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Groundwater well chemistry data for Niwot Saddle, C1 and Martinelli, 2005 - ongoing.
This is a summary of major ion concentrations and chemistry data for groundwater samples collected at groundwater wells on the Niwot saddle, C1 site and Martinelli site.
Soil interstitial water chemistry data for Green Lakes Valley, 1987 - 1992.
Soil interstitial water samples were collected from tension and zero-tension samplers at various locations within the Green Lakes Valley in the City of Boulder Watershed. Chemical analyses were performed on the samples in order to characterize soil solution concentrations and estimate fluxes of important chemical constituents. Samples were collected at several depths from within several soil types and topographic situations in this alpine/subalpine environment.
Snow horizon chemistry data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.
Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles in order to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season. Snow samples were collected and analyzed for cations and anions at the Mountain Research Station's Arikaree (formerly Kiowa) Laboratory.
SBC LTER: Land: Stream chemistry in the Santa Barbara Coastal drainage area, 2000 -2018
This data package contains stream water chemistry measurements taken in Santa Barbara area watersheds, between 2000 and 2018. We do not plan to update this dataset after 2018 because the watershed component of the research within SBCLTER was terminated. Stream water samples were collected weekly during non-storm flows in winter, and bi-weekly during summer. During winter storms, samples were collected hourly (rising limb) or at 2-4 hour intervals (falling limb). Analytes sampled in the SBC LTER watersheds included dissolved nitrogen (nitrate, ammonium, total dissolved nitrogen); soluble reactive phosphorus (SRP); particulate organic carbon, nitrogen and phosphorus; total suspended sediments; and conductivity. There were two tables in this dataset. Samples from "registered stations" (see geographic coverage) were in the first data table (see table name). Many other samples had been collected ad hoc, or as "stations of opportunity". These had been collected in a second table, designated "non-registered". The station codes for these samples may have been reused, and were not recorded in metadata (but station codes can be found in data).
SBC LTER: Ocean: Time-series: Mid-water SeaFET pH and CO2 system chemistry with surface and bottom Dissolved Oxygen at Arroyo Quemado Reef(ARQ), 2012-2017
Calibrated pH (Total scale, SeaFET sensor) an disoolved oxygen (miniDOT)data were collected from Arroyo Quemado Reef in the Santa Barbara Channel (site ID: ARQ). pH data are accompanied by in situ temperature and associated carbonate chemistry parameters. The SeaFET instrument is located about 4 meters from the surface, with other moored instruments. Associated carbonate chemistry parameters were calculated with the CO2calc programs from USGS, and include: partial pressure and fugosity of CO2, concentrations of bicarbonate, carbonate and hyrdroxide ion, Omega (saturation state) of calcite and aragonite. Dissolved oxygen sensors (miniDOT, PME) were added in 2014, and are mounted near the ocean surface and near the seafloor, and also report temperature. All data have been interpolated to a 20 minute time interval for compatibility with other SBC LTER moored instrument data. Data coverage is 2012-07-30 to 2017-03-10.All data from this site have been concatenated with the pH data from the other sites and merged into one data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sbc&identifier=6005
SBC LTER: Ocean: Time-series: Mid-water SeaFET pH and CO2 system chemistry with surface and bottom Dissolved Oxygen at Mohawk Reef(MKO), 2012 - 2017
