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19,493 results for “Populism”
Attack of the clones: population genetics reveals clonality of Colletotrichum lupini, the causal agent of lupin anthracnose
<p><em>Colletotrichum lupini</em>, causing lupin anthracnose, is one of the worst pathogens to lupin cultivation worldwide. Understanding its population structure and evolutionary potential is crucial to design successful disease management strategies. The objective of this study was to employ population genetics to investigate the diversity, evolutionary dynamics and molecular basis of host interaction of this notorious lupin pathogen. A collection of globally representative <em>C. lupini </em>isolates was genotyped through triple digest restriction-site associated DNA sequencing (3D-RADseq), resulting in a dataset of unparalleled resolution. Phylogenetic and structural analysis could distinguish four (I – IV) independent lineages. The strong population structure, low recombination and slow linkage decay strongly suggests that <em>C. lupini</em> reproduces clonally. Different morphologies and virulence patterns on white (<em>Lupinus albus</em>) and Andean lupin (<em>L. mutabilis</em>) were observed between and within clonal lineages. Isolates belonging to lineage II were shown to have a mini-chromosome which was also partly present in lineage III and IV, but not in lineage I isolates. Variation in the presence of this mini-chromosome could indicate a role in host interaction. All four lineages were present in the South American Andes region, which is concluded to be the center of origin of this species. Only members of lineage II have been found outside South America since the 1990s, indicating it as the current pandemic population. As a seed-borne pathogen, <em>C. lupini</em> has mainly spread through infected but symptomless seeds, stressing the importance of phytosanitary measures to prevent future outbreaks of strains that are yet confined to South America.</p>
Supplementary material for "Increased sensitivity of marine invertebrates to metal toxicity in the past two decades linked to Climate Change and Ocean Acidification: revelations from a natural population of sea urchins in the Mediterranean Sea." by "Davide Sartori, Guido Scatena, Cristina Vrinceanu, Andrea Gaion".
<p>Satellite observations of environmental factors and effect concentration 50 for copper to sea urchin, from 2003 to 2022.</p>
Population-adjusted pseudo global warming simulations over the Great Lakes Region
<p>Summaries of pseudo global warming simulations over the Great Lakes Region. Specifics below:</p> <p> </p> <p>Each CSV file provides regional summaries from PGW simulations, either over land, for urban grids, or rural grids. This includes cumulative hours above critical heat stress threholds as well as their population-adjusted values.</p> <p>The 'Future_sensitivities_land.csv' file calculates heat stress in the future by keeping all variables except one the same value as the historical run.</p> <p>Among the geotiffs, _perc_land files include percentage of hours in summer above the National Weather Service heat index and wet bulb globe temperature thresholds, _pop_land incorporate population adjusted heat stress above those thresholds, the TEMP_contribution files estimate changes in heat stress if only air temperature changed and all variables remained the same as historical values and the HI_ and WBGT_pop rasters include summer average heat stress and their corresponding populations. </p> <p>See Chakraborty et al. Under Review (will be updated on paper publication) for more details.</p>
Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps
