The business of open data
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The document discusses lessons learned from ODINE, a European Union program that provides funding and support to open data startups and SMEs. Some key lessons include: there is high demand for open data as it offers a unique value proposition; national open data ecosystems influence companies' success; defining the value of open data for solving customer problems is important early on; and various business models like freemium can be used. The program received many submissions and funded 6-month projects for ideas that mattered and were enabled by open data, showing there is potential for open data businesses.
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Horizon 2020 sme funding, euresearch, susanne daniel, march 2019
The document provides information on funding opportunities for companies through Horizon 2020, the European Union's research and innovation program. It describes several bottom-up funding programs including the SME Instrument, which provides grants up to €2.5 million to innovative small and medium enterprises. It also discusses Fast Track to Innovation, which funds demonstration and validation of innovations, and FET Open, which supports high-risk frontier technology research. The document provides details on eligibility, funding amounts, evaluation criteria and deadlines for applying to these bottom-up Horizon 2020 programs.
20171215 open innovation battistella
The document discusses an open innovation platform that matches small and medium enterprises (SMEs) with experts to help solve technical problems and generate new ideas. The platform crawls scientific literature and patent databases to identify experts best suited to a company's needs based on their relevant publications. It allows anonymous initial contact between companies and experts before they negotiate direct collaboration. A trial with SMEs resulted in over €2 million in research contracts and €16 million in total value generated for participating companies. The presentation encourages interested SMEs to participate in a free trial of the platform and its services.
Getting Digital Introduction Hannah Rudman
Welcome from Hannah who will give an overview of the AmbITion Scotland programme, explain the focus of the
event and show how digital developments create benefits and opportunities for any arts organisation.
http://getambition.com / http://consultrudman.com
Fueling the open data economy
This document summarizes the key aspects of an open data incubator program. The incubator provides resources like funding up to €100,000, computing resources, introductions to mentors and investors, and training to help projects make use of open data and develop businesses or services. It accepts applications on a rolling basis and projects are selected every two months for six month incubations. The goal is to help facilitate the commercialization of open data and data-driven businesses through various supports.
Digistart
This document provides an overview of the DIGISTART project which aims to create pathways for first-time entrepreneurs. It discusses six main initiatives: 1) a common open application for idea-stage startups, 2) a talent forum, 3) an MOOC, 4) research, 5) a student support center, and 6) workshops/training. It also outlines key focus areas and success metrics for partners in London, Malmo, and Lisbon and describes plans for some of the main initiatives going forward.
Horizon 2020 | Collaborative R&D Support Service | Claire Griffin
Laura McAuley: laura.mcauley@investni.com
Tel. 028 9069 8807
Wizer.Me - NOAH18 Tel Aviv
The document summarizes NOAH Advisors, a leading European corporate finance boutique focused on internet companies. It has completed over 30 successful M&A transactions totaling billions of euros, utilizing its expertise, network, and focus on entrepreneurship. It connects capital with entrepreneurs through conferences and facilitates the funding and sale process to minimize management distraction.
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Fp7 Frameworks NW - Funding for Collaborative Projects
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EU Startup Services Capital Week AMS presentation 25-9
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EDI: Open Call 3 for Startups, #StartupsKillTheVirus
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Webinar slides - Internet of Things Convergence: Preparatory Studies
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EDI: Federation of data services to foster the adoption of data-driven AI in ...
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The Megatrends Project and Online Education Consortia
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Horizon 2020 sme funding, euresearch, susanne daniel, march 2019
Horizon 2020 sme funding, euresearch, susanne daniel, march 2019
Horizon 2020 | Collaborative R&D Support Service | Claire Griffin
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More from Elena Simperl
When stars align: studies in data quality, knowledge graphs, and machine lear...
