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7,228 results for “modules”
Language Recognition for SSB modulated HF Radio Signals of Short Duration - Example Files
<p>These files are example files of the dataset used in our work "Language Recognition for SSB modulated HF Radio Signals of Short Duration". Two files are 10 second HF radio segments from Russian amateur radio communications. The other six files are a single recording from CommonLanguage (https://huggingface.co/datasets/common_language) and five modified versions of this file, created by applying the proposed HF radio simulation approach. Additional details can be found in the Paper.</p>
Data from: Kir2.1 modulation in macrophages sensitises dorsal root ganglion neurons through TNF secretion after nerve injury
<p>This data pertain to the manuscript titled "Kir2.1 modulation in macrophages sensitises dorsal root ganglion neurons through TNF secretion after nerve injury", currently in preprint on BioRxiv (https://doi.org/10.1101/2023.06.21.545843). The name of the data files correspond to the for each figure in the study. The data file in .csv format are organized so that they can easily be opened in R or other analysis language. To understand them and how they are labelled, it is advised to open the figure next to them and find the appropriate panel.</p> <p>Here are included:</p> <ul> <li>Example images of section of mouse dorsal root ganglion (DRG) after spared nerve injury (SNI), labelled for CX3CR1+ cells, Ki67 and MHC class II by immunohistochemistry.</li> <li>LC-MS-MS proteomic data set of CX3CR1+ cells from DRG of mice after SNI.</li> <li>Voltage clamp data of CX3CR1+ cells from DRG of mice after SNI</li> <li>Electrophysiological data sets (multi-electrode array, current clamp and voltage clamp) of dissociated DRG neurons treated with medium conditioned by CX3CR1+ or GFAP+ cells sorted from ipsilateral or contralateral DRG from mice after SNI. In addition, pharmacological treatments were added to the conditioned medium (CM).</li> </ul>
RNA-Seq data from: Hox genes modulate physical forces to differentially shape small and large intestinal epithelia
<p>Hox genes are highly conserved, master regulators of spatial patterning in the embryo, but how these factors trigger regional morphogenesis has largely remained a mystery. In the developing gut, Hox genes help demarcate identities of the small and large intestines early in embryogenesis, which ultimately leads to their specialization in both form and function. While the midgut forms villi, the hindgut develops flat, brain-like sulci that resolve into heterogeneous outgrowths. Combining mechanical measurements and mathematical modeling, we demonstrate that the posterior Hox gene Hoxd13 regulates biophysical phenomena that shape the hindgut lumen. We further show that Hoxd13 acts through the TGFβ pathway to thicken, stiffen, and promote isotropic growth of the subepithelial mesenchyme; together, these features lead to hindgut surface buckling. TGFβ, in turn, promotes collagen deposition to affect mesenchymal geometry and growth. We thus identify a cascade of events downstream of positional genetic identity that direct posterior intestinal morphogenesis. </p> <p>To identify genes and pathways that are directly or indirectly regulated by Hoxd13 to affect posterior gut morphogenesis in the chick, we compared mesodermal transcriptomes of wild-type midgut and hindgut intestinal samples, as well as mesodermal samples from a Hoxd13-overexpressing midgut at E12 and E14. Tissues were dissected and endoderm layers were removed manually before RNA extraction and downstream processing. Unbiased clustering was used to identify genes commonly differentially expressed in the hindgut and Hoxd13-misexpressing midgut. This submission contains bulk RNA-seq raw data (fastq.bz2 files) and processed .txt files with read counts. Experiment information is provided in .xlsx Metadata file used for NCBI GEO submission.</p>
The functions of cholera toxin subunit B as a modulator of silica nanoparticle endocytosis
<p>The gastrointestinal tract is the main target of orally ingested nanoparticles (NPs) and at 9 the same time exposed to noxious substances, such as bacterial components. We investigated the 10 interaction of 59 nm silica (SiO2) NPs with differentiated Caco-2 intestinal epithelial cells in the pres-11 ence of cholera toxin subunit B (CTxB) and compared the effects to J774A.1 macrophages. CTxB can 12 affect cellular functions and modulate endocytosis via binding to the monosialoganglioside (GM1) 13 receptor, expressed on both cell lines. After stimulating macrophages with CTxB, we observed no-14 table changes in the membrane structure but not in Caco-2 cells and no secretion of the pro-inflam-15 matory cytokine TNF-α was detected. Cells were then exposed to 59 nm SiO2 NPs and CtxB sequen-16 tially and simultaneously, resulting in a high NPs uptake in J774A.1 cells but no uptake in Caco-2 17 cells was detected. Flow cytometry analysis revealed that exposure of J774A.1 cells to CTxB resulted 18 in a significant reduction in the uptake of SiO2 NPs. In contrast, the uptake of NPs by highly selective 19 Caco-2 cells remained unaffected following CTxB exposure. Based on colocalization studies, CTxB 20 and NPs might enter cells via shared endocytic pathways, followed by their sorting into different 21 intracellular compartments. Our findings provide new insights into the CTxB function to modulate 22 SiO2 NPs uptake in phagocytic but not in differentiated intestine cells.</p>
