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edi48/100

PIE LTER stream chemistry characteristics studied for methane ebullition at four headwater streams in Massachusetts and New Hampshire.

Sediment, stream, and canopy characteristics were measured in four headwater streams in Massachusetts and New Hampshire associated with methane ebullition monitoring. Canopy cover, water depth, sediment organic matter content, the percent sediment less than 2mm in diameter, sediment depth, sediment percent carbon, and sediment percent nitrogen were measured. Other files to reference: WAT-Stream-Ebullition

openCC (other)Jul 2021View details →

zenodo44/100

iNRACM: Incorporating 15N into the Regional Atmospheric Chemistry Mechanism (RACM) for assessing the role photochemistry plays in controlling the isotopic composition of NOx, NOy, and atmospheric nitrate

15N compounds and reactions were incorporated into Regional Atmospheric Chemistry Mechanism (RACM), based on recent experimental or calculated values of isotope fractionation factors (α), to  simulate δ15N values in NOy compounds.

opencc-by-4.0May 2020View details →

zenodo44/100

DMS multiphase chemistry mechanism and model results

Open Access DMS multiphase chemistry mechanism This repository contains the complete DMS multiphase chemistry mechanism developed and applied in Wollesen de Jonge et al. (2021). The DMS multiphase mechanism is located in the folder named DMS_chemistry. The executable mechanism consist of a number of Fortran f90 files, which were generated with the kinetic pre-processor (KPP) (Damian et al., 2002) using the KPP input file DMSchem.def and DMSchem.kpp file. Both the executable Fortran code and the KPP input files are stored in the subfolder DMS_multiphase_chem. The DMSchem.def file list all reactions and reaction rates in the DMS multiphase chemistry mechanism similar to the supplementary Tables S1 in Wollesen de Jonge et al. (2021).  The executable DMS multiphase chemistry mechanism consist of the following Fortran f90 files: DMSchem_Main.f90 DMSchem_Function.f90 DMSchem_Initialize.f90 DMSchem_Integrator.f90 DMSchem_Jacobian.f90 DMSchem_JacobianSP.f90 DMSchem_LinearAlgebra.f90 DMSchem_mex_Fun.f90 DMSchem_mex_Jac_SP.f90 DMSchem_Model.f90 DMSchem_Monitor.f90 DMSchem_Parameters.f90 DMSchem_Precision.f90 DMSchem_Rates.f90 DMSchem_Util.f90 DMSchem_Global.f90 These f90-files can be linked and compiled with gfortran using the provided Makefile.   The subfolder photolysis contain vectors with absorption cross sections (cs), quantum yields (qy) and the spectral actinic flux of the UV-lamps in the AURA chamber. A simplified model setup is provided to illustrate how the DMS-multiphase chemistry routines can be run. The DMS multiphase chemistry mechanism is called and run from a program named main.f90. In the main.f90 program the temperature, humidity, pressure and initial concentrations of all gas and aqueous phase species in the DMS multiphase chemistry are declared. After this the main program call the subroutines getKVALUES and getJVALUES from the Fortran module reaction_rates.f90. <ul> <li>getKVALUES calculates a number of complex reaction rates (mainly pressure dependent three-body reactions).</li> <li>getJVALUES calculates all gas phase photolysis rates in the DMS multiphase chemistry mechanism using the absorption cross sections, quantum yields and the spectral actinic flux files stored in the photolysis subfolder</li> </ul> The main.f90 program saves the concentration of all species in a file called conc.dat, the time step vector (time.dat) and all species names in SPC_NAMES.dat. A short Matlab script called plot_concentrations.m is provided to illustrate how the concentrations of all species listed in SPC_NAMES.dat can be plotted along the saved time vector. The simplified model (only used for demonstration purpose) can be compiled and executed with GFortran using the provided Makefile by typing the following commands in the command line (terminal): make ./main.exe   Stored model results presented in Wollesen de Jonge et al. (2021) We have saved all model data from each simulated smog chamber experiment and atmospheric relevant base case and sensitivity run presented in Wollesen de Jonge et al. (2021) in the form of 'OutputTable' files. The columns in the tables related to the DMS smog chamber experiments are classified as follows: <ul> <li>1 time [h] (simulation time starting from -1 h hour and ending at 15 h, time = 0 h is defined as the time when the UV-lights were turned on in the AURA smog chamber).</li> <li>2 PN_1.7nm [#/cm^3] (Total particle number concentration of particles 1.7 nm in diameter).</li> <li>3 PN_2.5nm [#/cm^3] (Total particle number concentration of particles 2.5 nm in diameter)</li> <li>4 PN_10nm [#/cm^3] (Total particle number concentration of particles 10 nm in diameter)</li> <li>5 PM_SO4 [μg/m^3] (Sulfate particle mass)</li> <li>6 PM_CH3SO3 [μg/m^3] (Methane sulfonic acid (MSA) particle mass)</li> <li>7 PM_NH4 [μg/m^3] (Ammonium particle mass)</li> <li>8 DMS [ppbv] (Dimethyl sulfide gas phase concentration)</li> <li>9 O3 [ppbv] (Ozone gas phase concentration)</li> <li>10 PV [μm^3/cm^3] (Total particle volume concentration)</li> <li>11 PM [μg/m^3] (Total particle mass concentration)</li> <li>12 NH3 [ppbv] (Ammonia gas phase concentration)</li> <li>13 MSIA [#/cm^3] (Methane sulphinic acid gas phase concentration)</li> <li>14 SO2 [ppbv] (Sulfur dioxide gas phase concentration)</li> <li>15 DMSO [#/cm^3] (Dimethyl sulfoxide gas phase concentration)</li> <li>16 HPMTF [#/cm^3] (Hydroperoxymethyl thioformate  gas phase concentration)</li> <li>17 H2O2 [ppbv] (Hydrogen peroxide gas phase concentration)</li> <li>18 HO2 [#/cm^3] (Hydroperoxyl radical gas phase concentration)</li> <li>19 OH [#/cm^3] (Hydroxyl radical gas phase concentration)</li> <li>20-219 dN/dlogDp [#/m^3] (Particle number size distributions)</li> </ul>   The header line, rows 20-219, give the corresponding aerosol particle geometric mean diameters (Dp) in unit m, which were used to represent the modelled particle number size distributions (dN/dlogDp). The gas-phase concentrations given in unit ppb are given at the standard temperature and pressure of 273.15 K and 1E5 Pa.   The columns in the tables related to the atmospheric relevant runs are classified as follows: <ul> <li>1 time [h] ] (simulation time).</li> <li>2 DMS [#/cm^3]  (Dimethyl sulfide gas phase concentration)</li> <li>3 H2SO4 [#/cm^3] (Sulfuric acid gas phase concentration)</li> <li>4 MSA [#/cm^3] (Methane sulfonic acid gas phase concentration)</li> <li>5 HPMTF [#/cm^3] (Hydroperoxymethyl thioformate  gas phase concentration)</li> <li>6 MSIA [#/cm^3] (Methane sulphinic acid gas phase concentration)</li> <li>7 DMSO [#/cm^3] (Dimethyl sulfoxide gas phase concentration)</li> <li>8 UVflux [] (Relative UV light intensity, i.e. UVflux = 1 maximum sunlight, UVflux = 0 no sunlight)</li> <li>9 sinkCL [#/cm^3/s] (DMS loss rate by reactions with Cl radicals)</li> <li>10 sinkOHabs [#/cm^3/s] (DMS loss rate by reactions with OH via the abstraction pathway)</li> <li>11 sinkBrO [#/cm^3/s] (DMS loss rate by reactions with BrO radicals)</li> <li>12 sinkOHadd [#/cm^3/s] (DMS loss rate by reactions with OH via the addition pathway)</li> <li>13 sinkNO3 [#/cm^3/s] (DMS loss rate by reactions with NO3 radicals)</li> <li>14 sinkO3aq [#/cm^3/s] (DMS loss rate by reactions with O3 in the aqueous phase)</li> <li>15 PM_CH3SO3 [μg/m^3] (Methane sulfonic acid (MSA) particle mass)</li> <li>16 PM_SO4 [μg/m^3] (Sulfate particle mass)</li> <li>17 PM_NH4 [μg/m^3] (Ammonium particle mass)</li> <li>18 PM_NO3 [μg/m^3] (Nitrate particle mass)</li> <li>19-218 dN/dlogDp [#/m^3]. (Particle number size distributions)</li> </ul>   