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2,113 results for “Very High Resolution”
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/349/4. The abstract below was extracted from the Level 0 data package and is included for context: The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
Ramped Pyrolysis Oxidation (RPO) coupled radiocarbon (14C-DOC) and stable carbon (13C-DOC), high-resolution molecular composition (FT-ICR MS), and biodegradable dissolved organic carbon (BDOC) of groundwater, river water, and lagoon water in northeast Alaska, 2017
Supra-permafrost groundwater (SPGW), river water, and lagoon water were sampled near Kaktovik, AK to assess the reactivity and origin of dissolved organic matter (DOM) across interconnected hydrologic systems during late summer. Water samples were collected on August 17th 2017 from SPGW along the beach of Jago Lagoon (Jago GW), surface water from the Jago River’s main channel above tidal influence (Jago R), and from the water column of Kaktovik Lagoon at 2–3 m depth (KA LW). Measurements were made from grab samples for river and lagoon water, and from a composite sample for SPGW gathered from 10 individual shoreline locations. Data include dissolved organic carbon concentration (DOC, mg C L-1), Ramped Pyrolysis Oxidation (RPO) derived fraction compositions of 13C-DOC (δ13C ‰), 14C-DOC (in fraction modern), and method/instrumental error in the 14C and 13C results, and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) molecular composition and summarized compound classes. Biodegradable DOC (BDOC) bottle experiments were performed using all three sample types, where DOC concentration was subsequently measured at 2, 7, 14, and 28 days. FT-ICR MS composition was subsequently measured at the 28-day timepoint to track changes in molecular formulae and compound class relative abundance following biodegradation. Data from RPO serial thermal oxidation include temperature and normalized CO2 profiles for each background sample. Thermal-oxidation profiles of CO2 were transformed into non-parametric activation energy (E) distributions using an inverse model. Model output includes C mass of oxidized CO2 (µg C), Tmax (K), Emax (kJ mol-1), Emean (kJ mol-1), Estd (kJ mol-1), and p(0,E)max of user-defined sample fractions. FT-ICR MS results include a summary table of the relative abundance of compound classes (e.g., unsaturated phenolic, polyphenolic, aliphatic, condensed aromatics, peptide-like) and elemental groupings (e.g., CHO-type, CHON-type, CHOS-type, CHON
Cascade project at North Temperate Lakes LTER - High-resolution spatial analysis of CASCADE lakes during experimental nutrient enrichment 2015 - 2016
This dataset contains high-resolution spatio-temporal water quality data from two experimental lakes during a whole-ecosystem experiment. Through gradual nutrient addition, we induced a cyanobacteria bloom in an experimental lake (Peter Lake) while leaving a nearby reference lake (Paul Lake) as a control. Peter and Paul Lakes (Gogebic county, MI USA), were sampled using the FLAMe platform (Crawford et al. 2015) multiple times during the summers of 2015 and 2016. In 2015 nutrient additions to Peter Lake began on 1 June, and ceased on 29 June, Paul Lake was left unmanipulated. In 2016 no nutrients were added to either lake. Measurements were taken using a YSI EXO2 probe and a Garmin echoMap 50s. Sensor- data were collected continuously at 1 Hz and linked via timestamp to create spatially explicit data for each lake. Crawford, J. T., L. C. Loken, N. J. Casson, C. Smith, A. G. Stone, and L. A. Winslow. 2015. High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology. Environmental Science & Technology 49:442–450.
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format
The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
Cascade Project at North Temperate Lakes LTER – High-resolution Spatial Data for Whole Lake Experiments 2018 - 2019
Spatial measurements of water quality from Peter and Paul lakes in 2018 and 2019. In 2019, inorganic nitrogen and phosphorus were added to Peter Lake daily to cause an algal bloom while Paul Lake was an unmanipulated reference lake. In 2018, both lakes were sampled 1 time per week, while in 2019 lakes were sampled three times per week. Measurements were taken using the FLAMe sampling platform (Crawford et al. 2015, Environmental Science and Technology 49:442-450), which was driven in a grid pattern and recorded GPS coordinates and water measurements at 1Hz to create high resolution spatial maps.
