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74 results for “plant mapping”
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.
Distribution and habitat suitability maps for Central European steppe plants
<p>This dataset contains distribution maps for Central European steppe plants and coordinates of species occurrence points used by Divíšek et al. (2022) to calibrate habitat suitability models. These models were projected onto past climates and the resulting habitat suitability maps for 10 periods since the Last Glacial Maximum (LGM) are also included. These maps were further used as input data for simulations of species migration from climatically suitable areas in the LGM to identify those that may have served as a source for colonisation of the species' current ranges. For each species, we present maps of climatically suitable areas during the LGM and mid-Holocene (for the latter period, only areas accessible from the LGM are shown), as well as maps of the "source areas" from which the species may have colonised the regions occupied today.</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.
GIS70 Konza Prairie Woody Plant Mapping in Core Watersheds (1D, 20B, and 4B) in 2019
This dataset contains the point and polygon boundaries of shrubs and trees mapped in watersheds 1D, 4B, and 20B from May to August 2019. Datatype one (GIS700) defines the point locations of all trees mapped in these watersheds. Datatype two (GIS701) defines the point locations of all shrubs less than one meter wide in these watersheds. Datatype three (GIS702) defines the boundaries of select shrub species greater than one meter wide in these watersheds. These data are available to download as zipped shapefiles (.zip), and compressed Google Earth KML layers (.kmz).
Scored protein-protein interactions accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p><a href="http://plants.proteincomplexes.org/static/data/panplant_cfms_scores_annot.txt.gz">All scored pairwise protein-protein interactions with CF-MS scores (3,076,999 unique pairwise interactions)</a></p> <ul> <li>Description: Scores between Orthogroups with the corresponding CF-MS score and eggNOG generated orthogroup descriptions.</li> <li>Note: Only the highest scoring pairs are considered significant. A CF-MS score >= 0.509 corresponds to 10% FDR, >= 0.207 corresponds to 50% FDR</li> <li>Format: OrthogroupID1 [tab] OrthogroupID2 [tab] Score [tab] Annotation1 [tab] Annotation2</li> </ul>
Protein elution profiles accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p>Key to files</p> <p><strong>Experiment_Order.csv</strong></p> <ul> <li>Description: Meta details of each experiment.</li> <li>Format: experiment_name,ExperimentID_order,tissue,experiment_type,spec,ExperimentID</li> </ul> <p><strong>Fraction_Details.csv</strong></p> <ul> <li>Description: Meta details of each fraction</li> <li>Format:FractionID,frac_order,ExperimentID</li> </ul> <p><strong>plant_virNOG_orthology.csv.gz</strong></p> <ul> <li>Description: Conversion between orthogroup and protein IDs.</li> <li>Format:ID,ProteinID,spec</li> </ul> <p><strong>orthogroup_annotation.csv.gz</strong></p> <ul> <li>Description: Orthogroup annotations</li> <li>Format:ID,Annotation,arath_genenames,arath_Entries,arath_Entry_names,arath_Protein_names,disruptions,tair_disruptions,lloyd2012_LOFs,arath_functions,arath_misc,pathway,unipathway,BioCyc,Reactome,BRENDA,kegg_pws,ec,arath_masses,arath_protein_names,arath_GO,devstages,tissues,tair,araport,orysj_genenames,orysj_Entries,orysj_Entry_names,orysj_Protein_names,orysj_disruptions,orysj_functions,orysj_misc</li> </ul> <p><strong>panplant_tidy_elution_virNOG.csv.gz</strong></p> <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong> for all experiments. Includes parts per million in each fraction. </li> <li>Format: ExperimentID,FractionID,ID,Total_PeptideCount,spec,ExperimentID_order,FractionID_order,abundance_ppm</li> </ul> <p><strong>panplant_tidy_elution_protcount.csv.gz</strong></p> <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed <strong>proteins</strong> for all experiments. </li> <li>Format: ExperimentID,FractionID,ProteinID,ProteinCount,spec,ExperimentID_order,FractionID_order</li> </ul> <p><strong>panplant_wide_elution_virNOG.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong></li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>panplant_wide_elution_virNOG_annot.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong>, includes annotation columns.</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> <p><strong>panplant_wide_elution_expnorm.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong></li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>panplant_wide_elution_expnorm_annot.