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Type-A grounding system effective length database
<p>A dataset containing effective length values of a subset of type-A grounding system according to the IEC 62305-3 International Standard.</p> <p>Effective length values are computed in 880 simulations by varying three input parameters (features): soil resistivity, rise-time of the lightning current injected in the middle of the grounding system, and the burial depth of the grounding system.</p> <p>In the computation procedure soil ionization is disregarded.</p>
WiFeS observation of southern nearby Type Ia supernova host galaxies
<p>This repository contains all the reduced and spatially-binned spectral data used in this work. Refer to the README for more detailed information. </p>
Figure 1 in Original specimens and type localities of early described polychaete species (Annelida) from Norway, with particular attention to species described by O.F. Müller and M. Sars
Figure 1. Otto Friderich Müller. From drawing by Cornelius Høyer. Reproduced from Wolff (1994).
Figure 4 in Original specimens and type localities of early described polychaete species (Annelida) from Norway, with particular attention to species described by O.F. Müller and M. Sars
Figure 4. Michael Sars. Photography by P.M. Thomsen. Reproduced from Økland (1955).
Response of soil fungal communities and their co-occurrence patterns to grazing exclusion in different grassland types
<p>Overgrazing and climate change are the main causes of grassland degradation, and grazing exclusion is one of the most common measures for restoring degraded grasslands worldwide. Soil fungi can respond rapidly to environmental stresses, but the response of different grassland types to grazing control has not been uniformly determined. Three grassland types (temperate desert, temperate steppe grassland, and mountain meadow) that were closed for grazing exclusion for nine years were used to study the effects of grazing exclusion on soil nutrients as well as fungal community structure in the three grassland types. The results showed that (1) in the 0–5 cm soil layer, grazing exclusion significantly affected the soil water content of the three grassland types (<em>P</em><0.05), and the pH, total phosphorous (TP) and nitrogen-to-phosphorous ratio (N/P) changed significantly in all three grassland types (<em>P</em><0.05). Significant changes in soil nutrients in the 5–10 cm soil layer after grazing exclusion occurred in the mountain meadow grasslands (<em>P</em><0.05), but not in the temperate desert and temperate steppe grasslands. (2) For the different grassland types, Archaeorhizomycetes was most abundant in the montane meadows, and Dothideomycetes was most abundant in the temperate desert grasslands and was significantly more abundant than in the remaining two grassland types (<em>P</em><0.05). Grazing exclusion let to insignificant changes in the dominant soil fungal phyla and in α diversity but significant changes in the β diversity of soil fungi (<em>P</em><0.05). (3) Grazing exclusion areas have higher mean clustering coefficients and modularity classes than grazing areas. In particular, the highest modularity class is found in temperate steppe grassland grazing exclusion areas. (4) We also found that pH is the main driving factor affecting soil fungal community structure, that plant coverage is a key environmental factor affecting soil community composition, and that grazing exclusion indirectly affects soil fungal communities by affecting soil nutrients. The above results suggest that grazing exclusion may regulate microbial ecological processes by changing the soil fungal β diversity in the three grassland types. Grazing exclusion is not conducive to the recovery of soil nutrients in areas with mountain meadow but improves the stability of soil fungi in temperate steppe grassland. Therefore, the type of degraded grassland should be considered when formulating suitable restoration programmes when grazing exclusion measures are implemented. The results of this study provide new insights into the response of soil fungal communities to grazing exclusion, providing a theoretical basis for the management of degraded grassland restoration.</p>
Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets
<p>The uploaded files are source datasets for the scMTNI algorithm. scMTNI is a multi-task learning framework that integrates the cell lineage structure, scRNA-seq and scATAC-seq measurements to enable joint inference of cell type-specific GRNs. See more details at Zhang, S., Pyne, S., Pietrzak, S. et al. Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. Nat Commun 14, 3064 (2023). <a href="https://doi.org/10.1038/s41467-023-38637-9">https://doi.org/10.1038/s41467-023-38637-9</a></p> <p>The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/scMTNI_sourcedata.tar.gz?versionId=a19f69c1-5bb2-436e-9a30-a7efbf1ab3da">scMTNI_sourcedata.tar.gz</a> contains the following 3 parts:</p> <p>1) The cluster-specific scRNA-seq matrices and the prior networks for all three datasets and scMTNI inferred consensus networks.</p> <p>2) Gold standard human and mouse datasets for evaluation.</p> <p>3) Source data for scMTNI figures 2-8 and supplementary figures. The key for each figure and its corresponding file path is in SourceData_Key_v2.xlsx.</p> <p>The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/Buenrostro_Hematopoiesis.tar.gz">Buenrostro_Hematopoiesis.tar.gz</a> contains the scRNA-seq data for human hematopoietic differentiation downloaded from Data S2 of Buenrostro et al.</p> <p>The source data <a href="../api/files/5c75876b-c08a-4186-a08d-da9db78f64f3/RawMotifFiles.tar.gz">RawMotifFiles.tar.gz</a> contains the motif instance files and promoter files for human and mouse for generating prior networks using scATAC-seq data for scMTNI. Check <a href="https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md">https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md</a> for examples and scripts.</p> <p>The <a href="../api/records/11876980/draft/files/Buenrostro_priorNetwork_bamfiles.tar.gz/content">Buenrostro_priorNetwork_bamfiles.tar.gz</a> contains the raw bam files of scATAC-seq data for human hematopoietic differentiation downloaded from Buenrostro et al.</p>
Figure 1 in Genetic structure of Trypanosoma congolense "forest type" circulating in domestic animals and tsetse flies in the South-West region of Cameroon
Figure 1. Allelic frequency at each locus by host.
