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363 results for “stack”
Large-area periodically-poled lithium niobate wafer stacks optimized for high-energy narrowband terahertz generation - Dataset
<p>Dataset for the publication "Large-area periodically-poled lithium niobate wafer stacks optimized for high-energy narrowband terahertz generation".</p>
Biomarker indices and concentrations and biomarker-based temperature estimates from the Iberian Margin core MD95-2042, composite atmospheric temperature record from Greenland, and stacks of delta 18Oice and atmospheric temperature records from three Antarctic sites
<p>Core MD95-2042 alkenone and GDGT data: This dataset provides the following information for core MD95-2042: depth, age, summed OH-GDGT, iGDGT, and di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentrations, OH-GDGT-based, iGDGT-based, and alkenone-based paleothermometric indices, GDGT-2/GDGT-3 ratio, and biomarker-based sea surface temperature (SST) and 0‐ to 200‐m sea temperature (subT; gamma function probability distribution for target temperatures with a = 4.5 and b = 15) estimates. Sediment samples were taken every 5 cm from core MD95-2042 and homogenized before lipid extraction. The lipid extracts were splitted into two fractions: one for alkenone analysis by gas chromatography coupled to a flame ionization detector, and the other for GDGT analysis by high-performance liquid chromatography coupled to mass spectrometry. All GDGT analyses were done in duplicate. The 1σ analytical uncertainties from 37 replicate analyses of the core catcher sample from core MD95-2042 are 0.007 (0.4 °C) for RI-OH, 0.008 (0.2 °C) for RI-OH′, 0.003 (0.2 °C) for TEX<sub>86</sub>, 0.238 for GDGT-2/GDGT-3, and 0.010 (0.26 °C) for U<sup>K′</sup><sub>37</sub>. RI-OH′-SST estimates are from the following global calibration: SST = (RI-OH′ + 0.029)/0.0422 (Fietz et al., 2020). RI-OH-SST estimates are from the following global calibration: SST = (RI-OH − 1.11)/0.018 (Lü et al., 2015). TEX<sub>86</sub><sup>H</sup>-SST estimates are from the following regional paleocalibration: SST = 68.4 × TEX<sub>86</sub><sup>H</sup> + 33.0 (Darfeuil et al., 2016). U<sup>K′</sup><sub>37</sub>-SST estimates are from the following global calibration: SST = 29.876 × U<sup>K′</sup><sub>37</sub> − 1.334 (Conte et al., 2006). Bayesian calibrations were also used for TEX<sub>86</sub>-SST and TEX<sub>86</sub>-subT estimates (BAYSPAR; Tierney & Tingley, 2014, 2015) and for U<sup>K′</sup><sub>37</sub>-SST estimates (BAYSPLINE; Tierney & Tingley, 2018). Alkenone data covering the 160–70 and 70–0 ka BP periods are from Davtian et al. (2021) and Darfeuil et al. (2016), respectively. GDGT data covering the 160–45 ka BP period are from Davtian et al. (2021). The age model of core MD95-2042 for the 160–43 and 43–0 ka BP periods was obtained by tuning to Chinese speleothems (Cheng et al., 2016) and by recalibrating existing <sup>14</sup>C ages with the Marine20 calibration curve (Heaton et al., 2020), respectively. MIS, Marine Isotope Stage; GDGT, glycerol dialkyl glycerol tetraether; and N/A, not available.</p> <p>Greenland atmospheric temperature record: This dataset consists in a composite Greenland atmospheric temperature record, which was built with the following records: the GISP2 atmospheric temperature record by Kobashi et al. (2017) for the 10–0 ka BP period, the NGRIP atmospheric temperature record by Kindler et al. (2014) for the 120–10 ka BP period, and the NEEM atmospheric temperature record by NEEM community members (2013) for the 129–120 ka BP period. The NEEM temperature anomalies obtained by NEEM community members (2013) were shifted by –31 °C to obtain absolute air temperatures. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p> <p>Antarctic δ<sup>18</sup>O<sub>ice</sub> and atmospheric temperature stacks: This dataset consists in two stacks of three Antarctic records (EDC, EDML, and WD), one for δ<sup>18</sup>O<sub>ice</sub> and the other for atmospheric temperature: both stacks are provided with their stacking uncertainties. To build the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were resampled every 10 years before centering to zero means and normalization to unit standard deviations over the 140–0 ka BP period (68–0 ka BP for WD). To optimize the continuity between the portions with and without the WD ice core, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were centered to zero means over the 68–67 ka BP period. The resulting Antarctic δ<sup>18</sup>O<sub>ice</sub> records were then averaged and stacking uncertainties were calculated as the pooled standard deviation of the stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records divided by the square root of the number of stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records. The final Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, expressed in ‰, has the same standard deviation as the δ<sup>18</sup>O<sub>ice</sub> record from EDML over the 140–0 ka BP period, and has a zero mean over the 1–0 ka BP. The Antarctic atmospheric temperature stack was built like the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, except that the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were corrected for seawater δ<sup>18</sup>O<sub>ice</sub> variations before conversion into atmospheric temperature. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p>
