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5,145 results for “CO₂”
Ultrafast Infrared Transient Absorption Spectroscopy of Gas-Phase Ni(CO)4 Photodissociation at 261 nm
<p>This is the data repository for the following publication:</p> <p>Neil C. Cole-Filipiak, Jan Tross, Paul Schrader, Laura M. McCaslin, and Krupa Ramasesha, "Ultrafast infrared transient absorption spectroscopy of gas-phase Ni(CO)4 photodissociation at 261 nm," J. Chem. Phys. 156, 144306 (2022), https://doi.org/10.1063/5.0080844.</p> <p><strong>Abstract:</strong></p> <p>We employ ultrafast mid-infrared transient absorption spectroscopy to probe the rapid loss of carbonyl ligands from gas-phase nickel tetracarbonyl following ultraviolet photoexcitation at 261 nm. Here, nickel tetracarbonyl undergoes prompt dissociation to produce nickel tricarbonyl in a singlet excited state; this electronically excited tricarbonyl loses another CO group over tens of picoseconds. Our results also suggest the presence of a parallel, concerted dissociation mechanism to produce nickel dicarbonyl in a triplet excited state, which likely dissociates to nickel monocarbonyl. Mechanisms for the formation of these photoproducts in multiple electronic excited states are theoretically predicted with one-dimensional cuts through the potential energy surfaces and computation of spin–orbit coupling constants using equation of motion coupled cluster methods (EOM-CC) and coupled cluster theory with single and double excitations (CCSD). Bond dissociation energies are calculated with CCSD, and anharmonic frequencies of ground and excited state species are computed using density functional theory (DFT) and time-dependent density functional theory (TD-DFT).</p> <p> </p> <p><strong>Experimental Data:</strong></p> <p>All data are saved as a .csv file. The first column contains frequencies (in cm<sup>-1</sup>) while the first row indexes each time delay (in ps). High-resolution transient spectra at select time delays are similarly structured. Each transient .csv file is labeled according to molecule, pump wavelength, file contents, pump laser power, pressure, and a date (<em>e.g.</em> NT261_trans_1mW_1.5torr_17Feb2021.csv).</p> <p> </p> <p><strong>Computational Data:</strong></p> <p>This data repository consists of 7 directories, which contain the data used in the main paper. Computational data published in the supplementary material may be requested from the corresponding authors.</p> <p>Anharmonic frequencies and DFT energies can be obtained in the directory "VPT2", where the files are labelled nicoX_*_anharm.out, where X=3,2,1 (the compound) and * corresponds to the electronic state for which the calculation was performed.</p> <p>EOM-CC calculations of the spin-orbit coupling constants at the geometries reported are found within the "SOCC" directory using the naming convention nicoX_[]_so_*.out, where X=3,2, []=an indication of the geometry, and * corresponds to the electronic state for which the calculation was performed.</p> <p>Calculations of the minimum energy crossing points (MECPs) can be found in the directory "MECP" using the naming convention nicoX_min*.out, where X=4,3,2,1, and * corresponds to the two electronic states for which the MECP is calculated (e.g. s0s1).</p> <p>The following 4 directories contain all the EOM-CC output files needed to reproduce the curves from Figure 2: 4to3, 3to2, 4to2, and 2to1, corresponding to panels a, b, c, and d, respectively. The naming conventions for the files within these directories are X_yz.out, where X=the name of the directory, y=the value of the reaction coordiante, and z=s (singlet) or t (triplet).</p>
Data from "CO Line Emission Surfaces and Vertical Structure in Mid-Inclination Protoplanetary Disks"
<p>CO line emission image cubes ("[DISK]_CO_cube.fits"), line+continuum image cubes ("[DISK]_CO_cube_wcont.fits"), and zeroth moment maps ("[DISK]_CO_M0.fits") associated with Law et al., 2022, "CO Line Emission Surfaces and Vertical Structure in Mid-Inclination Protoplanetary Disks," The Astrophysical Journal</p> <p>CO line emission image cubes from the DSHARP ALMA Large Program (for HD 142666, MY Lup, GW Lup, WaOph 6, DoAr 25) can be found at: https://bulk.cv.nrao.edu/almadata/lp/DSHARP/ and are not included here.</p> <p>The raw data are available on the ALMA archive (see Table 1 in the paper for a listing of the relevant ALMA project codes).</p>
