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1,093 results for “scripts”
Data and scripts used in "Atlantic Equatorial Undercurrent intensification counteracting warming induced deoxygenation"
<p>This file contains data and scripts for "Atlantic Equatorial Undercurrent intensification counteracting warming induced deoxygenation“.</p> <p>Data:</p> <ul> <li>Physical oceanography (CTD) during Meteor cruise M158</li> <li>ADCP current measurements (38 and 75 kHz) during Meteor cruise M158</li> <li>wind products (ASCAT, CCMP, JRA55do, QuikSCAT)</li> <li>Data of the mean 23W ship section between 5S-14N</li> <li>Velocity data from moored ADCP at 23W, 0N</li> <li>Velocity data from 25 individual ship sections along 23W</li> </ul> <p>For the scripts leading to Figures 1 to 3 see subdirectories:</p> <ul> <li>figure01/</li> <li>figure02/</li> <li>figure03/</li> </ul> <p>For further information on executable scripts and available data, please see the "Readme.txt" documents in the different subdirectories.</p> <p> </p>
Data and scripts related to: Rapid coordination of effective learning by the human hippocampus
<p>This data set contains intracranial EEG data (ASCII format), eye-tracking data from an EyeLink 1000 remote system (edf format), behavioral data, and MATLAB code to reproduce the analyses reported in the manuscript, “Rapid coordination of effective learning by the human hippocampus” published in <em>Science Advances.</em></p> <p>The file <strong>KragelEtal21_SciAdv.zip</strong> contains the raw data divided into folders according to content type, for each of the six participants in the study, and the MATLAB code necessary to reproduce all analyses. MATLAB live scripts provide examples of how to reproduce the main analyses reported in the manuscript.</p> <p>External datasets:</p> <p>In addition to the dataset provided here, three open-access datasets are analyzed in the manuscript.</p> <p> - The <a href="http://figrim.mit.edu/">FIGRIM Dataset</a> contains eye-tracking data during a continuous recognition task.</p> <p> - Two additional eye-tracking datasets during free viewing of repeated scenes are provided in “<a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.9pf75">An extensive dataset of eye movements during viewing of complex images</a>,” namely the Memory I and Memory II datasets.</p> <p>To reproduce region of interest analyses outside of the hippocampus, both the seven-network cortical parcellation developed by <a href="https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation_Yeo2011">Yeo, Krienen et al.</a>, and the <a href="https://identifiers.org/neurovault.image:1702">Harvard-Oxford cortical atlas</a> are required.</p> <p>Stimuli:</p> <p>The scenes used in this study are part of <a href="https://cocodataset.org">Microsoft COCO</a>. Scenes were selected from the 2017 Train images. Image identifiers are maintained.</p> <p>Salience model:</p> <p>To reproduce analyses that consider the visual salience of each scene, DeepGaze II model predictions for each stimulus are required. Tensorflow models and a Jupyter notebook demonstrating their use are available for <a href="https://deepgaze.bethgelab.org/">download</a>.</p> <p>Software dependencies:</p> <p>The code in this project was developed using MATLAB r2017b. The following external packages are required for code execution. Some external packages are included in the repository.</p> <p>- fieldtrip (<a href="https://github.com/fieldtrip/fieldtrip">https://github.com/fieldtrip/fieldtrip</a>)<br> - spm12 (<a href="https://github.com/spm/spm12">https://github.com/spm/spm12</a>)<br> - BOSC (<a href="https://doi.org/10.1016/j.neuroimage.2010.08.064">https://doi.org/10.1016/j.neuroimage.2010.08.064</a>)<br> - Edf2Mat (<a href="https://github.com/uzh/edf-converter">https://github.com/uzh/edf-converter</a>)<br> - boundedline (<a href="https://github.com/kakearney/boundedline-pkg">https://github.com/kakearney/boundedline-pkg</a>)<br> - export_fig (https://github.com/altmany/export_fig)</p> <p>License:</p> <p>The included code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version. See the file COPYING for more details. The release of this software includes functions from other toolboxes that are covered under their respective licenses.</p>
