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zenodo48/100

The data for "Accurate Infrared Line Lists for 20 Isotopologues of Carbon Disulfide (CS2) at Room Temperature"

[Updates on 2025-03-02: energy levels and line lists of CS2 323 and 333 isotopologues are corrected; energy levels of 224, 223, and 232 isotopologues are extended to 0.1 au (ZPE included); partition function (Q) of first 4 isotopologues by direct summation up to 4000 K; number of 323 and 333 iso lines in natural line list are updated; natural line lists are updated] The paper was published online at <a href="https://iopscience.iop.org/article/10.3847/1538-4365/ad3809">ApJS</a> with open access to public, DOI: 10.3847/1538-4365/ad3809 First-generation data product and IR line lists for Carbon Disulfide (CS2), including an isotopologue-independent ab initio PES of Carbon Disulfide refined with selected HITRAN energy levels below 7000 cm-1, an ab initio DMS fitted with CCSD(T)/aug-cc-pV(T/Q/5+d)Z dipoles computed up to 20,000 cm-1 above potential minimum and extrapolated to one-electron basis set limit, room temperature IR line lists for 20 individual isotopologues of 12/13C and 32/33/34/36S, denoted Ames-296K, and a "natural" CS2 list with intensities scaled by their terrestrial abundances.  This project is funded by NASA Grant 18-2XRP18_2-0046 through NASA/SETI Institute Co-operative Agreement 80NSSC19M0121.  See https://huang.seti.org/CS2/cs2.html for data format and abundance information. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.  The line profile parameters and room temperature simulations are supported through 80NSSC20K1596. List of Files, supplement to article " Accurate IR Line Lists for CS2 and Isotopologues at Room Temperature" <ol> <li>Ames-1.PES.zip:  Ames-0 and Ames-1 PES subroutine & coefficient files; PES.refinement.files.zip: PES refinement related files including reference energy level list and refinement output.</li> <li>J=0-200 energy level lists of 12C32S2 and 19 minor isotopologues, computed on the Ames-1 PES. The .zip file contains 20 compressed .tgz (or .xz) files, and partition function of 222, 224, 223, and 232 isotopologues.</li> <li>Ames-1.DMS.zip:  Ames-1 DMS subroutine & coefficient files, and ab initio data;</li> <li>cs2.xxx.Ames-1.296K.1E-31.dat.tgz (or .xz) : 20 files, "xxx" is the S-C-S isotope mass unit number. These are the Ames-296K IR line lists for 12C32S2 and 19 minor isotopologues, each with 100% abundance. Computed using Ames-1 DMS and rovibrational wavefunctions for those energy levels acquired on Ames-1 PES;</li> <li>cs2.20iso.Ames.natural.296K.1E-31.10Kcm-1.dat.updated.tgz:  A "natural" Ames-296K IR line list for CS2, including 10,018,977 transitions from all 20 isotopologues with their 296K intensities scaled by terrestrial abundances, covering the range of 0 - 10,000 cm-1. Computed on the Ames-1 PES and DMS.</li> <li>cs2.222.A+I.296K.ames+heff.natural.tgz:  A(mes)+I(AO).296K line list for the main isotopologue 222, with terrestrial abundance. Ames-296K intensity prediction is combined with the more accurate energy levels (and line positions) from Effective Hamiltonian model.</li> <li>cs2.iso2-20.Ames-1.natural.1E-31.dat.iso2-4_use_HITRAN2020_purified.v5.xz:  the Ames "natural" line list for minor isotopologues #2 - #20, in which the energy levels of 224, 223 and 232 are replaced with reliable values in HITRAN2020.  Therefore, the final composite line list = 6) + 7)</li> <li>Heff.and.HITRAN.energy.level.matches.and.line.list.update.zip:  the short FORTRAN programs for energy level matches between Ames-1 PES levels and Heff model levels, and the subroutines to use Heff and HITRAN energy levels and line positions. Lists of matched Ames vs HITRAN/H_eff levels are also included.</li> <li>ORIGIN project file for related analysis and figures. Use Origin Viewer to open on PC and MAC, <a href="https://www.originlab.com/viewer/dl.aspx">https://www.originlab.com/viewer/dl.aspx</a></li> <li>a Python program to generate line-broadening parameters for rovibrational CS2 molecule</li> <li>CS2 cross-section data of PNNL, HITRAN and Ames line lists.  </li> </ol>  # Iso   #Lines    #in"natural"   abundance    1 222 1,903,882 1,856,648  0.892811      2 224 3,983,009 2,159,579  0.0792103    3 223 3,745,299 1,328,631  0.0140944    4 232 1,925,377   658,645   0.100306      5 424 1,940,490   439,254   1.207E-3      6 234 4,211,645   698,654   8.151E-4       7 324 3,671,708   589,323   6.510E-4      8 226 4,155,423   558,540   3.566E-4      9 233 3,918,753   410,542   1.439E-4     10 323 1,937,950   290,799  5.142E-5     11 434 2,080,912   138,209  1.692E-5   12 426 3,852,415   223,268  1.976E-5     13 334 4,062,765   172,858  6.773E-6   14 236 4,742,198   158,941  4.630E-6     15 326 4,113,292   137,128  3.075E-6   16 333 1,988,991    80,862   6.803E-7     17 436 4,434,008    58,478   1.361E-7    18 626 1,986,823    21,692   3.572E-8     19 336 4,613,173    32,681   3.528E-8    20 636 2,194,332     4,245    4.28E-10     Line List Data Format: (CS2 is the 53rd molecule in HITRAN, we use iso# from table below, e.g., 1 - 222; 2 - 224; ...; 10 - 323; ....; 20 - 636) <ol> <li>in the original line list files: cs2.xxx.Ames-1.296K.1E-31.dat iso  wavenumber S(Ames) A21(Ames)  E"(cm-1)  v1v2l2v3'  v1v2l2v3"    JPS' #root'   JPS" #root" J'  J" e/f_symmetry  2     6.165375 1.492856E-30 4.851961E-12   876.91172  0  2  2 0  0  2  2 0  29 1 2     4  28 2 2     4  29  28 e e</li> <li>in cs2.iso2-20.Ames-1.natural.1E-31.dat.iso2-4_use_HITRAN2020_purified.v5, original Ames-1 line position and the difference = Heff - Ames are appended to the end of each line of iso #2 (224), iso #3 (223), and iso #4 (232). </li> <li>in cs2.222.AI-296K.ames+heff.natural.dat.v2, two integers are added to each line to keep the record for the number of cycles after which a match (or no match) was made for upper and lower levels, "0-41" for "matched",  '99' for "not matched", "-1" for out of range, i.e. > 9000 cm-1. The differences between the original Ames and corrected/replaced transition wavenumber, E', and E" are also appended at the end. The relation is wv/E'/E" (Heff) + diff = wv/E'/E" (Ames). For example, in the transition below, E''(Ames) = 3445.4018+0.7865 = 3446.1883 cm-1.    1    36.666580 1.538246E-31 2.306988E-07  3445.40177  0  4  2 1  1  6  2 0  57 1 2    31  58 2 2    34  57  58 e e   3   3   -0.7207    0.0658    0.7865</li> </ol>