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong>, including columns with annotations</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> <p><strong>[experiment_name].virNOG.wide.gz</strong></p> <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong> in one experiment</li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <ul> </ul> <p><strong>[experiment_name].protcount.wide.gz</strong></p> <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) counts for all observed <strong>proteins </strong>in one experiment</li> <li>Format: ProteinID,[Fractions]</li> </ul> <p><strong>[species]_specconcat.virNOG.wide.gz</strong></p> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups </strong>from a particular species. Only present for species with more than one experiment. </li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>[species]_specconcat.protcount.wide.gz</strong></p> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>proteins</strong> from a particular species. Only present for species with more than one experiment. </li> <li>Format: ProteinID,[Fractions]</li> </ul> <p> </p> <ul> </ul> <p>Species codes</p> <p>|Code | Species | Common name | Use |<br> |---|---|---|<br> | arath | Arabidopsis Thaliana | Arabidopsis | <br> | braol | Brassica oleracea | Broccoli |<br> | cansa | Cannabis sativa | hemp | <br> | cerri | Ceratopteris richardii | C-fern | <br> | chlre | Chlamydomonas reinhardtii | Chlamydomonas |<br> | chqui | Chenopodium quinoa | Quinoa | <br> | orysj | Oryza sativa var. japonica | Rice |<br> | selml | Selaginella moellendorffii | Selaginella | <br> | sollc | Solanum lycopersicum | Tomato | <br> | wheat | Triticum Aestivum | Wheat | <br> | soybn | Glycine max | Soybean | <br> | cocnu | Cocos nucifera | Coconut | </p> <p>| maize | MAIZE | maize | </p> <p> </p>
Planting Date Maps (Low Resolution) and County Data for the US Corn Belt
<p>Aggregated dataset on planting dates for maize and soybean crops in the US Corn Belt from 2000-2020. This dataset was generated from Landsat satellite data and covers 12 states. The methodology is described in <a href="https://doi.org/10.1016/j.rse.2023.113551">Deines et al. 2023 </a>in Remote Sensing of Environment (open access).</p> <p>Note: Version 2 is identical to Version 1, but includes a file missing from v1 (soybeans_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv)</p> <p><strong>Preferred citation:</strong></p> <p>Deines, J.M., A. Swatantran, D. Ye, B. Myers, S. Archontoulis, & D.B. Lobell. 2023. Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020. <em><strong>Remote Sensing of Environment. </strong></em><a href="https://doi.org/10.1016/j.rse.2023.113551">https://doi.org/10.1016/j.rse.2023.113551</a></p> <p><strong>Contents</strong></p> <ol> <li>Gridded map datasets of planting dates aggregated to 10 km resolution. Map datasets are contained in zip files by crop type. Each zip file contains 21 annual geotiff rasters covering the full study region. <ol> <li>Maize_plantingDates_2000-2020_10000m_DeinesEtAl_vRSE.zip</li> <li>Soybeans_plantingDates_2000-2020_10000m_DeinesEtAl_vRSE.zip</li> </ol> </li> <li>Tabular county planting date statistics derived from the high-resolution (30 m) planting date maps. This data is presented in .csv format and includes the day-of-year when 10, 25, 50, 75, and 90 percent of crop area was planted for each year. There is one record for each county-year; data files are separated by crop type. <ol> <li>maize_countyStats_doyPercentPlanted_2000-2020_DeinesEtAl_vRSE.csv</li> <li>soybeans_countyStats_doyPercentPlanted_2000-2020_DeinesEtAl_vRSE.csv</li> </ol> </li> <li>Tabular planting progress by county. These .csv's present the area planted for each crop by county for each day. They can be considered the "raw" version of item #2 (county stats) and can therefore be used to derive other planting progress percentiles other than those provided in item #2. <ol> <li>maize_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv</li> <li>soybeans_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv</li> </ol> </li> </ol> <p><strong>Metadata</strong></p> <p>1. Planting date maps</p> <ul> <li>Value: day-of-year of planting</li> <li>No Data value = -999 (grid cells outside of the study area or lacking maize/soybeans that year)</li> <li>Map projection: EPSG:5070, CONUS Albers Equal Area</li> <li>Maps for 2008-2018 use the USDA's Cropland Data Layers to identify maize pixels; maps for 1999-2007 use the Corn-Soy Data Layer produced by <a href="https://www.nature.com/articles/s41597-020-00646-4">Wang et al. 2000</a> (data + manuscript are open access).</li> </ul> <p>2. Tabular datasets</p> <ul> <li>GEOID = 5 digit county FIPS, or the unique identifier assigned to each county by the US TIGER county dataset</li> <li>county_code = 3 digit county identifier. Represents places 3-5 in the 5 digit code.</li> <li>state_code = 2 digit state identifier. Represents places 1-2 in the 5 digit code.</li> <li>DOY = day-of-year</li> <li>area_daily_m2 = area of that crop planted that day in that county, in square meters</li> <li>doy_X = day-of-year when X% of area was planted that year</li> </ul> <p><br> </p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl Plants 49)
<p>Ensembl Plants 49 derived ID mapping database for use with BridgeDb.<br> The scripts used to create these databases based on Ensembl BioMart can be found at <a href="https://github.com/bridgedb/create-bridgedb-genedb">https://github.com/bridgedb/create-bridgedb-genedb</a>.</p> <p>This work was funded by the <a href="https://fairplus-project.eu/">FAIRplus project</a> (grant agreement no 802750) and <a href="https://www.nwo.nl/en/researchprogrammes/open-science/open-science-fund/open-science-fund-2021-awarded-grants">NWO Open Science Fund</a> (grant no <a href="https://www.nwo.nl/en/projects/203001121">203.001.121</a>).</p>
Text-fig. 2. Map of the Bolca area, showing all the relevant fossil sites, and map of Italy, with the Bolca area marked by a star. in A Whole-Plant Specimen Of The Marine Macroalga Pterigophycos From The Eocene Of Bolca (Veneto, N-Italy)
Text-fig. 2. Map of the Bolca area, showing all the relevant fossil sites, and map of Italy, with the Bolca area marked by a star.
