Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
518
datasets available to search
ShareScore release 0.9.0
Dataset results
518 results for “useful plants”
Dataset for Accuracy of Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and CNN-LSTM Model
<p>This research paper introduces a deep learning hybrid model employing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting.The proposed method integrates the Variational Mode Decomposition (VMD) algo-rithm with the CNN-LSTM model to predict PV power generation from a solar farm in Boussada, Algeria, from January 1, 2019, to December 31, 2020. The performance of the developed model is benchmarked against other deep learning models (VMD-CNN, VMD-LSTM, CNN-LSTM) across various time horizons (15, 30, and 60 minutes) to provide a comprehensive evaluation. Our findings exhibit greater performance of the developed model compared to other architectures, showcasing promising results in solar power forecasting. This research contributes to the main goal of enhancing EMS by providing accurate solar energy forecasts.</p>
May to July 2018 regions of interest (ROIs) of tidal marsh and tidal forest plant species to be used as ground reference data in habitat mapping
We collected field data from sites distributed in habitats along the salinity axis of the Altamaha River estuary and the Duplin River to be used as ground reference data for habitat mapping. Regions of interest (ROIs) for tidal marsh (salt, brackish, tidal fresh) and tidal fresh forest vegetation species were generated near ground control points (GCP) by digitizing vegetation areas in ArcGIS 10.4 based on field maps.These observations will be used to create habitat maps from aerial photographs of the Altamaha River estuary, GA taken following Hurricane Irma to better understand how the storm surge affected tidal vegetation and to examine any shifts in vegetation type.
Estimation of Abundance and Distribution of Salt Marsh Plants from Images Using Deep Learning
Recent advances in computer vision and machine learning, most notably deep convolutional neural networks (CNNs), are exploited to identify and localize various plant species in salt marsh images. Three different approaches are explored that provide estimations of abundance and spatial distribution at varying levels of granularity in terms of spatial resolution. In the coarsest-grained approach, CNNs are tasked with identifying which of six plant species are present/absent in large patches within the salt marsh images. CNNs with diverse topological properties and attention mechanisms are shown capable of providing accurate estimations with > 90% precision and recall in the case of the more abundant plant species whereas the performance of the CNNs is observed to decline in the case of less common plant species. Estimation of percent cover of each plant species is performed at a finer spatial resolution, where smaller image patches are extracted and the CNNs tasked with identifying the plant species or substrate at the center of the image patch. In an ecological setting, several image patches (~100) are extracted and classified using this approach to estimate the percent cover of the various plant species in the image. For the percent cover estimation task, the CNNs are observed to exhibit a performance profile similar to that for the presence/absence estimation task, but with an ~ 5–10% reduction in precision and recall. Finally, estimation of the spatial distribution of the various plant species is performed via semantic segmentation of the input images at the finest level of granularity in terms of spatial resolution. The Deeplab-V3 semantic segmentation architecture is observed to provide very accurate estimations for abundant plant species; however, a significant degradation in performance is observed in the case of less abundant plant species and, in extreme cases, rare plant classes are seen to be ignored entirely. Overall, a clear trade-off is observed between
Taxonomic list of Brazilian fruit-bearing plants for human use