MPM-334-1: Fossil basicranium and endocranial volumes and CT-stack (Enantiornithes, Avialae)
<p>Among terrestrial vertebrates, only crown birds (Neornithes) rival mammals in terms of relative brain size and behavioural complexity. Relatedly, the anatomy of the avian central nervous system and associated sensory structures, such as the vestibular system of the inner ear, are highly modified with respect to those of other extant reptile lineages. However, a dearth of three-dimensional Mesozoic fossils has limited our knowledge of the origins of the distinctive endocranial structures of crown birds. Traits such as an expanded, flexed brain, a ventral connection between the brain and spinal column, and a modified vestibular system have been regarded as exclusive to Neornithes. Here, we demonstrate all of these 'advanced' traits in an undistorted braincase from an Upper Cretaceous enantiornithine bonebed in south-eastern Brazil. Our discovery suggests that these crown bird-like endocranial traits may have originated prior to the split between Enantiornithes and the more crownward portion of avian phylogeny over 140 million years ago, while coexisting with a remarkably plesiomorphic cranial base and posterior palate region. Altogether, our results support the interpretation that the distinctive endocranial morphologies of crown birds and their Mesozoic relatives are affected by complex trade-offs between spatial constraints during development.</p>
Investigating the Point of View of Project Management Practitioners on Technical Debt - A Study on Stack Exchange
<p>Investigating the Point of View of Project Management Practitioners on Technical Debt - A Study on Stack Exchange.</p> <p>Journal of Software Engineering Research and Development (JSERD) 2023.</p> <p> </p>
Kirchhoff pre-stack depth migration images of the multi-channel seismic data, SO190, RV. SONNE
<p>The dataset consists of four newly processed 2-D pre-stack depth migrated multi-channel seismic lines (BGR06_303, BGR06_305, BGR06_311 and BGR06_313) collected by GEOMAR and BGR in 2006. The dataset reveals the subducted oceanic reliefs and detailed accretionary wedge structure offshore eastern Java, Bali, Lombok, and Sumbawa islands, along the Sunda arc. The dataset is saved in standard SEGY format and could be loaded in open-source or commercial software. </p>
Input/Output files for 800-kyr planktonic 𝜹18O stack for the West Pacific Warm Pool
<p>Input and output files for an 11 core, planktonic d18O West Pacific Warm Pool stack constructed using alignment software BIGMACS.</p>
Calibration Dataset - HPOSS: A hierarchical portfolio optimization stacking strategy to reduce the generalization error of ensembles of models
<p>Calibration dataset for the study case presented in the paper "HPOSS: A hierarchical portfolio optimization stacking strategy<br> to reduce the generalization error of ensembles of models".</p> <p>It encompasses a .h5 file with a dataset called "Calibrations_LHS", which consists of a 80x5 numpy array of float numbers corresponding to (d1(m),d2(m),d3(m),d4(m),zeta_max(Pa)), where d_i, i = 1,...,4 are dimensions (in meters) of the I-beam and zeta_max is the maximum bending stress (in Pascals) developed in a simply such supported beam with 1 m of length after a point load of 1000N is applied at its center.</p>
Supplemental materials for studying GPU programming with Stack Overflow posts
<p>It includes the data used in our study of GPU programming and the complete results obtained from the study.</p>
Zebrafish with lyz:EGFP expressing neutrophils: Mesh Well Inserts Z-stack 3
<p>96-well plate z-stack of zebrafish with lyz:EGFP expressing neutrophils acquired with a multi-camera array microscope (MCAM)(Ramona Optics Inc., Durham, NC, USA). Mesh well inserts are used and half of the zebrafish on the plate were injected with csf3r morpholino. The overall z-stack is broken into four files.</p> <p> </p> <p>HDF5 files can be opened using open source Python software: https://docs.xarray.dev/</p>
Zebrafish with lyz:EGFP expressing neutrophils: Mesh Well Inserts Z-stack 2
<p>96-well plate z-stack of zebrafish with lyz:EGFP expressing neutrophils acquired with a multi-camera array microscope (MCAM)(Ramona Optics Inc., Durham, NC, USA). Mesh well inserts are used and half of the zebrafish on the plate were injected with csf3r morpholino. The overall z-stack is broken into four files.</p> <p> </p> <p>HDF5 files can be opened using open source Python software: https://docs.xarray.dev/</p>
