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FIGURE 1 in A phylogenetic framework for the Hylocereeae (Cactaceae) and implications for the circumscription of the genera
FIGURE 1. Stem and flower morphology of the Hylocereeae. A) Acanthocereus chiapensis (Arias 1021); B) A. tetragonus (Arias 2166); C) Disocactus quezaltecus (Veliz 42515); D) D. phyllanthoides (Arias 1432); E) Aporocactus flagelliformis (Arias 1236); F) Epiphyllum cartagense (Hammel 19793); G) E. chrysocardium (cultivated, Botanical Garden UNAM); H) E. hookeri subsp. hookeri (Arias 1560); I) E. thomasianum (Hammel 22020); J) Pseudorhipsalis himantoclada (Hammel 20806); K) P. ramulosa (Ferrufino 718); L) Hylocereus monacanthus (Hammel 26600); M) H. undatus (cultivated, Botanical Garden UNAM); N) Selenicereus anthonyanus (Hammel, cultivated); O) S. dorschianus (Arias 2221); P) S. pteranthus (Garcia 874); Q) Weberocereus glaber (Bravo s.n.) [photos by S. Arias (A–H, K, M, O–Q), and B. Hammel (I, J, L, N)].
FIGURE 4. Strophocactus. A in A phylogenetic framework for the Hylocereeae (Cactaceae) and implications for the circumscription of the genera
FIGURE 4. Strophocactus. A) S. wittii, flowering specimen (Schmidt s.n., cultivated at the Botanic Garden Bonn). B) Strophocactus wittii (left) and Strophocactus (Pseudoacanthocereus) sicariguensis (right), both forming flattened stems [photos by W. Barthlott, LotusSalvinia.de].
FIGURE 3 in A phylogenetic framework for the Hylocereeae (Cactaceae) and implications for the circumscription of the genera
FIGURE 3. Convergent fern-like flattened stems in A) Epiphyllum chrysocardium, B) Weberocereus imitans, C) Epiphyllum anguliger, D) Selenicereus (Cryptocereus) anthonyanus (photos by W. Barthlott, Lotus-Salvinia.de).
The data for "Noise-Resilient Forward Transient Analysis for Pipe Burst Localization in Complex Pipe Networks: Theoretical Framework"
<p>The data is for supporting our research paper titled "Noise-Resilient Forward Transient Analysis for Pipe Burst Localization in Complex Pipe Networks: Theoretical Framework".</p>
Online repository input data collection framework
Open the record for dataset details and reuse information.
Reproduction package for the paper "Transient study using LoTSS – framework development and preliminary results"
<p>This is a basic reproduction package for the paper "Transient study using LoTSS – framework development and preliminary results" by <a href="https://doi.org/10.1093/mnras/stae1458" target="_blank" rel="noopener">de Ruiter et al. (2024).</a> The folders analysis, P164+55, simulations, source_finder and transient_rates have been compressed.</p> <p> </p> <h2>Raw Data</h2> <p>The visibilities that are used to create the subtraction images for the LoFAR Two-metre Sky Survey (LoTSS) can be found in the following <a href="https://repository.surfsara.nl/collection/lotss-dr2" target="_blank" rel="noopener">Surf repository</a>.</p> <p> </p> <h2>Software</h2> <p>A <a href="https://github.com/mhardcastle/ddf-pipeline/blob/master/scripts/sub-sources-outside-region.py" target="_blank" rel="noopener">script</a> to perform the sky model subtraction exists within the pipeline for the LoTSS processing. The imaging software WSClean can be downloaded <a href="https://gitlab.com/aroffringa/wsclean/" target="_blank" rel="noopener">here </a> and a manual to get started can be found <a href="https://wsclean.readthedocs.io/en/latest/" target="_blank" rel="noopener">here</a>. The command that was used to create the subtraction images is (as described in Table 1 of the paper):</p> <p> </p> <blockquote> <p>wsclean -name subtract_images_8sec_P164+55_pad/P164+55 -weight briggs -0.25 -padding 2.0 -no-reorder -niter 0 -size 2200 2200 -scale 6asec -pol I -intervalsout 3600 -no-dirty P164+55_object.dysco.sub.shift.avg.weights.ms.archive0</p> </blockquote> <p> </p> <p>The scripts for source finding and filtering of the transient candidates are given in source_finder/ and analysis/. A detailed description of how to use this is given in subtraction images transient pipeline.pdf. Also the required for python packages are described in detail here.</p> <p> </p> <p> </p> <h2>Figures and Tables</h2> <h3>simulations/</h3> <p>The jupyter notebook visualise_simulated_transients.ipynb can be used to visualize the 8 second and 1 hour simulated transient sources.</p> <p> </p> <h3>transient_rates/</h3> <p>The jupyter notebook transient_rates.ipynb can be used to recreate the limits on the transient surface density (Figure 13).</p> <p> </p> <p> </p> <h2>Intermediate data products </h2> <p>This subtraction images for a single pointing are available in this reproduction package in P164+55/ The subtraction images for all the pointings have a total size of 2.8 TB, therefore, we only include the P164+55 pointing. The subtraction images for the 1hr snapshots, 2 minute snapshots and most of the 8 second snapshots (up to 50 GB) have been included. The subtraction images for other pointings can be requested by sending an email to <a href="mailto:irisderuiter7@gmail.com">irisderuiter7@gmail.com</a></p> <p> The subtraction images with simulated transients are included in simulations/</p>