F-Type ATP synthase rotor ring stoichiometry predictions
<p>Stoichiometry prediction data for:</p> <p>1) rotor rings with experimental structures available</p> <p>2) representative c subunits</p> <p>3) c subunits with unexpectedly high predicted stoichiometry</p> <p>4) all c subunits via matching with corresponding representative sequence</p>
Repurposing Type I-A CRISPR-Cas3 for a robust diagnosis of human papillomavirus (HPV)
<p>R-loop-triggered collateral single-stranded DNA (ssDNA) nuclease activity within Class 1 Type I CRISPR-Cas systems holds immense potential for nucleic acid detection. However, the hyperactive ssDNase activity of Cas3 introduces unwanted noise and false-positive results. In this study, we identified a novel Type I-A Cas3 variant derived from <em>Thermococcus siculi</em>, which remains in an auto-inhibited state until it is triggered by Cascade complex and R-loop formation. This Type I-A CRISPR-Cas3 system not only exhibits an expanded protospacer adjacent motif (PAM) recognition capability but also demonstrates remarkable intolerance towards mismatched sequences. Furthermore, it exhibits dual activation modes—responding to both DNA and RNA targets. The culmination of our research efforts has led to the development of the Hyper-Active-Verification Establishment (HAVE, 惠父). This innovation enables swift and precise <em>human papillomavirus</em> (HPV) diagnosis in clinical samples, providing a robust molecular diagnostic tool based on the Type I-A CRISPR-Cas3 system. Our findings contribute to understanding type I-A CRISPR-Cas3 system regulation and facilitate the creation of advanced diagnostic solutions with broad clinical applicability.</p>
Data for Cell-type-specific alternative splicing in the cerebral cortex of a Schinzel-Giedion Syndrome patient variant mouse model
<p><span><strong>data.tar.gz </strong>contains all files from the data directory (except for sam outputs from STAR) associated with the 230926_EJ_Setbp1_AlternativeSplicing GitHub project and includes the following files:</span></p> <p> </p> <p><span><strong>./marvel: </strong>- </span><span>This directory contains rds and Rdata objects that were created using the MARVEL R package</span></p> <p><span>cell_type_goresults.rds - This is the go results split by cell type</span></p> <p><span>marvel_04_split_counts.Rdata - This R data includes all environment objects from MARVEL script 04, and is used for downstream plotting</span></p> <p><span>normalized_sj_expression.Rds - This object is the normalized splice junction expression</span></p> <p><span>Setbp1_marvel_aligned.rds - Final prepared MARVEL object before any SJU analyses have been run</span></p> <p><span>significant_tables.RData - For those who do not want to load multiple massive files, this includes all significant SJU results for each cell type</span></p> <p><span>sj_usage_cell_type.rds - This data object has splice junction usage calculated for each cell type</span></p> <p><span>sj_usage_condition.rds - This data object has splice junction usage calculated for each cell type and also split by condition</span></p> <p> </p> <p><strong><span>./seurat: </span></strong><span>- This directory contains all intermediate and final Seurat single-cell gene expression objects</span></p> <p><span>annotated_brain_samples.rds - This is the final iteration of the processing in Seurat for a final annotated object. Please use this object for any Seurat or single-cell gene expression analyses.</span></p> <p><span>clustered_brain_samples.rds - This is the clustered Seurat object, before cell type annotation based on canonical markers.</span></p> <p><span>filtered_brain_samples_pca.rds - This is the filtered Seurat object, before clustering but after PCA.</span></p> <p><span>filtered_brain_samples.rds - This is the filtered Seurat object, before PCA.</span></p> <p><span>integrated_brain_samples.rds - This the integrated Seurat object, before other steps.</span></p> <p> </p> <p><span><strong>./star: </strong>- </span><span>All files in the STAR directory are outputs from STARsolo, as described in our methods. Each output directory contains the same files, so only one example is included here for brevity. Intermediate SAM files were removed to optimize space.