Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

datasets available to search

ShareScore release 0.9.0

Dataset results

61 results for “DMS”

Learn how ShareScore rates datasets ↗

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

DMS-SAT_GLOBAL_MONTHLY_DMS_DMSPt_CLIM_v1.0.0

README file for the global DMS and DMSPt fields derived with DMS-SAT Martí Galí Tàpias, 2019-02-06 Questions and requests can be addressed to: marti.gali.tapias@gmail.com Content and data sources ======================== This dataset contains gobal fields of dimethylsulfide (DMS) and dimethylsulfoniopropionate in the sea surface layer, estimated with different remote sensing algorithms. It is based on ocean colour data publicly available at https://oceancolor.gsfc.nasa.gov/. Monthly climatological data are provided at 9 km, 1 and 5 degree resolutions on lat-lon grids. Different satellite products have been used for chlorophyll_a and euphotic layer depth, as indicated in the names of the files: {Variable}_{PERIOD}_{algorithm}_{spatial resolution}_{chlorophyll product}_{euphotic layer product}.nc {chlorophyll product}: CHL corresponds to OC4-CI, GSM to Garver-Siegel-Maritorena. {euphotic layer product}: KD490 corresponds to diffuse attenuation coefficient at 490 nm, ZLEE correspond to Lee et al. algorithm. Note that CHL is not used in VS07 although it appears in the file name. Please check Galí et al. 2018 Biogeosciences for details. This dataset is related to <a href="https://doi.org/10.5281/zenodo.2205131">doi.org/10.5281/zenodo.2205131</a> References ========== Galí, M., Devred, E., Levasseur, M., Royer, S. J., & Babin, M. (2015). A remote sensing algorithm for planktonic dimethylsulfoniopropionate (DMSP) and an analysis of global patterns. Remote Sensing of Environment, 171, 171-184. https://doi.org/10.1016/j.rse.2015.10.012 Galí, M., Levasseur, M., Devred, E., Simó, R., & Babin, M. (2018). Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales.  Biogeosciences, 15(11), 3497-3519. https://doi.org/10.5194/bg-15-3497-2018 How to cite =========== This dataset can be freely distributed, but please cite it using its DOI. When relevant, cite also the journal articles mentioned above. Acknowledgments ================ I acknowledge NASA’s Ocean Biology Processing Group (OBPG) for making data freely available. ====== Enjoy! Martí Galí Tàpias

opencc-by-4.0Feb 2019View details →

zenodo44/100

ARCFRIE Experiment 1: DMS - data, figures, packages

This is the complete data set and analysis of the ARCIRE experiement 1, including all data, figures, and packages.

opencc-by-4.0Feb 2019View details →

zenodo44/100

DMS emission dataset (latitudes >45N)

