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528 results for “equivalence”
Snow depth and snow water equivalent measurements along a road course and historic snow course in the Andrews Experimental Forest, 1978 to present
With an increase in emphasis on monitoring climate change impacts and change in the form of precipitation at HJ Andrews Experimental Forest, snow data collection within our climate monitoring program, a snow course to document depths of snow was designed around a dispersed sampling scheme rather than a point intensive scheme as previously employed in the historic Reference Stand snow course. Primary objectives are to document the presence/absence of snow, snow depth, and time of melt-off. Snow depths are verified using stakes placed near the road to allow for routine and frequent observation. Stakes are placed at different locations, elevations and aspects in paired forested/open sites. Time-lapse cameras were deployed at all the stakes to allow for daily measurements beginning in fall 2014. Truthing of points with snow core sampling for snow moisture content (snow water equivalent) is done when possible, usually 1-2 times per year. Cameras are set to take 3 readings per day (09:00, 12:00, 15:00 PST). One snow depth and coverage is extracted from the images per stake per day.
Snow water equivalent data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.
Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. This dataset contains derived values of SWE from snow profile measurements.
Dataset of "Glue-assisted Exfoliation of Two-dimensional Sulfur-rich Niobium Thiophosphate (Nb4P2S21) for Sulfur-equivalent Electrode Study in Lithium Storage"
<p>Two-dimensional (2D) layered thiophosphates have garnered attention for advanced batteries due to their open ionic diffusion channels, high capacity, and unique catalytic properties. However, their potential in energy storage applications remains largely unexplored. In this study, we report for the first time a 2D transition metal thiophosphate (Nb4P2S21) with high sulfur content. Nb4P2S21, synthesized via chemical vapor transport (CVT), is treated as a sulfur-equivalent material with better conductivity than sulfur, suitable for high-capacity lithium storage. The bulk material can be delaminated into high-quality nanoplates via glue-assisted grinding exfoliation, both displaying a layered quasi-one-dimensional (quasi-1D) morphology, which shortens the ion diffusion path and promises enhanced rate performance compared to larger lateral 2D materials. Density functional theory (DFT) calculations indicate that Nb4P2S21 has a direct bandgap of 1.64 eV (HSE06 method), with exfoliated counterparts showing near-infrared (NIR) photoluminescence at 755 nm, broadening potential applications to NIR-based devices. By tuning the working voltage window for lithium-ion batteries (LIBs) and controlling lithiation product formation, the material exhibits distinct electrochemical characteristics at 0 ~ 2.6 V, 0.5 ~ 2.6 V, 1.0 ~ 2.6 V, and 1.5 ~ 2.6 V. However, sulfur-rich electrodes in carbonate electrolytes demonstrate limited electrochemical potential due to polysulfide formation, leading to detrimental side reactions with carbonate-based electrolytes. Transitioning to ether-based electrolytes improves the initial reversible capacity and Coulombic efficiency of Nb4P2S21 by stabilizing the formed polysulfides. Despite this improvement, the material still mirrors the shuttle effect in lithium-sulfur batteries, diminishing active sulfur and undermining battery integrity. Further EDS and TOF-SIMS analyses of post-cycled electrode materials show significant sulfur loss and precipitation within the electrodes, exacerbating the shuttle effect and causing battery failure. Implementing strategies used in lithium-sulfur batteries, such as introducing polar host catalysts, could enhance the potential of these materials.</p>
Equivalent black carbon aerosol measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>The authors would highly appreciate to be contacted if the data is used for any purpose.</p> <p>We measured equivalent black carbon (eBC) with an aethalometer (model AE33, Magee Scientific) at a time resolution of one second during the Antarctic Circumnavigation Expedition (ACE). We report five-minute averaged data, cleaned from exhaust gas influence. Temporal coverage is from December 20, 2016 to April 10, 2017.</p> <p>The mass concentration of eBC, reported in ng m<sup>-3</sup>, reflects how far fossil fuel combustion or biomass burning contribute to the aerosol population over the Southern Ocean and between South Africa and Europe. Over the Southern Ocean there are no sources of eBC, except for ship emissions and (sub-)Antarctic station emissions, and hence an enhancement of eBC points towards long-range influence from Africa, Australia, New Zealand and South America. When plotted against latitude, eBC concentrations drop south of 60°S, indicating a more pristine environment. Elevated concentration around the equator are likely influenced by biomass burning in tropical Africa.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_equivalent_black_carbon_aerosol.