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Fig. 2. Selected photographs showing the sargassum habitat and Scyllaea fulva. A in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 2. Selected photographs showing the sargassum habitat and Scyllaea fulva. A, Sargassum bed at Tai She Wan; B, In situ swimming Scyllaea fulva; C, Dorsal view of Scyllaea fulva in a beaker; D, Lateral view of Scyllaea fulva in a beaker. Scale bar = 0.5 cm.
Fig. 3. Selected photographs showing Phestilla subodiosa. and its coral host. A in Fig. 1 in Parascorpaena poseidon Chou and Liao 2022
Fig. 3. Selected photographs showing Phestilla subodiosa. and its coral host. A, several individuals of Phestilla subodiosa. feeding on a fragment of Montipora peltiformis, with a clear feeding scar along the lower edge. B, Dorsal view of SCSMBC030984. C–D, Dorsal view and ventral view of SCSMBC030985, respectively; E, Dorsal view of SCSMBC030986. Scale bars: A = 5 mm; B–E = 1 mm.
Figure 3. 46 points are selected on face elements to describe the emotions.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>The number of points and the position of points are not standardized, but it is depending on<br> the features that will be extracted, and used for the classifier. Many researches use various number<br> of points and positions based on their view about the feature to be considered [13] [18] [19]. Figure<br> 3 shows the points we used.</p>
Figure 4. Adding a Sensing Module and selecting its type-Designing a Growing Functional Modules "Artificial Brain"
<p>GFM controllers learn to satisfy some predefined goals<br> while interacting with the environment and thus should be considered as artificial brains. An<br> example of the design process of a simple controller is provided herein to explain the inherent<br> methodology, to exhibit the components' interconnections and to demonstrate the control process.</p>
Figure 3. (Top): Illustration of the therapy selection main menu. This enables the user to select one of three options for the therapy. Stimuli sequence selectors; (Bottom): (a) Short distance – complete visual field; (b) Short distance – macular; (c) Middle-long distance.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>distance therapies for the prescribed time suggested by the ophthalmologist.<br> Note that the complete visual field therapy stimulates different parts in the entire visual field<br> whereas macular therapy stimulate only a small part of the visual field, only the first 10° of vision<br> range. In contrast, middle-long distance therapies are not developed inside the device; instead the<br> patient must sit watching a wall, where the stimuli will be presented. Figure 3 (Bottom) shows the<br> sequence selectors for the three different cases. The therapist will choose a desired number of<br> sequences according to the results of the examination to each patient; hence it is completely patient<br> dependent.<br> Once the therapist finishes the particular design of the stimuli sequence, the software<br> automatically displays a window where he can save the customized patient-specific details for future<br> use as a text file.</p>
BRAIN Journal-The Presence and Activity on Facebook of the Informative Travel Organizations in Romania-Figure 1. Selected Romanian counties in order to strengthen the target group of tourism organizations with informative role
<p>In this regard, we analysed the current state of presence and communication on Facebook for<br> 109 informative tourism entities located in 25 Romanian counties, selected on the basis of tourist<br> traffic indicators for the period between 2007 and 2013. The structure of the 109 organizations<br> analysed is: 43 tourist information centers (39.45%), 44 entities with the name of the association for<br> tourism promotion, ecotourism promotion, mountaineering promotion etc. (40.36%), 18 tourism<br> clubs (16.51%) and 4 tourist information points/offices (3.67%)</p>
Marriage Proportion, Age Specific Fertility, Births within Marriage ratios for US, Japan, and selected European countries
