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Fig 6b - FWHM vs distance - broken membrane
<p>The temperature profile on a broken patterned silicon membrane. (b) The line width versus the relative distance between the moving heating laser and the DBT-Ac nanocrystal. The error bars correspond to repeated laser scans, as explained in Sec. III A from the related publication. (c) The temperature profile as a function of the relative distance between the moving heating laser and the DBT-Ac nanocrystal. The red circles denote temperatures estimated from experimental measurements in (b) considering a calibration curve as described in Sec. III A. The solid lines represent simulated temperature profiles assuming different power laws of dependence of the thermal conductivity on the temperature, as well as the presence or lack thereof of the tear, as described in the text.</p>
The role of tropical rainfall in driving range dynamics for a long-distance migratory bird
<p>Predicting how the range dynamics of migratory species will respond to climate change requires a mechanistic understanding of the factors that operate across the annual cycle to control the distribution and abundance of a species. Here we use multiple lines of evidence to reveal that environmental conditions during the nonbreeding season influence range dynamics across the lifecycle of a migratory songbird, the American redstart (<em>Setophaga ruticilla</em>). Using long-term data from the nonbreeding grounds and breeding origin estimated from stable hydrogen isotopes in tail feathers, we found that the relationship between nonbreeding season survival and migration distance is mediated by precipitation, but only during dry years. A long-term drying trend throughout the Caribbean is associated with higher mortality for individuals from the northern portion of the species' breeding range, resulting in an approximate 500 km southward shift in breeding origins of this Jamaican population over the past 30 years. This shift in connectivity is mirrored by changes in the redstarts breeding distribution of abundance. These results demonstrate that the climatic effects on demographic processes originating during the tropical nonbreeding season is actively shaping range dynamics in a migratory bird.</p>
Distance from available surface water of mammals in Ruaha National Park
<p>In Africa, burgeoning human populations promote agricultural expansion and the associated demand for water. Water abstraction for agriculture from perennial rivers can be detrimental for wildlife, particularly when it reduces water availability in protected areas. Ruaha National Park (Ruaha NP) in southern Tanzania, one of the largest parks in Africa, contains important wildlife populations, including rare and endangered species. The Great Ruaha River (GRR) is the main dry-season water source for wildlife in the Park. Water offtake from this river for large-scale irrigation and livestock production up-stream of the Park has caused large expanses of this formerly perennial river within the Park to dry out during the dry season. The dry season distribution of a species in relation to surface water is considered an indicator of its dependence on water and ability to cope with the loss of surface water. We investigated how diminishing surface water availability during three dry seasons (2011–2013) affected herbivores' distance to water in Ruaha NP. The distance held by herbivores to water is shaped by a range of factors including dietary category. We determined changes in the locations of available surface water throughout the dry season using standardized ground transects, close to and leading away from the GRR, to map the locations of nine herbivore species. Functional responses of herbivores, i.e. their change in distance to water between early and late dry season, indicated that distance to water was (i) shortest in buffalo and waterbuck (grazers), (ii) similar for plains zebra (grazer), elephant and impala (mixed feeders), (iii) larger in giraffe and greater kudu (browsers) and (iv) largest in generalist feeders (warthog, common duiker). The substantial species' differences in surface water dependence broadly fit predicted species differences in their ability to cope with anthropogenic reduction in surface water in Ruaha NP.</p>
Distance functions of carabids in crop fields depend on functional traits, crop type and adjacent habitat: a synthesis
