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528 results for “equivalence”
Recommendations for reporting equivalent black carbon (eBC) mass concentrations based on long-term pan-European in-situ observations
<p>A reliable determination of equivalent black carbon (eBC) mass concentrations derived from filter absorption photometers (FAPs) measurements depends on the appropriate quantification of the mass absorption cross-section (MAC) for converting the absorption coefficient (babs) to eBC. This study investigates the spatial–temporal variability of the MAC obtained from simultaneous elemental carbon (EC) and babs measurements performed at 22 sites. We compared different methodologies for retrieving eBC integrating different options for calculating MAC including: locally derived, median value calculated from 22 sites, and site-specific rolling MAC. The eBC concentrations that underwent correction using these methods were identified as LeBC (local MAC), MeBC (median MAC), and ReBC (Rolling MAC) respectively. Pronounced differences (up to more than 50 %) were observed between eBC as directly provided by FAPs (NeBC; Nominal instrumental MAC) and ReBC due to the differences observed between the experimental and nominal MAC values. The median MAC was 7.8 ± 3.4 m2 g-1 from 12 aethalometers at 880 nm, and 10.6 ± 4.7 m2 g-1 from 10 MAAPs at 637 nm. The experimental MAC showed significant site and seasonal dependencies, with heterogeneous patterns between summer and winter in different regions. In addition, long-term trend analysis revealed statistically significant (s.s.) decreasing trends in EC. Interestingly, we showed that the corresponding corrected eBC trends are not independent of the way eBC is calculated due to the variability of MAC. NeBC and EC decreasing trends were consistent at sites with no significant trend in experimental MAC. Conversely, where MAC showed s.s. trend, the NeBC and EC trends were not consistent while ReBC concentration followed the same pattern as EC. These results underscore the importance of accounting for MAC variations when deriving eBC measurements from FAPs and emphasize the necessity of incorporating EC observations to constrain the uncertainty associated with eBC.</p>
Dataset for publication: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform
<p>This dataset provides the necessary data to get the images and results shown in the paper "Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform". </p> <p>Source Data Raw.zip has the entire data set used to generate the images.</p> <p>Source Data.zip contains the processed data from "Source Data Raw.zip". </p> <p>Files with extension .dream3d are accompained by a file with extension .xdmf. This files can be opened with Paraview. And their data can be accesible using python or matlab.</p> <p>For more information contact Proffesor Somnath Ghosh at Johns Hopkins University, Civil and Systems Engineering Department.</p>
Text-fig. 1. Line drawings of idealized murine left first molars, M1 above (a) and m1 below (b). Standard Cope-Osborn terminology is used for the cusps, with equivalent t1 to t12 terminology and specialized cingulum cusp names for m1. in Early Late Miocene Murine Rodents From The Upper Part Of The Nagri Formation, Siwalik Group, Pakistan, With A New Fossil Calibration Point For The Tribe Apodemurini (Apodemus/Tokudaia)
Text-fig. 1. Line drawings of idealized murine left first molars, M1 above (a) and m1 below (b). Standard Cope-Osborn terminology is used for the cusps, with equivalent t1 to t12 terminology and specialized cingulum cusp names for m1.
