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12,346 results for “derivatives”
Remote sensing of river discharge (RSQ) estimates derived from multi-temporal Landsat width observations and BAM/geoBAM discharge inversion algorithms
<p><strong>This repository provides three data files in CSV format:</strong><br> 1. Gauge name, lat/lon information<br> 2. Gauge name, date, and multi-temporal river width extracted from Landsat<br> 3. Gauge name, date, and BAM/geoBAM estimates of river discharge with monthly Q priors</p> <p>Note: the multi-temporal river width data were extracted from Landsat imageries using RivWidthCloud, where the river centerline/orthogonal line definition and the cross-section sampling strategies were made prior to, and different from Feng et al. (2022). So the width values may be different from Feng et al. (2022) at some locations due to these differences. The discharge estimates were derived from BAM/geoBAM algorithms with width-only observations. More details of the technical workflow and the inner workings of BAM/geoBAM were provided in the literature below and papers therein.</p> <p> </p> <p><strong>Reference:</strong></p> <p>Lin, P., D. Feng, C.J. Gleason, M. Pan, C.B. Brinkerhoff, X. Yang, H.E. Beck, R. Frasson (2023). Inversion of river discharge from remotely sensed river widths: a critical assessment at three-thousand global river gauges. <em>RSE</em>.</p> <p> </p> <p>Updated: 2022/6/17, 2023/1/19</p> <p> </p>
Satellite-derived chlorophyll-a concentrations for Lake Harsha (USA) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of Lake Harsha (USA) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
Soil greenhouse gas emissions (CO2 and N2O) data and metadata derived from H2020 Diverfarming project
<p>Soil greenhouse gas emissions (CO<sub>2</sub> and N<sub>2</sub>O) data and metadata of an almond crop diversified with <em>Thymus hyemalis </em>(diversification 1) and with<em> Capparis spinosa </em>(diversification 2). This data comes from WP5 "Environmental impact and delivery of ecosystem services by crop diversification", derived from H2020 Diverfarming project. This workpackage has been designed to provide sound and robust scientific understanding of the benefits and drawbacks of the tailored diversified cropping systems for improvement of the environmental quality and delivery of ecosystem services in each pedoclimatic region. http://www.diverfarming.eu</p>
A map of active cropland and short-term fallows across Northern Mozambique derived from PlanetScope data
<p><strong>Overview</strong></p> <p>A map of smallholder-dominated landscapes covering the provinces Niassa, Zambezia, Cabo Delgado, and Nampula in Northern Mozambique. The map includes active cropland and short-term fallows as separate classes, as well as five land cover classes (herbaceous vegetation, open woodlands, closed woodlands, non-vegetated land, water). The map is based on PlanetScope mosaics and consequently comes at 4.77m spatial resolution.</p> <p>The download contains the following files:</p> <ul> <li>ps_lc_nmoz.tif / .qml: land cover map and associated QGIS style file</li> <li>ps_lc_nmoz_probmargins.tif / .qml: probability margins and associated QGIS style file</li> <li>training.gpkg: training samples with class labels</li> <li>LICENSE.pdf: NICFI data program user license</li> </ul> <p><strong>Map accuracy</strong></p> <p>We conducted an area-adjusted accuracy assessment based on a stratified random sample, which yielded important insights regarding accuracies and error types. The area-adjusted overall accuracy of the map is 88.9%, but users should be aware of the most important error types:</p> <ul> <li>Active cropland were overestimated, whereas local topographical depressions with moist soils, and regions with exposed soils/rocks and sparse vegetation cover were found to be falsely classified.</li> <li>Short-term fallows were underestimated, particularly in regions with high growth rates and extensive land management, such as parts of the northern and north-eastern study region.</li> </ul> <p><strong>Further resources</strong></p> <p>The production of this map was made possible through the <a href="https://www.planet.com/nicfi/">NICFI data program</a>, providing the PlanetScope mosaics and the Google Earth Engine cloud computing platform for preprocessing of the satellite data and classification. As such, the use of the map falls under the <a href="https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf">NICFI data program license agreement</a> included in the download. The code for preprocessing the PlanetScope mosaics is based on the Google Earth Engine Python API and made available at <a href="https://github.com/philipperufin/eepypr/">https://github.com/philipperufin/eepypr/</a>.</p> <p>We advise map users to read the <a href="https://eartharxiv.org/repository/view/3174/">preprint</a> or the <a href="https://doi.org/10.1016/j.jag.2022.102937">open access paper</a> for detailed insights. In case of questions please consult these resources or contact the lead author of the work.</p>
