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3,673 results for “Temporal”
Multi-Temporal Cloud Gap Imputation With HLS Data Across CONUS
<p>This release contains the version 1.0 of the dataset which was used in <a href="https://arxiv.org/abs/2404.19609">Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model</a> and is included as one of the tasks in the <a href="https://madewithclay.org/challenge">AI for Earth Challenge 2024</a>.</p>
Community science approach reveals temporal and eutrophication-related spatial patterns in bladderwrack-associated invertebrate fauna
<p>Data related to the "Community science approach reveals temporal and eutrophication-related spatial patterns in bladderwrack-associated invertebrate fauna" paper by Salo, Nieminen, Salovius-Laurén and Rinne published in Estuarine, Coastal and Shelf Science in 2024. <a href="https://doi.org/10.1016/j.ecss.2024.108822">https://doi.org/10.1016/j.ecss.2024.108822</a></p> <p>The data describes the community data collected with the community science method described in the paper. </p>
Dautan et al 2024 " Gut-Initiated Alpha Synuclein Fibrils Drive Parkinson's Disease Phenotypes: Temporal Mapping of non-Motor Symptoms and REM Sleep Behavior Disorder"
<p><span>Parkinson’s disease (PD) is characterized by progressive motor as well as less recognized non-motor symptoms that arise often years before motor manifestation, including sleep and gastrointestinal disturbances. Despite the heavy burden on the patient’s quality of life, these non-motor manifestations are poorly understood. To elucidate the temporal dynamics of the disease, we employed a mice model involving injection of alpha-synuclein (αSyn) pre-formed fibrils (PFF) in the duodenum and antrum as a gut-brain model of Parkinsonism. Using anatomical mapping of αSyn PFF propagation and behavioral and physiological characterizations, we unveil a correlation between post-injection time the temporal dynamics of αSyn propagation and non-motor/motor manifestations of the disease. We highlight the concurrent presence of aggregates in key brain regions, expressing acetylcholine or dopamine and their functions in sleep duration, wakefulness, and particularly REM-associated atonia corresponging to REM behavioral disorder-like symptoms. This study presents a novel and in-depth exploration into the multifaceted nature of PD, unraveling the complex connections between α-synucleinopathies, gut-brain connectivity, and the emergence of non-motor phenotypes.</span></p>
Data repository for "Spatio-temporal trends of Holocene peat carbon accumulation in China: climatic and human drivers"
<p>Dating results collected from peatlands in China are used to calculate the spatiotemporal trends of the Holocene peat accumulation rate (PAR) and net carbon balance (NCB), including all original dating, calculated intermediate results, and final composite results. This file includes a total of 14 tables (Supplementary Tables S1-S14).</p>
A spatio-temporal dataset for ecophysiological monitoring of urban trees
<p>A dataset was produced for 117 urban trees in four monospecific tree rows in the city of Rennes, northwestern France. The trees were measured in nine 2- to 3-day measurement sessions from Apr-Sep 2021. The dataset includes (i) leaf traits (i.e., contents of pigments, water and dry matter) measured <em>in situ</em> and in the laboratory; (ii) plant area density measured <em>in situ</em> under the canopy and (iii) georeferenced data that describe the location, geometry and species of the trees. The dataset provides an original overview of dynamics of the contents of pigments, water and dry matter for four tree species grown under urban conditions. It can be used for several purposes, such as identifying trees’ responses/behaviors in relation to their urban environment or climate conditions.</p> <p>The repository comprised 3 files : </p> <ul> <li><strong>DATASET_PART1.csv</strong> : This file contains leaf trait measurements</li> <li><strong>DATASET_PART2.csv</strong> : This file contains plant area density measurements </li> <li><strong>DATASET_PART3.gpkg</strong> : This file contains two spatial vector layers: (1) <em>CROWN_EXTENT </em>that is<em> </em>a polygon layer describing tree crowns and (2) <em>TRUNK_LOCATION</em> that is a point layer describing tree location.</li> </ul> <p>More details on the study site, protocols and data can be found in the following reference:</p> <p>Théo Le Saint, Jean Nabucet, Cécile Sulmon, Julien Pellen, Karine Adeline, Laurence Hubert-Moy, A spatio-temporal dataset for ecophysiological monitoring of urban trees, Data in Brief, Volume 57, 2024, 111010, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.111010.</p>
