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12,346 results for “derivatives”
CLDF dataset derived from Tang and Her's "Quantitative typological data on classifiers and plural markers" from 2019
<p><strong>Tang, Marc and One-Soon Her. 2019. Insights on the Greenberg-Sanches-Slobin Generalization: Quantitative typological data on classifiers and plural markers. Folia Linguistica, 53(2): 297-331. https://doi.org/10.1515/flin-2019-2013.</strong></p>
CLDF dataset derived from Auderset's "Interrogatives as relativization markers in Indo-European" from 2020
<p><strong>Sandra Auderset. (2020). CLDF dataset from "Interrogatives as relativization markers in Indo-European” (Version v1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4071139.</strong></p>
Bacteria-Derived Extracellular Vesicle Microbiome in NAFLD Patients With and Without Obstructive Sleep Apnea
<p>These are the supplementary figures.</p>
Data and code for "Coral calcifying fluid aragonite saturation states derived from Raman spectroscopy"
<p>This file contains all the data and code for "Coral calcifying fluid aragonite saturation states derived from Raman spectroscopy" by DeCarlo et al. in Biogeosciences. Run the file, "run.R" in R to reproduce the analysis and create all the figures.</p> <p>Please see the published paper for methods and details: https://www.biogeosciences-discuss.net/bg-2017-194/</p>
Raw data for the manuscript: Multigenerational toxicity of microplastics derived from two types of agricultural mulching films to Folsomia candida
<p>Survival and reproduction data from multigenerational single species tests involving two types of plastic materials.</p>
Coastal bluff point clouds derived from SfM near Elwha River mouth, Washington from 2016-04-18 to 2020-05-08
<p>Point Clouds of an approximately 2.0 km alongshore reach of seaward-facing coastal bluff faces on the Strait of Juan de Fuca, Washington State, were derived using structure-from-motion (SfM) photogrammetry from digital photos collected at least quarterly between 2016 and 2022. The point clouds were derived to assess spatial and temporal patterns of erosion on the bluff face and deposition at the base of the bluff. Photos from Miller, et al. (2022) were aligned using a modified USGS published workflow (Over, et al., 2022) with Agisoft Metashape Professional 1.8.5. Photos were aligned within a single chunk in a 4D approach described by Wernette, et al. (2022), and the sparse point cloud was filtered by reconstruction uncertainty (Ru) and projection accuracy (Pa). Dense point clouds were generated independently for each survey date by disabling all cameras except for a single photo date and then generating the dense cloud. This was repeated for each of the 30 photo survey dates, resulting in 30 dense point clouds (one point cloud per photo survey date).</p>
Supplementary information for: Macrophage- and CD4+ T cell-derived SIV differ in glycosylation, infectivity, and neutralization sensitivity
<p>The human immunodeficiency virus (HIV) envelope protein (Env) mediates viral entry into host cells and is the primary target for the humoral immune response. Env is extensively glycosylated, and these glycans shield underlying epitopes from neutralizing antibodies. The glycosylation of Env is influenced by the type of host cell in which the virus is produced. Thus, HIV is distinctly glycosylated by CD4<sup>+</sup> T cells, the major target cells, and macrophages. However, the specific differences in glycosylation between viruses produced in these cell types have not been explored at the molecular level. Moreover, it remains unclear whether the production of HIV in CD4<sup>+</sup> T cells or macrophages affects the efficiency of viral spread and resistance to neutralization. To address these questions, we employed the simian immunodeficiency virus (SIV) model. Glycan analysis implied higher relative levels of oligomannose-type <em>N</em>-glycans in SIV from CD4<sup>+</sup> T cells (T-SIV) compared to SIV from macrophages (M-SIV), and the complex-type <em>N</em>-glycans profiles seem to differ between the two viruses. Notably, M-SIV demonstrated greater infectivity than T-SIV, even when accounting for Env incorporation, suggesting that host cell-dependent factors influence infectivity. Further, M-SIV was more efficiently disseminated by HIV-binding cellular lectins. We also evaluated the influence of cell type-dependent differences on SIV's vulnerability to carbohydrate-binding agents (CBAs) and neutralizing antibodies. T-SIV demonstrated greater susceptibility to mannose-specific CBAs, possibly due to its elevated expression of oligomannose-type <em>N</em>-glycans. In contrast, M-SIV exhibited higher susceptibility to neutralizing sera in comparison to T-SIV. These findings underscore the importance of host cell-dependent attributes of SIV, such as glycosylation, in shaping both infectivity and the potential effectiveness of intervention strategies.</p>
The derivative in economics, a dilemma of understanding: Systematizing experience.