opencc-by-nc-4.0Dec 2023View details →

zenodo48/100

Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362

Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for  the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe",  https://doi.org/10.3390/land10121362 from the the long term experiments  belonging in some of the SoilCare project partners.

opencc-by-4.0Dec 2021View details →

zenodo48/100

Data for "Modelling soil carbon stocks following reduced tillage intensity: a framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe"

Dataset of paired observations of conventional tillage (CT) with no tillage (NT) and reduced tillage (RT) from studies in temperate oceanic regions of Western Europe, extracted from a recent systematic review (Jordon et al. preprint, see DOI below). R code of modelling framework to estimate tillage rate modifiers (TRM) for simulating adoption of RT and NT using RothC-26.3, and meta-estimates of TRM across studies.

opencc-by-4.0Sep 2021View details →

zenodo48/100

Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe

Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale. The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1; Soil properties were predicted at fixed depths:     Surface soil = s0..0cm,     Subsoil 1 = s30..30cm,     Subsoil 2 = s60..60cm,     Subsoil 3 = s100..100cm. To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula. Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020; To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:     log.oc = 15 → 0.3% SOC;     log.oc = 20 → 0.6% SOC;     log.oc = 25 → 1.1% SOC;     log.oc = 30 → 1.9% SOC;     log.oc = 35 → 3.2% SOC;     log.oc = 40 → 5.3% SOC;     log.oc = 50 → 14.8% SOC;

opencc-by-sa-4.0May 2022View details →

zenodo48/100

Data for "Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis"

