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12,346 results for “derivatives”
Low molecular weight seaweed–derived polysaccharides lead to increased faecal bulk but do not alter human gut health markers
<p>Differential analysis of taxa before and after the consumption of either agar, alginate or maltodextrin showed no significant change at phylum or family level (<strong>Supplementary tables 1-2</strong>). </p> <p><em>Supplementary Table 1: Differential abundance analysis with ALDEX2 on family level</em></p> <p><em>Supplementary Table 2: Differential abundance analysis with ALDEX2 on phylum level</em></p>
Modelling Cyclic Stretch of Patient-Derived Alveolar Epithelial Cells from Organoids using a New Alveoli-on-Chip Platform
<p><span>The data originates from lung tumor samples obtained at the University Hospital Bern. Patient-derived alveolar epithelial type 2 cells (AEC2) were isolated and cultured into organoids. Grown organoids where dissociated and seeded onto a novel alveoli-on-chip system submerged in medium. After 24 h of either no mechanical stimulus (control) or cyclic breathing stretch (stretch), RNA was isolated and sequenced. The RNA-seq data underwent quality control, alignment to the reference genome, read counting, DGE and GSEA. All analyses were run in R version 4.2.1.</span></p>
Snow melt onset date estimates derived from CLARA-A2 SAL surface albedo dataset
<p>Snow melt onset date estimates for the Northern Hemisphere, 1982-2015. Derived from the CLARA-A2 SAL surface albedo dataset. Version for manuscript review.</p>
Value chain around Ecuadorian cocoa based on products and their derivatives.
<p><strong>Map all the actors in the supply chain of the Ecuadorian cocoa sector.</strong></p>
Plant Communities at the Eden Project, UK, derived from soil eDNA
<p>The project seeks to understand the potential for the use of eDNA collected from soil to characterise plant communities. To do so, soils were sampled at the Eden Project in Cornwall UK, within the two covered biomes where we have a good understanding of the structure and composition of plant communities (further quantified with above ground plant coverage inventories). 32 plots were established across 10 different plant assemblages, each of which experiences subtle differences in soil chemistry and microclimate. Each plot consists of a 2 x 2 m quadrat, with four soil aggregates collected at each corner. </p> <p>eDNA was then extracted and amplified following the methods detailed in Zinger et al. (2016) and Donald et al. (2021). The primers used targeted the P6 loop of thechloroplastic trnL intron [primer_fwd: GGGCAATCCTGAGCCAA, primer_rev: CCATTGAGTCTCTGCACCTATC] (Taberlet et al. 2007). 16 Extraction, 54 Sequencing, and 16 PCR controls are included so as to account for potential errors generated during the processing of samples, with a mock community (4 positive controls) of 10 known plant sequences also included to guide filtering thresholds. PCR products were pooled and sequencing libraries were constructed using the Illumina TruSeq NanoPCRFree kit following the supplier’s instructions (Illumina Inc., San Diego, California, USA), except that the ligation product was not PCR amplified to limit tag-jump biases (Taberlet et al 2018). The libraries were then sequenced on an Illumina Hiseq platform (San Diego, CA, USA).</p> <p>Sequencing was conducted by the GenoToul bioinformatics platform (Toulouse, France), with the OBITOOLS package (Boyer et al. 2016). Here, the produced sequence data was processed using the following steps. First, ‘illuminapairedend’ was used to assemble paired-end reads. This algorithm is based on an exact alignment algorithm that considers the quality scores at all positions during the assembly process. Subsequently, we used the ‘ngsfilter’ command to identify and remove the primers and tags on each read, and assign reads to their respective samples (NGS filter file provided: <strong>ngsfilter_TRNL_PLANTS_EDEN_PROJECTb.txt</strong>). This program was used with its default parameters tolerating two mismatches for each of the two primers and no mismatch for the tags. Following this, sequencing reads were dereplicated using the ‘obiuniq’ command. The produced <strong>data.uniq.fasta</strong> file is supplied here. Sequences were then further filtered to remove sequences of low quality (containing Ns or with paired-end alignment scores below 50), and sequences represented by only one read (singletons) using the ‘obigrep’ command. To remove PCR/sequencing errors as well as