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Fig. 3 in Reproductive tactics used by the Lambari Astyanax aff. fasciatus in three water supply reservoirs in the same geographic region of the upper Iguaçu River
Fig. 3. Histological slides of partially spawned ovary of Astyanax aff. fasciatus of Passaúna and Piraquara reservoirs, HE staining. Post ovulatory follicles (arrow). Scale bar = 80 µm.
Fig. 4 in Reproductive tactics used by the Lambari Astyanax aff. fasciatus in three water supply reservoirs in the same geographic region of the upper Iguaçu River
Fig. 4. Monthly distribution of the total (K) and somatic (K') condition factors of Astyanax aff. fasciatus in Iraí (a), Passaúna (b), and Piraquara (c) reservoirs. K = continuous line, K' = dotted line.
Fig. 2 in Reproductive tactics used by the Lambari Astyanax aff. fasciatus in three water supply reservoirs in the same geographic region of the upper Iguaçu River
Fig. 2. Maturation curve and monthly distribution of the percentage frequency of the stages of ovarian development of Astyanax aff. fasciatus females in the Iraí (a), Passaúna (b), and Piraquara (c) reservoirs. The numbers indicated above the bars refer to absolute frequency and the letters indicated gonadal development: B = maturation, C = mature, SD = partially spawned, and D = spawned.
Fig. 5 in Reproductive tactics used by the Lambari Astyanax aff. fasciatus in three water supply reservoirs in the same geographic region of the upper Iguaçu River
Fig. 5. Distribution of the percentage frequency in length classes of Astyanax aff. fasciatus individuals of Iraí, Passaúna and Piraquara reservoirs.
Fig. 1 in Reproductive tactics used by the Lambari Astyanax aff. fasciatus in three water supply reservoirs in the same geographic region of the upper Iguaçu River
Fig. 1. Location of Iraí (A), Passaúna (B), and Piraquara (C) reservoirs in the region of the upper Iguaçu River basin, Paraná State, Brazil.
EO-derived water quality parameters using aerial imaging spectrometry for Lake Mulargia (Sardinia, Italy) (2020/09/24)
<p>This dataset contains Hyspex-derived water quality (WQ) products of Lake Mulargia (Sardinia, Italy) for the 24 September 2020. The acquisition was done by CGR Spa (Italy). Available parameters are: True-color image (RGB), Colored Dissolved Organic Matter (CDOM), Chlorophyll-a (CHL), and Suspended Particulate Matter (SPM). WQ parameters have been calculated using CNR’s bio-optical model BOMBER parameterized with the inherent optical properties specific of the case study. The data are available as GeoTiff files in WGS 84 / UTM zone 32N (EPSG: 32632).</p>
Retrieval and Validation of Total Seasonal Liquid Water Amounts in the Percolation Zone of Greenland Ice Sheet Using L-band Radiometry
<p>This repository contains the dataset associated with the analyses presented in the following study:</p> <p>Hossan, A., Colliander, A., Vandecrux, B., Schlegel, N.-J., Harper, J., Marshall, S., and Miller, J. Z.: <em>Retrieval and validation of total seasonal liquid water amounts in the percolation zone of the Greenland Ice Sheet using L-band radiometry</em>, <strong>The Cryosphere</strong>, 19, 4237–4258, <a href="https://doi.org/10.5194/tc-19-4237-2025" target="_new">https://doi.org/10.5194/tc-19-4237-2025</a>, 2025.</p> <p>In this study, we demonstrated the capability of NASA's Soil Moisture Active Passive (SMAP) L-band radiometer to estimate surface and subsurface liquid water amounts (LWA) in the percolation zone of the Greenland Ice Sheet. The article presents our initial retrieval algorithm, validation results, and highlights the potential for developing a Greenland-wide LWA data product.</p> <p><strong>Contents of this Repository</strong></p> <p>This repository includes:</p> <ul> <li><strong>SMAP-retrieved daily, vertically integrated LWA gridded initial data products</strong> (2015–2023), derived from enhanced-resolution SMAP TB observations. These data include spatial coordinates, acquisition dates, and a melt flag indicator.</li> <ul> <li>SMAP_LWA_time_series_AWS contains daily time series at a AWS location (point observation)</li> <li>Samimi_EBM_LWA_time_series_AWS contains corresponding time series of LWA estimated by Samimi model forced by PROMICE AWS.</li> <li>GEMB_LWA_time_series_AWS contains corresponding time series of LWA estimated by GEMB model forced by PROMICE AWS</li> <li>The locations and name ID of the AWS are given in AWS.txt/xls file</li> <li>L_band_LWA_yyyy.nc files contain daily LWA and TB data over the entire percolation zone</li> </ul> <li><strong>Corresponding vertically polarized brightness temperature (TBV) data</strong>, including their winter mean and standard deviation.</li> <li><strong>Model-based LWA estimates used for validation</strong>, including outputs from:</li> <ul> <li>The locally calibrated <strong>Energy and Mass Balance (EMB)</strong> model.</li> <li>The <strong>Glacier Energy and Mass Balance (GEMB)</strong> model within NASA’s <strong>Ice-sheet and Sea-level System Model (ISSM)</strong>.</li> </ul> </ul> <p><strong>Retrieval and Validation Codebase</strong></p> <p>The MATLAB scripts and tools used for the microwave retrieval algorithm, radiative transfer modeling, inversion process, and comparative validation with in situ AWS-driven model outputs are available at the following GitHub repository:</p> <p>🔗 <a href="https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS" target="_new">https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS</a><br><em>(Last accessed: 17 September 2025)</em></p> <p>The codebase includes:</p> <ul> <li>Preprocessing routines for SMAP TB data.</li> <li>Implementation of the radiative transfer forward model.</li> <li>Inversion and threshold-based detection algorithms.</li> <li>Validation scripts for comparison against AWS-forced EMB and GEMB model outputs.</li> </ul> <p><strong>Relevance</strong></p> <p>These data and methods support ongoing efforts to improve surface mass balance (SMB) estimates and enhance projections of Greenland’s contribution to global sea level rise.</p> <p> </p>
