Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

Copper mineralization at Carajás mineral province - Brazil: geological, structural, and geophysical data

Gridded geological, structural, and geophysical data at the Carajás mineral province. A number of known Cu occurrences are provided. This dataset is suitable for experimenting with machine learning methods.

opencc-by-4.0Apr 2024View details →

Dataset to Schiedung et al. (2024): Millennial-aged pyrogenic carbon in high-latitude mineral soils

Dataset to Schiedung et al. (2024, Communications Earth & Environment): Pyrogenic Carbon is Aged at Millennial Scale in High-Latitude Mineral Soils DOI: <a href="https://doi.org/10.1038/s43247-024-01343-5">10.1038/s43247-024-01343-5</a> This repository includes the following files:  dd_all.csv - Includes all data for the individual samples that are presented in the manuscript. Var_names_dd_all.csv - Describes all variables in dd_all with corresponding unit  dd_site_average.csv - Includes all data that has been determined on composite samples for each site or the average of all samples per site  Var_names_dd_site_average.csv  -  Describes all variables in dd_site_average.csv with corresponding unit All .csv use "," as separator.  This data set is also connected to Schiedung et al. (2022, Catena <a href="https://doi.org/10.1016/j.catena.2022.106194"> https://doi.org/10.1016/j.catena.2022.106194</a> ) and the corresponding repository: <a href="../records/10609291">https://zenodo.org/records/10609291</a>

opencc-by-4.0Jan 2024View details →

Mineral spectral refractive index and bulk optical property dataset for aerosol studies

