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18,820 results for “northing”
Parish church (Parochiekerk Sint-Lambertus). Interior. North aisle. Altar. Detail. Statue of Madonna with Child. Ca 1750. 2
<u>File Name</u>: PM_141733_B_Parike <br><u>Sublocation</u>: Parike, parochiekerk Sint-Lambertus <br><u>Location</u>: Brakel <br><u>Province</u>: Oost-Vlaanderen <br><u>Country</u>: Belgium <br><u>Header</u>: Binnenzicht, het noordelijke zijaltaar, detail, beeld van een Madonna met Kind op maansikkel, detail, ca 1750 <br><u>Description</u>: Parish church (Parochiekerk Sint-Lambertus). Interior. North aisle. Altar. Detail. Statue of Madonna with Child. Ca 1750. <br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Author Mail</u>: PMRMaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert,pmrmaeyaert@gmail.com <br><u>Keywords</u>: Cultural heritage; Europe|Belgium|Vlaanderen streken|Vlaamse Ardennen; Europe|Belgium; Europe|Belgium|Oost-Vlaanderen; Europe|Belgium|Oost-Vlaanderen|Parike (Brakel); Europe|Belgium|Oost-Vlaanderen|Brakel; Cultural heritage|Techniques|Sculpture; Cultural heritage|Techniques <br><u>Date of Generation</u>: 2021-10-24T14:30:21+02:00
Cathedral (Cathédrale Notre-Dame). Exterior. West façade. West façade. North porch. A statue.
<u>File Name</u>: PM_144073_F_Reims <br><u>Sublocation</u>: Cathédrale Notre-Dame <br><u>Location</u>: Reims <br><u>Province</u>: Grand Est, Marne <br><u>Country</u>: France <br><u>Header</u>: Vue de l'extérieur, la façade occidentale, le portail nord, à gauche, "l'ange au sourire" <br><u>Description</u>: Cathedral (Cathédrale Notre-Dame). Exterior. West façade. West façade. North porch. A statue. <br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Author Mail</u>: PMRMaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert,pmrmaeyaert@gmail.com <br><u>Keywords</u>: Europe|France; Europe|France|Grand Est; Europe|France|Grand Est|Marne; Europe|France|Grand Est|Marne|Reims; Cultural heritage|Monuments|Cathedral; Cultural heritage|Monuments; Cultural heritage <br><u>Date of Generation</u>: 2022-04-28T12:31:08+02:00
Parish church (Parochiekerk Sint-Lambertus). Interior. North aisle. Altar. Detail. Statue of Madonna with Child. Ca 1750.
<u>File Name</u>: PM_141732_B_Parike <br><u>Sublocation</u>: Parike, parochiekerk Sint-Lambertus <br><u>Location</u>: Brakel <br><u>Province</u>: Oost-Vlaanderen <br><u>Country</u>: Belgium <br><u>Header</u>: Binnenzicht, het noordelijke zijaltaar, detail, beeld van een Madonna met Kind op maansikkel, detail, ca 1750 <br><u>Description</u>: Parish church (Parochiekerk Sint-Lambertus). Interior. North aisle. Altar. Detail. Statue of Madonna with Child. Ca 1750. <br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Author Mail</u>: PMRMaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert,pmrmaeyaert@gmail.com <br><u>Keywords</u>: Cultural heritage; Europe|Belgium|Vlaanderen streken|Vlaamse Ardennen; Europe|Belgium; Europe|Belgium|Oost-Vlaanderen; Europe|Belgium|Oost-Vlaanderen|Parike (Brakel); Europe|Belgium|Oost-Vlaanderen|Brakel; Cultural heritage|Techniques|Sculpture; Cultural heritage|Techniques <br><u>Date of Generation</u>: 2021-10-24T14:28:02+02:00
Parish church (Parochiekerk Sint-Lambertus). Interior. North aisle. Altar. Detail.
