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Go back to the Amazon S3 bucket page and upload this HTML file of yours. What’s the address? You should now see your file in the bucket’s file listing. Click on that file and then click the Properties tab again. The properties will now show the details of your new file. Let’s step back a bit. Sandbox solutions and user code. – can upload a business solution and upload it at the site collection level. User code gallery. Things to note about the 'Hello world' application - SharePoint style. This code requires a reference to.
More files, more problems?
Nearly all enterprises, regardless of industry, have to store files, whether they are backups, media content or specialized vertical application datasets. Managing and scaling on-premises infrastructure to provide online storage and distribution of such backup or content files is often burdensome and costly, requiring expensive hardware refreshes, expansion and software licensing. Such large file data repositories can be siloed in specialized file servers, NAS units or backup systems, limiting access for big data analytics or media processing applications.
AWS Storage Gateway's file interface, or file gateway, offers you a seamless way to connect to the cloud in order to store application data files and backup images as durable objects on Amazon S3 cloud storage. File gateway offers SMB or NFS-based access to data in Amazon S3 with local caching. It can be used for on-premises applications, and for Amazon EC2-based applications that need file protocol access to S3 object storage.
Why use AWS Storage Gateway and Amazon S3 for file storage
Traditional SMB, NFS, or S3 API interfaces
Scalability & flexibility of AWS
Once the file gateway has moved data into Amazon S3, you can manipulate, analyze and manage it using native AWS services via API. Additionally, from your Amazon S3 bucket, you can distribute that data to other regions around the world with Cross-Region Replication, apply storage management tools, use Lifecycle policies to migrate it to archive-tier Amazon Glacier cloud storage, and even deploy additional file gateways to access it from your other sites.
File gateway use cases
Online content repository
The file gateway allows you to cost-effectively and durably store large files and media assets on AWS. Local applications also benefit from a low-latency local cache of frequently used content. The result is tiered, hybrid cloud content storage, which can be accessed easily by on-premises applications via NFS or SMB, from wherever you deploy gateway appliances - including in Amazon EC2. Storage Gateway automatically preserves the file metadata as object metadata, and also preserves the directory structure by including it in the object name. Content stored in Amazon S3 can be manipulated by in-cloud services via API, for example to do automatic image resizing with an AWS Lambda function, or to index the files with Amazon Elasticsearch Service.
Backup to cloud
Many organizations start their cloud journey by moving secondary and tertiary data, such as backups, to the cloud. The file gateway’s SMB and NFS interfaces provide an ideal way for IT groups to simply transition their backup jobs from existing on-premises backup systems to the cloud. Backup applications, native database tools or scripts that can write to SMB or NFS can write to the file gateway, which will store the backups as Amazon S3 objects of up to 5TiB in size. With an adequately sized local cache, recent backups can be used for fast on-site recoveries, while long-term retention needs are addressed by tiering backups to low-cost S3 Standard, S3 Infrequent Access and Amazon Glacier cloud storage tiers.
Big data, machine learning & processing
The file gateway makes it easy for Business Intelligence, Analytics or other teams that use Machine Learning to easily move file-based data into Amazon S3. They can then use that data for analytics, either with in-place queries via services such as Amazon Athena, Amazon Redshift Spectrum, or load it into other cloud tools such Amazon EMR for Hadoop-based processing. Post-analysis, result sets can be stored back in the same bucket, and the storage gateway service can make those new results files (objects) visible to on-premises applications wherever you have deployed a file gateway.
Additionally, you can apply simple compute functions with AWS Lambda to process data files stored in S3 with the file gateway, or even apply Machine Learning services to the data, for instance using Amazon Rekognition to perform image recognition or flag objectionable content.
Vertical industry applications
Industries including Oil & Gas, Media & Entertainment, Design & Architecture, and Manufacturing have domain-specific applications that generate large specialized files. These files often need to be distributed, or at least accessible, from multiple sites. Over time, most of these files become infrequently accessed, and can be stored on lower cost, but durable online cloud storage, if not fully archived. File gateway allows such on-premises applications, across multiple locations, to use Amazon S3 and Amazon Glacier to store the files. It also enables migration of such file-based applications to Amazon EC2, by providing the central, globally accessible repository, based on Amazon S3 object storage.
Results that file gateway and AWS deliver
IT outcomes
- Reduce datacenter infrastructure footprint; Minimize storage and backup stacks
- Focus on strategic initiatives, applications, optimal architectures, and flexible efficient operations
- Reduce the operational burden of maintaining and refreshing hardware
- Global scalability with easy redundancy - without infrastructure management
- Data durability: Amazon S3 and Amazon Glacier cloud storage is designed for 99.999999999% of durability
Business results
- Flexibility to evolve business as needed
- Shift to OpEx and consumption purchasing model that can be aligned to line of business growth
- Eliminate large and unexpected CapEx outlays
- Lower total costs of storing and processing data
- Confidence that critical data is safe and secure, and that your organization is in compliance with necessary regulations
Featured customers
“Our immersive digital strategy is enabling us to exploit the immense potential of mRNA science to deliver transformative medicines for many diseases, helping position us as one of today’s most notable high-growth biotechs. Seamlessly integrated and orchestrated cloud-based IT systems are critical to manage and industrialize the complex planning and execution of our mRNA pipeline scale-up at every stage of drug development. AWS Storage Gateway has the promise to transform the way we move data into the cloud. The file interface lets us easily integrate data files from analytical instruments, and the transparent S3 storage lets us easily connect our cloud-based applications and leverage the powerful storage capabilities of S3. With the AWS File Gateway, we can now unleash the full power of AWS on our instrument data.”
-- Dave Johnson, PhD, Director, Informatics - Moderna Therapetuics
Learn more: how it works
AWS Storage Gateway resources
Developer resources
Whitepapers, webinars & more
Have more questions?
