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60 results for “2014 to 2015”
InnoVine WP3: 105 phenolic compound quantification of 2014 and 2015 mature grape berries from a core-collection of 279 irrigated and non-irrigated Vitis vinifera cultivars
<p>FP7/311775 InnoVine (Innovation in vineyard): Combining innovation in vineyard management and genetic diversity for a sustainable European viticulture</p> <p>WP3: Exploiting the genetic diversity in grapevine</p> <p>105 phenolic or related compounds, from 2014 and 2015 mature grape berries from a core-collection of 279 irrigated and non-irrigated <em>Vitis vinifera</em> cultivars, were quantified by UPLC-TQ-MRM Mass Spectrometry (Lambert M<em> et al., Molecules</em> <strong>2015</strong>, <em>20</em>(5), 7890-7914; doi:10.3390/molecules20057890 & Pinasseau L <em>et al.</em>, <em>Molecules</em> <strong>2016</strong>, <em>21</em>(10), 1409; doi:10.3390/molecules21101409).</p> <p>3 parameters were added:<br> - water/drought status (delta C13)<br> - sugar content (refractive index, brix degree)<br> - weight of 100 grape berries</p> <p>All plant material was collected at the Vassal repository: French National Grapevine Germplasm Collection, INRA Domaine de Vassal, 34340 Marseillan-Plage, France (Centre de Ressources Biologiques de la Vigne (CRB-Vigne) de Vassal-Montpellier).</p>
Mesocosm experiment on fresh marsh plant community responses to salinity pulses in 2014 and 2015
We established mesocosm communities using seven freshwater species (Zizaniopsis miliacea, Pontederia cordata, Persicaria hydropiperoides, Peltandra virginica, Ludwigia peploides, Sagittaria lancifolia and Eleocharis palustris) collected in the same freshwater marsh (31°20’16”N, 81°27’52”W) between Apr 19th and Apr 22nd, 2014, and the sediments were collected from a freshwater pond (31°23'54"N, 81°16'47"W) on Sapelo Island, GA at the same time. Our experimental unit was a plastic round swimming pool (h = 18 cm, r = 122 cm) filled to the top with homogenized sediments. Each unit consisted of 2 individuals of Zizaniopsis, Pontederia, Peltandra and Sagittaria, 5 – 8 Persicaria and Ludwigia separately. Eleocharis was mixed up with other species, so there were uneven numbers in each unit. From May 22 to August 19, 2014, the mesocosm communities were treated with three salinity levels (3, 5, and 10 psu: practical salinity units) and five exposure durations (5, 10, 15, 20 and 30 days per month), relative to a freshwater control (n=3 per treatment combination). Except for 30 days treatment which equals saline press treatment, plants were watered with daily freshwater after treated with saline water for 5, 10, 15 and 20 days. Different salinities were achieved by mixing tap water with sea water. We simulated tides during the treatment, by watering the plants every morning and then pumping out the excess water through a 6-in (15 cm) slotted well (constructed of PVC pipe) in the afternoon. From August 20, 2014 to June 30, 2015, all mesocosm communities were flushed with fresh tap water to recover.
Eriophorum vaginatum leaf length 2015-2017 from 2014 common gardens established at Toolik Lake, Coldfoot, and Sagwon - Alaska
Data on Eriophorum vaginatum leaf length collected from common gardens established at Toolik Lake, Coldfoot, and Sagwon in 2014 with tussocks from Coldfoot, Toolik Lake, and Sagwon. Data collected during the growing seasons of 2015, 2016, and 2017
Air and soil temperature in warmed and control plots of 2014 reciprocal transplant gardens Toolik Lake, Coldfoot, and Sagwon, Alaska 2015 and 2016
Air and soil temperatures from iButtons located at reciprocal transplant gardens at Toolik Lake, Coldfoot, and Sagwon in 2015 and 2016. The reciprocal transplant gardens at Coldfoot (CF), Toolik Lake (TL), Sagwon (SG) Each plot contains three tussocks, 30-50 centimeters apart
Institutional Dimensions of Restoring Everglades Water Quality -Interview Notes (FCE), September 2014-July 2015
These data were compiled through semi-structured and open-ended interviews as part of the Institutional Dimensions of Restoring Everglades Water Quality research project. The notes represent responses from farmers, agricultural extension agents, state and federal government officials, private water consultants, and nonprofit officials involved in the implementation of the Everglades Forever Act regulations. These mandate that farms in the Everglades Agricultural Area implement best management practices to reduce phosphorus enrichment. Since the regulations started in 1994, water quality has steadily improved. The research sought to explain why the regulations have been effective.
