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4,725 results for “normalization”
Long-term composited and land cover-adjusted Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2020
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Next, we corrected the underestimated ENDISI values of dark impervious surface cover and the overestimated ENDISI values of bright bare soils based on visible Landsat bands and 2020 land cover (Sabu et al. 2023). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 - Sabu, S., Frazier, A., & Rashid, B. (2023). Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020 [Dataset]. Environmental Data Initiative. https://doi.org/10.6073/PASTA/BF18E5856215BD2D4DAB3B024BA87A7E
Long-term composited Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 – from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Long-term composited Normalized Difference Vegetation Index (NDVI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
### overview This data package consists of multiple decades of normalized difference vegetation index (NDVI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). To serve as a proxy measurement of vegetation greenness and productivity across years and seasons, NDVI was derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Normalized Difference Vegetation Index (NDVI) derived from 2019 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices—Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2019 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, and 2017 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP study area boundary.
Speech disfluencies: Neurophysiological aspect in normal population
Open the record for dataset details and reuse information.
Triaxial accelerometer gait dataset: foot and lower back motion during normal and metronome walking
<p><strong>This dataset contains accelerometric data collected from young and older individuals walking in a controlled environment. The data were recorded using two triaxial accelerometers, one attached to the participant's lower back and the other attached to the foot. Participants were instructed to walk back and forth along a 205-meter corridor under two different conditions:</strong></p> <p><strong>Normal walking: </strong>Participants walked at their preferred walking speed, reflecting their natural gait and pace.</p> <p><strong>Metronome walking: </strong>Participants synchronized their walking pace to a metronome set to their preferred walking cadence. This condition introduced a rhythmic element to the walking pattern, allowing for the study of gait changes when adhering to an external tempo.</p> <p><strong>Another condition was also measured to introduce a more variable and dynamic walking pattern that reflects everyday pedestrian movement in a real-world context.</strong></p> <p><strong>Free outdoor walking:</strong> Older participants engaged in approximately 5 minutes of free walking in an urban environment, navigating city streets. During this activity, only the lumbar accelerometer was used to record data. </p>
Murreviikko: an Annotated and Normalized Corpus of Dialectal Finnish Tweets
<p>Murreviikko (literally 'Dialect week') is a campaign founded in the University of Eastern Finland to promote the use of Finnish dialects in social media. It started in 2020 and takes place mid-October.</p> <p>The original data was collected from Twitter with the search word murreviikko ('dialect week') and hashtag #murreviikko separately for 2020, 2021 and 2022. The current dataset combines all the original collections.</p> <p>The tweets are dialectologically annotated on two levels: following the East-West division of Finnish dialects, and following a seven-way division of Finnish dialects (South-West, Häme, Southern Ostrobothnia, Central and Northern Ostrobothnia, Far North, Savo, and South-East), appended with the Helsinki slang. There is also a class for dialectal tweets, which are not discernible (NA) because of contrasting or scarce dialectal features.</p> <p>The original tweets are normalized to a phonetic standard, but word order is not altered, or grammar rules of standard Finnish followed otherwise. This means that for instance standard Finnish possessive suffixes (minun kirja-ni 'my book-my') are not added if they are not present in the original tweet (minun kirja). Likewise, dialect words are not corrected to the standard alternative, even if such words would exist (pruukata > pruukata instead of standard tavata).</p> <p>Following the rules of the Twitter API, this repository only includes the tweet id's, dialect annotations and normalizations. The original tweets are available for scientific use by request, as granted by the European Union’s Digital Single Market directive (2019/790).</p>
30 m Normalized Difference Vegetation Index Maps of Pure Pixels over China for Estimation of Fractional Vegetation Cover (2014, 2018, 2022)
<p>Using multi-angle remote sensing data, we generated 30-m maps for the normalized difference vegetation index (NDVI) of fully-covered vegetation (<em>Vv</em>) and bare soils (<em>Vs</em>) across China in 2014, 2018 and 2022. These pixel-wise <em>Vv</em> and <em>Vs</em> maps can be integrated with the vegetation index (VI)-based model to facilitate the accurate and rapid estimation of fractional vegetation cover (FVC) across various spatial resolutions and large scales. The products were produced using a multi-angle algorithm (MultiVI), which effectively addressed the spatial variability inherent in <em>Vv</em> and <em>Vs</em> and enhanced the accuracy of FVC estimations in comparison to traditional statistical methods. The estimated FVC demonstrated a root mean square deviation (RMSD) of approximately 0.1 when evaluated against field-measured FVC across different experimental sites.</p>
Daily rainfall series and rainfall erosivity in Mexico for three climatic normals (1968-1997, 1978-2007, and 1988-2017)
As in many countries around the world, there are some issues in the Mexican rainfall series, such as missing values, short measurement periods, and series homogeneity (breaks due to station relocation and measurement mistakes), which further compound the challenge of using climate data. Furthermore, it is necessary to develop a complete and helpful rainfall series database by following an imputing and homogenization process of the rainfall series. This research has compiled and systematized a national dataset with daily rainfall and rainfall erosivity for three climatic normals CN (1968-1997, 1978-2007, and 1988-2017). We have used the "climatol" package to fill the data. After, we calculated daily rainfall erosivity using a power law model. As a result, we obtained 1370, 1679, and 1683 rainfall series for the CNs 1968-1997, 1978-2007, and 1988-2017, respectively. The median values of the rainfall erosivity for the three CNs were 3245, 3070, and 3327 MJ mm/ ha h yr, respectively. We are making this database available for public consultation for researchers and students, technical assistants, decision-makers, and others interested in environmental studies in Mexico.