In this case, the header line, rows 19-218, give the corresponding geometric mean diameters in unit m. The modelled smog chamber experiments result files were named according to the date when the DMS experiments were performed: Exp. DMS1 - 20180405 Exp. DMS2 - 20180519 Exp. DMS3 - 20180521 Exp. DMS4 - 20180523 Exp. DMS5 - 20180526 Exp. DMS6 - 20190226 Exp. DMS7 - 20190301   Details for each output table are given here: OutputTable_AtmMain: Base case atmospheric model run OutputTable_lowWindAtm: Atmospheric model run with 2 m/s wind speed OutputTable_PolAtm: Atmospheric model run with higher O3 and NOx concentrations OutputTable_woAqAtm: Atmospheric model simulation without aqueous phase chemistry reactions OutputTable_woCloudAtm: Atmospheric model simulation without clouds   OutputTable20180405_1: Base case smog chamber simulation experiment DMS1 OutputTable20180519_1: Base case smog chamber simulation experiment DMS2 OutputTable20180521_1: Base case smog chamber simulation experiment DMS3 OutputTable20180523_1: Base case smog chamber simulation experiment DMS4 OutputTable20180526_1: Base case smog chamber simulation experiment DMS5 OutputTable20190226_1: Base case smog chamber simulation experiment DMS6 OutputTable20190301_1: Base case smog chamber simulation experiment DMS7    OutputTable20180519_2: MSIA+OH rate analogues to Yin et al, exp. DMS2  OutputTable20180519_3: MSIA+OH rate analogues to Lucas & Prinn et al. , exp. DMS2  OutputTable20180519_4: MSIA+OH rate set to 0, exp. DMS2  OutputTable20180519_5: No gas partitioning to the liquid water film on the chamber walls, exp. DMS2  OutputTable20180519_6: MCM gas-phase chem. setup, exp. DMS2  OutputTable20180519_9: CH3SOO isomerization set to 0, exp. DMS2  OutputTable20180519_10: HPMTF pathway set to 0, exp. DMS2    OutputTable20190226_2: HPMTF pathway analogues to Veres et al. , exp. DMS6  OutputTable20190226_3: HPMTF pathway analogues to Yin et al. , exp. DMS6  OutputTable20190226_4: HPMTF patway set to 0 , exp. DMS6  OutputTable20190226_5: No gas partitioning to the liquid water film on the chamber walls , exp. DMS6  OutputTable20190226_6: CH3SOO isomerization set to 0 , exp. DMS6  OutputTable20190226_7: MSIA+OH rate set to 0 , exp. DMS6    OutputTable20180405_12: O3 wall accommodation coefficient = 1E-8, exp. DMS1  OutputTable20180519_12: O3 wall accommodation coefficient = 1E-8, exp. DMS2  OutputTable20180521_12: O3 wall accommodation coefficient = 1E-8, exp. DMS3  OutputTable20180523_12: O3 wall accommodation coefficient = 1E-8, exp. DMS4  OutputTable20180526_12: O3 wall accommodation coefficient = 1E-8, exp. DMS5  OutputTable20190226_12: O3 wall accommodation coefficient = 1E-8, exp. DMS6  OutputTable20190301_12: O3 wall accommodation coefficient = 1E-8, exp. DMS7    OutputTable20180405_13: O3 wall accommodation coefficient = 1E-6, exp. DMS1  OutputTable20180519_13: O3 wall accommodation coefficient = 1E-6, exp. DMS2  OutputTable20180521_13: O3 wall accommodation coefficient = 1E-6, exp. DMS3  OutputTable20180523_13: O3 wall accommodation coefficient = 1E-6, exp. DMS4  OutputTable20180526_13: O3 wall accommodation coefficient = 1E-6, exp. DMS5  OutputTable20190226_13: O3 wall accommodation coefficient = 1E-6, exp. DMS6  OutputTable20190301_13: O3 wall accommodation coefficient = 1E-6, exp. DMS7    OutputTable20180405_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS1  OutputTable20180519_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS2  OutputTable20180521_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS3  OutputTable20180523_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS4  OutputTable20180526_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS5  OutputTable20190226_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS6  OutputTable20190301_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS7    OutputTable20180405_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS1  OutputTable20180519_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS2  OutputTable20180521_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS3  OutputTable20180523_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS4  OutputTable20180526_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS5  OutputTable20190226_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS6  OutputTable20190301_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS7    OutputTable20180405_16: DMS wall accommodation coefficient = 1E-8, exp. DMS1  OutputTable20180519_16: DMS wall accommodation coefficient = 1E-8, exp. DMS2  OutputTable20180521_16: DMS wall accommodation coefficient = 1E-8, exp. DMS3  OutputTable20180523_16: DMS wall accommodation coefficient = 1E-8, exp. DMS4  OutputTable20180526_16: DMS wall accommodation coefficient = 1E-8, exp. DMS5  OutputTable20190226_16: DMS wall accommodation coefficient = 1E-8, exp. DMS6  OutputTable20190301_16: DMS wall accommodation coefficient = 1E-8, exp. DMS7    OutputTable20180405_17: DMS wall accommodation coefficient = 1E-6, exp. DMS1  OutputTable20180519_17: DMS wall accommodation coefficient = 1E-6, exp. DMS2  OutputTable20180521_17: DMS wall accommodation coefficient = 1E-6, exp. DMS3  OutputTable20180523_17: DMS wall accommodation coefficient = 1E-6, exp. DMS4  OutputTable20180526_17: DMS wall accommodation coefficient = 1E-6, exp. DMS5  OutputTable20190226_17: DMS wall accommodation coefficient = 1E-6, exp. DMS6  OutputTable20190301_17: DMS wall accommodation coefficient = 1E-6, exp. DMS7    OutputTable20180405_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS1  OutputTable20180519_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS2  OutputTable20180521_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS3  OutputTable20180523_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS4  OutputTable20180526_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS5  OutputTable20190226_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS6  OutputTable20190301_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS7    OutputTable20180405_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS1  OutputTable20180519_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS2  OutputTable20180521_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS3  OutputTable20180523_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS4  OutputTable20180526_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS5  OutputTable20190226_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS6  OutputTable20190301_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS7    OutputTable20180519_20: Liquid water content on walls (LWC wall) = 0.3 mg/m^3, exp. DMS2  OutputTable20190226_20: Liquid water content on walls (LWC wall) = 1.5 g/m^3, exp. DMS6  OutputTable20190301_20: Liquid water content on walls (LWC wall) = 15 g/m^3, exp. DMS7    OutputTable20180519_21: Liquid water content on walls (LWC wall) = 30 mg/m^3, exp. DMS2  OutputTable20190226_21: Liquid water content on walls (LWC wall) = 150 g/m^3, exp. DMS6  OutputTable20190301_21: Liquid water content on walls (LWC wall) = 1000 g/m^3, exp. DMS7   References Wollesen de Jonge, R., Elm, J., Rosati, B., Christiansen, S., Hyttinen, N., Lüdemann, D., Bilde, M., and Roldin, P.: Secondary aerosol formation from dimethyl sulfide – improved mechanistic understanding based on smog chamber experiments and modelling, Atmos. Chem. Phys. <a href="https://doi.org/10.5194/acp-2020-1324">https://doi.org/10.5194/acp-2020-1324</a> (2021)    Damian, V., Sandu, A., Damian, M., Potra, F., and Carmichael, G. R.: The kinetic preprocessor KPP-a software environment for solving chemical kinetics, Comput. Chem. Eng., 26, 1567–1579, https://doi.org/10.1016/S0098-1354(02)00128-X, 2002.