<u>File Name</u>: PM_141731_B_Parike <br><u>Sublocation</u>: Parike, parochiekerk Sint-Lambertus <br><u>Location</u>: Brakel <br><u>Province</u>: Oost-Vlaanderen <br><u>Country</u>: Belgium <br><u>Header</u>: Binnenzicht, het noordelijke zijaltaar, detail <br><u>Description</u>: Parish church (Parochiekerk Sint-Lambertus). Interior. North aisle. Altar. Detail. <br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Author Mail</u>: PMRMaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert,pmrmaeyaert@gmail.com <br><u>Keywords</u>: Cultural heritage; Europe|Belgium|Vlaanderen streken|Vlaamse Ardennen; Europe|Belgium; Europe|Belgium|Oost-Vlaanderen; Europe|Belgium|Oost-Vlaanderen|Parike (Brakel); Europe|Belgium|Oost-Vlaanderen|Brakel <br><u>Date of Generation</u>: 2021-10-24T14:02:03+02:00
Data for "Modelling soil carbon stocks following reduced tillage intensity: a framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe"
<p>Dataset of paired observations of conventional tillage (CT) with no tillage (NT) and reduced tillage (RT) from studies in temperate oceanic regions of Western Europe, extracted from a recent systematic review (Jordon et al. preprint, see DOI below).</p> <p>R code of modelling framework to estimate tillage rate modifiers (TRM) for simulating adoption of RT and NT using RothC-26.3, and meta-estimates of TRM across studies.</p>
Lagrangian overturning in the eastern subpolar North Atlantic Ocean - ORCA025-GJM189 Particle Trajectory Dataset
<p>This dataset contains the output of Lagrangian particle tracking experiments using 5-day mean velocity and hydrographic fields from the ORCA025-GJM189 ocean sea-ice model hindcast configured during the Drakkar project in which numerical particles are initialised along the northward inflows across the Overturning in the Subpolar North Atlantic Program (OSNAP) East section. Particles are advected using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool using the regular step-wise stationary advection scheme and an adapted implementation of the vertical turbulent mixing parameterisation created by Paris et al. (2013) for the Connectivity Modelling System Lagrangian particle tracking tool. This vertical turbulent mixing scheme only acts on particles found within the surface mixed layer (as evaluated along particle trajectories) and randomly reshuffles them according to a maximum vertical velocity of 10 cm/s - characteristic of vertical convective plumes. Note, particles cannot be artificially subducted across the base of the mixed layer into the ocean interior using this scheme.</p> <p>Particles are initialised on the first-available day of each month (based on the centre of the model fields 5-day mean windows) between 1976 and 2008 (inclusive) before being advected within the Iceland and Irminger Basins until any one of three termination conditions are met: 1) particles return southward across OSNAP East, 2) particles flow northward across the Greenland-Scotland Ridge, or 3) particles reach the maximum advection time of 7-years. The 7-year maximum advection time ensures >99.1% of all initialised particles meet one of conditions 1) or 2), hence only 0.9% of all particles are terminated between OSNAP East and the Greenland-Scotland Ridge.</p> <p>The number of particles initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 2.5 mSv (mSv == 1E-3 Sv), enabling the calculation of robust Lagrangian statistics.</p> <p>Particle locations (referenced to the original ORCA025 model grid) and properties (potential temperature, salinity, potential density and local mixed layer depth) are stored in the output files on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points.</p> <p>In total, four Lagrangian experiments were conducted at the Department of Earth Sciences, University of Oxford. Please see README.md for a full description of all Lagrangian experiments and the accompanying output files.</p> <p><strong>For a complete description of the ORCA025-GJM189 hindcast configuration see:</strong> https://github.com/meom-configurations/ORCA025.L75-GJM189.</p> <p><strong>For a complete description of TRACMASS v7.1 see</strong>: https://github.com/TRACMASS/tracmass</p>
Data on 'Gelatinous macrozooplankton diversity and distribution in the North Sea and Skagerrak/Kattegat during January-February 2021'
