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513 results for “prone”
Machine learning classifiers for species classification of fungi using error-prone long-reads on extended metabarcodes
<p>Machine learning models used in the decision tree of linked machine learning models (<a href="https://github.com/teenjes/fungal_ML">https://github.com/teenjes/fungal_ML</a>)</p>
Supporting data for managing fire-prone forests in a time of decreasing carbon carrying capacity
<p>These data and code include surface fuels and prescribed fire emissions data from the Teakettle Experimental Forest in the Sierra Nevada, California, USA. These data include transect data of surface fuels and the emissions from a 2017 prescribed burn. Emissions from the prescribed burn were calculated using a stock change approach by subtracting pre-burn surface fuels from post-burn surface fuels. We used these data and a Monte Carlo simulation approach to estimate the frequency of prescribed burning required to reduce surface fuels following a widespread overstory tree mortality event.</p>
Characterization and morphometry of prone and affected watersheds by hydro-geomorphological processes in the Serra do Mar Mountain Range, southeastern Brazil: foundation for planning and mitigation actions.
<p>Data: shapefile, tables, and kmz files. </p> <ol> <li>SHAPEFILES</li> </ol> <p>- Dataset with watersheds mapped in the Serra do Mar Paulista Region in the follow cities:</p> <ul> <li>Ubatuba (Abbvr. WU)</li> <li>Caraguatatuba (Abbvr. WC)</li> <li>São Sebastião (Abbvr. WSS)</li> <li>Bertioga (Abbvr. WB)</li> <li>Santos (Abbvr. WS)</li> <li>Praia Grande (Abbvr. WPG)</li> <li>Cubatão (Abbvr. WCUB)</li> <li>São Vicente (Abbvr. WSV)</li> <li>Itanhaém (Abbvr. WITA)</li> <li>Peruíbe (Abbvr. WPERU)</li> <li>Iguape (Abbvr. WIGUA)</li> <li>Itariri (Abbvr. WITR)</li> <li>Pedro de Toledo (Abbvr. WPDT)</li> <li>Iporanga (Abbvr. WIPORA)</li> <li>Apiaí (Abbvr. WAPI)</li> <li>Itaoca Abbvr. WITAO)</li> </ul> <p>- Each shapefile contain information about altitude (min., max, and mean), area (km²), and length (km). </p> <p>- Debris-flow Inventory shapefile.</p> <p> 2. TABLES</p> <ul> <li>Tables for the watersheds mapped in each cities also contain information about the morphometric parameters (melton ratio, basin relief, and relief ratio).</li> <li>Debris-flow inventory information. </li> </ul> <p> </p>
Vegetation structure and fuel dynamics in fire-prone, Mediterranean-type Banksia woodlands
<p>Increasing extreme wildfire occurrence globally is boosting demand to understand the fuel dynamics and fire risk of fire-prone areas. This is particularly pressing in fire-prone, Mediterranean climate-type vegetation, such as the Banksia woodlands surrounding metropolitan Perth, southwestern Australia. Despite an extensive wildland-urban interface and frequent fire occurrence, fuel accumulation and the spatial variation in fuel risk is not well quantified across the broad extent of this ecosystem. Using a space for time sampling approach to generate a chronosequence of time since fire, we selected sites that spanned across two distinct sandy soil types (Spearwood and Bassendean sands) and a rainfall gradient (550 to 750 mm north–south). We examined 82 sites in Banksia woodlands, southwestern Australia. Of the 82 sites, 44 burnt during the measurement period (2016 to 2021), which provided the opportunity for fuel measurements following fire (resulting in total N = 126). We wanted to answer two key questions: 1) How do measures of fuel load (mass) and arrangement (structure and continuity) vary across space and time, particularly with respect to time since the last fire? 2) How do biophysical drivers, such as soil type and rainfall, influence fuel accumulation and arrangement, and do these covariates improve litter fuel modelling beyond traditional asymptotic models? We found that fine surface fuel loads (litter and small twigs) differed between sand types, accumulating faster and reaching a higher peak on Spearwood sands (7–9 Mg ha−1) compared to Bassendean sands (6–7 Mg ha−1). Shrub layer fuel loads also accumulated faster on Spearwood sands than on Bassendean sands. While shrub layer fuels on Spearwood sands peaked at 14 years and declined thereafter, those on Bassendean sand did not decline over time but have lower overall connectivity. Total fine fuels (fine surface plus fine shrub layer fuels) had no significant decline over the same time period, on either sand type. Total fine fuel loads reached a peak of 9–10 Mg ha−1 between 13- and 20-years following fire, depending on the underlying sand type. Our quantitative fuel accumulation models confirmed the strength of time since fire as a predictor of hazard, but nonetheless included up to 40% unexplained variance. Importantly, while components fluctuated over time, the combined total of fine fuels did not decline with the long absence of fire, suggesting fire risk does not necessarily decrease in long unburned vegetation.</p>