<p><strong>The dataset<br></strong></p> <p>The Pest Sticky Traps (PST) dataset is a collection of yellow chromotropic sticky trap pictures specifically designed for training/testing deep learning models to automatically count insects and estimate pest populations.</p> <p>Images were manually annotated by some experts of the Department of Agriculture, Food and Environment of the University of Pisa (Italy) by putting a dot over the centroids of each identified insect. Specifically, we labeled insects as belonging to the category “whitefly” considering two different species, i.e., the sweet potato whitefly (<em>Bemisia tabaci</em>) (Gennadius) and the greenhouse whitefly (<em>Trialeurodes vaporariorum</em>) (Westwood).</p> <p>The dataset comprises two subsets:<br>- a subset we suggest using for the training/validation phases (contained in the `train/` folder)<br>- a subset we suggest using for the test phase (contained in the `test/` folder)</p> <p>Annotations of the two subsets are contained in `train/annotations.csv` and `test/annotations.csv`, respectively. They have the following columns:<br>- *imageName* - filename of the image containing the whiteflies,<br>- *X,Y* - 2D coordinates of the whitefly in the image space,<br>- *class* - class index of the insect (always 0 in this dataset).</p> <p> </p> <p><strong>Citing our work</strong></p> <p>If you found this dataset useful, please cite the following paper</p> <blockquote> <pre>@inproceedings{CIAMPI2023102384,<br> title = {A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps},<br> journal = {Ecological Informatics},<br> volume = {78},<br> pages = {102384},<br> year = {2023},<br> issn = {1574-9541}, doi = {10.1016/j.ecoinf.2023.102384}, url = {https://www.sciencedirect.com/science/article/pii/S1574954123004132}, year = 2023, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, } </pre> </blockquote> <p>and this Zenodo Dataset</p> <blockquote> <pre>@dataset{ciampi_2023_7801239, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, title = {Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps}}, month = apr, year = 2023, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.7801239}, url = {<a href="https://doi.org/10.5281/zenodo.7801239">https://doi.org/10.5281/zenodo.6560823</a>} } </pre> </blockquote> <p> </p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset or if you experience any issues downloading files, please contact us at <a href="mailto:mobdrone@isti.cnr.it">luca.ciampi@isti.cnr.it</a></p> <p> </p> <p> </p>
Lepidoptera genomics based on 88 chromosomal reference sequences informs population genetic parameters for conservation
<p>This repository contains (1) germline mutations called by the DeepVariant (v1.1.0) pipeline in VCF format; (2) rejected substitution scores calculated by the Genomic Evolutionary Rate Profiling (GERP++) software on each species and chromosome; and (3) the phylogenetic tree used as guide tree in the Cactus alignment.</p>
Data for: Patterns of shared signatures of recent positive selection across human populations
<p>Genome-wide summary stats for modified iHS scan in 1KG as reported in:</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/29459708/">Patterns of shared signatures of recent positive selection across human populations.</a></p> <p>Johnson KE, Voight BF.Nat Ecol Evol. 2018 Apr;2(4):713-720. doi: 10.1038/s41559-018-0478-6. Epub 2018 Feb 19.</p> <p>PMID: 29459708</p> <p>Code available at: https://github.com/bvoightlab/iHS_calc</p>
Data For: Identifying rare variants inconsistent with identity-by-descent in population-scale whole-genome sequencing data
<p>Simulation output and Genome-wide scan for nIBD variants in UK10K data as reported in:</p> <p>Identifying rare variants inconsistent with identity-by-descent in population-scale whole-genome sequencing data</p> <p>Johnson KE, Adams CJ, Voight BF. Methods Ecol Evol 2022 Nov;13(11): 2429–2442.</p> <p>Code available at: https://github.com/kelsj/EVICORD</p>
Genome-wide characterization of human minisatellite VNTRs: population-specific alleles and gene expression differences
<p>This repository consists of minisatellite VNTR genotypes for 2,800 samples (2,770 individuals). The raw VCF files were produced using <a href="https://github.com/yzhernand/VNTRseek">VNTRseek</a> on xxx data sources: <a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000_genomes_project/">30 high coverage WGS datasets</a> from the 1000 Genomes Project phase 3, <a href="https://www.internationalgenome.org/data-portal/data-collection/30x-grch38">2,504 unrelated genomes</a> from New York Genome Center (NYGC), <a href="https://www.internationalgenome.org/data-portal/data-collection/sgdp">253 genomes from Simons Diversity Genome Project</a> (SGDP), <a href="https://www.illumina.com/products/by-type/informatics-products/basespace-sequence-hub/apps/tumor-normal.html">two tumor-normal breast cancer samples</a> from Illumina Basespace, haploid genomes <a href="https://www.ncbi.nlm.nih.gov/sra/SRX652547">CHM1 </a>and <a href="https://www.ncbi.nlm.nih.gov/sra/SRX1009644">CHM13</a>, and seven genomes from the Personal Genome Project from the Genome In A Bottle Consortium (GIAB). Raw VCF files are provided for each data source separately.