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4,694 results for “data analysis”
ExcapeDB: An integrated large scale dataset facilitating Big Data analysis in chemogenomics
<p>ExcapeDB: An integrated large scale dataset facilitating Big Data analysis in chemogenomics</p> <p>Supplementary file (full dataset download)</p> <p>- v2 with SMILES errors fixed (19.01.2019)</p>
Data on robotic grinding of Inconel 718 part with 3M Cubitron II 984F belt for tool wear and material removal analysis
<p>Data on robotic grinding of Inconel 718 part for tool wear and material removal analysis</p> <p>Date data was obtained: May 2019</p> <p>The performance of a metal grinding operation with a robot has been studied, specifically how the grinding capability changes as time goes by and the tool gets worn. A pneumatic grinding tool has been implemented on the robot flange and abrasive belts of 3M Cubitron II 984F have been used.</p> <p>A rectangular metallic part of known dimensions has been attached to a load cell and has been grinded several times in consecutive tests. The grinding operation consists on a straight line along the longest side of the part.<br> During each test session, the same abrasive belt was used with fixed grinding conditions (tool angle, applied force, overlap, robot feed), until the grinding time reached 20 minutes.<br> The metal part has been weighed at regular intervals with the load cell, which allowed us to measure the evolution of the removed height of material for each pass, depending on grinding time.<br> The quantity of grinded material is measured as the height reduction in the part as the robot moves over the part at certain speed.</p> <p>Test sessions were designed for 4 tool angles (25, 35, 45, 75º related to the vertical), and were repeated three times for each angle. With 75º the tool was almost horizontal and it provided the smallest material removal capability. With 25º the tool was almost perpendicular to the area being grinded and it provided the highest material removal capability. </p> <p>The data of the tests is presented in the following units:<br> - Time: seconds:<br> - Removed material height per grinding tool pass: millimeters.</p> <p>The user of the data may easily convert the removed material height per pass into removed material volume or weight per pass. Considering that the width of the grinding belt is 12.5 mm, and the length of the grinded tool is 160 mm, if the height of the removed material is multiplied to the length and width the removed volume per pass can be calculated. Multiplying the volume with the density the weight of the removed material per pass can be calculated.</p> <p>The results of different tests presented in the .xlsx document, which can be accessed using free software such as OpenOffice or LibreOffice:<br> https://www.openoffice.org<br> https://www.libreoffice.org/</p> <p><br> TECHNICAL DESCRIPTION OF THE USED DEVICES AND CONDITIONS</p> <p>- Material of the grinded part: Inconel 718, density 8.19g/cm3. <br> - Dimensions of the grinded part: 160x90x40 mm.<br> - Robot: Stäubli TX90L.<br> - Belt grinding tool: AMTRU SwingBelt 120. https://www.amtru.com<br> - Applied pneumatic pressure on the grinding tool: 9 bars.<br> - Belt speed: Maximum possible speed obtained with 7 bars pneumatic mains in the workshop.<br> - Abrasive belt: 3M Cubitron II 984F, 610x12.5 mm, 36 grit (roughing).<br> - Load cell to measure the weight of the part: HBM SP4M, capacity 3 kg, precision 0.01 g.<br> - Overlap (tool lateral displacement between two passes): 6.25 mm.<br> - Robot feed: 75 mm/s (100 mm/s for the 45º test).</p> <p>IDEKO Research Centre<br> Address: <br> Arriaga kalea 2<br> 20870, Elgoibar, SPAIN<br> Contact:<br> Asier Barrios, abarrios@ideko.es<br> Patxi Hacala, phacala@ideko.es<br> Phone: (+34) 943 74 80 00</p>
Data and Analysis for Kaplanis, Denny, and Raimondi 2024, "Vertical distribution of rocky intertidal organisms shifts with sea-level variability on the Northeast Pacific Coast".
