More Like this

1. Model estimates of water yield and N yield under alternative N management practices across the Mississippi-Atchafalaya River basin during 1980-2017

   https://doi.org/10.25380/iastate.12779954.v1  

   Lu, Chaoqun; Zhang, Jien (August 2020, Iowa State University)

   This dataset contains maps of water yield and nitrogen (N) yield each year, covering the Mississippi/Atchafalaya River Basin (MARB) spanning from 1980 to 2017. These maps were reconstructed by aggregating from a daily model (Dynamic Land Ecosystem Model, DLEM) estimates and are at 5-min×5-min (0.08333° Lat × 0.08333° Lon) resolution. There are two subfolders, "TT" and "DT", within this folder. "TT" and "DT" respectively indicate "traditional timing" and "dynamic timing" of nitrogen fertilizer applications in regards to the model experiments in the main text. The "TT" folder contains the gridded model estimates of water yield (named by "Runoff") and nitrogen yield (named by "Nleach") at annual bases. TT reflects our best estimate of water and N fluxes within the context of multi-factor environmental changes including climate, atmospheric CO2 concentration, N deposition, land use, and human management practices (such as fertilizer use, tillage, tile drainage, etc.). The "DT" folder only contains the model estimates of nitrogen yield (“Nleach”) under an alternative N management practice. More details can be found in the linked publication. 

   more » « less
2. Gap-Filled Dissolved Oxygen Data from the Ocean Observatories Initiative Endurance Array Inshore Moorings

   https://doi.org/10.5281/zenodo.15742508  

   Cervantes, Brandy (January 2025, Zenodo)

   Introduction The National Science Foundation Ocean Observatories Initiative (OOI) collects continuous in-situ measurements of dissolved oxygen (DO) on the Endurance Array moorings in the inner shelf region of the Oregon and Washington coasts. Aanderaa Optode 4831 oxygen sensors were deployed at 7 meters depth on the near surface instrument frame (NSIF) and on the collocated coastal surface piercing profiler (CSPP) moorings. The sensors suffer from calibration drift due to biofouling, which can cause a dramatic increase in DO during daylight hours and corresponding decrease at night compared to the conditions in the water column (Palevsky et al., 2023). This enhanced diel signal, when present, is much more pronounced on fixed-depth sensors and usually begins to occur 1-2 months after a mooring is deployed. After this biofouling issue was identified, OOI began deploying UV lamps adjacent to the oxygen sensor in spring 2018, after which there was substantial improvement in DO data quality. Each file in this dataset contains the measured near surface DO and the corrected near surface DO at the Oregon and Washington inner shelf surface moorings (ISSM) with gaps from periods of biofouling replaced with the DO measured by the CSPP.  Methods OOI oxygen data Dissolved oxygen sensors on OOI CSPPs and at fixed-depths on moorings are named “DOSTA”, a contraction of DO Stable Response. The DOSTA data are downloaded on a deployment-by-deployment basis for all available data streams (telemetered and recovered for fixed-depth moorings; recovered only for CSPPs) from the OOI Gold Copy THREDDs catalog. Each deployment file additionally contains the practical salinity, seawater temperature, and pressure measured by the collocated CTD. The telemetered and recovered data streams are combined and interpolated to a common timebase with one-minute resolution.  Evaluate fixed-depth oxygen data The NSIF DO data are quality-controlled using both automated and manual methods to create flags that follow the Quality Assurance of Real-Time Oceanographic Data (QARTOD) standards. Endurance array team members perform a visual inspection of oxygen and ancillary data from each deployment to determine instrument failure from biofouling or other issues. Annotations from human-in-the-loop analyses of failed or suspect data generate the QARTOD flags.    Merge profiler oxygen data QARTOD flags are applied to the CSPP data to omit failed data points. CSPP DO data are averaged from 2-7 meters depth then interpolated to the one-minute timebase. The resulting CSPP time series shows good agreement with the NSIF during data overlaps. Finally, the NSIF DO data is replaced with the CSPP DO data during periods of biofouling or instrument failure, flags are generated for the hybrid DO dataset, and separate netCDF files are created for the Oregon and Washington locations. Files Filename: CE01ISSM-NSIF-DOSTA.nc Description Oregon Coastal Endurance Site CE01, Inner Shelf Surface Mooring, Near Surface Instrument Frame, Dissolved Oxygen Stable Response Geographic Range Latitude: 44.6598 to 44.6598 Longitude: -124.095 to -124.095 Time Range Start: 2014-10-10, 18:00:00 UTC End: 2025-06-24, 20:00:00 UTC Variables: "time", ”depth”, "sea_water_practical_salinity", "sea_water_practical_salinity_qartod_results", "sea_water_temperature",  "sea_water_temperature_qartod_results", "measured_dissolved_oxygen", "measured_dissolved_oxygen_qartod_results", "corrected_dissolved_oxygen", "corrected_dissolved_oxygen_qartod_results" Filename: CE06ISSM-NSIF-DOSTA.nc Description Washington Coastal Endurance Site CE06, Inner Shelf Surface Mooring, Near Surface Instrument Frame, Dissolved Oxygen Stable Response Geographic Range Latitude: 47.1336 to 47.1336 Longitude: -124.272 to -124.272 Time Range Start: 2015-04-10, 05:00:00 UTC End: 2025-06-24, 20:00:00 UTC Variables: "time", ”depth”, "sea_water_practical_salinity", "sea_water_practical_salinity_qartod_results", "sea_water_temperature",  "sea_water_temperature_qartod_results", "measured_dissolved_oxygen", "measured_dissolved_oxygen_qartod_results", "corrected_dissolved_oxygen", "corrected_dissolved_oxygen_qartod_results" 