Supplemental data for "Inequitable spatial and temporal patterns in the distribution of multiple environmental risks and benefits in Metro Vancouver"
<p><strong>DemoEnPoC2016.csv/DemoEnPoC2006.csv:</strong></p> <p>This is a table including environmental and demographic (Census variables) data at postal code level for Metro Vancouver in the year 2006 and 2016. The environmental data (SO2 metrics, PM2.5 metrics, Calculated ozone metrics, NO2 data, NDVI metrics, and Canadian Active Living Environments Index (Can-ALE) indexed to DMTI Spatial Inc. postal codes) were extracted from CANUE (Canadian Urban Environmental Health Research Consortium). The demographic data is extracted from Canadian Census analyzer (https://datacentre.chass.utoronto.ca/), the deprivation index is downloaded from from the Institut national de santé publique du Québec (INSPQ). </p> <p><strong>DGRwithLable:</strong></p> <p>This is the Dissemination Geographies Relationship File for the 2021 census year (Statistics Canada, 2021) with the lable of urban or rural, indicating which dissemination area (DA) is identified as urban and included in this study. The urban area is named as population certer. </p> <p><strong>Aggregation and SS Determination:</strong></p> <p>This script contains code for:</p> <ul> <li>Aggregating postal code level data to the Dissemination Area (DA) level.</li> <li>Eliminating rural DAs.</li> <li>Converting environmental data into ordinal categories using quartile and even break methods.</li> <li>Identifying sweet and sour spots for each DA based on these methods.</li> </ul> <p><strong>SSEJ Analysis:</strong></p> <p>This script includes code for:</p> <ul> <li>Creating violin and box plots to illustrate descriptive statistics of demographic groups across different environmental categories (sweet, sour, risky, and medium).</li> <li>Performing linear regression analyses between environmental categories and demographic variables.</li> </ul> <p><strong>SS Heatmap:</strong></p> <p>This script comprises code for:</p> <ul> <li>Summarizing the results of the linear regression analyses.</li> <li>Assessing changes in inequities among demographic groups between 2006 and 2016.</li> <li>Visualizing regression coefficients through heatmaps.</li> </ul> <p> </p>
Two-time correlation function based on speckle patterns from x-ray photon correlation spectroscopy associated with "Intermittent cluster dynamics and temporal fractional diffusion in a bulk metallic glass" (scientific article published in Nature Communications, 2024)
<p>This dataset consists of contrast data, i.e., the two-time correlation function, based on speckle patterns measured at the at the 8ID-E beamline of the Advanced Photon Source at Argonne National Laboratory.</p> <p>Experimental details are stated in the paper specified under "related work" and in the accompanying supplementary information.</p> <p>You are welcome to use this dataset in compliance with the CC BY 4.0 licence assigned to this dataset.</p> <p>Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.</p> <p>____________________________________________________________________</p> <p>The data consists of 32 text files in total, which correspond to the main and lower panel Figure 2 of the main publication. </p> <p>30 of these text files are contrast data, which are named "contrast_DT250s_nn.text" wiith "nn" as the identifier of consecutive data sets going from 1 to 30. Each data set consists of p rows and q columns, DT250s denotes the time resolution of data points, which is 250 s along both row and column values.</p> <p>The data set called "Time_Contrast_1to30s.txt" states the start time in seconds of the first data point of each of the thirty contrast data set.</p> <p>The data set called "ScatteredIntensity.txt" states the scattered intensity at full time resolution, i.e. 2.5 s.</p> <p>The files are plain text files with the data points separated by "space" along rows and "new line" along columns.</p>