<p>This repository contains all the data and R scripts used to produce all analyses and figures for Kaplanis, Denny, and Raimondi 2024, as well as all intermediate outputs and final figures. To access this content, download and unzip the intertidalvertdist folder (for intertidal vertical distribution). The R Project is titled "intertidalvertdist". All pertinent information needed to access data, replicate the analyses, and produce figures is contained within the README file, but a brief desciption is below.</p> <p><br>Directory Architecture:</p> <p>Data:<br>Contains all data. Within this folder are two subdirectories - Raw Data, and Processed Data. Raw Data are unmanipulated, straight from the data source. Processed Data are outputs from scripted data wrangling and transformations. </p> <p>Within each of these folders are two more subdirectories: Tide Gauge Data, and MARINe Data. These are the two data sources used in this manuscript - monthly sea-level data from The National Oceanic and Atmospheric Administration Center for Operational Oceanographic Products and Services (NOAA CO-OPS) tide gauge stations, and long-term rocky intertidal biological monitoring data from Multi-Agency Rocky Intertidal Network (MARINe) survey sites.</p> <p>Scripts:<br>All R scripts are contained within the Scripts folder. The scripts have the prefix IVD (for intertidal vertical distribution), then a name that indicates the major function of the code. The scripts either downloads data, manipulates data, conducts analyses, and/or produces a figure.</p> <p>Outputs:<br>Any figures and tables from preliminary analyses, but that are not used in the final manuscript, are saved in Outputs.</p> <p>Figures:<br>All final figures and tables are contained in the Figures folder. All figures are produced by scripts, except Figs. 1 and 2, which are schematics produced manually in a graphics editor. This folder contains two other folders: Supplemenatary Figures, and Partial Regression Plots. Partial Regression plots are the same as the final Figures 8-12, except they are grouped by taxa rather than by explanatory variable.</p> <p>Data Processing Workflow - Overview: <br>Tide Gauge Data (Data/Raw Data/Tide Gauge Data/individual stations) were downloaded using the NOAA Co-Ops API URL Builder (https://tidesandcurrents.noaa.gov/api-helper/url-generator.html), merged, then analyzed. Three MARINe data sets from the Coastal Biodiversity Survey (CBS) were accessed via data requests (https://marine.ucsc.edu/explore-the-data/contact/data-request-form.html). The first MARINe dataset (Data/Raw Data/MARINe Data/CBS_Percent Cover Data, both First Sample and Full Sample) was used to determine the top ten most abundant taxa (hereafter termed “dominant taxa”) across CBS survey sites during the monitoring period of 2001-01-01 to 2021-09-30. The second MARINe dataset (Data/Raw Data/MARINe Data/CBS_Elevation Data) was used to describe the upper limits of vertical distribution of dominant taxa through time. The third MARINe dataset (Data/Raw Data/MARINe Data/CBS_Presence Data) was used to visualize latitudinal distribution of taxa.</p> <p>Location information for Tide Gauge Stations and CBS Survey Sites were assembled into a table (Data/Raw Data/CBS_Tide Gauge_Data.csv)</p> <p>Tide Gauge Data were processed first, then MARINe Data. To replicate this workflow follow the steps described in the README file, in order.</p>
Data for Tradeoff Analysis of Recovery Pathways post Superstorm Sandy: a New Jersey Case Study - v0.1
<p>Data repository for the manuscript titled: <em>Tradeoff Analysis of Recovery Pathways post Superstorm Sandy: a New Jersey Case Study.</em></p> <p>This repository includes all of the input data, processed data, and output data used for the project. This is a working version of our Tradeoff Analysis ready for interested users, peer reviewers, and others to run. </p> <p>README file on GitHub: https://github.com/Laura-Geronimo/Geronimo-etal_2024_NaturalHazardsReview/blob/main/README.md</p> <p> </p>
Raw data for Infrastructure and Awareness Landscape Analysis in Latin America
