Find research datasets worth reusing

opencc-by-4.0Aug 2022View details →

In-situ grazing-incidence X-ray diffraction data of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) via employing an isopropanol antisolvent. Raw Data

The dataset contains 400 diffraction images from a 40 second in-situ grazing-incidence wide-angle X-ray scattering measurement of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) on a glass substrate. The crystallization is initiated via employing an isopropanol antisolvent during the spin-coating of the perovskite precursor solution. 40 µL of MAPbBr3 solution (4:1 DMF/DMSO solvent mixture) was applied on plasma-cleaned glass substrate in a chamber with kapton windows. The two-phase spin-coating regime included 10 seconds at 1000 rpm followed by 30 seconds at 2000 rpm, 200 µL of antisolvent was dispensed at t = 30 s.     The data was acquired at the P08 Beamline at PETRA III (DESY Hamburg). Acquisition parameters:   <ul> <li> X-ray wavelength: 0.6888 nm </li> <li> Sample detector distance: 809 mm </li> <li> Incidence angle: 0.5 deg. </li> <li> Detector model: XRD 1621 CN3 EHS </li> <li> Acquisition rate : 10 frames per second (10 Hz) </li> <li> Direct beam position (pixels): 545, 222 </li> </ul>

PARAGON 1 - KM2112 - Nitrogen In Situ Measurements

This dataset contains measurements of in situ nitrogen concentrations collected during the PARAGON 1 expedition (KM2112) in the North Pacific Subtropical Gyre. Measurements come from daily collections of whole seawater at 150 m using trace metal clean techniques. Samples for measurements of nitrogen concentrations were collected by subsampling 250 mL aliquots of whole seawater into high density polyethylene bottles and freezing upright at -20°C until analysis onshore. Nitrate plus nitrite (NO₃⁻ + NO₂⁻) concentrations were measured colorimetrically using segmented flow on a SEAL Autoanalyzer III with a high resolution detector as described by Foreman et al. (2019). Total nitrogen concentrations were measured similarly, following overnight UV oxidation of the sample according to Foreman et al. (2019). NO₂⁻ concentrations were measured using the high sensitivity chemiluminescent method described by Dore and Karl (1996) and Foreman et al. (2016). Ammonium (NH₄⁺) concentrations were determined using the high sensitivity fluorometric method of Holmes et al. (1999). For each analysis, reported values are the average of technical duplicate measurements from each sample. Timestamp is in UTC.

PARAGON 1 - KM2112 - Iron Uptake In Situ Measurements

This dataset contains measurements of in situ rates of dissolved iron (Fe) uptake collected during the PARAGON 1 expedition (KM2112) in the North Pacific Subtropical Gyre. Measurements come from daily collections of whole seawater at 150 m using trace metal clean techniques. Rates of iron uptake were measured by subsampling 250 mL aliquots of whole seawater into polycarbonate bottles and spiking with a final concentration of 0.4 nmol L⁻¹ ⁵⁵FeCl₃ with a specific activity of 2.74 Ci mmol⁻¹ of Fe (Perkin Elmer). A killed blank measurement was also made by spiking an additional 250 mL aliquot with a final concentration of 1% glutaraldehyde prior to spiking with ⁵⁵FeCl₃. All aliquots were then incubated in the dark at in situ temperature for 8-10 hours. Incubations were terminated by filtering the entire 250 mL aliquot for each sample through a 0.2 µm polycarbonate filter. In order to remove extracellularly bound Fe, filters were rinsed 3 times with an oxalate wash according to Tang and Morel (2006) followed by three rinses with 0.2 µm filtered seawater. Filters were then transferred to high density polyethylene scintillation vials and submerged in 10 mL of Ultima Gold LLT scintillation cocktail (Perkin Elmer). Radioactivity incorporated into microbial biomass was measured on a TriCarb 4910TR scintillation counter. Values reported are blank-corrected averages of triplicate measurements from each sample but have not been adjusted for isotope dilution resulting from unlabeled iron present in situ. Timestamp is in UTC.

