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2,960 results for “elements”
Data for: Combining stable isotopes, trace elements, and distribution models to assess the geographic origins of migratory bats
<p>The expansion of industrial-scale wind-energy facilities has increased the production of low-carbon emission energy but has also resulted in mortality of wildlife, including migratory bats. Management decisions can be limited by a lack of understanding of the geographic impact of bats killed at wind-energy facilities. Several studies have leveraged stable hydrogen isotope ratios (δ<sup>2</sup>H) of bat fur to illuminate this issue but are limited in the precision of conclusion because δ<sup>2</sup>H values vary primarily across latitudinal and elevational bands. One approach to increase the precision of geographic assignment is to combine independent inferences about spatial location from additional biomarkers and other related information. To test this possibility, we assigned known-origin individuals of three bat species commonly killed at on-shore wind-energy facilities in North America (<em>Lasiurus</em> <em>borealis</em>, <em>L</em>. <em>cinereus</em>, and <em>Lasionycteris</em> <em>noctivagans</em>) to probable origin using δ<sup>2</sup>H values, trace element concentrations, and species distribution models. We used cross-validation calibrated combined model tuning to determine the degree to which assignment probabilities improved when combining datasets. We found that combining markers typically performed better than single approaches. For <em>L. borealis</em> and <em>L. cinereus</em>, combining all three data sources outperformed any single or other combined approach. With an accuracy set at 80%, an average of 39.7% and 36.0% of each species' total geographic range was considered a potential origin, respectively; stable hydrogen alone included 51.8% and 50.6% of the total geographic area. In contrast, for <em>L. noctivagans</em>, including trace elements did not increase precision, and adding distribution data to δ<sup>2</sup>H values only improved precision by 0.6%. Thus, we found that a combination of multiple biomarkers typically, but not always, outperforms single marker approaches, and optimized combinations of different markers outperform equal weighting of each marker. From a practical perspective, δ<sup>2</sup>H values performed better than trace elements alone; in cases where cost is a limiting factor, the stable hydrogen should be the single biomarker used in conjunction with species distribution models. Overall, these results highlight the importance of validating methods for each species they are applied to and show that combining information from intrinsic biomarker approaches is a useful tool to document bat movements.</p>
Dataset for 'Discrete Element Analysis of Diametrical Compression of Annular Agglomerates'
<p>This dataset contains both numerical simulation data and experimental data describing on three-dimensional diametrical compression tests of annular agglomerates with various bonding strength and internal holes modelled by the Discrete Element Method (DEM) . Detailed study is presented in a journal paper entitled 'Discrete Element Analysis of Diametrical Compression of Annular Agglomerates'. We provide all the essential data in a single excel file with the description below:</p> <p>Dataset 1- Fig. 4. Evolution of forces for both top and bottom platens with strain.</p> <p>Dataset 2-Fig. 5. Evolution of average mechanical coordination number with strain.</p> <p>Dataset 3-Fig. 7. Force-strain curves during diametrical compression with different surface energies (λ=0.3).</p> <p>Dataset 4-Fig. 8. The peak forces against the surface energy of annular specimens.</p> <p>Dataset 5-Fig. 9. Variation of the tensile strength with surface energy of the annular specimens.</p> <p>Dataset 6-Fig. 11. Force-strain curves with different ratios of internal diameter to the external diameter (γ=15 J/m2).</p> <p>Dataset 7- Fig.12. The peak force on platens against the ratio of internal diameter to the external diameter of annular specimens.</p> <p>Dataset 8-Fig. 13. Variation of tensile strength with different ratios of internal diameter to the external diameter.</p> <p>Dataset 9-Fig. 15. Force-strain curves of disc specimens with different surface energies.</p> <p>Dataset 10-Fig. 16. The calculated tensile strength of disc specimens.</p> <p>Dataset 11-Fig. 17. Comparisons of current DEM results of data from experimental data</p> <p>Dataset 12-Fig. 18. Dimensionless parameter (σ_t R)/γ against the ratio of internal diameter to the external diameter λ.</p>