Supplementary Files for systematic review and meta-analysis: Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis

opencc-by-4.0Nov 2021View details →

zenodo48/100

Data from: "Damage deflection and subsequent damage diffusion in carbon-boron fibre hybrid composites under longitudinal compression"

The datset contains raw data used for the work presented in the journal paper "Damage deflection and subsequent damage diffusion in carbon-boron fibre hybrid composites under longitudinal compression". Specifically, it contains machine recorded data and video recordings (either SEM or with optical microscope) of the compression tests on small scale single edge notched specimens made of IM7/8552 (carbon/epoxy) and HyBor 52 FPI (carbon-boron fibre hybrid composite). It also contains specimens pictures taken during and after the tests (including SEM and optical micrographs). For more details, please refer to the full paper.

opencc-by-4.0Jul 2024View details →

zenodo48/100

Data from: Carbon and Water Balances in a Watermelon Crop Mulched with Biodegradable Films in Mediterranean Conditions at Extended Growth Season Scale

Abstract The uploaded data are relative to the investigation around (i) the carbon source/sink nature and, further, (ii) the water and carbon balances, of a drip-irrigated and mulched watermelon. The crop was cultivated under the semi-arid climate of the Apulia region, in south Italy. The used mulching films were biodegradable as indicate by the producer; plants and some non-standard fruits were left on the soil as green manure after harvesting, thus, the experiment spanned from planting to the subsequent crop (6 months of continuous measurement from June to November 2023). The results detailed in the original publication indicate that mulching films contribute to carbon sequestration in the soil (+19.3 gC m−2). However, this mulched watermelon represents a net carbon source, with a net biome exchange, as loss from ecosystems, equal to +230 gC m−2. This is primarily due to the substantial amount of carbon exported through marketable fruits. Fixed water scheduling led to water waste through deep percolation (approximately 1/6 of the water supplied), which also contributed to the loss of organic carbon via leaching (−4.3 gC m−2).   Methods Site and crop The field site was at the CREA-AA Research Unit experimental farm located in southern Italy (Rutigliano–Bari, 41 01’ N, 17°01’ E, altitude 147 m a.s.l.)., characterized by a Mediterranean semi-arid climate (average annual rainfall of 535 mm). The soil is classified as Lithic Rhodoxeralf, with a clay texture, stable structure, shallow profile (0.6–1.1 m) and rapid drainage due to an underlying cracked limestone subsoil. The SOC content averages around 12.0 g kg−1. The field capacity and the permanent wilting point volumetric water contents are 0.36 and 0.21 m3 m−3, respectively; with a bulk density of 1.15 Mg m−3, the available soil water ranges from 80 to 140 mm. The studied watermelon crop (seedless var. Lion king), followed a broccoli cabbage crop harvested in April and partially incorporated (0.81 kg m−2 of fresh biomass in a soil layer depth of 0.30 m, corresponding to 0.69 kgH2O m−2) as green manure on 25 May 2023. Main tillage at medium depth ploughing (0.30 m) and seedbed preparation were performed between 25 and 30 May 2023; the biodegradable film mulch (model PC 100 d8, BASF, Italy, 1 m width) was applied on 1 June 2023. On the same day, driplines (2.1 Lh−1 emitters, 0.60 m apart) and the main organic fertilization (Orga-Kem 6.11.8 + 11CaO, 300 kg ha−1) were also applied. The watermelon plants were transplanted on 9 June at a spacing of 2.70 m between rows and 1 m between plants, covering an area of about 4.0 ha, with a density of approximately 3200 plants ha−1. Every 6 rows, the inter-row distance was 5 m to facilitate machinery passage. The first irrigation was performed the day before planting. Crop management adhered to the usual treatments in the area including mechanical weed removal every 4 weeks, irrigation around three times per week to maintain optimal soil water conditions and monthly fertigation (ammonium sulphate 50 kg ha−1, magnesium nitrate 30 kg ha−1, calcium nitrate 60 kg ha−1, mycorrhizae 20 kg ha−1). The scalar harvest of marketable fruits occurred between 28 and 31 August 2023. After harvesting, on 25 September 2023, the fresh plant residues (0.6 kg m−2 of fresh biomass, corresponding to 0.49 kgH2O m−2), unharvested fruits (4.0 kg m−2 of fresh material, corresponding to 3.7 kgH2O m−2) and the mulching film were chopped by a tractor shredder and ploughed in two steps, on 2 and 13 October 2023, to a soil depth of 0.30 m. Measurements concluded at the end of November 2023, when tillage for the new winter crop commenced.   Measurements of H2O and CO2 fluxes; partitioning in evaporation, transpiration, photosynthesis and respiration The eddy covariance technique was employed to monitor water vapor (H2O) and carbon dioxide (CO2) fluxes. The equipment comprised a three-dimensional sonic anemometer (uSonic 3 Scientific, Metek GmbH, 25337 Elmshorn, Germany) and a fast response open-path infrared gas analyzer (LI-7500, Li-COR Inc., Lincoln, NE, USA). The three wind components, sonic temperature and atmospheric concentrations of CO2 and H2O were continuously measured at 1.5 m above the crop canopy, with the sensor height adjusted to follow crop growth, reaching a maximum of 1.75 m. Data were recorded at a frequency of 10 Hz on a dedicated computer using the MeteoFlux software (Servizi Territorio, S.n.c., Cinisello Balsamo, Italy) and were stored on an hourly scale. Post-processing and computation of hourly fluxes of H2O (mmol m−2 s−1) and CO2 (μmol m−2 s−1) were conducted using EddyPro software, v7.0.9 (<a href="http://www.licor.com/eddypro">http://www.licor.com/eddypro</a>), applying 60 min block averaging, double coordinate rotation, the statistical test, the maximum cross-covariance method, and the WPL density correction. H2O and CO2 fluxes were partitioned into transpiration, evaporation, photosynthesis and respiration, respectively, using the flux variance similarity method. This method utilizes the Monin–Obukhov similarity theory to separate stomatal (photosynthesis, Fp, and transpiration, Ft) from non-stomatal (respiration, Fr, and evaporation, Fe) processes (Palatella et al., 2014). the H2O and CO2 EC fluxes were partitioned using an adaptation of the code in Phyton provided by (Skaggs et al., 2018) and downloaded from  <a href="https://github.com/usda-arsussl/fluxpart">https://github.com/usda-arsussl/fluxpart</a> (V0.2.10).

