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5,145 results for “CO₂”
X-ray diffraction images for DPF3 tandem PHD fingers co-crystallized with an acetylated histone-derived peptide
<p>This submission includes a tar archive of bzipped diffraction images recorded with the ADSC Q315r detector at the Advanced Photon Source of Argonne National Laboratory, Structural Biology Center beam line 19-ID. Relevant meta data can be found in the headers of those diffraction images.</p> <p>Please find below the content of an input file XDS.INP for the program XDS (Kabsch, 2010), which may be used for data reduction. The "NAME_TEMPLATE_OF_DATA_FRAMES=" item inside XDS.INP may need to be edited to point to the location of the downloaded and untarred images.</p> <p>!!! Paste lines below in to a file named XDS.INP</p> <p>DETECTOR=ADSC MINIMUM_VALID_PIXEL_VALUE=1 OVERLOAD= 65000<br /> DIRECTION_OF_DETECTOR_X-AXIS= 1.0 0.0 0.0<br /> DIRECTION_OF_DETECTOR_Y-AXIS= 0.0 1.0 0.0<br /> TRUSTED_REGION=0.0 1.05<br /> MAXIMUM_NUMBER_OF_JOBS=10<br /> ORGX= 1582.82 ORGY= 1485.54<br /> DETECTOR_DISTANCE= 150<br /> ROTATION_AXIS= -1.0 0.0 0.0<br /> OSCILLATION_RANGE=1<br /> X-RAY_WAVELENGTH= 1.2821511<br /> INCIDENT_BEAM_DIRECTION=0.0 0.0 1.0<br /> FRACTION_OF_POLARIZATION=0.90<br /> POLARIZATION_PLANE_NORMAL= 0.0 1.0 0.0<br /> SPACE_GROUP_NUMBER=20<br /> UNIT_CELL_CONSTANTS= 100.030 121.697 56.554 90.000 90.000 90.000<br /> DATA_RANGE=1 180<br /> BACKGROUND_RANGE=1 6<br /> SPOT_RANGE=1 3<br /> SPOT_RANGE=31 33<br /> MAX_CELL_AXIS_ERROR=0.03<br /> MAX_CELL_ANGLE_ERROR=2.0<br /> TEST_RESOLUTION_RANGE=8.0 3.8<br /> MIN_RFL_Rmeas= 50<br /> MAX_FAC_Rmeas=2.0<br /> VALUE_RANGE_FOR_TRUSTED_DETECTOR_PIXELS= 6000 30000<br /> INCLUDE_RESOLUTION_RANGE=50.0 1.7<br /> FRIEDEL'S_LAW= FALSE<br /> STARTING_ANGLE= -100 STARTING_FRAME=1<br /> NAME_TEMPLATE_OF_DATA_FRAMES= ../x247398/t1.0???.img</p> <p>!!! End of XDS.INP</p> <p> </p> <p> </p>
Sentinel-1 T156 co-seismic interferogram of Kumamoto EQ
<p>Sentinel-1ascending co-seismic interferogram (wrapped) of Kumamoto Earthquake.</p> <p>Master acquisition time: 2016-04-08</p> <p>Slave acquisition time: 2016-04-20 </p> <p>Track: 156</p> <p>Perpendicular Baseline: 68m</p> <p>Multilooking: 10 Azimuth, 2 Range</p>
Sentinel-1 T163 co-seismic interferogram of Kumamoto EQ
<p>Sentinel-1descending co-seismic interferogram (wrapped) of Kumamoto Earthquake.</p> <p>Master acquisition time: 2016-03-27</p> <p>Slave acquisition time: 2016-04-20 </p> <p>Track: 163</p> <p>Perpendicular Baseline: 4m</p> <p>Multilooking: 10 Azimuth, 2 Range</p>
ALOS2 T197 co-seismic interferogram of Amatrice earthquake (Italy)
<p>ALOS2 T197 co-seismic interferogram (wrapped) of Amatrice earthquake (Italy). <br /> Data Type: wrapped Interferogram (radians)<br /> Observation interval: 09092015AL2_24082016AL2<br /> Sensor: ALOS2<br /> Wavelength: 23,6 [cm]<br /> Look Angle: 36.6 [deg]<br /> Projection: Geografic Lat-Long (WGS84)<br /> Applied Phase Filter: Goldstein 0.5<br /> Author: IREA - CNR</p> <p><em>Acknowledgments</em>: JAXA, ESA GEP, CNR-IREA, Italian DPC</p> <p> </p>
X-ray diffraction images for endothiapepsin co-crystallised with inhibitor H189 to 0.94 Angstrom resolution.
