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Annotation of metagenome-assembled genomes retrieved from Amazon river basin metagenomes
<p> </p> <p><strong>Annotation of metagenome-assembled genomes retrieved from Amazon river basin metagenomes</strong></p> <p> </p> <p> RELEASE MAG-2018/01<br> --------------------------------------</p> <p> </p> <p>1. INTRODUCTION</p> <p>Here is deposited the genes and proteins annotation from metagenome-assembled genomes (MAGs) retrieved from Amazon river basin metaganomes (SRP044326, PRJEB25171 and SRP039390) were deposited under European Nucleotide Archive - ENA project PRJEB25176. Briefly, metagenomes were coassembled in groups by geographical location with Megahit v.1.0 and the contigs were used to a reads mapping and binning with BWA-MEM (version 0.7.12-r1039), SamTools (version 1.3.1) and Metabat (v2.12.1). MAGs with overall quality greater than 50, calculated with CheckM (version 1.0.11), were selected for refining precedures. Contigs outliers were eliminated by using RefineM (version 0.0.23). Finished MAGs were then annotated by Prokka (version 1.11) pipeline, and with the other most completes databases up to date (KEGG, UniProtKB, dbCAN, PFAM, eggNOG and COG).</p> <p> </p> <p>2. LOCATION</p> <p> </p> <p> MAGs sequences are available under ENA project PRJEB25176.</p> <p> </p> <p> ENA_accession Isolate<br> -------------------- --------------<br> ERZ494218 AM_0118<br> ERZ494219 AM_0219<br> ERZ494220 AM_0226<br> ERZ494221 AM_0228<br> ERZ494222 AM_0233<br> ERZ494223 AM_0240<br> ERZ494224 AM_0244<br> ERZ494225 AM_0256<br> ERZ494226 AM_0268<br> ERZ494227 AM_0275<br> ERZ494228 AM_0466<br> ERZ494229 AM_0507<br> ERZ494230 AM_0510<br> ERZ494231 AM_0519<br> ERZ494232 AM_0528<br> ERZ494233 AM_0546<br> ERZ494234 AM_0608<br> ERZ494235 AM_0615<br> ERZ494236 AM_0616<br> ERZ494237 AM_0619<br> ERZ494238 AM_0621<br> ERZ494239 AM_0630<br> ERZ494240 AM_0643<br> ERZ494241 AM_0729<br> ERZ494242 AM_0764<br> ERZ494243 AM_0832<br> ERZ494244 AM_0849<br> ERZ494245 AM_0854<br> ERZ494246 AM_0876<br> ERZ494247 AM_0902<br> ERZ494248 AM_0936<br> ERZ494249 AM_1003<br> ERZ494250 AM_1104<br> ERZ494251 AM_1111<br> ERZ494252 AM_1205<br> ERZ494253 AM_1312<br> ERZ494254 AM_1409<br> ERZ494255 AM_1503<br> ERZ494256 AM_1603<br> ERZ494257 AM_1606<br> ERZ494258 AM_1801<br> ERZ494259 AM_1811<br> ERZ494260 AM_2104<br> ERZ494261 AM_2116<br> ERZ494262 AM_2124<br> ERZ494263 AM_2202<br> ERZ494264 AM_2207<br> ERZ494265 AM_2208<br> ERZ494266 AM_2324<br> ERZ494267 AM_2502<br> ERZ494268 AM_2804<br> </p> <p>3. ACKNOWLEDGEMENTS<br> </p> <p>This work is a joint effort of Laboratory of molecular biology from Federal<br> University of São Carlos, São Paulo, Brazil (LBM/UFSCAR) and Protists group<br> of Institut del Ciencias del Mar, Barcelone, Spain (ICM). We are grateful to<br> Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), as well as, the spanish funding organ Consejo Superior de Investigaciones Científicas (CSIC).</p> <p>This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.</p> <p> </p> <p>4. CONTACT INFORMATION</p> <p> Current curators:</p> <p> - Célio Dias Santos Júnior (celio.diasjunior@gmail.com)<br> - Flavio Henrique-Silva (dfhs@ufscar.br)<br> - Ramiro R. Logares (ramiro.logares@icm.csic.es)<br> </p> <p>5. COPYRIGHT NOTICE</p> <p> Amazon River Basin Metagenome-Assembled Genomes Annotation - AM/MAGs<br> Copyright (C) 2018 The AMnrGC consortium.</p> <p> This database is provided “as is” and without any warranty of any kind,<br> of openly available. You can redistribute and/or modify it<br> as you wish, under the terms of Creative Commons CC BY 4.0:</p> <p> https://creativecommons.org/licenses/by/4.0/</p> <p>___________________<br> Barcelone, Feb/2018</p>
Amazon Customer Review Data
<p><strong>Dataset</strong>: Amazon Customer Review Data for sentiment analysis</p> <p><strong>Size</strong>: 60889 appox.</p> <p><strong>Format</strong>: .CSV</p> <p><strong>Period</strong>: 2013 to 2019</p> <p><strong>Categories</strong>: 5…… (Mobiles, Smart TV, Books, Mobile Accessories, Refrigerator)</p> <p><strong>Unique_ID</strong>: Customized (Primary Key)</p> <p><strong>Review_Header</strong>: user’s comment in few words</p> <p><strong>Review_Text</strong>: User’s comment in details (3-4 lines)</p> <p><strong>Rating</strong>: (1- Very Low, 2 🡪 Low, 3🡪 Avg, 4 🡪 Good, 5 - Excellent)</p> <p><strong>Posting Period</strong>: 2013 to 2019</p> <p><strong>Own_Rating</strong>: for 1-2 🡪 Negative, 3🡪 Neutral, 4-5 🡪 Positive</p>
Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon: Implication for identifying trends in dry season rainfall
<h1>Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon.</h1>
Auswirkungen von Google, Apple, Facebook, Amazon und Microsoft auf Schule und Unterricht in Deutschland