High resolution pond velocity measurements, Idaho21
<p>These scientific data were obtained by Jeffrey Nielson and Stephen Henderson of Washington State University, working in collaboration with Sandra Mayne, Caren Goldberg and Jeffrey Manning. High-resolution current meters were used to obtain detailed measurements of water velocity, with supporting measurements of wind velocity and water temperature profiles. Overview of observations in referenced Henderson et al. (2024) L&O paper, more details in included files. </p>
Mediterranean Cyclone tracks between 1979-2018 (40 years) from a high-resolution perspective using ECMWF ERA5 dataset
<p>The present dataset presents the trajectories of the 13,157 cyclones identified within the Mediterranean Region (MR) between 1979 and 2018 (40 years). These cyclone tracks were obtained using the new Cyclone Detection and Tracking Method (CDTM) described in Aragão e Porcù (2021) to take advantage of the recent availability of a high-resolution reanalysis dataset of ECMWF ERA5. The CDTM uses hourly data of Geopotential Height at 1000 hPa with a spatial resolution of 0.25°x0.25°, and the analysis' domain covers the area within 15°W to 48° E and 21° N to 54°N. Additionally, trying to eliminate artificial low-pressure cores, short-living thermal-lows or too weak cyclones as much as possible, the present study only considered cyclones lasting more than 24h.<br> The dataset presents hourly information for all cyclones from the cyclogenesis time to the cyclolysis time. Each record presents: [1] Cyclone ID (integer, 8 digits), [2] Cyclone centre longitude position (°E, real, 8 digits, 3 decimal digits), [3] Cyclone centre latitude position (°N, real, 8 digits, 3 decimal digits), [4] Year (integer, 4 digits), [5] Month (integer, 2 digits), [6] Day (integer, 2 digits), [7] Hour (integer, 2 digits), [9] Cyclone centre Geopotential Height at 1000 hPa (m, real, 9 digits, 3 decimal digits).<br> The analyses presented in Aragão e Porcù (2021) revealed that the proposed CDTM is capable to capture almost the totality of the observed cyclones, as well as describing its respective area of cyclogenesis, trajectories, and durations. More than an adaptation to a high-resolution dataset, the method brings as its primary contribution a suitable set of parameters to systematically identify and track the cyclonic activities in the Mediterranean, where cyclones do not have sizeable horizontal pressure gradients and present a shorter lifetime compared to open-ocean cyclones.</p> <p>Cite this article</p> <p>Aragão, L., Porcù, F. Cyclonic activity in the Mediterranean region from a high-resolution perspective using ECMWF ERA5 dataset. <em>Clim Dyn</em> (2021). https://doi.org/10.1007/s00382-021-05963-x</p>
2005-2099 High resolution bioclimatic variables for the surface and bottom of the Mediterranean Sea.
<p><em><span>This dataset provides annual statistical descriptors (mean, minimum, maximum, range and standard deviation) of key biogeochemical and physical variables for the Mediterranean Sea. It covers the period 2005-2099 under the RCP8.5 scenario, with a spatial resolution of 1/24 degree (~4km²). Variables include temperature, salinity, pH, water velocity, nutrients (NO3, PO4, NH4), dissolved inorganic carbon, oxygen, and net primary production. Data are available for both surface and at bathymetry level. The original projections were generated using OGSTM-BFM and MFS16 models at daily time and 1/16 degree grid resolution. We downscaled these to 1/24 degree and applied Quantile Delta Mapping bias correction using CMEMS reanalysis products for 2005-2020. The dataset is provided in a user-friendly format, making it accessible for various ecological and environmental modelling applications.</span></em></p>
Extended data for the paper: "SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters"
<p>Extended data 1 to 4 for the software article:<br>SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters. </p> <p>The extended data is tables and a Figure output and input from/to SentemQC runs relevant for the SentemQC paper.</p>
Marine magnetic anomaly data from high resolution surveys off the SW Portuguese coast