2D Abaqus finite element model of the climbing drum peel test for fracture toughness and mode mixity determination
<p>Abaqus finite element model files published in connection with the below article:</p> <p>Jespersen, K.M and Toftegaard, H. L. (2023), Mode mixity for the fracture toughness obtained by climbing drum peel tests. Risø symposium 2023. <a href="https://doi.org/10.1088/1757-899X/1293/1/012032">doi.org/10.1088/1757-899X/1293/1/012032</a></p> <p>Uploads include the overall .cae file for Abaqus 2022 and the corresponding .odb (result) and .inp files. Videos showing the deformation of the model are included for the "CDP_mat-GFRP_cracklength-a10_BC-gripped" case.</p>
Selective control of transposable element expression during T cell exhaustion and anti-PD1 treatment
<p>Supplementary data from (Bonté et al.) which contains Human and Mouse RNA-seq data including : </p> <ul> <li><strong>Raw counts and TPM/FPKM expression matrices of data analyzed </strong>(genes, individual TEs, TE subfamilies) <ul> <li>raw.counts / fpkm.count / tpm.counts rds files</li> </ul> </li> <li><strong>SQuiRE raw output files </strong> <ul> <li>SQuiRE.results rds files</li> </ul> </li> <li><strong>Seurat processed single cell object</strong> (with genes, TEs, Subfamilies) <ul> <li>SC.object rds files</li> </ul> </li> </ul> <p>In details, Mouse and Human data are labelled as follows :</p> <ul> <li><strong>Mouse.SC :</strong> <ul> <li><strong>Type of data</strong> : Single cell RNA-seq data of sorted CD8+ TILs (model B16 melanoma tumors )</li> <li><strong>Label of samples </strong>: Naïve like, Early activated, EffectorMemory, Tpex, Tex</li> <li><strong>Origin of the data</strong> : S. J. Carmona, I. Siddiqui, M. Bilous, W. Held, D. Gfeller, Deciphering the transcriptomic landscape of tumor-infiltrating CD8 lymphocytes in B16 melanoma tumors with single-cell RNA-Seq. Oncoimmunology 9, 1737369 (2020).</li> </ul> </li> <li><strong>Mouse.tumor.TILs :</strong> <ul> <li><strong>Type of data</strong> : bulk RNA-seq data of sorted CD8+ TILs (model B16 melanoma tumors)</li> <li><strong>Label of samples </strong>: Tumor Slamf6+ or Tumor Tim3+</li> <li><strong>Origin of the data</strong> : B. C. Miller<em> et al.</em>, Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. <em>Nature Immunology</em> <strong>20</strong>, 326-336 (2019).</li> </ul> </li> <li><strong>Mouse.LCMV.TILs :</strong> <ul> <li><strong>Type of data</strong> : bulk RNA-seq data of sorted CD8+ TILs (model LCMV clone 13)</li> <li><strong>Label of samples </strong>: LCMV Slamf6+ or LCMV Tim3+</li> <li><strong>Origin of the data</strong> : B. C. Miller<em> et al.</em>, Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. <em>Nature Immunology</em> <strong>20</strong>, 326-336 (2019).</li> </ul> </li> <li><strong>Mouse.LCMV_Fli1.TILs :</strong> <ul> <li><strong>Type of data</strong> : bulk RNA-seq data of sorted CD8+ TILs (model LCMV clone 13)</li> <li><strong>Label of samples </strong>: WT or Fli1KO</li> <li><strong>Origin of the data</strong> : Z. Chen<em> et al.</em>, In vivo CD8(+) T cell CRISPR screening reveals control by Fli1 in infection and cancer. <em>Cell</em> <strong>184</strong>, 1262-1280 e1222 (2021).</li> </ul> </li> <li><strong>Human.TILs </strong>: <ul> <li><strong>Type of data</strong> : bulk RNA-seq data of sorted CD8+ TILs (NSCLC tumor tissue )</li> <li><strong>Label of samples </strong>: Tex or Tpex</li> <li><strong>Origin of the data</strong> : Bonté et al. (ongoing submission)</li> </ul> </li> </ul> <p> </p> <p>Type of features is labeled as follows:</p> <ul> <li><strong>Gene expression</strong> : genes</li> <li><strong>Individual TE expression</strong> : TEs</li> <li><strong>TE subfamiliy expression</strong> : SF</li> </ul> <p>Metadata corresponding to each expression matrix can be found <a href="https://github.com/pebonte/TE_TILs">here</a>.</p> <p> </p>
Discrete Element Method model results of fracturing and displacements above laccolith intrusions
