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222 Research products, page 1 of 23

  • Research data
  • 2018-2022
  • EU
  • Recolector de Ciencia Abierta, RECOLECTA
  • Repositorio Institucional Olavide

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  • Open Access
    Authors: 
    Palomar, T.; Martínez-Weinbaum, Marina; Aparicio, Mario; Maestro-Guijarro, Laura; Castillejo, Marta; Oujja, M.;
    Publisher: DIGITAL.CSIC
    Country: Spain
    Project: EC | IPERION HS (871034)

    The study was undertaken in eleven flashed glass samples, provided by LambertsGlas® consisting of a colorless base glass covered by layers of different colors and thicknesses. This dataset consists of images of the samples; Laser-induced Breakdown Spectrocopy (LIBS) spectra; Laser-induced Fluorescence (LIF) spectra; Optical Microscopy (OM) images; UV-Vis-IR spectra and Field Emission Scanning Electron Microscopy (FESEM) images and the assingment of the Energy-dispersive X-ray (EDS) analysis. This information allows characterizing the composition of both sides of the glasses and determining the chemilcal identification of chromophores responsible for the flashed glass coloration. Images are presented in JPG. All spectra are presented in cvs format, in a single page. Descriptions of the samples and the experimental conditions in which the spectra were taken and the name of the column values are included at the top of each page. For LIBS, 1 file per sample of elemental composition of the flashed glasses are included. Each file is composed of 2 columns (wavelength and intensity). For LIF, 1 file per sample of the analysis of fluorescent species of each flashed glass are included. Each file is composed of 2 columns (wavelength and intensity). For UV-Vis-IR spectroscopy, 1 file per sample of glass chromophores, just for the colored side. Each file is composed of 2 columns (wavelength and intensity). For FESEM-EDS, 2 files per sample. In the first one: "PHOTOS", 1 cross section image per sample is included. In the second group of files: "EDS", 1 file per sample of the assignment of the main elements. Each file is composed of 3 columns (the main elements, the results of the glass base and the colored layer in weight percentage, respectively). -- This dataset is subject to a Creative Commons Attribution 4.0 International (CC BY 4.0) License. There are 5 files which correspond to each technic employed for the analysis of the eleven different samples. The file title "PHOTOS" contains: Fig. 1_Flashedglasses_Photo; Fig. 2_OM_Photo. The file title “LIBS” contains: LIBS_Black-Baseglass; LIBS_Black-Coloredlayer; LIBS_Blue1-Baseglass; LIBS_Blue1-Coloredlayer; LIBS_Blue2-Baseglass; LIBS_Blue2-Coloredlayer; LIBS_Blue3-Baseglass; LIBS_Blue3-Coloredlayer; LIBS_Brown1-Baseglass; LIBS_Brown1-Coloredlayer; LIBS_Brown2-Baseglass; LIBS_Brown2-Coloredlayer; LIBS_Green1-Baseglass; LIBS_Green1-Coloredlayer; LIBS_Green2-Baseglass; LIBS_Green2-Coloredlayer; LIBS_Green3-Baseglass; LIBS_Green3-Coloredlayer; LIBS_Pink1-Baseglass; LIBS_Pink1-Coloredlayer; LIBS_Pink2-Baseglass; LIBS_Pink2-Coloredlayer. The file for “LIF” contains: LIF_Black-Baseglass; LIF_Black-Coloredlayer; LIF_Blue1-Baseglass; LIF_Blue1-Coloredlayer; LIF_Blue2-Baseglass; LIF_Blue2-Coloredlayer; LIF_Blue3-Baseglass; LIF_Blue3-Coloredlayer; LIF_Brown1-Baseglass; LIF_Brown1-Coloredlayer; LIF_Brown2-Baseglass; LIF_Brown2-Coloredlayer; LIF_Green1-Baseglass; LIF_Green1-Coloredlayer; LIF_Green2-Baseglass; LIF_Green2-Coloredlayer; LIF_Green3-Baseglass; LIF_Green3-Coloredlayer; LIF_Pink1-Baseglass; LIF_Pink1-Coloredlayer; LIF_Pink2-Baseglass; LIF_Pink2-Coloredlayer. For the “FESEM-EDS” there are two files inside. One title "EDS" which contains the documents: EDS_Black; EDS_Blue1; EDS_Blue2; EDS_Blue3; EDS_Brown1; EDS_Brown2; EDS_Brown2; EDS_Green1; EDS_Green2; EDS_Green3; EDS_Pink1; EDS_Pink2. And the other called "PHOTOS" which contains: FESEM_Black; FESEM_Blue1; FESEM_Blue2; FESEM_Blue3; FESEM_Brown1; FESEM_Brown2; FESEM_Green1; FESEM_Green2; FESEM_Green3; FESEM_Pink1; FESEM_Pink2. This is the experimental dataset used in the paper Appl. Sci., 12(11), 5760 (2022) (https://www.mdpi.com/2076-3417/12/11/5760). Flashed glasses are composed of a base glass and a thin colored layer and have been used since medieval times in stained glass windows. Their study can be challenging because of their complex composition and multilayer structure. In the present work, a set of optical and spectroscopic techniques have been used for the characterization of a representative set of flashed glasses commonly used in the manufacture of stained glass windows. The structural and chemical composition of the pieces were investigated by optical microscopy, field emission scanning electron microscopy-energy dispersive X-ray spectrometry (FESEM-EDS), UV-Vis-IR spectroscopy, laser-induced breakdown spectroscopy (LIBS), and laser-induced fluorescence (LIF). Optical microscopy and FESEM-EDS allowed the determination of the thicknesses of the colored layers, while LIBS, EDS, UV-Vis-IR, and LIF spectroscopies served for elemental, molecular, and chromophores characterization of the base glasses and colored layers. Results obtained using the micro-invasive LIBS technique were compared with those retrieved by the cross-sectional technique FESEM-EDS, which requires sample taking, and showed significant consistency and agreement. In addition, LIBS results revealed the presence of additional elements in the composition of flashed glasses that could not be detected by FESEM-EDS. The combination of UV-Vis-IR and LIF results allowed precise chemical identification of chromophores responsible for the flashed glass coloration. This research has been funded by the Spanish State Research Agency (AEI) through project PID2019-104124RB-I00/AEI/10.13039/501100011033, the Fundación General CSIC (ComFuturo Programme), by project TOP Heritage-CM (S2018/NMT-4372) from Community of Madrid, and by the H2020 European project IPERION HS (Integrated Platform for the European Research Infrastructure ON Heritage Science, GA 871034). Peer reviewed

  • Open Access English
    Authors: 
    Huertas, I. Emma; Flecha, Susana; García-Lafuente, Jesús;
    Publisher: DIGITAL.CSIC
    Country: Spain
    Project: EC | COMFORT (820989)

