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6,334 results for “Directivity”
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.
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.
EEG study of the attentional blink; before, during, and after transcranial Direct Current Stimulation (tDCS)
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>
Double-directional Multipath Data at 140 GHz
<p>This data set contains 140 GHz double-directional path data in an indoor hall environment. Details of the environment and data format are available in .txt and .ppt files in the same package as data. The data were derived from channel sounding along with a measurement-based ray-launcher, which is elaborated in the following paper.</p> <p>M. F. de Guzman, P. Koivumäki and K. Haneda, "Double-directional multipath data at 140 GHz derived from measurement-based ray-tracer," in Proc. 2022 Vehicular Technology Conference, Helsinki, Finland, June 2022.</p> <p>@INPROCEEDINGS{deGuzman22_VTCS,<br> author={de Guzman, Mar Francis and Koivum\"{a}ki, Pasi and Haneda, Katsuyuki},<br> booktitle={2022 95th Veh. Tech. Conf. (VTC2022-Spring)},<br> title={Double-directional multipath data at 140 {GHz} derived from measurement-based ray-launcher},<br> year={2022},<br> address={Helsinki, Finland},<br> month={June},<br> pages={1-6},<br> }</p>
Data and code for: Little directional change in the timing of Arctic spring phenology over the past twenty-five years
<p>Data and code accompanying the publication: Little directional change in the timing of Arctic spring phenology over the past twenty-five years.</p> <p>This resource contains 1. R-scripts to calculate yearly phenologies from raw temporally explicit flowerin, arthropod observation and bird nesting data from Zackenberg. The raw data is openly accessible through the Greenland Ecosystem Monitoring database (https://data.g-e-m.dk/), as well as an R-script to carry out most of the analyses presented in the publication. To facilitate the use of the analysis script, pre-produced annual phenologies of focal taxa are included as csv-tables.</p>
Directed Outflow Project Lower Trophic Study
The upper San Francisco Bay Estuary and Sacramento San Joaquin Delta is critical habitat for the endemic Delta Smelt (Hypomesus transpacificus), an endangered planktivorous fish with an annual, semi-anadromous life cycle. Freshwater outflow actions during the fall season are hypothesized to improve Delta Smelt habitat and therefore Delta Smelt condition before the spawning life stage. As part of efforts to evaluate the effectiveness of such actions to benefit Delta Smelt populations, various studies were initiated by the U.S. Bureau of Reclamation under the Directed Outflow Project (DOP). One component of the DOP was to evaluate how the Fall X2 freshwater outflow action changed the lower trophic prey available to Delta Smelt. The action is hypothesized to change abiotic and biotic aspects that can benefit Delta Smelt habitat by using freshwater outflow to maintain the position of X2 (defined as the distance in kilometers from the Golden Gate Bridge to the tidally averaged 2 ppt salinity isohaline) at around 70 km during wet or above normal precipitation water years. This moves the low salinity zone (0.5 - 6 ppt) further seaward into Suisun Bay which increases the area of preferred Delta Smelt habitat. Field sampling began the September of 2017 and occurred bi-weekly through the end of November, paired with the U.S. Fish and Wildlife Service Enhanced Delta Smelt Monitoring program. Three random sites were sampled per target region (Suisun Bay, Suisun Marsh, the Lower Sacramento River, the Cache Slough Complex, and the Sacramento Deep Water Ship Channel) per week. Sites were chosen using a Generalized Random Tessellation Stratified design. During 2018, sampling was increased to weekly and in 2019-2020 sampling began in April and ended in November. Sampling occurs for three different habitat types: shoal, channel surface and channel deep at each site depending on the depth of the water column. The shoal habitat is less than 10 feet, channel habitat is greater than 10 f
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.
Nitrogen addition alters plant competition directly more than indirectly through soil microbes.
Eutrophication, the excessive addition of nutrients to ecosystems, is a pervasive component of global environmental change that can alter community dynamics. Although nitrogen addition experiments have widely documented important declines in plant diversity and shifts in plant species composition, the underlying causes of these outcomes are widely debated. Nitrogen inputs may directly affect plant competition for light or soil water or may influence plant species indirectly by altering the composition of soil microbes. In a 28-year field nitrogen addition experiment, we tested whether nitrogen-induced changes to soil microbes could indirectly alter the outcome of competition between codominant foundation plant species. In the field, long-term addition of inorganic nitrogen slowed the competitive take-over of blue grama grass (Bouteloua gracilis) by black grama grass (B. eriopoda) and thereby stabilized the ecotone between two grassland ecosystems in central New Mexico, USA.
