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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.
Effects of community management on user activity in online communities
<p>Data and code needed to reproduce the results of the paper "Effects of community management on user activity in online communities", available in draft <a href="https://www.overleaf.com/13551865bzgswwbvkpgq#/52358582/">here</a>.</p> <p>Instructions:</p> <ol> <li>Unzip the files.</li> <li>Start with JSON files obtained from calling platform APIs: each dataset consists of one file for posts, one for comments, one for users. In the paper we use two datasets, one referring Edgeryders, the other to Matera 2019.</li> <li>Run them through edgesense (https://github.com/edgeryders/edgesense). Edgesense allows to set the length of the observation period. We set it to 1 week and 1 day for Edgeryders data, and to 1 day for Matera 2019 data. Edgesense stores its results in a file called JSON network.min.json, which we then rename to keep track of the data source and observation length.</li> <li>Launch Jupyter Notebook and run the notebook provided to convert the network.min.json files into CSV flat files, one for each netwrk file</li> <li>Launch Stata and open each flat csv files with it, then save it in Stata format.</li> <li>Use the provided Stata .do scripts to replicate results.</li> </ol> <p>Please note: I use both Stata and Jupyter Notebook interactively, running a block with a few lines of code at a time. Expect to have to change directories, file names etc.</p> <p> </p>
Fig. 3 in Effect of BAM-FX in developing a management program to control major insect pests of tomato: sweetpotato whitefly (Hemiptera: Aleyrodidae), thrips (Thysanoptera: Thripidae), and their transmitted viruses
Fig. 3. Mean number of western flower thrips (Frankliniella occidentalis) per 5 leaf sample of tomato treated with various treatments of BAM‑FX and N‑P‑K granular fertilizer in 2016. Bars represent standard error of the means. T1 = BAM‑FX applied on soil, no pesticide, no N‑P‑K fertilizer; T2 = BAM‑FX applied on foliage, no pesticide, no N‑P‑K fertilizer; T3 = BAM‑FX applied on soil, pesticide, no N‑P‑K fertilizer; T4 = BAM‑FX applied on foliage, pesticide, no N‑P‑K fertilizer; T5 = BAM‑FX applied on foliage, pesticide, N‑P‑K fertilizer; T6 = no BAM‑FX, pesticide, N‑P‑K fertilizer; T7 = no BAM‑FX, no pesticide, N‑P‑K fertilizer; D1 = first sampling date (14 Dec); D2 = second sampling date (21 Dec); D3 = third sampling date (21 Dec); D4 = fourth sampling date (28 Dec); D5 = fifth sampling date (4 Jan); D6 = sixth sampling date (11 Jan).
Fig. 2 in Effect of BAM-FX in developing a management program to control major insect pests of tomato: sweetpotato whitefly (Hemiptera: Aleyrodidae), thrips (Thysanoptera: Thripidae), and their transmitted viruses
Fig. 2. Mean number of common blossom thrips (Frankliniella schultzei) per 5 leaf sample of tomato treated with various treatments of BAM‑FX and N‑P‑K granular fertilizer in 2016. Bars represent standard error of the means. T1 = BAM‑FX applied on soil, no pesticide, no N‑P‑K fertilizer; T2 = BAM‑FX applied on foliage, no pesticide, no N‑P‑K fertilizer; T3 = BAM‑FX applied on soil, pesticide, no N‑P‑K fertilizer; T4 = BAM‑FX applied on foliage, pesticide, no N‑P‑K fertilizer; T5 = BAM‑FX applied on foliage, pesticide, N‑P‑K fertilizer; T6 = no BAM‑FX, pesticide, N‑P‑K fertilizer; T7 = no BAM‑FX, no pesticide, N‑P‑K fertilizer; D1 = first sampling date (14 Dec); D2 = second sampling date (21 Dec); D3 = third sampling date (21 Dec); D4 = fourth sampling date (28 Dec); D5 = fifh sampling date (4 Jan); D6 = 6th sampling date (11 Jan).
Fig. 5 in Gastrointestinal parasites of a reintroduced semi-wild plains bison (Bison bison bison) herd: Examining effects of demographic variation, deworming treatments, and management strategy
Fig. 5. The average sum of FECs counts by year, demonstrating and increasing trend in FECs between 2015 and 2019. Black horizontal lines denote median values, while the top and bottom of boxes denote the upper and lower interquartile ranges (75th and 25th percentiles). Extending "whiskers" denote values of 1.5 times the interquartile range; points outside of this range constitute outliers.
Fig. 4 in Gastrointestinal parasites of a reintroduced semi-wild plains bison (Bison bison bison) herd: Examining effects of demographic variation, deworming treatments, and management strategy
Fig. 4. The sum of FECs counted per gram of individual bison, demonstrating variation FECs between and among individuals. Black horizontal lines denote median values, while the top and bottom of boxes denote the upper and lower interquartile ranges (75th and 25th percentiles). Extending "whiskers" denote values of 1.5 times the interquartile range; points outside of this range constitute outliers.
Fig. 3 in Gastrointestinal parasites of a reintroduced semi-wild plains bison (Bison bison bison) herd: Examining effects of demographic variation, deworming treatments, and management strategy
Fig. 3. The sum of FECs types, including "STRONGs" (Strongyle-type), "COCCs" (Coccidia), "NEMAs" (Nematodirus), "TRICHs" (Trichuris), "MONs" (Moniezia) counted per gram of sample from bison of various age classes. Ages classes included "NC" (New Calf; 0–1), "YR" (Yearling; 1–2), "JA" (Juvenile to Adult Transition; 2–4), "YA" (Young Adult; 4–6), "PA" (Peak Adult; 6–9), "MA" (Mature Adult; 9+). Black horizontal lines denote median values, while the top and bottom of boxes denote the upper and lower interquartile ranges (75th and 25th percentiles). Extending "whiskers" denote values of 1.5 times the interquartile range; points outside of this range constitute outliers.
