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68 results for “high time resolution”
Data from: Oscillayers: a dataset for the study of climatic oscillations over Plio-Pleistocene time scales at high spatial-temporal resolution
Motivation: In order to understand how species evolutionarily responded to Plio-Pleistocene climate oscillations (e.g. in terms of speciation, extinction, migration and adaptation), it is first important to have a good understanding of those past climate changes per se. This, however, is currently limited due to the lack of global-scale climatic datasets with high temporal resolution spanning the Plio-Pleistocene. To fill this gap, I here present Oscillayers, a global-scale and region-specific bioclim dataset, facilitating the study of climatic oscillations during the last 5.4 million years at high spatial (2.5 arc-minutes) and temporal (10 kyr time periods) resolution. This data set builds upon interpolated anomalies (Δ layers) between bioclim layers of the present and the Last Glacial Maximum (LGM) that are scaled relative to the Plio-Pleistocene global mean temperature curve, derived from benthic stable oxygen isotope ratios, to generate bioclim variables for 539 time periods. Evaluation of the scaled, interpolated estimates of palaeo-climates generated for the Holocene, Last Interglacial and Pliocene showed good agreement with independent General Circulation Models (GCMs) for respective time periods in terms of pattern correlation and absolute differences. Oscillayers thus provides a new tool for studying spatial-temporal patterns of evolutionary and ecological processes at high temporal and spatial resolution. Main types of variable contained: 19 bioclim variables for time periods throughout the Plio-Pleistocene. Input data and R script to recreate all 19 bioclim variables. Spatial location and grain: Global at 2.5 arc-minutes (4.65 x 4.65 = 21.62 km2 at the equator). Time period and grain: The last 5.4 million years. The grain is 10 kyr (= 539 time periods). Level of measurement: Data are for terrestrial climates (excluding Antarctica) taking sea level changes into account. Software format: All data are available as ASCII (ESRI) grid files.
New high time resolution mutual impedance measurements
<p>=====================================================================================================<br> Author : L. Bucciantini<br> Date : 03/11/2022<br> Laboratory : CNRS-LPC2E, Orléans (France)<br> =====================================================================================================</p> <p>Dear reader, we thank you for your interest in our dataset. In this document, we describe what you will<br> find in it.</p> <p>In case you need help with using the dataset, or if you are interested in mutual impedance experiments,<br> do not hesitate to contact our team in Orléans (pierre.henri@cnrs-orleans.fr, pierre.henri@oca.eu)</p> <p>=====================================================================================================<br> Topic:<br> This dataset contains the outputs of numerical simulations and experimental tests performed<br> to test and validate two new mutual impedance instrumental modes: the chirp mode and the<br> multi-spectral mode. These modes have the objective of improving the time resolution of<br> mutual impedance measurements.</p> <p>These new instrumental modes are validated by comparing their measurements to the classic<br> instrumental mode of mutual impedance measuremetns, the so-called frequency sweep mode.</p> <p><br> Numerical model:<br> The numerical outputs are obtained from a numerical model based on the solution of the 1D-1V Vlasov-Poisson<br> system of equations. The scheme used to solve the model is the one developed<br> by Mangeney et al. (2002), A Numerical Scheme for the Integration of the Vlasov-Maxwell System of Equations. Journal of Computational Physics, (doi: https://doi.org/10.1006/jcph.2002.7071).<br> The 1D-1V Vlasov-Poisson version of this model is described in Henri, et al. (2010), Vlasov-Poisson<br> simulations of electrostatic parametric instability for localized Langmuir wave packets in the solar wind,<br> Journal of Geophysical Research (Space Physics), 115, 6106 (2010)</p> <p>Experimental testing facility:<br> The experimental outputs are obtained using the testing facility of CNRS-LPC2E space laboratory. It is<br> composed of a vacuum chamber, a pumping system and a plasma source. The pumps make vacuum in the<br> chamber (p=1e-5mbar). The plasma source generates a plasma flow that we use to test mutual impedance<br> instruments in a medium which is representative of Earth's ionosphere (100-2000 km of altitude).</p> <p>-----------------------------------------------------------------------------------------------------<br> Instrumental modes of interest</p> <p>frequency sweep (FS) mode : typical mode. Implemented for DFP-COMPLIMENT onboard Comet Interceptor</p> <p>chirp (CH) mode : new fast mode. It is based on the emission of only one period per frequency.</p> <p>multi-spectral (MS) mode : new fast mode. It is based on the simultaneous emission of multiple frequencies.</p> <p>-----------------------------------------------------------------------------------------------------<br> What is inside the dataset:</p> <p>The dataset is composed of 3 numerical and 6 experimental mutual impedance measurements.<br> The numerical measurements are obtained for the same plasma box but for different instrumental modes.<br> The experimental measurements are obtained for two different plasmas, monitored using the three different<br> instrumental modes.</p> <p>-----------------------------------------------------------------------------------------------------<br> List of directories:</p> <p><br> Numerical_CH, Numerical_MS: Each of these outputs corresponds to a mutual impedance measurement<br> Numerical_FS performed using a different instrumental mode.<br> For MS and FS, inside the directory each folder corresponds to the<br> emission of a different frequency.</p> <p><br> Experimental : It contains a txt file with inside the mutual impedance spectra obtained<br> for two different plasmas</p> <p><br> -----------------------------------------------------------------------------------------------------<br> List of folders inside the directories:</p> <p>In the case of the numerical measurements, folders begin with the name "000" and have increasing index.