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Data on ground ice, organic carbon and soluble cations in tundra permafrost and active-layer soils near Lac de Gras in the Slave Geological Province, N.W.T., Canada
<p>Data and computer code for producing figures for the manuscript:</p> <p>Subedi, R., Kokelj, S. V., and Gruber, S.: Ground ice, organic carbon and soluble cations <br> in tundra permafrost soils and sediments near a Laurentide ice divide in the Slave <br> Geological Province, N.W.T., Canada. The Cryosphere, accepted for publication in October 2020. </p> <p>Discussion paper and final version: https://doi.org/10.5194/tc-2020-33</p> <p> </p> <p>==========================================================================================<br> CONTENT OF DIRECTORIES<br> ==========================================================================================<br> -– data [input data to produce plots]<br> |–– BoreholesMeta.csv<br> |–– brackets_photos_ice.csv<br> |–– brackets_photos_thawed.csv<br> |–– Lac_de_Gras_permafrost_20200612.csv<br> |–– NordicanaD<br> <br> |–– ds_000582159 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]<br> |–– Cored_Drill_TCR.csv<br> |–– Cored_Drill_TCR.csv_ReadMe.txt<br> <br> |–– ds_000582163 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]<br> |–– Cored_Drill_Logs.csv_ReadMe.txt<br> |–– Cored_Drill_Logs.csv</p> <p>–– plot [R scripts write plots into this subdirectory]</p> <p>–– src [R scripts to generate plots]<br> |–– Combined_Plots.R [produces Figures 3–6]<br> |–– Eskers.R [helper function called by Combined_Plots.R]<br> |–– Organics.R [helper function called by Combined_Plots.R]<br> |–– plot_boreholes_DD_single.R [produces Figures S3]<br> |–– plot_boreholes_DD.R [produces raw Figure S2 for further graphic processing]<br> |–– Till.R [helper function called by Combined_Plots.R]<br> |–– Valley.R [helper function called by Combined_Plots.R]</p> <p><br> ==========================================================================================<br> RUNNING SCRIPTS<br> ==========================================================================================</p> <p>Adjust the variable 'path' in these scrips, then run: <br> Combined_Plots.R<br> plot_boreholes_DD_single.R<br> plot_boreholes_DD.R </p> <p>Tested with R version 3.6.3 (2020-02-29) -- "Holding the Windsock"</p> <p> </p> <p>==========================================================================================<br> REFRERENCE<br> ==========================================================================================<br> Please note that the data contained in data/NordicanaD is published as Gruber et al. (2018)<br> and only included here for convenience. The full reference for the authoritative copy is: <br> <br> Gruber, S., Brown, N., Stewart-Jones, E., Karunaratne, K., Riddick, J., Peart, C., <br> Subedi, R., Kokelj, S. 2018. Drill logs, visible ice content and core photos from 2015 <br> surficial drilling in the Canadian Shield tundra near Lac de Gras, Northwest Territories, <br> Canada, v. 1.0 (2015-2015). Nordicana D38, doi: 10.5885/45558XD-EBDE74B80CE146C6. <br> http://www.cen.ulaval.ca/nordicanad/dpage.aspx?doi=45558XD-EBDE74B80CE146C6 </p>
Roots Carbon Dynamics in Temperate forest roots, Thuringia, Germany
<p>These files contain radiocarbon, d13C, NSC concentrations, and CO2 efflux rates measured for aspen (<em>Populus tremula</em> hybrids) roots collected during 2018 growing season in the Großer Hermannsberg Mountain, Germany (50°42’50’’ N, 10°36’13’’ E, 616 m a.s.l).</p> <p>Coarse (> 2 mm) and fine (2 ≤ mm) roots collected from three 'treatments': before stem girdling (Pre-girdling), ~3 months after girdling (Girdling) and ~3 months after girdling but in un-girdled trees (Control). The files with the relevant results: '13C', '14C', 'CO2_efflux', 'NSC'.</p> <p>Few roots from the 'Pre-girdling' treatment were incubated for respiration measurements 7 d after harvest. The files with the relevant results: 'Repeated_incubations_isotopes', 'Repeated_incubations_fluxes'. </p> <p>Results of incubations used for Q10 calculations presented in the file 'CO2_efflux_Q10'.</p> <p>Temperature and rainfall in the site during 2018 growing season are presented in the file 'Field_temperature_rainfall'.</p> <p>Results used to reconstruct local atmospheric D14C-CO2 record are presented in the file 'Local_atmospheric_CO2_D14C'.</p> <p>The file 'Metadata' contains information about the headers in the other files.</p>
