downstream of the lake suggests the presence of individual fish versus lake-sourced DNA in the outlet stream discharging to the main river. Smallmouth Bass were later confirmed by visual observation during a snorkeling campaign, and angling. Our results, both eDNA and visual confirmation, definitively show Smallmouth Bass now occupy the main stem of the Southwest Miramichi. </p>
Optimal searching behaviour generated intrinsically by the central pattern generator for locomotion
<p>Efficient searching for resources such as food by animals is key to their survival. It has been proposed that diverse animals from insects to sharks and humans adopt searching patterns that resemble a simple Lévy random walk, which is theoretically optimal for 'blind foragers' to locate sparse, patchy resources. To test if such patterns are generated intrinsically, or arise via environmental interactions, we tracked free-moving <i>Drosophila</i> larvae with (and without) blocked synaptic activity in the brain, suboesophageal ganglion (SOG) and sensory neurons. In brain-blocked larvae we found that extended substrate exploration emerges as multi-scale movement paths similar to truncated Lévy walks. Strikingly, power-law exponents of brain/SOG/sensory-blocked larvae averaged 1.96, close to a theoretical optimum (µ = 2.0) for locating sparse resources. Thus, efficient spatial exploration can emerge from autonomous patterns in neural activity. Our results provide the strongest evidence so far for the intrinsic generation of Lévy-like movement patterns.</p>
Search Behavior can Affect Financial Decision Results: A Behavior Study of Google Trends Data and Linguistic Scale
As search engines have become the main information resources of our daily life, studies about search behavior on the internet have gained great popularity with the growing knowledge of how the search behavior itself can affect our daily decisions, e.g. what to purchase, where to travel and even how to define beauty. However, there is no consensus conclusion whether the search behavior itself or the linguistic meaning behind it that can affect their decision. After analyzing the linguistic meanings of 13,915 English words obtained from Google Trends and its profit gained from the US house market by automatic transactions. It is found that linguistic meanings can affect financial decision results as word clusters with supervised machine learning methods.
WW1 Search light position, Helsinki, Finland
First World War trench search light position in base XVIII:10 in [Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) land front defense line in Kasemattikallio park, Maununneva, Helsinki, Finland. The base was built in 1915 - 1918. The whole base contains: * 150 meters of thrench * 2 observation positions * 2 search light positions * At least 5 shelter rooms * 120 m² cave More info: * [Location in Google Maps](https://goo.gl/maps/2u5Yg7efC29X3urW9) * [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=112.1000013737) (only in finnish) Source: Objaverse 1.0 / Sketchfab
Searching for anthrax in the New York City subway metagenome.
<p>You can view the write up at the following link: http://read-lab-confederation.github.io/nyc-subway-anthrax-study/</p> <p>This data set includes the scripts and write up of the following GitHub repository: https://github.com/Read-Lab-Confederation/nyc-subway-anthrax-study</p> <p> </p> <p>In January 2015 Chris Mason and his team published<sup>1</sup> an in-depth analysis of metagenomic<sup>2</sup> data(environmental shotgun DNA sequence) from samples isolated from public surfaces in the New York City (NYC) subway system. Along with a ton of really interesting findings, the authors claimed to have detected DNA from the bacterial biothreat pathogens <em>Bacillus anthracis</em> (which causes anthrax) and <em>Yersinia pestis</em>(causes plague) in some of the samples. This predictably led to a huge interest from the press and scientists on social media. The authors followed up with an re-analysis of the data on microbe.net<sup>3</sup>, where they showed some results that suggested the tools that they were using for species identification overcalled anthrax and plague.</p> <p><em>B. anthracis</em> is a Gram-positive bacterium that forms tough spores as part of its lifecycle. The 5.2 M basepair (Mb) main chromosome is very similar to those of other bacteria in species informally called the ‘<em>Bacillus cereus</em> group’<sup>4</sup> (including <em>B. cereus</em>, <em>B. thuringiensis</em> and <em>B. mycoides</em>). <em>Bacillus cereus</em> group strains in general are commonly found in soil but <em>B. anthracis</em> itself is very rare and generally associated with livestock grazing sites with a past history of anthrax.</p> <p>What sets <em>B. anthracis</em> apart from close relatives is the presence of two plasmids: pXO1 (181kb), which carries the lethal toxin genes and pXO2 (94kb), which includes genes for a protective capsule. Without one of these plasmids, <em>B. anthracis</em> is considered attenuated in virulence and unable to cause classic anthrax. Other <em>B. cereus</em> group bacteria can have plasmids very similar to pXO1 and pXO2 but missing the important virulence genes. Rarely, other <em>B. cereus</em> group carry pXO1 and appear to cause anthrax-like disease. Its a confusing situation, not helped by the current overly-narrow species definitions. This recent review<sup>5</sup> gives more information.</p> <p>The NYC subway metagenome study raised very timely questions about using unbiased DNA sequencing for pathogen detection. We were interested in this dataset as soon as the publication appeared and started looking deeper into why the analysis software gave false positive results and indeed what exactly was found in the subway samples. We decided to wrap up the results of our preliminary analysis and put it on this site. This report focuses on the results for <em>B. anthracis</em> but we also did some preliminary work on <em>Y.pestis</em> and may follow up on this later.</p> <ol> <li>http://www.sciencedirect.com/science/article/pii/S2405471215000022</li> <li>http://en.wikipedia.org/wiki/Metagenomics</li> <li>http://microbe.net/2015/02/17/the-long-road-from-data-to-wisdom-and-from-dna-to-pathogen/</li> <li>http://genome.cshlp.org/content/22/8/1512</li> <li>http://www.annualreviews.org/doi/abs/10.1146/annurev.micro.091208.073255</li> </ol> <p> </p> <p> </p>