<p>Persistent Identifiers (PIDs), such as Digital Object Identifiers (DOIs), are foundational to connecting and enhancing the visibility of Latin American research within a global framework. Although the region is rich in diverse and impactful research, many repositories remain only partially integrated into international registries and aggregators, limiting their discoverability and reach. The adoption of PIDs across repositories in Latin America varies widely, underscoring the need for increased awareness about the role of open PIDs in advancing research accessibility and visibility.</p> <p>This dataset offers a comprehensive overview of the current landscape of repositories, publishing systems, and Open Science policies across Latin America, shedding light on the institutional and national efforts that support an open and inclusive research infrastructure. It highlights the importance of collaboration among researchers, institutions, funders, librarians, and government agencies in fostering Open Science practices and encouraging strategic PID adoption. By expanding these open practices and strengthening PID adoption, Latin American research can achieve greater integration and impact within the global research ecosystem.</p> <p>You can read the full report titled "Infrastructure and Awareness Landscape Analysis in Latin America" at <a href="https://doi.org/10.5281/zenodo.14010858" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14010858</a> </p>
Smarter open government data for Society 5.0: analysis of 51 OGD portals
<p>This dataset contains data collected during a study <a href="https://doi.org/10.3390/s21155204">"Smarter open government data for Society 5.0: are your open data smart enough"</a> (<em>Sensors</em>. 2021; 21(15):5204) conducted by Anastasija Nikiforova (University of Latvia).<br> It being made public both to act as supplementary data for "Smarter open government data for Society 5.0: are your open data smart enough" paper and in order for other researchers to use these data in their own work.</p> <p>The data in this dataset were collected in the result of the inspection of 60 countries and their OGD portals (total of 51 OGD portal in May 2021) to find out whether they meet the trends of Society 5.0 and Industry 4.0 obtained by conducting an analysis of relevant OGD portals.</p> <p>Each portal has been studied starting with a search for a data set of interest, i.e. “real-time”, “sensor” and “covid-19”, follwing by asking a list of additional questions.<br> These questions were formulated on the basis of combination of (1) crucial open (government) data-related aspects, including open data principles, success factors, recent studies on the topic, PSI Directive etc., (2) trends and features of Society 5.0 and Industry 4.0, (3) elements of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use Model (UTAUT).</p> <p>The method used belongs to typical / daily tasks of open data portals sometimes called “usability test” – keywords related to a research question are used to filter data sets, i.e. “real-time”, “real time” and “real time”, “sensor”, covid”, “covid-19”, “corona”, “coronavirus”, “virus”. In most cases, “real-time”, “sensor” and “covid” keywords were sufficient.<br> The examination of the respective aspects for less user-friendly portals was adapted to particular case based on the portal or data set specifics, by checking:<br> 1. are the open data related to the topic under question ({sensor; real-time; Covid-19}) published, i.e. available?<br> 2. are these data available in a machine-readable format?<br> 3. are these data current, i.e. regularly updated? Where the criteria on the currency depends on the nature of data, i.e. Covid-19 data on the number of cases per day is expected to be updated daily, which won’t be sufficient for real-time data as the title supposes etc.<br> 4. is API ensured for these data? having most importance for real-time and sensor data;<br> 5. have they been published in a timely manner? which was verified mainly for Covid-19 related data. The timeliness is assessed by comparing the dates of the first case identified in a given country and the first release of open data on this topic.<br> 6. what is the total number of available data sets?<br> 7. does the open government data portal provides use-cases / showcases? <br> 8. does the open government portal provide an opportunity to gain insight into the popularity of the data, i.e. does the portal provide statistics of this nature, such as the number of views, downloads, reuses, rating etc.?<br> 9. is there an opportunity to provide a feedback, comment, suggestion or complaint?<br> 10. (9a) is the artifact, i.e. feedback, comment, suggestion or complaint, visible to other users?</p> <p>***Format of the file***<br> .xls, .ods, .csv (for the first spreadsheet only)</p> <p>***Licenses or restrictions***<br> CC-BY</p> <p>For more info, see README.txt</p>
Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis - Imaging Flow Citometry Data