opencc-by-4.0Sep 2024View details →

zenodo48/100

Data from: Shift of bacterial and fungal communities upon soil amelioration is driven by carbon degradability of organic amendments

Microbial communities of bacteria and fungi have been analyzed in soil. Agricultural soil was amended with different organic amendments including straw, compost, biogas residues, and biochar, and incubated in the lab. After 6 months, DNA extracted from soil samples was analyzed via Illumia MiSeq DNA sequencing (16S V3V4 for bacteria, ITS1 for fungi) to evaluate changes to the microbial community structure. For details, please see the respective publication (DOI: 10.1007/s44378-024-00012-5).

opencc-by-4.0Feb 2024View details →

zenodo48/100

Global Carbon Budget 2022, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual Global ocean biogechemical models and surface ocean fCO2-based data-products

Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (data-products). There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs.   Note: These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. The best estimates of the annual global ocean carbon sink, based on the native grids of data-products and GOBMs and with the adjustments described in the Global Carbon Budget 2022 (https://doi.org/10.5194/essd-14-4811-2022, section C3), are available in the Global Carbon Budget 2022 spreadsheet. The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2022 paper (https://doi.org/10.5194/essd-14-4811-2022), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.17 GtC yr-1, Tropics: 0.16 GtC yr-1, South: 0.32 GtC yr-1, see GCB 2022 paper, section 2.4.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because adjustments were applied only for global fluxes. What is in the files? (1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly): fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south (2) The files for the GOBMs contain the following fields, for simulation A (‘contemporary simulation’, including effects of rising CO2, climate change and variability) and simulation B (‘control simulation’, constant CO2, no climate change and variability). Temporal resolution: monthly fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude (3) One file ‘GCB-2022_OceanModel_RegionalBreakdown_1959-2021.nc’ with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual. Fair data use statement: The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use. Citation: Please cite the Global Carbon Budget 2022 (Friedlingstein et al., 2022, ESSD, https://doi.org/10.5194/essd-14-4811-2022) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2022 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720). Acknowledgement: Please add the following text in the acknowledgement of your paper: “We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output.” Co-authorship: An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication. Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make additional output from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudget.org/

opencc-by-4.0Dec 2021View details →

zenodo48/100

Data on public understandings of and attitudes towards carbon-smart urban green infrastructure in Kumpula, Helsinki