Keynote at DQMLKG workshop at the 21st European Semantic Web Conference 2024
Knowledge engineering: from people to machines and back
Keynote at the 21st European Semantic Web Conference
This talk was not generated with ChatGPT: how AI is changing science
This talk discusses how AI is changing science by allowing huge amounts of data to be analyzed to discover patterns, increasing reproducibility of research, and supporting qualitative research. However, challenges also exist around developing best practices for AI, preventing misinformation, ensuring data remains in the public domain, and designing effective human-AI interaction. The talk calls for addressing these challenges to maximize AI's benefits for science.
Knowledge graph use cases in natural language generation
Keynote talk at INLG (International Natural Language Generation Conference) & SIGDial (Special Interest Group on Discourse and Dialogue), September 2023
Knowledge engineering: from people to machines and back
This document discusses knowledge engineering and how it sits between software engineering and artificial intelligence. It focuses on building and maintaining knowledge-based systems that capture human expertise to support decision making. The document examines challenges with involving more people in knowledge engineering processes and how automation could help. It explores options like games, online communities, and human computation to engage people while also discussing how artificial intelligence advances may assist with knowledge representation and engineering challenges.
The web of data: how are we doing so far
The document summarizes the current state of open data and the web of data. It discusses how data is being shared online through datasets, digital traces, and algorithms. While there is a lot of annotated data available, especially about locations and businesses, uptake of linked data and vocabulary reuse is still low. The document also reviews guidelines for improving data organization, discoverability, documentation, and engagement. Finally, it discusses ongoing research on data search behavior, sensemaking practices, and the potential for generative AI to help with data understanding and reuse.
What Wikidata teaches us about knowledge engineering
This document summarizes an expert talk about Wikidata and knowledge engineering. The key points are:
1) Wikidata is a collaborative knowledge graph with over 28,000 active users that contains over 97 million items and 1.6 billion edits. It allows both human and bot editors.
2) Studies of Wikidata show that a balanced mix of bot and human editors, as well as diversity in editor tenure and interests, leads to higher quality knowledge graph items and ontology.
3) Provenance or references are important for trust in Wikidata statements, but the quality of these references is not well understood and varies across languages. Further research is exploring how to better evaluate reference quality.
Open government data portals: from publishing to use and impact
The document discusses open government data portals and their evolution from initial publishing of data to supporting reuse and impact. It describes the key stages in developing portals, including the first portal launched over 13 years ago and the current European data portal. The document outlines work done to support the entire data value chain, analyze portal usage, develop guidelines to make portals more user-centric, and measure their effectiveness in promoting reuse. Examples are provided for how portals can better organize data, promote reuse, and co-locate documentation to support users.
Ten myths about knowledge graphs.pdf
This document discusses ten common myths about knowledge graphs. It summarizes each myth and provides evidence to debunk it with examples from Wikidata and other knowledge graphs. Some of the myths addressed include that Google invented knowledge graphs, that knowledge graphs are databases or GOFAI systems, that their quality is not good enough, and that they are not useful for applications like natural language processing. The document advocates that knowledge graphs are large, collaboratively built resources that can power machine learning and facilitate explanations from AI systems.
What Wikidata teaches us about knowledge engineering
This document provides an overview of a presentation about Wikidata and what it teaches about knowledge engineering. Some key points:
- Wikidata is a collaborative knowledge graph created by Wikimedia in 2012 with over 23,000 active users and billions of edits.
- It contains statements about items linked by properties, with items identified by Q codes and properties by P codes. Items can represent classes, entities, or values.
- Statements include optional qualifiers and required references, which can link internally or externally.
- The knowledge graph is co-edited by both humans and over 340 bots created by the community.
- Research on Wikidata explores how group composition, diversity, and provenance impact knowledge
Data commons and their role in fighting misinformation.pdf
The document discusses the role of data commons and open data in fighting misinformation. It notes that algorithms used to detect misinformation are only as good as the data they are trained on, and that data work faces challenges around transparency, accountability, and mitigating biases. However, open data initiatives involving contributions from many users can help by making more trusted data available to algorithms. Overall, participatory and transparent approaches to data are needed to build critical infrastructure for combating the spread of misinformation.