<p>Dataset to accompany the paper "Marital fertility patterns and nonmarital birth ratios: an integrated approach"</p> <p>Includes:</p> <p>US African American women and White American women data on age-specific fertility (5-year groups), age specific marital fertility (5-year groups), proportion of women with a first marriage (5-year groups), and the ratio of births within marriage (5-year groups) as well as calculated values from the paper. For ages 15-44 and years 1980, 1985, 1990, 1995, 2000.</p> <p>Selected European country women data on age-specific fertility (5-year groups), age specific marital fertility (5-year groups), proportion of women with a first marriage (5-year groups), and the ratio of births within marriage (5-year groups) as well as calculated values from the paper. For ages 15-44 and years 1991, 2001, and 2011 (data not available for all countries in all years).</p> <p>Japanese women data on age-specific fertility (5-year groups), age specific marital fertility (5-year groups), proportion of women with a first marriage (5-year groups), and the ratio of births within marriage (5-year groups) as well as calculated values from the paper. For ages 15-44 and years 1950, 1960, 1970, 1980, 1990, 1995, 2000, 2005, and 2010.</p> <p> </p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 9. Step by step drawing of the selected route solution
<p>When the solution that is desired to be viewed is double clicked, the connections between bus stops are drawn in turn, and the route is shown as can be seen in Figure 9.</p>
Data from Figures in "Selection rules for cavity-enhanced Brillouin light scattering from magnetostatic modes"
<p>Data from figures in our paper "Selection rules for cavity-enhanced Brillouin light scattering from magnetostatic modes" in Physical Review B. The figures are in an Origin file (OriginPro 2016). Matlab code (R2016b) that can be used to generate plots of the magneto-static modes is also included.</p>
Gaia data, Pan-STARRS photometry, and stream selection masks for the region around the GD-1 stream
<p>This file contains:</p> <ul> <li>relevant columns from Gaia DR2</li> <li>Pan-STARRS (PS1) photometry (grizy)</li> <li>de-reddened PS1 photometry (g0, r0, etc.)</li> <li>binary masks to apply to select out stars that pass our proper motion and color-magnitude diagram selection (pm_mask, gi_cmd_mask)</li> <li>a binary mask to apply to select out stars in the stream track defined in <a href="https://arxiv.org/abs/1805.00425">Price-Whelan & Bonaca (2018) </a>(stream_track_mask)</li> <li>GD-1 positional coordinates (phi1, phi2)</li> <li>Proper motions in the GD-1 coordinate system (pm_phi1_cosphi2, pm_phi2)</li> <li>Proper motions in the GD-1 coordinate system, corrected for solar reflex motion (pm_phi1_cosphi2_no_reflex, pm_phi2_no_reflex)</li> </ul> <p>To select out probable members of the GD-1 stream in, e.g., Python, use:</p> <pre><code class="language-python">from astropy.table import Table tbl = Table.read('gd1-with-masks.fits') tbl = tbl[tbl['pm_mask'] & tbl['gi_cmd_mask']]</code></pre> <p>To select out only stars within the stream track identified in <a href="https://arxiv.org/abs/1805.00425">Price-Whelan & Bonaca (2018)</a>, do:</p> <pre><code class="language-python">from astropy.table import Table tbl = Table.read('gd1-with-masks.fits') tbl = tbl[tbl['pm_mask'] & tbl['gi_cmd_mask'] & tbl['stream_track_mask']</code></pre> <pre> </pre>
Photoplethysmography in dogs and cats: selection of measurement sites for pet monitor
<p>The PPG measurements of the study Cugmas et al, 2018.</p> <p>Notes.txt include all information about the dataset.</p>
Auditory stream segregation and selective attention for cochlear implant listeners: Evidence from behavioral measures and event-related potentials
<p>Data set generated for the study "Auditory stream segregation and selective attention for cochlear implant listeners: Evidence from behavioral measures and event-related potentials" </p> <ol> <li><strong>behavioral.txt</strong>: d' scores obtained by the listeners on the deviant detection task. <ul> <li>subject: listener ID</li> <li>distractor: Electrode separation condition</li> <li>deviant: Deviant triplet</li> <li>d: d' scores</li> <li>exp: experimental session (BEH / ERP)</li> </ul> </li> <li><strong>ERP_by_condition.txt</strong>: <ul> <li>Subject: listener ID</li> <li>Type: Sound type (Target / Distractor)</li> <li>Dev: Deviant condition. Early = deviant triplets 1 or 2. Late = deviant triplet 3 or <em>none.</em></li> <li>rep: Triplet number</li> <li>sound: sound number within the triplet</li> <li>amplitude: amplitude difference between the active and the passive listening conditions.</li> </ul> </li> </ol> <p> </p>
