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245 results for “soil datasets”
Appendix dataset for "Distribution, storage, and factors influencing particulate and mineral-associated organic matter in paddy soils"
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Dataset of the bacterial fitness estimation values and mutation identifications from: Rapid decline of adaptation of Pseudomonas fluorescens to soil biotic environment
<p>Interactions between microbes can both constrain and enhance adaptation, but to date, most studies employ simplified communities and environments. We measured fitness of populations of the soil bacterium Pseudomonas fluorescens that have been evolved in both the presence and absence of a natural potting soil microbial community. Populations from both environments showed similar fitness increases with respect to the ancestor in the absence of the community, suggesting no significant cost of evolving with the community. By contrast, fitness in the presence of the community increased for community-evolved populations, but decreased considerably below the ancestral state for populations evolved in the absence of the community. This suggests some abiotic beneficial mutations are costly in the presence of the community, while others are not, with the former only selected against in the presence of the community. Most mutations underpinning fitness changes were clone specific, supporting the view that there are multiple genetic pathways to adaptation in this complex environment. Such extreme mutational effects have not been observed in comparable in vitro studies that employ much simpler abiotic and biotic environments, and suggest that a caution is need when extrapolating results from simplified in vitro systems to real-world contexts.</p>
Grand Bahama Groundwater and Soil Salinity Dataset
<p>This file contains groundwater salinity and water levels and soil salinity data collected post hurricane Dorian during the period of February 2020 - November 2020. </p>
Dataset for "Model diagnostic analysis in a cold basin influenced by frozen soils with the aid of stable isotope"
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Dataset of the effects of different levels of alpine wetland degradation on flora and soil
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Soil Erosion Modeling dataset
<p><strong><span>Descriptions of the soil erosion modeling dataset</span></strong></p> <p><span>Introduction</span></p> <p><span>The "Soil Erosion Modeling dataset.xlsx" file contains data on soil erosion modeling studies conducted in Ethiopia. It includes information about the study area, modeling methods, erosion agents, soil loss estimates, and model validation techniques. This dataset can be used to analyze trends in soil erosion modeling research in Ethiopia, identify the most used models and methods, and assess the effectiveness of different soil conservation practices. The dataset <span>focuses on studies related to soil erosion modeling and contains detailed information on various research articles. </span></span></p> <p><strong><span>Methodology</span></strong></p> <p><span>The data was collected from scientific peer-reviewed papers published between 2003 and 2023 in reputable<span>, Scopus-indexed international journals. We used the Scopus, ScienceDirect, and SpringerLink databases to retrieve and download all the articles in the present dataset. The dataset contains 38 attributes, 38 columns, and 371 rows, excluding the header. </span></span></p> <p><strong><span>Here's a brief description of the dataset:</span></strong></p> <p><span>Sheet Name: Sheet1</span></p> <p><span>Total Columns: 38</span></p> <p><span>Total Rows: Includes multiple rows<span>, each representing a distinct research study.</span></span></p> <p><strong><span>Column Descriptions</span></strong></p> <p><span>ID: Unique identifier for each study.</span></p> <p><span>Publication Year: The year the study was published.</span></p> <p><span>Publisher: The publishing entity.</span></p> <p><span>Journal: The journal where the study was published.</span></p> <p><span>DOI: Digital Object Identifier for the study.</span></p> <p><span>Authors: contains authors of each study.</span></p> <p><span>Title: Title of the research paper.</span></p> <p><span>Region of Study: Geographic region where the study was conducted.</span></p> <p><span>Name of the Study Area: Specific area within the region.</span></p> <p><span>Study Scale: The scale at which the study was conducted (e.g., Watershed, Plot).</span></p> <p><span>Erosion Agent: The agent at which the study was aimed</span></p> <p><span>Modeling Type: Modeled <span>erosion type (e.g., Rill and sheet, sediment yield)</span></span></p> <p><span>Quantification Synthesis: a <span>method of soil loss estimate (gross, net)</span></span></p> <p><span>The approach of soil loss estimate: The methodology (qualitative/quantitative)</span></p> <p><span>Average Soil loss (Mg ha-1 yr-1): The predicted average soil loss for each study</span></p> <p><span>Model Name: contains the name of all soil erosion models <span>applied in Ethiopia</span></span></p> <p><span>Modeling Aim: The aim at which the study was focused</span></p> <p><span>LUC/BMPs Status: The studies that focused on land use change or best management practices</span></p> <p><span>Modeled land use/cover (LU): The <span>land use/cover in which the study was modeled</span></span></p> <p><span>Land use/cover data sources: The sources of land use and land cover data for each study</span></p> <p><span>Modeled Period: The period when the study period was focused on</span></p> <p><span>Latitude/Longitude: The geographic coordinates of the study area</span></p> <p><span>Altmin/Altmax: The maximum and minimum altitude</span></p> <p><span>Area: Contains the area of each study</span></p> <p><span>Rainfall data indicative period: The period when the rainfall data was collected for each study</span></p> <p><span>Rainfall Data Type: <span>The type of rainfall data (point, gridded, etc.)</span></span></p> <p><span>Rainfall Time Resolution: Temporal resolution of the rainfall data (e.g., Daily, Event-based).</span></p> <p><span>Rainfall Amount (mm): Annual or seasonal rainfall amount.</span></p> <p><span>DEM Size for Topography: The size of Digital Elevation Model resolution.</span></p> <p><span>Fieldwork Activities: Whether fieldwork was conducted (Yes/No).</span></p> <p><span>Type of Fieldwork Activities: Specific types of fieldwork activities conducted.</span></p> <p><span>Soil Sampling Activities: <span>Was soil sampling conducted (Yes/No)?</span></span></p> <p><span>Type of Soil Information: <span>The soil information collected (e.g., Point, Maps).</span></span></p> <p><span>Validation of Model Results: Whether the model results were validated (Yes/No).</span></p> <p><span>Type of Validation: Method used for validation (e.g., Measured Sediment Yield).</span></p> <p><span>Model Calibration: Whether the model was calibrated (Yes/No).</span></p>
Land Use and Soil Datasets
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Dataset for the project "The role of expansive and invasive plant species in shaping the activity of microbial communities and carbon sequestration in the soil of post-mining spoil heaps."
