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691 results for “vegetation data”
Data from: Subalpine vegetation changes in the Eastern Sudetes (1973–2021): Effects of abandonment, conservation management and avalanches
<p>This dataset contains the original data used in the article:</p> <p>Klinkovská K., Kučerová A., Pustková Š., Rohel J., Slachová K., Sobotka V., Szokala D., Danihelka J., Kočí M., Šmerdová E. & Chytrý M. (2023) Subalpine vegetation changes in the Eastern Sudetes (1973–2021): effects of abandonment, conservation management and avalanches. <em>Applied Vegetation Science</em>, 26, e12711. <a href="https://doi.org/10.1111/avsc.12711">https://doi.org/10.1111/avsc.12711</a></p> <p>The data contain plant species composition from vegetation plots repeatedly surveyed in the Hrubý Jeseník Mountains (Eastern Sudetes, Czech Republic). Vegetation plots surveyed by Leoš Bureš and Zuzana Burešová in 1973–1978 were resurveyed in 2004–2010 by Martin Kočí and Leo Bureš and resurveyed again in 2021 by the authors of this dataset. Several new plots were also surveyed in 2004–2010 and resurveyed in 2021. In the 1970s, plot locations were related to patches of a detailed vegetation map. In the 2000s, plot locations were measured using GPS with an uncertainty of less than 10 m. In 2021, plots were localized using differential GPS with ~5 cm accuracy (Topcon HiPer SR with Getac PS336 Data Collector) in each corner of the plot. These coordinates are stored in the fields Longitude, Longitude2, Longitude3, Longitude4, Latitude, Latitude2, Latitude3 and Latitude4 in the dataset.</p> <p>The dataset contains records of 148 vegetation plots, of which 66 were sampled three times (1970s, 2000s, 2021) and 82 were sampled twice (2000s, 2021), i.e. 362 vegetation-plot records in total. Their locations are shown at <a href="https://arcg.is/0uSrz9">https://arcg.is/0uSrz9</a>.</p> <p>Plot size and shape varied by vegetation type. Repeated sampling always used the same plot size as the previous sampling. Most plots were squares of 100 m<sup>2</sup> in woodlands and 16 m<sup>2</sup> in grasslands or rectangles of 10 m<sup>2</sup> in springs. All species of vascular plants were recorded in each plot, and their cover was estimated using the nine-grade Braun-Blanquet scale (Westhoff & van der Maarel, 1978). Bryophytes and lichens were recorded in only some plots, focusing on the dominant species.</p> <p>In 2021, soil samples were collected from the mineral soil horizon at 5–10 cm depth from four places within each plot. We mixed these samples for each plot and measured the soil pH and electrical conductivity of a mixed sample in a soil-water suspension (weight ratio 1:2.5) using the HQ40D digital multimeter. These data are found in the fields Soil_ph and Conduct.</p> <p>Plots were divided into managed and unmanaged based on the overlay of plot coordinates with GIS layers indicating the areas managed for conservation purposes in the last ten years provided by the Administration of the Landscape Protected Area Jeseníky (field Mown). In addition, plots were divided into affected and unaffected by the 2019 avalanche based on the positions of damaged trees observed in the field (field Avalanch).</p> <p>The structure of header data follows the structure of the ReSurveyEurope Database (<a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>). In addition, the vegetation type of each plot record in the 1970s, 2000s and 2021 is given in the fields Class_2021, Class_2000 and Class_1970, respectively</p> <p>The data on species composition and environmental variables are provided in two formats:</p> <ul> <li>Turboveg 2 database (see <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) – file <strong>TurbovegDbBackup_Jeseniky_resurvey.zip</strong>. To use this dataset in Turboveg, the database dictionary (TurbovegDdBackup_Default dictionary.zip) and species list (TurbovegSlBackup_Czechia_slovakia_2015.zip) have to be installed.</li> <li>Two TXT files with columns separated by tabs: <ul> <li><strong>Jeseniky_resurvey_species.txt</strong> contains the percentage covers of plant species in plots. Plant nomenclature harmonized according to Kaplan et al. (2019). Bryophytes, vernal species <em>Anemone nemorosa </em>and <em>Cardamine pratensis</em>, hybrids and species that could not be accurately identified were excluded. Woody species occurring in different layers were merged within each plot.</li> <li><strong>Jeseniky_resurvey_head.txt</strong> includes information about the number of species in each plot (spe.nr), the number and proportion of threatened species (IUCN categories CR, EN, VU, columns end.nr and perc.end) and unweighted means of Ellenberg-type indicator values for light (Light_IV), temperature (Temp_IV), moisture (Moist_IV), soil reaction (React_IV) and nutrients (Nutr_IV) used to test changes in these variables through time.</li> </ul> </li> </ul> <p>These data are also stored in the Czech National Phytosociological Database (Chytrý & Rafajová 2003; <a href="https://botzool.cz/vegsci/phytosociologicalDb">https://botzool.cz/vegsci/phytosociologicalDb</a>) and the ReSurveyEurope database (<a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>).</p>
Data from: Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system
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Data from: Exploring the perceptions of physicians, caregivers and families towards artificial nutrition and hydration for people in permanent vegetative state: how can a photo-elicitation method help?
