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13,276 results for “IT management”
Figure 1 in Mowing inhibits the invasion of the alien species Solidago altissima and is an effective management strategy
Figure 1. (a) Coverage of S. altissima and (b) the average number of species per quadrat in the S. altissima-dominant areas or uninvaded areas. Data are presented as means ± standard errors of 14 replicates. Bars with different letters are significantly different at p <0.05 (Student's t-test).
Figure 2 in Mowing inhibits the invasion of the alien species Solidago altissima and is an effective management strategy
Figure 2. Number of shoots of S. altissima in March 2018 and April 2019. Mowing 1: mowed once in July; Mowing 2: mowed twice in May and September; Mowing 3: mowed three times in May, July, and September; Eco 200: 30% of shoots in the quadrats were cut near the ground, and the cut surfaces were covered with Eco 200 block. Data are presented as means ± standard errors of 14 replicates. Bars with different letters are significantly different at p <0.05 (ANOVA with post hoc Tukey's test).
Figure 2 in Clonal mechanisms that matter in Agave fourcroydes and A. sisalana invasions in drylands: implications for their management
Figure 2. Flowering, aerial bulbil, and shoot from rhizomes phenology of Agave fourcroydes (AF) and A. sisalana (AS) in the study zone. Wilcoxon signed-rank tests showed no statistical differences between species in any variable. Mean % floral buds: W = 29.0, p-value = 0.141; Mean % flowers: W = 2.0, p-value = 0.789; Mean % bulbils: W = 49.0, p-value = 0.834; Mean % fallen bulbils: W = 13.0, p-value = 0.083; Mean % new shoots from rhizomes: W = 16.0, p-value = 0.834. W = Wilcoxon statistic.
Figure 1 in Clonal mechanisms that matter in Agave fourcroydes and A. sisalana invasions in drylands: implications for their management
Figure 1. Diagram of a rosette of Agave fourcroydes or A. sisalana showing their clonal ramet types. BS: basal shoots, born directly from the rosette; RS: shoots from rhizomes, and aerial bulbils from floral scapes.
Figure 4 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 4: Crop sequence in rotation 3: standard sugar beets variety "Mixer"; Cereals; WOSRWWOSR; Cereals; Oil radish; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 8 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 8: SBN population dynamic under the semi-tolerant variety "Rosalinda" followed by non-host crops at The SBN initial population Pi (eggs g−1 soil) = 4, Tolerance (T) = 0.273, proportion of the population survived (s) = 0.35 and reproduction factor (Rf) = 3.8. The suggested number of waiting years by SBN-Watch to the next "Rosalinda" crop is four years (Pf <T).
Figure 2 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 2: Crop sequence in rotation 1: standard sugar beets variety "Mixer"; Cereals; Cereals; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 3 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 3: Crop sequence in rotation 2: standard sugar beets variety "Mixer"; Cereals; WOSR; Cereals; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 1 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 1: A screenshot of the user interface showing a selected crop rotation and the estimated final SBN population (Pf) values, sugar yield (tonnes/ha), income (SEK/ha) and the reproduction factor (Rf) values.
Figure 9 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 9: (A) relationship between initial SBN population (Pi eggs g−1 soil) and reproduction factors (Rf) of three sugar beets varieties estimated by SBNWatch; (B) relationship between initial SBN population Pi (eggs g−1 soil) and reproduction factors (Rf) of four sugar beets varieties sown in microplots in 2013–2014 (n = 4). The bars represent means of Rf ± Sd.
Figure 7 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 7: Crop sequence in rotation 6: tolerant sugar beets variety "Julietta"; Cereals; WOSR; Cereals; Oil radish; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
Adaptive forest management improves stand-level resilience of temperate forests under multiple stressors: Dataset
<p>This dataset is linked to the paper “Adaptive forest management improves stand-level resilience of temperate forests under multiple stressors" submitted to Science of The Total Environment.</p>
FIG. 3 in Response of bryophytes to disturbances in managed forests. A case study from a Polish forest
FIG. 3. — Co-correspondence analysis biplot illustrating the ordination of most frequent bryophyte species colonizing ground in relation to the statistically significant environmental variables (n=75). Abbreviations: Age, forest age; DBH.max, maximal trees diameter at breast height; DW.vol, dead wood volume; L, Ellenberg indicator value (EIV) for light; T, EIV for temperature; F, EIV for moisture; R, EIV for pH; N, EIV for nitrogen; the first four letters of species indicate genus name, next four indicate species names and next four letters subspecies names.