Data and Computer scripts for 'Place recognition using batlike sonar' (eLife)
<p>The paper ‘Place recognition using batlike sonar’ can be freely accessed online at http://dx.doi.org/10.7554/eLife.14188. Contents, including text, figures, and data, are free to reuse under a CC BY 4.0 license. </p> <p>The uploaded files contain all data (including raw data), scripts and supporting files used in preparing the manuscript</p> <ul> <li>360panoramas.tar: 360 panoramic pictures taken at the locations at the St Andrews site.</li> <li>PhotosSites.tar: Additional pictures taken at the different ensonification sites</li> <li>ProcessData.tar: This contains all Matlab code for processing and visualizing the data. Also, it contains the templates for all locations as used in the paper.</li> <li>RawData.7z.0xx: These files contain the raw acoustic data as recorded from the microphones for all locations at each of the three sites. This data is provided as Matlab arrays. Due to the file size limitation of Zenodo, the archive has been split into 17 parts. Your archive manager should be able to open all data by accessing RawData.7z.001.</li> <li>Latex.tar: The latex source code and images for the final manuscript.</li> </ul>
Board Leadership Database (U.S. Public Firms) + ML Script for Scaling Human Coded Data
<p>Files include: (1) an open sourced database of CEO duality and board chair orientations developed by scaling human coded data using supervised machine learning techniques (in both .dta and .csv formats), as well as (2) the accompanying training and scoring scripts to scale human coded data.</p> <p>Users may apply the scoring script to score the same variables from company proxy statements, or may adapt the training/scoring scripts and retrain models to scale human coded data of other constructs or measures. </p> <p>We note that early steps in the process to develop our database and script required web-scraping of company filings from SEC Edgar and text extraction from collected filings. We relied on other publicly available scripts to develop our own fetcher and extraction scripts. Users seeking to duplicate those parts of the process may benefit from the following resources from Kai Chen and pipy.org: </p> <p>For resources from Kai Chen: see <a href="https://urldefense.com/v3/__https:/www.kaichen.work/?p=681__;!!K6Z8K8YTIA!BizP9-ZnzgV0Pq7ck-UENJ1EBDrFkAkNoCaO34Ad1ezxH_okstsfxniagvxXdWudkDP44fPACbY9vaIG5A$">https://www.kaichen.work/?p=681</a> and <a href="https://urldefense.com/v3/__https:/www.kaichen.work/?p=946__;!!K6Z8K8YTIA!BizP9-ZnzgV0Pq7ck-UENJ1EBDrFkAkNoCaO34Ad1ezxH_okstsfxniagvxXdWudkDP44fPACbZhp5N_Tw$">https://www.kaichen.work/?p=946</a></p> <p>For resources from pipy.org, see <a href="https://urldefense.com/v3/__https:/pypi.org/project/sec-edgar-downloader/__;!!K6Z8K8YTIA!BizP9-ZnzgV0Pq7ck-UENJ1EBDrFkAkNoCaO34Ad1ezxH_okstsfxniagvxXdWudkDP44fPACbYxYSZGGw$">sec-edgar-downloader</a> and <a href="https://urldefense.com/v3/__https:/pypi.org/project/sec-api/__;!!K6Z8K8YTIA!BizP9-ZnzgV0Pq7ck-UENJ1EBDrFkAkNoCaO34Ad1ezxH_okstsfxniagvxXdWudkDP44fPACbZ_FwY6jw$">sec-api</a></p> <p> </p>
Suplementary data, results and scripts: "Reconstruction of Cell-specific Models Capturing the Influence of Metabolism on DNA methylation in Cancer"
<p>This repository contains supplementary data, models and scripts associated with "Reconstruction of Cell-specific Models Capturing the Influence of Metabolism on DNA methylation in Cancer".</p><p>Folders content:</p><p>'data_results_matlabscripts': data, result files and scripts (original python scripts and adapted MATLAB scripts)</p><p>'supplementary_figures': supplementary figures</p><p>'supplementary_tables': supplementary tables</p>
Data and scripts for the publication "Coil Optimization for Quasi-helically Symmetric Stellarator Configurations"
<p>Coils and VMEC configurations for the three stellarator configurations presented in "Coil Optimization for Quasi-helically Symmetric Stellarator Configurations", including the optimization scripts used to find the coils and plot scripts used to produce the figures in the text.</p>
Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in Dec 2023.
