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Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05) in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05)
FIGURE 2 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
FIGURE 2 (a) Principal component (PC) () C00, () C04, () C10, () C20, () C32, () E04, () E10, () E20, and () E32 and (b) linear discriminant (LD) scores for Salmo trutta treatment groups (C, control; E, exercise) across experimental weeks (i.e., age 00 (control sample before treatment initiation) to 32 (32 weeks of treatment); n = 6 individuals per group). PC1 and PC3, derived from a between-group PC analysis of Procrustes superimposed landmarks corrected for the arching artefact (PC2). LD1 and LD2, derived from a LD analysis on the corrected principal component scores. Ellipses demarcate 95% confidence intervals; O, group centroids. N.B. The change of direction for head size on LD1 resulting from a negative association with PC1 (see Table 2)
FIGURE 4 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
FIGURE 4 Box plots showing median (), 25th–75th percentiles () and range () of Salmo trutta condition at length (KÞ for exercised () and control () Salmo trutta cohorts across the experimental period (i.e., age) weeks 4–32 after treatment initiation (n = 6 per group). *, significant differences of pairwise least-squares means between exercised and control cohorts; different lower-case letters (black, exercise; grey, control) denote significant differences of pairwise least-squares means within treatments across the experimental period
A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation
<h1>Dataset Description for "A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation"</h1> <p>This dataset accompanies the research paper titled <strong>"A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation"</strong>, currently under review for the AGU Journal JAMES. The study introduces a novel Regional Climate Model (RCM) emulator focusing on high-resolution climate downscaling for the New Zealand region. For additional insights and access to the codebase utilized in this research, please refer to our <a href="https://github.com/nram812/A-Robust-Generative-Adversarial-Network-Approach-for-Climate-Downscaling" target="_new">GitHub repository</a>.</p> <h2>Aims</h2> <p>Our study's overarching goal was to assess the effectiveness of Generative Adversarial Networks (GANs) in a climate downscaling context and is structured around two aims. The first aim of our study is to examine whether GANs can overcome several important limitations of regression-based climate downscaling algorithms (i.e. underestimating the magnitude of extreme events). The second and most important aim of our study is to assess the robustness GAN performance to different training hyperparameters. Our robustness assessment thoroughly scrutinizes GANs for their application in climate downscaling contexts, ensuring that they can learn and capture regional climate processes</p> <h2>Geographic Focus</h2> <p>Our research focuses only on the New Zealand Region (165°E-184°W, 33°S-51°S).</p> <p> </p> <h2>Data Overview</h2> <h3>Training and Evaluation Data</h3> <p>The training data used in this study (for our RCM emulator) only spans the historical period of simulation. It comprises daily accumulated precipitation as the primary target variable, alongside large-scale predictor variables. </p> <ul> <li> <p><strong>Resolution:</strong> The target variable is presented at a 12km resolution, reflecting the highest resolution face of RCM for the New Zealand region. Predictor variables are coarsened to a 1.5-degree resolution from original CCAM outputs using conservative interpolation. </p> </li> <li> <p><strong>Period Coverage:</strong></p> <ul> <li>Training Data: 1960-2014</li> <li>Validation Data: 1986-2005</li> </ul> </li> <li> <p><strong>Models:</strong></p> <ul> <li>Training on: ACCESS-CM2</li> <li>Validated on: EC-Earth3, NorESM2-MM</li> </ul> </li> </ul> <h3>File Structure</h3> <ul> <li> <p><strong>Training Data:</strong></p> <ul> <li>Target/Ground Truth (Y): <code>predictor_ACCESS-CM2_hist.nc</code></li> <li>Predictor (X): <code>pr_ACCESS-CM2_hist.nc</code></li> </ul> </li> <li> <p><strong>Evaluation Data:</strong></p> <ul> <li><strong>NorESM2-MM:</strong> <ul> <li>Target (Y): <code>NorESM2-MM_historical_precip_compressed.nc</code></li> <li>Predictor (X): <code>NorESM2-MM_histupdated_compressed.nc</code></li> </ul> </li> <li><strong>EC-Earth3:</strong> <ul> <li>Target: <code>EC-Earth3_historical_precip_compressed.nc</code></li> <li>Predictor: <code>EC-Earth3_histupdated_compressed.nc</code></li> </ul> </li> </ul> </li> </ul> <h2>Methodological Insights</h2> <ul> <li> <p><strong>Regional Climate Model</strong>, Our Regional Climate Model training data is from the Conformal Cubic Atmospheric Model (CCAM) which is a global non-hydrostatic atmospheric model renowned for its variable-resolution cubic grid. . For more information about CCAM, please see the following <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD038530">paper</a>.