Dataset for "The first mitotic division of human embryos is highly error prone"
<p>Data for The first mitotic division of human embryos is highly error prone. [Dataset]<br> Research output available from https://omero.warwick.ac.uk/webclient/?show=project-8301<br> Details for how to access the dataset are available on the public data page: https://warwick.ac.uk/fac/sci/med/research/biomedical/facilities/camdu/publicdata/</p> <p>The dataset contains movies of human embryos consented to research progressing through the first two embryonic mitoses. The chromosomes have been visualised using SiR-DNA dye and imaged using a deltavision widefield microscope.The dataset consists only of time lapse imaging movies.</p>
Supplementary files for the manuscript "Assembly of Long Error-Prone Reads Using Repeat Graphs"
<p>Supplementary files for the manuscript "Assembly of Long Error-Prone Reads Using Repeat Graphs"</p> <p> </p> <p>Contents<br> ---------</p> <p>* `human_assemblues` - Flye assemblies of the human ONT sequencing data + QUAST benchmarking<br> of Flye, Canu and MaSuRCA assemblies. Scripts for assembly graph analysis are also included.</p> <p>* `nctc_assemblis` - Flye assemblies of the NCTC 21 bacterial dataset.</p> <p>* `yeast_assemblies` - working directories Flye, Canu, Falcon, Hinge and Miniasm assemblies of <br> yeast PB and ONT datasets + final assemblies + quast report. Some large files <br> (such as read alignments) were deleted.</p> <p>* `worm_assemblies` - working directories Flye, Canu, Falcon, Hinge and Miniasm assemblies of <br> the c. elegans dataset + final assemblies + quast report. Some large files <br> (such as read alignments) were deleted. `tandem_misassemblies` directory contain<br> the detailed analysis of nine tandem misassemblies. We recommend "gepard" dot-plotter for visualization.</p> <p>* `metagenome_assemblies` - Flye and Canu assemblies of a PacBio mock metagenome dataset.<br> In addition to metagenome assemblies, each bacteria was reassembled separately to<br> estimate the rate of divergence between the target genomes and the available references.</p> <p>* `simulated_data` - two assemblies of the simulated data illustrating Figure 1 (from Appendix I),<br> as well as simulated unbridged repeats benchmark.</p> <p><br> Software versions and parameters<br> --------------------------------</p> <p>* Flye - 2.3.5 (commit 20afeda)<br> * Canu - 1.7.1 (commit dfa60b8)<br> * Falcon - 0.3.0 (FALCON-Integrate commit 7498ef9)<br> * HINGE - 0.5.0 (commit 79fdf66)<br> * Miniasm - 0.2-r168-dirty (commit 40ec280) / Minimap2 2.8-r711-dirty (commit 8fc5f8d)<br> * Quast - 5.0.0 (commit de6973bb)</p> <p>Flye and Canu were run with the default parameters. The config files / scripts for<br> Falcon, HINGE and Miniasm could be found in the 'asm_config' archive folder.</p> <p>The HUMAN (but not the HUMAN+) assembly was generated with the earlier <br> Flye version 2.3.2 (released on Feb 20 2018) to provide a fair comparison <br> with the Canu and MaSuRCA assemblies (which were not updated since the release of Flye 2.3.2).<br> We note that the HUMAN assembly using the latest Flye version 2.3.5 has <br> NGA50 = 7.3 Mb and improves over the Flye 2.3.2 assembly (NGA50 = 6.3Mb). <br> HUMAN+ was assembled using the latest Flye and Canu versions (as of September 2018).</p> <p>The code for unbridged repeat resolution is currently available <br> in a separate 'flye-trestle' branch (commit 6100d32)</p>
Redistribution of the map with the flood prone areas in Flanders (status 2017-07-13)
<p>This is a redistribution of the data source '<a href="http://www.geopunt.be/catalogus/datasetfolder/f5b2c84c-0d78-4efa-a97d-7cd172726572">Overstromingsgevoelige gebieden 2017 - (Watertoets), correctie 13/07/2017</a>', originally published by 'Vlaamse Milieumaatschappij - afdeling Operationeel Waterbeheer' and ‘Departement Mobiliteit en Openbare Werken, afdeling Waterbouwkundig Laboratorium’, and distributed by 'Informatie Vlaanderen' under a CC-BY compatible license. It is redistributed for reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes.</p> <p>In the context of the Flemish Water Assay (Watertoets), a fourth version of a map has been created that shows flood prone areas up to the plot level for the entire Flemish Region. The map contains the effectively flood prone areas (‘effectief overstromingsgevoelig’) and the potentially flood prone areas (‘mogelijk overstromingsgevoelig’). </p> <p>In this new version, the effectively flood prone areas were processed with information from new and updated modeled flood areas, in addition to the registered local floods between 2006 and now. These modifications honour the changes to the implementing decision that the Flemish Government approved on 15 May 2017. Unlike previous versions that were raster files, the 2017 version is a vector file.