Code: The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems
<p>This is the code archive for the publication "The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems" in Earth's Future.</p> <p>Abstract:</p> <p>Studies have recently focused on using coupled natural–human systems (CNHS) to inform policymaking. However, model uncertainty can increase with model complexity and affect the variance of the model outcomes. Therefore, this study explores an uncertainty analysis of coupled hydrological and human decision models to better evaluate CNHS modeling properties. Five coupled models are proposed with different model complexities for human behavior settings (i.e., model structure and the number of calibrated parameters): one static, two adaptive, and two learning adaptive. Learning adaptive models (the most complex) have both a learning component (capturing long-term trends) and an adaptive component (capturing short-term variations), while adaptive models omit the learning component. The static model is the simplest, without learning or adaptive components. Applying the law of total variance, the model output uncertainty is decomposed into three sources: (1) climate change scenario uncertainty, (2) climate internal variability, and (3) different model configurations with parameter sets or model structures that are equally capable of producing similar outcomes. Our exploratory analysis demonstrated that model uncertainty would likely increase with model complexity given uncertain input data (e.g., climate forcing) and different model configurations; the inclusion of a learning mechanism in the human system can potentially offset the impact of the natural system on uncertainty through coupling natural and human systems. We also discuss other uncertainty sources, such as assumptions about model structure due to incomplete knowledge and metrics for calibration target selection for future studies.</p>
How nitrogen and phosphorus supply to nutrient-limited autotroph communities affects herbivore growth: testing stoichiometric and co-limitation theory across trophic levels
<p><span>Primary producer communities are often growth-limited by essential nutrients such as nitrogen (N) and phosphorus (P). The magnitude of </span><span>limitation and whether N, P, or both elements are limiting autotroph </span><span>growth depends on the supply and ratios of these essential nutrients. </span><span>Previous studies identified single, serial or co-limitation as predominant </span><span>limitation outcomes in autotroph communities by factorial nutrient </span><span>additions. Little is known about potential consequences of such scenarios </span><span>for herbivores and whether their growth is primarily affected by changes </span><span>in autotroph quantity or nutritional quality. We grew a community of </span><span>phytoplankton species differing in various food quality aspects in </span><span>experimental microcosms at varying N and P concentrations resulting in </span><span>three different N:P ratios. At carrying capacity, N, P, both nutrients or </span><span>none were added to reveal which nutrients were limiting. The nutrient supplied </span><span>communities were fed to the generalist herbivorous rotifer </span><span>Brachionus calyciflorus to investigate how changing phytoplankton </span><span>biomass and community composition affect herbivore abundance. We </span><span>found phytoplankton being growth-limited either by N alone (single </span><span>limitation) or serially, i.e. primarily by N and secondarily by P, altering </span><span>available food quantity for rotifers. Rotifer growth showed a different </span><span>response pattern compared to phytoplankton, suggesting that apart from </span><span>food quantity food quality aspects played a substantial role in the </span><span>transfer from primary to secondary production. The combined addition of </span><span>N and P to phytoplankton had generally a positive effect on herbivore </span><span>growth, whereas adding non-limiting nutrients had a rather detrimental </span><span>effect probably due to stoichiometrically imbalanced food in terms of </span><span>nutrient excess. Our experiment shows that adding various nutrients to </span><span>primary producer communities will not always lead to increased </span><span>autotroph and herbivore growth, and that differences between autotroph </span><span>and herbivore responses under co-limiting conditions can be partly well </span><span>explained by concepts of ecological stoichiometry theory.</span></p>
