Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data

<h2>Sub-dataset: WRB soil types probabilities (part 1)</h2> <h2>Disclaimer</h2> This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk. <h2>Description</h2> This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics. This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset. This data set includes: <ul> <li>Soil types classification and relative entropy: This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li>Soil types probabilities (part 1): This data includes 92 averaged probabilities (0-1) maps for classes from abruptic.acrisols to gleyic.arenosols. The probabilites were scaled 100x (0-100). </li> <li>Soil types probabilities (part 2): This data includes 93 averaged probabilities maps for classes from gleyic.cambisols to vitric.andosols. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li>Time period: long term.</li> <li>Type of data: Soil types classification and model probabilities.</li> <li>How the data was collected or derived: The data was derived using ensemble ML models.</li> <li>Statistical methods used: Relative entropy (Kullback-Leibler divergence)</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif, the fields are: <ol> <li>generic variable name: soil.types = soil types</li> <li>variable procedure combination: ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li>Position in the probability distribution/variable type: m = mean | c = class | p = probability</li> <li>Spatial support: 30m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20220101 = 2022-01-01</li> <li>Time reference end time: 20221231 = 2022-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: v20240917 = version from 2024-09-17</li> </ol>

opencc-by-4.0Sep 2024View details →

Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data

<h2>Sub-dataset: WRB soil types probabilities (part 2)</h2> <h2>Disclaimer</h2> This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk. <h2>Description</h2> This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics. This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset. This data set includes: <ul> <li>Soil types classification and relative entropy: This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li>Soil types probabilities (part 1): This data includes 92 averaged probabilities (0-1) maps for classes from abruptic.acrisols to gleyic.arenosols. The probabilites were scaled 100x (0-100). </li> <li>Soil types probabilities (part 2): This data includes 93 averaged probabilities maps for classes from gleyic.cambisols to vitric.andosols. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li>Time period: long term.</li> <li>Type of data: Soil types classification and model probabilities.</li> <li>How the data was collected or derived: The data was derived using ensemble ML models.</li> <li>Statistical methods used: Relative entropy (Kullback-Leibler divergence)</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif, the fields are: <ol> <li>generic variable name: soil.types = soil types</li> <li>variable procedure combination: ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li>Position in the probability distribution/variable type: m = mean | c = class | p = probability</li> <li>Spatial support: 30m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20220101 = 2022-01-01</li> <li>Time reference end time: 20221231 = 2022-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: v20240917 = version from 2024-09-17</li> </ol>

opencc-by-4.0Sep 2024View details →

Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data

<h2>Sub-dataset: WRB soil types classification and relative entropy</h2> <h2>Disclaimer</h2> This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk. <h2>Description</h2> This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics. This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset. This data set includes: <ul> <li>Soil types classification and relative entropy: This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li>Soil types probabilities (part 1): This data includes 92 averaged probabilities (0-1) maps for classes from abruptic.acrisols to gleyic.arenosols. The probabilites were scaled 100x (0-100). </li> <li>Soil types probabilities (part 2): This data includes 93 averaged probabilities maps for classes from gleyic.cambisols to vitric.andosols. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li>Time period: long term.</li> <li>Type of data: Soil types classification and model probabilities.</li> <li>How the data was collected or derived: The data was derived using ensemble ML models.</li> <li>Statistical methods used: Relative entropy (Kullback-Leibler divergence)</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif, the fields are: <ol> <li>generic variable name: soil.types = soil types</li> <li>variable procedure combination: ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li>Position in the probability distribution/variable type: m = mean | c = class | p = probability</li> <li>Spatial support: 30m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20220101 = 2022-01-01</li> <li>Time reference end time: 20221231 = 2022-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: v20240917 = version from 2024-09-17</li> </ol>

opencc-by-4.0Dec 2022View details →

Soil gas concentration and flux data from two peatland forest harvesting experiments in Southern Finland

