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6,175 results for “Composite”
Figure 1 in Composition and structure of plant communities in the Moist Temperate Forest Ecosystem of the Hindukush Mountains, Pakistan
Figure 1. Samping sites in Lalkoo valley.
Figure 11 in Investigation of roost composition of passerine birds in different environmental conditions
Figure 11. Roost composition in the Khanewal of UC-6. S.E. = Standard Error.
Figure 12 in Investigation of roost composition of passerine birds in different environmental conditions
Figure 12. Khanewal: UC 7; Roost composition of the four birds. S.E. = Standard Error.
Figure 7 in Investigation of roost composition of passerine birds in different environmental conditions
Figure 7. Roost composition of birds in Lahore Road, Sheikhupura. S.E. = Standard Error.
Figure 4 in Investigation of roost composition of passerine birds in different environmental conditions
Figure 4. Roost composition of birds in Toba Tek Singh (UC 286). S.E. = Standard Error.
Figure 2 in Investigation of roost composition of passerine birds in different environmental conditions
Figure 2. PARS Campus for the roost composition of birds. S.E. = Standard Error.
Fig. 1 in The herpetofauna of the Baja California Peninsula and its adjacent islands, Mexico: composition, distribution, and conservation status
Fig. 1. Geographic regions of the Baja California Peninsula, Mexico.
Fig. 10 in The herpetofauna of the Baja California Peninsula and its adjacent islands, Mexico: composition, distribution, and conservation status
Fig. 10. View of the Magdalena Region at La Purisima, Baja California Sur. Photo by Gerardo Marrón.
Fig. 1 in Zooplankton species composition of Cladocera and Cyclopoida (Copepoda) in the Vaya Lake
Fig. 1. Map of the Vaya Lake with the location of the stations.
Dataset - Biogas composition from agricultural sources and organic fraction of municipal solid waste
<p>Data in this file compiled by H. Madi, A. Calbry-Muzyka, F. Rüsch-Pfund, M. Gandiglio, and S. Biollaz as Supplementary Information for "Biogas composition from agricultural sources and organic fraction of municipal solid waste", 2021. Table format in this file adapted from D. Papadias et al. at Argonne National Labs, www.cse.anl.gov/FCs_on_biogas/Impurities%20-%20LFG.xls and www.cse.anl.gov/FCs_on_biogas/Impurities%20-%20WWTP.xls (see also doi.org/10.1016/j.energy.2012.06.031).</p>
A multimodal data-set of a unidirectional glass fibre reinforced polymer composite
<p>Please cite the following article when using the data-sets hereby shared:</p> <p>Emerson, M.J., Dahl, V.A., Conradsen, K., Mikkelsen, L.P. and Dahl, A.B., 2018. A multimodal data-set of a unidirectional glass fibre reinforced polymer composite. <em>Data in brief</em>, <em>18</em>, pp.1388-1393.</p> <p>These data-sets were used for validating the use of X-ray tomography and our dictionary-based probabilistic method for detection of individual fibres, for more information see the following article:</p> <p>Emerson, M.J., Dahl, V.A., Conradsen, K., Mikkelsen, L.P. and Dahl, A.B., 2018. Statistical validation of individual fibre segmentation from tomograms and microscopy. <em>Composites Science and Technology</em>, <em>160</em>, pp.208-215.</p>
The role of competition in structuring ant community composition across a tropical forest disturbance gradient