Earthquake Catalogue for: Illuminating the pre-, co-, and post-seismic phases of the 2016 M7.8 Kaikoura earthquake with 10 years of seismicity
<p><strong>0.2: Correction</strong></p> <p>This version corrects the original dataset which had some incorrect focal mechanisms. These mechanisms had incorrect rakes as a result of an error in the uncertainty calculation algorithm. The remainder of the catalogue is unchanged.</p> <p><strong>Code:</strong></p> <p>If you are looking for the code used for this project this is here: <a href="https://zenodo.org/record/5047794#.Y22Mvn5By-Y">https://zenodo.org/record/5047794#.Y22Mvn5By-Y</a> - the link in the paper appears to be wrong as of 11/11/2022.</p> <p><strong>Description</strong></p> <p>Earthquake catalogue generated around the rupture area of the 2016 M7.8 Kaikoura earthquake. For a full description see the associated paper submitted to JGR 2021.</p> <p>The catalogue is here in two forms:</p> <ol> <li>A QuakeML file with all picks, magnitudes, and locations included. This is quite a large file. When reading using ObsPy this will take a long time to read (>10 minutes), and expand in memory to > 8GB.</li> <li>A CSV file with the preferred origin and magnitude information for all events. Relocated events can be identified because they have a station count of 0: GrowClust does not return the number of stations used, whereas NonLinLoc (used for absolute locations) does.</li> </ol>
Three-dimensional Digital Outcrop Models of the Tullig Sandstone, Western Irish Namurian Basin, Co. Clare, Ireland
<p>Tullig Sandstone is part of the Tullig Cyclothem, Western Irish Namurian Basin, Co. Clare, Ireland. The Tullig Sandstone is a prominent sandstone interval that represents an ancient fluvial-deltaic system. </p> <p>Outcrops of the Tullig Sandstone were surveyed by an unmanned aerial vehicle (UAV, DJI Mavic Pro Platinum™). Three-dimensional digital outcrop models were generated from images collected from UAV using Agisoft Metashape™.</p>
The effect of co-location on human communication networks
<p>Representative dataset for "The effect of co-location on human communication networks." The files are serialized python objects pickled using python 3.8. dist_dict_* contains data on the pairwise distance between researchers, while undir_semiactive_* contains networks representing daily email counts between researchers.</p>
Co-located Multi-device Audio Experiences Dataset
<p>This dataset contains the survey responses obtained from the survey on co-located multi-device audio experiences, in the form of a .csv file.</p>
Microclimate simulation output: "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens"
<p>The following microclimate simulation dataset supports the paper "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens" by Mathias Schaefer, published in Urban Ecosystems (2022).</p> <p>"T0Simulation_11082020_output" contains data about the status quo simulation of the area of interest (500 m x 500 m x 60 m), whereas "T1Simulation_11082020_output" shows the results of the Green Infrastructure scenario described in the research article above. Please ensure enough memory space on your device, as both files have a size of approximately 25 GB (unzipped).</p> <p>The output files can be visualized with the ENVI-met Leonardo extension. The ENVI-met LITE-version is freely available and can be downloaded at the <a href="https://envi-met.info/doku.php?id=files:download">ENVI-met homepage</a>. Alternatively, the included .NETCDF files can be imported as a multidimensional raster dataset in ArcGIS Pro.</p> <p>Files in the folder "atmosphere" represent meteorological parameters such as potential air temperature [°C], relative humidity [%], or wind speed [m/s]. Air pollution calculations like particulate matter concentrations [µg/m³] can be found in the folder "pollutants". The folder "buildings" contains building data for 3D visualizations of surface temperatures [°C].</p>