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Data from: Discrepancies in vegetation phenology trends and shift patterns in different climatic zones in middle and eastern Eurasia between 1982 and 2015
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Data from: Responses of biomass allocation across two vegetation types to climate fluctuations in the northern Qinghai-Tibet Plateau
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Data from: Vegetation type controls root turnover in global grasslands
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Data from: Species richness and phylogenetic diversity of seed plants across vegetation zones of Mount Kenya, East Africa
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Data from: Consumer control as a common driver of coastal vegetation worldwide
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Data from: Mapping and exploring variation in post-fire vegetation recovery following mixed severity wildfire using airborne LiDAR
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Data from: Use of normalized difference vegetation index to assess N status and predict grain yield in rice
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SMEX04 Vegetation Data, Sonora, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set contains in situ data collected using a multispectral radiometer and a plant-canopy analyzer over the Soil Moisture Experiment 2004 (SMEX04) areas of Arizona, USA and Sonora, Mexico.
SMEX02 Watershed Vegetation Sampling Data, Walnut Creek, Iowa, Version 1
This data set contains vegetation parameters as part of the Soil Moisture Experiment 2002 (SMEX02).
SMEX05 Vegetation Water Content Data: Iowa, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set contains vegetation water content data of the Ames, Iowa, USA area. A vegetation sampling campaign was conducted in coordination with satellite calibration and correction to result in an estimated map of Vegetation Water Content (VWC).
SMEX04 Vegetation Water Content Data, Sonora, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set consists of Vegetation Water Content (VWC) data for two Soil Moisture Experiment 2004 (SMEX04) regional study areas: Arizona, USA and Sonora, Mexico. VWC was derived from the Normalized Difference Infrared Index (NDII) which was obtained from Landsat 5 Thematic Mapper (TM) imagery.
SMEX04 Vegetation Water Content Data, Arizona, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set consists of Vegetation Water Content (VWC) data for two Soil Moisture Experiment 2004 (SMEX04) regional study areas: Arizona, USA and Sonora, Mexico.
SMEX03 Soil Moisture, Meteorological, and Vegetation Data: Brazil, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set contains soil moisture, vegetation, and meteorological data. An extensive collection of photographs documenting the regional study areas is also included.
SMEX04 Vegetation Data, Arizona, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set contains in situ data collected using a multispectral radiometer and a plant-canopy analyzer over the Soil Moisture Experiment 2004 (SMEX04) areas of Arizona, USA and Sonora, Mexico.
Expression data from vegetative tissues of grain, sweet and bioenergy sorghums
GEO Series GSE49879. Saccharum hybrid cultivar; Zea mays subsp. mays; Sorghum bicolor. 78 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
Expression data from Aspergillus niger comparing aerial structures with vegetative mycelium
GEO Series GSE32123. Aspergillus niger; Aspergillus niger CBS 513.88. 6 samples. Type: Expression profiling by array.
SMAPVEX16 Manitoba In Situ Vegetation Data V001
This data set contains in situ measurements of crop density, height, and biomass collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.