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26,784 results for “M”
Predicted soil organic carbon stock at 30 m in t/ha for 0-100 cm depth global / update of the map of mangrove forest soil carbon
<p>This is the 2nd update of maps produced by <a href="https://doi.org/10.1088/1748-9326/aabe1c">Sanderman et al (2018)</a>. The improvements to the <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-prediction-of-soil-organic-carbon.html">3D spatial predictions</a> include:</p> <ul> <li> <p>new updated global mangrove coverage map (contact Thomas Worthington),</p> </li> <li> <p>spatiotemporal predictions to account for differences in spectral reflectance at the time of field work,</p> </li> <li> <p>additional SOC points <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41558-018-0162-5/MediaObjects/41558_2018_162_MOESM2_ESM.xlsx">published in Rovai et al. (2018)</a> used in model training (see gpkg file).</p> </li> </ul> <p>To open map in QGIS or similar, drag and drop the *.tif files. You can than add also the gpkg file contain the training points.</p> <p>Production steps (ensemble predictions using SuperLearner) are explained in detail at: </p> <ul> <li>R code: <a href="https://github.com/whrc/Mangrove-Soil-Carbon/">https://github.com/whrc/Mangrove-Soil-Carbon/</a> (see "R_code/GMW_mangroves_SOC_30m.R")</li> <li>Tutorial: <a href="https://envirometrix.github.io/PredictiveSoilMapping/soilmapping-using-mla.html#ensemble-predictions-using-superlearner-package">"Predictive Soil Mapping with R"</a></li> </ul> <p>Produced for the purpose of Mangrove Restoration Potential Map funded by The Nature Conservancy and IUCN. Contact TNC: Emily Landis <<a href="mailto:elandis@TNC.ORG">elandis@TNC.ORG</a>>. Contact IUCN / University of Cambridge: Thomas Worthington <<a href="mailto:taw52@cam.ac.uk">taw52@cam.ac.uk</a>>.</p> <ul> <li>The mangrove restoration potential map is available at: <a href="https://www.researchgate.net/deref/http%3A%2F%2Fmaps.oceanwealth.org%2Fmangrove-restoration%2F">http://maps.oceanwealth.org/mangrove-restoration/</a></li> </ul>
Sampled and simulated benthic invertebrate body-mass data from the Porcupine Abyssal Plain Sustained Observatory (4850 m, 48.83° N 16.50 °W, NE Atlantic)
<p>A dataset of sampled and simulated benthic invertebrate body-mass data from the Porcupine Abyssal Plain Sustained Observatory (4850 m, 48.83° N 16.50 °W, NE Atlantic) has been produced. It includes data on macro- and megabenthos derived from a randomly sampled power law distribution, seabed core samples, large-scale seabed photography, and seabed trawls.</p>
CNBH-10 m: A first Chinese building height at 10 m resolution
<p>Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, <em>R</em> = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/macao">Macau</a> has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 10<sup>8</sup> m<sup>3</sup>). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China.</p>
Soil organic carbon and associated uncertainty at 90 m resolution for peninsular Spain
Soil organic carbon (SOC) must be quantified and monitored to assess soil management practices, adapt policies, and evaluate environmental impacts. However, due to SOC spatial variability, soil surveys become a very challenging task because of the high costs of acquiring data, operational complexity, and updating. Digital soil mapping based on machine learning approaches in combination with remote sensing techniques have enabled soil carbon spatial distribution to be significantly improved, even with limited soil samples. A legacy soil database of 8,361 georeferenced profiles and a selection of environmental data-driven covariates intimately related to soil-forming factors (e.g., biota, climate, parent material) were used to generate SOC maps. Modeling of data was based on three supervised learning approaches: quantile regression forest, ensemble machine learning and auto-machine learning. For the final SOC spatial distribution maps, each pixel was assigned the prediction from the most accurate model, i.e., lowest uncertainty. We applied this modeling technique to generate cost-effective, high-resolution maps (90 m pixel resolution) of SOC distribution, and its associated spatially explicit uncertainty, in peninsular Spain. These maps showed 15.7 g.kg-1 mean SOC concentration at 0-30 cm and 3.6 g.kg-1 at 30-100 cm depth. The total SOC stock at its effective depth was 3.8 Pg C, storing the 74% in the upper 30 cm (2.82 Pg C). The correlation between SOC observed and predictions final values showed R2=0.68 for SOCc and R2=0.54 for SOCs at the upper 30cm. The methodology proposed in this study aims to improve benchmark SOC estimates in support of the National GHG Emissions Inventory Report
Meteorology and soil moisture data collected at multiple frequencies from the M-NORT NPP site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's M-NORT NPP site weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature at 5-minute frequency, and all sensors are measured at 30-minute, hourly and daily frequencies. Observed values include average/maximum/minimum air temperature, relative humidity, and total precipitation; soil moisture, temperature and conductivity. These are measured and calculated based on 1-second scan rate of all sensors located at an automated weather station, and a nearby soil substation, installed at the site. Air temperature and relative humidity is measured at approximately 2.5m. Soil sensors are installed at approximately 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the M-RABB NPP site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's M-RABB NPP site weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind at 5-minute frequency, and all sensors are measured at 30-minute, hourly and daily frequencies. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; total precipitation; average and total solar incoming and reflectance; average albedo; average net radiation; soil moisture, temperature and conductivity. These are measured and calculated based on 1-second scan rate of all sensors located at an automated weather station, and a nearby soil substation, installed at the site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, air temperature and relative humidity at approximately 2.5m, and solar at approximately 3m. Soil sensors are installed at approximately 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the M-WELL NPP site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's M-WELL NPP site weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind at 5-minute frequency, and all sensors are measured at 30-minute, hourly and daily frequencies. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; total precipitation; soil moisture, temperature and conductivity. These are measured and calculated based on 1-second scan rate of all sensors located at an automated weather station, and a nearby soil substation, installed at the site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at approximately 10, 20 and 30cm depths.