Normalized Difference Vegetation Index (NDVI) derived from 2021 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices —Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI)— from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2021 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, 2017, and 2019 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP LTER study area boundary of central Arizona, USA. The materials presented here include NDVI data with SAVI data presented in a companion dataset that is also available through the EDI.
Long-term composited Modified Normalized Difference Water Index (MNDWI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
Abstract ======== This data package consists of multiple decades of modified normalized difference water index (MNDWI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). By providing a metric by which to reliably identify bodies of open water, these MNDWI data are intended to facilitate analyses of land-based environmental variables (e.g., urbanization, vegetation, land surface temperature) and can also be used to track long-term and seasonal change in the coarse extent of open water as a land-cover type. MNDWI was derived, following the methods of Xu (2006), from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see \'Methods and Protocols\') and accompanying Javascript code. **Citations:** - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18--27. <https://doi.org/10.1016/j.rse.2017.06.031> - Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. *International Journal of Remote Sensing*, *27*(14), 3025--3033. <https://doi.org/10.1080/01431160600589179>
Centre frequencies and uncertainties for "Evidence for a kilometre-scale seismically slow layer atop the core-mantle boundary from normal modes"
<p>A table containing the centre frequencies and uncertainties used for the study presented in "Evidence for a kilometre-scale seismically slow layer atop the core-mantle boundary from normal modes". This table is the same as is contained in the supplementary materials of that paper.</p> <p>Russell, S., Irving, J. C. E., Jagt, L., & Cottaar, S. (2023). Evidence for a kilometer-scale seismically slow layer atop the core-mantle boundary from normal modes. Geophysical Research Letters, 50, e2023GL105684. <a href="https://doi.org/10.1029/2023GL105684">https://doi.org/10.1029/2023GL105684</a></p>
BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH
<p>This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. </p> <p> </p> <p>Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. </p> <p> </p> <p>The file name convention for the Level 3 NetCDF files is as follows.</p> <p> </p> <p>uas_L3_yyyymmdd_hhmmss_vx.x.nc</p> <p>where</p> <p>L3 = Level 3 data </p> <p>yyyymmdd = year,month,day</p> <p>hhmmss = hour,minute,second</p> <p>x.x = version number </p> <p>Time is the flight start time in UTC.</p> <p>Version number description is provided in the NetCDF global attributes.</p> <p> </p> <p>Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. </p> <p><strong>November 2023 update</strong>: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data. Version 2.1 adds data file uas_L3_20221018_171650_v2.1.nc that was missing in Version 2.0.</p> <p><strong>December 2023 update</strong>: Version 2.2 updated soil moisture data with a wet bias in v2.1 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.</p>
AIM aggregated dataset of normal driving behavior at intersections
<p>As part of the Application Platform for Intelligent Mobility (AIM), the traffic situation of an intersection in Braunschweig (Germany) was recorded in order to better understand the behavior of road users. In the project L3pilot, the normal driving behavior at the intersection was analyzed. We focused on kinematic and interaction behaviour of a vehicle (turning left or right, going straight) with oncoming road users (VRU and motorised vehicles) and with lead vehicle. Altogether, 30 days of trajectory data of different months of 2018 and 2019 of the relevant scenarios were analyzed. The datasets include the aggregated parameters in the following scenarios: </p> <ul> <li>Scenario L1: left turning following vehicle from West to North interacting with the lead vehicle from West to North.</li> <li>Scenario L2: left turning vehicle from West to North interacting with oncoming vehicle from East to West.</li> <li>Scenario L3: left turning vehicle from West to North interacting with oncoming bicycle.