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10,699 results for “easting”
St Dunstan In The East
The quiet garden containing the ruins of St Dunstan in the East church on St Dunstan's Hill near Lower Thames Street, London. Originally built in about 1100 and then destroyed in the blitz during World War 2. https://en.wikipedia.org/wiki/St_Dunstan-in-the-East 3371 photos taken in October 2020 with a Sony a6000 and processed in Agisoft Metashape. Railing fence reconstructed in places. Tops of tree canopy removed in places. Source: Objaverse 1.0 / Sketchfab
Leiston Abbey East Wall Buttress
Leiston Abbey was a Premonstatensian foundation in Suffolk, England, dissolved in the C16th. As with many such structures, valuable stone cladding was plundered for re-use in the locality. This example at Leiston shows an external buttress on the east end of the abbey with the rubble and flint infill that is typical of this area contrasting with cladding that remains in situ. You can see some graffiti carved into the cladding. FR and what looks like a temporary bench mark. Captured 25-07-2015 4pm Source: Objaverse 1.0 / Sketchfab
Drinking Fountain Trafalgar Square East
A drinking fountain in Trafalgar Square (East side), London. Date:1960 http://www.mdfcta.co.uk/details-f/f217.html Photos taken January 2019 with a Sony a6000 and processed in Agisoft Metashape. Source: Objaverse 1.0 / Sketchfab
East Runton Pill Box
A WW2 pillbox mostly burried in the sand on the beach between Cromer and East Runton. Uploaded with 3dScannerApp.com and scanned with an ipadpro 2020 Source: Objaverse 1.0 / Sketchfab
Rosedale East Engine Bed close
The remains of an engine bed and building at one of the former drift mine entrances at Rosedale East. The picture below shows the type of oil/diesel engine that might have sat here, with a massive single cylinder. This engine was installed by the Carlton Iron Company in 1900 and drove an electric generator for power for lighting, drills and haulage for the mine. Pictue by Thomas Smith:  Source: Objaverse 1.0 / Sketchfab
Shrimp Boat Wreck, Florida East Coast
Little remains of the early 20th century shrimp boat wreck. Intermittently exposed, only the remains of the stern were visible when we visited the site recently. Beach wrecks have several environmental issues to contend with that tend to rapidly degrade the resource. By modeling the remains we are helping to preserve this quickly vanishing resource for future researchers. Source: Objaverse 1.0 / Sketchfab
Choirokoitia - Nort East excavations
Choirocoitia is an archaeological site on the island of Cyprus dating from the Neolithic age. It has been listed as a World Heritage Site by UNESCO since 1998. The site is known as one of the most important and best preserved prehistoric sites of the eastern Mediterranean. Much of its importance lies in the evidence of an organised functional society in the form of a collective settlement, with surrounding fortifications for communal protection. The Neolithic aceramic period is represented by this settlement and around 20 other similar settlements spread throughout the island. See Wikipedia: https://en.wikipedia.org/wiki/Khirokitia Source: Objaverse 1.0 / Sketchfab
Herstmonceux Castle - East Sussex, England
Created from frames crowd sourced from [Youtube](https://www.youtube.com/watch?v=pXIxQoY1oz0) under Creative Commons. Herstmonceux Castle is a brick-built castle near Herstmonceux, East Sussex, England. From 1957 to 1988 its grounds were the home of the Royal Observatory, Greenwich. Today it is used by the Bader International Study Centre of Queen's University, Canada. - [Wikipedia](https://en.wikipedia.org/wiki/Herstmonceux_Castle) Created in RealityCapture by Capturing Reality from 150 images in 01h:00m:40s using high quality settings. 7.8m -> 1m Source: *Herstmonceux Castle Aerial / Drone View* by *Beautiful Landmarks* https://youtu.be/pXIxQoY1oz0 Source: Objaverse 1.0 / Sketchfab
Frog coin tree, Pressmennan Wood, East Lothian
We're a superstitious lot, but I don't know when the practice of hammering coins into trees, such as this carved tree stump began? Does anyone have any ideas? This example and many others can be seen in Pressmennan Wood, East Lothian, Scotland. More details here: https://www.woodlandtrust.org.uk/visiting-woods/wood/?woodId=4908&woodName=pressmennan-wood Model produced from 93 images taken on a Samsung S8. Source: Objaverse 1.0 / Sketchfab
Research Funding in the Middle East and North Africa: Analyses of Acknowledgments in Scientific Publications (unified funders)
<p>This dataset is the result of the unification of funder names acknowledged in scientific publications indexed in the Web of Science with at least one author affiliated to an institution located in the Middle East and North Africa.</p> <p>This list contains 1,039 unified names of funders from the 22 MENA countries as of 16 March 2023 along with their type and country.</p>
Anthropogenic disturbance driving population decline of a dominant tree in East Asia evergreen broadleaved forests over the last 11,000 years
<p><span>Current biodiversity loss was generally considered to be caused by </span><span>anthropogenic disturbance</span><span>, but when anthropogenic activities began to impact biodiversity loss is still </span><span>controversial</span><span>. One hypothesis suggested it was from the industrial era, while others proposed that the anthropogenic disturbance had already resulted in biodiversity decline since the Early Holocene. To test these hypotheses, we focus on subtropical East Asia, which has witnessed a land use and anthropogenic history since the Early Holocene. We selected the unique vegetation of evergreen broadleaved forests (EBLFs), using a genomic approach to infer the demographic history of a dominant plant (<em>Litsea</em> <em>elongata</em>) of EBLFs, and to further detangle the impact of climate change and/or anthropogenic disturbance on effective population size fluctuation. Nine well-defined geographical clades were identified within extant populations of <em>L</em>. <em>elongata</em>. The estimated historical population sizes of these clades all contracted, indicating persistent population decline over the last 11,000 years. Significant correlation was detected between demographic history and three anthropogenic disturbance factors, rather than the climate change in the Holocene. Therefore, we provided the <span>dataset used for<span> phylo</span></span>g<span>enomic analysis</span>,<span> </span><span> demographic history inference</span><span>, and correlation analysis in this study.</span></span></p>