   more » « less
3. Data for: The effect of Pliocene regional climate changes on silicate weathering

   https://doi.org/10.6078/D11H7D  

   Maffre, Pierre; Chiang, John; Swanson-Hysell, Nicholas (January 2023, Dryad)

   This dataset stores the data of the article The effect of Pliocene regional climate changes on silicate weathering: a potential amplifier of Pliocene-Pleistocene cooling P. Maffre, J. Chiang & N. Swanson-Hysell, Climate of the Past). This study uses a climate model (GCM) to reproduce an estimate of Pliocene Sea Surface Temperature (SST). The main GCM outputs of this modeling (with a slab ocean model) are stored in "GCM_outputs_for_GEOCLIM/", as well as the climatologies from ERA5 reanalysis. The other GCM outputs that were used in intermediary steps (coupled ocean-atmosphere, and fixed SST simulations) are stored in "other_GCM_outputs/". The forcing files (Q-flux) and other boundary conditions to run the "main" GCM simulations can be found in "other_GCM_outputs/Q-flux_derivation/", as well as the scripts used to generate them. Secondly, the mentioned study uses the GCM outputs in "GCM_outputs_for_GEOCLIM/" as inputs for the silicate weathering model GEOCLIM-DynSoil-Steady-State (https://github.com/piermafrost/GEOCLIM-dynsoil-steady-state/tree/PEN), to investigate weathering and equilibrium CO2 changes due to Pliocene SST conditions. The results of these simulations are stored in "GEOCLIM-DynSoil-Steady-State_outputs/". The purpose of this dataset is to provide the raw outputs used to draw the conclusions of Maffre et al. (2023), and to allow the experiments to be reproduced, by providing the scripts to generate the boundary conditions. 

   more » « less
4. NSF COLDEX Ice Penetrating Radar Derived Grids of the Southern Flank of Dome A:Subtitle

   https://doi.org/10.18738/T8/M77ANK  

   Young, Duncan A; Paden, John P; Yan, Shuai; Kerr, Megan E; Singh, Shivangini; Vega_Gonzàlez, Alejandra; Kaundinya, Shravan; Greenbaum, Jamin S; Chan, Kristian; Blankenship, Donald D; et al (January 2025, Texas Data Repository)