PARAGON 1 - KM2112 - Particulate Carbon and Nitrogen In Situ Measurements

This dataset contains measurements of particulate carbon and nitrogen concentrations collected during the PARAGON 1 expedition (KM2112) in the North Pacific Subtropical Gyre. Measurements come from daily collections of whole seawater at 150 m using trace metal clean techniques. Samples for measurements of particulate carbon and nitrogen concentrations were collected by filtering 4 L of whole seawater onto pre-combusted 25 mm glass fiber filters (GF/F, Whatman) under positive pressure. Filters were then transferred to polystyrene Petri dishes lined with pre-combusted aluminum foil and stored at -20°C until analysis onshore. Particulate carbon and nitrogen concentrations were then determined by high temperature combustion using an Exeter CE-440 Elemental Analyzer according to Grabowski et al. (2019). Particulate carbon concentrations are measurements of total particulate carbon, including both organic and inorganic carbon. Timestamp is in UTC.

PARAGON 1 - KM2112 - Total Organic Carbon In Situ Measurements

This dataset contains measurements of total organic carbon concentrations (TOC) collected during the PARAGON 1 expedition (KM2112) in the North Pacific Subtropical Gyre. Measurements come from daily collections of whole seawater at 150 m using trace metal clean techniques. Samples for measurements of TOC concentrations were collected by aliquoting 40 mL of whole seawater into pre-combusted borosilicate vials. Samples were acidified with 27 µL of 12N HCl (Optima grade, Fisher), capped with Teflon-lined silicone septa lids, and stored in the dark at room temperature until analysis on shore. Total organic carbon concentrations were determined by high temperature combustion on a modified Shimadzu TOC analyzer according to Carlson et al. (2010). Timestamp is in UTC.

PARAGON 1 - KM2112 - Leucine Incorporation In Situ Measurements

This dataset contains measurements of in situ rates of leucine incorporation as a measure of bacterial production collected during the PARAGON 1 expedition (KM2112) in the North Pacific Subtropical Gyre. Measurements come from daily collections of whole seawater at 150 m using trace metal clean techniques. Rates of leucine incorporation were measured by subsampling 1.5 mL aliquots of whole seawater into 2 mL microcentrifuge tubes (Axygen) and spiking with a final concentration of 20 nmol L⁻¹ leucine with a ³H specific activity of 53.3 Ci mmol⁻¹ of leucine (Perkin Elmer). A killed blank measurement was made by spiking an additional 1.5 mL aliquot with 100 µL of 100% (w/v) ice-cold trichloroacetic acid (TCA) prior to spiking with ³H-leucine. All aliquots were then incubated in the dark at in situ temperature for 4-6 hours after which live incubations were terminated with the addition of 100 µL of 100% (w/v) ice-cold TCA. Samples were processed at sea using the centrifugation method of Smith and Azam (1992), and radioactivity incorporated into bacterial protein was measured on a TriCarb 4910TR scintillation counter using Ultima Gold LLT scintillation cocktail (Perkin Elmer). Values reported are the blank-corrected averages of triplicate measurements from each sample. Timestamp is in UTC.

opencc-by-4.0Apr 2023View details →

MS and NMR data of in situ Captured Marine Exometabolites

This folder contains the raw data pertaining to the article In Situ Capture and Real Time Enrichment of Marine Chemical Diversity  <a href="https://doi.org/10.1021/acscentsci.3c00661">https://doi.org/10.1021/acscentsci.3c00661</a>Data are organized in folders corresponding to each figure. Briefly, this folder contains  the raw mass spectrometry (MS) data, the cytoscape files of the full molecular network (Fig3), the xcel spreadsheets of annotated MS spectra related to each investigated specialized exometabolites from the Mediterranean sponges Aplysina cavernicola (AC, Fig4), Spongia officinalis (SO, Fig5), and Agelas oroides (AO, Fig6), the raw 1H NMR data from each sponge exometabolite (EM) extract with their corresponding crude extract (CR).<ul><li>All MS2 data were acquired on a Bruker Impact II qTOF (ESI positive, collision energy 20-40eV) also deposited here : MSV000091465</li><li>SIRIUS software and CANOPUS were used to further annotate the chemodiversity of captured marine EMs</li><li>All NMR data were acquired on a BRUKER avance II+  instrument (600 MHz, cryoprobe) in CD3OD</li></ul>-------------------------References related to in silico MS annotation tools:<ul><li>Kai Dührkop, Louis-Félix Nothias, Markus Fleischauer, Raphael Reher, Marcus Ludwig, Martin A. Hoffmann, Daniel Petras, William H. Gerwick, Juho Rousu, Pieter C. Dorrestein and Sebastian Böcker Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nature Biotechnology, 2020.  https://doi.org/10.1038/s41587-020-0740-8</li><li>Yannick Djoumbou Feunang, Roman Eisner, Craig Knox, Leonid Chepelev, Janna Hastings, Gareth Owen, Eoin Fahy, Christoph Steinbeck, Shankar Subramanian, Evan Bolton, Russell Greiner, David S. Wishart ClassyFire: automated chemical classification with a comprehensive, computable taxonomy J Cheminf, 8, 2016.  https://doi.org/10.1186/s13321-016-0174-y</li><li>Kim, Hyun Woo and Wang, Mingxun and Leber, Christopher A. and Nothias, Louis-Félix and Reher, Raphael and Kang, Kyo Bin and van der Hooft, Justin J. J. and Dorrestein, Pieter C. and Gerwick, William H. and Cottrell, Garrison W. NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products. Journal of Natural Products, 84, 2021. https://doi.org/10.1021/acs.jnatprod.1c00399</li></ul>

