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1,453 results for “Outcome research”
Figure 1a from: Eisenhauer N (2018) Aboveground-belowground interactions drive the relationship between plant diversity and ecosystem function. Research Ideas and Outcomes 4: e23688. https://doi.org/10.3897/rio.4.e23688
Figure 1a - Importance of the duration of the experiment for its outcome. <br> The effect of plant diversity on plant productivity and on the performance of decomposers increases over time. Regression between the R² of the relationship between plant diversity and plant productivity and the R² of the relationship between plant diversity and decomposer biomass/density. Data from the Jena Experiment from different years [plant productivity in 2003 – 2009; microbial biomass in 2003 – 2009 (white circles); meso- (gray circles) and macroinvertebrate densities (black circles) in 2004, 2006 and 2008].
Figure 1b from: Hambly A, Stedmon C (2018) FluoRAS Sensor - Online organic matter for optimising recirculating aquaculture systems. Research Ideas and Outcomes 4: e23957. https://doi.org/10.3897/rio.4.e23957
Figure 1b An example of a fluorescence EEM "fingerprint" of drinking water. Multivariate modelling of EEM datasets allows the most important areas of the EEM to be identified, and used for simplified online fluorescence sensors. - EMM viewed as a waterfall plot
Figure 3 from: Eisenhauer N (2018) Aboveground-belowground interactions drive the relationship between plant diversity and ecosystem function. Research Ideas and Outcomes 4: e23688. https://doi.org/10.3897/rio.4.e23688
Figure 3 - Plant diversity effects on soil microbes more pronounced at elevated [CO2]. Microbial biomass (µg Cmic g-1 soil dry mass) and basal respiration (BR; µl O2 h-1 g-1 soil dry mass) as affected by plant species richness (SR) and CO2 concentrations. Dashed lines indicate ambient CO2 levels, solid lines elevated CO2 levels (+180 ppm). SR x CO2 for Cmic: p=0.007; SR x CO2 for BR: p=0.03). Data from August 2010. Means with SE. Redrawn after Eisenhauer et al. (2013).
Figure 4 from: Eisenhauer N (2018) Aboveground-belowground interactions drive the relationship between plant diversity and ecosystem function. Research Ideas and Outcomes 4: e23688. https://doi.org/10.3897/rio.4.e23688
Figure 4 - Conceptual figure showing how global change drivers like temperature increase and drought may increase plant diversity–ecosystem function relationships.
Figure 2 from: Eisenhauer N (2018) Aboveground-belowground interactions drive the relationship between plant diversity and ecosystem function. Research Ideas and Outcomes 4: e23688. https://doi.org/10.3897/rio.4.e23688
Figure 2 - Conceptual scheme of how aboveground–belowground interactions may influence the positive relationship between plant diversity and ecosystem functioning. The left part of the scheme illustrates how lower quantity and quality of plant inputs to the soil in species-poor plant communities (being low in resource use complementarity) may induce negative soil feedback effects. The right part of the scheme shows that higher quantity and quality of plant inputs in species-rich plant communities (being high in resource use complementarity) may cause the dominance of positive soil feedback effects. Mutualists will decrease (Wurst et al. 2008, Latz et al. 2012) and/or superimpose (Eisenhauer et al. 2012a) detrimental effects of antagonists on plants. The four proposed projects complement each other to explore the underlying mechanisms of this scheme across different experimental contexts.
Figure 1b from: Eisenhauer N (2018) Aboveground-belowground interactions drive the relationship between plant diversity and ecosystem function. Research Ideas and Outcomes 4: e23688. https://doi.org/10.3897/rio.4.e23688
Figure 1b - Importance of the duration of the experiment for its outcome. <br> Long-term plant diversity studies on soil biota are rare. Relationship between sampling time since the establishment of the biodiversity experiment, number of studies investigating soil biota and percentage of significant plant diversity effects on soil biota (Eisenhauer et al. 2012a). Size of the bubbles and respective numbers indicate percentage of significant plant diversity effects (regression between number of studies and time: R²=0.56, p=0.033, between time and significant plant diversity effects: R²=0.66, p=0.014, n=20 studies).
Figure 7e from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 7e Examples of use of the functions Duopoly and Star. - Example of use of the function Star. Parameter angle is set to 20º.
Figure 7b from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 7b Examples of use of the functions Duopoly and Star. - Example of use of the function Duopoly. Creation of a nefroid.
Figure 6e from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 6e Examples of different fractals produced with the functions of the LearnGeom package. The examples obtained with FractalSegment show how minimal modifications of the parameters can lead to very different curves. - A modificaction of the first five iterations of the Koch's (c) by changing parameter f from 1 to 2.
Figure 6b from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 6b Examples of different fractals produced with the functions of the LearnGeom package. The examples obtained with FractalSegment show how minimal modifications of the parameters can lead to very different curves. - First three first iterations of the Koch's curve.
Figure 6a from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 6a Examples of different fractals produced with the functions of the LearnGeom package. The examples obtained with FractalSegment show how minimal modifications of the parameters can lead to very different curves. - First seven iterations of the Sierpinski triangle.
Figure 5c from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 5c Different stages of the creation of a beehive structure with the aid of tessellations. - Once the contiguous hexagons are obtained, function Tessellation allows the creation of the structure.
Figure 5b from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 5b Different stages of the creation of a beehive structure with the aid of tessellations. - Creating two contiguous hexagons to the starting one. These hexagons are derived from the middle points of some of the sides of the initial hexagon.
Figure 5a from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 5a Different stages of the creation of a beehive structure with the aid of tessellations. - Creating a regular hexagon that works as the start of the tessellation.
Figure 4a from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 4a Partial results during the process of finding the circumcenter of the triangle of points (-1,0), (0,1) and (1,0). - Triangle creation and obtention of the middle points of the sides and three auxiliary points in the orthogonal direction of each of the sides.
Figure 6d from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 6d Examples of different fractals produced with the functions of the LearnGeom package. The examples obtained with FractalSegment show how minimal modifications of the parameters can lead to very different curves. - A modificaction of the first five iterations of the Koch's (c) by changing parameter angle from 60º to 90º.
Figure 3e from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 3e Examples of use of the functions included in the package that represent affine transformations in the plane. In all the pictures, the blue triangle, placed at the points A(0,0), B(2,0) and C(1,1), is the one passed to each of the functions, being the orange triangle the output resulting for each of the transformations. - A shear transformation.
Figure 3f from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 3f Examples of use of the functions included in the package that represent affine transformations in the plane. In all the pictures, the blue triangle, placed at the points A(0,0), B(2,0) and C(1,1), is the one passed to each of the functions, being the orange triangle the output resulting for each of the transformations. - A homothety.
Figure 3b from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 3b Examples of use of the functions included in the package that represent affine transformations in the plane. In all the pictures, the blue triangle, placed at the points A(0,0), B(2,0) and C(1,1), is the one passed to each of the functions, being the orange triangle the output resulting for each of the transformations. - A rotation.
Figure 7a from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 7a Examples of use of the functions Duopoly and Star. - Example of use of the function Duopoly. Creation of an astroid.
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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)
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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.