</p> <p>The raw VCF files were preprocessed (preprocess.sh) to extract genotypes and provided in VNTRseek_preprocessed_data.tar.gz (uncompressed size 10G). The R Markdown code to analyze the preprocessed data and produce figures and tables is also provided (tables_and_figures.Rmd). For more information see the ReadMe file.</p> <p>This work was supported in part by NSF grants IIS-1423022 and DBI-1559829.</p>
A lack of population structure characterizes the invasive Lonicera japonica in West Virginia and across eastern North America
<p>Figure S1. Mig-seq primers used in the current study.</p> <p>Dataset S1. SNP data in .vcf format for Lonicera japonica.</p> <p>Figure S2: STRUCTURE analyses. Left: Delta K plot showing the optional number of ancestral population clusters (based on Evanno et al. 2015 method). Right: Ancestry plots from analysis with ParallelStructure for k = 3 (above) and k = 5 (below). Colors correspond to each ancestral cluster.</p>
Dataset for "A computational fluid dynamics—Population balance equation approach for evaporating cough droplets transport"
<p>Dataset for figures and tables of the article "A computational fluid dynamics—Population balance equation approach for evaporating cough droplets transport" submitted to "International Journal of Multiphase Flow".</p>
Population-level health and economic impacts of introducing Vaccae vaccination in China: A modeling study
<p>Supplementary to "Population-level health and economic impacts of introducing Vaccae vaccination in China: A modeling study"</p>
Supporting information for "Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa"
<p><strong>Description</strong></p> <p>In this work, we use few-shot learning to segment the body and vein architecture of <em>P. trichocarpa</em> leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy and retain biologically realistic features. The leaf and vein segmentations are compared against a U-Net baseline model, and subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the "vein density" trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code, we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 <em>P. trichocarpa</em> genome for 1,419 genotypes.</p> <p><strong>Directories:</strong></p> <pre><code>Few-shot learning for p. trichocarpa leaf traits ├── data │ ├── genomes │ │ ├── Ptri_V4_Nisq1.[...].bed │ │ ├── Ptri_V4_Nisq1.[...].bim │ │ └── Ptri_V4_Nisq1.[...].fam │ ├── images │ │ └── *.jpeg │ ├── leaf_masks │ │ └── *.png │ ├── leaf_preds │ │ └── *.png │ ├── leaf_unet_preds │ │ └── *.png │ ├── results │ │ ├── digital_traits.tsv │ │ ├── gwas_results.csv │ │ ├── manual_traits.tsv │ │ ├── vein_density_blups.tsv │ │ └── vein_density_tps_adj.tsv │ ├── vein_bce_preds │ │ └── *.png │ ├── vein_bce_probs │ │ └── *.png │ ├── vein_fl_preds │ │ └── *.png │ ├── vein_fl_probs │ │ └── *.png │ ├── vein_masks │ │ └── *.png │ ├── vein_unet_bce_preds │ │ └── *.png │ ├── vein_unet_bce_probs │ │ └── *.png │ ├── vein_unet_fl_preds │ │ └── *.png │ ├── vein_unet_fl_probs │ │ └── *.png ├── figures │ └── *.png ├── logs │ ├── leaf_tracer_256.txt │ ├── leaf_unet_256.txt │ ├── vein_grower_bce_128.txt │ ├── vein_grower_fl_128.txt │ ├── vein_unet_bce_128.txt │ └── vein_unet_fl_128.txt ├── models │ ├── BuildCNN.py │ ├── BuildUNet.py │ ├── LeafTracer.py │ └── VeinGrower.py ├── notebooks │ ├── Figures.ipynb │ ├── GrowerInference.ipynb │ ├── GrowerTraining.ipynb │ ├── TracerInference.ipynb │ ├── TracerTraining.ipynb │ ├── UNetLeafSegmentation.ipynb │ └── UNetVeinSegmentation.ipynb ├── utils │ ├── GetLowestGPU.py │ ├── ImageLoader.py │ ├── LeafGenerator.py │ ├── ModelWrapperGenerator.py │ ├── TimeRemaining.py │ ├── TraceInitializer.py │ ├── UNetTileGenerator.py │ └── VeinGenerator.py └── weights ├── leaf_tracer_256_best_val_model.save ├── leaf_unet_256_best_val_model.save ├── vein_grower_bce_128_best_val_model.save ├── vein_grower_fl_128_best_val_model.save ├── vein_unet_bce_128_best_val_model.save └── vein_unet_fl_128_best_val_model.save </code></pre> <p><strong>Data:</strong></p> <p>The <code>data</code> folder includes all images, ground truth segmentations, predicted segmentations, and extracted leaf traits. All images encode the sample ID in the file name by indicating the treatment, block, row, position, and leaf side, respectively. For example, the file, <code>C_1_1_2_bot.jpeg</code>, indicates the control treatment, block 1, row 1, position 2, and the bottom side of the leaf. Tabulated results include position IDs as well as the corresponding genotype IDs.</p> <ul> <li>The <code>images</code> folder includes the 2,906 high-resolution leaf scans taken in the field.</li> <li>The <code>leaf_masks</code> folder includes 50 ground truth segmentations used for training the leaf tracing algorithm.</li> <li>The <code>leaf_preds</code> folder includes the 2,906 predicted segmentations from the leaf tracing algorithm.</li> <li>The <code>leaf_unet_preds</code> folder includes the 2,906 predicted segmentations from the U-Net model for leaf segmentation.</li> <li>The <code>vein_masks</code> folder includes 8 ground truth segmentations used for training the vein growing algorithm.</li> <li>The <code>vein_*_preds</code> folder includes the 1,453 predicted segmentations from the vein growing algorithm, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>vein_*_probs</code> folder includes the 1,453 predicted probability maps from the vein growing algorithm before thresholding, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>vein_unet_*_preds</code> folder includes the 1,453 predicted segmentations from the U-Net model for vein segmentation, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>vein_unet_*_probs</code> folder includes the 1,453 predicted probability maps from the U-Net model for vein segmentation before thresholding, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>genomes</code> folder includes the set of SNPs called against the v4 <em>P. trichocarpa</em> genome for 1,419 genotypes with a README file detailing the steps taken.</li> <li>The <code>results</code> folder includes <ul> <li>Raw values of the 68 predicted leaf traits in <code>digital_traits.tsv</code></li> <li>Manually measured values of petiole length and width in <code>manual_traits.tsv</code></li> <li>Thin plate spline (TPS) adjusted values of the vein density trait in <code>vein_density_tps_adj.tsv</code></li> <li>Best linear unbiased prediction (BLUP) adjusted values of the vein density trait in <code>vein_density_blups.tsv</code></li> <li>GWAS results for the vein density trait, including chromosome positions and corresponding P values, in <code>gwas_results.csv</code></li> </ul> </li> </ul> <p><strong>Figures:</strong></p> <p>The <code>figures</code> folder includes all figures and videos used in the manuscript. See <code>notebooks/Figures.ipynb</code> for the methods used to generate these figures.</p> <p><strong>Logs:</strong></p> <p>The <code>logs</code> folder includes logs of CNN convergence for the training and validation sets during model training for the leaf tracing CNN vein growing CNN, and U-Net models. The file names include the model, loss function (bce: binary cross-entropy, fl: focal loss), and size of the input window for each method (e.g., 128 for the vein growing CNN).</p> <p><strong>Models:</strong></p> <p>The <code>models</code> folder includes the CNN implementations in PyTorch as well as the leaf tracing and vein growing algorithms at inference time.</p> <ul> <li><code>BuildCNN.py</code> defines the CNN architecture for leaf tracing or vein growing, with user-specified input shape, output shape, layers, and output activation functions.</li> <li><code>BuildUNet.py</code> defines the U-Net architecture for leaf and vein segmentation, with user-specified input/output shape, layers, and output activation functions.</li> <li><code>LeafTracer.py</code> defines the leaf tracing algorithm at inference time.</li> <li><code>VeinGrower.py</code> defines the vein growing algorithm at inference time.