Contact usAdd to this registryIf you want to add a dataset or example of how to use a dataset to this registry, please follow the instructions on the.Unless specifically stated in the applicable dataset documentation, datasets available through the Registry of Open Data on AWS are not provided and maintained by AWS. Datasets are provided and maintained by a variety of third parties under a variety of licenses. Please check dataset licenses and related documentation to determine if a dataset may be used for your application.
Cancer genomic life sciencesTherapeutically Applicable Research to Generate Effective Treatments (TARGET) is the collaborativeeffort of a large, diverse consortium of extramural and NCI investigators. The goal of the effortis to accelerate molecular discoveries that drive the initiation and progression of hard-to-treatchildhood cancers and facilitate rapid translation of those findings into the clinic. TARGET projects provide comprehensive molecular characterization to determine the genetic changesthat drive the initiation and progression of childhood cancers.The dataset contains open ClinicalSupplement, Biospecimen.Usage examples.by.by Bolouri H, Farrar JE, Triche T Jr, et al.by.by Yu Liu, John Easton, Ying Shao, et al.by Lim EL, Trinh DL, Ries RE, et al. Disaster response earth observation geospatial natural resource satellite imagery sustainabilityThe isa land monitoring constellation of two satellites that provide high resolutionoptical imagery and provide continuity for the current SPOT and Landsat missions.The mission provides a global coverage of the Earth's land surface every 5 days,making the data of great use in on-going studies. L1C data are available fromJune 2015 globally.
L2A data are available from April 2017 over wider Europeregion and globally since December 2018.Usage examples.by.by.by.by.by. Disaster response earth observation geospatial imaging satellite imagery sustainabilityThis project creates a S3 repository with imagery acquiredby the China-Brazil Earth Resources Satellite (CBERS). Theimage files are recorded and processed by Instituto Nacional de PesquisaEspaciais (INPE) and are converted to Cloud Optimized Geotiffformat in order to optimize its use for cloud based applications.The repository contains all CBERS-4 MUX, AWFI, PAN5M andPAN10M scenes acquired sincethe start of the satellite mission and is daily updated withnew scenes.Usage examples.by.by.by.by.by. Climate earth observation environmental natural resource oceans satellite imagery sustainability water weatherA global, gap-free, gridded, daily 1 km Sea Surface Temperature (SST) dataset created by merging multiple Level-2 satellite SST datasets. Those input datasets include the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 (AMSR-2) on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. Data are available fro.Usage examples.by GHRSST Project.by.by.by.by.
Biodiversity biology earth observation ecosystems environmental life sciences sustainabilityThe eBird Status and Trends project generates estimates of birdoccurrence and abundance at a high spatiotemporal resolution.This dataset represents the primary modeled results from theanalysis workflow and are designed for further analysis,synthesis, visualization, and exploration.Usage examples.by Matt Strimas-Mackey, Tom Auer, and Daniel Fink.by Tom Auer and Daniel Fink.by Cornell Lab of Ornithology.by Cornell Lab of Ornithology.by Tom Auer and Daniel Fink. Biology cell imaging image processing machine learning microscopyThis bucket contains multiple datasets (as Quilt packages) created by theAllen Institute for Cell Science (AICS). The imaging data in this bucket containseither of the following:1) field of view images from glass plates2) cell membrane, DNA, and structure segmentations3) cell membrane, DNA and structure contours4) machine learning imaging predictions of the previously listed modalities.In addition, many of the datasets include CSVs that contain feature setsrelated to that data.Usage examples.by.by.by.by.by. Biology fluorescence imaging image processing life sciences microscopy neurobiology neuroimaging neuroscienceThis data set, made available by Janelia's FlyLight project, consists of fluorescence imagesof Drosophila melanogaster driver lines, aligned to standard templates, and stored in formatssuitable for rapid searching and visualization. Additional data will be added as it is published.A large release of Gen1 MCFO samples is coming at the beginning of May 2020.Usage examples.by Rob Svirskas.by Hideo Otsuna.by Rob Svirskas.by Geoffrey Meissner.by Jody Clements, Rob Svirskas, Hideo Otsuna, Cristian Goina, Konrad Rokicki. Cancer genomic life sciencesThe International Cancer Genome Consortium (ICGC) coordinates projects with the common aim of accelerating research into the causes and control of cancer.
The PanCancer Analysis of Whole Genomes (PCAWG) study is an international collaboration to identify common patterns of mutation in whole genomes from ICGC. More than 2,400 consistently analyzed genomes corresponding to over 1,100 unique ICGC donors are now freely available on Amazon S3 to credentialed researchers subject to ICGC data sharing policies.Usage examples.by.by.by.by.by. Computer vision disaster response earth observation geospatial machine learning satellite imagerySpaceNet, launched in August 2016 as an open innovation project offering a repository of freely availableimagery with co-registered map features. Before SpaceNet, computer vision researchers had minimal optionsto obtain free, precision-labeled, and high-resolution satellite imagery. Today, SpaceNet hosts datasetsdeveloped by its own team, along with data sets from projects like IARPA’s Functional Map of the World (fMoW).Usage examples.by.by.by Nick Weir.by.by. Array tomography biology electron microscopy image processing life sciences lightsheet microscopy magnetic resonance imaging neuroimaging neuroscienceThis bucket contains multiple neuroimaging datasets (as Neuroglancer Precomputed Volumes) across multiple modalities and scales, ranging from nanoscale (electron microscopy), to microscale (cleared lightsheet microscopy and array tomography), and mesoscale (structural and functional magnetic resonance imaging). Additionally, many of the datasets include segmentations and meshes.Usage examples.by J.
Vogelstein, E. Gray Roncal, V. Chandrashekhar, F. Seshamani, J. Manavalan, B. Chevillet, E. Bridgeford, D.
Vogelstein, K. Deisseroth, and R.
Vogelstein, and A. Szalay.by.by R. Manavalan, E. Grosenick, N. Deisseroth, M.