Version 1.3, updated 11/15/2024. Added a file with 27 regional dust sample mineral composition information 'NewRegionalSamples.xlsx', along with the refractive index data. All refractive index files here have 127 rows (wavelengths) and 27 columns (samples) 'kall27_coarse.dat' is the imaginary part of the coarse mode.  'kall27_fine.dat' is the imaginary part of the fine mode. 'nall27_coarse.dat' is the real part of the coarse mode. 'nall27_fine.dat' is the real part of the fine mode. Version 1.2, updated 04/23/2024. Major changes:  Changed all the data file names to new format: "mix"+{property name}+{number}, rearranged the number of mixing samples Updated all the bulk optical property data. This version use constant values of standard deviation in the lognormal size distribution settings for the coarse mode and the fine mode respectively. The phase matrices are separated from the other bulk properties due to their large file sizes. The readme file is updated correspondingly. The information of scattering angles (498 angles in total) is uploaded as "TAMUdust2020_Angle.dat". Added supplemental file data in 'Supplemental.tar.gz'. Additional refractive indices are zipped in 'AdditionalRefInd.tar.gz' Version 1.1, updated 03/14/2024. Major changes:  Added mixed bulk properties for "0 (99%coarse+1%fine)" and "11 (2.0 µm coarse+ 0.4 µm fine)"; Added "reff.dat" in the 'BulkProperties.tar.gz'. The data include four columns: fine mode fraction, bulk projected area <A>, bulk volume <V>, effective radius r_eff. The information is for mixed sample number 0 to 11, each corresponds to one row. Added refractive indices for chlorite, mica, smectite, pyroxene, vermiculite and pyroxenes. These groups can be applied in some other models. Version 1.0, uploaded 01/02/2024. This database include supplemental data and files for the publication of this paper: Sensitivities of Spectral Optical Properties of Dust Aerosols to their Mineralogical and Microphysical Properties. Yuheng Zhang, M. Saito, P. Yang, G. L. Schuster, and C. R. Trepte, J. Geophys. Res. Atmos. 2024.   ***************************************** The supplemental data include: 1) 'GroupRefInd.tar.gz' Mineral (group) refractive index files. E. g., 1All_Illite.dat contains the complex refractive index files of illite group. Format (from left to right columns): Wavelength (unit: µm), Real part (n), Imaginary part (k), standard deviation of n, standard deviation of k. The file 'fine_log.dat' includes the mean and standard deviation values of n and k for all the generated fine mode dust samples at 11,044 wavelengths from 0.2 to 50 micron. The file 'fine_log127.dat' only includes the values at 127 wavelengths from 0.2 to 50 micron (defined in 'swav.txt' and 'lwav.txt'), and is used for the bulk property computations. The files 'coarse_log.dat' and 'coarse_log127.dat' are for the coarse mode dust samples. 2) 'CompositionFraction.xlsx': Mineral composition data sources/references and composition data (mean and standard deviation values of each group). 'Vlog_coarse.dat': Randomly generated VOLUME FRACTION of 9 mineral groups for the coarse mode dust. Left to right: Illite, Kaolinite, Montmorillonite (Other clays), Quartz, Feldspar, Carbonate, Gypsum (Sulphate), Hematite, Goethite. 'Vlog_fine.dat': For the fine mode dust. 3) 'RefSources.xlsx': The data source references of mineral refractive indices. We didn't include the olivine, other silicates, soot and titanium-rich minerals in the paper, but the refractive indices are available for those who are interested. Chlorite, Mica and Vermiculite group are mentioned in some studies, and we included the refractive indices for these minerals as well. 4) 'DustSamples.tar.gz' Dust sample refractive index files. The files are enclosed in four folders: fine_sw/ fine_lw/ coarse_sw/ coarse_lw/. fine: fine mode. coarse: coarse mode. 'sw' means shortwave (< 4 µm, in total 76 wavelengths defined in 'swav.txt') while 'lw' means longwave (>= 4 µm, in total 51 wavelengths defined in 'lwav.txt'). All files start with 'rdn', which means that they are computed based on randomly generated composition (data given in sheet 2 of 'CompositionFraction.xlsx'). The four digit number after 'rdn' is the index of each dust sample. In total, there are 5,000 samples. The sample composition is the same for the same sample index in the same size mode (fine/coarse). Data file format (from left to right columns): real part, imaginary part. 5) 'BulkProperties.tar.gz' Bulk property files (excluding phase matrices) 'mixqx.dat' files format (from left to right columns): Extinction efficiency (Qext), Scattering efficiency (Qsca), Backscattering efficiency (Qbck), and Asymmetry coefficient (Qasy). To obtain asymmetry factor, use Qasy/Qsca. 'mixbkx.dat' files format (from left to right columns): P11(pi) P12(pi) P22(pi) P33(pi) P34(pi) P44(pi). 'x' refers to the number at the end of the file name. It can be 100 ~ 112, each represents a setting of coarse and fine mode effective radius and volume fraction (see details in "reff.dat") 'reff.dat' contains the effective radius information of the mixture. It has 7 columns: File number "x", Fine mode volume fraction, Fine mode effective radius (µm), Coarse mode effective radius (µm), Bulk projected area (µm^2), Bulk volume (µm^3), Bulk effective radius (µm). 6) 'PhaseMatrices.tar.gz' Phase matrices data 'mixphswx.dat' files contain phase matrix results at 532 nm (shortwave). From left to right: P11, P12, P22, P33, P34, P44. 'mixphlwx.dat' files contain phase matrix results at 10.5 µm (longwave). There are 635,000 rows in each data file. 635,000 rows = 127 wavelengths * 5,000 samples. Row 1~127 is sample 1, row 128~254 is sample 2, etc.. Suggest to use matlab function 'reshape(property, 127, 5000)' for each column when processing the data. 7) 'Supplemental.tar.gz' We also include data files mentioned in the supplemental file of the paper. The adjusted source data files of the nine mineral groups are included. The supplemental bulk property files are named based on the figure number. 8) 'AdditionalRefInd.tar.gz' We also include additional refractive indices for chlorite, smectite, vermiculite, mica, dolomite, titanium-rich minerals, pyroxenes and soot. These data can be useful in other models. For more detailed information and datasets, please contact: Yuheng Zhang, yuheng98@tamu.edu or yuhengz98@qq.com.

opencc-by-4.0Aug 2024View details →

Cirrus formation regimes - Data driven identification and quantification of mineral dust effect

This repository contains the data for the paper:  Authors: Kai Jeggle , David Neubauer , Hanin Binder and Ulrike Lohmann Titel: Cirrus formation regimes - Data driven identification and quantification of mineral dust effect Date: 2024 Note that the scripts can be found in the accompanying code repository (https://github.com/tabularaza27/cloud_clustering) Contents: ├── cirrus_cloud_trajectories.ftr ├── cluster_input_data.ftr ├── cluster_models │       └── temperature_clustering_k4_12 │           ├── cloud_ids.npy │           ├── model_params.json │           └── trained_model.hdf5 │       └── temperature_clustering_k4_24 │           ├── cloud_ids.npy │           ├── model_params.json │           └── trained_model.hdf5 ├── cluster_predictions.ftr └── readme.txt For more info, please have a look at the readme.txt This is an updated version of the data, containing updated models and predictions based on the Journal revisions

openCC (other)Feb 2025View details →

Field Evidence of Carbon and Nitrogen Stabilization through Mineral Associated Organic Matter Formation in Coastal Wetland Soils from Apalachicola, Florida, collected in June, 2022.