<p>Natural pest and weed regulation are essential for agricultural production, but the spatial distribution of natural enemies within crop fields and its drivers are mostly unknown. Using 28 datasets comprising 1,204 study sites across eight Western and Central European countries, we performed a quantitative synthesis of carabid richness, activity densities and functional traits in relation to field edges (i.e. distance functions). We show for the first time that distance functions of carabids strongly depend on carabid functional traits, crop type and, to a lesser extent, adjacent non-crop habitats. Richness of both predators and granivores and activity densities of small and granivorous species decreased towards field interiors, whereas the densities of large species increased. We found strong distance decays in maize and vegetables whereas richness and densities remained more stable in cereals, oilseed crops and legumes. We conclude that carabid assemblages in agricultural landscapes are driven by the complex interplay of crop types, adjacent non-crop habitats and further landscape parameters with great potential for targeted agroecological management. In particular, our synthesis indicates that a higher edge-interior ratio can counter the distance decay of carabid richness per field and thus likely benefits natural pest and weed regulation, hence contributing to agricultural sustainability.</p>
Energetic trade-offs in migration decision-making, reproductive effort, and subsequent parental care in a long-distance migratory bird
<p>Migratory species trade-off long-distance movement with survival and reproduction, but the spatiotemporal scales at which these decisions occur is relatively unknown. Technological and statistical advances allow fine-scale study of animal decision-making, improving our understanding of possible causes and therefore conservation management. We quantified effects of reproductive preparation during spring migration on subsequent breeding outcomes, breeding outcomes on autumn migration characteristics, and autumn migration characteristics on subsequent parental survival in Greenland white-fronted geese (<em>Anser albifrons flavirostris</em>). These are long-distance migratory birds with a ~50% population decline from 1999 to 2022. We deployed GPS-acceleration devices on adult females to quantify up to five years of individual decision-making throughout the annual cycle. Weather and habitat-use affected time spent feeding and overall dynamic body acceleration (i.e., energy expenditure) during spring and autumn. Geese that expended less energy and fed longer during spring were more likely to successfully reproduce. Geese with offspring expended more energy and fed for less time during autumn, potentially representing adverse fitness consequences of breeding. These behavioural comparisons among Greenland white-fronted geese improve our understanding of fitness trade-offs underlying abundance. We provide a reproducible framework for full annual cycle modelling using location and behaviour data, applicable to similarly studied migratory animals.</p>
Rare, long-distance dispersal underpins genetic connectivity in the pink sea fan, Eunicella verrucosa
<p>Characterising patterns of genetic connectivity in marine species is of critical importance given the anthropogenic pressures placed on the marine environment. For sessile species, population connectivity can be shaped by many processes, such as pelagic larval duration, oceanographic boundaries, and currents. This study combines restriction-site associated DNA sequencing (RADseq) and passive particle dispersal modelling to delineate patterns of population connectivity in the pink sea fan, <em>Eunicella verrucosa, </em>a temperate octocoral. Individuals were sampled from 20 sites covering most of the species' northeast Atlantic range, and a site in the northwest Mediterranean Sea to inform on connectivity across the Atlantic-Mediterranean transition. Using 7,510 neutral SNPs, a geographic cline of genetic clusters was detected, partitioning into: Ireland, Britain, France, Spain (Atlantic), and Portugal and Spain (Mediterranean). Evidence of significant inbreeding was detected at all sites, a finding not detected in a previous study of this species based on microsatellite loci. Genetic connectivity was characterised by an isolation by distance pattern (IBD) (<em>r<sup>2</sup></em> = 0.78, <em>p</em><0.001), which persisted across the Mediterranean-Atlantic boundary. In contrast, exploration of ancestral population assignment using the program ADMIXTURE indicated genetic partitioning across the Bay of Biscay, which we suggest represents a natural break in the species' range, possibly linked to a lack of suitable habitat. As the pelagic larval duration (PLD) is unknown, passive particle dispersal simulations were run for 14 and 21 days. For both modelled PLDs, inter-annual variations in particle trajectories suggested that in a long-lived, sessile species, range-wide IBD is driven by rare, longer dispersal events which act to maintain gene flow. These results suggest that oceanographic patterns may facilitate range-wide stepping-stone genetic connectivity in <em>E. verrucosa</em>, and highlight that both oceanography and natural breaks in a species' range should be considered in the designation of ecologically coherent MPA networks.</p>