Vogelstein, and R. Cancer genomicThe Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile study is the Multiple Myeloma Research Foundation (MMRF)’s landmark personalized medicine initiative.
CoMMpass is alongitudinal observation study of around 1000 newly diagnosed myeloma patients receiving variousstandard approved treatments. The MMRF’s vision is to track the treatment and results for eachCoMMpass patient so that someday the information can be used to guide decisions for newlydiagnosed patients.
CoMMpass checked on patients every 6 months for 8 years, collecting tissuesamples, gene.Usage examples.by Jonathan J Keats, PhD, Gil Speyer, Legendre Christophe, Christofferson Austin, KristiStephenson, BS, Ahmet Kurdoglu, Megan Russell, Aldrich Jessica, Cuyugan Lori, JonathanAdkins, Jackie McDonald, Adrienne Helland, Alex Blanski, Meghan Hodges, Dan Rohrer, SundarJagannath, MD, David Siegel, MD PhD, Ravi Vij, MD MBA, Gregory Orloff, MD, Todd Zimmerman,MD, Ruben Niesvizky, MD, Darla Liles, MD, Joseph W. Fay, Jeffrey L. Wolf, MD PhD, Robert M.Rifkin, Norma C Gutierrez, The MMRF CoMMpass Network, Jen Toups, Mary Derome, MS, WinnieLiang, PhD, Seunchan Kim, Daniel Auclair, PhD, Pamela G. Kidd, MD, Scott Jewell, PhD, JohnDavid Carpten, PhD, Sagar Lonial, MD.by Jonathan J Keats, PhD, Gil Speyer, Austin Christofferson, Christophe Legendre, PhD, JessicaAldrich, Megan Russell, Lori Cuyugan, Jonathan Adkins, Alex Blanski, Meghan Hodges, DanRohrer, Sundar Jagannath, MD, Ravi Vij, MD, Gregory Orloff, MD, Todd Zimmerman, MD, RubenNiesvizky, MD, Darla Liles, MD, Joseph W. Fay, Jeffrey L. Wolf, MD, Robert M Rifkin, Norma CGutierrez, MD PhD, Mmrf CoMMpass Network, Jennifer Yesil, MS, Mary Derome, MS, SeungchanKim, PhD, Winnie Liang, PhD, Pamela G.
Kidd, MD, Scott Jewell, PhD, John David Carpten, PhD,Daniel Auclair, PhD, Sagar Lonial, MD FACP.by.by Keats JJ, Craig DW, Liang W, Venkata Y, Kurdoglu A, Aldrich J, Auclair D, Allen K, HarrisonB, Jewell S, Kidd PG, Correll M, Jagannath S, Siegel DS, Vij R, Orloff G, Zimmerman TM, MMRFCoMMpass Network, Capone W, Carpten J, Lonial S.by Sagar Lonial, MD, Venkata D Yellapantula, Winnie Liang, PhD, Ahmet Kurdoglu, BS, JessicaAldrich, MSc, Christophe M. Legendre, MD, Kristi Stephenson, Jonathan Adkins, JackieMcDonald, Adrienne Helland, Megan Russell, Austin Christofferson, Lori Cuyugan, Dan Rohrer,Alex Blanski, Meghan Hodges, Mmrf CoMMpass Network, Mary Derome, Daniel Auclair, PhD, PamelaG. Kidd, MD, Scott Jewell, PhD, David Craig, PhD, John Carpten, PhD, Jonathan J.
Geospatial lidar solar sustainabilityReleased to the public as part of the Department of Energy's Open Energy Data Initiative, the National Renewable Energy Laboratory's (NREL) PV Rooftop Database (PVRDB) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for 128 metropolitan regions in the United States. The source lidar data and building footprints were obtained by the U.S. Department of Homeland Security Homeland Security Infrastructure Program for 2006-2014. Using GIS methods, NREL identified suitable roof surfaces based on their size, orientation, and shading.Usage examples.by Robert Margolis et al 2017 Environ.
12 074013.by.by Pieter Gagnon et al 2018 Environ. 13 024027.by.by.
Information retrieval machine learning natural language processingAmazon Customer Reviews (a.k.a. Product Reviews) is one of Amazon’s iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website.
Over 130+ million customer reviews are available to researchers as part of this dataset.Usage examples.by.by.by.by AWS. Atmosphere climate geospatial ice land machine learning model oceans sustainabilityThe Community Earth System Model (CESM) Large Ensemble Numerical Simulation (LENS) dataset includes a 40-member ensemble of climate simulations for the period 1920-2100 using historical data (1920-2005) or assuming the RCP8.5 greenhouse gas concentration scenario (2006-2100), as well as longer control runs based on pre-industrial conditions. The data comprise both surface (2D) and volumetric (3D) variables in the atmosphere, ocean, land, and ice domains. The total data volume of the original dataset is 500TB, which has traditionally been stored as 150,000 individual CF/NetCDF files on disk o.Usage examples.by Anderson Banihirwe, NCAR.by NCAR Science at Scale team.by Kay et al. (2015), Bull.
AMS, 96, 1333-1349.by Joe Hamman, NCAR. Bioinformatics biology deep learning genetic genomic life sciences machine learningThe Encyclopedia of DNA Elements (ENCODE) Consortium is an international collaboration ofresearch groups funded by the National Human Genome Research Institute (NHGRI). The goalof ENCODE is to build a comprehensive parts list of functional elements in the human genome,including elements that act at the protein and RNA levels, and regulatory elements thatcontrol cells and circumstances in which a gene is active.
