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FIG. 5 in Lichen community assemblages and functional traits as indicators of vegetation types in central Mexico, based on herbarium specimens
FIG. 5. — Non-metric Multidimensional Scaling analysis of lichen community functional traits and environmental variables in three vegetation types. * (p <0.05) and ** (p <0.005) represent the significance of functional traits related to the Xerophytic shrubland (X), Subtropical shrubland (S), and Quercus L. forest (Q).
FIG. 1 in Lichen community assemblages and functional traits as indicators of vegetation types in central Mexico, based on herbarium specimens
FIG. 1. — Amplitude and mean values of alpha diversity (species richness) per vegetation type. Symbols: black dot, outlier; white dots, mean values.
Supplemental material of "An annotated whole-genome multilocus sequence typing schema for scalable high resolution typing of Streptococcus pyogenes"
<p>This supplemental material includes the genome assemblies, associated metadata and analysis results for five datasets used to define a publicly available annotated wgMLST schema for <em>S. pyogenes</em> and to evaluate its suitability for high resolution typing. A brief description for each file in the dataset is available in the included README file. Raw sequencing data and sample metadata for the 265 isolates included in Dataset1 have been deposited in the European Nucleotide Archive (ENA) under Project <a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB49967?show=reads">PRJEB49967</a>.</p> <p>The wgMLST schema was created with <a href="https://github.com/B-UMMI/chewBBACA">chewBBACA</a> and is publicly available at <a href="https://chewbbaca.online/species/1/schemas/1">chewie-NS</a>, where a more detailed description of schema creation, annotation and curation can be found.</p>
FIG. 6 in Lichen community assemblages and functional traits as indicators of vegetation types in central Mexico, based on herbarium specimens
FIG. 6. — Correlation of lichen community functional traits and environmental variables in three vegetation types.
FIG. 2 in Lichen community assemblages and functional traits as indicators of vegetation types in central Mexico, based on herbarium specimens
FIG. 2. — Beta diversity and shared species per vegetation type: A, pairwise comparisons of the different elements of beta diversity (Xerophytic, xerophytic shrubland; Quercus, Quercus forest; Subtropical, subtropical shrubland); B, Venn diagram showing exclusive and shared species among the vegetation types included in this study.
Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses
<p>Raw data and code to reproduce figures in the manuscript "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses"</p> <p># README</p> <p>## Introduction</p> <p>This README provides essential information about the codebase for the manuscript titled "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses." The code in this repository is self-contained and is expected to run smoothly given the appropriate versions of the required libraries/packages.</p> <p>## Directory structure and execution details</p> <p>### R code</p> <p>- Main Figures 2A-2D, 3A-3C, and 4A-4E, as well as supplemental figures S2A-S2H, S3A-S3E, S4L, and S5A-S5I, were generated using R. Execute the `R_figs_master.r` script located in the `r_code` directory.<br> - All figures will be saved within the `r_code/code_generated_figures` directory.<br> - Note: Exact UMAP representations might vary across different hardware and operating systems, likely due to an issue with the UWOT package ([Reference Issue](https://github.com/satijalab/seurat/issues/5514)). If figures appear outside their designated plot ranges, set "FixAxes" to 'FALSE' in the `single_cell_variables.r` script.</p> <p>### MATLAB code</p> <p>- Main figures 1B, 1D-1F, and 6A-6H, as well as supplemental figures S1A-S1J and S6A-S6I, were generated using MATLAB (version 9.11.0.1809720 (R2021b) Update 1). Execute the `get_the_figs_matlab.m` script located in the `matlab_code` directory.<br> - All figures will be saved within the `matlab_code/code_generated_figures` directory.<br> - Required: [fca_readfcs, version 2020.06.22](https://ch.mathworks.com/matlabcentral/fileexchange/9608-fca_readfcs).</p> <p>### Python code</p> <p>- Figures 5B-5F panels were generated using Python (version 3.6.8). Run the `fig_5_analysis_code.py` script located in the `python_code` directory.<br> - All figures will be saved within the `python_code/code_generated_figures` directory.<br> - The preprocessed images located in `python_code/data_repository/Adamts2_processed`, `python_code/data_repository/Agmat_processed`, and `python_code/data_repository/Baz1a_processed` were generated using the ImageJ macro `python_code/cropped_to_processed_macro.ijm` from the raw images in `python_code/data_repository/Adamts2_cropped`, `python_code/data_repository/Agmat_cropped`, and `python_code/data_repository/Baz1a_cropped`.</p> <p>## Supplementary code (for reference only as raw data is not included)</p> <p>### Mapping code and genome construction code</p> <p>- Initial processing of Single-cell RNA-sequencing was performed with Cell Ranger, coordinated by the Python script:<br> `python_code/mapping_and_genome_construction/single_cell_mapping_pipeline.py`. Some components of this script are deprecated and were primarily used to pass .fastq files to Cell Ranger and organize the outputs.