РИС. 1. Схематичное иЗображение глаЗа наЗемного лёгочного моллюска. СокраЩениЯ: c – роговица; ec – глаЗнаЯ капсула; r – сетчатка; p – краЯ Зрачка; l – хрусталик, окруженный слоем стекловидного тела; L abs – абсолютное расстоЯние между Зрачком и наружной поверхностью хрусталика; D l – продольный диаметр хрусталика; А – абсолютный диаметр Зрачabs ка; D – поперечный диаметр глаЗа. FIG. 1. Schematic drawing of the eye of a terrestrial pulmonate mollusk. Abbreviation: c – cornea; ec – eye capsule; r – retina; p – edges of the pupil; l – lens, surrounded by a layer of the vitreous body; L abs – the absolute distance between the pupil and the outer surface of the lens; D l – the longitudinal diameter of the lens; А abs – the absolute diameter of the pupil; D e – the transverse diameter of the eye. in Зрачок камерных глаЗ наЗемных брюхоногих моллюсков (Heterobranchia, Stylommatophora)
РИС. 1. Схематичное иЗображение глаЗа наЗемного лёгочного моллюска. СокраЩениЯ: c – роговица; ec – глаЗнаЯ капсула; r – сетчатка; p – краЯ Зрачка; l – хрусталик, окруженный слоем стекловидного тела; L abs – абсолютное расстоЯние между Зрачком и наружной поверхностью хрусталика; D l – продольный диаметр хрусталика; А – абсолютный диаметр Зрачabs ка; D – поперечный диаметр глаЗа. FIG. 1. Schematic drawing of the eye of a terrestrial pulmonate mollusk. Abbreviation: c – cornea; ec – eye capsule; r – retina; p – edges of the pupil; l – lens, surrounded by a layer of the vitreous body; L abs – the absolute distance between the pupil and the outer surface of the lens; D l – the longitudinal diameter of the lens; А abs – the absolute diameter of the pupil; D e – the transverse diameter of the eye.
Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance
<p>The below data are associated with our paper entitled "Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance."</p> <p>1) SNP-gene link predictions generated by pgBoost and existing methods SCENT (Sakaue et al. 2024 <em>Nat Genet</em>), Signac (Stuart et al. 2021 <em>Nat Methods</em>), ArchR (Granja et al. 2021 <em>Nat Genet</em>), and Cicero (Pliner et al. 2018 <em>Mol Cell</em>).</p> <p><strong>pgBoost_scores.tsv.gz </strong>contains linking predictions made by pgBoost.</p> <p><strong>constituent_method_scores.tsv.gz</strong> contains linking predictions made by constituent methods.</p> <p><em><span>**NOTE: promoters (+/- 1kb from TSS) and candidate links >500kb are excluded from linking predictions (see manuscript)**</span></em></p> <p>Linking scores and percentiles are reported for each method (pgBoost score, SCENT FDR, Signac correlation, ArchR correlation, Cicero co-accessibility). Rank percentiles are computed as: 1 - (rank / n). When multiple links receive the same score, they are assigned the percentile of the top rank. Links unscored by each method (denoted by zeros* in the linking score column) are assigned a percentile equivalent to the percent of links unscored by the focal method. See the Methods section of the paper for further details on computing linking scores and summarizing scores across cell types and data sets.</p> <p>*Candidate links tested and assigned a co-accessibility of zero by the Cicero method are given a score of 1e-100 in the "Cicero" column to distinguish between unscored candidate links and candidate links assigned a partial correlation of zero (see Pliner et al. 2018 <em>Mol Cell</em>).</p> <p><em>NOTE: The predictions associated with this release (version 2) were generated using an expanded set of data sets, an expanded training set, and corrected TSS coordinates.</em></p> <p>2) GWAS-derived evaluation SNP-gene link evaluation set.</p> <p><strong>gwas_evaluation.tsv</strong>: GWAS-derived evaluation SNP-gene link evaluation set. Column 1 provides SNP coordinates in the format <chr-start-end>. This evaluation framework was proposed by Weeks et al. 2024 <em>Nature Genetics</em> based on fine-mapping results from Kanai et al. <em>medrxiv</em> (see Methods: <em>Evaluation data sets</em> of Dorans et al.). "True" links (gold = 1) are non-coding variants fine-mapped to a focal trait (PIP > 0.1) with a coding variant for exactly one candidate gene within 1 Mb attaining PIP > 0.5 for the same trait. "False" links (gold = 0) are candidate SNP-gene pairs involving a SNP with a "true" link. This file of SNP-gene links was adapted from credible set-gene links <a href="https://github.com/Deylab999/GWAS_benchmark_IGVF/blob/bb91d08cc02d59cdd829eb1430569057ac26c5fe/V2G/ENCODE_E2G_2023/UKBiobank.ABCGene.anyabc.tsv">here</a> (the "truth" column defines true/false links) by identifying SNPs with PIP > 0.1 within each credible set-gene link.</p>
