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1,544 results for “SPOT”
MCR LTER: Coral Reef: Population Dynamics: Mean Daily Recruitment of Three-spot Dascyllus to Gump Reef, 1992 to 2012
These data describe the abundance of settler-stage three-spot dascyllus (Dascyllus trimaculatus), a planktivorous damselfish to their juvenile microhabitat, the sea anemone Heteractis magnifica. Surveys were begun in 1993, and, except for 1994, have been conducted daily each year between June and September on a reef adjacent to the UC Berkeley Gump Research Station in Cooks Bay on the north shore of the island of Moorea in French Polynesia. This time series completed in 2012.
The SPOTS Models: A Grid of Theoretical Stellar Evolution Tracks and Isochrones For Testing The Effects of Starspots on Structure and Colors
<p><strong>The SPOTS Models: A Grid of Theoretical Stellar Evolution Tracks and Isochrones For Testing The Effects of Starspots on Structure and Colors</strong></p> <p>This repository contains the Stellar Parameters of Tracks with Starspots (SPOTS) grid of theoretical stellar evolutionary tracks and isochrones, presented in Somers, Pinsonneault, and Cao (2020, in prep). Our models were calculated with the Yale Rotating Evolution Code (e.g. van Saders & Pinsonneault, 2013, ApJ 776, 67), including updated which incorporate a treatment of surface starspots (Somers & Pinsonneault, 2015, ApJ 807, 174S). Modelling details can be found in these references. The purpose of this evolutionary suite is to provide the community with state-of-the-art predictions for the influence of starspots and magnetic activity on the structure of stars.</p> <p>The grid includes both isochrones and tracks. They can be downloaded individually from this repository, or in bulk by downloading the .zip files.</p> <p><strong>Isochrones (.isoc):</strong></p> <p>Each isochrone file contains a series of isochrones (stellar properties for a range of masses at fixed age) for ages between 1 Myrs and 4 Gyrs. Each file contains these isochrones for a different surface starspot covering fraction, given by the name of the file -- f000.isoc = 0% covering fraction, f017.isoc = 17% covering fraction, etc. Each isochrone contains several columns with different information, including,</p> <ol> <li>Fundamental properties: mass, age, luminosity, radius, logg, Teff, convective overturn timescale (TauCZ), lithium abundance relative to initial (Li/Li0).</li> <li>Starspot properties: Covering fraction (Fspot), ratio of spot temperature to ambient temperature (Xspot), the temperatures of hot and cool regions (T_hot, T_cool).</li> <li>Two-temperature colors, including Johnson BV, Cousins RI, 2MASS JHK, WISE W1, and Gaia G, BP, RP.</li> </ol> <p>Colors that fell outside of the calibrated range are listed as -99.0.</p> <p><strong>Tracks (.track):</strong></p> <p>We also include individual tracks for every combination of Fspot and Mass considered in the paper. Each .track file lists the mass and starspot covering fraction in the filename -- i.e. m055_f034.track is the model of mass 0.55Msun and with a 34% surface covering fraction. In addition to all the properties included in the isochrones, the track files also include:</p> <ol> <li>The total moment of interia of the model (total_I) and the moment of interia of the surface convection zone (CZ_I)</li> <li>The central and surface hydrogen abundances (X_cen, X_surf) and the surface metallicity (Z/X_surf)</li> <li>The deuterium abundance relative to initial (H2/H2_0)</li> </ol>
Brainport, Automated valet parking, RS camera parking spot occupancy
<p><strong>Scenario description</strong>:</p> <p>RS Camera parking spot occupancy detection and publication of the iot message from type AutoPilot.ParkingSpotDetection to the PMS via IoT platforms</p> <p><strong>Session description</strong>:</p> <p>A AD-car parks to the selected parking spot the rs camera detect the car at the parking spot and publish the occupancy information to the PMS for parking management purpose</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Fig. 5 in Not all spotted cats are leopards: evidence for a Hemilienardia ocellata species complex (Gastropoda: Conoidea: Raphitomidae)
Fig. 5. Records of the Hemilienardia ocellata species complex, based on material examined in the present paper. Filled cycles = H. ocellata (Jousseaume, 1884); triangles = H. acinonyx sp. nov.; black square = H. lynx sp. nov.; grey square = H. cf. lynx sp. nov.; diamonds = H. pardus sp. nov.
