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113 results for “Temporal resolution”
Snow cover and snow water equivalent for: How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction?
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Data for: Tracking the temporal dynamics of insect defoliation by high-resolution radar satellite data
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Denitrification losses in response to N fertiliser rates - integrating high temporal resolution N2O, in-situ 15N2O and 15N2 measurements and fertiliser 15N recoveries in intensive sugarcane systems
Denitrification is a key process in the global nitrogen (N) cycle, causing both nitrous oxide (N2O) and dinitrogen (N2) emissions. However, estimates of seasonal denitrification losses (N2O+N2) are scarce, reflecting methodological difficulties in measuring soil-borne N2 emissions against the high atmospheric N2 background and challenges regarding their spatio-temporal upscaling. This study investigated N2O+N2 losses in response to N fertiliser rates (0, 100, 150, 200 and 250 kg N ha-1) on two intensively managed tropical sugarcane farms in Australia, by combining automated N2O monitoring, in-situ N2 and N2O measurements using the 15N gas flux method and fertiliser 15N recoveries at harvest. Dynamic changes in the N2O/(N2O+N2) ratio (< 0.01 to 0.768) were explained by fitting generalised additive mixed models (GAMMs) with soil factors to upscale high temporal-resolution N2O data to daily N2 emissions over the season. Cumulative N2O+N2 losses ranged from 12 to 87 kg N ha-1, increasing non-linearly with increasing N fertiliser rates. Emissions of N2O+N2 accounted for 31–78% of fertiliser 15N losses and were dominated by environmentally benign N2 emissions. The contribution of denitrification to N fertiliser loss decreased with increasing N rates, suggesting increasing significance of other N loss pathways including leaching and runoff at higher N rates. This study delivers a blueprint approach to extrapolate denitrification measurements at both temporal and spatial scales, which can be applied in fertilised agroecosystems. Robust estimates of denitrification losses determined using this method will help to improve cropping system modelling approaches, advancing our understanding of the N cycle across scales.
Seeing the light: high temporal frequency (5-10min resolution) measurements of dissolved oxygen, photosynthetically active radiation, temperature, and depth used to estimate metabolism in restored and unrestored Baltimore streams.
The continually increasing global population residing in urban landscapes impacts numerous ecosystem functions and services provided by urban streams. Urban stream restoration is often employed to offset these impacts and conserve or enhance the various functions and services these streams provide. Despite the assumption that ‘if you build it, [the function] will come’, current understanding of the effects of urban stream restoration on stream ecosystem functions are based on short term studies which may not capture variation in restoration effectiveness over time. We quantified the impact of stream restoration on nutrient and energy dynamics of urban streams by studying 10 urban stream reaches (five restored, five unrestored) in the Baltimore, Maryland, USA, region over a two-year period. We measured gross primary production (GPP) and ecosystem respiration (ER) at the whole-stream scale continuously throughout the study and nitrate (NO3-N) spiraling rates seasonally (spring, summer, autumn) across all reaches. There was no significant restoration effect on NO3-N spiraling across reaches. However, there was a significant canopy cover effect on NO3-N spiraling, and directly comparing paired sets of unrestored-restored reaches showed that restoration does affect NO3-N spiraling after accounting for other environmental variation. Furthermore, there was a change in GPP:ER seasonality, with restored and open-canopied reaches exhibiting higher GPP:ER during summer. The restoration effect, though, appears contingent upon altered canopy cover, which is likely to be a temporary effect of restoration and is a driver of multiple ecosystem services, e.g., habitat, riparian nutrient processing. Our results suggest that decision-making about stream restoration, including evaluations of nutrient benefits, clearly needs to consider spatial and temporal dynamics of canopy cover and tradeoffs among multiple ecosystem services. Here we provide the raw dissolved oxygen, temperature, li
Data and models for "Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery" by Lagerquist et al.
