Find research datasets worth reusing

openCC (other)May 2024View details →

Hubbard Brook Experimental Forest: Chemistry of Precipitation – Monthly Fluxes, Watershed 9, 1995 - ongoing

These data are monthly fluxes of solutes in precipitation collected at the Hubbard Brook Experimental Forest and are a part of the Hubbard Brook Watershed Ecosystem Record (HBWatER), which is a long-term record of stream and precipitation chemistry and volume. The solute fluxes in precipitation are calculated as the product of precipitation volume and solute concentrations. There are nine gaged watersheds at the Hubbard Brook Experimental Forest, some of which have been subjected to experimental manipulations. The calculation of fluxes is currently supervised by John Campbell (US Forest Service). The long-term stream water record is collected and maintained by the US Forest Service. The collection and management of the long-term stream and precipitation chemistry record was initiated in 1963 by Gene E. Likens, F. Herbert Bormann, Robert S. Pierce, and Noye M. Johnson. HBWatER is currently sustained by Tammy Wooster (Cary IES) and Jeff Merriam (USFS) and the dataset is curated and maintained by a team of researchers: Chris Solomon (Cary IES), Emma Rosi (Cary IES), Emily Bernhardt (Duke), Lindsey Rustad (USFS), John Campbell (USFS), Bill McDowell (UNH), Charley Driscoll (Syracuse U.), Mark Green (Case Western), and Scott Bailey (USFS). Current Financial Support for HBWatER is provided by NSF LTREB # 1907683 and the USDA Forest Service Northern Research Station. 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 US Forest Service, Northern Research Station.

openCC (other)Nov 2025View details →

Soil water content measurements and rainfall data for plots with experimentally altered precipitation and nutrient inputs at the Jornada Basin LTER site, 2011-ongoing

This dataset contains soil volumetric water content data collected starting in 2011 for a long-term precipitation and nutrient manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs, and fertilization treatments to alter nitrogen input to 2.5 x 2.5 meter plots in a desert grassland. Soil sensors are installed at surface and deep soil layers in each plot and collect hourly averages of volumetric water content using a time-domain reflectometry method. This dataset contains daily averages. This is an ongoing study and the dataset will be updated yearly.

openCC (other)Mar 2025View details →

Dataset and analyses for publication entitled: “Acclimation of the nitrogen cycle to changes in precipitation”

This dataset contains data and analysis code for the paper entitled “Acclimation of the nitrogen cycle to changes in precipitation" by Currier et al. As the frequency of precipitation extremes are expected to increase, especially in arid regions, we asked how prolonged shifts in water availability facilitate acclimation of the N cycle in a semiarid grassland. Using natural abundances of stable nitrogen isotopes for dominant plants and soils and rainfall manipulation experiments, we tested the hypothesis that N cycling will interact with water availability further amplifying the openness of the N cycle through time. For the dominant plant species, we found the relationship for N availability vs. ambient annual precipitation to be significantly positive, contrary to global spatial models. We also considered the temporal dynamics of our experiments, which imposed directional rainfall manipulations in duration ranging from 5 to 14 years. The slopes of these relationships decreased (became less positive) with more time since the onset of the directional precipitation extremes. These data and metadata supplement long-term foliar and soil isotope data from the Jornada LTER (Dataset ID: knb-lter-jrn.210586001) with a large spatial dataset from NEON data package DP1.10026.001 and Craine et al. 2018 (https://doi.org/10.5061/dryad.v2k2607).

openCC0Jan 2026View details →

APT01 Daily precipitation amounts measured at multiple sites across konza prairie

Data set contains daily records of precipitation on 10 raingauges at 10 sites on Konza Prairie. Two sites (020A and 002C; SE) have 7-day clocks (one revolution per week), 7 have 24-hour clocks (one revolution per day), and the Headquarters raingauge generates daily data and 15 minute data. The Headquarters raingauge generates data year round. The remaining rain gauges are operated from April 1 to October 31. Precipitation amounts are recorded in mm. As of 2011, the HQ 1 (7-day clock) and HQ 2 (24-hour clock) raingauges have been discontinued and replaced with an Ott Pluvio2 rainguage that began data generation March 2010. APT011 - Precipitation on Konza Prairie collected at the Headquarter (HQ); APT012 - Precipitation on Konza Prairie collected at 10 rainfall gauges; APT013 - Precipitation on Konza Prairie collected at 8 rainfall gauges (HOBO Data Logging Rain Gauges).

openCC0May 2025View details →

Precipitation-productivity relationships in desert grassland: a test of the double asymmetry hypothesis.