Supplementary files for the manuscript "Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand", submitted to JGR Earth Surface
<p>This repository contains supplementary files to the manuscript ""Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand" submitted to JGR: Earth Surface. It contains: </p> <p>- The Matlab script used to find the optimal distance-from-fault and elevation windows ("elevation_distance_window_optimization"), and 3 text files used for input in this script ("data_erates" contains the erosion rates, "data_elev" the number of pixels in each elevation bin, "data_distAF" the number of pixels in each distance-from-fault bin). </p> <p>- An Excel spreadsheet with the same information that the input text files contain, but specifiying the elevation or distance from fault bin values ("elevation and distance from fault with bins")</p> <p>- A shapefile of catchment outlines ("WSAcatch") for the catchments sampled for CRN denudation rates</p> <p>- Raw CRN data ("Table 2_new_CRN_data")</p> <p>- Excel spreadsheet with the compilation of themochronometric cooling ages used in the age2exhume code (van der Beek & Schildgen, 2023; <a href="https://doi.org/10.5281/zenodo.7341603">https://doi.org/10.5281/zenodo.7341603</a>).</p> <p>CRN data and catchment outlines will also be uploaded to the OCTOPUS database (<a href="https://octopusdata.org/">https://octopusdata.org/</a>) after manuscript acceptance.</p>
Macrosystems EDDIE Module 5 version 2: Introduction to Ecological Forecasting (Instructor Materials)
Ecological forecasting is a tool that can be used for understanding and predicting changes in populations, communities, and ecosystems. Ecological forecasting is an emerging approach which provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Ecological forecasters develop and update forecasts using the iterative forecasting cycle, in which they make a hypothesis of how an ecological system works; embed their hypothesis in a model; and use the model to make a forecast of future conditions. When observations become available, they can assess the accuracy of their forecast, which indicates if their hypothesis is supported or needs to be updated before the next forecast is generated. In this Macrosystems EDDIE (Environmental Data-Driven Inquiry & Exploration) module, students will apply the iterative forecasting cycle to develop an ecological forecast for a National Ecological Observation Network (NEON) site. Students will use NEON data to build an ecological model that predicts primary productivity. Using their calibrated model, they will learn about the different components of a forecast with uncertainty and compare productivity forecasts among NEON sites. The overarching goal of this module is for students to learn fundamental concepts about ecological forecasting and build a forecast for a NEON site. Students will work with an R Shiny interface to visualize data, build a model, generate a forecast with uncertainty, and then compare the forecast with observations. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This EDI data package contains instructional materials necessary to teach the module. Intructional materials (instructor manual, introductory presentation for the module, and a presentation to introduce students and instructors to R Shiny) are provided in both pdf and editable formats with
Macrosystems EDDIE Module 1: Climate Change Effects on Lake Temperatures
Climate change is modifying the thermal structure of lakes around the globe. Because it is difficult to predict how lakes will respond to the many different aspects of climate change (e.g., altered temperature, precipitation, wind, etc.), many researchers are using models to manipulate climate scenarios and simulate lake responses. Lake simulation models provide a powerful tool for exploring the sensitivity of lake thermal structure characteristics to weather. In this module, students will learn how to set up a lake model (General Lake Model; GLM) and "force" the model with climate scenarios of their own design to test hypotheses about how lakes may change in the future. Once students have mastered running one climate scenario for their lake, they will learn how to use distributed computing tools to scale up and run hundreds of different climate scenarios for their lakes. The overarching goal of this module is for students to explore new modeling and computing tools while learning fundamental concepts about how climate change will affect lakes. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This dataset contains instructional materials and the files necessary to run the complete module. Readers are referred to the GLM science manual (Hipsey et al. 2014) for further details on model configuration.