# README file for (sub)Arctic DMS emission dataset (latitudes >45N), v1.0.0 =========================================================================== Martí Galí Tàpias, 2019-06-12 http://orcid.org/0000-0002-5587-1271 Address requests for additional data and questions to: marti.gali.tapias@gmail.com This dataset contains satellite-derived time series of: * dmspt (sea-surface dimethylsulfoniopropionate -DMSPt- concentration, in µmol/m3 or equivalent nmol/L) * dms (sea-surface dimethylsulfide -DMS- concentration, in µmol/m3 or equivalent nmol/L) * fdms (sea-air DMS flux, in µmol/m2/d) * ice concentration (0 to 1) The DMSPt dataset was produced as described by: Galí, M., Devred, E., Levasseur, M., Royer, S. J., & Babin, M. (2015). A remote sensing algorithm for planktonic dimethylsulfoniopropionate (DMSP) and an analysis of global patterns. Remote Sensing of Environment, 171, 171-184. https://doi.org/10.1016/j.rse.2015.10.012 The DMS dataset was produced as described by: Galí, M., Devred, E., Babin, M., & Levasseur, M. (2019). Decadal increase in Arctic dimethylsulfide emission. Accepted in PNAS. doi:10.1073/pnas.1904378116. Galí, M., Levasseur, M., Devred, E., Simó, R., & Babin, M. (2018). Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales. Biogeosciences, 15(11), 3497-3519. https://doi.org/10.5194/bg-15-3497-2018   Code for the DMSPt_SAT and DMS_SAT algorithms: https://github.com/mgali/DMS-SAT_ALGORITHM ## File format MetCDF files are self-describing: they contain one “variable of interest” and all the corresponding temporal and geographic dimensions. ## File naming convention variable_sensorYYYY_nD_28km.nc Both the DMSPt and DMS satellite datasets are based on ocean color data obtained from NASA Ocean Color website. Chlorophyll was derived from the GSM algorithm, hence _gsm was appended to the variable names. DMSPt and DMS were produced for 2 satellite sensors. * S = SeaWiFS, years 1998 to 2007 * A = MODIS-Aqua, years 2003 to 2016 (the last year is empty from October on) FDMS was calculated using ERA-Interim wind speed and SST, and ice cover from NSIDC. Two different gas exchange parameterizations were used, whose names are appended to the variable names: * W97 (Woolf 1997) * N00GBC (Nightingale et al. 2000 GBC –do not confound with Nightingale et al. 2000 GL) FDMS was also calculated using climatological DMS fields (W97 parameterization) while retaining the interannual variability of all the other variables involved in FDMS calculation: * S19982007 DMS climatology * A20032016 DMS climatology * L11 DMS climatology (interpolation based climatology of Lana et al. 2011 GBC) Ice cover (ice) is also provided for the full period 1998-2016. ## Directory naming convention Each directory is named using the names of variable it contains.

opencc-by-4.0Jun 2019View details →

zenodo44/100

DMS measurements dataset for manuscript "Classification of Volatile Organic Compounds by Differential Mobility Spectrometry Based on Continuity of Alpha Curves"

Differential mobility spectrometry dispersion plots collected for the manuscrit "Classification of Volatile Organic Compounds by Differential Mobility Spectrometry Based on Continuity of Alpha Curves". The measurement files are located in the folders that represent certain week and day of measurement. The folders containing measurements are named with the following pattern: [chemical abbreviation]_[dilution rate]. For example "2PEtOH_1o10k" means that the folder contains measurement of 2-phenylethanol diluted with propylene glycol in volumetric proportion 1/10 000. Another example is "nBuOH_1o100" - n-Butanol diluted with propylene glycol in volumetric proportion 1/100. Please find the abbreviations in the article referred. For the first five weeks only 1/100 dilutions were measured. The last two weeks (weeks 6 and 7) 1/10 000 dilutions were measured. However, Carvone 1/100 was measured again on week 6 due to suspicion of faulty measurements during the previous weeks. The faulty measurements were not confirmed, and thus there 25 more samples of Carvone with dilution rate 1/100.

opencc-by-4.0Aug 2024View details →

zenodo40/100

Measurements of Methanethiol (MeSH) and dimethyl sulfide (DMS) in surface seawater during 2022 at the monthly sampling observatory of Blanes Bay Microbial Observatory.

Global emissions of methanethiol are highly uncertain and the drivers influencing its seawater concentrations are quasi unexplored. Here we try to address this gap in our understanding by contributing new seasonally resolved seawater measurements. Mediterranean surface seawater sampling at the Blanes Bay Microbial Observatory was carried out monthly during 2022. January, March and April were not sampled due to technical difficulties. Seawater was hand-collected in glass sampling bottles around 11:00 am each day. The samples were kept in the dark and analysed at the ICM-CSIC for MeSH and DMS with the Vocus-PTR coupled to SFCE within 3 h of sample collection. This dataset contains MeSH and DMS surface seawater concentrations and relevant auxiliary data (chlorophyll a, sea surface temperature and sea surface salinity). Sea surface temperature and salinity were determined using a CTD profiler Model SD204 (SAIV A/S). The chlorophyll from the Blanes 2022 timeseries was measured on a fluorometer (10AU Turner fluorometer). Details on the measurement methodology can be found here: Wohl, C.; Güell-Bujons, Q.; Castillo, Y.M.; Calbet, A.; Simó, R. Volatile Organic Compounds Released by Oxyrrhis marina Grazing on Isochrysis galbana. Oceans 2023, 4, 151-169. https://doi.org/10.3390/oceans4020011   Definitions of acronyms, site abbreviations, or other project-specific designations: Lat=latitude (negative indicates south) Lon=longitude (negative indicates west ) Hourlybin = The mean measured concentration during each hour is shown, 30 min either side of the listed time. The timestamp indicates sampling time in UTC, expressed as  DD/MM/YYYY HH:MM MeSH_nM= methanethiol surface seawater concentration in nM, defined as nmol dm^(-3) DMS_nM= dimethyl sulfide surface seawater concentration in nM, defined as nmol dm^(-3) fluo = chlorophyll a concentration in mg m^(-3) SST= sea surface temperature in degrees Celsius SSS= sea surface salinity in practical salinity units (PSU)

