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800 results for “mixtures”
Data from: N-mixture models estimate abundance reliably: a field test on Marsh Tit using time-for-space substitution
<p>Imperfect detection in field studies on animal abundance, including birds, is common and can be corrected for in various ways. The binomial N-mixture (hereafter binmix) model developed for this task is widely used in ecological studies owing to its simplicity: it requires replicated count results as the input. However, it may overestimate abundance and be sensitive to even small violations of its assumptions. We used a 33-year dataset on the Marsh Tit, Poecile palustris, a sedentary forest passerine, from Białowieża Forest, Poland to validate inference from binmix models by comparing model-estimated abundances to the true number of breeding pairs within the plots, determined by exhaustive population study. The abundance estimates, derived from six springtime (April-May) counts of males on each plot in each year, were highly reliable: 116 out of 132 year-plot estimates (88%) included the true number of pairs within the 95% confidence intervals. Over- and underestimations were thus rare and similarly frequent (9 and 12 cases, respectively), with a tendency to overestimate at low densities and underestimate at high densities. Marsh Tits sing rarely but the frequency of countersinging increases with abundance, leading to non-independence in detections. When accounted for in a submodel for detection, the per-survey number of countersinging events positively affected detection probability but only weakly affected abundance estimates. Simulations further demonstrate that this property, overestimation at low densities and underestimation at high densities, may be a systematic bias of binmix model even if density-dependent detection is absent. While the behaviour of binmix models in specific situations requires more study, we conclude that these models are a valid tool to estimate abundance reliably when intensive population monitoring is not feasible.</p>
Input files, plotting scripts, and figures for "Smoldering combustion in cellulose and hemicellulose mixtures: Examining the roles of density, fuel composition, oxygen concentration, and moisture content"
<p>This bundle of files contains all the <a href="http://reaxfire.com/trac/gpyro">Gpyro</a> input files, data, plotting scripts, and figures for "Smoldering combustion in cellulose and hemicellulose mixtures: Examining the roles of density, fuel composition, oxygen concentration, and moisture content". The README.txt file contains additional details about the contents.</p>
Combined Biostimulant Applications of Trichoderma spp. with Fatty Acid Mixtures Improve Biocontrol Activity, Horticultural Crop Yield and Nutritional Quality
<p>Raw Data of "Combined Biostimulant Applications of Trichoderma spp. with Fatty Acid Mixtures Improve Biocontrol Activity, Horticultural Crop Yield and Nutritional Quality".</p>
Mixture effects in alkane/cycloalkane hydroconversion over Pt/HUSY : carbon number impact
<p>This repository includes characterization data (H<sub>2</sub>-O<sub>2</sub> titration and N<sub>2</sub> adsorption t-plot) associated with the publication: N.Korica, A.Ben Hassine, H.Dao Thi, L.Bergaoui, K.Van Geem, P.S.F.Mendes, J.De Clercq, J.W.Thybaut “Mixture effects in alkane/cycloalkane hydroconversion over Pt/HUSY : carbon number impact”, submitted to Fuel journal in December 2021.</p> <p>The impact of carbon number of reacting alkanes and cycloalkanes on mixture effects in hydroconversion over Pt/HUSY has been studied by experiments performed on high-throughput setup over Pt/HUSY catalyst with three different Pt loadings and HUSY zeolite with Si/Al molar ratio of 6. The dispersion of platinum over HUSY zeolite was determined by H<sub>2</sub>-O<sub>2</sub> titration, while the specific surface area was determined by t-plot N<sub>2</sub> adsorption. These data are classified based on the platinum loading and the characterization method.</p> <p>0.3 wt%Pt (H2-O2 titration) : The dispersion of 0.3 wt%Pt/HUSY</p> <p>0.3 wt%Pt (N2 ads t-plot) : The specific surface area of 0.3 wt%Pt/HUSY</p> <p>0.1 wt%Pt (H2-O2 titration) : The dispersion of 0.1 wt%Pt/HUSY</p> <p>0.1 wt%Pt (N2 ads t-plot) : The specific surface area of 0.1 wt%Pt/HUSY</p> <p>0.07 wt%Pt (H2-O2 titration) : The dispersion of 0.07 wt%Pt/HUSY</p> <p>0.07 wt%Pt (N2 ads t-plot) : The specific surface area of 0.07 wt%Pt/HUSY</p>