Contrasting plant adaptation strategies to latitude in the native and invasive range of Spartina alterniflora: geographic survey (2014) and Common garden (2015-2017)
We examined trait differences and evolution across geographic clines among continents of the intertidal grass Spartina alterniflora within its invasive and native ranges. Between September and November 2014, we sampled vegetative and reproductive traits in the field at 20 sites over 20° latitude in China (invasive range) and 28 sites over 17° latitude in the US (native range). We grew both Chinese and US plants in a greenhouse common garden for three years (2015 - 2017) to determine if differences in performance of S. alterniflora between the introduced and native ranges were due to genetic differences or differences in abiotic conditions.
Sea ice satellite and model data between October 2014 and December 2015
<p>These images contain monthly averages for Arctic sea ice concentration and thickness between October 2010 and December 2015 from two simulations, one with the TOPAZ4 ocean-sea ice data assimilation system (Sakov et al., 2012 and https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=ARCTIC_REANALYSIS_PHYS_002_003) and another with the S4K Arctic regional model. Image names are self-explanatory. Images with S4K results include a rectangle delimiting S4K domain, inserted in the larger TOPAZ4 domain. This was done to emphasize the transition between the two models, where the latter provides the boundary conditions to the former. </p> <p> </p>
Institutional Dimensions of Restoring Everglades Water Quality - Social Capital Analysis (FCE), Florida Everglades Agricultural Area from September 2014 to July 2015
These data were compiled through the Institutional Dimensions of Restoring Everglades Water Quality research project. One of the manuscripts generated by this project focused on the social capital dynamics in the Everglades Agricultural Area. These data represent different social capital aspects reflected by the responses of interview subjects. The purpose of analyzing social capital was to explore why and how farmers cooperated given that the state law, the Everglades Forever Act, which required the adoption of best management practices, relied on shared compliance for farmers to improve water quality. The study sought to undrestand how different aspects of social capital (broadly pro-social norms of reciprocity and trust) either encouraged or discouraged farmers to adopt BMPs effectively.
Net community production, nutrients, and hydrographic parameters in the South China Sea in October 2014 and June 2015
<p>Net community production (NCP) corresponds to the difference between photosynthesis and respiration and can be estimated based on the dissolved oxygen to argon ratio (O<sub>2</sub>/Ar) in the mixed layer. NCP is also an important proxy for the biological pump in the ocean. In order to figure out the influencing factors on NCP in the northern slope region of the South China Sea (SCS), we conducted high-resolution underway measurements of O<sub>2</sub>/Ar and hydrographic parameters using membrane inlet mass spectrometry (MIMS) and multi-parameter water quality logger (RBR Maestro) in the northern slope region of the SCS in October 2014 and June 2015, assisted by nutrients measurements. NCP in the mixed layer was estimated using the biological supersaturation of O<sub>2</sub>/Ar, Δ (O<sub>2</sub>/Ar), and gas transfer velocity (k). All the underway data were compiled into the 5-min interval. Surface water samples for the nutrients analysis were collected from Niskin bottles mounted on the conductivity–temperature–depth (CTD) rosette at sampling stations, the nutrients were then photometrically determined by an auto-analyzer. The mixed layer depth (MLD) and the euphotic depth (Z<sub>eu</sub>) were calculated at stations where CTD casts were made based on potential density and chlorophyll fluorescence profile, respectively.</p>
Kaneohe Bay Light Data 2014 and 2015 - superseded
<p>PAR light data from 2 meters depth on different patch reefs in Kaneohe Bay, Oahu Hawaii during 2014 and 2015.</p>
Kaneohe Bay Seawater Temperature Data 2014 and 2015
<p>Seawater temperature data from loggers placed at 2 meters depth at multiple patch reefs in Kaneohe Bay, Oahu, Hawaii.</p>
Dataset for paper: J. A. Cerón-Guzmán and E. León (2015), Detecting Social Spammers in Colombia 2014 Presidential Election. MICAI 2015. DOI: 10.1007/978-3-319-27101-9_9
<p>Dataset for paper: J. A. Cerón-Guzmán and E. León (2015), Detecting Social Spammers in Colombia 2014 Presidential Election. The 14th Mexican International Conference on Artificial Intelligence. DOI: 10.1007/978-3-319-27101-9_9</p>
Uganda Malaise trapping 2014–2015 background data
<p>This dataset contains the background data to the Uganda Malaise trapping 2014 – 2015. We trapped flying insects for a year in tropical forest in Kibale National Park. The sampling itself is described separately, in the <a href="https://doi.org/10.1098/rsos.190913">associated paper</a>. This dataset contains background data such as the weather, vegetation around our traps, maps and descriptions of the trap sites, and data on the 876 insect samples. It also contains the scripts used to process and clean up the data.</p> <p>The seven files in folder "1 Processed data" will usually be of the greatest interest. They contain ready-to-use data on GPS tracks and waypoints, vegetation, weather, trap sites and samples.</p>
Local physical oceanographic and oxygen isotopic observations at Palmyra Atoll of the 2014-15 and 2015-16 El Nino events
Open the record for dataset details and reuse information.