Haxby et al. (2001): Faces and Objects in Ventral Temporal Cortex (fMRI)
<pre><a href="http://data.pymvpa.org/datasets/haxby2001/">http://data.pymvpa.org/datasets/haxby2001/</a> This is a block-design fMRI dataset from a study on face and object representation in human ventral temporal cortex. It consists of 6 subjects with 12 runs per subject. In each run, the subjects passively viewed greyscale images of eight object categories, grouped in 24s blocks separated by rest periods. Each image was shown for 500ms and was followed by a 1500ms inter-stimulus interval. Full-brain fMRI data were recorded with a volume repetition time of 2.5s, thus, a stimulus block was covered by roughly 9 volumes. This dataset has been repeatedly reanalyzed. For a complete description of the experimental design, fMRI acquisition parameters, and previously obtained results see the references_ below. Terms Of Use ============ The original authors of :ref:`Haxby et al. (2001) <HGF+01>` hold the copyright of this dataset and made it available under the terms of the `Creative Commons Attribution-Share Alike 3.0`_ license. .. _Creative Commons Attribution-Share Alike 3.0: http://creativecommons.org/licenses/by-sa/3.0/</pre> <pre>References ========== :ref:`Haxby, J., Gobbini, M., Furey, M., Ishai, A., Schouten, J., and Pietrini, P. (2001) <HGF+01>`. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430. :ref:`Hanson, S., Matsuka, T., and Haxby, J. (2004) <HMH04>`. Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001). revisited: is there a “face” area? NeuroImage 23, 156–166. :ref:`O’Toole, A. J., Jiang, F., Abdi, H., & Haxby, J. V. (2005) <OJA+05>`. Partially distributed representations of objects and faces in ventral temporal cortex. Journal of Cognitive Neuroscience, 17, 580–590. :ref:`Hanke, M., Halchenko, Y.O., Sederberg, P.B., Olivetti, E., Fründ, I., Rieger, J.W., Herrmann, C.S., Haxby, J.V., Hanson, S. and Pollmann, S (2009) <HHS+09b>`. PyMVPA: a unifying approach to the analysis of neuroscientific data. Frontiers in Neuroinformatics, 3:3.</pre> <p> </p>
Dataset of E. huxleyi blooms: spatio-temporal distribution and their impact on high-latitudinal marine environments (1998-2016)
<p>Dataset of coccolithophore blooms in polar seas of the Northern Hemisphere, viz. the North, Labrador (with adjacent North Atlantic open waters), Norwegian, Barents, Greenland and Bering seas are presented for the period 1998-2016. Seas are divided into 4 regions, for each of them continuous data series (as 8-days composites) are published, including information about bloom spatial masks, coccolith concentration, particulate inorganic carbon content and CO<sub>2</sub> partial pressure in water increment driven by coccolithophores.</p> <p>Datasets are published as NetCDF files with full metadata/descriptions and with GDAL support.</p> <p>Additional information (regions configuration, data access instructions) is provided alongside the data.</p> <p>Naming convention is: <strong>niersc_cocco_<version of dataset>_<region>_<start date>_<end date>.nc</strong></p>
Dataset and replication package for Temporal Discounting in Software Engineering: A Replication Study
<p>Dataset and replication package for the paper Temporal Discounting in Software Engineering: A Replication Study (Fagerholm, F., Becker, C., Chatzigeorgiou, A., Betz, S., Duboc, L., Penzenstadler, B., Mohanani, R., Venters, C. (2019). Temporal Discounting in Software Engineering: A Replication Study. 13th ACM/IEEE International Symposium of Empirical Software Engineering and Measurement (ESEM 2019)). The dataset consists of answers to a questionnaire on temporal discounting in a technical debt context. Two questionnaire templates illustrate how to gather the data for professional and student participants. An analysis script is provided which shows the details of the calculations and analyses performed for the paper. More information is given in the description file.</p>
Temporally enhanced RSEI and Nighttime Lights Reveal Long-Term Ecological Changes and Effective Protection in China's Inaugural National Parks