Mixture effects in alkane/cycloalkane hydroconversion over Pt/HUSY : carbon number impact
<p>This repository includes experimental data associated with the publication: N.Korica, A.Ben Hassine, H.Dao Thi, L.Bergaoui, K.Van Geem, P.S.F.Mendes, J.De Clercq, J.W.Thybaut “Mixture effects in alkane/cycloalkane hydroconversion over Pt/HUSY : carbon number impact”, submitted to Fuel journal in December 2021.</p> <p>The impact of carbon number of reacting alkanes and cycloalkanes on mixture effects in hydroconversion over Pt/HUSY has been studied by experiments on high-throughput setup by feeding equimolar mixtures of n-octane and tert-butylcyclohexane, and n-decane and methylcyclohexane. In order to investigate the above-mentioned impact, the kinetic behavior was examined at various experimental conditions, over Pt/HUSY catalyst with three different Pt loadings and HUSY zeolite with Si/Al molar ratio of 6.</p> <p>The process conditions which were used for every feed are summarized:</p> <ul> <li>Pure n-octane <ul> <li>Catalysts : 0.07 and 0.1 wt%Pt/HUSY (poorly- and well-balanced catalyst for pure <em>n</em>-octane)</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of reactant, bar : 0.05</li> </ul> </li> <li>Pure tert-butylcyclohexane <ul> <li>Catalysts : 0.07 and 0.3 wt%Pt/HUSY</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of reactant, bar : 0.05</li> </ul> </li> <li>Equimolar mixture of n-octane and tert-butylcyclohexane <ul> <li>Catalysts : 0.07 and 0.3 wt%Pt/HUSY</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of each reactant, bar : 0.05</li> </ul> </li> <li>Pure n-decane <ul> <li>Catalysts : 0.07 and 0.3 wt%Pt/HUSY (poorly- and well-balanced catalyst for pure <em>n</em>-decane)</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of reactant, bar : 0.05</li> </ul> </li> <li>Pure methylcyclohexane <ul> <li>Catalysts : 0.07 and 0.1 wt%Pt/HUSY</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of reactant, bar : 0.05</li> </ul> </li> <li>Equimolar mixture of n-decane and methylcyclohexane <ul> <li>Catalysts : 0.07 and 0.3 wt%Pt/HUSY</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of each reactant, bar : 0.05</li> </ul> </li> </ul> <p>The kinetics of hydroconversion of different alkane/cycloalkane feeds were compared based on conversion of reactants and yields to isomers. The data are classified based on figures in the Article.</p> <p>Figure 5 : <em>n</em>-Octane conversion as a function of space time at 10 bar pressure - comparison of experiments with pure <em>n</em>-octane and in mixture with methylcycyclohexane and tert-butylcyclohexane</p> <p>Figure 6 : Octane isomer yields as a function of <em>n</em>-octane conversion - comparison of experiments with pure <em>n</em>-octane and in mixture with tert-butylcyclohexane</p> <p>Figure 7 : tert-Butylcyclohexane conversion as a function of space time at 10 bar pressure - comparison of experiments with pure tert-butylcyclohexane and in mixture with <em>n</em>-octane</p> <p>Figure 8 : Butylcyclohexane isomer yields as a function of tert-butylcyclohexane conversion comparison of experiments with pure tert-butylcyclohexane and in mixture with <em>n</em>-octane</p> <p>Figure 9 : <em>n</em>-Decane conversion as a function of space time at 10 bar pressure - comparison of experiments with pure <em>n</em>-decane and in mixture with methylcyclohexane</p> <p>Figure 10 : Decane isomer yields as a function of <em>n</em>-decane conversion - comparison of experiments with pure <em>n</em>-decane and in mixture with methylcyclohexane</p> <p>Figure 11 : Methylcyclohexane