ENCODE investigators employ avariety of assays and methods to identify functional elements. The discovery and annotationof gene elements is accomplished primarily by sequencing a.Usage examples.by.by.by.by. Disaster response earth observation geospatial meteorological satellite imagery sustainability weatherGOES satellites (GOES-16 & GOES-17) provide continuous weather imagery andmonitoring of meteorological and space environment data across North America.GOES satellites provide the kind of continuous monitoring necessary forintensive data analysis. They hover continuously over one position on the surface.The satellites orbit high enough to allow for a full-disc view of the Earth. Becausethey stay above a fixed spot on the surface, they provide a constant vigil for theatmospheric 'triggers' for severe weather conditions such as tornadoes, flash floods,hailstorms, and hurrican.Usage examples.by North Carolina State University’s North Carolina Institute for Climate Studies.by.by National Geographic.by Solcast. Air quality atmosphere earth observation environmental geospatial satellite imagery sustainabilityThis data set consists of observations from the Sentinel-5 Precursor (Sentinel-5P) satellite of the European Commission’s Copernicus Earth Observation Programme. Sentinel-5P is a polar orbiting satellite that completes 14 orbits of the Earth a day.
It carries the TROPOspheric Monitoring Instrument (TROPOMI) which is a spectrometer that senses ultraviolet (UV), visible (VIS), near (NIR) and short wave infrared (SWIR) to monitor ozone, methane, formaldehyde, aerosol, carbon monoxide, nitrogen dioxide and sulphur dioxide in the atmosphere. The satellite was launched in October 2017 and entered ro.Usage examples.by.by.by.by. Cancer genomic life sciencesBeat AML 1.0 is a collaborative research program involving 11 academic medical centers who workedcollectively to better understand drugs and drug combinations that should be prioritized forfurther development within clinical and/or molecular subsets of acute myeloid leukemia (AML)patients.
Beat AML 1.0 provides the largest-to-date dataset on primary acute myeloid leukemiasamples offering genomic, clinical, and drug response. This dataset contains open ClinicalSupplement and RNA-Seq Gene Expression Quantification data.Usage examples.by Haijiao Zhang, Samantha Savage, Anna Reister Schultz et al.by Jeffrey W.
Tyner, Cristina E. Tognon, Dan Bottomly et al.by. Cancer genomic life sciencesThe goal of the project is to identify recurrent genetic alterations (mutations, deletions,amplifications, rearrangements) and/or gene expression signatures. National Cancer Institute (NCI)utilized whole genome sequencing and/or whole exome sequencing in conjunction with transcriptomesequencing. Cancer genomicThe Foundation Medicine Adult Cancer Clinical Dataset (FM-AD) is a study conducted by FoundationMedicine Inc (FMI).
Genomic profiling data for approximately 18,000 adult patients with a diversearray of cancers was generated using FoundationeOne, FMI's commercially available, comprehensivegenomic profiling assay. This dataset contains open Clinical and Biospecimen data.Usage examples.by Ryan J. Hartmaier, Lee A.
Albacker, Juliann Chmielecki, Mark Bailey, Jie He, Michael E.Goldberg, Shakti Ramkissoon, James Suh, Julia A. Elvin, Samuel Chiacchia, Garrett M.Frampton, Jeffrey S.
Ross, Vincent Miller, Philip J. Stephens and Doron Lipson.by Beltran H, Yelensky R, Frampton GM, Park K, Downing SR, MacDonald TY, Jarosz M, Lipson D,Tagawa ST, Nanus DM, Stephens PJ, Mosquera JM, Cronin MT, Rubin MA.by. Disaster response elevation geospatial lidar sustainabilityThe goal of the (3DEP) is to collect elevation data in the form of light detection and ranging (LiDAR) data over the conterminous United States, Hawaii, and the U.S. Territories, with data acquired over an 8-year period. This dataset provides two realizations of the 3DEP point cloud data. The first resource is a public access organization provided in format, which a lossless, full-density, streamable octree based on (LAZ) encoding. The second resource is a of the same data in LAZ (Compressed LAS) format.
Resource names in bot.Usage examples.by.by.by. Genome wide association study genomic life sciences loftee vepVEP determines the effect of genetic variants (SNPs, insertions, deletions, CNVs or structural variants) on genes, transcripts, and protein sequence, as well as regulatory regions. The European Bioinformatics Institute produces the VEP tool/db and releases updates every 1 - 6 months. The latest release contains 267 genomes from 232 species containing 5567663 protein coding genes. This dataset hosts the last 5 releases for human, rat, and zebrafish. Also, it hosts the required reference files for the Loss-Of-Function Transcript Effect Estimator (LOFTEE) plugin as it is commonly used with VEP.Usage examples.by.by.by. Agriculture environmental food security life sciences machine learning sustainabilityThis dataset contains soil infrared spectral data and paired soil propertyreference measurements for georeferenced soil samples that were collectedthrough the Africa Soil Information Service (AfSIS) project, which lastedfrom 2009 through 2018.
In this release, we include data collected duringPhase I (2009-2013.) Georeferenced samples were collected from 19 countriesin Sub-Saharan African using a statistically sound sampling scheme,and their soil properties were analyzed using both conventional soiltesting methods and spectral methods (infrared diffuse reflectancespectroscopy). The two.Usage examples.by.by. Biology gene expression genetic image processing imaging life sciences machine learning neurobiology transcriptomicsThe Allen Mouse Brain Atlas is a genome-scale collection of cellular resolution gene expression profiles using in situ hybridization (ISH). Highly methodical data production methods and comprehensive anatomical coverage via dense, uniformly spaced sampling facilitate data consistency and comparability across 20,000 genes. The use of an inbred mouse strain with minimal animal-to-animal variance allows one to treat the brain essentially as a complex but highly reproducible three-dimensional tissue array.
The entire Allen Mouse Brain Atlas dataset and associated tools are available through an.Usage examples.by.by. Cancer genomic life sciencesThe Cancer Genome Characterization Initiatives (CGCI) program supports cutting-edge genomicsresearch of adult and pediatric cancers. CGCI investigators develop and apply advanced sequencingmethods that examine genomes, exomes, and transcriptomes within various types of tumors. TheBurkitt Lymphoma Genome Sequencing Project (BLGSP) aim is to create a databank of the manyalterations found in Burkitt lymphoma, an uncommon type of Non-Hodgkin lymphoma. The datasetcontains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification,miRNA-Seq Isoform Expression Quan.Usage examples.by.by Bruno M. Grande, Daniela S. Gerhard, Aixiang Jiang, Nicholas B.