Laboratory Dataset on Self-ignition of Carbon-Rich Soil
<p>The file attached contains a complete set of experimental data from carbon-rich soil self-heating ignition cubic basket experiments for a range of soil inorganic content (IC) ranging from 3% to 86%. The experiments were carried out in a thermostatically controlled oven with thermocouples for measuring the ambient and soil temperatures. The data reported includes the dates of experiments, volume of soil baskets being tested, oven ambient temperature, inorganic content present in the sample, bulk density of the soil and if the sample ignited or not. This data is in support of the journal paper:</p> <p>F. Restuccia, X. Huang, G. Rein, <strong>Self-ignition of Natural Fuels: Can Wildfires of Carbon-Rich Soil Start by Self-heating?</strong>, <em>Fire Safety Journal </em>2017, http://doi.org/10.1016/j.firesaf.2017.03.052.</p>
MAR2PROTECT - Coconut shell derived activated carbon for effective separation of greenhouse gases - DATASET
<p>The need for innovative and efficient adsorptive materials with enhanced structural characteristics that facilitate the selective capture of greenhouse gases (GHGs) is critical. Porosity and surface area play an important role in the adsorptive capture and separation of GHGs, enabling the design of processes that reduce GHGs emissions. This study shows how residual coconut shell (CS) biomass can be reused for the design of novel biomaterials (CS-CO<sub>2</sub>, CS-ZnCl<sub>2</sub>) with structural characteristics that promote the selective adsorption of GHGs. Additionally, the results are compared with those obtained with activated carbon monoliths (ACM) and a Metal-Organic Framework (MOF Fe-BTC) to understand the impact of different porous solid matrices on adsorptive GHG capture. In this context, the adsorption performance of difluoromethane (R-32), pentafluoroethane (R-125), 1,1,1,1-tetrafluoroethane (R-134a), 1,1,1,1-trifluoroethane (R-143a), carbon dioxide (CO<sub>2</sub>), and methane (CH<sub>4</sub>) on CS-CO<sub>2</sub>, CS-ZnCl<sub>2</sub>, ACM and Fe-BTC were measured by gravimetry at 283.15 K, 303.15 K and 323.15 K. The experimental data are correlated using the dual-site Langmuir adsorption model, and the selectivities of the commercial mixtures R-410A, R-407C, R-404A and CO<sub>2</sub>/CH<sub>4</sub> are calculated using the Ideal Adsorption Solution theory (IAST). CS-ZnCl<sub>2</sub> has a higher selectivity for R-125 over R-32 in the separation of R-410A at low pressure, and also a higher selectivity for R-407C due to its larger pore volume. In the separation of the R-404A refrigerant blend, CS-CO<sub>2</sub> adsorbs predominantly R-134a and R-143a over R-125. Finally, the ACM material preferentially adsorbs CO<sub>2</sub> over CH<sub>4</sub>, owing to its large and elongated micropores that favour the adsorption of the smaller molecule. This study introduces novel and innovative materials to enhance the separation of GHGs mixtures, contributing to a reduction in their emissions.</p>
Dataset to: Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada (CATENA) - Version 2 (Corrected)
<p><strong>Version update: Coordinates were not correct in previsous version and have been corrected now in version 2</strong></p> <p> </p> <p>Dataset to the manuscript: Schiedung et al. (2022, Catena) Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada ( <a href="https://doi.org/10.1016/j.catena.2022.106194">https://doi.org/10.1016/j.catena.2022.106194</a> )</p> <p>Data files, variables and parameter are described in <em>Var_names_dd_all.csv</em> for all data on each sample and <em>Var_names_dd_composites.csv </em>for all data on composited samples per site and depth. DRIFT data and corresponding explenation are in <em>Schiedung_CATENA_DRIFT_v1.1.zip.</em></p> <p> </p> <p><strong> </strong></p>
Carbon Price Scenarios: Projecting prices for emission certificates
<p>This dataset consists of three different carbon price development scenarios. Each is represented by two growth rates which results in a total of 6 time series. The time frame is from 2020 to 2050. The units of the values are given in € / t CO₂. All values are nominal.</p> <p>Overall, it should be noted that an estimate of the development of CO2 prices in the german nEHS and EU-ETS is subject to great uncertainty due to the major influence of regulatory intervention, a less liquid market towards 2030 and a lack of markets after 2030.</p> <p>The data provided is delivered in frictionless data format (see 2024-03-25_metadata_carbon-price-scenarios.package.json) and can be accessed using the frictionless software (https://frictionlessdata.io/).</p>