A public participatory GIS -survey dataset detailing public understandings of and attitudes towards carbon-smart urban green infrastructure in Kumpula, Helsinki, Finland.

opencc-by-4.0Feb 2023View details →

zenodo48/100

Global mangrove soil carbon data set at 30 m resolution for year 2020 (0-100 cm)

Global soil organic carbon stocks in mangrove forests at 30 m resolution, and predicted for 2020 using spatiotemporal ensemble machine learning. Soil organic carbon stock (t/ha) was derived using predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks. The "mangroves_tiles_SOC_predictions_2020.zip" file contains predictions of SOC content, Bulk Density (BD) and aggregated SOC stocks (t/ha) for 0—100 cm depth interval. Example of a tile: <ul> <li>089E_21N (89E to 90E, 21N to 22N): <ul> <li>sol_db.od_mangroves.typology_m_30m_s0..100cm_2020_global_v0.1.tif = predicted BD aggregated to 0—100 cm;</li> <li>sol_soc.wpct_mangroves.typology_m_30m_s0..0cm_2020_global_v1.1.tif = predicted SOC content (%) at 0 cm depth (surface soil);</li> <li>sol_soc.wpct_mangroves.typology_m_30m_s0..100cm_2020_global_v1.1.tif = predicted SOC content (%) for 0—100 cm;</li> <li>sol_soc.tha_mangroves.typology_m_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha (mean value);</li> <li>sol_soc.tha_mangroves.typology_l.std_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha lower 95% probability prediction interval;</li> <li>sol_soc.tha_mangroves.typology_u.std_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha upper 95% probability prediction interval;</li> </ul> </li> </ul> Example of a tile: <ul> <li>class      : RasterLayer</li> <li>dimensions : 4004, 4004, 16032016  (nrow, ncol, ncell)</li> <li>resolution : 0.00025, 0.00025  (x, y)</li> <li>extent     : 88.9995, 90.0005, 20.9995, 22.0005  (xmin, xmax, ymin, ymax)</li> <li>crs        : +proj=longlat +datum=WGS84 +no_defs</li> <li>source     : sol_db.od_mangroves.typology_m_30m_s0..0cm_2002_global_v0.1.tif</li> </ul> To load global mosaics Soil Carbon t/ha Maps (0—100cm) as COGs directly into QGIS or similar, best use: <ul> <li> <a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_m_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_m_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a> </li> <li> <a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_l.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_l.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a> </li> <li> <a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_u.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_u.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a> </li> </ul>

opencc-by-4.0Mar 2023View details →

zenodo48/100

Online Data for 'The role of wildfires in the interplay of forest carbon stocks and wood harvest in the contiguous United States during the 20th century'

This data file (.xlsx) contains all data used to create table 1, figures 1a-d, figure 2, figure S1, S2, and S5 of the study "The role of wildfires in the interplay of forest carbon stocks and wood harvest in the contiguous United States during the 20th century". Main article is available under: https://doi.org/10.1029/2023GB007813

opencc-by-4.0May 2023View details →

edi48/100

Factors Influencing Aboveground Carbon Storage in Mixed Oak-Pine Forests: USDA FIA Data from Southeastern U.S. (2009-2019)

This study explores factors affecting aboveground carbon (AGC) storage in mixed oak-pine forests across the Southeastern United States. Utilizing USDA Forest Inventory and Analysis (FIA) data from 2009 to 2019, the research spans nine states: Alabama, Mississippi, Florida, Georgia, North Carolina, South Carolina, Texas, Louisiana, and Virginia. Data processing in R included converting units to the metric system and calculating structural diversity using Shannon diversity indices. Climate data from the PRISM Climate Group were integrated with FIA data using longitude and latitude. The research aims to uncover how various factors influence AGC storage and contribute to informed forest management practices.

openCC (other)Sep 2024View details →

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AquaMatch Dissolved Organic Carbon Data from Water Quality Portal: ~1970-2024

