Amazon Web Services is Hiring. Amazon Web Services (AWS) is a dynamic, growing business unit within Amazon.com. We are currently hiring Software Development Engineers, Product Managers, Account Managers, Solutions Architects, Support Engineers, System Engineers, Designers and more. Visit our careers page to learn more. Issuu is a digital publishing platform that makes it simple to publish magazines, catalogs, newspapers, books, and more online. Easily share your publications and get them in front of Issuu’s. IMDb Everywhere Find showtimes, watch trailers, browse photos, track your Watchlist and rate your favorite movies and TV shows on your phone or tablet! IMDb Mobile site.

• Tv 30 12 Auf Aktienkurse Einzureichen Integration Amazon Deutsche

Epic Games uses AWS to deliver Fortnite to more than 200 million players around the world, support growth of more than 100 times in just 12 months, and perform analysis that helps it improve the game. Fortnite, one of the world’s most popular video games, runs nearly entirely on AWS, including its worldwide game-server fleet, backend services, databases, websites, and analytics pipeline and processing systems. Epic Games uses a range of AWS services to provide the availability it needs to support peak usage more than 10 times that of non-peak, as well as the scalability to host game events with all of its 200 million users invited. Chris Dyl, director of platform at Epic Games, spoke onstage at re:Invent 2018. Intercom designed, prototyped, tested, and deployed a stream-processing service in under two weeks using an AWS serverless architecture. Intercom offers a suite of messaging-first products that integrate seamlessly with other companies' websites and mobile apps to help them acquire, engage with, and support their own customers.

Intercom is using AWS Lambda and Amazon Kinesis Data Analytics to detect and shut down misbehaving customer integrations that might imperil availability by excessively updating Amazon DynamoDB. RedAwning used AWS to transform its contact center operations, enabling better customer service, an improved employee experience, and greater scalability—all while lowering costs 80 percent. RedAwning is the world’s largest supplier of vacation rentals to consumers and online booking sites with more than 140,000 properties in 10,000 locations.

The company uses Amazon Connect contact center as a service; Amazon Lex and Amazon Polly to enable a custom virtual agent that interacts naturally with customers; and AWS Lambda for looking up customer information. Using artificial intelligence, machine learning, and big data on AWS, Bigfinite helps pharmaceutical makers increase the accuracy and efficiency of their manufacturing processes while maintaining regulatory compliance. Bigfinite develops software-as-a-service (SaaS) applications for industrial processes in biotech and pharma and other regulated industries.

The company uses Amazon Athena for serverless querying of Amazon S3 data and incorporates advanced cloud technology into its big-data analytics platform, using AWS services including Amazon Elastic MapReduce, AWS Lambda, and Amazon Machine Learning. Using AWS, Liulishuo has created a stable and reliable online learning platform, which provides tens of millions of users with personalized English language learning services. Liulishuo is a leading AI-driven education technology company dedicated to providing each and every user with personalized and adaptive online learning courses. AWS services employed by Liulishuo include: Amazon EC2, Amazon S3, Amazon EMR, Amazon VPC, Amazon CloudWatch, Amazon RDS, Amazon ElastiCache, AWS Data Transfer, Amazon DynamoDB, AWS Config, AWS Storage Gateway, among others. Intowow can scale its in-app supply-side platform to support millions of apps thanks to the flexibility of AWS.

Intowow provides a platform that allows advertisers to run in-app video ads. The company provides mobile advertising SDK—which enables in-app video ads to operate and collects ad-performance data—on Amazon EC2 instances with Amazon EMR supporting parallel processing for data analytics, Amazon S3 for data storage, and Amazon Athena to run queries against the data in Amazon S3 using standard SQL. Innovaccer built a scalable, zero-latency data platform using AWS that is 81 percent more cost-effective than an on-premises equivalent. Innovaccer is a leading healthcare data platform company, empowering healthcare organizations in the United States with data-driven insights for faster clinical decision making and efficient care processes. The company uses a variety of AWS services including Amazon EC2 instances for computing power, Amazon S3 for storage, and Amazon RDS for user data to support their healthcare data platform. By using AWS to build a data lake, zipMoney can gather unique customer insights that vastly improve its underwriting process, pushing the boundaries of analytics with artificial intelligence and machine learning.