opencc-by-4.0Jul 2024View details →

zenodo40/100

Measurements of Methanethiol (MeSH) and dimethyl sulfide (DMS) in surface seawater during POLAR-CHANGE Antarctic expedition 2023 on board the BIO Hesperides.

Global emissions of methanethiol (MeSH) are highly uncertain and the drivers influencing its seawater concentrations are quasi unexplored. Here we try to address this gap in our understanding by contributing new seawater concentration measurements from the Southern Ocean. The POLAR-CHANGE cruise was conducted in February and March 2023 on board the BIO RV Hesperides A33 in waters of the Antarctic Peninsula and the Weddell Sea. A Vocus PTR-ToF (TOFWERK AG, Thun, Switzerland, Vocus Scout) coupled to a segmented flow coil equilibrator (SFCE) was used to measure DMS and MeSH in seawater from the underway seawater inlet. This dataset contains MeSH and DMS surface seawater concentrations and relevant auxiliary data (chlorophyll a, sea surface temperature (SST) and sea surface salinity (SSS)). Sea surface temperature (SST) and sea surface salitity (SSS) were measured in seawater from the underway water inlet located at 4-5 m depth. There was good agreement between the underway SST, SSS and the CTD sensors, confirming that the underway sensors were well calibrated. Underway seawater fluorescence measurements were not calibrated for total chlorophyll a (Chl a) concentration by frequent discrete measurements of Chl a from the on-board 10AU Turner fluorometer. For calibrated Chl a data, please get in touch with the autors.   From the beginning of the POLAR-CHANGE cruise we observed extremely high MeSH concentrations which immediately dropped down to within previously observed ranges, after cleaning the equilibrator daily with 10 % HCl. We decided to exclude all MeSH measurements before regular cleaning commenced from 26/02/2023 onwards as these datapoints are likely affected by biofouling. MeSH seawater concentrations prior to this date are replaced by -999 in the dataset shared here. Other gaps in the timestamp of data acquisition are due to calibrations or measuring other samples.   Details on the measurement methodology can be found here: Wohl, C.; Güell-Bujons, Q.; Castillo, Y.M.; Calbet, A.; Simó, R. Volatile Organic Compounds Released by Oxyrrhis marina Grazing on Isochrysis galbana. Oceans 2023, 4, 151-169. https://doi.org/10.3390/oceans4020011   Definitions of acronyms, site abbreviations, or other project-specific designations: Lat=latitude (negative indicates west and south) Lon=longitude (negative indicates west and south) Date=  The mean measured concentration during each hour is shown, 30 min either side of the listed time. The timestamp indicates sampling time in UTC, expressed as  DD/MM/YYYY HH:MM MeSH_nM= methanethiol surface seawater concentration in nM, defined as nmol dm^(-3) DMS_nM= dimethyl sulfide surface seawater concentration in nM, defined as nmol dm^(-3) fluo = surface fluorescence, a proxi for chlorophyll a concentration in mg m^(-3) SST= sea surface temperature in degrees Celsius SSS= sea surface salinity in practical salinity units (PSU)