</p> </li> <li> <p><strong>Predictor and Target Variables:</strong> Daily-averaged large-scale prognostic variables, including zonal wind, meridional wind, temperature, and specific humidity, are employed as predictors at the 500mb and 850mb pressure levels. These are normalized (see the GitHub repository for the mean and standard deviation fields). Precipitation is taken as is from CCAM and accumulated for each given day. Static predictors are also used in our model, which is stored in a GitHub repository.</p> </li> <li> <p><strong>Training Framework:</strong> Our dataset benefits from the "perfect framework" training strategy, which uses CCAM-coarsened predictor variables. For more information about the perfect and imperfect training frameworks, see the following <a title="review" href="https://journals.ametsoc.org/view/journals/aies/3/2/AIES-D-23-0066.1.xml">review</a></p> </li> </ul>
Low carbon energy R&D portfolios that are robust when models and experts disagree
<p>This data archive contains model runs and data analysis files to the research article</p> <p><strong>Low carbon energy R&D portfolios that are robust when models and experts disagree</strong></p> <p>by</p> <p>Franklyn Kanyako, Erin Baker, David Anthoff</p> <p> </p> <p><strong>All Model output and Non-Dominated Portfolios</strong>: This contains all expected values of all model outputs, used to determine the non-dominated portfolios under each policy.</p> <p><strong>Large Scale Expert Elicitation of R&D Investment</strong>: Contains samples of expert elicitation from each elicitation team.</p> <p> </p> <p> </p>
FIGURE 7 in Re-assessment of the Late Jurassic eusauropod dinosaur Hudiesaurus sinojapanorum Dong, 1997, from the Turpan Basin, China, and the evolution of hyper-robust antebrachia in sauropods
FIGURE 7. Right humerus of Rhomaleopakhus turpanensis gen. et sp. nov. (IVPP V11121-1; holotype). A, anterior view; B, lateral view. Abbreviations: dpc, deltopectoral crest; l.adp, lateral anterodistal process; m.adp, medial anterodistal process; mt, medial tuber. Note that it was not possible to remove the humerus from its cradle at the time these photographs were taken, so obtaining images of the posterior and medial surfaces was not possible. Scale bar equals 200 mm.
FIGURE 11 in Re-assessment of the Late Jurassic eusauropod dinosaur Hudiesaurus sinojapanorum Dong, 1997, from the Turpan Basin, China, and the evolution of hyper-robust antebrachia in sauropods
FIGURE 11. Phylogenetic relationships of Hudiesaurus sinojapanorum and Rhomaleopakhus turpanensis, gen. et sp. nov. A, topology based on EWP and EIW analyses of the Mannion et al. (2019a, b) matrix, with Wamweracaudia pruned a posteriori; B, topology based on EIW analysis of Moore et al. (2020) matrix. In both topologies, Hudiesaurus and Rhomaleopakhus are in bold font, the highlighted node represents 'Core Mamenchisaurus-like taxa' (CMTs), and eusauropods more derived than CMTs have been collapsed into a single lineage.
FIGURE 10 in Re-assessment of the Late Jurassic eusauropod dinosaur Hudiesaurus sinojapanorum Dong, 1997, from the Turpan Basin, China, and the evolution of hyper-robust antebrachia in sauropods
FIGURE 10. Articulated right manus of Rhomaleopakhus turpanensis, gen. et sp. nov. (IVPP V11121-1; holotype). A, anterior view; B, anterolateral view; C, anteromedial view; D, proximal (dorsal) view; and E, distal (ventral) view. Abbreviations: 1–2, phalanx number; ca, carpal; I–V, digit/metacarpal number; McX, metacarpal (number); PhX.Y, phalanx (number). Scale bars equal 100 mm.
FIGURE 6 in Re-assessment of the Late Jurassic eusauropod dinosaur Hudiesaurus sinojapanorum Dong, 1997, from the Turpan Basin, China, and the evolution of hyper-robust antebrachia in sauropods
FIGURE 6. Holotype right forelimb of Rhomaleopakhus turpanensis gen. et sp. nov. (IVPP V11121-1; holotype) with individual elements in approximate anatomical position, shown in anterior view. Scale bar equals 200 mm.
FIGURE 3 in Re-assessment of the Late Jurassic eusauropod dinosaur Hudiesaurus sinojapanorum Dong, 1997, from the Turpan Basin, China, and the evolution of hyper-robust antebrachia in sauropods
FIGURE 3. Posterior cervical vertebra of Hudiesaurus sinojapanorum (IVPP V11120; holotype). A, dorsal view; B, close up on anterior vertebral laminae supporting the diapophysis in right lateral view (not to scale). Abbreviations: ACDL, anterior centrodiapophyseal lamina; d.ACDL, dorsal branch of ACDL; l.ACDL, lateral branch of ACDL; dia, diapophysis;?epi, epipophysis; poz, postzygapophysis; PRDL.k, kink in PRDL; prz.p, pits on dorsal surface of prezygapophysis; SDF.c, pneumatic coel within spinodiapophyseal fossa; SPOL, spinopostzygapophyseal lamina. Scale bar equals 100 mm.