openCC0Mar 2022View details →

2018-2020 Laboratory measurements of inorganic carbon accompanied by sensor data measurements of in situ inorganic carbon from the Upper Clark Fork River (Montana, USA)

These data were collected to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) and Consortium for Research in Environmental Water Systems (CREWS) programs. The LTREB monitoring project consists of monthly and bi-weekly water quality monitoring across a 215-km river restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, heavy metal contamination, organic and inorganic carbon concentrations, and physicochemical parameters. The original analytical intent for these data was to assess the accuracy of calculating the partial pressure of carbon dioxide (pCO2) from electrochemical and spectrophotometric pH along with total alkalinity (AT). These data correspond to two parts: a tank study and a field application. The tank study was a set of controlled laboratory experiments that took place in a well-mixed temperature-controlled tank of freshwater. Data for the tank study are primarily measurements of electrochemical and spectrophotometric pH, AT, electrical conductivity, temperature, and ionic strength. The field application was used to demonstrate the real-world applicability of the tank study results in the Upper Clark Fork River (USGS HUC 17010201) at the Gold Creek site southeast of Missoula, MT, USA. Data from the field application are primarily high frequency measurements of carbon dioxide, pH, temperature, and electrical conductivity. Additional miscellaneous data were collected for quality control. These field data were collected using field deployments of SAMI sensors from Sunburst Sensors (Missoula, Montana, USA). Electrical conductivity data were collected with a HOBO sensor from Onset Computer Corporation (Bourne, Massachusetts, USA).

openCC (other)Feb 2023View details →

Time series of in situ Uv-Vis absorbance spectra and high-frequency predictions of total and soluble Fe and Mn concentrations measured at multiple depths in Falling Creek Reservoir (Vinton, VA, USA) in 2020 and 2021

High-frequency measurements of light absorbance were collected at multiple depths in Falling Creek Reservoir (FCR; Vinton, VA, USA) using a s::can Spectrolyser UV-Visible spectrophotometer coupled with a multiplexor pumping system. The system pumps water samples from individual depths into a flow-through cuvette where the UV-vis absorbance spectra of the sample are measured by the spectrophotometer. The system used in our study collected measurements of light absorbance every 2.5 nm wavelengths from 200 nm to 732.5 nm (optical path length of 10 mm) approximately at an hourly time step for seven monitoring depths in the reservoir. Data was collected during two periods; the first deployment (16 October to 9 November 2020) was to observe changes in Fe and Mn concentrations before, during, and after reservoir fall turnover and the second deployment (26 May to 21 June 2021) was to observe the effects of engineered hypolimnetic oxygenation on Fe and Mn concentrations. Partial least squares regression models were developed to generate predictions of total and soluble Fe and Mn concentrations based on the correlation between absorbance spectra and sampling data.

openCC (other)Sep 2025View details →

In Situ Carbon Dioxide and Methane Flux Measurements Using Opaque Chambers in a Sedge Fen Wetland (US-Los Lost Creek AmeriFlux Site, Wisconsin, Summer 2015)

This dataset contains in situ measurements of carbon dioxide (CO₂) and methane (CH₄) fluxes, collected using opaque closed chambers at the US-Los Lost Creek AmeriFlux fen shrub wetland site in northern Wisconsin during summer 2015. These data were collected to characterize variability of day-time mid-summer methane soil fluxes across the sampling area of the eddy covariance flux tower.