<p>Description of the project: Research on carbon sequestration in all types of ecosystems, including human-transformed oligotrophic ecosystems, has become more important today from a climate change perspective, as it can help mitigate its effects. Increasing the potential for C sequestration on brownfield sites can be achieved by improving biological processes and developing soil organic matter reservoirs. This may be particularly relevant for the functioning of oligotrophic ecosystems such as waste coal mine spoil heaps. In studies conducted to date on soil carbon dynamics, it has been found that carbon storage capacity is related to vegetation type, among other factors. This is mainly due to the fact that different plant species affect the physical and chemical properties of the soil, the chemical composition of the litter, detritus supply and rooting depth differently. Furthermore, plant species have been shown to influence the composition and biomass of soil microbial communities, and these microorganisms are responsible for the decomposition of organic compounds in the soil. There is little knowledge of soil microbial communities and their activities in the different plant communities of post-mining spoil heaps, especially with regard to the effects of specific microbial communities on soil carbon sequestration. The aim of the project was to compare the influence of a native expansive grass species (<em>Calamagrostis epigejos</em> (L.) ROTH) and an alien invasive species (<em>Solidago gigantea</em> AITION) on the activity and structure of soil microorganisms and carbon sequestration on post-mining spoil heaps spontaneously colonised by vegetation and subjected to reclamation. The study was carried out on a model post-mining heap, part of which has been reclaimed with overburdened soil, while the remaining part has not undergone any reclamation and is subject to spontaneous succession processes. An analysis of total organic carbon (TOC) in soil substrate samples was carried out to determine the effect of the plant species studied on carbon sequestration. TOC consists of organic compounds mainly derived from root exudates, microbial biomass and decomposition of plant litter and SOM by microorganisms. Therefore, the structure of soil microbial assemblages was also investigated by means of phospholipid fatty acid profiles and the activity of these microorganisms, by means of soil enzyme analysis and functional diversity of microorganisms using BIOLOG<sup>®</sup> Ecoplates. In addition, the in situ level of CO<sub>2</sub> release from the soil was also determined.</p>
Covariates dataset for "Temporal harmonization of a national dataset for spatial prediction of soil organic carbon"
<p>Environmental covariates used to predict the spatial distribution of soil organic carbon for the article 'Temporal harmonization of a national dataset for spatial prediction of soil organic carbon'</p>
Dataset for the publication: Moisture-driven divergence in mineral-associated soil carbon persistence
<p>This data accompanies the manuscript titled, "Moisture-driven divergence in mineral-associated soil carbon persistence". Descriptions of data sources and measurement methods are given in the manuscript.</p>
Datasets for the manuscript "Soil-Rock Interface Modulating Basal Shear Failure During Flow Sliding" by Kang et al.
<p>Datasets for the manuscript "Soil-Rock Interface Modulating Basal Shear Failure During Flow Sliding" by Kang et al, including all datasets and editable figure files shown in the Figures in the main text and in the supporting information.</p>
Dataset: Effects of flooding and nutrients on Spartina patens and Sagittaria lancifolia soil shear strength: a greenhouse study
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Dataset of the bacterial fitness estimation values and mutation identifications from: Rapid decline of adaptation of Pseudomonas fluorescens to soil biotic environment
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NASMo-TiAM 250m 16-day North America Surface Soil Moisture Dataset
This NASMo-TiAM (North America Soil Moisture Dataset Derived from Time-Specific Adaptable Machine Learning Models) dataset holds gridded estimates of surface soil moisture (0-5 cm depth) at a spatial resolution of 250 meters over 16-day intervals from mid-2002 to December 2020 for North America. The model employed Random Forests to downscale coarse-resolution soil moisture estimates (0.25 deg) from the European Space Agency Climate Change Initiative (ESA CCI) based on their correlation with a set of static (terrain parameters, bulk density) and dynamic covariates (Normalized Difference Vegetation Index, land surface temperature). NASMo-TiAM 250m predictions were evaluated through cross-validation with ESA CCI reference data and independent ground-truth validation using North American Soil Moisture Database (NASMD) records. The data are provided in cloud optimized GeoTIFF format.