Synthetic data (Part 2) for HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the rendered images and the segmentation masks that we use to train our model on HO3Dv2 dataset. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the rendered images for HO3Dv2.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Synthetic data (Part 1) for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed SDF samples. Meanwhile, we also include rendered data for HO3Dv2 here. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 rendered images.</div> <div>├── <a href="../api/records/13228003/draft/files/train_ho3d.zip/content" target="_blank" rel="noopener noreferrer">train_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 training set.</div> <div>├── <a href="../api/records/13228003/draft/files/full_test_dexycb.zip/content" target="_blank" rel="noopener noreferrer">full_test_dexycb.zip</a> - Contains the processed SDF files for DexYCB full test set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Processed data and trained models for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: <a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf">https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</a></p> <p>Link to the Arxiv article: <a href="https://arxiv.org/abs/2402.17062">https://arxiv.org/abs/2402.17062</a></p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed data of the interacting objects and SDF samples. Meanwhile, we also include the trained model weights here.</div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/11668766/draft/files/ckpts.zip/content" target="_blank" rel="noopener noreferrer">ckpts.zip</a> - Contains the trained weights model on different datasets (DexYCB and HO3Dv2)</div> <div>├── <a href="../api/records/11668766/draft/files/annotations.zip/content" target="_blank" rel="noopener noreferrer">annotations.zip</a> - Contains the preprocessed annotations of DexYCB and HO3Dv2 for efficient data loading.</div> <div>├── <a href="../api/records/11668766/draft/files/simple_ycb_models.zip/content" target="_blank" rel="noopener noreferrer">simple_ycb_models.zip</a> - Contains the preprocessed YCB objects for batched evaluation.</div> <div>├── <a href="../api/records/11668766/draft/files/test.zip/content" target="_blank" rel="noopener noreferrer">test.zip</a> - Contains the processed SDF files for DexYCB test set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_render_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_render_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set and rendering set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: <a href="https://github.com/amathislab/HOISDF">https://github.com/amathislab/HOISDF</a></div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Table S1. Uncorrected p distance matrix
<p>Uncorrected p distance values for combined COI and 16S sequences from phylogenetic analysis of <em>Helminthoglypta</em> land snails (Mollusca: Gastropoda: Helminthoglyptidae) from northern California and southern Oregon, USA.</p> <p>Note: Specimen numbers marked with * were missing COI sequences.</p> <p>Table in both Excel (*.xlsx) and comma-separated value (*.csv) formats.</p> <p> </p>
Final project report: The Side Effects of Forced Online Distance Education (FODE)
<p>The outbreak of COVID-19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID-19 epidemic and started the project with the working title: The Side Effects of Forced Online Distance Education (FODE). The aim of the project, was to investigate the response of university teachers and students to the new situation.</p>
Dataset of the study "Thirty seconds sit-to-stand test as an alternative for estimating peak oxygen uptake and six-minutes walking distance in women with breast cancer: a cross-sectional study"
<p>Data was collected to study the usefulness of the thirty seconds sit-to-stand test as an alternative for estimating peak oxygen uptake and six-minutes walking distance in women with breast cancer, which is a cross-sectional study derived from the ONCORE project (Randomized controlled trial on comprehensive exercise-based cardiac rehabilitation program for the prevention of anthracyclines and/or anti-HER2 antibodies-induced cardiotoxicity in breast cancer), ClinicalTrials.gov Identifier: NCT03964142</p> <p> </p> <p>DATASET FILE (xlsx) includes 4 sheets:<br> - Dataset_variables: all variables collected pre-post intervention<br> - Descriptive data (baseline): variables used for the descriptive analysis before the intervention (baseline)<br> - Data_CPET-30STS(pre-post): pooled data from CPET-30STS pre-post intervention<br> - Data_6MWD-30STS(pre-post): pooled data from 6MWD-30STS pre-post intervention</p>