Derivation of plant functional type (PFT) maps from the ESA CCI Land Cover product
<p><em>This package supplements the following paper submitted to ESSD: <strong>Gross and net land cover changes of the main plant functional types derived from the annual ESA CCI land cover maps (1992-2015).</strong></em></p> <p><em>Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A. and Peng, S.: Gross and net land cover changes based on plant functional types derived from the annual ESA CCI land cover maps, Earth Syst. Sci. Data Discuss., 1–23, doi:10.5194/essd-2017-74, 2017.</em></p> <p><em>This package contains the protocol of converting the original annual ESA CCI Land Cover product into plant functional types (PFTs) that can be used by land surface models and the corresponding cross-walking table.</em></p> <p><em>The original ESA LC class data and translated PFTs in 2000 as an example are attached in the .zip file. The annual ESA CCI PFT maps from 1992 to 2015 at half degree resolution are also added in a .zip file.</em></p>
Map of China Pumped Storage Hydropower Plant Geospatal Distribution
<p>A map of PSH geospatial distribution in China, updated as of April 2024.</p> <p>All information are collected by using open-source on Internet. If you want to cite this map, please retain the copyright of Jingcai Cai.</p> <p>This map will also be upload in ArcGIS online soon, also the dataset will be released soon.</p> <p>If you want to contact author, could sent e-mal to me to get the dataset and have deeper communication with him.</p>
Text-fig. 11. Geological map south of the Nel'ma Bay. 1 - granodiorite (Early Palaeogene); 2 – Eocene andesitic and dacitic tuff with plant-bearing argillitic lenses; 3 – Late Eocene to Early Miocene andesite-basalt (Kizi Volcanic Group); 4 – tuffogenous sedimentary plant-bearing lenses with plant fossils; 5 – Dacite neck (Early Oligocene) 1 km south of the Dembi Bay; 6 – Pliocene pebbles and conglomerates; 7 – Plateaubasalts (Sovgavan' Formation, Late Neogene–Quaternary); 8 – Quaternary alluvial deposits; 9 – localities with fossil plants: a – Sonje, b – Bui, c – Dembi. in Mid-Latitude Palaeogene Floras Of Eurasia Bound To Volcanic Settings And Palaeoclimatic Events - Experience Obtained From The Far East Of Russia (Sikhote-Alin') And Central Europe (Bohemian Massif)
Text-fig. 11. Geological map south of the Nel'ma Bay. 1 - granodiorite (Early Palaeogene); 2 – Eocene andesitic and dacitic tuff with plant-bearing argillitic lenses; 3 – Late Eocene to Early Miocene andesite-basalt (Kizi Volcanic Group); 4 – tuffogenous sedimentary plant-bearing lenses with plant fossils; 5 – Dacite neck (Early Oligocene) 1 km south of the Dembi Bay; 6 – Pliocene pebbles and conglomerates; 7 – Plateaubasalts (Sovgavan' Formation, Late Neogene–Quaternary); 8 – Quaternary alluvial deposits; 9 – localities with fossil plants: a – Sonje, b – Bui, c – Dembi.
Text-fig. 1. Location map and original field label of T 68, fragment of arsinoithere upper molar from Oued Grigema, Tunisia. in Arsinoitherium (Embrithopoda) And Other Large Mammals And Plants From The Oligocene Of Tunisia
Text-fig. 1. Location map and original field label of T 68, fragment of arsinoithere upper molar from Oued Grigema, Tunisia.