Macrosystems EDDIE Module 3: Teleconnections
Ecosystems can be influenced by teleconnections, in which meteorological, societal, and/or ecological phenomenon link remote regions via cause and effect relationships. Because it is difficult to predict how ecosystems will respond to drivers from remote regions, many researchers are using models to simulate different teleconnection scenarios and see how ecosystems respond. For example, lake simulation models provide a powerful tool for exploring how lake thermal structure and ice cover respond to climate teleconnections such as the El Nino/Southern Oscillation (ENSO). In this module, students will learn how to set up a lake model and "force" the model with climate scenarios to test hypotheses about how far-away drivers interact with local lake characteristics to affect lake temperatures and ice cover in different lakes. The overarching goal of this module is for students to explore new modeling and computing tools while learning fundamental concepts about how teleconnections affect lake temperatures and ice cover. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This dataset contains instructional materials and the files necessary to run the complete module. Readers are referred to the GLM science manual (Hipsey et al. 2014; 2019) for further details on model configuration.
LAGOS-US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous U.S.
This data package, LAGOS-US LOCUS v1.0, is one of the core data modules of the LAGOS-US platform that provides an extensible research-ready platform to study the 479,950 lakes and reservoirs larger than or equal to 1 ha in the conterminous US (48 states plus the District of Columbia). This data module contains information on the location, identifiers, and physical characteristics of lakes and their watersheds. The characteristics in this module include: variables that can be obtained from GIS data such as location and geometry; variables that can be derived using GIS processing such as lake watersheds and their geometry, lake glaciation history, and lake connectivity; and commonly used identifiers from GIS and other data products useful for linking with LAGOS-US. LOCUS is based on a snapshot of the high-resolution National Hydrography Dataset product available at the initiation of the project that provided the basis for locating, identifying, and characterizing the geometry of all lakes in LAGOS-US. The database design that supports the LAGOS-US research platform was created based on several important design features. Lakes are the fundamental unit of consideration, all lakes in the spatial extent must be represented (above a minimum size) and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other 2 core data modules that are part of the LAGOS-US platform: GEO (which includes geospatial ecological context at multiple spatial and temporal scales for lakes and their watersheds) and LIMNO (in situ lake surface-water physical, chemical, and biological measurements through time) that are each found in their own data packages.
LAGOS-NE-GIS v1.0: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 2013-1925
This data package, LAGOS-NE-GIS v1.0, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. Three of the data packages each contain different types of data for 51,101 lakes and reservoirs larger than 4 ha in 17 lake-rich U.S. states to support research on thousands of lakes. These three package are: (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes. (2) LAGOS-NE-GEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes. These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. And, (3) LAGOS-NE-LIMNO v1.087.1: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. The other two data packages contain supporting data for the LAGOS-NE database: (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NE-GEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of LAGOS-NE-LIMNO. The LAGOS-NE GIS v1.0 module includes GIS datasets for: lake polygons and their hydrologic classification; wetland polygons and their classification; streams as a line coverage and their classification by stream order; the zones used for this study (state and county; hydrologic units [at the 4, 8 and 12 scales]); and, lake watersheds (IWS). We also include boundaries of U.S. stat
JRC-EU-TIMES Hydrogen Module