The dataset contains data analyzed in a scientific manuscript Peltoniemi et al., Soil CO2, CH4 and N2O concentrations and fluxes in peatland forests are associated with water table level - implications of selection harvesting on soil emissions, in review. The data was collected from two peatland forests. The sites Lettosuo (60.63° N, 23.95° E ) and Paroninkorpi (61.01°N, 24.75°E) locate in southern Finland, and they are fertile drained spruce mires. Both sites have unharvested control and selection harvested treatments. The dataset contains CO2, CH4, N2O and O2 concentrations analyzed by gas chromatograph from soil gas samples collected with silicon rubber tubes inserted into soil at different depths over the year 2018-2020. The dataset additionally contains soil gas flux measurements in 2020 with a portable gas analyzer. Air temperature and water table level were measured at both sites, rainfall in nearby meteorological stations, and soil redox potential at one of the sites (Paroninkorpi). More information about the research sites and data is available in the manuscript, and Laurila et al., 2020 (<a href="http://urn.fi/URN:ISBN:978-952-380-191-2">http://urn.fi/URN:ISBN:978-952-380-191-2</a>).

opencc-by-4.0Feb 2023View details →

Satellite soil and vegetation water content data capturing main terrestrial ecosystem changes

I)SUMMARY This repository contains a harmonized database for the study of terrestrial ecosystem changes published in [Bueso et al., 2021]. It covers the period June 2010 - July 2020 and includes the following variables, which were harmonized to a common spatial scale of 25km and monthly temporal resolution and clustered as detailed in [Bueso et al., 2021]: - SM: soil moisture from SMOS-IC v2 - VOD: vegetation optical depth from SMOS-IC v2 - NDVI: Normalized Vegetation Difference Index from MODIS, product MOD13Q1 v6 - PREC: Rainfall from PERSIANN-CDR v2.2. Additionally, land cover information from MODIS MCD12Q1 collection 6 for years 2011 and 2019 is provided for each cluster. II) CONTACT For questions, please e-mail Diego Bueso at diego.bueso@uv.es III) DATABASE We provide the maps of identified clusters by quantile of SM and VOD and the code to generate Figs 1 and 2 of supplementary material in [Bueso et al., 2023]. We then provide for each identified cluster .mat files containing the variables described above. Further details are in the readme.txt file IV) CITE To properly acknowledge the dataset we kindly encourage users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference the following publication:  D. Bueso, M. Piles, P. Ciais, J-P. Wigneron, Á. Moreno-Martínez, G. Camps-Valls, "Soil and vegetation water content identify the main terrestrial ecosystem changes", National Science Review, 2023, <a href="https://doi.org/10.1093/nsr/nwad026">https://doi.org/10.1093/nsr/nwad026</a>

opencc-by-4.0Sep 2021View details →

Open Soil Atlas (OSA) - Raw data from Phase I (Mar-Aug 2021, Berlin)

The Open Soil Atlas citizen science project collected 77 data observations, cathegorized in a set of 10 inficators, which offer information about the quality and fertility of the soil. Area of investigation: Berlin. Data collection period: March-August 2021.

opencc-by-4.0Mar 2023View details →

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"

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: 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. 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). 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 in silico the agroecological transition by virtually pre-screening sustainable, manageable intercrop systems adapted to local conditions.