<p>X-ray diffraction images collected on 23rd May 2000 at the BW7B beamline of DESY (Hamburg). </p>
Figure 5. - ATruncatoflabellumincrustatum, holotype, USNM 40774, Albatross 5251, Philippines B Truncatoflabellumsphenodeum, lectotype, NZGS CO 681, Trilissick Basin, New Zealand (Duntroonian = Lower Oligocene) C Truncatoflabellumcrassum, USNM 1130686, Albatross 5270, Philippines D Truncatoflabellumaculeatum, USNM 40781, Albatross 5156, Philippines. Scale bars: all 10 mm.
Figure 5. - ATruncatoflabellumincrustatum, holotype, USNM 40774, Albatross 5251, Philippines B Truncatoflabellumsphenodeum, lectotype, NZGS CO 681, Trilissick Basin, New Zealand (Duntroonian = Lower Oligocene) C Truncatoflabellumcrassum, USNM 1130686, Albatross 5270, Philippines D Truncatoflabellumaculeatum, USNM 40781, Albatross 5156, Philippines. Scale bars: all 10 mm.
Figure 12. - APlacotrochidescylindrica, holotype, Museum of Tropical Queensland G55627, off Queensland BPlacotrochidesfrustum, holotype, USNM 36451, Lesser Antilles; paratype, NMC, Hudson 4B, Lesser Antilles C Placotrochuslaevis, USNM 81994, Great Barrier Reef, Australia D Falcatoflabellumraoulensis, upper image, holotype, Museum of New Zealand, CO 258, Kermadec Ridge; lower images, paratype, USNM 94313, Kermadec Ridge. Scale bars: 1mm (A); 2 mm (B); 10 mm (C), except for basal scar, which is 5 mm; 1 mm (D), except latera view, which is 5 mm.
Figure 12. - APlacotrochidescylindrica, holotype, Museum of Tropical Queensland G55627, off Queensland BPlacotrochidesfrustum, holotype, USNM 36451, Lesser Antilles; paratype, NMC, Hudson 4B, Lesser Antilles C Placotrochuslaevis, USNM 81994, Great Barrier Reef, Australia D Falcatoflabellumraoulensis, upper image, holotype, Museum of New Zealand, CO 258, Kermadec Ridge; lower images, paratype, USNM 94313, Kermadec Ridge. Scale bars: 1mm (A); 2 mm (B); 10 mm (C), except for basal scar, which is 5 mm; 1 mm (D), except latera view, which is 5 mm.
Dataset for a Mouse and Rat heart trancriptomic and co-expression network analysis
<ul> <li>mouse_heart_data and rat_heart_expression contain GEO expression matrix for mouse and rat experiments.</li> <li>gse_gsm_mouse.txt and gse_gsm_rat.txt contain experiment IDs and series IDs from GEO.</li> <li>heart_quantNormData_mouse.tsv and heart_quantNormData_rat.tsv contain normalised expression matrices.</li> <li>heart_quantNormData_mouse_1sd.tsv and heart_quantNormData_rat_1sd.tsv contain the normalised expression matrices restricted to genes with a standard deviation higher than 1.</li> <li>fileForSCHypeThreshold0.5_heart.txt and fileForSCHypeThreshold0.75_heart.txt are the input for SCHype. schype_output_0.5th_heart.nodes.txt, schype_output_0.5th_heart.edges.txt, schype_output_0.75th_heart.nodes.txt and schype_output_0.75th_heart.edges.txt are the outputs.</li> <li>geneLists.zip contains gene lists used for ontology analysis (ENSEMBL gene id)</li> </ul>
Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2024
<p><strong>Changelog:</strong></p> <p>v2: Updates the recalculation process to using daily ESA CCI-SSTv3 and updates the temperature sensivity to those in Humphreys (2024). OISST version of the recalculated SOCAT data are no longer produced.</p> <p>v1.1: Corrects an error in the ESA CCI-SSTv3 tsv file having all NaN's for the reanalysis temperature and fCO2(sw). No other files were affected.</p> <p>v1: Initial Release</p> <p> </p> <p><strong>Description</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) version 2024 dataset (Bakker et al., 2016; <a href="https://doi.org/10.25921/9wpn-th28">https://doi.org/10.25921/648f-fv35</a>) is a quality-controlled dataset containing 38.6 million surface ocean gaseous CO₂ measurements collated from thousands of individual submissions. These gaseous CO₂ measurements are typically collected at many different depths (of the order of several metres below the surface) using many different systems, and the sampling depth varies dependent upon the sampling platform and/or setup. Different platforms (e.g. ships of opportunity, research vessels) and systems will collect water samples at different depths, and the sampling depth can even vary dependent upon sea state. Therefore, the collated SOCAT dataset contains high quality data, but these data are all valid for different and inconsistent depths. Therefore, the SOCAT provided individual gaseous CO₂ measurements and gridded data are sub-optimal for calculating global or regional atmosphere-ocean gas exchange (and the resultant net CO₂ sinks) and sub-optimal for verifying gas fluxes from (or assimilation into) numerical models.