This data set was used to observe the role of Mineral Associated Organic Matter Formation (MAOM) on biogeochemical soil properties in three coastal wetlands in Apalachicola, Florida. One wetland was restored using beneficial dredged sediment, increasing the soil's inorganic matter content. Soil samples were collected in June 2022 from this wetland and two nearby reference wetlands: one with high organic matter and the other with higher inorganic matter content. The samples were analyzed at the University of Central Florida for biogeochemical properties to determine which properties were most related to MAOM pools.

openCC (other)Mar 2025View details →

Soil respiration rates, biogeochemical pools, and mineral-associated organic matter from high organic matter and high mineral content coastal wetland soils in Apalachicola, Florida, 2022

This data set was used to observe how the application of dredged sediment would impact soil respirations rates, biogeochemical pools, mineral associated organic matter of coastal wetland soils from Apalachicola, Florida. To achieve this, a combination of intact core and bottle incubations were used, comparing a high organic matter coastal wetland soil to a high mineral content wetland soil which were collected in June, 2022. All laboratory analysis was conducted at the University of Central Florida in Orlando, Florida.

openCC (other)Mar 2024View details →

Mass and Chemistry of Organic Horizons and Surface Mineral Soils on Watershed 6 at the Hubbard Brook Experimental Forest, 1976 - present

The forest floor of Watershed 6 was first sampled in 1969-70. These data include forest floor thickness, soil mass, organic matter content, and major-element composition for samples collected since 1976. Watershed 6 has been resampled at intervals varying from one to ten years. Sampling at five to ten year intervals is expected to continue. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Mar 2022View details →

Mass and Chemistry of Organic Horizons and Surface Mineral Soils on Watershed 1 at the Hubbard Brook Experimental Forest 1996-present

This data set includes chemistry of O-horizons ("forest floor") and the 0-10 cm mineral soil layer in Watershed 1 at Hubbard Book. Calcium in the form of wollastonite (CaSiO3) was added to Watershed 1 in October 1999. The application rate was 1028 kg Ca per ha, and the application was relatively uniform across the watershed. Pre-treatment forest floor surveys were completed in 1996 and 1998. The first post-treatment forest floor survey was completed in 2000. This data set includes mass and thickness data for the sampled layers. Chemical data include concentrations and pools of organic matter, C, N, Ca, Mg, K, P, Mn, Fe, Al, Cu, Pb, and Zn. Soil pH and exchangeable Al, Ca, Mg, K, and H are also included. Sampling is intended to continue at 4 or 5 year intervals. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Dec 2024View details →

Hubbard Brook Nitrogen Oligotrophication (HBNO): In-situ Nitrogen Mineralization and Nitrification, 2021-2023

The goal of this project is to test the overarching hypothesis that positive feedback mechanisms involving changes in seasonal cycles that diminish N availability to plants such that plant N demand is not met by soil N availability in northern forests. Specifically, we hypothesize that increasing N demand by plants (induced by increasing temperatures, longer growing seasons, and other environmental changes) leads to greater N resorption by trees in autumn, increased C:N in litter, and greater net immobilization of N by soil microbes in the following spring. However, the timing of snowmelt and soil freezing in spring may further affect net mineralization and N availability for plants. These hypotheses are being tested with a combination of observational, experimental, and modeling approaches at Hubbard Brook Experimental Forest in New Hampshire: 1) measurements at 14 previously established sites along an elevation/aspect climate gradient; 2) litter and snow manipulation experiments at six sites along the climate gradient to create variation in soil climate conditions and microbial N immobilization during spring. We leveraged 14 sites previously established along an elevation and aspect-driven climate gradient at Hubbard Brook as a “natural climate experiment" to test our hypothesis that a positive feedback between N cycling during fall senescence and spring contributes to declining N availability in northern forests. This elevation gradient encompasses variation in mean annual air temperature of ~2.5 °C that is similar to the change projected to occur with climate change over the next 50–100 years in the northeastern U.S. There is relatively little variation in soils along the gradient. We are utilizing three sites at higher elevation (~550-660 m, north facing) and three sites at lower elevation (~375-500 m, south facing) for the litter and snow manipulation experiments to maximize the differences in temperature among the 14 sites. Litterbox manipulation: The objecti

openCC (other)Apr 2023View details →

Nutrient mineralization from green leaves in litterbags of three mesh sizes in the LUQ-LTER Canopy Trimming 2 Experiment

