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1,063
datasets available to search
ShareScore release 0.9.0
Dataset results
1,063 results for “search”
Experiment artefacts: DTW search and evaluation
<p>Results from query-by-example spoken term detection (QbE-STD) tasks using a Dynamic Time Warping (DTW) search (<a href="https://github.com/fauxneticien/qbe-std_feats_eval/blob/master/docs/usage.md#3-dtw-search">see description of procedure and associated experiment code</a>) from main experiments with 54 feature extraction methods (BNF; MFCC; wav2vec 2.0 English/XLSR Encoder, Quantiser, and Transformer Layers 1-24)</p>
Supporting Data for "Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties" Manuscript
<p>Training data and xTB calculated properties of sampled structures from the generative active learning experiments reported in the manuscript.</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>
Figure 1. Flow chart of the stages followed in the bibliographic search.
<p>Figure 1. <em>Flow chart of the stages followed in the bibliographic search.</em></p>
The SLR Search Results
<p>The uploaded bib files are the search results used for our systematic literature review paper: Enactment of Adaptation in Data Stream Processing with Latency Implications - A Systematic Literature Review.</p> <p>The search results retrieved from ACM: ACM_Search_Result.bib</p> <p>The search results retrieved from ScienceDirect: ScienceDirect_Search_Result.bib</p>
Sequences used for building HMMER profiles to search for SNAREs proteins
<p>This file contains the four sequence datasets that were used to build HMMER search profiles in order to find all SNAREs protein present in the predicted proteome of Oopsacas minuta: a human SNARE profile, a Qa-SNAREs profile, a Qb-SNAREs profile, a Qc-SNAREs profile.</p>
Candidate gene polymorphisms are linked to dispersive and migratory behaviour: searching for a mechanism behind the "paradox of the great speciators"
<p>The "paradox of the great speciators" has puzzled evolutionary biologists for over half a century. A great speciator requires excellent dispersal <span>propensity</span> to explain <span>its</span> occurrence on multiple islands, but reduced dispersal ability to explain <span>its</span> high number of subspecies. A rapid reduction in dispersal ability is often invoked to solve this apparent paradox, but a proximate mechanism has not been identified yet. Here, we explored the role of six genes linked to migration and animal personality differences (<em>CREB1, CLOCK, ADCYAP1, NPAS2, DRD4, </em>and<em> SERT</em>) in 20 South Pacific populations of silvereye (<em>Zosterops</em> <em>lateralis</em>) that range from highly sedentary to partially migratory, to determine if genetic variation is associated with dispersal propensity and migration. We detected genetic associations in three of the six genes: i) in a partial migrant population, migrant individuals had longer microsatellite alleles at the <em>CLOCK</em> gene compared to resident individuals from the same population; ii) <em>CREB1</em> displayed longer average microsatellite allele lengths in recently colonised island populations (< 200 years), compared to evolutionarily older populations. Bayesian broken stick regression models supported a reduction in <em>CREB1</em> length with time since colonisation; and iii) like <em>CREB1</em>, <em>DRD4</em> showed differences in polymorphisms between recent and old colonisations but a <span>larger</span> sample is needed to confirm. <em>ADCYAP1</em>, <em>SERT</em>, and <em>NPAS2</em> were variable but that variation was not associated with dispersal propensity. The association of genetic variants at three genes with migration <span>and</span> dispersal ability in silvereyes provides the impetus for further exploration of genetic mechanisms underlying dispersal shifts and the prospect of resolving a long-running evolutionary paradox through a genetic lens.</p>
Searching for intra-cloud positive leaders in VHF
<p>The data needed for making the figures for the paper that will be submitted for publication in GRL, including the GLE-scipts for making the figures.</p>
Data for study "Device-dependent click-through rate estimation in Google organic search results based on clicks and impressions data"
<p>Data for the study "Device-dependent click-through rate estimation in Google organic search results based on clicks and impressions data".</p>
CRCMDS - Tutorial - Advanced search
<p>Tutorial - Advanced search linked to the CRCMDS's Disability and Deafhood Database</p> <p><a href="https://chairemediashandicaps.uqam.ca/database/">https://chairemediashandicaps.uqam.ca/database/</a></p>
CRCMDS - Tutorial - Simple search
<p>Tutorial - Simple search linked to the CRCMDS's Disability and Deafhood Database</p> <p><a href="https://chairemediashandicaps.uqam.ca/database/">https://chairemediashandicaps.uqam.ca/database/</a></p>
Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation
<p><strong>Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation</strong></p> <p> </p> <p>This artifact contains:</p> <p>1. The binary folder. Its instruction can be found in [./binary/README.md](./binary/README.md).</p> <p>2. All classes used in the experiments [./classes.csv](./classes.csv).</p> <p>3. The experimental data zip file ([experimental_data.zip](./experimental_data.zip)).</p> <p> </p>
Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation
<p><strong>Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation</strong></p> <p> </p> <p>This zip file is the artifact that contains the following:</p> <p>1. The binary. </p> <p>2. All classes used in the experiments.</p> <p>3. The experimental data.</p> <p> </p>
Dynamic interplay between target search and recognition for a Type I CRISPR-Cas system
<p>CRISPR-Cas effector complexes enable the defense against foreign nucleic acids and have recently been exploited as molecular tools for precise genome editing at a target locus. To bind and cleave their target, the CRISPR-Cas effectors have to interrogate the entire genome for the presence of a matching sequence. Here we dissect the target search and recognition process of the Type I CRISPR-Cas complex Cascade by simultaneously monitoring DNA binding and R-loop formation by the complex. We directly quantify the effect of DNA supercoiling on the target recognition probability and demonstrate that Cascade uses facilitated diffusion for its target search. We show that target search and target recognition are tightly linked and that DNA supercoiling and limited 1D diffusion need to be considered when understanding target recognition and target search by CRISPR-Cas enzymes and engineering more efficient and precise variants. </p>
Asking the Machine: A Literature Review and Research Agenda on Semantic Search
<p><strong>To be submitted to IEEE Access.</strong></p>
Data for "The use of a graph database is a complementary approach to a classical similarity search for identifying commercially available fragment merges"
<p>The input and output data to the filtering pipeline described in "The use of a graph database is a complementary approach to a classical similarity search for identifying commercially available fragment merges"; code available from https://github.com/oxpig/fragment_network_merges. </p>
Research Data for Beyond Keywords: Intent-Driven Semantic Code Search in Software Ecosystems
<p>This contains the software repository for the intent-enhanced code search engine as well as the baseline code search engine used in the Master Thesis: 'Beyond Keywords: Intent-Driven Semantic Code Search in Software Ecosystems'</p>
RescueSpeech: A German Corpus for Speech Recognition in Search and Rescue Domain
<p>Dear User,</p> <p>We are thrilled to introduce our latest release - the <strong>RescueSpeech</strong> audio dataset, comprising authentic German speech recordings obtained from simulated search and rescue (SAR) exercises. The dataset contains manually annotated recordings from native German speakers, which were initially captured at 44.1 kHz and later down-sampled to 16 kHz to obtain a set of mono-speaker-single channel audio recordings. In order to protect the identity of the speakers, their names have been anonymized.</p> <p>The RescueSpeech dataset is divided into two sets, each designed for different tasks: Automatic Speech Recognition (ASR) and Speech Enhancement.</p> <p>1. For the ASR task, the dataset spans a duration of 1 hour and 36 minutes. It comprises a collection of clean-noisy pairs, where the noisy utterances are created by introducing contaminations from five different noise types sourced from the AudioSet dataset. These noise types include emergency vehicle siren, breathing, engine, chopper, and static radio noise. To match the 2412 clean utterances in the dataset, we have synthesized an equal number of corresponding noisy utterances. Additionally, we have provided the noise waveform files used to create the noisy utterances, ensuring transparency and reproducibility in the research community.</p> <p>2. The Speech Enhancement task dataset is larger in size compared to the ASR dataset. The primary objective of this dataset is to facilitate the fine-tuning of speech enhancement models, particularly for the five SAR noise types mentioned earlier: emergency vehicle siren, breathing, engine, chopper, and static radio noise. Given the limited duration of clean audio available (1 hour and 36 minutes), we have synthesized multiple noisy utterances with varying noise types and signal-to-noise ratio (SNR) levels, all derived from a single clean utterance. This augmentation approach allows us to generate a more extensive dataset for speech enhancement purposes while preserving the original speaker distribution.</p> <p>By providing these diverse datasets, we aim to support advancements in ASR and Speech Enhancement research, enabling the development and evaluation of robust systems that can handle real-world scenarios encountered during search and rescue operations.<br> </p>
Semantic Table Search Dataset
<p>Semantic Table Search Dataset Resource: A Large Scale Test Corpus for Semantic Table Search<br> Check out the <a href="">README</a> for more information.</p>
Searches for ED and COVID-19 from Google Trends
<p><em>Searches for ED and COVID-19 from Google Trends (provided by Google as Relative Search Volumes (RSVs): they are normalized with a minimum value of 0 and a maximum value of 100) and weekly COVID-19 cases from WHO (in absolute numbers) in the four time periods. Please note that in the first time period Google provides search data RSVs monthly, while in the other three periods this is done weekly. Time Period 1: January 1, 2004 to December 31, 2016, Time Period 2: January 1, 2017 to December 31, 2019, Time Period 3: January 1, 2020 to December 31, 2021, Time Period 4: January 1, 2022 to December 31, 2022. </em></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.