<p>This dataset includes raw and analysed data from '<strong>Gelatinous macrozooplankton diversity and distribution in the North Sea and Skagerrak/Kattegat during January-February 2021</strong>'</p> <p><strong>Louise G. Køhler<sup>1</sup>, Bastian Huwer<sup>2</sup>, José Martín Pujolar<sup>1</sup>, Malin Werner<sup>3</sup>, Karolina Wikström<sup>3</sup>, Anders Wernbo<sup>3</sup>, Maria Ovegård<sup>3</sup>, Cornelia Jaspers<sup>1*</sup></strong></p> <p> </p> <p><sup>1</sup>Centre for Gelatinous Zooplankton Ecology and Evolution, National Institute of Aquatic Resources, Technical University of Denmark, Kemitorvet 202, 2800 Kgs. Lyngby, Denmark</p> <p><sup>2</sup>National Institute of Aquatic Resources, Technical University of Denmark, Kemitorvet 201, 2800 Kgs. Lyngby, Denmark</p> <p><sup>3</sup>Institute of Marine Research, Department of Aquatic Resources (SLU Aqua), Swedish University of Agricultural Sciences, Turistgatan 5, S- 453 30 Lysekil, Sweden</p> <p>* Corresponding author: <a href="mailto:coja@aqua.dtu.dk">coja@aqua.dtu.dk</a></p> <p>This dataset includes data on the qualitative and quantitative description of the gelatinous macrozooplankton community of the North Sea during January-February 2021. Sampling was conducted during the 1<sup>st</sup> quarter International Bottom Trawl Survey (IBTS) on board the Danish R/V DANA (DTU Aqua Denmark) and the Swedish R/V Svea (SLU Sweden), as part of the ichthyoplankton investigation during night-time. A total of 147 stations were investigated in the western, central and eastern North Sea as well as the Skagerrak and Kattegat. Sampling was conducted with a 13 m long Midwater Ring Net (MIK net, Ø 2 m, mesh size 1.6 mm, cod end with smaller mesh size of 500 µm), equipped with a flow meter. The MIK net was deployed in double oblique hauls from the surface to c. 5 m above the sea floor. Samples were visually analysed unpreserved on a light table and/or with a stereomicroscope or magnifying lamp within 2 hours after catch. A total of 13,610 individuals were counted/sized. Twelve gelatinous macrozooplankton species or genera were encountered, namely the hydrozoan <em>Aequorea vitrina</em>, <em>Aglantha digitale</em>, <em>Clytia</em> spp., <em>Leuckartiara octona,</em> <em>Tima bairdii, Muggiaea atlantica</em>; the scyphozoans <em>Cyanea</em> <em>capillata and Cyanea lamarckii</em> and the ctenophores <em>Beroe</em> spp., <em>Bolinopsis infundibulum</em>, <em>Mnemiopsis leidyi</em>, <em>Pleurobrachia pileus</em>. Abundance data are presented on a volume specific (m<sup>-3</sup>) and area specific (m<sup>-2</sup>) basis. Size data have been used to estimate wet weights based on published length-weight regressions (see reference column in the dataset). This dataset contributes baseline information about the gelatinous macrozooplankton diversity and its specific distribution pattern in the extended North Sea area during winter (January-February) 2021. These data can be an important contribution to address global change impacts on marine systems, especially considering gelatinous macrozooplankton abundance changes in relation to anthropogenic stressors.</p>
North Pacific Subtropical Gyre RCLV Atlas (Version 2)
<p>This dataset tracks Rotationally Coherent Lagrangian Vortices (RCLVs) at an 8-day resolution in the North Pacific Subtropical Gyre region around Hawai’i. The ‘lat’ and ‘lon’ variables represent the center coordinates of the vortex, or the extremum of integrated relative vorticity. The contour boundaries demark the edge of the RCLV based on 32-day backward-in-time Lagrangian trajectories. In other words, eddies in this dataset represents a fluid mass of substantial size that was coherent for at least 32 days. The fluid masses are tracked through time to assign IDs and RCLV ages. The software used to generate the dataset is publicly available at https://github.com/lexi-jones/RCLVatlas (DOI: <a href="https://doi.org/10.5281/zenodo.7702978" target="_blank" rel="noopener noreferrer">10.5281/ZENODO.7702978</a>).</p> <p>Version 1 of the dataset (https://simonscmap.com/catalog/datasets/RCLV_atlas; DOI: <a href="https://doi.org/10.5281/zenodo.8139149" target="_blank" rel="noopener noreferrer">10.5281/ZENODO.8139149</a>) only includes contours that represent features of age 32 days or older. Version 2 extends the dataset to include eddy genesis by advecting the particle sets backward-in-time to age 24, 16, and 8 days.</p>
Supplementary files for Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach
<p>Model input and output files associated with the manuscript entitled "Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach" that will be submitted to Journal of Geophysical Research.</p>
Cosmic Pi North Pole Dataset 2024
