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1,167 results for “speed”
Wind speed and direction data from benchmark stations at the HJ Andrews Experimental Forest, 1973 to present
A three-level hydro-climatological network for data monitoring was established in 1994. The networks at each level are nested to form a coordinated program of data acquisition and measurement. A future vision of linking the benchmark meteorological stations with regional weather stations to expand the future scope of studies was also considered in designing this network. The first-level in this top-down approach consists of Benchmark Meteorological Stations (BMS) and Benchmark Stream Stations. The BMS are designed to represent the environment across the Andrews. These stations are intended to provide complete, long-term, high temporal resolution, meso-scale hydroclimatological data. The location of the BMS network is based on factors such as elevation, aspect, vegetation gradients, and accessibility. Collected meteorological parameters are generally standardized across the BMS as well as methods and instrumentation. Secondary Meteorological Stations also follow standardized methods and serve similar purposes but are somewhat limited in meteorological parameters collected. The Primary Meteorological Station (PRIMET), Central Meteorological Station (CENMET), Upper Lookout Meteorological Station (UPLMET), and Vanilla Leaf Meteorological Station (VANMET) are the four Benchmark Stations, Climatic Station at Watershed 2 (CS2MET) and the Hi-15 Meteorological Station (H15MET) are Secondary Stations. These wind parameters were previously part of database code MS001, but were separated out into their own database in 2024.
High-frequency dissolved oxygen, water temperature, wind speed, and radiation data; stream and in-lake nutrient concentration data; and daily metabolism and nutrient loading estimates for 16 lakes in North America and Northern Europe.
In lakes, ecosystem structure and processes are influenced by gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP). The rates of these metabolic processes are often controlled by resource availability, which often reflects catchment loads. Although the relationship between catchment loads and in-lake nutrient concentrations may be well defined in specific lakes, we explored how watershed vs. in-lake predictors of metabolism compare across lake types. To do this, we combined stream loads of carbon (C), nitrogen (N), and phosphorus (P) with high frequency in situ monitoring of lake metabolism and in-lake C, N, and P concentrations from 16 lakes spanning a range of latitudes (39 to 64 degrees N), inflowing stream (0 - 6 streams), and trophic status (oligotrophic to eutrophic). The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP, R, and NEP derived from those data. In addition, the data package includes in-lake and stream concentrations of dissolved organic carbon, total nitrogen, and total phosphorus and stream discharge data. The package also includes estimates of daily carbon, nitrogen and phosphorus loading to each lake derived from the stream concentrations and discharge.
Hubbard Brook Experimental Forest: Wind Speed and Wind Direction Measurements, 1965 - present
Wind data have been measured by an anemometer mounted 3 m above the ground at Hubbard Brook Experimental Forest Headquarters since 1965. Prior to 1981, every mile of wind movement caused a tick mark on a strip-chart recorder, and wind direction as N, S, E, W, or a combination, was recorded continuously. From 1981-2003, wind speed and direction were measured with a MetOne wind speed sensor. In June 2003 the MetOne was replaced with a R.M. Young company wind speed direction sensor (model 05103). Since that time, wind direction (azimuth) is based on a 0 to 360 degree scale. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
HURRECON Model for Estimating Hurricane Wind Speed, Direction and Damage
HURRECON is a simple meteorological model that estimates hurricane surface wind speed and direction based on the track, size, and intensity of a hurricane and the surface type (land or water). The model also estimates Fujita-scale wind damage as a function of peak 1/4 mile wind speed and wind gust factor. Estimates can be generated for a single site or a rectangular region. The model is based on published empirical studies of many hurricanes. HURRECON can be used to study the impacts of individual hurricanes or to reconstruct the hurricane disturbance regime for a particular region. For more information on the most recent version of the model please see the published paper (Boose, E. R., K. E. Chamberlin and D. R. Foster. 2001. Landscape and regional impacts of hurricanes in New England. Ecological Monographs 71: 27-48). Additional information is contained in the documentation that accompanies the program. For an updated version of the HURRECON model in R and Python, please see HF446.
Spatial variability in water chemistry of four Wisconsin aquatic ecosystems - High speed limnology Environmental Science and Technology datasets
Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. We developed a new sensor platform to continuously samples surface water at a range of speeds (0 to > 45 km hr-1) resulting in high-density, meso-scale spatial data. Here, we archive data associated with an Environmental Science and Technology publication. Data include a single spatial survey of the following aquatic ecosystems: Lake Mendota, Allequash Creek, Pool 8 of the Upper Mississippi River, and Trout Bog. Data have been provided in three formats (raw, hydraulic-corrected, and tau-corrected).
Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging
Open the record for dataset details and reuse information.
Update of: The Global Fire Atlas of individual fire size, duration, speed and direction
<p>This is an updated and extended record of the Global Fire Atlas introduced by Andela et al. (2019). Input data (burned area and land cover products) are updated to the MODIS Collection 6.1 (the original version featured in Andela et al. (2019) was based on collection 6.0 burned area and collection 5.1 land cover products, respectively). The timeseries is extended to cover the period 2002 to August 2024.</p> <h2><strong>Methodological Notes:</strong></h2> <p>The method employed to create the dataset precisely follows the approach described by Andela et al. (2019).</p> <p>The input burned area product is MCD64A1 Collection 6.1. It is described by Giglio et al. (2018) and available at: https://lpdaac.usgs.gov/products/mcd64a1v061/. </p> <p>The input land cover product is MCD12Q1 Collection 6.1. It is described by Sulla-Menashe et al. (2019) and available at: https://lpdaac.usgs.gov/products/mcd12q1v061/. </p> <p>Note that while the methods have remained the same compared to Andela et al. (2019), we do observe small differences between the Global Fire Atlas products originating from differences between the MCD64A1 collection 6.1 burned area data used here and the collection 6 data used in the original product. In addition, we observe more substantial differences in the dominant land cover class associated with each fire due to the differences between the MCD12Q1 collection 6.1 data used here and collection 5.1 data used in the original product. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. For each MODIS tile, the fire season is defined as the twelve months centred on the month with peak burned area (see Andela et al., 2019). For example, for a MODIS tile with peak burned area in December, the 2023 fire season would be defined as the period from July 2023 to June 2024, with the current record ending in August 2024. This is particularly relevant in the Southern extratropics and the northern hemisphere subtropics, where the fire seasons often span the new year. The local definition of the fire season is based on climatological peak in burned area as described by Andela et al. (2019).</p> <p>Here we extended the time-series to include the fire season of 2002, and extended the time-series until February 2025.</p> <h2> </h2> <h2><strong>Usage Notes:</strong></h2> <h3><strong>Incomplete Observations for the Latest Fire Seasons:</strong></h3> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. As such, the time-series can be incomplete for the latest fire season (e.g. the "2024 fire season") and also for the penultimate fire season (e.g. the "2023 fire season") due to the way that fire seasons are defined (see above). For example, if the month with peak burned area for a tile is December, then full data covering the 2023 fire season in that tile are not available until midway through the 2024 calendar year. This contrasts with the original dataset from Andela et al. (2019), which only included the data for entire fire seasons between 2003 and 2016. </p> <h3><strong>Observational Outages:</strong></h3> <p>For the purpose of time-series analysis, we note that the 2002 product may have been affected by outages of Terra-MODIS (most notably, June 15 2001 - July 3 2001 and March 19 2002 - March 28 2002), which affects the burn date estimates and Global Fire Atlas product. Following the launch of Aqua-MODIS in May 2002 burn date estimates are more reliable as estimated from both MODIS sensors onboard Terra and Aqua. </p> <h3><strong>File Naming Convention:</strong></h3> <p>GFA_v<em>{time-stamp}</em>_<em>{data-type}</em>_<em>{fire_season}</em>.<em>{file_type}</em></p> <p><em>{time-stamp}</em><strong> </strong>= Date that code was run.</p> <p><em>{data-type}</em><strong> </strong>= “ignitions” or “perimeters” for vector files; “day_of_burn”, “direction”, “fire_line”, or “speed” for raster files.</p> <p><em>{fire_season} </em>= the locally-defined fire season in which the fire was ignited (see more below).</p> <p><em>{file_type} </em>= ".shp" for vector files; ".tif" for raster files. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. Hence the file GFA_v20240409_perimeters_2003.shp can include fires from the 2003 fire season that ignited in the calendar years 2002 or 2004. </p> <h3>Coordinate systems (Map Projections):</h3> <p>Vector data are provided on the WGS84 projection.</p> <p>Raster data are provided on the MODIS sinusoidal projection used in NASA tiled products. The WKT string defining this projection is:</p> <pre><code>'PROJCS["unnamed",GEOGCS["Unknown datum based upon the custom spheroid",DATUM["Not_specified_based_on_custom_spheroid",SPHEROID["Custom spheroid",6371007.181,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Sinusoidal"],PARAMETER["longitude_of_center",0],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]'</code></pre> <p> </p> <h2><strong>Data Layers:</strong></h2> <p><em><strong>Table 1: Overview of the Global Fire Atlas data layers. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire, while the underlying 500 m gridded layers reflect the day-to-day behavior of the individual fires. In addition, we provide aggregated monthly summary layers at a 0.25° resolution for regional and global analyses.</p> <table> <tbody> <tr> <td>File name</td> <td>Content</td> </tr> <tr> <td>SHP_ignitions.zip</td> <td>Shapefiles of ignition locations with attribute tables (see Table 2)</td> </tr> <tr> <td>SHP_perimeters.zip</td> <td>Shapefiles of final fire perimeters with attribute tables (see Table 2)</td> </tr> <tr> <td>GeoTIFF_direction.zip</td> <td>500 m resolution daily gridded data on direction of spread (8 classes)</td> </tr> <tr> <td>GeoTIFF_day_of_burn.zip</td> <td>500 m resolution daily gridded data on day of burn (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_speed.zip</td> <td>500 m resolution daily gridded data on speed (km/day)</td> </tr> <tr> <td>GeoTIFF_fire_line.zip</td> <td>500 m resolution daily gridded data on the fire line (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_monthly_summaries.zip</td> <td>Aggregated 0.25° resolution monthly summary layers. These files include the sum of ignitions, average size (km2), average duration (days), average daily fire line (km), average daily fire expansion (km2/day), average speed (km/day), and dominant direction of spread (8 classes). </td> </tr> </tbody> </table> <p> </p> <p><em><strong>Table 2: Overview of the Global Fire Atlas shapefile attribute tables. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire.</p> <table> <tbody> <tr> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>lat, lon</td> <td>Coordinates of ignition location (°)</td> </tr> <tr> <td>size</td> <td>Fire size (km2)</td> </tr> <tr> <td>perimeter</td> <td>Fire perimeter (km)</td> </tr> <tr> <td>start_date, start_DOY</td> <td>Start date (yyyy-mm-dd), start day of year (1-366)</td> </tr> <tr> <td>end_date, end_DOY</td> <td>End date (yyyy-mm-dd), end day of year (1-366)</td> </tr> <tr> <td>duration</td> <td>Duration (days)</td> </tr> <tr> <td>fire_line</td> <td>Average length of daily fire line (km)</td> </tr> <tr> <td>spread</td> <td>Average daily fire growth (km2/day)</td> </tr> <tr> <td>speed</td> <td>Average speed (km/day)</td> </tr> <tr> <td>direction, direc_frac</td> <td>Dominant direction of spread (N, NE, E, SE, S, SW, W, NW) and associated fraction</td> </tr> <tr> <td>MODIS_tile</td> <td>MODIS tile id</td> </tr> <tr> <td>landcover, landc_frac</td> <td>MCD12Q1 dominant land cover class and fraction (UMD classification), provided for 2002-2023</td> </tr> <tr> <td>GFED_regio</td> <td>GFED region (van der Werf et al., 2017; available at https://www.globalfiredata.org/)</td> </tr> </tbody> </table> <p> </p> <p> </p>
DNA Origami Raw AFM Data - NanoLocz: Image analysis platform for AFM, high-speed AFM and localization AFM
<p>The data file is in the original ARIS data format as captured on a Cypher VRS1250 AFM (Oxford Instruments)<br><br><br></p>
Satellite-observed surface flow speed within Russell sector, West Greenland, bi-weekly average of 2015-2019
<p>An average horizontal surface ice velocity of Russell sector (Greenland) with 2-week temporal and 150m spatial resolution. Derived from satellite images collected between 2015 and 2019 by Landsat-8, Sentinel-1, and Sentinel-2. The details on the data processing can be found in https://doi.org/10.5194/tc-2021-170.</p> <p><br> Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with maps of vx and vy velocity components, maps of associated uncertainties per velocity component (STD of the 2-weeks averaged raw satellite measurements), and map of number of averaged measurements.</p>
Bonanza Creek LTER: Hourly Wind Speed and Direction at Various Heights from 1988 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska
Hourly wind data from sefveral sites with the the Caribou Poker Creeks Research Watershed. Wind data should be used cautiously during winter months due to ice and snow build up on sensors.
Bonanza Creek LTER: Hourly Wind Speed and Direction at 3m and 10 m from 1988 to Present in the Bonanza Creek Experimental Forest near Fairbanks, Alaska
This dataset contains the hourly output from Wind sensors for the Bonanza Creek Experimental Forest (BCEF). This includes Level 3 weather stations as well as smaller scale and temporal studies. This data can be sorted and viewed by site, year, hour, height of measurement, mean, min, and max value. Updates of each site are different since some are still on going while other had only a 2-3 year life cycle. Winter measurements may be inaccurate due to snow cover.