<b>Description: </b><p>Leaf litter ant community composition and competition</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/34"><b>The role of competition in structuring ant community composition across a tropical forest disturbance gradient.</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=1">here</a></p><p><b>Data worksheets: </b>There are 3 data worksheets in this dataset:</p><ol><li><p><b>Ant community composition</b> (Worksheet Composition)</p><p>Dimensions: 430 rows by 69 columns</p><p>Description: Site x species matrix of ant community composition</p><p>Fields: </p><ul><li><b>Forest Type</b>: Shows the two forest types used in the study (Field type: Categorical)</li><li><b>Site</b>: Represents the site/day sampled. 10 Sampling sites were used in each forest type (Field type: Location)</li><li><b>Time</b>: Represents the time in the day points were sampled (Field type: Time)</li><li><b>Point</b>: Represents the column of 3 sampling points for each time of day (Field type: Replicate)</li><li><b>ID</b>: Represents individual sampling point. Order is: Site(Day)/Time/Type/Sampling point no. Logged ID's also have LF at the start (Field type: ID)</li><li><b>Method</b>: Method used to record community (Field type: Categorical)</li><li><b>Diacamma</b>: Number of individuals (Field type: Abundance)</li><li><b>Odontoponera</b>: Number of individuals (Field type: Abundance)</li><li><b>Pheidole</b>: Number of individuals (Field type: Abundance)</li><li><b>Leptogenys</b>: Number of individuals (Field type: Abundance)</li><li><b>Pheidologeton</b>: Number of individuals (Field type: Abundance)</li><li><b>Crematogaster</b>: Number of individuals (Field type: Abundance)</li><li><b>Odontomachus</b>: Number of individuals (Field type: Abundance)</li><li><b>Aphaenogaster</b>: Number of individuals (Field type: Abundance)</li><li><b>Acanthomyrmex</b>: Number of individuals (Field type: Abundance)</li><li><b>Nylanderia</b>: Number of individuals (Field type: Abundance)</li><li><b>Camponotus</b>: Number of individuals (Field type: Abundance)</li><li><b>Cardiocondyla</b>: Number of individuals (Field type: Abundance)</li><li><b>Anochetus</b>: Number of individuals (Field type: Abundance)</li><li><b>Technomyrmex</b>: Number of individuals (Field type: Abundance)</li><li><b>Monomorium</b>: Number of individuals (Field type: Abundance)</li><li><b>Recurvidris</b>: Number of individuals (Field type: Abundance)</li><li><b>Polyrhachis</b>: Number of individuals (Field type: Abundance)</li><li><b>Cladomyrma</b>: Number of individuals (Field type: Abundance)</li><li><b>Lophomyrmex</b>: Number of individuals (Field type: Abundance)</li><li><b>Harpegnathos</b>: Number of individuals (Field type: Abundance)</li><li><b>Carebara</b>: Number of individuals (Field type: Abundance)</li><li><b>Cataulacus</b>: Number of individuals (Field type: Abundance)</li><li><b>Pachycondyla</b>: Number of individuals (Field type: Abundance)</li><li><b>Lordomyrma</b>: Number of individuals (Field type: Abundance)</li><li><b>Myrmecina</b>: Number of individuals (Field type: Abundance)</li><li><b>Proatta</b>: Number of individuals (Field type: Abundance)</li><li><b>Euprenolepis</b>: Number of individuals (Field type: Abundance)</li><li><b>Rhytidoponera</b>: Number of individuals (Field type: Abundance)</li><li><b>Paratrechina</b>: Number of individuals (Field type: Abundance)</li><li><b>Paraparatrechina</b>: Number of individuals (Field type: Abundance)</li><li><b>Tetramorium</b>: Number of individuals (Field type: Abundance)</li><li><b>Paratopula</b>: Number of individuals (Field type: Abundance)</li><li><b>Strumigenys</b>: Number of individuals (Field type: Abundance)</li><li><b>Pyramica</b>: Number of individuals (Field type: Abundance)</li><li><b>Ponera</b>: Number of individuals (Field type: Abundance)</li><li><b>Hypoponera</b>: Number of individuals (Field type: Abundance)</li><li><b>Tetraponera</b>: Number of individuals (Field type: Abundance)</li><li><b>Emeryopone</b>: Number of individuals (Field type: Abundance)</li><li><b>Centromyrmex</b>: Number of individuals (Field type: Abundance)</li><li><b>Tapinoma</b>: Number of individuals (Field type: Abundance)</li><li><b>Myrmicaria</b>: Number of individuals (Field type: Abundance)</li><li><b>Rotrastruma</b>: Number of individuals (Field type: Abundance)</li><li><b>Prionopelta</b>: Number of individuals (Field type: Abundance)</li><li><b>Gnamptogenys</b>: Number of individuals (Field type: Abundance)</li><li><b>Eurhopalothrix</b>: Number of individuals (Field type: Abundance)</li><li><b>Myrmoteras</b>: Number of individuals (Field type: Abundance)</li><li><b>Oecophylla</b>: Number of individuals (Field type: Abundance)</li><li><b>Myopias</b>: Number of individuals (Field type: Abundance)</li><li><b>Pseudolasius</b>: Number of individuals (Field type: Abundance)</li><li><b>Plagiolepis</b>: Number of individuals (Field type: Abundance)</li><li><b>Dacetinops</b>: Number of individuals (Field type: Abundance)</li><li><b>Mystrium</b>: Number of individuals (Field type: Abundance)</li><li><b>Echinopla</b>: Number of individuals (Field type: Abundance)</li><li><b>Philidris</b>: Number of individuals (Field type: Abundance)</li><li><b>Vollenhovia</b>: Number of individuals (Field type: Abundance)</li><li><b>Rhoptromyrmex</b>: Number of individuals (Field type: Abundance)</li><li><b>Anillomyrma</b>: Number of individuals (Field type: Abundance)</li><li><b>Cryptopone</b>: Number of individuals (Field type: Abundance)</li><li><b>Aenictus</b>: Number of individuals (Field type: Abundance)</li><li><b>Calyptomyrmex</b>: Number of individuals (Field type: Abundance)</li><li><b>Amblyopone</b>: Number of individuals (Field type: Abundance)</li><li><b>Prenolepis</b>: Number of individuals (Field type: Abundance)</li></ul><br></li><li><p><b>Morphometrics</b> (Worksheet Morpho)</p><p>Dimensions: 72 rows by 4 columns</p><p>Description: Size classes for the genera</p><p>Fields: </p><ul><li><b>Genera</b>: Genus ID (Field type: Taxa)</li><li><b>Size.Min</b>: Minimum body size (Field type: Categorical Trait)</li><li><b>Size.Max</b>: Maximum body size (Field type: Categorical Trait)</li></ul><br></li><li><p><b>Competition</b> (Worksheet Competition)</p><p>Dimensions: 866 rows by 15 columns</p><p>Description: Outcome of competitive interactions among individuals of different genera</p><p>Fields: </p><ul><li><b>Forest Type</b>: Shows the two forest types used in the study (Field type: Categorical)</li><li><b>Site</b>: Represents the site/day sampled. 10 Sampling sites were used in each forest type (Field type: Location)</li><li><b>Time</b>: Represents the time in the day points were sampled (Field type: Time)</li><li><b>ID</b>: Represents individual sampling point. Order is: Site(Day)/Time/Type/Sampling point no. Logged ID's also have LF at the start (Field type: ID)</li><li><b>Method</b>: Method used to record interaction (Field type: Categorical)</li><li><b>Genera1</b>: Genus ID of the first interacting individual (Field type: Taxa)</li><li><b>Genera2</b>: Genus ID of the second interacting individual (Field type: Taxa)</li><li><b>TimeG1</b>: The arrival time of the first genus in the interaction to the bait card in seconds (Field type: Numeric)</li><li><b>TimeG2</b>: The arrival time of the second genus in the interaction to the bait card in seconds (Field type: Numeric)</li><li><b>IntG1</b>: The competitive status of the first genus in the interaction (Field type: Categorical Interaction)</li><li><b>IntG2</b>: The competitive status of the second genus in the interaction (Field type: Categorical Interaction)</li><li><b>Interaction</b>: The type of interaction occuring between the two genera (Field type: Categorical Interaction)</li><li><b>GroupG1</b>: Whether or not the first genus was part of a group of individuals when interacting on the bait card (Field type: Categorical)</li><li><b>GroupG2</b>: Whether or not the second genus was part of a group of individuals when interacting on the bait card (Field type: Categorical)</li></ul><br></li></ol><p><b>Date range: </b>2016-02-02 to 2016-06-05</p><p><b>Latitudinal extent: </b>4.7273 to 4.7463</p><p><b>Longitudinal extent: </b>116.9669 to 117.5969</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Hymenoptera<br> -  -  -  - Formicidae<br> -  -  -  -  - <i>Acanthomyrmex</i><br> -  -  -  -  - <i>Aenictus</i><br> -  -  -  -  - <i>Amblyopone</i><br> -  -  -  -  - <i>Anillomyrma</i><br> -  -  -  -  - <i>Anochetus</i><br> -  -  -  -  - <i>Aphaenogaster</i><br> -  -  -  -  - <i>Calyptomyrmex</i><br> -  -  -  -  - <i>Camponotus</i><br> -  -  -  -  - <i>Cardiocondyla</i><br> -  -  -  -  - <i>Carebara</i><br> -  -  -  -  - <i>Cataulacus</i><br> -  -  -  -  - <i>Centromyrmex</i><br> -  -  -  -  - <i>Cladomyrma</i><br> -  -  -  -  - <i>Crematogaster</i><br> -  -  -  -  - <i>Cryptopone</i><br> -  -  -  -  - <i>Dacetinops</i><br> -  -  -  -  - <i>Diacamma</i><br> -  -  -  -  - <i>Echinopla</i><br> -  -  -  -  - <i>Emeryopone</i><br> -  -  -  -  - <i>Euprenolepis</i><br> -  -  -  -  - <i>Eurhopalothrix</i><br> -  -  -  -  - <i>Gnamptogenys</i><br> -  -  -  -  - <i>Harpegnathos</i><br> -  -  -  -  - <i>Hypoponera</i><br> -  -  -  -  - <i>Leptogenys</i><br> -  -  -  -  - <i>Lophomyrmex</i><br> -  -  -  -  - <i>Lordomyrma</i><br> -  -  -  -  - <i>Monomorium</i><br> -  -  -  -  - <i>Myopias</i><br> -  -  -  -  - <i>Myrmecina</i><br> -  -  -  -  - <i>Myrmicaria</i><br> -  -  -  -  - <i>Myrmoteras</i><br> -  -  -  -  - <i>Mystrium</i><br> -  -  -  -  - <i>Nylanderia</i><br> -  -  -  -  - <i>Odontomachus</i><br> -  -  -  -  - <i>Odontoponera</i><br> -  -  -  -  - <i>Oecophylla</i><br> -  -  -  -  - <i>Pachycondyla</i><br> -  -  -  -  - <i>Paraparatrechina</i><br> -  -  -  -  - <i>Paratopula</i><br> -  -  -  -  - <i>Paratrechina</i><br> -  -  -  -  - <i>Pheidole</i><br> -  -  -  -  - <i>Pheidologeton</i><br> -  -  -  -  - <i>Philidris</i><br> -  -  -  -  - <i>Plagiolepis</i><br> -  -  -  -  - <i>Polyrhachis</i><br> -  -  -  -  - <i>Ponera</i><br> -  -  -  -  - <i>Prenolepis</i><br> -  -  -  -  - <i>Prionopelta</i><br> -  -  -  -  - <i>Proatta</i><br> -  -  -  -  - <i>Pseudolasius</i><br> -  -  -  -  - <i>Pyramica</i><br> -  -  -  -  - <i>Recurvidris</i><br> -  -  -  -  - <i>Rhoptromyrmex</i><br> -  -  -  -  - <i>Rhytidoponera</i><br> -  -  -  -  - [<i>Rotrastruma</i>]<br> -  -  -  -  - <i>Strumigenys</i><br> -  -  -  -  - <i>Tapinoma</i><br> -  -  -  -  - <i>Technomyrmex</i><br> -  -  -  -  - <i>Tetramorium</i><br> -  -  -  -  - <i>Tetraponera</i><br> -  -  -  -  - <i>Vollenhovia</i><br></div><p></p>
Data from a field plot experiment with the canola pathogen Leptosphaeria maculans including disease severity at the leaf spot and canker stages of the epidemic, and population composition as isolates infectivity pathotypes.
