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.
253
datasets available to search
ShareScore release 0.7.1
Dataset results
253 results for “alkalinity”
Dissolved Inorgainic Carbon concentration and Total Alkalinity from surface water samples collected in the GCE LTER domain near Sapelo Island, Georgia between May 2014 and December 2022.
Surface water samples were collected from GCE LTER sampling stations between May 2014 and December 2022. Monthly samples were collected from GCE 6 (high and low tide) and GCE 7 (high tide). Quarterly samples were collected from the remaining GCE sites, 4 sites along the Duplin River, and AL-02 ( the Altamaha River oceanic end-member station). These samples were analyzed for dissolved inorganic carbon (DIC) and total alkalinity (TA).
Little Rock Lake Experiment at North Temperate Lakes LTER: pH and Alkalinity 1983 - 2000
The Little Rock Acidification Experiment was a joint project involving the USEPA (Duluth Lab), University of Minnesota-Twin Cities, University of Wisconsin-Superior, University of Wisconsin-Madison, and the Wisconsin Department of Natural Resources. Little Rock Lake is a bi-lobed lake in Vilas County, Wisconsin, USA. In 1983 the lake was divided in half by an impermeable curtain and from 1984-1989 the northern basin of the lake was acidified with sulfuric acid in three two-year stages. The target pHs for 1984-5, 1986-7, and 1988-9 were 5.7, 5.2, and 4.7, respectively. Starting in 1990 the lake was allowed to recover naturally with the curtain still in place. Data were collected through 2000. The main objective was to understand the population, community, and ecosystem responses to whole-lake acidification. Funding for this project was provided by the USEPA and NSF. pH and alkalinity of the treatment and reference basins of Little Rock Lake are measured at one station in the deepest part of each basin at the top and bottom of the epilimnion, mid-thermocline, and top, middle, and bottom of the hypolimnion. During the course of the study, three different types of electrodes were used to measure pH. Sampling Frequency: varies - Number of sites: 2
North Temperate Lakes LTER: Alkaline Phosphatase Activity in Lake Mendota 2000 - 2001
Parameters characterizing the alkaline phosphatase activity of Lake Mendota. Samples were collected at one station in the deepest part of each lake from an integrated sample of the epilimnion. Sampling Frequency: fortnightly during ice-free season - every 6 weeks during ice-covered season Number of sites: 1
Dataset of "Impact of Carbon Corrosion and Denitrogenation on the Deactivation of Fe-N-C Catalysts in Alkaline Media"
<p>In this work, we use a gas diffusion electrode half-cell coupled with inductively coupled plasma mass spectrometry (GDE-ICP-MS) to quantify the Fe dissolution rates in the potential range between 0.93 and 1.5 VRHE. It is shown that Fe dissolution accelerates with increased anodic potential and temperature while it is independent on the presence/absence of O2. The onset potential of Fe dissolution at room temperature agrees with the reported onset potentials of carbon corrosion and denitrogenation, C and N being oxidized to gaseous COx and NOx species, respectively. This correlation supports that the electrochemical oxidation of the N-C matrix triggers the observed catalyst demetallation in these conditions. Using a set of ex situ physicochemical characterization techniques, including spectroscopy and microscopy, the various degrees of degradation under three sets of experimental conditions of interest (O2-RT, O2-HT, and Ar-HT, where RT = 22℃ and HT = 62℃) are rationalized. Combining the GDE-ICP-MS technique and post-mortem analyses, this work provides novel insights into the degradation pathways of various Fe, N, and C species during start-stop events, which may inspire the next generation of durable Fe-N-C catalysts for anion exchange membrane fuel cells.</p>
Dataset of "High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH"
<p>Novel simple and efficient method for synthesis of high entropy sulfides of iron group metals (Cr, Fe, Ni, Co, Zn) is describedThe created material was investigated as a catalyst for electrochemical water splitting in acidic, neutral and alkaline pH. Investigation of the electrocatalytic activity of the synthesized material shows its high efficiency for overall water splitting in alkaline media. </p>
Dataset for Activation of Glassy Carbon Surfaces by Alkaline Anodization Enhances Dopamine Adsorption and Electron-Transfer Kinetics
<p>This dataset provides the raw data to the manuscript</p><p><strong>"Activation of Glassy Carbon Surfaces by Alkaline Anodization Enhances Dopamine Adsorption and Electron-Transfer Kinetics"</strong></p><p>published in ChemElectroChem</p><p>Specifically, the following measurements are provided:</p><ul><li>Scanning electrochemical cell microscopy (SECCM). Cyclic voltammetry (E, i) data for each location across the sample. 5 cycles.</li><li>Chronoamperometry (i, t) for the anodization process.</li><li>Atomic Force Microscopy (AFM) topography.</li><li>Raman microscopy</li><li>X-ray photoelectron spectroscopy (XPS)</li><li>Scanning electron microscopy (SEM)</li></ul>
Surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios.