opencc-by-4.0Jun 2021View details →

zenodo44/100

Water chemistry of LTER-Europe research site Lake Paione Inferiore LTER_EU_IT_088 (1984-2013)

This dataset provides information about water chemical parameters for Lake Paione Inferiore LTER_EU_IT_088: pH, Total alkalinity, conductivity, total nitrogen, major cations (calcium, magnesium, sodium, potassium), major anions (sulphate, nitrate, chloride) and silica for the period 1984-2013. Lake Paione Inferiore (LPI) is a high altitude Alpine lake, located at 2002 m a.s.l. in the Bognanco Valley, Province of Verbania, Piedmont Region, Italy. It has a surface area of 0.86 ha and a maximum depth of 13.5 m. The Lake, together with Lake Paione Superiore (LPS), is included in the monitoring sites of the UN-ECE Program ICP WATERS (International Cooperative Programme on Assessment and Monitoring of Acidification of Rivers and Lakes) for which the CNR Water Research Institute is the National Focal Centre for Italy. This dataset includes the following files: Metadata LTER_EU_IT_088.xls and per each parameter one xls file with data records. Dataset for water chemistry of LPI for the  period 2014-2020 is available at <a href="https://doi.org/10.5281/zenodo.10519349">https://doi.org/10.5281/zenodo.10519349</a> Detailed description of the site LPI is available at https://deims.org/c128d2f9-beb0-45ba-89bb-df9e12f95b0f

opencc-by-4.0Feb 2023View details →

zenodo44/100

Carbonate Chemistry and δ18O-H2O from East and West Greenland fjords, August 2018 and 2016