The purpose of this data package is to provide the derived data and R code for analyses presented in the manuscript by Collins et al. Knowing the relationship between precipitation (PPT) and aboveground net primary productivity (ANPP) is essential for understanding and modeling the global carbon cycle. Across grassland to forest gradients, the PPT-ANPP relationship is well-defined and non-linear. Temporal patterns within a site over time, however, are more variable than spatial patterns and nearly always linear. Linear relationships, however, are inconsistent with positive asymmetry occurring when the increase in ANPP in a wet year is greater than the decline in a dry year. The double asymmetry model predicts that concave down non-linearities will occur when extreme high and low PPT years are included in a time series. We used long-term ANPP data from ambient plots, plus rainfall addition and reduction experiments to test the predictions of the double asymmetry model. By combining experimental drought, plus water and nitrogen addition experiments we found some support for the double asymmetry model. However, the response was concave up not down under high precipitation coupled with nitrogen addition. By experimentally extending the range of monsoon precipitation we generated a significant although weak, non-linear PPT-ANPP relationship, but only when nutrient limitation was alleviated. Our results demonstrate that multiple interacting factors govern the PPT-ANPP relationship within a site over time.

openCustomJan 2024View details →

Precipitation data for four sites on the eastern side of Delmarva Peninsula, 2001-2022

Rain gauges (Davis Instruments rain collector with HOBO Event Recorder) were installed at four sites latitudinally along the Eastern Shore, VA to measure instantaneous rainfall. The logger only records when there is a precipitation event (0.2 mm minimum). We calculated daily rain totals and provided only dates with rain. The data represents the time period of 8/9/01 on for Mill, Garg, and Mosq sites and 10/16/01 on for Wach site. The system was down between October 2008 and December 2010. It was replaced by an upgraded system in January 2011 with new Davis tipping-bucket rain gauges and data loggers, and primary responsibility for data collection and processing shifted to VCR/LTER staff. This file will be updated when additional data is downloaded from the rain gauges.

openCustomMay 2022View details →

Annual precipitation for the Virginia Coast Reserve 1837-2021

The long-term record of annual precipitation begins in 1837, the first year of systematic recording of precipitation amounts at Fort Monroe, Hampton Roads, VA. This remarkable record continued until 1985. Subsequent to 1985, precipitation records come from the NOAA meteorological station at the Norfolk International Airport.

opencc-by-4.0Jan 2022View details →

Hydrogeological data of groundwater and precipitation monitored in the Vögelsberg landslide catchment

Data contains hydrogeological data of precipitation and groundwater within the catchment of the Vögelsberg landslide (Tyrol, Austria) monitored between 2017-11-22 and 2021-07-05. The dataset provides time series of discharge, temperature, electrical conductivity and stable isotope ratios in groundwater and precipitation. Dataset is associated to following preprint: “Pfeiffer, J.; Zieher, T.; Schmieder, J.; Bogaard, T.; Rutzinger, M. and Spötl, C. (2021) Spatial assessment of probable recharge areas - Investigating the hydrogeological controls of an active deep-seated gravitational slope deformation, Natural Hazards and Earth System Sciences Discussions, Vol. 2021, p. 1-29, <a href="https://doi.org/10.5194/nhess-2021-388">https://doi.org/10.5194/nhess-2021-388</a>”. Accompanying readme file gives a detailed description of data fields contained in the published data.

opencc-by-4.0Jan 2022View details →

Resilience estimates of Amazon and Congo rainforests based on mean annual precipitation and root zone storage capacity

