Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
52
datasets available to search
ShareScore release 0.9.0
Dataset results
52 results for “Soil database”
International Soil Carbon Network version 3 Database (ISCN3)
The ISCN is an international scientific community devoted to the advancement of soil carbon research. The ISCN manages an open-access, community-driven soil carbon database. This is version 3-1 of the ISCN Database, released in December 2015. It gathers 38 separate data set contributions, totaling 67,112 sites with data from 71,198 soil profiles and 431,324 soil layers. For more information about the ISCN, its scientific community and resources, data policies and partner networks visit: http://iscn.fluxdata.org/. For information about processes used to construct the DB: https://iscn.fluxdata.org/data/data-information/.
Soil visible–near infrared (vis–NIR) spectra for the Biomes of Australian Soil Environments (BASE) soil microbial diversity database
<p>Visible–near infrared spectra of 695 soil samples collected in the Biomes of Australian Soil Environments (BASE) soil microbial diversity project (Bissett et al., 2016). The spectra represent reflectance values from 2151 wavelengths that range from 350 nm to 2500 nm with a 1 nm interval. The dataset has unique sample identification numbers and the date of sampling, which can be related to the BASE (Australian Microbiome) database (https://data.bioplatforms.com/organization/australian-microbiome)</p>
International Soil Radiocarbon Database v1.0
<p>Synthesized soils data associated with v1.0 of the International Soil Radiocarbon Database. Detailed information about this dataset can be found at <a href="http://soilradiocarbon.org">soilradiocarbon.org</a>.</p>
ChSPD Chilean soil profile database V2
<p>ChSPD is a soil profile database for Chile. The data was compiled from different published and unpublished sources. This new soil database covers a wide range of ecosystems and climate conditions. It comprises 20 different soil physical, hydraulic, and chemical properties. Each soil property has its own number of observations, which is determined by the soil horizons surveyed and the measurements taken at each point. The ChSPD_V2 includes 19769 georeferenced records, which represent 14029 soil profiles. The properties with the most records are organic matter (15797 data points), texture distribution (clay, sand, and silt content, 4978 data points), bulk density (5088 data points), field capacity (2020), and permanent wilting point (2012). </p>
The Brazilian Soil Spectral Library (VIS-NIR-SWIR-MIR) Database: Open Access
<p><strong>Abstract:</strong></p> <p>NEW VERSION V.002 (Some Lat Long Coordinates added).</p> <p>Soil spectroscopy has emerged as a solution to the limitations associated with traditional soil surveying and analysis methods, addressing the challenges of time and financial resources. Analyzing the soil's spectral reflectance enables to observe the soil composition and simultaneously evaluate several attributes because the matter, when exposed to electromagnetic energy, leaves a "spectral signature" that makes such evaluations possible. The Soil Spectral Library (SSL) consolidates soil spectral patterns from a specific location, facilitating accurate modeling and reducing time, cost, chemical products, and waste in surveying and mapping processes. Therefore, an open access SSL benefits society by providing a fine collection of free data for multiple applications for both research and commercial use.</p> <p><strong>BSSL Description and Usefulness</strong></p> <p>The Brazilian Soil Spectral Library (BSSL), available at <a href="https://bibliotecaespectral.wixsite.com/english">https://bibliotecaespectral.wixsite.com/english</a>, is a comprehensive repository of soil spectral data. Coordinated by JAM Demattê and managed by the GeoCiS research group, the BSSL was initiated in 1995 and published by Demattê and collaborators in 2019. This initiative stands out due to its coverage of diverse soil types, given Brazil's significance in the agricultural and environmental domains and its status as the fifth largest territory in the world (IBGE, 2023). In addition, a Middle Infrared (MIR) dataset has been published (Mendes et al., 2022), part of which is included in this repository. The database covers 16,084 sites and includes harmonized physicochemical and spectral (Vis-NIR-SWIR and MIR range) soil data from various sources at 0-20 cm depth. All soil samples have Vis-NIR-SWIR data, but not all have MIR data.</p> <p>The BSSL provides open and free access to curated data for the scientific community and interested individuals. Unrestricted access to the BSSL supports researchers in validating their results by comparing measured data with predicted values. This initiative also facilitates the development of new models and the improvement of existing ones. Moreover, users can employ the library to test new models and extract information about previously unknown soil properties. With its extensive coverage of tropical soil classes, the BSSL is considered one of the most significant soil spectral libraries worldwide, with 42 institutions and 61 researchers participating. However, 47 collaborators from 29 institutions have authorized the data opening. Other researchers can also provide their data upon request through the coordinator of this initiative.