Greenland’s fjords and coastal waters are highly productive and sustain important fisheries, but retreating glaciers and increasing meltwater supply are changing fjord circulation and biogeochemistry, which may threaten the future productivity of these unique ecosystems. The freshening of Greenland fjords caused by unprecedented melting of the Greenland Ice Sheet has the potential to alter carbonate chemistry in coastal waters, which would influence CO2 uptake as well as have biological consequences from acidification. However, few studies to date explore the current acidification state in Greenland coastal waters. Here we present the first-ever large-scale measurements of carbonate system parameters and δ18O measurements in 16 Greenlandic fjords and by combining datasets from two August cruises. HDMS Lauge Koch and RV Sanna cruises sampled on the East and West coast of Greenland in August 2018 and 2016 respectively. This dataset consists of 52 carbonate chemistry sample sites where dissolved inorganic carbon (DIC), total alkalinity (TA), and δ18O-H2O were measured throughout the water column. Water samples were collected in Niskin bottles and were transferred directly into triplicate 12 ml exetainers with a gas tight Tygon tubes, allowing overflow of at least 3 times the volume of the exetainer. Triplicate exetainers were collected for both DIC and TA at each depth. Samples were preserved with HgCl2 (saturated solution) to a final concentration of 0.02%. TA was measured on an Apollo SciTech AS-ALK2 total alkalinity titrator based on the Gran titration procedure for samples in West Greenland. While samples for East Greenland were measured on automatic titrator (Metrohm 888 Titrando), and a combined Metrohm glass electrode (Unitrode). DIC samples were analyzed on Apollo SciTech's AS-C3 analyzer for both cruises, using a sample volume of 0.5 ml. Routine analysis of Certified Reference Materials (provided by A. G. Dickson, Scripps Institution of Oceanography) verified that the accuracy of DIC and TA measurements. Coalescing this dataset therefore provides the first-ever analysis of wide-scale Greenland Fjord carbonate chemistry.   Environmental variables recorded by CTD instruments are available for cruises from the West and East coasts respectively at the following DOIs: <a href="https://doi.org/10.5281/zenodo.4062024">https://doi.org/10.5281/zenodo.4062024</a>  and <a href="https://doi.org/10.5281/zenodo.5572329">https://doi.org/10.5281/zenodo.5572329</a>.   We would like to thank the crew on board the HDMS Lauge Koch and RV Sanna for their collaboration. The Cruises were funded by Danish Centre for Marine Science (Grants: 2016-05 and 2017-06) and by the EU Horizon2020 funded project INTAROS (grant no. 727890) and the Danish Cooperation for Environment in the Arctic. This dataset is a contribution the project FACE-IT (The Future of Arctic Coastal Ecosystems – Identifying Transitions in Fjord Systems and Adjacent Coastal Areas). FACE-IT has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 869154.

opencc-by-4.0Jun 2022View details →

zenodo44/100

Dataset for "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b"

This is the supplemental materials for the Astronomy & Astrophysics publication "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b". Please refer to "README.md" for details.

opencc-by-4.0Nov 2021View details →

zenodo44/100

Supporting model output for article "Assessing the potential impact of river chemistry on Arctic coastal production"