</li> <li>Scenario R1: right turning following vehicle from East to North interacting with the lead vehicle from East to North.</li> <li>Scenario R2: right turning vehicle from East to North interacting with bicycle from East to West.</li> <li>Scenario S: straight driving following vehicle from East to West interacting with the lead vehicle from East to West.</li> </ul> <p> </p> <table> <caption>meta table of car following</caption> <tbody> <tr> <td>col</td> <td>colname</td> <td>unit</td> <td>description</td> </tr> <tr> <td>1</td> <td>id</td> <td>-</td> <td>trial id</td> </tr> <tr> <td>2</td> <td>subscenario</td> <td>-</td> <td>the following vehicle stopped (stop) or not (non-stop)</td> </tr> <tr> <td>3</td> <td>m(v)</td> <td>m/s</td> <td>average velocity of following vehicle</td> </tr> <tr> <td>4</td> <td>max(v)</td> <td>m/s</td> <td>maximum velocity of following vehicle</td> </tr> <tr> <td>5</td> <td>sd(v)</td> <td>m/s</td> <td>standard deviation of velocity of following vehicle</td> </tr> <tr> <td>6</td> <td>mdn(v)</td> <td>m/s</td> <td>median velocity of following vehicle</td> </tr> <tr> <td>7</td> <td>m(ax)</td> <td>m/s²</td> <td>average longitudinal acceleration of following vehicle</td> </tr> <tr> <td>8</td> <td>max(ax)</td> <td>m/s²</td> <td>maximum longitudinal acceleration of following vehicle</td> </tr> <tr> <td>9</td> <td>min(ax)</td> <td>m/s²</td> <td>minimum longitudinal acceleration of following vehicle</td> </tr> <tr> <td>10</td> <td>sd(ax)</td> <td>m/s²</td> <td>standard deviation of longitudinal acceleration of following vehicle</td> </tr> <tr> <td>11</td> <td>mdn(ax)</td> <td>m/s²</td> <td>median longitudinal acceleration of following vehicle</td> </tr> <tr> <td>12</td> <td>duration</td> <td>s</td> <td>duration</td> </tr> <tr> <td>13</td> <td>m(d)</td> <td>m</td> <td>average distance to lead vehicle</td> </tr> <tr> <td>14</td> <td>min(d)</td> <td>m</td> <td>minimum distance to lead vehicle</td> </tr> <tr> <td>15</td> <td>m(THW)</td> <td>s</td> <td>average time headway</td> </tr> <tr> <td>16</td> <td>min(THW)</td> <td>s</td> <td>minimum time headway</td> </tr> <tr> <td>17</td> <td>m(TTC)</td> <td>s</td> <td>average time to collision</td> </tr> <tr> <td>18</td> <td>min(TTC)</td> <td>s</td> <td>minimum time to collision</td> </tr> <tr> <td>19</td> <td>v_min(TTC)</td> <td>m/s</td> <td>velocity of following vehicle at the minimum time to collision</td> </tr> <tr> <td>20</td> <td>a_min(TTC)</td> <td>m/s²</td> <td>acceleration of following vehicle at the minimum time to collision</td> </tr> <tr> <td>21</td> <td>THW_min(TTC)</td> <td>s</td> <td>time headway at the minimum time to collision</td> </tr> <tr> <td>22</td> <td>d_min(TTC)</td> <td>m</td> <td>distance to lead vehicle at the minimum time to collision</td> </tr> </tbody> </table> <p> </p> <table> <caption>meta table of crossing</caption> <tbody> <tr> <td>col</td> <td>colname</td> <td>unit</td> <td>description</td> </tr> <tr> <td>1</td> <td>id</td> <td>-</td> <td>trial id</td> </tr> <tr> <td>2</td> <td>subscenario</td> <td>-</td> <td>vehicle yielded (yielding) or didn't yield (non-yielding) to the oncoming road user</td> </tr> <tr> <td>3</td> <td>m(v)</td> <td>m/s</td> <td>average velocity of following vehicle</td> </tr> <tr> <td>4</td> <td>max(v)</td> <td>m/s</td> <td>maximum velocity of following vehicle</td> </tr> <tr> <td>5</td> <td>sd(v)</td> <td>m/s</td> <td>standard deviation of velocity of following vehicle</td> </tr> <tr> <td>6</td> <td>mdn(v)</td> <td>m/s</td> <td>median velocity of following vehicle</td> </tr> <tr> <td>7</td> <td>m(ax)</td> <td>m/s²</td> <td>average longitudinal acceleration of following vehicle</td> </tr> <tr> <td>8</td> <td>max(ax)</td> <td>m/s²</td> <td>maximum longitudinal acceleration of following vehicle</td> </tr> <tr> <td>9</td> <td>min(ax)</td> <td>m/s²</td> <td>minimum longitudinal acceleration of following vehicle</td> </tr> <tr> <td>10</td> <td>sd(ax)</td> <td>m/s²</td> <td>standard deviation of longitudinal acceleration of following vehicle</td> </tr> <tr> <td>11</td> <td>mdn(ax)</td> <td>m/s²</td> <td>median longitudinal acceleration of following vehicle</td> </tr> <tr> <td>12</td> <td>duration</td> <td>s</td> <td>duration</td> </tr> <tr> <td>13</td> <td>PET</td> <td>s</td> <td>post encroachment time</td> </tr> <tr> <td>14</td> <td>m(TAdv)</td> <td>s</td> <td>average time advantage</td> </tr> <tr> <td>15</td> <td>min(TAdv)</td> <td>s</td> <td>minimum time advantage</td> </tr> <tr> <td>16</td> <td>v_min(TAdv)_id1</td> <td>m/s</td> <td>Oncoming object’s velocity the moment of minimum time