Hurricanes generate disturbances in forests that alter physicochemical characteristics of the habitat by opening the canopy and depositing fresh wood and leaves. Our objectives were to evaluate the effects of simulated hurricane driven changes to nutrient fluxes from litter to soil immediately following canopy disturbance. This study used three complete replicated blocks with two canopy treatments, control and trim+debris. Measurements were made in three 5 x 5 m subplots within 20 x 20 m plots nested in the 30 x 30 m treatment areas. Anion and cation resin membranes were inserted into the fermentation layer at the litter-soil interface and retrieved after one week. The measurement intervals were 2-4 weeks before canopy trimming, 0-1, 1-2, 2-3 and 4-5 weeks after trimming. Nutrient mineralization differed significantly between control and trim+detritus. Total N and P fluxes occurred at 4-5 weeks after canopy trimming. Litter decomposition depends primarily on the interaction among climate, litter quality and biota, so consequently any change in habitat will result in changes in these factors. Our objectives were to evaluate the effects of hurricane driven changes to forests on green litter decomposition, invertebrate communities and nutrient mineralization. This study used three complete replicated blocks with two canopy treatments, control and trim+debris. Measurements were made in three 5 x 5 m subplots within 20 x 20 m plots nested in the 30 x 30 m treatment areas. Green leaves were enclosed in litterbags of three different mesh sizes in each subplot. Litterbags were retrieved after 21, 35, 84 and 168 days; decomposer fauna was extracted and identified, mineralized nutrients were measured using ion resin membranes, and weight loss was determined. Arthropod abundance differed significantly through time. In addition, the number of arthropod taxonomic groups and nutrient mineralization differed significantly between control and trim+detritus, and nutrient mineralizat

zenodo48/100

Map of soil organic carbon loss of mineral soils in Estonia

The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. The map was generated to evaluate soil organic carbon (SOC) loss in Estonian agricultural soils. It is directly related to SERENA project WP3, T3.2, D3.3 with the aim of applying cookbooks to assess soil threats or ecosystem services. This map is the outcome of applying a cookbook developed by ISRIC (Genova, G., Poggio, L., Kempen, B., & Colman, B. DSM Workflow Seedling. ISRIC - World Soil Information. https://doi.org/10.17027/ISRIC-FSX2-2691). The generated map of SOC loss expressed as absolute sequestration rate (t C ha-1 a-1) between 2015 and 2021 is in GEOTIFF format at the resolution of 100m. The input data for the cookbook was from the PANDA database, which contains regular soil monitoring and voluntary soil sampling data by farmers in Estonia. To achieve the aim for accounting SOC loss in agricultural soils temporal pairs were selected resulting in 1037 paired points where the interval between second sampling was more than 5 years. SOC stocks were calculated for the depth of 20 cm using the equation by Adams (1973) to calculate soil bulk density. The calculated SOC stock for time0 and time2 (> 5 years resampled locations) were used as input points for digital soil mapping, that is the ISRIC cookbook.

opencc-by-4.0Oct 2024View details →

zenodo48/100

Catalog of synthetic seismic records from mineral physics and travel-time tables from Waszek et al., 2021, Nature Geoscience