Data for: Low velocity impact resistance of thin and toughened carbon fibre reinforced epoxy
<p><em><strong>Version v2:</strong> <br></em>Added tiff-image stacks</p> <p><em><strong>Version v1:</strong><br></em>The data is supplementary to the publication "Low velocity impact resistance of thin and toughened carbon fibre reinforced epoxy", DOI: <a href="https://doi.org/10.1016/j.compscitech.2022.109362">10.1016/j.compscitech.2022.109362</a> as well as to the dissertation: "Morphology and Fracture of Block Copolymer and Core-Shell Rubber Particle Modified Epoxies and their Carbon Fibre Reinforced Composites", urn: <a href="https://nbn-resolving.org/urn:nbn:de:hbz:386-kluedo-63437">urn:nbn:de:hbz:386-kluedo-63437</a></p> <p>Key words: Polymer-matrix composites (PMCs), Impact behaviour, Low velocity impact, Barely visible impact damage, Damage tolerance, X-ray computed tomography, Fractography, Carbon fibre reinforced composite (CFRP)</p> <p>The data set is a collection of TXRM data of several low energy impact damages in CFRP specimens. The data was acquired via XCT (X-Ray Computed Tomography).</p> <p>Material details:</p> <ul> <li>Carbon fibre reinforced composite</li> <li>Thickness: ~ 1.65mm</li> <li>Matrix polymer: Epoxy-based (DGEBA): Sika CR144 + Anhydride curing agent (Huntsman Aradur917) + 1-Methylimidazole</li> <li>Carfon-fibre fabric: ECC Carbon fabric Style 763, based on Toho Tenax HTA40 E13, 140g/m²</li> <li>Layup: 13 layers, stacking sequence (45/-45/45/-45/90/0/90)s, (15% 0°/23% 90°/62% ± 45°) </li> <li>the average carbon fibre volume content was 52.5 ± 1.8 vol.-%</li> <li>cured ply-thickness: 126.1 μm</li> <li>Impact energies: 1J, 3J, 7J, 9J, 13J</li> <li>manufactured via autoclaving</li> </ul> <p>The reasearch received funding from the German Academic Exchange Service (DAAD) within the funding program “Kurzstipendien fuer Doktoranden” (grant number: 57438025).</p>
A multi-method study of femtosecond laser modification and ablation of amorphous hydrogenated carbon coatings
<p>We report here the optical constants of ECR (MW) and RF generated a-C:H layers before and after laser irradiation. The work is described in the following publication:</p> <p><a title="A multi-method study of femtosecond laser modification and ablation of amorphous hydrogenated carbon coatings" href="https://doi.org/10.1007/s00339-024-07980-z" target="_blank" rel="noopener">https://doi.org/10.1007/s00339-024-07980-z</a></p> <p>The data uploaded are the optical constants (n and k) of the a-C:H layers before (base) and after (ROIx) laser irradiation. Please see the article for the nomenclature of the data and for the methods applied ot produce the layers, laser shots, and OK data.</p>
Climate change and terrigenous inputs decrease the efficiency of the future Arctic Ocean's biological carbon pump
<p>This repository contains the post-processed model outputs underlying the main figures in the paper "Climate change and terrigenous inputs decrease the efficiency of the future Arctic Ocean’s biological carbon pump" by Oziel et al. in Nature Climate Change (https://doi.org/10.1038/s41558-024-02233-6). The repository also contains the jupyter notebooks (python) scripts used to produce the figures, the custom model code as well as the mesh informations to reproduce the model run.</p>
Data from: Carbon accumulation of cool season sports turfgrass species in distinctive soil layers