intraspecific variability, we built OTUs (Operational Taxonomic Units) using the ‘sumaclust’ clustering algorithm (Mercier et al. 2013), which considers the most abundant sequence of each cluster as the cluster representative. OTUs were set at a sequence similarity threshold of 95%. To assign a taxon to plant OTUs, we built a reference sequence database using the ecoPCR programme (Ficetola et al. 2010) on the European Molecular Biology Laboratory (EMBL; release 141). OTUs were then assigned a taxonomy, using OBITOOL’s ecotag programme (Boyer et al. 2016), which performs a global alignment of each OTU sequence (the query) against each reference. The reference taxon assigned to each OTU corresponds to the Last Common Ancestor of all the best-match sequences for the query. </p> <p><br> Datasets were subsequently filtered to remove contaminants as well as artefacts such as PCR chimeras and remaining sequencing errors, using routines implemented in the metabaR R package (Zinger et al 2021), in R version 3.6.1 (R Development Core Team, 2013). The filtering process consisted of four steps: (i) a negative control-based filtering. OTUs whose maximum abundance was found in extraction/PCR negative controls were removed from the dataset, as they were likely to be reagent/aerosol contaminants, better amplified in the absence of competing DNA fragments as it is the case in biological samples. (ii) a reference-based filtering. OTUs which are too dissimilar from sequences available in reference databases are potential chimeras generated during sequencing and amplification. In this study, we chose to set similarity thresholds at 100%. (iii) an abundance-based filtering. This procedure targets incorrect assignment of a few numbers of sequences corresponding to true OTUs occurring to the wrong sample, a phenomenon called “tag-switching”. It consists in setting OTUs abundances to 0 in samples where their abundance represents < 0.03% of the total OTU abundance in the entire dataset. (iv) Finally, we conducted a PCR-based filtering by considering any PCR reaction that yielded less than 1000 reads as non-functional, and removed them from the dataset. The script used for implementing this is provided (<strong>metabaR_Eden_Plants_100sim.html)</strong>, with sequence data processed to remove contaminants, OTUs of low taxonomic resolution, and PCRs with too low a read count. The clean data is provided (<strong>eden_plant_postclean_100sim.rds</strong>).</p> <p>References:</p> <p>Boyer, F. <em>et al.</em> (2016) ‘obitools: a unix-inspired software package for DNA metabarcoding’, <em>Molecular Ecology Resources</em>, 16(1), pp. 176–182. doi:<a href="https://doi.org/10.1111/1755-0998.12428">10.1111/1755-0998.12428</a>.</p> <p>Donald, J. <em>et al. (2021) '</em>‘Multi-taxa environmental DNA inventories reveal distinct taxonomic and functional diversity in urban tropical forest fragments.‘ <em>Global Ecology and Conservation</em> 29 (2021): e01724.</p> <p>Mercier, C. <em>et al.</em> (2013) ‘SUMATRA and SUMACLUST: fast and exact comparison and clustering of sequences’, in <em>Programs and Abstracts of the SeqBio 2013 workshop. Abstract</em>. Citeseer, pp. 27–29.</p> <p>Taberlet, P. <em>et al.</em> (2007) ‘Power and limitations of the chloroplast trn L (UAA) intron for plant DNA barcoding’, <em>Nucleic Acids Research</em>, 35(3), pp. e14–e14. doi:<a href="https://doi.org/10.1093/nar/gkl938">10.1093/nar/gkl938</a>.</p> <p>Taberlet, P. <em>et al.</em> (2018) <em>Environmental DNA: For Biodiversity Research and Monitoring</em>. Oxford University Press.</p> <p>Team, R.C. (2013) <em>R: A language and environment for statistical computing</em>. Vienna, Austria.</p> <p>Zinger, L. <em>et al.</em> (2016) ‘Extracellular DNA extraction is a fast, cheap and reliable alternative for multi-taxa surveys based on soil DNA’, <em>Soil Biology and Biochemistry</em>, 96, pp. 16–19.</p> <p>Zinger, L. et al. (2021) ‘metabaR: An r package for the evaluation and improvement of DNA metabarcoding data quality’, Methods in Ecology and Evolution. DOI: <a href="https://doi.org/10.1111/2041-210X.13552">https://doi.org/10.1111/2041-210X.13552</a></p>
Analyzing Satellite-Derived 3D Building Inventories and Quantifying Urban Growth towards Active Faults: A Case Study of Bishkek, Kyrgyzstan