openCC (other)Dec 2024View details →

Hubbard Brook Nitrogen Oligotrophication (HBNO): In-situ Nitrogen Mineralization and Nitrification, 2021-2023

The goal of this project is to test the overarching hypothesis that positive feedback mechanisms involving changes in seasonal cycles that diminish N availability to plants such that plant N demand is not met by soil N availability in northern forests. Specifically, we hypothesize that increasing N demand by plants (induced by increasing temperatures, longer growing seasons, and other environmental changes) leads to greater N resorption by trees in autumn, increased C:N in litter, and greater net immobilization of N by soil microbes in the following spring. However, the timing of snowmelt and soil freezing in spring may further affect net mineralization and N availability for plants. These hypotheses are being tested with a combination of observational, experimental, and modeling approaches at Hubbard Brook Experimental Forest in New Hampshire: 1) measurements at 14 previously established sites along an elevation/aspect climate gradient; 2) litter and snow manipulation experiments at six sites along the climate gradient to create variation in soil climate conditions and microbial N immobilization during spring. We leveraged 14 sites previously established along an elevation and aspect-driven climate gradient at Hubbard Brook as a “natural climate experiment" to test our hypothesis that a positive feedback between N cycling during fall senescence and spring contributes to declining N availability in northern forests. This elevation gradient encompasses variation in mean annual air temperature of ~2.5 °C that is similar to the change projected to occur with climate change over the next 50–100 years in the northeastern U.S. There is relatively little variation in soils along the gradient. We are utilizing three sites at higher elevation (~550-660 m, north facing) and three sites at lower elevation (~375-500 m, south facing) for the litter and snow manipulation experiments to maximize the differences in temperature among the 14 sites. Litterbox manipulation: The objecti

openCC (other)May 2025View details →

MCR LTER: Coral Reef: In Situ Light Data, ongoing since 2021

This time series describes light (PAR, 400-700 nm) recorded in the air, on the surface, and at depth (17.1 m depth) at the LTER2 fore reef site. Surface data originate from twin cosine collectors (Hobo/Onset brand, model S-LIA-M003) attached to USB micro weather stations (Hobo/Onset brand, model H21-USB). Sensors are attached to the roof of the waterfront building at the UCB Gump station (-17.490598, -149.826208) with the sensors ~ 6 m above sea level. In situ light is recorded with a single PME miniPAR logger fitted with a Li-COR LI-192 cosine correct PAR sensor attached to a PME miniWiper. The sensor is mounted underwater with the top of the sensor at 17.1 m depth. The objective is to measure K dPAR , the diffuse attenuation coefficient for PAR wavelengths, over long periods. The technique relies on the assumption that ~96% of surface PAR is transmitted through the air-sea interface when sun altitudes are > 46° and wind speeds are < 5 m s-1 (Gregg and Carder 1990). Departures from this assumption degrade the estimation of subsurface PAR from surface PAR, which is required to calculate K dPAR .

openCC (other)Nov 2022View details →

Globally distributed lake surface water temperatures collected in situ and by satellites; 1985-2009

Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global dataset of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985-2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues. This dataset accompanies a data publication in the journal Scientific Data

opencc-by-4.0Nov 2020View details →

Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase (supplementary data)

This a dataset of scanning transmission electron microscopy data showing Pt clusters nucleating in an ionic liquid. For each of the 4 movies there is the raw data (uncompressed .tif and compressed as .avi) and denoised versions (uncompressed .tif and compressed as .avi). This data is for the article "Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase" published in ChemNanoMat (2020), by Trond R. Henninen, Debora Keller and Rolf Erni. (https://onlinelibrary.wiley.com/doi/full/10.1002/cnma.202000503) Movie 1: Homogeneous nucleations of two clusters in a suspended thin film of ionic liquid.  Movie 2: Heterogeneous nucleation of a ca 8-9 atom cluster near the edge of a nanodroplet supported on a carbon film. Movie 3: Heterogeneous nucleation of multiple clusters in a nanodroplet. Shortly after nucleation, they coalesce to form disordered nanoclusters. Movie 4: Heterogeneous nucleation and dissolution cycles of spherical particles in a nanodroplet.