</span></p> <p><span>J1/ - This directory contains outputs for brain sample J1</span></p> <p><span>J13/ - This directory contains outputs for brain sample J13</span></p> <p><span>J15/ - This directory contains outputs for brain sample J15</span></p> <p><span>J2/ - This directory contains outputs for brain sample J2</span></p> <p><span>J3/ - This directory contains outputs for brain sample J3</span></p> <p><span>J4/ - This directory contains outputs for brain sample J4</span></p> <p><span>K1/ - This directory contains outputs for kidney sample K1</span></p> <p><span>K2/ - This directory contains outputs for kidney sample K2</span></p> <p><span>K3/ - This directory contains outputs for kidney sample K3</span></p> <p><span>K4/ - This directory contains outputs for kidney sample K4</span></p> <p><span>K5/ - This directory contains outputs for kidney sample K5</span></p> <p><span>K6/ - This directory contains outputs for kidney sample K6</span></p> <p> </p> <p><span><strong>./star/genome:</strong> - This directory contains outputs from running STAR genomeGenerate. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>chrLength.txt</span></p> <p><span>chrNameLength.txt</span></p> <p><span>chrName.txt</span></p> <p><span>chrStart.txt</span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>Genome</span></p> <p><span>genomeParameters.txt</span></p> <p><span>Log.out</span></p> <p><span>SA</span></p> <p><span>SAindex</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p> <p> </p> <p><span><strong>./star/J1:</strong> - This is the head STAR directory for sample J1. It contains logs, basic QC, and gene and splice junction counts. For more information about the STAR pipeline and its outputs, please refer to the STAR documentation</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>Log.final.out</span></p> <p><span>Log.out</span></p> <p><span>Log.progress.out</span></p> <p><span>SJ.out.tab</span></p> <p><span>Solo.out/</span></p> <p><span>STARgenome/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out:</strong>- This directory contains the outputs used for downstream analysis</span></p> <p><span>Barcodes.stats</span></p> <p><span>GeneFull_Ex50pAS/</span></p> <p><span>SJ/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS: </strong>- This directory contains the filtered and raw barcodes, features, and matrix files for gene expression (including introns)</span></p> <p><span>Features.stats</span></p> <p><span>filtered/</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p><span>UMIperCellSorted.txt</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/filtered: </strong>- This directory contains the filtered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages)</span></p> <p><span>barcodes.tsv.gz - This file contains filtered cell barcodes</span></p> <p><span>features.tsv.gz - This file contains filtered features (genes)</span></p> <p><span>matrix.mtx.gz - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/raw: </strong>- This directory contains the unfiltered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages). Files are the same as previously described for filtered.</span></p> <p><span>barcodes.tsv</span></p> <p><span>features.tsv</span></p> <p><span>matrix.mtx</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ: </strong>- This directory contains the QC and raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>Features.stats</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ/raw:</strong> - This directory contains the raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>barcodes.tsv - This file contains filtered cell barcodes</span></p> <p><span>features.tsv - This file contains filtered features (splice junctions)</span></p> <p><span>matrix.mtx - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/_STARgenome:</strong> - This directory contains the STARgenome created and used by STAR for this sample. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p>
Confronting sparse Gaia DR3 photometry with TESS for a sample of around 60,000 OBAF-type pulsators
<p>This listing contains the results produced as part of the paper "Confronting sparse Gaia DR3 photometry with TESS for a sample of around 60,000 OBAF-type pulsators" by Hey & Aerts, 2024.</p> <p>A description of the tables is as follows;</p> <ol> <li><strong>prewhiten_all.json</strong>: The frequencies, amplitudes, and phases of all targets in the sample prewhitened to a 1% false alarm level.</li> <li><strong>prewhiten_all_cleaned.json</strong>: The same as Table 1, where combination frequencies have been removed.</li> <li><strong>full_sample_prediction_paper.csv</strong>: The results of the classification for each variability class.</li> <li><strong>ranked_table_revised.csv:</strong> The final ranking of the targets for future studies.</li> </ol>
Figure 5 in First record of Oligosarcus itau (Characiformes: Characidae) outside the type locality, and second after the description
Figure 5. La Ciénaga dam 24°25'50.10''S, 65°16'41.0''W. El Carmen Department, Jujuy Province.