Hubbard Brook Experimental Forest: 15 Minute Wind Speed and Direction Measurements, 2012 – present
Wind speed and direction have been recorded at 15-minute resolution by an R.M. Young company wind sensor at three locations within Hubbard Brook Experimental Forest since 2012. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
PIE LTER 15-minute Wind speed and direction in the lower Plum Island Sound at the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2023.
Wind speed and direction measurements for 2023 at Ipswich Bay Yacht Club, Ipswich, MA. Wind speed is measured every 5 seconds and reported as an average in 15 minute intervals. Maximum wind speed is also reported for each 15 minute interval with a timestamp. Wind direction is measured every 15 minutes.
HomogWS-se: A century-long homogenized dataset of near-surface wind speed observations since 1925 rescued in Sweden
<p>Creating a century-long homogenized near-surface wind speed (WS) observation dataset is essential to improve our knowledge about the uncertainty and causes of WS stilling and recovery. We rescued paper-based WS records dating back to the 1920s at 13 stations in Sweden and established a four-step homogenization procedure to generate the first 10-member centennial homogenized WS dataset (HomogWS-se) for community uses among climatology, ecology, hydrology and energy industry. HomogWS-se can be used to study the WS variability and change, assess climate reanalysis, and constrain climate simulations for better future projection of changes in the WS and wind energy potential. HomogWS-se contains 13 individual text files with 10-member century-long homogenized monthly WS series, as well as the member-mean series.</p>
Data for: What's in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed
<p>The aggregate data in these datasets were used in analyses for "What’s in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed".</p>
Vertical profiles of urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland
<p><strong>Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland</strong></p> <p>=================================</p> <p>README version 1.3, 21/07/2022</p> <p>==================================</p> <p>Contact info:</p> <p>Paul Leahy, University College Cork</p> <p>paul.leahy@ucc.ie | +353 21 4902017</p> <p>================================</p> <p> </p> <p><strong>Contents</strong></p> <p><strong>1. Measurement location and time period</strong></p> <p><strong>2. What is measured (brief description)</strong></p> <p><strong>3. Instrumentation</strong></p> <p><strong>4. CSV file detailed descriptions</strong></p> <p>================================</p> <p> </p> <p><strong>1. Measurement location and time period: </strong></p> <p>North roof of Kane Building, University College Cork (UCC), Ireland.</p> <p>Lat 51 d 53 m 34 s N.</p> <p>Long 8 d 29 m 39 s W.</p> <p>Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building).</p> <p>The measurements were taken over a time period of several months in the years 2013 / 2014.</p> <p>=================================</p> <p><strong>2. What is measured (brief description):</strong></p> <p>* LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5 altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level.</p> <p>* Air temperature, atmospheric pressure, relative humidity.</p> <p>* Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level).</p> <p>* 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided.</p> <p>See 'CSV file detailed description' below for detailed information.</p> <p>* Diagnostic information.</p> <p>=================================</p> <p><strong>2.1 Surrounding terrain:</strong></p> <p>Surrounding area is urban/suburban. The aspect is northerly.</p> <p>To the West: 2-5 storey buildings, open spaces, suburban.</p> <p>To the South: 2-3 storey buildings, open spaces, trees, river.</p> <p>To the East: 2-3 storey buildings, open spaces.</p> <p>To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban.</p> <p>=================================</p> <p><strong>3. Instrumentation:</strong></p> <p>ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS.</p> <p>=================================</p> <p><strong>4. CSV files detailed description:</strong></p> <p><strong>4.1 Data on 10-minute averages:</strong></p> <p>Filename 05092013-03122013_10min_res.csv contains:</p> <p>10 minute averaged data from 05/09/2013 to 03/12/2013.</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level.</p> <p> </p> <p>Filename 03122013-07082014_10min_res.csv contains:</p> <p>10 minute averaged data from: 03/12/2013 to 07/08/2014.</p> <p>Measurement altitudes: 148 m, 90 m, 50 m, 35 m, 15 m above instrument level.</p> <p>Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).</p> <p> </p> <p>The first two rows contain header information.</p> <p>Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds.</p> <p>Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= number of scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] </p> <p>Horizontal min [m/s] </p> <p>Horizontal max [m/s] </p> <p>TI (turbulence intensity) []</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the averaging interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer).</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p> </p> <p><strong>4.2 Data with high time resolution (~23 s):</strong></p> <p> </p> <p>Filename 05092013-11112013_23s_res.csv contains:</p> <p>High resolution data from 05/09/2013 to 11/11/2013</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m.