Expanding the Diversity of Linkages in Covalent Organic Frameworks through Staudinger Cycloaddition: Synthesis of cis-β-Lactam and cis-β-Thiolactam COFs
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A simple framework for agent-based modeling with extracellular matrix: Simulation results
<h1>Data for "A simple framework for agent-based modeling with extracellular matrix"</h1> <p> </p> <div>Metzcar, J., Duggan, Ben S., Fischer, B., Murphy, M., Heiland, R., Macklin, P. A simple framework for agent-based modeling with extracellular matrix. bioRxiv. doi: 10.1101/2022.11.21.514608</div> <div> </div> <div><a href="https://www.biorxiv.org/content/10.1101/2022.11.21.514608">Link to preprint</a></div> <div> <p>This repository contains the data for each subfigure (and video) in the preprint cited and linked abvoe, as well as the original figures and videos themselves. We have tried to include original model file (<code>PhysiCell_settings.xml</code> or other <code>*.xml</code> file) used to generate each results with each file set as well as the code (see python scripts) to make images and videos that appear in the pre-print - both within the files and base modules and examples in a separate folder.</p> <p>The data can be regenerated using release 2.0, <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.1">2.1</a>, and <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.2.1">2.2.1</a>. Release 2.1 is recommended for reproducing more exactly the results for the fibrosis, invasive carcinoma, and series of leader-follower results as the results archived here were produced using a set of two random number generators (one from PhysiCell and one from BioFVM). 2.2.1, used to produce the invasive cellular front results, consolidates the use of random number generators to just one (the PhysiCell one). As such, in 2.2.1, the random number generator seed may need changed to produce stochastic replicates, even when multithreading.</p> </div> <p> </p> <div>The following file sets are in this download:</div> <ul> <li>Fig2_SM_3_simple_tests.zip <ul> <li>Has results for Figure 2 and SM Figure 3</li> </ul> </li> <li>Fig3_fibrosis.zip <ul> <li>Results from fibrosis simulation (originally Figure 3, now Figure 4)</li> </ul> </li> <li>Fig4_invasive_carcinoma.zip <ul> <li>Results from invasive carcinoma simulation (originally Figure 4, now Figure 5)</li> </ul> </li> <li>Fig5_collective_migration_initial_tests.zip <ul> <li>Results from initial leader follower model development (originally Figure 5, now Figure 6)</li> </ul> </li> <li>Fig6_instant_remodeling.zip <ul> <li>Results from instant remodeling leader follower model scenariods (originally Figure 6, now Figure 7)</li> </ul> </li> <li>Fig7b_leader_follower.zip <ul> <li>Results from leader-follower collective migration scenario (origianlly Figure 7b, now Figure 8b)</li> </ul> </li> <li>images_and_vidoes_for_paper.zip <ul> <li>has all the images, videos, and some figures from the main body of the paper and the supplmental material. Updated in this version.</li> </ul> </li> <li>Invasive_cellular_front.zip <ul> <li>Has results for each ECM scenario: random, parallel, and perpindicular orientations and mixed ECM conditions. New to this data repository.</li> </ul> </li> <li>python_imaging.zip <ul> <li>Base modules and examples for producing images (tested on Python 3.9). Updated in in this verison</li> </ul> </li> <li>SM_Fig_4b_leader_follower_decreased_remodeling.zip</li> <li>stochastic_replicates.zip <ul> <li>Contains stochastic replicates for the collective migration, fibrosis, and invasive carcinoma models and source code for all.</li> </ul> </li> <li>stochastic_replicates_invasive_cellular_front.zip <ul> <li>Contains stochastic replicates for the invasive cellular front scenarios. New to this data repository.</li> </ul> </li> </ul>
PFAS Adsorption Using Viologen-Modified Covalent Organic Frameworks: Synergistic Adsorption Mechanisms Provide a Robust Approach for Water Purification
<p>Dataset for manuscript.</p>
Figure of Violence Against Women in The Philippines Based On The Ecological Framework
<p>This article contributes to IR studies, especially on gender violence in ASEAN. The Philippines was chosen because there are many cases in this country. The literature review data and figures in this article are important as references for other researchers who have the same focus on women's violence in the ASEAN region, empowering them to further their research.</p>
Dataset related to "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations"
<p>This is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). It contains the resulting data of the manipulated SSCx network model, such as benchmarks, fitted stochastic models, manipulated connectomes, structural validations, as well as simulation data and analysis results.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <p><strong>Update 25/09/2024 (v2):</strong> Added benchmark results for assessing strong and weak scaling behavior of connectome rewiring.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