</p> <p>The data from the BSSL project can also help wet labs to improve their analytical capabilities, contributing to developing hybrid wet soil laboratory techniques and digital soil maps while informing decision-makers in formulating conservation and land use policies. The soil's capacity for different land uses promotes soil health and sustainability.</p> <p><strong>Coverage</strong></p> <p>The BSSL data covers all regions of Brazil, including 26 states and the Federal District. It is in a <em>.xlsx</em> format and has a total size of 305 Mb. The table is structured in sheets with rows for observations, and columns, representing various soil attributes in the surface layer, from 0 to 20 cm depth. The database includes environmental and physicochemical properties (22 columns and 16,084 rows), Vis-NIR-SWIR spectral bands (2151 columns and 16,084 rows), and MIR channels (681 columns and 1783 rows). An ID unique column can merge the sheet for each attribute or spectral range.</p> <p><strong>Accessing original data source</strong></p> <p>Using these data requires their reference in any situation under copyright infringement penalty. Three mechanisms are available for users to reach the original and complete data contributors:</p> <p>a) Refer to sheet two for name and code-based searches;</p> <p>b) Visit the website <a href="https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes">https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes</a> or locate the contributors' list by Brazilian state;</p> <p>c) Visit the website of the Brazilian Soil Spectral Service – Braspecs <a href="http://www.besbbr.com.br/">http://www.besbbr.com.br/</a>, an online platform for soil analysis that uses part of the current SSL (Demattê et al., 2022) - It was developed and managed by GeoCiS. There, owners from all over the country can be found.</p> <p><strong>Proceeding to data analysis</strong></p> <p>We registered and organized the samples at the ESALQ/USP Soil Laboratory. Some samples arrived without preliminary data analyses, so we analyzed them for soil organic matter (SOM), granulometry, cation exchange capacity (CEC), pH in water, and the presence of Ca, Mg, and Na, following the recommendations of Donagemma et al. (2011).</p> <p>The GeoCiS research group performed spectral analyses following the procedures described by Bellinaso et al. (2010). Demattê et al. (2019) provide detailed methods for sampling, preparation, and soil analyses, including reflectance spectroscopy. Latitude and longitude data can be requested directly from the data owner. In summary, the following steps are involved in data acquisition.</p> <p>a) We subjected the soil samples to a preliminary treatment, which involved drying them in an oven at 45°C for 48 hours, grinding them, and sieving them through a 2mm mesh;</p> <p>b) We placed the samples in Petri dishes with a diameter of 9 cm and a height of 1.5 cm;</p> <p>c) We homogenized and flattened the surface of the samples to reduce the shading caused by larger particles or foreign bodies, making them ready for spectral readings;</p> <p>d) The spectral analyses took place in a darkened room to avoid interference from natural light. We used a computer to record the electromagnetic pulses through an optical fiber connected to the sensor, capturing the spectral response of the soil sample;</p> <p>e) We obtained reflectance data in the Visible-Near Infrared-Shortwave Infrared (Vis-NIR-SWIR) range using a FieldSpec 3 spectroradiometer (Analytical Spectral Devices, ASD, Boulder, CO), which operates in the spectral range from 350 to 2500 nm;</p> <p>f) The sensor had a spectral resolution of 3 nm from 350-700 nm and 10 nm from 700-2500 nm, automatically interpolated to 1 nm spectral resolution in the output data, resulting in 2151 channels (or bands); and</p> <p>g) We positioned the lamps at 90° from each other and 35 cm away from the sample, with a zenith angle of 30°.</p> <p>The sensor captured the light reflected through the fiber optic cable, which was positioned 8 cm from the sample's surface.</p> <p>We used two 50W halogen lamps as the power source for the artificial light. It's important to note that we took three readings for each sample at different positions by rotating the Petri dish by 90°.</p> <p>Each reading represents the average of 100 scans taken by the sensor. From these three readings, we calculated the final spectrum of the samples. Notably, the laboratory's equipment and procedures for soil sample spectral analyses followed the ASD's recommendations, particularly about sensor calibration using a white spectralon plate as a 100% reflectance standard.