Spectro-photometric distances and self-calibrated abundances for Apogee DR16 RGB stars within the Milky Way disk
<p>The data file contains 48,853 RGB stars from Apogee DR16 within the Milky Way disk, for which we determine spectro-photometric parallax estimates (as described in Hogg et al. 2019, AJ, 158, 147), as well as self-calibrated stellar element abundances. The data set is described in detail and analyzed in Eilers et al. 2022 (arXiv: 2112.03295).</p>
Supplementary material for: Phylogeny and biogeography of the ancient spider family Filistatidae (Araneae) is consistent both with long-distance dispersal and vicariance following continental drift
<p>Raw data and input files for phylogenetic and biogeographic analysis of the article "<strong>Phylogeny and biogeography of the ancient spider family Filistatidae (Araneae) is consistent both with long-distance dispersal and vicariance following continental drift</strong>".</p> <p><strong>Supplementary material S1. </strong>Matrix of phenotypic characters in .ss format.</p> <p><strong>Supplementary material S2. </strong>Alignment of COI sequences in fasta format..</p> <p><strong>Supplementary material S3. </strong>Alignment of H3 sequences in fasta format.</p> <p><strong>Supplementary material S4. </strong>Alignment of 16S sequences in fasta format before trimming with gblocks.</p> <p><strong>Supplementary material S5. </strong>Alignment of 28S sequences in fasta format before trimming with gblocks.</p> <p><strong>Supplementary material S6. </strong>Input for running parsimony analysis using TNT (phenotypic data only).</p> <p><strong>Supplementary material S7. </strong>Input for running Bayesian inference using MrBayes (phenotypic data only).</p> <p><strong>Supplementary material S8. </strong>Input for running parsimony analysis using TNT (sequence data only).</p> <p><strong>Supplementary material S9. </strong>Input for running Bayesian inference using MrBayes (sequence data only).</p> <p><strong>Supplementary material S10. </strong>Input for running parsimony analysis using TNT (total evidence).</p> <p><strong>Supplementary material S11. </strong>Input for running Bayesian inference using MrBayes (total evidence).</p> <p><strong>Supplementary material S12. </strong>Input for running parsimony analysis using TNT (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S13. </strong>Input for running Bayesian inference using MrBayes (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S14. </strong>Input for running Bayesian inference using MrBayes (total evidence) and estimating node ages using tip-dating.</p> <p><strong>Supplementary material S15. </strong>Input for running Bayesian inference using Beast (sequence data only) and estimating node ages using node-dating.</p> <p><strong>Supplementary material S16. </strong>Raw geographic distances among areas in each time slice and dispersal probability matrices for each biogeographic model.</p> <p><strong>Supplementary material S17. </strong>Inputs for estimating ancestral ranges and performing biogeographic stochastic maps for our dataset.</p> <p><strong>Supplementary material S18. </strong>Consensus tree found with parsimony analysis using TNT (phenotypic data only).</p> <p><strong>Supplementary material S19. </strong>Consensus tree found with Bayesian inference using MrBayes (phenotypic data only).</p> <p><strong>Supplementary material S20. </strong>Consensus tree found with parsimony analysis using TNT (sequence data only).</p> <p><strong>Supplementary material S21. </strong>Consensus tree found with Bayesian inference using MrBayes (sequence data only).</p> <p><strong>Supplementary material S22. </strong>Consensus tree found with parsimony analysis using TNT (total evidence).</p> <p><strong>Supplementary material S23. </strong>Consensus tree found with Bayesian inference using MrBayes (total evidence).</p> <p><strong>Supplementary material S24. </strong>Consensus tree found with parsimony analysis using TNT (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S25. </strong>Consensus tree found with Bayesian inference using MrBayes (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S26. </strong>Consensus tree found with Bayesian inference using MrBayes (total evidence) and with node ages estimated using tip-dating.</p> <p><strong>Supplementary material S27. </strong>Maximum clade credibility tree found with Bayesian inference using Beast (sequence data only) and with node ages estimated using node-dating.</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>
Morphological adaptations linked to flight efficiency and aerial lifestyle determine natal dispersal distance in birds