Figure 4 in First record of Oligosarcus itau (Characiformes: Characidae) outside the type locality, and second after the description
Figure 4. Las Pavas River at 24º26'37.7''S, 65º13'01.3"W, El Carmen Department, Jujuy Province.
Pest species preyed upon by bats and the crop types affected by them.
<p>This database compiles the pest species consumed by bats and the crop types they attack, as part of the supplementary material of the article entitled "<strong>Pest suppression by bats and management strategies to favour it: a global review</strong>", published in the journal Biological Reviews.</p> <p>The crop types were classified into several categories this work: cereals (e.g. wheat, maize, corn, rice, barley, sorghum); forest (e.g. beech, oak, poplar, willow); fruit crops (e.g. apple, pear, apricot, strawberry, cranberry); grasses (e.g. sugarcane, turfs, pastures); legumes (e.g. pea, bean, alfalfa, soybean); ornamental (e.g. garden species); other (cotton, tea, tobacco, hop, flax, rubber tree, hemp, peppermint, jute, rapeseed, kenaf, ashwagandha, mushrooms, honeybees); stored products (e.g. stored cereals, stored tobacco, dried fruits); and vegetables (e.g. tomato, lettuce, spinach, potato, onion).</p>
Fig. 6 in The Impact Of Forest Harvesting Machines On Understorey Vegetation In Hylocomiosa Forest Type In Zemgale, Latvia
Fig. 6. The proportion of evergreen and summer green plant species in the herbaceous' layer.
Fig. 4 in The Impact Of Forest Harvesting Machines On Understorey Vegetation In Hylocomiosa Forest Type In Zemgale, Latvia
Fig. 4. The projective coverage of herbaceous' and moss' layers (mean values are shown ± SE).
Fig. 3 in The Impact Of Forest Harvesting Machines On Understorey Vegetation In Hylocomiosa Forest Type In Zemgale, Latvia
Fig. 3. The number of species of herbaceous and moss layers (mean values are shown ± SE).
Seed dormancy types and germination response of 15 plant species in temperate montane peatlands
<p>Despite their crucial role in determining the fate of seeds, the type and breaking mode of seed dormancy in peatland plants in temperate Asia with a continental monsoon climate are rarely known. Fifteen common peatland plant species were used to test their seed germination response to various dormancy-breaking treatments, including dry storage (D), gibberellin acid soaking (GA), cold stratification (CS), warm followed cold stratification (WCS), GA soaking + cold stratification (GA+CS) and GA soaking + warm followed cold stratification (GA+WCS). Germination experiment, viability and imbibition test, and morphological observation of embryos were conducted. Of the 15 species, nine showed physiological dormancy (PD), with non-deep PD being the dominant type. Four species, <em>Angelica pubescens</em>, <em>Cicuta virosa</em>, <em>Iris laevigata </em>and<em> Iris setosa</em> exhibited morphological physiological dormancy. Two species, <em>Lycopus uniflorus</em> and<em> Spiraea salicifolia</em>, demonstrated non-dormancy of seeds. Overall, the effect hierarchy of dormancy-breaking is: CS > GA > WCS > GA+CS > D > GA+WCS. Principal component analysis demonstrated that seed traits, including embryo length: seed length ratio, seed size, and monocot/eudicot divergence, are more likely to influence seed dormancy than environmental factors. Our study suggests that nearly 90% of the tested peatland plant species in the Changbai Mountains demonstrated seed dormancy, and seed traits (e.g. embryo to seed ratio and seed size) and abiotic environmental factors (e.g. pH and temperature seasonality) are related to germination behavior, suggesting seed dormancy being a common adaptation strategy for the peatland plants in the temperate montane environment.</p>
Fig. 27 in Type Specimens of Halacarid Mites by Dr. N. G. Makarova Relocated in the Collection of the National Museum of Nature and Science, Tsukuba, Japan
Fig. 27.ɹAgaue kurilensis, glass slide prepared by Dr. Makarova.
Fig. 25 in Type Specimens of Halacarid Mites by Dr. N. G. Makarova Relocated in the Collection of the National Museum of Nature and Science, Tsukuba, Japan
Fig. 25.ɹCopidognathus kamchaticus [nomen nudum], glass slide prepared by Dr. Makarova.
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.