<p>Imaging flow citometry (IFC) datasets analysed in "Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis" (under revision).</p> <p>The folders contain acquisitions of giant unilamellar vesicles (GUVs) for lipid exchange and content exchange controls, with file naming convention DATE_SAMPLE_REPLICATE.rif, content exchange is indicated by CE samples in the 20230228_CE.zip folder, lipid exchange by LE samples in the 20221222_LE.zip folder. 24 samples per set are included, triplicates of isolated P1 (DOPE Af488 0.6% in LE; Dex-Af488 40 uM for CE), P2 (DOPE Cy50.6% in LE; Dex-Af647 10 uM for CE), NC (P1 + P2 1:1), PC (DOPE Af488 0.3% + DOPE Cy5 0.3 in LE; Dex-Af488 20 uM + Dex-Af647 5 uM for CE), and M samples numbered 1 to 4, prepared by mixing P1, P2 and PC in different ratios (M1= 1:1:1; M2= 1:1:0.5; M3= 1:1:0.1; M4= 1:1:0.05).</p> <p>Only .rif files are provided, they have to be elaborated via compensation and application of an analysis template using the Amnis IDEAS software. Compensation matrices for lipid exchange (20230217_LEcom.ctm) and content exchange (20230217_CEcomp.ctm) are included, as well as the analysis template (Lipid_exchange_analysis_6.2.ast). Gating in the latter may have to be adjusted to analyse LE and CE experiments.</p> <p>10000 objects in the GUV population or 50000 objects in total were acquired in each file. The files were elaborated in batch mode, outputting the statistic reports (Statistics report CE.txt for CE; Statistics report LE.txt for LE) that were elaborated using an R scirpt (included, IFC_analysis.R) </p>
Multi-Dimensional Data Viewer (MDV) user manual for data exploration: "Systematic analysis of YFP traps reveals common discordance between mRNA and protein across the nervous system"
<table> <tbody> <tr> <td> <p> Please also see the latest version of the repository:<br> <a href="https://doi.org/10.5281/zenodo.6374011">https://doi.org/10.5281/zenodo.6374011</a> and<br> our website: <a href="https://ilandavis.com/jcb2023-yfp">https://ilandavis.com/jcb2023-yfp</a></p> </td> </tr> </tbody> </table> <p> </p> <p>The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV) -<a href="https://mdv.molbiol.ox.ac.uk/projects/mdv_project/7012?view=RNA+%2F+Protein+Distribution">link</a>, a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP traps reveals common discordance between mRNA and protein across the nervous system (<a href="https://doi.org/10.1083/jcb.202205129">eprint link</a>). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code <a href="https://github.com/ilandavislab/Annotate.OMERO.Fig">(github link)</a> used to annotate the figures, which could be adapted to any other kind of data annotation task.</p>
MITgcm Dataset for paper: Sensitivity analysis of a data-driven model of ocean temperature
<p>MITgcm dataset used in paper, Sensitivity analysis of a data-driven model of ocean temperature, made available here. The dataset comes from running a sector config of the MITgcm model, briefly described in the paper. This dataset is used to train the regression model described in the paper.</p> <p>Updated to include ncra_cat_tave.nc file which was accidentally missed on first version.</p>
Data to reproduce analysis in "Systematic analysis of transcriptional and epigenetic effects of genetic variation in Kupffer cells enables discrimination of cell intrinsic and environment-dependent mechanisms"
<p>Here you can find the datasets necessary to reproduce all analyses described in the Glass lab paper by <a href="https://www.biorxiv.org/content/10.1101/2022.09.22.509046v1">Bennett et al</a>. The python and R code for reproducing analysis and figures can be found on our linked <a href="https://github.com/HunterBennett/KupfferCell_NaturalGeneticVariation">github repository.</a></p> <p>Briefly, this paper explores the effect of natural genetic variation <em>in vivo</em>, using Kupffer cells as a model cell type. We collect and analyze transcriptional and epigenetic data (ATAC-seq, H3K27Ac ChIP-seq) to identify putative <em>trans</em> regulators driving differential gene expression across inbred strains of mice. Additionally, we provide evidence that <em>trans</em> effects control a majority of strain differential genes at homeostasis while <em>cis</em> effects dominate the transcriptional response to an external signal (lipopolysaccharide).</p> <p>References:</p> <p>Hunter Bennett, Ty D. Troutman, Enchen Zhou, Nathanael J. Spann, Verena M. Link, Jason S. Seidman, Christian K. Nickl, Yohei Abe, Mashito Sakai, Martina P. Pasillas, Justin M. Marlman, Carlos Guzman, Mojgan Hosseini, Bernd Schnabl, Christopher K. Glass bioRxiv 2022.09.22.509046; doi: <a href="https://doi.org/10.1101/2022.09.22.509046">https://doi.org/10.1101/2022.09.22.509046</a></p> <p> </p>
Solar and interplanetary magnetic field data analyzed in "Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator"
