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Datasets and example code for "Tuning perception and decisions to temporal context"
<p>Here are the datasets and examples of code of the paper "Tuning perception and decisions to temporal context"</p> <p> </p> <p>The datasets of both experiment are stored in a .csv file, organized in a long format.<br> Each row corresponds to a trial, while columns are variables.</p> <p>See the README.txt document for further information.</p>
D. viride occupancy in Compiegne forest at two spatial scales with temporal survey at fine scale, used in Percel et al. study
<p>This repository contains two datasets in text format, to be imported in codes related to Percel et al. study of D. viride colonization process within Compiegne forest. The dataset "Data_Dviride.2017_AVCHESS_d14.txt" contains the data regarding the occupancy turnover between two surveys in three stands of the forest. The dataset "Data_Dviride.2018_foret.txt" contains the data regarding the coarse grained spatial distribution of D. viride at the forest scale.</p> <p>This repository also contains a basic script used to compute statistics reported in Table 1 of Percel et al. article.</p>
Multi-temporal landslide inventory for southern Sikkim State, India
<p>The <em>Multi-temporal landslide inventory for southern Sikkim State, India</em>, is based on two data-sources (mapped extent given in <strong>Shapefile A</strong>): Google Earth images (<strong>Shapefiles B–C</strong>) and stereoscopic Cartosat-1 satellite images (<strong>Shapefiles D–F</strong>). The landslide inventories were collected for the purpose of mapping landslide domains (regions with similar physical and environmental characteristics that specifically drive landslide style) and the data was used to give a general idea of landslides occurring in the region rather than a detailed overview. The landslide inventories are given as shapefiles with two sources of data described separately, after which a summary of all shapefiles is given.</p> <p>Google Earth landslides are mapped using images from 2002 to 2019 with a mapped extent of approximately 3000 km<sup>2</sup> and was ground-truthed during a 12-day field visit from 23 February to 6 March 2019. The resultant landslide inventory contains 440 landslides with three main landslide types identified: translational slides, debris flows, and rockfalls. Translational slides include debris slides, rock slides, and unclassified translational slides. In the landslide inventory, debris flows and rockfalls are mapped as points representing their source area and translational slides are mapped as polygons representing both the source and depositional area. A complete description of the landslide types and mapping is given in Heijenk (2022, Chapter 3, section 3.4.2) The final landslide inventory (refer to how they would access it here, so a reference, or shapefile) includes the following:</p> <ul> <li><strong>Year</strong>, the year of the first image that the landslide appears in is taken,</li> <li><strong>Geology</strong>, the geological unit that the landslide occurs in is taken from Mottram <em>et al.</em> (2004),</li> <li><strong>Area</strong>, for translational slides the area is given,</li> <li><strong>Topographic data</strong> (elevation, aspect, slope, and curvature), which is taken from ASTER GDEM (Version 3.0, 2018, 30 m horizontal resolution, 30 m vertical resolution).</li> </ul> <p>The Cartosat landslide inventory contains 44 features mapped from one pair of stereoscopic Cartosat-1 images (National Remote Sensing Centre, Cartosat-1 ID 197823411, https://www.nrsc.gov.in/, 2.5 m x 2.5 m) captured on 30 September 2011 with extents of 851 km<sup>2</sup> and 957 km<sup>2</sup>. Three main landslide types have been mapped: deep-seated landslides, multi-temporal landslide areas, and rockfall areas. For deep-seated landslides, the scarp is mapped separately from the depositional area. A complete description of the methodology is given in Heijenk (2022, Chapter 3, section 3.4.3).</p> <p>The following shapefiles are included in this dataset:</p> <ol> <li><strong>Google_Earth_mapped_extent_21Dec2021.shp</strong>: Shapefile with a polygon that denotes the mapped extent of southern Sikkim State.</li> <li><strong>Google_Earth_landslides_polygon_21Dec2021.shp</strong>: Shapefile with 255 polygon features, where the polygon includes the source and depositional area of translational landslides.</li> <li><strong>Google_Earth_landslides_point_21Dec2021.shp</strong>: Shapefile with 185 point features that denote the source area of both debris flows and rockfalls.</li> <li><strong>Cartosat_197823411_extents.shp</strong>: Shapefile with 2 polygon features that denote the extent of the Cartosat-1 image pair captured on 30 September 2011.</li> <li><strong>Cartosat_landslides_21Dec2021.shp</strong>: Shapefile with 67 polygon features that describe 44 landslide features. Some landslide features have been mapped with separate polygons for the scarp and the depositional area.</li> <li><strong>Cartosat_197823411clouds.shp</strong>: Shapefile with 5 polygon features that show an estimated area of the clouds that block landslide mapping in the 30 September 2011 Cartosat-1 image pair.</li> </ol> <p>All shapefiles are in an WGS 84 EPSG:3857 projection.</p> <p>This research was funded by the UK Natural Environment Research Council (NERC, Grant # NE/R012148/1) and the British Geological Survey (BGS, BUFI).</p> <p>References: </p> <p>Heijenk, R.A. (2022). Landslide Variables, Inventories, and Domains in Data-Poor Regions: A Case Study in East Sikkim, India. [PhD thesis]. King’s College London.</p>
