Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,121
datasets available to search
ShareScore release 0.9.0
Dataset results
1,121 results for “weathering”
PIE LTER year 2020, meteorological data, 15 minute intervals, from the PIE LTER Marshview Farm weather station located in Newbury, MA
Year 2020 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
Year 2007, meteorological data, 15 minute intervals, from weather station located at the Governor's Academy, Byfield, MA then moved to MBL Marshview Farm, Newbury, MA.
Year 2007 meteorological measurements at Governor's Academy and MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
Year 2000, meteorological data, 15 minute intervals, from weather station located at the Governor's Academy located in Byfield, MA.
Year 2000 meteorological measurements at Governor's Academy of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
Year 2001, meteorological data, 15 minute intervals, from weather station located at the Governor's Academy located in Byfield, MA.
Year 2001 meteorological measurements at Governor's Academy of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
Year 2002, meteorological data, 15 minute intervals, from weather station located at the Governor's Academy located in Byfield, MA.
Year 2002 meteorological measurements at Governor's Academy of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
GNSS tomography data for assimilation into the Weather Research and Forecasting model
<p>The data set contains GNSS troposphere tomography estimations of 3D wet refractivity fields for a part of Central Europe (mostly Germany and Czech Republic), for the period of 29 May–14 June 2013 when heavy-precipitation events were observed. The refractivity fields were estimated using two different GNSS tomography models: ATom software package (https://github.com/GregorMoeller/ATom) developed at TU Wien, and the TOMO2 model (Rohm and Bosy, 2011; Rohm et al., 2014; Trzcina and Rohm, 2019) developed at the Wrocław University of Environmental and Life Sciences. Further description of the GNSS tomography processing can be found in the paper by Hanna et al. (2019).</p>
Participant Notes from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>Compilation of electronic meeting notes made by attendees at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes.</p> <p>Files are provided for Days 1-3 of the meeting. Day 4 inputs are included in Discussion notes under a separate doi.</p> <p>The Chapman Conference was supported by NSF Award AGS 1848885 and NASA grants 936723.02.01.09.14 and 936723.02.01.11.21</p>
NUS Republican China Weather Database
<p>This project compiled 463,530 instrumental observations of daily temperature, precipitation, and sunshine at up to 319 stations in China between 1912-51. The principal sources are monthly reports of the Institute of Meteorology, Nanjing, observatories in Japanese-occupied Manchuria, and the Japanese Army in North China. Gross errors, rounding errors, and inhomogeneities are identified. The new dataset is temporally and spatially consistent with existing datasets of monthly temperature, but reports higher precipitation and sunshine. Please refer to the readme.txt in the zip file for terms of use and description of individual files in the dataset.</p> <p>Please ensure the related paper is cited appropriately when reusing this dataset: Png Paak Liang Ivan, Chen Yeh-Ning, Chu Junhong, Feng Yikang, Lin Kuanhui Elaine, Tseng Wan-Ling (in press). Temperature, Precipitation, and Sunshine Across China, 1912-51: A New Daily Instrumental Dataset. Geoscience Data Journal.</p>
Canadian Fire Weather System - WRF/NOA
<p>The <strong>Canadian FWI System </strong>(Van Wagner, 1987) is a sub-system of the Canadian Fire Danger Rating System (Stocks et al., 1989), comprised of <strong>five components</strong> that consider the effects of fuel moisture and wind on fire behavior. The first three components, called <strong>fuel moisture codes</strong>, are numeric ratings of <strong>(1) </strong>the moisture content of surface litter and other fine fuels (fine fuel moisture code; <strong>FFMC</strong>), <strong>(2)</strong> the average moisture content of loosely compacted organic material (duff moisture code; <strong>DMC</strong>), and <strong>(3)</strong> the average moisture content of deep layers of organic material (drought code; <strong>DC</strong>). The remaining two components are related to <strong>fire behavior and spread</strong>: <strong>(1)</strong> the initial spread index (<strong>ISI</strong>), which represents the rate of spread, and <strong>(2) </strong>the build-up index (<strong>BUI</strong>), which represents the potential fuel availability. The above five parameters are finally combined for computing the <strong>fire weather index</strong> (hereafter referred to as the <strong>FWI</strong>), which represents the intensity of a spreading fire per unit length of fire front and is used as an indicator of fire danger. </p> <p>Computation of the Canadian Fire Weather System components was carried out using <strong>12 UTC meteorological data</strong> extracted from <strong>high-resolution (~12 km) WRF simulations</strong>, focussing on the <strong>Euro-Mediterranean region</strong>. These data include the 24 h accumulated precipitation (mm), 2 m air temperature (<sup>o</sup>C) and relative humidity (%), and 10 m wind speed (km h<sup>-1</sup>). It should be noted that normally, FWI is computed using meteorological data at 12 local time. The use of the 12 UTC data in our study is dictated by the fact that the Euro-Mediterranean encompasses different time zones (e.g. Bedia et al., 2012, 2018; Herrera et al., 2013).</p> <p>Details: </p> <ul> <li>File format: netcdf4</li> <li>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326)</li> <li>Longitude range: [-9.91, +36.4]</li> <li>Latitude range: [+32.1, +48.93]</li> <li>Temporal resolution: 1 day (at 12 UTC)</li> <li>Spatial resolution: 0.12 degrees (~12 Km)</li> <li>Spatial coverage: Euro-Mediterranean</li> <li>Time span: May 1 to September 30, for the years 1987-2016</li> <li>Stream: Regional climate simulations (driven by the WRF model)</li> </ul>
JRC Phased Array Weather Radar Observational Data
<p>These datasets were observed by Phased Array Weather Radar from 0755 to 0810 JST on October 12, 2019. Data type is NetCDF. Data variables are radar reflectivity and Doppler velocity.</p>
Figure 3 in Investigating the influence of habitat type and weather conditions on the population dynamics of land snails Vertigo angustior Jeffreys, 1830 and Vertigo moulinsiana (Dupuy, 1849). A case study from western Poland
Figure 3. Diagram of one-way analysis of covariance test comparing a logarithmized number of individuals of Vertigo angustior (F = 92.16; p <0.01) and Vertigo moulinsiana (F = 8.165; p <0.01) in the Ilanka and Pliszka sites in 2009. Middle line: mean; box range: standard error; whiskers: standard deviation.
Figure 2 in Investigating the influence of habitat type and weather conditions on the population dynamics of land snails Vertigo angustior Jeffreys, 1830 and Vertigo moulinsiana (Dupuy, 1849). A case study from western Poland
Figure 2. Precipitation in the studied sites in consecutive months of 2009; dashed bars – sampling months. (B) and (C) Abundance of individuals: juveniles (white bars) and adults (black bars) of Vertigo angustior (B) and Vertigo moulinsiana (C) in each sampling event in the Ilanka and Pliszka sites in 2009.
Data from the paper "RFI flagging in solar and space weather low frequency radio observations' by Zhang et al. 2023
<p>Data from the paper "RFI flagging in solar and space weather low frequency radio observations' by Zhang et al. 2023</p>
PV and Weather Datasets from Demosites in Oslo, Touzer, and Sevilla
<p>We have gathered data on the power generation of seven different PV modules from three demonstration sites in Oslo, Touzer, and Sevilla for a comparitive analysis. This data was sourced from TIGO cloud for the PV modules and Solcast, an open-source platform, for historical weather information. The data set is spanning from May 2021 to November 2023. These datasets are characterized by high-resolution recordings taken every 5 minutes.</p> <p> </p> <p>Other contributors: <span> Alexander G Ulyashin (SINTEF Industry)</span><span>, Xiang Ma (SINTEF Industry)</span><span>, Alicia Arce (Ayesa Engineering SA)</span><span>, Rebeca Gutierrez (Ayesa Engineering SA) </span><span>, </span><span>Hélène Ben Khemis (Higher Institute of Technological Studies in Touzer, T)</span><span>, Zaher Khantouch (Higher Institute of Technological Studies in Touzer, T) </span><span>and Ahmet Soylu</span> (OsloMet)</p> <p> </p>
Early-season biomass and weather enable robust cereal rye cover crop biomass predictions