<h3>Lista taxonômica de plantas frutíferas para consumo humano, com curadoria da equipe do projeto <a href="https://www.inaturalist.org/projects/pomar-urbano">Pomar Urbano</a>. </h3> <p><em>[see English description below]</em></p> <p><br>As planilhas estão organizadas da seguinte forma:</p> <p><strong>PT_lista_especies_aceitas_v.3.0</strong>: contém os nomes de todas as espécies atualmente indexadas na base de dados do <a href="https://www.inaturalist.org/projects/pomar-urbano">Pomar Urbano</a>.</p> <p><strong>PT_lista_especies_adicionadas_v.3.0</strong>: contém os nomes das novas espécies que passam a integrar a base de dados do Pomar Urbano a partir da versão 3.0.</p> <p><strong>PT_lista_especies_removidas_v3.0</strong>: contém os nomes das espécies removidas da versão 3.0 da lista, e que portanto não fazem mais parte do banco de dados do projeto. </p> <p> </p> <p><strong>Metadados usados nas planilhas:</strong></p> <ul> <li><em>Nome científico</em>: O nome científico completo, com autoria e data, se conhecidos.</li> <li><em>Família</em>: O nome científico completo da família.</li> <li><em>Nome vernacular</em>: nome comum, popular.</li> <li><em>Origem</em><strong>: </strong>Declaração sobre se um organismo foi introduzido em um local e tempo específicos por meio da atividade direta ou indireta dos seres humanos modernos.</li> <li><em>Distribuição geográfica</em>: área geográfica ou região onde uma espécie ocorre no Brasil. Foram considerados como valores válidos para este campo apenas as macrorregiões do Brazil, a saber: S = Sul, SE = Sudeste, CO = Centro-Oeste, NE = Nordeste, N = Norte.</li> <li><em>Última atualização</em>: A data mais recente em que a entrada no catálogo foi alterada, atualizada ou modificada.</li> </ul> <h3>--------------------------------------------------------------------------------------------------------------------------------------<br><br>Taxonomic list of fruit-bearing plants for human consumption, curated by the <a href="https://www.inaturalist.org/projects/pomar-urbano">Pomar Urbano project</a></h3> <p><em>[Vernacular names are presented only in Portuguese; for properly processing in data management tools, downloading a Portuguese language package might be necessary]</em></p> <p>The spreadsheets are organized as follows:</p> <p>EN_list_accepted_species_v.3.0: contains the names of all species currently indexed in the <a href="https://www.inaturalist.org/projects/pomar-urbano">Pomar Urbano</a> database.</p> <p>EN_new_added_species_v.3.0: contains the names of new species that are included in the Pomar Urbano database starting from version 3.0.</p> <p>EN_removed_species_v.3.0: contains the names of species that were present in the version 2.0 of the list and are therefore no longer part of the version 3.</p> <p> </p> <p><strong>Metadata used in the spreadsheets</strong>:</p> <p><em>Scientific Name</em>: The complete scientific name, including authorship and date, if known. <em>ExactMatch</em>: <a href="http://rs.tdwg.org/dwc/terms/scientificName">dwc:scientificName</a>. </p> <p><em>Family</em>: The full scientific name of the family. <em>ExactMatch</em>: <a href="http://rs.tdwg.org/dwc/terms/family">dwc:family.</a></p> <p><em>Vernacular Name</em>: Common or popular name. <em>ExactMatch</em>: <a href="http://rs.tdwg.org/dwc/terms/vernacularName">dwc:vernacularName</a></p> <p><em>Establishment Means</em>: Statement about whether an organism has been introduced to a specific place and time through the direct or indirect activity of modern humans. <em>ExactMatch</em>: <a href="http://rs.tdwg.org/dwc/terms/establishmentMeans">dwc:establishmentMeans</a></p> <p><em>Higher geography</em>: The geographical area or region where a species occurs in Brazil. Only the macroregions of Brazil are considered valid values for this field within this dataset, namely: S = South, SE = Southeast, CO = Central-West, NE = Northeast, N = North. <em>CloseMacth</em>: <a href="http://rs.tdwg.org/dwc/terms/higherGeography">dwc:higherGeography</a></p> <p><em>Last Update</em>: The most recent date on which the catalog entry was changed, updated, or modified. <em>ExactMatch</em>: <a href="http://purl.org/dc/terms/modified">dct:modified</a></p> <p> </p>
May to July 2018 ground control points GPS coordinates of tidal marsh and tidal forest plant species to be used as ground control points in habitat mapping.