opencc-by-4.0Jul 2023View details →

Data on soil compounds, respiration and incorporation of 13C-labeled substrate

Root exudation increases the concentration of readily available carbon (C) compounds in its immediate environment. This creates ‘hotspots’ of microbial activity characterized by accelerated soil organic matter turnover with direct implications for nutrient availability for plants. However, we still lack a deeper understanding of the microbial metabolic processes that occur in the immediate vicinity of the roots during and after a root exudation event. Even though theoretical concepts have been developed, the direct consequences of root exudation on microbial metabolism and nutrient availability have never been measured in their immediate environment in intact soil. Here, we used reverse microdialysis to simulate root exudation by releasing a 13C-labelled mix of low-molecular-weight organic C compounds at discrete, mm-sized locations in undisturbed soil in combination with 13C stable isotope tracing. This approach allowed us to investigate the fine-scale temporal and spatial response of microbial metabolism and soil chemistry to root exudation at the mm-scale, and to trace microbial respiration and uptake of exuded compounds. Our results show that a 9-hour simulated root exudation pulse leads to i) a large local respiration event and ii) alteration of the temporal dynamics of soil metabolites over the following twelve days right at the spot of exudate release. Notably, we observed an approximately threefold increase in ammonium concentrations twelve hours after the pulse and increased nitrate concentrations five days after the pulse. We also observed an increase of various short-chain fatty acids, such as acetate, propionate and formate over the following days, indicating altered microbial metabolic pathways and activity. Phospholipid and neutral lipid fatty acids (PLFAs and NLFAs) of all major microbial groups were significantly enriched in 13C within a radius of 5 mm around the microdialysis probes, but not beyond. The highest relative 13C enrichment was observed in fungal NLFAs, indicating that a significant proportion of the exuded compounds had been incorporated into fungal storage compounds. Our findings indicate that the punctual release of low-molecular weight organic C compounds into intact soil significantly changes microbial metabolism and activity in its immediate surroundings, which lead to enhanced mineralisation of native organic nitrogen (N). Our observations emphasise the versatility of microbial metabolic pathways that underlie the response of soil microbes to rapidly altered C availability. They furthermore demonstrate the effectiveness of this response, as triggered by root exudation pulses, to increase nutrient availability for plants around the root.

openCC (other)Apr 2022View details →

Soil microbial community composition (16S) data from a laboratory redox fluctuation experiment conducted with an Oxisol and Mollisol

To test the response of microbial communities to periodic oxygen limitation, we conducted a laboratory experiment where two contrasting soils (a rainforest Oxisol from Puerto Rico, and an Iowa cropland Mollisol) were incubated under headspace treatments where oxygen availability varied cyclically over time. Treatments consisted of 0, 2, 4, 8, or 12 d of anoxic conditions (dinitrogen headspace) followed by 4 d of oxic conditions (i.e., ambient oxygen concentrations), and these treatments were repeated for a total of 384 d. At 0, 48, and 384 days, DNA was extracted from replicates from each treatment for sequencing of 16S rRNA amplicons. Companion biogeochemical measurements from this experiment were published previously by Huang et al. (2021a,b). These data support the Hall et al. (2022) manuscript published in Frontiers in Microbiology.

openCC0Aug 2022View details →

Nevada Desert FACE Facility Soil Organic Carbon Data

This data set is the result of soils analysis from the Nevada Desert Free-Air CO2 Enrichment Facility (NDFF) experiment in the Mojave Desert and reports soil organic carbon (%C) and delta 13C stable isotope values. These soils were collected at the end of the NDFF experiment in 2007 and stored at Cornell University until analysis in 2018. Soils were harvested from 6 cover types (5 perennial vegetation covers and unvegetated interspace soils) from 0-100 cm in the soil profile in 20 cm increments. Soils were pretreated for inorganic carbon removal using an acid fumigation technique with HCl. Bulk density from NDFF plots is provided (kg soil* ha ^ -1) so that SOC stocks may be calculated. These data provide the basis for a publication challenging the prevailing idea that arid ecosystems will increase soil organic carbon stocks under long term elevated CO2.

Pre- and Post- Hurricane Maria Dry Soil Collection (GUAN, LAJA) (repackaging of occurrences published by the NEON Biorepository Data Portal)

These samples from Soil Collection (Distributed Periodic) (NEON-SOIC-DP) were collected at NEON GUAN and LAJA sites in Puerto Rico before (July and November 2017) and after (July 2018) Hurricane Maria, a Category 5 storm that affected the Carribean and occurred September 16, 2017 – October 2, 2017.