    SAMIpH_Database_2012_2017.csv provides data for the years 2012 and 2017.DATE_UTC and TIME_UTC are the day and time at which the measurement was taken, in UTC. pHConstSal35 is the pH at a constant salinity of 35. TEMPERATURE and SALINITY are the water temperature (in degrees Celsius) and the water salinity (practical salinity units, i.e., no units) respectively. SAMICO2_Database_2013_2017.txt provides data between the years 2013 and 2017. DATE are the day and time at which the measurement was taken, in UTC and CO2 is the in situ pCO2 value (in uatm) recorded by the SAMI device The database provides measurements of the carbon system parameters pH and pCO2 obtained with SAMI sensors (Sunburst Sensors, LLC) attached to a mooring line deployed in the Strait of Gibraltar between the years 2012 and 2017. Temperature and salinity data were obtained with a Conductivity-Temperature probe (CT Seabird SBE37-SMP) also installed in the line. Sampling interval was initially set to 60 min, but a battery run off happened in summer 2013 (which caused a six-month data gap) advised changing the interval to 120min to extend the battery life. This research was supported by the COMFORT project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820989 (project COMFORT, "Our common future ocean in the Earth system – quantifying coupled cycles of carbon, oxygen, and nutrients for determining and achieving safe operating spaces with respect to tipping points).” Funding was also provided by grant EQC2018-004285-P funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. Peer reviewed

  • Research data . 2022
    Open Access English
    Authors: 
    Reñé, Albert; Timoneda Solé, Natàlia; Sarno, Diana; Zingone, Adriana; Margiotta, Francesca; Passarelli, Augusto; Gallia, Roberto; Tramontano, Ferdinando; Montresor, Marina; Garcés, Esther;
    Publisher: CSIC - Instituto de Ciencias del Mar (ICM)
    Country: Spain
    Project: EC | ASSEMBLE Plus (730984)

    The presence of phytoplankton parasites along the water column was explored at the Long Term Ecological Station MareChiara (LTER-MC) in the Gulf of Naples (Mediterranean Sea) in October 2019. Microscopy analyses showed diatoms dominating the phytoplankton community in the upper layers (0-20 m). Metabarcoding data from the water column showed the presence of Chytridiomycota predominantly in the upper layers coinciding with the vertical distribution of diatoms. Laboratory incubations of natural samples enriched with different diatom cultures confirmed parasitic interactions of some of those chytrids – including members of Kappamyces – with diatom taxa. The temporal dynamics of diatoms and chytrids was also explored in a three-year metabarcoding time-series (2011-2013) from surface waters of the study area and in sediment samples. Chytrids were recurrently present at low relative abundances, and some taxa found to infect diatoms in the incubation experiments were also identified in the ASV time-series. However, co-occurrence analyses did not show any clear or recurrent pairing patterns for chytrid and diatom taxa along the three years. The chytrid community in the sediments showed a clearly different species composition compared to the recorded in the water column samples, with higher diversity and relative abundance. The combination of observations, incubations and metabarcoding confirmed that parasites are a common component of marine protist communities at LTER-MC. Host-parasite interactions must be determined and quantified to understand their role and the impact they have on phytoplankton dynamics File1: VERDI_samples_parameters.xlsx - Physico-chemical variables obtained from CTD profile - Inorganic nutrients concentrations - Chlorophyll-a concentrations - Organic carbon and nitrogen concentrations - Phytoplankton abundances - Detections of chytrids File 2: VERDI_asv_table.tbl: ASV abundances from natural samples and incubations File 3: VERDI_tax_table.tbl: Taxonomic assignments of ASVs File 4: VERDI_asv_seqs.fa: Sequences of ASVs File 5: VERDI_incubations_images.zip - Compilation of images taken during incubations with diatoms - Physico-chemical variables obtained from CTD profile - Inorganic nutrients concentrations - Chlorophyll-a concentrations - Organic carbon and nitrogen concentrations - Phytoplankton abundances - Detections of chytrids - Metabarcoding ASV abundances from natural samples and incubations - Metabarcoding Taxonomic assignments of ASVs - Metabarcoding Sequences of ASVs - Compilation of images taken during incubations with diatoms - European Union’s Horizon 2020 research and innovation programme under grant agreement No 730984, ASSEMBLE Plus project. - Spanish MICINN Project SMART (PID2020-112978GB-I00) - The research program LTER-MC is funded by the Stazione Zoologica Anton Dohrn Peer reviewed

  • Open Access
    Authors: 
    Arrayago, Itsaso; Rasmussen, Kim J.R.;
    Publisher: Universitat Politècnica de Catalunya
    Country: Spain
    Project: EC | New GeneSS (842395)

    Data was generated using the general purpose finite element software ABAQUS and performing advanced nonlinear analyses. The database is comprised of vertical and lateral system stiffness values corresponding to different random samples of six different nominal stainless steel frames under gravity and gravity plus wind load combinations. The values of the random variable assignments are given for each case. The full details of the finite element model can be found in: Arrayago, I.; Rasmussen, K.J.R. Reliability of stainless steel frames designed using the Direct Design Method in serviceability limit states. Journal of Constructional Steel Research 196, 107425, 2022. DOI: https://doi.org/10.1016/j.jcsr.2022.107425 The data included in the dataset corresponds to the vertical & lateral stiffness of each frame under different load conditions. Although the data has been generated using the finite element software ABAQUS, no special software is required to read or interpret the data. {"references": ["Arrayago I., Rasmussen K.J.R. Reliability of stainless steel frames designed using the Direct Design Method in serviceability limit states Journal of Constructional Steel Research 196, 107425, 2022. DOI: https://doi.org/10.1016/j.jcsr.2022.107425"]}

  • Open Access
    Authors: 
    Joy Goodman-Deane; Sam Waller; Elisabet Roca Bosch;
    Publisher: Universitat Politècnica de Catalunya
    Country: Spain
    Project: EC | DIGNITY (875542)

    This dataset contains data from a population-representative survey examining various factors relating to digital exclusion (particularly digital mobility exclusion). The survey was conducted with 1002 adult participants in Italy in 2020. This dataset is part of a series of 5 datasets which used the same questionnaire (translated into different languages) in different countries in Europe.

  • Open Access
    Authors: 
    Goodman-Deane, Joy; Waller, Sam; Roca Bosch, Elisabet; Delespaul, Sam;
    Publisher: Iniciativa Digital Politecnica
    Country: Spain
    Project: EC | DIGNITY (875542)

    This dataset contains data from a population-representative survey examining various factors relating to digital exclusion (particularly digital mobility exclusion). The survey was conducted with 418 adult participants in Flanders region in Belgium in 2021. This dataset is part of a series of 5 datasets which used the same questionnaire (translated into different languages) in different countries in Europe.

  • Open Access Catalan; Valencian
    Authors: 
    Goodman-Deane, Joy; Waller, Sam; Roca Bosch, Elisabet; Lazzarini, Boris; Wybraniec, Bartosz Maciej; Villares Junyent, Míriam;
    Publisher: Universitat Politècnica de Catalunya
    Country: Spain
    Project: EC | DIGNITY (875542)

    This dataset contains data from a population-representative survey examining various factors relating to digital exclusion (particularly digital mobility exclusion). The survey was conducted with 601 adult participants in the Barcelona Metropolitan Area of Spain in 2020. This dataset is part of a series of 5 datasets which used the same questionnaire (translated into different languages) in different countries in Europe.