Data for replication of the publication: Probabilistic leak localization in water distribution networks using a hybrid data-driven and model-based approach
<p>20 to 30% of drinking water produced is lost due to leaks in water distribution pipes. In times of water scarcity, losing so much treated water comes at a significant cost, both environmentally and economically. In this paper, we propose a hybrid leak localization approach combining both model-based and data-driven modeling. Pressure heads of leak scenarios are simulated using a hydraulic model, and then used to train a machine-learning based leak localization model. A key element of our approach is that discrepancies between simulated and measured pressures are accounted for using a dynamically calculated bias correction, based on historical pressure measurements. Data of in-field leak experiments in operational water distribution networks were produced to evaluate our approach on realistic test data. Two problematic settings for leak localization were examined. In the first setting, an uncalibrated hydraulic model was used. In the second setting, an extended version of the water distribution network was considered, where large parts of the network were insensitive to leaks. Our results show that the leak localization model is able to reduce the leak search region in parts of the network where leaks induce detectable drops in pressure. When this is not the case, the model still localizes the leak but is able to indicate a higher level of uncertainty with respect to its leak predictions.</p>
Modulating the Surface Properties of Lithium Niobate Nanoparticles by Multifunctional Coatings Using Water-in-Oil Microemulsions
<p>Raw data associated to the study entitled <em>Modulating the Surface Properties of Lithium Niobate Nanoparticles by Multifunctional Coatings Using Water-in-Oil Microemulsions.</em></p> <p>Metadata file</p> <p>DLS measurements</p> <p>FTIR characterization</p> <p>Labbook</p> <p>NMR characterization</p> <p>TEM and EDX analysis</p> <p>XRD measurements</p>
Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use
<p>Changes in climate and land-use and land-cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (1) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios to altered nutrient loads, (2) sensitivity (i.e., how responsive estuaries are to altered nutrient loads), and (3) adaptive capacity (i.e., how the socio-ecological system can use existing resources to reduce the impacts associated with increased exposure). The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increase is expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Our findings illustrate the benefits of integrating natural and socio-ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries. A<a href="https://lisemontefiore.shinyapps.io/estuarine_vulnerability/"> web-based application</a> has been developed to visualize and download the data.</p>
Figure 5 in First data on water mite (Acari, Hydrachnidia) assemblages of Point Rosa Marsh, Harrison Township, Michigan, USA, and their use as environmental bioindicators of aquatic health
Figure 5 Frequency of water mite genera collected from habitats surrounding Point Rosa Marsh including Lake St. Clair. Comparable samples were collected on ten collection dates during 2017, 2018 and 2019. Graphs are arranged (left to right, and then by row) in the order of the overall frequency of each genus. Each bar graph shows the number of taxa collected on the six collection dates with bars color-coded to assist in comparing graphs on various dates. Dark blue (B) [Oct. 18 2017], red (A) [Oct. 20 2017 (1)], light green (B) [Oct. 20 2017], dark green (D&C) [Oct. 27 2017], black (A) [Aug. 7 2018], orange (A) [Aug. 21 2018], grey (D&C) [Aug. 31 2018], yellow (A) [Sept. 16 2019], light
Figure 4 in First data on water mite (Acari, Hydrachnidia) assemblages of Point Rosa Marsh, Harrison Township, Michigan, USA, and their use as environmental bioindicators of aquatic health
Figure 4 Frequency of water mite genera collected in Point Rosa Marsh. Comparable samples were collected on nine collection dates during 2017, 2018 and 2019. Graphs are arranged (left to right, and then by row) in the order of the overall frequency of each genus. Each bar graph shows the number of taxa collected on the six collection dates with bars color-coded to assist in comparing graphs on various dates. Dark blue (1&2) [Oct. 18 2017], red (4) [Oct. 19 2017], light green (3) [Oct. 18 2017 (2)], no data (1&2) [Aug. 7 2018], black (1&2) [Aug. 21 2018],
Detecting Lunar and Martian Water via Backscattered Cosmic Particles using Muon Tomography