The variability of mass concentrations and source apportionment analysis of equivalent black carbon across urban Europe
<p>This study analyzed the variability of equivalent black carbon (eBC) mass concentrations and their sources in urban Europe to provide insights into the use of eBC as an advanced air quality (AQ) parameter for AQ standards. This study compiled eBC mass concentration datasets covering the period between 2006 to 2022 from 50 measurement stations, including 23 urban background (UB), 18 traffic (TR), 7 suburban (SUB), and 2 regional background (RB) sites. The results highlighted the need for the harmonization of eBC measurements to allow for direct comparisons between eBC mass concentrations measured across urban Europe. The eBC mass concentrations exhibited a decreasing trend as follows: TR > UB > SUB > RB. Furthermore, a clear decreasing trend in eBC concentrations was observed in the UB sites moving from Southern to Northern Europe. The eBC mass concentrations exhibited significant spatiotemporal heterogeneity, including marked differences in eBC mass concentration and variable contributions of pollution sources to bulk eBC between different cities. Seasonal patterns in eBC concentrations were also evident, with higher winter concentrations observed in a large proportion of cities, especially at UB and SUB sites. The contribution of eBC from liquid fossil fuel combustion, mostly traffic (eBC<sub>T</sub>) was higher than that of residential and commercial sources (eBC<sub>RC</sub>) in all European sites studied. Nevertheless, eBC<sub>RC</sub> still had a substantial contribution to total eBC mass concentrations at a majority of the sites. eBC trend analysis revealed decreasing trends for eBC<sub>T</sub> over the last decade, while eBC<sub>RC</sub> remained relatively constant or even increased slightly in some cities.</p>
Daily snow water equivalent and snow depth data from the valley Wattental in the Tuxer Alpen, Tyrol, Austria [dataset]
<p>The herein published dataset contains daily snow depth (HS) and daily snow water equivalent (SWE) data from the catchment of the Lizumbach in the valley bottom of Wattental in the Tuxer Alpen, Tyrol, Austria (N47.16820, E11.63858). The measurements are obtained at the tree line in an altitude of 1995m a.s.l. Measurement data are available from January 11 2010 until September 30 of 2022. The repository will be updated during the coming years. </p> <p> </p>
Carbon storage and carbon-equivalent albedo impact for US forests, by age and forest type
<p>These tables document estimates of carbon storage (Mg/ha +/- Standard Error) and carbon-equivalent albedo impacts (same units) of US forests by age and forest type (Healey et al., in review). Carbon estimates are derived from field measurements made by the USDA Forest Service on approximately 125,000 forested field plots (Domke et al., 2022). Soil organic carbon is omitted from these estimates, but all other above- and below-ground pools are included. Albedo impacts (time-dependent emissions equivalent, TDEE; Bright et al., 2016) were developed by applying atmospheric kernels (Bright and O'Halloran) to a new Landsat blue sky albedo product for the Landsat archive (Erb et al., 2022), as described by Healey et al. (in review). Standard error is supplied for each age/forest type bin for carbon storage, but upper and lower standard error bounds are specified for TDEE because log transformation creates an asymmetrical uncertainty envelope. </p> <p> </p> <p>Bright, Bogren, Bernier, Astrup, (2016). Carbon-equivalent metrics for albedo changes in land management contexts: Relevance of the time dimension. <em>Ecol. Appl.</em> 26, 1868–1880</p> <p>Bright, R. M., & O'Halloran, T. L. (2019). Developing a monthly radiative kernel for surface albedo change from satellite climatologies of Earth's shortwave radiation budget: CACK v1. 0. <em>Geoscientific Model Development, </em>12(9), 3975-3990.</p> <p>Domke, Walters, Nowak, Greenfield, Smith, Nichols, Ogle, Coulston, Wirth (2022). Greenhouse Gas Emissions and Removals From Forest Land, Woodlands, Urban Trees, and Harvested Wood Products in the United States, 1990–2020. (US Dept. Ag. For. Service, Madison, WI; <a href="https://doi.org/10.2737/FS-RU-382">https://doi.org/10.2737/FS-RU-382</a>).</p> <p>Erb, Li, Sun, Paynter, Wang, & Schaaf, (2022). Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain. <em>Remote Sensing</em>, <em>14</em>(21), 5320.</p> <p>Healey, Yang, Erb, Bright, Domke, Frescino, Schaaf, (in review) New satellite observations expose albedo dynamics offsetting half of carbon storage benefits in US forests.</p>
24-hour Oral Morphine Equivalent Based Opioid Prescribing After Surgery
ClinicalTrials.gov study NCT04043143. IPD Sharing: NO. Countries: 1. Publications: 7.
Snow cover and snow water equivalent for: How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction?
Open the record for dataset details and reuse information.
SPIReS-MODIS-ParBal snow water equivalent reconstruction: Western USA, water years 2001–2024
Open the record for dataset details and reuse information.
Data from: Different aspects of dominance are not equivalent when testing for trade-offs in ant communities
Open the record for dataset details and reuse information.
Snow water equivalent data for C1 Snotel, 1981 - 2010.
The U.S. Soil Conservation Service (SCS) operates hundreds of Snotel sites throughout the western United States. One of these is on Niwot Ridge at C-1, located approximately 1.6 km, by road, from the University of Colorado Mountain Research Station at an approximate elevation of 3000 meters. The Snotel installations operate on the principle that the snowpack exerts pressure on liquid-filled stainless steel "pillows" that rest on the ground. Pressure transducers output a digital signal that is sent via radio telemetry to one of two receiving stations. The transducer output is calibrated by measuring snowpack depth and density on or about the 25th of each month from December through April. A Mt. Rose snow sampler was used to determine the depth and density of the snowpack at each of the "pillow's" four corners. These four values were averaged to determine the monthly snow water equivalent (SWE) in inches. NOTE: The LTER data portal display does not display important maintenance/log information or other EML metadata features. Please be sure to view the EML file (a text file that contains XML tags) which is included in the zip archive (click on "Download zip archive") pertaining to each dataset. The EML file name will have the following format: knb-lter-nwt.[3 digit dataset number].[version number].xml. Most web browsers can parse the EML so it's easier to read.