opencc-by-4.0Mar 2024View details →

Rating curves based on satellite altimetry and in-situ discharge data

<h1>Context: </h1> The ESA river discharge Climate Change Initiative (CCI) project is a precursor study. It aims to derive long term climate data records (at least over 20-years) of river discharge for some selected river basins (and some locations in the river network) using satellite remote sensing observations (altimetry and multispectral images) and ancillary data. It aims to provide a proof-of-concept for the feasibility for a potential River Discharge ECV product to meet the requirements for the <a href="https://gcos.wmo.int/en/essential-climate-variables/rivers/" target="_blank" rel="noopener">Global Climate Observing System</a>. This project covers precursor activities towards the production of data products that address the GCOS-defined requirements for the River Discharge ECV. <h1>Data description :</h1> Just as in-situ stage measurements can be used to gauge river discharge, altimetry-derived water surface elevation (WSE) can serve as an alternative means of estimating river discharge when discharge time series data is available. Several methodologies have been documented for deriving discharge time series from multimission altimetry observations and supplementary data (Biancamaria et al., 2024). At least two approaches will be used, depending on the available in situ discharge and altimetry water surface elevation (WSE) time series: ⋅ Method 1: The preferred approach relies on the altimetry water surface elevation time series and in situ discharge time series to create a rating curve (RC) characterized by a power relationship between these two variables following a Bayesian approach (Rantz et al., 1982). However, this method necessitates a significant overlap period between discharge data and radar altimetry measurements (e.g., Biancamaria et al., 2011; Papa et al., 2012), or it requires the assumption that the rating curve remains valid and consistent when discharge data is only available prior to the altimetry observation period. ⋅ Method 2: The final option, in cases where there is no temporal overlap between in-situ or simulated discharge and water surface elevation data, assumes that the validity and stability of the rating curve persist across the various time periods covered by the two datasets. Both of these time periods should be sufficiently long to encompass a wide range of events. With this assumption, Tourian et al. (2013, 2017) introduced a method for calculating the rating curve, not based on the time series of discharge and water surface elevation, but on the distribution of their quantiles. This method has been adopted by a limited number of recent studies (e.g., Belloni et al., 2021). However, it’s important to note that this methodology naturally introduces higher errors when compared to the preferred approach. For this reason, this methodology will be validated over some stations with various hydrological dynamics and satisfying previous methods (overlap period exists between WSE and Q). <h1>Approaches to derive Rating Curve (RC) :</h1> <h2>Bayesian Approach :</h2> The Bayesian method is a robust statistical approach used for constructing a rating curve, frequently applied in the field of hydrology when the goal is to estimate unknown parameters from observed data, while taking into consideration the associated uncertainty in these estimates.  According to this, the estimation of the rating curve using the Bayesian method involves several steps: <ul> <li>The initial step entails defining a probabilistic model that describes the relationship between observed data and the parameters we aim to estimate. In many hydrological applications, the relationship between discharge data (Q) and water surface elevation data (WSE) is often expressed as a power function:</li> </ul>                                                                                       Q = a⋅(WSE-z0)b Here, a, z0 and b are the parameters of the rating curve. a, is a scaling coefficient governing the magnitude of the Q-WSE relationship, b, characterizes the nature of this relationship, and z0, represents the height of the free surface above the reference point, corresponding to the river bottom's altitude. The power relationship is especially pertinent due to its consistency with numerous hydrodynamic phenomena. The exponent b within the equation allows for the representation of distinctive flow characteristics, including factors like roughness and channel geometry. Moreover, it offers adaptability in modelling to accommodate variations in flow characteristics, whether they are turbulent or laminar. This relationship, despite its mathematical simplicity, facilitates the fine-tuning of model adjustments in accordance with observed data (Chow, 1959). <ul> <li>The second step involves the use of prior normal distributions, reflecting our prior knowledge about these parameters. These distributions can either be informative or uninformative, depending on our level of knowledge. The limits and ranges for a, z0 and b can vary depending on the specific context of the study, the dataset used, and the characteristics of the river or channel being analysed.