Griner, Jeremy S. Abramson,Thomas B. Alexander, Hilary Allen, Leona W.
Ayers, Jeffrey M. Bethony, Kishor Bhatia,Jay Bowen, Corey Casper, John Kim Choi, Luka Culibrk, Tanja M.
Davidsen, Maureen A.Dyer, Julie M. Gastier-Foster, Patee Gesuwan, Timothy C. Greiner, Thomas G. Gross, BenjaminHanf, Nancy Lee Harris, Yiwen He, John D.
Irvin, Elaine S. Jaffe, Steven J. Jones,Patrick Kerchan, Nicole Knoetze, Fabio E. Leal, Tara M. Lichtenberg, Yussanne Ma, Jean PaulMartin, Marie-Reine Martin, Sam M. Mbulaiteye, Charles G.
Mullighan, Andrew J. Mungall,Constance Namirembe, Karen Novik, Ariela Noy, Martin D. Ogwang, Abraham Omoding, JacksonOrem, Steven J. Reynolds, Christopher K.
![Amazon S3 File Upload Api Composites World Amazon S3 File Upload Api Composites World](/uploads/1/2/7/1/127194343/708816497.png)
Rushton, John T. Sandlund, Roland Schmitz, CynthiaTaylor, Wyndham H. Wilson, George W. Wright, Eric Y.
Zhao, Marco A. Marra, Ryan D.
Morin,Louis M. Climate earth observation meteorological sustainability weatherERA5 is the fifth generation of ECMWF atmospheric reanalyses of the global climate, and the first reanalysis produced as an operational service. It utilizes the best available observation data from satellites and in-situ stations, which are assimilated and processed using ECMWF's Integrated Forecast System (IFS) Cycle 41r2.The dataset provides all essential atmospheric meteorological parameters like, but not limited to, air temperature, pressure and wind at different altitudes, along with surface parameters like rainfall, soil moisture content and sea parameters like sea-surface temperatu.Usage examples.by.by. Bioinformatics health life sciences natural language processing usMIMIC-III (‘Medical Information Mart for Intensive Care’) is a large,single-center database comprising information relating to patientsadmitted to critical care units at a large tertiary care hospital.Data includes vital signs, medications, laboratory measurements,observations and notes charted by care providers, fluid balance,procedure codes, diagnostic codes, imaging reports, hospital lengthof stay, survival data, and more.
The database supports applicationsincluding academic and industrial research, quality improvement initiatives,and higher education coursework. The MIMIC-I.Usage examples.by James Wiggins, Alistair Johnson.by. Aerial imagery earth observation geospatial natural resource regulatory sustainabilityThe National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. This 'leaf-on' imagery andtypically ranges from 60 centimeters to 100 centimeters in resolution and is available from the naip-analytic Amazon S3 bucket as 4-band (RGB + NIR) imagery in MRF format, on naip-source Amazon S3 bucket as 4-band (RGB + NIR) in uncompressed Raw GeoTiff format and naip-visualization as 3-band (RGB) Cloud Optimized GeotTiff format.
NAIP data is delivered at the state level; every year, a number of states receive updates, with.Usage examples.by.by. Earth observation energy geospatial meteorological solar sustainabilityReleased to the public as part of the Department of Energy's Open Energy Data Initiative,the isa serially complete collection of hourly and half-hourly values of the threemost common measurements of solar radiation – global horizontal, directnormal, and diffuse horizontal irradiance — and meteorological data. Thesedata have been collected at a sufficient number of locations and temporal andspatial scales to accurately represent regional solar radiation climates.Usage examples.by Manajit Sengupta, Yu Xe, Anthony Lopez, Aron Habte, Galen Maclaurin, James Shelby.by Manajit Sengupta, Yu Xe, Anthony Lopez, Aron Habte, Galen Maclaurin, James Shelby, Paul Edwards.
Disaster response earth observation environmental geospatial satellite imagery sustainabilityThe S1 Single Look Complex (SLC) dataset contains Synthetic Aperture Radar (SAR) data in the C-Band wavelength. The SAR sensors are installed on a two-satellite (Sentinel-1A and Sentinel-1B) constellation orbiting the Earth with a combined revisit time of six days, operated by the European Space Agency. The S1 SLC data are a Level-1 product that collects radar amplitude and phase information in all-weather, day or night conditions, which is ideal for studying natural hazards and emergency response, land applications, oil spill monitoring, sea-ice conditions, and associated climate change effec.Usage examples.by Cheryl W. Tay, Sang-Ho Yun, Shi Tong Chin, Alok Bhardwaj, Jungkyo Jung & Emma M. Geospatial satellite imagery sustainabilityThe Terra Basic Fusion dataset is a fused dataset of the original Level 1 radiancesfrom the five Terra instruments.
They have been fully validate to contain the originalTerra instrument Level 1 data. Each Level 1 Terra Basic Fusion file contains one fullTerra orbit of data and is typically 15 – 40 GB in size, depending on how much data wascollected for that orbit. It contains instrument radiance in physical units; radiancequality indicator; geolocation for each IFOV at its native resolution; sun-view geometry;bservation time; and other attributes/metadata. It is stored in HDF5, conformed to CFconventions, and accessible by netCDF-4 enhanced models.
It’s naming conventionfollows: TERRABFL1BOXXXXYYYYMMDDHHMMSSF000V000.h5. A concise description of thedataset, along with links to complete documentation and available software tools, canbe found on the Terra Fusion project page:.Terra is the flagship satellite of NASA’s Earth Observing System (EOS). It was launchedinto orbit on December 18, 1999 and carries five instruments.