ZipMoney is an Australian fintech startup offering instantaneous, virtual lines of credit to consumers upon checkout at stores or on e-commerce sites. The firm relies on Amazon EMR and Amazon Elasticsearch Service to process and query vast amounts of data, Amazon S3 buckets to store such data, and Amazon DynamoDB to support its applications with low latency. By developing its SaaS platform on AWS instead of on premises, Shoptimize has reduced IT costs by at least 30 percent and in turn, increased customers’ profitability. Shoptimize delivers an end-to-end ecommerce platform to leading brands; to establish their online presence and grow website sales.

Its solution spans across technology, marketing, analytics and marketplace management. The company’s SaaS platform runs on Amazon EC2 instances with Amazon RDS providing database services and Amazon Kinesis delivering clickstream data from website visits, online marketing campaigns, and social-media interactions. Fatture in Cloud migrated from its existing cloud provider to AWS to improve performance by 100 percent, deliver a highly reliable service to customers, and reduce costs by 50 percent. Based in Italy, the company provides customers with invoicing and billing services 24x7 from any device—disrupting a largely traditional market by offering features such as real-time access to data. Using services such as Auto Scaling and Elastic Load Balancing, Fatture in Cloud supports a doubling of growth year-over-year. By migrating to the AWS Cloud, Halodoc delivers new product features to market 30 percent faster than before, and it has cut operations costs by 20–30 percent. Halodoc operates a holistic healthcare application enabling patients in Indonesia remote consult with doctors, order a home delivery pharmacy service, and experience at-home laboratory testing.

The startup uses Amazon EC2 to run its mobile app, Amazon RDS for database administration, and Amazon S3 to store documents and images. Its third line of business—Lab—was built completely on AWS Lambda. Using AWS, V-Count grew its revenue by 92 percent in the second half of 2017 by delivering cutting-edge retail analytics and people-counting products in a highly secure environment that keeps customer data safe. The company manufactures patented visitor-tracking devices and business-analytics tools to help its clients understand their customers’ behavior and maximize the effectiveness of their marketing campaigns. Using the Amazon CloudWatch monitoring service, the V-Count team can spend less time on system administration and more time developing new products. ShareChat has grown its userbase ten-fold in the last 12 months without any scaling issues by using AWS—gaining a low latency and high-performance infrastructure to provide customers with a reliable and responsive service across multiple network speeds. ShareChat provides an Android-based ShareChat app that enables Indians to chat online and share content in local languages.

The company uses Amazon EC2 instances for its main servers, AWS Lambda for running serverless app code and Amazon DynamoDB to store content attributes and user’s social graph. Hulu is redefining the television experience for viewers by using AWS to support the addition of more than 50 live channels for its Hulu with Live TV offering. Hulu is an American subscription video-on-demand service owned by Hulu LLC, a joint venture with The Walt Disney Company, 21st Century Fox, Comcast, and Time Warner. Running its live TV service on AWS’s reliable and secure infrastructure allows Hulu to deliver a great viewer experience, even in times of rapid spikes in viewership and traffic. By using AWS, Launchmetrics can expand its infrastructure capacity by 15 times, meaning a faster, more user-friendly experience for its customers during major fashion events. The company provides an integrated marketing platform with advanced analytical tools, which helps fashion designers and luxury and cosmetic brands launch its products successfully. Launchmetrics uses AWS CloudFormation to create and reengineer its own development environments, which is a key part of its new, agile, cloud-based strategy, and save time at every stage of the development process.

Digital ReLab adapted its digital-asset-management platform to the cloud by taking advantage of flexible AWS storage options. Digital ReLab provides software solutions that let users find, update, and share vast quantities of digital assets seamlessly and affordably, anywhere, through a secure central database. Digital ReLab uses Amazon EC2 for compute, Amazon S3 for long-term file storage, and Amazon EFS for a cloud-based file system that seamlessly integrates with cloud and on-premises installations of its solution.

Using AWS, Zoona cut IT costs in half while increasing the size of its infrastructure and the number of IT services it supports. The company, which helps people in sub-Saharan Africa gain access to financial services for the first time, has more than 1.6 million customers using Zoona to make electronic money transfers. Zoona built a microservices architecture on AWS with Amazon EC2 instances supporting all applications, Amazon RDS supporting MySQL and PostgreSQL databases, and Amazon DynamoDB running Datomic, a distributed database. Omada Health built a digital version of a proven diabetes-prevention program on AWS, enabling it to scale cost-effectively and achieve clinically significant results. Omada Health helps people change their habits, improve their health, and reduce their risk of chronic disease through intensive behavioral-change programs that are clinically supported and evidence-based. The company runs its services on AWS—including Amazon EC2, Amazon RDS, and Amazon S3—and improves outcomes with advanced data science using Amazon Redshift and AWS Elastic Beanstalk.