Dimethylsulfoniopropionate-derived compound concentrations, volatile organic compound concentrations, and microorganism abundances around two corals and a seaweed in the reefs of Moorea (French Polynesia)
<p>These data belong in the paper: </p> <p>M. Masdeu-Navarro, J-F. Mangot, L. Xue, M. Cabrera-Brufau, S.G. Gardner, D.J. Kieber, J.M. González, R. Simó (2022). Spatial and diel patterns of volatile organic compounds, DMSP-derived compopunds and planktonic microorganisms around a tropical scleractinian coral colony. <em>Frontiers in Marine Science</em>.</p> <p>Concentrations of DMSP, acrylate, DMSO, DMS, DMDS, COS, CS2, isoprene, CH3I, CH2ClI, CH2Br2 and CHBr3 in seawater samples around colonies of the corals Acropora pulchra and Pocillopora sp., and the brown seaweed Turbinaria ornata. Abundances of high-DNA and low-DNA bacteria, Prochlorococcus, Synechococcus, picoeukaryotes and nanoeukaryotes in the same samples, as determined by flow cytometry. All samples were collected in April 2018 in the coral reefs of Mo'orea, French Polynesia. </p> <p>The upper set of data contains concentrations at the distance of 0.5 cm from the coral polyps on the branch tips or verrucae, as well as from the seaweed thalli (samples IN), and 2 m away, downcurrent (samples OUT). The second set of data corresponds to A. pulchra only, and contains seawater samples IN, OUT and AL, the latter being sampled at 0.5 cm from the base of the dead branches colonized by a turf alga. IN, OUT and AL samples were collected over an entire diel cycle, every 6 hours for a period of 30 hours.</p>
ecochange: An R-package to derive ecosystem change indicators from freely available earth observation products
<p>This release includes the R code necessary to reproduce Figures 2-4 in the Application paper entitled: "ecochange: An R-package to derive ecosystem change indicators from freely available Earth Observation products."</p>
Washover morphometry: lidar-derived and reported in literature
<p>This portfolio includes three sets of data, used and explained in Lazarus, Williams & Goldstein (2022, <a href="https://doi.org/10.1029/2022GL100098">https://doi.org/10.1029/2022GL100098</a>):</p> <ol> <li>washover morphometry measured from lidar-derived topographic change along the coastline of New Jersey, USA, following Hurricane Sandy (2012) ('NJ_Sandy_metrics.csv');</li> <li>the geospatial data layers used to generate those measurements ('WashoverGIS.zip');</li> <li>and a compilation of washover morphometry reported in the literature ('washover_LAV_literature_examples.csv').</li> </ol> <p> </p> <p><strong>Washover morphometry datasets</strong></p> <ul> <li><strong>NJ_Sandy_metrics.csv</strong> – The lidar-derived washover morphometry dataset includes: deposit width (m), intrusion length (m), deposit area (m<sup>2</sup>), deposit volume (m<sup>3</sup>), deposit perimeter (m), built fraction, the storm event (Sandy 2012), and a general location note.</li> <li><strong>washover_LAV_literature_examples.csv</strong> – Also included here are 35 measurements of washover morphometry reported in the literature by six different studies, sampling different storm events in different coastal barrier settings (Carruthers et al., 2013; Williams, 2015; Jamison-Todd et al., 2020; Rodriguez et al. 2020; Hansen et al., 2021; Williams & Rains, 2022). The literature-based dataset includes: intrusion length (m), deposit area (m<sup>2</sup>), deposit volume (m<sup>3</sup>), the reference (dataset) in which the measurements were reported, and additional notes.</li> </ul> <p> </p> <p><strong>Geospatial data layers ('WashoverGIS' [zipped])</strong></p> <p>The lidar data underpinning the geospatial data layers here are available from the NOAA Digital Coast Data Viewer (<a href="https://coast.noaa.gov/dataviewer/#/">https://coast.noaa.gov/dataviewer/#/</a>): "2012 USGS EAARL-B Lidar: Pre-Sandy" (pre-storm), and "2012 USGS EAARL-B Lidar: Post-Sandy" (post-storm).</p> <p>Geospatial analysis was done in QGIS version 3.22.5. We masked both the pre- and post-storm surfaces to isolate only positive elevations, and subtracted the pre-storm surface from the post-storm surface to calculated the difference between them; we then retained only the positive differences in the resulting surface to isolate sites of sediment deposition. We manually digitized the perimeters of depositional forms we interpreted as washover, corroborated by aerial imagery (<a href="https://storms.ngs.noaa.gov/">https://storms.ngs.noaa.gov/</a>).</p> <p>Basic geometric characteristics (perimeter, area) were taken directly from the washover polygons; washover length and width were taken from oriented minimum bounding boxes around each polygon. Volume for each washover polygon was measured using the Volume Calculation Tool (version 0.4) plugin for QGIS (<a href="https://github.com/REDcatch/Volume_calculation_for_QGIS3">https://github.com/REDcatch/Volume_calculation_for_QGIS3</a>). In built settings, each washover deposit was associated with a locally estimated built fraction (Lazarus et al., 2021). Elements of the built environment (i.e., buildings) were isolated by creating a binary mask of the pre-storm surface, such that all elevations ³5 m were set to a value = 1, and all elevations <5 m set to zero. Minimum enclosing circles were drawn around each washover polygon, and the total built area (masked value = 1) within each circle summed using the QGIS Zonal Statistics tool. Here, local built fraction is the total built area within a minimum enclosing circle divided by the area of that circle.