<p><span><span><span>The file geocenter_models.tar contains all models comprising the GeoCenter ensemble: 3 convolutional neural networks (CNN), 3 isotonic-regression files (one for correcting each CNN’s mean estimate), and 3 more isotonic-regression files (one for correcting each CNN’s ensemble spread). Every model is found in a subdirectory whose names indicate which infrared (IR) wavelengths are used as input to the CNN. For example:</span></span></span></p> <ul> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/model.weights.h5: An HDF5 file containing the trained CNN that uses data from bands 7, 10, 16 (corresponding to 3.9, 7.34, and 13.3 microns on the GOES ABI imager). The trained CNN can always be read by neural_net_utils.read_model() in the ml4tccf library (https://doi.org/10.5281/zenodo.15116854).</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/model_metadata.p: A Pickle file containing metadata for the trained CNN. This file is needed to read the CNN itself with neural_net_utils.read_model(). Otherwise, you will probably never need to access this metafile directly.</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/isotonic_regression/isotonic_regression.dill: A Dill file </span></span></span><span><span><span>containing isotonic-regression models used to bias-correct the ensemble mean from the same CNN. </span></span></span><span><span><span> The trained isotonic-regression models can always be read by scalar_isotonic_regression.read_file() in the ml4tccf library. Note that there are technically two isotonic-regression models for every CNN’</span></span></span><span><span><span>s ensemble mean</span></span></span><span><span><span>: one that bias-corrects the </span></span></span><em><span><span><span>x</span></span></span></em><span><span><span>-coordinate of the TC-center, another that bias-corrects the </span></span></span><em><span><span><span>y</span></span></span></em><span><span><span>-coordinate.</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/</span></span></span><span><span><span>uncertainty_calibration</span></span></span><span><span><span>/</span></span></span><span><span><span>uncertainty_calibration.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble spread from the same CNN. In the ml4tccf code, I make a distinction between “isotonic_regression” (correcting the ensemble mean) and “uncertainty_calibration” (correcting the ensemble spread), but note that both models are isotonic regression and use the sklearn.isotonic.IsotonicRegression class. The trained uncertainty-calibration models can always be read by scalar_uncertainty_calibration.read_file() in the ml4tccf library. Again, note that there are technically two uncertainty-calibration models per CNN: one for spread in the </span></span></span><span><span><span><em>x</em></span></span></span><span><span><span>-coordinate, one for spread in the </span></span></span><span><span><span><em>y</em></span></span></span><span><span><span>-coordinate.</span></span></span></p> </li> </ul> <p><span> </span></p> <p><span><span><span>As mentioned above, every trained CNN can be read by neural_net_utils.read_model(). Also, every trained CNN can be applied to new data (inference mode) by neural_net_utils.apply_model(). The input argument model_object should be the object returned by neural_net_utils.read_model(), and I suggest setting num_examples_per_batch = 10 to avoid out-of-memory errors. The only other input argument is predictor_matrices, which is a list of two numpy arrays. The first numpy array contains IR imagery centered at the first-guess TC center, and the second numpy array contains ATCF scalars. The first numpy array should have dimensions S (number of TC samples) x </span></span></span><span><span><span>3</span></span></span><span><span><span>00 (grid rows) x </span></span></span><span><span><span>3</span></span></span><span><span><span>00 (grid columns) x </span></span></span><span><span><span>9</span></span></span><span><span><span> (lag times) x 3 (wavelengths). Lag times should be in the following order: </span></span></span><span><span><span>240, 210, </span></span></span><span><span><span>180, 150, 120, 90, 60, 30, 0 min ago. Wavelengths should be in the order indicated by the subdirectory name. The numpy array itself should contain </span></span></span><em><span><span><span>normalized</span></span></span></em><span><span><span> brightness temperatures at the given lag times and wavelengths, following the grid specifications laid out in the journal paper (a </span></span></span><em><span><span><span>plate carrée</span></span></span></em><span><span><span> grid with 2-km spacing). The original IR data (brightness temperatures) must be normalized to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-scores using the same normalization parameters as in the journal paper, </span></span></span><em><span><span><span>i.e.,</span></span></span></em><span><span><span> those based on the training data. See details below. The second numpy array in predictor_matrices should have dimensions S (number of TC samples) x 9 (variables). The variables must in the order: absolute latitude, cosine of longitude, sine of longitude, TC intensity, minimum central pressure, tropical flag, subtropical flag, extratropical flag, disturbance flag. The journal paper contains details on all these variables in one table. These variables must come from A-deck files at the </span></span></span><span><span><span>second-</span></span></span><span><span><span>most recent synoptic time. Like the IR data, these ATCF scalars must be normalized to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-scores using the same normalization parameters as in the journal paper. See details below.