<p>Farmers need accurate estimates of winter cover crop biomass to make informed decisions on termination timing or to estimate potential release of nitrogen from cover crop residues to subsequent cash crops. Utilizing data from an extensive experiment across 11 states from 2016 to 2020, this study explores the most reliable predictors for determining cereal rye cover crop biomass at the time of termination. Our findings demonstrate a strong relationship between early-season and late-season cover crop biomass. Employing a random forest model, we predicted late-season cereal rye biomass with a margin of error of approximately 1,000 kg ha<sup>-1</sup> based on early-season biomass, growing degree days, cereal rye planting and termination dates, photosynthetically active radiation, precipitation, and site coordinates as predictors. Our results suggest that similar modeling approaches could be combined with remotely sensed early-season biomass estimations to improve the accuracy of predicting winter cover crop biomass at termination for decision support tools.</p>
Madrid Retiro Weather Dataset Compliant with NGSI-LD and DCAT-AP
<h2>Description</h2> <p>Dataset that presents the NGSI-LD entity of Madrid Weather Station adapted to the NGSI-LD standard and the automatic generated metadata compliant with DCAT-AP v2 generated by the <a href="https://github.com/ging/fiware-draco/blob/master/docs/processors_catalogue/ngsi_ckan_sink.md">NGSIToCKAN</a> and <a href="https://github.com/ging/fiware-draco/blob/master/docs/processors_catalogue/update_ckan_metadata.md">UpdateCKANMetadata</a> Nifi processors and the <a href="https://github.com/YourOpenDAta/ckanext-dcatapedp">ckanext-dcatapedp CKAN extension</a></p> <h2>Paper</h2> <ul> <li>Paper: <a href="https://arxiv.org/abs/2402.06693">Fostering the integration of European Open Data into Data Spaces through High-Quality Metadata</a></li> <li>Cite:</li> </ul> <p><code>@misc{conde2024fostering,</code><br><code> title={Fostering the integration of European Open Data into Data Spaces through High-Quality Metadata}, </code><br><code> author={Javier Conde and Alejandro Pozo and Andrés Munoz-Arcentales and Johnny Choque and Álvaro Alonso},</code><br><code> year={2024},</code><br><code> eprint={2402.06693},</code><br><code> archivePrefix={arXiv},</code><br><code> primaryClass={cs.DB}</code><br><code>}</code></p>
Structure of the Canadian Forest Fire Weather Index System: the model and its components
<p>This material is part of:</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br><strong>Structure of the Canadian Forest Fire Weather Index system: the model and its components</strong> — The <a href="../record/10806780#preview-iframe">figure below</a> (formats: <a href="../record/10806780/files/FWI-sys_simple_diagram.png?download=1">PNG</a> or <a href="../record/10806780/files/FWI-sys_simple_diagram.pdf?download=1">PDF</a>) shows the logical subdivision of the Canadian Forest Fire Weather Index system (FWI-sys) in components.</p> <p> </p> <p>The Canadian FWI-sys (De Groot,1987; Van Wagner,1987) is an index of fire danger by weather designed to consider the effects on vegetation fuels of the sequence of weather conditions. It is designed to estimate a uniform numerical rating for the relative fire potential accounting for the local sequence of temperature, wind speed, relative humidity, and precipitation, for the day in which the rating is estimated but also modelling the dynamics of the previous days. In addition, the variable amount of possible drying due to the varying solar irradiation in different seasons is taken into account by adjusting the parameters per each month of the year.<br><br>The system is standardised to consider the behaviour of a reference typology of vegetation fuel (mature pine stand) regardless of other non-weather factors which may locally influence the fire danger, such as the specific topography or the pattern, composition, and structure of vegetation assemblages. Therefore, FWI-sys is suitable to support the harmonised comparison among variable weather conditions, either spatially (comparing different spatial regions) or temporally (comparing the same region over time).<br><br>The FWI-sys components are organised in three layers, processing at the daily frequency weather information (either from observations, reanalysis, forecast, or climate scenarios) and estimating from it a final standard aggregated numerical rating of fire intensity.<br><br>The required input variables are</p> <ul> <li>Temperature T (nominally, FWI-sys requires T at noon)</li> <li>Wind speed W (nominally, FWI-sys requires T at noon)</li> <li>Relative humidity</li> <li>Precipitation (24-hour rainfall)</li> <li>Month of the year</li> </ul> <p>The FWI-sys was originally designed to fit the Candian conditions. Following its success, adaptations of the system were studied for different areas of the globe. This implies that the parameters used inside the FWI-sys globally also depend on the latitude (Alexander, 2008).