Revegetation of landslides, vegetation <0.1m (Small landslide plots at the Luquillo Experimental Forest)
The purpose of this study is to document the recovery of vegetation on new landslides in the Luquillo Experimental Forest, in particular seedlings less then 1m tall. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Revegetation of landslides, vegetation > 1.0m (Large landslide plots at the Luquillo Experimental Forest)
Here we use permanent plot data sampled from 16 landslide to documents temporal successional pathways in landslide patches (without the use of a chronosequence, cf. Guariguata 1990) and address the following questions: (1) What are the successional pathways of landslide and what species define them? How much pathway variation of individual plots is there within these landslides? (2) How similar are pathways among landslide? Is there any evidence that, with time, landslides either converge to a common vegetative enpoint or slow in the rate of successional change? The purpose of this study is to document the recovery of vegetation on new landslides in the Luquillo Experimental Forest. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Larval euphausiids collected using a 1 x 1 m square frame net with 333-μm mesh aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 1993-2013
Euphausiids (krill) are abundant along the Western Antarctic Peninsula where they have important impacts on the marine ecosystem and biogeochemical cycling. Euphausiids develop through a series of morphologically distinct larval stages within the first year of their life cycle. The calyptopis stages are followed by the furcilia stages before individuals recruit to the post-larval population. Larvae collected during Janury were most likely spawned during the preceding weeks or months of Antarctic spring/summer. Euphausia superba and Thysanoessa macrura are the most abundant euphausiid species along the Western Antarctic Peninsula. Euphausia crystallorophias, Euphausia frigida, and Euphausia triacantha are present but less abundant in the region. Samples were collected with a 1 x 1 m square frame net with 333-μm mesh towed obliquely to a depth of typically 300 m. Density of total calyptopis and furcilia larvae (all species combined) was determined for a subset of samples collected on the annual Palmer LTER cruises to cover the latitudinal and cross-shelf gradients of the study region. Larval euphausiid abundance varies spatially as spawning output is not homogeneous across the region. Larval euphausiid abundance also varies year-to-year due to changes in population demographics and environmental conditions.
Mesozooplankton taxonomic density collected using a 1-m diameter ring net with 200-μm mesh at Palmer Station, Antarctica during Palmer LTER field seasons, 2017-2020
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton in surface waters and thus provide a link between primary producers and higher trophic levels. The numerical density of common mesozooplankton taxa was determined at Palmer LTER Stations B and E. Samples were collected with a 1-m diameter, 200-μm mesh ring net towed obliquely from the surface to a target depth of 50 m and back. Tows were conducted during daytime, and sampling frequency was nominally twice weekly while personnel were at Palmer Station between the months of November and March. The preserved samples were size-fractionated with nested sieves into five size classes (0.2−0.5, 0.5−1, 1−2, 2−5, and >5 mm) prior to microscopic enumeration. Data are provided for the following taxa: copepods Oithona spp., Calanoides acutus (>1 mm only), Calanus propinquus (>1 mm only), Rhincalanus gigas (>1 mm only), and small calanoids (0.2−1 mm), chaetognaths, asteroid larvae, nemertean larvae, and foraminifera (not quantified in all years). Individual size fractions were split and subsampled such that at least 100 individuals of the most abundant taxon were present. Density varies across taxa, seasonally, among years, and between sampling stations. Units of density are individuals per cubic meter.