The following is a summary of processed model output data from a series of HiLAT model runs. A description of the model runs and visualization of model output and analysis can be found in the accompanying manuscript. Gibson G. A., Elliott, S., Piliouras, A. Clement Kinney, J., Jeffery, N. (2022) Assessing the potential impact of river nitrate on coastal production in the Arctic. Frontiers in Marine Science: Coastal Ocean Processes. This work was supported by the Regional and Global Model Analysis (RGMA) program of the US Department of Energy’s Office of Science as a contribution to the HiLAT project. Additional support for this project was provided by the National Science Foundation, under award #173886.   River Nutrient Forcing The experiments involved modifying the nutrient concentrations in the river nutrient forcing files. The river nutrient files are specified during model setup. For use in the HiLAT model, GNEWS annual river nutrient inputs were partitioned into twelve monthly forcing values. The nearest ocean grid point to each of the GNEWS river mouth locations was identified and then, as with the runoff, the nutrient inputs for each river basin were spatially mapped to surface ocean model grid cells, which are 10 meters thick, such that the spatial pattern of river nutrient dispersion follows river water inputs to the oceans. The experiments were: i) The baseline model simulation: The 12 monthly values for each grid cell were constant in time. river_nutrients_GNEWS2000_gx1v6.nc ii) an experiment in which baseline Arctic River Nitrogen (NO 3 and NH 4 ) concentrations were doubled. river_nutrients_GNEWS2000_gx1v6_x2Arctic.nc iii) an experiment in which baseline Arctic River Nitrogen (DON and DIN) concentrations were scaled to the river volume discharge contained in runoff.daitren.iaf.20120419.nc river_nutrients_GNEWS2000_gx1v6_scaled_climatology.nc iv) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by two months. river_nutrients_GNEWS2000_gx1v6_shifted2m_climatology.nc v) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by a month and doubled in concentration. river_nutrients_GNEWS2000_gx1v6_shifted_climatology_x2.nc River nutrient fluxes are in units of nmol/cm2/s Only concentrations within the domain TLONG>=60 &TLONG <=340 & TLAT >=60 were modified in concentration/timing. Variables of interest: din_riv_flux: dissolved inorganic nitrogen river flux don_riv_flux: dissolved organic nitrogen river flux Each of the experiments is described in detail in Gibson et al (2022). --------------------------------------------- There are multiple versions of most output file types, corresponding to the river nutrient experiments that were conducted. Many variables in the output files are regional averages where model regions are indicated by a number *note - for aesthetics, the numbering used in the model output files differs slightly from the numbering used in the accompanying manuscript. The numbers assigned in the analysis files aligns with the numbers assigned to regions within the region mask provided in the grid file. Grid File/region masks gx1v6_polar_mask_coast.5.22.20c.nc This file is an updated version of the standard grid file. It has been updated to include the addition of a coastal Arctic region variable ‘Arctic_Coast_Mask’ which indicates which grid cells are in the coastal regions used in the analysis and the Arctic_Region variable which indicates which grid cells are in the broader regions. Variables contained in this file are: Arctic_Coast_Mask: contains values 0-9 indicating which (if any) coastal region a grid cell is in Arctic_Region Mask: contains values 0-11 indicating which (if any) region a grid cell is in TLAT: latitude of grid cell TLONG: longitude of grid cell TAREA: Area of grid cell HT: Bathymetry of grid cell   <table> <tbody> <tr> <td> </td> <td> Arctic_Region (seas) </td> <td> Arctic_Coast_Mask (coast) </td> </tr> <tr> <td> Bering Sea </td> <td> 1 </td> <td> 1 </td> </tr> <tr> <td> Chukchi Sea </td> <td> 2 </td> <td> 2 </td> </tr> <tr> <td> East Siberian Sea </td> <td> 3 </td> <td> 3 </td> </tr> <tr> <td> Laptev Sea </td> <td> 4 </td> <td> 4 </td> </tr> <tr> <td> Beaufort Sea </td> <td> 5 </td> <td> 5 </td> </tr> <tr> <td> Barents Sea </td> <td> 6 </td> <td> 6 </td> </tr> <tr> <td> Canadian Basin </td> <td> 7 </td> <td> - </td> </tr> <tr> <td> Eurasian Basin </td> <td> 8 </td> <td> - </td> </tr> <tr> <td> Nordic Seas </td> <td> 9 </td> <td> 7 </td> </tr> <tr> <td> Labrador Sea </td> <td> 10 </td> <td> 8 </td> </tr> <tr> <td> Kara Sea </td> <td> 11 </td> <td> 9 </td> </tr> </tbody> </table> --------------   Model outputs that were analyzed in the manuscript are contained in three different kinds of output file. For each file type a file exists for each river nutrient experiment. The following series of files contains variables related to the particulate organic carbon flux to the sediment, demineralization and remineralization rates. bgc_T62_gx1GIF_nut-riv-BASELINE-coast_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-month-shiftx2-coast_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-2xArcticN-coast_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-mon-clim-coast_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-2mon-shift-coast_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv_2XDC-coast_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-BASELINE-seas_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-month-shiftx2-seas_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-2xArcticN-seas_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-mon-clim-seas_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-2mon-shift-seas_region-sed-137-157.nc Variables contained in these are: POCTOSED_AVG* : Particulate organic carbon flux to sediment PONTOSED_AVG* : Particulate organic nitrogen flux to sediment SEDDENITRIF_AVG* : Sediment denitrification rate POC_PROD_AVG* : Production of Particulate organic carbon POC_FLUX_AVG* : Particulate organic carbon flux into layer/cell DON_REMIN_AVG* : Dissolved Organic Nitrogen remineralization rate DOC_REMIN_AVG* : Dissolved Organic Carbon remineralization rate DIAT_N_LIM_AVG* : Diatom nitrogen limitation DIAT_N_LIM_AVG* : Diatom nitrogen limitation DIAT_P_LIM_AVG* : Diatom phosphorous limitation DIAT_FE_LIM_AVG* : Diatom iron limitation DIAT_LIGHT_LIM_AVG*: Diatom light limitation SP_N_LIM_AVG* : Small phytoplankton nitrogen limitation SP_P_LIM_AVG* : Small phytoplankton phosphorous limitation SP_FE_LIM_AVG* : Small phytoplankton iron limitation SP_LIGHT_LIM_AVG* : Small phytoplankton light limitation Where * represents the coastal region number. ---------------------------------------- The following series of files contains primary production for the small and large phytoplankton groups and the zooplankton biomass. Coastal regional averages – based on regions marked in the Arctic_Coast_Mask variable bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-ACM-137-157.nc bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-ACM-137-157.nc bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-ACM-137-157.nc bgc_T62_gx1GIF_riv-BASELINE-region-prod-ACM-137-157.nc bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-ACM-137-157.nc bgc_T62_gx1GIF_nut-riv-2XDC-region-prod-ACM-137-157.nc Regional seas averages – based on regions marked in the Arctic_Region variable bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-seas-137-157.nc bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-seas-137-157.nc bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-seas-137-157.nc bgc_T62_gx1GIF_riv-BASELINE-region-prod-seas-137-157.nc bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-seas-137-157.nc Variables contained in these files are: TAREA_SUM* – total area of the region PPSP_REGSUM* – sum of primary production by small phytoplankton in a region PPDIAT_REGSUM*– sum of primary production by diatoms in a region ZOOC_AVG*– sum of zooplankton biomass in a region ---------------------------------------- The following series of files contains ice associated variables and mixed layer nutrients bgc_T62_gx1GIF_nut_riv-2xArcticN-ice_coastal-137-157.nc bgc_T62_gx1GIF_nut-riv-mon-clim-ice_coastal -137-157.nc bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_coastal -137-157.nc bgc_T62_gx1GIF_nut-riv-BASELINE-ice_coastal -137-157.nc bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_coastal -137-157.nc bgc_T62_gx1GIF_nut-riv-2XDC-ice_coastal -137-157.nc bgc_T62_gx1GIF_nut-riv-2xArcticN-ice_seas-137-157.nc bgc_T62_gx1GIF_nut-riv-mon-clim-ice_seas -137-157.nc bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_seas -137-157.nc bgc_T62_gx1GIF_nut-riv-BASELINE-ice_seas -137-157.nc bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_seas-137-157.nc HI_REGAVG* : Regional averaged ice depth HS_REGAVG* : Regional averaged snow depth ICEAREA_REGSUM* : Regional sum ice area ICEVOL_REGSUM*: Regional sum volume area MLAM_REGAVG* : Regional average ammonium concentration in mixed layer MLNIT_REGAVG* : Regional average nitrate concentration in mixed layer PP_REGAVG* : Regional average primary production (ice algae) PP_REGSUM* : Regional total primary production (ice algae) TAREA_SUM* : Total area of region TIME : time

