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4,681 results for “light”
Summary of measured and modeled light curve parameters for diffuse, direct, and intermediate light curves for 14 whole-canopy 1mx1m plots sampled near the shrub LTER sites at Toolik Field Station, Alaska, summer 2012.
14 1m x 1m shrub plots were sampled the summer of 2012 under direct and diffuse light conditions. Light response curves were measured under each light condition for each plot using a Li-Cor 6400 to measure net ecosystem exchange (NEP); these measurements were modelled using a saturatingMichaelis-Menton formula. The best fit parameters for those models are contained here (Pmax, K, RE, Eo, and light compensation point) for each individual NEP light response curve (direct, diffuse, and intermediate light conditions) measured with corresponding NDVI , LAI, diffuse light fraction, and average temperature. Sorting variables and curve ID numbers for each curve match the corresponding data in the flux data file.
Light response curves measured from shoots harvested at three levels in the canopy from 19 1m x 1m plots dominated by S. pulchra or B. nana shrubs near LTER Shrub plots at Toolik Field Station, AK the summer of 2012.
This dataset contains light response curves and modeled light curve parameters from shoots clipped from low, mid, and the top parts of tall, shrub canopies dominated either by Salix pulchra or Betula nana. Six shoots were harvested from each 1m x 1m plot, two from each level in the canopy in plots located near the LTER shrub plots at Toolik Field Station, AK the summer of 2012. The species harvested were chosen based on the species present in each plot, thus the species from each segment of the canopy may not be the same. Additional information about each shoot can be found in the "2012_GS_ITEX_PF_ShootA-CiData" and "2012_GS_ITEX_PF_ShootHarvestData" pages, regarding the A-Ci response, area, mass, leaf area index, and leaf nitrogen content of each shoot. The file "2012_GS_ITEX_PercentCover" contains the species cover data for each plot.
Primary production estimates from 14C uptake (in situ), determined by the incorporation of inorganic carbon into particulate organic carbon (POC) due to photosynthesis at selected light levels from CCE LTER process cruises in the California Current System, 2006 - 2021 (ongoing).
Primary productivity samples of seawater are taken each day shortly before noon on the CTD rosette up-cast during the CCE Process crusies (since 2006, ongoing). Light penetration below the surface is estimated from the Secchi disk depth. Niskin bottles from depths with ambient light intensities corresponding to light levels simulated by on-deck incubators are identified and sampled. Primary production is estimated from 14C uptake using this simulated in situ technique (followed by filtering) by which the assimilation of dissolved inorganic carbon by phytoplankton yields a measure (in µg/L/day) of the rate of photosynthetic primary production (particulate organic carbon, POC) at selected light levels in the euphotic zone within the CCE study area.
Percent light penetration: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
Air temperature and light intensity measurements within and near the Coweeta basin from 2011 to 2013
In this study 50 HOBO data loggers were deployed at 61 locations within and near the Coweeta basin to record air temperature and light intensity observations. The loggers were deployed between April 2011 and April 2012. The duration of observation at each location varied. All loggers were removed by February 2013. In April 2012, radiation shields constructed of two inverted plastic funnels were added to a subset of loggers in an attempt to reduce the effect of solar radiation on daytime temperature observations. Light intensity data collected by the loggers were used as a means of flagging potentially erroneous observations when the logger was exposed to direct sunlight. A light intensity threshold above which observations were flagged was determined by comparing logger observations to those collected by nearby (<5m) sensors protected with commercial gill style radiation shields. Despite attempts to flag potentially erroneous daytime temperature observations, it is likely that errors remain and these data should be used accordingly.