<p>This dataset contains simultaneous measurements of the interplanetary magnetic field magnitude <B> and the sun's radio flux at 10.7 cm <F10.7>. <B> measurements come from a series of spacecraft located at the L1 point, while <F10.7> was measured by the ongoing monitoring program by Canada's Dominion Radio Astrophysical Observatory. Bartels rotation-averaged data were downloaded from NASA's OMNIWeb, https://omniweb.gsfc.nasa.gov/html/ow_data.html. The file contains other solar wind plasma parameters that were not used in the analysis.</p>
Quaia data products for cosmolgy analysis
<p>The files below contain the data that were used in the cosmological analysis of the Gaia-unWISE quasar catalog (Quaia) in combination with CMB lensing data from <em>Planck</em> (<a href="https://arxiv.org/abs/2306.17748">Alonso et al. 2023</a>).</p> <p>The files included are:</p> <ul> <li><strong>cls_quaia_planckPR4_s8.fits</strong>: the complete set of power spectra and associated metadata (covariance matrix, redshift distributions) needed to reproduce the fiducial analysis of the paper. The file uses the sacc format. A short notebook showing how to interpret the data can be found <a href="https://github.com/damonge/quaia-cosmo-s8/blob/main/notebooks/CellReading.ipynb">here</a>.</li> <li><strong>selection_function_NSIDE64_G20.5_zsplit2binX.fits</strong>: the selection function map for bin X (0 and 1 for the Low-z and High-z bins used in the analysis, respectively), in <a href="https://healpix.jpl.nasa.gov/">HEALPix</a> projection with N<sub>side</sub>=64.</li> </ul> <p>To reproduce the power spectra used in the analysis, you will need to combine the selection functions above with the public <a href="https://zenodo.org/record/8060755">Quaia catalog</a>, and the PR4 CMB lensing maps from the <a href="https://pla.esac.esa.int">Planck legacy archive</a>.</p>
Missing data in the analysis of multilevel and dependent data (Examples)
<p>Example data sets and computer code for the book chapter titled "Missing Data in the Analysis of Multilevel and Dependent Data" submitted for publication in the second edition of "Dependent Data in Social Science Research" (Stemmler et al., 2015). This repository includes the computer code (".R") and the data sets from both example analyses (Examples 1 and 2). The data sets are available in two file formats (binary ".rda" for use in R; plain-text ".dat").</p> <p>The data sets contain simulated data from 23,376 (Example 1) and 23,072 (Example 2) individuals from 2,000 groups on four variables:</p> <p><code>ID</code> = group identifier (1-2000)<br> <code>x</code> = numeric (Level 1)<br> <code>y</code> = numeric (Level 1)<br> <code>w</code> = binary (Level 2)</p> <p>In all data sets, missing values are coded as "NA".</p>
Datasets and results from: "Random Forest Classification and Solar Flares Data: Analysis and Validation"
<p><strong>Instructions for the data and code repository</strong></p> <p>Results, post-processing workflow, and datasets for the research paper titled "Random Forest Classification and Solar Flares Data: Analysis and Validation".</p> <p>The folder contains three .csv files: the complete dataset (dataset.csv), the balanced training dataset (train_dataset.csv), and the testing dataset (test_dataset.csv).</p> <p>The folder also contains the result files from the research (.csv output files with predictions and .html files with evaluation metrics, etc.) exported from the JASP software. The number in each file name corresponds to the number of trees utilized in Random Forest modelling.</p> <p>In addition, the Python script for the post-processing workflow is provided, with comments located in the script.</p> <p>The soft range X-ray irradiance and VLF amplitude data were obtained from:<br> National Centers for Environmental Information (NCEI) Available online: https://www.ncei.noaa.gov/. Accessed on: 24th June 2023. <br> Worldwide archive of low-frequency data and observations (WALDO) Available online: https://waldo.world/. Accessed on: 24th June 2023.</p>
Śaiva and Buddhist rhetoric in copperplate grants of the Eastern Deccan: textual analysis data
<p>This is a textual analysis dataset derived from copperplate grants of the Eastern Cālukyas, Viṣṇukuṇḍis and Śrīmūla.<br> The data accompany the following forthcoming publication (title and date of publication subject to change):<br> Balogh, Dániel. 2024. ‘Can We Discern a Śaiva and a Buddhist Rhetoric in Royal Eulogies?’ In Śaiva-Buddhist Encounters in Early Medieval East India, edited by Florinda De Simini. Studies on the History of Śaivism. Naples: UniorPress.<br> Details of the methodology are presented in other publications by Balogh to appear in 2024 (forthcoming or in preparation when this dataset is released).<br> The parent of this dataset is available at https://zenodo.org/record/7900312<br> The present dataset has been extended by adding analytical data from Viṣṇukuṇḍi and Śrīmūla charters, but simultaneously reduced by discarding all data not pertaining to the issuing sovereigns.<br> This dataset, the underlying research and the relevant publication are results of the project DHARMA ‘The Domestication of “Hindu” Asceticism and the Religious Making of South and Southeast Asia’. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no 809994).</p>
