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Figure 10 in Metal-Age Maritime Culture at Jareng Bori Rockshelter, Pantar Island, Eastern Indonesia
Figure 10. Representative sherds for the decorative styles on pottery from Jareng Bori: Appliqué (1–2); Burnishing (3–5); Incising (6–10); Moulding (11); Moulding & Incising (12); Moulding-carving (13–15); Moulding-carving & Incising (16); Impressing (17–18); Punctation (19); Black-burnished (19–20); Black slipped (21–22); Grey slipped (23–24); Red slipped (25).
Figure 7 in Metal-Age Maritime Culture at Jareng Bori Rockshelter, Pantar Island, Eastern Indonesia
Figure 7. Cultural dental filing of the labial and occlusal surfaces of the upper first and second incisors recorded on the human burial at Jareng Bori (photo: Fayeza Shasliz Arumdhati).
Figure 2. Jareng Bori rockshelter, A in Metal-Age Maritime Culture at Jareng Bori Rockshelter, Pantar Island, Eastern Indonesia
Figure 2. Jareng Bori rockshelter, A: view from the beach facing west, B: the rockshelter facing east towards the beach, C: excavation site plan.
Figure 1 in Metal-Age Maritime Culture at Jareng Bori Rockshelter, Pantar Island, Eastern Indonesia
Figure 1. Location of Jareng Bori rockshelter, Pantar Island, eastern Indonesia in relation to recent sites excavated on nearby Alor.
Figure 4. Jareng Bori late historic burial c. 0–429 in Metal-Age Maritime Culture at Jareng Bori Rockshelter, Pantar Island, Eastern Indonesia
Figure 4. Jareng Bori late historic burial c. 0–429 cal. BP layers 4–5 (spits 6–9). Scale bar: 20 cm units.
Figure 8 in Metal-Age Maritime Culture at Jareng Bori Rockshelter, Pantar Island, Eastern Indonesia
Figure 8. Shell scrapers manufactured from (A–D) Asaphis violascens (spits 5, 8, 10 and 15); (E) Pitar sp. (spit 15); and (F) Cellana testudinaria (spit 16) from Jareng Bori.
Figure 6 The age-specific predation rate k in Long-term effects of saffron pollen on development, reproduction and predation capacity of Neoseiulus cucumeris (Acari: Phytoseiidae)
Figure 6 The age-specific predation rate k (x), and age-specific net predation rateqx() of Neoseiulus cucumeris when fed on saffron pollen for 10 and 20 generations (G10, G20) then switched toTetrany-
Bone Age Assessment from Articular Surface and Epiphysis using Deep Neural Networks
<p>This is our research dataset of "Bone Age Assessment from Articular Surface and Epiphysis in Hand Radiography using Deep Neural Networks"</p>
Phyloseq 16S v3v4 object accompanying paper: Host genotype affects endotoxin release in excreta of broilers at slaughter age
<p>This ready to load <strong>phyloseq</strong> R S4 object contains the ASV table, taxonomy table and sample metadata. This data was build using the DaDa2 (version 1.18.0) and phyloseq (version 1.34.0) R packages using the SILVA 138.1 nr99 database and raw MiSeq PE300 sequencing data deposited at NCBI-SRA under BioProject: PRJNA975731.</p> <p>F. Marcato, J.M.J. Rebel, S.K. Kar, I. Wouters, D. Schokker, A. Bossers, F. Harders, J.W. van Riel, M. Wolthuis-Fillerup and I.C. de Jong. </p> <p>The main goal of the current study was to investigate whether or not it was possible to modulate the fecal microbiome and thereby reducing endotoxin concentrations in the excreta of broiler chickens. An experiment was carried out with a 2 x 2 x 2 factorial arrangement including 3 factors, 1) genetic strain (fast-growing Ross 308 vs. slower-growing Hubbard JA757), 2) no vs. combined use of probiotics and prebiotics in the diet and drinking water, 3) early feeding at the hatchery vs. non early feeding. A total of 624 Ross 308 and 624 Hubbard JA757 day-old male broiler chickens were included until d 37 and d 51 of age, respectively. Broilers (26 chicks per pen) were housed in a total of 48 pens, and there were 6 replicate pens/treatment group. Pooled cloacal swabs (10 chickens per pen) for microbiome and endotoxin analyses were collected at a target body weight (BW) of 200 g, 1 kg and 2.5 kg.</p>
Data from: Ageing of juvenile coral grouper (Plectropomus maculatus) reveals year-round spawning and recruitment: Implications for seasonal closures
