Data Access Agreement Template: Fill & Download for Free

SaaS is the abbreviation for “software as a service”. A SaaS agreement is simply the name used for the agreement between a SaaS supplier and a SaaS customer which sets out the terms under which SaaS software may be accessed.The fundamental obligation of the Saas supplier is:to make its software accessible to the customer via the internet as a service - mandatory;to provide support services - optional;to ensure that it complies with certain requirements in relation to the maintenance of the software - optional.The customer:is granted with a right (license) to use the software; andis obliged to comply with the restrictions and prohibitions, set by SaaS supplier.The data supplied by the SaaS customer are the property of the SaaS customer and are subject to a standard CalOPPA / GDPR - friendly data processing clause. The customer data may be specified to be confidential.In consideration for the undertaking to provide the services, the SaaS customer agrees to pay to the SaaS supplier the relevant charges.If you are looking for a contractual document or a tailor-made SaaS agreement template, consider AXDRAFT. It’s free if you use this link. No strings attached.

There are legal things every business need to take care of and legal things specific for opening and running a drop ship business. I assume that you want to open a online e-commerce shop and use a drop shipping supplier to fill up your product catalog.Top 10 legal things to keep in mind when opening an ecommerce shop the drop shipping way.Consumer law and consumer rights. Keep in mind consumers are well protected in many countries when buying something online. In for example the EU people are allowed to cancel an order without reason within a x amount of days. Not only you need care that you follow the rules you also need to make sure you inform your customers about it.Mailings: Keep in mind that you may not spam people without their permission. Make sure that you register when and where people allowed you send them newsletters. The fines you get for not doing that are not funny.Taxes. Make sure you collect the tax the way is should be done. Address it to you customer and ensure that you can prove that you are sure you collected the right tax. Selling to the EU? You need at least two independent location markers to prove that you collected the right tax. Make also you understand all the taxes and if they are correctly addressed to the products you sell.EU cookie law. Using Google analytics or other analytic tools to analyze customers and visitor behaviour? Make sure you inform you EU visitors before they visit your website. The penalties are severe for not doing it.Website/ecommerce shop security and data protection. Yes you are legally responsible for ensure your website or e-commerce shops is constructed in such a way that user data is encrypted and the website as all the security basics in place to prevent hacking as much as possible. Storing creditcard numbers, e-mail address, names etc. then you need to ensure that the data is encrypted and is useless when somebody would try to steal it my hacking the database. Also read more about PCI compliance.Trademark, copyright, patents: duty to investigate. Often forgotten but if you are selling products through your webshop you need to make sure that you did all to investigate that the products are not fake and that you are allowed to sell them. Some drop shippers from for example China sell great copied speakers from certain brands that are actually protected. You as a webshop owner can be criminally prosecuted if you did not any research. Maybe you need certain licenses or permits to be allowed to sell the product.Insurances. Make sure you have all the insurances in place just in case. There are multiple types of insurance for small businesses, including general liability, product liability, professional liability, commercial liability and home-based insurance. Reach out to your local provider to find out which type would be best for your business and location.Shipping restrictions. Make sure that you are not selling products to countries that are not allowed. Maybe you drop-ship supplier does not care but you will end up with all the trouble paying back the customer and talking with customs.Contact and business information. Make sure you have all the information that is legally needed accessible on your website. Depending on the country you are running the business and the countries you are selling to this can differ. For example in the EU you need to clearly state your business license, contact data and tax number. Don’t forget your Terms and Conditions.Invoices. Make sure the invoices you send are legally okay and contain all the information that is legally needed. There are many rules to follow. Make sure you implement them the right way. Sending wrong invoices can cause a lot of trouble.NOT TO FORGET!Because you as a ecommerce shop will depend a lot on your dropship supplier regarding the fulfillment, it is important that you have a good written agreement with your supplier. Sometimes the dropship online platform you are using already took care of it. If not, here is a sample of a drop ship agreement that can help you leverage any risks: Premium Drop Shipping Agreement Template