</span></span></span></p> <p> </p> <p><span><span><span>Once you have predictions (estimated TC-center locations) from a CNN, you can bias-correct these predictions. To read the isotonic-regression model for the given CNN’s ensemble mean, use scalar_isotonic_regression.read_file() in the ml4tccf library. To apply the same model, use scalar_isotonic_regression.apply_models(). For the CNN’s ensemble spread, use scalar_uncertainty_calibration.read_file() and scalar_uncertainty_calibration.apply_models().</span></span></span></p> <p> </p> <p><span><span><span>To normalize the IR data, you will need the file ir_satellite_normalization_params.tar included with this dataset. Within the tar file is a single zarr file. You can read the zarr file with normalization.read_file() in the ml4tccf library; then you can normalize new data with normalization.normalize_data().</span></span></span></p> <p> </p> <p><span><span><span>To normalize the ATCF data, you will need the file a_deck_normalization_params.nc included with this dataset. This is a NetCDF file, containing the full set of training values for all 5 ATCF variables that are normalized (the binary storm-type flags are not normalized). You can read this file using any of the standard Python methods for reading NetCDF files, such as xarray.open_dataset(). To normalize new ATCF data, you can use the method normalization._normalize_one_variable(), where the argument actual_values_training is the list of training values from a_deck_normalization_params.nc for the given variable, while actual_values_new is the list of values to be normalized (currently in physical units, to be converted to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-score units).</span></span></span></p>
BreizhSR: multi-temporal cross-sensor super-resolution of satellite imagery
<h1>BreizhSR, a super-resolution Sentinel-2 to SPOT-6/7 dataset </h1> <h2>1. Dataset motivation</h2> <p><strong>BreizhSR</strong> is a dataset targetting super-resolution of (RGB bands of) Sentinel-2 images by providing time series colocated in space and time with SPOT-6/7 acquisitions. This dataset is composed of cloud free Sentinel-2 time series (visible bands at 10m resolution) and SPOT-6/7 pansharpened color images resampled 2.5m resolution. The study area is the region of Brittany (Breizh in the local language), located on the northwestern coast of France with an oceanic climate. The dataset covers about 35 000 km² with mostly agricultural areas (about 80 %). All acquisitions are from 2018 in the Brittany region of France.</p> <h2>2. Dataset organization</h2> <p>The dataset folder follows the structure detailed below :</p> <p><code>BreizhSR</code><br><code>├── dataset_test.pkl</code><br><code>├── dataset_train.pkl</code><br><code>├── README.md</code><br><code>├── x</code><br><code>├── x_test</code><br><code>├── y</code><br><code>└── y_test</code></p> <p>The <code>README.md</code> file contains the same information as this description.</p> <p>Actual image patches are stored in the <code>x</code> and <code>x_test</code> folders for Sentinel-2 patches, and in the <code>y</code> and <code>y_test</code> folders for ground truth SPOT patches. Subfolders are organized using a integer identifier (e.g. <code>8355</code>) that denote the series identifier. Therefore, for the S2 series <code>x/8355</code>, the corresponding SPOT patch is in subfolder <code>y/8355</code>.</p> <p>This organization and additional metadata are described in two <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html">Pandas Dataframes</a> : <code>dataset_train.pkl</code> and <code>dataset_test.pkl</code>. These files are Dataframes serialized using the <a href="https://docs.python.org/3/library/pickle.html">pickle Python serialization protocol</a>. The columns available in these Dataframes are described in the table below.</p> <table> <tbody> <tr> <td>x</td> <td>y</td> <td>wkt</td> <td>spot6_name</td> <td>sen2_acquisitions</td> <td>dates_sen2</td> <td>dates_spot6</td> <td>split</td> </tr> <tr> <td>Latitude of the center point (expressed in Lambert 93 CRS)</td> <td>Longitude of the center point (expressed in Lambert 93 CRS)</td> <td>Area of interest geometry in well-known text format</td> <td>Path to the SPOT ground truth</td> <td>Paths to the Sentinel-2 input series</td> <td>Acquisition dates for the Sentinel-2 images</td> <td>Acquisition date for the SPOT ground truth</td> <td>`train` or `test`</td> </tr> </tbody> </table> <h2>3. Data collection and preprocessing</h2> <h3>Sentinel-2</h3> <p>Sentinel-2 constellation has twin satellites launched by the European Space Agency (ESA) in 2015 and 2017 that cover all Earth’s surfaces every five days at the equator. Level-2A images of the BreizhSR dataset are gathered via the THEIA platform, which employs the MAJA pre-processing algorithm to obtain atmospherically corrected ground reflectance. To match the SPOT-6 spectral characteristics, only RGB bands at a 10-meter spatial resolution (B4, B3,and B2) are used in the analysis. The images were collected for the nine tiles covering the Brittany region from the 1st of April 2018 to the 31st of August 2018, filtering images with a cloud cover under 5 %. Since the SPOT-6 data was acquired in the summer of 2018, the Sentinel-2 time period was chosen to include images from before and after the SPOT-6 acquisitions while staying in a range of similar seasonal and climate conditions.