</p> <p> </p> <p>Note on time resolution:</p> <p>The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete.</p> <p>The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values.</p> <p> </p> <p>Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] not defined as measurement interval is too short.</p> <p>Horizontal min [m/s] not defined as measurement interval is too short. </p> <p>Horizontal max [m/s] not defined as measurement interval is too short. </p> <p>TI (turbulence intensity) [] not defined as measurement interval is too short.</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the scanning interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer.</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p><strong>4.3 Quality control indicators:</strong></p> <p> </p> <p>9998 atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog</p> <p>9999 high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path</p> <p>Status Flag 'Green' => good</p> <p>=======================================================</p> <p> </p>
Influence of Anode Immersion Speed on Current and Power in Plasma Electrolytic Polishing
<p><span>Plasma electrolytic polishing (PeP) is mainly used to improve the surface quality and thus </span><span>the performance of electrically conductive parts. It is usually used as an anodic process, i.e., the </span><span>workpiece is positively charged. However, the process is susceptible to high current peaks during</span> <span>the formation of the vapour–gaseous envelope, especially when polishing workpieces with a large </span><span>surface area. In this study, the influence of the anode immersion speed on the current peaks and the</span> <span>average power during the initialisation of the PeP process is investigated for an anode the size of a </span><span>microreactor mould insert. Through systematic experimentation and analysis, this work provides</span> <span>insights into the control of the initialisation process by modulating the anode immersion speed. The </span><span>results clarify the relationship between immersion speed, peak current, and average power and</span> <span>provide a novel approach to improve process efficiency in PeP. The highest peak current and average </span><span>power occur when the electrolyte splashes over the top of the anode and not, as expected, when the </span><span>anode touches the electrolyte. By immersion of the anode while the voltage is applied to the anode</span> <span>and counterelectrode, the reduction of both parameters is over 80 %.</span></p>
Run-to-failure data set of ball bearings subjected to time-varying load and speed conditions
<p>This data set consist of experimental data collected during 17 run-to-failure experiments on ball bearings subjected to time-varying load and speed conditions. No defect was initiated in the bearings before the experiments. A detailed description file is enclosed.</p> <p>Version 2024-04-02: All experiments B01 through B17 are uploaded. Furthermore, Figure 1 of the description file has been updated.</p> <p>Please also cite our paper, when using this data set: Javanmardi, A., Aimiyekagbon, O. K., Bender, A. ., Kimotho, J. K., Sextro, W., & Hüllermeier, E. (2024). Remaining Useful Lifetime Estimation of Bearings Operating under Time-Varying Conditions. <em>PHM Society European Conference</em>, <em>8</em>(1), 9. https://doi.org/10.36001/phme.2024.v8i1.4101</p>
MATLAB codes for : "Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach".
<p>The package contains all the materials needed to reproduce the findings of our paper. The paper is published by MDPI Applied Sciences journal and its details are as follow.</p> <p>Berghout, T.; Benbouzid, M. Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach. <em>Appl. Sci.</em> <strong>2023</strong>, <em>13</em>, 10916. https://doi.org/10.3390/app131910916</p> <p>1) Please you need to download the dataset from original link provided by introductory paper (Please read the above paper to find out about the datset used).<br> 2) Put the data in folders "RawData" for both experments.<br> 3) Please run the files for each experiment as provided, in alphabetical order.</p>
HURRECON Model for Estimating Hurricane Wind Speed, Direction, and Damage (R and Python)
The HURRECON model estimates wind speed, wind direction, enhanced Fujita scale wind damage, and duration of EF0 to EF5 winds as a function of hurricane location and maximum sustained wind speed. Results may be generated for a single site or an entire region. Hurricane track and intensity data may be imported directly from the US National Hurricane Center's HURDAT2 database. HURRECON is available in R and Python. The R version is available on CRAN as HurreconR. The model is an updated version of the original HURRECON model written in Borland Pascal for use with Idrisi (see HF025). New features include support for: (1) estimating wind damage on the enhanced Fujita scale, (2) importing hurricane track and intensity data directly from HURDAT2, (3) creating a land-water file with user-selected geographic coordinates and spatial resolution, and (4) creating plots of site and regional results. The model equations for estimating wind speed and direction, including parameter values for inflow angle, friction factor, and wind gust factor (over land and water), are unchanged from the original HURRECON model. For more details and sample datasets, see the project website on GitHub (https://github.com/hurrecon-model).
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.