Since moving to the AWS IoT environment, Centratech Systems has reduced training time for new customers from six hours to one, and has vastly expanded its customer base through an estimated 66 percent reduction in device costs. Centratech Systems is an Australian provider of wireless monitoring and control systems used by local governments to manage water, pumping, and electricity applications. The company relies on Amazon EC2 instances to host its software used with legacy hardware, and it has recently shifted to the AWS IoT platform to manage newer, lighter, and less costly smart devices in the field. Judo Capital (Judo) avoids the capital expenditure of IT by using AWS, while gaining the flexibility to scale and add new services without disruption to operations.

Judo provides loans to small and medium-sized enterprises within Australia. Today, Judo manages its loan-origination processes through an infrastructure built on the AWS Cloud that features Amazon EC2 compute instances, Amazon RDS for loan-based data, and AWS Lambda for serverless computing-based integrations—with third parties supporting the origination workflow. ICHEF has reduced its IT management overhead by 13 percent using AWS, while also bringing down its overall IT costs to just 7 percent of the monthly fee it charges customers to use its point-of-sale (POS) service. ICHEF provides a POS service for restaurants across Southeast Asia, where employees use the app’s interface through Apple iPads. The company runs the backend infrastructure supporting the POS on the AWS Cloud, using Amazon EC2 instances for compute, Amazon RDS for transactional database services, and AWS Lambda to run daily data integrations for customers with multiple establishments. With an ambition to connect 20 million cars across Europe by 2020 through its SPARK technology, Springworks needed a highly scalable service that allowed its developers to get features to market fast. It chose to build its Internet of Things (IoT) platform in the AWS Cloud, benefitting from the robust security that’s vital to its partners.

One such partner is TeliaSonera, the largest mobile operator in Sweden, which uses SPARK to power its IoT application, Telia Sense. Telia Sense gives drivers a wealth of useful information about their cars, including service alerts and location tracking, and it opens revenue streams for the mobile operator by linking car owners to service providers such as insurance firms and car repair shops.

As a three-man startup born in the AWS Cloud, CleverTap has gone from processing 50 million events per month to 55 billion in just 3.5 years, with a lean staffing model and a heavy reliance on automation. CleverTap is a mobile app analytics and user engagement platform, offering clients advanced segmentation and targeted marketing campaigns. The company has been able to scale rapidly using memory-intensive Amazon EC2 instances on its proprietary NoSQL database. It uses Elastic Load Balancing to distribute often spiky traffic and AWS CloudFormation to deploy an array of AWS resources such as Amazon CloudWatch, Amazon CloudFront, and Amazon S3 for storage. Using AWS, Tink can focus on innovation rather than infrastructure management and test ideas 85 percent faster than before.

The Sweden-based startup launched its app in 2012 with the aim of giving users an easy way to control their personal finances, connecting their bank accounts and credit cards to help them keep track of their money. AWS technologies help to ensure that Tink complies with data-security standards and give the company a robust platform that is trusted by business partners and customers. Vivino used the AWS Cloud to build the most downloaded wine app in the world, attracting 22 million users across the U.S., South America, Europe, and Asia. The app gives wine lovers easy access to a database of more than 10.3 million wines and to related information such as ratings, reviews, and where to buy.

The company ensures it has the capacity to handle traffic increases of 300 percent between Christmas and New Year’s using Auto Scaling with Amazon EC2, and it uses Amazon SES and Amazon SNS to drive wine promotions to millions of users in seconds. Fourdesire successfully maintains business growth of 500 percent in two years with the support of AWS. The company builds online games that are informative and interactive, promoting better health and environmental awareness. Fourdesire uses AWS Elastic Compute Cloud instances to run its game code as well as Amazon Route 53 and Elastic Load Balancing to direct and distribute incoming gaming traffic.

Transcend print to craft media-rich publications for iPad and different pill devices. Work blazingly quick on complicated files in Adobe Photoshop® and Illustrator®. Use acquainted tools in Adobe InDesign® for larger management and potency for page layout and preparation for high-quality print production. What's in Adobe Creative Suite: • Adobe Photoshop • Adobe Illustrator • Adobe InDesign • Adobe AcrobatX Pro • Adobe Bridge • Adobe Media Encoder What's New in Adobe CS6: Retouching and transforming Content-Aware Move tool The Content-Aware Move tool lets you quickly recompose images without complex layering or slow, precise selections. Freelancer multiplayer id keygen for mac free.