Fig. 4 in Not all spotted cats are leopards: evidence for a Hemilienardia ocellata species complex (Gastropoda: Conoidea: Raphitomidae)
Fig. 4. Marginal radular teeth of some species of Hemilienardia. A. H. malleti (Récluz, 1852) (from Kantor & Taylor 2002). B–C. H. ocellata (Jousseaume, 1884). Specimen from the Loyalty Islands, Lifou, Baie du Santal, Atelier LIFOU 2000, stn 1429, 20°47.5' S, 167°07.1' E, 8–18 m, 4.4 mm long. D. H. acinonyx sp. nov. Specimen from the Loyalty Islands, Lifou, Baie du Santal, Atelier LIFOU 2000, stn 1448, 20°45.8' S, 167°01.65' E, 20 m, 5.0 mm long.
Fig. 3 in Not all spotted cats are leopards: evidence for a Hemilienardia ocellata species complex (Gastropoda: Conoidea: Raphitomidae)
Fig. 3. Protoconch and shell morphology in the Hemilienardia ocellata complex. A–C. H. ocellata (Jousseaume, 1884). A. Specimen from the Maldives, Ari Atoll, Maagau Kandu, 25 m, 3.1 mm long. B–C. Specimen from New Caledonia, Expedition MONTROUZIER, stn 1319, 20°44.7' S, 164°15.5' E, 15–20 m, 3.6 mm long. D–E. H. acinonyx sp. nov. Specimen from the Loyalty Islands, Lifou, Baie du Santal, Atelier Lifou 2000, stn 1448, 20°45.8' S, 167°01.65' E, 20 m, 5.0 mm long. F–G. H. lynx sp. nov. Holotype, MNHN IM-2013-5489, Madang District, off Kranket Island, PAPUA NIUGINI stn PP14, 05°12' S, 145°50' E, 100–120 m, 2.75 mm long. H–I. H. pardus sp. nov. Specimen from the Loyalty Islands, Lifou, Baie du Santal, Atelier LIFOU 2000, stn 1454, 20°56.65' S, 167°02.0' E, 15–18 m, 5.2 mm long.
Fig. 2 in Not all spotted cats are leopards: evidence for a Hemilienardia ocellata species complex (Gastropoda: Conoidea: Raphitomidae)
Fig. 2. Species of the Hemilienardia ocellata complex. The SEM image with no letter denoted shows standard measurements. A–D. Hemilienardia ocellata (Jousseaume, 1884). A–B. Syntype, MNHN IM-2000-3128, Mauritius, 4.0 mm. C. Loyalty Islands, Lifou, Baie du Santal, Atelier Lifou 2000, stn 1429, 20°47.5' S, 167°07.1' E, 8–18 m, 4.4 mm. D. New Caledonia, Secteur de Koumac, Expedition Montrouzier, stn 1319, 20°44.7' S, 164°15.5' E, 15–20 m, 3.6 mm. E–F. Hemilienardia acinonyx sp. nov. E. Holotype, MNHN IM-2013-33593, Philippines, 8.1 mm. F. Loyalty Islands, Lifou, Baie du Santal, Atelier Lifou 2000, stn 1441, 20°46.4' S, 167°02.0' E, 20 m, 5.4 mm. G–H. Hemilienardia lynx sp. nov., holotype, MNHN IM-2013-5489, Papua New Guinea, 2.75 mm. I–M. Hemilienardia pardus sp. nov. I. BMOO 17147, Society Islands, Moorea. K. Holotype, MNHN IM-2000-31661, 5.8 mm. L–M. Loyalty Islands, Lifou, Baie du Santal, Atelier Lifou 2000, stn 1454, 20°56.65' S, 167°02.0' E, 15–18 m, 5.2 mm.