0.0 0.5 1.0 1.5 RDA 1 FIG. 4 in Response of bryophytes to disturbances in managed forests. A case study from a Polish forest
0.0 0.5 1.0 1.5 RDA 1 FIG. 4. — Redundancy analysis biplot illustrating the ordination of most frequent bryophyte species colonizing tree trunks in relation to the statistically significant environmental variables for the plots with the presence of tree host species (n=41). Abbreviations as in Fig. 3.
FIG. 6 in Response of bryophytes to disturbances in managed forests. A case study from a Polish forest
FIG. 6. — Redundancy analysis biplot illustrating the ordination of most frequent bryophyte species colonizing dead wood in relation to the statistically significant environmental variables for the plots with the presence of decaying logs (n=80). Abbreviations as in Fig. 3
FIG. 5 in Response of bryophytes to disturbances in managed forests. A case study from a Polish forest
FIG. 5. — Redundancy analysis biplot illustrating the ordination of most frequent bryophyte species colonizing tree bases in relation to the statistically significant environmental variables for the plots with the presence of tree host species (n=96). Abbreviations as in Fig. 3.
FIG. 2 in Response of bryophytes to disturbances in managed forests. A case study from a Polish forest
FIG. 2. — Frequency of occurrence of bryophytes generally and in the four types of substrates considered.
Performance of a durum wheat diversity panel under different management systems
<p>Raw data of a 3-year durum wheat trial carried out at Martonvásár, Hungary, from 2020 to 2022 under three different management systems, i.e. conventional high-input, conventional low-input and certified organic.</p>
Solutions for Reproducibility in Empirical Research: Virtual Machines, Containers, Environment Management Packages, and Cloud Platforms
<p>This image provides a comprehensive overview of various technologies and platforms used to enhance the reproducibility of empirical research. It is divided into several sections:</p> <ol> <li><strong>Virtual Machines (VMs): </strong>the left section of the image illustrates the architecture of VMs with Type 1 and Type 2 hypervisors. <br> - <em>Type 1 Hypervisor </em>runs directly on the hardware, providing high efficiency and performance. Examples include VMware ESXi, <strong>Microsoft Hyper-v</strong>, and Xen Project.<br> - <em>Type 2 Hypervisor</em> runs on an existing operating system, offering flexibility at the cost of some performance. Examples include <strong>Oracle VirtualBox</strong>, VMware Workstation, and Parallels.</li> <li><strong>Containers: </strong>the middle section of the image explains the containerization concept, which shares the host operating system's kernel, making containers more lightweight than VMs. Technologies like <strong>Docker</strong> and <strong>Kubernetes</strong> are shown as popular solutions for container orchestration.</li> <li><strong>Environment Management Packages: </strong>the top right section focuses on tools for managing software dependencies and environments. <strong>renv</strong> (for R) and <strong>Conda</strong> (for Python and other languages) are highlighted as key tools for creating reproducible research environments.</li> <li>Cloud Platforms: the bottom right section features various cloud-based platforms that facilitate reproducible research by providing scalable and shareable computational environments. Platforms include <strong>Google Colab</strong>, <strong>Posit Cloud</strong>, JupyterHub, <strong>Binder</strong>, Nextjournal, OpenShift, and <strong>Code Ocean</strong>.</li> </ol> <p>Together, these solutions provide a robust framework for ensuring that empirical research can be reliably reproduced and validated by others, addressing the challenges of dependency management, environment consistency, and computational resource availability.</p>
Figure 2 in Exploring sterile pollen technique as a novel tool for management of Palmer amaranth (Amoronthus polmeri)
Figure 2. Effect of different irradiation doses on seed set of Amoronthus polmeri inflorescences in 2020 with back-transformed means and SE. Abbreviations: I, irradiated pollen; I+NI, irradiated pollen followed by hand pollination with non-irradiated pollen; NI+I, non-irradiated pollen followed by hand pollination with irradiated pollen; NI, non-irradiated pollen; Open, open pollination. LSD0.05 is the least-square distance for significance level 0.05; error bars indicate SE; shaded areas for Open and NI are mean ± SE.
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.