NEON HQ Soil Archive (Megapit) (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains soil samples collected from the megapit at each terrestrial site (NEON sample class: mgp_perarchivesample). During the construction of all 47 terrestrial field sites, "Megapit" soils were collected from multiple horizons at a single soil pit that was up to 2m deep. These samples serve as a reference of soil physical and chemical conditions at the time the NEON site was constructed. The Megapit Archive is curated at the NEON program headquarters in Boulder, CO. Megapit soil samples are available upon request (https://www.neonscience.org/samples/soil-archive).

NEON Biorepository Soil Collection (Distributed Periodic) (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains air-dried soil samples collected during periodic soil sampling at NEON terrestrial sites (NEON sample class: sls_bgcSubsampling_in.bgcArchiveID). Soil biogeochemical samples are collected once every 5 years, with three unique sampling locations per plot and ten plots per site. Soil sampling is conducted to a maximum depth of 30 ± 1 cm where possible. When organic (O) and mineral (M) horizons are present within a single profile they are separated prior to analysis and archiving. However, other sub-horizons are not separated. Soil from the O horizon is homogenized and non-soil material is removed by hand (no sieving), whereas soil from the M horizon is homogenized and sieved to 2 mm. Prior to archiving, all soil samples are air-dried, then placed into glass jars and stored at room temperature. See links below for NEON data products that provide various physical, chemical, and biological measurements (pH, moisture, carbon and nitrogen content and stable isotopes, inorganic nitrogen pools and net transformation rates, microbial community composition and biomass) for these same soils. In addition, a more detailed characterization of the dominant soil types at each site, including taxonomy, texture, bulk density, and geochemical properties, occurred during the construction period of NEON through two projects. These data are available in NEON data products Soil physical and chemical properties, distributed initial characterization (DP1.10047.001) and Soil physical and chemical properties, Megapit (DP1.00096.001).

NEON Biorepository Soil Microbe Collection (Bulk Subsamples) (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains samples collected during periodic soil sampling and frozen at ultra-low temperatures in order to provide material for microbial sequencing or other microbial analyses (NEON sample classes: sls_soilCoreCollection_in.geneticArchiveSample1ID, sls_soilCoreCollection_in.geneticArchiveSample2ID, sls_soilCoreCollection_in.geneticArchiveSample3ID, sls_soilCoreCollection_in.geneticArchiveSample4ID, sls_soilCoreCollection_in.geneticArchiveSample5ID,sls_metagenomicsPooling_in.compositeSampleID). Archive samples are collected during each soil sampling bout and are promptly frozen. Three unique locations are sampled per plot, with ten plots per site. Bouts occur three times per year in order to capture the prevailing conditions at the site during different seasons, except in Alaska where only 1 bout is possible. Soil sampling is conducted to a maximum depth of 30 ± 1 cm, and when organic (O) and mineral (M) horizons are present within a single profile, they are separated prior to analysis and archiving. However, other sub-horizons are not separated. During the majority of bouts, only the top horizon (O if present, else M) is collected and archived. Soils are homogenized and non-soil material is removed by hand in the field, then subsamples are immediately frozen on dry ice. They are maintained in ultra-low temperature freezers until shipment to the Biorepository. See links below for NEON data products that provide physical, chemical, and biological measurements for these same soils (soil pH and moisture are always measured; chemical properties as well as microbial community composition and biomass are determined only for a subset of collection bouts). In addition, a more detailed characterization of the dominant soil types at each site, including taxonomy, texture, bulk density, and geochemical properties, occurred during the construction period of NEON through two projects. These data are available in NEON data products Soil physical and chemical

NEON Biorepository Soil Microbe Collection (DNA Extracts ) (repackaging of occurrences published by the NEON Biorepository Data Portal)