<p>Temporal patterns in spawning and juvenile recruitment can have major effects on population size and the demographic structure of coral reef fishes. For harvested species, these patterns are crucial in determining stock size and optimising management strategies such as seasonal closures. For the commercially important coral grouper (<em>Plectropomus</em> spp.) on the Great Barrier Reef, histological studies indicate peak spawning around the summer new moons. Here we examine the timing of spawning activity for <em>P. maculatus</em> in the southern Great Barrier Reef by deriving age in days for 761 juvenile fish collected between 2007 and 2022, and back-calculating settlement and spawning dates. Age-length relationships were used to estimate spawning and settlement times for a further 1,002 juveniles collected over this period. Unexpectedly, our findings indicate year-round spawning activity generates distinct recruitment cohorts that span several weeks to months. Peak spawning varied between years with no clear association with environmental cues, and little to no alignment with existing seasonal fisheries closures around the new moon. Given the variability and uncertainty in peak spawning times, this fishery may benefit from additional and longer seasonal closures, or alternative fisheries management strategies, to maximise the recruitment contribution from periods of greatest reproductive success.</p>
Combined Effects of Rotation and Age Spreads on Extended Main-Sequence Turn Offs - Isochrones and Stellar Tracks
<p>Theoretical isochrones and EEP stellar tracks featured in <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...887..199G/abstract">Gossage et al. 2019</a> (Combined Effects of Rotation and Age Spreads on Extended Main-Sequence Turn Offs). These 1D stellar evolution models are based on <a href="https://docs.mesastar.org/en/release-r22.11.1/">MESA</a> r7503, and utilize the same physical assumptions as in MIST v1.0 (<a href="https://ui.adsabs.harvard.edu/abs/2016ApJ...823..102C/abstract">Choi et al. 2016</a>).</p> <p>The models are described in <a href="https://ui.adsabs.harvard.edu/abs/2018ApJ...863...67G/abstract">Gossage et al. 2018</a> and <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...887..199G/abstract">Gossage et al. 2019</a>\(^1\), but salient features include:</p> <ul> <li>Metallicities [Fe/H] = -0.45, -0.30, -0.15, 0.0, 0.15, 0.30, 0.45 (assuming <a href="https://ui.adsabs.harvard.edu/abs/2009ARA%26A..47..481A/abstract">Asplund et al. 2009</a> protosolar abundances \(\rm Z_{\odot}\) = 0.0142).</li> <li>Rotation rates from v/v<sub>crit </sub>= 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 (initiated at ZAMS)</li> <li>Masses from 0.1 to 8 solar masses with spacings of: <ul> <li>\(\rm \Delta M = 0.05 M_{\odot}\) for \(\rm M < 1 M_{\odot}\)</li> <li>\(\rm \Delta M = 0.04 M_{\odot}\) for \(\rm 1 M_{\odot} \leq M < 2 M_{\odot}\)</li> <li>\(\rm \Delta M = 0.2 M_{\odot}\) for \(\rm 2 M_{\odot} \leq M \leq 8 M_{\odot}\)</li> </ul> </li> <li>Models are evolved up to the first thermal pulse.</li> </ul> <p><strong>File name convention:</strong> zip files, e.g., \(\texttt{MIST_v1.0_feh_p0.15_rotating_tracks.zip}\), contain theoretical models at a particular metallcity ([Fe/H] = 0.15 in this case), at all rotation rates and masses. The file \(\texttt{MIST_v1.0_feh_m0.15_rotating_tracks.zip}\) contains models at [Fe/H] = -0.15, and so on. \(\texttt{*_tracks.zip}\) contain EEP stellar track files and \(\texttt{*_isochrones.zip}\) files contain theoretical isochrone files, both in the same format as EEP track and theoretical isochrones found on the <a href="https://waps.cfa.harvard.edu/MIST/index.html">MIST website</a>. Zip files labeled, e.g.,\(\texttt{*_isochrones_FILTERSET.zip}\) contain observable isochrones, with synthetic photometry produced for a particular filter set (as on the <a href="https://waps.cfa.harvard.edu/MIST/model_grids.html">MIST packaged grids</a> site, which contains more information on the filter sets), computed without extinction (\(A_V=0\)). At the moment, filter sets for \(\texttt{UBVRIplus}\) (containing Gaia filters), \(\texttt{HST_WFC3}\), \(\texttt{HST_ACSWF}\), \(\texttt{HST_WFPC2}\), and \(\texttt{CFHTugriz}\) are included, but more can be added upon request\(^2\). </p> <p><strong>Notes:</strong></p> <p>\(^1\) These are the same models as featured in <a href="https://ui.adsabs.harvard.edu/abs/2018ApJ...863...67G/abstract">Gossage et al. 2018</a>, but in that work, [Fe/H] = -0.60 and -0.75 were featured as well and only included models up to v/v<sub>crit </sub>= 0.6. We include these models from <a href="https://ui.adsabs.harvard.edu/abs/2018ApJ...863...67G/abstract">Gossage et al. 2018</a> here as well. Models at [Fe/H] = -0.60 have been extended to v/v<sub>crit </sub>= 0.9, while [Fe/H] = -0.75 only includes up to the original v/v<sub>crit </sub>= 0.6.