</li> </ul> <p><strong>Notebooks:</strong></p> <p>The <code>notebooks</code> folder includes Jupyter notebooks used for model training, model inference, and figure generation.</p> <ul> <li><code>Figures.ipynb</code> is used to generate all of the manuscript figures.</li> <li><code>GrowerTraining.ipynb</code> is used to train the vein growing CNN.</li> <li><code>GrowerInference.ipynb</code> is used to apply the vein growing algorithm to the 1,453 leaf bottom images.</li> <li><code>TracerTraining.ipynb</code> is used to train the leaf tracing CNN.</li> <li><code>TracerInference.ipynb</code> is used to apply the leaf tracing algorithm to the 2,906 leaf top and bottom images.</li> <li><code>UNetLeafSegmentation.ipynb</code> is used to train and apply U-Net for leaf segmentation.</li> <li><code>UNetVeinSegmentation.ipynb</code> is used to train and apply U-Net for vein segmentation.</li> </ul> <p><strong>Utils:</strong></p> <p>The <code>utils</code> folder includes utility scripts implemented in Python that assist in model training and inference.</p> <ul> <li><code>ImageLoader.py</code> loads image/mask pairs for sampling training/validation tiles.</li> <li><code>LeafGenerator.py</code> generates inputs/outputs for the leaf tracing CNN.</li> <li><code>VeinGenerator.py</code> generates inputs/outputs for the vein growing CNN.</li> <li><code>UNetTileGenerator.py</code> generates inputs/outputs for the U-Net model.</li> <li><code>GetLowestGPU.py</code> identifies available GPUs using the <code>nvidia-smi</code> command and selects the one with lowest memory usage, if none available the device is set to CPU.</li> <li><code>ModelWrapperGenerator.py</code> wraps the PyTorch CNN and data loaders with similar functionality to the Keras Model class in TensorFlow (e.g., model.fit(...)).</li> <li><code>TimeRemaining.py</code> is used by the model wrapper to estimate remaining time left per epoch.</li> <li><code>TraceInitializer.py</code> is used by the tracing algorithm at inference time to initialize the leaf trace using automatic thresholding.</li> </ul> <p><strong>Weights:</strong></p> <p>The <code>weights</code> folder includes the CNN parameters from the epoch resulting in the best validation error. The file names include the model, loss function (bce: binary cross-entropy, fl: focal loss), and size of the input window for each method (e.g., 128 for the vein growing CNN). The weights are loaded into the CNN models for inference.</p> <p><strong>Citation:</strong></p> <pre><code>@article{ doi:10.34133/plantphenomics.0072, author = {John Lagergren and Mirko Pavicic and Hari B. Chhetri and Larry M. York and Doug Hyatt and David Kainer and Erica M. Rutter and Kevin Flores and Jack Bailey-Bale and Marie Klein and Gail Taylor and Daniel Jacobson and Jared Streich }, title = {Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa}, journal = {Plant Phenomics}, volume = {0}, number = {ja}, pages = {}, year = {}, doi = {10.34133/plantphenomics.0072}, URL = {https://spj.science.org/doi/abs/10.34133/plantphenomics.0072}, eprint = {https://spj.science.org/doi/pdf/10.34133/plantphenomics.0072}, }</code></pre>
Supporting Data Ferguson, Camenzind, et al., "Measurement-induced induced population switching", Phys. Rev. Research 5, 023028 (2023)
<p>This repository contains data for the publication "Measurement-induced population switching", Phys. Rev. Research 5, 023028 (2023) by Ferguson, Camenzind, <em>et al</em>.</p> <p><strong>Abstract</strong></p> <p>Quantum information processing is a key technology in the ongoing second quantum revolution, with a wide variety of hardware platforms competing toward its realization. An indispensable component of such hardware is a measurement device, i.e., a quantum detector that is used to determine the outcome of a computation. The act of measurement in quantum mechanics, however, is naturally invasive as the measurement apparatus becomes entangled with the system that it observes. This always leads to a disturbance in the observed system, a phenomenon called quantum measurement backaction, which should solely lead to the collapse of the quantum wave function and the physical realization of the measurement postulate of quantum mechanics. Here we demonstrate that backaction can fundamentally change the quantum system through the