<p><strong>Data set</strong></p> <p>Data are from an experiment simulating how differences in <em>Brassica napus</em> resistance deployment strategies and landscape connectivity influence epidemic severity and pathogen population composition of the fungus <em>Leptosphaeria maculans</em> on field plots inoculated with combinations of stubble in 2016 at CSIRO Canberra, ACT, Australia. Disease severity was assessed on the 60 field plots [Data_severity.csv] and 1490 isolates were sampled and assessed for infectivity [Data_infectivity.csv]. This dataset is described and analyzed in Bousset et al. (2018).</p> <p>Treatments were factorial combination of Resistance, Genetic Connectivity and Spatial Connectivity, replicated in 4 blocks (B1 to B4). Resistance has 3 categories (Rlm4, Rlm6 LepR1) differing by the resistance genes in oilseed rape varieties. Genetic Connectivity has 2 levels (HighGC, LowGC) differing by the pre-adaptation of the stubble populations to the host variety. Spatial Connectivity has two levels (HighSC, LowSC) differing by the stubble load. Control plots had NoStubble.</p> <p><strong>Data files</strong></p> <p>[Data_severity.csv] Disease severity was assessed on the 60 field plots at leaf spot and canker stages of the epidemic. Leaf spots data are counts. Canker data are numbers of stems in 12 categories defined by the cankered area on cross section (0 = no canker to 100 = fully cankered).</p> <p>[Data_infectivity.csv] Two types of isolates (122 from 3 stubble sources with contrasting preadaptation and 1368 from leaves sampled on 50 field plots) were tested for infectivity response (V = infective; A = non-infective) on the three host varieties (Rlm4, Rlm6 LepR1), at the seedling stage in greenhouse.</p> <p><strong>Associated publication</strong></p> <p>Bousset L, Sprague S, Thrall PH, Barrett LG (2018). Spatio-temporal connectivity and host resistance influence evolutionary and epidemiological dynamics of the canola pathogen <em>Leptosphaeria maculans. Evolutionary Applications</em> [ DOI: 10.1111/eva.12630 ].</p> <p><strong>Funding information</strong></p> <p>This work benefited from the financial support of INRA – the French National Institute for Agronomical Research, a CSIRO Sir Frederick McMaster fellowship to L. Bousset (Impact of inoculum carry-over on landscape dynamics of the blackleg canola pathogen) and the Grains Research & Development Corporation (GRDC Grant CSP00192)</p>
Ex-situ X-ray computed tomography data from two regions of non-crimp fabric based fibre composite under fatigue loading
<p>Ex-situ X-ray CT fatigue testing data published with data in brief: </p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). <em>Ex-situ X-ray computed tomography, tension clamp and in-situ transilluminated white light imaging data of non-crimp fabric based fibre composite under fatigue loading. Data in Brief.</em></p> <p>as a part of the below journal paper.</p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). Uncovering the fatigue damage initiation and progression in uni-directional non-crimp fabric reinforced polyester composite. Composites Part A.</p> <p>If using the data, please refer to one of the two.</p>
Tension clamp X-ray CT data of non-crimp fabric based fibre composite under fatigue loading
<p>Tension clamp X-ray CT fatigue testing data published with data in brief: </p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). <em>Ex-situ X-ray computed tomography, tension clamp and in-situ transilluminated white light imaging data of non-crimp fabric based fibre composite under fatigue loading. Data in Brief.</em></p> <p>as a part of the below journal paper.</p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). Uncovering the fatigue damage initiation and progression in uni-directional non-crimp fabric reinforced polyester composite. Composites Part A.</p> <p>If using the data, please refer to one of the two.</p>
Additional data for the "In-situ full field measurement during inter-facial debonding in single fiber composite under transverse load"