<p>#############################################################################################################<br> Datasets supporting the publication:<br> Analyzing satellite-derived 3D building inventories and quantifying urban growth towards active faults:<br> a case study of Bishkek, Kyrgyzstan.<br> <a href="https://doi.org/10.3390/rs14225790">https://doi.org/10.3390/rs14225790</a></p> <p>-Please refer to the publication for details on the production of each dataset.<br> -Datasets are ordered following the publication figures.<br> -Please cite the publication and this dataset repository when using the data.<br> #############################################################################################################</p> <p>------------------------<br> Structure:<br> File ID<br> -[fields:] description<br> ------------------------</p> <p>KH9_1979_builtup.shp<br> -KH9 1979 built-up area classification</p> <p>S2_2021_builtup.tif<br> -Sentinel-2 2021 built-up area classification.</p> <p>S2_2021_corine_land_cover_class.tif<br> -Sentinel-2 2021 land cover classification in Corine 2018 land-cover classes.</p> <p>S2_KH9_DN_change_aggregated.shp<br> -Proportional DN change aggregated to a 1 km^2 grid for areas ≥50% built-up.</p> <p>building_characteristics.shp<br> -build_count: building count in 500 m square grid cell.<br> -mean_area: mean building size (m^2) in 500 m square grid cell.<br> -median_area: median building size(m^2) in 500 m square grid cell.<br> -cell_coverage: %building coverage of 500 m square grid cell.</p> <p>pleiades_buildings_all.shp<br> -All building detections from Pleiades data. Confidence values are output from the deep learning model.</p> <p>pleiades_buildings_heights.shp<br> -Building detections from the Pleiades data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m)</p> <p>wv2_buildings_all.shp<br> -All building detections from WorldView-2 data. Confidence values are output from the deep learning model.</p> <p>wv2_buildings_heights.shp<br> -Building detections from the WorldView-2 data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m)</p> <p>trained_rcnn.zip<br> -ArcGIS Pro deep learning model (DLPK) used to extract building footprints.</p>
Data and Code for figures of "Long-range transport and fate of DMS-oxidation products in the free troposphere derived from observations at the high-altitude research station Chacaltaya (5240 m a.s.l.) in the Bolivian Andes"
<p>This database includes the material to create the figures in "Measurement Report: Long-range transport and fate of DMS-oxidation products in the free troposphere derived from observations at the high-altitude research station Chacaltaya (5240 m a.s.l.) in the Bolivian Andes" and the analyzed time series of all atmospheric variables presented.</p>
Experimental addition of marine-derived nutrients affects wildflower traits in a coastal meta-ecosystem
<p>Organismal movement can bring individuals, resources, and novel interactions across ecosystem boundaries and into recipient habitats, thereby forming meta-ecosystems. For example, Pacific salmon ecosystems receive large marine-derived nitrogen subsidies during annual spawning events, which can have a wide range of effects on aquatic and terrestrial plant species and communities. In this study, we evaluate the effects of cross-ecosystem nutrient subsidies on terrestrial plant growth and reproduction. We conducted a large-scale field experiment with four treatments: (1) addition of a pink salmon (<em>Oncorhynchus</em> <em>gorbuscha</em>) carcass, (2) addition of the drift seaweed rockweed (<em>Fucus</em> <em>distichus</em>), (3) addition of both salmon + rockweed, and (4) a control. We examined treatment effects on leaf nitrogen and fitness-associated floral traits in four common estuarine wildflower species. We found elevated leaf ∂<sup>15</sup>N in all plant species and all sampling years in treatments with salmon carcass additions but did not observe any differences in leaf percent nitrogen. We also observed larger leaf area in two species, a context-dependent increase in floral display area in two species, and a limited increase in plant seed set in response to both salmon carcass treatments. In sum, our study suggests that marine nutrients can affect terrestrial plant growth and reproduction.</p>
Figure 2 in Silicon derivatives induced host plant resistance against Tetranychus urticae (Acari: Tetranychidae) in eggplants farms
Figure 2. (A) Silicon leaf, total protein and phenol contents, (B) Activity of POD, CAT, and PPO of S. melongena- treated plants. Means followed by the same letter are not significantly different using Tukey's HSD Test at P <0.05. T1 = Control, T2 = OSAB 2 mL L−1, T3= OSAB 4 mL L−1, T4= Silica K 2 mL L−1, and T5 = Silica K 4 mL L−1.