<p>This data set contains scripts and data in support of the publication: Morand A., Poppe S., Harnett C., Cornillon A., Heap M., Mège D. (2023). Fracturing and dome-shaped surface displacements above laccolith intrusions: Insights from Discrete Element Method modeling. <em>submitted</em>, Journal of Geophysical Research: Solid Earth, July 2023.</p> <p>The data set is composed of results of two-dimensional (2D) Discrete Element Method (DEM) modeling performed by Morand et al., with the licensed commercial <em>Particle Flow Code </em>2D version 7.0 (PFC2D 7.0) from Itasca Consulting Group, Ltd. The DEM modeling simulates a new injection of magma into a preexisting laccolith intrusion and tracks the effect of host rock toughness, stiffness and the source depth. The data set corresponds to exported DEM particle and crack properties of the model results into ASCII files (.txt) and processed particle maximum finite shear strain calculated from particle displacements.</p>
Data for the project: Climatic impacts on the availability and transfer of chemical elements through Arctic terrestrial food-chains: a pilot study
<p>Uploaded data contain trace element concentrations measured in various biological samples collected at Zackenberg Research station (northeast Greenland) in August 2021</p> <p>Fifty (50) sample plots were visited to collect invertebrates (using pitfalls) and sample soil, vegetation, muskox feces and wool.</p> <p>The sheet PlotInfo provides information on the sample plots, which were placed using a stratified random placement procedure across 5 vegetation types and the elevational gradient</p> <p>The sheet Soil contains trace element concentrations (ppm) as well as N and C (%) measured in the soil samples across all 50 sample plots</p> <p>The sheet Vegetation contains trace element concentrations (ppm) as well as N and C (%) measured in the vegetation samples across all 50 sample plots</p> <p>The sheet Insects contains trace element concentrations (ppm) measured in the various insect groups captured in pitfall traps in 9 sample plots</p> <p>The sheet Muskox feces contains trace element concentrations (ppm) measured in the feces samples collected at all 50 sample plots</p> <p>The sheet Muskox Wool contains trace element concentrations (ppm) measured in 25 wool samples collected throughout the sampling area (not at specific sample plots).</p> <p> </p> <p>Trace element concentrations were measured at the ICP-MS platform at the Observatoirie Midi-Pyrenees, Toulouse, France</p> <p>N and C analyses were done at Department of Biology, University of Copenhagen, Denmark</p> <p> </p> <p>For further information on the project, samples, methods used contact flbe@ecos.au.dk</p>
Dataset for automated image-based generation of finite element models for masonry buildings
<p>This repository contains the dataset used for computing finite element models for masonry buildings via image-based approach. The method that uses this data set was presented in the paper "Automated image-based generation of finite element models for masonry buildings" by Pantoja-Rosero et., al. (2023)" https://doi.org/10.1007/s10518-023-01726-7</p>
Fig. 1 in Quantitative variations of usnic acid and selected elements in terricolous lichen Cladonia mitis Sandst., with respect to different environmental factors - A chemometric approach
Fig. 1. Relationship between usnic acid content in Cladonia mitis and the latitude of the collection sites (R = 0.547, p =0.019). The circles denote samples from open area, while squares denote samples from forest area.
Fig. 4 in Quantitative variations of usnic acid and selected elements in terricolous lichen Cladonia mitis Sandst., with respect to different environmental factors - A chemometric approach
Fig. 4. The projection of samples on the plane defined by the first two latent components of the PLS model. The circles denote samples from open area, while squares denote samples from forest area.
Fig. 2 in Quantitative variations of usnic acid and selected elements in terricolous lichen Cladonia mitis Sandst., with respect to different environmental factors - A chemometric approach
Fig. 2. Relationship between usnic acid content in Cladonia mitis and the altitude (in the range of 50 and 500 m above sea level) of the collection sites (n = 13). The circles denote samples from open area, while squares denote samples from forest area.