https://pdfs.semanticscholar.org/cec0/bdd0060141e64e9dfe5a56ea49009b48e0ea.pdf“Abstract—An analog implementation of a deep machinelearning system for ef¿cient feature extraction is presented in this work. It features online unsupervised trainability and non-volatile Àoating-gate analog storage. It utilizes a massively parallel recon¿gurable current-mode analog architecture to realize ef¿cient computation, and leverages algorithm-level feedback to provide robustness to circuit imperfections in analog signal processing. A 3-layer, 7-node analog deep machine-learning engine was fabricated in a 0.13 μm standard CMOS process, occupying 0.36 mm2 active area. At a processing speed of 8300 input vectors per second, it consumes 11.4 μW from the 3 V supply, achieving 1×1012 operation per second per Watt of peak energy ef¿ciency. Measurement demonstrates real-time cluster analysis, and feature extraction for pattern recognition with 8-fold dimension reduction with an accuracy comparable to the Àoating-point software simulation baseline.”Http://ftp://ftp.gunadarma.ac.id/.upload/Communication-ACM/November-2003/p50foster.pdf“DATA INTEGRATION IN A BANDWIDTH-RICH WORLD Inexpensive storage and wide-area bandwidth (with prices for both declining at least as fast as Moore’s Law) drive demand for middleware to integrate, correlate, compare, and mine local, remote, and distributed data. Exponential advances in sensors, storage systems, and computers are producing data of unprecedented quantity and quality. Multi-terabyte and even petabyte (1,000TB) data sets are emerging as major assets. For example, the climate science community has access to hundreds of terabytes of observational data from NASA’s Earth-observing system and simulation data from high-performance climate models; these data sources can yield new insights into global change. The World-Wide Telescope linking hundreds of digital sky surveys is revolutionizing astronomy [11]. And in industry, multi-terabyte (soon to be petabyte) data warehouses of consumer transactional data are increasingly common.IN-SPIRALING MERGER OF TWO BLACK HOLES. SWIRLING RED TENDRILS ARE OUTWARD-TRAVELING GRAVITATIONAL WAVES. (SIMULATION DATA: PETER DIENER AND THOMAS RADKE, BOTH MAX PLANCK INSTITUTE FOR GRAVITATIONPHYSICS, ALBERT EINSTEIN INSTITUTE/POTSDAM GERMANY; VISUALIZATION: JOHN SHALF USING THE VISAPULT TOOL DEVELOPED BY WES BETHEL, LAWRENCE BERKELEY NATIONAL LABORATORY)Requirements and Technologies Distributed data sources can be diverse in their formats, schema, quality, access mechanisms, ownership, access policies, and capabilities. Overcoming this multi-tiered Tower of Babel to achieve distributed data integration requires technical solutions and standards in three closely related areas: data discovery and access; data exploration and analysis; and resource management, security, and policy. Data discovery and access. The first step in integrating data is discovering data that may be relevant, often through middleware that examines metadata. Metadata can be represented, federated, and accessed in a variety of ways. Relevant technologies include Web services mechanisms. For example, there’s the Web Services Description Language specifications; Gridenabled data access and integration services [2]; directory services (such the Lightweight Directory Access Protocol); XML and relational databases; Semantic Web technologies [5]; and text-based Web search mechanisms applied to unstructured text-based metadata. Having identified data sets that might be relevant, the next step for the user is to access the data to see whether it is likely to be relevant and actually worth investigating. Data formats, schema, and access mechanisms span a broad range. Widely adopted access mechanisms include: the Open source project for a Network Data Access Protocol (OPeNDAP) in the environmental community; Storage Resource Broker (SRB) [3] in scientific projects; Data Web protocols for data mining (the Data Space Transfer Protocol, or DSTP); and GridFTP for high-performance and striped data movement. The OGSA-based Data Access and Integration (OGSA-DAI) [2] standards emerging from the Global Grid Forum seek to integrate these and other approaches. Data access can demand high transport performance and require parallel data access and movement. For example, if remote data is being delivered at a rate of 1Gbps, and a particular application’s data-integration activity involves reading 10 local bytes per remote byte received and performing 100 operations per local byte read, then the application requires 10Gbps local read bandwidth and 1Teraops/sec. of local computing to keep up with data delivery (a substantial and necessarily parallel resource). Striping data using multiple network connections linking pairs of nodes in distributed clusters is becoming a core technique in high-performance data transport [10]. The GridFTP extensions to the popular FTP protocol represent a standard approach to exploiting parallelism in data transfers, allowing multiple data channels to be coordinated via FTP control channel commands. Also relevant is the work on advanced protocols described in the article by Falk et al. in this section. We anticipate the emergence of data access services supporting