Figure 1 in Silicon derivatives induced host plant resistance against Tetranychus urticae (Acari: Tetranychidae) in eggplants farms
Figure 1. Mean number ± SE of the different stages of T. urticae on S. melongena leaves 10, 30 and 50 days after spraying (DAS). Means followed by the same letter are not significantly different using Tukey's HSD at P <0.05. T1 = Control, T2 = OSAB 2 mL L−1, T3 = OSAB 4 mL L−1, T4 = Silica K 2 mL L−1, and T5 = Silica K 4 mL L−1.
Dataset derived from GBIF data about Insecta.
<p>Dataset derived from GBIF data about Insecta. </p> <p>This dataset has been used to obtain the results presented in the paper "GBIF falls shorts of providing a representative picture of the global distribution of insects", submited to journal Systematic Entomology</p> <p> </p> <p>The dataset has been registered as derived dataset on GBIF, and the citation is:</p> <p><em>Derived dataset GBIF.org (22 December 2022) Filtered export of GBIF occurrence data https://doi.org/10.15468/dd.5bhqp3</em></p> <p> </p>
Io hot spots map derived by Juno/JIRAM orbits: 10, 11, 16, 17, 18, 20, 24, 25, 26, 27, 32, 33
<p>Io hot spots map GIS shapefile derived by Juno/JIRAM orbits: 10, 11, 16, 17, 18, 20, 24, 25, 26, 27, 32, 33</p>
SfM-MVS derived orthomosaics of the Otemma glacier forefield (2020)
<p><strong>Orthomosaics (2020) of the Otemma glacier forefield derived from SfM-MVS photogrammetry</strong></p> <p>This dataset includes the orthomosaics of the Otemma glacier forefield generated through SfM-MVS photogrammetry. This dataset is based upon the images collected during summer 2020. Details on the image acquisition and image processing can be found in Roncoroni et al. (2022) at this URL: https://doi.org/10.1080/01431161.2022.2079963</p> <p>Details:</p> <ul> <li>Format: .tif </li> <li>Name format: mmddyyyy<em>x</em>m_Orthomosaic (where mm is the month, dd the day, yyyy the year, and xm is AM or PM)</li> <li>Coordinate system: CH1903+ LV95 (EPSG:2056)</li> <li>Spatial resolution: 0.05 m</li> </ul> <p> </p>
N-containing carbons derived from microporous coordination polymers for use in post-combustion flue gas capture
<p>Datasets for the plots shown in the article entitled: N-containing carbons derived from microporous coordination polymers for use in post-combustion flue gas capture </p>
Dataset related to the article "Epicardial Adipose Tissue-Derived IL-1β Triggers Postoperative Atrial Fibrillation"
<p>This record contains raw data related to the article "Epicardial Adipose Tissue-Derived IL-1β Triggers Postoperative Atrial Fibrillation"</p> <p><strong>Background and aims:</strong> Post-operative atrial fibrillation (POAF), defined as new-onset AF in the immediate period after surgery, is associated with poor adverse cardiovascular events and a higher risk of permanent AF. Mechanisms leading to POAF are not completely understood and epicardial adipose tissue (EAT) inflammation could be a potent trigger. Here, we aim at exploring the link between EAT-secreted interleukin (IL)-1β, atrial remodeling, and POAF in a population of coronary artery disease (CAD) patients. <strong>Methods:</strong> We collected EAT and atrial biopsies from 40 CAD patients undergoing cardiac surgery. Serum samples and EAT-conditioned media were screened for IL-1β and IL-1ra. Atrial fibrosis was evaluated at histology. The potential role of NLRP3 inflammasome activation in promoting fibrosis was explored <em>in vitro</em> by exposing human atrial fibroblasts to IL-1β and IL-18. <strong>Results:</strong> 40% of patients developed POAF. Patients with and without POAF were homogeneous for clinical and echocardiographic parameters, including left atrial volume and EAT