These are theModerate-resolution Imaging Spectroradiometer (MODIS), the Multi-angle ImagingSpectroRadiometer (MISR), the Advanced Spaceborne Thermal Emission and ReflectionRadiometer (ASTER), the Clouds and Earth’s Radiant Energy System (CERES), and theMeasurements of Pollution in the Troposphere (MOPITT).The Terra Basic Fusion dataset is an easy-to-access record of the Level 1 radiancesfor instruments on.Usage examples.by.by University of Illinois. Climate earth observation meteorological sustainability weatherMeteorological data reusers now have an exciting opportunity to sample, experiment and evaluateMet Office atmospheric model data, whilst also experiencing a transformative method of requestingdata via Restful APIs on AWS. For information about the data see the.For examples of using the data check out the.If you need help and support using the data please raise an issue on the examples repository. Please note: Met Office continuously improves and updates its operational forecast models.Our last update became effective. Please find the deta.Usage examples.by Jacob Tomlinson.by Jacob Tomlinson.
Electrophysiology image processing life sciences machine learning neurobiology neuroimaging signal processingThe Allen Brain Observatory – Visual Coding is a large-scale, standardized survey of physiological activity across the mouse visual cortex, hippocampus, and thalamus. It includes datasets collected with both two-photon imaging and Neuropixels probes, two complementary techniques for measuring the activity of neurons in vivo. The two-photon imaging dataset features visually evoked calcium responses from GCaMP6-expressing neurons in a range of cortical layers, visual areas, and Cre lines. The Neuropixels dataset features spiking activity from distributed cortical and subcortical brain regions, c.Usage examples.by.
Biology cell imaging cell painting fluorescence imaging high-throughput imaging life sciences microscopyThe Cell Painting Image Collection is a collection of freelydownloadable microscopy image sets. Cell Painting is anunbiased high throughput imaging assay used to analyzeperturbations in cell models.
In addition to the imagesthemselves, each set includes a description of the biologicalapplication and some type of 'ground truth' (expected results).Researchers are encouraged to use these image sets as referencepoints when developing, testing, and publishing new imageanalysis algorithms for the life sciences. We hope that thethis data set will lead to a better understanding of w.Usage examples.by. Autonomous vehicles computer vision lidar mapping robotics transportation urban weatherThis research presents a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18. The vehicles The vehicles were manually driven on an average route of 66 km in Michigan that included a mix of driving scenarios like the Detroit Airport, freeways, city-centres, university campus and suburban neighbourhood, etc. Each vehicle used in this data collection is a Ford Fusion outfitted with an Applanix POS-LV inertial measurement unit (IMU), four HDL-32E Velodyne 3D-lidar scanners, 6 Point Grey 1.3 MP Cameras arranged on the.Usage examples.by Ford Motor Company. Cancer genomic life sciencesThe Human Cancer Models Initiative (HCMI) is an international consortium that is generating novel,next-generation, tumor-derived culture models annotated with genomic and clinical data.HCMI-developed models and related data are available as a community resource. The NCI iscontributing to the initiative by supporting four Cancer Model Development Centers (CMDCs).
CMDCsare tasked with producing next-generation cancer models from clinical samples. The cancer modelsinclude tumor types that are rare, originate from patients from underrepresented populations, lackprecision therapy, or lack ca.Usage examples.by. Climate meteorological sustainability weatherGlobal Historical Climatology Network - Daily is a dataset from NOAA that contains daily observations over global land areas. It contains station-based measurements from land-based stations worldwide, about two thirds of which are for precipitation measurement only.
Other meteorological elements include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, snowfall and snow depth. It is a composite of climate records from numerous sources that were merged together and subjected to a common suite of quality assurance reviews.
Some data are more.Usage examples.by Conor Delaney. Biology encyclopedic genomic health life sciences machine learning medicineTabula Muris is a compendium of single cell transcriptomic data from the model organism Mus musculus comprising more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations, and allow for direct and controlled comparison of gene expression in cell types shared between tissues, such as T-lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3’-end counting, enabled the s.Usage examples.by. Automatic speech recognition denoising machine learning speaker identification speech processingVOiCES is a speech corpus recorded in acoustically challenging settings,using distant microphone recording. Speech was recorded in real rooms with variousacoustic features (reverb, echo, HVAC systems, outside noise, etc.).
Adversarial noise,either television, music, or babble, was concurrently played with clean speech.Data was recorded using multiple microphones strategically placedthroughout the room. The corpus includes audio recordings, orthographic transcriptions,and speaker labels.Usage examples.by M.A. RoboticsThis project primarily aims to facilitate performance benchmarking in robotics research. The dataset provides mesh models, RGB, RGB-D and point cloud images of over 80 objects.
The physical objects are also available via the. The data are collected by two state of the art systems: UC Berkley's scanning rig and the Google scanner. The UC Berkley's scanning rig data provide meshes generated with Poisson reconstruction, meshes generated with volumetric range image integration, textured versions of both meshes, Kinbody files for using the meshes with OpenRAVE, 600.Usage examples.by.by.by.by. Cyber security internet intrusion detection network trafficThis dataset is the result of a collaborative project between the Communications Security Establishment (CSE) and The Canadian Institute for Cybersecurity (CIC) that use the notion of profiles to generate cybersecurity dataset in a systematic manner. It incluides a detailed description of intrusions along with abstract distribution models for applications, protocols, or lower level network entities. The dataset includes seven different attack scenarios, namely Brute-force, Heartbleed, Botnet, DoS, DDoS, Web attacks, and infiltration of the network from inside. The attacking infrastructure incl.
Bioinformatics biology coronavirus COVID-19 health life sciences medicine MERS SARSA centralized repository of up-to-date and curated datasets on or related to the spread and characteristics of the novel corona virus (SARS-CoV-2) and its associated illness, COVID-19. Globally, there are several efforts underway to gather this data, and we are working with partners to make this crucial data freely available and keep it up-to-date. Hosted on the AWS cloud, we have seeded our curated data lake with COVID-19 case tracking data from Johns Hopkins and The New York Times, hospital bed availability from Definitive Healthcare, and over 45,000 research articles about COVID-19 and rela.Usage examples.by.by.by. Climate coastal earth observation environmental sustainability weatherThis dataset contains historical and projected dynamically downscaled climate data for the State of Alaska and surrounding regions at 20km spatial resolution and hourly temporal resolution. Select variables are also summarized into daily resolutions.
