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FIGURE 6. Map showing estimated collection area. Points are field localities and 15 meter buffer shows estimated prospecting areas for 2008 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 6. Map showing estimated collection area. Points are field localities and 15 meter buffer shows estimated prospecting areas for 2008 (yellow), 2009 (green), and 2010 (blue). Precise locality information is available to qualified researchers upon request from JODA's museum program.
FIGURE 9. All field collections from 2013 - early 2019 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 9. All field collections from 2013 - early 2019 to be used for tracking previous collection area. Points outside of JODA boundaries are BLM, USFS, or private localities. Precise locality information is available to qualified researchers upon request from JODA's museum program.
FIGURE 1 in Reproductive biology of Parona signata (Actinopterygii: Carangidae), a valuable economic resource, in the coastal area of Mar del Plata, Buenos Aires, Argentina
FIGURE 1 | Monthly variation of the gonadosomatic index (GSI) of females (black continuous line), standard deviation (dashed lines) and temperature (in Celsius degrees) (gray continuous line) based on an annual cycle.
FIGURE 3 in Reproductive biology of Parona signata (Actinopterygii: Carangidae), a valuable economic resource, in the coastal area of Mar del Plata, Buenos Aires, Argentina
FIGURE 3 | Photomicrographs of Parona signata. A: primary growth oocytes (P); B: cortical alveoli oocytes (CA); C: yolked oocytes (YO) and a post-ovulatory follicle (large arrow); D: details of a yolked oocyte (r: radiate zone; g: granulosa cells; t: theca cells); E: hydrated oocyte (arrow); F: details of a post-ovulatory follicle (arrow); G: primary growth oocytes in an adult ovary (notice the thick wall of the ovary – OW); H: atresic follicle (arrow).
FIGURE 2 in Reproductive biology of Parona signata (Actinopterygii: Carangidae), a valuable economic resource, in the coastal area of Mar del Plata, Buenos Aires, Argentina
FIGURE 2 | Monthly relative frequency of the different gonadal development stages observed in females of Parona signata on the annual cycle.
Fig. 11 in Reproductive biology of Cetengraulis edentulus (Cuvier, 1829), the major fishery resource in Guanabara Bay, Brazil
Fig. 11. Relation between the oocytes mean diameter with the gonadosomatic index (A) and with the condition factor (B) of Cetengraulis edentulus in Guanabara Bay. Lines represent the generalized additive models selected by the Akaike information criterion.
Fig. 10 in Reproductive biology of Cetengraulis edentulus (Cuvier, 1829), the major fishery resource in Guanabara Bay, Brazil
Fig. 10. Relation between fecundity with total length (A) and fullness index (B) and between fecundity residuals (after controlling for the length effect) with gonadosomatic index (C) for Cetengraulis edentulus in Guanabara Bay. Lines represent the generalized additive models selected by the Akaike information criterion.
Fig. 7 in Reproductive biology of Cetengraulis edentulus (Cuvier, 1829), the major fishery resource in Guanabara Bay, Brazil
Fig. 7. Mean values (± standard error) of the condition factor among months and seasons (black square = females; white circle = males) of Cetengraulis edentulus in Guanabara Bay.
Fig. 4 in Reproductive biology of Cetengraulis edentulus (Cuvier, 1829), the major fishery resource in Guanabara Bay, Brazil
Fig. 4. Mean values (± standard error) of gonadosomatic index (GSI) among months and seasons (black square = females; white circle = males) of Cetengraulis edentulus in Guanabara Bay.
Fig. 6 in Reproductive biology of Cetengraulis edentulus (Cuvier, 1829), the major fishery resource in Guanabara Bay, Brazil
Fig. 6. Variation of the index of reproductive activity (IRA) for female Cetengraulis edentulus among months and seasons in Guanabara Bay.
Fig. 3 in Reproductive biology of Cetengraulis edentulus (Cuvier, 1829), the major fishery resource in Guanabara Bay, Brazil
Fig. 3. Seasonal variation in percent (%) frequency of occurrence of gonad maturation stages (GMS) for females (white column) and males (black column) of Cetengraulis edentulus in Guanabara Bay.
Fig. 1 in Ecomorphology and resource use by dominant species of tropical estuarine juvenile fishes
Fig. 1. Map indicating the location of the study area (rio Mamanguape estuary) on the coast of northeastern Brazil. CMR= Camboa da Marcação; CMA= Camboa dos Macacos; CTA= Camboa dos Tanques; CPO= Curva do Pontal Beach; PON= Pontal Beach; CAM= Campina Beach.