TOMCAT model data & IASI satellite data of O3, CO, H2O, CH4 and OH/derived OH for 2010 and 2017
<p>Monthly mean data of ozone (O3), carbon monoxide (CO), water vapour (H2O), methane (CH4) and the hydroxyl radical (OH) for 2010 and 2017.</p> <p>Model data is from the 3D chemical transport model TOMCAT (Chipperfield, 2006).</p> <p>Satellite observations are from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite and retrieved using schemes developed by the Rutherford Appleton Laboratory (RAL). The Ch4 is from RAL's CH4 retrieval scheme (Siddans et al. 2020) and the O3, CO and H2O retrievals are from the extended version of RAL’s Infrared and Microwave Sounding (IMS-extended) scheme (Pope et al. 2021). </p> <p>Full description of the data can be found in Pimlott et al. (2022) (preprint: https://doi.org/10.5194/acp-2022-79) which has now been accepted for publication in ACP. </p>
Data for: Co-evolution of dormancy and dispersal in spatially autocorrelated landscapes
<p>The evolution of dispersal can be driven by spatial processes, such as landscape structure, and temporal processes, such as disturbance. Dormancy, or dispersal in time, is generally thought to evolve in response to temporal processes. In spite of broad empirical and theoretical evidence of trade-offs between dispersal and dormancy, we lack evidence that spatial structure can drive the evolution of dormancy. Here, we develop a simulation-based model of the joint evolution of dispersal and dormancy in spatially heterogeneous landscapes. We show that dormancy and dispersal are each favored under different landscape conditions, but not simultaneously under any of the conditions we tested. We further show that, when dispersal distances are short, dormancy can evolve directly in response to landscape structure. In this case, selection is primarily driven by benefits associated with avoiding kin competition. Our results are similar in both highly simplified and realistically complex landscapes.</p>
PUMA IV: CO(2-1) channel maps
<p>CO(2-1) channel maps of the 25 ULIRGs systems (38 individual nuclei) of the <em>Physics of ULIRGs with MUSE and ALMA</em> (PUMA) sample (Perna et al., 2021; Pereira-Santaella et al., 2021). These figures are an extended appendix to the paper Lamperti et al (2022), published in A&A (arXiv:2209.03380).</p>
Text-fig. 8. Scanning electron micrographs (a–f, h) and X-ray microtomographic orthoslices (g) of flowers from Zliv-Řídká Blana locality. a: Taxon 27, epigynous flower, no. NM-F 4504; b: Taxon 30, epigynous flower, remains of two thick sepals, a massive nectary disk (arrowhead) and two styles, no. NM-F 3199; c: Taxon 29, flower with stamens have long filament, calyx (ca) and corolla (co), no. NM-F 3198; d: Taxon 32, flower, no. NM-F 4503; e–h: Taxon 14, e – hypogenous flower, no. NM-F 3196, f – floral bud with a thick pedicel, no. NM-F 3196, g – flower with gynoecium showing central placentation and several seeds, no. NMF 3196, h – flower with gynoecium showing several seeds, no. NM-F 4505. in Plant Mesofossils From The Late Cretaceous Klikov Formation, The Czech Republic
Text-fig. 8. Scanning electron micrographs (a–f, h) and X-ray microtomographic orthoslices (g) of flowers from Zliv-Řídká Blana locality. a: Taxon 27, epigynous flower, no. NM-F 4504; b: Taxon 30, epigynous flower, remains of two thick sepals, a massive nectary disk (arrowhead) and two styles, no. NM-F 3199; c: Taxon 29, flower with stamens have long filament, calyx (ca) and corolla (co), no. NM-F 3198; d: Taxon 32, flower, no. NM-F 4503; e–h: Taxon 14, e – hypogenous flower, no. NM-F 3196, f – floral bud with a thick pedicel, no. NM-F 3196, g – flower with gynoecium showing central placentation and several seeds, no. NMF 3196, h – flower with gynoecium showing several seeds, no. NM-F 4505.