</p> <p>Sentinel-2 tiles are cropped into 3x74x74 patches. The dataset is preprocessed with a min-max normalization, using the 2% and 98% percentile as an estimation of minimum and maximum values of Sentinel-2 data to take into account the presence of outliers due to artifacts such as clouds and their shadows.</p> <h3>SPOT-6/7</h3> <p>Orthorectified SPOT data under the Licence Ouverte is collected from the <a href="https://openspot-dinamis.data-terra.org/">DINAMIS</a> platform. Multispectral images at 6m resolution are pansharpened using the panchromatic 1.5m reference using the RCS algorithm <a href="https://www.orfeo-toolbox.org/CookBook/Applications/app_BundleToPerfectSensor.html">Orfeo ToolBox</a>, similar to the Brovey pansharpening algorithm. The pansharpened tiles are preprocessed with a min-max normalization, downsampled at 2.5m resolution and patches are finally cropped with dimensions 3x296x296.</p> <h2>4. License</h2> <p>SPOT images and the Sentinel-2 Theia L2A products are released under the <a href="https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf">Licence Ouverte 2.0</a> from the French government. This dataset contains modified Coprnicus Sentinel data from 2018, made available under free access by EU law. Other files in the dataset are licensed under Creative Commons Attribution 4.0 (CC BY 4.0).</p> <h3>Acknowledgements</h3> <p>We thank the support of GDR IASIS for funding this work under the SESURE project, the DINAMIS consortium, CNES/Airbus and IGN for access to the SPOT-6 data, and ESA for access to Sentinel-2 data. During the conduct of this research, Simon Donike received a European scholarship to engage in Master Copernicus in Digital Earth, Erasmus Mundus Joint Master Degree (EMJMD). We thank Dirk Tiede (Uni. Salzburg) for his help and feedback on BreizhSR. This work was performed using HPC resources from GENCI–IDRIS (grant 2022-AD011013003).</p>
High temporal resolution microclimate records
<b>Description: </b><p>Microclimate records collected at very high temporal resolution (10 sec) at a small number of sites</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/111"><b>Microclimate stratification in modified forests</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=52">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>High temporal resolution microclimate records</b> (Worksheet Data)</p><p>Dimensions: 229330 rows by 7 columns</p><p>Description: Microclimate records collected at very high temporal resolution (10 sec) at a small number of sites</p><p>Fields: </p><ul><li><b>Plot</b>: Location of record (Field type: Location)</li><li><b>time</b>: Date and time of record (Field type: Datetime)</li><li><b>Temp</b>: Air temperature 1 m above ground (Field type: Numeric)</li><li><b>RH</b>: Relative humidity. (Field type: Numeric)</li><li><b>LoggerType</b>: Make of datalogger that was used (Lascar or iButton) (Field type: ID)</li><li><b>LoggerID</b>: Unique reference number for the datalogger (Field type: ID)</li></ul><br></li></ol><p><b>Date range: </b>2015-12-11 to 2016-12-07</p><p><b>Latitudinal extent: </b>4.7104 to 4.7523</p><p><b>Longitudinal extent: </b>116.9484 to 117.6278</p>
Event-based hyperspectral EELS: towards nanosecond temporal resolution
<p>Here we present the two data sets presented in the work <a href="https://arxiv.org/abs/2110.01706">Event-based hyperspectral EELS: towards nanosecond temporal resolution</a>. Data was processed using Rust.</p> <p>In ASI Cheetah Timepix3, we have two different kinds of events: electron and TDC events (little-endian). Data chunk package is a 8-byte data that begins with "TPX3". Electron hit is a 8-byte packet that contains "0xb" in 60-63 bits. TDC data packet is a 8-byte packet that contains "0x6" in 60-63 bits. 56-59 bits identify if it is comes from TDC Line 1 or 2 and also identifies if it is a falling or a rising edge.</p>
Tracing pathways from high-resolution tractography, transcription, and temporal dimensions
<p>The neural circuits supporting human cognition are topics of enduring interest. The lack of tools available to map circuits has precluded our ability to trace the evolution of the human connectome. We harnessed high-resolution connectomic, anatomic, and transcriptomic data to develop enhanced tools to test for modifications in developmental programs across species. We found corresponding ages across species and transcriptionally define neurons with stereotypical projections in humans and macaques. We used these data to test for modifications in frontal cortex circuit. Frontal cortex circuitry development is extended in primates, which is concomitant with an expansion in cortico-cortical pathways compared with mice in adulthood. These parameters varied little across humans and macaques. We identify a collection of conserved features in frontal cortex circuits in studied primates. We demonstrate that the integration of transcriptional and connectomic data across temporal dimensions is a robust approach to trace the evolution of connections in primates. This dataset contains scripts as well as diffusion MR scans of mouse brains.</p>