Resilience refers to the capacity of the ecosystem to absorb perturbations and remain in its native stable state. Here, we quantified forest resilience of South American and African ecosystems using mean annual precipitation and root zone storage capacity (2000-2019). We adopted Hirota et al. (2011) methodology for calculating resilience using logistic regression.  This logistic regression predicts the probability of forest (tree cover > 50%) as a function of the independent variable. The predicted resilience estimates range between 0 to 1, where 1 represents the highest probability of finding forest – interpreted as highly resilient forest ecosystems. For more information, check: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16115">https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16115</a>

opencc-by-4.0Jul 2024View details →

The extrAIM dataset: A merged satellite-based daily precipitation dataset for the Mediterranean region (including an ensemble of 20 synthetic realisations)

extrAIM dataset is a new merged daily precipitation product (extraim_merged_data.nc) for the Mediterranean region with the following characteristics: <ul> <li>Dataset format: NetCDF</li> <li>Spatial resolution: 25 x 25 km</li> <li>Temporal resolution: 1 day</li> <li>Spatial coverage: Longitude: from -6.25 to 38.25, Latitude: 27.75 to 49</li> <li>Temporal coverage: 01-01-2007 to 30-09-2021</li> <li>Merging approach: Two-step merging (classification and regression) <ul> <li>Algorithm: Random Forest for both classification and regression</li> <li>Training strategy: Full training strategy</li> </ul> </li> <li>Merged precipitation products: SM2Rain-ASCAT and GPM Late Run</li> <li>Reference precipitation product: EMO5</li> <li>Static covariates: Longitude, Latitude and Elevation, in both classification and regression step <ul> <li>Classification step: probability dry and probability dry of the 5 neighboring points around the target locations</li> <li>Regression step: mean, standard deviation and skewness of daily precipitation, of the entire series and non-zero amounts, as well as mean precipitation of the 5 neighboring points around the target locations</li> </ul> </li> </ul> In addition, an ensemble of 20 synthetic realizations (equiprobable and bias-adjusted) of the merged dataset is provided (files named: “extraim_realisation_XX.nc”). The synthetic realisations were produced using the extrAIM’s uncertainty-quantification approach and the associated conditional sampling method.

opencc-by-4.0Apr 2019View details →

Data for "Nano-scale characterisation of sheared β'' precipitates in a deformed Al-Mg-Si alloy"

This dataset contains data used in the publication entitled "Nano-scale characterisation of sheared β'' precipitates in a deformed Al-Mg-Si alloy". This publication concerns how β'' precipitates are sheared by dislocations during deformation. The data contained in this repository are data acquired on various transmission electron microscopes of specimens of the aluminium alloy AA6060 in peak aged condition after uniaxial compression to 5%, 10%, and 20%, in addition to the undeformed reference alloy. There are five main types of data: <ul> <li>Transmission electron microscopy (TEM) images</li> <li>High-resolution TEM images</li> <li>High angle annular dark field (HAADF) scanning TEM (STEM) images</li> <li>Scanning precession electron diffraction (SPED) data.</li> <li>Cross-sectional data of precipitates in undeformed and 20% compressed conditions.</li> </ul> Data for the TEM, HRTEM, and STEM images are kept in zipped folders due to the large number of images (several hundreds for each compression condition). Folders are named following the format of "<alloy>_<compression>_<technique>", where technique refers to TEM, HRTEM, or STEM. Images are provided in both .hdf format and .jpg format (to aid in navigating the data). Please see <a href="https://www.hdfgroup.org/">HDF Group</a> for more information regarding the HDF file format, and <a href="https://www.hdfgroup.org/downloads/hdfview/">HDF View</a> for softaware to read and show HDF data. The Python package <a href="http://hyperspy.org/">HyperSpy</a>, is also useful for loading the HDF data for inspection, analysis, and presentation. For some STEM images, a stack of short-exposure STEM images acquired and analysed using the <a href="http://lewysjones.com/software/smart-align/">SmartAlign</a> plugin to <a href="http://www.gatan.com/products/tem-analysis/gatan-microscopy-suite-software">Gatan Digital Micrograph</a> is available. SmartAlign offers the possibility of rigidly and non-rigidly aligning the STEM images in the stack in order to reduce effect of specimen drift and scan noise during acquisition. The conventional STEM images are found in the zip archive labelled "STEM". When the filenames of the STEM images include "SAstack" and/or "SAimage", a STEM SmartAlign stack or the average through a non-rigidly aligned stack is available of the same field of view. In such cases, both the SmartAlign stack and the through-stack image is provided in the metadata in the .hdf file (note that not all stacks have been aligned, and in such cases no through-stack image is available). In addition, the SmartAlign stacks themselves are available in the subfolder "STEM\SmartAlign\" within each STEM folder. The through-stack images of the smart align stacks are also provided separately in the subfolder "STEM\SmartAlign\Aligned\". For the 20% compressed case, a lowloss electron energy loss spectroscopy (EELS) spectrum and thickness maps of the imaged areas are also provided, in the subfolder "STEM\EELS\". The SPED data, acquired using the ASTAR system of <a href="https://www.nanomegas.com/">NanoMegas</a>, is provided as .hdf5 files in the root directory of the repository. They should be read using and <a href="https://github.com/pyxem/pyxem">pyXem</a>. The attached Jupyter Notebook "SPED_data_inspection.ipynb" can be used to access the SPED datasets. These datasets are 4D datasets, with two spatial and two reciprocal dimensions. They have been decomposed using the non-negative matrix factorization algorithm (NMF) used in HyperSpy. These decomposition results are included in the .hdf5 files. In addition, parameters used in the preprocessing of the datasets are attached in the metadata in these files. The metadata of these files are also provided separately as .txt files. Finally, measurements of the precipitate cross-sectional area and circularity is available as .csv files with the first column being the row index, the second the cross-sectional areas of precipitates measured in nanometers squared, the third column is the perimeters of the precipitates measured in nanometers, and column four is the <a href="https://imagej.nih.gov/ij/plugins/circularity.html">circularity</a> of the precipitates.

