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13,276 results for “IT management”
Figure 1 in Management of invasive, plague-carrying signal crayfish by physical exclusion barriers
Figure 1. Overview of the study area and the known crayfish distribution in 2014 (A, B), and location and pictures of the three exclusion barriers (modified pipe culverts; panel C; please note the rotated map); fill color indicates barrier functionality with pink = flow-based and yellow = waterfall-based barriers. Blue arrows indicate the direction of stream flow. The red arrow in A denotes the study area.
Figure 4 in Management of the invasive Nuttall's pondweed (Elodea nuttallii) in Lough Arrow, a Natura 2000 designated lake in Western Ireland
Figure 4. Boxplot of E. nuttallii % cover from control, single and double jute matting treatments pre and post application.
Figure 1 in Management of the invasive Nuttall's pondweed (Elodea nuttallii) in Lough Arrow, a Natura 2000 designated lake in Western Ireland
Figure 1. Lough Arrow, location, aerial map and treatment location (Loughbrick Bay). Photo credit: Darren Garland.
Figure 2 in Environmental DNA as a tool to help inform zebra mussel, Dreissena polymorpha, management in inland lakes
Figure 2. The mean number of cycles needed to detect DNA of zebra mussels from water samples collected at the surface, mid-column and bottom of Lake Minnetonka directly above a known zebra mussel population. A lower number of cycles indicates a greater amount of DNA. Bars represent the 95% confidence intervals.
Figure 3 in Environmental DNA as a tool to help inform zebra mussel, Dreissena polymorpha, management in inland lakes
Figure 3. Structural Equation Model for zebra mussels in two lakes near Alexandria, Minnesota: Lake Le Homme Dieu (A) and Maple Lake (B). Nodes are environmental DNA copy numbers of zebra mussel DNA (eDNA), habitat, depth, lake and ash-free dry weight (AFDW). AFDW is log(AFDW + 0.1). eDNA is log(copy number eDNA + 0.1). Numbers next to a line between two nodes represents the correlation between the two nodes. The r2 values in boxes correspond % variance of dependent variable explained by the independent variable. Values with an asterisk (*) indicate significant correlation between nodes. Our significance level was established at α ≤ 0.05.
Figure 5 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 5: Crop sequence in rotation 4: tolerant sugar beets variety "Julietta"; Cereals; Cereals; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 6 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 6: Crop sequence in rotation 5: tolerant sugar beets variety "Julietta"; Cereals; WOSR; Cereals; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
Fig. 6 in Ecophysiological responses to the effect of annual management on an endemic viviparous fish in central plateau of México
Fig. 6. Bimonthly structure of the population of G. multiradiatus in San Martin. Shows the curves of growth with the von Bertalanffy model for highly seasonal cycles. Upon reaching the asymptotic curve determines the final class of each age cohort.
Fig. 5 in Ecophysiological responses to the effect of annual management on an endemic viviparous fish in central plateau of México
Fig. 5. General structure of the population of San Martín mexcalpique. The numbers in parentheses indicate the percentage of each size class of the total population.
Fig. 8 in Ecophysiological responses to the effect of annual management on an endemic viviparous fish in central plateau of México
Fig. 8. Percentages of each food components found in the digestive tract of G. multiradiatus during a hydrological cycle in san Martín dam.
Fig. 4 in Ecophysiological responses to the effect of annual management on an endemic viviparous fish in central plateau of México
Fig. 4. Number of individuals (bars) and mean biomass of the population (line curve) of mexcalpiques during a hydrological cycle.
Fig. 2 in Ecophysiological responses to the effect of annual management on an endemic viviparous fish in central plateau of México
Fig. 2. Ombrothermic diagram for San Martin dam, Amealco, Qro. It shows hydrological periods of importance to the life cycle of mexcalpique.
Figure 1 in The effects of weather and reed management on nesting parameters of the Great Reed Warbler, Acrocephalus arundinaceus (Aves: Sylviidae)
Figure 1. The relationship between mean water depth and nest density of the Great Reed Warbler at the Bager Pond for the period 2008-2016.
Figure 2 in The effects of weather and reed management on nesting parameters of the Great Reed Warbler, Acrocephalus arundinaceus (Aves: Sylviidae)
Figure 2. The relationship between the amount of precipitation and nesting success of the Great Reed Warbler at the Bager Pond for the period 2008-2016.
Figure 4 in The effect of grassland management on diversity of spider assemblages in the Mátra mountain
Figure 4. Ordination diagram of a distribution of guilds (hunting strategy) in the habitats. Guild types: +-Shaft diggers, O-Orb weavers, □-Space web spiders, ●-Diurnal runnings, *-Nocturnal runnings, ■-Crab spiders,▲- Jumping spiders.
Figure 2 in The effect of grassland management on diversity of spider assemblages in the Mátra mountain
Figure 2. Correlation of (A) number of individuals and (B) species richness between control and mowed habitats
Figure 5 in The effect of grassland management on diversity of spider assemblages in the Mátra mountain
Figure 5. (A) Cluster analysis and (B) Non-metric MDS by Bray-Curtis similarity of spider assemblages between the different habitat types
Figure 3 in The effect of grassland management on diversity of spider assemblages in the Mátra mountain
Figure 3. The values of Shannon-Wiener diversity (H) and Simpson's index (1-D) in the different habitat types.
Fig.6 in Reaching The Climate Objectives Of Forest Management In Latvia Within The Scope Of European Climate Policy
Fig.6. CO2 emissions in Latvia by sources in 2016, CO2 kt eq (Source: Created by authors after National GHG inventories).
Fig.2 in Reaching The Climate Objectives Of Forest Management In Latvia Within The Scope Of European Climate Policy
Fig.2. Scheme of LFSRI Silava changes in forest resources projections process based on Nation Forest Inventory data (Source: LFSRI Silava).
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.