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23,670 results for “Site”
Data from Phenocam (PHE) measurements of above-canopy vegetation (hartheim1) at Hartheim Forest Research Site (DE-Har) from 2021-01-01 to 2021-12-31 [RAW]
<div> <p>Phenocam images from "hartheim1" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2021. </p> <p>Phenocam "hartheim1" shows the view from the main tower at 29.6m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of the top of the canopy consisting of pinus sylverstris and pinus nigra.</p> <p> </p> </div>
Data from Phenocam (PHE) measurements of above-canopy vegetation (hartheim1) at Hartheim Forest Research Site (DE-Har) from 2020-01-01 to 2020-12-31 [RAW]
<p>Phenocam images from "hartheim1" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2020. </p> <p>Phenocam "hartheim1" shows the view from the main tower at 29.6m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of the top of the canopy consisting of pinus sylverstris and pinus nigra.</p>
Data from Phenocam (PHE) measurements of above-canopy vegetation (hartheim1) at Hartheim Forest Research Site (DE-Har) from 2019-01-01 to 2019-12-31 [RAW]
<p>Phenocam images from "hartheim1" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2019. </p> <p>Phenocam "hartheim1" shows the view from the main tower at 30m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of the top of the canopy consisting of pinus sylverstris and pinus nigra.</p>
LOBSTER (Ligand Overlays from Binding SiTe Ensemble Representatives)
<p>LOBSTER ("Ligand Overlays from Binding SiTe Ensemble Representatives") is a dataset of ligand overlays designed to evaluate small molecule superposition tools.</p> <p><br>Based on all structures from the RCSB PDB, the dataset generation and filtering protocols are fully automated to avoid subjectivity in the selection of protein-ligand complexes and to gain the largest possible set of refined compounds. Affinity and activity data have been processed to select ligands with a high ligand efficiency.<br>Ligands were superimposed in their crystal pose by aligning the corresponding binding pockets to so-called ensembles. For poses generated in benchmark experiments, this offers an objective comparison to the superimposed ligand crystal poses. A clustering of ensembles created with the same protein-ligand complexes ensures the diversity of the LOBSTER set.<br>The 671 ligand ensembles comprise a total of 3212 unique ligands from 3521 different protein-ligand complexes. A total of 72 734 ligand pairs have been derived from the ensembles. Ten subsets were generated from the pairs according to the shape overlap of the pairs, quantified by the Shape Tversky Index.</p>
Portable/on site devices (i.e. Specim IQ) spectral data on spice/pepper
<p>The dataset from the analysis of spices with portable/on-site devices (i.e. Specim IQ) based on light spectroscopy. Measurements are taken for the authentication of spice (i.e. pepper) using an on-site/portable/handheld device as part of WP3 (Task 3.1): Implementation of innovations in food authenticity. Measurements (reflectance values) are averaged per sample and only the final average spectral data is provided in the Excel sheets. The data is useful for anyone working with spectral data and its use for the authentication of spices.</p>
An hourly ground temperature dataset for 16 high-elevation sites (3493–4377 m a.s.l.) in the Bale Mountains, Ethiopia (2017–2020)
<p>This is a multiannual ground temperature dataset covering sixteen high elevation sites (3493-4377 m a.s.l.) in the Bale Mountains, southern Ethiopian Highlands</p> <p>The dataset is described in detail in the corresponding data paper by Groos et al. 2021 (https://doi.org/10.5194/essd-2021-268)</p> <p>The repository contains a readme file ("readme.txt"), a GeoPackage ("Data_Logger_Location.gpkg"), a thermal infrared time-lapse video ("thermal_infrared_time-lapse_video.mp4"), a metadata file for the video ("video_metadata.txt"), and two sub-folders: "raw_data" and "processed_data"</p> <p>The GeoPackage provides information on the location and environmental setting of each logger and can be easily opened and displayed in a Geographic Information System. The coordinate reference system is WGS84 / Geographic (EPSG code: 4326).</p> <p>The thermal infrared time-lapse video (<a href="https://vimeo.com/676294827">https://vimeo.com/676294827</a>) visualises the phenomenon of nocturnal cold air drainage and ponding in the Bale Mountains (for more information see the metadata file and Appendix C in the corresponding data paper).</p> <p>The folder "raw_data" contains the original logfiles of all GT and TM data loggers (see Table 1) in a tab-delimited text format with the logger ID and download date encoded in the file name. The date format of the GT data loggers is YYYY.MM.DD hh:mm:ss East Africa Time (EAT). The date format of the TM data loggers is DD.MM.YYYY hh:mm:ss EAT.</p> <p>The folder "processed_data" contains the followings two files:</p> <p>"Information_Sheet_Data_Gap-Filling.ods": An overview table with relevant information regarding the filling of (longer) data gaps in the ground temperature time series. The gap-filling procedure based on simple linear regression models is described individually for each logger.</p> <p>"Hourly_Ground_Temperatures.csv": Compilation of hourly ground temperature data from all GT and TM data loggers installed in the Bale Mountains (see Table 1 in the data paper). The dataset covers the period from 1 January 2017 to 31 January 2020, but individual time series may be shorter or contain data gaps (see Fig. 3 in the data paper). We use the international date format (ISO 8601): YYYY-MM-DD hh:mm:ss EAT. The following numerical indices (or a combination of them) in the columns starting with "Flag_*" are used to provide additional information on the post-processing of each hourly measurement of each time series:</p> <p>0 no data available<br> 1 original data (no post-processing)<br> 2 data interpolated to full hour<br> 3 erroneous data corrected<br> 4 erroneous data removed<br> 5 data gap-filled</p> <p>The meteorological data from the ten automatic weather stations in the Bale Mountains, which are operated since 2017, are currently post-processed and analysed in the framework of the DFG Research Unit 2358 "The Mountain Exile Hypothesis". The data will be made publicly available at some point in the future. However, individual access to the weather station data may be granted before on request to the coordination board of the research unit (bale@staff.uni-marburg.de).</p>