detection process. For quantum information processing, this means that the readout alters the system in such a way that a faulty measurement outcome is obtained. Specifically, we report a backaction-induced population switching, where the bare presence of weak, nonprojective measurements by an adjacent charge sensor inverts the electronic charge configuration of a semiconductor double quantum dot system. The transition region grows with measurement strength and is suppressed by temperature, in excellent agreement with our coherent quantum backaction model. Our result exposes backaction channels that appear at the interplay between the detector and the system environments, and opens new avenues for controlling and mitigating backaction effects in future quantum technologies.</p>
Dataset of the article "A new and almost perfectly accurate approximation of the eigenvalue effective population size of a dioecious population: comparisons with other estimates and detailed proofs"
<p>Dataset of the article "A new and almost perfectly accurate approximation of the eigenvalue effective population size of a dioecious population: comparisons with other estimates and detailed proofs" (https://doi.org/10.5281/zenodo.7927968), recommended by PCI Evol Biol (https://evolbiol.peercommunityin.org/articles/rec?id=651)</p>
Data from: Trends in butterfly populations in UK gardens – new evidence from citizen science monitoring
<p>This data package describes the annual abundance indices and trend estimates for 22 butterfly species in UK gardens for the period 2007-2020.</p> <p>These data form the basis of the results presented in: Plummer, K.E., Dadam, D., Brereton, T., Dennis, E.B., Massimino, D., Risely, K. et al. (2023) Trends in butterfly populations in UK gardens—New evidence from citizen science monitoring. <em>Insect Conservation and Diversity</em>, 1– 13. Available from: <a href="https://doi.org/10.1111/icad.12645">https://doi.org/10.1111/icad.12645</a></p> <p>Please refer to the paper for an explanation of the underlying BTO Garden BirdWatch (GBW) data and modelling protocols used to produce the datasets included here.</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Datasets, reproducible codes, and results for evaluating differential expression analysis methods on population-level RNA-seq data
<p>This upload contains the necessary R codes and data to reproduce the FDR and Power results described in our correspondence "Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives" to Li Y, Ge X, Peng F, Li W, Li JJ, Exaggerated false positives by popular differential expression methods when analyzing human population samples, <em>Genome Biology</em> 23, 79, 2022, DOI: 10.1186/s13059-022-02648-4.</p>
State of the art of studies on earthworm populations in the state of Paraná
<p>In this review, we included all the studies performed in the state of Paraná, Brazil, which had data on earthworms. We reviewed the literature for all publications (journal articles, dissertations, theses, conference proceedings, book chapters) carried out in the state of Paraná, Brazil, which had data on earthworms. The period evaluated ranged from 1986 (earliest date in the state) to 2020. Searches were performed in online databases including Sicence Direct, Scielo, CAPES and the digital collection of dissertations and theses from Brazilian Universities (BDTD). </p> <p>Overall 51 publications had earthworm data, including abundance, biomass, species, richness or just presence/absence. Data were extracted from these publications and compiled into an excel file. The dataset contains information gathered from 62 of the 399 municipalities in Paraná, and includes separation in ten geopolitical regions (IBGE 2010), as well as topographic regions and climate (Köppen, 1931). The ten geopolitical mesoregions are: West (WE), Northwest (NW), Center West (CW), Center North (CN), North Pioneer (NP), Center East (CE), Metropolitan (MT), Center South (CS), Southeast (SE) and Southwest (SW). The three topographic regions include the First, Second and Third Plateaus, and the Coastal Lowland. </p> <p>Earthworm data are presented as total abundance (number of individual m<sup>-2</sup>), fresh biomass (in g m<sup>-2</sup>) and species richness (total number). We also provide information on each species encountered, its ecological category, and whether it is native or exotic to the state of Paraná. For earthworm species, ecological category information follows the classification of Bouché (1977), including the intermediate categories: e.g., anecic, epigeic, endogeic, polyhumic endogeic, mesohumic endogeic, epi-endogeic, endo-epigeic. For each species, full names (when available), and species author(s) and year of the description are provided.