</p> <p>\(^2\) These tracks and isochrones may be converted into observable tracks and isochrones (w/ gravity darkening effects if desired) via Aaron Dotter's <a href="https://github.com/aarondotter/iso">iso code</a>.</p> <p>\(^3\) Although astroseismic \(\nu_{max}\) and \(\Delta \nu\) are included in the included history files, be aware that these are based on scaling relations reliant on solar values, assuming the Coriolis and centrifugal forces do not play a role. While an OK assumption for the Sun, this is less applicable models with rapid rotation and their values should not be trusted in such regimes.</p>
Data for: Age structure eliminates the impact of coinfection on epidemic dynamics in a freshwater zooplankton system
<p>Parasites often coinfect host populations, and, by interacting within hosts, might change the trajectory of multi-parasite epidemics. However, host-parasite interactions often change with host age, raising the possibility that within-host interactions between parasites might also change, influencing the spread of disease. We measured how heterospecific parasites interacted within zooplankton hosts and how host age changed these interactions. We then parameterized an epidemiological model to explore how age-effects altered the impact of coinfection on epidemic dynamics. In our model, we found that in populations where epidemiologically relevant parameters did not change with age, the presence of a second parasite altered epidemic dynamics. In contrast, when parameters varied with host age (based on our empirical measures), there was no longer a difference in epidemic dynamics between singly and coinfected populations, indicating that variable age structure within a population eliminates the impact of coinfection on epidemic dynamics. Moreover, infection prevalence of both parasites was lower in populations where epidemiologically relevant parameters changed with age. Given that host-population age structure changes over time and space, these results indicate that age-effects are important for understanding epidemiological processes in coinfected systems and that studies focused on a single age group could yield inaccurate insights.</p>
Forest cover, age and densification rate for Southern China at 30 m resolution
<p>The dataset includes Landsat based forest cover maps from 1986-2020 that were used to calculate forest age and densification rate.</p> <p>Forest probability as the output from a Random Forest model was used to reflect forest cover, and shows how likely an area resembles a dense forest. We set a threshold of 50% probability to define an area as dense forest, and the number of years from the year the threshold is crossed until 2018 as the forest age. Note that the area must remain above 50% until 2018 to qualify as forest. The years before the threshold is crossed are used to calculate the densification rate. It is defined as the mean probability change per year in the period where an area is between a probability of 20% and 50%. If the area falls below 20% the count is reset.</p>
Supporting Material for "Lunar Surface Model Age Derivation: Comparisons Between Automatic and Human Crater Counting Using LRO-NAC And Kaguya TC Images"
<p>Supporting Material for "Lunar Surface Model Age Derivation: Comparisons Between Automatic and Human Crater Counting Using LRO-NAC And Kaguya TC Images"</p> <p>Contents of this material</p> <ul> <li>Supplemental Text S1 and Text S2.</li> <li>Figures S1, S2, S2, S4, S5.</li> <li>Tables S1, S2</li> </ul> <p>For any questions email JHF (john.h.fairweaher@gmail.com).</p>
Enhanced diffusion barrier layers for avoiding degradation in SOFCs aged for 14000 h during 2 years