<p>This dataset contains <strong>magnetic anomaly grids</strong> that result from the full processing of marine magnetic data collected off the SW Portuguese coast between 2014 and 2019. A total area of ~4400 km<sup>2</sup> was surveyed with average line spacing of 1 nautic mile. Surveys covered the continental shelf and in some regions reaching up to 2500 m bathymetric levels. Total magnetic field data were acquired with a G882 Cesium vapor marine magnetometer towed, towed at sea surface.</p> <p><strong>Full processing</strong> of magnetic data included: layback correction; noise removal; IGRF subtraction; base station correction; line leveling; minimum curvature gridding. The resulting sea level magnetic anomaly grid was further processed for upward continuation and reduction to the pole, providing additional outputs. </p> <p>The following grids are provided in <strong>georeferenced geotiff format</strong>:</p> <ul> <li>Magnetic anomaly (sealevel)</li> <li>Magnetic anomaly reduced to the pole (sealevel)</li> <li>Magnetic anomaly upward continued to 200 m height </li> <li>Magnetic anomaly upward continued to 200 m height, reduced to the pole</li> <li>Magnetic anomaly upward continued to 3000 m height </li> <li>Magnetic anomaly upward continued to 3000 m height, reduced to the pole</li> </ul> <p><strong>Published in</strong>: Neres, M., P. Terrinha, J. Noiva, P. Brito, M. Rosa, L. Batista, C. Ribeiro (2023). <em>New Late Cretaceous and CAMP magmatic sources off West Iberia, from high-resolution magnetic surveys on the continental shelf.</em> <strong>Tectonics</strong>. doi: 10.1029/2022TC007637</p> <p> </p>
Sample data for "A weakly supervised framework for high resolution crop yield forecasts"
<p>This dataset includes sample data for the United States to run the weakly supervised framework as described in the paper titled <em>A weakly supervised framework for high resolution crop yield forecasts</em>, accessible at </p> <table summary="Additional metadata"> <tbody> <tr> <td><a href="https://doi.org/10.48550/arXiv.2205.09016">https://doi.org/10.48550/arXiv.2205.09016</a></td> </tr> </tbody> </table> <p> </p> <p>The updated paper (including results from the US) is published in Environmental Research Letters:</p> <p><a href="https://doi.org/10.1088/1748-9326/acf50e">https://doi.org/10.1088/1748-9326/acf50e</a></p> <p> </p> <p>The software implementation of the machine learning baseline is available at: https://github.com/BigDataWUR/MLforCropYieldForecasting/tree/weaksup.</p> <p> </p> <p>Data</p> <p>1. County data (county-data.zip) for county-level strongly supervised models:</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p> <p>* CSSF_COUNTY_US.csv: Crop productivity indicators including total above-ground production (kg ha<sup>-1</sup>), total weight of storage organs (kg ha<sup>-1</sup>), development stage (0-2). Source: de Wit et al. (2022).</p> <p>* METEO_COUNTY_US.csv: Meteo data including maximum, minimum, average daily air temperature (℃); sum of daily precipitation (PREC) (mm); sum of daily evapotranspiration of short vegetation (ET0) (Penman-Monteith, Allen et al., (1998)) (mm); climate water balance = (PREC - ET0) (mm). Source: Boogaard et al. (2022).</p> <p>* REMOTE_SENSING_COUNTY_US.csv: Fraction of Absorbed Photosynthetically Active Radiation (Smoothed) (FAPAR). Source: Copernicus GLS (2020).</p> <p>* SOIL_COUNTY_US.csv: Soil water holding capacity. Source: WISE Soil Property Database (Batjes, 2016).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p> </p> <p>2. 10-km grid data (grid-data.zip) for grid-level strongly supervised models:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level (similar to county data above).</p> <p>* METEO_GRIDs_US.csv: Meteo data at 10km grid level (similar to county data above).</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level (similar to county data above).</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level (similar to county data above).</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021), Lobell et al. (2020).</p> <p> </p> <p>3. County labels and 10-km grid inputs (dscale-US.zip) for weak supervision:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level.</p> <p>* METEO_GRIDs_US.csv: Meteo indicators at 10km grid level.</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level.</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level.</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p>
High-resolution orthoimagery of the Altamaha River estuary in November 2017 and October 2018
We acquired two high-resolution (0.15-m per pixel) color digital images of the salinity gradient of the Altamaha River estuary in November 2017 (1 month after Hurricane Irma) and October 2018. Flights ranged from the mouth of the estuary all the way up to the tidal fresh forest habitat. The 1:1200 scale orthoimagery is comprised of 4-bands (Red, Green, Blue, Near Infrarad (RGBNIR)) collected with a Ground Sample Distance (GSD) of 0.5 foot using a Leica ADS100 airborne digital sensor. Individual tiles were georectified using ground control points and merged into a domain-wide mosaic.