the flexible creation and manipulation of views on data sources (whether files or tables) and access to those views using a variety of operations, including database-style operations (such as SQL “select”) and other more general operations (such as attribute selection, row selection via range queries, and record selection via sampling). Integrating these mechanisms with high-performance transport protocols remains a major unresolved problem. Data exploration and analysis. Data rendered accessible can be analyzed in detail. Here, data exploration services are needed to address the challenges inherent in finding relevant data that can be combined with local data or with other remote data to achieve new discoveries. These services can provide basic statistical summaries, enable visual exploration of data, and support standard exploratory functions (such as building clusters), computing the regression of one variable on another. Efficient integration of distributed data requires protocols and services for managing the data records constituting data archives. Unlike files of bits, data archives of records have attributes, attribute metadata, keys, and missing values. Mechanisms for providing attribute- and record-based access to remote and distributed data include: SQL-based access methods for relational data; protocols designed to work with remote data (such as the Data Web Transfer Protocol [10], OPeNDAP, and OGSA-DAI [2]); and protocols designed to work with remote and distributed semistructured data (such as XPath). Data Webs support the exploration and mining of distributed data using templated data-mining operations. The transformation, analysis, and synthesis performed during data integration can be complex and 54 November 2003/Vol. 46, No. 11 COMMUNICATIONS OF THE ACM CCM3 data in Chicago (xi, ri, si, ti) (yi, ri, si, ti) Vegetation data in Amsterdam. Computer scientists have a good understanding of how to perform relational joins when data is at rest in a single location. An important method for data integration is to join distributed data in motion to look for patterns across data sets. An experiment at the iGrid 2002 Conference in Amsterdam integrated (on the fly) climate data from Chicago with vegetation data in Amsterdam at transfer rates greater than 2.4Gbps, a land-speed record at the time; integration involved two distributed three-node clusters and employed the SABUL data-transport protocol. computationally intensive. Data-transformation primitives incorporated into data middleware cannot capture arbitrary computations but can express many common data-preparation operations [10]. More general workflow services are also required to support the integration and scheduling of arbitrary user- and community-defined transformations. Users benefit from tools that record, organize, and exploit knowledge about how these activities derive new data from old. Virtual data systems aim to capture this information so as to allow reuse of generated data, explanation of data provenance, and other activities [8]. Resource management, security, and policy. Being famiiliar with today’s bandwidth- and data-poor world, users often assume only standard schema and access methods are required to render remote data accessible. But the distributed analysis of large quantities of data is computationally (and bandwidth) intensive, and a high-performance Internet can expose popular data resources to the risk of essentially unlimited loads. Efficient petascale data integration can require the harnessing and coordinated management of multiple computational and network resources at multiple sites. Thus, clients (and brokers acting on their behalf) need to negotiate service level agreements (SLAs) with computers, storage systems, and networks. They also need to deploy applications able to achieve desired end-to-end performance across these resources, as well as monitor performance and adapt to performance problems at either the network or SLA level [7]. For example, an application might request an end-to-end optical network plus associated computing and storage resources, use the resources to integrate remote and local data, then release them. Another effective optimization is to decouple data movement and computation so the data is staged to locations “near” (in terms of some access cost metric) to where it is required [12]. Data replication and distribution of data across the network [4] are also effective techniques. Along with the data itself, the physical resources employed for data integration are frequently precious and thus subject to access controls. Data-integration middleware must therefore provide comprehensive security, policy, and resource management solutions. These solutions are required at multiple levels, ranging from the individual user (“Can I access this file?”), to the user community (“How many Gb-hours is this community allocated?”), and from the local (“Allocate me 1Gbps bandwidth”), to the end-to-end (“Allocate resources to achieve 10Gbps throughput for this pipeline”), to the global (“Ensure that the most popular data sets are replicated”). Security and policy solutions must address the concerns of both the institutions that own specific resources and the communities wishing to achieve distributed analysis.”