To boost the gaming experience, it has in-memory Amazon ElastiCache and Amazon CloudFront to maximize data transfer speeds across the web. Kit Check provides an automation solution that combines cloud software with Internet of things (IoT) technology to help hospitals track medications more effectively, improve compliance and safety in the operating room, and dramatically reduce the time required to restock commonly used drugs. Kit Check uses AWS to connect proprietary on-site RFID equipment with a web-based service that tracks the usage and expiration dates of medicines.

By using AWS, the startup company launched and quickly expanded a profitable niche service across the U.S. Healthcare industry while avoiding the expense and overhead of traditional IT systems. Using AWS, Mojo Networks reduces the cost of operating its cloud infrastructure by up to 30 percent while reducing management time by 25 percent. Mojo Networks provides cloud-managed Wi-Fi services to companies worldwide.

Keen to overcome the limitations of its existing cloud-service provider, Mojo Networks switched to AWS, running customer Wi-Fi services in Amazon VPC environments, with traffic directed by Amazon Route 53, compute power delivered through Amazon EC2, and backups held in Amazon S3. Workday supports enterprise applications for more than 1,300 businesses—including more than 120 Fortune 500 companies—using AWS. Workday produces cloud-based applications that unify finance, HR, and planning into one seamless system for better business performance. It chose AWS as its preferred public cloud provider and plans to continue expanding its AWS footprint into new regions, starting with Canada in 2017 and the United States and Europe in subsequent years. Aneel Bhusri, CEO and co-founder, and David Clarke, SVP Technology and Infrastructure, spoke onstage at re:Invent 2016. Hudl ingests and encodes more than 39 hours of video every minute, boosts video upload speeds by 20 percent, and improves data analysis using AWS. Hudl is a software company that provides a video and analytics platform for coaches and athletes to quickly review game footage to improve team play.

The company runs its video platform and data-analysis solutions on the AWS Cloud, using Amazon ElastiCache for Redis to provide millions of coaches and sports analysts with near-real-time data feeds to help drive their teams to victory. Assignar provides software that streamlines the way organizations run their assets, field workers, and operations in highly regulated industries, using a single dashboard that delivers information in real time.

The Australian startup uses AWS to run its software, including a mobile application that enables workers and managers to capture and submit compliance and safety information from the field. This has enabled Assignar to serve 35 clients in about 12 months versus 10 clients in a physical infrastructure, achieve 99.999 percent availability, and reduce the time needed to onboard new clients from two weeks to less than 10 seconds. Provides insurers, building inspectors, and economists with information about commercial and residential structures across the United States. The company uses Amazon Machine Learning to create predictive models used for tasks such as estimating the age of roofs in a particular region so insurers can establish policies based on probable replacement costs. By using Amazon Machine Learning, BuildFax needs just a few weeks to create models that took six months or more in the past to build, and can offer new analytics services to its customers.

Tv 30 12 Auf Aktienkurse Einzureichen Integration Amazon Deutsche

DoApp provides more than 460 web and mobile applications for news organizations in 150 markets across the United States, with support for the iOS, Android, Amazon Fire, and mobile web platforms. The company uses Amazon Web Services to publish, update, and serve content to apps, particularly news apps that can be customized for individual organizations. By using AWS, the company’s 12 employees—including just three backend developers—have been able to provide content with uninterrupted availability for nearly five years while continually innovating and winning new clients across the globe. PaymentSpring provides payment services for organizations such as nonprofits and small ecommerce companies.

The startup had several platform requirements for its solution, including high availability and scalability, cost effectiveness, and features that would support compliance with Payment Card Industry (PCI) standards. PaymentSpring turned to AWS, launching a service that quickly scaled to millions of dollars while offering its customers the reliability and security expected of a payment system. Arterys offers a medical imaging solution that enables radiologists and cardiologists to improve the process of diagnosing and staging cardiovascular disease in patients.

The company is using AWS to render, analyze, and store multi-dimensional models of MRI scans each producing 5 to 10 GB of data. By using AWS, the company can render multi-dimensional models of the heart across all device types in 10 minutes or less instead of the 90-minute industry standard, and scale the platform to handle its growing storage needs.

Y-cam Solutions is a provider of high quality, affordable, and easy-to-use indoor and outdoor security cameras for residential and small business use. As the company prepared to launch a new surveillance video storage service in 2011, it needed a flexible infrastructure that could be launched quickly across North America and Europe without requiring a large capital investment or revenue commitment. By using AWS, Y-cam was able to build its service five months instead of 15 months as originally estimated, and estimates reducing the cost of infrastructure by 80% over a three-year period.