Dataset for paper "Soil-Rock Interface Modulating Basal Shear Failure within Landslide Shear Zones"
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Long time-series (1980-2020) high-resolution (1km) and multi-depth soil organic carbon dataset in China
<p>unit: kg C m-2 (soil oganic carbon density)</p><p>0100: denote 0-100 cm</p><p>020: denote 0-20 cm</p><p>Example 1980: 1980-1984 (five years mean soc)</p><p> </p><p> </p><p> </p>
Dataset for McClelland et al. "Crop-yield tradeoffs reduce the climate change mitigation potential of soil"
<p>The zip file in this record contains the post-processed model data for recreating analyses and figures from the manuscript "Crop-yield tradeoffs reduce the climate change mitigation potential of soil." The associated R scripts can be found at a separate Zenodo record, 10.5281/zenodo.13327482.</p>
Dataset of global climatic soil water contents and consecutive dry days
<p>The climatic water content is calculated as previously described [1, 2] using the average number of consecutive dry days obtained from precipitation timeseries (MSWEP [3]), potential evapotranspiration [4] (based on WorldClim [5]) and soil information (SoilGrids [6]).<br> The maps are generated at 0.1° resolution with a global extent of -180 to 180 °E and -60 to 90 °N.</p> <p>References<br> [1] Bickel, Samuel, Xi Chen, Andreas Papritz, and Dani Or. “A Hierarchy of Environmental Covariates Control the Global Biogeography of Soil Bacterial Richness.” Scientific Reports 9, no. 1 (August 20, 2019): 1–10. https://doi.org/10.1038/s41598-019-48571-w.<br> [2] Bickel, Samuel, and Dani Or. “Soil Bacterial Diversity Mediated by Microscale Aqueous-Phase Processes across Biomes.” Nature Communications 11, no. 1 (January 8, 2020): 1–9. https://doi.org/10.1038/s41467-019-13966-w.<br> [3] Beck, Hylke E., Eric F. Wood, Ming Pan, Colby K. Fisher, Diego G. Miralles, Albert I. J. M. van Dijk, Tim R. McVicar, and Robert F. Adler. “MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment.” Bulletin of the American Meteorological Society 100, no. 3 (March 2019): 473–500. https://doi.org/10.1175/BAMS-D-17-0138.1.<br> [4] Jensen, M. E., and H. R. Haise. “Estimating Evapotranspiration from Solar Radiation.” Proceedings of the American Society of Civil Engineers, Journal of the Irrigation and Drainage Division 89, no. 0023 (1963): 15–41.<br> [5] Fick, Stephen E., and Robert J. Hijmans. “WorldClim 2: New 1-Km Spatial Resolution Climate Surfaces for Global Land Areas: New Climate Surfaces for Global Land Areas.” International Journal of Climatology 37, no. 12 (October 2017): 4302–15. https://doi.org/10.1002/joc.5086.<br> [6] Hengl, Tomislav, Jorge Mendes de Jesus, Gerard B. M. Heuvelink, Maria Ruiperez Gonzalez, Milan Kilibarda, Aleksandar Blagotić, Wei Shangguan, et al. “SoilGrids250m: Global Gridded Soil Information Based on Machine Learning.” PLOS ONE 12, no. 2 (February 16, 2017): e0169748. https://doi.org/10.1371/journal.pone.0169748.</p> <p> </p>
Dataset for effects of shear zone thickness in root-reinforced soils: Willow F X-ray CT scans and digital volume correlation data
<p>Dataset for effects of shear zone thickness in root-reinforced soils: Willow F X-ray CT scans and digital volume correlation data</p>
Global Soil Moisture Dataset From a Multitask Model (GSM3)
<p>Global Soil Moisture Dataset From a Multitask Model (GSM3).</p> <p>Shortname: GSM3<br> Longname: Global Soil Moisture Dataset From a Multitask Model<br> Version: 1.0<br> Format: GeoTIFF<br> Spatial Coverage: Global<br> Temporal Coverage: 2015-01-01 to 2020-12-31<br> File Size: ~10.3 MB per file<br> Data Resolution<br> Spatial: 9-km<br> Temporal: Daily<br> Coordinate Reference System (CRS): EPSG:6933 - WGS 84 / NSIDC EASE-Grid 2.0 Global</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.