Equivalent Black Carbon Emission Factors from Ships Within a Sulfur Emission Control Area
<p>Equivalent black carbon (eBC) emission factors, for ship plumes sampled in the Port of Gothenburg during two measurement campaigns in autumn 2014 and autumn 2015 respectively. </p> <p>Measurement site coordinates: N57.6849, E11.838</p> <p>Equivalent black carbon measured with a Multi Angle Absorption Photometer (MAAP, Thermo Fisher Scientific), wavelength 637 nm. CO2 sampled with a non-dispersive infrared gas analyzer (LI840, LI-COR).</p> <p>Unit (EF_BC): ug (kg fuel)^-1</p> <p>File creator: Stina Ausmeel<br> Contact e-mail: stina.ausmeel@nuclear.lu.se</p>
Experimental evidence for the incorporation of two metals at equivalent lattice positions in mixed metal organic frameworks. Dataset related to publication. Version: 1
<p>Experimental evidence for the incorporation of two metals at equivalent lattice positions in mixed metal organic frameworks. Dataset related to publication. Version: 1</p>
Ultimate strength assessment of stiffened panel using non-linear mechanical behavior of an equivalent single layer: grillage FE model used for analysis
<p>This example shows how the ESL can be applied in the ultimate strength structural analysis in Abaqus finite element software. In other words, ESL methodology is applied only in some parts of the structure while larger structural supporting components like girders and webframes are still modeled explicitly. FIles include also the Full_3D_FEM model used for validating the ESL model.</p> <p>Dataset includes following files:</p> <p>1. ESL_nonlinear_grillage.inp - this is Abaqus input file for running the ESL nonlinear grillage model.</p> <p>2. ugensFINALv_master.for - this defines the nonlinear stiffness or ABD matrix. This is called by input file (ESL_nonlinear_grillage.inp ).</p> <p>3. Full_3D_FEM.inp - Full_3D_FEM model used for validating the ESL model.</p> <p> </p>
Compiler Equivalence
<p>Identifying equivalent mutants remains the largest impediment to the widespread uptake of mutation testing. Despite being researched for more than three decades, the problem remains. We propose Trivial Compiler Equivalence (TCE) a technique that exploits the use of readily available compiler technology to address this long-standing challenge. TCE is directly applicable to real-world programs and can imbue existing tools with the ability to detect equivalent mutants and a special form of useless mutants called duplicated mutants. We present a thorough empirical study using 6 large open source programs, several orders of magnitude larger than those used in previous work, and 18 benchmark programs with hand-analysis equivalent mutants. Our results reveal that, on large real-world programs, TCE can discard more than 7% and 21% of all the mutants as being equivalent and duplicated mutants respectively. A human-based equivalence verification reveals that TCE has the ability to detect approximately 30% of all the existing equivalent mutants.</p>
Northern Hemisphere historical in-situ Snow Water Equivalent dataset (NorSWE, 1979-2021)
<p><strong>Description</strong> (in English, French follows)</p> <p>The Northern Hemisphere historical in situ snow water equivalent dataset (NorSWE) includes snow water equivalent (SWE, or water equivalent of snow cover by WMO, 2018) observations from manual snow surveys, snow pillows, automated passive gamma radiation sensors (GMON), and from airborne passive gamma radiation surveys for the period 1979-2021 compiled from nine different sources covering North America, Russia, Finland, Norway and Switzerland. Exceptionally, to expand coverage over Europe we also include single point manual SWE observations (type_mes=1) from eleven sites in Switzerland. SWE is the primary variable of interest. Snow depth (SD) is included when available and derived bulk snow density is calculated from SD and SWE. NorSWE is described in detail in Mortimer and Vionnet (in prep). Sites intersecting the Global Mountain Biodiversity Assessment (GMBA) Mountain Inventory v2 (Snethlage et al., 2022; https://www.earthenv.org/mountains) with a 25 km buffer or a 2° slope mask derived from the GETASSE30 DEM are assigned a mountain mask flag of 1.