. 2.5k Downloads.

Abstract The AlpArray programme is a multinational, European consortium to advance our understanding of orogenesis and its relationship to mantle dynamics, plate reorganizations, surface processes and seismic hazard in the Alps–Apennines–Carpathians–Dinarides orogenic system. The AlpArray Seismic Network has been deployed with contributions from 36 institutions from 11 countries to map physical properties of the lithosphere and asthenosphere in 3D and thus to obtain new, high-resolution geophysical images of structures from the surface down to the base of the mantle transition zone.

With over 600 broadband stations operated for 2 years, this seismic experiment is one of the largest simultaneously operated seismological networks in the academic domain, employing hexagonal coverage with station spacing at less than 52 km. This dense and regularly spaced experiment is made possible by the coordinated coeval deployment of temporary stations from numerous national pools, including ocean-bottom seismometers, which were funded by different national agencies. They combine with permanent networks, which also required the cooperation of many different operators. Together these stations ultimately fill coverage gaps. Following a short overview of previous large-scale seismological experiments in the Alpine region, we here present the goals, construction, deployment, characteristics and data management of the AlpArray Seismic Network, which will provide data that is expected to be unprecedented in quality to image the complex Alpine mountains at depth.

3.1 Seismological Observations in the Alps 3.1.1 Observatory Versus Research Campaign The seismological investigation of the European Alps developed essentially along two tracks. On the one hand, national or regional observatories, mandated to monitor seismicity, targeted strategic positions with permanent sensors to build various seismic networks serving global, regional or local purposes. In general, these sites are carefully prepared in a time-intensive manner, with infrastructure that is appropriate for broadband, short-period or accelerometric sensors. On the other hand, research institutions usually took occasional initiatives to carry out temporary campaigns, with targeted ideas about the network geometry, operation duration and other characteristics.

Because of their temporary nature — and sometimes the need for building numerous sites within a short time — these sites are generally selected and installed more quickly than permanent ones with a consequence of lower signal-to-noise measuring conditions. As a result, observatory and research targets generally do not coincide. Further, it has been challenging to realize the integrated curation of data sets. While much was learned about particular aspects of the Alpine convergence, these separate initiatives typically lacked the critical mass to resolve the larger-scale structure in a coherent manner such that even many first-order questions such as the one on the polarity of the subduction below parts of the Alps remained unresolved. 3.1.2 Brief History of Seismological Investigations The early history of devices able to record seismic motions is difficult to trace, but they existed in Europe at the latest by the early eighteenth century (Ferrari ). Seismometers targeting long-period motions began being installed across the Alps around the turn of the nineteenth–twentieth century, e.g., in 1897 in Ljubljana, or in 1911 at Degenried, Zurich, at a site that is still occupied today (CH.ZUR). In the mid-1980s digital seismometry began in earnest, and increased disc capacities made the operation of continuously recording temporary stations a possibility, and greatly simplified the operation of permanent stations.

Within the AlpArray area, the earliest permanent stations that are still in operation today were installed or upgraded to digital in the 1980s and early 1990s. Fortunately, by the time of inception of the AlpArray project, many countries in the Alpine region operated dense and mature permanent broadband seismic networks. Fig. 3 Topographical map of the greater Alpine area and the geometry of the AlpArray Seismic Network. The permanent and temporary broadband stations (respectively red triangles and orange circles) cover the area within 250 km of the smoothed 800-m altitude line of the Alps (outer and inner thick white lines). Status as of August 2017 The AASN’s skeleton is the network of permanent stations operated in the area with (1) three-component recordings (vertical and typically N-S and E-W), (2) broadband sensors at least up to 30-s period responses and (3) open access data with a sampling rate of at least 100 samples per second. This background network was an ever-moving target during the 6 years of AASN planning, as new stations were installed, existing ones closed, and some stations were made open, resulting in a ca.

50% increase from 234 stations in summer 2011 to 352 in summer 2017. This growth rate is beyond the typical evolution of mature permanent networks. The AlpArray initiative coincided with the development of the European Integrated Data Archive (EIDA), which encouraged open access to broadband seismic data sets from permanent networks across Europe.

Nevertheless, the AlpArray initiative created additional momentum to encourage the opening of data sets and the construction of new stations in the Alpine area, thus creating a legacy beyond the lifetime of the ongoing temporary experiment. The design target for the sites of the AASN temporary network was to obtain homogeneously spaced coverage with the minimum number of stations, while exploiting the existing permanent stations as well as possible.