Open the record for dataset details and reuse information.
Genotypes of Aedes aegypti mosquitoes derived from SNP chip and low-coverage whole genome sequencing for platform cross-validation
<p>The mosquito <em>Aedes aegypti </em>is the primary vector of many human arboviruses such as dengue, yellow fever, chikungunya, and Zika, which affect millions of people world-wide. Population genetics studies on this mosquito have been important in understanding its invasion pathways and success as a vector of human disease. The Axiom aegypti1 SNP chip was developed from a sample of geographically diverse <em>Ae. aegypti </em>populations to facilitate genomic studies on this species. Here we evaluate the utility of the Axiom aegypti1 SNP chip for population genetics and compare it with a low-depth shot-gun sequencing approach using mosquitoes from the species' native (Africa) and invasive range (outside Africa). These analyses indicate that the results from the SNP chip are highly reproducible and have a higher sensitivity to capture alternative alleles than a low-coverage whole-genome sequencing approach. Although the SNP chip suffers from ascertainment bias, results from population structure, ancestry, demographic, and phylogenetic analyses using the SNP chip were congruent with those derived from low coverage whole genome sequencing, and consistent with previous reports on Africa and outside Africa populations using microsatellites. More importantly, we identified a subset of SNPs that can be reliably used to generate merged databases, opening the door to combined analyses. We conclude that the Axiom aegypti1 SNP chip is a convenient, more accurate, low-cost alternative to low-depth whole genome sequencing for population genetic studies of <em>Ae. aegypti</em> that do not rely on full allelic frequency spectra. Whole genome sequencing and SNP chip data can be easily merged, extending the usefulness of both approaches. </p>
Supplementary File 8; The full data set derived from formal quantitative surveys of land cover types and activities of humans, livestock and wildlife (Section 2.3) that were used for the analyses described in sections 2.4, 2.5 and 2.7
Open the record for dataset details and reuse information.
CSRM-derived: A 3-D crustal composition model in continental China
<p>This dataset contains a three-dimensional crustal composition model of the SiO2 content in the crustal bedrocks beneath continental China, derived from the <strong>CSRM-1.0 </strong>(<strong><a href="https://doi.org/10.5281/zenodo.11098135">model link</a></strong>)<strong>. </strong>The spatial resolution of the composition model is <strong>~60</strong> km in the north-south seismic belt and trans-North China Orogen, and <strong>~120</strong> km in the rest of the continental China.<strong> </strong>This composition model results from a project of constructing the high-resolution China Seismological Reference Model (<strong>CSRM-1.0</strong>) in the top 100 km of the crust and uppermost mantle in continental China (<a href="https://doi.org/10.1029/2024JB029520">Xiao et al, JGR, 2024</a>).</p> <p>本数据库包含了利用 CSRM-1.0(<a href="https://doi.org/10.5281/zenodo.11098135">模型链接</a>)估算的中国大陆区域地壳内不同深度岩石内二氧化硅含量的模型。该模型在南北地震带和华北造山带的分辨率约为 <strong>60</strong> km,在中国大陆的其他区域约为 <strong>120</strong> km。该成分模型源于构建中国大陆区域高精度地壳和上地幔顶部 100 公里三维地震学模型 (<strong>CSRM-1.0</strong>) 的工作 (<a href="https://doi.org/10.1029/2024JB029520">Xiao et al, JGR, 2024</a>) 。</p>
UCI CTM model simulations used for deriving the spillover of tropospheric ozone into the stratosphere
<p>The world has made great strides in phasing out the halocarbons that drive ozone loss, such as the chlorofluorocarbons 11 and 12. While living with the well-documented depletion of the ozone layer, we are now watching the slow recovery (increase) of stratospheric ozone over this century after our phaseout of halocarbon production and use. Projecting this recovery date also depends on the impact of other changing greenhouse gases on stratospheric chemistry as well as changes in tropospheric ozone. Both observations and models identify tropospheric ozone as increasing due to air-quality pollution in the lower atmosphere. Here, using a global chemistry-transport model, we find that this ozone increase carries over into the stratosphere at rates affecting the recovery expected from the decay of atmospheric halocarbons. This process is inherently included in our chemistry-climate models but is not diagnosed as such. The ozone assessments need to consider that what happens in the troposphere does not stay in the troposphere, complicating our interpretation of ozone changes over this century.</p>
Model output derived from SWE pacemaker experiments using MIROC6-AGCM