<p>Google, Apple, Facebook, Amazon und Microsoft (GAFAM) sind einige der größten Unternehmen der IT-Welt. Viele ihrer Produkte werden von Millionen von Personen nahezu täglich genutzt. Ihre Produkte werden unter Anderem auch in Schulen und im Unterricht eingesetzt. Um zu untersuchen, welche Auswirkungen GAFAM auf Schule und Unterricht aus Sicht der Lehrkräften haben, wurde eine Online-Befragung mit Lehrer*innen durchgeführt.</p> <p> </p>
Ventas de laptops y computadores en Amazon España durante abril del 2023
<p>El dataset extraído de la página de Amazon España se titula "Ventas de laptops y computadores en Amazon España durante abril del 2023", son datos de la categoría de electrónica y la subcategoría informática. En particular, aquellos relacionados con laptops y computadores publicados durante el mes de abril de 2023. Los datos incluyen nombre del computador, el precio, tamaño de pantalla, descripción del disco duro, velocidad del cpu y tamaño de la memoria, la valoración promedio de los clientes y la imagen de cada computador. Con el dataset extraído de Amazon España sobre ventas de laptops y computadores durante el mes de abril de 2023, se pueden realizar diversos análisis. Por ejemplo, se pueden identificar los productos más vendidos en esta categoría durante ese periodo y analizar las características que los hacen más populares entre los clientes. También se podría investigar la relación entre la valoración promedio de los clientes y las ventas de los productos, para determinar si hay una correlación entre la satisfacción del cliente y el éxito de ventas de un producto. Además, se podría realizar un análisis de imagen para ver cómo la apariencia de los productos influye en su popularidad y ventas. Todo esto podría ayudar a los fabricantes y vendedores de laptops y computadores a tomar decisiones informadas sobre cómo mejorar la calidad, el diseño y la promoción de sus productos para aumentar sus ventas y satisfacer a los clientes. Para extraer estos datos, es necesario realizar web scraping a la página de Amazon España.</p>
River Sediment Database-Amazon (RivSed-Amazon)
<p>The River Sediment Database-Amazon (RivSed-Amazon) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the Amazon River Basin that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of SWOT River Database (SWORD, Altenau et al., 2021) centerlines (median reach length = 10 km) where high quality river water pixels were detected within each Landsat image from 1984-2018. </p> <p>The methods used to produce this database were initially developed in the following publications:</p> <ul> <li>Gardner, J., Pavelsky, T. M., Topp, S., Yang, X., Ross, M. R., & Cohen, S. (2023). Human activities change suspended sediment concentration along rivers. <em>Environmental Research Letters. </em><a href="https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8">https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8</a> <strong>and </strong></li> <li>Gardner et al. (2020). The color of rivers. Geophysical Research Letters. <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL088946">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL088946</a></li> </ul> <p>The publication associated with RivSed-Amazon is in review.</p> <p><strong>Files:</strong></p> <p>1) Metadata (rivSed_Amazon_metadata_v1.01.pdf): Description key data files associated with this repository. </p> <p>2) RiverSed (RiverSed_Amazon_v1.1.txt). Table of SSC and associated data that is joinable to SWORD based on the ""reach_id".</p> <p>3) Shapefile of river centerlines over South America to which the reflectance data can be attached (SWORD_SA.shp).</p> <p>4) Shapefile of the reach polygons associated with SWORD_SA over the Amazon Basin. (reach_polygons_amazon.shp).</p> <p>5) SSC-Landsat matchup database with extended metadata on locations and in-situ data (train_full_v1.1.csv).</p> <p>6) The final training data used to build the xgboost machine learning model (train_v1.1.csv).</p> <p>7) The xgboost model that can make SSC predictions over inland waters in USA using Landsat bands/band combinations (tssAmazon_model_v1.1.rds and .rda). The model can only be loaded and used in R at this time.</p> <p>8) The correction coefficients applied to Landsat 5 and 8 to harmonized surface reflectance across Landsat 5,7,8 and over all bands to enable time series analysis.</p> <p> </p> <p> </p>
Fig. 6 in A new species of Paralithoxus (Siluriformes: Loricariidae: Ancistrini) from the highlands of Serra da Mocidade, Roraima State, Brazilian Amazon
Fig. 6. Type-locality of Paralithoxus mocidade in Ajarani River, tributary of the Branco River basin, located immediately downstream of a large waterfall. Photo by Marcos Amend.