  • Open Access Dutch; Flemish
    Authors: 
    Joy Goodman-Deane; Sam Waller; Elisabet Roca Bosch; Nick van Apeldoorn; Lisette Hoeke;
    Publisher: Universitat Politècnica de Catalunya
    Country: Spain
    Project: EC | DIGNITY (875542)

    This dataset contains data from a population-representative survey examining various factors relating to digital exclusion (particularly digital mobility exclusion). The survey was conducted with 423 adult participants in the Netherlands in 2020 and 2021. This dataset is part of a series of 5 datasets which used the same questionnaire (translated into different languages) in different countries in Europe.

  • Open Access
    Authors: 
    Zhu, Xiaomin; Zhang, Ziliang; Wang, Qitong; Peñuelas, Josep; Sardans, Jordi; Li, Na; Liu, Qing; Yin, Huajun; Liu, Zhanfeng; Lambers, Hans;
    Publisher: Dryad
    Country: Spain
    Project: EC | IMBALANCE-P (610028)

    [Methods] Isolation of roots and mycelia using ingrowth cores: To isolate roots and mycelia, we adopted an ingrowth-core technique modified from Zhang et al. (2018) and Keller et al. (2021). Ingrowth cores (6 cm inner diameter and 15 cm depth) were wrapped with a mesh with different pore sizes: mesh size of 2000 µm allowed the ingrowth of fine roots and mycelia (both roots and mycelia accessible); 48-µm mesh permitted the growth of mycelia but not of fine roots (only mycelia accessible), and 1-µm mesh excluded the growth of both roots and mycelia (only the soil) (Fig. 2). The C source in the 2-mm mesh cores was mainly derived from roots, mycelia and litter leachates, that of the 48-µm mesh cores was derived from mycelia and litter leachates, while the 1-µm mesh cores received C only from litter leachates. The soil was collected from the mineral layer (0-15cm) at each plot. After removing the visible roots, the soil from the same plot was homogenized and sieved through a 5-mm mesh. The sieved soil was filled into ingrowth cores corresponding to the soil bulk density at 0-15 cm depth (0.796 g cm-3, approximately 337 g per core). Six sets of ingrowth cores with different mesh-size (1-µm, 48-µm and 2000-μm) were installed in each treatment plot. In total, 108 ingrowth cores (2 N levels * 3 replicates * 6 sets * 3 mesh-sizes) were installed in this coniferous forest. Ingrowth cores were randomly placed in the topmost mineral horizon (0-15cm depth) in each plot in July 2017. The bottom of the ingrowth cores was covered with the corresponding size of the mesh to prevent inputs of roots and mycelia, respectively, and the top was covered by multiple layers of the corresponding size of the mesh to block the entry of coniferous litter but to allow gas and water exchange. When the cores were retrieved, we did not detect any external litter in the cores. To block the influx of new C derived from the saprophytic mycelia outside the cores, we spread a 2 mm-thick layer of silica sand around the cores. Silica sand as a growth substrate effectively reduces the disturbance of saprophytic hyphae (Hagenbo et al., 2017). Ingrowth cores were harvested in August 2019 and August 2020, respectively. Two sets of ingrowth cores were collected in each plot at each sampling date. Cores were transported to the laboratory within the icebox. After the removal of roots, soils inside the cores were sieved through a 2-mm mesh and divided into two subsamples: one subsample stored in -4 °C was used for the analyses of enzyme activities and microbial community composition; the second subsample was air-dried to perform soil aggregate fractionation, SOC determination, and soil biomarkers analysis. Root and mycelium biomass: Roots inside the 2000-µm mesh cores were manually picked out, washed thoroughly, oven-dried at 60°C for 48 hours and then weighed to determine the total root biomass. The ectomycorrhizal mycelium biomass was estimated using mesh bags (2 cm inner diameter, 15 cm depth; mesh size: 48 µm) filled with different particle sizes of HCl-washed silica sand (60 g, 0.36-2 mm) (Wallander et al., 2001). The mesh bags were randomly buried into the 0-15 cm soil depth in each plot in July 2017, and recovered at the same time as the ingrowth cores. The concentration of ergosterols was measured to characterize the biomass of ectomycorrhizal mycelia in the mesh bags (see details in the Supplementary Methods) (Parrent & Vilgalys, 2007). Soil aggregate fractionation and SOC concentration: To understand the physico-chemical protection of SOC in the RP and MP under N addition, soils were physically fractionated into three size fractions to examine the allocation of C and biomarkers among macroaggregates (Macro: 250~2000 µm), microaggregates (Micro: 53~250 µm) and slit-clay (< 53 µm) by using the wet-sieving technique (Six et al., 1998). The proportions of SOC and the concentrations of biomarkers in the three fractions were measured to characterize the role of physical protection by aggregates. The SOC and total N (TN) concentrations in bulk soil and size fractions were analyzed using an elemental analyzer (Vario MACRO, Elementar Analysensysteme GmbH, Hanau, Germany). To assess the protection of SOC by minerals, two forms of Fe and Al oxides, oxalate-extractable Fe/Al oxides (Feo + Alo) and dithionite-extractable Fe/Al (Fed + Ald) were measured by using the extraction method proposed by Gentsch et al (2018). The Fed + Ald indicates the amount of pedogenic Fe and Al within oxides, silicates and organic complexes, whereas Feo + Alo represents poorly crystalline oxyhydroxides (Gentsch et al., 2018). The concentrations of Fe and Al oxides in extracts were determined by inductively coupled plasma-optical emission spectrometry (ICP-OES, Optima 8300, Perkin Elmer, USA). SOC chemical composition: A range of major biomarkers, which are widely accepted to trace plant-derived and microbial-derived C, respectively, were selected to reveal the changes of the chemical composition of SOC in two pathways under N addition (Barré et al., 2018; Liang et al., 2019). Air-dried soil (1 g) was sequentially extracted (solvent extraction, base hydrolysis, and CuO oxidation) to isolate solvent-extractable free lipids (long-chain fatty acids), cutin- and suberin-derived compounds and lignin-derived phenols (vanillyls, syringyls and cinnamyls), respectively, according to standard protocols (Otto & Simpson, 2007; Tamura & Tharayil, 2014). Since the direct contribution of microbial living biomass to soil amino sugars is negligible, amino sugars are good indicators of microbial necromass (Liang et al., 2017, Joergensen, 2018). Four types of amino sugars, including glucosamine, galactosamine, manosamine, and muramic acid, were tested in this study. By assessing them in soils, we can investigate microbial necromass dynamics at the community-level (i.e., fungi and bacteria) and evaluate the contributions of necromass to SOC storage under different environmental conditions (Joergensen, 2018; Liang et al., 2019). The detailed chemical extractions and analyses of plant and microbial biomarkers are provided in Supplementary Methods. Microbial community composition: Soil microbial community composition was characterized using the phospholipid fatty acids (PLFAs) methods (see details in Supplementary Methods) (Bossio & Scow, 1998). The identification of the extracted fatty acid was based on a MIDI peak identification system (Microbial ID Inc., Newark, DE, USA). The PLFAs i15:0, α15:0, i16:0, i17:0, α17:0 were used to indicate the relative biomass of Gram-positive (G+) bacteria. The PLFAs 16:1ω9c, 16:1ω7c, 18:1ω7c, cy17:0, cy19:0 were used to indicate the relative biomass of Gram-negative (G-) bacteria. The PLFA 18:2ω6c was used as an indicator of saprotrophic fungal biomass. The PLFAs 10Me16:0, 10Me17:0 and 10Me18:0 were used to indicate actinomycete (AC) biomass. Microbial community composition was assessed by the ratio of saprotrophic fungal biomass to bacterial biomass (F/B ratio). Extracellular enzyme activity: The activities of three extracellular enzymes involved in the decomposition of lignin and fungal residues were measured as described by Saiya-Cork et al. (2002) (see details in Supplementary Methods). The β-N-acetyl-glucosaminidase(NAG)participates in chitin and peptidoglycan degradation, hydrolyzing chitobiose to glucosamine (Sinsabaugh et al., 2009). NAG activity was measured fluorometrically using 4-methylumbelliferyl N-acetyl-β-D-glucosaminide as the substrate. Phenol oxidases (POX) and peroxidases (PER) play an important role in degrading polyphenols, and their activities were measured colorimetrically using L-dihydroxyphenylalanine (DOPA) as the substrate. Data calculation and statistical analysis: To isolate the effects of root and mycelium on the SOC dynamics and associated microbial characteristics (i.e., SOC, biomarkers concentrations, fungal and bacterial biomass, and enzymes activities), net changes of the observations mediated by the root-pathway and mycelium-pathway were quantified by the difference of corresponding variables between the 2-mm mesh cores and 48-µm mesh cores, or between the 48-µm cores and 1-µm mesh cores, respectively (Fig. 2). The recent concept proposed by Zhu et al (2020) highlighted the contribution of microbial necromass to the SOC pool (i.e., MCP efficacy). Based on this concept, the changes of MCP efficacy (i.e., the contribution of increased microbial residual C to the increased SOC) under N addition were calculated as follow: Changes of MCP efficacy (% SOC) under N addition = , where MRCN, SOCN, MRCCK, and SOCCK represent the concentration of microbial residual C and SOC in the N-addition plots and the non-N addition plots, respectively. Additionally, the contribution of increased plant-derived C to the increased SOC induced by N addition was calculated using Eq. 1 but replacing microbial residual C with plant-derived C. Plant roots and associated mycorrhizae exert a large influence on soil carbon (C) cycling. Yet, little was known whether and how roots and ectomycorrhizal extraradical mycelia differentially contribute to soil organic C (SOC) accumulation in alpine forests under increasing nitrogen (N) deposition. Using ingrowth cores, the relative contributions of the root-pathway (RP) (i.e., roots and rhizosphere processes) and mycelium-pathway (MP) (i.e., extraradical mycelia and hyphosphere processes) to SOC accumulation were distinguished and quantified in an ectomycorrhizal-dominated forest receiving chronic N addition (25 kg N ha-1 yr-1). Under the non-N addition, the RP facilitated SOC accumulation, while the MP reduced SOC accumulation. Nitrogen addition enhanced the positive effect of RP on SOC accumulation from +18.02 mg C g-1 to +20.55 mg C g-1 but counteracted the negative effect of MP on SOC accumulation from -5.62 mg C g-1 to -0.57 mg C g-1, as compared to the non-N addition. Compared to the non-N addition, the N-induced SOC accumulation was 1.62~2.21 mg C g-1 and 3.23~4.74 mg C g-1, in the RP and the MP, respectively. The greater contribution of MP to SOC accumulation was mainly attributed to the higher microbial C pump (MCP) efficacy (the proportion of increased microbial residual C to the increased SOC under N addition) in the MP (72.5%) relative to the RP (57%). The higher MCP efficacy in the MP was mainly associated with the higher fungal metabolic activity (i.e., the greater fungal biomass and N-acetyl glucosidase activity) and greater binding efficiency of fungal residual C to mineral surfaces than those of RP. Collectively, our findings highlight the indispensable role of mycelia and hyphosphere processes in the formation and accumulation of stable SOC in the context of increasing N deposition. National Natural Science Foundation of China, Award: 32171757. The Chinese Academy of Sciences (CAS) Interdisciplinary Innovation Team, Award: xbzg-zysys-202112. The Second Tibetan Plateau Scientific Expedition and Research, Award: 2019QZKK0301. European Research Council Synergy project, Award: SyG-2013-610028 IMBALANCE-P. The Spanish Government, grant, Award: PID2019-110521GB-I00. National Natural Science Foundation of China, Award: 31901131. National Natural Science Foundation of China, Award: 42177289. The Spanish Government, grant, Award: PID2020-115770RB-I00. These data were generated to investigate how N addition affect SOC accural and chemical composition through the root-pathway and mycelium-pathway in an alpine coniferous forest. Samples of plant and soil were collected from each treatment plots (non-N addition and N-addition) in 2019 and 2020. Therefore, each parameter has 6 replicates (n = 3 replicates for each treatment * 2 sampling date =6),except for the plant-derived C in different soil size fractions (only measured the samples collected in 2019). Peer reviewed