</p> <p>Processing and quality control generally follows that described in Vionnet et al. (2021). Quality control involved range thresholding: ranges for SD, SWE and bulk density are 0-3 m (0-8 m where mmask = 1), 0-3000 kg m-2 (0-8000 kg m-2 where mmask =1), and 25-700 kg m-3. Where station elevation was not included in the original station metadata or it was deemed to be erroneous, elevation was taken from the United States Geological Survey’s National Elevation Dataset (Gesh et al., 2022). NorSWE was originally compiled to support evaluation of gridded SWE products over the modern satellite era (1979-2021) and focused on observations from snow course and airborne gamma SWE. In v2, we expanded the dataset to include automated data over North America to support hydrological modelling applications. In v3, we added data from Norway and Switzerland (Marty, 2020).</p> <p>NorSWE is provided as a netCDF (NorSWE-NorEEN_1979-2021_v3.nc, compressed into zip file) and following the conventions of the Canadian Snow Water Equivalent Dataset (CanSWE) described in Vionnet et al. (2021) with the addition of a mountain mask variable. The final dataset includes 10 153 locations spanning the years 1979 to 2021.</p> <p> </p> <p><strong>Description</strong> (Francais)</p> <p>Cet ensemble de données de l’Équivalent en Eau de la couverture Neigeuse (EEN, OMM, 2018) comprend des observations manuelles des lignes de neige, des mesures automatiques des coussins à neige et des capteurs gamma passif (GMON), et des estimations de l’EEN issues de mesures de radiation gamma aéroportée pour la période 1979-2021. Cette base de données compile des données issues de neuf sources couvrant l’Amérique du Nord, la Russie et la Finlande. L’EEN est la quantité d’intérêt principal. L’information sur la hauteur de neige (HN) est incluse lorsqu’elle est disponible et la masse volumique moyenne du manteau neigeux est calculée à partir de l’HN et de l’EEN. NorEEN est décrit en détail dans Mortimer and Vionnet (en préparation). Les sites montagneux sont indiqués par le code mmask (valeur = 1). Le masque de montagne combine le Global Mountain Biodiversity Assessment (GMBA) Mountain Inventory v2 (Snethlage et al., 2022; https://www.earthenv.org/mountains) (plus une zone tampon de 25 km) et un masque de topographie complexe (2°) calculée selon le modèle numérique de terrain (MNT) GETASSE30.</p> <p>Le traitement des données et le contrôle de qualité (CQ) suivent la méthodologie proposée par Vionnet et al. (2021). Pour le CQ, les observations en dehors de plages de valeurs prédéterminées ont été exclues : HN 0-3 m (0-8 m mmask = 1), EEN 0-3000 kg m-2 (0-8000 kg m-2 mmask = 1), et masse volumique moyenne du manteau neigeux 25-700 kg m-3. Si l’altitude de la station manquait ou était erronée, l’altitude du site a été extraite du fichier national d’élévation de la Commission Géologique des USA (Gesh et al., 2022). Originalement, la base de données décrite dans ce document a été mise en place pour évaluer de produits d’EEN sur grille d’échelle moyennes à large (4-50 km) couvrant la période moderne de la télédétection satellitaire (1979-2021). Pour cette raison, seules les observations des lignes de neige et les estimations de EEN dérivées de mesures de radiation gamma aéroportées avaient été incluses dans la version 1. Pour la version 2, les données historiques des stations automatiques couvrants l’Amérique du Nord ont été incluses en support des applications hydrologiques. Pour la version 3, les données historiques des stations de stations en Norvège et la Swisse ont été incluses (Marty, 2020).</p> <p>La base de données est distribuée au format NetCDF (NorSWE-NorEEN_1979-2021_v3.nc, comprimée dans une archive zip) selon les conventions de la base de données historiques canadiennes d’Équivalent en Eau de la Neige (CanEEN, Vionnet et al., 2021) et comprenant une variable supplémentaire indiquant les sites montagneux. La base de données inclut des mesures issues de 10,153 sites uniques durant la période de 1979 à 2021.</p> <p><strong>References/Références</strong></p> <p>Beaudette, D., Skovlin, J., Roecker, S., and Brown, A.: soilDB: Soil Database Interface. R package version 2.8.5, [codebase] https://CRAN.R-project.org/package=soilDB, 2024.