Text-fig. 3. SRXTM images of Miranthus elegans gen. et sp. nov.; Mira locality, Portugal. a, b: Volume renderings of flower bud in two different lateral views showing long pedicel, distinct calyx (ca) with almost equiaxial epidermal cells and corolla (co) with nearly smooth surface. c–e: Transverse sections (c, orthoslice xy1500; d, orthoslice xy1760; e, orthoslice xy1850) through flower bud at levels below the anthers showing stamen filaments (yellow) opposite the corolla lobes (co) and smaller staminodes (orange) in Early Flowers Of Primuloid Ericales From The Late Cretaceous Of Portugal And Their Ecological And Phytogeographic Implications
Text-fig. 3. SRXTM images of Miranthus elegans gen. et sp. nov.; Mira locality, Portugal. a, b: Volume renderings of flower bud in two different lateral views showing long pedicel, distinct calyx (ca) with almost equiaxial epidermal cells and corolla (co) with nearly smooth surface. c–e: Transverse sections (c, orthoslice xy1500; d, orthoslice xy1760; e, orthoslice xy1850) through flower bud at levels below the anthers showing stamen filaments (yellow) opposite the corolla lobes (co) and smaller staminodes (orange)
Text-fig. 7. SEM (a) and SRXTM (b–e) images of Miranthus kvacekii sp. nov.; Mira locality, Portugal. a: Lateral view of flower bud showing corolla lobes extending beyond calyx; note surface of pedicel, calyx and corolla with small equiaxial epidermal cells and indumentum of densely spaced, short stiff trichomes. b, c: Longitudinal sections through floral bud in two directions perpendicular to each other (a, orthoslice yz1024; b, orthoslice xz0950) showing corolla (co), calyx (ca), stamens (st) and semi-inferior ovary with thin ovary wall (ow) and central mushroom-shaped globose placenta (pl) bearing numerous ovules (ov). d, e: Transverse sections through floral bud above placenta (d, orthoslice xy0915; e, orthoslice xy1095) showing calyx (ca), corolla (co), ovary wall (ow) and ovules (ov); yellow outlines indicate the positions of anthers (d) and filaments (e); orange outlines indicate the position of three of the possible staminodes. Specimen, Mira 100-S170157 (a–e, holotype). Scale bars = 600 µm (a–c), 300 µm (d, e). in Early Flowers Of Primuloid Ericales From The Late Cretaceous Of Portugal And Their Ecological And Phytogeographic Implications
Text-fig. 7. SEM (a) and SRXTM (b–e) images of Miranthus kvacekii sp. nov.; Mira locality, Portugal. a: Lateral view of flower bud showing corolla lobes extending beyond calyx; note surface of pedicel, calyx and corolla with small equiaxial epidermal cells and indumentum of densely spaced, short stiff trichomes. b, c: Longitudinal sections through floral bud in two directions perpendicular to each other (a, orthoslice yz1024; b, orthoslice xz0950) showing corolla (co), calyx (ca), stamens (st) and semi-inferior ovary with thin ovary wall (ow) and central mushroom-shaped globose placenta (pl) bearing numerous ovules (ov). d, e: Transverse sections through floral bud above placenta (d, orthoslice xy0915; e, orthoslice xy1095) showing calyx (ca), corolla (co), ovary wall (ow) and ovules (ov); yellow outlines indicate the positions of anthers (d) and filaments (e); orange outlines indicate the position of three of the possible staminodes. Specimen, Mira 100-S170157 (a–e, holotype). Scale bars = 600 µm (a–c), 300 µm (d, e).
Fig. 2 in . Study of Nosema spp. in the Tomsk region, Siberia: co-infection is widespread in honeybee colonies
Fig. 2. Distribution of Nosema species in bee colonies (Apis mellifera) throughout the Tomsk region (dots A–I). Bee colonies not infected by Nosema are indicated in yellow; bee
Fig. 1. The 80 in Importance of Srepok Wildlife Sanctuary, Cambodia, for the endangered green peafowl: implications of co-occurrence near human use areas
Fig. 1. The 80-point count listening post locations within the core and outer core area of Srepok Wildlife Sanctuary.
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).
Data associated to the manuscript "Co-Ion Desorption as the Main Charging Mechanism in Metallic 1T-MoS2 Supercapacitors"
<p>Supporting data for the article:</p> <p>Co-Ion Desorption as the Main Charging Mechanism in Metallic 1T-MoS<sub>2</sub> Supercapacitors</p> <p>Sheng Bi, Salanne Mathieu, <em>ACS Nano</em>, 2022</p> <p>https://pubs.acs.org/doi/10.1021/acsnano.2c07272</p> <p>The folders <em>slab </em>and <em>slit</em> contain typical MetalWalls input files used to perform the simulations for two electrode geometries.</p>
Supplementary material 1 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Supplementary material 1 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
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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.