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> <p>The data file listed above contains five-minute averaged values of equivalent black carbon (eBC) measured during the Antarctic Circumnavigation Expedition. Timestamps are the end of the five-minute period over which the eBC values were averaged. Latitude and longitude are average values of the position of the measurement during the five-minute interval.</p> <p>NaN values of eBC denote missing values because of e.g., ship exhaust contamination, maintenance, instrument failure or signal noise levels exceeding 200 ng/m<sup>3</sup>. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period.</p> <p><strong>Dataset license</strong></p> <p>This equivalent black carbon dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Snow Water Equivalent Dataset for the South Fork of the San Joaquin River (2018/2021) and Senales (2019/2021)
<p>The dataset is related to: Premier, V., Marin, C., Bertoldi, G., Barella, R., Notarnicola, C., & Bruzzone, L. (2022). Exploring the Use of Multi-source High-Resolution Satellite Data for Snow Water Equivalent Reconstruction over Mountainous Catchments. <em>The Cryosphere Discussions</em>, 1-42.</p> <p>It contains three hydrological seasons - from 1st of October 2018 to 30th of September 2021 - of snow water equivalent (SWE) for the South Fork of the San Joaquin river in California (USA) and two hydrological seasons - from 1st of October 2019 to 30th of September 2021 - for the Schnals/Senales basin in South Tyrol (Italy). The product is daily and with a spatial resolution of 25 m. SWE values are in mm. Snow cover area (SCA) can be derived from the same by thresholding pixel containing SWE greater than 0 mm. Further information about the reference system is contained in the attributes of the netcdf files. Please, contact the authors for further questions. Information about the methodology and the input data for producing these time series is contained in the related article.</p>
Marcell Experimental Forest biweekly snow depth, frost depth, and snow water equivalent, 1962 - ongoing
This data table contains snowpack and frost data measured at the Marcell Experimental Forest from 1962–ongoing. The data came from five peatland/upland forest watersheds instrumented for hydrologic monitoring. Frost thickness and snowpack (snow water content, snowpack depth) are measured at 10 snowcourses that encompass three cover types (conifer, deciduous, open). The Marcell Experimental Forest in Itasca County, Minnesota, is operated and maintained by the USDA Forest Service, Northern Research Station, and was formally established in 1962 to study the ecology and hydrology of peatlands.
Snow depth, snow water equivalent, ice thickness in Fuglebekken and Revdalen catchments collected in the SnowPilot campaign in Spring 2022
<p>File SnowPilot_snowdepth_along_the_GPR_profile_2022 contains snow depth measurements taken along the GPR profile performed during the SIOS SnowPilot campaign in Spring 2022. File SnowPilot_snowdepth_swe_2022 contains depth, snow water equivalent and basal ice thickness. Snowpits were dug on GPR profile crossings in the Fuglebekken and Revdalen catchments in the Hornsund fiord, Spitsbergen catchment. Snow density was measured with an IG PAS snow tube, and snow depth and basal ice (ice forming on the ground surface) thickness were measured with an avalanche probe. Point locations measured. with handheld GPR reciever.</p>
Downscaled 8km March Snow Water Equivalent Estimates for the Western US, 1901-2010
<p>Downscaled estimates of March mean snow water equivalent at approximately 8km resolution across the western United States for the years 1901-2010. Data downscaled from the CERA-20c reanalysis using UA-SWE daily observations. Downscaled data using both the CERA-20c ensemble mean as well as each individual ensemble member as predictors are included. Units are in millimeters of snow water equivalent.</p>
Spatial distribution of snow water equivalent for the Niwot Ridge, 1996 - 2019
This dataset provides a daily estimation of snow water equivalent for the Niwot Ridge during snow melting period from 1997 to 2019 at 30-meter spatial resolution. The dataset includes two series of SWE data: 1) 1996-2007 daily SWE dataset is generated by Jepsen et al., (2012); 2) 2008-2019 daily SWE dataset is generated by Dr. Kehan Yang following the same method used by Jepsen et al., (2012). In brief, a physically based reconstruction model is used to calculate daily SWE backward from snow disappearance date to peak snow accumulation. The infilled hourly climate data set for C1, Saddle and D1 (data available at https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-nwt.168.2) is interpolated and used as the meteorological forcing in the snow energy balance calculation of SWE reconstruction. The shortwave radiation is estimated by downscaling hourly product of the Geostationary Operational Environmental Satellite (GOES) using TOPORAD tool. The USGS Landsat Level-3 fractional snow-covered area product is used to proportion potential energy flux for snowmelt at the pixel scale. Please see detailed methods included with this data package for more details and references.