advantage</td> </tr> <tr> <td>17</td> <td>a_min(TAdv)_id1</td> <td>m/s²</td> <td>Oncoming object’s acceleration in heading the moment of minimum time advantage</td> </tr> <tr> <td>18</td> <td>v_min(TAdv)_id2</td> <td>m/s</td> <td>vehicle’s velocity the moment of minimum time advantage</td> </tr> <tr> <td>19</td> <td>a_min(TAdv)_id2</td> <td>m/s²</td> <td>vehicle’s acceleration in heading the minimum time advantage</td> </tr> <tr> <td>20</td> <td>THW_min(TAdv)_id1</td> <td>s</td> <td>Timeheadway oncoming object to the crossing area’s entering part the moment of minimum time advantage</td> </tr> <tr> <td>21</td> <td>THW_min(TAdv)_id2</td> <td>s</td> <td>Timeheadway vehicle to the crossing area’s entering part the moment of minimum time advantage</td> </tr> <tr> <td>22</td> <td>d_min(TAdv)_id1</td> <td>m</td> <td>Distance oncoming object to the crossing area’s entering part the moment of minimum time advantage</td> </tr> <tr> <td>23</td> <td>d_min(TAdv)_id2</td> <td>m</td> <td>Distance vehicle to the crossing area’s entering part the moment of minimum time advantage</td> </tr> </tbody> </table> <p> </p>
Data for the article "Computational micromagnetics based on normal modes: Bridging the gap between macrospin and full spatial discretization"
<p>Data for the article "Computational micromagnetics based on normal modes: Bridging the gap between macrospin and full spatial discretization".</p> <p>Link to publisher: https://www.sciencedirect.com/science/article/abs/pii/S0304885321009197</p> <p>Link to Arxiv preprint: https://arxiv.org/abs/2105.08829</p>
Dataset for the published article "ITER relevant multi-emissive sheaths at normal magnetic field inclination"
<p>The data contained in the zip files constitute the main research data of the publication entitled as "<a href="https://iopscience.iop.org/article/10.1088/1741-4326/acaabd">ITER relevant multi-emissive sheaths at normal magnetic field inclination</a>" [1]. All the datasets constitute post-processed output from the 2D3V SPICE2 Particle-In-Cell (PIC) code. All the PIC simulations have been performed by M. Komm and A. Podolnik. The input is specified by the plasma density, the electron temperature and the surface temperature. The plasma parameters are relevant to partially mitigated ITER edge-localized modes (ELMs). The output concerns the incident plasma current densities, the emitted electron current densities and their standard deviation, the normal wall electrostatic field, the average electron incident energy, the average electron incident angle with respect to the wall normal and the virtual cathode depth. </p> <p>The assumptions below are followed in all simulations: (i) The Bohm pre-sheath structure is unaltered by the escaping emitted electrons, since the ions are injected at the plasma boundary with a speed distribution satisfying the Bohm criterion. (ii) Irrespective of the emission, the wall is biased with respect to the plasma boundary with a magnitude fixed by the ambipolarity of the plasma fluxes. (iii) The sheath is collisionless. (iv) The wall is perfectly planar. (v) A homogeneous quasi-neutral plasma boundary and an infinite emitting wall with a homogeneous prescribed surface temperature are considered.</p> <p>Sheaths that form between plasma-facing components (PFCs) and standard scrape-off-layer plasmas can be described by the classical model of one-dimensional magnetized multi-positive ion sheaths. There are various conditions that need to be satisfied for this model to be valid such as negligible cross-field drifts, low collisionality and weak electron emission.</p> <p>In contemporary metallic tokamaks, the weak emission condition is violated in the divertor region during intra-ELM as well as inter-ELM periods; thermionic emission being an effective electron emission mechanism from hot tungsten PFCs. As a result of the localized ELM-wetted area, the incident plasma currents can be assumed to remain nearly ambipolar and thus the non-ambipolar current should be equal to the emitted current that escapes to the Bohm pre-sheath. This escaping current density generates a strong volumetric Lorentz force that drives melt layer motion leading to macroscopic PFC erosion. At very elevated surface temperatures, the nominal thermionic current densities are so large that they become incompatible with the classical Bohm pre-sheath structure. As a consequence, space charge accumulation in the sheath leads to the formation of a virtual cathode that limits the escaping thermionic current to a constant value causing the recapture of a fraction of the