Lake snow removal experiment buoy, light, and chlorophyll data, 2019-2021
Although it is a historically understudied season, winter is now recognized as a time of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging research points to a future of reduced ice cover duration and changing snow conditions that will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice conditions, and subsequent changes to under-ice light environment, might impact ecosystem dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019 season served as a reference year, and snow was not removed from the lake. This dataset represents chlorophyll, light, and high frequency buoy data collected from this project. Related datasets are: https://doi.org/10.6073/pasta/962fa57959ff9828eb6f1cbda79b82c0 https://doi.org/10.6073/pasta/f6e271634a04819e25bc7c913cd67155 https://doi.org/10.6073/pasta/9a26e819522152e878d802df76cf90d7
Brainport, Platooning, platoon with live traffic light
<p><strong>Scenario description</strong>:</p> <p>Platoon formation and platooning, from Helmond to Eindhoven and back to the Automotive Campus.<br> - Starting in urban area with speed limits of 15 and 30 km/h.<br> - Driving East on the Europaweg with speed limits of 50 and 70 km/h. This includes 3 crossings with traffic lights.<br> - Driving on the the N270, along the Automotive Campus. One crossing with traffic lights, just before the A270.<br> - Driving on the A270 (speed limit 100 km/h). Interrupted by one traffic light.<br> - U-turn at the fly-over or at the end of the A270, to return the same way to the Automotive Campus.</p> <p><strong>Session description</strong>:</p> <p>Platoon formation and platooning, with live traffic light data included in planner.<br> - Live traffic light data available for planner<br> - Driver uses the Android app<br> - Starting at default locations<br> - Platooning (CACC and lane keeping) on the A270 when possible.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_Platooning_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatoonFormation</strong>: Data sent from PlatoonService to vehicle</p> <p>This dataset contains information about the route and speed for a specific vehicle for forming a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningAction</strong>: Data logged by vehicle</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningEvent</strong>: Data logged by vehicle</p> <p>This dataset contains information about the identifiers used for each specific platooning event</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatoonStatus</strong>: Data sent by vehicle to PlatoonService</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystemResample</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PSInfo</strong>: Data sent by PlatoonService to the vehicle</p> <p>This dataset contains speed and route information for the vehicle to create a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Target</strong>: Data from sensors on the vehicle</p> <p>Target detection in the vicinity of the host vehicle, by a vehicle sensor or virtual sensor</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Brainport, Platooning, formation improvement by traffic light data
<p><strong>Scenario description</strong>:</p> <p>Platoon formation with live traffic light data included in planner.<br> - Enabled live traffic light data included in planner<br> - Not using the Android app<br> - Starting at default locations<br> - This test was filmed, including the GUI.</p> <p><strong>Session description</strong>:</p> <p>Platoon formation improvement by traffic light data.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_Platooning_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatoonFormation</strong>: Data sent from PlatoonService to vehicle</p> <p>This dataset contains information about the route and speed for a specific vehicle for forming a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningAction</strong>: Data logged by vehicle</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningEvent</strong>: Data logged by vehicle</p> <p>This dataset contains information about the identifiers used for each specific platooning event</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatoonStatus</strong>: Data sent by vehicle to PlatoonService</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystemResample</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PSInfo</strong>: Data sent by PlatoonService to the vehicle</p> <p>This dataset contains speed and route information for the vehicle to create a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Target</strong>: Data from sensors on the vehicle</p> <p>Target detection in the vicinity of the host vehicle, by a vehicle sensor or virtual sensor</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Dataset for sound source localization with 101 Blinky sound-to-light conversion sensors
<p>Blinkies are sound-to-light conversion devices that can be used to monitor the sound level over large areas. The data from the sensors is harvested using a video camera. This dataset contains seven videos that were recorded in the gymnastical hall of Tokyo Metropolitan University, Hino Campus on July 3rd 2018. In the video, 101 Blinkies are spread on the ground of the gymnastic hall. A bluetooth speaker mounted on a remote controlled car runs between the Blinkies, causing them to change intensity. The file `pyramic_json` is a JSON format file containing all the meta-data necessary such as sensor locations, room dimensions, and segmentation information.</p> <p>This dataset was used to demonstrate sound source localization in the paper "Blinkies: Open source sound-to-light conversion sensors for large-scale acoustic sensing and applications" by Robin Scheibler and Nobutaka Ono (to appear).</p>
EU Lighting Efficacy Policy History Tool
<p>As of 2020, the EU has published <a href="https://ec.europa.eu/energy/en/topics/energy-efficiency/energy-efficient-products/list-regulations-product-groups-energy-efficient-products">three regulations on lighting efficiency</a>. This Excel tool can be used to calculate the corresponding luminous efficacy [lm/W] for each regulation.</p> <p>Compiled as part of the research project <a href="https://web.archive.org/web/20220920225758/https://www.ceenrg.landecon.cam.ac.uk/research/climate-change-and-energy-policy/what-factors-drive-innovation-in-energy-technologies-the-role-of-technology-spillovers-and-government-investment">"What factors drive innovation in energy technologies? The role of technology spillovers and government investment"</a>, funded by the Alfred P. Sloan Foundation.</p>
FIG. 15 in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals
FIG. 15. — Upper molar proportions in Euungulata, "Condylarthra", SANUs, and the kollpaniines from Tiupampa described here. Molar proportions are plotted in the developmental 'morphospace' (Kavanagh et al. 2007; Polly 2007) where the white region is consistent with the IC model; the broken line is the relationship predicted for lower molar of murine rodents (see Material and methods and Table 8). Abbreviations: Kalith., Kalitherium.