This release is associated with the accepted publication in Nature Geoscience: Waszek L., Tauzin B., Schmerr N., Ballmer M. and Afonso J.C. A poorly mixed mantle transition zone and its thermal state inferred from seismic waves. Nature Geoscience, 2021. This dataset must be used in conjunction with the NoLimit software package (https://zenodo.org/record/5512805). Both the software and dataset allow the prediction of synthetic seismic waveforms for SS and PP-precursors from mineral physics models, as well as their processing for reconstructing the surface of seismic boundaries associated with major mineralogical phase transitions in the Earth’s mantle (namely, the 410-km and 660-km depth discontinuities). For technical reasons (storage and quick access), the catalog is downsampled with respect to the one in Waszek et al. (2021), and it is provided with the HDF5 format. For more advanced applications such as changing mantle composition, or generating waveforms for deeper earthquakes, please contact Benoit Tauzin (benoit.tauzin@univ-lyon1.fr) and Lauren Waszek (lauren.waszek@jcu.edu.au). The dataset includes: * A fixed mantle composition, which is a mechanical mixture of basalt and harzburgite with a fraction of basalt f=0.2. * A downsampled catalog of synthetic waveforms for event depths between 0 and 80 km by step of 10 km (enough for reproducing the processing of observed SS and PP precursors waveforms). * Adiabatic temperature gradients with potential temperature Tpot between 1200 and 2100 K by step of 100 K. This catalog and associated travel-time tables will allow any user to generate synthetic waveforms for any moment tensor, and events within the pre-defined depth interval.   How to cite this material? Any use of the datasets or software must refer to: The reference paper: Waszek L., Tauzin B., Schmerr N., Ballmer M., Afonso J.C. A poorly mixed mantle transition zone and its thermal state inferred from seismic waves. Nature Geoscience. 2021. Software: Tauzin, Benoit, & Waszek, Lauren. (2021). NoLiMit MATLAB package v1.0. Non-Linear Bayesian partition Modeling of the Earth's Mantle Transition zone (Version 1). Zenodo. https://doi.org/10.5281/zenodo.5512805 Datasets: Tauzin, Benoit, Waszek, Lauren, & Afonso, Juan Carlos. (2021). Catalog of synthetic seismic records from mineral physics and travel-time tables from Waszek et al., 2021, Nature Geoscience (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5512035

opencc-by-4.0Sep 2021View details →

zenodo48/100

Supplement for Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland

Supplement to Jackisch et al., 2021: Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland. <a href="https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html">https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html</a> Data set contains 3D model in dxf file, additional images, selected handheld spectra. Publication summary: We integrate UAS-based magnetic and remote sensing mineral exploration data with legacy exploration data of a Ni-Cu-PGE prospect on Disko Island, West Greenland. The basalt unit has a complex magnetization, and we use a 3D magnetic vector inversion on the UAS magnetics to estimate magnetic properties and spatial dimensions of the mineralized unit. Our 3D modelling reveals a horizontal sheet and a strong remanent magnetization component. We highlight the advantage of UAS in rugged terrain.

opencc-by-4.0Oct 2021View details →

openCC (other)Feb 2025View details →

Carbon decomposition and nitrogen mineralization in Marsden agroecosystem diversification experiment, Iowa, 2021

This dataset contains measurements of soil carbon decomposition and nitrogen mineralization from 36 soils in the Marsden agroecosystem diversification experiment. Soil samples were collected in fall 2021 from a long-term experiment initiated in 2002 at Iowa State University's Marsden Farm. The dataset includes laboratory data on soil organic carbon (SOC) content, soil total nitrogen content, the carbon-to-nitrogen ratio of soil, time-series CO2 fluxes from SOC decomposition in a 13.5-month lab incubation, and soil nitrogen mineralization rates. It also provides model simulations of SOC decomposition from different carbon pools using three process-based models: the Agricultural version of the Integrated Biosphere Simulator (Agro-IBIS), CN-SIM, and the Microbial-ENzyme Decomposition (MEND) models.

openCC (other)Jun 2025View details →

Tree cores from three species along a natural nitrogen mineralization gradients in Michigan Lower Peninsula

Mycorrhizal fungi are understood to exhibit mutualistic relationships with trees. This study assessed this relationship via the growth of individual trees associated with different mycorrhizal communities along a gradient of N availability.

openCC0May 2025View details →

Sediment organic phosphorus mineralization through extreme drought experiment, Poyang Lake, China, 2022

Sediment samples from Poyang Lake before and after the extreme drought were analyzed by FT ICR-MS, and the samples were named Pre-drought and Post-drought, respectively; initial samples from the simulated drought experiment and samples from the treatment groups at the end of the drought were analyzed by FT ICR-MS, and the samples were named Initial, Light, Light+, respectively. 16S rRNA gene sequence analysis was performed on samples from each bacterial treatment group at the end of the drought and the initial samples, which were named Initial, Micerbe, Microbe+light, respectively.