Data from: Inference of selective force on house mice genomes during secondary contact in East Asia
<p>The house mouse (<em>Mus musculus</em>), commensal to humans, has spread globally via human activities, leading to secondary contact between genetically divergent subspecies. This pattern of genetic admixture can provide insights into the selective forces at play in this well-studied model organism. Our analysis of 163 house mouse genomes, mainly from East Asia, revealed substantial admixture between the subspecies<em> castaneus</em> and <em>musculus</em>, particularly in Japan and southern China. We revealed, despite the admixture, that all Y chromosomes in the East Asian samples belonged to the <em>musculus</em>-type haplogroup, potentially explained by genomic conflict under sex ratio distortion due to varying copy numbers of ampliconic genes on sex chromosomes. We also investigated the influence of natural selection on the post-hybridization of the subspecies <em>castaneus</em> and <em>musculus</em> in Japan. Even though the genetic background of most Japanese samples closely resembles the subspecies<em> musculus</em>, certain genomic regions overrepresented the <em>castaneus</em>-like genetic components, particularly in immune-related genes. Furthermore, a large genomic block containing a vomeronasal/olfactory receptor gene cluster predominantly harbored <em>castaneus</em>-type haplotypes in the Japanese samples, highlighting the possible role of olfaction-based recognition in shaping hybrid genomes.</p>
Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP5-RCP8.5)
<p>As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.</p> <p>This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 5 and the representative concentration pathway (RCP) used was RCP 8.5. More information about SSP and RCP can be referred to O'Neill et al. (2020).</p> <p>Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.</p> <p>More information about the six selected CMIP6 GCMs:</p> <p>ACCESS-CM2 -<br>http://dx.doi.org/10.1071/ES19040<br>BCC-CSM2-MR -<br>https://doi.org/10.5194/gmd-14-2977-2021<br>CNRM-ESM2-1-<br>https://doi.org/10.1029/2019MS001791<br>MPI-ESM1-2-HR -<br>https://doi.org/10.5194/gmd-12-3241-2019<br>MRI-ESM2-0 -<br>https://doi.org/10.2151/jmsj.2019-051<br>NorESM2-MM -<br>https://doi.org/10.5194/gmd-13-6165-2020</p> <p>Additional references:<br>O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.<br>Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0<br>Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.</p> <p><strong>Please cite the following if this data is used in any research or project:</strong></p> <p><em><strong>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: <a href="http://dx.doi.org/10.1145/3600100.3626637" target="_blank" rel="noreferrer noopener">10.1145/3600100.3626637</a></strong></em></p> <p> </p> <p><strong>Cross-Model Version:</strong></p> <div> <div> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719204, Feb 2024. [<a href="10719204" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719178, Feb 2024. [<a href="../records/10719178" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10698921, Feb 2024. [<a href="../records/10698921" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (Cross-Model version-SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10420668, Dec 2023. [<a href="../records/10420668" target="_blank" rel="noopener">Data</a>] </p> <p> </p> <p><strong>Model-specific Version:</strong></p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729277, Feb 2024. [<a href="../records/10729277" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729279, Feb 2024. [<a href="../records/10729279" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729223, Feb 2024. [<a href="../records/10729223" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729201, Feb 2024. [<a href="../records/10729201" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729157, Feb 2024. [<a href="../records/10729157" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729199, Feb 2024. [<a href="../records/10729199" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8335814, Sept 2023. [<a href="../records/8335814" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8338548, Sept 2023. [<a href="../records/8338548" target="_blank" rel="noopener">Data</a>] </p> </div> </div> <p> </p> <p><strong>Representative Cities Version:</strong></p> <p>Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F6939750%23.YwYzp3bMKUk&data=05%7C01%7Clif2%40ornl.gov%7C26cbed91b56e40d4014708dbc0976975%7Cdb3dbd434c4b45449f8a0553f9f5f25e%7C1%7C0%7C638315528798318118%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=S8Z0mjWDMqelFJkp2mfNBVqaiDCdM3AXjQ7PDPEBIu4%3D&reserved=0">Data</a>]</p>
Insights into Internal Solitary Waves East of Dongsha Atoll from Integrating Geostationary Satellite and Mooring Observations
<p>The dataset used to produce the figures in the manuscript. All the data are in MATLAB file format. </p>
Figure 26 in Molecular and morphological confirmation of Isometrus maculatus (DeGeer 1778) (Scorpiones Buthidae) from Northeast India and East Asia
Figure 26. Distribution of Isometrus specimens analysed here (see Figs 24–25).
