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1,154 results for “Pooling”
Figure 2 in Homing behaviour of rock pool blenny Parablennius parvicornis (Pisces: Blenniidae)
Figure 2. Logistic model for the probability of returning to the home pool [Prob(return home)] as a function of distance and current direction. Note that for all distances the probability of returning against the current (A) is higher than when the current is favourable (F).
Modified pool system based on the IEEE RTS-96 system incl. 39 wind power producers
<p>This is the data-set associated with the numerical simulations in the paper "A. Papakonstantinou, P. Pinson, <em>Population Dynamics for Renewables in Electricity Markets: A Minority Game View</em>". The paper will be presented in 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) in Oct. 16-20, 2016 in Beijing, China.</p> <p>We modify the original data-set [1] by adding the marginal costs for conventional generation introduced by [2] and flexible generators capable of providing up and down regulation following [3]. The cost of up-regulation is assumed to be 10% higher than the day-ahead cost and the cost of down-regulation 9% less than the day-ahead ahead costs.</p> <p>Furthermore, regarding stochastic generation, we assume zero marginal and cost free spilling action, while load shedding induces a cost of 1000 EUR/MWh. Finally, we assume that the total demand is at 80% of the conventional generation [3], while the total capacity of the 39 stochastic producers is at 30% of the demand.</p> <p>Within the data file the specific data used for the analysis in the paper are under pes_input().</p> <p>[1] IEEE RTS Task Force of APM Subcommittee, “The ieee reliability test system-1996.” IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 1010–1020, 1999.</p> <p>[2] D. Kirschen. Unit commitment data for modernized ieee rts-96. Accessed: 10-03-2016. [Online]. Available: http://www.ee.washington.edu/research/real/library.html</p> <p>[3] A. J. Conejo, M. Carri ́on, and J. M. Morales, Decision Making Under Uncertainty in Electricity Markets. Springer, 2010.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset of Hyperspectral Melt Pool Signatures and Thermal Anomalies in DED of 316L steel
<p><strong>Description of the dataset</strong><br>The dataset includes in-situ melt pool signatures (hyperspectral NIR images) during the Directed Energy Deposition of 316L steel for several classes of thermal anomalies. Thermal anomalies were created during the process by varying the scanning speed.</p> <p>Samples were printed on the MiCLAD machine at the Vrije Universiteit Brussel (Belgium).</p> <p>Process and acquisition parameters:</p> <ul> <li>Hardware: <ul> <li>Machine: MiCLAD (Vrije Universiteit Brussel)</li> <li>Laser: High-YAG BIMO 1064nm, 2.55mm fibre, flat-top</li> <li>Nozzle: Harald-Dickler HighNo 4.0</li> </ul> </li> <li>Process parameters: <ul> <li>Laser power: 600 W</li> <li>Scanning speed: 500/700/900/1100/1300 mm/min</li> <li>Powder: 316L 45-105 um</li> <li>Powder flow rate: 3.5 g/m</li> <li>Layer thickness: 0.2 mm</li> </ul> </li> <li>Image characteristics: <ul> <li>Camera: 3D-One Avior AX-M25NIR</li> <li>Hyperspectral filter layout: 5x5 (25 wavelengths per image)</li> </ul> </li> </ul> <p><strong>Description of the files</strong></p> <ul> <li>CSV dataset (hyperspectral_nir_meltpool_dataset.csv): List of filename, sample, label, time (ms), X and Z position (mm) and local scanning speed (mm/min) for all melt pool signatures. Thermal anomalies are labelled accordingly: <ul> <li>0 : baseline</li> <li>1 : edge</li> <li>2 : underheat</li> <li>3 : strong underheat</li> <li>4 : overheat</li> <li>5 : strong overheat</li> </ul> </li> <li>Melt pool signatures (hyperspectral_nir_meltpool_images_*.zip): Raw .tif thermal images of the melt pool taken in-situ. The raw images must debayered to retrieve the spectral information, see the Python function and example script. </li> <li>Python debayer function (debayer.py): Debayering function to retrieve the spectral information from the raw images. </li> </ul>
Data from: Comparison of pooled semen insemination and single colony insemination as sustainable honeybee breeding strategies