FIG. 11 in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals
FIG. 11. — Pucanodus gagnieri: partial right mandible with m2-3 (MHNC 13869): A, stereophotograph of occlusal view; B, the same in labial view. Scale bar: 5 mm.
FIG. 4 in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals
FIG. 4. — Molinodus suarezi: partial maxilla (MHNC 13870) with incomplete M1-2 and complete M3. Stereophotograph of occusal view. Scale bar: 5 mm.
FIG. 7 in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals
FIG. 7. — Simoclaenus sylvaticus: partial right mandible with alveolus of p1, root of p2-3, p4 and m1 (MHNC 13872): A, stereophotographs of occlusal view; B, the same in lateral view; C, the same in medial view. Scale bar: 5 mm.
FIG. 2 in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals
FIG. 2. — Partial left mandible of Molinodus suarezi (MHNC 13867) bearing p3-m3: A, stereophotographs of the occlusal view; B, lingual view; C, labial view. Scale bar: 5 mm.
FIG. 13. — A, B in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals
FIG. 13. — A, B, Lamegoia conodonta; C, didolodontidae indet.; A, occlusal view of a left m2 of Lamegoia conodonta (cast of holotype MNRJ 1463-V); B, occlusal view of a right M2 (reversed) of Lamegoia conodonta (cast of MNRJ 1465-V); C, occlusal view of a left M2 (cast of MNRJ 1464-V) of and undetermined didolodont (referred by Paula Couto [1952a] to L. conodonta). Scale bar: 5 mm.
FIG. 5 in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals
FIG. 5. — Molinodus suarezi: partial maxilla (MHNC 13870): A, occlusal view; B, lingual view. Scale bar: 5 mm.
Light micrographs on the morphological response to heat stress in the filamentous Zygnematophyceae Mougeotia sp. and Spirogyra pratensis
<p>Herein are the light micrographs of <em>Mougeotia</em> sp. and <em>Spirogyra pratensis</em> that were used to obtain the quantitative information for Figure 1 in the article "Heat stress response in the closest algal relatives of land plants reveals conserved stress signaling circuits" published in The Plant Journal, doi: 10.1111/tpj.14782</p>
Data of "Deterministic creation of entangled atom–light Schrödinger-cat states"
<p>Data published in "<em>Deterministic creation of entangled atom–light Schrödinger-cat states</em>"</p> <p>Nature Photonics <strong>volume 13</strong>, pages110–115(2019)</p>
Distribution of interplanetary dust detected by the Juno spacecraft and its contribution to the Zodiacal Light
<p>The Zodiacal light is sunlight reflected by dust in the inner solar system. Variations in the Zodiacal light with ecliptic latitude reveal discrete bands of dust orbiting near the ecliptic plane. The Juno spacecraft, in transit from earth to Jupiter, recorded a sufficient number of impacts with this dust to characterize their distribution in space for the first time. </p> <p>This dataset (filename IDP_List.txt) contains a time-ordered list of all IDP impact detections along with supplementary engineering and ephemeris information. The file is an ASCII text file and the file format is described in the word document (IDP_List_Format.docx).</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.