Fig. 1 in Not all spotted cats are leopards: evidence for a Hemilienardia ocellata species complex (Gastropoda: Conoidea: Raphitomidae)
Fig. 1. Relationships of the Hemilienardia ocellata complex as inferred by the molecular phylogenetic analysis. A. Bayesian tree based on the analysis of 61 Raphitomidae COI sequences. Black circles indicate nodes with 0.9 0.7.
Seagrasses excretes sugars to their rhizosphere making them the sweet spots in the sea
<p>This repository includes datasets published Sogin et al., Seagrasses excretes sugars to their rhizosphere making them the sweet spots in the sea. Datasets include: </p> <ol> <li>Dissolved organic carbon concentrations</li> <li>The percent of dissolve organic matter composed of polyphenols</li> <li>Porewater sugar concentrations</li> <li>Sediment oxygen concentrations</li> <li>Sediment incubation results</li> </ol> <p>Data was connected primarily from underneath a seagrass meadow in SantAndra Bay, Elba, Italy (42.808304,10.142891). However, additional sugar concentrations are also obtained from seagrass meadows in Belize and Germany. </p> <p> </p> <p> </p> <p> </p> <p>for exploring the concentrations of sugars, dissolve organic carbon, oxygen and polyphenols occurring in sediments inside and surrounding a <em>Posidonia oceanica</em> seagrass meadow in SantAndra Bay, Italy. </p>
Sign-specific stimulation "hot" and "cold" spots in Parkinson's disease validated with machine learning
<p><strong>Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become a standard therapy for Parkinson’s disease (PD). Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific parkinsonian signs when informed by precise locations of stimulation.</strong></p> <p> </p> <p><strong>We studied 275 PD patients who underwent STN-DBS between 2003 and 2018. We selected pre-DBS and best available post-DBS scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to DBS. Volumes of tissue activated (VTAs) were computed and weighted by i) tremor, ii) rigidity, iii) bradykinesia, and iv) axial signs changes. Then, sign-specific sites of optimal (“hot spots”) and suboptimal efficacy (“cold spots”) were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (n=51 STN-DBS patients from another institution).</strong></p> <p><strong> </strong></p> <p><strong>Tremor and rigidity hot spots were largely located outside and dorsolateral to STN whereas hot spots for bradykinesia and axial signs had larger overlap with STN. Using VTA overlap with sign-specific hot and cold spots, support vector machine (SVM) classified patients into quartiles of efficacy with ≥92% accuracy. The accuracy remained high (68-98%) when only considering VTA overlap with hot spots but was markedly lower (41-72%) when only using cold spots. The model also performed poorly (44-48%) when using only stimulation voltage, irrespective of stimulation location. Out-of-sample validation accuracy was ≥96% when using VTA overlap with the sign-specific hot and cold spots.</strong></p> <p><br> <strong>In two independent datasets, distinct brain areas could predict sign-specific clinical changes in PD patients with STN-DBS. With future prospective validation, these findings could individualize stimulation delivery to optimize quality of life improvement. </strong></p> <p><strong>Hot and cold spots for each sign are publicly available as binary labels in NIfTI format. </strong></p>
Data from: Species distribution models of the Spotted Wing Drosophila (Drosophila suzukii, Diptera: Drosophilidae) in its native and invasive range reveal an ecological niche shift
<p>The Spotted Wing Drosophila (<em>Drosophila</em> <em>suzukii</em>) is native to Southeast Asia. Since its first detection in 2008 in Europe and North America, it has been a pest to the fruit production industry as it feeds and oviposits on ripening fruit. Here we aim to model the potential geographical distribution of <em>D. suzukii</em>. We performed an extensive literature review to map the current records. In total, 517 documented occurrences (96 native and 421 invasive) were identified spanning 52 countries. Next, we constructed three species distribution models (SDMs) based on occurrence records in: 1) the native range (SDMnative), 2) the invasive range in Europe (SDMEurope) and 3) a global model of all records (SDMglobal). The models aimed to investigate, whether this species will be able to occupy additional ecological niches beyond its native range and expand its current geographic distribution both globally and in Europe. The SDMs were generated using Maximum Entropy algorithms (Maxent) based on present occurrence records and bioclimatic variables (WorldClim). Predictions