Calibrated pH (Total scale, SeaFET sensor) an disoolved oxygen (miniDOT)data were collected from Mohawk Reef in the Santa Barbara Channel (site ID: MKO). pH data are accompanied by in situ temperature and associated carbonate chemistry parameters. The SeaFET instrument is located about 4 meters from the surface, with other moored instruments. Associated carbonate chemistry parameters were calculated with the CO2calc programs from USGS, and include: partial pressure and fugosity of CO2, concentrations of bicarbonate, carbonate and hyrdroxide ion, Omega (saturation state) of calcite and aragonite. Dissolved oxygen sensors (miniDOT, PME) were added in 2014, and are mounted near the ocean surface and near the seafloor, and also report temperature. All data have been interpolated to a 20 minute time interval for compatibility with other SBC LTER moored instrument data. Data coverage is 2012-01-11 to 2017-12-19.The update of this dataset was terminated in 2019. All data from this site have been concatenated with the pH data from the other sites and merged into one data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sbc&identifier=6005
SBC LTER: Ocean: Time-series: Mid-water SeaFET pH and CO2 system chemistry with surface and bottom Dissolved Oxygen at Santa Barbara Harbor/Stearns Wharf(SBH), 2012-2017
Calibrated pH (Total scale, SeaFET sensor) an disoolved oxygen (miniDOT)data were collected from Santa Barbara Harbor/Stearns Wharf in the Santa Barbara Channel (site ID: SBH). pH data are accompanied by in situ temperature and associated carbonate chemistry parameters. The SeaFET instrument is located about 4 meters from the surface, with other moored instruments. Associated carbonate chemistry parameters were calculated with the CO2calc programs from USGS, and include: partial pressure and fugosity of CO2, concentrations of bicarbonate, carbonate and hyrdroxide ion, Omega (saturation state) of calcite and aragonite. Dissolved oxygen sensors (miniDOT, PME) were added in 2014, and are mounted near the ocean surface and near the seafloor, and also report temperature. All data have been interpolated to a 20 minute time interval for compatibility with other SBC LTER moored instrument data. Data coverage is 2012-09-15 to 2016-09-14. The update of this dataset was terminated in 2019. All data from this site have been concatenated with the pH data from the other sites and merged into one data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sbc&identifier=6005
Vector-LabPics dataset for images of materials in vessels in the chemistry lab
<p><strong>LabPics 2: A newer and larger a version (But harder to use) can be found here: <a href="../record/4736111"> https://zenodo.org/record/4736111</a></strong></p> <p>The Vector-LabPics V1 dataset contains 2187 images of chemical experiments with materials within mostly transparent vessels in various laboratory settings and in everyday conditions such as beverage handling. Each image in the dataset has an annotation of the region of each material phase and its type. In addition, the region of each vessel and its labels, parts, and corks are also marked.</p> <p>For more details see:</p> <p><a href="https://pubs.acs.org/doi/10.1021/acscentsci.0c00460">https://pubs.acs.org/doi/10.1021/acscentsci.0c00460</a></p> <p> </p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@<a href="https://twitter.com/SuperScienceGrl?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor">SuperScienceGrl</a> Twitter), <a href="https://cen.acs.org/sections/chemistry-in-pictures.html">Chemical and Engineering Science chemistry in pictures</a>, YouTube channels dedicated to chemistry experiments: <a href="https://www.youtube.com/channel/UCIgKGGJkt1MrNmhq3vRibYA">NurdRage</a>, <a href="https://www.youtube.com/user/TheRedNile">NileRed</a>, <a href="https://www.youtube.com/user/DougsLab">DougsLab</a>, <a href="https://www.youtube.com/channel/UCsJHe4uMbquncMpe1PiLa2A/videos">ChemPlayer</a>, and <a href="https://www.youtube.com/user/koen2all">Koen2All</a>. Additional sources for images include Instagram channels <a href="https://www.instagram.com/chemistrylover_/">chemistrylover_</a>(Joana Kulizic),<a href="https://www.instagram.com/chemistry.shz/?hl=en">Chemistry.shz</a> (Dr.Shakerizadeh-shirazi), <a href="https://www.instagram.com/ministryofchemistry/?hl=en">MinistryOfChemistry</a>, <a href="https://www.instagram.com/chemistryandme/?hl=en">Chemistry And Me</a>,<a href="https://www.instagram.com/explore/tags/chemistrylifestyle/?hl=en"> ChemistryLifeStyle</a>, <a href="https://www.instagram.com/explore/tags/vacuumdistillation/?hl=en">vacuum_distillation</a>, and <a href="https://docs.google.com/document/d/16QkXuIesB80gDONX-YVNFP4C6Mmj-14ZDrTmLrnNfms/edit#organic_chemistry_lab">Organic_Chemistry_Lab</a>. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support.</p> <p>Images of the dataset were taken from images and videos shared on Youtube and Instagram, Twitter and Tumblr channels and other contributors; we do not have copyright for the images. Any commercial or none academic use of the images depends on acquiring permission from the owner of the images. Note that the name of each image contains the image source. For any non-academic use of the images, please contact their sources for permission. We like to thank the following channels for sharing the images used in this dataset.</p> <p>We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@<a href="https://twitter.com/SuperScienceGrl?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor">SuperScienceGrl</a> Twitter), <a href="https://cen.acs.org/sections/chemistry-in-pictures.html">Chemical and Engineering Science chemistry in pictures</a>, YouTube channels dedicated to chemistry experiments: <a href="https://www.youtube.com/channel/UCIgKGGJkt1MrNmhq3vRibYA">NurdRage</a>, <a href="https://www.youtube.com/user/TheRedNile">NileRed</a>, <a href="https://www.youtube.com/user/DougsLab">DougsLab</a>, <a href="https://www.youtube.com/channel/UCsJHe4uMbquncMpe1PiLa2A/videos">ChemPlayer</a>, and <a href="https://www.youtube.com/user/koen2all">Koen2All</a>. Additional sources for images include Instagram channels <a href="https://www.instagram.com/chemistrylover_/">chemistrylover_</a>(Joana Kulizic),<a href="https://www.instagram.com/chemistry.shz/?hl=en">Chemistry.shz</a> (Dr.Shakerizadeh-shirazi), <a href="https://www.instagram.com/ministryofchemistry/?hl=en">MinistryOfChemistry</a>, <a href="https://www.instagram.com/chemistryandme/?hl=en">Chemistry And Me</a>,<a href="https://www.instagram.com/explore/tags/chemistrylifestyle/?hl=en"> ChemistryLifeStyle</a>, <a href="https://www.instagram.com/explore/tags/vacuumdistillation/?hl=en">vacuum_distillation</a>, and <a href="https://docs.google.com/document/d/16QkXuIesB80gDONX-YVNFP4C6Mmj-14ZDrTmLrnNfms/edit#organic_chemistry_lab">Organic_Chemistry_Lab</a>. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support. Images from C&EN's Chemistry in Pictures (<a href="http://cen.chempics.org/">cen.chempics.org</a>) used here with permission from C&EN and ACS. All rights reserved. Please contact cenchempics@acs.org to inquire about republishing.</p>
Supporting data for "Influence of an ionic comonomer on polymerization-induced self-assembly of diblock copolymers in non-polar media" (Polymer Chemistry, 2020, doi:10.1039/D0PY00101E)
<p>SAXS data [Q / (1/Å), I(Q) / Arb. unit, Uncertainty I(Q) / Arb. unit] provided as *.txt files</p> <p>DLS histograms [Diameter / nm, Proportion / %] provided as *.csv files</p>
Fig. 2 in Discrimination of habitat use between two sympatric species of mullets, Mugil curema and Mugil liza (Mugiliformes: Mugilidae) in the rio Tramandaí Estuary, determined by otolith chemistry
Fig. 2. Means and standard deviations (SD) of (a) Sr86:Ca43 (mmol.mol-1); and (b) Ba137:Ca43 ratio (µmol.mol-1) in otoliths of the inner 20 measurements (core) and the outer 20 measurements (edge) of Mugil curema and M. liza caught in the Tramandaí River Estuary, Brazil. Different letters within a spruce stand denote significant differences between species (Mann-Whitney U test, p<0.05).
Fig. 4 in Discrimination of habitat use between two sympatric species of mullets, Mugil curema and Mugil liza (Mugiliformes: Mugilidae) in the rio Tramandaí Estuary, determined by otolith chemistry
Fig. 4. Otolith transects of Mugil liza measured by LA-ICP-MS from the core to the edge. Ba137:Ca43 (line) and Sr86:Ca43 (dashed line). The identification code and total length (mm) of each fish are indicated on the graph.
Fig. 3 in Discrimination of habitat use between two sympatric species of mullets, Mugil curema and Mugil liza (Mugiliformes: Mugilidae) in the rio Tramandaí Estuary, determined by otolith chemistry
Fig. 3. Otolithtransectsof Mugilcurema measuredbyLA-ICP-MSfromthecoretotheedge. 43 (line) 86 43 (dashedline). Theidentificationcodeand totallength (mm) ofeachfishareindicatedonthegraph.