Empirical relationship between calcium triplet equivalent widths and [Fe/H] using Gaia photometry
<p>I present a new empirical relationship for red giant branch stars between the overall metallicity of the star and the sum of equivalent widths of the near-infrared calcium triplet (CaT) spectral lines. This method takes advantage of the all-sky photometry and astrometry of the Gaia mission, and the archival spectra from 2050 red giant branch stars from 18 globular clusters (-0.69>[Fe/H]>-2.44) acquired with the Anglo-Australian Telescope's AAOmega spectrograph.</p> <ul> <li><cluster_name>.tar.gz <ul> <li>Raw and reduced spectra for 18 globular clusters (NGC104, NGC6752, NGC6809, NGC288, NGC7099, NGC362, NGC6218, NGC4590, IC4499, NGC1904, ESO452-SC06, ESO280-SC12, NGC1851, NGC6624, NGC2298, Pal 5, NGC5024, Terzan 8, NGC5053)</li> <li>Lists of likely members</li> <li>Measured equivalent widths and radial velocities</li> </ul> </li> <li>code.tar.gz <ul> <li>running.py – calculates the equivalent widths and radial velocities of the stars from the spectra</li> <li>cat_new_params.py – calculates the best fitting empirical relationship</li> </ul> </li> <li>paper.tar.gz <ul> <li>The LaTeX source for the submitted RNAAS paper.</li> </ul> </li> </ul> <p> </p>
Library of equivalent widths of H I , He I , and He II lines of Massive Stars
<p>Library with the equivalent widths of the Balmer lines λλ 3835, 3889,<br> 3970, 4101, 4349, 4861; the He II lines λλ 4541 and 4200; the He I lines λλ 4471, 4387, 4144; and the He I +He II blended lines λ4026, measured in 45,000 CMFGEN models (Zsargó et al. 2020), and 202 PoWR models (Hainich et al. 2019) of OB stars.</p>
DATASET: TESTING NICHE EQUIVALENCE IN AMPHIDROMOUS FISH POPULATIONS
<p>Presence records of<em> Galaxias maculatus</em> were collected from 12 locations across five river basins in central-southern Chile during March, May, August, and November of 2019 as part of a study on fish sampling and processing (Ramírez-Álvarez et al. 2022. doi.org/10.1038/s41598-022-06936-8)</p> <p>Isotopic niches defined using a standard ellipse area (SEAc) in isotopic space, represented by a 2D ellipsoidal space (δ13C - δ15N) (see Supporting Information: Standard ellipse area functions - Ramírez-Álvarez et al. 2024. doi:10.1007/s10750-024-05738-5) - Empirical Bayesian Kriging</p> <p>Database of varibles used for niche modelling: isotopic niche and seven abiotic variables selected from 23 predictor variables: (1) 19 climate variables representing 1950–2000 climate averages from WorldClim (http://www.worldclim.org/). (2) Four spatially continuous topographic and hydrological variables from the EarthEnv Project adjusted to the HydroSHEDS river network (http://www.earthenv.org/) (Domisch et al. 2015). Selection of variables was accomplished by analysis of covariance and multicollinearity, using the ENMeval R package (Muscarella et al. 2014): (1) principal component analysis (PCA) to explore relationships among all predictors, evaluating the composition of components (component variables) that accounted for ≥65% of variance explained, (2) pairwise comparisons to detect pairs of variables with strong correlations (Pearson correlation coefficients <0.8), groups with a correlation of less than 0.8 were considered independent, and (3) variance inflation factor (VIF) <10, to reduce the effect of collinearity between predictors (Listed below). A VIF greater than 10 indicates collinearity problems in the model. The vifcor and vifstep functions were employed by calculating two different strategies to exclude highly collinear variables using a stepwise procedure (Muscarella et al. 2014).</p> <div><em>Variable description and ecological question associated, selected variables are marked in bold.