Data and code for: A quantitative model for spatio-temporal dynamics of root gravitropism
<p>This repository contains the experimental data presented in "A quantitative model for spatio-temporal dynamics of root gravitropism" and Python scripts for the presented root model.</p>
TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation
<p><strong>TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation</strong></p> <p> </p> <p><strong>Description:</strong></p> <p>The global monthly GOME-2A SIF dataset (2007–2021) with correction of temporal degradation. The corrected global GOME-2 SIF dataset can be obtained in two types. The daily level2 dataset is provided in hdf5 format(compressed in the zip files named "{Year}{Quater}.zip"). The name of the hdf5 files was SIF_daily_YYYYMMDD.h5, YYYY, MM, and DD represent the year, month, and date, respectively. The level3 datasets which were aggregated monthly from the level2 dataset, have a spatial resolution of 0.5°and were saved in TIFF format in chronological order from 2007 to 2021 (compressed in the file "Level3.zip"). The name of the files was SIFpar_evi_monthly _YYYYMM.tif, where SIF was product type, par, and evi represented upscaled parameters, monthly represented temporal scale, YYYY and MM was the year and month, respectively. The SIF output was stored in the hdf5 files along with other variables of interest for further processing and visualization. See the appendix for the structure of the hdf5 file.</p> <p> </p> <p><strong>cloud_fraction</strong><strong>[float]</strong>:</p> <p>Description: Effective cloud fraction derived from GOME-2 Level1B product.</p> <p>Units: none</p> <p><strong>latitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center latitude.</p> <p>Units: degrees N</p> <p><strong>longitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center longitude.</p> <p>Units: degrees E</p> <p><strong>latitude_bounds</strong><strong>[float]</strong>:</p> <p>Description: Latitude of the boundary corners for each pixel.</p> <p>Units: degrees N</p> <p><strong>longitude _bounds</strong><strong>[float]</strong>:</p> <p>Description: Longitude of the boundary corners.</p> <p>Units: degrees E</p> <p><strong>SIF_740</strong><strong>[float]</strong>:</p> <p>Description: SIF signal at 740nm retrieved using the 735–758 nm fitting window.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>SIF_daily</strong><strong> [float]</strong>:</p> <p>Description: SIF signal at 740nm with correction of day-length.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>Sigma_i</strong><strong>[float]</strong>:</p> <p>Description: The squre of single retrieval error of SIF_740.</p> <p>Units: (mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>)<sup>2</sup></p> <p><strong>Solar_zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar zenith angle.</p> <p>Units: degrees</p> <p><strong>Solar_azimuth_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar azimuth angle.</p> <p>Units: degrees</p> <p><strong>Viewing _zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Viewing zenith angle.</p> <p>Units: degrees</p> <p><strong>Viewing_azimuth_angle</strong><strong>[float]</strong>:</p> <p>Description: Viewing azimuth angle.</p> <p>Units: degrees</p> <p><strong>chi2</strong><strong>[float]</strong>:</p> <p>Description: The reduced chi-square value calculated based on the the fitting residuals.</p> <p>Units: None</p> <p><strong>Rad_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average radiance within the 735~758 nm window</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>ps_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within at around 680 nm ().</p> <p>Units: None</p> <p><strong>ps_red</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within the 665~680 nm window</p> <p>Units: None</p> <p><strong>NDVI</strong><strong>[float]</strong>:</p> <p>Description: Calculated by the TOA reflectance at red band (around 680 nm) and near-infrared band (around 780nm).</p> <p>Units: None</p> <p><strong>QA</strong><strong>[int]</strong>:</p> <p>Description: Quality_flag.</p> <p>0= Bad (ineffective original data)</p> <p>1= Good (passed all quality-filtering criteria)</p> <p>2= Good and the cloud fraction is lower than 0.3</p> <p>Units:None</p>
TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation
<p><strong>TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation</strong></p> <p> </p> <p><strong>Description:</strong></p> <p>The global monthly GOME-2A SIF dataset (2007–2021) with correction of temporal degradation. The corrected global GOME-2 SIF dataset can be obtained in two types. The daily level2 dataset is provided in hdf5 format(compressed in the zip files named "{Year}{Quater}.zip"). The name of the hdf5 files was SIF_daily_YYYYMMDD.h5, YYYY, MM, and DD represent the year, month, and date, respectively. The level3 datasets which were aggregated monthly from the level2 dataset, have a spatial resolution of 0.5°and were saved in TIFF format in chronological order from 2007 to 2021 (compressed in the file "Level3.zip"). The name of the files was SIFpar_evi_monthly _YYYYMM.tif, where SIF was product type, par, and evi represented upscaled parameters, monthly represented temporal scale, YYYY and MM was the year and month, respectively. The SIF output was stored in the hdf5 files along with other variables of interest for further processing and visualization. See the appendix for the structure of the hdf5 file.</p> <p> </p> <p><strong>cloud_fraction</strong><strong>[float]</strong>:</p> <p>Description: Effective cloud fraction derived from GOME-2 Level1B product.</p> <p>Units: none</p> <p><strong>latitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center latitude.</p> <p>Units: degrees N</p> <p><strong>longitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center longitude.</p> <p>Units: degrees E</p> <p><strong>latitude_bounds</strong><strong>[float]</strong>:</p> <p>Description: Latitude of the boundary corners for each pixel.</p> <p>Units: degrees N</p> <p><strong>longitude _bounds</strong><strong>[float]</strong>:</p> <p>Description: Longitude of the boundary corners.</p> <p>Units: degrees E</p> <p><strong>SIF_740</strong><strong>[float]</strong>:</p> <p>Description: SIF signal at 740nm retrieved using the 735–758 nm fitting window.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>SIF_daily</strong><strong> [float]</strong>:</p> <p>Description: SIF signal at 740nm with correction of day-length.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>Sigma_i</strong><strong>[float]</strong>:</p> <p>Description: The squre of single retrieval error of SIF_740.</p> <p>Units: (mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>)<sup>2</sup></p> <p><strong>Solar_zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar zenith angle.</p> <p>Units: degrees</p> <p><strong>Solar_azimuth_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar azimuth angle.</p> <p>Units: degrees</p> <p><strong>Viewing _zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Viewing zenith angle.</p> <p>Units: degrees</p> <p><strong>Viewing_azimuth_angle</strong><strong>[float]</strong>:</p> <p>Description: Viewing azimuth angle.</p> <p>Units: degrees</p> <p><strong>chi2</strong><strong>[float]</strong>:</p> <p>Description: The reduced chi-square value calculated based on the the fitting residuals.</p> <p>Units: None</p> <p><strong>Rad_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average radiance within the 735~758 nm window</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>ps_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within at around 780 nm.</p> <p>Units: None</p> <p><strong>ps_red</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within the 665~680 nm window</p> <p>Units: None</p> <p><strong>NDVI</strong><strong>[float]</strong>:</p> <p>Description: Calculated by the TOA reflectance at red band (around 680 nm) and near-infrared band (around 780nm).</p> <p>Units: None</p>
Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
<p>Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"<br> URL: https://github.com/qianghuangwhu/benchtemp</p> <p>Openreview: https://openreview.net/forum?id=rnZm2vQq31</p> <p><br> There are 19 (15+4) benchmark temporal graph datasets:<br> reddit,<br> wikipedia,<br> mooc,<br> lastfm,<br> enron,<br> SocialEvo,<br> uci,<br> CollegeMsg,<br> TaobaoSmall,<br> CanParl,<br> Contacts,<br> Flights,<br> UNtrade,<br> USLegis,<br> UNvote,</p> <p>DGraphFin,</p> <p>TaobaoLarge,</p> <p>YoutubeReddit,</p> <p>YoutubeRedditLarge</p> <p> </p> <p><br> Each dataset has three files:<br> 1. ml_{data_name}.csv - the csv file of the Temporal Graph.</p> <p>This file have five columns with properties:</p> <p>'u': the id of the user.<br> 'i': the id of the item.<br> 'ts': the timestamp of the interaction (edge) between the user and the item.<br> 'label': the label of the interaction (edge).<br> 'idx': the index of the interaction (edge).<br> For example:</p> <p>,u,i,ts,label,idx<br> 0,1,2,0.0,0.0,1<br> 1,1,3,0.0,0.0,2<br> 2,1,4,0.0,0.0,3<br> 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph..</p> <p>3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.</p>
Temporal Talbot effect of optical dark pulse trains
<p>The data and code used to produce the results in the paper "<em>Temporal Talbot effect of optical dark pulse trains</em>".</p>
Code and data: Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems
<p>Code and data to reproduce the results in Siqueira et al. (submitted) published as a Preprint (https://doi.org/10.32942/osf.io/mpf5x)</p> <p>The full set of results, including those made available as supplementary material, can be reproduced by running five scripts in the <strong>R_codes</strong> folder following this sequence:</p> <ul> <li>01_Dataprep_stability_metrics.R</li> <li>02_SEM_analyses.R</li> <li>03_Stab_figs.R</li> <li>04_Stab_supp_m.R</li> <li>05_Sensit_analysis.R</li> </ul> <p>and using the data available in the <strong>Input_data</strong> folder.</p> <p>The original raw data made available include the abundance (individual counts, biomass, coverage area) of a given taxon, at a given site, in a given year. See details here https://doi.org/10.32942/osf.io/mpf5x</p> <p>However, this is a collaborative effort and not all authors are allowed to share their raw data. One data set (LEPAS), out of 30, was not made available due to data sharing policies of The Ohio Division of Wildlife (ODOW). So, in code "01_Dataprep_stability_metrics.R" all data made available are imported, except the LEPAS data set. For this specific data set, code "01_Dataprep_stability_metrics.R" imports variability and synchrony components estimated using the methods described in Wang et al. (2019 Ecography; doi/10.1111/ecog.04290), diversity metrics (alpha and gamma diversity), and some variables describing the data set.</p> <p>A protocol for requesting access to the LEPAS data sets can be found here:<br> https://ael.osu.edu/researchprojects/lake-erie-plankton-abundance-study-lepas</p> <p>Dataset owner: Ohio Department of Natural Resources – Division of Wildlife, managed by Jim Hood, Dept. of Evolution, Ecology, and Organismal Biology, The Ohio State University. Email: hood.211@osu.edu</p> <p>Anyone who wants to reproduce the results described in the preprint can just download the whole R project (that includes code and data) and run codes from 01 to 05.</p> <p>I am making the whole R project folder (with everything needed to reproduce the results) available as a compressed file.</p>
FIGURE 4 Temporal estimates derived from a in Split distribution, biogeography and morphological and genetic diversity of the Iberobathynellini Tribe in the family Parabathynellidae (Crustacea, Malacostraca, Bathynellacea)
FIGURE 4 Temporal estimates derived from a relaxed molecular clock analysis. Divergence times are depicted in the coalescence-based MCC tree. Units of X-axis address Mya and blue bars at nodes indicate the 95% highest posterior density intervals (HPD).
Pattern of seasonal variation in rates of predation between spider families is temporally stable in a food web with widespread intraguild predation
<p>Intraguild predation (IGP) – predation between generalist predators (IGPredator and IGPrey) that potentially compete for a shared prey resource – is a common interaction module in terrestrial food webs. Understanding temporal variation in webs with widespread IGP is relevant to testing food web theory. We investigated temporal constancy in the structure of such a system: the spider-focused food web of the forest floor. Multiplex PCR was used to detect prey DNA in 3,300 adult spiders collected from the floor of a deciduous forest during spring, summer, and fall over four years. Because only spiders were defined as consumers, the web was tripartite, with 11 consumer nodes (spider families) and 22 resource nodes: 11 non-spider arthropod taxa (order- or family-level) and the 11 spider families. Most (99%) spider-spider predation was on spider IGPrey, and ~90% of these interactions were restricted to spider families within the same broadly defined foraging mode (cursorial or web-spinning spiders). Bootstrapped-derived confidence intervals (BCI's) for two indices of web structure, restricted connectance and interaction evenness, overlapped broadly across years and seasons. A third index, % IGPrey (% IGPrey among all prey of spiders), was similar across years (~50%) but varied seasonally, with a summer rate (65%) ~1.8x higher than spring and fall. This seasonal pattern was consistent across years. Our results suggest that extensive spider predation on spider IGPrey that exhibits consistent seasonal variation in frequency, and that occurs primarily within two broadly defined spider-spider interaction pathways, must be incorporated into models of the dynamics of forest-floor food webs. </p>
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: Drivers of wood decay in tropical ecosystems: Termites vs. microbes along spatial, temporal and experimental precipitation gradients
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Data from: The duration of high spring light for understory plants: contrasting responses to spatial and temporal temperature variation
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Niche expansion of capuchin monkeys to forest floor on guild-reduced islands increases interspecific spatio-temporal overlap
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Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
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Data from: Temporal variation of soil microarthropods in different forest types and regions of Central Europe
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Temporally consistent species differences in parasite infection but no evidence for rapid parasite-mediated speciation in Lake Victoria cichlid fish
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Temporal trends in the spatial bias of species occurrence records
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Anthropogenic noise and light alter temporal but not spatial breeding behavior in a wild frog
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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.