Macrozooplankton taxonomic density collected using a 1 x 1 m square net with 700-μm mesh at Palmer Station, Antarctica during Palmer LTER field seasons, 2017-2020
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton in surface waters and thus provide a link between primary producers and higher trophic levels. The numerical density of common macrozooplankton taxa was determined at Palmer LTER Stations B and E. Samples were collected with a 1 x 1 m square, 700-μm mesh Metro net towed obliquely from the surface to a target depth of 50 m and back. Duplicate tows typically were conducted at each sampling site. Tows were conducted during daytime, and sampling frequency was nominally twice weekly while personnel were at Palmer Station between the months of November and March. The catch was sorted and counted live. Data are provided for the following taxa, which dominated biomass: the euphausiids Euphausia superba and Thysanoessa macrura, the thecosome pteropod Limacina rangii, gymnosome pteropods, the salp Salpa thompsoni, amphipods, and larval fishes. Density varies across taxa, seasonally, among years, and between sampling stations. Units of density are individuals per cubic meter.
Size-fractionated zooplankton dry weight collected using a 1-m diameter ring net with 200-μm mesh at Palmer Station, Antarctica during Palmer LTER field seasons, 2017-2020
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton in surface waters and thus provide a link between primary producers and higher trophic levels. The density of zooplankton dry weight for five size fractions was determined at Palmer LTER Stations B and E. Samples were collected with a 1-m diameter, 200-μm mesh ring net towed obliquely from the surface to a target depth of 50 m and back. Tows were conducted during daytime, and sampling frequency was nominally twice weekly while personnel were at Palmer Station between the months of November and March. One-half of the catch was size-fractionated with nested sieves into the following five size classes for biomass analysis: 0.2−0.5, 0.5−1, 1−2, 2−5, and >5 mm. Individual size fractions were concentrated on preweighed 200 μm mesh filters and frozen at −20°C until analysis. Samples were thawed, weighed to determine wet biomass, dried at 60°C for at least 24 h, and weighed again to determine dry biomass. Zooplankton density varies across size groups, seasonally, among years, and between sampling stations. Units of biomass density are milligrams dry weight per cubic meter.
GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery
<p>A novel global 30-m land-cover product with a fine classification system for the year 2015 (GLC_FCS30-2015). The product was produced by combining time-series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the MCD43A4 NBAR and CCI_LC land-cover products. Secondly, a local adaptive random forest model was built for each 5°×5° geographical tile by using the multi-temporal Landsat spectral and textures features of the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile.</p>
The S&M-HSTPM2d5 dataset: High Spatial-Temporal Resolution PM 2.5 Measures in Multiple Cities Sensed by Static & Mobile Devices
<p>This S&M-HSTPM2d5 dataset contains the high spatial and temporal resolution of the particulates (PM2.5) measures with the corresponding timestamp and GPS location of mobile and static devices in the three Chinese cities: Foshan, Cangzhou, and Tianjin. Different numbers of static and mobile devices were set up in each city. The sampling rate was set up as one minute in Cangzhou, and three seconds in Foshan and Tianjin. For the specific detail of the setup, please refer to the Device_Setup_Description.txt file in this repository and the data descriptor paper.</p> <p>After the data collection process, the data cleaning process was performed to remove and adjust the abnormal and drifting data. The script of the data cleaning algorithm is provided in this repository. The data cleaning algorithm only adjusts or removes individual data points. The removal of the entire device's data was done after the data cleaning algorithm with empirical judgment and graphic visualization. For specific detail of the data cleaning process, please refer to the script (Data_cleaning_algorithm.ipynb) in this repository and the data descriptor paper.</p> <p>The dataset in this repository is the processed version. The raw dataset and removed devices are not included in this repository.</p> <p>The data is stored as a CSV file. Each CSV file which is named by the device ID represents the data that was collected by the corresponding device. Each CSV file has three types of data: timestamp as the China Standard Time (GMT+8), geographic location as latitude and longitude, and PM2.5 concentration with the unit of microgram per cubic meter. The CSV files are stored in either Static or Mobile folder which represents the devices' type. The Static and Mobile folder are stored in the corresponding city's folder.</p> <p>To access the dataset, any programming language that can access CSV files is appropriate. Users can also open the CSV file directly. The get_dataset.ipynb file in this repository also provides an option of accessing the dataset. To successfully execute ipynb file, Jupyter Notebook with Python 3.0 is required. The following python library is also required:</p> <p>get_dataset.ipynb:<br> 1. os library<br> 2. pandas library</p> <p>Data_cleaning_algorithm.ipynb:<br> 1. os library<br> 2. pandas library<br> 3. datetime library<br> 4. math library</p> <p>The instruction of installing the libraries above can be found online. After installing the Jupyter Notebook with Python 3.0 and the required libraries, users can try to open the ipynb file with Jupyter Notebook and follow the instruction inside the file. </p> <p>For questions or suggestions please e-mail Xinlei Chen <xinlei.chen@sv.cmu.edu></p>