<p>This raw dataset was gathered using a pair of Cosmic Pi detectors carried by the Transglobal Car Expedition across North America, to the North Pole and back to Greenland. To our knowledge this is the first ground level measurement of muons at the North Pole, beating the record previously set by the PolarquEEEst mission, which sailed up to a latitude of 82◦07′ (see https://cds.cern.ch/record/2743921/files/ncc11933(1).pdf for details).</p> <p>The dataset comprises a portable Influxdb 1.8 export, with several sub datasets. </p> <p>We have the following data stored in the CosmicPiV1.8.1 measurement table:</p> <ul> <li>measurement='CosmicPiV1.8.1',</li> <li>DeviceID=cosmicdict['tags']['id'], Unit ID</li> <li>latitude=cosmicdict['fields']['lat'], Latitude</li> <li>longitude=cosmicdict['fields']['lon'], Longitude</li> <li>Temp=cosmicdict['fields']['Temp'], Temperature in C</li> <li>Hum=cosmicdict['fields']['Hum'], Relative Humidity in %</li> <li>Accelx=cosmicdict['fields']['Accelx'], Acceleration, X axis in m/s/s</li> <li>Accely=cosmicdict['fields']['Accely'], Acceleration, Y axis in m/s/s</li> <li>Accelz=cosmicdict['fields']['Accelz'], Acceleration, Z axis in m/s/s</li> <li>Magx=cosmicdict['fields']['Magx'], Magnetic field, X axis in Gauss</li> <li>Magy=cosmicdict['fields']['Magy'], Magnetic field, Y axis in Gauss</li> <li>Magz=cosmicdict['fields']['Magz'], Magnetic field, Z axis in Gauss</li> <li>Pressx=cosmicdict['fields']['Press'], Atmospheric pressure in mbar</li> <li>Altx=cosmicdict['fields']['Alt'], Altitude in meters</li> <li>timestamp=cosmicdict['time'] Time</li> </ul> <p>And we also have the frequency data (how many cosmic rays detected per second) here:</p> <ul> <li>measurement='CosmicPiV1.8.1_freq',</li> <li>DeviceID=cosmicdict['tags']['id'], Unit ID</li> <li>geohash_pos="""+cosmicdict['fields']['geohash']+""", The geohash, a short code that represents lat & lon</li> <li>event_count=cosmicdict['fields']['event_count'], The number of events in this second</li> <li>timestamp=cosmicdict['time']) Time</li> </ul> <p>Some basic analysis code is presented here: https://github.com/CosmicPi/TGCEWorkshop</p>
Mesoscale Low-Level Jet Climatology for the North and Baltic Seas
<p><strong>Mesoscale Low-Level Jet Climatology for the North and Baltic Seas</strong></p> <p>This dataset contains a mesoscale low-level jet (LLJ) climatology for the Baltic and North Seas.</p> <p>The dataset consists of many individual raster layers of LLJ characteristics zipped in the "llj_climatology.zip" file. Each layer is a netCDF4 file, which can be read directly by QGIS, Python, and many other tools. In the "figures" folder, plots showing most of the layers can be found. A more comprehensive description of the layers is given below.</p> <p>Some examples of layers contained:</p> <ul> <li>LLJ rate-of-occurrence</li> <li>LLJ height</li> <li>LLJ duration</li> <li>Wind speed and direction for at LLJ peak</li> <li>Max shear above and below the LLJ peak</li> <li>Wind speed and direction at 100, 150, 200 m</li> <li>Rotor-equivalent wind speed (REWS) for IEA 15 MW reference turbine</li> </ul> <p>Because of strong seasonality in offshore LLJ occurrences, most layers come as long-term means, including the full five years and seasonality-averaged layers. Several aggregate statistics are available for each layer, such as mean, median, and standard deviation. </p> <p>The data was created using the Weather Research and Forecasting model v4.2.1 running a five-year hindcast from 2019-06-26 to 2024-06-26. A two-domain setup was used to downscale ERA5 boundary data to 3 km horizontal grid spacing. See the associated paper for a full data generation process and validation description.</p> <p>Based on user feedback, future versions could be expanded to hold additional layers/variables, such as sector-wise Weibull parameters or time-series samples for representative points. Contact btol@dtu.dk for feedback and requests for future versions. </p> <p><strong>Full list of variables</strong></p> <ul> <li><strong>ws100, ws150, ws200</strong>: wind speed at 100, 150, and 200 meters</li> <li><strong>wd100, wd150, wd200</strong>: wind direction at 100, 150, 200 meters</li> <li><strong>rews_iea15mw</strong>: rotor-equivalent wind speed for the IEA 15 MW reference turbine</li> <li><strong>cf_iea15mw</strong>: capacity factor for the IEA 15 MW reference turbine</li> <li><strong>llj_rate</strong>: LLJ detection rate </li> <li><strong>height_of_llj_max</strong>: height of LLJ peak in meters</li> <li><strong>llj_ws_max</strong>: wind speed of LLJ peak in meters per second</li> <li><strong>llj_wind_direction</strong>: wind direction of LLJ peak in degree</li> <li><strong>llj_duration</strong>: LLJ duration in hours</li> <li><strong>llj_most_prevalent_hour</strong>: most prevalent hour-of-day during LLJ events as hour integers (0-23)</li> <li><strong>llj_most_prevalent_hour_freq</strong>: relative frequency of most prevalent hour-of-day during LLJ events</li> <li><strong>llj_most_prevalent_season</strong>: most prevalent month-of-year during LLJ events as 0-based month integers (0-11 JAN-DEC)</li> <li><strong>llj_most_prevalent_season_freq</strong>: relative frequency of most prevalent month-of-year during LLJ events </li> <li><strong>llj_max_shear_below</strong>: maximum shear between the LLJ peak and the minimum below</li> <li><strong>llj_min_shear_above</strong>: minimum (maximum negative) shear between the LLJ peak and the minimum above</li> <li><strong>height_of_max_shear_below_llj</strong>: height of maximum shear detected below the LLJ peak in meters</li> <li><strong>height_of_min_shear_above_llj</strong>: height of minimum shear detected above the LLJ peak in meters</li> <li><strong>llj_depth</strong>: the depth of the LLJ measured from "height_of_max_shear_below_llj" to "height_of_min_shear_above_llj" in meters</li> <li><strong>llj_rews_iea15mw</strong>: rotor-equivalent wind speed for the IEA 15 MW reference turbine during LLJ events</li> <li><strong>llj_cf_iea15mw</strong>: capacity factor for the IEA 15 MW reference turbine during LLJ events</li> <li><strong>llj_abs_falloff_above</strong>: absolute wind speed fall-off above the LLJ peak in meters per second</li> <li><strong>llj_rel_falloff_above</strong>: relative wind speed fall-off above the LLJ peak </li> <li><strong>llj_abs_falloff_below</strong>: absolute wind speed fall-off below the LLJ peak in meters per second</li> <li><strong>llj_rel_falloff_below</strong>: relative wind speed fall-off below the LLJ peak </li> </ul> <p><strong>Several layers exist for different aggregation and seasons for each variable. Suffixes describe the aggregation (_mean, _median, _std) and season (_DJF, _MAM, _JJA, _SON)</strong></p> <p>This work is part of the FLOW project and was supported by the European Union Horizon Europe Framework Programme (HORIZON-CL5-2021-D3-03-04) under grant agreement no. 101084205.</p>
Functional traits database for North American birds
<p>Estimation of functional diversity in biological communities requires extensive and complete data on numerous functional traits of species or even individuals. When estimating functional diversity at large scales, this fact possesses an issue that may be hard to overcome: for many species, there might not be sufficient data on their functional traits. In such cases, even if there is missing information on functional trait value for one species in a community, this makes the trait impossible to use for the estimation of the functional diversity of a community. On the other hand, there are available datasets on the functional traits of all extant species within certain lineages across the world, but such datasets are often limited to very few functional traits, missing some dimensions of species' ecological niches. In this dataset, I compiled the available data from various sources that describe 23 functional traits of 703 bird species that occur in Canada, the United States, and Mexico. These functional traits include the following: diet type, diurnal and nocturnal feeding, diet items, feeding methods, feeding substrate, nest type, nest substrates, breeding system, chick development at hatching, nest aggregation, clutch size, first breeding age, number of clutches a year, breeding success, adult annual survival, mean biomass, maximum lifespan, hand-wing index, kleptoparasitism, nest parasitism, and the extent of dependency on other species for building a nest.</p>
Raw Data for Mapping Repositories and their Institutional Open Science Policies in the Middle East and North Africa (MENA)
<div> <p>Persistent Identifiers (PIDs), particularly Digital Object Identifiers (DOIs), are crucial for establishing a robust and globally accessible research infrastructure. In the Middle East and North Africa (MENA) region, a diverse array of research outputs and resources are produced and published in repositories. However, a significant number of these repositories, and outputs remain undiscoverable in global registries and aggregators. <br><br>These three datasets provides comprehensive information on the adoption of repositories, Open Access mandates, and DOIs adoption in MENA countries. It includes detailed records from different registry sources and repository platforms.<br><br>You can read the full report titled 'Mapping Repositories and their Institutional Open Science Policies in MENA' at <a href="https://doi.org/10.5281/zenodo.11370031">https://doi.org/10.5281/zenodo.11370031</a></p> </div>