</p> <p>The data source is a coproduction of the Hydraulic Engineering Laboratory of the Department of Mobility and Public Works (Departement Mobiliteit en Openbare Werken, afdeling Waterbouwkundig Laboratorium) and the division Operational Water Management of the Flemish Environmental Agency (Vlaamse Milieumaatschappij - VMM, afdeling Operationeel Waterbeheer), and is owned and administered by the latter.</p>
Awake Prone Positioning to Reduce Invasive VEntilation in COVID-19 Induced Acute Respiratory failurE
ClinicalTrials.gov study NCT04347941. IPD Sharing: YES. Countries: 1. Publications: 2.
Pre-ciliated tubal epithelial cells are prone to initiation of high-grade serous ovarian carcinoma
Open the record for dataset details and reuse information.
Supporting data for managing fire-prone forests in a time of decreasing carbon carrying capacity
Open the record for dataset details and reuse information.
Vegetation structure and fuel dynamics in fire-prone, Mediterranean-type Banksia woodlands
Open the record for dataset details and reuse information.
Effectiveness of prone positioning in non-intubated ICU patients with moderate to severe ARDS
<p><strong>Data and R code from the paper</strong></p> <p><em><strong>Effectiveness of prone positioning in non-intubated ICU patients with moderate to severe ARDS</strong></em></p> <p>Authors:</p> <p>Manuel Taboada, Ph.D. Mariana González, M.D. Antía Álvarez, M.D., Irene González, M.D, Javier García, M.D., María Eiras. M.D., María Diaz Vieito, M.D., Alberto Naveira, M.D., Pablo Otero, M.D., Olga Campaña, M.D., Ignacio Muniategui, M.D., Ana Tubio, M.D., Jose Costa, M.D., Salomé Selas, M.D., Agustín Cariñena, M.D., Adrián Martínez, M.D., Sonia Veiras, M.D. Ph.D, Francisco Aneiros, M.D., Valentín Caruezo, M.D., Aurora Baluja, M.D. Julian Alvarez, Ph.D<br> <br> <strong>Background</strong>:<br> In the treatment for severe ARDS from COVID-19, the WHO recommends prone positioning (PP) during mechanical ventilation for periods of 12-16 hours per day to potentially improve oxygenation and survival. In this prospective observational study we evaluated the ability of long PP sessions to improve oxygenation in awake ICU patients with moderate or severe ARDS due to COVID-19.</p> <p><strong>Methods</strong>:<br> The study was approved by the ethics committee of Galicia (code No. 2020-188), and all patients provided informed consent. In this case series, awake patients with moderate or severe ARDS by COVID-19 admitted to the ICU at University Hospital of Santiago from March 21 to April 5, 2020 were prospectively analyzed. Patients were instructed to remain in PP as long as possible and not stop until the they felt too tired to maintain that position. If needed, light sedation was administered. The following information were collected: StO2 and blood gases (PaO2, PaO2/FiO2, PaCO2, pH) in ICU admission, number and duration of PP sessions, StO2 and blood gases before, during and following a PP session, need of mechanical ventilation, duration of ICU admission and ICU outcome. Linear mixed effects models (LMM) were adjusted to estimate changes of oxygenation parameters from baseline,. Patients were added as a random effect to account for inter-patient random variability in their own baseline measurements.</p> <p><strong>Results</strong>:<br> Seven patients with moderate or severe ARDS by COVID-19 were included. All patients received at least one PP session. A total of 16 PP sessions were performed in the 7 patients during the period study. The median duration of PP sessions was 10 hours. For PP sessions of more than 4 hours, dexmedetomidine was used for sedation. Oxygenation increased in all sixteen sessions performed in the seven patients. Prone positioning was associated to a net increase from baseline in PaO2, PaO2/FiO2 and StO2 (65, mmHg 110 and 2.6% respectively) PaO2/FiO2 increased during PP (Δ PaO2/FiO2 110.42 mmHg [28.173; 192.67]) and after PP (37.75 [-1.57; 77.07] compared with previous supine position (baseline PaO2FiO2 median-IQR97.8 [97.2 – 99.4]). Two patients required intubation. Six patients were discharged from the ICU and one remains on mechanical ventilation.</p> <p><strong>Conclusions</strong>:<br> We found that PP improved oxygenation and may avoid intubation in ICU patients with COVID-19 and moderate or severe ARDS. PP was relatively well tolerated in our patients. and may be a simple strategy to improve oxygenation trying to reduce patients in mechanical ventilation and the length of stay in the ICU, especially in the COVID-19 pandemic.</p> <p><strong>GitHub repository</strong>:</p> <p>https://github.com/medicalc/prone_sars</p>