of habitat suitability vary greatly depending on the origins of occurrence records. According to all models, precipitation and low temperatures were key limiting factors for the distribution of <em>D. suzukii</em>, which suggests that this species requires a humid environment with mild winters in order to establish a permanent population in its invasive range. Several regions in the invasive range, not presently occupied by this species, were predicted highly suitable, especially in northern Europe, suggesting that <em>D. suzukii</em> is not occupying its full fundamental niche yet. Synthesis and applications. Based on these models of potential geographic distribution of the Spotted Wing Drosophila (<em>Drosophila</em> <em>suzukii</em>), we show a shift in the ecological niche in <em>D. suzukii</em> populations, emphasizing the importance of using presence and local environmental data. Further investigation regarding new occurrences is recommended to secure optimal pest management. Despite a continuing expansion, many countries still lack proper surveillance schemes, and we urge policymakers to initiate appropriate management programs.</p>
Data from: Independently evolved and gene flow‐accelerated pesticide resistance in two‐spotted spider mites
<p>Pest species are often able to develop resistance to pesticides used to control them, depending on how rapidly resistance can emerge within a population or spread from another resistant population. We examined the evolution of bifenazate resistance in China in the two‐spotted spider mite (TSSM) <em>Tetranychus</em> <em>uticae</em> Koch (Acari: Tetranychidae), one of the most resistant arthropods, by using bioassays, detection of mutations in the target <em>cytb</em> gene, and population genetic structure analysis using microsatellite markers. Bioassays showed variable levels of resistance to bifenazate. The <em>cytb</em> mutation G126S, which confers medium resistance in TSSM to bifenazate, had previously been detected prior to the application of bifenazate and was now widespread, suggesting likely resistance evolution from standing genetic variation. G126S was detected in geographically distant populations across different genetic clusters, pointing to the independent origin of this mutation in different TSSM populations. A novel A269V mutation linked to a low‐level resistance was detected in two southern populations. Widespread resistance associated with a high frequency of the G126S allele was found in four populations from the Beijing area which were not genetically differentiated. In this case, a high level of gene flows likely accelerated the development of resistance within this local region, as well as into an outlying region distant from Beijing. These findings, therefore, suggest patterns consistent with both local evolution of pesticide resistance as well as an impact of migration, helping to inform resistance management strategies in TSSM.</p>
Autonomic Provisioning and Application Mapping on Spot Cloud Resources
<p>1. Attached files: </p> <p>500_100_fmincon_0.95_1.mat <br /> Experiment with 500 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>500_100_heuristic_0.95_1.mat<br /> Experiment with 500 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_70_fmincon_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, exact algorithm.</p> <p>2000_70_heuristic_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.9_1.mat<br /> Experiment with 2000 users, 100ms max response time, 90% availability, exact algorithm.</p> <p>2000_100_heuristic_0.9_1.mat <br /> Experiment with 2000 users, 100ms max response time, 90% availability, our algorithm.</p> <p>2000_100_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>2000_100_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, exact algorithm.</p> <p>2000_100_heuristic_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, our algorithm.</p> <p>2000_300_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, exact algorithm.</p> <p>2000_300_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, our algorithm.</p> <p>10000_100_fmincon_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>10000_100_heuristic_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2. Data format:</p> <p>MATLAB data format, can be load from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Supplemental dataset for Northern Spotted Owl (<i>Strix occidentalis caurina</i>) genome assembly version 1.0