Jensen et al. 2024 - Biodiversity and distribution of gelatinous macrozooplankton in the North Sea and adjacent waters dataset from winter 2022 - raw dataset
<p><span>The diversity and distribution of gelatinous macrozooplankton is described by presenting qualitative and quantitative data of the jellyfish and comb jelly community encountered in the North Sea and Skagerrak/Kattegat during January/February 2022.<span> </span>Data were generated<span> </span>as part of the North Sea Midwater Ring Net survey (MIK), an ichthyoplankton survey conducted at night-time during the quarter 1 (Q1) International Bottom Trawl Survey (IBTS), aboard the Danish R/V DANA (DTU Aqua) and the Swedish R/V Svea (SLU) at a total of 100 stations. This dataset accompanies the Data in Brief Article below and should be cited when using this dataset. <br></span></p> <p><span>Jensen, C.J.D., Køhler, L.G., Huwer, B., Werner, M., Cieters, L., <strong>Jaspers, C.</strong> (submitted) Biod</span><span>iversity and distribution of gelatinous macrozooplankton in the North Sea and adjacent waters dataset from winter 2022. <em>Data in Brief. </em></span></p>
The Potential of Fragipans in Sustaining Pearl Millet during Drought Periods in North-Central Namiba (dataset)
<p><strong>Abstract</strong></p> <p>Sandy soils with fragipans are usually considered poorly suited for agriculture. However, these soils are cultivated in north-central Namibia as they can secure a minimum harvest during droughts. To understand the hydrological influence of fragipans in these soils, Ehenge, and what makes them valuable, their soil moisture content was measured over a time span of four months. These data were then compared to a deep soil without fragipan, Omutunda, which is more productive during normal years, but less productive during droughts. The results illustrate that the combination of sandy topsoil and shallow fragipan has beneficial effects on plant available water during dry periods, because of three reasons: (i) The high infiltration rate in the sandy topsoil, (ii) the prevention of deep drainage of water by the fragipan, and (iii) the limited evaporation losses through the capillary rise in the sand. The results also confirm the disadvantages of Ehenge during wet periods.</p> <p><strong>Description of Dataset</strong></p> <p>The dataset comprises a .pdf-file with a detailed data description, seven .csv-files (relevant datasets), and two txt-files with the R-code to produce the relevant Figures 4 and 5 from the paper “The Potential of Fragipans in Sustaining Pearl Millet during Drought Periods in North-Central Namibia” by Prudat et al. 2021.</p> <p><strong>File description<br> Rainfall.csv</strong>: daily rainfall in mm at two locations (<em>Omutunda</em> & <em>Ehenge</em>)<br> <strong>NDOB13_ehenge.csv/ NDOB13_omutunda.csv</strong>: soil temperature and soil moisture per minute<br> <strong>NDOB13_ehenge_daily.csv/ NDOB13_omutunda_daily.csv</strong>: soil temperature and volumetric soil moisture content (θ<sub>TDR</sub>) aggregated per day using the arithmetic mean<br> <strong>NDOB13_ehenge_RASW_daily.csv/ NDOB13_omutunda_RASW_daily.csv</strong>: The relative available soil water (RASW) was calculated based on the measured plant available water,</p> <p><strong>Scripts</strong><br> (1) <strong>RASW_rainfall_daily</strong>: Script to calculate and plot the relative available soil water (RASW) at the two stations and the rainfall<br> (2) <strong>Max_soil_surface_temperature</strong>: Script to calculate and plot the maximum soil surface temperature at the two stations</p> <p> </p><p><strong>References</strong></p> <p></p> <p>Cobos, D., Campbell, C., 2007. Correcting temperature sensitivity of ECH2O soil moisture sensors. Decagon Devices Pullman WA.</p> <p>Hillel, D., 1998. Environmental soil physics: Fundamentals, applications, and environmental considerations. Academic press.</p>
SOUNDSCAPE North Adriatic Underwater Noise Sound Pressure Levels