  • Research data . 2022
    Open Access English
    Authors: 
    Alabort Martínez, Carles; Sanchis Saez, Javier; Catalán Martínez, David; Serra Alfaro, José Manuel;
    Publisher: Universitat Politècnica de València
    Country: Spain
    Project: EC | iCAREPLAST (820770)

    The aim of iCAREPLAST project is to provide a cost and energy-efficient alternative to recycle and valorise non-recycled plastic waste (ca. 70% of European plastic waste) that, due to their characteristics or their contamination, are currently disposed into landfills (27%) or underexploited through energy recovery (42%). iCAREPLAST project, summarized in Fig.1, combines pyrolysis, catalytic treatment and membrane separation technologies to obtain high added-value chemicals, as they are (alkyl-)aromatics (BTXs and medium to long-chain alkyl-aromatics), that can be used to produce virgin-quality polymers or as raw materials for other processes in petrochemicals, fine chemicals and surfactants industries. As in other production processes, process control represents a key issue in the efficient operation of the proposed plant. WP5: “Plant modelling, control and optimization” will try to apply some state-of-art control and optimization techniques with the purpose of manage the iCAREPLAST process as a whole, taking into account several key information related to yields, energy use, sustainability indicators, etc. to drive the process towards its optimum operating point. In iCAREPLAST project, several approaches of process model simulations are considered. The first one is known as steady-state simulation, where iCAREPLAST partners can model different scenarios by handling with design parameters. This type of simulation is typically done during the conceptual phase of a project in an effort to gain a better understanding of how a design can be altered to get the most out of the process from both a business standpoint and an engineering perspective. The datasets presented belongs to this kind of simulations where a steady-state model has been developed in Aspen Plus and several sensitivity analysis has been performed. The datasets comprises raw data from steady-state model simulations performed using Aspen Plus Software. Each dataset corresponds with each unit or subprocess belonging to the entire iCARPLAST process.