<p>Carbon sequestered by turfgrasses may contribute to reducing atmospheric CO<sub>2 </sub>levels, to improved soil health and to increased turfgrass quality. Therfore in a field study conducted in the Netherlands, the amount of soil C accumulated by nine cool season turfgrass monocultures and 12 mixtures of turfgrass species during the first three years of establishment was analysed and compared. Thatch, mat and other soil layers and the layers were sampled and thickness of these layers was quantified. From these samples, dry matter, C and N concentrations, and CN ratio were measured.</p> <p>The study was conducted on a 3 years old turfgrass field of the turfgrass seed company DLF. The site was located in the Netherlands (51°32´N, 4°20´E), on a sandy soil (Hortic Anthrasol as described in the FAO/Unesco soil map of the world (2006)). The monocultures consisted of different varieties of the (sub)species <em>Lolium perenne (lp), Poa pratensis (Pp), Festuca arundinacea (Fa), Festuca rubra commutata (Frc), Festuca rubr trichophylla (Frt), Festuca rubra rubra (Frr), Festuca ovina duriuscala (Fod), Festuca ovina vulgaris (Fov), Agrostis stolonifera (As). </em>Varieties were treated as replicates per (sub)species, which resulted in some variation in the number of replicates, as not all species were available in the same number of varieties.<em> </em>Varieties of the<em> (s</em>ub)species and mixtures were on the market as commercial turfgrass seeds. </p> <p>In 2016 a soil profile sampler with a depth of 20 cm, a horizontal length of 10 cm and a width of 2 cm was used to take an undisturbed soil profile in each plot and the thickness of each layer, thatch, matt and remainder soil, was measured using the protocol as described in Evers et al. (2024). Plant biomass in the plots was quantified by taking cores of the top 20 cm of the soil with a core sampler (diameter 28 mm). Cores were divided into thatch, mat, the remainder soil till 10 cm depth, and 10-20 cm depth, respectively, based on the earlier measurement of layer thicknesses in the field. Sediment of each section was then carefully washed out with tap water, after which the remaining below-ground (dead and living) plant biomass was dried at 65°C until stable weight and weighed. Total C and N analyses were carried out at the General Instrumentation Department of Radboud University with a Vario Micro Cube Element Analyzer (Elementar, Langenselbold, Germany), from which C and N concentrations (in % of dry matter or in mg cm<sup>-3</sup> C from total plant biomass in a layer) and CN ratios were calculated.</p> <p>Statistical analyses were carried out using the open source program R version 3.5.2 (2018-12-20). Differences in thickness of thatch and mat as well as differences in the C accumulation and C- and N concentration in thatch, mat and soil layers between (sub)species of turfgrasses in were based on the calculated means per species. Normality of residuals and the equality of variances was checked with diagnostic plots and Levene’s test, respectively. Non-normal and heteroscedastic data were either log transformed in linear models from the car package, or general least square (gls) models using varIdent from the nlme package were used. All data were further analyzed with ANOVA-type3 from the car package, followed by the Tukey post hoc test of the emeans package. Correlations between thatch and mat thickness were analyzed with linear regression models in R of the ggplot package. Similar procedures were performed for correlation between thatch, mat or soil thickness and C accumulation as well as for the correlation between C concentration and N concentration on C accumulation in a particular layer.</p>
COMPAIR carbon footprint calculations and greenhouse gas emissions reduction scenarios
<p>Citizens' carbon footprint calculation results and citizen-created scenarios on how Greenhouse Gas emissions can be reduced by 55% by 2030 are available that were gathered as part of the <a href="https://cordis.europa.eu/project/id/101036563">EU Horizon2020 COMPAIR project</a> in Europe. The pilot cities/regions are Berlin, Athens, Sofia, Plovdiv, and Flanders.</p>
The Blue Carbon of Southern South West Atlantic salt marshes and their biotic and abiotic drivers
<p>Organic carbon stocks, salt marsh plant biomass, crab burrow abundances and diameters. This data was generated by sampling at 11 salt marsh sites along 3000 km of coastal line in southern SW Atlantic coast in South America. Organic carbon stocks and burial rates extarcted from other publications and used to update global estimates are also included with their respective references. Values of biotic and abiotic drivers included in the Structural Equation Model (SEM) to evaluate their roles in belowground organic carbon stocks.</p>
The data for "Accurate Infrared Line Lists for 20 Isotopologues of Carbon Disulfide (CS2) at Room Temperature"