conversion as a function of space time at 10 bar pressure - comparison of experiments with pure methylcyclohexane and in mixture with <em>n</em>-decane</p> <p>Figure S9 : <em>n</em>-Octane conversion as a function of space time at 20 bar pressure - comparison of experiments with pure <em>n</em>-octane and in mixture with methylcycyclohexane and tert-butylcyclohexane</p> <p>Figure S10 : <em>n</em>-Octane conversion as a function of space time - comparison of experiments with pure <em>n</em>-octane and in mixture with methylcycyclohexane and tert-butylcyclohexane</p> <p>Figure S13 : tert-Butylcyclohexane conversion as a function of space time at 20 bar pressure - comparison of experiments with pure tert-butylcyclohexane and in mixture with <em>n</em>-octane</p> <p>Figure S14 : tert-Butylcyclohexane conversion as a function of space time - comparison of experiments with pure tert-butylcyclohexane and in mixture with <em>n</em>-octane</p> <p>Figure S15 : <em>n</em>-Decane conversion as a function of space time at 20 bar pressure - comparison of experiments with pure <em>n</em>-decane and in mixture with methylcyclohexane</p> <p>Figure S16 : <em>n</em>-Decane conversion as a function of space time - comparison of experiments with pure <em>n</em>-decane and in mixture with methylcyclohexane</p> <p>Figure S17 : Methylcyclohexane conversion as a function of space time at 20 bar pressure - comparison of experiments with pure methylcyclohexane and in mixture with <em>n</em>-decane</p> <p>Figure S18 : Methylcyclohexane conversion as a function of space time - comparison of experiments with pure methylcyclohexane and in mixture with <em>n</em>-decane</p>
Development of Cost-Effective High-Modulus Asphalt 5. Report Date Aug. 2021 Concrete (HMAC) Mixtures Using Crumb Rubber and Local Construction Materials in Louisiana
<p>One of the emerging solutions to enhance the durability of asphalt pavements is the use of a French asphalt mix<br> known as “High-Modulus Asphalt Concrete (HMAC).” This mix uses a hard asphalt binder, high binder content<br> (about 6%), and low air voids content as compared to Superpave mixtures. The key objective of this study was<br> to develop a cost-effective HMAC mixture using crumb rubber and local materials in Louisiana. To achieve this<br> objective, four HMAC mixtures were prepared using two asphalt binders (PG 82-22 and PG 76-22 plus 10%<br> crumb rubber) and two Reclaimed Asphalt Pavement (RAP) contents (20% and 40%); additionally, a<br> conventional Superpave mixture in Louisiana was prepared as a control mixture. The laboratory performance<br> of these five mixtures was evaluated in terms of workability, dynamic modulus, rutting resistance, and cracking<br> resistance. The AASHTOWare Pavement ME Design software was also used to estimate the long-term field<br> performance of these mixtures. Results indicated that the HMAC mixture prepared with 10% crumb rubber and<br> 20% RAP successfully met the French mix design specifications for HMAC and LaDOTD specifications. This<br> HMAC mix outperformed the control Superpave mix in terms of dynamic modulus, rutting resistance, and<br> cracking resistance. Additionally, this HMAC mixture can reduce the required asphalt thickness by 1.5 or 2<br> inches based on traffic level. The cost-effectiveness analysis indicated that this HMAC mixture was more costeffective<br> than conventional Superpave mixtures in Louisiana. In addition, this mixture is environmentallyfriendly<br> since it can reduce the disposal of scrap tires in landfills.</p>
Chiral flow in a binary mixture of two-dimensional active disks - Supplementary Data
<p>Supplementary data for the paper: "Chiral flow in a binary mixture of two-dimensional active disks"</p> <p> </p> <ol> <li>*_raw_trajectories.pkl.xz files contain tracking data for each experiment as a pickled Pandas DataFrame object (xz compression) in pixel units (particle diameter=77px)</li> <li>*_w.pkl.xz files contain self-rotation velocity for each experiment as a pickled Pandas DataFrame object (xz compression) in rad/s</li> <li>experiments_properties.dat is a summary table containing the average fields (including vorticity and kinetic energy) for all experiments. Also subdivided by species.</li> </ol>