<br> - A custom genome was constructed to account for the expression of CaMPARI2 in the single-cell RNA-sequencing dataset:<br> `python_code/mapping_and_genome_construction/campari2_genome_construction.py`.<br> - Processing of Bulk RNA-sequencing, either single or paired-end, was executed through Python:<br> `python_code/mapping_and_genome_construction/bulk_single_end_mapping.py` and `python_code/mapping_and_genome_construction/bulk_paired_end_mapping.py`.<br> - A custom genome was constructed to account for the expression of various artificial promoter viruses:<br> `python_code/mapping_and_genome_construction/bulk_seq_genome_construction.py`.</p>
Magnetite in Muong Nong-type Australasian tektites from South China
<p>This supporting information provides the raw data that are used in the main article. Note that the research paper contained all raw data updated on the zenodo data repository is under review.</p>
Magnetic structure and field-dependent magnetic phase diagram of Ni2In-type PrCuSi
<p>Data sets for original figures in the article 'Magnetic structure and field-dependent magnetic phase diagram of Ni<sub>2</sub>In-type PrCuSi' published in <a href="https://iopscience.iop.org/article/10.1088/1361-648X/aae28d/meta">J. Phys.:Condens. Matter 30 (43) 2018</a>. The file name of each xls file corresponds to the figure number in the published article. The files can be opened using the Excel program. If there are sub-figures, or multiple frames in each figure, the data of each sub-figure is stored in separate sheets within one xls file. The files with the file extension 'vesta' can be opened using the freely available program <a href="https://jp-minerals.org/vesta/en/">VESTA</a>.</p>
Single-Band or Dual-Band Pi-Type Rat-Race and Branch-Line Coupler Designer
<p>Using this software, one can design single or dual-band rat-race or branch-line coupler using Pi topology.</p> <p> </p> <h2>How to use Pi Coupler Designer for Single/Dual-Band BLC and RRC:</h2> <ol> <li>Download <table> <tbody> <tr> <td> <div><a href="../api/records/8278306/draft/files/Pi_Coupler_Designer.rar/content" target="_blank" rel="noopener noreferrer">Pi_Coupler_Designer.rar</a></div> </td> </tr> </tbody> </table> </li> <li>Extract the rar file.</li> <li>Open the Pi_Coupler_Designer.exe file in Windows 11</li> <li>Maximize the window. Display resolution 1200X600 or more is preferred.</li> <li>Users can switch between BLC and RRC using the Coupler menu.</li> <li>At first, select the band: Single-Band/Dual-Band.</li> <li>Select PS for RRC: Ideal-Phase-Inv/C-Section/Pi-Section/T-Section/ex-Pi-section.</li> <li>Enter f0, n, phi and TLip: Ch imp/ El len for single band design.</li> <li>Then, enter Z_beta or theta_beta and other parameters.</li> <li>Enter f1,f2, n1,n2 for dual-band design.</li> <li>Press the Design button to get the design schematic. </li> <li>Press the analysis button to get the S-parameters results. Users can switch the excitation port and Response type.</li> <li>For additional queries, write to r.sinha30@gmail.com</li> </ol>
Fine mapping of 5 chronic pain types
<p>Fine mapping was run with CAUSALdb-finemapping-pip using clumped GWAS data of 5 chronic pain types: chronic neck, back, hip and knee pain and chronic headaches.</p>
Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms - Datasets
<p>Datasets for reproducibility of the results found in Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms. This study utilized data from the following 4 journals:</p> <p>Lake, B.B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun 10, 2832 (2019).</p> <p>Liao, J., Yu, Z., Chen, Y. et al. Single-cell RNA sequencing of human kidney. Sci Data 7, 4 (2020).</p> <p>Menon, R. et al. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight 5, e133267 (2020).</p> <p>Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J Am Soc Nephrol 29: 2069–2080 (2018).</p> <p>Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).</p>
Data and code for: Spatial cell type enrichment predicts mouse brain connectivity
<p>A fundamental neuroscience topic is the link between the brain's molecular, cellular and cytoarchitectonic properties and structural connectivity (SC). Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. Here, we utilize whole-brain mapping of neuronal and non-neuronal subtypes via the Matrix Inversion and Subset Selection (MISS) algorithm to model inter-regional connectivity as a function of regional cell-type composition with machine learning. We deployed random forest algorithms for predicting connectivity from cell type densities, demonstrating surprisingly strong prediction accuracy of cell types in general and particular cells like oligodendrocytes. We found evidence of a strong distance-dependency in the cell-connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia are salient for short-range connections. Our results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.</p>
Noise production by a Savonius type wind turbine- a comparison between single and five segment rotor.