<p><strong>This data set is model output derived from numerical simulations using MIROC6-AGCM (Onuma et al., 2024, Journal of Climate).</strong></p> <p><strong>The contents are as below.</strong></p> <p>- <strong>python</strong>: Python scripts for the visualization<br><br>- <strong>figure</strong>: PNG files created by the Python scripts<br><br>- <strong>data</strong>: model output data for the Python scripts. The data directory includes the below contents.<br><br>-- <strong>LMIP</strong>: Output data simulated using MATSIRO6 with GSWP3 reanalysis data (LMIP experiment) are included. Monthly time-series data and regional averages for 1901-2010 derived from LMIP experiment (netCDF and csv files, respectively).</p> <p>-- <strong>AMIP</strong>: Output data simulated using MIROC6 with prescribed sea surface temperature and sea ice concentration (AMIP experiment) are included. Monthly time-series data and regional averages for 1901-2010 derived from AMIP ensemble experiments (netCDF and csv files, respectively).</p> <p>-- <strong>AMIP+SWE</strong>: Output data simulated using MIROC6 with prescribed sea surface temperature, sea ice concentration and snow water equivalent (AMIP+SWE experiment) are included. Monthly time-series data and regional averages for 1901-2010 derived from AMIP+SWE ensemble experiments (netCDF and csv files, respectively). Difference in a surface energy budget between AMIP+SWE and AMIP is also included (feedback_*.csv).</p> <p>-- <strong>AS-A</strong>: Statistical results analyzed from output data in AMIP+SWE and AMIP experiments (csv files). R_a (R_b) means the correlation coefficients of the outputs in AMIP+SWE (AMIP) for inter-annual change from 1901 to 2010 with those in LMIP. P means p-value. RMSD_a (RMSD_b) means the root mean square difference of the outputs in AMIP+SWE (AMIP) for inter-annual change from 1901 to 2010 with those in LMIP. Tr_a (Tr_b, Tr_ref) means the inter-annual trends from 1901 to 2010 in AMIP+SWE (AMIP, LMIP).<br><br>-- <strong>else</strong>: CMIP6 amip multi-models output, GlobSnow3 observation data and mask files for the regional averages. CMIP6 and GlobSnow3 data have been processed for analysis.</p> <p>*Regarding AMIP and AMIP+SWE data, the uploaded data are limited to ensemble mean data and run01 data due to data size that can be uploaded. If you need model outputs from run02 to run10, please contact Yukihiko Onuma (yonuma613@gmail.com). </p> <p><strong>The abbreviations of the output files are below.</strong><br>alb: land surface albedo<br>clt: total cloud cover fraction<br>ebflx: soil evaporation<br>gdt: surface air temperature (GSWP3)<br>hfls: latent heat flux<br>hfss: sensible heat flux<br>mrsos: surface soil moisture<br>pr: total precipitation<br>rlds: downward longwave radiation at the surface<br>rldscs: clear-sky downward longwave radiation at the surface<br>rls: net longwave radiation at the surface<br>rlus: upward longwave radiation at the surface<br>rsds: downward shortwave radiation at the surface<br>rsdscs: clear-sky downward shortwave radiation at the surface<br>rss: net shortwave radiation at the surface<br>rsus: upward shortwave radiation at the surface<br>snc: snow cover fraction<br>snow: snow water equivalent<br>T2: surface air temperature<br>tsl_z0: surface soil temperature (top 0.05 m)<br>tsl_z1: sub-surface soil temperature (top 0.05-0.25 m)</p>
Acrylohydrazides derivatives NMR for PeerJ
<p>The raw material provided is intended for publication in ChemRxiv and PeerJ Organic Chemistry. This submission aims to contribute valuable insights to the scientific community within the realm of organic chemistry, fostering collaborative research and advancing our understanding of the field.</p>
Datasets accompanying "Deciphering the heterogeneity of differentiating hPSC-derived corneal limbal stem cells through single-cell RNA-sequencing"
<p>Datasets include two seurat objects: one with all unfiltered cells and raw expression data (seu_unfiltered.rds) and one dataset with normalised expression and latest annotations (differentiation_object_latest.rds). Additionally, a single-cell object containing post-(py)SCENIC analysis is added (ipsc_scenic.h5ad).</p>
High temperature properties incuding creep, creep crack growth rate and thermal fatigue linked with chemical composition of alloys derived from 1.4848 refractory stainless steels
<p>The following set of data results from cast alloys modifying chemical composition taking as reference 1.4848 alloy and getting sound samples that have been tested to calculate creep, creep crack growth rate and thermal fatigue data. </p>
Supplementary data to "High-Resolution Raman Imaging of >300 Patient-Derived Cells from Nine Different Leukemia Subtypes: A Global Clustering Approach"
<p>Compressed ".feather" files including the entire dataset of 319 Raman maps of the same number of cells from 19 patients affected by nine distinct leukemia subtypes.<br>Raw data have been pre-processed as follows using custom software (LabVIEW, National Instruments Corp., TX): a) cosmic rays removal by singular value decomposition (SVD); b) camera offset subtraction; c) CCD response correction (intensity and etaloning) using a tungsten halogen light with known emission (Avalight-HAL, Avantes BV, NL)); d) wavenumber calibration using the zero-wavenumber laser line, toluene and argon-mercury emission (CAL-2000, Ocean Optics, Germany); e) denoising by SVD.<br>More details in the open access published article and supplementary material (10.1021/acs.analchem.4c00787).</p>
Derived environmental temperatures at Jezero crater from Air Temperature Sensors' measurements on the Perseverance rover.