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  • Open Access
    Authors: 
    Palomar, T.; Martínez-Weinbaum, Marina; Aparicio, Mario; Maestro-Guijarro, Laura; Castillejo, Marta; Oujja, M.;
    Publisher: DIGITAL.CSIC
    Country: Spain
    Project: EC | IPERION HS (871034)

    The study was undertaken in eleven flashed glass samples, provided by LambertsGlas® consisting of a colorless base glass covered by layers of different colors and thicknesses. This dataset consists of images of the samples; Laser-induced Breakdown Spectrocopy (LIBS) spectra; Laser-induced Fluorescence (LIF) spectra; Optical Microscopy (OM) images; UV-Vis-IR spectra and Field Emission Scanning Electron Microscopy (FESEM) images and the assingment of the Energy-dispersive X-ray (EDS) analysis. This information allows characterizing the composition of both sides of the glasses and determining the chemilcal identification of chromophores responsible for the flashed glass coloration. Images are presented in JPG. All spectra are presented in cvs format, in a single page. Descriptions of the samples and the experimental conditions in which the spectra were taken and the name of the column values are included at the top of each page. For LIBS, 1 file per sample of elemental composition of the flashed glasses are included. Each file is composed of 2 columns (wavelength and intensity). For LIF, 1 file per sample of the analysis of fluorescent species of each flashed glass are included. Each file is composed of 2 columns (wavelength and intensity). For UV-Vis-IR spectroscopy, 1 file per sample of glass chromophores, just for the colored side. Each file is composed of 2 columns (wavelength and intensity). For FESEM-EDS, 2 files per sample. In the first one: "PHOTOS", 1 cross section image per sample is included. In the second group of files: "EDS", 1 file per sample of the assignment of the main elements. Each file is composed of 3 columns (the main elements, the results of the glass base and the colored layer in weight percentage, respectively). -- This dataset is subject to a Creative Commons Attribution 4.0 International (CC BY 4.0) License. There are 5 files which correspond to each technic employed for the analysis of the eleven different samples. The file title "PHOTOS" contains: Fig. 1_Flashedglasses_Photo; Fig. 2_OM_Photo. The file title “LIBS” contains: LIBS_Black-Baseglass; LIBS_Black-Coloredlayer; LIBS_Blue1-Baseglass; LIBS_Blue1-Coloredlayer; LIBS_Blue2-Baseglass; LIBS_Blue2-Coloredlayer; LIBS_Blue3-Baseglass; LIBS_Blue3-Coloredlayer; LIBS_Brown1-Baseglass; LIBS_Brown1-Coloredlayer; LIBS_Brown2-Baseglass; LIBS_Brown2-Coloredlayer; LIBS_Green1-Baseglass; LIBS_Green1-Coloredlayer; LIBS_Green2-Baseglass; LIBS_Green2-Coloredlayer; LIBS_Green3-Baseglass; LIBS_Green3-Coloredlayer; LIBS_Pink1-Baseglass; LIBS_Pink1-Coloredlayer; LIBS_Pink2-Baseglass; LIBS_Pink2-Coloredlayer. The file for “LIF” contains: LIF_Black-Baseglass; LIF_Black-Coloredlayer; LIF_Blue1-Baseglass; LIF_Blue1-Coloredlayer; LIF_Blue2-Baseglass; LIF_Blue2-Coloredlayer; LIF_Blue3-Baseglass; LIF_Blue3-Coloredlayer; LIF_Brown1-Baseglass; LIF_Brown1-Coloredlayer; LIF_Brown2-Baseglass; LIF_Brown2-Coloredlayer; LIF_Green1-Baseglass; LIF_Green1-Coloredlayer; LIF_Green2-Baseglass; LIF_Green2-Coloredlayer; LIF_Green3-Baseglass; LIF_Green3-Coloredlayer; LIF_Pink1-Baseglass; LIF_Pink1-Coloredlayer; LIF_Pink2-Baseglass; LIF_Pink2-Coloredlayer. For the “FESEM-EDS” there are two files inside. One title "EDS" which contains the documents: EDS_Black; EDS_Blue1; EDS_Blue2; EDS_Blue3; EDS_Brown1; EDS_Brown2; EDS_Brown2; EDS_Green1; EDS_Green2; EDS_Green3; EDS_Pink1; EDS_Pink2. And the other called "PHOTOS" which contains: FESEM_Black; FESEM_Blue1; FESEM_Blue2; FESEM_Blue3; FESEM_Brown1; FESEM_Brown2; FESEM_Green1; FESEM_Green2; FESEM_Green3; FESEM_Pink1; FESEM_Pink2. This is the experimental dataset used in the paper Appl. Sci., 12(11), 5760 (2022) (https://www.mdpi.com/2076-3417/12/11/5760). Flashed glasses are composed of a base glass and a thin colored layer and have been used since medieval times in stained glass windows. Their study can be challenging because of their complex composition and multilayer structure. In the present work, a set of optical and spectroscopic techniques have been used for the characterization of a representative set of flashed glasses commonly used in the manufacture of stained glass windows. The structural and chemical composition of the pieces were investigated by optical microscopy, field emission scanning electron microscopy-energy dispersive X-ray spectrometry (FESEM-EDS), UV-Vis-IR spectroscopy, laser-induced breakdown spectroscopy (LIBS), and laser-induced fluorescence (LIF). Optical microscopy and FESEM-EDS allowed the determination of the thicknesses of the colored layers, while LIBS, EDS, UV-Vis-IR, and LIF spectroscopies served for elemental, molecular, and chromophores characterization of the base glasses and colored layers. Results obtained using the micro-invasive LIBS technique were compared with those retrieved by the cross-sectional technique FESEM-EDS, which requires sample taking, and showed significant consistency and agreement. In addition, LIBS results revealed the presence of additional elements in the composition of flashed glasses that could not be detected by FESEM-EDS. The combination of UV-Vis-IR and LIF results allowed precise chemical identification of chromophores responsible for the flashed glass coloration. This research has been funded by the Spanish State Research Agency (AEI) through project PID2019-104124RB-I00/AEI/10.13039/501100011033, the Fundación General CSIC (ComFuturo Programme), by project TOP Heritage-CM (S2018/NMT-4372) from Community of Madrid, and by the H2020 European project IPERION HS (Integrated Platform for the European Research Infrastructure ON Heritage Science, GA 871034). Peer reviewed

  • Open Access English
    Authors: 
    Huertas, I. Emma; Flecha, Susana; García-Lafuente, Jesús;
    Publisher: DIGITAL.CSIC
    Country: Spain
    Project: EC | COMFORT (820989)