thickness. POAF was not associated with atrial fibrosis at histology. No significant difference was observed in serum IL-1β and IL-1ra levels between POAF and no-POAF patients. EAT-mediated IL-1β secretion and expression were significantly higher in the POAF group compared to the no-POAF group. The <em>in vitro</em> study showed that both IL-1β and IL-18 increase fibroblasts' proliferation and collagen production. Moreover, the stimulated cells perpetuated inflammation and fibrosis by producing IL-1β and transforming growth factor (TGF)-β. <strong>Conclusion:</strong> EAT could exert a relevant role both in POAF occurrence and in atrial fibrotic remodeling.</p>
Sentinel-2 derived Sphagnum and herbaceous CI, GCC, NDVI, MSI, SL2P10 LAI, and hourly temperature, water table depth, PAR on the Bernadouze peatland from 2017 to 2021 and 2D scans LAI over 2021.
<p>This release contains data from field campaign over the Bernadouze Peatland and satellite sentinel-2 derived vegetation indices from 2017-01-01 to 2021-12-31.</p> <p>Sentinel-2 derived Sphagnum and herbaceous chlorophyll index, green chromatic coordinate, normalised difference index, moisture soil index retrived on google earth engine from 2017-01-01 to 2021-12-31 on the Bernadouze peatland.</p> <p>Sentinel-2 sphagnum and herbaceous leaf area index (m².m-²) computed with the SL2P10 algorithm from 2017-01-01 to 2021-12-31 thanks to google earth engine.</p> <p>Sphagnum leaf area index (m².m-²), measured with a 2D-scan (LI3100 Area Meter) over the 2021 season on the Bernadouze peatland.</p> <p>Reflectance over the 12 bands of Sentinel-2 on two areas of the Bernadouze peatland : one dominated by Sphagnum mosses and the other by herbaceous vegetation. Data related to an image acquired the 2021-07-21.</p> <p>Hourly air temperature (°C) and photosynthetically active radiations (umol.m-².s-1) derived from the S2M (SAFRAN–SURFEX, ISBA–Crocus–MEPRA) reanalysis chain on the Bernadouze peatland. Vertical resolution of 300m on the 'Couseran' massif.</p> <p>Hourly water table depth (m) from 10 piezometers (PZ1, ..., PZ10) over the Bernadouze peatland. Measured with 10 Orpheus Mini Water Level Logger, OTT HydroMet, Germany.</p> <p>Growth primary productivity of dominant peatland vegetation (umol.m-².s-1) calculated by the difference of measured net primary productivity and of measured ecosystem respiration flux under dark conditions. Measurements of GPP and ER are made with a soil chamber connected to a LI-COR LI-7810 analyser from 2017-01-01 to 2021-12-31 on the Bernadouze peatland.</p>
Data: More than 1000 genotypes are required to derive robust relationships between yield, yield stability and physiological parameters: a computational study on wheat crop
<p>APSIM-Wheat <strong>(</strong><a href="">www.apsim.info</a><strong>)</strong> was used to simulate a data set (for details, see Casadebaig<em> et al.</em>, 2016) with 9100 virtual genotypes (<em>N</em><sub>gen</sub>= 9100) grown under 9000 environments (<em>N</em><sub>env</sub>=9000). In short, virtual genotypes were created by varying the value of 90 independent physiological parameters in a range of ±20% from the reference cultivar <em>Hartog</em>. Environments in the dataset contain historical climate data of 125 years (1889-2013) in four locations (Emerald, Narrabri, Yanco and Merredin) in Australia, in combination with two CO<sub><sup>2</sup></sub> levels (380 and 555 ppm), three nitrogen levels (low: 50%, control: 100% and high fertilization: 100% plus 50 kg‧ha<sup>-1</sup>) and three sowing dates (early, control and late).</p>
Supplementary videos for the "Remote-refocusing light-sheet fluorescence microscopy enables 3D imaging of electromechanical coupling of hiPSC-derived and adult cardiomyocytes in co-culture" manuscript