<p><strong>Introduction</strong></p> <p>The search for water on the Lunar and Martian surfaces is a cornerstone of space exploration, playing a key role in expanding our understanding of the history and evolution of these celestial bodies. Despite its importance, current knowledge about the distribution, concentration, origin, and migration of water on the Moon and Mars is still limited. This study aims to address these gaps by employing a novel approach that leverages cosmic-ray muon detectors and backscattered radiation. Through the use of advanced muon tracking systems and preliminary simulations conducted with GEANT4, the research suggests that muon tomography holds significant promise for improving our understanding of water-ice content on the Lunar and Martian surfaces.</p> <p><strong>Data Description</strong></p> <p>Data and detector models were generated using GEANT4. The simulations include:</p> <ul> <li>Lunar and Martian dry regolith</li> <li>Lunar and Martian regolith with water-ice beneath the surface</li> </ul> <p><strong>Contents</strong></p> <p>This record includes:</p> <ul> <li><code>*.csv</code>: Output raw files from GEANT4, including 5D information, scattering angle, detector plate position, and particle type.</li> <li><code>backscatter_eventselection.py</code>: Python code to filter events and generate a CSV file of selected backscattered events.</li> <li><code>*.tiff</code>: Visualization files depicting Lunar and Martian scenarios, including detector geometry and particle events.</li> <li><code>ml_classifier.py</code>: Python code for machine learning tasks to classify backscattered events.</li> <li><code>OP_Muographers_2023.pdf</code>: Detailed description of chemical composition and simulated scenarios.</li> <li>Tracking_EKF: Performs track reconstruction and computes track lengths using extended Kalman Filter.</li> </ul> <p><strong>Disclaimer</strong></p> <p>The provided datasets are simulated samples suitable for conceptual R&D and performance studies. They have not been calibrated against real data and should not be used for physics projections about the detectors.</p>
Phtometry measurements using spectroscopic ellipsometer of PAAO containing samples in air and water (AoI 45 deg., 60 deg., varying polarizer and analyser angles)
<p>The file contains raw intensity data measured in reflection mode for various material combinations: porous anodized aluminium oxide (PAAO), gold nanoparticles (Au NPs), diamond-like carbon with silver nanoparticles composite (DLC:Ag), quartz, and silicon.</p> <p>List of investigated samples:</p> <table> <thead> <tr> <th scope="col">Sample name</th> <th scope="col">Sample description</th> </tr> </thead> <tbody> <tr> <td>AJ1</td> <td>Aluminium substrate, ~241 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 16 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ2</td> <td>Aluminium substrate, ~259 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 37 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ3</td> <td>Aluminium substrate, ~293 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 8 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ4</td> <td>Aluminium substrate, ~317 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 30 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ5</td> <td>Aluminium substrate, ~345 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 57 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ6</td> <td>Aluminium substrate, ~276 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 42 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>AJ7</td> <td>Aluminium substrate, ~316 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 25 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>AJ8</td> <td>Aluminium substrate, ~345 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 5 min 4 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>Q140</td> <td>Quartz substrate, ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>Si140</td> <td>Silicon substrate, ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> </tbody> </table> <p>Ellipsometer: rotating compensator GES5-E (Semilab).</p> <p>Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum.</p> <p>Detector: UV-Vis CCD with 0.8 nm resolution.</p> <p>Spectral range: approximately 230-960 nm.</p> <p>Mode: photometry.</p> <p>Light incidence angles: 45°, 60°.</p> <p>Polarizer angles: 0°, 90°.</p> <p>Analyzer angles: 0°, 45°, 90°.</p> <p>Light beam size: microspot (365 × 470 μm<sup>2</sup> at 75° angle of incidence).</p> <p>The reference was measured from aluminum mirror provided with the ellipsometer for each variation of measurement set-up.</p> <p>Each sample was first measured in air and then after immersion in water. Focusing was performed before each measurement.</p> <p>.smdx and .mup files are generated by the ellipsometer and can be opened using Semilab software. Their file names contain sample name, polarizer angle (Pol), analyzer angle (Ana), incidence angle (ang), and surrounding medium (air/water). .csv files are exported data related to each sample. .jpg files are for illustrative purposes to show the spectra. .rar file contains all the files in this upload.</p> <p> </p>
Data from: Can IR images of the water surface be used to quantify the energy spectrum and the turbulent kinetic energy dissipation rate?
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Life history plasticity and water use trade-offs associated with drought resistance in a clade of California jewelflowers
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Data for: Land use change and coastal water darkening drive synchronous dynamics in phytoplankton and fish phenology on centennial time scales
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Evaluation of point-of-use treatments and biochar to reduce 1,2,3-trichloropropane (TCP) contamination in drinking water
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Data for fitness analyses used in: Environmentally-induced DNA methylation is inherited across generations in water fleas (Daphnia magna)
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Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use
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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.