<p><strong>StrOccCau_1.0_nuc.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the file that we deposited at DDBJ/ENA/GenBank as a Whole Genome Shotgun (WGS) project under accession NIFN00000000. It is is the file that you will most likely want to download if you would like to perform an alignment to this genome assembly. This file is the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without any contigs and scaffolds less than 1,000 nt and also without the contigs and scaffolds that we identified either as the mitochondrial genome sequence or as contaminant sequences.</p> <p><strong>StrOccCau_1.0_nuc_masked.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the repeat-masked (hard-masked) assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without any contigs and scaffolds less than 1,000 nt and also without the contigs and scaffolds that we identified either as the mitochondrial genome sequence or as contaminant sequences.</p> <p><strong>StrOccCau_1.0_mito.fa</strong> : This FASTA format file is the mitochondrial-genome-derived scaffold from the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012).</p> <p><strong>StrOccCau_1.0.gff.bz2</strong> : This gff format file compressed this file with bzip2 contains the gene annotations of StrOccCau_1.0_nuc.fa.</p> <p><strong>StrOccCau_1.0_transcripts.fa.bz2</strong> : This FASTA format file compressed this file with bzip2 contains the sequences of the gene transcript sequences of the genes annotated in StrOccCau_1.0.gff.</p> <p><strong>StrOccCau_1.0_proteins.fa.bz2</strong> : This FASTA format file compressed this file with bzip2 contains the protein sequences of the genes annotated in StrOccCau_1.0.gff.</p> <p><strong>StrOccCau_1.0_RM_homology_includes_LowComplexity.out.bz2</strong> : This file provides the repeat annotations produced by the homology-based masking of StrOccCau_1.0_nuc.fa that included masking of low complexity regions and simple repeats. We compressed this file with bzip2.</p> <p><strong>StrOccCau_1.0_RM_DeNovo_includes_LowComplexity.out</strong> : This file provides the repeat annotations produced by the de novo masking (which followed after first performing homology-based masking) of StrOccCau_1.0_nuc.fa that included masking of low complexity regions and simple repeats.</p> <p><strong>StrOccCau_1.0_RM_homology_no_LowComplexity.out.bz2</strong> : This file provides the repeat annotations produced by the homology-based masking of StrOccCau_1.0_nuc.fa that did not include masking of low complexity regions and simple repeats. We compressed this file with bzip2.</p> <p><strong>StrOccCau_1.0_RM_DeNovo_no_LowComplexity.out</strong> : This file provides the repeat annotations produced by the de novo masking (which followed after first performing homology-based masking) of StrOccCau_1.0_nuc.fa that did not include masking of low complexity regions and simple repeats.</p> <p><strong>StrOccCau_1.0_alignments_of_light_associated_genes.txt</strong> : This file provides alignments of light-associated gene orthologs as well as assemblies of transcriptome sequences in NEXUS format.</p> <p><strong>StrOccCau_1.0_nuc_masked_SpottedBarredOwl_variant_file.vcf.bz2</strong> : This is a raw, unfiltered variant call format file compressed with bzip2 that was generated after aligning both spotted owl and barred owl short read data aligned to StrOccCau_1.0_nuc_masked.fa.</p> <p><strong>StrOccCau_0.1.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012).</p> <p><strong>StrOccCau_0.1_masked.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the repeat-masked assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012).</p> <p><strong>StrOccCau_0.2.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without any contigs and scaffolds less than 1,000 nt.</p> <p><strong>StrOccCau_0.2_masked.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the repeat-masked assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without any contigs and scaffolds less than 1,000 nt.</p> <p><strong>StrOccCau_GapCloser_output_NoContamNoMito.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without the contigs and scaffolds that we later identified either as the mitochondrial genome sequence or as contaminant sequences.</p> <p><strong>Citations</strong> - if you utilize these data, please include these citations:</p> <p>Hanna ZR., Henderson JB., Wall JD., Emerling CA., Fuchs J., Runckel C., Mindell DP., Bowie RCK., DeRisi JL., Dumbacher JP. 2017a. Supplemental dataset for Northern Spotted Owl (<em>Strix occidentalis caurina</em>) genome assembly version 1.0. <em>Zenodo</em>. DOI: 10.5281/zenodo.822859.</p> <p>Hanna ZR., Henderson JB., Wall JD., Emerling CA., Fuchs J., Runckel C., Mindell DP., Bowie RCK., DeRisi JL., Dumbacher JP. 2017b. Northern Spotted Owl (Strix occidentalis caurina) Genome: Divergence with the Barred Owl (<em>Strix varia</em>) and Characterization of Light-Associated Genes. <em>Genome Biology and Evolution</em> 9:2522–2545. DOI: 10.1093/gbe/evx158.</p>