<p>Supplementary videos for preprint manuscript: </p> <p><em>Remote-refocusing light-sheet fluorescence microscopy enables 3D imaging of electromechanical coupling of hiPSC-derived and adult cardiomyocytes in co-culture</em><br> Liuba Dvinskikh, Hugh Sparks, Liliana Brito, Kenneth T MacLeod, Sian E Harding, Christopher Dunsby<br> bioRxiv 2023.01.28.526043; doi: https://doi.org/10.1101/2023.01.28.526043</p> <p>All videos have been rendered with JPEG compression.</p> <p>Shortened video captions (Please see supplementary information document for full caption)<br> <strong>Video 1:</strong> 3D LSFM timelapse of hiPSC-CM undergoing spontaneous calcium transients. <br> <strong>Video 2:</strong> Widefield transillumination timelapse of hiPSC-CM and adult-CM <br> <strong>Video 3:</strong> Widefield fluorescence timelapse of hiPSC-CM and adult CM with synchronized spontaneous calcium transients. <br> <strong>Video 4a:</strong> 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture undergoing synchronized spontaneous transients. <br> <strong>Video 4b</strong>: Depth-encoded MIPs of the 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture undergoing synchronized spontaneous transients. <br> <strong>Video 5a:</strong> 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture undergoing synchronized spontaneous transients in a sample without NBleb. <br> <strong>Video 5b</strong>: Depth-encoded MIPs of the 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture without NBleb undergoing synchronized spontaneous transients. <br> <strong>Video 6a</strong>: 3D LSFM timelapse of hiPSC-CM and adult-CM co-culture undergoing synchronized spontaneous transients in a sample treated with NBleb. <br> <strong>Video 6b:</strong> Depth-encoded MIPs of the 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture with NBleb undergoing synchronized spontaneous transients. <br> <strong>Video 7a:</strong> 3D LSFM timelapse of hiPSC-CM and adult-CM day 0 co-culture undergoing synchronized spontaneous transients in a sample without NBleb. <br> <strong>Video 7b: </strong>Depth-encoded MIPs of the 3D LSFM timelapse of hiPSC-CM and adult-CM day 0 co-culture without NBleb. </p> <p> </p>
Raw Data for the article: Current Perspectives on Adult Mesenchymal Stromal Cell-Derived Extracellular Vesicles: Biological Features and Clinical Indications
<p>Extracellular vesicles (EVs) constitute one of the main mechanisms by which cells communicate with the surrounding tissue or at distance. Vesicle secretion is featured by most cell types, and adult mesenchymal stromal cells (MSCs) of different tissue origins have shown the ability to produce them. In recent years, several reports disclosed the molecular composition and suggested clinical indications for EVs derived from adult MSCs. The parental cells were already known for their roles in different disease settings in regulating inflammation, immune modulation, or transdifferentiation to promote cell repopulation. Interestingly, most reports also suggested that part of the properties of parental cells were maintained by isolated EV populations. This review analyzes the recent development in the field of cell-free therapies, focusing on several adult tissues as a source of MSC-derived EVs and the available clinical data from in vivo models.</p>
Rates of greenhouse gas (carbon dioxide, methane and nitrous oxide) fluxes, denitrification-derived N2O and N2 fluxes and nitrification-derived N2O fluxes from salt marsh soils in Quebec, Canada and Louisiana, U.S. under ambient and elevated temperature and nutrient loading.