SBC LTER: Ocean: High resolution water temperature at Mohawk and Arroyo Quemado, ongoing since 2018
Ocean in-situ temperature data were collected at two nearshore locations: Mohawk and Arroyo Quemado. There are three sites at each of the locations: inshore, offshore, and east of reef. Temperature was recorded using hobo sensors at 1-meter interval along the water column, and the sampling interval is 2 minutes.
A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures
Open the record for dataset details and reuse information.
Effects of Phase Regression on High-Resolution Functional MRI of the Primary Visual Cortex
Open the record for dataset details and reuse information.
Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.
<h2>Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.</h2><h4>Daniel Buscombe, Marda Science LLC</h4><p>There are two datasets in this data release:</p><p>1. <strong>Model training dataset</strong>. A manually (or semi-manually) labeled image dataset that was used to train and evaluate a machine (deep) learning model designed to identify subaerial accumulations of large wood, alluvial sediment, water, and vegetation in orthoimagery of alluvial river corridors in forested catchments. </p><p>2. <strong>Model output dataset</strong>. A labeled image dataset that uses the aforementioned model to estimate subaerial accumulations of large wood, alluvial sediment, water, and vegetation in a larger orthoimagery dataset of alluvial river corridors in forested catchments. </p><p>All of these label data are derived from raw gridded data that originate from the U.S. Geological Survey (<i>Ritchie et al., 2018</i>). That dataset consists of 14 orthoimages of the Middle Reach (MR, in between the former Aldwell and Mills reservoirs) and 14 corresponding Lower Reach (LR, downstream of the former Mills reservoir) of the Elwha River, Washington, collected between the period 2012-04-07 and 2017-09-22. That orthoimagery was generated using SfM photogrammetry (following <i>Over et al., 2021</i>) using a photographic camera mounted to an aircraft wing. The imagery capture channel change as it evolved under a ~20 Mt sediment pulse initiated by the removal of the two dams. The two reaches are the ~8 km long Middle Reach (MR) and the lower-gradient ~7 km long Lower Reach (LR). </p><p>The orthoimagery have been labeled (pixelwise, either manually or by an automated process) according to the following classes (inter class in the label data in parentheses):</p><p>1. vegetation / other (0)</p><p>2. water (1)</p><p>3. sediment (2)</p><p>4. large wood (3)</p><h3>1. Model training dataset.</h3><p>Imagery was labeled using a combination of the open-source software Doodler (<i>Buscombe et al., 2021</i>; <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler</a>) and hand-digitization using QGIS at 1:300 scale, rasterizeing the polygons, and gridded and clipped in the same way as all other gridded data. Doodler facilitates relatively labor-free dense multiclass labeling of natural imagery, enabling relatively rapid training dataset creation. The final training dataset consists of 4382 images and corresponding labels, each 1024 x 1024 pixels and representing just over 5% of the total data set. The training data are sampled approximately equally in time and in space among both reaches. All training and validation samples purposefully included all four label classes, to avoid model training and evaluation problems associated with class imbalance (<i>Buscombe and Goldstein, 2022</i>). </p><p>Data are provided in geoTIFF format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels.</p><p>Pixel-wise labels measurements such as these facilitate development and evaluation of image segmentation, image classification, object-based image-analysis (OBIA), and object-in-image detection models, and numerous potential other machine learning models for the general purposes of river corridor classification, description, enumeration, inventory, and process or state quantification. For example this dataset may serve in transfer learning contexts for application in different river or coastal environments or for different tasks or class ontologies.