</p> <p>Accurate calculations of CO₂ flux between the atmosphere and oceans require CO₂ concentrations at the top and bottom of the mass boundary layer, the ~100 μm deep layer that forms the interface between the ocean and the atmosphere (Woolf et al., 2016). Ignoring vertical temperature gradients across this very small layer can result in significant biases in the concentration differences and the resulting gas fluxes (e.g. ~5 to 29% underestimate in global net CO₂ sink values; (Dong et al., 2022, 2024; Ford et al., 2024; Watson et al., 2020; Woolf et al., 2016)). It is currently impossible to measure the CO₂ concentrations either side of this very thin layer, but it is possible to calculate the concentrations either side of this layer using the SOCAT data, satellite observations and knowledge of the carbonate system.</p> <p>Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a recalculation methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the recalculation methodology is described in detail in Goddijn-Murphy et al. (2015). The recalculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes.</p> <p>This data submission contains a recalculation of the fugacity of CO₂ (fCO₂<sub> (sw)</sub>) from the SOCAT version 2024 dataset to a consistent sub-skin temperature field. The recalculation was performed using a tool that is distributed within the FluxEngine open source software toolkit (https://github.com/oceanflux-ghg/FluxEngine) (Holding et al., 2019; Shutler et al., 2016). The recalculated SOCAT dataset was produced with a climate quality and depth consistent (0.2 m) temperature dataset, the European Space Agency (ESA) Climate Change Initiative sea surface temperature (CCI-SST) product V3 (Embury et al., 2024; Good & Embury, 2024)</p> <p>Following the recommendations in Dong et al (2022), the CCI-SST was bias corrected with respect to SST drifters (~0.2m) to remove a cool bias (0.04K) identified in the CCI-SST validation report (Embury, 2023) and a climate data record intercomparison (Atkinson et al., 2023). This bias correction was applied as a globally fixed value. The daily CCI-SST data (0.05 º; ~5km at the equator) were linearly interpolated to the SOCAT observations, providing both an SST and SST uncertainty values representative for each individual SOCAT observation. The SOCAT fCO<sub>2 (sw)</sub> was then recalculated to the CCI-SST temperature using the updated temperature sensitivities described in Humphreys (2024).</p> <p>We have applied the recalculation process to both the main SOCAT dataset (data flags A,B,C,D).</p> <p><strong> </strong></p> <p><strong>Data records</strong></p> <p>The resulting reanalysed data are provided as a tab-separated value file (individual cruise points) and as netCDF-5 file (gridded monthly means). These are the same file formats as provided by SOCAT and analogous to the SOCAT single data point and gridded data. Each row in the tab-separated value file corresponds to a row in the original SOCAT version 2024 dataset.</p> <p>The original SOCAT version 2024 data are included in full, with five additional columns containing the reanalysed data:</p> <p>* T_subskin - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* T_subskin_uncertainty - The uncertainty (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_subskin.</p> <p>* pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature.</p> <p>* pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_subskin.</p> <p>The netCDF gridded version of the reanalysed dataset contains monthly mean data, binned into a 1º by 1º grid and uses the same units, missing value indicators and time and space resolution as the original SOCAT gridded product to maximise compatibility. The gridding is performed using the SOCAT gridding methodology (Sabine et al., 2013). As a consistency check to confirm the gridding method and precision, values within the gridded dataset are cross-checked against the original SOCAT gridded dataset. The unweighted SOCAT fCO2 (sw) showed a mean absolute difference of 0.02 μatm and for the cruise weighted fCO2 (sw) a difference of 0.21 μatm (N = 370920). Within the unweighted data, ~1200 monthly 1 degree regions (~0.3 %) have a difference greater than ±1 μatm, which occur in locations where SOCAT fCO<sub>2 (sw)</sub> observations could not be matched to the satellite reference data and therefore were not included in the gridding. The cruise-weighted data has ~10,000 of the monthly 1 degree regions (~2 %) with a difference greater than ±1 μatm, which occur in the same regions as the unweighted data.