<p>Dataset used in <a href="https://link.springer.com/article/10.1007/s10533-023-01104-0?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20231214&utm_content=10.1007/s10533-023-01104-0#citeas">Elevated temperature and nutrients lead to increased N<sub>2</sub>O emissions from salt marsh soils from cold and warm climates</a>.</p> <p>The dataset contains fluxes calculated from headspace gas samples taken over a 24 hour period from intact soil cores, as well as corresponding environmental data. Intact soil cores (0-15 cm depth, 2.5 cm diameter) were taken at five sampling locations along a 20 m transect using a soil auger or piston corer. Samples were collected along a transect in four marsh sites in Quebec, Canada (La Pocatière: 47°22'24.7"N 70°03'26.3"W) and Louisiana, U.S. (Barataria Basin: 29°33'47.3"N 90°04'22.8"W and 29°29'52.2"N 89°55'00.2"W) from two vegetation types (<em>Sporobolus alterniflorus</em> formerly known as <em>Spartina alterniflora </em>and<em> Sporobolus pumilus</em> formerly known as<em> Spartina patens</em>). In Quebec, the two vegetation zones were in the same marsh whereas in Louisiana two separate marshes, dominated by the relevant vegetation, were chosen. Soil samples were collected on the 20-21<sup>st</sup> July 2021 from Louisiana and the 9-10<sup>th</sup> August 2021 from Quebec. Environmental data was collected including <em>in-situ</em> soil temperature and salinity, and gravimetric soil moisture, extractable soil dissolved organic carbon (DOC), extractable soil total dissolved nitrogen (TDN), extractable soil nitrate, extractable soil ammonium, extractable soil soluble reactive phosphate, soil total carbon, soil total nitrogen, soil carbon to nitrogen ratio, soil d<sup>13</sup>C and soil d<sup>15</sup>N determined from additional 0-15 cm core samples. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement no. 838296, a NSERC Discovery Grant and a Natural Environment Research Council grant number (NE/T012323/1).</p> <p>Stable <sup>15</sup>N tracers were added to the intact soil cores so that at each location, at each treatment level (ambient and elevated, described below), there was one core receiving no tracer for greenhouse gas fluxes, one core receiving <sup>15</sup>N-NO<sub>3</sub><sup>‑ </sup>for denitrification rates and one core receiving <sup>15</sup>N-NH<sub>4</sub><sup>+</sup> for nitrification rates. The cores were incubated at ambient temperature (16 ℃ and 28.1 ℃ for Quebec and Louisiana, respectively) and nutrient concentrations (3.2 NO<sub>3</sub><sup>-</sup>, 2.0 NH<sub>4</sub><sup>+</sup>; 2.9 NO<sub>3</sub><sup>-</sup>, 2.5 NH<sub>4</sub><sup>+</sup>; 0.5 NO<sub>3</sub><sup>-</sup>, 7.3 NH<sub>4</sub><sup>+ </sup>and 5.7 NO<sub>3</sub><sup>-</sup>, 2.8 NH<sub>4</sub><sup>+</sup> mg g wet soil<sup>-1</sup> for Quebec <em>S. alterniflorus</em>, Quebec <em>S. pumilus</em>, Louisiana <em>S. alterniflorus</em> and Louisiana <em>S. pumilus</em>, respectively), and elevated temperature (ambient temperature +5 ℃) and nutrient concentration (double ambient concentration). Gas samples were collected from the headspace of 0-15 cm intact cores in a 20 cm high PVC pipe, capped at the top and bottom to create a 5 cm headspace. Gas samples were analysed for greenhouse gases (GHGs: N<sub>2</sub>O, CH<sub>4</sub>, CO<sub>2</sub>) and <sup>15</sup>N in denitrification-derived N<sub>2</sub>O, denitrification-derived N<sub>2</sub> and nitrification-derived N­<sub>2</sub>O.