Cyclic tensile-compressive tests on thin concrete boundary elements with a single layer of reinforcement prone to out-of-plane instability
<p>The growing need for residential housing in Latin American countries has led to the construction of reinforced concrete buildings with wall thicknesses as low as 8-10 cm. Such walls have typically only a single layer of vertical rebars and are therefore particularly susceptible to out-of-plane failure. To investigate the response of the corresponding wall boundary elements, twelve reinforced concrete columns with a single layer of vertical rebars were tested under tension-compression cycles. The objective of this test series was to gain insights into parameters triggering wall instability and out-of-plane failure. The experimental tests investigate the effect of thickness, reinforcement ratio, and eccentricity of the longitudinal rebars with respect to the element axis. This paper summarises the results of the test campaign. The specimen response is analysed at the global and local level, and the influence of the crack pattern on the out-of-plane response of the column and the conditions leading to out-of-plane failure are described. Furthermore, the differences between members with a single layer of vertical rebars to members with two layers are discussed. The influence on the response of the parameters analysed in the experimental campaign is addressed, showing that section with small thickness and large reinforcement content are more prone to out-of-plane failures. Finally, the predictions of existing models are compared to the new experimental data. The entire data set is publically available.</p>
Proxy-based hail-prone-day climatology and trends for Australia 1979-2021
<p><strong>Climatology of and trends on hail-prone days over Australia from 1979-2021</strong></p> <p>Hail-prone day climatology is calculated using the proxy of Raupach et al., 2023a (DOI: 10.1175/MWR-D-22-0127.1) applied to ERA5 pressure-level reanalysis data (DOI: 10.24381/cds.bd0915c6) at gridded 0.25 degree resolution for 1979-2021 (daily) and March 1979-May 2022 (seasonally). A day was considered hail-prone if a hail-prone atmosphere was detected at 03, 06, or 09 UTC. Trends in hail-prone days are calculated as in Raupach et al., 2023b (DOI: 10.1038/s41612-023-00454-8).</p> <p>The climatology shows the mean annual hail-prone days per grid point over 1979-2021, while seasonal climatologies are over March 1979-May 2022. The trends information shows changes in annual hail-prone days over the same period.</p> <p>NB: The seasonal climatology values differ slightly from those in Supp. Figure 3 in Raupach et al 2023, owing to an improved calculation of the climatology. The differences for each season are less than hail-prone 0.5 days. For details of the change see https://github.com/traupach/era5_hail_climatology/blob/main/analysis/climatology_for_zenodo.ipynb.</p> <p>These data are licensed with a Creative Commons Attribution 4.0 International license (CC-BY-4.0, https://creativecommons.org/licenses/by/4.0/legalcode). These results contain modified Copernicus Climate Change Service information 2022. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p>If using these data, please inform T. Raupach at t.raupach@unsw.edu.au, and please cite the following paper for which these data were calculated: </p> <p>Raupach, T.H., Soderholm, J.S., Warren, R.A. <em>et al.</em> Changes in hail hazard across Australia: 1979–2021. <em>npj Clim Atmos Sci</em> <strong>6</strong>, 143 (2023). https://doi.org/10.1038/s41612-023-00454-8</p> <p>Acknowledgements: This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government.</p>
Plant trait matrix for fire-prone Mediterranean chronosequences
<p>A database of 23 traits for 117 Mediterranean plant species. It includes three groups of traits: i) four whole-plant traits (bud height, sclerophylly, xerophily and resprouting ability), ii) ten above-ground traits (plant height, flowering time and span, leaf size, area, weight, thickness, specific leaf area, seed size and dispersal distance), and iii) nine below-ground traits (root length, depth, laterality, root depth:laterality ratio, root weight, specific root length, root C and N concentration and the root CN ratio).</p>
SPAligner: alignment of long error-prone reads to assembly graphs