</li> </ul> - Coefficient “a”:  adjustment parameter for the rating curve representing the scaling factor for discharge. Its value can significantly fluctuate based on various factors such as the characteristics of the river or channel, hydraulic conditions, and other influencing factors. Consequently, "a" must be non-negative and constrained within a sensible range specific to the system under study. Following the Manning equation, “a” must be equal to W/n*S1/2 (Chow et al., 1988) where W is the river’s width (m), n the Manning’s roughness coefficient and S the slope (m/m). Given the considerable variability in river width and slope across different stations, a feasible range for this coefficient can be considered as:                                                                                       a ∈ [0; 3000] - Coefficient “b”: adjustment parameter representing the exponent of the rating curve and indicating the hydraulic condition of the study site. Like "a," this value must comply with physical constraints and cannot be negative. Following the Manning equation, “b” must be equal to 5/3 for reference hydraulic condition (Rantz et al., 1982). To accommodate the variability in system characteristics across sites, the following range values can be considered for this coefficient:                                                                                       b ∈ [0; 5] - Coefficient “z0”: offset or the elevation at which discharge begins. It should be within the range of elevations relevant to your study. For this reason, the value cannot exceed the minimum value of water surface elevation (WSE) and the range value need to consider of the variability in term of water depth over the sites. A feasible range for this coefficient can be considered as:                                                                                       z0 ∈ [min(WSE)-30; min(WSE)] <ul> <li>The final step involves parameter estimation. The posterior distribution of the parameters yields probabilistic estimates of the rating curve parameters in the form of mean values (optimal values) and credibility intervals (95th percentiles). This accounts for the uncertainty associated with these parameters and is achieved through Markov Chain Monte Carlo (MCMC) sampling from the posterior distribution. Two commonly employed MCMC algorithms are "NUTS" (No-U-Turn Sampler) and "Metropolis-Hastings." The Metropolis-Hasting sampler "MH" algorithm, which is relatively simple and efficient where a balance between exploration and exploitation is desired. This algorithm can be adapted to sample from discrete state spaces.</li> </ul> <h2>Quantile approach : </h2> The Quantile approach employs statistical modelling using quantile functions to create a rating curve, eliminating the necessity for overlapping measurements. This algorithmic method enables the estimation of river discharge using satellite altimetry, even in instances where there are no in situ measurements within the altimeter's timeframe. This approach has undergone application and validation in diverse river basins spanning different climatic zones, such as the Amazon, Brahmaputra, Danube, Niger, and Ob (Tourian et al., 2013). Assuming a stationary flow behaviour and no modification in the river bathymetry both at the altimetry virtual station and at the in-situ gage, this approach ensures the utilization of historical in situ data in current applications. This method computes the quantile functions of the altimetry water surface elevation on one hand and of the discharge time series on the other hand. Then a scatter plot of these in-situ discharge quantiles versus altimetry water surface elevation quantiles is computed to establish the rating curve using the bayesian approach described previously. <h1>File description :</h1> <table> <tbody> <tr> <td>Column name</td> <td>Description</td> </tr> <tr> <td>basin-station</td> <td>Basin name in capital letters and Station name in capital letters separated by "_" and where spaces have been replaced by "-".</td> </tr> <tr> <td>lon</td> <td>Longitude in decimal degrees [-180,180] with 4 decimals - corresponding to the insitu discharge station.</td> </tr> <tr> <td>lat</td> <td>Latitude in decimal degrees [-90,90] with 4 decimals – corresponding to the insitu discharge station.</td> </tr> <tr> <td>a</td> <td>Adjustment parameter for the rating curve representing the scaling factor for discharge. Number with 3 decimals.</td> </tr> <tr> <td>b</td> <td>Adjustment parameter representing the exponent of the RC and indicating the hydraulic condition of the study site. Number with 3 decimals.</td> </tr> <tr> <td>z0</td> <td>Offset of the elevation at which discharge begins. Number with 3 decimals.</td> </tr> <tr> <td>a_sd</td> <td>Standard deviation of the coefficient "a". Number with 3 decimals.</td> </tr> <tr> <td>b_sd</td> <td>Standard deviation of the coefficient "b". Number with 3 decimals.</td> </tr> <tr> <td>z0_sd</td> <td>Standard deviation of the coefficient "z0". Number with 3 decimals.</td> </tr> <tr> <td>period</td> <td>Period used to compute the rating curve under the format %Y-%m-%d where the start and the end dates are separated by ":"</td> </tr> <tr> <td>nb</td> <td>Number of overlap dates to compute the rating curve.</td> </tr> <tr> <td>Methodology</td> <td>Methodology used to compute the rating curve. The first part describes the approach used to compute the RC and the second part, separated by “_”, describes the algorithm used. To avoid any issue for the reader the spaces have been replaced by “-”. At the end 2 approaches has been used: “Overlap-approach” or “Quantile-approach” and 2 algorithms: “Bayesian-algorithm” or “Multiple-algorithms” designed for Arctic rivers experiencing frozen periods. </td> </tr> <tr> <td>Source</td> <td>In-situ data sources to compute the rating curve. If multiple sources has been used, the sources are separate by "/"</td> </tr> </tbody> </table> --------- THE DATASET IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.