Satellite-derived chlorophyll-a concentrations for Lake Mulargia (Sardinia, Italy) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of Lake Mulargia (Sardinia, Italy) for the period 29 Mar. 2013 - 31 Jan. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
Discovering molecular regulators of ageing using mixture models with RNA-sequencing data
<p>Identifying the molecular regulators that control ageing is challenging because the ageing process is influenced by a combination of genetic and environmental factors which makes it difficult to source the contribution of a single gene. Multiple studies have demonstrated that as humans age, increased gene expression heterogeneity results in the dysregulation of key regulators and pathways. Given the dynamic nature of gene expression, it is vital that this data be modelled by statistical approaches that can appropriately account for changes in variability to understand the contribution of heterogeneity during the aging process and properly identify its regulators. This study demonstrates the utility of using mixture models to model biological variability of gene expression occurring during ageing and how novel potential regulators of ageing can be identified.</p> <p>Our mixture modelling approach was applied to gene expression data from the Genotype-Tissue Expression (GTEx) cohort. For every gene, the expression profile was modelled using a mixture model across the cohort where the subset of donors corresponding to each mode was tested for a significant change in age group. The multi-tissue aspect of GTEx was leveraged to find ageing regulators based on this mixture model approach genes that were common across multiple tissues, suggesting that the regulation of ageing may also be controlled through a set of genes that have non-tissue-specific activity.</p> <p>Our approach identified well-documented ageing regulators <em>mTOR </em>and <em>RICTOR</em> and other potential ageing regulators such as <em>IL4</em> and <em>GPR4</em> which were detected only by our approach. Genes identified by edgeR, DESeq2 and the mixture model-based approach were enriched for similar biological pathways. This suggests that while the specific ageing regulators identified from our approach may be distinct, they generally belong in the same pathways as the genes identified by standard approaches. Overall, these results indicate that modelling gene expression variability using mixture models in conjunction with standard differential gene expression can help uncover new regulators that have a potential role for understanding human ageing.</p> <p>I</p>
Results of surface hot-in place recycling (remix) of modified and alternative asphalt mixtures in Finland. Part II: Bitumen scale
<p>The following material is included in a digital appendix.</p> <ul> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Appendix%201.%20FT-IR.pdf?versionId=6ea761b1-a830-4e01-a761-f103373497ce">Appendix 1</a>:</strong> FT-IR spectra of all bitumens before and after REM/RUT.</li> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Appendix%202.%20Gradations.pdf?versionId=b8b1b0b0-d421-42f8-84ef-728cb1acce33">Appendix 2</a>:</strong> Gradations of the specimens before and after REM/RUT (two specimens per material).</li> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Appendix%203.%20DSR%20test%20results.pdf?versionId=cd3ccf9e-a264-458c-9d54-fcfbca40ec1f">Appendix 3</a>:</strong> DSR test data. Complex shear modulus (G*), phase angle (δ), and Black diagram for all materials before and after REM/RUT.</li> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Appendix%204a.%20DSR%20master%20curves%20(8-mm).pdf?versionId=44f9c881-912f-4f35-b4c7-193d88d5572c">Appendix 4a</a>:</strong> DSR shifted data, and master (fitted) curves, 8-mm, summary before and after REM/RUT.