Boundary Fire: Sapling measurements inside and outside mammalian exclosures in 2013, 2014 and 2015
This dataset contains measurements from tagged saplings of trembling aspen, paper birch, and black spruce inside and outside mammalian exclosures set-up along the Steese Highway in the Boundary Fire. Density of dominant woody species is included along with height, diameter at basal and breast height, crown measurements, survival, health, leaf length, and presence of browsing damage on all tagged saplings.
CEDS_GBD-MAPS: Data Snapshot (2014 - 2015)
<p>2014-2015 Snapshot of the full CEDS_GBD-MAPS dataset (available at: https://doi.org/10.5281/zenodo.3754964)</p> <p>CEDS_GBD-MAPS Snapshot: Annual anthropogenic emissions of 7 key atmospheric pollutants from Dec 2014 - Feb 2015, produced using the <a href="http://www.globalchange.umd.edu/ceds/">Community Emissions Data System</a>, updated for the Global Burden of Disease - Major Air Pollution Sources project (<a href="https://github.com/emcduffie/CEDS/tree/CEDS_GBD-MAPS">CEDS_GBD-MAPS</a>).</p> <p>Emissions are provided for NO<sub>x</sub>, SO<sub>2</sub>, CO, NH<sub>3</sub>, NMVOCs, Black Carbon (BC), and Organic Carbon (OC) from 11 anthropogenic sectors and four fuel categories as both annual country totals and global gridded emission fluxes (0.5 x 0.5 degree resolution).<br> Note: The CEDS_GBD-MAPS inventory does not include emissions from open fires or aircraft.<br> <strong>Sectors: </strong><br> 1. Agriculture (non-combustion sources only, excludes open fires)<br> 2. Energy (transformation and extraction)<br> 3. Industry (combustion and non-combustion processes)<br> 4. On-Road Transportation<br> 5. Off-Road/Non-Road Transportation (rail, domestic navigation, other)<br> 6. Residential Combustion<br> 7. Commercial Combustion<br> 8. Other Combustion<br> 9. Solvents<br> 10. Waste (disposal and handling)<br> 11. International Shipping<br> <strong>Fuel Categories:</strong><br> 1. Total Coal Combustion (hard coal + brown coal + coal coke)<br> 2. Solid Biofuel Combustion<br> 3. Liquid Fuel (light oil + heavy oil + diesel oil) plus Natural Gas Combustion<br> 4. CEDS Process Source Categories (see McDuffie, et al., (ESSD) 2020) for further details.<br> Note: Total anthropogenic emissions = the sum of fuel categories 1-4</p> <p><strong>Zip File Details:</strong><br> Consistent with the full CEDS_GBD-MAPS inventory, emissions are available here in three different formats:</p> <p>1. <em>CEDS_GBD-MAPS_Snapshot_annual_country_total_emissions_by_sector_fuel_2014-2015.zip</em></p> <ul> <li>Zip file contains 7 .csv files that each contain total annual anthropogenic emissions of each compound from each country for 2014 and 2015, as a function of 11 anthropogenic sectors and 4 fuel categories.</li> <li>Emissions are in units of kt yr<sup>-1</sup> and include NO<sub>x</sub> (as NO<sub>2</sub>), CO, SO<sub>2</sub>, NH<sub>3</sub>, total NMVOCs, BC, and OC</li> <li>NOTE: Emissions include the entire Jan 2014 - Dec 2015 time period </li> </ul> <p>2. <em>CEDS_GBD-MAPS_Snapshot_gridded_emissions_by_sector_fuel_DJF_2014-2015.zip</em></p> <ul> <li>Zip file contains 145 netCDF files of anthropogenic global gridded emission fluxes, reported as a function of 11 anthropogenic sectors and 5 fuel categories (1 file per compound per fuel category, plus 1 file for the sum of all fuel categories)</li> <li>Monthly data are provided for Dec 2014 - Feb 2015 and have been formatted for use in the GEOS-Chem model (<a href="http://acmg.seas.harvard.edu/geos/">http://acmg.seas.harvard.edu/geos/</a>).