Thanks to the aforementioned progress in the permanent networks during the design phase, the daunting task of manually planning the location of temporary sites to best fill the gaps had to be repeated several times. The rewarding result was that not only could the several participants agree on the common plan of deployment, but also that the number of planned sites could be matched with the number of available instruments. Furthermore, agreements were rapidly made to share seismic instruments and manpower between project partners in order to achieve the desired coverage. The final strategy for locating temporary sites within AASN was to adopt a hexagonal compact packing strategy (Fig. Instead of a rectangular grid, this geometry could better adapt to the existing permanent network stations and ensure that “voids” which are easily created in a grid are filled. Furthermore, only a 3-km radius area around each planned site location was allowed for station installation by the deploying teams. In case of larger deviation from a planned site, the neighbouring site locations were at least reconsidered.

With this procedure, no point within the targeted area is farther than 30 km away from an AASN station. In newly covered regions each AASN station is at 52 km distance from 6 neighbouring sites, which is tighter and more compact than previous large networks around the world (see above). Fig. 4 Map showing distance to the closest broadband seismological station: a spacing at the beginning of AlpArray planning in summer 2011 with 234 stations; b principle of positioning newly installed temporary stations; c spacing of the complete 628 stations of the AlpArray Seismic Network in August 2017 Despite the thorough planning on land, three gaps remained within the targeted zone: (1) the largest one is naturally in the Ligurian Sea and the neighbouring Gulf of Lions, which we aimed to cover with ocean-bottom seismometers right at the start of the project conception. Following varying plans and station availabilities, the final configuration covers most of the Ligurian Sea, only leaving empty areas with less than 1000 m water depth, which cannot be instrumented as they are prone to bottom trawling. (2) The second major gap is in the Adriatic Sea, whose northern part is also too shallow for secure OBS operations. To minimize this gap, we planned the sites on land as close as possible to the coast. Furthermore, the CASE Complementary Experiment aims at closing the gap on the SE edge of the AASN.

(3) In the SE corner of the AASN, gaps in the Dinarides were unavoidable, because stations could not be deployed safely in some areas. The AASN planning was optimized in this area following the local boundary conditions.

4.2 Installation Preparation The technical planning of the tremendous AASN operation was initiated as early as the scientific planning. A working group with various backgrounds (observatories, research institutes, network operators, mobile station pools, field experts, IT experts, etc.) was formed and developed a Technical Strategy for the AASN. This document sets “compulsory”, “best practice” and “recommended” rules in numerous aspects of the AASN installation and operation, such as equipment and settings; site selection, vault types and noise levels; communications and maintenance schedule; data recovery and security; data formats, access and coordination. This document was adopted as the standard for the operation of the AASN early on and was closely followed throughout the years.

For further details, we refer to the document, available on the AlpArray website ( ). Prior to the overall deployment a station naming convention was also adopted following the Standard for the Exchange of Earthquake Data (SEED) (IRIS ). A unique station name is composed of 5 alphanumeric characters: a leading “A” (for AlpArray), followed by three digits from a range of numbers distributed for each country, ending with an “A” for the initial site and changed to subsequent letters (B, C, etc.) in case of major ( 10 m) site changes. In this way the deploying groups could work in parallel and independently.

The ranges of station names for the ten countries of temporary station deployment as well as the OBS component are shown in Table. Country of deployment Range of numbers AT Austria 001–049 BH Bosnia–Herzegovina 050–059 CH Switzerland 060–069 CZ Czech Republic 070–099 DE Germany 100–149 and 350–399 FR France 150–249 HR Croatia 250–259 HU Hungary 260–279 IT Italy 280–329 SK Slovakia 330–349 OBS OBS component 400–449 AlpArray temporary stations belong to the Z3 network, a code reserved with the International Federation of Digital Seismograph Networks (FDSN). Stations of permanent networks were generally not altered, neither in their names nor following the rules of the AlpArray Technical Strategy. However, to simplify data access, the entire AASN network data (both permanent and temporary stations) are available under a unique virtual network code “ALPARRAY” (see Sect. 4.3 Installation.

The official start date of the AASN was 1 January 2016. Installation of the temporary stations had already started in summer 2015 and reached more than 81% on land by mid-2016 and more than 92% by the end of 2016 (Fig. The AASN was fully completed in early July 2017, with the deployment of the OBS component and of the last land station. The main cause for this extended schedule was the heterogeneous funding scheme and logistic boundary conditions across the participating countries. The evolution of the AASN installation is shown in an animation provided as Online Resource 1. We strived to keep common categories regarding housing and soil types of the installed stations.