Fig. 2 in Gastrointestinal parasites of a reintroduced semi-wild plains bison (Bison bison bison) herd: Examining effects of demographic variation, deworming treatments, and management strategy
Fig. 2. The sum of FECs counted per gram of sample from bison of various age classes, including "NC" (New Calf; 0–1), "YR" (Yearling; 1–2), "JA" (Juvenile to Adult Transition; 2–4), "YA" (Young Adult; 4–6), "PA" (Peak Adult; 6–9), "MA" (Mature Adult; 9+). Black horizontal lines denote median values, while the top and bottom of boxes denote the upper and lower interquartile ranges (75th and 25th percentiles). Extending "whiskers" denote values of 1.5 times the interquartile range; points outside of this range constitute outliers.
Fig. 1 in Gastrointestinal parasites of a reintroduced semi-wild plains bison (Bison bison bison) herd: Examining effects of demographic variation, deworming treatments, and management strategy
Fig. 1. Aerial image of the Crane Trust bison pastures. The smaller North metapopulation was continuously grazed in the Visitor Center ("VC" – 50 acres) pasture (outlined in pink). The larger South metapopulation was rotated through Ruge-South Brown ("RS" – 387 acres) pasture (outlined in orange), Calving-Office ("CO" – 267 acres) pasture (outlined in yellow), and North Meadow ("NM" – 177 acres) pasture (outlined in green). The North (orange) and South (pink) metapopulation pastures were separated by a minimum distance of 200 m, including an 80 m channel of the Platte River. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1. A in The effectiveness of field pest management and culling at harvest for risk mitigation of two fruit flies affecting citrus in China
Fig. 1. A logic chart illustrating work flow and calculating efficacies of pest management and culling at harvest (systems approach efficacy = the efficacy of the 2 measures together).
BEE-STEWARD: a research and decision support software for effective land management to promote bumblebee populations
<p><span><span>The demand for agent-based models to explore the effects of environmental change on pollinator population dynamics is growing. However, models need a simple yet flexible interface to enable adoption by a wide range of stakeholders. </span></span><span><span>We introduce BEE-STEWARD: a research and decision-support software tool, enabling researchers, policy-makers, land management advisors, and practitioners to predict and compare the effects of bee-friendly management interventions on bumblebee populations over several years. </span></span><span><span>BEE-STEWARD integrates the BEESCOUT and <i>Bumble</i>-BEEHAVE agent-based models of bumblebee behaviour, colony growth and landscape exploration into a user-friendly interface, with reconstructed code, and expanded functionality. Bespoke automatic reports can be created to illustrate how different land management interventions can affect the densities of bumblebees and their colonies over time. </span></span><span><span>BEE-STEWARD could be an important virtual test-bed for scientists exploring the impacts of different stressors on bumblebees and used by those with little or no modelling experience, enabling a shared methodology between research, policy, and practice.</span></span></p>
Simulated treatment effects on bird communities inform landscape‐scale dry conifer forest management
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Effects of different types of low-intensity management on plant-pollinator interactions in Estonian grasslands
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BEE-STEWARD: a research and decision support software for effective land management to promote bumblebee populations
Open the record for dataset details and reuse information.
Can we manage a future with more fire? Effectiveness of defensible space treatment depends on housing amount and configuration
Context: Fire in forested wildland urban interface (WUI) landscapes is increasing throughout the western United States. Spatial patterns of fuels treatments affect fire behavior, but it is unclear how fire risk and fuel treatment effectiveness will change under future conditions. Objectives: (1) How do area burned, forest and fuel characteristics, and fire risk change over time under 21st-century climate? (2) When defensible space fuels treatments are applied around all houses, which scenarios of WUI housing amount and configuration minimize fire risk? Methods: In generic 10,000-ha US Northern Rocky Mountain subalpine forest landscapes, we simulated 21 scenarios differing in fuels treatment, housing amount and configuration (neutral landscape models), and projected future climate using the process-based model iLand. We compared fire risk at three scales: 1-ha home ignition zone (HIZ), 9-ha safe suppression zone (SSZ), and landscape. Results: Under warm-dry climate, annual area burned increased, but area burned at high fire intensity peaked in the 2060s and then declined sharply; fire risk followed similar trends. Defensible space treatments maintained low flame lengths in HIZs. Clustered housing was more effective at reducing SSZ risk compared to dispersed housing. At landscape scales, treating more of the landscape reduced fire risk but configuration was unimportant. Conclusions: The most effective strategy for reducing fire risk depends on the scale at which risk is assessed. Clustering WUI developments and treating between 10 to 30% of the landscape every 10 years can reduce fire risk across multiple scales.
Long-term effectiveness of sustainable land management practices to control runoff, soil erosion, and nutrient loss and the role of rainfall intensity in Mediterranean rainfed agroecosystems
<p>This data set corresponds to the open-access article "Long-term effectiveness of sustainable land management practices to control runoff, soil erosion, and nutrient loss and the role of rainfall intensity in Mediterranean rainfed agroecosystems" published in CATENA. (<a href="https://doi.org/10.1016/j.catena.2019.104352">https://doi.org/10.1016/j.catena.2019.104352</a>), funded by he European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. </p>
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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.