<p>The dataset contains all the data used for the publication called Enhanced diffusion barrier layers for avoiding degradation in SOFCs aged for 14000 h during 2 years, Bernadet et al., Journal of Power Sources, 555, 2023, 232400 (10.1016/j.jpowsour.2022.232400) as well as for the supplementary information.</p> <p>Data is labeled with the figure number-letter and sample type.</p> <p>All the figures related to the nano-XRF data (Fig4, FigS1-4) were obtained from the same dataset, shared for Fig4 and FigS1.</p>
Morphology of Arctic cod (Boreogadus saida) assessed according to habitat preference and age in the Beaufort Sea
<p><span>Arctic cod (<em>Boreogadus</em> <em>saida</em>) represents the most widespread and abundant fish in the Arctic and is a critical trophic link in its ecosystems. Like many species endemic to the region, it has lost essential habitat as the extent and thickness of sea ice </span><span>have </span><span>declined substantially in recent </span><span>decades. Extreme warming induced by climate change continues to deteriorate polar marine environments. Thus, understanding how Arctic cod uses and partitions its habitat/environment is central to its conservation. We assessed Arctic cod functional morphology using traditional (including gill rakers) and geometric morphometrics and explored whether these differed among habitats and age classes using multivariate techniques. While distinct ecotypes have been proposed, these were not detected in our analyses. Rather, results show similar patterns in the external morphology of Arctic cod across habitats and age classes in the Beaufort Sea. However, analysis of gill rakers revealed concurrent habitat- and age-specific changes likely associated with dietary preferences. Findings indicate that although Arctic cod does not specialise in external morphological features in any habitat, important aspects of its internal feeding morphology </span><span>shift </span><span>as it grows, likely underpinning important distributional changes and the species' critical role in transferring energy in Arctic marine ecosystems.</span></p>
Multicenter dataset of simulated neuroimaging features - quadratic relationship with age
<p>Let <span class="math-tex">\(y_{ijf}\)</span> be the one-dimensional array of the simulated feature <span class="math-tex">\(f\)</span>, for the single-center <span class="math-tex">\(i\)</span>, and participant <span class="math-tex">\(j\)</span>, for a total of <span class="math-tex">\(k\)</span> single-center datasets,<span class="math-tex">\(n_i\)</span> participants for each center, and <span class="math-tex">\(V\)</span> features. We simulated MRI-derived cortical thickness (CT) and fractal dimension (FD) data for <span class="math-tex">\(k = 3, 10, 36\)</span> single-centers. Each single-center dataset provided the same number of participants (i.e., <span class="math-tex">\(n_i = n\)</span>), with <span class="math-tex">\(n\)</span> assuming the values <span class="math-tex">\(25, 50, 100, 250\)</span>. Totally, we did 24 experiments, i.e., we simulated 24 different multicenter datasets (12 for the CT features and 12 for the FD measures).</p> <p>Each <span class="math-tex">\(y_{ijf}\)</span> was generated using the model proposed by Johnson and colleagues [1] and recently used for neuroimaging features’ simulation by Chen and collaborators [2]:</p> <p><span class="math-tex">\(y_{ijf} = \alpha_f + \beta_{f1}x_{ij} + \beta_{f2}x_{ij}^2 + \gamma_{if} + \delta_{if}\epsilon_{ijf}\)</span></p> <p>where <span class="math-tex">\(\alpha_f\)</span> is the average value of the feature <span class="math-tex">\(f\)</span> in the single-center ICBM dataset [3], <span class="math-tex">\(\beta_{f1}\)</span>and <span class="math-tex">\(\beta_{f2}\)</span> are the linear and quadratic effects of the age on the feature <span class="math-tex">\(f\)</span>, respectively, and <span class="math-tex">\(x_{ij}\)</span> is a simulated age variable drawn from a uniform distribution <span class="math-tex">\(X \sim uniform([20, 90])\)</span>. The mean site effect <span class="math-tex">\(\gamma_{if}\)</span> was drawn from a normal distribution with zero mean and standard deviation equal to 0.1, while the variance site effect <span class="math-tex">\(\delta_{if}\)</span> was drawn from a center-specific inverse gamma distribution with chosen parameters. For our simulations, we chose to distinguish the site-specific location factors by assuming independent and identically distributed (i.i.d.) normal distributions and scaling factors using the parameters described as follows. We set the value of the inverse gamma shape, for each center, as {46, 51, 56}, respectively, when k = 3, as {40, 42, .., 58} when k = 10, and as {10, 12, .., 40, 41, .., 50, .., 70} when k = 36. In all cases, the inverse gamma scale was set to 50.