opencc-by-4.0Jan 2022View details →

zenodo44/100

The Surface Water Chemistry (SWatCh) database

This is the dataset presented in the following manuscript: The Surface Water Chemistry (SWatCh) database: A standardized global database of water chemistry to facilitate large-sample hydrological research, which is currently under review at Earth System Science Data. Openly accessible global scale surface water chemistry datasets are urgently needed to detect widespread trends and problems, to help identify their possible solutions, and determine critical spatial data gaps where more monitoring is required. Existing datasets are limited in availability, sample size/sampling frequency, and geographic scope. These limitations inhibit the answering of emerging transboundary water chemistry questions, for example, the detection and understanding of delayed recovery from freshwater acidification. Here, we begin to address these limitations by compiling the global surface water chemistry (SWatCh) database. We collect, clean, standardize, and aggregate open access data provided by six national and international agencies to compile a database containing information on sites, methods, and samples, and a GIS shapefile of site locations. We remove poor quality data (for example, values flagged as “suspect” or “rejected”), standardize variable naming conventions and units, and perform other data cleaning steps required for statistical analysis. The database contains water chemistry data for streams, rivers, canals, ponds, lakes, and reservoirs across seven continents, 24 variables, 33,722 sites, and over 5 million samples collected between 1960 and 2022. Similar to prior research, we identify critical spatial data gaps on the African and Asian continents, highlighting the need for more data collection and sharing initiatives in these areas, especially considering freshwater ecosystems in these environs are predicted to be among the most heavily impacted by climate change. We identify the main challenges associated with compiling global databases – limited data availability, dissimilar sample collection and analysis methodology, and reporting ambiguity – and provide recommended solutions. By addressing these challenges and consolidating data from various sources into one standardized, openly available, high quality, and trans-boundary database, SWatCh allows users to conduct powerful and robust statistical analyses of global surface water chemistry.

opencc-by-4.0Feb 2021View details →

zenodo44/100

Signatures of Nitrogen Chemistry in Hot Jupiter Atmospheres - Posteriors

Supplementary material for 'Signatures of Nitrogen Chemistry in Hot Jupiter Atmospheres', ApJL, 2017. Contains the posterior probability distributions resulting from atmospheric retrievals of WASP-31b, WASP-63b, and HD 209458b.

opencc-by-4.0Nov 2017View details →

zenodo44/100

Liquid-Jet Photoemission Spectroscopy as a Structural Tool: Site-Specific Acid-Base Chemistry of Vitamin C

Dataset pertaining to the article "Liquid-Jet Photoemission Spectroscopy as a Structural Tool: Site-Specific Acid-Base Chemistry of Vitamin C", submitted to Physical Chemistry Chemical Physics. Here, we demonstrate how liquid-jet photoemission spectroscopy can be systematically used for chemical analysis, probing tautomeric forms and deprotonation sites in aqueous vitamin C. We also present a fast and reliable computational protocol to model the spectra.   Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see https://www.nexusformat.org/ https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format: * nexpy (distributed with python) * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium) The following files are provided: Photoemission data pertaining to vitamin C PES measurements: vitamin-C.h5   Computational data for Figures 3, 5, and 6: binding energy values (plain text) from which the spectra were modeled, optimized molecular geometries (XYZ coordinates, distances in angstroms) used in the calculations, and a sample input for the binding energy calculation. All data for each figure are packed in a zip file. COMPUTATIONAL-DATA-Fig3.zip COMPUTATIONAL-DATA-Fig5.zip COMPUTATIONAL-DATA-Fig6.zip Numeric representations of the traces shown in the article's figures (space-separated or comma-separated ascii-files): Figure 3: EXPT.dat TAUTOMER-A.dat TAUTOMER-B.dat C2.dat C3.dat C5.dat C6.dat C2+C3.dat C2+C5.dat C2+C6.dat C3+C5.dat C3+C6.dat C5+C6.dat Figure 5: EXPT.dat TAUTOMER-A.dat C3.dat C2+C3.dat Figure 6: EXPT.dat C3.dat C3-UFF-ensemble.dat C3-Bondi-single.dat C3-Bondi-ensemble.dat TAUTOMER-A.dat TAUTOMER-A-ensemble.dat C2+C3.dat C2+C3-ensemble.dat Figure S1: EXPT.dat EXPT-850eV.dat   Version history: 4: Data from version 2 reuploaded 3: Experimental NeXus-data added 2: Computational data and figure traces added 1: Initial upload Contact person for questions regarding this data set: Lukas Tomanik, tomanikl@vscht.cz . If you use these data for your scientific work we are curious to learn about it.

opencc-by-4.0Jun 2024View details →

zenodo44/100

Seasonal Carbonate Chemistry Variability in Marine Surface Waters of the Pacific Northwest. Data Archive.