<p>Instrumental insemination of honeybees allows for two opposing breeding strategies. In single colony insemination (SCI), all drones to inseminate a queen are taken from one colony. In pooled semen insemination (PSI), sperm of many genetically diverse drones is mixed and queens are fertilised from the resulting drone pool. While SCI allows for maximum pedigree control, proponents of PSI claim to reduce inbreeding and maintain genetic variance. Using stochastic simulation studies, we compared genetic progress and inbreeding rates in small honeybee populations under SCI and PSI. Four different selection criteria were covered: estimated breeding values (EBV), phenotypes, true breeding values (TBV), and random selection. Under EBV-based truncation selection, SCI yielded 9.0% to 44.4% higher genetic gain than PSI, but had vastly increased inbreeding rates. Under phenotypical or TBV selection, the gap between SCI and PSI in terms of genetic progress narrowed. Throughout, PSI yielded lower inbreeding rates than SCI, but the differences were only substantial under EBV truncation selection. As a result, PSI did not appear as a viable breeding strategy due to its incompatibility with modern methods of genetic evaluation. Instead, SCI is to be preferred but instead of strict truncation selection, strategies to avoid inbreeding need to be installed.</p>
In vivo parameter maps for: Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools
<p>Quantitative magnetization transfer and relaxometry maps as described in the Paper <em>Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools</em>.</p> <p>.</p>
Data from: Abiotic and biotic drivers on tadpoles in seasonal rock pools of Western Ghats rock outcrops, India
<p>We assessed the influence of abiotic (pool size, monsoon progression) and biotic (predator abundances) factors on occurrence and abundance of three species of tadpoles by periodically monitoring rock pools in lateritic plateaus. The dataset generated from this study is published here. </p> <p>Species Coverage: <em>Euphlyctis jaladhara, Microhyla nilphamariensis, Polypedates maculatus</em>; four predator groups (Pisaurid Spiders, Crabs, Water Beetles, Dragonfly Larvae)</p> <p>Geographic Coverage: Devi Hasol plateu of Ratnagiri District, Maharashtra State, India. (16°44'–16°45'N; 73°25–73°27'E)</p> <p>Temporal Coverage: July, August, September (2022).</p> <p> </p> <p><strong>Methods:</strong></p> <p>Nighttime rock pool surveys were conducted for tadpoles of three species (<em>Euphlyctis jaladhara, Microhyla nilphamariensis, Polypedates maculatus</em>). Pools were monitored eight times during the study period between 1900–2300 hr, usually in clear weather, barring occasional rain incidences. The pool water was clear during all the observation occasions. For large (>1003 cm<sup>3</sup>) pools, the observer gently walked along the bank and scanned the pool to record all animals. Care was taken not to recount the same schools of tadpoles, and a red light was used while approaching the pool to avoid light disturbance. The observer enumerated tadpoles of the three species and their potential predators (fishing spiders, crabs, dragonfly larvae, and water beetles) by counting them using head and hand-held torch lights. Following microhabitat variables were recorded at four occasions: Pool maximum length and width (cm), water depth (cm) at three points, humus cover (%), submerged vegetation cover (%), and edge vegetation cover (%). The percentage covers of vegetation and humus were visually estimated. </p> <p> </p> <p><strong>Funding:</strong></p> <ol> <li>On the Edge (UK)</li> <li>The Habitats Trust (India)</li> <li>The Bombay Environmental Action Group (India)</li> </ol>
California's forest carbon offset buffer pool is severely undercapitalized
<p>A copy of the input, intermediate, and final output data files underlying the analysis and figures of Badgley et al., "California’s forest carbon offset buffer pool is severely undercapitalized."</p>
Geochemical and genomic data from the NEOM Brine Pool (Gulf of Aqaba)
<p>This workbook contains the raw data acquired from the sampling of cores and water collected at 1,800 m depth in the NEOM Brine Pool, Gulf of Aqaba. The file captures the geochronology of the long core (S1-S2), X-ray fluorescence scanning (S3), X-ray diffraction (S4), geochemical analyses of the core sediments and overlying bine, plus associated uncertainties, and measurement precision (S5), and genomics (S6).</p>
Data for "Vegetation complexity and pool size predict species richness of forest birds"