</p><h4>Files:</h4><p>1. Labels_used_for_model_training_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 63 MB, label tiffs</p><p>2. Model_<i>training_</i> images1of4.zip, 1.5 GB, imagery tiffs</p><p>3. Model_<i>training_</i> images2of4.zip, 1.5 GB, imagery tiffs</p><p>4. Model_<i>training_</i> images3of4.zip, 1.7 GB, imagery tiffs</p><p>5. Model_<i>training_</i> images4of4.zip, 1.6 GB, imagery tiffs</p><h3>2. Model output dataset.</h3><p>Imagery was labeled using a deep-learning based semantic segmentation model (<i>Buscombe, 2023</i>) trained specifically for the task using the Segmentation Gym (<i>Buscombe and Goldstein, 2022</i>) modeling suite. We use the software package Segmentation Gym (<i>Buscombe and Goldstein, 2022</i>) to fine-tune a Segformer (<i>Xie et al., 2021</i>) deep learning model for semantic image segmentation. We take the instance (i.e. model architecture and trained weights) of the model of <i>Xie et al. (2021)</i>, itself fine-tuned on ADE20k dataset (<i>Zhou et al., 2019</i>) at resolution 512x512 pixels, and fine-tune it on our 1024x1024 pixel training data consisting of 4-class label images.</p><p>The spatial extent of the imagery in the MR is [455157.2494695878122002,5316532.9804129302501678 : 457076.1244695878122002,5323771.7304129302501678] (NAD83(2011) / UTM zone 10N). Imagery width is 15351 pixels and imagery height is 57910 pixels. The spatial extent of the imagery in the LR is [457704.9227139975992031,5326631.3750646486878395 : 459241.6727139975992031,5333311.0000646486878395] (NAD83(2011) / UTM zone 10N). Imagery width is 12294 pixels and imagery height is 53437 pixels. Data are provided in Cloud-Optimzed geoTIFF (COG) format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels. All grids have been clipped to the union of extents of active channel margins during the period of interest.</p><p>Reach-wide pixel-wise measurements such as these facilitate comparison of wood and sediment storage at any scale or location. These data may be useful for studying the morphodynamics of wood-sediment interactions in other geomorphically complex channels, wood storage in channels, the role of wood in ecosystems and conservation or restoration efforts. </p><h4>Files:</h4><p>1. Elwha_MR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 9.67 MB, label COGs from Elwha River Middle Reach (MR)</p><p>2. Elwha<i>MR_ imagery_ part1_ of</i>_<i> </i>2.zip, 566 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>3. Elwha<i>MR_ imagery_ part2_ of</i>_<i> </i>2.zip, 618 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>3. Elwha_LR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 10.96 MB, label COGs from Elwha River Lower Reach (LR)</p><p>4. ElwhaL<i>R_ imagery_ part1_ of</i>_<i> </i>2.zip, 622 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>5. ElwhaL<i>R_ imagery_ part2_ of</i>_<i> </i>2.zip, 617 MB, imagery COGs from Elwha River Middle Reach (MR)<br> </p><p>This dataset was created using open-source tools of the Doodleverse, a software ecosystem for geoscientific image segmentation, by Daniel Buscombe (<a href="https://github.com/dbuscombe-usgs">https://github.com/dbuscombe-usgs</a>) and Evan Goldstein (<a href="https://github.com/ebgoldstein">https://github.com/ebgoldstein</a>). Thanks to the contributors of the Doodleverse!. Thanks especially Sharon Fitzpatrick (<a href="https://github.com/2320sharon">https://github.com/2320sharon</a>) and Jaycee Favela for contributing labels. </p><h3>References</h3><p>• Buscombe, D. (2023). <strong>Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery (v1.0)</strong> [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8172858">https://doi.org/10.5281/zenodo.8172858</a></p><p>• Buscombe, D., Goldstein, E. B., Sherwood, C. R., Bodine, C., Brown, J. A., Favela, J., et al. (2021).