Fig. 2. A in Ecomorphology and resource use by dominant species of tropical estuarine juvenile fishes
Fig. 2. A representative species, Menticirrhus littoralis, with sixteen morphological variables: total length (TL), standard length (SL), body height (BH), mean body height (MHB), body width (BW), head length (HL), head height (HH), relative eye height (ERH), pectoral fin length (PFL), pectoral fin width (PFW), caudal fin height (CFH), caudal peduncle length (CPL), caudal peduncle height (CPH), caudal peduncle width (CPW), mouth width (WM) and mouth height (HM).
Fig. 1 in Reducing mowing frequency increases floral resource and butterfly (Lepidoptera: Hesperioidea and Papilionoidea) abundance in managed roadside margins
Fig. 1. Effects of mowing treatment (no mowing, mowing every 6 wk, and mowing every 3 wk) on butterfly abundance and mortality. (a) Live butterflies were counted every other week and summed for the section replicates of each mowing treatment. (b) Dead butterflies were counted weekly and summed for the section replicates of each mowing treatment. (c) The relative butterfly mortalities were calculated every 3 wk as ΣDead / (ΣDead + ΣLive) for the section replicates of each mowing treatment. The gray box on each x-axis indicates when the interrupted 6 wk treatment (6* wk) was added due a mowing error in the 6 wk treatment sections in Site 2. The 6* wk treatment was split from the 6 wk treatment for the whole time period in all sites for longitudinal reasons, i.e., to avoid an unnatural drop in the 6 wk treatment afer the mowing error. The 6* wk treatment was split afer the mowing error in Site 2 in the statistical analysis. Black vertical lines represent the knots that separated the data into spline sections for the statistical analyses.
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 6. Comparison on resource utilization.
<p>Figure 6 shows resource utilization in different system loads and as shown in it, in ICDA<br> resource utilization is more efficient than other methods especially in higher system load which is<br> due to tradeoff and sharing factors.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 4. Sharing and tradeoff factor effect on resource utilization
<p>In Figure 4 we consider tradeoff and sharing factors in providers. The result illustrates that<br> by using these factors providers improve resource utilization. Higher resource utilization motivates<br> more providers to participate in the cloud and also enables the cloud market to handle more<br> consumers which influences market efficiency.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 1. Resource allocation schema in proposed method
<p>We assume that the resources allocation satisfies the following conditions:<br> • The quantity of a resource can be measured in arbitrary units (e.g. 60 units of resource<br> A).<br> • A resource can be divided into an arbitrary fraction (e.g. a resource of 60 units is divided<br> into 20 units for consumer 1 and 40 units for consumer 2).<br> • A resource request of a service can be divided into sub-requests and acquired from<br> multiple providers (e.g. a resource request of 40 units utilized as 10 units from provider<br> 1 and 30 units from provider 2).<br> Figure 1 shows a cloud computing environment with the proposed mechanism.</p>
Database of Pines from the Forests paper: "Intraspecific Variation in Pines from the Trans-Mexican Volcanic Belt Grown Under Two Watering Regimes: Implications for Management of Genetic Resources"
<p>Raw data from the Forests paper: "Intraspecific Variation in Pines from the Trans-Mexican Volcanic Belt Grown under Two Watering Regimes: Implications for Management of Genetic Resources" Forests <strong>2018</strong> <em>9</em>(2), 71. doi:<a href="http://dx.doi.org/10.3390/f9020071">10.3390/f9020071. </a></p> <p>The database correspond to seedlings of four Mexican pines: <em>P. oocarpa, P. patula</em> and <em>P. pseudostrobus</em>, that were submitted to two watering treatments: Field Capacity (FC) and Drought-Stress (DS), during 90 days. Growth and biomass, survival and ontogenetic score were measured.</p>
Taking advantage from phenotype variability in a local animal genetic resource: identification of genomic regions associated with the hairless phenotype in Casertana pigs
<p>Ped and Map files for 96 Casertana breed pigs genotyped with Illumina BeadChip 60K Porcine.<br> The first field of the ped file contains the id of the farm (1az-6az).<br> The hairless phenotype, in the ped phenotype field, is codified as 1, the hairy phenotype is codified as 2.</p>
Intentional Forgetting in Organizations: The Positive Effects of Decision Support Systems on Mental Resources and Well-being
<p>This dataset contains raw data collected in an experimental study at the University of Muenster, Germany. The study is part of a larger research project and examined “intentional forgetting” effects in a simulated sales planning scenario. Intentional forgetting was operationalized via computer-based decision support system that enabled users to forget decision-relevant background information. We assumed that such intentional forgetting not only enhances decision quality but also decreases strain of decision makers and releases memory capacities for additional tasks.</p>
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.