</p> <p>------------------------------------------------------------------------------------------------------<br> List of files inside the folders for the numerical measurements:</p> <p>density_e.npz : electron density inside the box, in function of time (tempo)</p> <p>density_p.npz : ion density inside the box, in function of time (tempo)</p> <p>E.npz : electric field in the box, in function of time (tempo)</p> <p>qrho.npz : electric potential in the box, in function of time (tempo)</p> <p>qrho_imposed.npz : electric charge imposed at the emitting antennas, in function of time (tempo)</p> <p>tempo.npz : time-vector for density, electric field, electric potential and charge vectors</p> <p>TEST_Luca.dat : parameters describing the characteristics of the simulated plasma box</p> <p><br> [<br> Note : the previous files can be opened as follows</p> <p>import numpy as np</p> <p>vector_file_name = np.load('file_name.npz') # Use these for the .npz files<br> characteristics_of_the_box = np.genfromtxt('TEST_Luca.dat',skip_header=1)</p> <p>]</p> <p>------------------------------------------------------------------------------------------------------<br> List of files inside the folders for the experimental measurements:</p> <p>data.txt : frequencies and amplitudes associated to the measurements.</p> <p>[<br> Note : the previous file can be directly included into a python script</p> <p>]</p> <p>-------------------------------------------------------------------------------------------------------<br> Characteristics of the simulated plasma box (TEST_Luca.dat)</p> <p>nx : amount of spatial grid points</p> <p>xl : physical size of the spatial box, expressed in Debye length</p> <p>tt_w : time resolution for ion and electron density, electric field, electric potential and charge</p> <p>rap_m : ion-to-electron mass ratio</p> <p>R_p : ion-to-electron temperature ratio</p> <p>dt : time step used to evolve in time the numerical simulation</p> <p>emission : emission frequency</p> <p>power : amplitude of the electric charge imposed at the emitting antennas</p> <p><br> (Note : all parameters not listed here but present inside the TEST_Luca.dat file correspond to additional<br> functionalities of the model. For the use of this dataset, they can be discarded.)</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Datasets supporting the publication: Evaluating night-time light sources and correlation with socio-economic development using high-resolution multi-spectral Jilin-1 satellite imagery of Quito, Ecuador
<p>##################################</p> <p><strong>Datasets supporting the publication:</strong></p> <p><em>Evaluating night-time light sources and correlation with socio-economic development using high-resolution multi-spectral Jilin-1 satellite imagery of Quito, Ecuador.</em></p> <p>International Journal of Remote Sensing. <a href="https://doi.org/10.1080/01431161.2023.2205983">https://doi.org/10.1080/01431161.2023.2205983</a></p> <p>C. Scott Watson<sup>a</sup>*, John R. Elliott<sup>a</sup>, Marco Córdova<sup>b</sup>, Jonathan Menoscal<sup>b</sup>, Santiago Bonilla-Bedoya<sup>c</sup></p> <p><em><sup>a</sup>COMET, School of Earth and Environment, University of Leeds, LS2 9JT, UK</em></p> <p><em><sup>b</sup>Facultad Latinoamericana de Ciencias Sociales, FLACSO, Quito, Ecuador</em></p> <p><em><sup>c</sup>Research Center for the Territory and Sustainable Habitat, Universidad Tecnológica Indoamérica,Machala y Sabanilla, 170301, Quito, Ecuador</em></p> <p><strong>-Please refer to the publication for details on the production of each dataset.<br> -Please cite the publication and this dataset repository when using the data.</strong></p> <p>##################################</p> <p><strong>Data:</strong></p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>J1_mosaic_max.tif</td> <td>Mosaicked Jilin-1 multi-spectral night-time image of Quito, Ecuador. Acquisition: 8th July 2021 at ~10:30 UTC (05:30 local time)</td> </tr> <tr> <td>corine_landcover_S2_20210705T153621_20210705T154215_T17MQV.tif</td> <td>Land cover classification applied to a Sentinel-2 image (5th July 2021)</td> <td> </td> </tr> <tr> <td>corine_landcover_symbology_qgis.txt</td> <td>Land cover classification symbology for QGIS</td> </tr> <tr> <td>light_type_classification.tif</td> <td>Light type classification: class 1 = LED, class 10 = HPS.</td> </tr> <tr> <td>classified_light_locations.shp</td> <td>Classified light source (point) locations</td> </tr> </tbody> </table>
High-time-resolution chemical composition and source apportionment of PM2.5 in northern Chinese cities: implications for policy
<p>Three real-time measurement campaigns were conducted in Xi’an, Shijiazhuang, and Beijing to investigate the chemical characteristics and source contributions of PM<sub>2.5</sub> and explore the formation progress of heavy pollution for policy implications. Chemeical components of PM<sub>2.5</sub> including OA, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup>, NH<sub>4</sub><sup>+</sup>, Cl-, BC, and elements were monitored by Q-ACSM, AE33, and Xact, respectively. The results show that chemical compositions of PM<sub>2.5 </sub>in the three cities are all dominated by organic aerosol (OA) and nitrate (NO<sub>3</sub><sup>-</sup>). And results of source apportionment analyzed by the hybrid environmental receptor model (HERM) show that the secondary nitrate plus sulfate contributed higher to PM<sub>2.5</sub> compared to other primary sources. Morevoer, the potential formation mechanisms of secondary aerosol in three cities were further explored by establishing the correlations between secondary nitrate plus sulfate and aerosol liquid water content (ALWC), and O<em><sub>x</sub></em> (O<sub>3</sub> + NO<sub>2</sub>). The results show that photochemical oxidation and aqueous-phase reaction were two important pathways of secondary aerosol fromation. Additionally, this study compared the changes in chemical composition and source contributions of PM<sub>2.5</sub> in past decades, and suggested that not only the activities of clean energy replacements for the rural household is urgently enhanced to reduce the primary source emissions in northern China, but also collaborative control ozone and particulate matter need to be contiouslty promoted to weaken the atmosphere oxidation capacity for reducing secondary aerosol formation.</p>
Data from: Ancient mitochondrial DNA provides high-resolution time scale of the peopling of the Americas
Open the record for dataset details and reuse information.