</li> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Appendix%204b.%20DSR%20master%20curves%20(4-mm).pdf?versionId=30952a8f-029b-409f-ba87-da9d9972880b">Appendix 4b</a>:</strong> DSR shifted data, and master (fitted) curves, 4-mm, summary before and after REM/RUT.</li> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Appendix%205.%20WLF%20and%20sigmoidal.pdf?versionId=fb826979-868d-47f4-8acd-697550000f87">Appendix 5</a>:</strong> WLF fitting constants (Table A1), and parameters for sigmoidal curves (Table A2).</li> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Data%20II%20-%20Read%20me.pdf?versionId=c40871da-deff-456f-b4d6-a6136e9c294a">Database</a>:</strong> Laboratory data (Excel files, brief read-me guide) of all tests at bitumen scale. </li> </ul>
Results of surface hot-in place recycling (remix) of modified and alternative asphalt mixtures in Finland. Part I: Mixture scale
<p>The following material is included as a digital appendix:</p> <ul> <li><strong><a href="https://zenodo.org/api/files/ecff4755-8652-4c3b-9dcf-28cf09002c98/Appendix.%20Cores%20and%20plots%20of%20test%20results.pdf?versionId=39468d84-e556-41c4-b86c-0ccdb2d5a36e">Appendix</a>. </strong>Images of selected cores, and test results for all specimens at the mixture scale (plots): bulk density, air voids, strength, stiffness, Prall abrasion, and creep permanent deformation.</li> <li><a href="https://zenodo.org/api/files/ecff4755-8652-4c3b-9dcf-28cf09002c98/Data%20I%20-%20Read%20me.pdf?versionId=e500484c-10df-417a-bc1c-d2017d70d64f"><strong>Database</strong></a> with laboratory test data and measurements (mixture scale). Two Excel files and brief read-me guide.</li> </ul>
The design of optimal mixtures from atom groups using Generalized Disjunctive Programming
<p>The files contain all the mixture design problems implemented in GAMS for this publication.</p> <p>All problems are solved in GAMS version 24.8.3 and are run on a single core of a dual 6 core Intel Xeon E5-1660 machine at<br> 3.30 GHz.</p> <p> </p>
Replication Data for: Boolean Circuits in Colloidal Mixtures of ZnO and Proteinoids
<h2><span>Replication Data for: Boolean Circuits in Colloidal Mixtures of ZnO and Proteinoids</span></h2>
FIGURE 3 in Mixture formation in a partially stratified directly injected natural gas engine
FIGURE 3: The effect of varied gall density (low vs high) on leaves of T. cordata and T. tomentosa infested by P. tetratrichus on the total level of flavonols (a), anthocyanins (b) and tannins (c). Different letters (lower case for T. cordata and capital for T. tomentosa) above the bars indicate statistically significant differences among treatments (Kruskal-Wallis non-parametric test or Tukey's HSD test, P=0.05). The asterisks indicate statistically significant differences among the control treatments for T. cordata and T. tomentosa (Student's t-test; * – P<0.05; ** – P<0.01). Means SD of untransformed data are shown.
FIGURE 2 in Mixture formation in a partially stratified directly injected natural gas engine
FIGURE 2: The cross-sections through the edgerolling on T. cordata (a – d) and erineum on T. tomenosa (e-f) leaves at the early phase of development (a-c) and at the more expanded phase (d – f). Phenolics (deep-red in colour) are localised in the cells of the outer layer of the nutritive tissue of both gall types. Starch grains (arrow) dominate within the cells of hypertrophied parenchyma (d). The P. tetratrichus specimens are localised within distinguishable cavities of roll-gall (b, c, d). The presence of red coloured deposits within eriophyoid bodies (d, arrow) suggests that phenolics can be sequestered. Magnification: 90x (a, b, c, e, f); 180x (d).
FIGURE 1 in Mixture formation in a partially stratified directly injected natural gas engine
FIGURE 1: The effect of P. tetratrichus feeding on linden leaf morphology: edgerollings on T. cordata leaves at a low (a) and high (b) density and erinea on T. tomentosa leaves at a low (c) and high (d) density visible from the upper side of the leaf blade.
