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Fig. 2 in Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica)
Fig. 2. Schematic distribution of the fish species examined in the present study for larval of C. osculatum sp. D and C. osculatum sp. E, along the continental shelf of the Ross Sea coastal ecosystem. Arrows indicating preferred preys and the diet preference for each fish species are reported according to the literature (La Mesa et al., 2004). The represented pelagic organisms comprise species of euphausiids and fish juveniles, benthic and epibenthic organisms are polychaetes, amphipods, decapods and gastropods. A pie chart with the relative proportions of C. osculatum sp. D and C. osculatum sp. E is given for each fish species. Squares and circles represent the hypothetical distribution of C. osculatum sp. D and C. osculatum sp. E larvae in their intermediate hosts.
Fig. 3 in Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica)
Fig. 3. Schematic representation of the hypothetic life-cycle of C. osculatum sp. D (a) and C. osculatum sp. E (b) in the Ross Sea.
Рис. 2. ΔоΛговременная Αинамика весенней чисΛенности трех виΑов уток (A — трескунка; B — касатки; C — шиΛохвости) на ΑебеΑинском стационаре Хинганского заповеΑника (показаны уровень значимости и 95-процентный ΑоверитеΛьный интерваΛ) Fig. 2. Long-term spring number dynamics of three duck species at the Lebedinsky Station of Khingansky State Nature Reserve with p-values and 0.95 confidence intervals. A — Gargany; B — Falcated Duck; C — Pintail in The results of long-term observation of waterfowl spring migration in Khingan Nature Reserve, Eastern Russia
Рис. 2. ΔоΛговременная Αинамика весенней чисΛенности трех виΑов уток (A — трескунка; B — касатки; C — шиΛохвости) на ΑебеΑинском стационаре Хинганского заповеΑника (показаны уровень значимости и 95-процентный ΑоверитеΛьный интерваΛ) Fig. 2. Long-term spring number dynamics of three duck species at the Lebedinsky Station of Khingansky State Nature Reserve with p-values and 0.95 confidence intervals. A — Gargany; B — Falcated Duck; C — Pintail
Dataset - ESG Dimensions and Corporate Value of the Brazilian Companies
<p><span>This dataset is the population</span><span> of the public companies from the Brazilian Stock Market (</span><span>B3, 2023</span><span>). The authors use the list of 100 more liquidity companies on B3. Once the authors collected the 100 most </span><span>liquid</span><span> companies, based on the trade shares during the period of 5 years (2017 to 2021), the authors </span><span>analysed</span><span> the database </span><span>and</span><span> eliminated all the companies with missing data during the period of 5 years. In the end of the depuration process, the authors have 93 publicly traded companies collected from the Economatica database system from the year 2017 to 2021, performing a dataset of 465 observations for 5 years. The authors observed 81 companies linked to at least one ESG-related </span><span>index</span><span> and 12 companies </span><span>without</span><span> connection to ESG. The companies used </span><span>were</span><span> only the ones </span><span>that</span><span> have all data available in all years. </span></p>
Fig. 3 Trees obtained under the implied weighting using three concavity values k in First phylogenetic analysis of the tribe Oligaphorurini (Collembola: Onychiuridae) inferred from morphological data, with implications for generic classification
Fig. 3 Trees obtained under the implied weighting using three concavity values k = 6 (a), 9 (b), and 12 (c)
Radiative-transfer dataset for "Distilling machine learning's added value: Pareto fronts in atmospheric applications"
<p>This dataset goes with the journal paper "Distilling machine learning's added value: Pareto fronts in atmospheric applications" by T. Beucler, A. Grundner, S. Shamekh, P. Ukkonen, M. Chantry, and R. Lagerquist.</p> <p>Subdirectory "training" contains unnormalized (in physical units) training data. Subdirectories "validation" and "testing" contain unnormalized validation and testing data. Subdirectory "training/for_pareto_paper_2024/simple" contains training data from the simple (clear-sky) dataset discussed in the paper; subdirectory "training/for_pareto_paper_2024/complex" contains training data from the complex (multi-cloud) dataset discussed in the paper. Subdirectories "validation/for_pareto_paper_2024/simple" and "validation/for_pareto_paper_2024/complex" are analogous but for the validation data; subdirectories "testing/for_pareto_paper_2024/simple" and "testing/for_pareto_paper_2024/complex" are analogous but for the testing data.</p> <p>Subdirectories beginning with "normalized_predictors" -- "normalized_predictors/training", "normalized_predictors/validation", "normalized_predictors/testing", "normalized_predictors/training/for_pareto_paper_2024/simple", "normalized_predictors/training/for_pareto_paper_2024/complex", etc. -- are analogous to the above but containing normalized predictors (in z-scores rather than physical units).</p> <p>Every file -- after unzipping, so that the extension is ".nc" rather than ".nc.gz" -- can be read by `example_io.read_file` in the ml4rt library (https://github.com/thunderhoser/ml4rt).</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 2. bid value for time factor