</em></div> <div><em>Series 1: Temperature, temperature variations and interaction with precipitation.</em></div> <div>bio1: Annual Mean Temperature; Is the temperature usually suitable? </div> <div><strong>bio2: Mean Diurnal Range; Are the days too warm or too cold?</strong></div> <div>bio3: Isothermality; Do temperatures fluctuate greatly over the course of a month? </div> <div>bio4: Temperature Seasonality (standard deviation); Do temperatures fluctuate greatly over the course of a year?</div> <div>bio5: Min Temperature of Coldest Month; Is the maximum temperature too high?</div> <div>bio6: Min Temperature of Coldest Month; Is the temperature constantly too high?</div> <div><strong>bio7: Temperature Annual Range; Do temperatures fluctuate greatly over the course of a year?</strong></div> <div><strong>bio8: Mean Temperature of Wettest Quarter; Is it too cold or too warm during the rainy season?</strong></div> <div>bio9: Mean Temperature of Driest Quarter; Is it too cold or too warm during the dry season?</div> <div>bio10: Mean Temperature of Warmest Quarter; Are the warmer months too cold?</div> <div><strong>bio11: Mean Temperature of Coldest Quarter; Are the colder months too warm?</strong></div> <div><em>Series 2: Precipitation and Rainfall Patterns</em></div> <div>bio12: Annual Precipitation; Does it rain enough in a year?</div> <div>bio13: Precipitation of Wettest Month; Does it rain a lot during the wettest month?</div> <div><strong>bio14: Precipitation of Driest Month; Does it rain poorly during the driest month?</strong></div> <div>bio15: Precipitation Seasonality (Coefficient of Variation); Would rainfall fluctuate much between seasons?</div> <div>bio16: Precipitation of Wettest Quarter; Does it rain a lot in the rainy season?</div> <div>bio17: Precipitation of Driest Quarter; Is rainfall low during dry seasons?</div> <div>bio18: Precipitation of Warmest Quarter; Does it rain enough during the warmer months?</div> <div>bio19: Precipitation of Coldest Quarter; Does it rain enough during the colder months?</div> <div><em>Series 3: Topology and hydrology</em></div> <div><strong>dem: Average elevation; Does altitude play a role as a topological factor?</strong></div> <div><strong>slope_av: Average slope; Is the slope suitable for the accumulation of small ponds?</strong></div> <div>flow_ac_ac: Accumulation flow; Does enough water accumulate or does it drain too quickly?</div> <div>flow_ac_le: Accumulation flow direction; Does the flow direction support the formation of small ponds?</div> <p>Domisch, S., G. Amatulli & W. Jetz, 2015. Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Scientific Data 2(1):150073 doi:10.1038/sdata.2015.73.</p> <p>Muscarella, R., P. J. Galante, M. Soley‐Guardia, R. A. Boria, J. M. Kass, M. Uriarte & R. P. Anderson, 2014. ENM eval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in ecology and evolution 5(11):1198-1205</p>
Strain path change characterization of dual-phase steel (DP780) using non-linear strain path experiments: true strain-stress and equivalent strain-stress data
<p>Strain path change characterization of dual-phase steel (DP780) using non-linear strain path experiments: true strain-stress and equivalent strain-stress data.</p>
Data and code for: Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent
<p>Code and data to reproduce figures in manuscript entitled "Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent" published in Hydrology and Earth System Sciences (https://hess.copernicus.org/preprints/hess-2022-136/).</p> <p>The contents include three folders, "Codes", "Data", and "Figures". In "Codes" folder, R scripts are listed in the order needed to reproduce the figures. All code is written in R version 4.2.0. Data sets needed to reproduce figures are provided in "Data" folder (Rdata format). The pdf files in "Figures" folder are outputs generated from the corresponding R scripts. Note that final figures in the article were produced by combining multiple figures using a vector graphics software (Inkscape) or PowerPoint. Please contact Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>) with any questions. </p> <p>Preferred citation: Cho, E., Vuyovich, C. M., Kumar, S. V., Wrzesien, M. L., Kim, R. S., and Jacobs, J. M. (2022). Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent, Hydrol. Earth Syst. Sci., https://doi.org/10.5194/hess-2022-136.</p> <p>Corresponding author: Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>; <a href="mailto:escho@umd.edu">escho@umd.edu</a>)</p>
Bi-equivalent planar graphs data files
<p>The archive file contains files describing every bi-equivalent planar graph.<br>Each folder of the type LM contains the graph of valencies L and M.<br>Each sub-folder in these folder correspond to 1 graph.<br>Their content is:<br>- f.par : the PGC for the graph<br>- a txt file: file describing the connectivity of the graph (see below for the format).<br>- a svg file: vector graphic file for the graph.</p> <p>Syntax of the txt file:<br>Line 1 : the number of nodes for the graph<br>Line 2 : the nodes for the outside face (usually "0 1 2 P-1")<br>Following lines except last 2 lines: "n1 n2" : the indices of 2 nodes linked together.<br>Line -2 from the end: "N0 n1 n2 ... nn" : the list of nodes of the 1st type.<br>Last line: "N1 n1 n2 ... nn", the list of nodes of the 2nd type.</p>
CSV equivalent of LOFAR ACC files
<p>These are conversions of LOFAR ACC files by Griffin Foster from <a href="https://zenodo.org/record/840405/files/20120513_052251_acc_512x192x192.dat">https://zenodo.org/record/840405</a> to demonstrate the use of the ACC to CSV converter developed at DIAS</p>
Manual in-situ measurements of snow depth and snow water equivalent at the Polish Polar Station Hornsund - winter seasons 2021/2022and 2022/2023
<p>The dataset presents manual measurements of snow depth and snow water equivalent collected at the Polish Polar Station Hornsund in Svalbard during the winter seasons of 2021/2022 and 2022/2023.</p> <p>Snow depth measurements have been conducted at the same location by the Station's overwintering personnel since August 1982. Snow depth is calculated from a mean of three snow stakes to avoid the effects of the drifting snow. Measurements are taken manualy, on a daily basis. </p> <p>Snow water equivalent measurements have also been carried out at the same points by the Station's overwintering crew since October 1982. These measurements are performed every five days using a VS-43 snow tube. However, measurements are not taken when the snow depth is less than 5 cm.</p>
Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"
<p>This datasset contains data to reproduce the following figures of the paper <em>Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site</em>:</p> <ul> <li> <p>Time series data of Figures 1c and 2</p> </li> <li> <p>Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4)</p> </li> <li>Pléiades snow depth map (Figure S1)</li> <li> <p>Data used for plotting Figure S2</p> </li> </ul> <p> </p>
NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series and the regionalisation of the ΔSNOW model