Data from: Oscillayers: a dataset for the study of climatic oscillations over Plio-Pleistocene time scales at high spatial-temporal resolution
Open the record for dataset details and reuse information.
High-resolution time series of leaf length and leaf thickness of strawberry and tomato measured in a growth chamber
<p>This dataset captures leaf thickness and leaf length of two strawberry plants and one tomato seeding. Leaf thickness measurements are only available of the strawberry plants. Additional data included light intensity data, soil water content (of one plant), relative humidity and temperature data. All sensors were read every second over a period of two weeks.</p>
Long time-series (2020-2100) high-resolution (1km) multi-scenario and multi-depth soil organic carbon dataset in China
<p>unit: kg C m-2 (soil oganic carbon density)</p><p>0100: denote 0-100 cm</p><p>020: denote 0-20 cm</p><p>Example 2020: 2020-2024 (five years mean soc)</p>
Fig. 2. A-B in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)
Fig. 2. A-B: (A) Derivative melt curves of T. copemani, T. noyesi G8, T. vegrandis G7; (B) Derivative melt curves of T. microti, T. cruzi and T. rangeli.
Fig. 6. A-D in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)
Fig. 6. A-D: (A) Derivative melt curves of wildlife samples containing T. copemani and T. vegrandis G7 infections from woylie tissue and the (B) the respective normalised melt domains; (C) Derivative melt curves of wildlife samples containing mixed infections with T. copemani and T. noyesi G8 in woylie blood; (D) Derivative melt curves showing wildlife samples containing mixed infections of T. copemani, T. noyesi G8 and T. vegrandis G7 in woylie tissue.
Schmidtea polychroa high resolution developmental transcriptomic time-course
GEO Series GSE64653. Schmidtea polychroa. 132 samples. Type: Expression profiling by high throughput sequencing.
Rapid high-resolution measurement of DNA replication timing by droplet digital PCR
GEO Series GSE114480. Homo sapiens. 2 samples. Type: Other.
Caenorhabditis elegans high resolution developmental transcriptomic time-course
GEO Series GSE60755. Caenorhabditis elegans. 139 samples. Type: Expression profiling by high throughput sequencing.
High temporal resolution RNA-seq time series of serum-starved T98G human glioblastoma cells responding to serum stimulation
GEO Series GSE138662. Homo sapiens. 41 samples. Type: Expression profiling by high throughput sequencing.
Amphimedon queenslandica high resolution developmental transcriptomic time-course
GEO Series GSE60483. Amphimedon queenslandica. 124 samples. Type: Expression profiling by high throughput sequencing.
Dual RNA-seq – High-resolution comparative Dual RNA-seq time-course
GEO Series GSE67758. Homo sapiens; Salmonella enterica subsp. enterica serovar Typhimurium str. SL1344. 51 samples. Type: Expression profiling by high throughput sequencing.
Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis (time series)
GEO Series GSE29642. Arabidopsis thaliana. 48 samples. Type: Expression profiling by array.
Intricate genetic program underlying hypoxia-induced dormancy in Mycobacterium tuberculosis revealed by high-resolution transcriptional time-course
GEO Series GSE116353. Mycobacterium tuberculosis. 81 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome dynamics in early zebrafish embryogenesis determined by high-resolution time course analysis
GEO Series GSE83395. Danio rerio. 180 samples. Type: Expression profiling by array.
High resolution time course transcriptome of Plasmodium falciparum parasite 3D7 strain by RNA-seq
GEO Series GSE150484. Plasmodium falciparum. 24 samples. Type: Expression profiling by high throughput sequencing.
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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.