<p>Natal dispersal—the movement from birthplace to breeding location—is often considered the most significant dispersal event in an animal's lifetime. Natal dispersal distances may be shaped by a variety of intrinsic and extrinsic factors, and remain poorly quantified in most groups, highlighting the need for indices that capture variation in dispersal among species.</p> <p>In birds, it is hypothesized that dispersal distance can be predicted by flight efficiency, which can be estimated using wing morphology. However, the use of morphological indices to predict dispersal remains contentious and the mechanistic links between flight efficiency and natal dispersal are unclear.</p> <p>Here, we use phylogenetic comparative models to test whether hand-wing index (HWI, a morphological proxy for wing aspect ratio) predicts natal dispersal distance across a global sample of 114 bird species. In addition, we assess whether HWI is correlated with flight usage in foraging and daily routines.</p> <p>We find that HWI is a strong predictor of both natal dispersal distance and a more aerial lifestyle.</p> <p>Our results support the use of HWI as a valid proxy for relative natal dispersal distance, and also suggest that evolutionary adaptation to aerial lifestyles is a major factor connecting flight efficiency with patterns of natal dispersal.</p>
Path-finding algorithm as a dispersal assessment method for invasive species with human-vectored long-distance dispersal event
<p><strong>Aim</strong>: An assessment method that can precisely represent human-vectored long-distance dispersals (HVLDD) is currently in need for effective management of invasive species. Here, we focused on HVLDD happening along roads and proposed a path-finding algorithm as a more precise dispersal assessment tool than the most widely used Euclidean distance method by using pine wilt disease (PWD) as a case study.</p> <p><strong>Location</strong>: Busan Metropolitan City, Republic of Korea</p> <p><strong>Methods</strong>: A path-finding algorithm, which calculates distances by considering spatial distribution of road networks, was tested for its effectiveness in estimating dispersal distances of HVLDD events. To this end, annual HVLDD cases were classified from entire PWD occurrence data from 2016 to 2019 and their dispersal distances were calculated using the path-finding algorithm and the Euclidean distance method. We constructed potential dispersal ranges based on the occurrence points in 2016, 2017, and 2018 using the respective year's mean dispersal distance for both methods, and their performances in accounting for each subsequent year's HVLDD cases were compared to determine which method calculated more precise distances. The information on which road class contributed more to dispersal occurrences and distances was analysed as well using the proposed algorithm.</p> <p><strong>Results</strong>: The potential dispersal ranges of the path-finding algorithm accounted for more future anthropogenic infection cases than the ones that used the Euclidean distance method, validating its higher functionality. It also revealed that most HVLDDs started and ended on small roads, and large roads constituted the majority of the total dispersal length.</p> <p><strong>Main Conclusions</strong>: The path-finding algorithm has proven to be a more effective dispersal assessment method for HVLDD events. It can help design effective control strategies. Thus, we encourage using the path-finding algorithm for dispersal assessment of invasive species that move along road networks, as well as for the development of more powerful HVLDD prediction models.a</p>
Designing of Fiber Bragg Gratings for Long-distance Optical Fiber Sensing Networks
<p>Research data of <em>Modelling and Simulation in Engineering </em>journal article “Designing of Fiber Bragg Gratings for Long-distance Optical Fiber Sensing Networks”.</p> <p>Most optical sensors on the market are optical fiber Bragg grating (FBG) sensors with low reflectivity (typically 7-40%) and low side-lobe suppression (SLS) ratio (typically SLS <15dB), which prevents these sensors from being effectively used for long-distance remote monitoring and sensor network solutions. This research is based on designing the optimal grating structure of FBG sensors and estimating their optimal apodization parameters necessary for sensor networks and long-distance monitoring solutions. Gaussian, sine and raised sine apodizations are studied to achieve the main requirements, which are - maximally high reflectivity (at least 90%) and side-lobe suppression (at least 20 dB), as well as maximally narrow bandwidth (FWHM<0.2 nm), FBGs with uniform (without apodization). Results gathered in this research propose high-efficiency FBG grating apodizations, which can be further physically realized for optical sensor networks and long-distance (at least 40 km) monitoring solutions.</p> <p> </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.