This data was produced using the Weather Research and Forecasting (WRF) model (Version 3.5). We downscaled both ERA-Interim historical reanalysis data (1979-2015) and both historical and projected runs from 2 GCM’s from the Coupled Model Inter-comparison Project 5 (CMIP5): GFDL-CM3 and NCAR-CCSM4 (historical run: 1970-2005 and RCP 8.5: 2006-2100).
AstronomyThe data are from observations with the Murchison Widefield Array (MWA) which is aSquare Kilometer Array (SKA) precursor in Western Australia. This particulardataset is from the Epoch of Reionization project which is a key science driverof the SKA. Nearly 2PB of such observations have been recorded to date, this isa small subset of that which has been exported from the MWA data archive inPerth and made available to the public on AWS. The data were taken to detectsignatures of the first stars and galaxies forming and the effect of these earlystars and galaxies on the evolution of the u. Aerial imagery demographics disaster response geospatial image processing machine learning population satellite imagery sustainabilityPopulation data for a selection of countries, allocated to 1 arcsecond blocks and provided in a combination of CSVand Cloud-optimized GeoTIFF files. This refinesusing machine learning models on high-resolution worldwide Digital Globesatellite imagery.
CIESIN population counts aggregated from worldwide censusdata are allocated to blocks where imagery appears to contain buildings. Disaster response earth observation geospatial meteorological satellite imagery sustainability weatherHimawari-8, stationed at 140E, owned and operated by the Japan Meteorological Agency (JMA), is a geostationary meteorological satellite, with Himawari-9 as on-orbit back-up, that provides constant and uniform coverage of east Asia, and the west and central Pacific regions from around 35,800 km above the equator with an orbit corresponding to the period of the earth’s rotation. This allows JMA weather offices to perform uninterrupted observation of environmental phenomena such as typhoons, volcanoes, and general weather systems. For questions regarding Himawari-8 imagery specifications, visit.
Autonomous vehicles computer vision deep learning machine learning roboticsDataset and benchmarks for computer vision research in the context of autonomous driving. The dataset has been recorded in and around the city of Karlsruhe, Germany using the mobile platform AnnieWay (VW station wagon) which has been equipped with several RGB and monochrome cameras, a Velodyne HDL 64 laser scanner as well as an accurate RTK corrected GPS/IMU localization unit. The dataset has been created for computer vision and machine learning research on stereo, optical flow, visual odometry, semantic segmentation, semantic instance segmentation, road segmentation, single image depth predic. Computer vision machine learning multimediaThe Multimedia Commons is a collection of audio and visual features computed for the nearly 100 million Creative Commons-licensed Flickr images and videos in the YFCC100M dataset from Yahoo!
Labs, along with ground-truth annotations for selected subsets. The International Computer Science Institute (ICSI) and Lawrence Livermore National Laboratory are producing and distributing a core set of derived feature sets and annotations as part of an effort to enable large-scale video search capabilities. They have released this feature corpus into the public domain, under Creative Commons License 0, s. Deep learning machine learning natural language processingSome of the most important datasets for NLP, with a focus on classification, includingIMDb, AG-News, Amazon Reviews (polarity and full), Yelp Reviews (polarity andfull), Dbpedia, Sogou News (Pinyin), Yahoo Answers, Wikitext 2 and Wikitext103, and ACL-2010 French-English 10^9 corpus.
This is part of thefast.ai datasets collection hosted by AWS for convenience of fast.aistudents. See documentation link for citation and license details for eachdataset.
Aerial imagery climate disaster response sustainability weatherIn order to support NOAA's homeland security and emergency response requirements, the National Geodetic Survey Remote Sensing Division (NGS/RSD) has the capability to acquire and rapidly disseminate a variety of spatially-referenced datasets to federal, state, and local government agencies, as well as the general public. Remote sensing technologies used for these projects have included lidar, high-resolution digital cameras, a film-based RC-30 aerial camera system, and hyperspectral imagers. Examples of rapid response initiatives include acquiring high resolution images with the Emerge/App. Climate meteorological sustainability weatherThe Global Ensemble Forecast System (GEFS), previously known as the GFS Global ENSemble (GENS), is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental Prediction (NCEP) started the GEFS to address the nature of uncertainty in weather observations, which is used to initialize weather forecast models.
The GEFS attempts to quantify the amount of uncertainty in a forecast by generating an ensemble of multiple forecasts, each minutely different, or perturbed, from the original observations. With global coverage, GEFS is produced fo. Climate disaster response environmental meteorological sustainability weatherThe Global Forecast System (GFS) is a weather forecast model producedby the National Centers for Environmental Prediction (NCEP). Dozens ofatmospheric and land-soil variables are available through this dataset,from temperatures, winds, and precipitation to soil moisture andatmospheric ozone concentration. The entire globe is covered by the GFSat a base horizontal resolution of 18 miles (28 kilometers) between gridpoints, which is used by the operational forecasters who predict weatherout to 16 days in the future. Horizontal resolution drops to 44 miles(70 kilometers) between grid point.
Climate meteorological sustainability weatherThe Integrated Surface Database (ISD) consistsof global hourly and synoptic observationscompiled from numerous sources into a gzippedfixed width format. ISD was developed as a jointactivity within Asheville's Federal ClimateComplex. The database includes over 35,000 stationsworldwide, with some having data as far backas 1901, though the data show a substantialincrease in volume in the 1940s and again inthe early 1970s. Currently, there are over14,000 'active' stations updated daily in thedatabase. The total uncompressed data volume isaround 600 gigabytes; however, it. Agriculture climate disaster response environmental sustainability transportation weatherThe NOAA National Water Model Reanalysis dataset contains outputfrom multi-decade retrospective simulations.