<p>Surface maps and basin mean/total of annual mean surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios and for underlying the baseline projection (RCP4.5).</p> <p>Details on simulations and alkalinisation strategies are given in the reference article below.</p> <p> </p> <p>Reference:</p> <p>Butenschön, M., Lovato, T., Masina, S., Caserini, S., Grosso, M., 2021. Alkalinization Scenarios in the Mediterranean Sea for Efficient Removal of Atmospheric CO2 and the Mitigation of Ocean Acidification. Front. Clim. 3. <a href="https://doi.org/10.3389/fclim.2021.614537">https://doi.org/10.3389/fclim.2021.614537</a></p>
Pitfalls in Sample Preparation of Metalloproteins for Low-Temperature EPR: The Example of Alkaline Myoglobin
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements (EPR and UV-vis), computer simulations and data analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DTA</strong>, <strong>m</strong>, <strong>mat</strong>, <strong>ods</strong>,<strong> tif</strong></li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements have filename extensions <strong>DSC</strong> and <strong>DTA</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong></li> <li>UV-vis spectroscopic measurements have filename extensions <strong>ods</strong></li> <li>Processed data ready for simulation/figure preparation have filename extension <strong>mat </strong>(both for EPR and UV-vis)</li> <li>High-quality figures published in main text and supplementary are provided as <strong>tif</strong> files</li> </ul> </li> <li>CW-EPR measurements were generated with a Bruker ELEXSYS E580 X-band spectrometer equipped either with an Oxford CF935 continuous flow cryostat and a Bruker ER4118 SPT-N1 resonator or with an Oxford ESR 900 continuous-flow cryostat and a Bruker ER 4122 SHQ resonator.</li> <li>Pulse EPR experiments were performed with an Oxford CF935 continuous flow cryostat and a Bruker ER4118 SPT-N1 resonator.</li> <li>Simulations of EPR spectra and fitting were performed with the Easyspin software (v. 6.0.0-dev.26) implemented in Matlab (MathWorks, R2020b)</li> <li><strong>The dataset</strong>Files with extension <strong>m</strong> normally recall files with extension <strong>mat</strong> which should be stored in the same working folder <ul> <li>Files in <strong>PARACAT_WP3_20210929_01_CW</strong> folder include subfolders organised by topic: <ul> <li>“Alternative cryoprotectants” contains: ready-to-plot / ready-to-simulate data in <strong>mat</strong> format; simulation scripts in <strong>m</strong> format.</li> <li>“Glycerol effects - different buffers” contains: ready-to-plot / ready-to-simulate data in <strong>mat</strong> format; simulation scripts in <strong>m</strong> format.</li> </ul> </li> <li>Files in <strong>PARACAT_WP3_20210929_02_PULSE</strong> folder include pulse EPR spectroscopic measurements; original data are in <strong>DSC</strong> and <strong>DTA</strong> formats; processed data and fitting are in <strong>m</strong> format. Files in <strong>m</strong> format recall original data files, therefore they should be stored in the same working folder.</li> <li>Files in <strong>PARACAT_WP3_20210929_03_UV-VIS</strong> folder include UV-vis spectroscopic measurements; original and basic processed data are in <strong>ods</strong> format; ready-to-plot data are in <strong>mat</strong> format; scripts for figure preparation are in <strong>m</strong> format.</li> <li>Files in <strong>PARACAT_WP3_20210929_04_FIGURES</strong> folder include high-quality figures published in main text and supplementary, provided as <strong>tif</strong> files</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Information on</strong>: <ul> <li><strong>Specialized abbreviations:</strong> <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong><em>T<sub>m</sub></em></strong> or <strong><em>T<sub>2</sub></em></strong>– phase memory time, <strong>CAPS</strong> - N-cyclohexyl-3-aminopropanesulfonic acid, <strong>CHES</strong> - N-Cyclohexyl-2-aminoethanesulfonic acid</li> <li><strong>Definitions of variables:</strong> magnetic field (<strong>mT</strong> - milliTesla), pH (pH units), UV-vis absorbance intensity (<strong>A.U.</strong> – arbitrary units), EPR intensity (<strong>A.U.</strong> – arbitrary units), Hahn Echo Intensity (<strong>A.U.</strong> – arbitrary units), <em>g</em>-values (adimensional)</li> <li><strong>Units of measurements:</strong> <ul> <li>Concentration: <strong>mM</strong> (millimolar), <strong>µM</strong> (micromolar), <strong>% v/v</strong> (percentage volume/volume)</li> <li>Volume: <strong>mL</strong> (milliliters), <strong>µL</strong> (microliters)</li> <li>Wavelength: <strong>nm</strong> (nanometers)</li> <li>Temperature: <strong>°C</strong> (Celsius degrees), <strong>K</strong> (Kelvin degrees)</li> <li>Time: <strong>ns</strong> (nanoseconds)</li> <li>Frequency: <strong>GHz</strong> (gigahertz)</li> <li>Power: <strong>mW</strong> (milliwatt)</li> </ul> </li> </ul> </li> </ul>
Datasets and codes for the peer review article "Human and natural impacts on the U.S. freshwater salinization and alkalinization: A machine learning approach"