<p>This repository contains benchmarking datasets and scripts for the manuscript "SPAligner: alignment of long error-prone reads to assembly graphs". </p> <p>Graph representation of genome assemblies has been recently used in different applications — from gene finding to haplotype separation. While many of these applications are based on aligning DNA and protein sequences to assembly graphs, existing software tools for finding such alignments have important limitations. We present a novel SPAligner (Saint Petersburg Aligner) tool for aligning long reads to assembly graphs and demonstrate that it generates accurate alignments.</p> <p> </p> <p> </p>
Sequencing data: Fungi, fire and insects: Protea infructescences as reservoirs for fungal biodiversity in fire-prone environments
<p>This is the dataset for a submitted manuscript entitled "Fungi, fire and insects: <em>Protea</em> infructescences as reservoirs for fungal biodiversity in fire-prone environments".</p> <p>The upload contains all forward and reverse paired end .fastq files. The files are already demultiplexed. The metadata.xlsx file contains sample information, gps coordinates, etc.</p> <p> </p>
GROOT (chanGe pROneness Open daTaset)
<p>GROOT (chanGe pROneness Open daTaset)</p> <p>General Information</p> <p>Data Set Characteristics: Multivariate</p> <p>Associated Tasks: Classification</p> <p>Number of Instances: 4183</p> <p>Number of Attributes: 8</p> <p>Missing Values? No</p> <p>Area: Software Engineering</p> <p>Source</p> <p>Cristiano Sousa Melo</p> <p>Matheus Mayron Lima da Cruz</p> <p>Antônio Diogo Forte Martins</p> <p>José Maria da Silva Monteiro Filho</p> <p>Javam de Castro Machado</p> <p>Data Set Information</p> <ul> <li>The data set of this work is generated from the backend source code of a WEB application started in 2013, and until 2018 were collected 8 releases to analyze change-prone classes</li> <li>The dependent variable "will change" has obtained according to [1]</li> <li>The dependent variable "will change" is imbalanced, containing 3871 "not change" (0) labels and 312 "will change" (1) label</li> </ul> <p>Attribute Information</p> <p><strong>instanceID</strong>: It is responsible for identifying each instance of the dataset uniquely.</p> <p><strong>classID</strong>: This column indicates the class from which the information of the row was extracted. It is a value obtained by hashing the name of the class.</p> <p><strong>releaseID</strong>: This column indicates the release from which the information of the row was extracted.</p> <p><strong>CBO (Coupling Between Object) [2]:</strong> A class is coupled to another one if it used its member functions and/or instance variables. CBO provides the number of classes to which a given class is coupled. Range: 0 - 162</p> <p><strong>CC (Cyclomatic Complexity) [3]:</strong> It is a software metric used to indicate the complexity of a program. It is a quantitative measure of the number of linearly independent paths through a program's source code. Range: 0 - 488.0</p> <p><strong>DIT (Depth of Inheritance Tree) [2]:</strong> It is defined as the maximum depth of the inheritance graph of each class. Range: 0 - 7</p> <p><strong>LCOM (Lack of Cohesion on Methods) [2]:</strong> This is the number of pairs of member functions without shared instance variables, minus the number of pairs of members functions with shared instance variables. Range: 0 - 1</p> <p><strong>LOC:</strong> Number of lines of codes. Imports and comments are not included. Range: 0 - 1369</p> <p><strong>NOC (Number Of Children) [2]:</strong> It is the number of direct descendants for each class. Range: 0 - 189</p> <p><strong>RFC (Response For a Class) [2]:</strong> This is the number of methods that can potentially be executed in response to a message received by an object of that class. Range: 0 - 413</p> <p><strong>WMC (Weighted Methods per Class) [2]:</strong> It is the number of methods of a class.Range: 0 - 56</p> <p><strong>class_frequency:</strong> It is the number of appearences that the class has through all the releases. Range: 0 - 8</p> <p><strong>number_of_changes:</strong> It is the number of changes that a class suffered through all the releases. Range: 0 - 7</p> <p><strong>will_change:</strong> It is the dependent variable, the indicator of a class change prone.</p> <p><strong>change_probability:</strong> It is the number of changes divided by the class frequency. Range: 0 - 1</p> <p>References</p> <p>[1] Lu, H., Zhou, Y., Xu, B., Leung, H., and Chen, L. (2012). The ability of object-oriented metrics to predict change-proneness: a meta-analysis. Empirical Software Engineering, 17(3)</p> <p>[2] S. R. Chidamber and C. F. Kemerer, "A metrics suite for object oriented design," in IEEE Transactions on Software Engineering, vol. 20, no. 6, pp. 476-493, June 1994.</p> <p>[3] T. J. McCabe, "A Complexity Measure," in IEEE Transactions on Software Engineering, vol. SE-2, no. 4, pp. 308-320, Dec. 1976.</p>