Wind tunnel distributed temperature sensing with actively heated fibers and microstructures for detecting wind direction
<p>Wind tunnel tests were performed using distributed temperature sensing with actively heated fibers that had microstructures attached in opposing directions on neighboring fibers. These microstructures created a temperature difference between fibers that depended on wind speed, providing a prototype for distributed sensing of wind direction. These data are connected to a publication detailing this work and method, <a href="https://www.atmos-meas-tech-discuss.net/amt-2019-188/">"Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables"</a>.</p> <p>Data are stored in a netcdf format and includes the instrument reported temperature ('instr_temp') and calibrated temperature ('cal_temp') with the various parameters tested in the linked paper available as coordinates, labeled along an 'expname' dimension.</p> <p>The included ipython notebooks provide examples and explanations for using these laboratory data.</p>
Computational Supporting Information for How Chemical Environment Activates Anthralin and Molecular Oxygen for Direct Reaction
<p>The updated version of the dataset contains all original computational results, including validation of the level of theory, molecular structures, and analysis spreadsheets that are in support of our experimental observations of spontaneous reactivity of anthralin/dithranol molecule with molecular oxygen without any catalyst or co-substrate.<br> The paper was published in Journal of Organic Chemistry, 2020, 85(2), 1315–1321 (DOI: 10.1021/acs.joc.9b03133).</p> <p>In the meantime, the science was also also presented at the 8th ELSI Symposium, Tokyo Institute of Technology, Tokyo (Japan); February 3-7, 2020 in the context of molecular catalysis and their role in the chemical evolution of the building blocks of life.</p> <p>This version also has an important update that is being exclusively published here on Zenodo. The selected level of theory (MN15 functional with triple-zeta quality basis set supplemented with BOTH diffuse and polarization basis functions) is further confirmed to be one of the most reasonable one among 98 commonly used functionals.</p>
Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase (supplementary data)
<p>This a dataset of scanning transmission electron microscopy data showing Pt clusters nucleating in an ionic liquid. For each of the 4 movies there is the raw data (uncompressed .tif and compressed as .avi) and denoised versions (uncompressed .tif and compressed as .avi).</p> <p>This data is for the article "Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase" published in ChemNanoMat (2020), by Trond R. Henninen, Debora Keller and Rolf Erni. (https://onlinelibrary.wiley.com/doi/full/10.1002/cnma.202000503)</p> <p><strong>Movie 1:</strong> Homogeneous nucleations of two clusters in a suspended thin film of ionic liquid. </p> <p><strong>Movie 2: </strong>Heterogeneous nucleation of a ca 8-9 atom cluster near the edge of a nanodroplet supported on a carbon film.</p> <p><strong>Movie 3: </strong>Heterogeneous nucleation of multiple clusters in a nanodroplet. Shortly after nucleation, they coalesce to form disordered nanoclusters.</p> <p><strong>Movie 4:</strong> Heterogeneous nucleation and dissolution cycles of spherical particles in a nanodroplet.</p>