<p>Ongoing salinization and alkalinization in U.S. rivers have been attributed to inputs of road salt and effects of human-accelerated weathering in previous studies. Salinization poses a severe threat to human and ecosystem health, while human derived alkalinization implies increasing uncertainty in the dynamics of terrestrial sequestration of atmospheric carbon dioxide. A mechanistic understanding of whether and how human activities accelerate weathering and contribute to the geochemical changes in U.S. rivers is lacking. To address this uncertainty, we compiled dissolved sodium (salinity proxy) and alkalinity values along with 32 watershed properties ranging from hydrology, climate, geomorphology, geology, soil chemistry, land use, and land cover for 226 river monitoring sites across the coterminous U.S. Using these data, we built two machine-learning models to predict monthly-aggregated sodium and alkalinity fluxes at these sites. The sodium-prediction model detected human activities (represented by population density and impervious surface area) as major contributors to the salinity of U.S. rivers. In contrast, the alkalinity-prediction model identified natural processes as predominantly contributing to variation in riverine alkalinity flux, including runoff, carbonate sediment or siliciclastic sediment, soil pH and soil moisture. Unlike prior studies, our analysis suggests that the alkalinization in U.S. rivers is largely governed by local climatic and hydrogeological conditions.</p>
Laboratory experiments testing pH, alkalinity and particle impacts on Mn removal
Laboratory experiments were conducted to investigate impacts of pH, alkalinity, and presence of particles on Mn removal in freshwater. The dataset includes monitoring data from: 1) a 14-day experiment in Mn(II) solutions in nanopure water, 2) a 24-hour experiment in Mn(II) solutions in nanopure water, and 3) a 10-day experiment in water from two drinking water reservoirs. The 14-day pH and alkalinity laboratory experiment was conducted starting October 30, 2022 and included sample collection and pH monitoring on day 0, 1, 4, 7, 10, and 14. The 24-hour pH and alkalinity laboratory experiment was conducted starting February 20, 2023 and included sample collection and pH monitoring at 0, 1, 2, 6, 12, and 24 hours. The reservoir water laboratory experiment was conducted starting March 22, 2023 and included sample collection and pH monitoring on day 0, 1, 4, 7, and 10. This experiment tested Mn removal in water collected from the lower water column of Falling Creek Reservoir (FCR) and Carvins Cove Reservoir (CCR), located in Vinton, Virginia, USA and Roanoke, Virginia, USA respectively. Both reservoirs are owned and operated by the Western Virginia Water Authority and are managed as drinking-water sources for the city of Roanoke, VA, USA.
Water year 2019 monitoring of the inorganic carbon system (pH and total alkalinity) in the Upper Clark Fork River (Montana, USA)
These data were collected by the University of Montana and Montana State University to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) program. The original analytical intent for these data was to assess the response of river inorganic carbon system to the floodplain restoration. Data are lab analyses of pH and total alkalinity in samples of well-mixed river thalweg water. Data are from the 2019 water year (1 Oct 2018 to 30 Sep 2019). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at 13 project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA. Lab analyses include high-precision colorimetric pH analysis and gran titration of alkalinity.
Water year 2017-18 monitoring of the inorganic carbon system (pH and total alkalinity) in the Upper Clark Fork River (Montana, USA)
These data were collected by the University of Montana and Montana State University to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) program. The original analytical intent for these data was to assess the response of river inorganic carbon system to the floodplain restoration. Data are lab analyses of pH and total alkalinity in samples of well-mixed river thalweg water. Data are from the 2017 and 2018 water years (1 Oct 2016 to 30 Sep 2018). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA. Lab analyses include high-precision colorimetric pH analysis and gran titration of alkalinity.
Water year 2020 monitoring of the inorganic carbon system (pH and total alkalinity) in the Upper Clark Fork River (Montana, USA)
These data were collected by the University of Montana and Montana State University to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) program. The original analytical intent for these data was to assess the response of river inorganic carbon system to the floodplain restoration. Data are lab analyses of pH and total alkalinity in samples of well-mixed river thalweg water. Data are from the 2020 water year (1 Oct 2019 to 30 Sep 2020). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA. Lab analyses include high-precision colorimetric pH analysis and gran titration of alkalinity.