opencc-by-4.0Jan 2022View details →

In situ Bragg Coherent X-ray Diffraction Imaging of Corrosion in a Co-Fe alloy microcrystal

Here we present the final crystal reconstructions and analysis scripts for the paper titled "In situ Bragg coherent X-ray diffraction imaging of corrosion in a Co–Fe alloy microcrystal" published in CrystEngComm, 24(7), 1334-1343, on 18/01/2021.  <a href="https://doi.org/10.1107/S1600577520016264">https://doi.org/10.1107/S1600577520016264</a>

opencc-by-4.0May 2022View details →

In situ smartphone radiometry of Lake Balaton, MONOCLE H2020 project

This dataset includes in situ radiometric data collected from Lake Balaton and Kis-Balaton, Hungary during July 3rd – July 5th, 2019 within the framework of Horizon 2020 MONOCLE project. The Python code used to analyse the data and produce the summary tables, and which should be used to read the data in, can be found here: <a href="https://github.com/burggraaff/smartphone-water-colour">https://github.com/burggraaff/smartphone-water-colour</a> The data are structured as follows: "Balaton_2019070x" - These folders contain the RAW and JPEG smartphone images, sorted by station, time, and smartphone. Each low-level subfolder includes the RAW and JPEG images and a CSV file with the derived radiance, R_rs, and other values. "Discarded_data" - These folders contain RAW and JPEG smartphone images that were not used, organised in the same way as "Balaton_2019070x". They are included here for completeness. Each subfolder contains a file explaining why the data were not used. "Greycard" - This folder contains the RAW smartphone images used to characterise the angular response of the grey card. Also included are the derived mean/uncertainty values in .NPY format. "BALATON_2019_STATION_LOG" - This worksheet contains the station log of the MONOCLE field campaign at Lake Balaton in 2019, during which our field data were taken. "balaton_xxx_18pct.csv" - These CSV files contain summaries of the data derived from the smartphone RAW images, including radiance, R_rs, etc. They are most easily read in using the Python code linked above. These files were generated by stacking the individual CSV files from each station/time/smartphone. The filename indicates the smartphone and data type. "balaton_Samsung_Galaxy_S8_raw_replicates.csv" - This CSV file contains the relative uncertainty (R/G/B and band ratios, in %) and absolute uncertainty (hue angle and FU) in replicate Galaxy S8 measurements, as described in the paper. "greycard_data_Maine.csv" - This CSV file contains the data used to determine the spectral response of the grey card. It includes spectral measurements of the surface irradiance on a white panel and the grey card, and the downwelling irradiance measured with a cosine collector. "README.txt" - Description of the data. "So-Rad_Balaton2019.csv" - This CSV file contains the (ir)radiance data and R_rs from the So-Rad on 3 July 2019, processed using the 3C method, as described in the paper. "wisp_Balaton_20190703_20190705_table.csv" - This CSV file contains the (ir)radiance data and R_rs from the WISP-3 on 3-5 July 2019, processed using the Mobley method, as described in the paper.