A new and highly robust light-responsive Azo-UiO-66 for highly selective and low energy post-combustion CO2 capture and its application in a mixed matrix membrane for CO2/N2 separation
<p>Supporting information for publication in Journal of Materials Chemistry A, <a href="https://dx.doi.org/10.1039/C8TA03553A">https://dx.doi.org/10.1039/C8TA03553A </a></p>
bin3C - GTDB metadata associated with reference genomes selected for the simulated community
<p>Supplementary data table S1 from the manuscript</p> <p>bin3C : Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)</p> <p>A simulated community was constructed for ground truth validation of bin3C results. This table lists the GTDB metadata associated with each of the 63 selected genomes.</p>
USP5 Zf-UBD Co-Crystal Structure with Compound XSR00035795a & Testing Selectivity
<p>Growth of well-diffracting co-crystals of USP5 zinc finger ubiquitin binding domain (Zf-UBD) and compound XSR00035795a to solve the structure to determine ligand interactions in the binding pocket. A surface plasmon resonance assay was used to determine if methyl group on carboxylic chain of compound XSR00035795a confers selectivity to USP5 versus HDAC6. </p>
SNP genotypes for 524 wild mice and selected laboratory strains
<p>SNP genotypes from the Mouse Universal Genotyping Array for 524 wild mice and 12 selected laboratory strains. Data are provided in PLINK binary format (*.bed/*.bim/*.fam files) with an accompanying sample manifest (comma-separated text.)</p>
Selected WRF output for Langtang catchment, years 2011, 2012, 2013
<p>Includes selected variables for the Langtang catchment of the WRF run used in <em>Contrasting meteorological drivers of the glacier mass balance between the Karakoram and central Himalaya, </em>by PNJ Bonekamp, RJ de Kok, E Collier and WW Immerzeel. </p> <p>WRF version 3.8.1 is used for the simulations. The outer domain is forced with 6-hourly ERA-Interim data (0.75 o x0.75o), and grid analysis nudging is applied to the horizontal wind, water vapour mixing ratio and potential temperature fields in the highest 15 vertical levels. In all domains, the land cover dataset is updated using the climate change initiative dataset (CCI, Defourny et al. (2017)) of the European Space Agency (ESA), which has a spatial resolution of 300 m. The soil moisture, soil temperatures and skin temperatures is initialized with the GLDAS dataset (0.25x0.25, Rodell et al. 2004).</p> <p>Important parameter settings are:<br> - One way nesting<br> - Model top pressure<br> - Morrison microphysics<br> -YSU planetary boundary layer scheme wit topo_wind option=1<br> - Noah-MP scheme<br> -RRTGM radiation scheme<br> -MM5 Similarity Scheme<br> - Rayleigh damping at the top boundary<br> - Diffusion is calculated in physical space<br> <br> Included variables [unit']:<br> Time [hours from 1 December 2010], total simulation time is till 1 January 2014. All data are hourly variables. <br> ALBEDO [-]<br> GLW [Wm-2], longwave downward radiation<br> GRAUPELNC [m], non convective graupel<br> GRDFLX [Wm-2], ground heat flux <br> HFX [Wm-2], Sensible heat flux<br> HGT [m], altitude above sea level<br> LH [Wm-2], latent heat flux<br> LU_INDEX [-] #24 categories<br> LWDNB [Wm-2], longwave downward radiation<br> LWUPB [Wm-2], longwave upward radiation<br> RAINC [m], convective rainfall<br> RAINNC [m], non convective rainfall<br> SNOWC [-], snow cover<br> SNOWNC [-], non convective snowfall<br> SWDOWN [Wm-2], shortwave downwards radiations<br> T2 [K], 2-meter temperature<br> TSK [K], surface temperature<br> U10 [ms-1], wind in U-direction at 10-m<br> V10 [ms-1], wind in V direction at 10-m</p> <p>See for the full description of the variables Chapter 5 of the WRF user guide</p>
Selected WRF output for Shimshal catchment, years 2011, 2012, 2013