</p> <p>The original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed SOCAT data. Statistical sample mean, minimum, maximum, standard deviation and count data for each grid cell are included, with unweighted and cruise-weighted versions (following the convention used by SOCAT). In addition the full satellite SST fields at monthly 1 degree resolution are included within the netCDF file (‘sst_subskin_full’), so that the SST data can be used in any further processing (e.g. used with fCO<sub>2 (sw)</sub><sup> </sup>interpolation approaches). Full meta data are included within the file.</p> <p><strong> </strong></p> <p><strong>Quick-start Guide</strong></p> <p><strong>Individual cruise data (.tsv files)</strong></p> <p>The individual cruise data files include the original SOCAT data as well as recalculated fCO<sub>2 (sw)</sub> (‘fCO2_reanalysed [uatm]’ column) with their paired temperatures (‘T_subskin [C]’ column). The original SOCAT data columns for fCO<sub>2 (sw)</sub> (‘fCO2_rec [uatm]’) and SST (‘SST [deg C]’) can be replaced with the recalculated columns as a quick start.</p> <p> </p> <p><strong>Gridded cruise data (monthly 1 degree; .nc files)</strong></p> <p>The gridded cruise data files are the original SOCAT netcdf files (unweighted and cruise weighted), that have been appended with the recalculated values.</p> <p>If users use the unweighted SOCAT fCO<sub>2</sub><sub> </sub><sub>(sw)</sub> (‘fco2_ave_unwtd’) with its paired temperature (‘sst_ave_unwtd’). These variables can be replaced with the recalculated fCO<sub>2 (sw)</sub> (‘fco2_reanalysed_ave_unwtd’) and the paired temperature (‘sst_subskin_unweighted’).</p> <p>If users use the cruise-weighted SOCAT fCO<sub>2</sub><sub> </sub><sub>(sw)</sub> (‘fco2_ave_weighted’) with its paired temperature (‘sst_ave_weighted’). These variables can be replaced with the recalculated fCO<sub>2 (sw)</sub> (‘fco2_reanalysed_ave_weighted’) and the paired temperature (‘sst_subskin_weighted’).</p> <p><strong> </strong></p> <p><strong>Additional information</strong></p> <p>1. Due to the temporal range of the ESA CCI-SST the recalculated values are only available from 1980 onwards.</p> <p>2. This submission contains two files contained within a single zip file: Fordetal_SOCATv2024_ESACCIv3_biascorrected_Humpherys_daily_v2.nc</p> <p>Fordetal_SOCATv2024_ESACCIv3_biascorrected_Humpherys_daily_unc_withheader_v2.tsv</p> <p>3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.</p> <p> </p> <p><strong>How to cite these data </strong></p> <p>Please cite the DOI of this dataset, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), the FluxEngine toolbox which was used to perform the reanalysis (Holding et al., 2019; Shutler et al., 2016) and the original SOCAT dataset (Bakker et al., 2016) and/or gridded equivalent (Sabine et al., 2013).</p> <p><strong> </strong></p> <p><strong>Previous versions</strong></p> <p>v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a></p> <p>v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a></p> <p>v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a></p> <p>v2022: <a href="https://doi.org/10.5281/zenodo.8228585">https://doi.org/10.5281/zenodo.8228585</a></p> <p>v2023: <a href="https://doi.org/10.5281/zenodo.8229316">https://doi.org/10.5281/zenodo.8229316</a></p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p>These data were produced with funding from the Ocean ICU project (<a href="https://ocean-icu.eu/">https://ocean-icu.eu/</a>), the Convex Seascape Survey (<a href="https://convexseascapesurvey.com/">https://convexseascapesurvey.com/</a>) and the European Space Agency Ocean Carbon 4 Climate project (OC4C; 3-18399/24/I-NB). This work was funded by the European Union under grant agreement no. 101083922 (OceanICU) and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10054454, 10063673, 10064020, 10059241, 10079684, 10059012, 10048179]. The views, opinions and practices used to produce this dataset/software are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p> <p> </p> <p><strong>References</strong></p> <p>Atkinson, C., Rayner, N., Kennedy, J., Sikorski, T., Bonino, G., Quilestino-Olario, R., et