</p> <p>Geospatial files here include:</p> <ul> <li><strong>NJ_north_wash_metrics.shp</strong> // <strong>NJ_south_wash_metrics.shp </strong>– shapefiles of the digitized washover deposits, with morphometric characteristics compiled in their attribute tables</li> <li><strong>NJ_north_BBs.shp</strong> // <strong>NJ_south_BBs.shp</strong> – oriented bounding boxes to determine deposit intrusion length & width</li> <li><strong>NJ_north_MECs.shp</strong> // <strong>NJ_south_MECs.shp</strong> – minimum enclosing circles, used for calculating local built fraction</li> <li><strong>NJ_north_dSandy_POS.tif</strong> // <strong>NJ_south_dSandy_POS.tif </strong>– positive [post-storm - pre-storm] elevation differences</li> <li><strong>NJ_north_rooftops_th05.tif</strong> // <strong>NJ_south_rooftops_th05.tif </strong>– binary mask based on the pre-storm lidar layer ("2012 USGS EAARL-B Lidar: Pre-Sandy") used for calculating built fraction, in which all topographic elements >= 5 m are set = 1, and all < 5 m are set = 0</li> </ul> <p> </p>
Desired properties of recycling-derived fertilisers from an end-user perspective
<p>This data refers to responses to questions asked to farmers and farm advisors in seven different North-West European countries, relating to the desired properties of the mineral fertiliser substitutes, recycling derived fertilisers (RDFs). In total, 1225 participants responded from Belgium, France, Germany, Ireland, Luxembourg, the Netherlands and the United Kingdom. The types of questions asked included the participants' demographics and farming activities, and the parameters, properties and qualities the respondents were looking for, to determine the desired properties of RDFs.</p>
Derived Data from "Expanding European protected areas through rewilding"
<p>We present the major derived data obtained through the study "Expanding European protected areas through rewilding" published in Current Biology.</p> <p>Data refer to three shapefiles and it is structured as: </p> <p>1) "Rewilding Patches" folder - presenting European rewilding patches (human footprint <=5), classified by area</p> <p>2) "Marxan Solutions" folder - presenting optimized solutions to expand current European protected areas through rewilding such to achieve ,in each country, 30% area with protected areas ("PA_all" sub-folder) and 10% area with strict protected areas ("PA_strict" sub-folder)</p> <p>For detail on data, users are adviced to read the "Readme" files in each folder.</p>
Dataset for article: Pro-cognitive effects of dual tacrine derivatives acting as cholinesterase inhibitors and NMDA receptor antagonists
<p>Figure 1. Chemical structures of tacrine (<strong>a</strong>) and its derivatives created by introducing substituents on the aromatic core and/or altering the size of the cycloalkyl moiety attached to the aromatic region: 7-MEOTA (<strong>b</strong>), K1578 (7-chloro-1<em>H</em>,2<em>H</em>,3<em>H</em>-cyclopenta[<em>b</em>]quinolin-9-amine; <strong>c</strong>), K1592 (1-chloro-6<em>H</em>,7<em>H</em>,8<em>H</em>,9<em>H</em>,10<em>H</em>-cyclohepta[<em>b</em>]quinolin-11-amine; <strong>d</strong>), K1594 (6-methyl-1,2,3,4-tetrahydroacridin-9-amine; <strong>e</strong>), and K1599 (7-methoxy-1<em>H</em>,2<em>H</em>,3<em>H</em>-cyclopenta[<em>b</em>]quinolin-9-amine; <strong>f</strong>). Compounds in this study were used in the form of hydrochloride salts.</p> <p>Figure_2_values. Test results. Morris water maze: scopolamine-induced model of cognitive deficit in the acquisition and reversal phases. The graphs show the effects of K1578 (<strong>a</strong>), K1592 (<strong>b</strong>), K1594 (<strong>c</strong>), and K1599 (<strong>d</strong>) on escape latency during the acquisition phase, where none of the compounds ameliorated the deficit of spatial learning. The remaining graphs display the effects of K1578 (<strong>e</strong>), K1592 (<strong>f</strong>), K1594 (<strong>g</strong>), and K1599 (<strong>h</strong>) in the reversal phase, where K1578 (1 mg/kg) and K1599 (at both doses), and marginally K1592 (1 mg/kg; see in the text), mitigated the scopolamine-induced deficit of reversal learning. VEH – vehicle, the numbers in brackets denote the dose applied (mg/kg). Data are presented as the mean + SEM, * vs. VEH, * p < 0.05, ** p < 0.01, *** p < 0.001. <em>n</em> = 6–9 animals per group. Statistical significance was determined using two-way repeated measures ANOVA (a–d) or ANOVA (e, f, h) followed by Dunnett’s multiple comparisons tests.