<p>Includes selected variables for the Langtang catchment of the WRF run used in <em>Contrasting meteorological drivers of the glacier mass balance between the Karakoram and central Himalaya, </em>by PNJ Bonekamp, RJ de Kok, E Collier and WW Immerzeel. </p> <p>WRF version 3.8.1 is used for the simulations. The outer domain is forced with 6-hourly ERA-Interim data (0.75 o x0.75o), and grid analysis nudging is applied to the horizontal wind, water vapour mixing ratio and potential temperature fields in the highest 15 vertical levels. In all domains, the land cover dataset is updated using the climate change initiative dataset (CCI, Defourny et al. (2017)) of the European Space Agency (ESA), which has a spatial resolution of 300 m. The soil moisture, soil temperatures and skin temperatures is initialized with the GLDAS dataset (0.25x0.25, Rodell et al. 2004).</p> <p>Important parameter settings are:<br> - One way nesting<br> - Model top pressure<br> - Morrison microphysics<br> -YSU planetary boundary layer scheme wit topo_wind option=1<br> - Noah-MP scheme<br> -RRTGM radiation scheme<br> -MM5 Similarity Scheme<br> - Rayleigh damping at the top boundary<br> - Diffusion is calculated in physical space<br> <br> Included variables [unit']:<br> Time [hours from 1 December 2010], total simulation time is till 1 January 2014. All data are hourly variables. <br> ALBEDO [-]<br> GLW [Wm-2], longwave downward radiation<br> GRAUPELNC [m], non convective graupel<br> GRDFLX [Wm-2], ground heat flux <br> HFX [Wm-2], Sensible heat flux<br> HGT [m], altitude above sea level<br> LH [Wm-2], latent heat flux<br> LU_INDEX [-] #24 categories<br> LWDNB [Wm-2], longwave downward radiation<br> LWUPB [Wm-2], longwave upward radiation<br> RAINC [m], convective rainfall<br> RAINNC [m], non convective rainfall<br> SNOWC [-], snow cover<br> SNOWNC [-], non convective snowfall<br> SWDOWN [Wm-2], shortwave downwards radiations<br> T2 [K], 2-meter temperature<br> TSK [K], surface temperature<br> U10 [ms-1], wind in U-direction at 10-m<br> V10 [ms-1], wind in V direction at 10-m</p> <p>See for the full description of the variables Chapter 5 of the WRF user guide</p>
Testing Selectivity of USP5 Zf-UBD Analogues with SPR Assay
<p>A surface plasmon resonance (SPR) assay is used to determine binding affinities of commercial compound analogues against the zinc finger ubiquitin binding domain (Zf-UBD) of USP5 and test for selectivity against HDAC6 Zf-UBD </p>
Community-weighted mean traits in old-growth and selectively logged forest
<p><strong>Description: </strong></p> <p>Community-weighted mean traits from tree species that make up more than 80% basal area in plots in selectively logged forest at SAFE and in old-growth forest in Danum Valley and Maliau Basin. Sampled during the BALI project traits campaign</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/55"><strong>Biodiversity and land-use impacts on tropical ecosystem function (BALI): Quantifying functional trait distributions across the disturbance gradient</strong></a></p> <p><strong>Funding: </strong>These data were collected as part of research funded by:</p> <ul> <li>NERC (Standard grant, NE/K016253/1)</li> </ul> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <p> </p> <p><strong>Permits: </strong>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2.2(385))</li> </ul> <p> </p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3247602">here</a></p> <p><strong>Files: </strong>This dataset consists of 3 files: Both_CWM_traits.xlsx, CSP_protocol_Chlorophyll_and_Carotenoids.pdf, CSP_protocol_Phenols_Tannins_Analysis.pdf</p> <p><strong>Both_CWM_traits.xlsx</strong></p> <p>This file contains dataset metadata and 1 data tables:</p> <ol> <li> <p><strong>CMW_traits</strong> (described in worksheet CMW_traits)</p> <p>Description: Community-weighted mean traits of tree in plots in SAFE , Danum Valley and Maliau Basin sampled during the BALI project traits campaign</p> <p>Number of fields: 36</p> <p>Number of data rows: 8</p> <p>Fields:</p> <ul> <li><strong>location</strong>: Location (Field type: Categorical)</li> <li><strong>forest_type</strong>: Forest type (Field type: Categorical)</li> <li><strong>forestplots_name</strong>: Plot name coherent with forestplots database (Field type: ID)</li> <li><strong>plot_name_trait_campaign</strong>: Plot name used during the BALI trait campaign (Field type: ID)</li> <li><strong>CWM_total_K_mg.g_log</strong>: CWM foliar potassium concentration in mg per g dry weight, log transformed data (Field type: Numeric)</li> <li><strong>CWM_total_Ca_mg.g_log</strong>: CWM foliar calcium concentration in mg per g dry weight, log transformed data (Field type: Numeric)</li> <li><strong>CWM_total_Mg_mg.g_log</strong>: CWM foliar magnesium concentration in mg per g dry weight, log transformed data (Field type: Numeric)</li> <li><strong>CWM_total_P_mg.g_log</strong>: CWM foliar phosporus concentration in mg per g dry weight, log transformed data (Field type: Numeric)</li> <li><strong>CWM_N_perc</strong>: CWM foliar nitrogen concentration (Field type: Numeric)</li> <li><strong>CWM_15N_per_mil</strong>: CWM foliar 15N isotope concentration (Field type: Numeric)</li> <li><strong>CWM_C_perc</strong>: CWM foliar carbon concentration (Field type: Numeric)</li> <li><strong>CWM_13C_per_mil</strong>: CWM foliar 13C isotope concentration, expressed relative to Vienna Pee Dee Belemnite (VPDB) as δ13C in units of per mil [‰] (Field type: Numeric)</li> <li><strong>CWM_DR</strong>: CWM dark respiration measured on leaf attached to a branch that is cut under water and remains in water (Field type: Numeric)</li> <li><strong>CWM_Asat</strong>: CWM light-saturated net photosynthesis measured on leaf attached to a branch that is cut under water and remains in water. (Field type: Numeric)</li> <li><strong>CWM_Amax</strong>: CWM maximum photosynthetic capacity measured on leaf attached to a branch that is cut under water and remains in water. (Field type: Numeric)</li> <li><strong>CWM_leaf_thickness_mm_log</strong>: CWM thickness of leaf, log transformed data (Field type: Numeric)</li> <li><strong>CWM_dry_weight_mg_log</strong>: CWM leaf oven-dried weight, log transformed data (Field type: Numeric)</li> <li><strong>CWM_LA_mm2_log</strong>: CWM leaf area (LA) calculated from fresh leaves collected from branches, scanned immediately, log transformed data (Field type: Numeric)</li> <li><strong>CWM_SLA_mm2_mg</strong>: CWM specific leaf area (SLA) determined as the one-sided area of a fresh leaf, divided by its oven-dry mass. (Field type: Numeric)</li> <li><strong>CWM_LDMC_mg.g</strong>: CWM leaf dry-matter content (LDMC) is the oven-dry mass (mg) of a leaf, divided by its water-saturated fresh mass (g) mg g–1 (Field type: Numeric)</li> <li><strong>CWM_chla_mg.g</strong>: CWM foliar chlorophyll a content (Field type: Numeric)</li> <li><strong>CWM_chlb_mg.g</strong>: CWM foliar chlorophyll b content (Field type: Numeric)</li> <li><strong>CWM_carot_mg.g</strong>: CWM foliar carotenoids content (Field type: Numeric)</li> <li><strong>CWM_Fp_N_mm_log</strong>: CWM force to punch leaf, dividing the observed force (N) required to puncture the leaf lamina by the circumference of the instrument's rod, log transformed data (Field type: Numeric)</li> <li><strong>CWM_specific_Fp_log</strong>: CWM specific force to punch (Fp divided by lamina thickness), log transformed data (Field type: Numeric)</li> <li><strong>CWM_WD_B</strong>: CWM branch wood density from branch segment with bark (Field type: Numeric)</li> <li><strong>CWM_hemicellulose_perc</strong>: CWM foliar hemicellulose concentration (Field type: Numeric)</li> <li><strong>CWM_cellulose_perc</strong>: CWM foliar cellulose concentration (Field type: Numeric)</li> <li><strong>CWM_lignin_recalcitrants_perc</strong>: CWM foliar lignin and recalcitrants concentration (Field type: Numeric)</li> <li><strong>CWM_total_tannin_mg.g</strong>: CWM foliar tannin concentration (Field type: Numeric)</li> <li><strong>CWM_total_phenol_mg.g</strong>: CWM total foliar phenol concentration (Field type: Numeric)</li> <li><strong>CWM_chla_mg.mm2</strong>: CWM foliar chlorophyll a content expressed on leaf area basis (Field type: Numeric)</li> <li><strong>CWM_chlb_mg.mm2</strong>: CWM foliar chlorophyll b content expressed on leaf area basis (Field type: Numeric)</li> <li><strong>CWM_carot_mg.mm2</strong>: CWM foliar carotenoids content expressed on leaf area basis (Field type: Numeric)</li> <li><strong>CWM_N_mg.mm2</strong>: CWM foliar nitrogen concentration expressed on leaf area basis (Field type: Numeric)</li> <li><strong>CWM_total_P_mg.mm2.l</strong>: CWM foliar phosporus concentration expressed on leaf area basis, log transformed data (Field type: Numeric)</li> </ul> </li> </ol> <p><strong>CSP_protocol_Chlorophyll_and_Carotenoids.pdf</strong></p> <p>Description: Methodology of chlorophyll and carotenoids analysis, Carnegie Spectranomics protocol: https://drive.google.com/file/d/0B58dyv8L3FpMdGw0QWtiZElHQzQ/view</p> <p><strong>CSP_protocol_Phenols_Tannins_Analysis.pdf</strong></p> <p>Description: Methodology of phenols and tannins analysis, Carnegie Spectranomics protocol: https://drive.google.com/file/d/0B58dyv8L3FpMcTBHblQwRHdyRE0/view</p> <p><strong>Date range: </strong>2014-05-01 to 2018-09-01</p> <p><strong>Latitudinal extent: </strong>4.5000 to 5.0700</p> <p><strong>Longitudinal extent: </strong>116.7500 to 117.8200</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.