</p> <p>For the analysis in the Middle Infrared (MIR) spectral region, we followed the procedures outlined by Mendes et al. (2022). We milled the soil fraction smaller than 2 mm, sieved it to 0.149 mm, and scanned it using a Fourier Transform Infrared (FT-IR) alpha spectroradiometer (Bruker Optics Corporation, Billerica, MA 01821, USA) equipped with a DRIFT accessory.</p> <p>The spectroradiometer measured the diffuse reflectance using Fourier transformation in the spectral range from 4000 cm<sup>-1</sup> to 600 cm<sup>-1</sup>, with a resolution of 2 cm<sup>-1</sup>. We conducted these measurements in the Geotechnology Laboratory of the Department of Soil Science at Esalq-USP. We took the average of 32 successive readings to obtain a soil spectrum. Sensor calibration took place before each spectral acquisition of the sample set by standardizing it against the maximum reflectance of a gold plate.</p> <p> </p> <p><strong>Dataset characterization</strong></p> <p>The database, named BSSL_DB_Key_Soils, has five sheets containing the key soil attributes, Vis-NIR-SWIR and MIR datasets, descriptions of the contributors and the proximal sensing methods used for spectral soil analysis. The sheets can be linked by "ID_Unique" columns, which bring the corresponding rows according to the data type. Some cells are empty because collaborators have already provided data in this way. However, we have decided to keep them in the database because they have other soil key attributes. Every Column in the data sheets is described as follows:</p> <p> </p> <p><strong>Sheet 1. BSSL_Soil_Attributes_Dataset</strong></p> <p>Column 1. <strong>ID_unique</strong>: Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data;</p> <p>Column 3. <strong>Vis_NIR_SWIR_availability</strong>: availability of spectral data in visible, near-infrared, and shortwave infrared ranges;</p> <p>Column 4. <strong>MIR_availability</strong>: availability of spectral data in the middle infrared range;</p> <p>Column 5. <strong>Sampling</strong>: type of soil sampling;</p> <p>Column 6. <strong>Depth_cm</strong>: soil surface layer depth in centimeters; </p> <p>Column 7. <strong>Lat</strong>: Latitude; </p> <p>Column 8. <strong>Lat</strong>: Longitude; </p> <p>Column 9. <strong>Region</strong>: Brazilian geographical region of samples' source;</p> <p>Column 10. <strong>Municipality</strong>: Brazilian municipality of samples' source;</p> <p>Column 11. <strong>State</strong>: Brazilian Federation Unit of samples' source;</p> <p>Column 12. <strong>Vegetation</strong>: type of vegetal covering;</p> <p>Column 13. <strong>Biome</strong>: groupings of ecosystems that share similar characteristics and span different regions;</p> <p>Column 14. <strong>Geology</strong>: type of rock matter from local soil sampling;</p> <p>Column 15. <strong>Sand_gkg</strong>: Content of the soil fraction with grain size between 2 and 0.053 mm, expressed in grams per kilogram;</p> <p>Column 16. <strong>Clay_gkg</strong>: Content of soil fraction with grain size smaller than 0.002 mm, expressed in grams per kilogram;</p> <p>Column 17. <strong>SOM_gkg</strong>: Soil organic matter content, expressed in grams per kilogram;</p> <p>Column 18. <strong>pH_H2O</strong>: Soil hydrogen ion potential measured in water;</p> <p>Column 19. <strong>Ca_mmolkg</strong>: Exchangeable calcium content in the soil, expressed in millimoles per kilogram;</p> <p>Column 20. <strong>Mg_mmolkg</strong>: Exchangeable magnesium content in the soil, expressed in millimoles per kilogram;</p> <p>Column 21. <strong>Na_mmolkg</strong>: Exchangeable sodium content in the soil, expressed in millimoles per kilogram; and</p> <p>Column 22. <strong>CEC_Ph7_mmolkg</strong>: Cation exchange capacity of the soil at neutral pH, expressed in millimoles per kilogram.</p> <p> </p> <p><strong>Sheet 2. BSSL_Vis_NIR_SWIR_Dataset</strong></p> <p>Column 1. <strong>ID_Unique</strong>: Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data; and</p> <p>Column 3 – 2153. <strong>350 – 2500</strong>: Reflectance in 2151 spectral bands in nanometers from visible and near-infrared to shortwave infrared range (350 – 2500 nm).</p> <p> </p> <p><strong>Sheet 3. BSSL_MIR_Dataset</strong></p> <p>Column 1. <strong>ID_Unique:</strong> Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner_code:</strong> Acronym assigned to each contributor who allowed access to their proprietary data; and</p> <p>Column 3 – 683. <strong>4000 – 600:</strong> Reflectance in 681 spectral bands in centimeters in the middle infrared range (4000 – 600 cm<sup>-1</sup>).</p> <p> </p> <p><strong>Sheet 4. Contributors</strong></p> <p>Column 1. <strong>Owner_code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data, which identifies and links it to datasets;</p> <p>Column 2. <strong>Owner</strong>: Name of the collaborator who agreed to the availability of the data;</p> <p>Column 3. <strong>E-mail</strong>: Contact the e-mail of the owner for more information or a data request;</p> <p>Column 4. <strong>Institution</strong>: Contributor's affiliation;</p> <p>Column 5. <strong>Samples NIR</strong>: Number of Vis-NIR-SWIR samples sent to the BSSL collection;</p> <p>Column 6. <strong>Samples MIR</strong>: Number of MIR samples sent to the BSSL collection;</p> <p> </p> <p><strong>Sheet 5. Metadata</strong></p> <p>Column 1. <strong>Material and Methods</strong>: Description of procedures performed for soil data analyses</p> <p> </p> <p><strong>Expectation and Social Relevance</strong></p> <p>These data can impact various disciplines such as soil surveying, soil attribute mapping, soil analysis, soil mineralogy, soil management zones, precision agriculture, development of new datasets and scientific groups, and others. We expect this contribution to be valuable and useful to the soil research community in promoting this non-renewable natural resource's conservation and sustainable use.</p>