<p>All hydrogen related input data of the JRC-EU-TIMES model. The full JRC-EU-TIMES is open on Zenodo: <a href="http://doi.org/10.5281/zenodo.3544900">http://doi.org/10.5281/zenodo.3544900</a>. Associated datasets of JRC-EU-TIMES are made open as separate data sources for non-TIMES users. These datasets consist of a selection of the JRC-EU-TIMES input files with information on one specific topic. These datasets are not stand-alone models. In the file "Hydrogen Inputs JRC-EU-TIMES July 2019.xlsx", all hydrogen data are grouped. This grouping is cleaner as the subres/scenario files because all the data are in one place with additional metadata for improved understanding. You will find technologies related to hydrogen production, but also consumption and transformation. The two other files are the TIMES Subres files for hydrogen production, storage, transport and distribution. In JRC-EU-TIMES, one of the hydrogen production routes is transforming electricity surpluses with electrolysers. Different from the enclosed paper [Blanco H. et al., 2019], the electricity surpluses of the open JRC-EU-TIMES are based on a country-specific analysis with an hourly model outside JRC-EU-TIMES. Some more information on this is available in the other enclosed paper [Pavičević M., 2019].</p>
Abiotic stress mediated modulation of chromatin landscape in Arabidopsis thaliana
<p>This dataset include figures and supplementary material for the manuscript entitled<strong> </strong>"Abiotic stress mediated modulation of chromatin landscape in <em>Arabidopsis thaliana" </em>to be published in Journal of Experimental Botany special issue focused on Chromatin.</p> <p><strong>Supplementary File 1:</strong> Table describing read count, mapping percentage and genome coverage from each sample in FAIRE-seq and DNase-seq.</p> <p><strong>Supplementary File 2:</strong> List of DHSs obtained from control and stress subjected samples.</p> <p><strong>Supplementary File 3:</strong> List of FIRs obtained from control and stress subjected samples.</p> <p><strong>Supplementary File 4:</strong> List of uniquely merged OCRs with respective chromatin accessibility score in cold, heat, salt and drought stress.</p> <p><strong>Supplementary File 5:</strong> List of GO terms enriched in nrOCRs, SRCRs, and SACRs.</p> <p><strong>Supplementary File 6:</strong> List of GO terms enriched in overlapping nrOCRs, SRCRs, and SACRs.</p> <p><strong>Supplementary File 7:</strong> List of digital footprints (DFPs) obtained from nrOCRs regions of control-cold, control-heat, control-salt and control-drought pairs.</p> <p><strong>Supplementary File 8: </strong>Annotation details of the chromatin regions which were either found to be in state of accessible (CAS > 0.2) or inaccessible (CAS < -0.2) upon exposure to all of the stresses studied (heat, cold, salt and drought stress).</p> <p><strong>Supplementary Fig S1: Overlap of DHSs in control sample of present study with previously published studies.</strong></p> <p>A Venn diagram showing overlap of DNase hypersensitive sites (DHSs) found in control sample of present study and Zhang et al 2010 (<strong>A</strong>) and Sullivan et al 2014 (<strong>B</strong>). The statistical significance of overlap is calculate using hypergeometric Fischer`s exact test.</p> <p><strong>Supplementary Fig S2: Genomic locations of DHSs and FIRs</strong></p> <p>A line diagram representing the genomic location of unique DHSs and FIRs over each chromosome. DHSs/FIRs identified from each sample were merged to generate unique non-redundant subset of DHSs/FIRs before plotting over genome.</p> <p><strong>Supplementary Fig S3: Validation of correlation between OCRs and gene expression using microarray.</strong></p> <p>Box plot representing expression of genes (log10(normalised expression)) whose various structual elements fall in OCRs.</p> <p><strong>Supplementary Fig S4: First exons are highly enriched in both DHSs and FIRs</strong></p> <p> A bar plot showing presence of uFIRs, uDHSs, and ovOCRs in various positions of exon in Arabidopsis genes. The X-axis represent the exon number whereas Y-axis represent the fraction of OCRs found in each exon number.</p> <p><strong>Supplementary Fig S5: Validation of correlation between Ha-SACRs/Ha-SRCRs and gene expression using microarray.</strong></p> <p>Relative expression of genes (log2 fold change) corresponding to Ha-SACRs (Top) and (Ha-SRCRs (bottom) in cold (A), heat (B), salt (C) and drought (D) stress are plotted as box plot (p- value from Mann-Whitney test). To further compare RNA-seq data of salt stress with microarray, RNA-seq data was down-sampled to include genes which were also present in microarray data (E).</p> <p><strong>Supplementary Fig S6: Genomic location of SACRs and SRCRs found in Drought sample.</strong></p> <p>A snapshot of Integrative Genome Viewer (IGV) showing genomic location of stress activated chromatin regions (SACRs) and stress repressed chromatin region (SRCRs) in drought sample. The location of the centromere on each chromosome is shown as green bar IGV track.</p>
Dataset for Recurrent Rossby wave packets modulate the persistence of dry and wet spells across the globe