The various housing classes and the number of corresponding sites are listed in Table, along with the distribution of soil types on which the sensors sit. Regarding real-time communication of the stations, about 75% of the land stations could be equipped with an online communication device (Table ). Among the permanent sites all except two stations are online. Full station details with photographs and further notes are kept up to date on the European Station Book hosted by the Orfeus Data Centre ( ). OBS ocean-bottom seismometer The completion of the AASN was achieved by a joint French–German OBS deployment cruise campaign in June 2017. The 30 stations deployed in the Ligurian Sea were offline by nature.

Following a joint German–French OBS cruise in February 2018, 27 stations have been successfully recovered, 1 has been released from the ocean-bottom but not yet found, 1 could not be released but includes an automatic trigger set for October 2018. The 30th OBS was successfully recovered mid-March 2018 by a French cruise.

Orientations of the horizontal components will be known after initial processing of the data. 4.4 Challenges The principal challenge in establishing the AASN was to achieve a coherent schedule. The main reason for this was the lack of unified European funding on par with that which enabled USArray or IberArray. Instead, funding to run AlpArray came from national sources (Austria, Croatia, Czech Republic, France, Germany, Hungary, Switzerland) complemented by institutional internal funding sources (Italy). Ultimately, all these initiatives were successful, but in some countries several re-submissions were required, such that the project durations in different countries did not always overlap, causing coordination headaches to ensure the design goal of all stations being operational at the same time.

This was exacerbated by the complexity of instrument availability in numerous mobile pools, including the purchase of new hardware (new stations in Germany, France, Hungary). Therefore, the multivariate equation system comprising politics and logistics was resolved iteratively and required both cooperation and pragmatism over the years to result in the AASN as described here. The successful cooperation between many partners called for internal rules and guidelines, which were not straightforward to realize. The AlpArray project has its own Memorandum of Collaboration for the overarching science programme ( ). The AlpArray Seismic Network has developed rules for the participating institutions ( ); for example, an institute needs to operate at least ten stations to become a full member. Finding acceptable and respected rules for both observatories and universities, and for parties with different levels of access to new data, was a challenge, but also a prerequisite to beginning deployment and field operation.

Although the technical standards were clearly set from the beginning, it was not practical for some sites to meet certain recommendations, particularly with regard to background noise levels. An example are sites in the Po Plain, where the background noise level is very high, and finding an optimal site that meets the agreed noise level targets and also fitting the overall network geometry was impossible such that either the geometry or (in most cases) the noise criteria had to be relaxed. In other regions, several sites have not met the agreed noise criteria and have already been moved, which requires careful monitoring and responsive field teams. Funding the OBS component with matching cruise schedules in Germany and France was an additional task, followed by further challenges regarding permits and ship routes, including last minute changes due to intervention by national navies. 4.5 The Completed AASN The AASN is composed of 628 stations in total: 352 permanent and 276 temporary seismometers, the latter including 30 ocean-bottom sensors. By August 2017 already 20 temporary stations had to be moved to occupy more favourable sites nearby, or because of changes in permitting situation.

Full details of permanent and temporary stations, together with a GoogleEarth file, are provided as Online Resources 2, 3 and 4. Fig. 7 AlpArray Seismic Network vicinity: a map of distance to the nearest other seismological station; b histogram of these distances.

As on previous figures, triangles denote permanent stations and circles represent temporary ones. Status as of August 2017 The AASN is expected to operate until the end of 2018 at least, to ensure simultaneous data acquisition at land stations over 2 years. The recording period is obviously shorter for the OBS component (ca.

8 months) due to their autonomy related to current battery technology, as well as to the schedule of research vessels. The AASN goes beyond previous large-scale initiatives in offering a higher station density and not following a rolling site occupation but simultaneous operation of all stations for a minimum period of 2 years. It is a measure of its success that it focuses on a geological target rather than being governed by political boundaries. 4.6 Data Examples, Access and Network Perspectives.

Fig. 8 Waveform examples across the AASN. A Teleseismic waves following the 2017-09-08T04:49:19 (UTC) M W 8.2 Mexico earthquake. Waveforms are band-pass filtered between 0.01 and 0.5 Hz. Theoretical teleseismic phase arrival times (see colour legend) are calculated by the Crazyseismic code (Yu et al. B Local and regional waves following the 2017-03-06T20:12:07 (UTC) M L 4.6 Urnerboden (Switzerland earthquakes). Waveforms are band-pass filtered between 0.04 and 2 Hz. On both figures waveforms are shown for stations available for download on 20 March 2018, represented by green dots on the maps in the lower right corners.