</p> <p>Figure-3_values. Test results. Morris water maze: MK-801-induced model of cognitive deficit in the acquisition phase. The graphs illustrate the effects of the compounds K1578 (<strong>a</strong>), K1592 (<strong>b</strong>), K1594 (<strong>c</strong>), and K1599 (<strong>d</strong>) on escape latency. Only K1599 (1 mg/kg) ameliorated the MK-801-induced deficit of spatial learning. VEH – vehicle, the numbers in brackets denote the dose (mg/kg). Data are presented as the mean + SEM, * vs. VEH, * p < 0.05, ** p < 0.01. <em>n</em> = 5–7 animals per group. Statistical significance was determined using two-way repeated measures ANOVA followed by Dunnett’s multiple comparisons tests.</p> <p>Figure_4_values. Open field test. The results demonstrate the effects of K1578 (<strong>a</strong>), K1592 (<strong>b</strong>), K1594 (<strong>c</strong>), and K1599 (<strong>d</strong>) on the distance moved by intact and MK-801-treated animals. VEH – vehicle, the numbers in brackets denote the dose (mg/kg). Data are presented as the mean + SEM, * vs. VEH group of the corresponding phenotype, * p < 0.05, ** p < 0.01, **** p < 0.0001. <em>n</em> = 6–14 animals per group. A significant effect of both factors (treatment and phenotype) was determined using two-way ANOVA, followed by Dunnett’s multiple comparisons tests.</p> <p>Figure-5_values. Acetylcholinesterase activity. The results document the effect of the compounds (1 mg/kg ip) on AChE activity in the hippocampus (<strong>a</strong>), prefrontal cortex (<strong>b</strong>), striatum (<strong>c</strong>), and whole brain sample (<strong>d</strong>). K1578 and K1599 decreased AChE activity in the striatum. VEH – vehicle. Data are presented as the median with minimum to maximum range, * vs. VEH, *** p < 0.001, **** p < 0.0001. VEH samples AChE enzyme activities reached the following absolute values (a) 15.19 ± 3.09 U/mg protein, (b) 9.810 ± 1.54 U/mg protein, (c) 26.05 ± 3.27 U/mg protein, and (d) 27.38 ± 3.36 U/mg protein. Significance was determined by ANOVA (graphs c, d), followed by Dunnett’s multiple comparisons tests.</p> <p>Figure_6_values. Electrophysiology: Inhibition of GluN1/GluN2A receptors by K1599. Representative whole-cell patch-clamp recordings measured from HEK293 cells expressing the GluN1/GluN2A receptors held at a membrane voltage of −80 mV and +60 mV; 30 μM K1599 was applied as indicated. Results summarizing the relative inhibition induced by 30 µM K1599, measured at the indicated membrane potentials. <em>n</em> ≥ 5 cells per each condition.</p> <p>Table_1. The rats were pseudo-randomly assigned to one of the 18 treatment groups listed in. Each group received two injections: one containing the study compound and another containing either MK-801 or scopolamine, as indicated by the group name. The vehicle group (VEH) received the DMSO vehicle (2.5 mL/kg) and saline. The “scopolamine” and “MK-801” groups received scopolamine or MK-801, respectively, along with the DMSO vehicle (2.5 mL/kg).</p> <p>Table 2. Treatment groups and <em>n</em> in biochemical experiments - AChE activity assay.</p>
Genome-wide de novo L1 Retrotransposition Connects Endonuclease Activity with Replication: insertion data and derivative models
<p>This data repository provides access to the LINE-1 (L1) insertion site data from Flasch, et al., 2019:<br><a href="https://www.sciencedirect.com/science/article/pii/S0092867419302338?via%3Dihub">https://www.sciencedirect.com/science/article/pii/S0092867419302338?via%3Dihub</a></p> <p>Please see file <strong>L1_actual_and_random_insertions_help.docx</strong> for more detailed information.</p> <p>Files <strong>weighted_model_142_102917_corrected.txt</strong> and <strong>weighted_model_142_102917_uncorrected.txt</strong> carry a list of all possible 7-mer insertion sites, one per row, with site weights calculated based on observed L1 insertion sites. The files are either corrected or uncorrected for the frequency of those sites as found in the human genome, respectively. A header line defines the columns.</p> <p>The weight files described above were used to construct the simulated <strong>hg19 </strong>insertions sets described below.</p> <p>Archive <strong>all_insertion_sets.tar</strong> contains a series of insertion files, each with the complete data set used to analyze insertions from the named cell line.</p> <p>Column 5 is the iteration number, where:</p> <ul> <li>iteration == 0 identifies the actual, observed insertions</li> <li>each iteration > 0 identifies one round of simulation, up to 10K total simulations</li> <li>each iteration has the same number of insertions</li> </ul> <p>Column 4 is the insertion number within each actual or simulated insertion set.</p> <p>Please see the companion Zenodo data set:<br>10.5281/zenodo.12538130<br>for more information about creating insertion site models from your own insertion data.</p>
CLDF dataset derived from von Rosenberg's "De Mentawei-Eilanden en Hunne Bewoners" from 1853
<p>Cite the source of the dataset as:</p> <blockquote> <p>Rosenberg, Carl Benjamin Hermann von. 1853. De Mentawei-Eilanden en Hunne Bewoners. <em>Tijdschrift voor Indische Taal-, Land- en Volkenkunde</em> 1. 403–440. Available online at <a href="https://www.digitale-sammlungen.de/en/view/bsb10433845?page=450,451" target="_blank" rel="noopener">https://www.digitale-sammlungen.de/en/view/bsb10433845?page=450,451</a></p> </blockquote>
Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data
<p>Overview:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br>The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate difference of ERA5-Land - aggregated CHELSA <br>3. interpolate differences with a Gaussian filter to 30 arc seconds <br>4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to monthly averages.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month):<br><code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code></p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 82:00:30N<br>south: 18N<br>west: 32:00:30W<br>east: 70E</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>Monthly</p> <p>Pixel values:<br>Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br>GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2</p> <p>Original ERA5-Land dataset license:<br><a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br>Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://doi.org/10.5281/zenodo.7427021">https://doi.org/10.5281/zenodo.7427021</a></p>
Image-derived indicators of phytoplankton community responses to Pseudo-nitzschia blooms
<p>Data associated with the manuscript "Image-derived indicators of phytoplankton community responses to <em>Pseudo-nitzschia</em> blooms" submitted to the journal <em>Harmful Algae</em>. There is an additional R script that calculates an interaction metric as described in the paper. </p>
CLDF dataset derived from Hale's "Wordlists in Selected Languages of Nepal" from 1973
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hale, Austin (1973): Clause, sentences, and discourse patterns in selected languages of Nepal. Kathmandu: Institute of Nepal and Asiatic Studies.</p> </blockquote>
CLDF dataset derived from Greenhill and Gray's "Bantu Basic Vocabulary Database" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Simon Greenhill and Russell Gray, 2015. Bantu Basic Vocabulary Database</p> </blockquote>
CLDF dataset derived from Bowern and Atkinson's "Internal Structure of Pama-Nyungan" from 2012
<p>Cite the source of the dataset as:</p> <blockquote> <p>Bowern, Claire, & Atkinson, Quentin. (2012). Computational Phylogenetics and the Internal Structure of Pama-Nyungan: Dataset [Data set]. Language. http://doi.org/10.1353/lan.2012.0081</p> </blockquote>
Concordant inter-laboratory derived concentrations of ceramides in human plasma reference materials via authentic standards
<h1>Concordant inter-laboratory derived concentrations of ceramides in human plasma reference materials via authentic standards</h1> <p>In this community effort, we compared measurements between 34 laboratories from 19 countries, utilizing mixtures of labelled authentic synthetic standards, to quantify by mass spectrometry four clinically used ceramide species in the NIST (National Institute of Standards and Technology) human blood plasma Standard Reference Material (SRM) 1950, as well as new suite of candidate plasma reference materials (RM 8231). Participants either utilized a provided validated method (SOP) and/or their method of choice (OTHER). Mean concentration values, and intra- and inter-laboratory coefficients of variation (CV) were calculated using single-point and multi-point calibrations, respectively.</p> <p>The attached file "ILS-Ceramide-Ring-Trial-Datasets.csv" and the table below map the lab number (LabNum) used in the manuscript in all plots to the originally assigned submission Id (LabId) used in <a href="https://github.com/lifs-tools/ils-ceramide-ring-trial/tree/main/data/original-reports" target="_blank" rel="noopener">the anonymized reports </a>containing the peak areas submitted by each lab for their SOP (Standard) and / or OTHER (Preferred) workflow. The table below provides further information on the separation used (LC), the mass analyzer type (QQQ=Triple Quads and Traps, Orbitrap, TOF) and the associated mass analyzer resolution (LowRes, HighRes), and links each LabNum to the corresponding dataset name and Zenodo DOI, if available. In order to retain the anonymity of all participating labs w.r.t. the submitted datasets, only the converted mzML files are provided in the linked submissions. Please note that labs were free to choose whether they wanted to disclose their MS data or not. Thus, missing datasets indicate that the corresponding lab did not provide their raw / mzML data. </p> <p>All reports together with the code for analysis and visualization, reproducing the figures in the manuscript, are available under the following doi: <a href="../doi/10.5281/zenodo.10081970" target="_blank" rel="noopener">https://zenodo.org/doi/10.5281/zenodo.10081970</a>. This links to releases of the following GitHub repository: <a href="https://github.com/lifs-tools/ils-ceramide-ring-trial">https://github.com/lifs-tools/ils-ceramide-ring-trial</a>. </p> <h2>Ring Trial mzML