opencc-by-4.0Jul 2024View details →

zenodo40/100

Рис. 16–20. Lixus pulverulentus, гоΛова Λичинки и ротовые органы. 16 – виΔ сверху; 17 – усик; 18 – максиΛΛоΛабиаΛьный компΛекс; 19 – Λабрум и кΛипеус; 20 – эпифаринкс. Figs 16–20. Lixus pulverulentus, larval head and mouth parts. 16 – dorsal view; 17 – antenna; 18 – maxillolabial complex; 19 – labrum and clypeus; 20 – epipharynx. at – antenna, cl – clypeus, stp – stipes, ma – mala, plb – praelabium, plsb – postlabium; setae: als – anteriolateral, ams – anteriomedial, cls – clypeal, des – dorsal epicranial, dms – dorsal marar, fs – frontal, les – lateral epicranial, mbs – malabasiventral, mds – mandibular dorsal, mes – median, pfs – palpiferal, pslbs – postlabial, vms – ventral malar. in Description of the preimaginal stages and biology of the weevil Lixus (Dilixellus) pulverulentus (Scopoli, 1763) (Coleoptera: Curculionidae: Lixini)

Рис. 16–20. Lixus pulverulentus, гоΛова Λичинки и ротовые органы. 16 – виΔ сверху; 17 – усик; 18 – максиΛΛоΛабиаΛьный компΛекс; 19 – Λабрум и кΛипеус; 20 – эпифаринкс. Figs 16–20. Lixus pulverulentus, larval head and mouth parts. 16 – dorsal view; 17 – antenna; 18 – maxillolabial complex; 19 – labrum and clypeus; 20 – epipharynx. at – antenna, cl – clypeus, stp – stipes, ma – mala, plb – praelabium, plsb – postlabium; setae: als – anteriolateral, ams – anteriomedial, cls – clypeal, des – dorsal epicranial, dms – dorsal marar, fs – frontal, les – lateral epicranial, mbs – malabasiventral, mds – mandibular dorsal, mes – median, pfs – palpiferal, pslbs – postlabial, vms – ventral malar.

opencc-by-4.0Dec 2017View details →

zenodo40/100

Data and Code for figures of "Long-range transport and fate of DMS-oxidation products in the free troposphere derived from observations at the high-altitude research station Chacaltaya (5240 m a.s.l.) in the Bolivian Andes"

This database includes the material to create the figures in "Measurement Report: Long-range transport and fate of DMS-oxidation products in the free troposphere derived from observations at the high-altitude research station Chacaltaya (5240 m a.s.l.) in the Bolivian Andes" and the analyzed time series of all atmospheric variables presented.

opencc-by-4.0Sep 2022View details →

zenodo40/100

Concentrations of dissolved dimethyl sulfide (DMS), methanethiol and other trace gases in context of microbial communities from the temperate Atlantic to the Arctic Ocean

This archive contains bioinformatic code and data files to reproduce bacterial analyses from Gros et al. (Biogeosciences; https://doi.org/10.5194/bg-2022-150). In this study, seawater samples collected along an Atlantic-Arctic transect (55-80°N) were evaluated for microbial community composition and trace gas concentrations. Microbes were sampled underway using "AUTOFIM" automatic filtration; and bacterial communities amplicon-sequenced using 16S rRNA gene primers. Raw sequence data is available at ENA under BioProject PRJEB50492. The archive contains:   - Rmarkdown for primer-clipping, ASV generation and taxonomy assignment: 1_DADA.Rmd   - Rscript for ASV and metadata processing; alpha-diversity calculations: 2_DataLoad.R   - Rscript for generation of manuscript figures: 3_Results.R   - Rscript for generation of supplementary figures: 4_Supplement.R   - Metadata and environmental parameters as .txt files

opencc-by-4.0Feb 2023View details →

zenodo36/100

Secondary_data_Maximum chemotactic indices (Icmax) to DMSP, DMS and acrylate

Dataset containing secondary data regarding Chemotactic Indexes (Ic). This data was used for the generation the plot about Ic related to the article: The distinctive chemotactic responses of three marine herbivore protists to DMSP and related compounds. Therefore, provides information about the chemotaxis indexes obtained three cultured dinoflagellates (Karlodinium armiger, Gyrodinium dominans and Oxyrrhis marina) responding to gradients of DMSP, DMS and and acrylate diffusing from a microcapillary.  Nomenclature: Cap: Capillary (C=control, E=experimental (with the chemical cue). D=DMSP/ M=DMS/ A=ACRYLATE 2=2uM / 20= 20uM / 200= 200uM _1, _2, _3 = replicates 1, 2 or 3

opencc-by-4.0May 2024View details →

zenodo36/100

Measurements of Methanethiol (MeSH) and dimethyl sulfide (DMS) in surface seawater digitalised from AMT-7 1998.