Data for a leaf litter decomposition study and soil density fractionation analysis at a whole-watershed fertilization experiment in a temperate forest
To assess how elevated N deposition influences leaf litter decomposition dynamics and soil organic matter formation in a temperate deciduous forest, we coupled a reciprocal transplant leaf litter decomposition study with an analysis of the distribution of soil organic matter in mineral associated and particulate organic matter fractions at a long-term, whole-watershed, N fertilization experiment. We found that nearly 30 years of N additions slowed decay rates by about 11% for leaf litter decomposed in the fertilized watershed, regardless of the watershed from which the initial litter was collected. An apparent consequence of the altered rates of decomposition was that the soil in the fertilized watershed had about a 40% greater fraction of SOM in light particulate organic matter compared to the reference watershed, which was positively correlated with the bulk soil carbon to nitrogen ratio. Collectively, our results suggest that under conditions of N saturation, the physical transfer pathway of SOM formation is favored, which can have important implications for the future of the soil organic matter stock and nutrient cycling.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IX: metrics derived from All Raw Data Collected Plus Data from Previous Studies on the 2004 Alaska Wildfires Included in Analysis 2022
This data set includes metrics derived from field and lab data collected for deciduous and mixed deciduous-confier plots collected in the summer of 2022 (Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019)), as well as additional data for conifer plots from previous studies of the Taylor Highway Complex (2004), Dall Creek/Yukon Crossing (2004), and Boundary (2004) fires. Those additional data were acquired from: https://www.lter.uaf.edu/d1/d1-detail/id/773 and https://daac.ornl.gov/ABOVE/guides/ABoVE_Plot_Data_Burned_Sites.html. From this complete data set of 333 plots, 311 plots were used in analyses in Black at al. (NCC) paper: "Increased deciduous tree dominance reduces wildfire carbon losses in boreal forests". Plots excluded (from 2022 FiSL data) were poplar-dominated, mixed poplar/conifer dominated, missing soil C data, or conifer-dominated (adventituous root heights were not recorded consistently at sites in 2022 making it impossible to estimate pre-fire conifer stand organic soil C pools for 2022-collected conifer plots). Only 2005-collected conifer plots were used in NCC paper analyses. For all plots, in addition to field/lab derived site characteristics and combustion metrics, post hoc remotely sensed metrics were derived: pre-fire NDVI/EVI-2 trends, 1980-2010 climate normals, and DOB weather metrics.
Spartina alterniflora aboveground biomass patterns from Landsat 5 TM imagery (1984-2011) and external driver data used in multivariate analysis.
We used Landsat 5 TM satellite imagery to derive aboveground biomass estimates for the three height classes (tall, medium, short) of Spartina alterniflora on the Centeral Georgia Coast. We used geospatial techniques to scale up in situ measurements of aboveground S. alterniflora aboveground biomass to landscape level estimates using 294 Landsat images acquired between 1984 to 2011. For each scene we extracted data from the same 63 sampling polygons, containing 1,222 pixels covering 1.1 million m^2. Using univariate and linear multiple regression tests, we compared Landsat derived biomass estimates for three S. alterniflora size classes against a suite of abiotic drivers. Drivers included monthly mean values for Altamaha River Discharge, Palmer Drought Severity Index, Standardized Precipitation Index, Mean Sea Level, Precipitation, and Temperature.
BIDS Data for "A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation"
<p>Base data package for the “"A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation” article, formatted corresponding to the Brain Imaging Data Structure.</p>
Analysis raw data
<p>Analysis raw data of feedstock used for biochar and COMBI production and products obtained by pyrolysis and composting processes.</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.