</p> <p>Each CSV file contains the following columns:</p> <ul> <li>SITE: simulated imaging site label (i.e., a, b, c, ..., j)</li> <li>age: each subject's simulated age, expressed in years</li> <li>cortex_CT (FD): CT (or FD) of the cerebral cortical gray matter (GM).</li> <li>lh_cortex_CT (FD), rh_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM.</li> <li>lh_frontal_cortex_CT (FD), rh_frontal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the frontal lobe.</li> <li>lh_temporal_cortex_CT (FD), rh_temporal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the temporal lobe.</li> <li>lh_parietal_cortex_CT (FD), rh_parietal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the parietal lobe.</li> <li>lh_occipital_cortex_CT (FD), rh_occipital_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the occipital lobe.</li> </ul> <p><em>References</em></p> <p>[1] Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. <em>Biostat. Oxf. Engl.</em> <strong>8</strong>, 118–127 (2007).</p> <p>[2] Chen, A. A. <em>et al.</em> Mitigating site effects in covariance for machine learning in neuroimaging data. <em>Hum. Brain Mapp.</em> <strong>43</strong>, 1179–1195 (2022).</p> <p>[3] 1000 Functional Connectomes Project (FCP) – <a href="http://fcon_1000.projects.nitrc.org/fcpClassic/FcpTable.html">ICBM dataset</a>.</p>
Multivariate Genome-wide association summary statistics for shared aging factor
<p>This dataset contains genome-wide summary statistics (autosomal variants) computed from a multivariate genome-wide association study of five aging-related phenotypes using Genomic Structural Equation Modeling (https://github.com/GenomicSEM/GenomicSEM). The effective sample size is calculated to be 1,958,774. Column descriptions are included in the accompanying README file. </p> <p>The summary statistics are provided on an "AS-IS" basis, without any type of warranty, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose.</p> <p>If investigators use these data, any and all consequences are entirely their responsibility. The user agrees that to cite the appropriate publication in any communications or publications arising directly or indirectly from these data by downloading and and using these data,. The user also agrees to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles and also agree that they will never attempt to identify any participant.</p> <p> </p>
Similarity network fusion to identify phenotypes of small for gestational age fetuses
<p>Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results <em>confirm</em> the existence of two subtypes of human FGR with distinct molecular and clinical features based on multi-omic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures. </p>
Perovskite Solar Cells Ageing Dataset
<p>This dataset contains cleaned 2,245 ageing test traces (time vs. MPPT PCE/ maximum power point tracking power conversion efficiency) for perovskite solar cells with various device stacks and architectures in the pickle (.pkl) format.</p> <p>The dataset can be loaded with the following commands on Python.</p> <pre><code class="language-python">import pickle5 as pickle import pandas as pd import numpy as np with open('20230303_mySeriesDrop.pkl', "rb") as fh: mySeriesDrop = pickle.load(fh)</code></pre> <p>The following command can be used to call a specific row (row 0) within the dataset.</p> <pre><code class="language-python">mySeriesDrop[0]</code></pre> <p>The next steps to use the dataset is using scaling/ normalisation (for instance using sklearn.preprocessing.MaxAbsScaler) and smoothing (for instance using Savitzky-Golay filter).</p> <p>The code to run the complete analysis, including self-organising map clustering, can be accessed here: <a href="https://doi.org/10.5281/zenodo.8181602">https://doi.org/10.5281/zenodo.8181602</a>.</p>
ScienceDex guides
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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.