</p> <p>Geographic location is provided when possible, with latitude, longitude and altitude, soil types according to the Sistema Brasileiro de Classificação de Solos - SiBCS (Santos et al. 2018). Sampling date and season are also provided, when available.</p> <p>When known, the sampling method(s) used were given. These included quantitative methods involving handsorting, such as 1) the standard Tropical Soil Biology and Fertility (TSBF) Programme method (Anderson & Ingram 1993), in which soils are handsorted from monoliths 25x25 cm square to depths ranging from 10 to 40 cm (identified as TSBF in the spreadsheets); or 2) other monolith dimensions like 20x20, 40x40 and 50x50 cm (identified as Handsorting in the spreadsheets). Qualitative sampling (e.g. Bartz et al. 2014) included: 1) collecting in various niches like deeper soil layers, litter, under rocks, in and under rotting logs, next to water bodies like streams, lakes and swamps; 2) chemical extraction using a diluted formalin solution (usually over an area 50x50 cm), following recommendations of ISO 23611-1 (2017), and pouring of the solution either on the soil surface, or at the bottom of the pit; 3) electrical extraction using a modifed apparatus (Azevedo et al. 2010), based on the Octet-Method (Thielemann 1986).</p> <p>The determination of LUS was based on Nadolny et al. (2020), which characterized Native Vegetation, Forest Plantation (including forest with <em>Pinus</em> sp. and <em>Eucalyptus</em> sp.), Pasture, Integrated Systems (e.g., agropastoral, silvopastoral or agrosilvopastoral systems) and agricultural areas (Conventional Tillage, No-Tillage and Minimum Tillage). In addition to these, Perennial Crops, Grass Lawns and Agroforestry Systems were included.</p> <p>The soil chemical and physical analysis data were included in the dataset when performed in the same places as the earthworm sampling. Chemical data included: pH, H+Al, K, Ca, Mg, P, C, sum of Bases, CEC, Base saturation, N, Na. Physical data included: sand, clay and silt proportions, texture, porosity, density and resistance to penetration.</p> <p>All data are provided in excel format and include 5 tabs: Readme, Legend, Earthworms + environment, Species distribution and References. The Readme tab provides information on the associated publication in the Revista Brasileira de Ciência do Solo authored by Dudas et al. (see https://doi.org/10.36783/18069657rbcs20220159). The Legend tab provides a description of the variables used in the other (data) tabs. Earthworms + environment has information on the sampling methods used, the earthworm abundance, biomass and richness found at the different sampling sites in Paraná, and the data on soil and environmental variables. The Species distribution tab provides information on the species found, places of origin, ecological category, sampling method used and LUS. The References tab provides detailed bibliographic information on the sources of the data used to build the tables and the dataset.</p> <p>Overall, in the state of Paraná, 90 species of earthworms were found in 51 counties of the state, of which 66 were native and 24 were exotic. A large number of species (46) are likely new and still must be formally described. Higher species richness was found in native vegetation, which also had a higher proportion of native species (75%). The other LUS with more native species were: Forest Plantation (FP) and No-Tillage (NT), while Conventional Tillage (CT) sites had only 17% native species. Earthworm abundance and biomass were highest in less disturbed LUS such as agroforestry systems, native vegetation, forestry plantation,<br> grass lawns, and permanent crops, compared to the highest disturbance LUS including soil preparation (MT and CT), where the lowest abundance and biomass were found. However, as only 16% of the 399 counties in Paraná have been sampled so far, much further research is needed in order to adequately assess the relationships between earthworms and land use. </p>