<p>Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in NHESS journal in Dec 2023.</p> <p>For further technical details read the file READMEdata in the zipfile. The script MAKEFIG remakes all the figures from the data.</p> <p>For scientific details read the associated article preprint on the NHESS egusphere website.</p>
[Dataset & scripts] to "Spatial scales of kinetic energy in the Arctic Ocean", dataset from Caili Liu
<p>## "Spatial scales of kinetic energy in the Arctic Ocean"</p> <p>Available dataset for each figure (1~9) and figure10 in the main text, including Jupyter notebook scripts (Fig1, Fig2, Fig5, Fig10) and Matlab scripts (Fig3, Fig4, Fig6, Fig7, Fig8, Fig9).</p> <p>## Description</p> <p>This dataset is as the supplementary to the manuscript "Spatial scales of kinetic energy in the Arctic Ocean", including jupyter notebook scripts and matlab scripts of visualization directly for figures1~9.</p> <p>1) Jupyter notebook scripts for visualization<br>the MESH and BG are used for visualization, and *.mat are the dataset for Fig1/2/5/10. The load path in the script should be changed to your files accordingly.</p> <p>2) Matlab scripts for plots<br>All figures/panels are directly produced, but it is composed of panels for Fig7/8/9 additionally.</p>
Data, plotting scripts, and figures for "A physics-based ignition model with detailed chemical kinetics for live fuel burning studies"
<p>This repository contains the data, plotting scripts, and figures associated with the paper "A physics-based ignition model with detailed chemical<br>kinetics for live fuel burning studies" by Diba Behnoudfar and Kyle E. Niemeyer.</p> <p>See the README file for additional details.</p>
Worldwide Soundscapes project metadata and analysis scripts
<p>The Worldwide Soundscapes project is a global, open inventory of spatio-temporally replicated passive acoustic monitoring meta-datasets (i.e. meta-data collections). This Zenodo entry comprises the data tables that constitute its (meta-)database, as well as their description. Additionally, R scripts are provided to replicate the analysis published in [placeholder].</p> <p>The overview of all sampling sites and timelines can be found on the corresponding project on <a href="https://ecosound-web.de/ecosound_web/collection/index/106">ecoSound-web</a>, as well as a <a href="https://ecosound-web.de/ecosound_web/collection/show/49">demonstration collection</a> containing selected recordings. The recordings of this collection were annotated and analysed to explore macro-ecological trends.</p> <p>The audio recording criteria justifying inclusion into the meta-database are:</p> <ul> <li>Stationary (no transects, towed sensors or microphones mounted on cars)</li> <li>Passive (unattended, no human disturbance by the recordist)</li> <li>Ambient (no directional microphone or triggered recordings, non-experimental conditions)</li> <li>Spatially and/or temporally replicated (i.e. multiple sites sampled at the same time and/or multiple days - covering the same daytime - sampled at the same site)</li> </ul> <p>The individual columns of the provided data tables are described in the following. Data tables are linked through primary keys; joining them will result in a database. The data shared here only includes validated collections.</p> <p><strong>Changes from version 4.0.0</strong></p> <p>Added link to the published synthesis.</p> <p><strong>Meta-database CSV files</strong></p> <p><strong>collections</strong></p> <ul> <li>collection_id: unique integer, primary key</li> <li>name: name of the dataset. if it is repeated, incremental integers should be used in the "subset" column to differentiate them.</li> <li>ecoSound-web_link: link of validated meta-collection on ecoSound-web</li> <li>primary_contributors: full names of people deemed corresponding contributors who are responsible for the dataset</li> <li>secondary_contributors: full names of people who are not primary contributors but who have significantly contributed to the dataset, and who could be contacted for in-depth analyses</li> <li>date_added: when the datased was added (YYYY-MM-DD)</li> <li>URL_open_recordings: internet link for openly-available recordings from this collection</li> <li>URL_project: internet link for further information about the corresponding project</li> <li>DOI_publication: Digital Object Identifiers of corresponding publications</li> <li>core_realm_IUCN: The main, core realm of the dataset according to IUCN Global Ecosystem Typology (v2.0): https://global-ecosystems.org/</li> <li>medium: the physical medium the microphone is situated in</li> <li>locality: optional free text about the locality</li> <li>contributor_comments: free-text field for comments by the primary contributors</li> </ul> <p><strong>collections-sites</strong></p> <ul> <li>dataset_ID: primary key of collections table</li> <li>site_ID: primary key of sites table</li> </ul> <p><strong>sites</strong></p> <ul> <li>site_ID: unique integer, primary key</li> <li>site_name: internal name or code of sampling site as used in respective projects</li> <li>latitude_numeric: site's numeric degrees of latitude</li> <li>longitude_numeric: site's numeric degrees of longitude</li> <li>blurred_coordinates: whether latitude and longitude coordinates are inaccurate, boolean. Coordinates may be blurred with random offsets, rounding, snapping, etc. Indicate the blurring method inside the comments field</li> <li>topography_m: vertical position of the microphone relative to the sea level. for sites on land: elevation. For marine sites: depth (negative). in meters. Only indicate if the values were measured by the collaborator.</li> <li>freshwater_depth_m: microphone depth, only used for sites inside freshwater bodies that also have an elevation value above the sea level</li> <li>realm: Ecosystem type: main realm according to IUCN GET https://global-ecosystems.org/</li> <li>biome: Ecosystem type: main biome according to IUCN GET https://global-ecosystems.org/</li> <li>functional_group: Ecosystem type: main functional group according to IUCN GET https://global-ecosystems.org/</li> <li>contributor_comments: free text field for contributor comments</li> <li>GADM_0: Global ADMinistrative Database level 0 classification of terrestrial site or marine site that is within territorial waters. Source: https://gadm.org/download_world.html</li> <li>IHO: International Hydrographic Organization classification of marine site. Source: https://marineregions.org/downloads.php</li> <li>WDPA: World Database on Protected Areas classification of the site. Source: https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA</li> </ul> <p><strong>deployments</strong></p> <ul> <li>dataset_ID: primary key of datasets table</li> <li>deployment: identical subscript letters to denote rows that belong to the same deployment. For instance, you may use different operation times and schedules for different target taxa within one deployment.</li> <li>subset_site_ID: If the deployment was not done in all the sites of the corresponding collection, site IDs where the deployment was conducted</li> <li>start_date: date of deployment start</li> <li>start_time_mixed: deployment start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset). Corresponds to the recording start time for continuous recording deployments. If multiple start times were used, you should mention the latest start time (corresponds to the earliest daytime from which all recorders are active). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")</li> <li>permanent: whether the deployment is permanent, boolean</li> <li>end_date: date of deployment end (date when last scheduled operation starts)</li> <li>end_time_mixed: deployment end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Corresponds to the recording end time for continuous recording deployments.</li> <li>operation_mode: continuous: recording takes place from the deployment start date-time to deployment end date-time.<br>periodical: recording takes place periodically (i.e., with duty cycle) from the deployment start date-time to deployment end date-time.<br>scheduled: recording takes place during scheduled daily time intervals (optionally with duty cycle)</li> <li>duty_cycle_minutes: duty cycle of the recording (i.e. the fraction of minutes when it is recording), written as "recording(minutes)/period(minutes)". empty if no duty cycle is used. For example: "1/6" if the recorder is active for 1 minute and standing by for 5 minutes</li> <li>operation_start_time_mixed: only for scheduled recordings: start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")</li> <li>operation_duration_minutes: only for scheduled recordings: duration of operation in minutes, if constant</li> <li>operation_end_time_mixed: only for scheduled recordings: end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Only required if durations are variable. Do not use when end times are ambiguous (for instance, if a recording could be 1 hour or 25 hours long because the end is on the next day). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")</li> <li>high_pass_filter_Hz: frequency of the high-pass filter of the recorder if applied, in Hz. Otherwise, write "none". This may be called a "low-cut" filter too.