Data for: "Direct photochemical control of imine exchange reactions"
<div>This dataset is all of the data produced which relates to the text "Direct photochemical control of imine exchange reactions"</div> <div> </div> <div>The data set is separated loosely into </div> <div> </div> <div>1) Computational-data : All of the simulated data<br> <br>2) Kinetics : All of the data which lead to the nmr-time monitored experiments, where samples were equilibrated then irradiated and heated<br> <br>3) Photophysical-characterisation : All UV-VIS spectra and luminance spectra<br> <br>4) Synthetic-data-and-charcterisation : the details of the synthetises, and the 1H NMR, 13C NMR, IR, Mass-Spec, and Elemental analysis data<br> <br> <br>Generally within these folders, subfolders, subsubfolders etc. the folders contain zipped HTML copies of the lab notebooks, images of the graphs which result from them, and code which has generated them. Within further folders will be data which produces these graphs.<br> <br> <br>The way to interact with the compressed HTML lab notebooks, is to unzip them, and then open the HTML files.<br> <br> <br>Warning: The code for generating the graphs has not been cleaned up; it is presented as it was at time of publication. It will take some time for you to follow it, not because it is complex, but because it includes a lot of unnecessary diversions. Often I was working out how to process the data as I programmed them.<br> </div> <div> </div> <div> </div> <div>Where to find this data for each figure is given as follows:</div> <div> </div> <div>Figure 1: -not data-</div> <div> </div> <div>Figure 2: .\photophysical-characterisation\Imines-UV-VIS</div> <div> </div> <div>Figure 3: .\Kinetics\Kinetic-main</div> <div> </div> <div>Figure 4: .\Kinetics\Kinetic-temperature</div> <div> </div> <div>Figure 5: .\Computational-data</div> <div> </div> <div>Figure S1: .\photophysical-characterisation\Amines-UV-VIS</div> <div> </div> <div>Figure S2: .\Kinetics\Supplementary-kinetic</div> <div> </div> <div>Figure S3: .\Kinetics\Supplementary-temperature-kinetic</div> <div> </div> <div>Figure S4: .\Computational-data</div> <div> </div> <div>Figure S5: .\Kinetics\Kinetic-main\nmr\A-4-20-1mnova.mnova</div> <div> </div> <div>Figure S6: .\Kinetics\Kinetic-main\nmr\A-4-21-1mnova.mnova</div> <div> </div> <div>Figure S7: .\Synthetic-data-and-characterisation\[compound-data]\1H-NMR</div> <div> </div> <div>Figure S8: .\Synthetic-data-and-characterisation\[compound-data]\1H-NMR</div> <div> </div> <div>Figure S9: .\photophysical-characterisation\LED-Luminence</div> <div> </div> <div>Figure S10: .\photophysical-characterisation\LED-Luminence</div> <div> </div> <div>Figure S11: -not data-</div> <div> </div> <div>Figure S12: -not data-</div> <div> </div> <div>Figure S13: .\Synthetic-data-and-characterisation\Characterisation_A-imine\1H-NMR</div> <div> </div> <div>Figure S14: .\Synthetic-data-and-characterisation\Characterisation_MA-imine\1H-NMR</div> <div> </div> <div>Figure S15: .\Synthetic-data-and-characterisation\Characterisation_DMMA-imine\1H-NMR</div> <div> </div> <div>Figure S16: .