<p>The following document is an extension of the <em>In-situ full field measurement during inter-facial debonding in single fiber composite under transverse load</em> publication. It contains guidelines for the experimental results for the single fiber experiment of epoxy matrix and PTFE fiber, epoxy matrix and galvanized steel matrix, modified epoxy matrix and PTFE fiber and modified epoxy matrix and galvanized steel matrix. The detailed data from the experiments is provided with this document as <em>CSV </em>files.</p>
Dataset on the characterization of the flavor of two red wine varieties using sensory descriptive analysis, volatile organic compounds quantitative analysis by GC-MS and odorant composition by GC-MS-O
<p>The dataset contains data that were collected on 2 sets of 8 French red wines from two grape varieties, Pinot Noir (PN) and Cabernet Franc (CF). It provides, for the 16 wines, (i) sensory descriptive data obtained with a trained panel, (ii) volatile organic compounds (VOC) quantification data obtained by Gas Chromatography–Mass Spectrometry (GC-MS) and (iii) odorant composition obtained by Gas Chromatography–Mass Spectrometry–Olfactometry (GC-MS-O).</p> <p> </p> <p>The dataset is a Microsoft Excel Worksheet containing 8 sheets.</p> <p>- Sheet 1: Information</p> <p>Gives information about the sheets contained in this .xlsx file</p> <p>- Sheet 2: Experimental_factors</p> <p>Each row represents a wine</p> <p>Each column corresponds to an experimental factors of the wines (Grape variety, Vintage and Protected Designation of Origin)</p> <p>- Sheet 3: List_sensory_descriptors</p> <p>Lists the 33 sensory descriptors used for the sensory descriptive analysis of the wines</p> <p>- Sheet 4: Sensory_descriptive_analysis</p> <p>Each row represents a wine</p> <p>Each column corresponds to a condition (2640 columns)</p> <p>Senso_(ortho or retro)_(Panelist1 to Panelist 16)_(1 to 33 Sensory descriptors)_(1 to 3 repetitions for ortho and 1 to 2 repetitions for retro)</p> <p>For the ortho (orthonasal) measurements, there is 16 panelists, 33 sensory descriptors and 3 repetitions = 1584 columns</p> <p>For the retro (retronasal) measurements, there is 16 panelists, 33 sensory descriptors and 2 repetitions = 1056 columns</p> <p>Each cell contains a sensory measurement for the corresponding condition in the corresponding wine</p> <p>- Sheet 5: List_VOC</p> <p>Lists the 45 VOC quantified in the wines with their corresponding CAS number</p> <p>VOC: Volatil Organic Compounds</p> <p>- Sheet 6: VOC_quantification</p> <p>Each row represents a wine</p> <p>Each column corresponds to a VOC (45 columns)</p> <p>Each cell contains the quantification of the corresponding VOC in the corresponding wine</p> <p>- Sheet 7: List_GC-MS-O</p> <p>Lists the 49 odor-active compounds identified with their corresponding CAS number and the 34 compounds identified by their apex indice</p> <p>- Sheet 8: GC-MS-O</p> <p>Each row represents a wine</p> <p>Each column corresponds to an odor-active compound identified by its CAS number or by its Apex indice if the compound was not identify (81 odor-active compounds) + the number of judges who smelled the compound and its description (by 8 judges) = 9 columns per odor-active compound for a total of 729 columns</p>
Influence of temperature on the molecular composition of ions and charged clusters during pure biogenic nucleation
<p>Data of Figures in manuscript:</p> <p>Influence of temperature on the molecular composition of ions and charged clusters during pure biogenic nucleation.</p> <p>Frege et al., Atmos. Chem. Phys. 18, 65–79, 2018</p> <p>https://doi.org/10.5194/acp-18-65-2018</p> <p> </p>
Alite-Belite-Ye'elimite cements: effect of dopants on the clinker composition and properties
