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14,198 results for “14”
Crown Geometry Measurements of 14 Species in the CTFS-ForestGEO Plot at Harvard Forest 2013
Tree crown geometry and height, especially when coupled with remotely sensed data, can aid in the characterization of tree and forest structure. In this study, we collected crown geometry data (tree height, crown radius, and crown depth) in order to develop mixed-effects model allometric equations. We leveraged the already existing Center for Tropical Forest Science (CTFS) and Smithsonian Institute’s Forest Global Earth Observatory (ForestGEO) MegaPlot on Prospect Hill at Harvard Forest, Massachusetts to apply allometric equations. In total, we sampled 374 trees across 14 species. Developed allometry was applied to 2014 CTFS-ForestGEO census data to develop allometric canopy height models, which were compared to a lidar canopy height model acquired by NASA’s G-LiHT.
WSC - Water surface elevation (WSE) and water table depth (WTD) from 14 points at the Wibu field site, 2012-2013 growing seasons
Observation wells were installed for the purpose of continuously monitoring the water table level during the 2012 and 2013 growing seasons at the Wibu field site. These data were then used to study the yield response of corn to water table depth, soil texture, and growing season weather conditions (Zipper et al., in prep). The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site. The 2012 growing season was characterized by severe drought, and the water table fell below the bottom of most wells in late June/early July.
Summary of soil temperature, moisture, and thaw depth for 14 chamber flux measurements sampled near Arctic LTER shrub sites at Toolik Field Station, Alaska, summer 2012.
Soil temperature at 5cm and 10cm depth, volumetric water content (VWC) and depth of thaw for 14 shrub canopy flux plots measured in vicinity of the Arctic LTER shrub site, Toolik Field Station, AK in 2012.
CSM01 Seasonal summary of numbers of small mammals on 14 LTER traplines in prairie habitats at Konza Prairie
Data set contains seasonal summaries (spring and autumn) of the number of individuals of each species of small mammal captured (relative abundance) on each grassland trapline. Each record contains year, season, trapline and number of individuals captured of each species. These live trap records are based on daily captures during two 4-day trapping periods in spring (late February to early April) and autumn (early October to mid-November) for each of 14 permanent traplines established on seven fire-grazing treatments (two traplines per treatment). These seven fire-grazing treatments include three sites that are grazed by bison (1 unburned, 1 annual burn and 1 4-year burn) and four sites that are not grazed by bison (1 unburned, 1 annual burn and 2 4-year burn).
Base segundo año 13_14 Validación SEQ EDU2012-32725
<p>Base de datos de resultados obtenidos de una muestra de 805 sujetos de tres universidades de la ciudad de Valencia: Universitat de Valènica, Universitat Politècnica de València y Universidad Católica de Valencia.</p><p>Esta muestra se utilizó para la validación del cuestionario SEQ (Study Engagement Questionnaire, de Kember y Leung (2005) en población española.</p><p>La base de datos, en SPSS, está integrada por 23 variables: Universidad, Rama, Titulación, Curso, Sexo, Edad, PsCrit (Pensamiento crítico), PesCrea (Pensamiento creativo), AprenAut (Aprendizaje autogestionado), Adapt (Adaptabilidad), Respro (Resolución de problemas), HabCom (Habilidades de comunicación), HabIn (Habilidades intergrupales), ManTec (Manejo nuevas tecnologías), AprenAct (Aprendizaje activo), EnCom (Enseñanza para la comprensión), Feedback (Feedback aprendizaje), Evaluación (Evaluación), RelProAl (Relación profesor-alumno), CarTrab (Carga de trabajo), RelEst (Relación con otros estudiantes), AprenCoop (Aprendizaje cooperativo), Coherencia (Coherencia plan de estudios)</p><p>Aviso: a partir de la variable Edad, se ha incluido el Nombre de la variable y la Etiqueta de la variable</p><p> </p><p> </p><p> </p><p> </p><p> </p>
Datasets of "Carbide coating on nickel to enhance the stability of supported metal nanoclusters" Nanoscale, 2022, 14, 3589-3598
<p>These are the datasets related to the publication "Carbide coating on nickel to enhance the stability of supported metal nanoclusters", Nanoscale, 2022, 14, 3589-3598 (<a href="https://doi.org/10.1039/D1NR06485A">https://doi.org/10.1039/D1NR06485A</a>). They are saved as NeXus/HDF5 files according to the nxstm NeXus application definition (<a href="https://doi.org/10.5281/zenodo.5792930">https://doi.org/10.5281/zenodo.5792930</a>).</p>
Weather dataset from Otemma glacier forefield, Switzerland (from 14 July 2019 to 18 November 2021)