al. (2023, November 30). ESA CCI Phase 3 Sea Surface Temperature (SST): Climate Assessment Report D5.1 v1.1. Retrieved July 18, 2024, from https://climate.esa.int/documents/2370/SST_CCI_D5.1_CAR_v1.1-signed.pdf</p> <p>Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O’Brien, K. M., Olsen, A., et al. (2016). A multi-decade record of high-quality fCO<sub>2</sub> data in version 3 of the Surface Ocean CO<sub>2</sub> Atlas (SOCAT). <em>Earth System Science Data</em>, <em>8</em>(2), 383–413. https://doi.org/10.5194/essd-8-383-2016</p> <p>Dong, Y., Bakker, D. C. E., Bell, T. G., Huang, B., Landschützer, P., Liss, P. S., & Yang, M. (2022). Update on the Temperature Corrections of Global Air‐Sea CO<sub>2</sub> Flux Estimates. <em>Global Biogeochemical Cycles</em>, <em>36</em>(9). https://doi.org/10.1029/2022GB007360</p> <p>Dong, Y., Bakker, D. C. E., Bell, T. G., Yang, M., Landschützer, P., Hauck, J., et al. (2024). Direct observational evidence of strong CO<sub>2</sub> uptake in the Southern Ocean. <em>Science Advances</em>, <em>10</em>(30), eadn5781. https://doi.org/10.1126/sciadv.adn5781</p> <p>Embury, O. (2023). SST CCI Product Validation and Intercomparison Report. https://climate.esa.int/documents/2369/SST_CCI_D4.1_PVIR_v2.1-signed.pdf</p> <p>Embury, O., Merchant, C. J., Good, S. A., Rayner, N. A., Høyer, J. L., Atkinson, C., et al. (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. <em>Scientific Data</em>, <em>11</em>(1), 326. https://doi.org/10.1038/s41597-024-03147-w</p> <p>Ford, D. J., Shutler, J. D., Blanco-Sacristán, J., Corrigan, S., Bell, T. G., Yang, M., et al. (2024). Enhanced ocean CO<sub>2</sub> uptake due to near-surface temperature gradients. <em>Nature Geoscience</em>. https://doi.org/10.1038/s41561-024-01570-7</p> <p>Goddijn-Murphy, L. M., Woolf, D. K., Land, P. E., Shutler, J. D., & Donlon, C. (2015). The OceanFlux Greenhouse Gases methodology for deriving a sea surface climatology of CO<sub>2</sub> fugacity in support of air-sea gas flux studies. <em>Ocean Science</em>, <em>11</em>(4), 519–541. https://doi.org/10.5194/os-11-519-2015</p> <p>Good, S. A., & Embury, O. (2024). ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis product, version 3.0 [Application/xml]. NERC EDS Centre for Environmental Data Analysis. https://doi.org/10.5285/4A9654136A7148E39B7FEB56F8BB02D2</p> <p>Holding, T., Ashton, I. G., Shutler, J. D., Land, P. E., Nightingale, P. D., Rees, A. P., et al. (2019). The FluxEngine air–sea gas flux toolbox: simplified interface and extensions for in situ analyses and multiple sparingly soluble gases. <em>Ocean Science</em>, <em>15</em>(6), 1707–1728. https://doi.org/10.5194/os-15-1707-2019</p> <p>Humphreys, M. P. (2024). Temperature effect on seawater <em>f</em> CO<sub>2</sub> revisited: theoretical basis, uncertainty analysis and implications for parameterising carbonic acid equilibrium constants. <em>Ocean Science</em>, <em>20</em>(5), 1325–1350. https://doi.org/10.5194/os-20-1325-2024</p> <p>Sabine, C. L., Hankin, S., Koyuk, H., Bakker, D. C. E., Pfeil, B., Olsen, A., et al. (2013). Surface Ocean CO<sub>2</sub> Atlas (SOCAT) gridded data products. <em>Earth System Science Data</em>, <em>5</em>(1), 145–153. https://doi.org/10.5194/essd-5-145-2013</p> <p>Shutler, J. D., Land, P. E., Piolle, J. F., Woolf, D. K., Goddijn-Murphy, L., Paul, F., et al. (2016). FluxEngine: A flexible processing system for calculating atmosphere-ocean carbon dioxide gas fluxes and climatologies. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>33</em>(4), 741–756. https://doi.org/10.1175/JTECH-D-14-00204.1</p> <p>Watson, A. J., Schuster, U., Shutler, J. D., Holding, T., Ashton, I. G. C., Landschützer, P., et al. (2020). Revised estimates of ocean-atmosphere CO<sub>2</sub> flux are consistent with ocean carbon inventory. <em>Nature Communications</em>, <em>11</em>(1), 1–6. https://doi.org/10.1038/s41467-020-18203-3</p> <p>Woolf, D. K., Land, P. E., Shutler, J. D., Goddijn-Murphy, L. M., & Donlon, C. J. (2016). On the calculation of air-sea fluxes of CO<sub>2</sub> in the presence of temperature and salinity gradients. <em>Journal of Geophysical Research: Oceans</em>, <em>121</em>(2), 1229–1248. https://doi.org/10.1002/2015JC011427</p> <p> </p>
Dataset for Exploring Co-benefits Between PM2.5 Control and Carbon Reduction
Open the record for dataset details and reuse information.