Supporting datasets PubFig05 for: "Heterogeneous Ensemble Combination Search using Genetic Algorithm for Class Imbalanced Data Classification"
<p><strong>Faces Dataset: PubFig05</strong></p> <p>This is a subset of the ''PubFig83'' dataset [1] which provides 100 images each of 5 most difficult celebrities to recognise (referred as class in the classification problem). For each celebrity persons, we took 100 images and separated them into training and testing sets of 90 and 10 images, respectively:</p> <p><strong>Person: </strong>Jenifer Lopez; Katherine Heigl; Scarlett Johansson; Mariah Carey; Jessica Alba</p> <p> </p> <p><strong>Feature Extraction</strong></p> <p>To extract features from images, we have applied the HT-L3-model as described in [2] and obtained 25600 features.</p> <p><strong>Feature Selection</strong></p> <p>Details about feature selection followed in brief as follows:</p> <ol> <li> <p><strong>Entropy Filtering:</strong> First we apply an implementation of Fayyad and Irani's [3] entropy base heuristic to discretise the dataset and discarded features using the minimum description length (MDL) principle and only 4878 passed this entropy based filtering method.</p> </li> <li> <p><strong>Class-Distribution Balancing:</strong> Next, we have converted the dataset to binary-class problem by separating into 5 binary-class datasets using one-vs-all setup. Hence, these datasets became <em>imbalanced</em> at a ratio of 1:4. Then we converted them into <em>balanced binary-class</em> datasets using random sub-sampled method. Further processing of the dataset has been described in the paper.</p> </li> <li> <p><strong>(alpha,beta)-k Feature selection:</strong> To get a good feature set for training the classifier, we select the features using the approach based on the (alpha,beta)-k feature selection [4] problem. It selects a minimum subset of features that maximise both within class similarity and dissimilarity in different classes. We applied the entropy filtering and (alpha,beta)-k feature subset selection methods in three ways and obtained different numbers of features (in the Table below) after consolidating them into binary class dataset.</p> </li> </ol> <ul> <li> <p><strong>UAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the <em>union</em> of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features.</p> </li> <li> <p><strong>IAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the <em>intersection</em> of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features.</p> </li> <li> <p><strong>UEAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets. Then, we applied the entropy filtering and (alpha,beta)-k feature set selection method on each of the balanced binary-class datasets. Finally, we took the <em>union</em> of selected features for each <em>balanced binary-class</em> datasets and get a set of features.</p> </li> </ul> <p>All of these datasets are inside the compressed folder. It also contains the document describing the process detail.</p> <p> </p> <p><strong>References</strong></p> <p>[1] Pinto, N., Stone, Z., Zickler, T., & Cox, D. (2011). Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on (pp. 35–42).</p> <p>[2] Cox, D., & Pinto, N. (2011). Beyond simple features: A large-scale feature search approach to unconstrained face recognition. In Automatic Face Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on (pp. 8–15).</p> <p>[3] Fayyad, U. M., & Irani, K. B. (1993). Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In International Joint Conference on Artificial Intelligence (pp. 1022–1029).</p> <p>[4] Berretta, R., Mendes, A., & Moscato, P. (2005). Integer programming models and algorithms for molecular classification of cancer from microarray data. In Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38 (pp. 361–370). 1082201: Australian Computer Society, Inc.</p> <p> </p>
3rd ACSE Robot Rescue and Search Competition
<p>X. Dai, S.A. Tafrishi, and Y. Kuang. “3rd ACSE Robot Rescue and Search Competition”. University of Sheffield, First Runner-up Team, Master Shifu Robot, May<br /> 2013.</p>
Europe PubMed Central Lite Metadata search index
<p>Metadata of all 1,264,182 Open Access articles from the Europe PubMed Central Lite dataset, parsed from the fulltext XML (ftp://ftp.ebi.ac.uk/pub/databases/pmc/oa/) and converted to bibJSON (http://okfnlabs.org/bibjson/), then indexed with search-index (https://github.com/fergiemcdowall/search-index), and finally exported as a snapshot.</p> <p><span>An example record is included in the `additional notes` field below.</span></p> <p><span>To use this, you'll need to import the snapshot into a search-index instance: </span>https://github.com/fergiemcdowall/search-index/blob/master/doc/replicate.md.</p>
Literature search: Remote sensing in conservation and ecology
<p>This file provides the raw data of a literature search that was conducted to demonstate the growing relevance and rapid devleopment of remote sensing in relation to conservation and ecology within academia. The search was performed using Scopus, a database of peer-reviewed literature, using the string "remote sensing" AND ["conservation" OR "ecology"].</p>
Figure 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI 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 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI 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 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 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 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 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.
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.