ArcGIS GDB for CPBS Report 23UCB01 - A Context-sensitive Street Classification Framework for Speed Limit Setting
<p>ArcGIS .gdb geodatabase files and a data dictionary in Excel format.</p>
Assemblies generated in the manuscript "Geometric deep learning framework for de novo genome assembly"
<p>Assemblies evaluated in the manuscript "Geometric deep learning framework for de novo genome assembly". All the assemblies were generated by us, except CHM13.ONT.Flye-2.9.fa.gz which was generated by <a href="https://www.nature.com/articles/s41587-019-0072-8">Kolmogorov et al. (2019)</a>.</p>
Appendix A for the paper title : ArtInsight: A Multimodal AI Framework for Interpreting Children's Drawings and Enhancing Emotional Understanding
<p> Appendix A in this paper includes the specific prompt used for annotating the children's drawings with ChatGPT. This prompt was meticulously designed to ensure that the AI generated consistent and relevant descriptions and assessment reports for each image. The prompt guides the AI to focus on several key areas:</p> <ul> <li><strong>Description of the Artwork</strong>: Encouraging a detailed observation of the visual elements in the drawing, such as colors, shapes, figures, and any notable features.</li> <li><strong>Psychological Interpretation</strong>: Guiding the AI to interpret the possible emotional and psychological themes represented in the artwork based on the visual cues.</li> <li><strong>Possible Emotional Themes</strong>: Identifying underlying emotions or feelings that the child might be expressing through their drawing.</li> <li><strong>Recommended Interventions</strong>: Suggesting appropriate actions or conversations that therapists, educators, or parents could initiate to support the child's emotional well-being.</li> </ul>
Supplementary Dataset for V8s: An Edge/Cloud Deployable Kubernetes Task Offloading Framework for Smart Vehicles
<p>The supplementary dataset related to the plots in the paper "V8s: An Edge/Cloud Deployable Kubernetes Task Offloading Framework for Smart Vehicles" is published on this page. You can download the zip file with the following contents:</p> <p> </p> <p>exec-time/<br>├── edge-face-detect.csv<br>├── edge-object-recognition.csv<br>├── nano-face-detect.csv<br>├── nano-gpu-object-recognition.csv<br>├── nano-object-recognition.csv<br>├── rahti-face-detect.csv<br>├── rahti-object-recognition.csv<br>├── rahti2-face-detect.csv<br>├── rahti2-object-recognition.csv<br>├── ras-face-detect.csv<br>└── ras-object-recognition.csv<br>latency/<br>├── 5g.csv (when accessing the edge server using 5GTN connectivity)<br>├── edge.csv (when accessing the edge server using WiFi and VPN)<br>└── rahti-cloud.csv (when accessing the Rahti using WiFi)</p> <p> </p> <p> </p> <p>There are two folders in the dataset, one is for the latency information when using 5G connection and WiFi connection when using the resources. The other folder is for the execution time of the two implemented applications --- object recognition and face detection --- using the V8s framework in various execution environments.</p> <p>The file size is about 209 MiB.</p>
Digital Design and Discovery of Biological Metal-Organic Frameworks for Gas Signaling
<p>This repository contains the structures, features and compositions of Bio-hMOFs database.</p> <ol> <li>Fragments and Composition: Contains the building block fragments used to generate the Bio-hMOFs.</li> <li>Structures-CIFs: Contains the structures of Bio-hMOFs</li> <li>Geometric and RACs: Contains the geomtric and RACs features of Bio-hMOFs</li> <li>Adsorption Capacity: Contains the adsorption uptake of NO and CO adsorption simulated under 298K under 1 bar and 10 bar.</li> <li>Mechanical Properties; Computed mechanical properties</li> </ol>
ADSCAN: AI-Powered Multi-modal Framework for Enhanced Ad Detection and Web Safety
<p>We published codes and datasets (SITE-D, TEXT-D and part of IMG-D) for paper "ADSCAN: AI-Powered Multi-modal Framework for Enhanced Ad Detection and Web Safety."</p>
Data from: Spatiotemporal diversification of the true frogs (Genus Rana): a historical framework for a widely studied group of model organisms