The white vertical line across the waveforms marks the origin time of the respective earthquake Over 18 GB of data is collected each day by the AASN, and the final size of the data archives is expected to be on the order of 15 TB. The seismological waveforms of the AASN are distributed through the European Integrated Data Archive (EIDA, ), where the data from the 36 institutions that collect seismological data are archived across seven different nodes in five countries. Data from the temporary sites are available under the Z3 network code, while the virtual network “ALPARRAY” comprises all stations of the AASN, permanent and temporary. The AASN data are distributed with password protection. Data access is immediate for the Core Group of the AASN members. Registered seismological observatories with monitoring and alerting duties can use the Z3 data with real-time access and may report phase picks in their catalogues; however, waveforms shall not be published.

The use of data for research by AlpArray participants requires a priori submission and approval of the research topic by the AASN Core Group. The waveform data will be freely shared among the entire AlpArray Working Group at most 1 year after AASN operation has ended (currently planned for 1 January 2020).

The waveform data will be freely available to the public 3 years after AASN operation has ended (currently planned for 1 January 2022). The quality and completeness of AASN data have been, are and will be periodically reviewed and will be the scope of a forthcoming manuscript.

For the earliest set of site noise characterization in different parts of the AASN, we refer to Fuchs et al. ( ), Molinari et al. ( ), Govoni et al. ( ) and Vecsey et al. The AASN can be cited by referring to this paper and the temporary component (Z3) by the AlpArray Seismic Network ( ) seismic network DOI. The AASN demonstrates the current capability of the community to integrate mobile data within infrastructures traditionally built for permanent network archives — which has been an ORFEUS focus for many years and has been funded through the EU projects NERIES, NERA and now EPOS-IP and SERA. More secure and professional management of the archives allows better curation and dissemination of these data sets and benefits the wider scientific community.

AASN data will be used for numerous seismological applications, which will be subject to forthcoming publications. The depth targets of these will range from shallow (e.g., sedimentary basins, landslides) to deep (e.g., mantle transition zone) depth.

The methodologies expected to be applied include body and surface wave tomography, receiver functions, ambient-noise tomography, anisotropy studies, seismicity analysis, attenuation structure, various joint inversions and more. Some of the high-resolution tomography approaches employing body wave full waveform inversion expect to resolve length scales below 20 km. Thanks to its high spatial density and uniform coverage, the AASN is suitable for deep Earth imaging applications such as mapping ultra-low-velocity zones or reflectors near the core-mantle boundary, testing the large low-velocity anomaly beneath Africa or upper and mid-mantle seismic discontinuities in regions beneath India or the Atlantic Ocean. 5 Conclusions and Perspective. A European geoscience initiative, coined AlpArray, has become a reality some 6 years after planning began in the summer of 2011. National funding from eight countries and the matching of national seismological instrument pools in time and space has enabled deployment of 276 temporary stations, including 30 ocean-bottom sensors, to fill spatial gaps between 352 permanent stations of the greater Alpine area across 11 countries.

With a total of 628 stations, the AlpArray Seismic Network is now up and running, yielding unprecedented homogeneous and dense coverage to image orogenic structures and processes in 3D. We expect that the great effort invested in this broadband collaboration will not only be fruitful for basic and applied research (e.g., seismic hazard), but will also set a precedent for exemplary scientific cooperation across borders. Although ultimately successful, the diverse multinational funding streams had presented a real risk to this initiative. We expect that this experiment will be followed in the future by other groups of neighbouring countries to investigate their common geodynamic region and also by European observatories and research institutions to work jointly elsewhere. We therefore encourage funding agencies in Europe and elsewhere to think of mechanisms how proposal submission and funding decisions for such large-scale initiatives can be coordinated. In the meanwhile, we expect a wealth of new images of local, regional and pan-Alpine structures to be revealed, as seen by seismological methods using the AASN and complementary experiments. These will foster the integration of other geophysical data (e.g., gravity) and the synthesis of numerous interpretative studies in the domains of geology and modelling, which ultimately lead the community to a much improved understanding of Alpine orogeny.

Activities on specific research topics are coordinated inside thematic research groups (e.g., surface waves, ambient-noise and full waveform inversion; receiver functions; gravity; seismicity, seismotectonics and local earthquake tomography; seismic anisotropy), guaranteeing mutual collaboration and efficient updates on issues related to data quality, applied methodologies and scientific results.