</p> <p>Carroll, T.R. Airborne Gamma Radiation Snow Survey Program: A user's guide, Version 5.0. National Operational Hydrologic Remote Sensing Center (NOHRSC), Chanhassen, 14, 2001. https://www.nohrsc.noaa.gov/special/tom/gamma50.pdf</p> <p>Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M., and Tyler, D.: The National Elevation Dataset, Photogramm. Eng. Rem. S., 68, 5–32, 2002.</p> <p>Marty, C.: GCOS SWE data from 11 stations in Switzerland, EnviDat, [data Set], https://www.doi.org/10.16904/15, last updated 2024 (last access: February 2025), 2020.</p> <p>Snethlage, M.A., Geschke, J., Spehn, E.M., Ranipeta, A., Yoccoz, N. G., Körner, Ch., Jetz, W., Fischer, M., and Urbach, D.: A hierarchical inventory of the world’s mountains for global comparative mountain science, Sci. Data, 9, 149, https://doi.org/10.1038/s41597-022-01256-y, 2022.</p> <p>Snethlage, M.A., Geschke, J., Spehn, E.M., Ranipeta, A., Yoccoz, N. G., Körner, Ch., Jetz, W., Fischer, M., and Urbach, D.: GMBA Mountain Inventory v2 [data set], GMBA-EarthEnv., https://doi.org/10.48601/earthenv-t9k2-1407, 2022, accessed June 2023.</p> <p>Vionnet, V., Mortimer, C., Brady, M., Arnal, L., and Brown, R.: Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020), Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, 2021.</p> <p>Vionnet, V., Mortimer C., Brady, M., Arnal, L., and Brown R.: Canadian historical Snow Water Equivalent dataset (CanSWE 1928-2022), Version 5, Zenodo, https://zenodo.org/records/7734616 , 2021, updated 13 March 2023.</p> <p>WMO (Ed.): Guide to instruments and methods of observation: Volume II - Measurement of Cryospheric Variables, 2018th ed., World Meteorological Organization, Geneva, WMO-No. 8, 52 pp., 2018. <strong><br></strong></p>
Global snow water equivalent product derived from machine learning model trained with in situ measurement data
<p>This dataset is a global snow water equivalent dataset using machine learning trained with in-situ measurements. The temporal resolution of the SWEML product is daily, and the spatial resolution is 0.25˚ (approximately 25km). It covers latitudes of 90S to 90N and longitudes of 180W to 180E with global scales, excluding Antarctica. The dataset is provided in NetCDF format, organized by year. Each year contains daily SWE data, including leap days in leap years.</p>
Dataset of paper "Phosphate recovery from urine-equivalent solutions for fertiliser production for plant growth"
<p>Dataset of paper "Phosphate recovery from urine-equivalent solutions for fertiliser production for plant growth"</p>
Canadian historical Snow Water Equivalent dataset (CanSWE, 1928-2024)
<p><strong>Description</strong> (in English, French follows)</p> <p>The Canadian historical Snow Water Equivalent dataset (CanSWE) includes manual and automated pan-Canadian observations of Snow Water Equivalent (SWE) collected by national, provincial and territorial agencies, hydropower companies and their partners, as well as academic institutions. Snow depth and derived bulk snow density are also included when available. A code describes the SWE measurement method for each site following World Meteorological Organization (WMO) standards (WMO, 2019). This new dataset supersedes the most recent update of the Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al. (2019) and available at <a href="https://doi.org/10.18164/cf337b6b-9a87-4ffd-a8e5-41e6498b1474">https://doi.org/10.18164/cf337b6b-9a87-4ffd-a8e5-41e6498b1474</a>. The creation of CanSWE used the 2019 CHSSD update as a starting point and involved three main steps: (i) correction and cleaning of the 2019 CHSSD update (correction of metadata, removal of duplicates), (ii) update of this cleaned dataset until July 2020 and addition of snow data from new stations and agencies, and (iii) consistent quality control of the final dataset. The version 6 of CanSWE includes over one million SWE measurements from 2945 different locations across Canada over the snow seasons 1928 – 2024 where a snow season is defined as starting August 01 and ending July 31. CanSWE is described in detail in Vionnet et al. (2021).</p> <p>The data are distributed in 2 formats: a NetCDF file (CanSWE-CanEEN_1928-2024_v7.nc) and a zip file containing a csv version of CanSWE (CanSWE-CanEEN_1928-2024_v7.zip). More details about the dataset, the file format and the update made in CanSWEv7 are given in the files ReadMe_CanSWE_v7.pdf (in English) and LisezMoi_CanEEN_v7.pdf (in French).