<p>Within the Interreg Italy-Croatia <strong>SOUNDSCAPE</strong> <strong>project</strong> a basin-scale, cross-national, long-term underwater monitoring in the Northern Adriatic Sea was carried out (https://www.italy-croatia.eu/web/soundscape). A broad network of nine monitoring stations, characterized by different natural conditions and anthropogenic pressures, ensured acoustic data collection from March 2020 to June 2021, including the first full lockdown period related to the COVID-19 pandemic (March–April 2020). Calibrated stationary recorders featured with an omnidirectional Neptune Sonar D60 Hydrophone recorded continuously 24h a day (48 kHz, 16 bit).</p> <p>Data were analysed to Sound Pressure Levels (SPLs, dB re 1 uPa) that are here released as a dataset composed of 20 and 60 seconds averaged SPL output files for each station. Data are archived using structured hdf5 files, each one containing metadata and SPL data, according to ICES (International Council for the Exploration of the Sea) continuous noise data specification (https://www.ices.dk/data/data-portals/Pages/Continuous-Noise.aspx).</p> <p>A research object with a jupyter notebook developed to post process SPL data is available at https://doi.org/10.24424/hrhm-8849</p> <p>If data are used, please cite also Petrizzo, A., Barbanti, A., Barfucci, G. <em>et al.</em> Author Correction: First assessment of underwater sound levels in the Northern Adriatic Sea at the basin scale. <em>Sci Data</em> <strong>10</strong>, 179 (2023). https://doi.org/10.1038/s41597-023-02099-x</p>
Occurrence dataset for the subspecies of the American badger (Taxidea taxus berlandieri) in the north-central region of Mexico
<p>The subspecies of American badger (<em>Taxidea taxus berlandieri </em>Baird, 1858), also called tlalcoyote (Figure 1), is distributed in north-central Mexico. However, its occurrence records are scarce and the few that exist are uncertain due to incorrect georeferencing or identification of the taxonomic unit. In view of this, we disgned a spatial sampling in part of the states of Coahuila de Zaragoza, Durango, Nuevo León, San Luis Potosí and Zacatecas. In this north-central protion of Mexico, we generated a grid of squares measuring 5 × 5 km over the entire study area using QGIS® 3.10 software. Subsequently, we excluded squares that included urban settlements, agricultural land, or water bodies in more than 30% of their extension; we also descarted squares located at an altitude over 2,250 meters above sea level. To perform this filtering, we used both the land use and vegetation chart of the INEGI [Instituto Nacional de Estadística, Geografía e Informática] (2018) and the Digital Elevation Model (DEM) downloaded from the USGS page [United States Geological Survey] (2019) as a basis. As result, we obtained 3,471 squares separated by at least 5 km. Then, through simple random sampling, 177 (≈5%) squares were selected, where we generated centroids to be used as sampling sites. </p> <p>In field work, between 2009 and 2015, at these 177 sites we traced a 10 × 100 m transect, where we searched for<em> T. t. berlandieri</em> signs (i.e., burrows and scratching posts). In this case, their burrows and scratching posts are easily observed and quantified, and there is no chance of mistaking them for burrows of other species (Long 1973; Merlin 1999). Also, we recorded possible sightings, as other studies (e.g., Merlin 1999; Elbroch 2003). As result, we only found 33 with signs of occurrence. </p> <p><a href="https://zenodo.org/api/files/9a8452c6-15e2-43cd-9c07-27b7fc1d422a/Figure%201.%20Taxidea%20taxus%20Berlandieri%20Baird%2C%201858.jpeg">Figure 1.</a> Individual of tlalcoyote (<em>Taxidea taxus Berlandieri</em>). Photo obtained from Naturalista (2023) and uploaded by David Molina©. All rights reserved (CC BY-NC-ND).</p> <p>To increase the number of records, we included occurrence data from GBIF [Global Biodiversity Information Facility portal] (2022). We downloaded only the records that included coordinates and that their basis of registration was "preserved specimen". This, because they are correctly identified as specimens from biological collections (Maldonado <em>et al.</em> 2015). In addition, we only selected records for Mexico. Subsequently, we filtered the downloaded database, discarding records that were incorrectly georeferenced, with atypical and duplicate coordinates, as well as with low geospatial accuracy (e.g., less than three decimals of precision).