<p>[<strong>Updates on 2025-03-02</strong>: 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]</p> <p>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</p> <p>First-generation data product and IR line lists for Carbon Disulfide (CS2), including an isotopologue-independent <em>ab initio</em> PES of Carbon Disulfide refined with selected HITRAN energy levels below 7000 cm-1, an <em>ab initio </em>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.</p> <p><strong>List of Files</strong>, supplement to article " Accurate IR Line Lists for CS2 and Isotopologues at Room Temperature"</p> <ol> <li>Ames-1.PES.zip: Ames-0 and Ames-1 PES subroutine & coefficient files;<br>PES.refinement.files.zip: PES refinement related files including reference energy level list and refinement output.</li> <li><em>J</em>=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 <em>ab initio</em> 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> <p><strong> # Iso #Lines #in"natural" abundance </strong><br> 1 222 1,903,882 1,856,648 0.892811 <br> 2 224 3,983,009 2,159,579 0.0792103 <br> 3 223 3,745,299 1,328,631 0.0140944 <br> 4 232 1,925,377 658,645 0.100306 <br> 5 424 1,940,490 439,254 1.207E-3 <br> 6 234 4,211,645 698,654 8.151E-4 <br> 7 324 3,671,708 589,323 6.510E-4 <br> 8 226 4,155,423 558,540 3.566E-4 <br> 9 233 3,918,753 410,542 1.439E-4 <br>10 323 1,937,950 290,799 5.142E-5 <br>11 434 2,080,912 138,209 1.692E-5 <br>12 426 3,852,415 223,268 1.976E-5 <br>13 334 4,062,765 172,858 6.773E-6 <br>14 236 4,742,198 158,941 4.630E-6 <br>15 326 4,113,292 137,128 3.075E-6 <br>16 333 1,988,991 80,862 6.803E-7 <br>17 436 4,434,008 58,478 1.361E-7 <br>18 626 1,986,823 21,692 3.572E-8 <br>19 336 4,613,173 32,681 3.528E-8 <br>20 636 2,194,332 4,245 4.28E-10 </p> <p><strong>Line List Data Format: </strong>(CS2 is the 53rd molecule in HITRAN, we use iso# from table below, e.g., 1 - 222; 2 - 224; ...; 10 - 323; ....; 20 - 636)</p> <ol> <li>in the original line list files: cs2.xxx.Ames-1.296K.1E-31.dat<br>iso wavenumber S(Ames) A21(Ames) E"(cm-1) <em>v1v2l2v3' v1v2l2v3" JPS' #root' JPS" #root" J' J" e/f</em>_symmetry<br> 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. <br><em> 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</em></li> </ol> <p> </p>
Dataset to manuscript: Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India
<p>Raw data to the manuscript entitled "Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India" by Severin-Luca Bellè, Jean Riotte, Muddu Sekhar, Laurent Ruiz, Marcus Schiedung and Samuel Abiven.</p> <p>Data files include all raw data of soil cores (20211111_Raw_data.zip), data measured on composited samples (20211111_Composite_data.zip) and DRIFT spectra (20211111_DRIFT_data.zip).</p> <p>Files ending with var_names are the README files.</p>
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
<p>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. </p>
Carbon Sequestration Capacity Groups
<p>Marginal Lands (MLs) as detected by MaiL Project were classified in Carbon Sequestration Capacity (CSC) Groups. The methodology based on multicriteria GIS analysis with data including tree species maps (Brus et al., 2011), land cover maps (Malinowski, et al., 2020) and Aboveground Biomass maps (Spawn, Sullivan, Lark, & Gibbs, 2020). The aim was to estimate potential suitable species for afforestation for each Marginal Land as well species’ Above Ground Biomass Carbon (AGBC) and proceed to classification into CSC groups.<br> In order to estimate CSC for MLs and classify in CSC groups, it is crucial to estimate potential suitable species for afforestation and their Aboveground Biomass Carbon. The MLs as calculated on Task 2.3 of MAIL project is the basemap, where the most frequent species from neighbor forested areas, both dominant 1 and 2 species, and species’ Aboveground Biomass Carbon values are assigned. Dominant 1 and 2 species of neighbor forested areas are adapted to the ecological and climatological conditions and therefore are considered to be the most suitable for afforestation projects. Through classification into CSC groups, we get a better understanding regarding the relative interconnections between groups and each one's potential trend.The frequency distribution of the formula’s results is presented in a histogram. Classification into CSC groups was done by manually defining classes ranges, in such a way so each class to cover approximately the same area across Europe, with the exception of higher and lower sequestration groups, Group A and Group E respectively. Group A represents higher sequestration MLs, covering 5% of Europe’s total MLs and on the other side Group E represents lower sequestration MLs covering 31% of Europe’s MLs.</p>