We collected field data from sites distributed in habitats along the salinity axis of the Altamaha River estuary and the Duplin River to be used as ground control points (GCP) and ground reference data for habitat mapping. GCPs for tidal marsh (salt, brackish, tidal fresh) and tidal fresh forest vegetation species were acquired. A real time kinematic (RTK) GPS survey of GPS coordinates and ground elevations for tidal marsh vegetation was carried out in May of June of 2018. A handheld GPS was used to collect GPS coordinates for tidal forest plant species in July of 2018. A total of 101 GCPs were collected in tidal habitats, with 26 in salt, 28 in brackish and 29 in tidal fresh marsh, and another 18 in tidal fresh forest. These observations will be used to create habitat maps from aerial photographs of the Altamaha River estuary, GA taken following Hurricane Irma to better understand how the storm surge affected tidal vegetation and to examine any shifts in vegetation type.
WPE01 Assessing the value added of NEON for using machine learning to quantify vegetation mosaics and woody plant encroachment at Konza Prairie
Woody encroachment, or invasion of woody plants, is rapidly shifting tallgrass prairie into shrub and evergreen dominated ecosystems, mainly due to exclusion of fire. Tracking the pace and extent of woody encroachment is difficult because shrubs and small trees are much smaller than the coarse resolution (>10m2) of common remote sensed images. However, the US government has been investing in finer resolution (<2m2) remote sensing through USDA NAIP and the National Ecological Observatory Network (NEON), both of which cost multi-million dollars each year and contain different remote sensed products. We compared two methods of classification (random forests and support vector machines) with these two freely available remotely sensed aerial images to determine if and how much NEON adds to classification accuracy and determine which method of machine learning was more accurate. All models have very high overall classification accuracy (>91%), with the NEON image a few percent more accurate than NAIP. The NEON image significantly relies on canopy height (LiDAR) to make classifications, but the importance of bands is more evenly distributed during NAIP classification. Lastly, accuracy for Eastern Red Cedar specifically is high with NEON (78-84%), compared to the relatively low classification accuracy using NAIP imagery (55-61%).
Brachypodium distachyon images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Brachypodium distachyon images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Euphorbia peplus images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Euphorbia peplus images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Arabidopsis thaliana images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Arabidopsis thaliana</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Oryza sativa images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Oryza sativa</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Solanum lycopersicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Solanum lycopersicum</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Ocimum basilicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Ocimum basilicum</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Scanning the horizon for invasive plant threats using a data-driven approach
<p>This repository holds the data and code for the manuscript "Scanning the horizon for invasive plant threats using a data-driven approach". </p> <p><strong>Contents</strong></p> <ul> <li>code: descriptions below</li> <li>data: descriptions below</li> <li>intermediate-data: datasets produced by processing original data (see code) or produced through horizon scan process (descriptions below)</li> <li>fl-plants-horizon-scan.Rproj: RStudio project for running R scripts</li> </ul> <p> </p> <table> <thead> <tr> <th scope="col">code</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>gcw_processing.R</td> <td>R script to format data downloaded from the Global Compendium of Weeds</td> </tr> <tr> <td>native_introduced_ranges.R</td> <td>R script to create map of native and introduced ranges of taxa on the final list</td> </tr> <tr> <td>random_draws_plant_families.R</td> <td>R script to evaluate over- and underrepresentation of plant families in initial