<strong> Human-in-the-loop segmentation of Earth surface imagery</strong>. Earth and Space Science, 9, e2021EA002085. <a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a></p><p>• Buscombe, D., & Goldstein, E. B. (2022). <strong>A reproducible and reusable pipeline for segmentation of geoscientific imagery.</strong> Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p><p>• Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, <strong>Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation</strong>: U.S. Geological Survey Open-File Report 2021–1039, 46 p., <a href="https://doi.org/10.3133/ofr20211039">https://doi.org/10.3133/ofr20211039</a>.</p><p>• Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, <strong>Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals</strong>: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/F7PG1QWC">https://doi.org/10.5066/F7PG1QWC</a>.</p><p>• Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M. and Luo, P., 2021. <strong>SegFormer: Simple and efficient design for semantic segmentation with transformers</strong>. Advances in Neural Information Processing Systems, 34, pp.12077-12090.</p><p>• Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A. and Torralba, A., 2019. <strong>Semantic understanding of scenes through the ade20k dataset</strong>. International Journal of Computer Vision, 127, pp.302-321.</p><p><br> </p>
Wollestraat 29, Bruges (BE): high-resolution images of dry wood cores taken form a medieval floor joists, for tree-ring analysis
<ul><li>Dry-wood cores taken from historical timbers of a floor joists in the medieval building 'De Oude Steen', Wollestraat 29, Bruges (Belgium).</li><li><a href="https://id.erfgoed.net/erfgoedobjecten/29956 ">https://id.erfgoed.net/erfgoedobjecten/29956 </a></li><li>The cores were sampled at 22/02/2023 with a dry-wood borer (internal diameter 12 mm, external diameter 19 mm).</li><li>The cores were surfaced with increasingly finer sanding papers, from P60 up to P4000.</li><li>The cores were photograpphed with a Sony alpha7R IV full frame camera and FE 90 mm F/2.8G macro lens.</li><li>The<a href="https://www.wsl.ch/en/services-produkte/skippy/"> Skippy</a> system served as the image capturing platform.</li><li>The individual digital macro-photos were stitched with PTGui into a mosaic image (.tiff).</li><li>The mosaic images have a resolution of ~4 µm.</li></ul>
A Missing Piece of the E-Region Puzzle: High-Resolution Photoionization Cross Sections and Solar Irradiances in Models
<p>Dataset corresponding to the associated publication, "A Missing Piece of the E-Region Puzzle: High-Resolution Photoionization Cross Sections and Solar Irradiances in Models." The dataset includes high-resolution photoionization and photoabsorption cross section for O and N<sub>2</sub> as well as high-resolution solar spectrum. Photoionization rates from model runs obtained from AURIC and the Meier photoionization code are also included. Please refer to the readme for information on the data structure.</p> <p><strong>***Please note that the paper is under review and has not been accepted yet.***</strong></p>
A Danish high-resolution dataset for six office rooms with occupancy, indoor environment , heating, ventilation, lighting and room control monitoring
<p>A dataset containing measurement data for six office rooms in Aalborg Denmark.<br>All the measurements have been resampled to 5 minute resolution<br>The measurements consists of:</p> <ul> <li>BMS data for the rooms</li> <li>Occupancy for the rooms (from cameras)</li> <li>BMS data for the AHU supplying the rooms</li> <li>BMS data for the Heating system supplying the rooms</li> </ul> <p>Changes from v2<br>It was found that the pressure difference measurements across the exhaust fan was faulty and the following variables have therefore been removed:</p> <ul> <li>Ventilation:Fan__air_flow__exhaust</li> <li>Ventilation:Fan__pressure_difference__exhaust</li> </ul> <p>More data has been added, now increasing the dataset to span the rest of 2023. To better handle the changes between standard time and daylight-saving time the column named "timestamp" has been adjusted so the datetime format now follows the ISO 8601 format YYYY-MM-DDThh:mm:ss+hhmm. the +hhmm changes between 0100 (Danish standard time) and 0200 (Danish daylight-saving time).</p> <p> </p>