    SAMIpH_Database_2012_2017.csv provides data for the years 2012 and 2017.DATE_UTC and TIME_UTC are the day and time at which the measurement was taken, in UTC. pHConstSal35 is the pH at a constant salinity of 35. TEMPERATURE and SALINITY are the water temperature (in degrees Celsius) and the water salinity (practical salinity units, i.e., no units) respectively. SAMICO2_Database_2013_2017.txt provides data between the years 2013 and 2017. DATE are the day and time at which the measurement was taken, in UTC and CO2 is the in situ pCO2 value (in uatm) recorded by the SAMI device The database provides measurements of the carbon system parameters pH and pCO2 obtained with SAMI sensors (Sunburst Sensors, LLC) attached to a mooring line deployed in the Strait of Gibraltar between the years 2012 and 2017. Temperature and salinity data were obtained with a Conductivity-Temperature probe (CT Seabird SBE37-SMP) also installed in the line. Sampling interval was initially set to 60 min, but a battery run off happened in summer 2013 (which caused a six-month data gap) advised changing the interval to 120min to extend the battery life. This research was supported by the COMFORT project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820989 (project COMFORT, "Our common future ocean in the Earth system – quantifying coupled cycles of carbon, oxygen, and nutrients for determining and achieving safe operating spaces with respect to tipping points).” Funding was also provided by grant EQC2018-004285-P funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. Peer reviewed

  • Research data . 2022
    Open Access English
    Authors: 
    Reñé, Albert; Timoneda Solé, Natàlia; Sarno, Diana; Zingone, Adriana; Margiotta, Francesca; Passarelli, Augusto; Gallia, Roberto; Tramontano, Ferdinando; Montresor, Marina; Garcés, Esther;
    Publisher: CSIC - Instituto de Ciencias del Mar (ICM)
    Country: Spain
    Project: EC | ASSEMBLE Plus (730984)

    The presence of phytoplankton parasites along the water column was explored at the Long Term Ecological Station MareChiara (LTER-MC) in the Gulf of Naples (Mediterranean Sea) in October 2019. Microscopy analyses showed diatoms dominating the phytoplankton community in the upper layers (0-20 m). Metabarcoding data from the water column showed the presence of Chytridiomycota predominantly in the upper layers coinciding with the vertical distribution of diatoms. Laboratory incubations of natural samples enriched with different diatom cultures confirmed parasitic interactions of some of those chytrids – including members of Kappamyces – with diatom taxa. The temporal dynamics of diatoms and chytrids was also explored in a three-year metabarcoding time-series (2011-2013) from surface waters of the study area and in sediment samples. Chytrids were recurrently present at low relative abundances, and some taxa found to infect diatoms in the incubation experiments were also identified in the ASV time-series. However, co-occurrence analyses did not show any clear or recurrent pairing patterns for chytrid and diatom taxa along the three years. The chytrid community in the sediments showed a clearly different species composition compared to the recorded in the water column samples, with higher diversity and relative abundance. The combination of observations, incubations and metabarcoding confirmed that parasites are a common component of marine protist communities at LTER-MC. Host-parasite interactions must be determined and quantified to understand their role and the impact they have on phytoplankton dynamics File1: VERDI_samples_parameters.xlsx - Physico-chemical variables obtained from CTD profile - Inorganic nutrients concentrations - Chlorophyll-a concentrations - Organic carbon and nitrogen concentrations - Phytoplankton abundances - Detections of chytrids File 2: VERDI_asv_table.tbl: ASV abundances from natural samples and incubations File 3: VERDI_tax_table.tbl: Taxonomic assignments of ASVs File 4: VERDI_asv_seqs.fa: Sequences of ASVs File 5: VERDI_incubations_images.zip - Compilation of images taken during incubations with diatoms - Physico-chemical variables obtained from CTD profile - Inorganic nutrients concentrations - Chlorophyll-a concentrations - Organic carbon and nitrogen concentrations - Phytoplankton abundances - Detections of chytrids - Metabarcoding ASV abundances from natural samples and incubations - Metabarcoding Taxonomic assignments of ASVs - Metabarcoding Sequences of ASVs - Compilation of images taken during incubations with diatoms - European Union’s Horizon 2020 research and innovation programme under grant agreement No 730984, ASSEMBLE Plus project. - Spanish MICINN Project SMART (PID2020-112978GB-I00) - The research program LTER-MC is funded by the Stazione Zoologica Anton Dohrn Peer reviewed

  • Open Access
    Authors: 
    Arrayago, Itsaso; Rasmussen, Kim J.R.;
    Publisher: Universitat Politècnica de Catalunya
    Country: Spain
    Project: EC | New GeneSS (842395)

    Data was generated using the general purpose finite element software ABAQUS and performing advanced nonlinear analyses. The database is comprised of vertical and lateral system stiffness values corresponding to different random samples of six different nominal stainless steel frames under gravity and gravity plus wind load combinations. The values of the random variable assignments are given for each case. The full details of the finite element model can be found in: Arrayago, I.; Rasmussen, K.J.R. Reliability of stainless steel frames designed using the Direct Design Method in serviceability limit states. Journal of Constructional Steel Research 196, 107425, 2022. DOI: https://doi.org/10.1016/j.jcsr.2022.107425 The data included in the dataset corresponds to the vertical & lateral stiffness of each frame under different load conditions. Although the data has been generated using the finite element software ABAQUS, no special software is required to read or interpret the data. {"references": ["Arrayago I., Rasmussen K.J.R. Reliability of stainless steel frames designed using the Direct Design Method in serviceability limit states Journal of Constructional Steel Research 196, 107425, 2022. DOI: https://doi.org/10.1016/j.jcsr.2022.107425"]}

  • Open Access
    Authors: 
    Joy Goodman-Deane; Sam Waller; Elisabet Roca Bosch;
    Publisher: Universitat Politècnica de Catalunya
    Country: Spain
    Project: EC | DIGNITY (875542)

    This dataset contains data from a population-representative survey examining various factors relating to digital exclusion (particularly digital mobility exclusion). The survey was conducted with 1002 adult participants in Italy in 2020. This dataset is part of a series of 5 datasets which used the same questionnaire (translated into different languages) in different countries in Europe.

  • Open Access
    Authors: 
    Goodman-Deane, Joy; Waller, Sam; Roca Bosch, Elisabet; Delespaul, Sam;
    Publisher: Iniciativa Digital Politecnica
    Country: Spain
    Project: EC | DIGNITY (875542)

    This dataset contains data from a population-representative survey examining various factors relating to digital exclusion (particularly digital mobility exclusion). The survey was conducted with 418 adult participants in Flanders region in Belgium in 2021. This dataset is part of a series of 5 datasets which used the same questionnaire (translated into different languages) in different countries in Europe.

  • Open Access Catalan; Valencian
    Authors: 
    Goodman-Deane, Joy; Waller, Sam; Roca Bosch, Elisabet; Lazzarini, Boris; Wybraniec, Bartosz Maciej; Villares Junyent, Míriam;
    Publisher: Universitat Politècnica de Catalunya
    Country: Spain
    Project: EC | DIGNITY (875542)

    This dataset contains data from a population-representative survey examining various factors relating to digital exclusion (particularly digital mobility exclusion). The survey was conducted with 601 adult participants in the Barcelona Metropolitan Area of Spain in 2020. This dataset is part of a series of 5 datasets which used the same questionnaire (translated into different languages) in different countries in Europe.