<p>Each consumer is looking for utilizing its requested service with minimum price before its<br> deadline. To utilize a service all required resources should be allocated before deadline and<br> otherwise service failed to utilize and consumer must pay penalty to providers for all other<br> resources which is allocated to it. So Consumer should adjust its bid price rapidly to the acceptable<br> price of the market. Since consumers are generally sensitive to deadline in acquiring requested<br> service, it is intuitive to consider deadline time when formulating the bid price. Consumer agent<br> time dependent bid price formula is determined in.</p>
Figure 7. Adding an Acting Module, its configuration values and its input connections-Designing a Growing Functional Modules "Artificial Brain"
<p>The fourth step consists of adding an Acting Module, its configuration values and input<br> connection as shown in figure 7. A type “CI” is assigned because it functionality will consist of<br> triggering a steering command in accordance with the perception from the Sensing Module and in<br> order to satisfy the input request from the Global Goal. Consequently, the feedback is set to “1 18”<br> where “1” is the reference to the Sensation “free” and “18” to the perception in output of the<br> Sensing Module. The identifier “18” for this perception is computed as at the total number of<br> Sensation plus one (first sensing module). Identifiers and their references are automatically updated<br> when a Sensation is added or deleted.</p>
Figure 4. (a) Therapy player software screen, where a) is the stimuli time, b) is the total therapy time, c) is the file path, d) displays the numeric values of each sequence of the therapy, e) shows the current value, and f) shows the current lag angle for zenith and azimuth values; (b) USB mechanism for conversion, where a) USB-UART converter, and b) USB-Zigbee converter.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>Where tt time expended by the servomotors to point the laser to a given position and execute<br> a laser beam sequence; tspin is the time that a servomotor needs to spin one degree; ttol is a given the<br> tolerance time; θservo is the addition of degrees that both servos in a laser driver need to spin point<br> the laser in a given position; tstimuli is the time expended in execute a laser beam, between 250 and<br> 605 ms (Weiskrantz et al., 1991); T is the total time of all repetitions in a therapy, suggested<br> between 20 and 60 minutes and N is the number of repetitions in a therapy.</p>
Figure 3. Distribution of Q3 values-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The estimated accuracy for the α- helices (QH), β- strands (QE), C-coil states (QC), and three<br> state together (Q3) for the system is shown in Figure 3.</p>
Figure 4. A graphic on values of TP, TN, FP, and FN for each different application process.-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>This study has proposed a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM) and Cognitive Development Optimization Algorithm (CoDOA). In this approach, the training process of the SVM has been supported with the CoDOA and after determining the most optimum sigma (σ) parameter of the Gauss (RBF) kernel function (so the most optimum SVM), a better classification formation has been tried to be achieved. In the context of the study, diabetes data set, which is related to Pima Indians, has been used for evaluating effectiveness of the proposed approach and after six different application processes, it was seen that the approach is well-enough on classification, which means being capable of determining diabetes. There are also some future works regarding the developed CoDOA-SVM based approach. In this context, there will be some more works for improving classification accuracy and also setting different optimization plans on i.e. different parameters of the kernel function. Additionally, it is aimed to evaluate the approach with datasets belonging to different diseases.</p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 3. Average values in acceleration and clicks
<p>However, when looking at the median values of acceleration and clicks, a clear difference appears between the states: more clicks for relaxation, higher acceleration for stress (Figure 3). </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 9. Illustration of three different wavelets with three waves that have high amplitude values, as indicated by the dashed red circles
<p>The mother wavelet of Coiflet 5 contained triple-high oscillation amplitude (i.e., Figure 9a). We considered that this mother wavelet was inappropriate for our data because overall our data possibly contained only a few matches with the mother wavelet of Coiflet 5. Moreover, the Symlet 10 (i.e., Figure 9b) and 20 (i.e., Figure 9c) also provided supportive results that were lower than others in ANNSVM_WLHT because their mother wavelets also had a similar shape as that of Coiflet 5. For similar reasons, the Haar wavelet was not proper because it is a step function. </p>