<p>Time series of daily Snow Water Equivalent (SWE) and Snow Density over the Northern Hemisphere, based on in-situ station observations of snow depth converted to SWE using the ΔSNOW model (Winkler et al., 2021) and regionalised parameters. </p> <p>An extensive description of the dataset and the method to generate it can be found in the data descriptor manuscript published in the journal Earth System Science Data: <a href="https://essd.copernicus.org/preprints/essd-2023-31/">https://essd.copernicus.org/articles/15/2577/2023/essd-15-2577-2023</a> </p> <p><strong>Dataset:</strong> A total of 11,0071 time series of modelled SWE and estimated snow density at the point scale, spanning 1950-2022, at daily resolution.<em> "NH-SWE_dataset_MAP.png"</em> shows a Northern Hemisphere map with the location of all stations in the NH-SWE dataset and their elevation in meters. </p> <p><strong>Files: </strong>The dataset is provided in two different formats:</p> <ol> <li>Individual <em>.csv</em> files for each station in the NH-SWE dataset at <em>"NH_SWE_dataset_vector_files.zip"</em></li> <li>Full-dataset <em>.csv </em>matrices with dates as rows and NH-SWE stations as columns at <em>"NH_SWE_dataset_matrix_files.zip"</em></li> </ol> <p><strong>Metadata:<em> </em></strong><em>"NH_SWE_METADATA.csv"</em> Includes information on NH-SWE stations location (ID, country, station name, coordinates, elevation), data source, length of time series, model parameters and the climate variables used to estimate them, and average snow climatology such as average maximum snow depth, average peak SWE and average maximum snow cover duration. More details and units in the <em>"README_fileformats.txt"</em> file. </p> <p><strong>ΔSNOW model parameter regionalisation: </strong>The code to obtain the ΔSNOW model parameters based on climate variables for all the stations in the NH-SWE dataset is shared in<em><strong> </strong>"DeltaSNOW_parameter_regionalisation.zip"</em>. The method is extensively described in the data descriptor manuscript by Fontrodona-Bach et al., (2023) submitted to Earth System Science Data. More details in the <em>"README_regionalisation.txt"</em> file. </p> <p><strong>Data use: </strong>Free, provided adequate citation of both the data descriptor manuscript and the zenodo record. See <em>"README_datausage.txt"</em></p> <p><strong>Version history:</strong><br>v1: Initial upload. The ΔSNOW model regionalisation was missing.<br>v2: Manuscript submission version. Updated dataset and includes the ΔSNOW model regionalisation code.</p> <p><strong>Reported errors:</strong><br>The dataset accidentally contains one station from the Southern Hemisphere (NH-SWE ID 500001), located in Antarctica (Country code AY). <br>The longitude of a few stations exceeds +180 decimal degrees. To obtain the correct value within the [-180,180] decimal degree longitude bounds, the value exceeding +180 needs to be added to -180 degrees (e.g. +181.0 degrees is actually -179.0 degrees).<br>Swedish stations have two different country codes, SE for the ECA&D stations, and SW for the GHCNd stations. <br>Japan country code is "JA" in the metadata, although the official country code should be JP. </p>
Buddhist Transliteration Terms in Vetter's Lexicographical Study of An Shigao, with Gandhari Equivalents
<p>This dataset consists of two files; the basic one is based on Tilmann Vetter's Lexicographical Study of An Shigao’s Translations, collecting all instances of words that are transliterations of Indic terms. The extended file adds the Gandhari equivalent - when they exist - of the Sanskrit / Pali terms, according to Baums & Glass. This dataset can be used to study the correspondence between Chinese characters and the Indic terms they represent.</p> <p><strong>Bibliography</strong></p> <p>Baums, Stefan, and Andrew Glass. n.d. ‘Gandhari.Org – Gāndhārī Language and Literature’. Accessed 31 January 2023. <a href="https://www.gandhari.org/">https://www.gandhari.org/</a>.</p> <p>Vetter, Tilmann. 2012. <em>A Lexicographical Study of An Shigao’s and His Circle’s Chinese Translations of Buddhist Texts</em>. Studia Philologica Buddhica. Monograph Series 28. Tokyo: International Institute for Buddhist Studies of the International College for Postgraduate Buddhist Studies.</p> <p> </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.