Harmonized Soil Database of Ecuador 2021
In Ecuador there have been two main projects that have collected national soil information. These projects are: a) “Generación de Geoinformación para la Gestión de territorio y valoración de tierras rurales de la Cuenca del Río Guayas, escala 1:25.000” (2007-2015) developed by the Instituto Espacial Ecuatoriano (IEE), and b) “Generación De Geoinformación Para La Gestión Del Territorio A Nivel Nacional" (2009-2012), developed by Sistema Nacional de Información de Tierras Rurales e Infraestructura Tecnológica (SIGTIERRAS). These projects followed a similar methodology to collect and analyze soil information. However, the resulting databases have different data structures, and they show differences in the way these projects store and present soil information. Only a portion of the original databases was digitized. Most of the available data was only available in PDF files. These PDF files need to be digitized into an easy-to-manage format (e.g., *csv). The difficulty is that each PDF represents one soil profile containing morphological and analytical soil information. Thus, given the volume of soil information available in hundreds of PDF files, manual extraction (e.g., capturing soil data one by one) was not feasible. Therefore, automatic extraction of soil information from each PDF file was developed using open-source programming for data management and statistical computing (in Python and R). This process was developed to optimize data extraction from PDF files. The soil information from both projects in PDF files has been digitized and unified into one harmonized database. We present a new database for Ecuador containing soil information from 13,542 soil profiles, 5 368 are from the IEE project and 8 174 profiles from the SIGTIERRAS project. The new database includes 5368 are from the IEE project and 8174 profiles from the SIGTIERRAS project. The new database includes data from 51,692 soil horizons and information of about 20 morphological and 46 analytical variabl
Database to: Cover crops affect pool specific soil organic carbon in cropland – A meta‐analysis
<p>Database to a meta-analysis studying the effects of cover crops on the mineral-associated organic carbon pool (MAOC), the particulate organic carbon pool (POC) and the microbial biomass carbon pool (MBC). Consists of:<br>1. information on the database<br>2. legend<br>3. list of included studies, all extracted data necessary for response ratio calculation and moderator analysis, and additional information</p>
Revised database of the Soil Information System of Latin America and the Caribbean, SISLAC
<p>This dataset contains the revised version of the SISLAC database in three formats: comma-separated values (.csv), microsoft access (.mdb) and PostGIS database (.backup). This database was reviewed and the inconsistencies found in the profiles and in the description of their horizons were corrected. Consists of two tables, one for the description of the profiles and the other with the description of the horizons and their properties. The key field between both tables is the profile identifier, column <strong><em>profile_id</em></strong>.</p>
A time series database of available P concentrations and grass growth in soils receiving DPW fertilizers
<p>This data set contains a time-series database of available and exchangeable P concentrations and grass dry matter yield in soils receiving struvites and hydrochar produced from DPS</p>
Global Soil Bulk Density DataBase (GSBDDB)
<p>We complied the Global Soil Bulk Density DataBase (GSBDDB). This database inlcudes 162,470 soil samples (35,805 sampling sites) with bulk density (BD) and soil organic cabron (SOC) for the globle. Among them, 96,705 soil samples have soil particle size fractions (i.e. clay, silt and sand) as well. In addtion, this dataset also records spatial coordinates, elevation, mean annual precipitation, mean annual temperature, potential evapotranspiration and aridity index.</p> <p>This dataset is asscoated to the "Towards improved pedotransfer functions for estimating soil bulk density using the global soil bulk density database (DSBDDB)" by Chen et al. (in preparation).</p> <p>Manuscript citation: Chen, S., Dai, L, Shuai Q., Xue, J., Zhang, X., Xiao, Y., et al. Towards improved pedotransfer functions for estimating soil bulk density using the global soil bulk density database (DSBDDB). In preparation.</p> <p>When using the data, please cite repositories as well as the original manuscript.</p> <p>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