The data for The Simulation of the Equinox Asymmetry of Ionospheric Electrodynamics at East-Asian Sector
<p>The data is used to describe the figure in the paper "The Simulation of the Equinox Asymmetry of Ionospheric Electrodynamics at East-Asian Sector"</p>
Data for: Subglacial freshwater driven speedup of East Antarctic outlet glacier retreat
<p>Recent studies have revealed the presence of a complex freshwater system underlying the Aurora Subglacial Basin (ASB), a region of East Antarctica that contains ~7 m of global sea level potential in ice mainly grounded below sea level. Yet, the impact that subglacial freshwater has on driving the evolution of the dynamic outlet glaciers that drain this basin has yet to be tested in a coupled ice sheet-subglacial hydrology numerical modeling framework. Here, we project the evolution of the primary outlet glaciers draining the ASB (Moscow University Ice Shelf, Totten, Vanderford, and Adams Glaciers) in response to an evolving subglacial hydrology system and to ocean forcing through 2100, following low and high CMIP6 emission scenarios. By 2100, ice-hydrology feedbacks enhance the ASB's 2100 sea level contribution by ~30% (7.50 mm to 9.80 mm) in high emission scenarios and accelerate retreat of Totten Glacier's main ice stream by 25 years. Ice-hydrology feedbacks are particularly influential in the retreat of the Vanderford and Adams Glaciers, driving an additional 10 km of retreat in fully-coupled simulations relative to uncoupled simulations. Hydrology-driven ice shelf melt enhancements are the primary cause of domain-wide mass loss in low emission scenarios, but are secondary to ice sheet frictional feedbacks under high emission scenarios. The results presented here demonstrate that ice-subglacial hydrology interactions can significantly accelerate retreat of dynamic Antarctic glaciers and that future Antarctic sea level assessments that do not take these interactions into account might be severely underestimating Antarctic Ice Sheet mass loss. </p> <p>In this data publication, we present the model output and results associated with the following manuscript recently submitted to the Journal of <em>Geophysical Research: Earth Surface</em>: "Subglacial discharge accelerates ocean driven retreat of Aurora Subglacial Basin outlet glaciers over the 21st century". We include yearly ice sheet model output between 2017-2100 for eight numerical ice-subglacial hydrology model runs. We also include the ice sheet and subglacial hydrology model initial states. In addition, we include all ocean forcing time-series (temperature and salinity for the low emission and high emission climate forcing scenarios for three glacial regions), which are used as input into the melt parameterization. Lastly, we include a MATLAB script that contains the code used to couple the ice-subglacial hydrology models as well as a "readme" file with further information on all data in this publication.</p>
Figure 4 in First record of four snapper fishes (Perciformes: Lutjanidae) from Odisha, East coast of India
Figure 4. Lutjanus vitta (Quoy & Gaimard).
Figure 1 in First record of four snapper fishes (Perciformes: Lutjanidae) from Odisha, East coast of India
Figure 1. Lutjanus guilcheri (Fourmanoir).
Figs 1 – 5 in NEW RECORD OF SCARAB-BEETLE OF THE SUBFAMILY DYNASTINAE (COLEOPTERA: SCARABAEIDAE) FROM THE RUSSIAN FAR EAST
Figs 1 – 5. Male of Oryctes nasicornis (collected in Primorsky krai, Lazo, 27.VI 2023):
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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.