<p>China's inaugural national parks play a crucial role in preserving biodiversity and maintaining ecosystem services. These protected areas are characterized by diverse landscapes and sensitive ecological environments. Over recent decades, the interplay between intensified human activities and global climate change has posed significant challenges to the ecological quality of these regions. Accurate and scientific assessment of ecological quality is essential for informed management and policy-making.</p> <p>This dataset is based on multiple MODIS datasets, incorporating NDVI, LST, WET, and NDBSI as indicators. Using principal component analysis (PCA), we produced the Improved Remote Sensing Ecological Index (RSEI) for these parks from 2000 to 2022 at a 500m spatial resolution.</p> <p>The RSEI was calculated using four component indices: greenness, heat, dryness, and wetness. Data for dryness and wetness were derived from the 8-day composite 500m resolution surface reflectance product MOD09A1. Heat was calculated using the 8-day composite 1km resolution land surface temperature product MOD11A2, which was resampled to 500m resolution. Greenness was derived from the 16-day composite 500m resolution vegetation index product MOD13A1.</p> <p>The improved RSEI calculation method enhances the temporal stability and comparability of the data, making it more suitable for long-term ecological monitoring.</p> <p>The improved RSEI effectively integrates dynamic changes of multiple variables and offers better temporal comparability for long-term ecological monitoring. Our results indicate that the ecological environment quality within the inaugural national parks significantly improved over the study period, with more noticeable improvements following the implementation of pilot conservation programs.</p> <p>This dataset provides foundational information for understanding the long-term ecological trends in China's national parks. It serves as a crucial resource for researchers, policymakers, and conservationists dedicated to the sustainable management and development of these vital ecological regions.</p> <p>The dataset contains five RAR compressed files, each corresponding to one of the national parks. These files include the Remote Sensing Ecological Index (RSEI) data from 2000 to 2022 for each respective park:</p> <ul> <li><strong>NTLNP-RSEI.rar</strong>: Contains the RSEI data for the Northeast Tiger and Leopard National Park (NTLNP) from 2000 to 2022.</li> <li><strong>HTRNP-RSEI.rar</strong>: Contains the RSEI data for the Hainan Tropical Rainforest National Park (HTRNP) from 2000 to 2022.</li> <li><strong>WNP-RSEI.rar</strong>: Contains the RSEI data for the Wuyishan National Park (WNP) from 2000 to 2022.</li> <li><strong>SNP-RSEI.rar</strong>: Contains the RSEI data for the Sanjiangyuan National Park (SNP) from 2000 to 2022.</li> <li><strong>GPNP-RSEI.rar</strong>: Contains the RSEI data for the Giant Panda National Park (GPNP) from 2000 to 2022.</li> </ul> <p>Each of these compressed files includes the improved RSEI calculations for the respective national park, providing a comprehensive view of the ecological quality changes over the 22-year period.</p> <p>The details of the data are as follows:</p> <ul> <li><strong>Data Format</strong>: GeoTiff</li> <li><strong>Pixel Values</strong>: Represent RSEI, ranging from 0 to 1, with no units.</li> <li><strong>Compatibility</strong>: The data can be directly opened and processed using remote sensing and GIS software such as ENVI and ArcGIS.</li> <li><strong>Data Quality</strong>: Due to the application of water and snow masks to remove the influence of water bodies and snow/ice on the WET component, there are some missing data areas.</li> </ul> <p>These datasets offer valuable insights into the ecological quality changes within each national park over the specified period, making them essential for researchers, policymakers, and conservationists involved in the sustainable management and development of these protected areas.</p> <p>For using the data and code provided in this dataset, please cite the following paper:</p> <p>Wen, C., Long, T., He, G., Jiao, W., & Jiang, W. (2025). Temporally enhanced RSEI and nighttime lights reveal long-term ecological changes and effective protection in China’s inaugural national parks. <em>Ecological Indicators, 170</em>, 112981. <a href="https://doi.org/10.1016/j.ecolind.2024.112981" target="_new" rel="noopener">https://doi.org/10.1016/j.ecolind.2024.112981</a></p> <p>The calculation of the RSEI is completed using Google Earth Engine. The link to the calculation code is:</p> <p><a href="https://code.earthengine.google.com/fab5452cd224d1f06226aece4c1a1016">https://code.earthengine.google.com/089d74f423e91a0da9490f5098c55021</a></p>