</p> <p><strong>Description</strong> (Francais)</p> <p>La base données historiques canadiennes d’Equivalent en Eau de la Neige (CanEEN) comprend des observations manuelles et automatiques de l’Equivalent en Eau de la Neige (EEN) à l’échelle du Canada collectées par des agences nationales, provinciales et territoriales, des compagnies productrices d’hydroélectricité et leurs partenaires ainsi que par des universités. Les informations sur la hauteur de neige et la masse volumique moyenne du manteau neigeux sont incluses lorsqu’elles sont disponibles. Un code qui suit les règles de l’Organisation Mondiale de la Météorologie (OMM, 2019) décrit la méthode de mesure de l’EEN pour chaque site. Cette nouvelle base de données remplace le jeu de données des Relevés Nivométriques Canadiens (RNC) publié par Brown et al. (2019) et disponible à l’adresse : <a href="https://doi.org/10.18164/cf337b6b-9a87-4ffd-a8e5-41e6498b1474">https://doi.org/10.18164/cf337b6b-9a87-4ffd-a8e5-41e6498b1474</a>. La création de CanEEN se base sur la version de 2019 des RNC et se décompose en 3 étapes principales : (i) correction et nettoyage de la version 2019 des RNC (correction des métadonnées, suppression des duplicata), (ii) mise à jour de ce jeu de données nettoyé avec des données disponibles jusqu’en Juillet 2020 et ajout de données historiques provenant de nouvelles stations et de nouveaux partenaires, (iii) contrôle qualité appliqué à l’ensemble du jeu de données. La version 7 de CanEEN inclut plus d’un million de mesures de l’EEN collectées dans 2945 stations à travers le Canada pour les années nivologiques 1928 à 2023 où une année nivologique est définie pour la période allant du 1 août au 31 juillet. CanEEN est décrit en détail dans Vionnet et al. (2021).</p> <p>Les données sont distribuées sous deux formats: un fichier au format NetCDF (CanSWE-CanEEN_1928-2024_v7.nc) et une archive zip contenant un fichier au format csv (CanSWE-CanEEN_1928-2024_v7.csv). Des informations complémentaires sur le jeu de données, leur format ainsi que les modifications apportées dans la version 6 sont fournies dans les fichiers ReadMe_CanSWE_v7.pdf (en Anglais) et LisezMoi_CanEEN_v7.pdf (en Francais).</p> <p><strong>References/Références: </strong></p> <p>Brown, R. D., Fang, B., and Mudryk, L.: Update of Canadian historical snow survey data and analysis of snow water equivalent trends, 1967–2016. Atmos. Ocean, 57, 149 156, <a href="https://doi.org/10.1080/07055900.2019.1598843">https://doi.org/10.1080/07055900.2019.1598843</a>, 2019</p> <p>Vionnet, V., Mortimer, C., Brady, M., Arnal, L., and Brown, R.: Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020), Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, 2021.</p> <p>WMO (World Meteorological Organization): Global Cryosphere Watch: Improvements in the international reporting of Snow Depth, WIGOS Newsletter, 5, 3-4, <a href="https://community.wmo.int/wigos-newsletters-archive">https://community.wmo.int/wigos-newsletters-archive</a>, 2019</p>
Dataset for: Neutrons on Rails -- trans-regional monitoring of soil moisture and snow water equivalent
<p>Using the railway system for regular environmental monitoring could extend the measurement capability to trans-regional and nationwide scales. Cosmic-ray neutron detectors in trains respond to spatial patterns of water content in their environment. Three distinct real world experiments support a proof of concept for soil and snow water monitoring using trains on short and long-range tracks across Germany:</p> <ul> <li>Supplement S4: Data (raw and processed) for the train journey from Leipzig to Berlin.</li> <li>Supplement S5: Data (raw and processed) for the train journey from Dessau to Zerbst, the subsequent car-borne Rover measurements, and the TDR measurements.</li> <li>Supplement S6: Data (raw and processed) for the train journey from Garmisch-Partenkirchen to Munich to Leipzig.</li> </ul> <p>This is the dataset supplementing the corresponding GRL publication "Neutrons on Rails -- trans-regional monitoring of soil moisture and snow water equivalent", preprint available from: https://doi.org/10.1002/essoar.10507363.1</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.