</p> <p>The first layer of components (the <em>fuel moisture codes</em>: Fine Fuel Moisture Content, FFMC; Duff Moisture Code, DMC; Drought Code, DC) is composed by dynamic variables. This means that the value of each component for a given day depends also on the value of the same component the day before. The dynamic components with longer memory of their past history also approximate the seasonal changes in solar radiation, by considering the month of the year (see Figure, bottom left).</p> <ul> <li><strong>Fine Fuel Moisture Code (FFMC)</strong> : provides a numerical rating of the moisture content of the top litter and other cured fine fuels, indicating the relative ease of ignition and flammability of fine fuel.</li> <li><strong>Duff Moisture Code (DMC)</strong> : models a standard moisture content of loosely-compacted organic layers of moderate depth (duff layers and medium-sized woody material). This component of the FWI-sys represents wooden fuels of intermediate thickness.</li> <li><strong>Drought Code (DC)</strong> : models a standard moisture content of deeper, compact, organic layers. This component of the FWI-sys is able to track seasonal drought effects on coarse wooden fuels.</li> </ul> <p> </p> <p>The second layer of components (the<em> fire behaviour indices</em>: Initial Spread Index, ISI; Buildup Index, BUI; Fire Weather Index, FWI) mathematically is composed by stateless D-TM components. This means that these components do not have an internal memory of the past conditions, while instead they rely on the combined information offered by the different temporal inertia of the fuel moisture codes, which they process as input information.</p> <ul> <li><strong>Initial Spread Index (ISI)</strong> : represents the expected rate of fire spread. It considers the combined effects of wind and the FFMC on the rate of spread. However, it excludes the influence of fuel moisture and availabity for the coarser wooden fuels.</li> <li><strong>Buildup Index (BUI)</strong> : combines DMC and DC to model the total amount of fuel available for combustion to the spreading fire.</li> <li><strong>Fire Weather Index (FWI)</strong> : offers a standard aggregated numerical rating of fire intensity which combines ISI and BUI.</li> </ul> <p><br>Given its structure, the model can also be interpreted as a recurrent neural network (RNN) where the input variables are transformed into the final aggregated numerical rating (FWI) by means of two hidden layers: the <em>fuel moisture codes</em> (three nodes/neurons); and the <em>fire behaviour indices</em> (two nodes/neurons).</p> <p>Note that this structure is not a simple feedforward network, as the first hidden layer is made by dynamic components (FFMC, DMC, DC, see highlighted feedack loops in <a href="../record/10806780/files/FWI-sys_simple_diagram_recurrent.png?download=1">PNG</a> format). The activation functions are complex, and the D-TM components (either dynamic or stateless) generally mix physically-based and empirical aspects. A consequence of the complexity of the FWI-sys activation functions is that a neural network with standard (e.g. sigmoidal) activation functions would need to exploit disproportionally many more additional neurons for the same FWI-sys D-TM complexity to be reasonably approximated.</p> <p> </p> <p>An additional FWI-sys component is a simple transfromation of the aggregated FWI values to better account for the nonlinear increase of fire control effort with increasing FWI values (Van Wagner, 1987):</p> <ul> <li><strong>Daily Severity Rating (DSR)</strong>: this transformation of FWI is meant to provide a measure of control difficulty:<br> DSR = 0.0272 ⋅ FWI <sup>1.77</sup><br>which easily invertible:<br> FWI = ( DSR / 0.0272 ) <sup>1 / 1.77</sup></li> </ul> <p><br><br>To cite the Figure, please refer to:<br><br>de Rigo, 2016. <strong>Structure of the Canadian Forest Fire Weather Index System: the model and its components</strong>. https://doi.org/10.5281/zenodo.6558576</p> <p>which is part of</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br> </p> <p> </p> <p><strong>References</strong></p> <p>De Groot, W.J., 1987. <strong>Interpreting the Canadian Forest Fire Weather Index (FWI) System</strong>. In: <em>Fourth Central Regional Fire Weather Committee