Molecular dynamics trajectories for "Structure and chemistry of graphene oxide in liquid water from first principles"
<p>This dataset contains molecular dynamics (MD) trajectories from the paper <a href="https://doi.org/10.1038/s41467-020-15381-y">“Structure and chemistry of graphene oxide in liquid water from first principles”, F. Mouhat, F.-X. Coudert and M.-L. Bocquet, <em>Nature Commun.</em>, <strong>2020</strong>, <em>11</em>, 1566, 10.1038/s41467-020-15381-y</a></p> <p> </p>
Geographical distribution of co-authors of Nobel laureates 1994-2018 in Physics, Chemistry and Physiology or Medicine
<p>Geographical distribution of co-authors of Nobel laureates 1994-2018 in Physics, Chemistry and Physiology or Medicine. Appendix to the article «Quantitative analysis of the co-publications of Ukrainian scientists with the Nobel laureates 1994-2018 in Science».</p>
Nectar chemistry is not only a plant's affair: floral visitors affect nectar sugar and amino acid composition
<p>This dataset contains data used in the analyses performed in the article entitled "Nectar chemistry is not only a plant’s affair: floral visitors affect nectar sugar and amino acid composition". The Excel file contains three sheets. 'Raw data' contains concentration of sugars, amino acids, pollen grains and yeast cells measured in several flowers and plants of <em>Gentiana lutea</em> subsp. <em>symphyandra</em>, belonging to different experimental treatments. 'Amino acid diversity' contains the concentration of specific protein and non-protein amino acids found in a subset of the above mentioned flowers. 'Pollen suspension test' contains the concentration of the same amino acids found in nectar after suspension of pollen of <em>G. lutea</em> at different time intervals (0, 1, 4, and 24 hours).</p>
Chemistry of the Surface of Mars
<p>Concentration of 11 major elements at the surface of Mars (SiO2, TiO2, Al2O3, FeO,MnO,MgO, CaO, Na2O, K2O, P2O5 and Cr23O3) following the approach presented in the following article :</p> <p>Baratoux, D., H. Samuel, C. Michaut, M. J. Toplis, M. Monnereau,<br /> M. Wieczorek, R. Garcia, and<br /> K. Kurita (2014), Petrological constraints on the density of the Martian crust, <em>J. Geophys. Res. Planets, 119, </em>1707-1727, doi:10.1002/2014JE004642. </p>
Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS - Dataset
<p>These files are the dataset for the following paper "Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS". Authors: Prihadi Sumintadireja<sup>1</sup>, Dasapta Erwin Irawan<sup>1</sup>, Yuano Rezky<sup>2</sup>, Prana Ugiana Gio<sup>3, </sup>Anggita Agustin<sup>1</sup></p>
Products and Models for "Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b"
<p>Hot Jupiters are among the best-studied exoplanets, but it is still poorly understood how their chemical composition and cloud properties vary with longitude. Theoretical models predict that clouds may condense on the nightside and that molecular abundances can be driven out of equilibrium by zonal winds. Here we report a phase-resolved emission spectrum of the hot Jupiter WASP-43b measured from 5-12 μm with JWST's Mid-Infrared Instrument (MIRI). The spectra reveal a large day-night temperature contrast (with average brightness temperatures of 1524±35 and 863±23 Kelvin, respectively) and evidence for water absorption at all orbital phases. Comparisons with three-dimensional atmospheric models show that both the phase curve shape and emission spectra strongly suggest the presence of nightside clouds which become optically thick to thermal emission at pressures greater than ~100 mbar. The dayside is consistent with a cloudless atmosphere above the mid-infrared photosphere. Contrary to expectations from equilibrium chemistry but consistent with disequilibrium kinetics models, methane is not detected on the nightside (2σ upper limit of 1-6 parts per million, depending on model assumptions).</p>
Models for rapid estimates of leaf litter chemistry using reflectance spectroscopy
<p>Measuring the chemical traits of leaf litter is important for understanding plants' roles in nutrient cycles, including through nutrient resorption and litter decomposition, but conventional leaf trait measurements are often destructive and labor-intensive. Here, we develop and evaluate the performance of partial least-squares regression (PLSR) models that use reflectance spectra of intact or ground leaves to estimate leaf litter traits, including carbon and nitrogen concentration, carbon fractions, and leaf mass per area (LMA). Our analyses included more than 300 samples of senesced foliage from 11 species of temperate trees, including needleleaf and broadleaf species. Across all samples, we could predict each trait with moderate-to-high accuracy from both intact-leaf litter spectra (validation <em>R<sup>2</sup></em> = 0.543-0.941; %RMSE = 7.49-18.5) and ground-leaf litter spectra (validation <em>R<sup>2</sup></em> = 0.491-0.946; %RMSE = 7.00-19.5). Notably, intact-leaf spectra yielded better predictions of LMA. Our results support the feasibility of building models to estimate multiple chemical traits from leaf litter of a range of species. In particular, the success of intact-leaf spectral models allows non-destructive trait estimation in a matter of seconds, which could enable researchers to measure the same leaves over time in studies of nutrient resorption.</p>
Data for Hays et al Nature Chemistry 2022 NCHEM-21050864A
<p>Source data for Hays et al Nature Chem 2022<br> NCHEM-21050864A</p> <p>The source data relating to Figures 1-3 of the main article as well as source data for the Figures in the Supplementary Information are presented in separate directories, each with their own explicative "readme.txt" or "readme.doc". The main experimental and theoretical data are present in the "Figure 3" directory and its subdirectories.</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.