Datasets</h2> <table> <tbody> <tr> <th>LabNum</th> <th>LabId</th> <th>Protocol</th> <th>LC</th> <th>MassAnalyzerType</th> <th>MassAnalyzerResolution</th> <th>DatasetName</th> <th>DOI</th> </tr> <tr> <td>1</td> <td>02b</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_01_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134264" target="_blank" rel="noopener">10.5281/zenodo.13134264</a></td> </tr> <tr> <td>2</td> <td>3</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_02_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13145059" target="_blank" rel="noopener">10.5281/zenodo.13145059</a></td> </tr> <tr> <td>3</td> <td>4</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_03_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134437" target="_blank" rel="noopener">10.5281/zenodo.13134437</a></td> </tr> <tr> <td>4</td> <td>5</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_04_OTHER</td> <td> </td> </tr> <tr> <td>5</td> <td>7</td> <td>OTHER</td> <td>RP</td> <td>Orbitrap</td> <td>HighRes</td> <td>Lab_05_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13134439" target="_blank" rel="noopener">10.5281/zenodo.13134439</a></td> </tr> <tr> <td>6</td> <td>9</td> <td>OTHER</td> <td>FIA</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_06_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13134441" target="_blank" rel="noopener">10.5281/zenodo.13134441</a></td> </tr> <tr> <td>7</td> <td>10a</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_07_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134445" target="_blank" rel="noopener">10.5281/zenodo.13134445</a></td> </tr> <tr> <td>7</td> <td>10b</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_07_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13134443" target="_blank" rel="noopener">10.5281/zenodo.13134443</a></td> </tr> <tr> <td>8</td> <td>12</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_08_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134447" target="_blank" rel="noopener">10.5281/zenodo.13134447</a></td> </tr> <tr> <td>9</td> <td>13</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_09_OTHER</td> <td> </td> </tr> <tr> <td>10</td> <td>14</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_10_SOP</td> <td> </td> </tr> <tr> <td>11</td> <td>15</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_11_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134449" target="_blank" rel="noopener">10.5281/zenodo.13134449</a></td> </tr> <tr> <td>12</td> <td>16</td> <td>OTHER</td> <td>RP</td> <td>Orbitrap</td> <td>HighRes</td> <td>Lab_12_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13134451" target="_blank" rel="noopener">10.5281/zenodo.13134451</a></td> </tr> <tr> <td>13</td> <td>17a</td> <td>OTHER</td> <td>RP</td> <td>TOF</td> <td>HighRes</td> <td>Lab_13_OTHER</td> <td> </td> </tr> <tr> <td>14</td> <td>18a</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_14_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135617" target="_blank" rel="noopener">10.5281/zenodo.13135617</a></td> </tr> <tr> <td>14</td> <td>18b</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_14_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135617" target="_blank" rel="noopener">10.5281/zenodo.13135617</a></td> </tr> <tr> <td>15</td> <td>19</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_15_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135625" target="_blank" rel="noopener">10.5281/zenodo.13135625</a></td> </tr> <tr> <td>16</td> <td>20a</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_16_SOP</td> <td> </td> </tr> <tr> <td>16</td> <td>20b</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_16_OTHER</td> <td> </td> </tr> <tr> <td>17</td> <td>21</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_17_SOP</td> <td> </td> </tr> <tr> <td>18</td> <td>22</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_18_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135627" target="_blank" rel="noopener">10.5281/zenodo.13135627</a></td> </tr> <tr> <td>19</td> <td>23</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_19_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135629" target="_blank" rel="noopener">10.5281/zenodo.13135629</a></td> </tr> <tr> <td>20</td> <td>24</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_20_SOP</td> <td> </td> </tr> <tr> <td>21</td> <td>25</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_21_SOP</td> <td><a