Scientific and Technical Seminar, Proceedings</em>. Winnipeg, Manitoba, Canada, pp. 3-14. <a href="https://purl.org/INRMM-MiD/c-14176512">https://purl.org/INRMM-MiD/c-14176512</a> </p> <p>Van Wagner, C.E., 1987. <strong>Development and structure of the Canadian Forest Fire Weather Index System</strong>. <em>Forestry Technical Report</em>. Canadian Forestry Service, Ottawa, Canada. <a href="https://purl.org/INRMM-MiD/c-14168337">https://purl.org/INRMM-MiD/c-14168337</a> </p> <p>Alexander, M.E., 2008. <strong>Latitude considerations in adapting the Canadian Forest Fire Weather Index System for use in other countries</strong>. In: Lawson, B.D., Armitage, O.B. (Eds.), <em>Weather Guide for the Canadian Forest Fire Danger Rating System</em>. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, Canada, pp. 67–73. ISBN:978-1-100-11565-8 <a href="https://purl.org/INRMM-MiD/z-MBDA6A6I">https://purl.org/INRMM-MiD/z-MBDA6A6I</a></p> <p> </p>
A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation
<h1>Dataset Description for "A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation"</h1> <p>This dataset accompanies the research paper titled <strong>"A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation"</strong>, currently under review for the AGU Journal JAMES. The study introduces a novel Regional Climate Model (RCM) emulator focusing on high-resolution climate downscaling for the New Zealand region. For additional insights and access to the codebase utilized in this research, please refer to our <a href="https://github.com/nram812/A-Robust-Generative-Adversarial-Network-Approach-for-Climate-Downscaling" target="_new">GitHub repository</a>.</p> <h2>Aims</h2> <p>Our study's overarching goal was to assess the effectiveness of Generative Adversarial Networks (GANs) in a climate downscaling context and is structured around two aims. The first aim of our study is to examine whether GANs can overcome several important limitations of regression-based climate downscaling algorithms (i.e. underestimating the magnitude of extreme events). The second and most important aim of our study is to assess the robustness GAN performance to different training hyperparameters. Our robustness assessment thoroughly scrutinizes GANs for their application in climate downscaling contexts, ensuring that they can learn and capture regional climate processes</p> <h2>Geographic Focus</h2> <p>Our research focuses only on the New Zealand Region (165°E-184°W, 33°S-51°S).</p> <p> </p> <h2>Data Overview</h2> <h3>Training and Evaluation Data</h3> <p>The training data used in this study (for our RCM emulator) only spans the historical period of simulation. It comprises daily accumulated precipitation as the primary target variable, alongside large-scale predictor variables. </p> <ul> <li> <p><strong>Resolution:</strong> The target variable is presented at a 12km resolution, reflecting the highest resolution face of RCM for the New Zealand region. Predictor variables are coarsened to a 1.5-degree resolution from original CCAM outputs using conservative interpolation. </p> </li> <li> <p><strong>Period Coverage:</strong></p> <ul> <li>Training Data: 1960-2014</li> <li>Validation Data: 1986-2005</li> </ul> </li> <li> <p><strong>Models:</strong></p> <ul> <li>Training on: ACCESS-CM2</li> <li>Validated on: EC-Earth3, NorESM2-MM</li> </ul> </li> </ul> <h3>File Structure</h3> <ul> <li> <p><strong>Training Data:</strong></p> <ul> <li>Target/Ground Truth (Y): <code>predictor_ACCESS-CM2_hist.nc</code></li> <li>Predictor (X): <code>pr_ACCESS-CM2_hist.nc</code></li> </ul> </li> <li> <p><strong>Evaluation Data:</strong></p> <ul> <li><strong>NorESM2-MM:</strong> <ul> <li>Target (Y): <code>NorESM2-MM_historical_precip_compressed.nc</code></li> <li>Predictor (X): <code>NorESM2-MM_histupdated_compressed.nc</code></li> </ul> </li> <li><strong>EC-Earth3:</strong> <ul> <li>Target: <code>EC-Earth3_historical_precip_compressed.nc</code></li> <li>Predictor: <code>EC-Earth3_histupdated_compressed.nc</code></li> </ul> </li> </ul> </li> </ul> <h2>Methodological Insights</h2> <ul> <li> <p><strong>Regional Climate Model</strong>, Our Regional Climate Model training data is from the Conformal Cubic Atmospheric Model (CCAM) which is a global non-hydrostatic atmospheric model renowned for its variable-resolution cubic grid. . For more information about CCAM, please see the following <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD038530">paper</a>.