Surface water DIC, total alkalinity, and pH for the September 2002 through December 2004 Georgia Coastal Ecosystems LTER oceanographic surveys
Surface water samples for total dissolved inorganic carbon (DIC), total alkalinity (TAlk), and pH were collected from the Altamaha River, Doboy Sound, Sapelo River and the Duplin River (anchor station near Marsh Landing) during the Georgia Coastal Ecosystems LTER oceanographic surveys from September 2002 through December 2004. DIC was measured using a custom automated DIC analyzer. Total alkalinity was determined by Gran titration. pH of surface water at stations was measured on board using a glass electrode. This study was part of the GCE oceanographic monitoring program, and will be repeated periodically.
Surface water DIC, total alkalinity, and pH for the March 2001 Georgia Coastal Ecosystems LTER oceanographic survey
Surface water samples for total dissolved inorganic carbon (DIC), total alkalinity (TAlk), and pH were collected from the Altamaha River, Doboy Sound, Sapelo River and the Duplin River (anchor station near Marsh Landing) during March 19-21, 2001. DIC was measured using a custom automated DIC analyzer. Total alkalinity was determined by Gran titration. pH of surface water at stations was measured on board using a glass electrode. This study was part of the GCE oceanographic monitoring program, and will be repeated periodically.
Surface water DIC, total alkalinity, and pH for the June 2001 Georgia Coastal Ecosystems LTER oceanographic survey
Surface water samples for total dissolved inorganic carbon (DIC), total alkalinity (TAlk), and pH were collected from the Altamaha River, Doboy Sound, Sapelo River and the Duplin River (anchor station near Marsh Landing) during June 26-28, 2001. DIC was measured using a custom automated DIC analyzer. Total alkalinity was determined by Gran titration. pH of surface water at stations was measured on board using a glass electrode. This study was part of the GCE oceanographic monitoring program, and will be repeated periodically.
Surface water DIC, total alkalinity, and pH for the October 2001 Georgia Coastal Ecosystems LTER oceanographic survey
Surface water samples for total dissolved inorganic carbon (DIC), total alkalinity (TAlk), and pH were collected from the Altamaha River, Doboy Sound, Sapelo River and the Duplin River (anchor station near Marsh Landing) during October 11-14, 2001. DIC was measured using a custom automated DIC analyzer. Total alkalinity was determined by Gran titration. pH of surface water at stations was measured on board using a glass electrode. This study was part of the GCE oceanographic monitoring program, and will be repeated periodically.
Surface water DIC, total alkalinity, and pH for the November 2001 Georgia Coastal Ecosystems LTER oceanographic survey
Surface water samples for total dissolved inorganic carbon (DIC), total alkalinity (TAlk), and pH were collected from the Altamaha River, Doboy Sound, Sapelo River and the Duplin River (anchor station near Marsh Landing) during November 26-29, 2001. DIC was measured using a custom automated DIC analyzer. Total alkalinity was determined by Gran titration. pH of surface water at stations was measured on board using a glass electrode. This study was part of the GCE oceanographic monitoring program, and will be repeated periodically.
Dissolved inorganic carbon and alkalinity of discrete water column samples, collected aboard Palmer LTER annual cruises of the Western Antarctic Peninsula, 1993 - 2019.
Dissolved inorganic carbon (DIC or total CO2 – TCO2) and total alkalinity (TALK) are two of the four parameters defining the carbonate system in seawater. DIC is composed of dissolved CO2 gas, which dissociates into carbonate, 〖CO〗_3^(2-), and bicarbonate, 〖HCO〗_3^- in seawater. About 90% of the DIC is in the form of bicarbonate, ~10% is carbonate, and ~1% is CO2. The dissociation of CO2 dissolved in seawater into carbonate and bicarbonate gives seawater its great capacity to absorb CO2 from the atmosphere. Alkalinity (also known as “buffer capacity”) is a measure of the capacity of water to neutralize acids. Alkalinity is a complex product of the concentrations of (in decreasing order of importance) the DIC components, borate, hydroxide, phosphate, silicate and dissolved ammonium. Ocean biology regulates the alkalinity through the uptake and release of the DIC and the macronutrients N, P and Si. We measure surface DIC and ALK to understand the exchange of CO2 across the air-sea interface in our study area. With DIC and dissolved CO2, we can also derive estimates of ocean pH and thus monitor the extent and evolution of ocean acidification. Analytical methods and QC are presented under the Methods and Protocols tab.
IODP Expedition 391 Alkalinity and pH
Alkalinity was determined by Gran titration with an autotitrator (Metrohm 794 basic Titrino) using 0.1 M HCl at 20 degrees C. Report includes alkalinity, correction factor (if applicable), and pH.
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.