This dataset, “AquaMatch Dissolved Organic Carbon Data from Water Quality Portal ~1970-2024”, is a component of a forthcoming update to AquaSat (Ross et al., 2019), AquaSat version 2 (“V2”). The overarching purpose of AquaSat V2 is to emphasize the individual parts of the AquaSat pipeline that make-up the matchups between satellite and in-situ measurements. As such, we have greatly expanded and improved upon the AquaSat dissolved organic carbon dataset in two ways: First, we have incorporated additional recent in situ data beyond what was available at the publication of AquaSat. Second, we have created a data quality tiering system to provide end-users with more guidance on data usage. In this schema we have three tiers: restrictive data that are verifiably self-similar across organizations and time-periods and can be considered highly reliable; narrowed data that we have good reason to believe are self-similar, but for which we cannot verify full compatibility across data providers; and inclusive data, which are assumed to be reliable and are harmonized to our best ability given the information available from the data provider. We have also added flag columns to help users understand complexities of the available depth and field sampling data. This dataset is a derived data product created using records downloaded from the Water Quality Portal (WQP) spanning January 5, 1970, to June 27, 2024. The WQP is a data warehouse for water-related data measured or observed within the United States and US territories managed by the Environmental Protection Agency, United States Geological Survey, and the National Water Quality Monitoring Council. The dataset does not contain remote sensing matchups but can be paired with Landsat surface reflectances using the pipeline presented in Ross et al. (2019). Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D. et al. (2019). AquaSat: A data set to enable remote sensing of water quality for inland waters. Water

openCC0Nov 2024View details →

edi48/100

Short-term high-frequency water dissolved carbon dioxide, temperature, dissolved oxygen, salinity and pH data from 8 Estonian lakes in year 2014

This dataset was used in the analysis described in the manuscript by Khan, H., A. Laas, R. Marcé, B. Obrador. Major effects of alkalinity on the relationship between metabolism and dissolved inorganic carbon dynamics in lakes. In review: Ecosystems. This dataset was used to calculate short term changes of dissolved inorganic carbon (DIC) concentrations and its variability in Estonian lakes. DIC data were compared with measured dissolved oxygen (DO) data in lakes covering a range of different alkalinity levels. Our results suggest that a large part of the measured variability in DO and DIC reflects non-metabolic processes. In lakes of lower alkalinity, DIC dynamics appear to be mostly driven by aquatic metabolism, whereas in lakes of higher alkalinity calcite precipitation plays a major role on DIC dynamics and needs to be considered along with metabolism.

openCC (other)Oct 2019View details →

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Carbon dioxide response curve, dark respiration, specific leaf area, and leaf nitrogen data for the 2014 Eriophorum vaginatum reciprocal transplant gardens at Toolik Lake and Sagwon, AK, collected in 2016.

Transplant gardens at Toolik Lake and Sagwon were established in 2014. At each location, 60 tussocks each from ecotypes of Eriophorum vaginatum from Coldfoot (CF, 67°15′32″N, 150°10′12″W), Toolik Lake (TL, 68°37′44″N, 149°35′0″W), and Sagwon (SAG, 69°25′26″N, 148°42′49″W) were transplanted. Half the transplanted tussocks were grown under ambient conditions, while the other half were exposed to passive warming supplied by open-top chambers (OTC). Data were collected in late June through July 2016 include carbon dioxide response curve data, dark respiration, specific leaf area, and leaf nitrogen content.

openCC (other)Jan 2020View details →

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Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Half-hourly soil moisture and temperature data, 2008-2024

The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes half-hourly values of surface moisture content (gravimetric, 0-5cm), depth-integrated soil moisture (volumetric, 0-20 cm), and soil temperature in winter warming, summer warming, and control treatment plots at CiPEHR.

openOpenMar 2025View details →

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Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Weekly thaw depth data, 2009-2024

The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes weekly thaw depth measurements collected from winter warming, summer warming, and control treatment plots at CiPEHR. Additional measurements from on-plot gas flux wells, water table monitoring wells, and off-plot locations are also reported. Note that the experimental warming portion of this experiment concluded in 2022. These data are a continuation of measurements taken at previously warmed plots but plots were not actively manipulated after 2022.

openOpenJun 2025View details →

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Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Weekly thaw depth data, 2011-2022

This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warming affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieved using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. This data set includes weekly ground thaw measurements.

openOpenJun 2025View details →

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Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Seasonal water table depth data, 2011-2022

openOpenNov 2023View details →

ScienceDex guides

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

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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