Comparison of occlusal dental wear and degenerative alterations of the temporomandibular joint in two medieval populations from central Europe
<p>This repository contains the radiographs of the mandibular condyles published in the paper titled "Comparison of dental status parameters and degenerative alterations of the temporomandibular joint in central European medieval skeletal finds". The radiographs were obtained with a portable X-ray unit (Kavo Nomad-Pro).</p> <p>Selected CT reconstructions of the condyles are available for a sub-group of individuals.</p> <p>Files naming convention:</p> <p>- Files name start with the burial number (ex. B10; Z98);</p> <p>- R and L stand for "right" and "left";</p> <p>- AP: anteroposterior view; LM: lateromedial view.</p>
Open-population models for estimating roadkill rates - Data and R Code
<p>Roadkill carcass capture-recapture data, capture histories for four and eight-occasion designs, and R code (with JAGS code) for roadkill rates estimation.</p>
FUME Local population projections in destination cities
<p>FUME data on projected distributions of migrants at local level between 2030 and 2050.</p> <p>The dataset contains a folder of data for each destination city as a gridded dataset at 100m resolution in GeoTIFF format. The examined destination cities are: Amsterdam, Copenhagen, Krakow and Rome. The dataset is provided as 100m grid cells based on the Eurostat GISCO grid of the 2021 NUTS version, using ETRS89 Lambert Azimuthal Equal-Area (EPSG: 3035) as coordinate system. The file names consist of the projected year, the corresponding scenario, and the reference migrant group. The projections have been performed for the years 2030, 2040 and 2050. The investigated scenarios are the following:<br> • benchmark (bs),<br> • baseline (bs),<br> • Rising East (re),<br> • EU Recovery (eur),<br> • Intensifying Global Competition (igc), and<br> • War (war).</p> <p>The migration background is derived from data about the Region of Origin (RoO) for migrants in Copenhagen and Amsterdam, and from Region of Citizenship (CoC) for migrants in Krakow and Rome.</p> <p>The case study of <strong>Copenhagen</strong> covers the two central NUTS3 areas (DK011, DK012) and the groups presented are the following:<br> • total population (totalpop),<br> • native population (DNK),<br> • Eastern EU European migrants (EU_East),<br> • Western EU Europeans migrants (EU_West),<br> • Non-EU European migrants (EurNonEU),<br> • migrants from Turkey (Turkey),<br> • the MENAP countries (MENAP; excluding Turkey),<br> • other non-Western (OthNonWest), and<br> • other Western countries (OthWestern).</p> <p>The case study of <strong>Amsterdam</strong> covers one NUTS3 area (NL329) and the presented groups are the following:<br> • total population (totalpop),<br> • native population (NLD),<br> • Eastern EU European migrants (EU East),<br> • Western EU European migrants (EU West),<br> • migrants from Turkey and Morocco (Turkey + Morocco),<br> • migrants from the Middle East and Africa (Middle East + Africa),<br> • migrants from the former colonies (Former Colonies), and<br> • migrants from the rest of the world (Other Europe etc).</p> <p>The case study of <strong>Krakow</strong> covers the Municipality of Krakow, and the presented groups are the following:<br> • total population (totalpop),<br> • native population (POL),<br> • EU/EFTA European migrants (EU),<br> • non-EU European migrants (Europe_nonEU), and<br> • migrants from the rest of the world (Other).</p> <p>The case of <strong>Rome</strong> covers the Municipality of Rome, and the presented groups are the following:<br> • total population (totalpop),<br> • native population (ITA),<br> • migrants from Romania (ROU),<br> • Philippines (PHL),<br> • Bangladesh (BGD),<br> • the EU (EU; excluding Romania),<br> • Africa (Africa),<br> • Asia (Asia; excluding Philippines and Bangladesh) and<br> • America (America).</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.