</p> </li> <li> <p><strong>Predictor and Target Variables:</strong> Daily-averaged large-scale prognostic variables, including zonal wind, meridional wind, temperature, and specific humidity, are employed as predictors at the 500mb and 850mb pressure levels. These are normalized (see the GitHub repository for the mean and standard deviation fields). Precipitation is taken as is from CCAM and accumulated for each given day. Static predictors are also used in our model, which is stored in a GitHub repository.</p> </li> <li> <p><strong>Training Framework:</strong> Our dataset benefits from the "perfect framework" training strategy, which uses CCAM-coarsened predictor variables. For more information about the perfect and imperfect training frameworks, see the following <a title="review" href="https://journals.ametsoc.org/view/journals/aies/3/2/AIES-D-23-0066.1.xml">review</a></p> </li> </ul>
A dataset of three vine water status indicators, weather records and soil water capacity components collected from a rain-fed Mediterranean vineyard
<p>This dataset contains three key indicators of vine water status: vine shoot growth index (iG-Apex), predawn leaf water potential (Ψpd), and carbon isotope ratio (δ13C). Additionally, it includes weather data and soil measurements. Spatial data files of studied fields, plots, and some vines’ locations are also provided. The data were collected from a rain-fed vineyard in Southern France, 4 km north of the Mediterranean Sea. Measurements were made at the plot level, with each plot consisting of 10 adjacent grapevines (Vitis Vinifera). The iG-Apex was recorded weekly from June 10 to August 22, 2022, across 70 vine plots. Ψpd was measured weekly between 3 a.m. and 5 a.m. in 12 of these plots using a pressure chamber. On August 23, 2022, 100 berries were sampled from each plot, and 1.5 mL of grape juice was extracted for δ13C analysis using a carbon analyzer and mass spectrometer. The dataset contains 761 iG-Apex measurements (70 time series), 720 Ψpd measurements (60 time series), and 70 single-date δ13C measurements. Weather data were recorded daily in 2022 from a weather station located at the vineyard's center, providing five parameters: cumulative rainfall, relative humidity, and three air temperatures (mean, minimum, and maximum). Soil available water capacity components (horizon thickness, field capacity, permanent wilting point, bulk density, and rock fragment content) were measured in 5 of the 70 plots after prior soil profile wall analyses. Monitoring vine water status is essential for optimizing grape yield and wine quality. While Ψpd and δ13C are considered reference methods, they are expensive and prone to logistical constrains. In contrast, iG-Apex can be collected with minimal time and financial investment. This dataset enables not only the exploration of statistical relationships between the plant-based and soil-based indicators, but also the modeling of Ψpd and/or δ13C using iG-Apex, while accounting for weather and soil influences. All data were georeferenced, allowing future integration of ancillary spatial data sources, like multi-spectral remote sensing images or yield data.</p>
Phenotypic, weather, soil, and imputed genomic data for the apple REFPOP
<p>Supporting datasets for the article "Integrative multi-environmental genomic prediction in apple" by Jung et al. (2024)<em>.</em></p> <p>Pheno_raw.xlsx – Eleven traits were assessed during up to five years from 2018 to 2022 (Year) at up to five locations* (Country). The traits evaluated were floral emergence (Flowering_begin), flowering intensity (Flowering_intensity), harvest date (Harvest_date),<strong> </strong>total fruit weight (Fruit_weight), fruit number (Fruit_number), single fruit weight (Fruit_weight_single), titratable acidity (Acidity), soluble solids content (Sugar), fruit firmness (Firmness), red over color (Color_over), and russet frequency (Russet_freq_all).</p> <p>Weather_raw.xlsx – Hourly measurements from 2018 to 2022 (Date) of temperature (Temperature), relative humidity (Humidity), and global radiation (Radiation) were obtained at five locations* (Location).</p> <p>Soil_raw.xlsx – Soil characteristics (Variable) were measured at five locations* (Group.1) and two soil depths (Group.2) in 2016.</p> <p>SNPs_final_2022.bed, SNPs_final_2022.bim, SNPs_final_2022.fam – imputed genomic dataset of 303,239 biallelic SNPs in the PLINK format.</p> <p>*The locations correspond to Belgium (BEL), Switzerland (CHE), Spain (ESP), France (FRA) and Italy (ITA).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.