Data For Scalco et al. Clinicopathological correlates of quantitative Amyloid-B Pathology in the Temporal Cortex: Machine learning analysis of 131 cases from an ADRC
<p>Dataset containing 131 de-identified whole slide images (WSIs) with a respective data dictionary. </p> <p><strong>Paper</strong>: Scalco, R., Oliveira, L.C., Lai, Z. et al. Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases. acta neuropathol commun 12, 134 (2024). https://doi.org/10.1186/s40478-024-01827-7</p> <p><strong>Details</strong>: A total of 131 .svs. WSIs, de-identified using svs-deidentifier v 0.9.1-beta (https://github.com/pearcetm/svs-deidentifier/releases). Dataset is uploaded in batches due to Zenodo data upload limitations.</p> <p><strong>Slide curation/preparation</strong>: All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (<a href="https://www.ucdmc.ucdavis.edu/alzheimers/">https://www.ucdmc.ucdavis.edu/alzheimers/</a>). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 between 20x and 40x magnification.</p> <p><strong>Code:</strong> Please refer to <a href="https://github.com/ucdrubinet/BrainSec">https://github.com/ucdrubinet/BrainSec</a> and <a href="https://github.com/keiserlab/plaquebox-paper">https://github.com/keiserlab/plaquebox-paper</a></p>
Data from "Resource pulses drive spatio-temporal dynamics of non-native bark beetles and wood borers"
<p>This is a compilation of datasets that were used for the publication entitled "Resource pulses drive spatio-temporal dynamics of non-native bark beetles and wood borers" by Eckehard G. BROCKERHOFF, Stephanie L. SOPOW, and Martin K.-F. BADER, published in the Journal of Applied Ecology, 'in press' in October 2024.</p> <p>Note: The date format is either (i) season (spring/summer/autumn/winter) plus a two-figure short form for the year (e.g., "autumn08" stands for autumn 2008), or (ii) just the year for an annual total in either four- or two-figure form in the file name (e.g., "reg2010sums.csv" or "reg10sums.csv" for the year 2010).</p> <p>1. File "mean_trap_catches.csv" = Data used for Fig. 1 - Mean trap catch data of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus over time in Kaingaroa forest stands 378 ("F2006"), 377 ("F2009"), and 383 ("F2010"). For further explanations see methods of Brockerhoff et al. (2024).</p> <p>2. File "reg2010sums.csv" = Data used for Fig. 2 - Year 2010, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 2 in Brockerhoff et al. (2024).</p> <p>3. File "reg2010sums.csv" = Data used for Fig. 2 - Year 2011, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 2 in Brockerhoff et al. (2024).</p> <p>4. File "reg2010sums.csv" = Data used for Fig. 2 - Year 2012, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 2 in Brockerhoff et al. (2024).</p> <p>5. File "reg10sums.csv" = Data used for Fig. 3 - Year 2010, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 3 in Brockerhoff et al. (2024).</p> <p>6. File "reg11sums.csv" = Data used for Fig. 3 - Year 2011, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 3 in Brockerhoff et al. (2024).</p> <p>7. File "reg12sums.csv" = Data used for Fig. 3 - Year 2012, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 3 in Brockerhoff et al. (2024).</p> <p>8. File "hylu2010-fitted_dispersal_to_5km-Version_23May2024.csv" = Data shown in Fig. 4 - Extension of the prediction range to 5 km of Hylurgus ligniperda dispersal data, using a generalised additive mixed model (GAMM) with beta distributed errors and the default logarithmic link. For details see caption of Fig. 4 and methods in Brockerhoff et al. (2024).</p> <p> </p>
Temporal study of Santa Cruz Mountain bats using environmental DNA and acoustic data
<p>Data and R scripts for a study of niche partitioning in a bat community in California's Santa Cruz Mountains using environmental DNA and bioacoustic data collected over a roosting season.</p> <p>Associated with the publication "Temporal study of environmental DNA and acoustic data reveals coexistence of sympatric bat species in a North American ecosystem" in <em>Environmental DNA. </em></p>
Experimental results dataset reported in the paper: "Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement."