  • Open Access Dutch; Flemish
    Authors: 
    Joy Goodman-Deane; Sam Waller; Elisabet Roca Bosch; Nick van Apeldoorn; Lisette Hoeke;
    Publisher: Universitat Politècnica de Catalunya
    Country: Spain
    Project: EC | DIGNITY (875542)

    This dataset contains data from a population-representative survey examining various factors relating to digital exclusion (particularly digital mobility exclusion). The survey was conducted with 423 adult participants in the Netherlands in 2020 and 2021. This dataset is part of a series of 5 datasets which used the same questionnaire (translated into different languages) in different countries in Europe.

  • Open Access
    Authors: 
    Zhu, Xiaomin; Zhang, Ziliang; Wang, Qitong; Peñuelas, Josep; Sardans, Jordi; Li, Na; Liu, Qing; Yin, Huajun; Liu, Zhanfeng; Lambers, Hans;
    Publisher: Dryad
    Country: Spain
    Project: EC | IMBALANCE-P (610028)

    [Methods] Isolation of roots and mycelia using ingrowth cores: To isolate roots and mycelia, we adopted an ingrowth-core technique modified from Zhang et al. (2018) and Keller et al. (2021). Ingrowth cores (6 cm inner diameter and 15 cm depth) were wrapped with a mesh with different pore sizes: mesh size of 2000 µm allowed the ingrowth of fine roots and mycelia (both roots and mycelia accessible); 48-µm mesh permitted the growth of mycelia but not of fine roots (only mycelia accessible), and 1-µm mesh excluded the growth of both roots and mycelia (only the soil) (Fig. 2). The C source in the 2-mm mesh cores was mainly derived from roots, mycelia and litter leachates, that of the 48-µm mesh cores was derived from mycelia and litter leachates, while the 1-µm mesh cores received C only from litter leachates. The soil was collected from the mineral layer (0-15cm) at each plot. After removing the visible roots, the soil from the same plot was homogenized and sieved through a 5-mm mesh. The sieved soil was filled into ingrowth cores corresponding to the soil bulk density at 0-15 cm depth (0.796 g cm-3, approximately 337 g per core). Six sets of ingrowth cores with different mesh-size (1-µm, 48-µm and 2000-μm) were installed in each treatment plot. In total, 108 ingrowth cores (2 N levels * 3 replicates * 6 sets * 3 mesh-sizes) were installed in this coniferous forest. Ingrowth cores were randomly placed in the topmost mineral horizon (0-15cm depth) in each plot in July 2017. The bottom of the ingrowth cores was covered with the corresponding size of the mesh to prevent inputs of roots and mycelia, respectively, and the top was covered by multiple layers of the corresponding size of the mesh to block the entry of coniferous litter but to allow gas and water exchange. When the cores were retrieved, we did not detect any external litter in the cores. To block the influx of new C derived from the saprophytic mycelia outside the cores, we spread a 2 mm-thick layer of silica sand around the cores. Silica sand as a growth substrate effectively reduces the disturbance of saprophytic hyphae (Hagenbo et al., 2017). Ingrowth cores were harvested in August 2019 and August 2020, respectively. Two sets of ingrowth cores were collected in each plot at each sampling date. Cores were transported to the laboratory within the icebox. After the removal of roots, soils inside the cores were sieved through a 2-mm mesh and divided into two subsamples: one subsample stored in -4 °C was used for the analyses of enzyme activities and microbial community composition; the second subsample was air-dried to perform soil aggregate fractionation, SOC determination, and soil biomarkers analysis. Root and mycelium biomass: Roots inside the 2000-µm mesh cores were manually picked out, washed thoroughly, oven-dried at 60°C for 48 hours and then weighed to determine the total root biomass. The ectomycorrhizal mycelium biomass was estimated using mesh bags (2 cm inner diameter, 15 cm depth; mesh size: 48 µm) filled with different particle sizes of HCl-washed silica sand (60 g, 0.36-2 mm) (Wallander et al., 2001). The mesh bags were randomly buried into the 0-15 cm soil depth in each plot in July 2017, and recovered at the same time as the ingrowth cores. The concentration of ergosterols was measured to characterize the biomass of ectomycorrhizal mycelia in the mesh bags (see details in the Supplementary Methods) (Parrent & Vilgalys, 2007). Soil aggregate fractionation and SOC concentration: To understand the physico-chemical protection of SOC in the RP and MP under N addition, soils were physically fractionated into three size fractions to examine the allocation of C and biomarkers among macroaggregates (Macro: 250~2000 µm), microaggregates (Micro: 53~250 µm) and slit-clay (< 53 µm) by using the wet-sieving technique (Six et al., 1998). The proportions of SOC and the concentrations of biomarkers in the three fractions were measured to characterize the role of physical protection by aggregates. The SOC and total N (TN) concentrations in bulk soil and size fractions were analyzed using an elemental analyzer (Vario MACRO, Elementar Analysensysteme GmbH, Hanau, Germany). To assess the protection of SOC by minerals, two forms of Fe and Al oxides, oxalate-extractable Fe/Al oxides (Feo + Alo) and dithionite-extractable Fe/Al (Fed + Ald) were measured by using the extraction method proposed by Gentsch et al (2018). The Fed + Ald indicates the amount of pedogenic Fe and Al within oxides, silicates and organic complexes, whereas Feo + Alo represents poorly crystalline oxyhydroxides (Gentsch et al., 2018). The concentrations of Fe and Al oxides in extracts were determined by inductively coupled plasma-optical emission spectrometry (ICP-OES, Optima 8300, Perkin Elmer, USA). SOC chemical composition: A range of major biomarkers, which are widely accepted to trace plant-derived and microbial-derived C, respectively, were selected to reveal the changes of the chemical composition of SOC in two pathways under N addition (Barré et al., 2018; Liang et al., 2019). Air-dried soil (1 g) was sequentially extracted (solvent extraction, base hydrolysis, and CuO oxidation) to isolate solvent-extractable free lipids (long-chain fatty acids), cutin- and suberin-derived compounds and lignin-derived phenols (vanillyls, syringyls and cinnamyls), respectively, according to standard protocols (Otto & Simpson, 2007; Tamura & Tharayil, 2014). Since the direct contribution of microbial living biomass to soil amino sugars is negligible, amino sugars are good indicators of microbial necromass (Liang et al., 2017, Joergensen, 2018). Four types of amino sugars, including glucosamine, galactosamine, manosamine, and muramic acid, were tested in this study. By assessing them in soils, we can investigate microbial necromass dynamics at the community-level (i.e., fungi and bacteria) and evaluate the contributions of necromass to SOC storage under different environmental conditions (Joergensen, 2018; Liang et al., 2019). The detailed chemical extractions and analyses of plant and microbial biomarkers are provided in Supplementary Methods. Microbial community composition: Soil microbial community composition was characterized using the phospholipid fatty acids (PLFAs) methods (see details in Supplementary Methods) (Bossio & Scow, 1998). The identification of the extracted fatty acid was based on a MIDI peak identification system (Microbial ID Inc., Newark, DE, USA). The PLFAs i15:0, α15:0, i16:0, i17:0, α17:0 were used to indicate the relative biomass of Gram-positive (G+) bacteria. The PLFAs 16:1ω9c, 16:1ω7c, 18:1ω7c, cy17:0, cy19:0 were used to indicate the relative biomass of Gram-negative (G-) bacteria. The PLFA 18:2ω6c was used as an indicator of saprotrophic fungal biomass. The PLFAs 10Me16:0, 10Me17:0 and 10Me18:0 were used to indicate actinomycete (AC) biomass. Microbial community composition was assessed by the ratio of saprotrophic fungal biomass to bacterial biomass (F/B ratio). Extracellular enzyme activity: The activities of three extracellular enzymes involved in the decomposition of lignin and fungal residues were measured as described by Saiya-Cork et al. (2002) (see details in Supplementary Methods). The β-N-acetyl-glucosaminidase(NAG)participates in chitin and peptidoglycan degradation, hydrolyzing chitobiose to glucosamine (Sinsabaugh et al., 2009). NAG activity was measured fluorometrically using 4-methylumbelliferyl N-acetyl-β-D-glucosaminide as the substrate. Phenol oxidases (POX) and peroxidases (PER) play an important role in degrading polyphenols, and their activities were measured colorimetrically using L-dihydroxyphenylalanine (DOPA) as the substrate. Data calculation and statistical analysis: To isolate the effects of root and mycelium on the SOC dynamics and associated microbial characteristics (i.e., SOC, biomarkers concentrations, fungal and bacterial biomass, and enzymes activities), net changes of the observations mediated by the root-pathway and mycelium-pathway were quantified by the difference of corresponding variables between the 2-mm mesh cores and 48-µm mesh cores, or between the 48-µm cores and 1-µm mesh cores, respectively (Fig. 2). The recent concept proposed by Zhu et al (2020) highlighted the contribution of microbial necromass to the SOC pool (i.e., MCP efficacy). Based on this concept, the changes of MCP efficacy (i.e., the contribution of increased microbial residual C to the increased SOC) under N addition were calculated as follow: Changes of MCP efficacy (% SOC) under N addition = , where MRCN, SOCN, MRCCK, and SOCCK represent the concentration of microbial residual C and SOC in the N-addition plots and the non-N addition plots, respectively. Additionally, the contribution of increased plant-derived C to the increased SOC induced by N addition was calculated using Eq. 1 but replacing microbial residual C with plant-derived C. Plant roots and associated mycorrhizae exert a large influence on soil carbon (C) cycling. Yet, little was known whether and how roots and ectomycorrhizal extraradical mycelia differentially contribute to soil organic C (SOC) accumulation in alpine forests under increasing nitrogen (N) deposition. Using ingrowth cores, the relative contributions of the root-pathway (RP) (i.e., roots and rhizosphere processes) and mycelium-pathway (MP) (i.e., extraradical mycelia and hyphosphere processes) to SOC accumulation were distinguished and quantified in an ectomycorrhizal-dominated forest receiving chronic N addition (25 kg N ha-1 yr-1). Under the non-N addition, the RP facilitated SOC accumulation, while the MP reduced SOC accumulation. Nitrogen addition enhanced the positive effect of RP on SOC accumulation from +18.02 mg C g-1 to +20.55 mg C g-1 but counteracted the negative effect of MP on SOC accumulation from -5.62 mg C g-1 to -0.57 mg C g-1, as compared to the non-N addition. Compared to the non-N addition, the N-induced SOC accumulation was 1.62~2.21 mg C g-1 and 3.23~4.74 mg C g-1, in the RP and the MP, respectively. The greater contribution of MP to SOC accumulation was mainly attributed to the higher microbial C pump (MCP) efficacy (the proportion of increased microbial residual C to the increased SOC under N addition) in the MP (72.5%) relative to the RP (57%). The higher MCP efficacy in the MP was mainly associated with the higher fungal metabolic activity (i.e., the greater fungal biomass and N-acetyl glucosidase activity) and greater binding efficiency of fungal residual C to mineral surfaces than those of RP. Collectively, our findings highlight the indispensable role of mycelia and hyphosphere processes in the formation and accumulation of stable SOC in the context of increasing N deposition. National Natural Science Foundation of China, Award: 32171757. The Chinese Academy of Sciences (CAS) Interdisciplinary Innovation Team, Award: xbzg-zysys-202112. The Second Tibetan Plateau Scientific Expedition and Research, Award: 2019QZKK0301. European Research Council Synergy project, Award: SyG-2013-610028 IMBALANCE-P. The Spanish Government, grant, Award: PID2019-110521GB-I00. National Natural Science Foundation of China, Award: 31901131. National Natural Science Foundation of China, Award: 42177289. The Spanish Government, grant, Award: PID2020-115770RB-I00. These data were generated to investigate how N addition affect SOC accural and chemical composition through the root-pathway and mycelium-pathway in an alpine coniferous forest. Samples of plant and soil were collected from each treatment plots (non-N addition and N-addition) in 2019 and 2020. Therefore, each parameter has 6 replicates (n = 3 replicates for each treatment * 2 sampling date =6),except for the plant-derived C in different soil size fractions (only measured the samples collected in 2019). Peer reviewed