Electrochemical ammonia recovery and co-production of chemicals from manure wastewater
<p class="MsoNormal"><span>Livestock manure wastewater, containing high level of ammonia, is a major source of water contamination, posing serious threats to aquatic ecosystems. Because ammonia is an important nitrogen fertilizer, efficiently recovering ammonia from manure wastewater would have multiple sustainability gains from both the pollution control and the resource recovery perspectives. Here, we develop an electrochemical strategy to achieve this goal by using an ion-selective potassium nickel hexacyanoferrate (KNiHCF) electrode as a mediator. The KNiHCF electrode spontaneously oxidizes organic matter and uptakes ammonium ions (NH₄⁺) and potassium ions (K⁺) in manure wastewater with a nutrient selectivity of ~100%. Subsequently, nitrogen- and potassium-rich fertilizers are produced alongside the electrosynthesis of H₂ (green fuel) or H₂O₂ (disinfectant) while regenerating the KNiHCF electrode. The preliminary techno-economic analysis indicates that the proposed strategy has notable economic potential and environmental benefits. This work provides a powerful strategy for efficient nutrient (NH₄⁺ and K⁺) recovery and decentralized fertilizer and chemical production from manure wastewater, paving the way to sustainable agriculture.</span></p>
Characterizing ambient air quality and oil and gas air pollution emissions in Broomfield County, CO
<p>Unconventional oil and natural gas development (UOGD) has expanded rapidly across the United States in recent decades and raised concerns about associated air quality impacts. While significant effort has been made to quantify methane emissions, relatively few observations have been made of Volatile Organic Compounds (VOCs), especially during drilling and completion of new wells. Extensive air monitoring during development of several large, multi-well pads in Broomfield, Colorado, in the Denver-Julesburg Basin, provides a novel opportunity to examine changes in local air toxics and other VOC concentrations during well drilling and completions and production.</p>
Targeted DNA-seq analysis was performed on sorted population of CD45+/CD34+ HSPCs from control or FLI-1 modified mRNA treated mPB after co-culture with vascular niche cells
<p>Human mPB HSPCs were harvested isolated and transduced with either control or FLI-1 modified mRNA. HSPCs were introduced into co-culture with vascular niche ECs. Cultures were harvested and CD45+/CD34+ HSPCs were resoerted and processed for trageted DNA-seq analysis. Contains raw FASTQ sequencing files, unfiltered VCFs, and curated results in an excel.</p>
University of Turin IRIS-registered Publications for co-authorship networks
<p>Data extracted from the University of Turin (UNITO) IRIS publication database (available at <a href="https://iris.unito.it/">www.iris.unito.it</a>) regarding publication authorship.<br>This data was generated in order to produce co-authorship networks of authors inside of UNITO.<br><br>The JSON-formatted dataset includes data regarding UNITO affiliated authors that have published from the year 2012 to roughly September 2023, along with a list of all of their publications in the same time period.The dataset encompasses 15807 authors and 66313 articles.<br><br>The JSON has the following structure:</p><ul><li>`authors` (list): A list of objects representing authors, each with the following structure:<ul><li>`name` (string): The author's given name, all in lowercase letters. This field is always populated;</li><li>`surname` (string): The author's family name, all in lowercase letters. This field is always populated;</li><li>`affiliation` (string): The string "university of turin";</li><li>`department` (string or NULL): Empty (`null`) or with a string indicating the author's <strong>current</strong> (as of data collection, roughly September 2023) work department;</li><li>`id` (string): Unique UUID4 of the author.</li></ul></li><li>`papers` (list): A list of object representing published articles, each with the following structure:<ul><li>`id` (string): Unique IRIS ID of the publication. This field is always populated;</li><li>`title` (string): Title of the publication. This field is always populated;</li><li>`year` (numeric): Year of the article's publication date. This field is always populated;</li><li>`authors` (list): A non-empty list of strings. Each string is one ID of one of the authors in the `authors` list.</li></ul></li></ul><p>The data was kindly provided by the IRIS office in September 2023 and preprocessed by Luca Visentin to a digestible JSON.</p>