and final list</td> </tr> <tr> <td>review_process_comparison.R</td> <td>R script to evaluate differences in scores before and after peer-review and consensus-building</td> </tr> <tr> <td>scores_certainty_pathways.R</td> <td>R script to create figures of scores, certainty, and pathways for final list</td> </tr> <tr> <td>risk_scores_analys.R</td> <td>R script to evaluate final risk scores</td> </tr> <tr> <td>pathways_process.R</td> <td>R script to process pathways to introduction data</td> </tr> <tr> <td>taxa_list_processing.R</td> <td>R script to create list used for rapid risk assessments from an initial list</td> </tr> </tbody> </table> <p> </p> <table> <thead> <tr> <th scope="col">data</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>cab_list_full.csv</td> <td>list of potential invasive species to Florida generated by CABI Horizon Scan Tool on November 15, 2019</td> </tr> <tr> <td>GCW_full_list_020420.csv</td> <td>Global Compendium of Weeds downloaded on February 4, 2020</td> </tr> <tr> <td>PlantAtlasDataExport-20191211-194219.csv</td> <td>Atlas of Florida plants downloaded December 11, 2019</td> </tr> <tr> <td>Taxon_x_List_GloNAF_vanKleunenetal2018Ecology_121119.csv</td> <td>GloNAF 1.2 database downloaded December 11, 2019</td> </tr> <tr> <td>the-plant-list</td> <td>The Plant List Database downloaded August 3, 2021</td> </tr> </tbody> </table> <p> </p> <table> <thead> <tr> <th scope="col">intermediate-data</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>federal_noxious_weed_list.csv</td> <td>manually formatted version of the USDA Federal Noxious Weed List downloaded March 16, 2020</td> </tr> <tr> <td>first_round_assessments_050120.csv</td> <td>rapid risk assessments for horizon scan pre-peer-review</td> </tr> <tr> <td>fl_prohibited_plants.csv</td> <td>manually compiled list of prohibited plants in Florida based on the Florida Noxious Weed List, Florida Prohibited Plants list, and Florida Invasive Species Council (all downloaded March 9, 2020)</td> </tr> <tr> <td>horizon_scan_plants_full_reviews_080321.csv</td> <td>rapid risk assessments for horizon scan post-peer-review and consensus-building</td> </tr> </tbody> </table> <p> </p>
Alignment used in "A phylogenomically informed five-order system for the closest relatives of land plants"
<p>Alignment that served as the basis for the phylogenomic analyses presented in "A phylogenomically informed five-order system for the closest relatives of land plants" — preprint on bioRxiv doi: https://doi.org/10.1101/2022.07.06.499032</p>
Taxonomy, occurrences, phylogeny, traits and uses of the entire plant genus Scleria (Cyperaceae)
<p>This resource includes several datasets:</p> <p>(1) Taxonomy (261 species): Updated taxonomy of the genus Scleria at the species level based on Bauters et al. 2016 and 2019.</p> <p>(2) Occurrences (latitude/longitude): data was compiled using observations from the Global Biodiversity Information Facility, Red List, research grade identifications from iNaturalist (accessed 13/08/2023) and records for collections from BR, K, GENT, L, MO, NY, P, US and WAG which were georeferenced using Google Earth. The dataset includes 22,759 observations from 248 species. Methodology follows Larridon et al. (2021).</p> <p>(3) Phylogeny of the genus based on three markers (ITS, ndhF, rps16) from Larridon et al. (2021) (136 species).</p> <p>(4) Traits. (i) We measured maximum height, maximum blade length, maximum blade width, stem width, nutlet length and nutlet width from 1,254 specimens of 209 species housed at Royal Botanic Gardens, Kew and the Muséum National d'Histoire Naturelle in Paris. (ii) We also compiled another dataset of 16 continuous and categorical traits for all 261 Scleria species derived from protologues and descriptions from regional floras. (iii) We measured nutlet weight for 141 species.</p> <p>(5) Uses & ecology: ethnobotany (mostly medicinal uses) and references to its ecology in several ecosystems (e.g., pollination, dispersal, ecological role). This data was gathered from several bibliographical sources, also provided.</p> <p>(6) Pictures of nutlets from 141 species.</p>
Annotation of Phytozome V12 protein plant sequences using the ragp pipeline for hydroxyproline-rich glycoprotein mining