This archive includes two .nc files (NetCDF format) containing observational data (discrete and mooring) from marine surface waters of the Pacific Northwest that have not yet been submitted to a long-term data repository. These data contributed to the development of seasonal cycle data products described in the manuscript by Fassbender et al. A metadata file is provided for the discrete data subset (upper 10 m of discrete observational data); however, the complete cruise datasets and metadata will be submitted for archival in the National Centers for Environmental Information’s (NCEI) Ocean Carbon and Acidification Data repository (<a href="https://www.nodc.noaa.gov/oceanacidification/">https://www.nodc.noaa.gov/oceanacidification/</a>). Data subsets are provided here for accelerated public access. Data users are encouraged to download the complete datasets from NCEI once they are available (<a href="https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html">https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html</a>). Metadata for the University of Washington Oceanic Remote Chemical/Optical Analyzer (ORCA) mooring observations used by Fassbender et al., including the temperature and salinity data from the Dabob Bay and Twanoh moorings, are not provided here. Quality control protocols applied to the ORCA mooring data are outlined in the Quality Assurance Project Plan (<a href="http://nwem.ocean.washington.edu/ORCA_QAPP.pdf">http://nwem.ocean.washington.edu/ORCA_QAPP.pdf</a>; Newton and Devol, 2012).

opencc-by-4.0Feb 2018View details →

zenodo44/100

Data accessibility in the chemical sciences: an analysis of recent practice in organic chemistry journals

<div> Data is the analysis of the data outputs of 240 randomly selected research papers from 12 top-ranked journals published in early 2023. We investigate author compliance with recommended (but not compulsory) data policies, whether there is evidence to suggest that authors apply FAIR data guidance in their data publishing, and if the existence of specific recommendations for publishing NMR data by some journals encourages compliance. Files in the data package have been provided in both human and machine-readable forms. The main dataset is available in the Excel file Data worksheet.XLSX, the contents of which can also be found in Main_dataset.CSV, Data_types.CSV, and Article_selection.CSV with explanations of the variable coding used in the studies in Variable_names.CSV, Codes.CSV, and FAIR_variable_coding.CSV. The R code used for the article selection can be found in Article_selection.R. Data about article types from the journals that contain original research data is in Article_types.CSV. Data collected for analysis in our sister paper[4] can be found in Extended_Adherence.CSV, Extended_Crystallography.CSV, Extended_DAS.CSV, Extended_File_Types.CSV, and Extended_Submission_Process.CSV. A full list of files in the data package and a short description for each is given in README.TXT. </div>

opencc-by-4.0Oct 2024View details →

zenodo44/100

Datasets supplementing journal article "Probing dynamic covalent chemistry in a 2D boroxine framework by in-situ near-ambient pressure X-ray photoelectron spectroscopy" in Nanoscale 2022

Datasets supporting the Nanoscale journal article "Probing dynamic covalent chemistry in a 2D boroxine framework by in-situ near-ambient pressure X-ray photoelectron spectroscopy". NAP-XPS.zip: Near-ambient pressure X-ray photoelectron spectroscopy, Figures 2, 3. (NEP 101007417) STM.zip: Scanning tunneling microscopy, Figure 5a, inset. (NEP 101007417) TPD.zip: Temperature programmed desorption, Figure 1a. UHV-XPS.zip: X-ray photoelectron spectroscopy, Figure 1b,c. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007417, having benefited from the access provided by by ALBA in Barcelona (Spain) and CNR-IOM in Trieste (Italy) within the framework of the NFFA-Europe Pilot Transnational Access Activity, proposal ID075.

opencc-by-4.0Nov 2022View details →

zenodo44/100

Water chemistry of LTER-Europe research site Lake Paione Superiore LTER_EU_IT_089 (1984-2013)

This dataset provides information about water chemical parameters for Lake Paione Superiore LTER_EU_IT_089: pH, Total alkalinity, conductivity, total nitrogen, major cations (calcium, magnesium, sodium, potassium), major anions (sulphate, nitrate, chloride) and silica for the period 1984-2013. Lake Paione Superiore (LPS) is a high altitude Alpine lake, located at 2269 m a.s.l. in the Bognanco Valley, Province of Verbania, Piedmont Region, Italy. It has a surface area of 0.68 ha and a maximum depth of 11.5 m. The Lake, together with Lake Paione Inferiore (LPI), is included in the monitoring sites of the UN-ECE Program ICP WATERS (International Cooperative Programme on Assessment and Monitoring of Acidification of Rivers and Lakes) for which the CNR Water Research Institute is the National Focal Centre for Italy. This dataset includes the following files: Metadata LTER_EU_IT_089.xls and per each parameter one xls file with data records. Detailed description of the site LPS is available at https://deims.org/7e5837a9-ee27-4e27-822a-f50e5217c313 Dataset for water chemistry of LPS for the  period 2014-2020 is available at https://doi.org/10.5281/zenodo.10519126

opencc-by-4.0Dec 2022View details →

zenodo44/100

Dynamically coupled kinetic chemistry in brown dwarf atmospheres I. Performing global scale kinetic modelling

Gifs and Exo-FMS GCM output from the 3D brown dwarf atmospheric simulations in Lee, Tan and Tsai (2023).  Animated gifs for each effective temperature (Teff - first number in filename) of the brown dwarf (OLR and CH4 VMR). The gifs frames are every hour of simulation for 4 simulated days. Exo-FMS GCM output in netCDF format containing the 3D T-p structure and chemical results from the coupled mini-chem and GCM model for each Teff simulation (number in filename). `average' is the averaged output of the last 100 days. `daily' is the snapshot at the end of the simulation.