<p>The following data set contains acoustic evaluation results from experimental studies performed on Savonius wind turbine. </p> <p>Noise from a typical single segment Savonius wind turbine has been compared to that of its five segment counterpart. This comparison is mainly conducted for three case:</p> <p> </p> <p>1. when rotor is loaded (giving us max coefficient of performance)</p> <p>2. when the rotor is not loaded (free rotation)</p> <p>3. when the rotor is stopped. </p> <p> </p> <p>Power characteristics of the two rotors have also been plotted, allowing to establish a comparison between noise and power produced.</p> <p> </p> <p> </p> <p> </p>
Meta-GWAS of Age at Type 1 Diabetes Diagnosis
<p>7,923 subjects with type 1 diabetes from five studies (SDRNT1BIO, DCCT, CACTI, WESDR and EDC) were included in this analysis. This dataset includes summary stats for 8,154,711 autosomal SNPs.</p>
Meta-GWAS of C-peptide in Type 1 Diabetes
<p>7,252 subjects with type 1 diabetes from four studies (SDRNT1BIO, DCCT, CACTI and WESDR) were included in this analysis. This dataset includes summary stats for 8,150,646 autosomal SNPs.</p>
Carbon storage and carbon-equivalent albedo impact for US forests, by age and forest type
<p>These tables document estimates of carbon storage (Mg/ha +/- Standard Error) and carbon-equivalent albedo impacts (same units) of US forests by age and forest type (Healey et al., in review). Carbon estimates are derived from field measurements made by the USDA Forest Service on approximately 125,000 forested field plots (Domke et al., 2022). Soil organic carbon is omitted from these estimates, but all other above- and below-ground pools are included. Albedo impacts (time-dependent emissions equivalent, TDEE; Bright et al., 2016) were developed by applying atmospheric kernels (Bright and O'Halloran) to a new Landsat blue sky albedo product for the Landsat archive (Erb et al., 2022), as described by Healey et al. (in review). Standard error is supplied for each age/forest type bin for carbon storage, but upper and lower standard error bounds are specified for TDEE because log transformation creates an asymmetrical uncertainty envelope. </p> <p> </p> <p>Bright, Bogren, Bernier, Astrup, (2016). Carbon-equivalent metrics for albedo changes in land management contexts: Relevance of the time dimension. <em>Ecol. Appl.</em> 26, 1868–1880</p> <p>Bright, R. M., & O'Halloran, T. L. (2019). Developing a monthly radiative kernel for surface albedo change from satellite climatologies of Earth's shortwave radiation budget: CACK v1. 0. <em>Geoscientific Model Development, </em>12(9), 3975-3990.</p> <p>Domke, Walters, Nowak, Greenfield, Smith, Nichols, Ogle, Coulston, Wirth (2022). Greenhouse Gas Emissions and Removals From Forest Land, Woodlands, Urban Trees, and Harvested Wood Products in the United States, 1990–2020. (US Dept. Ag. For. Service, Madison, WI; <a href="https://doi.org/10.2737/FS-RU-382">https://doi.org/10.2737/FS-RU-382</a>).</p> <p>Erb, Li, Sun, Paynter, Wang, & Schaaf, (2022). Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain. <em>Remote Sensing</em>, <em>14</em>(21), 5320.</p> <p>Healey, Yang, Erb, Bright, Domke, Frescino, Schaaf, (in review) New satellite observations expose albedo dynamics offsetting half of carbon storage benefits in US forests.</p>
zEPHYR - Noise and performance correlation of a Savonius type vertical axis wind turbine.