FUI Water Color product of inland waters in China at 30-m in 2015
<p>The first 30-meters FUI water color product of China. The product was developed using time-series Landsat 8 imagery and FUI water color retrieval method. Taking into account the huge amount of computational and storage space required for the national-scale water color mapping, the high-performance Google Earth Engine (GEE) cloud-based platform was introduced to support the computation. First, a cloud-free composite in China for the summer of 2015 was generated using time-series Landsat-8 imagery and the Best-Available-Pixel (BAP) compositing algorithm. Then, the first 30-merters FUI water color product of China was developed using the generated BAP composite and the Google Earth Engine computing platform. The first 30-meters FUI water color product can promote the understanding of the water color of water bodies in China, and provide very important information for preserving and restoring inland water quality.</p> <p>The details of the product is described in "<a href="https://zenodo.org/api/files/59060333-b9fc-45ad-b381-3b05a866de6c/FUI_WaterColor_2015China_Readme_V1.1.docx?versionId=44bb5bbb-407f-4dc8-b183-fa1a3843488f">FUI_WaterColor_2015China_Readme_V1.1.docx</a>".</p>
Supporting data to M. Cavallaro, et al., 3'-5' crosstalk contributes to transcriptional bursting, 2019
<p>This repository contains supporting data to reference [1]. Please cite [1] if you find this repository useful. The data include:</p> <ul> <li>Flow cytometry data of HBB and HIV transgenes' expression in `.fcs` format.</li> <li>NanoString data for HIV expression.</li> <li>smFISH data for HBB and Akt1 gene expression.</li> </ul> <p>[1] M. Cavallaro, <em>et al.</em>, 3'-5' interactions contribute to transcriptional bursting, bioR$\chi$iv 514174. <a href="https://doi.org/10.1101/514174">https://doi.org/10.1101/514174</a></p>
Red-optical spectra of three nearby M dwarfs with SALT HRS
<p>We conducted observations of three nearby mid-M dwarfs with the High-Resolution Spectrograph (HRS) at the Southern African Large Telescope (SALT, DDT proposal code: 2019-2-DDT-006). We obtained spectra in its red arm over a wavelength range of 5,500-8,900 Angström with a spectral resolution of about 40,000 in medium-resolution mode. The observations were carried out on February 08 and February 09, 2020. The data were reduced with the PEPSI data reduction software (Strassmeier et al. 2018). The reduction followed the standard steps of bias overscan detection and subtraction, scattered light extraction from the inter-order space and subtraction, definition of échelle orders, optimal extraction of spectral orders, wavelength calibration, and a self-consistent continuum fit to the full two-dimensional (2D) image of extracted orders.</p> <p><strong>Files in this dataset</strong></p> <p>salt1.txt: TIC 44984200 (2MASS J08380224-5855583)<br> salt2.txt: TIC 277539431 (2MASS J10551532-7356091)<br> salt3.txt: TIC 300741820 (2MASS J07404497-6648318)</p> <p><strong>Columns in each file from left to right:</strong></p> <p>1 Wavelength in Angstrom<br> 2 Normalized flux<br> 3 Uncertainty on normalized flux</p> <p><strong>Corresponding author</strong></p> <p>Ekaterina Ilin, eilin@aip.de, Leibniz Institute for Astrophysics Potsdam (AIP)</p>
ELC10: European 10 m resolution land cover map 2018
<p>Refer to preprint here: https://arxiv.org/abs/2104.10922</p> <p>A land cover classification for Europe at 10 m resolution produced with a machine learning workflow driven by Sentinel optical and radar satellite imagery. The classification model was trained on land cover reference data form the LUCAS (Land Use/Cover Area frame Survey) dataset. The map represents conditions in 2018.</p> <p>The methodology is currently under review, but this will be updated as soon as the paper is available online. Please refer to the publication for accuracy estimates and usage guidelines.</p> <p>The map is split up into a number of raster tiles with the coordinate reference system "EPSG:3035 - ERTS89 / LAEA Europe".The filename of each tile is in the form baseFilename-yMin-xMin where xMin and yMin are the coordinates of each tile within the overall bounding box of the entire ELC10 image.</p> <p>The pixel values, their definitions and suggested hex color codes include: 0 (not mapped #000000), 1 (Artificial land, #CC0303), 2 (Cropland, #CDB400), 3 (Woodland, #235123), 4 (Shrubland, #B76124), 5 (Grassland, #92AF1F), 6 (Bare land, #F7E174), 7 (Water/permanent snow/ice, #2019A4), 8 (Wetland, #AEC3D6).</p>
Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in 'winning' hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.
<p>Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in ‘winning’ hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.</p>
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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.