Fig. 1 in A new species of Paralithoxus (Siluriformes: Loricariidae: Ancistrini) from the highlands of Serra da Mocidade, Roraima State, Brazilian Amazon
Fig. 1. Paralithoxus mocidade, holotype, INPA 54745, female, 50.5 mm SL, in lateral, dorsal and ventral views.
Fig. 3 in Upstream dam impacts on gilded catfish Brachyplatystoma rousseauxii (Siluriformes: Pimelodidae) in the Bolivian Amazon
Fig. 3. Mean annual fishing effort and capture per unit effort (CPUE) of Brachyplatystoma rousseauxii (black bars) per month in the Ichilo River before (1998–2007) and after dam closure (2015–2017) using catfish-specific fishing methods. Mean monthly water level (MWL) in Puerto Villarroel for the two sample periods is shown (SENAMHI 2018), with vertical lines representing standard deviation. The horizontal bars show the period when commercial fishing is prohibited.
Fig. 1 in Upstream dam impacts on gilded catfish Brachyplatystoma rousseauxii (Siluriformes: Pimelodidae) in the Bolivian Amazon
Fig. 1. Upper Madeira River basin, showing landing site Puerto Villarroel and fishing area in the Ichilo River (Bolivian Amazon), as well as the Santo Antônio and Jirau dams (Brazilian Amazon).
Figure 2. Combined species discovery curve for 726 in Canopy assemblages and species richness of planthoppers (Hemiptera: Fulgoroidea) in the Ecuadorian Amazon
Figure 2. Combined species discovery curve for 726 planthopper canopy fogging samples from Onkone Gare (three collecting years) including select estimators of diversity. Total observed morphospecies was 573, with 26% represented as singletons. The averaged value of the diversity estimators is 740. Curves for species observed and diversity estimators failed to reach an asymptote.
Figures 1–13 in New records and diagnostic notes on large carpenter bees (Hymenoptera: Apidae: genus Xylocopa Latreille), from the Amazon River basin of South America
Figures 1–13. Dorsal habitus photographs of pinned, preserved specimens of females of Xylocopa (Neoxylocopa) species from the Amazon River basin, from the USNM collection. 1) X. (N.) aeneipennis. 2) X. (N.) amazonica. 3) X. (N.) aurulenta. 4) X. (N.) carbonaria. 5) X. (N.) cearensis. 6) X. (N.) fimbriata. 7) X. (N.) frontalis. 8) X. (N.) grisescens. 9) X. (N.) hirsutissima. 10) X. (N.) orthogonaspis. 11) X. (N.) similis. 12) X. (N.) suspecta. 13) X. (N.) tegulata.
Fig. 7 in The changing course of the Amazon River in the Neogene: center stage for Neotropical diversification
Fig. 7. Growth of mega-wetlands in northern South America. Geological time scale at top. Eustatic sea-level estimates from Zachos et al. (2001). Area estimates of for Atlantic and Caribbean draining mega-wetlands from paleogeographic reconstructions in Wesselingh, Hoorn (2010) and Hoorn et al. (2017), and for the Orinoco basin by Jaramillo et al. (2017). Caribbeandraining Andean foreland basins in orange; Atlantic-draining basins contributing to transcontinental Amazon in yellow. Areas estimated using ImageJ (Abràmoff et al., 2004). Curves smoothed using a third-order Bezier Spline.
Fig. 4 in A new species of Tetragonopterus (Characiformes: Characidae) from Central Amazon lowlands, Brazil
Fig. 4. Map of northern South America showing the distribution of Tetragonopterus manaos; yellow circle represents the holotype and black circles represent paratypes.