The presented data set consists of noise measurement and performance characteristics obtained as a part of the experimental campaign in the Open Jet Facility (OJF) at Delft University of Technology. The observations have been recorded for a typical Savonius vertical axis wind turbine. Refer to the file "setup" for further experimental campaign details.
Replication package for the paper: "Machine Learning for the Identification and Classification of Technical Debt Types on StackOverflow Discussions"
<p>This is the replication package for the article "Machine Learning for the Identification and Classification of Technical Debt Types on StackOverflow Discussions". The article was published in the Research Track of the third Brazilian Workshop on Intelligent Software Engineering (ISE'23).</p> <p>The replication package consists of 8 files:<br> 1) dataset.csv, 2) code_anayses.ipynb and 3) example_test_balanced.csv and the others are results of word cloud generation.</p> <p>In dataset.csv, we provide the data for future replications.</p> <p>In code_anayses.ipynb, we provide the code we use to arrive at the results.</p> <p>In example_test_balanced.csv, we provide an example input dataset for training the models.</p> <p>For future references in this article, please contact lead author Eliakim Gama, or one of the co-authors.</p>
Aggregation of symbionts on hosts depends on interaction type and host traits
<p>Symbionts tend to be aggregated on their hosts, such that few hosts harbor the majority of symbionts. This ubiquitous pattern can result from stochastic processes, but aggregation patterns may also depend on the type of host-symbiont interaction, plus traits that affect host exposure and susceptibility to symbionts. Untangling how aggregation patterns both within and among populations depend on stochastic processes, interaction type and host traits remains an outstanding challenge. Here, we address this challenge by using null models to compare aggregation patterns in a neutral system of Balanomorpha barnacles attached to patellid limpets and a host-parasite system of Trinidadian guppies (Poecilia reticulata) and their Gyrodactylus spp. monogeneans. We first used a model to predict patterns of symbiont-host aggregation due to random partitioning of symbionts to hosts. This null model accurately predicted the aggregation of barnacles on limpets, but the degree of aggregation varied across 303 quadrats. Quadrats with larger limpets had less aggregated barnacles, whereas aggregation increased with variation in limpet size. Across 84 guppy populations, Gyrodactylus spp. parasites were significantly less aggregated than predicted by the null model. As in the neutral limpet-barnacle system, aggregation decreased with mean host size. Parasites were also significantly less aggregated on males than females because male guppies tended to have higher prevalence and lower parasite burdens than predicted by the null model. Together, these results suggest stochastic processes can explain aggregation patterns in neutral but not parasitic systems, though in both systems host traits affect aggregation patterns. Because the distribution of symbionts on hosts can affect symbiont evolution via intraspecific interactions, and reciprocally host behavior and evolution via host-symbiont interactions, identifying the drivers of aggregation enriches our understanding of host-symbiont interactions.</p>
FIGURE 2 Different habitat types that were sampled during the 2018 in Taxonomy, ecology and distribution of the mosquitoes (Diptera: Culicidae) of the Dutch Leeward Islands, with a key to the adults and fourth instar larvae
FIGURE 2 Different habitat types that were sampled during the 2018 survey. Natural habitats: a) Bromelias, b) inflorescences of Heliconia sp. c) rock pool/beach pool, d) crab hole; Semi-natural habitats: e) temporary/permanent pond, Artificial habitats: f) bucket, g) well, h) car tire, i) abandoned swimming pool, j) cistern. Photos courtesy of K.-D.B. Dijkstra, J.G. van der Beek and K. Hulshof.
ScienceDex guides
Understand access before you commit
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.