True frogs of the genus Rana are widely used as model organisms in studies of development, genetics, physiology, ecology, behavior, and evolution. Comparative studies among the more than 100 species of Rana rely on an understanding of the evolutionary history and patterns of diversification of the group. We estimate a well-resolved, time-calibrated phylogeny from sequences of six nuclear and three mitochondrial loci sampled from most species of Rana, and use that phylogeny to clarify the group's diversification and global biogeography. Our analyses consistently support an "Out of Asia" pattern with two independent dispersals of Rana from East Asia to North America via Beringian land bridges. The more species-rich lineage of New World Rana appears to have experienced a rapid radiation following its colonization of the New World, especially with its expansion into montane and tropical areas of Mexico, Central America, and South America. In contrast, Old World Rana exhibit different trajectories of diversification; diversification in the Old World began very slowly and later underwent a distinct increase in speciation rate around 29–18 Ma. Net diversification is associated with environmental changes and especially intensive tectonic movements along the Asian margin from the Oligocene to early Miocene. Our phylogeny further suggests that previous classifications were misled by morphological homoplasy and plesiomorphic color patterns, as well as a reliance primarily on mitochondrial genes. We provide a phylogenetic taxonomy based on analyses of multiple nuclear and mitochondrial gene loci.
Data from: A modelling framework to assess the effect of pressures on river abiotic habitat conditions and biota
River biota are affected by global reach-scale pressures, but most approaches for predicting biota of rivers focus on river reach or segment scale processes and habitats. Moreover, these approaches do not consider long-term morphological changes that affect habitat conditions. In this study, a modelling framework was further developed and tested to assess the effect of pressures at different spatial scales on reach-scale habitat conditions and biota. Ecohydrological and 1D hydrodynamic models were used to predict discharge and water quality at the catchment scale and the resulting water level at the downstream end of a study reach. Long-term reach morphology was modelled using empirical regime equations, meander migration and 2D morphodynamic models. The respective flow and substrate conditions in the study reach were predicted using a 2D hydrodynamic model, and the suitability of these habitats was assessed with novel habitat models. In addition, dispersal models for fish and macroinvertebrates were developed to assess the re-colonization potential and to finally compare habitat suitability and the availability / ability of species to colonize these habitats. Applicability was tested and model performance was assessed by comparing observed and predicted conditions in the lowland Treene River in northern Germany. Technically, it was possible to link the different models, but future applications would benefit from the development of open source software for all modelling steps to enable fully automated model runs. Future research needs concern the physical modelling of long-term morphodynamics, feedback of biota (e.g., macrophytes) on abiotic habitat conditions, species interactions, and empirical data on the hydraulic habitat suitability and dispersal abilities of macroinvertebrates. The modelling framework is flexible and allows for including additional models and investigating different research and management questions, e.g., in climate impact research as well as river restoration and management.
Data from: An informational diversity framework, illustrated with sexually deceptive orchids in early stages of speciation
Reconstructing evolutionary history for emerging species complexes is notoriously difficult, with newly isolated taxa often morphologically cryptic and the signature of reproductive isolation often restricted to a few genes. Evidence from multiple loci and genomes is highly desirable, but multiple inputs require 'common currency' translation. Here we deploy a Shannon information framework, converting into diversity analogue, which provides a common currency analysis for maternally inherited haploid and bi-parentally inherited diploid nuclear markers, and then extend that analysis to construction of minimum-spanning networks for both genomes. The new approach is illustrated with a quartet of cryptic congeners from the sexually deceptive Australian orchid genus Chiloglottis, still in the early stages of speciation. Divergence is more rapid for haploid plastids than for nuclear markers, consistent with the effective population size differential (Nep < Nen), but divergence patterns are broadly correlated for the two genomes. There are nevertheless intriguing discrepancies between the emerging plastid and nuclear signals of early phylogenetic radiation of these taxa, and neither pattern is entirely consistent with the available information on the sexual cues used by the orchids to lure the pollinators enforcing reproductive isolation. We describe possible extensions of this methodology to multiple ploidy levels and other types of markers, which should increase the range of application to any taxonomic assemblage in the very early stages of reproductive isolation and speciation.
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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.