<p>This xlsx file contains data needed to replicate analyses in our article on species richness of Australian passerine birds.</p> <p>AUTHOR of data files: Vladimir Remes<br> CONTACT: vlad.remes/at/gmail,com<br> AUTHORS of the MS: V. Remes, L. Harmacková, B. Matysiokova, L. Rubacova, E. Remesova<br> DATE CREATED: 8 June 2022</p> <p>The file was saved in MS Excel for Mac 16.42.</p> <p>The file is named "data_SR_AU_passerines.xlsx" and has two sheets:<br> "data": contains the data<br> "legend": contains explanations of data columns</p> <p>The related article is:</p> <p>Remeš V, Harmáčková L, Matysioková B, Rubáčová L and Remešová E (2022) Vegetation complexity and pool size predict species richness of forest birds. Front. Ecol. Evol. 10:964180. doi: 10.3389/fevo.2022.964180</p>
Elemental Content in P Pools from Murderkill Watershed and CMB Modeling
<p>Spreadsheet contains elemental concentrations from ICP-OES of the H<sub>2</sub>O-P, HNO<sub>3</sub>-P, and NaOH-P pools of particulate matter from the river and tributaries and farm, residential, and forest soils in the Murderkill watershed. Chemical mass balance modeling input parameters using the elemental concentrations of sources (soils) and sinks (particulate matter sample sites) are also provided.</p>
Supplementary data for: Comparison of transcriptomic profiles between HFPO-DA and prototypical PPARa, PPARg, and cytotoxic agents in mouse, rat, and pooled human hepatocytes
<p>Like many per- or polyfluorinated alkyl substances (PFAS), toxicity studies with HFPO-DA (ammonium,2,3,3,3-tetrafluoro-2-(heptafluoropropoxy)-propanoate), a short-chain PFAS used in the manufacture of some types of fluorinated polymers, indicate that the liver is the primary target of toxicity in rodents following oral exposure. Although the current weight of evidence supports the PPARa mode of action (MOA) for liver effects in HFPO-DA-exposed mice, alternate MOAs have also been hypothesized including PPARg or cytotoxicity. To further evaluate the MOA for HFPO-DA in rodent liver, transcriptomic analyses were conducted on samples from primary mouse, rat and pooled human hepatocytes treated for 12, 24 or 72 hours with various concentrations of HFPO-DA, or agonists of PPARa (GW7647), PPARg (rosiglitazone), or cytotoxic agents (i.e., acetaminophen or d-galactosamine). Concordance analyses of enriched pathways across chemicals within each species demonstrated greatest concordance between HFPO-DA and PPARa agonist GW7647-treated hepatocytes compared to the other chemicals evaluated. These findings were supported by benchmark concentration modeling and predicted upstream regulator results. In addition, transcriptomic analyses across species demonstrated a greater transcriptomic response in rodent hepatocytes treated with HFPO-DA or agonists of PPARa or PPARg, indicating rodent hepatocytes are more sensitive to HFPO-DA or PPARa/g agonist treatment. These results are consistent with previously published transcriptomic analyses and further support that liver effects in HFPO-DA-exposed rodents are mediated through rodent-specific PPARa signaling mechanisms as part of the MOA for PPARa activator-induced rodent hepatocarcinogenesis. Thus, effects observed in mouse liver are not appropriate endpoints for toxicity value development for HFPO-DA in human health risk assessment.</p>
Figure 4 in Herbicide-resistance management: a common pool resource problem?
Figure 4. Attributes of resource users associated with cooperative behavior and self-governance. Adapted from Schlager (2004, 152).
Figure 3 in Herbicide-resistance management: a common pool resource problem?
Figure 3. Attributes of common pool resources associated with cooperative behavior and self-governance. Adapted from Schlager (2004, 151–152).
Figure 1 in Herbicide-resistance management: a common pool resource problem?
Figure 1. Diagram of pesticide resistance as common property resource based on Miranowski and Carlson (1986). In this conceptualization, the common property resource is pest susceptibility, which is composed of a stock variable and a flow variable. Pest resistance is initially a renewable resource but becomes depleted over time through repeated use of chemicals. Thus, the actions of certain individuals may deplete the resource stock for others.
Figure 2 in Herbicide-resistance management: a common pool resource problem?