Text-fig. 1. Locality map and Keshin Formation section at Cape Tsvetkov, East Taimyr (after Kazakov et al. 2002). 1 – tuff conglomerate, 2 – sandstone, 3 – grained siltstone, 4 – siltstone, 5 – mudstone, 6 – foraminifers, 7 – conchostracans, 8 – plant megafossils, 9 – locality of described plants, 10 – Tsvetkov Cape (East Taimyr). in Taimyria Gen. Nov., A New Genus Of Evolutionary Advanced Gymnosperms From Triassic Of The Taimyr Peninsula, Siberia, Russia
Text-fig. 1. Locality map and Keshin Formation section at Cape Tsvetkov, East Taimyr (after Kazakov et al. 2002). 1 – tuff conglomerate, 2 – sandstone, 3 – grained siltstone, 4 – siltstone, 5 – mudstone, 6 – foraminifers, 7 – conchostracans, 8 – plant megafossils, 9 – locality of described plants, 10 – Tsvetkov Cape (East Taimyr).
Mapping of solar panels and Fukushima Daiichi Nuclear Power Plant Accident-associated radioactive waste storage in 2022 and 2023, Fukushima, Japan
<p>The policy of reconstruction after the Fukushima Daiichi Nuclear Plant accident has led to a radical transformation of the landscapes of Fukushima Prefecture especially in two aspects (Asanuma-Brice et al., 2023). The first is related to an extensive decontamination policy which resulted in the removal of more than 13 million m<sup>3</sup> of contaminated soil (MOEJ, 2021). The second is the widespread installation of solar panels, which demonstrate the transition decided by the Prefecture and the inhabitants in terms of energy policy.</p> <p>A systematic mapping of these features was carried out from the satellite imagery of Google Map (2023) within the boundaries of Fukushima Prefecture. The objective was to highlight the evolution of specific land use features that are captured imprecisely by automatic detection mapping. We focused on the main visible change in the landscape in terms of land use since Fukushima Daiichi nuclear accident: contaminated waste disposal areas and solar panel fields. These zones were delineated allowing a calculation of the corresponding surface areas (m<sup>2</sup>).<strong> The dataset is composed of 4 shapefile layers: contaminated waste deposits in 2022 and 2023, solar panels in 2022 and 2023. For the year 2022 the last update was conducted in July 2022 and for the year 2023 the last update took place in March 2023.</strong></p> <p>As the land use is in constant and rapid transition (Asanuma-Brice, 2021), we considered as contaminated waste deposits, the permanent storage centers as well as the sites where there are still bags of contaminated waste in varying numbers, knowing that they will be removed and stored on other dedicated sites (Evrard et al., 2019). This choice was made to potentially identify, when the map was updated, the future uses of the land where this waste was stored temporarily.</p> <p>This dataset is part of a larger project that aims to provide the community with an interactive tool (https://mitatelab.cnrs.fr/mitate-labs-map-of-solar-panel-and-contaminated-wasted-land/) that makes available various types of information essential to the analysis of the reconstruction, such as: the delineation of the evacuated zone (which evolved throughout time), the delineation of the municipality boundaries affected by the reconstruction policy, the main services found in these localities, the location of the memorials of the disaster in the zone, as well as the geo-localization of the soil/sediment samples collected by other Mitate lab researchers in order to investigate the redistribution of radionuclides in the environment (Evrard et al., 2021).</p>
Plant Survey of Current Vegetation: MAP OF SONORAN DESERT PLANT COMMUNITY DISTRIBUTION IN THE CAPLTER STUDY AREA, PHOENIX, ARIZONA
This study represents an effort to map the distribution of plant community types across the Central Arizona - Phoenix Long Term Ecological Research (CAP-LTER) site centered in metropolitan Phoenix using Landsat ETM data. Vegetation classification was carried out using field data collected from within the study area describing woody plant species. A system was devised which represented a compromise between providing floristic information and enabling maximum spectral discrimination between community types. Image classification used reference spectra derived from training sites in the field and was carried out on subsets defined by soil surface texture in order to control for the strong background soil signature inherent to arid regions. While groundtruthing revealed that vegetation on clayey soils was mapped to 91% accuracy, other sections produced maps with less accuracy. The results of this study demonstrate that image classification of desert vegetation using only Landsat ETM data is problematic and may not be practical without other supporting data, such as radar imaging.This project attempts to produce a vegetation distribution map across undeveloped parcels of outlying desert wilderness, as well as remnant mountain parks throughout the city, contained within the Central Arizona Phoenix Long Term Ecological Research (CAPLTER) study area. This effort seeks to create the first successful classification map of Sonoran Desert vegetation derived from satellite imagery. The map would also be the first fine-scale map of plant community types in the Phoenix region. The depiction would allow for a calculation of the land area covered by each vegetation class, and which communities are exposed to development pressures. This map potentially provides a basis from which researchers can measure vegetative biomass distribution across the landscape and attempt to incorporate this component into ecological models of energy flows and biogeochemical cycling in the CAP-LTER site. I