National High-Resolution Cropland Classification of Japan with Agricultural Census Information and Multi-temporal Multi-modality datasets
<p>Multi-modality datasets offer advantages for processing frameworks with complementary information, particularly for large-scale cropland mapping. Extensive training datasets are required to train machine learning algorithms, which can be challenging to obtain. To alleviate the limitations, we extract the training samples from the agricultural census information. We focus on Japan and demonstrate how agricultural census data in 2015 can map different crop types for the entire country. Due to the lack of Sentinel-2 datasets in 2015, this study utilized Sentinel-1 and Landsat-8 collected across Japan and combined observations into composites for different prefecture periods (monthly, bimonthly, seasonal). Recent deep learning techniques have been investigated the performance of the samples from agricultural census information.<br> Finally, we obtain nine crop types on a countrywide scale (around 31 million parcels) and compare our results to those obtained from agricultural census testing samples as well as those obtained from recent land cover products in Japan. The generated map accurately represents the distribution of crop types across Japan and achieves an overall accuracy of 87% for nine classes in 47 prefectures.</p>
A dynamic modelling approach to quantify pollution contributions from critical source areas within watersheds at fine temporal resolutions
<p>The support data for <em>A dynamic modelling approach to quantify pollution contributions from critical source areas within watersheds at fine temporal resolutions</em></p>
Tracing pathways from high-resolution tractography, transcription, and temporal dimensions
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BErkeley High Resolution (BEHR) OMI NO2 Prototype High Temporal Resolution Product
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Formation binning: a new method for increased temporal resolution in regional studies, applied to the Late Cretaceous dinosaur fossil record of North America
<p>The advent of palaeontological occurrence databases has allowed for detailed reconstruction and analyses of species richness through deep time. While a substantial literature has evolved ensuring that taxa are fairly counted within and between different time periods, how time itself is divided has received less attention. Stage-level or equal-interval age bins have been frequently used for regional and global studies in vertebrate palaeontology. However, when assessing diversity at a regional scale, these resolutions can prove inappropriate with the available data. Herein, we propose a new method of binning geological time for regional studies that intrinsically incorporates the chronostratigraphic heterogeneity of different rock formations to generate unique stratigraphic bins. We use this method to investigate the diversity dynamics of dinosaurs from the Late Cretaceous of the Western Interior of North America prior to the Cretaceous–Palaeogene mass extinction. Increased resolution through formation binning pinpoints the Maastrichtian diversity decline to between 68–66 Ma, coinciding with the retreat of the Western Interior Seaway. Diversity curves are shown to exhibit volatile patterns using different binning methods, supporting claims that heterogeneous biases in this time-frame affect the pre-extinction palaeobiological record. We also show that apparent high endemicity of dinosaurs in the Campanian is a result of non-contemporaneous geological units within large time bins. This study helps to illustrate the utility of high-resolution, regional studies to supplement our understanding of factors governing global diversity in deep time and ultimately how geology is inherently tied to our understanding of past changes in species richness.</p>
Data from: Drivers of assemblage-wide calling activity in tropical anurans and the role of temporal resolution
<p>1. Temporal scale in animal communities is often associated with seasonality, despite the large variation in species activity during a diel cycle. A gap thus remains in understanding the dynamics of short-term activity in animal communities.</p> <p>2. Here we assessed calling activity of tropical anurans and addressed how species composition varied during night activity in assemblages along gradients of local and landscape environmental heterogeneity.</p> <p>3. We investigated 39 anuran assemblages in the Pantanal wetlands (Brazil) with passive acoustic monitoring during the peak of one breeding season and first determined changes in species composition between night periods (early, mid, and late) using two temporal resolutions (1-hour and 3-hour intervals). Then, we addressed the role of habitat structure (local and landscape heterogeneity variables from field-based and remote sensing metrics) and ecological context (species richness and phylogenetic relatedness) in determining changes in species composition (i) between night periods and (ii) across days.