href="../doi/10.5281/zenodo.13244007" target="_blank" rel="noopener">10.5281/zenodo.13244007</a></td> </tr> <tr> <td>22</td> <td>26a</td> <td>OTHER</td> <td>FIA</td> <td>Orbitrap</td> <td>HighRes</td> <td>Lab_22_OTHER</td> <td> </td> </tr> <tr> <td>22</td> <td>26b</td> <td>OTHER</td> <td>FIA</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_22_OTHER</td> <td> </td> </tr> <tr> <td>23</td> <td>27</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_23_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135632" target="_blank" rel="noopener">10.5281/zenodo.13135632</a></td> </tr> <tr> <td>24</td> <td>28</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_24_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135634" target="_blank" rel="noopener">10.5281/zenodo.13135634</a></td> </tr> <tr> <td>25</td> <td>29a</td> <td>SOP</td> <td>RP</td> <td>TOF</td> <td>HighRes</td> <td>Lab_25_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13136167" target="_blank" rel="noopener">10.5281/zenodo.13136167</a></td> </tr> <tr> <td>25</td> <td>29b</td> <td>OTHER</td> <td>SFC</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_25_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13135638" target="_blank" rel="noopener">10.5281/zenodo.13135638</a></td> </tr> <tr> <td>26</td> <td>30</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_26_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13136171" target="_blank" rel="noopener">10.5281/zenodo.13136171</a></td> </tr> <tr> <td>27</td> <td>31</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_27_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13136176" target="_blank" rel="noopener">10.5281/zenodo.13136176</a></td> </tr> <tr> <td>28</td> <td>32</td> <td>OTHER</td> <td>RP</td> <td>TOF</td> <td>HighRes</td> <td>Lab_28_OTHER</td> <td> </td> </tr> <tr> <td>29</td> <td>33</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_29_OTHER</td> <td> </td> </tr> <tr> <td>30</td> <td>34</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_30_SOP</td> <td> </td> </tr> <tr> <td>31</td> <td>35</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_31_OTHER</td> <td> </td> </tr> <tr> <td>32</td> <td>36</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_32_SOP</td> <td><a href="../doi/10.5281/zenodo.13166454" target="_blank" rel="noopener">10.5281/zenodo.13166454</a></td> </tr> <tr> <td>33</td> <td>37</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_33_OTHER</td> <td><a href="../doi/10.5281/zenodo.13732469" target="_blank" rel="noopener">10.5281/zenodo.13732469</a></td> </tr> <tr> <td>34</td> <td>38</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_34_SOP</td> <td><a href="../doi/10.5281/zenodo.13324807" target="_blank" rel="noopener">10.5281/zenodo.13324807</a></td> </tr> </tbody> </table>
Satellite-derived monthly Arctic winter sea ice thickness, snow depth, freeboards, ice draft, and bulk ice density (2011-2022) and validation datasets
<h1><strong>[Description]</strong></h1> <p>This dataset is curated for a manuscript published in Earth and Space Science by Hoyeon Shi and his colleagues in April 2024. </p> <blockquote> <p>Shi, H., Tonboe, R., Lee, M., Dybkjær, G., Sohn, J., Singha, S., & Baordo, F. (2024). A Simple and Robust CryoSat-2 Radar Freeboard Correction Method Dedicated to TFMRA50 for the Arctic Winter Snow Depth and Sea Ice Thickness Retrieval. <em>Earth and Space Science</em>, <em>11</em>(10), e2024EA003715. https://doi.org/10.1029/2024EA003715</p> </blockquote> <p>Here, version 2 is uploaded, corresponding to the revised manuscript during the revision. The main changes compared to version 1 are:<br> 1) Update of the CryoSat-2 radar freeboard dataset (from v2p4 to v2p6)<br> 2) Update of the coefficients for the radar freeboard correction equations<br> 3) Extension of the retrieval period for the CS2IS2 method (April is now included)<br> 4) Removal of OIB data points used for the regression from the validation datasets<br> 5) Inclusion of the Fram Strait mooring dataset in the validation dataset</p> <p>It consists of three directories, each described below.</p> <h2><strong>01_retrieval_results</strong></h2> <p>This directory includes CryoSat-2-based monthly fields of Arctic sea ice thickness, snow depth, total freeboard, ice freeboards, ice draft, and bulk sea ice density for the winter months of the 2011-2022 period (January-March for alpha method and January-April for CS2IS2 method). Those variables are obtained using six combinations of two retrieval methods and three radar freeboard correction methods.</p> <p><em>Retrieval methods</em></p> <ul> <li>alpha method: A simultaneous retrieval method based on Shi et al. (2020) and Shi et al. (2023), combining CryoSat-2, AVHRR, and AMSR data</li> <li>CS2IS2 method: A simultaneous retrieval method based on Kwok and Marcus (2018) and Kwok et al. (2020), combining CryoSat-2 and ICESat-2 data</li> </ul> <p><em>Radar freeboard correction methods</em></p> <ul> <li>Wave speed correction method: Mallet et al. (2020)</li> <li>Empirical correction method: An empirical correction derived from the CS2_OIB_matchup data, using snow depth as a predictor</li> <li>Bias correction method: An empirical correction derived from the CS2_OIB_matchup data, doing bias correction</li> </ul> <p>The datasets used for generating this dataset are as follows:</p> <ul> <li>CryoSat-2 <br>- AWI CryoSat-2 sea ice thickness v2p6 (doi: <a href="https://doi.org/10.5281/zenodo.10044554" target="_blank" rel="noopener">10.5281/zenodo.10044554</a>)</li> <li>ICESat-2<br>- NSIDC ATL20 dataset (doi: <a href="https://doi.org/10.5067/ATLAS/ATL20.004" target="_blank" rel="noopener">10.5067/ATLAS/ATL20.004</a>)</li> <li>AVHRR<br>- Copernicus Marine Service's surface temperature datasets (doi: <a href="https://doi.org/10.48670/MOI-00130" target="_blank" rel="noopener">10.48670/MOI-00130</a>, doi: <a href="https://doi.org/10.48670/MOI-00123" target="_blank" rel="noopener">10.48670/MOI-00123</a>)</li> <li>AMSR<br>- JAXA AMSR-E 6.9 GHz brightness temperature (doi: <a href="https://doi.org/10.57746/EO.01gs73ayng11rpwk7n54aynyj1" target="_blank" rel="noopener">10.57746/EO.01gs73ayng11rpwk7n54aynyj1</a>)<br>- JAXA AMSR2 6.9 GHz brightness temperature (doi: <a href="https://doi.org/10.57746/EO.01gs73b1nzeh3g66jr4p04mr0j" target="_blank" rel="noopener">10.57746/EO.01gs73b1nzeh3g66jr4p04mr0j</a>)</li> <li>Auxiliary data<br>- Sea ice concentration: OSI SAF (doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_0013" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_0013</a>, doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_0014" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_0014</a>)<br>- Sea ice type: OSI SAF (doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_NRT_2006" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_NRT_2006</a>)</li> </ul> <p>The naming convention is 'RetrievalMethod_CorrectionMethod_yyyymm.bin'. The 'RetrievalMethod' is either 'alpha' or 'CS2IS2', and the 'CorrectionMethod' is either 'WaveSpeed,' 'Empirical,' or 'BiasCorrection.' The data format is a 32-bit floating point array in the shape of 6 x 448 x 304 (25 km polar stereographic grid). The first dimension indicates the variables (in the order of snow depth (0), sea ice thickness (1), ice freeboard (2), total freeboard (3), sea ice draft (4), and bulk sea ice density (5)). For example, to read the sea ice thickness of January 2020 based on the alpha method with an empirical correction, you may write this Python command:</p> <p><code>import numpy as np</code><br><code>data = np.fromfile('alpha_Empirical_202001.bin', dtype=np.float32).reshape(6,448,304)</code><br><code>hi = data[1,:,:]</code></p> <p>The unit of thickness-related variable is cm, and the unit of density is kg/m3. The 25 km polar stereographic grid information is available on the NSIDC website (doi: <a href="https://doi.org/10.5067/N6INPBT8Y104" target="_blank" rel="noopener">10.5067/N6INPBT8Y104</a>).</p> <h2><strong>02_valdiation data </strong></h2> <p>This directory includes reference data used for quality assessment of retrievals. There are three sub-directories:</p> <p>'Mooring_draft_psn25_monthly' includes sea ice draft measurements from the moorings in the Beaufort Sea (https://www2.whoi.edu/site/beaufortgyre/data/mooring-data/), Fram Strait (doi: <a href="https://doi.org/10.21334/npolar.2022.b94cb848" target="_blank" rel="noopener">10.21334/npolar.2022.b94cb848</a>), and the Laptev Sea (doi: <a href="https://doi.org/10.1594/PANGAEA.912927" target="_blank" rel="noopener">10.1594/PANGAEA.912927</a>, doi: <a href="https://doi.org/10.1594/PANGAEA.899275" target="_blank" rel="noopener">10.1594/PANGAEA.899275</a>).</p> <p>'OIB_SD_psn25_monthly' and 'OIB_TFB_psn25_monthly' include airborne snow depth and total freeboard measurements from NASA's Operation IceBridge campaign (doi: <a href="https://doi.org/10.5067/G519SHCKWQV6" target="_blank" rel="noopener">10.5067/G519SHCKWQV6</a>, doi: <a href="https://doi.org/10.5067/GRIXZ91DE0L9" target="_blank" rel="noopener">10.5067/GRIXZ91DE0L9</a>).</p> <p>Original data were processed to become monthly gridded data to make a comparison with satellite retrievals. The OIB data points used for the regression were excluded when processing the monthly gridded data. The naming convention of each file is 'Var_yyyymm.bin,' where 'Var' is the variable name (SD: snow depth, TFB: total freeboard, Di: ice draft). For example, you can use the following code to read the OIB snow depth in March 2014.</p> <p><code>import numpy as np</code><br><code>hs = np.fromfile('SD_201403.bin', dtype=np.float32).reshape(448,304)</code></p> <h2><strong>03_CS2_OIB_matchup</strong></h2> <p>This directory includes a match-up of AWI's CryoSat-2 L2P track data and OIB track data. The matching was done by resampling two high-resolution data on a coarser-resolution common grid (25 km polar stereographic grid) using a drop-in-a-bucket resampling method. The file format is CSV, and it is straightforward to understand when it is opened.</p> <h1><strong>[Abbreviations]</strong></h1> <p>AMSR: Advanced Microwave Scanning Radiometer<br>AVHRR: Advanced Very High Resolution Radiometer<br>AWI: Alfred Wegener Institute<br>CS2: CryoSat-2<br>JAXA: Japan Aerospace Exploration Agency<br>NASA: National Aeronautics and Space Administration<br>NSIDC: National Snow and Ice Data Center<br>OIB: Operation IceBridge<br>OSI SAF: Ocean and Sea Ice Satellite Application Facility</p> <p> </p>
CLDF dataset derived from Tolmie and Dawson's "Comparative Vocabulary of the Indigenous Peoples in British Columbia" from 1884
<p>Cite the source of the dataset as:</p> <blockquote> <p>Tolmie, Fraser W. and Dawson, George M. (1884). Comparative vocabularies of the Indian tribes of British Columbia, with a map illustrating distribution. Montreal: Dawson Brothers.</p> </blockquote>
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