<p><strong>Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement.</strong></p> <p>Experimental results data-set (as reported in Figure 2 of the main paper).</p> <p>Caption of the Figure: Classical bound violation ∆S<em><sup>(l)</sup></em>(n) =S<em><sup>(l)</sup></em>(2n)−(2n−2) (<em>l=T,H,S</em>) for the multi-parameter <em>CHSH</em> inequalities in the temporal (red), spatial (grey) and “hybrid” (blue) domain. The dots represent the experimental results, with the uncertainty bars evaluated as statistical fluctuations among repeated measurement sets, while the solid curves show the theoretically-expected values (for the correlations belonging to the spatial domain, deviations from the ideal case due to the V<em><sub>s</sub></em>= 0.982 estimated visibility of the generated |<em>ψ<sub>−</sub></em>〉state were considered)</p> <p>Data-set description:</p> <p>Column 1 = X-coordinate: n (Number of Settings)</p> <p>Column 2 = Y-coordinate: ∆S<em><sup>(S)</sup></em>(n), Space-like (S) CHSH Violation [Grey color in the figure on the paper]</p> <p>Column 3 = Uncertainty on ∆S<em><sup>(S)</sup></em>(n) (Space-like, S, Grey in the figure on the paper), </p> <p>Column 4 = Y-coordinate: ∆S<em><sup>(H)</sup></em>(n), Hybrid (H) CHSH Violation [Blue color in the figure on the paper]</p> <p>Column 5 = Uncertainty on ∆S<em><sup>(H)</sup></em>(n) (Hybrid, H, Blue in the figure on the paper)</p> <p>Column 6 = Y-coordinate: ∆S<em><sup>(T)</sup></em>(n), Time-like (T) CHSH Violation [Red color in the figure on the paper]</p> <p>Column 7 = Uncertainty on ∆S<em><sup>(T)</sup></em>(n) (Time-like, T, Red in the figure on the paper)</p>
A Spatio-temporal Atlas of the Developing Fetal Brain with Spina Bifida Aperta
<p>This version contains two zipped folders.</p> <ol> <li><a href="https://zenodo.org/api/files/c84cc018-9d2d-4adb-9558-9ab96649c922/spina_bifida_atlas.zip">spina_bifida_atlas.zip</a> contains a copy of our spina bifida aperta fetal brain atlas.<br> This folder is available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication), as indicated in the LICENSE file in this folder.<br> This is the same version of the atlas as the one available on synapse (<a href="https://www.synapse.org/#!Synapse:syn25887675/wiki/611424">https://www.synapse.org/#!Synapse:syn25887675/wiki/611424</a>, DOI: 10.7303/syn25887675).</li> <li><a href="https://zenodo.org/api/files/c84cc018-9d2d-4adb-9558-9ab96649c922/LucasFidon/spina-bifida-MRI-atlas-0.1.0.zip?versionId=31751dfa-7b17-4c84-89d5-2769d648eed8">LucasFidon/spina-bifida-MRI-atlas-0.1.0.zip</a> is a copy of the code that was used to compute the fetal brain atlas for spina bifida aperta in this repository.<br> This folder is available under BSD-3-Clause license, archived from GitHub, as indicated in the LICENSE file in this folder.</li> </ol> <p><strong>How to cite:</strong><br> If you find the data in this folder useful for your research please cite:</p> <p>L. Fidon, E. Viola, N. Mufti, A. L. David, A. Melbourne, P. Demaerel, S. Ourselin, T. Vercauteren, J. Deprest, M. Aertsen. A Spatio-temporal Atlas of the Developing Fetal Brain with Spina Bifida Aperta, 2021.</p>