</li> <li>Emission fluxes are in units of kg m<sup>-2</sup> s<sup>-1</sup> and include NO<sub>x</sub> (as NO), CO, SO<sub>2</sub>, NH<sub>3</sub>, 25 speciated VOCs, BC, and OC</li> <li>Example: ALD2-em-liquid-fuel-plus-natural-gas_CEDS_DJF_2014-2015.nc provides emission fluxes (Dec 2014, Jan 2015, Feb 2015) for the subVOC ALD2 that result from the combustion of liquid fuel and natural gas in each of the 11 source sectors.</li> </ul> <p>3. <em>CEDS_GBD-MAPS_Snapshot_gridded_total_anthro_emissions_by_sector_input4CMIP_DJF_2014-2015.zip</em></p> <ul> <li>Zip file contains 29 netCDF files (1 per compound) of anthropogenic global gridded emission monthly fluxes for Dec 2014 - Feb 2015, as a function of 11 anthropogenic sectors only (no disaggregation of fuel categories)</li> <li>netCDF files follow the CEDS CMIP6 gridded emissions format. More information available at: <br> <a href="http://www.globalchange.umd.edu/ceds/ceds-cmip6-data/">http://www.globalchange.umd.edu/ceds/ceds-cmip6-data/</a></li> <li>Emission fluxes are in units of kg m<sup>-2</sup> s<sup>-1</sup> and include NO<sub>x</sub> (as NO<sub>2</sub>), CO, SO<sub>2</sub>, NH<sub>3</sub>, 25 speciated VOCs, BC, and OC</li> </ul> <p>*Additional data details are provided in the README.txt file*</p> <p> </p> <p>*Version 2020_sv1.0 of this dataset was produced to accompany the following manuscript:<br> McDuffie, E. E., S. J. Smith, P. O'Rourke, K. Tibrewal, C. Venkataraman, E. A. Marais, B. Zheng, M. Crippa, M. Brauer, R. V. Martin, <strong>A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel- specific sources (1970- 2017): An application of the Community Emissions Data System (CEDS)</strong>, <em>Earth System Science Data, Submitted</em></p>
Stream Temperature Dataset, Fall 2014 - Summer 2015, Rocky Mountian National Park, Colorado, USA
<p>Dataset describing stream temperatures at 18 sites across two adjacent stream networks located in Rocky Mountain National Park, Colorado, USA. Temperature data collected with Onset Pendant waterproof loggers. DateTime format is UTC-7, with no daylight savings time. Temperature units are degrees C. Status column indicates the assumed location of the logger (Active= in water, Inactive = out of water). Elevation, UAA (Upstream Accumulated Area), X, and Y describe characteristics of the logger site. Lake_fraction column describes the fraction of the area draining to a site which is estimated to pass through one or more lakes, and UAA_weighted_mean_lake_elevation describes the mean elevation of upstream lakes, weighted by the UAA of each lake. This dataset accompanies the following publication, wherein it is described in greater detail:</p> <p>Carlson, S. P., & Poole, G. C. (2021). Describing alpine lake influence on stream network temperatures: A statistical modeling approach. <em>Hydrological Processes</em>, hyp.14072. <a href="https://doi.org/10.1002/hyp.14072">https://doi.org/10.1002/hyp.14072</a></p>
Kaneohe Bay Seawater Temperature Data 2014 and 2015 - superseded
<p>Seawater temperature data from loggers placed at multiple patch reefs in Kaneohe Bay, Oahu, Hawaii.</p>
Kaneohe Bay Light Data 2014 and 2015- superseded
<p>Light data (PAR) collected at 2 meters depth from various patch reefs in Kaneohe Bay<br> </p>
Kaneohe Bay Light Data 2014 and 2015
<p>Light data (PAR) collected at 2 meters depth from various patch reefs in Kaneohe Bay.</p>
ScienceDex guides
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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.