Figure 2. This diagram conceptualizes herbicide resistance as a common pool resource problem. Importantly, two conjoined common pool resources—herbicides and the weed gene pool—make up this resource system. Following common pool resource theory, this diagram illustrates the interconnectedness of four stock variables: (1) supply of a herbicide; (2) supply of a weed gene pool susceptible to a herbicide; (3) supply of a weed gene pool resistant to a herbicide; and (4) supply of herbicide efficacy on a weed gene pool. We have also diagramed corresponding flow variables or resource units (RU). In a generalized way, the use of a herbicide application (F1) influences the weed gene pool. However, the weed gene pool (S2 and S3) also acts independently of herbicide use and is influenced by both biological dynamics and social dynamics. Importantly, dynamics involving the weed gene pool are complex and include spatial and temporal variability in both the plant population and weed seedbank. The characteristics of the weed gene pool (S2 and S3) then affect the efficacy of the herbicide (S4) and whether its effectiveness is renewable or whether it becomes a finite stock resource. The quality of the herbicide (S4) may ultimately affect the supply of the herbicide (S1), if declining efficacy takes away from the herbicide's economic and chemical utility. In particular, the quality of these two common pool resources and not simply the quantity makes it a very complex resource arrangement. Factors adding complexity include that the weed gene pool is simultaneously both a pest and a resource. Furthermore, when the weed gene pool is characterized as a resource (its susceptibility to herbicides), the quality of this resource depends primarily upon provisioning practices of the common pool resource that keep the quality intact.In other words, following resource practices that do not allow internal or external resistance into the gene pool is key to maintaining its quality. The lack of quality from underprovisioning may result in a finite stock supply of the resource (i.e., weed gene pool susceptible to herbicides).Overappropriation (i.e., quantity or overharvesting of the resource) is a concern,in that it can be connected to poor provisioning practices.Aside from using a resource unit of herbicide in an application, the resource user does not directly appropriate or harvest from the system. This schematic only covers a generalized scenario, and more finescale analysis is needed to tease apart the complex relationships existing among herbicides and the weed gene pool.
Total data for global pattern of organic carbon pools in forest soil
<p>Understanding the mechanisms of soil organic carbon (SOC) sequestration in forests is vital to ecosystem carbon budgeting, and helps gain insight in the functioning and sustainable management of world forests. An explicit knowledge of the mechanisms driving global SOC sequestration in forests is still lacking because of the complex interplays between climate, soil and forest type in influencing SOC pool size and stability. Based on a synthesis of 1179 observations from 292 studies across global forests, we quantified the relative importance of climate, soil property and forest type on total SOC content and the specific contents of physical (particulate vs. mineral-associated SOC) and chemical (labile vs. recalcitrant SOC) pools in upper 10 cm mineral soils, as well as SOC stock in the O horizons. The variability in the total SOC content of the mineral soils was better explained by climate (47~60%) and soil factors (26%~50%) than by NPP (10~20%). The total SOC content and contents of particulate (POC) and recalcitrant SOC (ROC) of the mineral soils all decreased with increasing mean annual temperature because SOC decomposition overrides the C replenishment under warmer climate. The content of mineral-associated organic carbon (MAOC) was influenced by temperature, which directly affected microbial activity. Additionally, the presence of clay and iron oxides physically protected SOC by forming MAOC. The SOC stock in the O horizons was larger in the temperate zone and Mediterranean regions than in the boreal and sub/tropical zones. Mixed forests had 64% larger SOC pools than either broadleaf or coniferous forests, because of i) higher productivity, and ii) litter input from different tree species resulting in diversification of molecular composition of SOC and microbial community. While climate, soil and forest type jointly determine the formation and stability of SOC, climate predominantly controls the global patterns of SOC pools in forest ecosystems.</p>
Fig. 4 in Determination Of Sexual Dimorphism And Morphological Variation Of Pool Barb, Puntius Sophore (Cypriniformes, Cyprinidae), Using Landmark Based Geometric Morphometric Analysis
Fig. 4. Change of body shape along principal component axis (PC 1 = 43.827 %, and PC 2 = 20.578 %). Left side is the lollipop plots. Right side is the transformation grids of shape change.
Fig. 3, a in Determination Of Sexual Dimorphism And Morphological Variation Of Pool Barb, Puntius Sophore (Cypriniformes, Cyprinidae), Using Landmark Based Geometric Morphometric Analysis
Fig. 3, a — eigenvalues plot of the proportion of variance described by each PC, b — scatter plot showing scores on the first two PCs for the sample of non-breeding season and breeding season fish population (female in red, male in blue and non-breeding season population in green).
Fig. 1, a in Determination Of Sexual Dimorphism And Morphological Variation Of Pool Barb, Puntius Sophore (Cypriniformes, Cyprinidae), Using Landmark Based Geometric Morphometric Analysis
Fig. 1, a — male individual in breeding season; b — digitized image of P. sophore with the 14 landmarks (red points) used for the geometric morphometric analysis: c — scatter plot of 14 landmarks configurations after Procrustes Superimposition.
Fig. 2 in Determination Of Sexual Dimorphism And Morphological Variation Of Pool Barb, Puntius Sophore (Cypriniformes, Cyprinidae), Using Landmark Based Geometric Morphometric Analysis
Fig. 2. Distribution of non-breeding season population and the breeding season (male and female) population along first and second canonical variate axes (female in red, male in blue and non-breeding season population in green).
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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.