Comparison among three different Digital Surface Models and their respective hydraulic outcomes in the flood-prone urban area of Navaluenga (Ávila, Spain)
<p>Three different Digital Surface Models (DSMs) generated from LiDAR data are presented. The LiDAR information has been considered as raw data (DSM3) and subjected to some transformations to better represent the urban environment (DSM1). DSM2 is an intermediate state between DSM1 and DSM3. </p> <p>On the other hand, a hydraulic model has been run for each DSM and for two return periods (25 and 500 years), obtaining in all cases the graphical outputs of depths, velocities, Froude numbers and hazard. </p> <p>The different DSMs are named DSM1, DSM2 and DSM3, which can be downloaded in TIN format. The hydraulic outputs associated with the different DSMs can be downloaded in raster format and are named as follows: the Digital Surface Model to which it refers, the return period considered and the type of hydraulic output (depth, velocity, Froude number and hazard).</p> <p>DSM1: Digital Surface Model 1 (TIN format).<br> dsm1_25depth: Depths obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25froud: Froude numbers obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25haz: Hazard obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25veloc: Velocities obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format). <br> dsm1_500depth: Depths obtained when considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500froud: Froude numbers obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500haz: Hazard obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500veloc: Velocities obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).</p> <p>DSM2: Digital Surface Model 2 (TIN format).<br> dsm2_25depth: Depths obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25froud: Froude numbers obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25haz: Hazard obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25veloc: Velocities obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format). <br> dsm2_500depth: Depths obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500froud: Froude numbers obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500haz: Hazard obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500veloc: Velocities obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).</p> <p>DSM3: Digital Surface Model 2 (TIN format).<br> dsm3_25depth: Depths obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25froud: Froude numbers obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25haz: Hazard obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25veloc: Velocities obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format). <br> dsm3_500depth: Depths obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500froud: Froude numbers obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500haz: Hazard obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500veloc: Velocities obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).</p>
ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data (repository for simulated ONT RNA-seq data)
<p>Simulated ONT direct RNA and 1D cDNA sequencing data of varying sequencing depths (0.5 million, 1 million, 3 million, and 5 million simulated reads) used for benchmark evaluations of transcript discovery and quantification in our paper "ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data". All details can be found in the <strong>Materials and Methods</strong> section of the paper. </p> <p><em>HEK293T_DirectRNA.transcriptome_quantification.tsv</em> and <em>HEK293T_DirectRNA.transcriptome_quantification.tsv </em>are tab-separated files containing estimated raw read counts and normalized abundance values (in TPM) of transcripts annotated in GENCODE v34lift37. Transcript quantification was done using NanoSim (version 3.1.0). </p> <p><em>HEK293T_DirectRNA.NanoSim_500k.fastq.gz</em>,<em> </em><em>HEK293T_DirectRNA.NanoSim_1M.fastq.gz</em>, <em>HEK293T_DirectRNA.NanoSim_3M.fastq.gz</em>, and<em> HEK293T_DirectRNA.NanoSim_5M.fastq.gz </em>are gzip compressed FASTQ files containing 0.5 million, 1 million, 3 million, and 5 million simulated ONT direct RNA sequencing reads respectively. </p> <p><em>HEK293T_1DcDNA.NanoSim_500k.fastq.gz</em>,<em> HEK293T_1DcDNA.NanoSim_1M.fastq.gz</em>, <em>HEK293T_1DcDNA.NanoSim_3M.fastq.gz</em>, and<em> HEK293T_1DcDNA.NanoSim_5M.fastq.gz </em>are gzip compressed FASTQ files containing 0.5 million, 1 million, 3 million, and 5 million simulated ONT 1D cDNA sequencing reads respectively. </p>
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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.