opencc-by-4.0May 2024View details →

Dataset of "Selective Precipitation of REE-Rich Aluminum Phosphate with Low Lithium Losses from Lithium Enriched Slag Leachate"

Currently, recycling of spent lithium-ion batteries is carried out using mechanical, pyrometallurgical and hydrometallurgical methods and their combination. The aim of this article is to study a part of pyro-hydrometallurgical processing of spent lithium-ion batteries which includes lithium slag hydrometallurgical treatment and refining obtained leachate. Lithium slag intended for leaching experiments contains 3,68 % of Li; 11,02 % of Al; 1,17 % of Co; 1,71 % of Cu and other metals in minority content. Leaching step was realized via dry digestion that is an effective method capable of transferring over 99% of the present metals such as Li, Al, Co, Cu and others to the leachate. The highest content in leachate reached Al (2666 µg/mL) and Li (2239 µg/mL). Extraction of metals from leachate can be conducted using various methods, with precipitation being the most used. In this work, the influence of two types of precipitation agent (NaOH, Na3PO4) on precipitation efficiency of Al and Li losses was investigated. It was found that the precipitation of aluminium with NaOH can result in the co-precipitation of lithium, causing total lithium losses up to 40 %. As suitable precipitating agent for complete Al removal from Li leachate with a minimal loss of lithium (less than 2 %), crystalline Na3PO4 was determined under following condition: pH = 3, 400 rpm, 10 minutes, room temperature. Analysis confirmed that, in addition to aluminium, the precipitate also contains REE La (3.4%), Ce (2.5%), Y (1.3%), Nd (1%) and Pr (0.3%), which selective recovery will be the subject of further study.

openCC (other)Jan 2025View details →

Inter- and intra-annual temperature and precipitation variability (1950-2022) across the ranges of non-migratory birds and their association with generation length

While environmental variability is theorized to impact the life history characteristics of organisms, these hypotheses have not been thoroughly tested with empirical data. To fill this gap, we synthesized a global data set of environmental variability metrics and life history characteristics across the ranges of 7,477 non-migratory, non-marine avian species. These data are derived from the ERA5 climate reanalysis, AVONET, BirdTree, and BirdLife databases as well as previously published research. By extracting environmental variability values across individual species' ranges, this data set allows users to evaluate avian species' pace of life in response to environmental change.

openCC (other)Feb 2025View details →

Great Bay Estuary, NH/ME, Box Model Water Chemistry, Flow, Precipitation, and Seagrass Coverage Data, 2008 - 2023.