</p> <p>Soil temperature (YSI 30, Baton Rouge, USA or DeltaTrak 11050, Pleasanton, USA) and porewater salinity (YSI 30, Baton Rouge, USA or portable ATC refractometer) were measured in-situ or in the laboratory using the portable refactometer. Additional soil samples were used for multiple analyses; one subsample was extracted with ultrapure water (18.2 MΩ) for DOC and TDN analysis, one subsample was extracted with 2M KCl for NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup>, one subsample was extracted with Olsen-P solution (0.5 M NaHCO<sub>3</sub>, pH 8.5), for soluble reactive phosphate analysis and one subsample was weighed and dried for soil moisture and then finely ground and analysed for total carbon, total nitrogen, d<sup>13</sup>C and d<sup>15</sup>N.</p> <p>N<sub>2</sub>O, CH<sub>4</sub> and CO<sub>2</sub> concentrations were measured in the gas samples using a gas chromatograph interfaced with a PAL3 autosampler (Agilent 7890A, Agilent Technologies Ltd, USA) fitted with a flame ionisation detector (FID) for CH<sub>4</sub> analysis and a micro electron capture detector (mECD) for N<sub>2</sub>O analysis. CO<sub>2</sub> was methanised to CH<sub>4</sub> before analysis on the FID. The instrument precision as the relative standard deviation was < 5 % for all of the gases, while the minimum detectable concentration difference (MDCD) was 9 ppb N<sub>2</sub>O, 72 ppb CH<sub>4 </sub>and 31 ppm CO<sub>2</sub>. Potential GHG fluxes were calculated from the linear portion or where the highest production was observed in the concentration-time series ( https://doi.org/10.2134/jeq2003.2436). If fluxes were below the MDCD they were set to zero see (https://doi.org/10.1002/2017JG003783). The <sup>15</sup>N content of the N<sub>2</sub> and N<sub>2</sub>O was determined using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with a trace-gas pre-concentrator inlet with autosampler (isoFLOW GHG; Elementar Analysensysteme GmbH, Hanau, Germany), with a standard deviation of d<sup>15</sup>N < 0.05 %. Extractable dissolved organic carbon and total dissolved nitrogen were analysed in soil extractant (ultrapure water 18.2 MΩ, 7:1 of extractant to soil) on a TOC/TDN analyser (TOC VCSn + TMN-1, Shimadzu, Kyoto, Japan), with 50 mg C l<sup>-1</sup> and 10 mg l<sup>-1</sup> standards resulting in accuracy and precision of 0.3 and ±0.3 mg C l<sup>-1</sup>, and 0.5 and ±0.3 mg N l<sup>-1</sup>, respectively. Extractable nitrate+nitrite (assumed to be nitrate) and ammonium were analysed in soil extractant (2M KCl, 5:1 of extractant to soil) using a microplate reader and methods in Sims et al., 1995 (<a href="https://doi.org/10.1080/00103629509369298">https://doi.org/10.1080/00103629509369298</a>) with a limit of detection of 0.1 ppm and accuracy of ±5 %. Extractable phosphate was analysed in soil extractant (Olsen-P solution 0.5M NaHCO­<sub>3</sub>, pH 8.5, 10:1 of extractant to dry soil) using a microplate reader and methods in Jeannotte et al., 2004 (https://doi.org/10.1007/s00374-004-0760-4) with a limit of detection of 1 mg P l<sup>-1</sup> and accuracy of ±6 %. Soil total carbon, total nitrogen, d<sup>13</sup>C and d<sup>15</sup>N analysis was performed using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with an elemental analyser (EA) inlet (vario PYRO cube; Elementar Analysensysteme GmbH, Hanau, Germany). The precision was < 5 % for both C and N and the precision as a standard deviation was < 0.06 % for both d<sup>13</sup>C and d<sup>15</sup>N. Results from the experiments were entered into an Excel spreadsheet for ingestion into the Zenodo data repository.</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)
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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.