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ShareScore release 0.9.0
Dataset results
1,855 results for “Autonomous”
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-07-29/2018-08-22
<p>2018-07-29/2018-08-22</p>
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-06-10/2018-06-12
<p>2018-06-10/2018-06-12</p>
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-04-02/2018-04-05
<p>2018-04-02/2018-04-05</p>
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-07-12/2018-07-17
<p>2018-07-12/2018-07-17</p>
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-07-18/2018-07-28
<p>2018-07-18/2018-07-28</p>
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-06-03/2018-06-06
<p>_</p>
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-04-06/2018-04-26
<p>2018-04-06/2018-04-26</p>
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-04-27/2018-05-03
<p>2018-04-27/2018-05-03</p>
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-05-04/2018-05-30
<p>2018-05-04/2018-05-30</p>
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-06-07/2018-06-09
<p>2018-06-07/2018-06-09</p>
Raw EK80 high-frequency echosounder data collected by autonomous sailbuoy in Lofoten/Vesterålen 2018-05-31/2018-06-02
<p>2018-05-31/2018-06-02</p>
FIGURE 61 in Review and contribution to the stoneflies (Insecta: Plecoptera) of Guangxi Zhuang Autonomous Region, China, with new species and new records from South China
FIGURE 61. Distribution of Styloperla wui Chao, 1947 in prefectural cities of Guangxi.
FIGURE 41 in Review and contribution to the stoneflies (Insecta: Plecoptera) of Guangxi Zhuang Autonomous Region, China, with new species and new records from South China
FIGURE 41. Stonefly species richness occurring within six families in Guangxi Province.
Autonomous Self-burying Seed Carriers for Aerial Seeding
<p>The datasets </p>
Autonomous Aerial Inspection using Visual-Inertial Robust Localization and Mapping
<p>This video illustrates the content of the paper referenced below.<br> <br> <strong>Reference:</strong><br> Lucas Teixeira, Ignacio Alzugaray and Margarita Chli, "Autonomous Aerial Inspection using Visual-Inertial Robust Localization and Mapping", in Proceedings of the International Conference on Field and Service Robotics (FSR), 2017.</p> <p><strong>Abstract:</strong></p> <p>With recent technological breakthroughs bringing fully autonomous inspection using small Unmanned Aerial Vehicles (UAVs) closer to reality, the community of Robotics has actively been developing the real-time perception capabilities able to run onboard such constraint platforms. Despite good progress, realistic deployment of autonomous UAVs in GPS-denied environments is still rudimentary. In this work, we propose a novel system to generate a collision-free path towards a user-specified inspection direction for a small UAV using monocular-inertial sensing only and performing all computation onboard. Estimating both the previously unknown scene and the UAV’s trajectory on the fly, this system is evaluated on real experiments outdoors in the presence of wind and poorly structured environments. Our analysis reveals the shortcomings of using sparse feature maps for planning, highlighting the importance of robust dense scene estimation proposed here.</p>
data from Enhancing Autonomous Driving through Intelligent Navigation
Open the record for dataset details and reuse information.
Children and young adults with epilepsy exhibit an interictal autonomic dysfunction: a prospective exploratory study
Open the record for dataset details and reuse information.
Protistan metabolism across the western North Atlantic Ocean revealed through autonomous underwater profiling
<p>Metatranscriptomic assembly, predicted open reading frames, counts, and annotation files from seawater samples obtained in the western North Atlantic Ocean. GitHub notebooks are located here: <a href="https://github.com/cnatalie/BATS">https://github.com/cnatalie/BATS</a>. Assembly was created using the <em>eukrhythmic</em> pipeline: <a href="https://github.com/AlexanderLabWHOI/eukrhythmic">https://github.com/AlexanderLabWHOI/eukrhythmic</a></p> <ul> <li>merged_merged.fasta.gz = Final assembly, merged across 44 metatranscriptomes using 4 different assemblers</li> <li>merged.fasta.transdecoder.pep.zip = Open reading frames of final assembly, predicted by Transdecoder </li> <li>merged.fasta.transdecoder-estimated-taxonomy.out.zip = EUKulele-derived taxonomic annotations of ORFs using a combined EukProt, PhyloDB, and RefSeq reference database</li> <li>newtaxa.eukprot.merged.fasta.transdecoder-estimated-taxonomy.out.zip = similar to above, but manually curated mid-level taxonomy for supergroups of interest</li> <li>eggnog.emapper.annotations.zip = eggnog-mapper annotations of ORFs</li> <li>table.tab.zip = counts associated with ORFs (merged.fasta.transdecoder.pep) generated with Salmon</li> <li>TPM_table.tab.zip = community-wide TPM (normalized) counts associated with ORFs (merged.fasta.transdecoder.pep) generated with Salmon</li> <li>copiesperL_ORFs_FactorIncluded.csv.zip = raw counts associated with ORFs (merged.fasta.transdecoder.pep) converted to copies per L taking into account spiked-in RNA standard concentration (copies), standard reads mapped, volume of seawater filtered, and dilution factor used in library preparation</li> <li>assembly.table.tab.zip = counts associated with final assembly (merged_merged.fasta) generated with Salmon</li> <li>SamplesViewReportCLIO_AE1913merged_trans210506_updated220606exclusive.zip = Exclusive spectral counts associated with ORFs (merged.fasta.transdecoder.pep). Peptide-spectrum matches were performed using Sequest algorithm within IseNode Proteome Discoverer 2.2.0.388 (Thermo Fisher Scientific). Scaffold 5.1.2 (Proteome Software) was used for protein grouping and exclusive spectral counting. Note, the (+x) data has been removed from protein names, which indicates whether (and how many) proteins sharing peptides were designated into the same protein group. </li> <li>cds.length2.tab.zip = Length of proteins (ORFs) in nucleotide base pairs</li> <li>CTD.zip = CTD files from cruise AE1913</li> </ul>
Autonomously Shaping Natural Climbing Plants: A Bio-hybrid Approach
<p>Plant shaping experiments data.</p>
FIGURE 12 in A remarkable new species of Nemoura (Plecoptera: Nemouridae) from Chuxiong Yi Autonomous Prefecture of Yunnan Province, China
FIGURE 12. Type locality, Zixi Mountain, Chuxiong Yi Autonomous Prefecture, Yunnan Province, China.
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.