SIA-BRA: The carbon and nitrogen stable isotope ratios of animals of Brazilian biomes and coastal marine areas
<p>SIA-BRA is a compilation of C and N stable isotope ratios of terrestrial and aquatic animals sampled in Brazilian biomes and coastal-marine areas.</p> <p>Version 1.0 contains isotopic data of c. 21,804 non-captive wildlife specimens, excluding livestock production or laboratory<br> experiments. They were 13,881 vertebrates and 7,923 invertebrates. There are 11 phyla, with a clear dominance of Chordata (64%) and Arthropoda (29%), 36 classes, 154 orders, 473 families, 894 genera and 1,157 species.</p> <p>They were divided into the following habitats: terrestrial (30% of the total), freshwater (27%), oceanic (40%)<br> and estuarine (4%) (see <a href="https://doi.org/10.1111/geb.13449">https://doi.org/10.1111/geb.13449</a>)</p> <p>Software format: Data are supplied as delimited text files (.csv).</p>
Dataset supporting the paper "Electronic decoupling of polyacenes from the underlying metal substrate by sp3 carbon atoms. Communications Physics 3, 159 (2020)"
<p>Dataset corresponding to theoretical calculations of the paper "Electronic decoupling of polyacenes from the underlying metal substrate by sp3 carbon atoms". Communications Physics 3, 159 (2020). <a href="https://doi.org/10.1038/s42005-020-00425-y">https://doi.org/10.1038/s42005-020-00425-y</a> </p> <p>Two folders corresponding to pentacene and dihydroheptacene structures on Ag(001):</p> <ul> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>)</li> <li>.siesta files: STM images in WsXM format (<a href="http://www.wsxm.eu/">http://www.wsxm.eu/</a>) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).<br> </li> </ul> <p> </p>
Chlorophyll a concentration, particulate organique carbon, and particle mean size index [gamma; 0.2 - 20 µm] measured using an hyperspectral spectrophotometer [ACS, Wetlabs] during the Tara Pacific Expedition 2016-2018
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide the continuous dataset originating from the hyperspectral and multispectral spectrophotometers [ACS] instruments acquiring continuously during the full course of the campaign. Surface seawater was pumped continuously through a hull inlet located 1.5 m under the waterline using a membrane pump (10 LPM; Shurflo), circulated through a vortex debubbler, a flow meter, and distributed to a number of flow-through instruments. An [ACS] spectrophotometer (WETLabs) measured hyper-spectral (4 nm resolution) attenuation and absorption in the visible and near infrared except between Panama and Tahiti where an AC-9 multispectral spectrophotometer (WETLabs) was used instead. The flow was automatically directed through a 0.2 µm filter for 10 minutes every hour before being circulated through the spectrophotometer to eliminate the impact of biofouling and instrument drift and estimate particulate absorption [ap] and attenuation [cp] (Slade et al. 2010). Chlorophyll a content was estimated from particulate absorption line height at 676 nm (Boss et al. 2001). The particulate organic carbon concentration [poc] was estimated using an empirical relation (Gardner et al. 2006) between measured [poc] and measured [cp]. An indicator for size distribution of particles between 0.2 and ~20 µm [gamma] was calculated from [cp] (Boss et al 2001). The data was processed with custom software for underway optical data (InLineAnalysis software available on GitHub). The detailed information regarding the data processing is given in the processing report attached with the data and in Lombard et al. (In prep.). These results are preliminary: no matchup with in-situ chlorophyll from HPLC or [poc] measurements were performed.</p>
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"
<p>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).</p> <p>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.</p>
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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.
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.
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.
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.
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.