<p>Hydroxyproline aware annotation of hydroxyproline-rich glycoprotein (HRGP) sequences was performed on sequence data from 62 plant proteomes obtained from Phytozome database (<a href="https://phytozome.jgi.doe.gov/pz/portal.html">https://phytozome.jgi.doe.gov/pz/portal.html</a>, version 12) using the ragp R package (<a href="https://github.com/missuse/ragp">https://github.com/missuse/ragp</a>, version 0.3.0.0001). </p> <p>In each archive a single comma separated value table (.csv) is present along with a README.txt file describing the contents of the corresponding .csv file. The archives are:</p> <p>- phytozome_V12.tar.gz - sequences from 62 plant proteomes (phytozome V12) with a total of 2797062 protein sequences.</p> <p>- phytozome_V12_phobius.tar.gz -<strong> </strong> Signal peptide prediction using Phobius (<a href="http://phobius.sbc.su.se/">http://phobius.sbc.su.se/</a>) on sequences present in phytozome_V12.tar.gz.</p> <p>- phytozome_V12_signalp.tar.gz -<strong> </strong> Signal peptide prediction using SignalP 4.1 (<a href="http://www.cbs.dtu.dk/services/SignalP-4.1/">http://www.cbs.dtu.dk/services/SignalP-4.1/</a>) on sequences present in phytozome_V12.tar.gz.</p> <p>- phytozome_V12_targetp.tar.gz - Signal peptide prediction using TargetP 1.1 (<a href="http://www.cbs.dtu.dk/services/TargetP/">http://www.cbs.dtu.dk/services/TargetP/</a>) on sequences present in phytozome_V12.tar.gz.</p> <p>- phytozome_V12_predict_hyp.tar.gz - Probability of proline hydroxylation for each proline from 266135 protein sequences which were predicted to be secreted by a majority vote (using Phobius, SignalP 4.1 and TargetP 1.1).</p> <p>- phytozome_V12_maab.tar.gz - Motif and amino acid bias (MAAB) classification of hydroxyproline-rich glycoproteins performed on 266135 protein sequences which were predicted to be secreted by a majority vote (using Phobius, SignalP 4.1 and TargetP 1.1). The number of predicted hydroxyprolines in each sequence is also indicated (based on predictions provided in phytozome_V12_predict_hyp.tar.gz).</p> <p>- phytozome_V12_scan_ag.tar.gz. - Hydroxyproline aware arabinogalactan motif scan performed on 266135 protein sequences which were predicted to be secreted by a majority vote (using Phobius, SignalP 4.1 and TargetP 1.1). Hydroxyproline predictions are provided in phytozome_V12_predict_hyp.tar.gz. </p> <p>- phytozome_V12_scan_ag_hmmscan.tar.gz - Detection of domains in a subset of protein sequences which were found to contain arabinogalactan motifs (a subset of phytozome_V12_scan_ag.tar.gz).</p> <p>The list of the 62 plant species is provided in phytozome_V12.tar.gz README.txt.</p> <p>For questions contact mdragicevic@ibiss.bg.ac.rs.</p> <p> </p> <p> </p>
Resource use strategies, resistance and tolerance to aerial biomass removal in Argentina mid-west native plants
<p>Dataset of the PhD Thesis from Lucas D. Gorné:<br> - Gorné LD. 2018. Estrategias de uso de recursos, resistencia y tolerancia a la remoción de biomasa aérea en plantas nativas del centro-oeste de Argentina. Tesis del Doctorado en Ciencias Biológicas. Facultad de Ciencias Exactas, Físicas y Naturales. Universidad Nacional de Córdoba. Córdoba, Argentina. https://ri.conicet.gov.ar/handle/11336/87925.</p>
Reproducibility package for Using root economics traits to predict biotic plant soil-feedbacks
<p>Using root economics space to predict biotic plant soil-feedbacks presents a novel framework linking below ground ecological theory to plant soil feedback effects. We show how to calculate root functional distance and location of two plant species in root economics space and how these measures can help to predict the strength and direction of the plant soil feedback between them. </p> <p>Contains data and scripts to reproduce analysis and figures for the manuscript (https://github.com/ggpmrutten/linkingRES-PSF)</p>
Data, code and software to reproduce the article entitled "Modeling soil-plant functioning of intercrops using comprehensive and generic formalisms implemented in the STICS model"