Thesesimulations used observed rainfall as input and ingested otherrequired meteorological input fields from a weather reanalysisdataset. The output frequency and fields available in thishistorical NWM dataset differ from those contained in thereal-time forecast model. One application of this dataset isto provide historical context to current real-time streamflow,soil moisture and snowpack NWM conditions.
The reanalysis datacan be used to infer flow frequencies and perform temp. Agriculture climate disaster response environmental sustainability transportation weatherThe National Water Model (NWM) is a water resources model that simulates and forecasts waterbudget variables, including snowpack, evapotranspiration, soil moisture and streamflow, overthe entire continental United States (CONUS).
The model, launched in August 2016, is designedto improve the ability of NOAA to meet the needs of its stakeholders (forecasters, emergencymanagers, reservoir operators, first responders, recreationists, farmers, barge operators, andecosystem and floodplain managers) by providing expanded accuracy, detail, and frequency of waterinformation. It is operated by NOA. Climate coastal disaster response environmental meteorological oceans sustainability water weatherThe Operational Forecast System (OFS) has been developed to serve the maritime user community. OFS was developed in a joint project of the NOAA/National Ocean Service (NOS)/Office of Coast Survey, the NOAA/NOS/Center for Operational Oceanographic Products and Services (CO-OPS), and the NOAA/National Weather Service (NWS)/National Centers for Environmental Prediction (NCEP) Central Operations (NCO). OFS generates water level, water current, water temperature, water salinity (except for the Great Lakes) and wind conditions nowcast and forecast guidance four times per day. Biology imaging life sciences neurobiology neuroimagingOpenNeuro is a database of openly-available brain imaging data.
The data are shared according to a Creative Commons CC0 license, providing a broad range of brain imaging data to researchers and citizen scientists alike. The database primarily focuses on functional magnetic resonance imaging (fMRI) data, but also includes other imaging modalities including structural and diffusion MRI, electroencephalography (EEG), and magnetoencephalograpy (MEG). OpenfMRI is a project of the. Development of the OpenNeuro resource has been funded by th.
Art culture encyclopedic history museumThe Smithsonian’s mission is the 'increase and diffusion of knowledge' and has been collecting since 1846. The Smithsonian, through its efforts to digitize its multidisciplinary collections, has created millions of digital assets and related metadata describing the collection objects. On February 25th, 2020, the Smithsonian released over 2.8 million CC0 interdisciplinary 2-D and 3-D images, related metadata, and additionally, research data from researches across the Smithsonian. The 2.8 million 'open access' collections are a subset of the Smithsonian’s 155 million objects. Digital preservation free software open source software source codeis the largestexisting public archive of software source code and accompanyingdevelopment history. The Software Heritage Graph Dataset is a fullydeduplicated Merkle DAG representation of the Software Heritage archive.The dataset links together file content identifiers, source codedirectories, Version Control System (VCS) commits tracking evolution overtime, up to the full states of VCS repositories as observed by SoftwareHeritage during periodic crawls.
The dataset’s contents come from majordevelopment forges (including GitHub and GitLab), FOSS distributions (e.g.,Deb. Biology encyclopedic genomic health life sciences machine learning medicine single-cell transcriptomicsTabula Muris Senis is a comprehensive compendium of single cell transcriptomic data from the model organism Mus musculus comprising more than 500,000 cells from 18 organs and tissues across the mouse lifespan. We discovered cell-specific changes occurring across multiple cell types and organs, as well as age related changes in the cellular composition of different organs.
Using single-cell transcriptomic data we were able to assess cell type specific manifestations of different hallmarks of aging, such as senescence, changes in the activity of metabolic pathways, depletion of stem-cell populat. Genetic genomic life sciencesThe Genome Institute at Washington University has developed a high-throughput, fault-tolerant analysis information management system called the Genome Modeling System (GMS), capable of executing complex, interdependent, and automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. GMS includes a full system image with software and services, expandable from one workstation to a large compute cluster.
Life sciencesThe NIH-funded Human Microbiome Project (HMP) is a collaborative effort of over 300 scientists from more than 80 organizations to comprehensively characterize the microbial communities inhabiting the human body and elucidate their role in human health and disease. To accomplish this task, microbial community samples were isolated from a cohort of 300 healthy adult human subjects at 18 specific sites within five regions of the body (oral cavity, airways, urogenital track, skin, and gut). Targeted sequencing of the 16S bacterial marker gene and/or whole metagenome shotgun sequencing was performe. Genetic maps life sciences population genetics recombination maps simulationsContains all resources (genome specifications, recombination maps, etc.) required for species specific simulation with the stdpopsim package. These resources are originally from a variety of other consortium and published work but are consolidated here for ease of access and use. If you are interested in adding a new species to the stdpopsim resource please raise an issue on the stdpopsim GitHub page to have the necessary files added here.
Disaster response geospatial natural resource satellite imagery sustainabilityData from the Moderate Resolution Imaging Spectroradiometer (MODIS), managed bythe U.S. Geological Survey and NASA. Five products are included:MCD43A4 (MODIS/Terra and Aqua Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid),MOD11A1 (MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid),MYD11A1 (MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid),MOD13A1 (MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid),and MYD13A1 (MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid).MCD43A4 has global coverage, all.Usage examples.by.
Computer vision urban usThe Multiview Extended Video with Activities (MEVA) dataset consistsvideo data of human activity, both scripted and unscripted,collected with roughly 100 actors over several weeks. The data wascollected with 29 cameras with overlapping and non-overlappingfields of view. The current release consists of about 328 hours(516GB, 4259 clips) of video data, as well as 4.6 hours (26GB) ofUAV data. Other data includes GPS tracks of actors, camera models,and a site map. We have also released annotations for 22 hours ofdata.
Further updates are planned.
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