   <p>NSF COLDEX performed two airborne campaigns from South Pole Station over the Southern Flank of Dome A and 2022-23 and 2023-24, searching for a potential site of a continuous ice core that could sample the mid-Pleistocene transition. Ice thickness data extracted from the MARFA radar system has allow for a new understanding of this region.</p> <p>Here we generate crustal scale maps of ice thickness, bed elevation, specularity content, subglacial RMS deviation and fractional basal ice thickness with 1 km sampling, and 10 km resolution. We include both masked and unmasked grids.</p> <p> The projection is in the SCAR standard ESPG:3031 polar stereographic projection with true scale at 71˚S.</p> <p>These geotiffs were generated using performed using GMT6.5 (<a href="https://doi.org/10.1029/2019GC008515">Wessel et al., 2019</a>) using the pygmt interface, by binning the raw data to 2.5 km cells, and using the <a href="https://github.com/sakov/nn-c"> nnbathy </a> program to apply natural neighbor interpolation to 1 km sampling. A 10 km Gaussian filter - representing typical lines spacings - was applied and then a mask was applied for all locations where the nearest data point was further than 8 km. </p> Ice thickness, bed elevation and RMS deviation @ 400 m length scale (<a href="http://dx.doi.org/10.1029/2000JE001429">roughness</a>) data includes the following datasets: <ul> <li> UTIG/CRESIS <a href="https://doi.org/10.18738/T8/J38CO5">NSF COLDEX Airborne MARFA data</a></li> <li> British Antarctic Survey <a href="https://doi.org/10.5285/0f6f5a45-d8af-4511-a264-b0b35ee34af6">AGAP-North</a></li> <li> LDEO <a href="https://doi.org/10.1594/IEDA/317765"> AGAP-South </a></li> <li> British Antarctic Survey <a href="https://doi.org/10.5270/esa-8ffoo3e">Polargap</a></li> <li> UTIG Support Office for Airborne Research <a href="https://doi.org/10.15784/601588">Pensacola-Pole Transect (PPT) </a></li> <li> NASA/CReSIS <a href="https://doi.org/10.5067/GDQ0CUCVTE2Q"> 2016 and 2018 Operation Ice Bridge </a> </li> <li> ICECAP/PRIC <a href="https://doi.org/10.15784/601437"> SPICECAP Titan Dome Survey </a> </ul> <p>Specularity content (<a href="https://doi.org/10.1109/LGRS.2014.2337878">Schroeder et al. 2014</a>) is compiled from <a href="https://doi.org/10.18738/T8/KHUT1U"> Young et al. 2025a </a> and <a href="https://doi.org/10.18738/T8/6T5JS6"> Young et al. 2025b</a>.</p> <p>Basal ice fractional thickness is complied from manual interpretation by Vega Gonzàlez, Yan and Singh. </p> <p>Code to generated these grids can be found at <a href="https://github.com/smudog/COLDEX_dichotomy_paper_2025"> at github.com </a></p> 

   more » « less
5. Data from: Inheritance of DNA methylation differences in the mangrove Rhizophora mangle

   https://doi.org/10.5281/zenodo.5102718  

   Mounger, Jeannie; Boquete, M. Teresa; Schmid, Marc W.; Granado, Renan; Robertson, Marta H.; Voors, Sandy A.; Langanke, Kristen L.; Alvarez, Mariano; Wagemaker, Cornelis A.M.; Schrey, Aaron W.; et al (January 2021, Zenodo)

   {"Abstract":["This record contains supplementary information for the article "Inheritance of DNA methylation differences in the mangrove Rhizophora mangle" published in Evolution&Development. It contains the barcodes (barcodes.txt), the reference contigs (contigs.fasta.gz), the annotation of the reference contigs (mergedAnnot.csv.gz), the SNPs (snps.vcf.gz), the methylation data (methylation.txt.gz), and the experimental design (design.txt). All data are unfiltered. Short reads are available on SRA (PRJNA746695). Note that demultiplexing of the pooled reads (SRX11452376) will fail because the barcodes are already removed and the header information is lost during SRA submission. Instead, use the pre-demultiplexed reads that are as well linked to PRJNA746695.<\/p>\n\n\n <\/p>\n\nTable S13 (TableS13_DSSwithGeneAnnotation.offspringFams.csv.gz): <\/strong><\/p>\n\nDifferential cytosine methylation between families using the mother data set. The first three columns fragment number ("chr"), the position within the fragment ("pos"), and the sequence context ("context"). Columns with the pattern FDR_<X>_vs_<Y> contain false discovery rates of a test comparing population X with population Y. Average DNA methylation levels for each population are given in the columns "AC", "FD", "HI", "UTB", "WB", and "WI". The remaining columns contain the annotation of the fragment, for example whether it matches to a gene and if yes, the gene name ID and description are provided.<\/p>"]} 

   more » « less