<p>This is the data, code and software to reproduce the article entitled " Modeling soil-plant functioning of intercrops using comprehensive and generic formalisms implemented in the STICS model". Here is a summary of the paper:</p> <p>The growing demand for sustainable agriculture is raising interest in intercropping for its multiple potential benefits to avoid or limit the use of chemical inputs or increase the production per surface unit. Predicting the existence and magnitude of those benefits remains a challenge given the numerous interactions between interspecific plant-plant relationships, their environment and the agricultural practices. Soil-crop models are critical in understanding these interactions in dynamics during the whole growing season, but few models are capable of accurately simulating intercropping systems.</p> <p>In this study, we propose a set of simple and generic formalisms for simulating key interactions in intercropping systems that can be readily included into existing dynamic crop models. This requires simulating important processes such as development, light interception, plant growth, N and water balance, and yield formation in response to management practices, soil conditions, and climate. These formalisms were integrated into the STICS soil-crop model and evaluated using observed data of intercropping systems of cereal and legumes mixtures, including Faba bean-Wheat, Pea-Barley, Sunflower-Soybean, and Wheat-Pea mixtures. We demonstrate that the proposed formalisms provide a comprehensive simulation of soil-plant interactions in various types of bispecific intercrops. The model was found consistent and generic under a range of spring and winter intercrops (nRMSE = 25% for maximum leaf area index, 23% for shoot biomass at harvest, and 18% for yield).</p> <p>This is the first time a complete set of formalisms has been developed and published for simulating intercropping systems and integrated into a soil-crop model. With its emphasis on being generic, sufficiently accurate, simple, and easy to parameterize, STICS is well-suited to help researchers designing <em>in silico</em> the agroecological transition by virtually pre-screening sustainable, manageable intercrop systems adapted to local conditions.</p> <p> </p> <p> </p> <p> </p>
The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems: scripts, model output, and parameter files
<p>This repository contains the model outputs and R scripts used to process the data to analyze the impact of the plant hydraulic parameterization of the manuscript: "The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems". The following is a detailed description of the content of this repository:</p> <p>model_output.zip: This compressed file contains the results of all the individual numerical experiments per experimental site as produced by the Comunity Land Model version 5. The files are stored in NETCDF format per year. The folder is arranged with subfolders containing the individual results from each experimental site as follows:</p> <ul> <li>rc: model output with the results of the resistant configuration of experiment 1 (RC)</li> <li>vc: model output with the results of the vulnerable configuration of experiment 1 (VC)</li> <li>k_dc: model output with the results of the default configuration used for experiments 1 and 2 (DC or DC<em>k</em><sub>max</sub>)</li> <li>k_rc: model output with the results of the low plant hydraulic conductance (L<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_irc: model output with the results of the intermediate low plant hydraulic conductance (IL<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_vc: model output with the results of the high plant hydraulic conductance (H<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_ivc: model output with the results of the intermediate high plant hydraulic conductance (IH<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_iirc: model output with the results of the additional intermediate low plant hydraulic conductance (IIL<em>k</em><sub>max</sub>) for experiment 2</li> <li>ko_dc: model output with the results of the best <em>k</em><sub>max</sub> and the default configuration of the PVC used in experiment 3</li> <li>ko_rc: model output with the results of the best <em>k</em><sub>max</sub> and the resistant configuration of the PVC used in experiment 3</li> <li>ko_vc: model output with the results of the best <em>k</em><sub>max</sub> and the vulnerable configuration of the PVC used in experiment 3</li> </ul> <p>The scripts were written for use in RStudio, and each contains a detailed description of the data requirements and outputs. Each script was developed to read directly the netcdf files of the model output and the csv files containing the transpiration estimates calculated from the SAPFLUXNET per experimental site (script 1).</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.