Supplement to "The virtual spot approach: a simple method for image U-Pb carbonate geochronology by high-repetition rate LA-ICP-MS" by Hoareau et al
<p>This repository contains supplementary data, excel datasets and python / R codes as supplement to the publication by Hoareau et al.</p> <p>Files are:</p> <ul> <li>pdf with Supplementary material (S1 to S6)</li> <li>Excel spreadsheet with all image ratio and CPS data (Table S7)</li> <li>Iolite4 python plugin for virtual spot calculation (Iolite4_plugin_virtual_spot.py)</li> <li>Jupyter .ipynb file for U-Pb age calculation (Multiage.ipynb, R language)</li> <li>Jupyter .ipynb file for cp-sat minimum rectangle problem calculation (Rects.ipynb, Python language)</li> </ul>
Large Spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation
<p>A complete DeepMIB project with a synthetic dataset generated for quick tests of semantic segmentation approaches.<br>The dataset includes a trained DeepLabV3-Resnet18 network for detection of large spots on a black background. </p><p>The network can be opened by loading "2D_LargeSpots_2cl_DeepLabV3.mibCfg" file by</p><ul><li><i>MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load </i></li><li>Drag and drop of the config file into DeepMIB window</li></ul><p>Microscopy Image Browser: <a href="https://mib.helsinki.fi">https://mib.helsinki.fi</a></p>
Data from: The spotted parrotfish genome provides evolutionary insight into the ecological adaptation of a keystone dietary specialist
<p>With over 600 valid species, the wrasses (family Labridae) are among the largest and most successful of the marine teleosts. They feature prominently on coral reefs where they are known not only for their impressive diversity in colouration and form, but also in their functional specialization and ability to occupy a wide variety of trophic guilds. Among the wrasses, the parrotfishes (tribe Scarini) display some one of the most dramatic examples of trophic specialization. Using abrasion-resistant biomineralized teeth, parrotfishes are able to mechanically extract protein-rich micro-photoautotrophs growing in and amongst reef carbonate material, a dietary niche that is inaccessible to most other teleost fishes. This ability to exploit an otherwise untapped trophic resource is thought to have played a role in the diversification and evolutionary success of the parrotfishes. In order to better understand the key evolutionary innovations leading to the success of these dietary specialists, we sequenced and analysed the genome of a representative species, the spotted parrotfish (<em>Cetoscarus ocellatus</em>). We find significant expansion, selection, and duplication within several detoxification gene families and a novel poly-glutamine expansion in the enamel protein ameloblastin, and we consider their evolutionary implications. Our genome provides a useful resource for comparative genomic studies investigating the evolutionary history of this highly specialized teleostean radiation.</p>
Figure 2 in Development and life table parameters of the Phytoseius corniger Wainstein (Acari: Phytoseiidae) feeding on the two-spotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae) under laboratory conditions
Figure 2. The age-specific survival rate (lx), and fecundity (mx) of Phytoseius corniger fed on Tetranychus urticae under laboratory conditions (25 ± 2 °C, 55 ± 5% of RH, and 16L: 8D h photoperiod).
Figure 1 in Development and life table parameters of the Phytoseius corniger Wainstein (Acari: Phytoseiidae) feeding on the two-spotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae) under laboratory conditions
Figure 1. Age-stage specific survival rate (sjx) of the parent cohort of bisexual Phytoseius corniger fed on Tetranychus urticae under laboratory conditions (25 ± 2 °C, 55 ± 5% of RH, and 16L: 8D h photoperiod). Note: L stands for larva, N1 for protonymph, and N2 for deutonymph, respectively.
Fig.ç6.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, ventral view; B, segments 4 and 5, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; pac, pachycyclus; pf, pectinate fringe; rss, rounded sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç6.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, ventral view; B, segments 4 and 5, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; pac, pachycyclus; pf, pectinate fringe; rss, rounded sensory spot.
ScienceDex guides
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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.