FIGURE 4 in Re-assessment of the Late Jurassic eusauropod dinosaur Hudiesaurus sinojapanorum Dong, 1997, from the Turpan Basin, China, and the evolution of hyper-robust antebrachia in sauropods
FIGURE 4. Posterior cervical vertebra of Hudiesaurus sinojapanorum (IVPP V11120; holotype). Close-up on the right lateral side of the neural spine in dorsolateral view to show pneumatic coels and accessory laminae within the spinodiapophyseal fossa (not to scale). Abbreviations: AHL, accessory horizontal lamina; lig, ossified intervertebral ligament; PODL, postzygodiapophyseal lamina; SPOL, spinopostzygapophyseal lamina; SPRL, spinoprezygapophyseal lamina.
FIGURE 2 in Re-assessment of the Late Jurassic eusauropod dinosaur Hudiesaurus sinojapanorum Dong, 1997, from the Turpan Basin, China, and the evolution of hyper-robust antebrachia in sauropods
FIGURE 2. Posterior cervical vertebra of Hudiesaurus sinojapanorum (IVPP V11120; holotype). A, right lateral view; B, left lateral view; C, anterior view; D, posterior view. Abbreviations: acc.proc, accessory process; ACDL, anterior centrodiapophyseal lamina; CPOF, centropostzygapophyseal fossa; CPOL, centropostzygapophyseal lamina; CPRF, centroprezygapophyseal fossa; CPRL, centroprezygapophyseal lamina; dia, diapophysis; lig, ossified intervertebral ligament; mp, metapophysis; mt, median tubercle; PCDL, posterior centrodiapophyseal lamina; POCDF, postzygapophyseal centrodiapophyseal fossa; PODL, postzygodiapophyseal lamina; poz, postzygapophysis; pp, parapophysis; PRCDF, prezygocentrodiapophyseal fossa; PRDL, prezygodiapophyseal lamina; PRDL.k, kink in PRDL; prz, prezygapophysis; SDF, spinodiapophyseal fossa; SPOF, spinopostzygapophyseal fossa; SPOL, spinopostzygapophyseal lamina; SPRL, spinoprezygapophyseal lamina; TPOL, interpostzygapophyseal lamina; TPRL, interprezygapophyseal lamina. Scale bars equal 100 mm.
FIGURE 8 in Re-assessment of the Late Jurassic eusauropod dinosaur Hudiesaurus sinojapanorum Dong, 1997, from the Turpan Basin, China, and the evolution of hyper-robust antebrachia in sauropods
FIGURE 8. Right humerus of Rhomaleopakhus turpanensis gen. et sp. nov. (IVPP V11121-1; holotype). A, proximal end view (damaged); B, distal end view. Abbreviations: l.adp, lateral anterodistal process; m.adp, medial anterodistal process. Scale bars equal 100 mm.
FIGURE 9 in Re-assessment of the Late Jurassic eusauropod dinosaur Hudiesaurus sinojapanorum Dong, 1997, from the Turpan Basin, China, and the evolution of hyper-robust antebrachia in sauropods
FIGURE 9. Right ulna and radius of Rhomaleopakhus turpanensis, gen. et sp. nov. (IVPP V11121-1; holotype). A–F, right ulna in anterior (A), lateral (B), posterior (C), posteromedial (D), proximal (E), and distal (F) views. G–L, right radius in anterior (G), lateral (H), posterior (I), medial (J), proximal (K), and distal (L) views. Note that in E, F, K, and L that anterior is towards the top of the page. Abbreviations: alp, anterolateral process of proximal ulna; amf, anteromedial fossa on distal ulna; amp, anteromedial process of proximal ulna; amr, anteromedial ridge on distal ulna; bev, beveled condyles of distal radius; con, concavity between olecranon and anteromedial processes on proximal ulna; dc, distal condyles; exp.p, posterior expansion of distal ulna; ole, olecranon process; plr, posterolateral ridge of distal radius; pmr, posteromedial ridge of proximal radius; post.pr., posterior process of proximal ulna; rad.f, radial fossa. Scale bars equal 200 mm (A–D, G–J) or 100 mm (E, F, K, L).