ANE Site Placemarks for Google Earth
<p>ANE.kmz is a set of site placemarks for Google Earth of a selection of the most important archaeological sites in the Ancient Near East. ANE.kmz works with Google Earth Pro, which first has to be downloaded for free. When opened inside Google Earth Pro, ANE.kmz gives, to the left, an alphabetic list of ancient sites and, to the right, on the satellite images the same sites marked. For the moment, there are some 2500 sites with modern names; among them some 400 have ancient names. Additions of more sites are planned. Ancient names are written without parenthesis. Modern names are within parenthesis. Most sites have been identified on the satellite images.</p> <p>ANE Waters.kmz is an experimental set of provisional water placemarks for Google Earth covering Mesopotamia up to modern time.</p> <p>ANE Picture.jpg is just illustrating the appearence of ANE.kmz before zooming in and is not for use.</p>
Data: Weak Cation Selectivity in HCN Channels Results from K+-mediated release of Na+ from selectivity filter binding sites
<p>Complementary data for the paper: Weak Cation Selectivity in HCN Channels Results from K+-mediated release of Na+ from selectivity filter binding sites.</p>
Historical Sea Surface Temperature (SST) data and thermal stress indices of the Tara Pacific Expedition's coral reef sampling sites, from May 1st 2002 to August 31st 2018.
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems at 111 sampling sites around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis.</p> <p>Here we provide a high-resolution historical dataset that spans from 2002 to each sites’ sampling date and gives an overview of past climate variability and heatwaves experienced by corals sampled at each site. Ocean skin temperature (11 and 12 µm spectral bands longwave algorithm) was extracted from 1km resolution level-2 MODIS-Aqua and MODIS-Terra from 2002 to the sampling date and from level-2 VIIRS-SNPP from 2012 to the sampling date. Day and night overpasses were used to maximize data recovery. Following recommendations from NASA Ocean Color (OB.DAAC), only SST products of quality 0 and 1 were used. The 9 closest pixels to the sampling sites of each scene were extracted. All the extracted pixels from the 3 satellites were then averaged daily to obtain daily SST averages and standard deviations time series for each sampling site, from 2002 to the sampling date.</p> <p>Each time series was first averaged on a Julian day basis to provide a seasonal average. This yearly seasonal average was triplicated and concatenated into a 3-year seasonal cycle to apply a digital low pass filter on the middle year without generating artifacts. A digital low pass filter (filter order 3, pass band ripple 0.1; “filfilt” function in matlab) with 36 Julian days windows was applied to the concatenated time series to remove high frequency noise. The middle year was then extracted from the concatenated time series to recover the seasonal cycle. The sea surface temperature anomaly was calculated as the SST minus the seasonal cycle over the full time series. Considering the short periods of missing data (mean of the 95th percentile of the duration of consecutive days with missing data: 9.8 ± 4.1 days), the missing values in the SST and SST anomaly time series were linearly interpolated in order to calculate thermal stress indices. The SST anomaly frequency was calculated as the number of days over the past 52 weeks when the SST anomaly is greater than or equal to 1 °C. Thermal stress indices relevant to coral reef health were then calculated using methodology developed for the Coral Reef Temperature Anomaly Database (CoRTAD) data base (Saha et al. 2019). Events of cold temperature accumulation were also reported to cause bleaching and mortality (Lirman et al. 2011; González-Espinosa & Donner 2020), therefore, the same set of indices were calculated for cold stress adapting the CoRTAD method, but using the minimum weekly climatologies.</p> <p>A condensed table containing single values associated with each sampling site was created ('TaraPacific_SST_timeseries_mean_products') extracting the minimum, maximum, sum, averages, standard deviations, and value recorded at the sampling day of each of these indices (detailed in the readme file provided with the dataset 'README_TaraPacific_historical_SST.md'). Additional metrics of the last heating and cooling events as well as the time of recovery is also provided to represent the state of thermal stress at the day of sampling.</p>
An External Replication on the Effects of Test-driven Development Using a Multi-site Blind Analysis Approach
<p>This dataset contains the <strong>unblinded </strong>version of the data collected and analyzed for the experiment reported in the paper. </p> <p>The semantics of the data can be found in the spreadsheet. For the formulas on how to obtain this data from the raw data, please see the paper. </p>
Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2018-11-05 to 2018-12-31 [RAW]
<p>Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2022. The phenocam "hartheim2" was put into operation on November 5, 2018. There are no phenocam images before that date at this site.</p> <p>Phenocam "hartheim2" shows the view from the main tower at 8.4 m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of in-canopy vegetation.</p>
Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2020-01-01 to 2020-12-31 [RAW]
<p>Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2020. </p> <p>Phenocam "hartheim2" shows the view from the main tower at 8.4 m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of in-canopy vegetation.</p>
Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2021-01-01 to 2021-12-31 [RAW]
<div> <p>Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2021. </p> <p>Phenocam "hartheim2" shows the view from the main tower at 7m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosystem Site DE-Har, Germany</a> recording the phenology and state of in-canopy vegetation.</p> </div>
Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2022-01-01 to 2022-12-31 [RAW]
<div> <p>Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2022. </p> <p>Phenocam "hartheim2" shows the view from the main tower at at 8.4 m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosystem Site DE-Har, Germany</a> recording the phenology and state of in-canopy vegetation.</p> <p> </p> </div>
Pauni (पौनि Bhandārā district) Maharashtra. Stone head at the site of the stūpa.