High-resolution images from a low-cost imaging device for hyphae in soil
<p>This dataset contains high-resolution images produced by a low-cost imaging device for hyphae in soil called <em>Hyphascope</em>. Using a digital microscope camera (DMC; 600× magnification),<em> </em>the device takes detailed images (0.83 × 0.62 mm imaged area) of a soil profile from evenly spaced camera positions within a user-defined volume. Repeated imaging of a soil profile with <em>Hyphascope</em> enables researchers to observe and quantify changes in the amount, distribution, and morphology of hyphae.</p> <p>Individual images were combined using the <em>Grid/Collection stitching</em> plugin of the <em>Fiji</em> distribution of <em>imageJ</em> (Preibisch et al. 2009). All images are supplied in the JPG format to limit their file size. For more details on the assembly and application of <em>Hyphascope</em>, see <a href="https://doi.org/10.17504/protocols.io.bp2l6xo3zlqe/v1">this protocol on protocols.io</a>. For information on the development, limitations, and expected outcomes of the protocol, see <a href="https://doi.org/10.1371/journal.pone.0318083">this article</a> published in PLOS ONE. </p> <p> </p> <div> <h2>Image set 1: 10 × 10 mm soil profile area at 20 - 30 mm soil depth</h2> <p>Imaged at 0.65 μm px<sup>-1</sup> (39200 dpi)* in a <em>Quercus serrata</em> grove on 2023/05/25 during a period of high hyphal density in the soil.</p> <h3>Individual images (18 rows × 14 images each)</h3> <ul> <li> <p><em>set1_foc00000.zip</em> (focus depth 0 mm)</p> </li> </ul> <h3>Combined images</h3> <ul> <li> <p><em>set1_foc00000_combined.jpg</em> (focus depth 0 mm)</p> </li> </ul> <h2>Image set 2: 5 × 5 mm soil profile area at 100 - 105 mm soil depth</h2> <p>Imaged at 0.52 μm px<sup>-1</sup> (49000 dpi) in a <em>Quercus serrata</em> grove on 2023/09/25.</p> <h3>Individual images (9 rows × 7 images each)</h3> <ul> <li> <p><em>set1_foc00000.zip</em> (focus depth 0 mm)<em><br></em></p> </li> <li> <p><em>set1_foc00025zip</em> (focus depth 0.025 mm)</p> </li> <li><em>set1_foc00050.zip</em> (focus depth 0.05 mm)</li> </ul> <h3>Combined images</h3> <ul> <li> <p><em>set1_foc00000_combined.jpg</em> (focus depth 0 mm)</p> </li> <li> <p><em>set1_foc00025_combined.jpg</em> (focus depth 0.025 mm)</p> </li> <li><em>set1_foc00050_combined.jpg</em> (focus depth 0.05 mm)</li> </ul> <h2>Image set 3: 5 × 5 mm soil profile area at soil surface level</h2> <p>Imaged at 0.52 μm px<sup>-1</sup> (49000 dpi) in a <em>Quercus serrata</em> grove on 2023/10/14 during a rain event.</p> <h3>Individual images (9 rows × 7 images each)</h3> <ul> <li> <p><em>set2_foc00000.zip</em> (focus depth 0 mm)<em><br></em></p> </li> <li> <p><em>set2_foc00025zip</em> (focus depth 0.025 mm)</p> </li> <li><em>set2_foc00050.zip</em> (focus depth 0.05 mm)</li> </ul> <h3>Combined images</h3> <ul> <li> <p><em>set2_foc00000_combined.jpg</em> (focus depth 0 mm)</p> </li> <li> <p><em>set2_foc00025_combined.jpg</em> (focus depth 0.025 mm)</p> </li> <li><em>set2_foc00050_combined.jpg</em> (focus depth 0.05 mm)</li> </ul> <p> </p> <p> </p> <p><em>*Units of imaging resolution: </em></p> <ol> <li><em>pixel width (μm px-1), i.e. the horizontal or vertical distance on the imaged surface covered by a single pixel; </em></li> <li><em>dots per inch (dpi), i.e. the number of pixels along a horizontal or vertical distance of 25.4 mm on the imaged surface.</em></li> </ol> </div>
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.