</p> <p>4. Nocturnal calling activity of anuran assemblages varied more within the 1-hour resolution than the 3-hour resolution. Differences in species composition between early and late-night periods were related to local habitat structure and phylogenetic relatedness, while a low variation in compositional changes across days was associated with low-heterogeneous landscapes. None of these relationships were observed using the coarser temporal resolution (3-hour).</p> <p>5. Our findings on the variation of calling activity in tropical anuran assemblages suggest potential trades-off mediated by fine-temporal partitioning. Local and landscape heterogeneity may provide conditions for spatial partitioning, while the relatedness among co-signaling species provides cues on the ecological overlap of species with similar requirements. These relationships suggest a role of niche dimensional complementarity on the structuring of these anuran assemblages over fine temporal scales. We argue that fine-temporal differences between species in breeding activity can influence the outcome of species interaction and that addressing temporal scaling issues can improve our understanding of dynamics of animal communities.</p>
Data from: The effects of temporal resolution on species turnover and on testing metacommunity models
Patterns of low temporal turnover in species composition found within long paleoecological time series contrast with the high turnover predicted by dispersal-limited neutral metacommunity models and thus have been used to support non-neutral models. However, predictions assume temporal resolution on the scale of a season or year whereas individual fossil assemblages are typically time-averaged to decadal or centennial time scales. Here, we simulate the effects of time averaging by building time-averaged assemblages from local dispersal-limited non-averaged (living) assemblages and compare the predicted species turnover with observed patterns in mollusk and ostracod fossil records. Time averaging substantially reduces temporal turnover such that neutral predictions converge with those of trade-off and density-dependent models, and tends to decrease species dominance and increase the proportion of rare species. Observed turnover rates are comparable to an appropriately scaled neutral model: patterns of high community stability can be produced or reinforced by time averaging alone. The community attributes of local time-averaged assemblages approach those of the metacommunity. Time-averaged assemblages are thus unlikely to capture attributes arising from processes operating at small spatial scales, but should do well at capturing the turnover and diversity parameters of metacommunities, and thus will be a valuable basis for analyzing the large-scale processes that determine metacommunity evolution.
Alpha-band oscillations and visual temporal resolution: An expansion and partial replication of Samaha & Postle's 2015 study: "The Speed of Alpha-Band Oscillations Predicts the Temporal Resolution of Visual Perception"
<p>Data for the study "Alpha-band oscillations and visual temporal resolution: An expansion and partial replication of Samaha & Postle’s 2015 study: “The Speed of Alpha-Band Oscillations Predicts the Temporal Resolution of Visual Perception”"</p> <p>Contents consist of curated EEG data for statistical analysis, statistical analysis workflow and code and Matlab code for the behavioural flash fusion task.</p>
Supplementary Movie 1 from: A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution
<p>This record contains<strong> Supplementary Movie 1</strong> from:</p> <p><strong>A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution</strong></p> <p>Andreas Kaczmarczyk, Simon van Vliet, Roman Peter Jakob, Raphael Dias Teixeira, Inga Scheidat, Alberto Reinders, Alexander Klotz, Timm Maier, Urs Jenal</p> <p>Biozentrum, University of Basel, 4056 Basel, Switzerland</p> <p>Correspondence to: urs.jenal[at]unibas.ch, andreas.kaczmarczyk[at]unibas.ch</p> <p> </p>
Characterisation and calibration of PM sensors at high temporal resolution
<p>This repository contains the data used for the Chapter 7 of my PhD thesis:</p> <blockquote> <p>Bulot, Florentin (2022) Systematic studies of commodity particulate matter air pollution sensors. <em>University of Southampton, Doctoral Thesis</em>, 272pp. http://eprints.soton.ac.uk/id/eprint/458166</p> </blockquote> <p>It contains three files:</p> <ul> <li>fidas_10s.csv - contains the PM concentration data from the Fidas 200S</li> <li>rh_temperature.csv - contains the relative humidity and temperature data recorded by each of the SHT35 sensors in each air quality monitor</li> <li>sensors_10s_avg.zip - a zip file containing sensors_10s_avg.csv which contains the PM concentration data measured by the commodity PM sensors from Plantower PMS5003 and Sensirion SPS30 models</li> </ul>
Spatio-temporal plant hormonomics: From tissue to subcellular resolution
<p>Repository files for the review publication "Spatio-temporal plant hormonomics: From tissue to subcellular resolution".</p> <p>Repository_File_1: Statistical methods</p> <p>Repository_File_2: Raw data from Web of Science for targeted plant hormone analysis</p> <p>Repository_File_3: Raw data from Web of Science for untargeted plant hormone analysis</p>
ScienceDex guides
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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.