<p>Weather data collected in the Otemma forefield (Switzerland) from 14 July 2019 to 18 November 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:<br> tom.muller.1@unil.ch<br> bettina.schaefli@giub.unibe.ch</p> <p><strong>Description of data : </strong>WeatherData.csv</p> <p>Time span of data : 14 July 2019 to 18 November 2021<br> Time step : homogenized 10 minutes-averaged data</p> <p>Location of data (Coordinate in SWISS LV95 (EPSG:2056)<br> - Glacier snout Station : 2598615 / 1087375<br> - Glacier center Station : 2600495 / 1088631<br> - Floodplain Station : 2598096 / 1087087</p> <p>STRUCTURE OF DATA : tidy dataframe with following headers :<br> - <em>date </em>: local date (UTC+01 with daylight saving time)<br> - <em>variable </em>: parameter of interest, with following classes :<br> Air_humidity : Air humidity in percent of air saturation [%]<br> Air_temperature : Air temperature in [°C]<br> Atm_pressure : Atmospheric pressure in [hPa]<br> Incoming_radiation : Incoming shortwave radiation in [W/m2]<br> Precipitation : Liquid precipitation measured [mm]<br> - <em>name </em>: location of data (see coordinates above)<br> - <em>dateUTC </em>: date with UTC timezone</p> <p>Device used for data acquisition :<br> - Air_humidity/Air_temperature/Atm_pressure : Decagon VP-4<br> - Incoming_radiation : Apogee Instruments SP-11<br> - Precipitation : Double tipping buckets rain gauge from Davis Instruments (resolution 0.2 mm)</p> <p> </p> <p><strong>Description of data : </strong>RainComposite_Otemma_Arolla.csv</p> <p>Time span of data : 01 July 2019 to 18 November 2021<br> Time step : homogenized 10 minutes-averaged data</p> <p>The dataset compiles the measured rain during summer at the closest weather station (Glacier snout).<br> For the winter period (and few gaps during the summer), the solid precipitations (snow) from the closest MeteoSwiss weather stations (SwissMetNet) were used.<br> Gaps where filled with 1) MeteoSwiss Otemma and if data were still missing filled with 2) MeteoSwiss Arolla<br> <br> Location of data (Coordinate in SWISS LV95 (EPSG:2056)<br> - Glacier snout Station : 2598615 / 1087375<br> - Otemma camp Station : 2597508 / 1086653<br> - MeteoSwiss Station : 2596476 / 1085864<br> - MeteoSwiss Arolla : 2603507 / 1095832</p> <p>STRUCTURE OF DATA : tidy dataframe with following headers :<br> - date : local date (UTC+01 with dst)<br> - variable : parameter of interest<br> Precipitation : Liquid and solid precipitation [mm]. Composite dataset composed of melted snow (snow Water Equivalent, in mm, from MeteoSwiss station) and Rain (in mm from Glacier station).<br> - Location : location of data (see above)<br> - dateUTC : date in UTC timezone<br> <br> Device used for data acquisition :<br> - Glacier snout Station : Double tipping buckets rain gauge from Davis Instruments (resolution 0.2 mm)<br> - Otemma camp Station : Double tipping buckets rain gauge, Spectrum WatchDog 1120 (resolution 0.25 mm)<br> - MeteoSwiss : see <a href="https://www.meteoswiss.admin.ch/home/measurement-and-forecasting-systems/land-based-stations/automatisches-messnetz.html">SwissMetNet</a> project</p> <p> </p> <p><strong>Description of data :</strong> Otemma_weather_Plot_alldata.html</p> <p>An interactive plot generated with python plotly (open in web browser) containing all above described data.</p>
body_finishing_14
This is the process of making a carafe Bontemps. The step depicted is called "body finishing"(from the Mingei project).
necking_14
This is the process of making a carafe Bontemps. The step depicted is called "necking"(from the Mingei project).
carafe_foot_laying_14
This is the process of making a carafe Bontemps. The step depicted is called "carafe_foot_laying"(from the Mingei project).
carafe_leg_laying_14
This is the process of making a carafe Bontemps. The step depicted is called "carafe_leg_laying"(from the Mingei project).
From_Blowpipe_to_Punty_14
This is the process of making a carafe Bontemps. The step depicted is called "From Blowpipe to Punty"(from the Mingei project).
beak_cutting_14
This is the process of making a carafe Bontemps. The step depicted is called "beak_cutting"(from the Mingei project).
cord_finishing_14
This is the process of making a carafe Bontemps. The step depicted is called "cord_finishing"(from the Mingei project).
handle_finishing_14
This is the process of making a carafe Bontemps. The step depicted is called "handle_finishing"(from the Mingei project).
Annealing_14
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
handle_laying_14
This is the process of making a carafe Bontemps. The step depicted is called "handle_laying"(from the Mingei project).
Finition_14
This is the process of making a carafe Bontemps. The step depicted is called "Finition"(from the Mingei project).
beak_finishing_14
This is the process of making a carafe Bontemps. The step depicted is called "beak_finishing"(from the Mingei project).
MAMEM EEG SSVEP Dataset III (14 channels, 11 subjects, 5 frequencies presented simultaneously)
<p>EEG signals with 14 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation, and the Emotiv EPOC, using 14 wireless channels has been used for capturing the signals</p>
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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.