</p> <p>We loaded the remaining data into the QGIS® software and performed a spatial filtering, where we excluded data that were outside the study area, located in unlikely areas (e.g., human settlements, bodies of water, agricultural areas) and with a distance of less than 5 km from the records obtained in the field. This gave a total of 10 records from the GBIF portal. Finally, we loaded the raster layers of elevation (Elev; INEGI 2007), normalized difference vegetation index (NDVI, USGS 2019) and the slope of the terrain into the software to extract the pixel values based on the GBIF records and those obtained in the field. With this, we generated a new global dataset to which we performed environmental filtering to find environmental outliers. We plotted the normality distribution of the data for each variable and the dispersion of the data among the variables. In this filtering, we conserve all records. Figure 2 shows the normality distribution of the records as a function of Elev. Figure 3 shows the dispersion of the data between Elev and NDVI.</p> <p><a href="https://zenodo.org/api/files/9a8452c6-15e2-43cd-9c07-27b7fc1d422a/Figure%202.%20Normal%20distribution.png">Figure 2.</a> Normality distribution of <em>T. t. berlandieri</em> occurrence records as a function of the elevation variable (Elev).</p> <p><a href="https://zenodo.org/api/files/9a8452c6-15e2-43cd-9c07-27b7fc1d422a/Figure%203.%20Scatter%20plot.png">Figure 3.</a> Scatter plot of <em>T. t. berlandieri</em> occurrence records as a function of elevation (Elev) and normalized difference vegetation index (NDVI).</p> <p>For the north-central region of Mexico, we present the global database (i.e., <a href="https://zenodo.org/api/files/9a8452c6-15e2-43cd-9c07-27b7fc1d422a/Tatabe_joint.csv">Tatabe_joint.csv</a>), as well as the database that contains only the field evidence records (i.e., <a href="https://zenodo.org/api/files/9a8452c6-15e2-43cd-9c07-27b7fc1d422a/Tatabe_first_order.csv">Tatabe_first_order.csv</a>) and another one with the filtered GBIF records (i.e., <a href="https://zenodo.org/api/files/9a8452c6-15e2-43cd-9c07-27b7fc1d422a/Tatabe_GBIF.csv">Tatabe_GBIF.csv</a>).</p>
CESM2 Idealized Experiment Output: Summer atmospheric response to zero May North American snow cover
<p>The National Center for Atmospheric Research’s Community Earth System Model version 2.2 (CESM2) (Danabasoglu et al., 2020) was run in the Atmospheric Model Intercomparison Project (AMIP) configuration. SSTs and sea-ice were prescribed as monthly varying seasonal cycles based on the observed climatology from 2005 to 2015 (i.e., component set: F2010climo) (Hurrell et al., 2008). We employed the Community Atmosphere Model version 6 (CAM6) (Bogenschutz et al., 2018)<span> </span>as the atmospheric component and the Community Land Model version 5 (CLM5) (Lawrence et al., 2019) as the land-surface component. Each model was run with a horizontal resolution of 0.9˚ latitude by 1.25˚ longitude.</p> <p>We ran a control simulation in this configuration for ten consecutive years. We then modified the land-surface restart files for May 1st of each year by reducing the snow cover over North America to zero. Using these modified files, we then completed a reduced snow simulation by rerunning three-month simulations from May through July for each of the ten years. </p>
Water quality in a basin for drinking water in the North-West of Italy (2022-2023)
<p>Information about water quality in La Loggia basin was collected in different seasons in 2022 and 2023, both on the basin surface and at different depths.</p> <p>Samples were collected and analysed in lab, for the following parameters:</p> <ul> <li>Total chlorophyll</li> <li>Blue-green algae</li> <li>Diatoms</li> <li>Green algae</li> <li>Planktothrix</li> <li>Transparency</li> <li>Temperature</li> <li>Dissolved oxygen</li> <li>pH</li> <li>Conductivity</li> <li>Turbidity</li> <li>Bromide</li> <li>Bromate</li> <li>Chloride</li> <li>Chlorite</li> <li>Chlorate</li> <li>Fluoride</li> <li>Nitrite</li> <li>Nitrate</li> <li>Orthophosphate</li> <li>Sulfates</li> </ul> <p>Samples were collected in the same dates of Sentinel-2 passages, in order to be used for the of satellite derived water quality products.<br> The shared data are not representative of drinking water distributed to users, since a multi-step treatment is performed on raw water in order to assure water safety and law-compliant quality standards.</p> <p><br> The coordinates of the sampling points are also available in the dataset.</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) January 2014 - December 2014
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from January 2014 to December 2014 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 20 m [°C]; Salinity @ 20 m [psu].</p>
ScienceDex guides
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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.