Wood preservatives utilizing low-value olive oil production by-products: Analysis
<p>The objective of this study was to develop and assess the efficacy of two experimental methodologies for the maleinisation of lampante oil to be used for wood protection.</p> <p>Two maleinisation techniques were used to chemically modify low-value lampante oil in an attempt to limit leaching, increase hydrophobicity, and impart some level of antimicrobial performance when impregnated in wood. Pine and beech wood specimens were treated with the modified oils and underwent leaching, accelerated weathering, and fungi tests. The following analysis assessed the efficacy of the modified oil treatments in improving these characteristics.</p>
Time series of detonation velocity for Fickett's model for various values of activation energy
<p>This dataset contains several time series of detonation velocity for Fickett's model.</p> <p>Parameters are: q=4, resolution per unit lenth is 1280.</p> <p>Activation energies (theta) are 0.95, 1, 1.004, 1.055, 1.065, 1.089.</p>
Data and R-Scripts for: Value of crowd-based water level class observations for hydrological model calibration
<p>This dataset corresponds to the study<br> "Value of crowd-based water level class observations for hydrological model calibration"<br> submitted to Water Resources Research in August 2019.</p> <p>Please use the R-Scripts in ascending numbers and adapt the paths to where you stored the files.<br> The helpfunctions.R will be used by some of the scripts and you might<br> want to adapt a path in line 356 for it to be used correctly with the scripts 8a and 8b.</p> <p>The parameter ranges used for the HBV calibration can be found in the "Parameters and parameter ranges.pdf"</p> <p>If you do not wish to calibrate the model, and just perform some statistics<br> start with script 7 and use the<br> - CrossValidation_stats_all.txt in the LUT Tables folder which contains<br> all model performances.<br> - CrossValidation_stats_WP1.txt contains also results of the upper benchmark<br> (only those labelled with no error and hourly).<br> - RandomParamPerformance_Validation.txt contains the results of the random parameters<br> (lower benchmark).<br> - The folders Validation Results and Calibration Results contain the files in HBV-format after the model<br> calibration and validatin were completed. The results of the Calibration and Validation files are also summarized<br> in the aforementioned txt-files within script 6 -HBV CrossValidation.R.<br> Please be aware that for the study only the catchments Murg, Guerbe, Mentue, and Verzasca were used!</p> <p><br> If you run into trouble using the data please contact simon.etter[at]outlook.com.</p> <p>Co-authors are:<br> Prof. Dr. Jan Seibert - jan.seibert[at]geo.uzh.ch<br> Dr. Ilja (H.J.) van Meerveld - ilja.vanmeerveld[at]geo.uzh.ch<br> Barbara Strobl - barbara.strobl[at]geo.uzh.ch</p>
Figure. Mean pre-adult development time (in days) values for all strains. Vertical bars denote 0.95 confidence intervals. in Effects of artificial migration of susceptible individuals on resistance and fitness of a fenitrothion-resistant strain of Musca domestica (L.) Diptera
Figure. Mean pre-adult development time (in days) values for all strains. Vertical bars denote 0.95 confidence intervals.
Figure. The proportion of benthic abundance for different invertebrate orders relative to the total benthic abundance [%] of the Munzur and Pülümür rivers, representing the mean value of the 2 sites per river. in Macroinvertebrate composition in the metarhithral zones of the Munzur and Pülümür rivers: a preliminary study
Figure. The proportion of benthic abundance for different invertebrate orders relative to the total benthic abundance [%] of the Munzur and Pülümür rivers, representing the mean value of the 2 sites per river.
Figure. Comparison of longest primary feather, tail length, and chest circumference in male and female common snipe (* = p <0.05; **= p <0.01). Table 3. Weight of gut variables in male and female common snipe. in Revision of common snipe, Gallinago gallinago in morphometric analysis and building the standard reference haematological values for further studies
Figure. Comparison of longest primary feather, tail length, and chest circumference in male and female common snipe (* = p <0.05; **= p <0.01). Table 3. Weight of gut variables in male and female common snipe.
Fig. 5 in Value of forest remnants for montane amphibians on the livestock grazed Mount Mbam, Cameroon
Fig. 5. Non-metric dimensional scaling plot of amphibian community structure divided by land use type on Mount Mbam based on visual encounter surveys with equal effort for each land use. The PERMANOVA p-value is shown in the top right corner.
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.