<p>This dataset is used in the following study: "Recurrent Rossby wave packets modulate the persistence of dry and wet spells across the globe."</p> <p>Dataset includes:</p> <ul> <li>Dry and wet spells for the Northern and the Southern Hemisphere respectively.</li> <li>The output of the statistical model for each season (MJJASO/NDJFMA) and for each spell type (dry/wet) for both the hemispheres (NH/SH).</li> </ul> <p>File naming used belongs to mainly two categories: one for naming spell file, and second for naming the output file from the statistical model. An example from each category is explained below. The rest of the files follow the same naming style.</p> <ol> <li><em>Spell file</em>;<strong> NH_1.0mm_dry_spells_all_months_gap_1_days_no_spell2_check.nc</strong>: Northern Hemisphere 1.0mm threshold dry spell for all months with a gap of 1 day</li> <li><em>Statisical model output file</em>; <strong>NH_weibull_MJJASO_drythresh_1_min_spell_count_40_time_steps_min_spell_length_5D_1D_N_1980_2016.nc</strong>: Northern Hemisphere Weibull model output for MJJASO using dry threshold of 1.0 mm with a minimum spell count of 40 time-steps and a minimum spell length of 5 days for the period 1980-2016</li> </ol> <p>This dataset alone is sufficient for reproducing the analysis presented in the study. Open source tools like Python, R, etc can be used to read '.nc' file type. Additional code help in the form of Jupyter notebooks reproducing figures made from this dataset can be viewed <a href="https://github.com/avatar101/RRWPS-extremes">here on GitHub.</a></p>
Data of "A quantum-logic gate between distant quantum-network modules"
<p>Data published in "<em>A Quantum-Logic Gate between Distant Quantum-Network Modules</em>"</p> <p>Science</p>
Video Results of visual preprocessing module
<p>These videos titled ellip-detect show the ellipse detection results.</p> <p>The results are obtained by further detecting ellipses on the patches detected by the visual preprocessing module.</p> <p>These videos demonstrate the developed arc-based ellipse detector can handle the occlusion very well and perform robustly in real conditions.</p> <p> </p> <p>These videos titled tracking show the visual tracking results.</p> <p>Object, after initialization by template matching, will not be re-detected again.</p> <p>So this is a one-passing evaluation. </p> <p> </p> <p>These videos titled detect show the results of the visual preprocessing module.</p> <p>Object, after initialization by template matching, will be re-detected again if tracking fails.</p>
Madagascar data for deforestprob Python module
<p>This is a data-set for Madagascar to be used with the deforestprob Python module (https://ghislainv.github.io/deforestprob) to compute the spatial probability of deforestation for the year 2010. The data-set includes the following variables:</p> <ul> <li>forest/non-forest for the period 2000-2010 (1)</li> <li>distance to forest edge in 2010, distance to previous deforestation (period 1990-2000) (1)</li> <li>altitude, slope, aspect (2)</li> <li>distance to road, town, river (3)</li> <li>protected area network (4)</li> </ul> <p><strong>Sources</strong></p> <ol> <li>http://bioscenemada.cirad.fr, forest maps derived from Harper et al. 2007 and Hansen et al. 2013</li> <li>http://srtm.csi.cgiar.org, SRTM 90m Digital Elevation Database v4.1</li> <li>http://www.geofabrik.de, data extracts from the OpenStreetMap project for Madagascar,</li> <li>http://rebioma.net, SAPM ("Système des Aires Protégées à Madagascar"), 20/12/2010 version</li> </ol> <p>The variables have been computed using the gdal tools and executing a bash script available at https://github.com/ghislainv/deforestprob/blob/master/notebook/scripts/dataMada.sh</p>
Tutorial Photonics Explorer Module 7: Interference and Diffraction
<p>Photonics Austria (PhAu) has conducted Teacher Training Programmes about Photonics - the Photonics Explorer - in order to promote the potential of photonics to enliven physics lessons. This video shows several experiments on the subject of interference and diffraction.</p>
Tutorial Photonics Explorer Module 5: Polarisation
<p>Photonics Austria (PhAu) has conducted Teacher Training Programmes - the Photonics Explorer- about Photonics in order to promote the potential of photonics to enliven physics lessons. This video is concerned with the topic polarisation and optical activity. </p>
Tutorial Photonics Explorer Module 1: total internal reflection
<p>Photonics Austria (PhAu) has conducted Teacher Training Programmes about Photonics - the Photonics Explorer - in order to promote the potential of photonics and to enliven physics lessons. This video tutorial demonstrates and explains the principals of total internal reflection.</p> <p> </p>
Tutorial Photonics Explorer Module 3 part 2: lenses, imaging rules, optical setups and telescopes
<p>Photonics Austria (PhAu) has conducted Teacher Training Programmes about Phoronics - the Photonics Explorer- in order to promote the potential of photonics to enliven physics lessons. This video is concerned with the topic polarisation and optical activity.</p> <p> </p>
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.