This data repository contains compiled surface water (tributary and estuarine), wet deposition, and wastewater effluent chemistry, along with discharge, precipitation totals, and monthly effluent flows necessary for the completion of solute budgets for Great Bay, a subregion of Great Bay Estuary, NH/ME, USA. These datasets are part of on-going monitoring programs in the Great Bay Estuary and Lamprey River Hydrological Observatory. A subset of the monitoring data for the 2008 to 2023 period was compiled. The tributary and estuarine monitoring data were requested from the NH Department of Environmental Services Environmental Monitoring Database as part of the Tidal Tributary and Estuary Water Quality Monitoring Programs. The wet deposition chemistry record is maintained as part of the Lamprey River Hydrologic Observatory. Wastewater effluent chemistry was downloaded from the EPA's Enforcement and Compliance History Online Database. The annual (1996 - 2023) seagrass coverage dataset for Great Bay Estuary reflects coverage of Zostera marina seagrass only and was compiled from annual monitoring reports. Mean daily instantaneous discharge data for the three tidal tributaries used in the load calculations are available from the USGS National Water Information System. Hourly precipitation volume data for the Durham, NH SSW station are available from the NCDC U.S. Climate Reference Network, with minor hourly gaps filled using the University of New Hampshire Durham weather station (https://www.weather.unh.edu).

openCC (other)Oct 2025View details →

Removal of Aqueous Uranyl and Arsenate Mixtures by Natural Limestone and Hydroxyapatite Precipitates, Rio Paguate, NM, 2022-2023

This dataset documents a series of laboratory batch experiments investigating the removal of aqueous uranyl (U) and arsenate (As) mixtures using natural limestone and precipitated hydroxyapatite (HAp, Ca₁₀(PO₄)₆(OH)₂) as reactive materials. The main objective of the study was to address the challenge of simultaneous removal of uranyl cations and arsenate oxyanions by using mineral-based adsorbents, such as limestone. The precipitation of HAp enhanced As removal while maintaining high uranium immobilization efficiency.The archived data include measurements of aqueous U and As at trace-level concentrations under varying experimental conditions, including pH (ranging from 7 to 11), initial contaminant concentrations (0.05–1 mM), and the addition of calcium (Ca²⁺) and phosphate (PO₄³⁻) to promote HAp precipitation. Experiments were conducted in triplicate to ensure reproducibility, and solid-phase characterization data (from pXRD, SEM/EDX, and electron microprobe analysis) are also included to support the interpretation of removal mechanisms. A key finding revealed from the data is that near-complete removal of U (>97%) with As removal between 30 and 98% were achieved under pH conditions around 9. This dataset provides a comprehensive record of solution chemistry and treatment performance, serving as a fundamental resource for evaluating the effectiveness of natural mineral-based approaches for remediating co-contaminated waters.

openCC (other)Oct 2025View details →

Marcell Experimental Forest 15-minute precipitation, 2011 - ongoing

This data publication contains 15-minute precipitation data collected from 2011-ongoing at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota, which is operated and maintained by the USDA Forest Service, Northern Research Station. The data come from three long-term meteorological monitoring stations.

openOpenApr 2022View details →

Bonanza Creek LTER: Hourly Precipitation Measurements from 1993 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska

Precipitation is measured at many sites withinCPCRW using TE525 tipping buckets. These sensors are able to detect .254mm of precipitation and record hourly totals of rainfall or throughfall. It should be noted the tipping buckets within forested sites are subject to clogging by forest debris whcih can result in under reporting of rain events as well as over reporting upon release of the collected precipitation.

openOpenApr 2022View details →

Bonanza Creek LTER: Hourly Precipitation Weighing Bucket Measurements from 1988 to Present in the Bonanza Creek Experimental Forest near Fairbanks, Alaska

This data set includes year round precipitation values from LTER1 and LTER2. Sampling buckets are filled with antifreeze to melt and prevent snow build up. While these sensors work year round, tipping bucket data seems more accurate for snow free periods.

openOpenNov 2023View details →

Bonanza Creek LTER: Hourly Precipitation Weighing Bucket Measurements from 2008 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska

This data set includes year round precipitation values from sampling buckets filled with antifreeze to melt and prevent snow build up. While these sensors work year round, tipping bucket data seems more accurate for snow free periods.