opencc-by-4.0Oct 2022View details →

diFUME In-situ meteorological dataset

Description Air temperature and vapor pressure are measured on a micrometeorological tower located near the centre of Basel (BKLI). Vapor Pressure Deficit (VPD) is then calculated based on saturated vapor pressure estimation. Direct and diffuse incoming radiation are measured with a 2-axis sun tracker (INTRA, BRUSAG) at the roof-level in an unobscured location next to the micrometeorological tower. Measured shortwave radiant flux densities (W m-2) are converted to photon flux densities (μmol m-2 s-1) within PAR (i.e. photosynthetic active radiation, solar shortwave radiation between 400 – 700 nm) using a standard conversion factor. Soil temperature (oC) and volumetric water content (m3 m-3) are continuously measured at three different locations of the study area at 10 cm below surface. The locations have different characteristics, BKLI is at a street canyon, BKFP is at a non-irrigated park location and BSMP is at an irrigated park location. Soil temperature is also continuously measured at a station outside the city center (BLER) and soil volumetric water content is estimated from lysimeter measurements from another station outside the city centre (BIN). Data format: comma separated values (csv) Time step: 60 min (aggregated) <table> <tbody> <tr> <td> Station acronym </td> <td> Geographic location </td> <td> Variables </td> <td> Units </td> <td> Sensor type - model </td> <td> Sensor height (m a.g.l.) </td> </tr> <tr> <td> BKLI </td> <td> 47.56173 °N, 7.58049 °E </td> <td> Air temperature (Tair) </td> <td> Degrees Celsius </td> <td> Thermo-HYGrometer (Thygan) </td> <td> 38 </td> </tr> <tr> <td>   </td> <td>   </td> <td> Vapor pressure (WVP) </td> <td> kPa </td> <td> Thermo-HYGrometer (Thygan) </td> <td> 38 </td> </tr> <tr> <td>   </td> <td>   </td> <td> Saturation vapor pressure (es) </td> <td> kPa </td> <td> Estimated by Thygan measurements </td> <td> 38 </td> </tr> <tr> <td>   </td> <td>   </td> <td> Vapor pressure deficit (VPD) </td> <td> kPa </td> <td> Estimated by Thygan measurements </td> <td> 38 </td> </tr> <tr> <td>   </td> <td>   </td> <td> Direct radiation </td> <td> W / m2 </td> <td> Pyrheliometer (CHP1, Kipp & Zonen) </td> <td> 21   </td> </tr> <tr> <td>   </td> <td>   </td> <td> Diffuse radiation </td> <td> W / m2 </td> <td> Pyranometer (CM21, Kipp & Zonen)   </td> <td> 21   </td> </tr> <tr> <td>   </td> <td>   </td> <td> PAR direct </td> <td> μmol / m2 / s </td> <td> Estimated by Pyrheliometer measurements </td> <td> 21 </td> </tr> <tr> <td>   </td> <td>   </td> <td> PAR diffuse </td> <td> μmol / m2 / s </td> <td> Estimated by Pyranometer measurements </td> <td> 21 </td> </tr> <tr> <td>   </td> <td>   </td> <td> PAR global </td> <td> μmol / m2 / s </td> <td> Estimated by Pyrheliometer and Pyranometer measurements </td> <td> 21 </td> </tr> <tr> <td>   </td> <td>   </td> <td> Soil temperature (Tsoil) </td> <td> Degrees Celsius </td> <td> Thermistor (CS655, Campbell Scientific Inc.) </td> <td> -0.1 </td> </tr> <tr> <td>   </td> <td>   </td> <td> Soil moisture (theta) </td> <td> m3/m3 </td> <td> Water content reflectometer (CS655, Campbell Scientific Inc.) </td> <td> -0.1 </td> </tr> <tr> <td> BKFP </td> <td> 47.56595 °N,  7.56921 °E </td> <td> Soil temperature (Tsoil) </td> <td> Degrees Celsius </td> <td> Thermistor (CS655, Campbell Scientific Inc.) </td> <td> -0.1 </td> </tr> <tr> <td>   </td> <td>   </td> <td> Soil moisture (theta) </td> <td> m3/m3 </td> <td> Water content reflectometer (CS655, Campbell Scientific Inc.) </td> <td> -0.1 </td> </tr> <tr> <td> BSMP </td> <td> 47.5529 °N,  7.57456 °E </td> <td> Soil temperature (Tsoil) </td> <td> Degrees Celsius </td> <td> Thermistor (CS655, Campbell Scientific Inc.) </td> <td> -0.1 </td> </tr> <tr> <td>   </td> <td>   </td> <td> Soil moisture (theta) </td> <td> m3/m3 </td> <td> Water content reflectometer (CS655, Campbell Scientific Inc.) </td> <td> -0.1 </td> </tr> <tr> <td> BLER </td> <td> 47.5923 °N, 7.6493 °E </td> <td> Soil temperature (Tsoil) </td> <td> Degrees Celsius </td> <td> Thermistor </td> <td> -0.1 </td> </tr> <tr> <td> BIN </td> <td> 47.5411 °N, 7.5835 °E </td> <td> Soil moisture (theta) </td> <td> m3/m3 </td> <td> Estimated by weighable lysimeter measurements </td> <td> -2 </td> </tr> </tbody> </table>