Zebrafish with lyz:EGFP expressing neutrophils: csf3r_MO injected stack
<p>96-well plate z-stack of zebrafish with lyz:EGFP expressing neutrophils acquired with a multi-camera array microscope (MCAM)(Ramona Optics Inc., Durham, NC, USA). Zebrafish larvae have been injected with csf3r morpholino.</p> <p> </p> <p>HDF5 files can be opened using open source Python software: https://docs.xarray.dev/</p>
Zebrafish WT: Z-stack
<p>96-well plate z-stack of wild type zebrafish acquired with a multi-camera array microscope (MCAM)(Ramona Optics Inc., Durham, NC, USA).</p> <p> </p> <p>HDF5 files can be opened using open source Python software: https://docs.xarray.dev/</p>
Zebrafish with lyz:EGFP expressing neutrophils: Dibutyl phthalate Stack
<p>96-well plate z-stack of zebrafish with lyz:EGFP expressing neutrophils acquired with a multi-camera array microscope (MCAM)(Ramona Optics Inc., Durham, NC, USA). Fish were treated with Dibutyl pthalate or DMSO prior to imaging.</p> <p> </p> <p>HDF5 files can be opened using open source Python software: https://docs.xarray.dev/</p>
Zebrafish with lyz:EGFP expressing neutrophils: Mesh Well Inserts Z-stack 1
<p>96-well plate z-stack of zebrafish with lyz:EGFP expressing neutrophils acquired with a multi-camera array microscope (MCAM)(Ramona Optics Inc., Durham, NC, USA). Mesh well inserts are used and half of the zebrafish on the plate were injected with csf3r morpholino. The overall z-stack is broken into four files.</p> <p> </p> <p>HDF5 files can be opened using open source Python software: https://docs.xarray.dev/</p>
Zebrafish with lyz:EGFP expressing neutrophils: Non-injected stack
<p>96-well plate z-stack of zebrafish with lyz:EGFP expressing neutrophils acquired with a multi-camera array microscope (MCAM)(Ramona Optics Inc., Durham, NC, USA).</p> <p> </p> <p>HDF5 files can be opened using open source Python software: https://docs.xarray.dev/</p>
Data for Local atomic stacking and symmetry in twisted graphene trilayers
<p>Data for <a href="https://arxiv.org/abs/2303.09662">Local atomic stacking and symmetry in twisted graphene trilayers</a></p> <p>Please refer to <a href="https://github.com/bediakolab/bediakolab_scripts">bediakolab_scripts</a> (relevant code in TrilayerTEM) and <a href="https://github.com/bediakolab/pyInterferometry">pyInterferometry</a>. </p> <p>key.txt contains further information regarding format and labeling of this data. </p>
Replication Package for "The Double-edged Sword of Banning Generative AI on Online Question-and-Answer Communities: Evidence from Stack Exchange"
<p>This is a replication package for "The Double-edged Sword of Banning Generative AI on Online Question-and-Answer Communities: Evidence from Stack Exchange".</p>
GPT vs Stack Overflow: data collection (A2I2 T2 2023)
<p><strong>About</strong></p> <p>The dataset components produced by <a href="https://github.com/MHLoppy/A2I2-T2-2023">this repo</a>. Please see the documentation there for more information.</p> <p>Each CSV has been individually zipped so that you only have to download the specific file(s) that you want.</p> <p> </p> <p><strong>Overview of Files</strong></p> <p>From using the <a href="https://archive.org/details/stackexchange">Stack Exchange Data Dump</a> as the data source (these zip files have a <strong>DD_</strong> prefix):</p> <ul> <li>Raw dataset before processing: <strong>saved_dataset.csv (DD_saved_dataset.zip)</strong></li> <li>Completed tag count: <strong>tag_count.csv (DD_tag_count.zip)</strong></li> <li>Processed dataset with completed evaluations: <strong>dataset_results.csv (DD_dataset_results.zip)</strong></li> </ul> <p>From using Google BigQuery as the data source (these zip files have a <strong>BQ_</strong> prefix):</p> <ul> <li>Raw dataset before processing: <strong>saved_dataset.csv (BQ_saved_dataset.zip)</strong></li> <li>Completed tag count: <strong>tag_count.csv (BQ_tag_count.zip)</strong></li> <li><em>No large-scale evaluation was completed when using BigQuery as a data source.</em></li> </ul> <p>As noted in the linked repo, the use of Google BigQuery as a data source is not recommended for this work, but the working code and dataset have nonetheless been provided for completeness.</p> <p> </p> <p><strong>License</strong></p> <p>This dataset is licensed under the <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0 license</a>, the same license used by the Stack Exchange Data Dump.</p>
5-stack data
<p>Flux data used to measure the transmission and energy selection characteristics of a 5-layer energy analyzer</p>
Stacking Exercises Aid the Decline in FVC and Sick Time
ClinicalTrials.gov study NCT01999075. IPD Sharing: NO. Countries: 1. Publications: 2.
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.