</li> <li>bit_depth: sampling bit depth of the recordings. Often constant for a particular recorder</li> <li>channels: number of recorded audio channels</li> <li>sampling_frequency_kHz: frequency at which the microphone signal was sampled by the recorder (sounds of half that frequency will be recorded)</li> <li>recorder: recorder used for deployment</li> <li>microphone: microphone used for deployment</li> <li>target_taxa: main IUCN animal taxa that were studied with this deployment, using the exact IUCN Red list names (http://www.iucnredlist.org/), separated by commas. Only genera, families, orders, and classes are accepted. Empty if there was no taxonomic focus (i.e., general soundscapes were the study focus).</li> <li>contributor_comments: free text field for contributor comments</li> <li>exact_recordings: whether the deployment data here have been superseded by inserting more exact recording date-time ranges into the meta-collection on ecoSound-web</li> </ul> <p><strong>recordings (partial download from <a href="https://ecosound-web.de/">ecoSound-web</a>)</strong></p> <ul> <li>recording_id: primary key of the recordings table</li> <li>collection_id: ID of the collection the recording belongs to</li> <li>name: name of the recording</li> <li>site_id: site ID the recording belongs to:</li> <li>recorder_id: ID of the recorder used for the recording (internal ecoSound-web code)</li> <li>microphone_id: ID of the microphone used for the recording (internal ecoSound-web code)</li> <li>recording_gain:recording gain applied for amplifying the audio signal, in decibels</li> <li>duty_cycle_recording: fraction of the recording periode when the recorder is actively recording audio</li> <li>duty_cycle_period: period of the duty cycle, i.e., time between the starts of two subsequent recordings</li> <li>note: comments (contains the target taxon)</li> <li>file_date: date of the recording start</li> <li>file_time: local time of the recording start</li> <li>sampling_rate: audio sampling rate in Hz</li> <li>bitdepth: depth in bits for each audio sample</li> <li>channel_num: number of channels</li> <li>duration: duration of the recording in seconds. Note: duty-cycled recordings cover only a proportion of this duration<strong><br></strong></li> </ul> <p><strong>affiliations</strong></p> <ul> <li>affiliation_id: primary key of affiliations table</li> <li>lab_research_group: Laboratory or research group name</li> <li>department_school_institute: department, school, or institute name</li> <li>university_institution: University or institution name</li> <li>street_address: street address</li> <li>region_state_province_city: region, state, province, or city name</li> <li>postal_code: postal code</li> <li>country: country name</li> </ul> <p><strong>primary_contributors</strong></p> <ul> <li>First_name: First, given name, anonymised when contributor is technically accepted but has not yet given publication authorisation</li> <li>Last_name: Last, family name, anonymised when contributor is technically accepted but has not yet given publication authorisation</li> <li>ORCiD</li> <li>affiliation_IDs: primary keys of the affiliations' table corresponding affiliations, separated by comma</li> <li>first_tier_position: Author position in first-tier</li> <li>publication_agreement: Has contributor explicitly agreed to share her/his meta-data in the collaboration agreement?</li> <li>co_author_first_synthesis: Has contributor confirmed co-authorship intention in the collaboration agreement?</li> </ul> <p>The following columns describe the contributor's role in the project accordint to <a href="https://credit.niso.org/">CRediT</a> taxonomy.</p> <p><strong>Auxiliary files for reproducing analysis</strong></p> <p><strong>R scripts</strong></p> <ul> <li><strong>acoustic analysis.R: </strong>reproduces the result of the soundscape case studies</li> <li><strong>metadata analysis.R:</strong> reproduces the metadata analysis results in the publication</li> </ul> <p><strong>Data from the demonstration collection (download from ecoSound-web)</strong></p> <ul> <li><strong>demo_recordings.csv:</strong> metadata of the recordings, see recordings table</li> <li><strong>demo_sites.csv: </strong>metadata of the sampling locations, see sites table</li> <li><strong>demo_tags.csv: </strong>data describing annotations made in demonstration recordings for the biophony, anthropophony, geophony, and unknown sound sources</li> <li><strong>spectrograms.zip:</strong> contains PNG format spectrograms used in generating Figure 5</li> </ul> <p><strong>Externally sourced data</strong></p> <ul> <li><strong>GET_areas_2.1.1.csv: </strong>raw data obtained from Keith et al. 2023 (https://doi.org/10.5281/zenodo.10081251), then summarized in QGIS to obtain areas per