This collection contains genetic extracts from soil microbes collected during periodic soil sampling at NEON terrestrial sites (NEON sample class: mic_dnaExtraction_in.soilDnaSampleID). Three unique locations are sampled per plot with ten soil plots per site. Bouts occur three times per year in order to capture the prevailing conditions at the site during different seasons, except in Alaska where there only 1 bout is possible. However, the frequency of genetic analysis and thus DNA archiving varies by site type. Soil sampling is conducted to a maximum depth of 30 ± 1 cm, and when organic (O) and mineral (M) horizons are present within a single profile, they are separated prior to analysis and archiving. However, other sub-horizons are not separated. During the majority of bouts, only the top horizon (O if present, else M) is analyzed for genetic content. Soils are homogenized and non-soil material is removed by hand in the field, then subsamples are immediately frozen on dry ice. They are maintained in ultra-low temperature freezers until they are shipped to an analytical facility for DNA extraction, sample preparation and sequencing. During peak greenness bouts, subsamples from each of the 3 sampling locations per plot are combined to form a plot-level composite that is used for metagenomics analysis. Laboratory metadata are delivered to NEON for QC testing and acceptance, and then formatted for upload to public sequence repositories. Genetic extracts are shipped from the analytical facility in 96-well plates to the NEON Biorepository to be archived at -80 degrees Celsius. See links below for NEON data products that provide physical, chemical, and biological measurements for these same soils (soil pH, moisture, and microbial properties are always measured; chemical properties are determined only for a subset of collection bouts). The metabarcoding protocol used by Battelle Applied Genomics is available in the NEON document library (https://data.neonscience.org/docu

Ayres 2019: Quantitative Guidelines for Establishing and Operating Soil Archives (repackaging of occurrences published by the NEON Biorepository Data Portal)

Ayres, E. 2019. Quantitative Guidelines for Establishing and Operating Soil Archives. Soil Science Society of America Journal, 83(4): 973-981. https://doi.org/10.2136/sssaj2019.02.0050  

openCC (other)Jul 2023View details →

Soil physical, chemical, and root data from forest stands at Thompson Farm in Durham, NH

In July 2019, quantitative soil pits were excavated alongside replicate power cores from three mature oak-pine forest stands at Thompson Farm (Durham, NH) to characterize soil physical and chemistry properties as well as root depth profiles in control and treatment plots associated with the Thompson Farm DroughtNet project, as well as in the footprint of the Ameriflux tower. Mean total sampling depth across all pits and cores was 84cm, though half of samples reached 100 cm or deeper. Chemical and isotopic analyses for each sample layer include pH, total N, total C, and stable isotope ratios of N and C. Physical data include soil mass, rock volume, bulk density, and soil texture. Oven-dried root mass in each sample layer was recorded in multiple size categories (from pits) or sorted to species (from power cores).

openCC0Jan 2019View details →

Soil moisture data across a Florida scrub and sandhill landscape collected from 1998-2018 at Archbold Biological Station

This project was initiated 1998 to examine the variation in percent soil moisture in relation to rainfall, vegetation type, gaps, and time-since-fire in upland habitats at Archbold Biological Station, in south-central Florida. Data were collected from 78 sampling points across four vegetation types (rosemary scrub, scrubby flatwoods, oak-hickory scrub and sandy roadsides) with different time-since-fires (2-3 years or >20 years post-fire). In January 2006, 30 additional sampling points were added to include a fourth vegetation type (southern ridge sandhill). Data were collected at three depths below the soil surface (10, 50 and 90 cm) weekly (1 October 1998 – 9 June 1999), then bi-weekly (23 June 1999 – 3 October 2001), monthly (17 October 2001 – 15 June 2011), and every other month thereafter until the project ended on 16 July 2018.

openCC (other)Feb 2020View details →

American Residential Macrosystems - Soil chemistry data within parks and residential yards in six major metropolitan areas in the United States, 2017-2018

"In six major U.S. metropolitan cities (Boston, Baltimore, Los Angeles, Miami, Minneapolis St. Paul, and Phoenix), 1 meter soil cores were collected to evaluate soil microbial carbon and nitrogen cycle processes that are sensitive to land management. Laboratory methods followed those used by Raciti et al. (2011a,b) to measure microbial biomass carbon and nitrogen content, microbial respiration, potential net nitrogen mineralization, potential net nitrifcation, potential denitrifcation, and pools of extractable ammonium and nitrate. "