thermo-electrons. Thus, there is a transition from a monotonic to a non-monotonic potential profile, with the latter known as the space-charge limited (SCL) regime of the emissive sheath. In the case of oblique magnetic field inclination angles, the SCL transition is still realized, but further complications arise due to the suppression of the nominal thermionic current by recapture during Larmor gyration. In contemporary tokamaks, this transition generally occurs at temperatures below the tungsten melting point, thus particular attention has been paid to the SCL sheaths, since they nearly exclusively surround the molten tungsten PFCs. The thermionic emissive sheath in the SCL regime has been thoroughly investigated in our previous works, where an accurate semi-empirical expression for the limited value of the escaping thermionic current as function of the plasma conditions and magnetic field inclination angle was constructed on the basis of systematic PIC simulations [2-4].</p> <p>On the other hand, during ITER intra-ELM periods, the predicted elevated electron temperatures and high plasma densities of the pre-sheath edge should have a strong impact on the emissive sheath established above hot tungsten PFCs. In particular, the high plasma electron temperatures could enable significant contributions from electron-induced electron emission (secondary electron emission and electron backscattering), the intense normal surface electrostatic fields indicate that thermionic emission is coupled with field emission (in the Schottky regime) and the strong plasma currents suggest that virtual cathodes are formed at much higher surface temperatures (so that the monotonic potential profile regime is of primary interest for melt motion). In order to explore this novel multi-emissive sheath regime, a a comprehensive tungsten electron emission model has been implemented that features accurate analytical descriptions of the yields, energy and angular distributions for the processes of field-assisted thermionic emission, secondary electron emission and electron backscattering [5]. In the present publication [1], at normal magnetic field inclinations, highly accurate analytical semi-empirical expressions are provided for the secondary electron emission current, electron backscattering current and thermionic current in the monotonic regime as well as for the total escaping current in the SCL regime. These semi-empirical expressions have been benchmarked against comprehensive PIC simulations, whose primary post-processed data are provided herein.</p> <p>[1] P. Tolias, M. Komm, S. Ratynskaia and A. Podolnik, "ITER relevant multi-emissive sheaths at normal magnetic field inclination", Nucl. Fusion 63 (2023) 026007.<br> [2] M. Komm, S. Ratynskaia, P. Tolias, J. Cavalier, R. Dejarnac, J. P. Gunn and A. Podolnik, "On thermionic emission from plasma-facing components in tokamak-relevant conditions", Plasma Phys. Control. Fusion 59 (2017) 094002.<br> [3] M. Komm, P. Tolias, S. Ratynskaia, R. Dejarnac, J. P. Gunn, K. Krieger, A. Podolnik, R. A. Pitts and R. Panek, "Simulations of thermionic suppression during tungsten transient melting experiments", Phys. Scr. T170 (2017) 014069.<br> [4] M. Komm, S. Ratynskaia, P. Tolias and A. Podolnik, "Space-charge limited thermionic sheaths in magnetized fusion plasmas", Nucl. Fusion 60 (2020) 054002.<br> [5] P. Tolias, M. Komm, S. Ratynskaia and A. Podolnik, "Origin and nature of the emissive sheath surrounding hot tungsten tokamak surfaces", Nucl. Mater. Energy 25 (2020) 100818.</p> <p> </p>
Normalized Difference Vegetation Index (NDVI) derived from 2010 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2010 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2010-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Normalized Difference Vegetation Index (NDVI) derived from 2013 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2013 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2013-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Normalized Difference Vegetation Index (NDVI) derived from 2015 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2015 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2015-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Normalized Difference Vegetation Index (NDVI) derived from 2017 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2017 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2017-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
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.