Global emissions of methanethiol are highly uncertain and the drivers influencing its seawater concentrations are quasi unexplored. Here we have digitalised measurements from 1998 taken during AMT-7 (Atlantic Meridional Transect), which may be useful for future investigators. The measurements are originally presented in the following reference: Kettle, A. J., Rhee, T. S., von Hobe, M., Poulton, A., Aiken, J., and Andreae, M. O.: Assessing the flux of different volatile sulfur gases from the ocean to the atmosphere, J. Geophys. Res., 106, 12193–12209, https://doi.org/10.1029/2000JD900630, 2001. Measurements from AMT-7 were extracted from the graphs in the manuscript using an image extraction technique (https://apps.automeris.io/wpd/, last accessed July 2024). To ensure concurrency of the measurements and deal with the error in the time stamp related to the use of an image extraction technique, data from AMT-7 was binned in 6-hourly bins. Data gaps are listed with an improbable number of -999.   Definitions of acronyms, site abbreviations, or other project-specific designations: Lat=latitude (negative indicates south) Lon=longitude (negative indicates west) t_Kettle01 = The mean measured concentration during each 6 hour bin is shown, 3 hours either side of the listed time. The timestamp indicates sampling time in local solar time, expressed as DD/MM/YYYY HH:MM MeSH_nM= methanethiol surface seawater concentration in nM, defined as nmol dm^(-3) DMS_nM= dimethyl sulfide surface seawater concentration in nM, defined as nmol dm^(-3) Chla_mg_m3 = surface chlorophyll a concentration in mg m^(-3) SST_C= sea surface temperature in degrees Celsius SSS_PSU= sea surface salinity in practical salinity units (PSU)

opencc-by-4.0Jul 2024View details →

zenodo36/100

Global sea-surface DMS concentrations estimated through data-driven approaches

DMS_ML_average.nc and DMS_SAT-OPT_average.nc represent the 10-year (2011-2020) average monthly seawater DMS concentrations (1°x1°) calculated using two different methods, ML and SAT-OPT, respectively.

opencc-by-4.0Sep 2023View details →

ClinicalTrials.gov36/100

Fluid Management of Acute Decompensated Heart Failure Subjects Treated With Reprieve Decongestion Management System (DMS)

ClinicalTrials.gov study NCT05174312. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

zenodo32/100

Model code and output of CSIBv4 pan-Arctic sea ice-ocean DMS simulation

here i deposit the model source code and output of the manuscript "Spatio-temporal variability in modelled bottom-ice and sea-surface dimethylsulfide concentrations and fluxes in the Arctic during 1979-2015" by Hayashida et al. (2020). See "readme.txt" for information. For question, contact Hakase Hayashida.

opencc-by-4.0Mar 2020View details →

zenodo32/100

ESCOTT results on ProteinGym DMS Substitutions dataset

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2024View details →

ClinicalTrials.gov32/100

Differential Mobility Spectrometry (DMS) Based Oral Tumor Analysis

ClinicalTrials.gov study NCT05902455. IPD Sharing: NO. Countries: 1. Publications: 6.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov32/100

Differential Mobility Spectrometry (DMS) Based Skin Tumor Analysis

ClinicalTrials.gov study NCT05247710. IPD Sharing: NO. Countries: 1. Publications: 7.

closedIPD-NOFeb 2026View details →

geo24/100

DMS-MaPseq: A genome-wide or targeted approach for RNA structure probing in vivo

GEO Series GSE84537. Saccharomyces cerevisiae; Homo sapiens; Drosophila melanogaster. 29 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenSep 2016View details →

geo24/100

DMS-MaPseq chemical probing of pri-miRNA and human mRNA segments

GEO Series GSE262014. synthetic construct. 33 samples. Type: Other.

openGEO-OpenMar 2025View details →

ScienceDex guides

Understand access before you commit

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