Identifying Spatial Co-occurrence in Healthy and InflAmed tissues (ISCHIA)
<p>Integrated IBD scRNASeq reference: Seurat object and cell type markers. </p><p>Molecular Cartography (Resolve) data data of human colon samples. 6 ulcerative colitis colon resections: 3 inflamed, 3 non-inflamed areas (4 patients in total). Segmented object (Seurat), and imaging data used for figures: B2-1 and C1-2 (healthy samples), C2-1 and C1-4 (inflamed samples) </p><p>Visium (10X) spatial transcriptomics data of human colon samples. 4 ulcerative colitis colon resections: 2 inflamed, 2 non-inflamed areas (3 patients in total). </p><p>Supplementary Table 1: positively co-occurring ligands and receptors in composition class 5.</p><p> </p><p> </p>
Artificial night-time lighting and nutrient enrichment synergistically favour the growth of alien ornamental plant species over co-occurring native plant species
<ol> <li>Insights into ecological drivers of alien plant invasions can be gained through comparative studies of growth and fecundity of invasive alien plants versus those of co-occurring non-invasive alien plants and native plants across environmental conditions in common garden settings. Habitats that harbour alien plant species in many ecosystems globally are presently experiencing light pollution resulting from artificial light at night (ALAN) and increased rates of nutrient enrichment of the soil. However, the potential interactive effects of ALAN and nutrient enrichment on invasiveness of alien plant species remain unknown.</li> <li>Here, we performed a common-garden experiment to test the interactive effects of ALAN and soil nutrient enrichment on the growth of a random set of 10 alien (five invasive and five naturalized) and seven co-occurring native ornamental plant species that are commonly cultivated within urban and peri-urban areas of Nairobi city in Kenya. We predicted that a simultaneous increase in photoperiod via ALAN and nutrient enrichment will favor growth of invasive alien plant species over that of non-invasive alien and native plant species. We grew the 17 plant species under natural daylight (ALAN-) vs natural daylight followed by ALAN (ALAN+) and fully crossed with two levels of nutrient enrichment (low vs high) and competition (competition vs no-competition against a native plant <em>Ocimum</em> <em>gratissimum</em>) treatments.</li> <li>Under simultaneous high-nutrient and no-competition treatments, ALAN enhanced mean total biomass of invasive and naturalized alien species by 61.1% and 131.4%, respectively but decreased that of native plant species by 34%. In contrast, under simultaneous high-nutrient and competition treatments, ALAN enhanced mean total biomass of invasive alien plant species by 68.6% and that of naturalized alien species by 51.9% and native species by 35.4%. High-nutrient treatment enhanced flower formation more strongly in invasive and naturalized alien plants than in native plants. The invasive and naturalized alien species grew taller than native species across the light, nutrient, and competition treatments.</li> <li> <em>Synthesis</em>: The present findings suggest that light pollution and nutrient enrichment may jointly confer growth advantage to invasive alien plant species over that of co-occurring native plant species and enhance invasiveness of alien plant species.</li> </ol>
Keyterm Co-Occurence Network
<p>A bibliometric map displaying various nodes (word items) and the links by which they are connected conceived on VOSviewer. In this publication, we focused on words indicative of deficit thinking (at risk, disorder, risk, deficit, psychopathology, impairment, abnormality, difficulty, problem, risk factor, delay, dysfunction, cognitive impairment, conduct disorder, brain abnormality, cognitive deficit, failure, anomaly, attention problem, behavior problem, cognitive dysfunction, environmental risk factor, neuropsychological deficit, executive dysfunction). This map can be viewed and explored by uploading the attached json file on app.vosviewer.com. Readers can evaluate specific nodes by using the "find" feature on the left-handside menu. </p>
Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans
<h3>Contact:</h3><h3>Ilya Verzhbinsky</h3><h3>ilya@health.ucsd.edu</h3><p> </p><p>This is the processed data used to generate the results in the manuscript:</p><p>Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans. <i>PNAS</i> (2023).</p><p>To analyze this data, please first access the code at the following repository: <a href="https://github.com/iverzh/coripple-prediction"><strong>https://github.com/iverzh/coripple-prediction</strong></a></p><p>All downloaded zip files should be uncompressed and placed in a directory named <i>out/ </i>in the <i>CoRipplePredictionPNAS/</i> folder.</p><p> </p><p> </p><p> </p>
CO and CO2 mixing ratios and flux measurements from the Amazon tropical rainforest
<p>CO and CO2 mixing ratio and flux measurements from the Amazon tropical rainforest</p><p>This dataset belongs to the manuscript 'The emission of CO from tropical rain forest soils', submitted to the journal Biogeosciences in December 2023.</p><p>More details on this dataset can be found in this manuscript:</p><p>https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2746/egusphere-2023-2746.pdf</p><p> For questions, please reach out to Hella van Asperen: hasperen@bgc.mpg-jena.de</p><p>########################################</p><p>Plateau tower CO and CO2 mixing ratio measurements</p><p>Plateau tower CO and CO2 mixing ratio measurements took place in a dry season campaign (28 Sep- 7 Oct 2020) and a wet season campaign (11-18 May 2021) at the K34 tower at field site ZF2 in the Amazon rain forest (-2.60898, -60.209106). Due to a problem in the beginning of the dry season campaign, the measurements at the tower were continued until outside the campaign period, until 18 October 2020. Measurements were performed by a Spectronus FTIR analyzer. Concentrations were measured at 3 heights (5,15 and 36m) every half hour. Canopy height is ~28m.</p><p>########################################</p><p>Valley CO and CO2 mixing ratio measurements</p><p>Valley CO and CO2 mixing ratio measurements took place in a dry season campaign (28 Sep- 7 Oct 2020) and a wet season campaign (11-18 May 2021) at a valley close to the K34 tower at field site ZF2 (-2.600026, -60.217079). Since no electricity was available, automatic battery-driven bag sampling was performed during the night at 3h time intervals, with 4 measurements per night from a 1m height inlet (~18:00, ~21:00, ~0:00, ~3:00). Bag samples were measured the following morning by a Spectronus FTIR-analyzer. </p><p>########################################</p><p>Plateau and valley chamber CO and CO2 fluxes</p><p>Flux chamber measurements were performed over soil and litter together on the plateau and in the valley at field site ZF2, in a dry season campaign (28 Sep- 7 Oct 2020) and a wet season campaign (11-18 May 2021). Five soil collars were installed in the valley, and five on the plateau. Each collar was measured 3 times during each campaign week (on different days). Measurements from the same collar are indicated as (for example) V1A, V1B, V1C. After each flux chamber measurement, soil moisture and soil temperature was measured.</p>
Data from: programming co-assembled peptide nanofiber morphology via anionic amino acid type: insights from molecular dynamics simulations
<p>Co-assembling peptides can be crafted into supramolecular biomaterials for use in biotechnological applications, such as cell culture scaffolds, drug delivery, biosensors, and tissue engineering. Peptide co-assembly refers to the spontaneous organization of two different peptides into a supramolecular architecture. Here we use molecular dynamics simulations to quantify the effect of anionic amino acid type on co-assembly dynamics and nanofiber structure in binary CATCH(+/-) peptide systems. CATCH peptide sequences follow a general pattern: CQCFCFCFCQC, where all C's are either a positively charged or a negatively charged amino acid. Specifically, we investigate the effect of substituting aspartic acid residues for the glutamic acid residues in the established CATCH(6E-) molecule, while keeping CATCH(6K+) unchanged. Our results show that structures consisting of CATCH(6K+) and CATCH(6D-) form flatter β-sheets, have stronger interactions between charged residues on opposing β-sheet faces, and have slower co-assembly kinetics than structures consisting of CATCH(6K+) and CATCH(6E-). Knowledge of the effect of sidechain type on assembly dynamics and fibrillar structure can help guide the development of advanced biomaterials and grant insight into sequence-to-structure relationships.</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.