\Synthetic-data-and-characterisation\Characterisation_FLUR-imine\1H-NMR</div> <div> </div> <div> </div> <div> </div> <div> </div> <div>The dataset contains details of the following compounds:</div> <div> </div> <div>Article name: A-Imine</div> <div> </div> <div>IUPAC name: (E)-N-phenyl-1-(thieno[3,2-b]thiophen-2-yl)methanimine</div> <div> </div> <div>SMILES Code: C1(/N=C/C2=CC(SC=C3)=C3S2)=CC=CC=C1</div> <div> </div> <div>SLN: C[2](N=[S=I]CC[8]=CC(SC=C[16])=C@16S@9)=CC=CC=C@3</div> <div> </div> <div>InChI: 1S/C13H9NS2/c1-2-4-10(5-3-1)14-9-11-8-13-12(16-11)6-7-15-13/h1-9H/b14-9+</div> <div> </div> <div>InChI key: FMENSGUUZYOJTA-NTEUORMPSA-N</div> <div> </div> <div> </div> <div>Article name: DMMA-Imine</div> <div> </div> <div>IUPAC name: (E)-N,N-dimethyl-4-((thieno[3,2-b]thiophen-2-ylmethylene)amino)aniline</div> <div> </div> <div>SMILES Code: CN(C)C1=CC=C(/N=C/C2=CC(SC=C3)=C3S2)C=C1</div> <div> </div> <div>SLN: CN(C)C[1]=CC=C(N=[S=I]CC[9]=CC(SC=C[17])=C@17S@10)C=C@2</div> <div> </div> <div>InChI: InChI=1S/C15H14N2S2/c1-17(2)12-5-3-11(4-6-12)16-10-13-9-15-14(19-13)7-8-18-15/h3-10H,1-2H3/b16-10+</div> <div> </div> <div>InChI key: XWBXQJOJYSCJCK-MHWRWJLKSA-N</div> <div> </div> <div> </div> <div>Article name: FLUR-Imine</div> <div> </div> <div>IUPAC name: (E)-N-(9H-fluoren-2-yl)-1-(thieno[3,2-b]thiophen-2-yl)methanimine</div> <div> </div> <div>SMILES Code: C1(C=CC=C2)=C2C(C=CC(/N=C/C3=CC(SC=C4)=C4S3)=C5)=C5C1</div> <div> </div> <div>SLN: C[1](C=CC=C[13])=C@13C(C=CC(N=[S=I]CC[16]=CC(SC=C[23])=C@23S@16)=C[8])=C@9C@2</div> <div> </div> <div>InChI: InChI=1S/C20H13NS2/c1-2-4-17-13(3-1)9-14-10-15(5-6-18(14)17)21-12-16-11-20-19(23-16)7-8-22-20/h1-8,10-12H,9H2/b21-12+</div> <div> </div> <div>InChI key: RSZKSUGPHCONDB-CIAFOILYSA-N</div> <div> </div> <div> </div> <div>Article name: MA-Imine</div> <div> </div> <div>IUPAC name: (E)-1-(thieno[3,2-b]thiophen-2-yl)-N-(p-tolyl)methanimine</div> <div> </div> <div>SMILES Code: CC1=CC=C(/N=C/C2=CC(SC=C3)=C3S2)C=C1</div> <div> </div> <div>SLN: CC[1]=CC=C(N=[S=I]CC[9]=CC(SC=C[17])=C@17S@10)C=C@2</div> <div> </div> <div>InChI: InChI=1S/C14H11NS2/c1-10-2-4-11(5-3-10)15-9-12-8-14-13(17-12)6-7-16-14/h2-9H,1H3/b15-9+</div> <div> </div> <div>InChI key: XARWNOWDJRWLJY-OQLLNIDSSA-N</div> <div> </div> <div> </div> <div>IUPAC name: (E)-4-((thieno[3,2-b]thiophen-2-ylmethylene)amino)benzonitrile</div> <div> </div> <div>SMILES Code: N#CC1=CC=C(/N=C/C2=CC(SC=C3)=C3S2)C=C1</div> <div> </div> <div>SLN: N#CC[5]=CC=C(N=[S=I]CC[8]=CC(SC=C[16])=C@16S@9)C=C@6</div> <div> </div> <div>InChI: InChI=1S/C14H8N2S2/c15-8-10-1-3-11(4-2-10)16-9-12-7-14-13(18-12)5-6-17-14/h1-7,9H/b16-9+</div> <div> </div> <div>InChI key: MTCNXZQWYYVDCG-CXUHLZMHSA-N</div> <div> </div> <div> </div> <div>IUPAC name: (E)-N-(4-methoxyphenyl)-1-(thieno[3,2-b]thiophen-2-yl)methanimine</div> <div> </div> <div>SMILES Code: COC1=CC=C(/N=C/C2=CC(SC=C3)=C3S2)C=C1</div> <div> </div> <div>SLN: COC[5]=CC=C(N=[S=I]CC[8]=CC(SC=C[16])=C@16S@9)C=C@6</div> <div> </div> <div>InChI: InChI=1S/C14H11NOS2/c1-16-11-4-2-10(3-5-11)15-9-12-8-14-13(18-12)6-7-17-14/h2-9H,1H3/b15-9+</div> <div> </div> <div>InChI key: WIBJKKCQZPFCIZ-OQLLNIDSSA-N</div> <div> </div> <div> </div> <div>Article name: A-Amine</div> <div> </div> <div>IUPAC name: Benzenamine</div> <div> </div> <div>Common name: Aniline</div> <div> </div> <div>SMILES Code: NC1=CC=CC=C1</div> <div> </div> <div>SLN: NC[2]=CC=CC=C@3</div> <div> </div> <div>InChI: InChI=1S/C6H7N/c7-6-4-2-1-3-5-6/h1-5H,7H2</div> <div> </div> <div>InChI key: PAYRUJLWNCNPSJ-UHFFFAOYSA-N</div> <div> </div> <div>CAS no: 62-53-3</div> <div> </div> <div> </div> <div> </div> <div>Article name: MA-Amine</div> <div> </div> <div>IUPAC name: 4-Aminotoluene</div> <div> </div> <div>Common name: p-toludine</div> <div> </div> <div>SMILES Code: NC1=CC=CC=C1</div> <div> </div> <div>SLN: NC[2]=CC=CC=C@3</div> <div> </div> <div>InChI: InChI=1S/C6H7N/c7-6-4-2-1-3-5-6/h1-5H,7H2</div> <div> </div> <div>InChI key: PAYRUJLWNCNPSJ-UHFFFAOYSA-N</div> <div> </div> <div>CAS no: 106-49-0</div> <div> </div> <div> </div> <div>Article name: DMMA-Amine</div> <div> </div> <div>IUPAC name: N1,N1-dimethylbenzene-1,4-diamine</div> <div> </div> <div>SMILES Code: NC1=CC=C(N(C)C)C=C1</div> <div> </div> <div>SLN: NC[2]=CC=C(N(C)C)C=C@3</div> <div> </div> <div>InChI: InChI=1S/C8H12N2/c1-10(2)8-5-3-7(9)4-6-8/h3-6H,9H2,1-2H3</div> <div> </div> <div>InChI key: BZORFPDSXLZWJF-UHFFFAOYSA-N</div> <div> </div> <div>CAS no: 99-98-9</div> <div> </div> <div> </div> <div>Article name: FLUR-Amine</div> <div> </div> <div>IUPAC name: 9H-fluoren-2-amine</div> <div> </div> <div>SMILES Code: NC1=CC(CC2=C3C=CC=C2)=C3C=C1</div> <div> </div> <div>SLN: NC[2]=CC(CC[8]=C[9]C=CC=C@9)=C(@10)C=C@3</div> <div> </div> <div>InChI: InChI=1S/C13H11N/c14-11-5-6-13-10(8-11)7-9-3-1-2-4-12(9)13/h1-6,8H,7,14H2</div> <div> </div> <div>InChI key: CFRFHWQYWJMEJN-UHFFFAOYSA-N</div> <div> </div> <div>CAS no: 153-78-6</div> <div> </div> <div> </div> <div>IUPAC name: 4-aminobenzonitrile</div> <div> </div> <div>SMILES Code: NC1=CC=C(C#N)C=C1</div> <div> </div> <div>SLN: NC[2]=CC=C(C#N)C=C@3</div> <div> </div> <div>InChI: InChI=1S/C7H6N2/c8-5-6-1-3-7(9)4-2-6/h1-4H,9H2</div> <div> </div> <div>InChI key: YBAZINRZQSAIAY-UHFFFAOYSA-N</div> <div> </div> <div>CAS no: 873-74-5</div> <div> </div>
xPore: Identification of differential RNA modifications from nanopore direct RNA sequencing
<p>xPore is a Python package for identification and quantification of differential RNA modifications from direct RNA sequencing.</p> <p>The detailed usage is documented at <a href="https://xpore.readthedocs.io/en/latest/">https://xpore.readthedocs.io/en/latest</a>, while all scripts and source code are available at <a href="https://github.com/GoekeLab/xpore">https://github.com/GoekeLab/xpore</a>.</p> <p>All the preprocessed datasets used in the paper are provided here. </p> <p>Please cite our paper below when using these data.<br> Ploy N. Pratanwanich et al. "Detection of differential RNA modifications from direct RNA sequencing of human cell lines." bioRxiv (2020).</p>
Annotations to direct and indirect image rotation estimation methods of orthopedic X-ray images
<p>The annotation file contains labels for AP wrist images of the MURA dataset on the center line of the radius bone. The annotations are stored in json format. For each annotated image file of the MURA dataset an entry is provided with the coordinates of the start and end point of the radius' center line.</p>
Figure 2: Direct and indirect paths of knowledge transfer to New Zealand to manage sand drifting in the nineteenth and twentieth centuries.
<p>Figure 2 of article: Managing Coastal Sand Drift in the Anthropocene: A Case Study of the Manawatū-Whanganui Dune Field, New Zealand, 1800s–2020s</p> <p>DOI zenodo: 10.5281/zenodo.5075980</p>
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.