MAST: Phylogenetic inference with mixtures across sites and trees (revised)
<p>Hundreds or thousands of loci are now routinely used in modern phylogenomic studies. Concatenation approaches to tree inference assume that there is a single topology for the entire dataset, but different loci may have different evolutionary histories due to incomplete lineage sorting, introgression, and/or horizontal gene transfer; even single loci may not be treelike due to recombination. To overcome this shortcoming, we introduce an implementation of a multi-tree mixture model that we call MAST. This model extends a prior implementation by Boussau et al. (2009) by allowing users to estimate the weight of each of a set of pre-specified bifurcating trees in a single alignment. The MAST model allows each tree to have its own weight, topology, branch lengths, substitution model, nucleotide or amino acid frequencies, and model of rate heterogeneity across sites. We implemented the MAST model in a maximum-likelihood framework in the popular phylogenetic software, IQ-TREE. Simulations show that we can accurately recover the true model parameters, including branch lengths and tree weights for a given set of tree topologies, under a wide range of biologically realistic scenarios. We also show that we can use standard statistical inference approaches to reject a single-tree model when data are simulated under multiple trees (and vice versa). We applied the MAST model to multiple primate datasets and found that it can recover the signal of incomplete lineage sorting in the Great Apes, as well as the asymmetry in minor trees caused by introgression among several macaque species. When applied to a dataset of four Platyrrhine species for which standard concatenated maximum likelihood and gene tree approaches disagree, we observe that MAST gives the highest weight (i.e. the largest proportion of sites) to the tree also supported by gene tree approaches. These results suggest that the MAST model is able to analyse a concatenated alignment using maximum likelihood while avoiding some of the biases that come with assuming there is only a single tree. We discuss how the MAST model can be extended in the future.</p>
Statistical Analysis of Selected Mixtures of Alkali-activated Materials Exposed to Thermal
<p>Alkaline-activated materials have come to the forefront of the interest of construction experts in recent decades.<br>It is a composite material, an alternative to concrete, where the binder component can be a by-product (with pozzolanic or<br>latent hydraulic properties) of some industrial processes instead of ecologically disadvantageous cement. The work is<br>focused on the statistical analysis of the compressive strength of alkali-activated materials exposed to temperature stress,<br>where the first part contains three mixtures of alkali-activated composites that were subjected to the frost resistance test,<br>and the second part follows the functional dependence of the compressive strength of the selected mixture of the alkaliactivated system on exposure to high temperatures with the selection of the optimal statistical model.</p>
Following the mixtures of organic micropollutants with in-vitro bioassays in a large lowland river from source to sea - bioassays CRC
<p>Supportive material for the submitted paper: </p> <p><strong><em><span>Following the mixtures of organic micropollutants with in-vitro bioassays in a large lowland river from source to sea </span></em></strong></p> <p><span>from Hommel et al.</span></p> <p><span>The data includes the with R automated evaluation of the AhR-CALUX, AREc32, ERa-GeneBLAzer and SH-SY5Y assay with respective plots and excel files of the concentrations response curves.</span></p>
Thermodynamic (p, ρ, T) characterization of a reference high-calorific natural gas mixture when hydrogen is added up to 20 % (mol/mol)
<p>File: 1-s2.0-S0360319924017166-mmc1.docx</p> <p>This file (DOCX) is a supplementary file that contains the mixture preparation chart and auxiliary mixture validation data.</p> <p>File: 2024_IJHE_Results_Repository.xlsx</p> <p>This file (XLSX) contains tables with the density data of the systems investigated.</p> <p>File: 2024_IJHE_Manuscript_repository.docx</p> <p>This is an author-created, un-copyedited version of an article accepted for publication in the International Journal of Hydrogen Energy (2024, 70, 118-135). The editor of the Journal is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher-authenticated, Open-Access version is available online at: https://doi.org/10.1016/j.ijhydene.2024.05.028</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.