<p>Pauni (पौनि Bhandārā district) Maharashtra. Stone head at the site of the <em>stūpa</em>, kept in Jagannāth temple.</p>
GPP at FLUXNET Tier 1 sites from P-model
<p>Gross primary production, simulated by the P-model for each FLUXNET 2015 Tier 1 site. The model was driven by site-specific meteorological forcing and MODIS FPAR, extracted for the pixel corresponding to the site location.</p> <p>The CSV files contain simulated GPP values from different model setups conducted with the P-model and used for the publication Stocker et al. <em>Geosci. Mod. Dev. </em>(in review). One file is given for each temporal aggregation level (daily, 8-daily, annual, spatial [= mean annual value by site], and mean seasonal cycle [= mean per day-of-year]. Each file contains output from all model setups presented in Stocker et al. (2019), as given by column <em>setup</em>.</p> <p>The data differs slightly for each file:</p> <p><strong>Daily</strong> gpp_pmodel_fluxnet2015_stocker19gmd_daily.csv:</p> <ul> <li><em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015.</li> <li><em>date: </em>YYYY-MM-DD), date_start (in _8daily, YYYY-MM-DD specifying the first day of the respective 8-day period), year (in _annual, YYYY), doy (in __meanseason, specifying the day-of-year),</li> <li><em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup></li> <li><em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below.</li> </ul> <p><strong>8-daily</strong> gpp_pmodel_fluxnet2015_stocker19gmd_8daily.csv:</p> <ul> <li><em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015.</li> <li><em>date_start</em> : YYYY-MM-DD specifying the first day of the respective 8-day period</li> <li><em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup></li> <li><em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below.</li> </ul> <p><strong>Annual</strong> gpp_pmodel_fluxnet2015_stocker19gmd_annual.csv:</p> <ul> <li><em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015.</li> <li><em>year: </em>YYYY</li> <li><em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> yr<sup>-1</sup></li> <li><em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below.</li> </ul> <p><strong>Spatial</strong> gpp_pmodel_fluxnet2015_stocker19gmd_spatial.csv:</p> <ul> <li><em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015.</li> <li><em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> yr<sup>-1</sup></li> <li><em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below.</li> </ul> <p><strong>Mean seasonal cycle</strong> gpp_pmodel_fluxnet2015_stocker19gmd_meanseason.csv:</p> <ul> <li><em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015.</li> <li><em>doy: </em>day-of-year</li> <li><em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup></li> <li><em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below.</li> </ul> <p> </p>
Photos from WIDER UPTAKE case study site in the Czech Republic (Camera 1)
<p><span>Hourly photos from camera 1 at the WIDER UPTAKE H2020 project's case study site in the Czech Republic. </span></p>
Photos from WIDER UPTAKE case study site in the Czech Republic (Camera 3)
<p>Hourly photos from camera 3 at the WIDER UPTAKE H2020 project's case study site in the Czech Republic.</p>
Photos from WIDER UPTAKE case study site in the Czech Republic (Camera 2)
<p>Hourly photos from camera 2 at the WIDER UPTAKE H2020 project's case study site in the Czech Republic.</p>
Heritage Site Database Limpopo National Park
<p>Accompanying datafile with list of sites, chronological period, and assessment as explained in the following papers:</p> <p>An assessment system for archaeological sites, the example of Limpopo Valley (in review)</p> <p>Anneli Ekblom, Solange Macamo, Peter Bechtel, Frederico Regala, Susana Carvalho, Mussa Raja, Michel Notelid (2024) A Framework for Cultural Heritage Management in National Parks, Mozambique. Bull. Mus. Anthropol. préhist. Monaco, n° 63. </p> <p> </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.