opencc-by-4.0Apr 2023View details →

zenodo44/100

Towards a mechanistic description of autoxidation chemistry: from precursors to atmospheric implications (data)

The files contain the raw data from simulations of the plots for main manuscript figures 2 & 3. Any data not archived here (mainly ADCHEM simulations) can be provided upon request (contact: lukas.pichelstorfer@helsinki.fi) On the data naming, content and structure: file names are structured as <Facility/experiment type>_<experimental conditions>_<data_content>.dat "..._time.dat": contains all data output times during the simulation [s] "...T.dat": gas phase temperature during the simulation [K] for all output times (columns) "..._DIAMETER.dat" contains the diameters [nm] of all (100) size bins (lines) for each output time (columns); "...SPC_NAMES.dat": contains the chemical species names "...N_bins.dat": particle number concentration [1/m3] for each size bin (lines) and each output time (columns) "...conc.dat": contains the gas-phase concentrations [1/ccm] of chemical species for all output times (number of species * number of times) "mass_spec.dat": contains the particle phase mass ([µg/m3] for all chemical species -> lines) sum over 100 size bins and for all output times (columns) "FlowTube_autoAPRAM_composition.dat": composition C/H/O/N for each autoAPRAM-fw species "FlowTube_autoAPRAM_names.dat": autoAPRAM-fw species

opencc-by-4.0Jun 2023View details →

zenodo44/100

RMG-DB-11: Enumerating Reaction Space for Small Molecule Chemistry

This repository presents approximately 750 million atom-mapped reaction SMILES. Reactions are generated by applying templates from the Reaction Mechanism Generator (RMG) database to a subset of the species from GDB11. Thus, we refer to this dataset as RMG-DB-11 i.e., the Reaction Mechanism Generator Database whose species contain up to 11 heavy atoms. All SMILES have been canonicalized by RDKit. All reactions are labeled with their corresponding RMG template. This data serves as a crucial starting point for quantitative predictive chemistry. Many methods that search for transition state structures require atom-mapped SMILES, which this repository provides. This data is also well-suited for unsupervised pre-training of various machine learning models. To parse the data with Python, start with import pandas as pd. Reactions with 1-8 heavy atoms can be parsed using the following code snippet: pd.read_csv(<filepath>). Reactions with 9 heavy atoms can be parsed using pd.read_pickle(<filepath>, compression='zip'). The file names below include the word "zip" as a helpful hint to use the compression argument. Due to the large number of reactions with 10 and 11 heavy atoms, these are split into smaller chunks. First untar the file using tar -xvf <tar_archive> to obtain several zipped pickle files that can each be parsed using the same method as with 9 heavy atoms.

opencc-by-4.0Dec 2022View details →

edi44/100

Mountain Lake Chemistry and Physics Profile Data since 2015 at Castle Lake

This data set contains long-term limnology data from Castle Lake (located 5440 ft above sea level in Northern California). The data contained can be broadly grouped into two different types: chemical and physical data. This data set contains dissolved oxygen saturation, dissolved oxygen concentration, chlorophyll-a concentration, CDOM, phycocyanin concentration, conductivity, specific conductivity, pH, salinity, water temperature, pressure, turbidity, sea pressure, density anomaly, and speed of sound at various depths. Sampling Frequency: Continuous measurements were made and recorded down to milliseconds. Sampling dates vary from year to year, starting as early as February and as late as November. This data collection is part of an ongoing project funded by the US National Science Foundation, private donors, US AID, and the University of Nevada's Global Water Center. This package was updated in February 2023 with data from 2021 and 2022. Dissolved O2 concentration data in the 2020 data file was updated to values in units of mg/L instead of umol/L and specific conductivity units in all files was revised to be in microSiemens per centimeter and not milliSiemens per centimeter.

openCC (other)Feb 2023View details →

edi44/100

LAGOS-NE – Lake nutrient chemistry and geospatial data to measure spatial structure of ecosystem properties in a 17-state region of the U.S.

This dataset includes data for the lake water quality and geospatial variables that describe climate, hydrology, land use land cover, and lake characteristics that were used to study spatial structure in lake properties at the sub-continental scales (Lapierre et al. Quantifying spatial structure to improve understanding of the relationships between climate, landscape, and lake ecosystem properties, to be submitted to Ecology). All observations came from LAGOS-NELIMNO v. 1.054.1 and LAGOS-NEGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS-NE contains a complete census of lakes great than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for the LAGOS-NELIMNO v. 1.054.1 dataset and were mostly generated by government agencies (state, federal, tribal) and universities. In this analysis, we compiled lake water quality data from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOS-NELIMNO v. 1.054.1 (2002-2011). We report the median total nitrogen, total phosphorus, secchi depth, and chlorophyll values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics including variables related to lake morphometry, climate, hydrology, atmospheric deposition, land use and land cover.

openCC (other)Jul 2017View details →

edi44/100

Greenhouse Gas and Water Chemistry Data from Ponds in the Twin-Cities area of Minnesota, 2021

Freshwaters are significant contributors of greenhouse gases to the atmosphere, including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Small waterbodies such as ponds are now recognized to have disproportionate greenhouse gas emissions relative to their size, but recorded emissions from ponds have varied by several orders of magnitude. To assess drivers of variation in pond greenhouse gas dynamics, this study measured concentrations and emissions of CO2, CH4, and N2O across 26 ponds in Minnesota, USA during the ice-free season. The studied ponds ranged in land-use, from urban stormwater ponds to natural forested ponds. Water chemistry variables were measured with sonde profiles as well as surface water samples. Greenhouse gas emsisions of CO2 and CH4 were measured with a floating chamber at three locations on each pond, and gas concentrations of CO2, CH4, and N2O were measured using a headspace equilibrium technique in both the surface waters and bottom waters of each pond.

openCC (other)Sep 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record