Fig. 3 in Quantitative variations of usnic acid and selected elements in terricolous lichen Cladonia mitis Sandst., with respect to different environmental factors - A chemometric approach
Fig. 3. The weights of the first two latent components of the partial least square model. Usnic acid and Pb concentrations are response parameters, all other parameters are predictors.
Video demonstration of the Turning in place behaviour element in the Interactive group (behaviour element ID code: ILTP) for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour element: Turning in place in the interactive experimental group, as exhibited by a social robot (behaviour element ID code: <strong>ILTP</strong>). The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <strong><em>How do you do the things that you do? - Ethological approach to the description of robot behaviour</em></strong> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, P.</p>
Video demonstration of the Turning in place behaviour element in the Minimally interactive group (behaviour element ID code: MLTP) for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour element: Turning in place in the minimally interactive experimental group, as exhibited by a social robot (behaviour element ID code: <strong>MLTP</strong>). The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <em><strong>How do you do the things that you do? - Ethological approach to the description of robot behaviour</strong></em> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, P.</p>
Video demonstration of the Leaving table behaviour element in the Interactive group (behaviour element ID code: ILLT) for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour element: Leaving table in the interactive experimental group, as exhibited by a social robot (behaviour element ID code: <strong>ILLT</strong>). The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <em><strong>How do you do the things that you do? - Ethological approach to the description of robot behaviour</strong></em> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, P.</p>
Video demonstration of the Moving to checkpoint (behaviour element ID code: MLMC); and Head orientation during locomotion (behaviour element ID code: MHOL) behaviour elements in the Minimally interactive group for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour elements: Moving to checkpoint (behaviour element ID code: <strong>MLMC</strong>); and Head orientation during locomotion (behaviour element ID code: <strong>MHOL</strong>) in the minimally interactive experimental group, as exhibited by a social robot. The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <em><strong>How do you do the things that you do? - Ethological approach to the description of robot behaviour</strong></em> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, P.</p>
Video demonstration of the Looking at human face (behaviour element ID code: IILH); and Greeting sound (behaviour element ID code: IVGR) behaviour elements in the Interactive group for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour elements: Looking at human face (behaviour element ID code: <strong>IILH</strong>); and Greeting sound (behaviour element ID code: <strong>IVGR</strong>) in the interactive experimental group, as exhibited by a social robot. The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <em><strong>How do you do the things that you do? - Ethological approach to the description of robot behaviour</strong></em> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, P.</p>
Video demonstration of the Gaze alternation behaviour element in the Interactive group (behaviour element ID code: IIGA) for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour element: Gaze alternation in the interactive experimental group, as exhibited by a social robot (behaviour element ID code: <strong>IIGA</strong>). The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <em><strong>How do you do the things that you do? - Ethological approach to the description of robot behaviour</strong></em> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, P.</p>
Video demonstration of the Greeting behaviour element in the Minimally interactive group (behaviour element ID code: MIGR) for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour element: Greeting in the minimally interactive experimental group, as exhibited by a social robot (behaviour element ID code: <strong>MIGR</strong>). The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <em><strong>How do you do the things that you do? - Ethological approach to the description of robot behaviour</strong></em> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, P.</p>
Video demonstration of the Looking down at tray (behaviour element ID code: IODT); and Higher intensity attention getting (behaviour element ID code: IVHA) behaviour elements in the Interactive group for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour elements: Looking down at tray (behaviour element ID code: <strong>IODT</strong>); and Higher intensity attention getting (behaviour element ID code: <strong>IVHA</strong>) in the interactive experimental group, as exhibited by a social robot. The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <em><strong>How do you do the things that you do? - Ethological approach to the description of robot behaviour</strong></em> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, P.</p>
Video demonstration of the Standing to table (behaviour element ID code: ILST); and Lower intensity attention getting (behaviour element ID code: IVLA) behaviour elements in the Interactive group for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour elements: Standing to table (behaviour element ID code: <strong>ILST</strong>); and Lower intensity attention getting (behaviour element ID code: <strong>IVLA</strong>) in the interactive experimental group, as exhibited by a social robot. The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <em><strong>How do you do the things that you do? - Ethological approach to the description of robot behaviour</strong></em> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, 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.