Resampled FY4A GIIRS radiances from targeted observations for Typhoon Maria(2018) with high temporal resolution of 15 minutes
<p><strong>GIIRS_SSEC_Maria.tar.gz</strong> is the FY-4A GIIRS targeted observations for Typhoon Maria(2018) with 15 minutes temporal resolution, 00z – 23z 10 July, 2018. The radiances are re-sampled data generated at Space Science and Engineering Center of the University of Wisconsin-Madison. These are the data used in the study of Ma et al.(2021).</p> <p>The HDF files are the original GIIRS targeted observations for Typhoon Maria (2018) with 15 minutes temporal resolution which are used in the study of Yin et al.(2021). These radiances have not been re-sampled and for details please refer to Version 1.0 of this dataset: http://doi.org/10.5281/zenodo.4656877 (Han & Yin, 2021).</p>
Spatio-temporal water surplus and evapotranspiration in the catchment area of the Vögelsberg landslide (Tyrol, Austria)
<p>Multi-temporal maps of daily water surplus and evapotranspiration in the catchment area of the Vögelsberg landslide (Tyrol, Austria) based on the SVAT model LWF-Brook90 from 01/01/2008 to 31/12/2019. The spatio-temporal results represent the hydrological forcing of acceleration phases of the deep-seated landslide (see also Pfeiffer et al. 2021, <a href="https://doi.org/10.1002/esp.5129">https://doi.org/10.1002/esp.5129</a>). To investigate the feasibility of a modified land cover as nature-based solutions to reduce the landslide's activity, three land cover scenarios were considered (under current climatic conditions):</p> <p>- Current land cover conditions classified based on air-borne laser scanning data</p> <p>- Forest scenario: catchment area completely covered by forests (hypothetical scenario)</p> <p>- Pole timber scenario: open land above agricultural areas is replaced by areas of pole timber (considered realistic)</p> <p>Three land cover classes (open land, pole timber, mature forest), 11 soil types and 5 vertical meteorological domains were distinguished. Maps were produced with a spatial resolution of 10m (Projection: Austria GK West, EPSG: 31254). The maps are provided as raster stacks in tif-format with each layer representing one day.</p> <p>For further details see OPERANDUM deliverables D4.5 and D4.6.</p>
Multi-temporal digital terrain models of the active deep-seated Vögelsberg landslide (OAL-Austria)
<p>Multi-temporal digital terrain models of the active deep-seated Vögelsberg landslide in OAL Austria (Lat: 47.272°, Lon: 11.597°) with a spatial resolution of 50cm. Raster were derived from classified 3D point clouds acquired with a Riegl VUX-1LR unmanned aerial vehicle laser scanner on August 3<sup>rd</sup> 2018, August 14<sup>th</sup> 2019 and November 6<sup>th</sup> 2020 (Projection: EPSG 31254). Ground classification after Axelsson (2000). Data was used to assess topographic changes at the toe of the active deep-seated Vögelsberg landslide in OAL-Austria.</p>
Temporal Distortion for Angry Faces: Testing Visual Attention and Action Preparation Accounts
<p>Datasets for Experiment 1 and Experiment 2 of "Temporal Distortion for Angry Faces: Testing Visual Attention and Action Preparation Accounts"</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.