functional group</li> <li><strong>Havlik_sites.csv:</strong> data obtained from Havlik et al. 2022 supplementary material (https://www.frontiersin.org/articles/10.3389/fmars.2022.919418), originally named "Data Sheet 1.CSV"</li> <li><strong>Sugai_sites_updated.csv:</strong> data obtained from Sugai et al. 2019 (https://doi.org/10.1093/biosci/biy147), personal communication with permission</li> <li><strong>taxonomy.csv:</strong> raw data obtained from IUCN Red List for all animal taxa (https://www.iucnredlist.org/)</li> <li><strong>topography_range_latitude.csv:</strong> raw topography from GEBCO sub-ice data (https://www.gebco.net/data_and_products/gridded_bathymetry_data/), summarised by bins of 10 latitudinal rows</li> </ul>
Raw Data and Scripts for manuscript submitted to Oikos as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in a Subalpine Grassland Ecosystems'
<p>Raw Data and Scripts for manuscript submitted to PCI as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in Subalpine Grassland Ecosystems'</p>
Data and script: Community size can affect the signals of ecological drift and niche selection on biodiversity
<p>Updated version of the code. Data files are the same. This is the final version of the code, associated with a manuscript published in Ecology (doi: 10.1002/ecy.3014). A preprint is also available: https://www.biorxiv.org/content/10.1101/515098v1.abstract</p> <p>This is a unique dataset on insect communities sampled identically in a total of 200 streams in climatically highly different regions (100 in Brazil and 100 in Finland). The sampling design included 5 streams (communities) per watershed and provided us replicates of metacommunities (watersheds). Data also include information on in-stream variables (such as current velocity (m/s), depth (cm), stream width (cm), % of sand (0.25-2 mm), gravel (2-16 mm), pebble (16-64 mm), cobble (64-256 mm), and boulder (256-1024 mm), % of canopy cover by riparian vegetation, pH, conductivity, total nitrogen, and total phosphorus) and catchment level variables (such as average slope, % of native forest cover, pasture, agriculture, planted forests, urban areas, mining, water bodies, bare soil, secondary forest cover, and mixed land uses).</p> <p>In addition to the dataset, here we also provide and R code used to investigate the relationship between beta diversity and community size. This code calculates 4 types of beta-diversity metric for each of 100 watersheds (5 streams) in Brazil and Finland. Beta diversity: Sorensen and Bray-Curtis dissimilarity between all pairs. Beta deviation from null models: Raup-Crick (vegan version) and Bray-Curtis beta-deviation (based on the scripts by Chris Catano and Jonathan Myers). These beta diversity metrics are modelled against community size, environmental heterogeneity and spatial extent.</p> <p> </p>
Data and script: Catchment scale deforestation increases the uniqueness of subtropical stream communities
<p>These are datasets on benthic diatom and insect communities sampled in 100 streams along a gradient of land use intensification, ranging from streams in pristine forests to agricultural catchments in southeast subtropical Brazil. Data also include information on instream and land-use variables.</p> <p>In addition to the datasets, we also provide the R codes used to investigate whether compositional uniqueness (LCBD) and species contribution to beta diversity (SCBD) of stream diatoms and insects can be predicted by instream and land-use characteristics and by species traits and taxonomic relatedness, respectively. </p>
Data plotted in Figures in O'Connor et al. paper on methane forcing, including plotting scripts
<p>The datasets included here are of the plotted data from the figures of the paper entitled "Apportionment of the Pre-Industrial to Present-Day Climate Forcing by Methane using UKESM1" submitted for publication to J. Adv. Modeling Earth Sys as csv files. Scripts used for plotting also included. </p>
Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot
<p>Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot</p> <blockquote> <p><strong>X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil </strong>- [<a href="https://doi.org/10.1002/ppj2.20036">https://doi.org/10.1002/ppj2.20036</a>]</p> </blockquote> <p>The ZIP file contains:</p> <ul> <li><code>MCT1_Rcode.R</code> - Statistics script for candidate single-timepoint experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.</li> <li><code>MCT1... .csv</code> - 3 CSV data files required by the R script.</li> <li><code>MCT2_Rcode.R</code> - Statistics script for time-series experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.</li> <li><code>MCT2... .csv</code> - 3 CSV data files required by the R script.</li> <li><code>R_RooThProcessing.R</code> - R code for aggregating root traits from RooTh software.</li> <li><code>Modelling folder</code> - OpenSimRoot with model parameters and root data used in manuscript.</li> </ul>