  • Research data . 2022
    Open Access English
    Authors: 
    Alabort Martínez, Carles; Sanchis Saez, Javier; Catalán Martínez, David; Serra Alfaro, José Manuel;
    Publisher: Universitat Politècnica de València
    Country: Spain
    Project: EC | iCAREPLAST (820770)

    The aim of iCAREPLAST project is to provide a cost and energy-efficient alternative to recycle and valorise non-recycled plastic waste (ca. 70% of European plastic waste) that, due to their characteristics or their contamination, are currently disposed into landfills (27%) or underexploited through energy recovery (42%). iCAREPLAST project, summarized in Fig.1, combines pyrolysis, catalytic treatment and membrane separation technologies to obtain high added-value chemicals, as they are (alkyl-)aromatics (BTXs and medium to long-chain alkyl-aromatics), that can be used to produce virgin-quality polymers or as raw materials for other processes in petrochemicals, fine chemicals and surfactants industries. As in other production processes, process control represents a key issue in the efficient operation of the proposed plant. WP5: “Plant modelling, control and optimization” will try to apply some state-of-art control and optimization techniques with the purpose of manage the iCAREPLAST process as a whole, taking into account several key information related to yields, energy use, sustainability indicators, etc. to drive the process towards its optimum operating point. In iCAREPLAST project, several approaches of process model simulations are considered. The first one is known as steady-state simulation, where iCAREPLAST partners can model different scenarios by handling with design parameters. This type of simulation is typically done during the conceptual phase of a project in an effort to gain a better understanding of how a design can be altered to get the most out of the process from both a business standpoint and an engineering perspective. The datasets presented belongs to this kind of simulations where a steady-state model has been developed in Aspen Plus and several sensitivity analysis has been performed. The datasets comprises raw data from steady-state model simulations performed using Aspen Plus Software. Each dataset corresponds with each unit or subprocess belonging to the entire iCARPLAST process.

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