Spatial mapping of root systems reveals diverse strategies of soil exploration and resource contest in grassland plants
<p>1. When foraging and competing for belowground resources, plants have to coordinate the behaviour of thousands of root tips in a manner similar to that of eusocial animal colonies. While well described in animals, we know little about the spatial behaviour of plants, particularly at the level of individual roots.</p> <p>2. Here, we employed statistical methods previously used to describe animal ranging behaviour to examine root system overlap and the efficiency of root positioning in eight grassland species grown in monocultures and mixtures along a gradient of neighbour densities.</p> <p>3. Species varied widely in their ability to distribute roots efficiently, with the majority of species showing significant root aggregation at very fine spatial scales. Extensive root system overlap was observed in species mixtures, indicating a lack of territoriality at the level of whole root systems. However, with increasing density of competitors, several species withdrew roots from the periphery of foraging ranges and increased intraplant root aggregation in the remaining area, which may indicate consolidation of foraging areas under competitive pressure.</p> <p>4. Several species exhibited responses consistent with resource contest in species mixtures where encounters with competitors' roots triggered increased root aggregation at the expense of foraging efficiency. Such responses only occurred in mixtures of species with comparable competitive abilities but were absent in asymmetric species combinations.</p> <p>5. Synthesis. Combining fine-scale measurement of plant root distributions with spatial statistics yields new insights into plant behavioural strategies with significant potential to impact resource foraging efficiency and productivity.</p>
QTL mapping for seedling and adult plant resistance to stripe and leaf rust in two winter wheat populations
<p><span>The two recombinant inbred lines (RIL) populations developed by crossing Almaly × Avocet S (206 RILs) and Almaly × Anza (162 RILs) were used to detect the novel genomic regions associated with adult plant resistance (APR) and seedling or all-stage resistance (ASR) to yellow rust (YR) and leaf rust (LR). Both the populations were evaluated for YR APR in two environments (2018 and 2019) and LR APR in three environments (2018, 2019, and 2020) in the Anza population and two environments (2018 and 2019) in the Avocet population; both the populations were phenotyped for one environment during 2020 for LR and YR ASR and genotyped using high throughput DArTseq technology. A set of 51 QTLs including 22 for YR APR, nine for LR APR, nine for YR ASR, and 11 for LR ASR were identified. Also, a set of 13 stable QTLs including nine QTLs (<em>QYR-APR-2A.1, QYR-APR-2A.2, QYR-APR-4D.2, QYR-APR-1B, QYR-APR-2B.1, QYR-APR-2B.2, QYR-APR-3D, QYR-APR-4D.1, </em>and<em> QYR-APR-4D.2</em>) for YR APR and four QTLs (<em>QLR-APR-4A, QLR-APR-2B, QLR-APR-3B, </em>and<em> </em></span><em>QLR-APR-5A.2</em>) <span>for LR APR were identified. </span><span>In silico analysis revealed that the key putative candidate genes such as <em>Cytochrome P450</em></span><em><span>, Protein kinase-like domain superfamily</span><span>, Zinc-binding ribosomal protein</span><span>, SANT/Myb domain</span><span>, WRKY transcription factor</span><span>, Nucleotide-sugar transporter,</span></em><span> and <em>NAC</em> </span><em><span>domain superfamily</span></em><span> were in the QTL regions and involved in the regulation of host response towards the pathogen infection. </span><span>The stable QTLs identified in this study are useful for developing rust-resistant varieties through marker-assisted selection (MAS).</span></p>
Text-fig. 1. Geological map of South Bohemian Basins. in Plant Mesofossils From The Late Cretaceous Klikov Formation, The Czech Republic
Text-fig. 1. Geological map of South Bohemian Basins.
Text-fig. 2. Location map of the Bohemian Forest lakes (CR) and Wielki Staw Lake (Poland). in Quillwort (Isoëtes), A Mysterious Plant From The Czech Republic
Text-fig. 2. Location map of the Bohemian Forest lakes (CR) and Wielki Staw Lake (Poland).
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.