<p><strong>Material from Version 2</strong> extends derived Air Temperature Sensor data to the first 700 sols of the Mars 2020 mission used in the analysis of <em>Munguira et al. (2024). "One Martian Year of Near-Surface Temperatures at Jezero from MEDA measurements on Mars2020/Perseverance". Journal of Geophysical Research: Planets. [in revision]. </em>We also include the tables needed to generate and reproduce the figures in the paper. Most importantly, the tables include the results from different analyses of temperatures through Fourier series and Reynolds averaging. </p>
ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from MIOST geostrophic velocities (1/24°).
<p>This product provides the Finite-size Lyapunov Exponents derived from surface geostrophic velocities result from the application of the MIOST algorithm (Ubelmann et al., 2019; https://doi.org/10.1029/2020JC016560) to altimetry L3-data (https://doi.org/10.5281/zenodo.10648981) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Size Lyapunov Exponents was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/24º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>fsle (2D)</p>
The SPHINX M-dwarf Spectral Grid. I. Benchmarking New Model Atmospheres to Derive Fundamental M-Dwarf Properties
<p><strong>NEW UPDATE:::::::::::::VERSION 4</strong></p> <p>MODEL GRID AND SUPPLEMENTARY FIGURES for <strong>The SPHINX M-dwarf Spectral Grid. I. Benchmarking New Model Atmospheres to Derive Fundamental M-Dwarf Properties:</strong></p> <p><strong>(1) MODEL GRID:</strong> Zip file titled 'SPHINX_MODELS_MLT_1.zip' contains a directory (376MB on disk) that is divided into three folders: ATMS, SPECTRA, and ABUNDANCES. All models here assume mixing length parameter of 1. For more info, we direct the reader to the paper.</p> <p>The ATMS directory contains thermal profiles/atmospheres of all models. (Temperature in K, Pressure in bars)</p> <p>The SPECTRA directory contains synthetic spectra. (Wavelength--0.1 to 20 microns, Flux in W/m2/m, R~250)</p> <p>The ABUNDANCES directory contains mixing ratios of all atomic and molecular species included in these models.</p> <p><strong>(2) SUPPLEMENTARY FIGURES:</strong></p> <p>The plot files are named as 'target name' _ corner.</p> <p>Each file is a corner plot of posterior probability distributions from grid-model fit of low-resolution spectra of benchmark M dwarfs using the SPHINX model grid. We also include posterior distributions of Starfish (Czekala et al.2015) hyperparameters to properly constrain model and data systematics. The vertical blue lines in the plots indicate values from observations (empirically derived [M/H] from Mann et al. 2013 and interferometrically measured radii from Boyajian et al. 2012b).</p> <p>File named Gl436_mixinglength shows difference in grid model fit assuming mixing length parameter 1 vs 0.5.</p> <p>File named Gl725B_spot shows difference in grid model fit assuming stellar photospheric heterogeneity vs without.</p> <p><strong>If you use our stellar atmosphere models, please remember to cite both the zenodo doi for the open source data as well as the paper. Thank you!</strong></p> <p> </p> <p>--> link to <a href="https://iopscience.iop.org/article/10.3847/1538-4357/acabc2/meta">published paper</a></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.