Revised database of the Soil Information System of Latin America and the Caribbean, SISLAC version 1.2
<p>The SISLAC_database version 1.2 contains the revised version of the SISLAC database in comma-separated values (csv) format. This database was reviewed and the inconsistencies found in the profiles and in the description of their horizons were corrected. The key field between both tables is the profile identifier, column <strong><em>profile_id</em></strong>.</p>
A database of soil type and soil depth across Everglades National Park, Florida, USA
This dataset contains record of soil type and soil depth across Everglades National Park. This data was obtained through published literature, unpublished field data, and inter-agency collaboration. Data come from either soil cores to bedrock or refusal (as specified by data source), cores that did not reach bedrock, a soil probe to determine depth, or the depth of installed surface elevation tables. Soil type was classified either along a gradient of organic matter content when data was available: mineral (<40%), intermediate (40-70%), or organic (<70%). When data on organic matter content wasn’t available but qualitative descriptions of soil type were, the type unit of soil was classified as either organic or mineral based on the descriptive lithology. When lithologic descriptions were provided and indicated the soil profile was not entirely one soil type (e.g. peat), depth of peat within core was recorded. When data source had specific coordinates, those coordinates were used. For older studies that only provided a figure showing the location of each estimate at a broad scale, location was estimated by georeferencing the figure in ArcMap, and dropping a point at each location. Records were cross-referenced with a recent vegetation map developed by the National Park Service in order to obtained vegetation community at each location (Ruiz et al. 2017).
Database of geochemical analyses of soils and rocks related to the Bosumtwi impact crater
<p>This excel file contains geochemical analyses of soils and rocks related to the Bosumtwi impact crater and its surroudings geological units - all data included in this database have been extracted from peer-reviewed literature.</p>
Fig. 3 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight
Fig. 3. The ten-year mean values (1997–2006) of roe deer antler weights for each game management unit in Hungary. Source: National Game Management Database of Hungary
Fig. 2 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight
Fig. 2. The spatial distribution of main soil types, based on the soil types and subtypes from the Agrotopography Map of Hungary. (* For abbreviations see Table 1). Source: Hungarian Academy of Sciences, Research Institute of Soil Science and Agricultural Chemistry
Fig. 5 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight
Fig. 5. The means and standard deviations of soil-evaluation-numbers (SEN; soil fertility index representing the natural fertility of different soils in the percentage of the fertility of the most fertile soil) and percentages of land-use types (agriculture and forest) for each main soil types* in Hungary.
Fig. 1 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight
Fig. 1. The values of soil-evaluation-number (representing the natural fertility of different soils in the percentage of the fertility of the most fertile soil) from the Agrotopography Map of Hungary. (* 1, <10%; 2, 10–20%; 3, 20–30%; 4, 30–40%; 5, 40–50%; 6, 50–60%; 7, 60–70%; 8, 70–80%; 9, 80–90%; 10, 90–100%). Source: Hungarian Academy of Sciences, Research Institute of Soil Science and Agricul-
Database Manuscript Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics - Gonzalez Dominguez et al
<p>The database contained the data used in the manuscript <strong>Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics, by Gonzalez Dominguez et al. </strong></p>
Figure 1 in Building a global database of soil microbial biomass and function: a call for collaboration
Figure 1. Sampling locations and underrepresented environmental conditions in the dataset as of 19 November, 2019. For each pixel, we measured the percentage of environmental conditions (e.g. climate, soil characteristics, topographic information, vegetation indices) captured by the current dataset. Hot deserts, tropical rainforests and sub-Arctic regions are the least represented.
Georeferenced Spanish Soil Profile Database (SODES)
<p>The Georeferenced Spanish Soil Profile Database (SODES) contains soil information and data values for 1683 georereferenced soil profiles, compiled from an extensive bibliographic review. SODES allows the characterisation of the different peninsular Spanish soil types and is a suitable tool for assessments and decision making purposes dealing with different issues.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.