Dataset [Amazônia and Amazon: domain analysis with IRaMuTeQ in Scopus and LISA databases]
<p>The study reports the comparative analysis between the results of the search queries for the terms<em> Amazônia</em> and <em>Amazon</em> in Scopus and LISA databases, in the period from 2008 to 2018. Concept Theory and Domain Analysis were used in conjunction with IRaMuTeQ software in order to identify, quantify and analyse semantic distances in a sample consisting of 80 abstracts from retrieved articles.</p> <p>DOI: <a href="https://doi.org/10.5771/9783956507762-522">https://doi.org/10.5771/9783956507762-522</a></p> <p> </p>
Fig. 2 in Integrative taxonomy reveals two new cryptic species of Hyphessobrycon Durbin, 1908 (Teleostei: Characidae) from the Maracaçumé and middle Tocantins River basins, Eastern Amazon region
Fig. 2. Hyphessobrycon frickei Guimarães, Brito, Bragança, Katz & Ottoni sp. nov. (CICCAA 02388), 17.7 mm SL; jaw suspensory. A. Premaxillary. B. Maxilla. C. Dentary. Scale bar: 1 mm
Fig. 8 in Integrative taxonomy reveals two new cryptic species of Hyphessobrycon Durbin, 1908 (Teleostei: Characidae) from the Maracaçumé and middle Tocantins River basins, Eastern Amazon region
Fig. 8. Topology of the ultrametric tree performed in BEAST ver. 1.8.4 including unique haplotypes summarizing the results of GMYC, bPTP and ABGD. Numbers above and below branches are posterior probability values. The star indicates the Hyphessobrycon copelandi clade.
Catchment-scale estimates of Amazon evapotranspiration
<p>Amazon basin-mean monthly estimates of evapotranspiration estimated from catchment-balance analysis, satellites (MODIS, P-LSH and GLEAM), reanalysis (ERA5) and the CMIP5 and CMIP6 climate models. Catchment level estimates of climate variables that influence ET are also included (precipitation, radiation and leaf area index). Full details of all source datasets are provided in 'Evapotranspiration in the Amazon: spatial patterns, seasonality and recent trends in observations, reanalysis and CMIP models' in Hydrology and Earth System Sciences, <a href="https://doi.org/10.5194/hess-2020-523">https://doi.org/10.5194/hess-2020-523</a>.</p>
Bioclimatic data for species distribution modelling in the Amazon Basin
<p>In this dataset, bioclimatic data regarding the Amazon Basin, in the near of the cities of Manaus and Manacapuru are available. There are 11 environmental data variables, referring to temperature, atmospheric pressure, concentration of pollutants and aerosols, such as carbon monoxide, ozone, carbon dioxide, among others. These were collected by the G-159 Gulfstream aircraft during its two periods of operation (IOP1 and IOP2), available in the GOAmazon (Green Ocean Amazon) project's data repository. A spatial interpolation methodology (linear barycentric interpolation) was applied to each variable, in order to obtain a larger area of data. The species occurrence data were collected from the repositories of the ICMBio (Instituto Chico Mendes de Conservação da Biodiversidade) Portal da Biodiversidade and GBIF (Global Biodiversity Information Facility), referring to the same date and location of the environmental data. <br> </p>
Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change (public)
<p><strong>Title</strong>: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>This repository contains</strong>:</p> <ol> <li>Zipped folder:<strong> Fig1_data_input.zip</strong> - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1a_f_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "<strong><driver>_assessment_v2.csv</strong>". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g. 0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period. The folder also contains the output regrowth models seen in Figure 1 in the format "<strong>regrowth_model_<driver_threshold>.RData" </strong>where driver_threshold refers to the driving variable name and the associated threshold limit for the given driver.</li> <li>Zipped folder:<strong> Fig2_regions_outline.zip</strong> - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile. </li> <li>Zipped folder: <strong>Fig1g_2b_e_variable_importance.zip</strong> - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_plot.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed<NUM> - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files are the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this. </li> <li>Zipped folder: <strong>Fig3_data_input.zip</strong> - all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "<strong><REGION>-Group.csv</strong>". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The code takes data in AGB and converts to AGC. The folder also contains the output regrowth models seen in Figure 3 in the format <strong>"regrowth_model_<region_disturbance_type>.RData" </strong>where region_disturbance refers to the region and the type of disturbance experienced. </li> <li> Zipped folder: <strong>Fig4_5_carbon_sink_2017.zip </strong>- Contains two subfolders: a) <strong>Map_aggre_0.1deg</strong> -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) <strong>secondary_forest_by_region_and_disturbance </strong>- this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_plot.R" in the code repository (see below). </li> </ol> <p><strong>Code: </strong>The corresponding code mentioned here can be access here: <a href="https://github.com/heinrichTrees/secondary-forest-regrowth-amazon-public">heinrichTrees/secondary-forest-regrowth-amazon-public (github.com)</a></p> <p><strong>Data usage: </strong>When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper as well as the DOI of this repository. </p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</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.