In vitro cell cycle oscillations exhibit a robust and hysteretic response to changes in cytoplasmic density
<p>Cells control the properties of the cytoplasm to ensure proper functioning of biochemical processes. Recent studies showed that cytoplasmic density varies in both physiological and pathological states of cells undergoing growth, division, differentiation, apoptosis, senescence, and metabolic starvation. Little is known about how cellular processes cope with these cytoplasmic variations. Here, we study how a cell cycle oscillator comprising cyclin-dependent kinase (Cdk1) responds to changes in cytoplasmic density by systematically diluting or concentrating cycling <em>Xenopus</em> egg extracts in cell-like microfluidic droplets. We found that the cell cycle maintains robust oscillations over a wide range of deviations from the endogenous density: as low as 0.2× to more than 1.22× relative cytoplasmic density (RCD). A further dilution or concentration from these values arrested the system in a low or high steady state of Cdk1 activity, respectively. Interestingly, diluting an arrested cytoplasm of 1.22× RCD recovers oscillations at lower than 1× RCD. Thus, the cell cycle switches reversibly between oscillatory and stable steady states at distinct thresholds depending on the direction of tuning, forming a hysteresis loop. We propose a mathematical model which recapitulates these observations and predicts that the Cdk1/Wee1/Cdc25 positive feedback loops do not contribute to the observed robustness, supported by experiments. Our system can be applied to study how cytoplasmic density affects other cellular processes.</p>
Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events
<p>Data, code and supplementary Figures for paper "Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events".</p>
A robust method for the measurement of social reward in adult mice
<p>Dataset contains four files.</p> <p><strong>File 1. Harda_et_al._2022_all_data.xlsx</strong></p> <p>All data used in the publication. Results of the social conditioned place preference test perofmed on adult female laboratory mice (strain: C57BL/6).</p> <p><strong>File 2. Harda_et_al._2022_initial_pref_30_70%.xls</strong></p> <p>All data contained in File 1, except for animals that showed initial preference for any of the contexts exceeding 70%.</p> <p><strong>File 3. Harda_et_al._2022_initial_pref_30_70%_trimmed.xlsx</strong></p> <p>Data contained in File 2, randomly trimmed to the same number of animals for each social context. Trimming was performed separately for each experimental group. Data were trimmed by custom R script (File 4).</p> <p><strong>File 4. trimmer.R</strong></p> <p>R script used to randomly trimm data to the same number of animals for each social context.</p>
Supplementary Data to *Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data*
<p>Supplementary code and data to <strong>Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data</strong> by <strong>Y. Schaelte et al., 2021</strong>.</p> <p>The archive contains a <strong>README.rst </strong>for information on what is where and how to execute the study and generate the figures. The underlying code without the data can be found at the repository https://github.com/yannikschaelte/study_abc_rad, of which this archive is a snapshot.</p> <p> </p>
Group and individual social network metrics are robust to changes in resource distribution in experimental populations of forked fungus beetles
<p>Social interactions drive many important ecological and evolutionary processes. It is therefore essential to understand the intrinsic and extrinsic factors that underlie social patterns. A central tenet of the field of behavioral ecology is the expectation that the distribution of resources shapes patterns of social interactions.</p> <p>We combined experimental manipulations with social network analyses to ask how patterns of resource distribution influence complex social interactions.</p> <p>We experimentally manipulated the distribution of an essential food and reproductive resource in semi-natural populations of forked fungus beetles (Bolitotherus cornutus). We aggregated resources into discrete clumps in half of the populations and evenly dispersed resources in the other half. We then observed social interactions between individually marked beetles. Half-way through the experiment, we reversed the resource distribution in each population, allowing us to control any demographic or behavioral differences between our experimental populations. At the end of the experiment, we compared individual and group social network characteristics between the two resource distribution treatments.</p> <p>We found a statistically significant but quantitatively small effect of resource distribution on individual social network position and detected no effect on group social network structure. Individual connectivity (individual strength) and individual cliquishness (local clustering coefficient) increased in environments with clumped resources, but this difference explained very little of the variance in individual social network position. Individual centrality (individual betweenness) and measures of overall social structure (network density, average shortest path length, and global clustering coefficient) did not differ between environments with dramatically different distributions of resources.</p> <p>Our results illustrate that the resource environment, despite being fundamental to our understanding of social systems, does not always play a central role in shaping social interactions. Instead, our results suggests that sex differences and temporally fluctuating environmental conditions may be more important in determining patterns of social interactions.</p>
A tradeoff between robustness to environmental fluctuations and speed of evolution
<p>The ability of a species to cope with both long-term and short-term environmental fluctuations might vary with the species' life history. While some life-history characteristics promote large and stable population sizes despite interannual environmental fluctuations, other life-history strategies might allow to evolve quickly in response to long-term gradual changes. In a theoretical study, we show that there is a tradeoff between both properties. Life-history characteristics that promote fast rates of evolution come at the expense of a poor response to short-term environmental fluctuations, and vice versa. We demonstrated the presence of this tradeoff by the use of a mathematical analysis and individual-based simulations.</p>
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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)
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.