Detrital Carbonate Minerals in Earth's Element Cycles (Data & Scripts)
<p>Earth surface conditions, including climate and sea level, are largely controlled by the cycling of carbon and biogeochemically coupled elements. However, most elemental budgets cannot be consentaneously balanced for the present state. Here, we investigate the possible role of riverine carbonate minerals in biogeochemical cycles. We derive individual river basin export fluxes, the global export flux to the ocean and its reduction by human influence, utilizing state-of-the-art regression techniques and published global-scale datasets. Results point to a significance of riverine detrital carbonates for the global mass balances of carbon, calcium, alkalinity and strontium, which might help solving this long-standing problem. </p> <p>[Plain Language summary from: Müller et al. 2022, Detrital Carbonates in Earth's Element Cycles, GBC, <a href="https://doi.org/10.1002/essoar.10508409.1">https://doi.org/10.1002/essoar.10508409.1</a> ].</p> <p>Here data and scripts on which these investigations are based can be accessed.</p> <p> </p> <p>Funding:<br> This work was carried out under the umbrella of the Netherlands Earth System Science Centre (NESSC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie, grant agreement No 847504. Funding was also provided by BMBF-project PALMOD (Ref 01LP1506C) through the German Federal Ministry of Education and Research (BMBF) as Research for Sustainability inititative (FONA). AS thanks the European Research Council for Consolidator Grant 771497.</p>
Data, plotting scripts, and figures for "Assessing diffusion model impacts on enstrophy and flame structure in lean premixed flames"
<p>This repository contains the data, plotting scripts, and figures associated with the paper "Assessing diffusion model impacts on enstrophy and flame structure in lean premixed flames" by Aaron J. Fillo, Peter E. Hamlington, and Kyle E. Niemeyer.</p> <p>See the README file for additional details.</p>
Dataset and scripts for the paper with title Evaluating Programming Models for the HPC GPU Ecosystem
<p>Dataset and scripts for the paper with title Evaluating Programming Models for the HPC GPU Ecosystem</p>
Analysis scripts for the evaluation of a low-cost high-throughput plant phenotyping system
<p>Data analyses to complement "Image dataset for the evaluation of a low-cost high-throughput plant phenotyping system" (DOI: 10.5281/zenodo.5725224). "README_SetupAndAnalyses.pdf" contains instructions for setting up the high-throughput phenotyping (HTP) system and analyzing the resulting image datasets. The analyses are split into two parts. First, the automatically acquired HTP and manually acquired (DSLR) images are processed using the Python script labeled "finalGreennessAnalyses.py". The csv file labeled "labelTable.csv" is used to rename the DSLR images in terms of the date acquired and experimental conditions and must be included for the Python script to process the DSLR images. The output of the Python script includes "greennessGoProTable.txt" containing tab-delimited data regarding foliar size and greenness for each HTP image and "greennessDSLRTable.txt" containing tab-delimited data regarding foliar size and greenness for each DSLR image. The second step of the analyses includes inferential statistics (e.g., correlations and linear mixed effects modeling) and is based on the R script labeled "ghGoProAndDSLR_toPublish2.R". The csv file labeled "parAllBenches.csv" includes average solar daily light integral (solar DLI) data that were used as part of the linear mixed effects models in R.</p>
Experimental data and scripts used for the paper "Experiments and low-order modelling of intermittent transitions between clockwise and anticlockwise spinning thermoacoustic modes in annular combustors"
<p>The folder contains the experimental data, the scripts an the instructions to generate the figures of the paper.</p> <p>Because of difficulties for uploading large files on zenodo, the heaviest files, which are the acoustic measurement files (.TDMS format), are not included in the zip file, but are put aside of it.</p> <p>For the scripts to work correctly, all the tdms files should be moved in the folder Faure-BeaulieuA_StochasticTransitionsAzimuthalMode_PROCI_20200713/01_input_data/</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.