<p><strong>ABSTRACT:</strong> Clinkers with alite-belite-ye´elimite (ABY) are considered as eco-friendly cements. Clinkering (2 Kg) of two types of ABY clinkers was optimized: standard-ABY, with CaF<sub>2</sub> and ZnO as dopants (32.6 wt% alite, 30.4 wt% b-belite and 15.6 wt% ye'elimite) and αABY, with CaF<sub>2</sub>, ZnO, B<sub>2</sub>O<sub>3</sub>, Na<sub>2</sub>O (14.4 wt% alite, 37.1 wt% b-belite, 18.5 wt% α'<sub>H</sub>-belite and 7.5 wt% ye'elimite). Zn and F were mainly incorporated in alite and ferrite, while S sulfur was mostly incorporated into Fluor-ellestadite, ye’elimite and, in lower percentages, in belite and ferrite, determined by EDS semi-quantitative. The effect of dopants on the hydration of the cement mortars has been tested. Thus, standard-ABY develops 29.9 MPa compressive strengths at 1 day while αABY does 16.8 MPa, mainly due to the higher amount of alite of the former. The presence of α’<sub>H</sub>-belite in αABY increases mechanical strengths at 28 days of hydration from 44.7 MPa of standard-ABY to 74.9 MPa.</p>
A combined experimental/numerical investigation on hygrothermal aging of fiber-reinforced composites
<p>The x-ray scan data and videos used in the following paper to where the references should be given:</p> <p>Rocha, I.B.C.M., van der Meer, F.P., Raijmaekers, S., Lahuerta, F., Nijssen, R.P.L., Mikkelsen, L.P., Sluys, L.J., A combined experimental/numerical investigation on hygrothermal aging of fiber-reinforced composites, European Journal of Mechanics / A Solids, doi: <a href="https://doi.org/10.1016/j.euromechsol.2018.10.003">https://doi.org/10.1016/j.euromechsol.2018.10.003</a>, 2018.</p> <p>More details on the scans can be found in the paper.</p> <p>The following relations is between the files and the figures from the paper</p> <p>Fig 7: YouTube:<a href="https://youtu.be/o4NLVGRgTW8">https://youtu.be/o4NLVGRgTW8</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/LFoV-Ref-036_036-LFOV-2.5mm-x4_recon.mpg?versionId=d8c523f6-9cf7-4e38-8e8c-1e6fe0bd2876">LFoV-Ref-036_036-LFOV-2.5mm-x4_recon.mpg</a><br> Fig 8a: YouTube:<a href="https://youtu.be/hbxVToJD7Mc">https://youtu.be/hbxVToJD7Mc</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/SFoV-Ref-Surf-165b-side-0.5-20x_recon.mpg?versionId=edb0f8c2-6eb8-42c1-a968-ed44f247f53a">SFoV-Ref-Surf-165b-side-0.5-20x_recon.mpg </a><br> Fig 8b: YouTube:<a href="https://youtu.be/keVhRX78m7o">https://youtu.be/keVhRX78m7o</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/SFoV-Ref-Center-165b-center-0.5-20x_recon.mpg?versionId=ec6fb064-14f2-40f4-9aba-e3eb9416e427">SFoV-Ref-Center-165b-center-0.5-20x_recon.mpg </a><br> Fig 9: YouTube:<a href="https://youtu.be/N-ji_cDbEXc">https://youtu.be/N-ji_cDbEXc</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/LFoV-65C-179a-2863-179a_recon.mpg?versionId=527e02a2-9a69-4ad5-bc51-a6096dd0ecc6">LFoV-65C-179a-2863-179a_recon.mpg </a><br> Fig 10a: YouTube:<a href="https://youtu.be/T-Yq3fkGxzA">https://youtu.be/T-Yq3fkGxzA</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/SFoV-65C-Surf-179a-center-and-side_179a-side-0.5mm-x20_recon.mpg?versionId=9a15a8ad-c55b-44d0-bece-6ee8f093179c">SFoV-65C-Surf-179a-center-and-side_179a-side-0.5mm ...</a><br> Fig 10b: YouTube:<a href="https://youtu.be/4fZqXQY2VOs">https://youtu.be/4fZqXQY2VOs</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/SFoV-65C-Center-179a-center-and-side_179a-center-0.5mm-x20_recon.mpg?versionId=33951d51-6763-4cf1-a88b-8e793813d4be">SFoV-65C-Center-179a-center-and-side_179a-center-0 ...</a><br> Fig 11a: YouTube:<a href="https://youtu.be/4L8BCa6tgFA">https://youtu.be/4L8BCa6tgFA</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/SFoV-50C-Surf-2863-165a-side-0.5-20x_recon.mpg?versionId=147fb3ff-3b7a-4aed-971d-8085bcc821da">SFoV-50C-Surf-2863-165a-side-0.5-20x_recon.mpg </a><br> Fig 11b: YouTube:<a href="https://youtu.be/uBWqHxYG3GU">https://youtu.be/uBWqHxYG3GU</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/SFoV-50C-Center-Spec-2863-165a-2863-165a-middle-0.5-20x_recon.mpg?versionId=f09a97f4-91a2-40a4-9109-4d2ef518933b">SFoV-50C-Center-Spec-2863-165a-2863-165a-middle-0. ...</a></p> <p> </p> <p> </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.