Are our knowledge graphs trustworthy?
The document discusses the trustworthiness of knowledge graphs. Knowledge graphs are machine-readable data organized for general purpose use in applications like search engines, recommender systems, and virtual assistants. While knowledge graphs are used alongside machine learning in AI systems as a source of domain knowledge, it is unclear if they can be trusted. To determine if knowledge graphs are trustworthy, we must consider both the process of knowledge engineering used to create them as well as the knowledge graph as an end product. Factors like understanding the data's provenance, auditing it for bias, transparency in its creation and use are important for establishing trust.
The web of data: how are we doing so far?
This document summarizes Elena Simperl's presentation on "The web of data: how are we doing so far?". Some key points:
- The web has shaped our understanding and interactions with data in many ways like answering questions, sharing data online, and publishing data for others to use.
- However, the theory and practice of the web of data are different, and we are at a crucial moment in how data is published and used on the web.
- Open data portals need to improve in areas like adopting standards, co-locating documentation, and making data more usable and discoverable in order to increase data reuse.
Crowdsourcing and citizen engagement for people-centric smart cities
This document discusses how crowdsourcing and citizen science can be used to improve smart cities through collective intelligence. It describes how crowdsourcing involves solving problems through open crowds, using both paid microtasks and unpaid volunteering. Citizen science involves scientific research conducted by amateur scientists. The document outlines examples like using crowdsourcing to audit urban areas through a virtual street view tool. It emphasizes that citizen engagement projects must consider people's diverse motivations for participating and align incentives appropriately to encourage sustained involvement. Assessments of data quality and understanding participant behaviors are also important.
Pie chart or pizza: identifying chart types and their virality on Twitter
This document summarizes a study on identifying chart types from images shared on Twitter and predicting how viral (retweeted and liked) the images will be. The study used a convolutional neural network to identify 11 different chart types from images. It also used a multi-modal neural network to jointly predict retweets and likes by using features from the images as well as author metadata. The study introduced two new datasets (DataTweet+ and DataTweet) of charts collected from Twitter and found the models performed best when trained on these realistic datasets compared to benchmark datasets.
High-value datasets: from publication to impact
This document summarizes research on how people search for and interact with open data. It describes several studies analyzing logs of user activity on open data portals. The studies found that users conduct exploratory searches using keywords and filters, with location and category being popular filters. Successful searches tended to use both keywords and filters. The research also provided implications for open data portal designers, such as improving filters, location data granularity, and linking related content and datasets. Future work could include studies of user information needs and sharing granular activity data between portals.
The story of Data Stories
This document summarizes the work of the Data Stories project, which aimed to make data more accessible and engaging for the public. It developed tools and frameworks to communicate data through art, games and storytelling. The project examined how varying levels of localization, participation and shareability impact public engagement with factual information. It delivered guidance to help experts share data in inspiring and informative ways, countering the rise of misinformation.
The human face of AI: how collective and augmented intelligence can help sol...
This document summarizes a talk on how collective and augmented intelligence can help solve societal problems. It discusses how AI depends on human input, how collective intelligence benefits AI, and provides examples of using human computation and crowdsourcing to support disaster relief and conduct urban auditing. It also describes challenges in making crowdsourcing sustainable and assessing data quality, and emphasizes the need for iterative design of human-AI systems to bring together human, collective, and computational intelligence.
Qrowd and the city: designing people-centric smart cities
The document discusses designing smart cities that are people-centric by bringing together human and computational intelligence. It describes the QROWD project which uses a mix of crowdsourcing approaches like participatory sensing, paid microtasks, and mobile volunteers to collect data for smart city applications. Evaluation of pilots in two cities found that the virtual city explorer tool achieved good data coverage and accuracy comparable to OpenStreetMap. Ongoing work focuses on optimizing task design and incentives to improve crowdsourcing.
One does not simply crowdsource the Semantic Web: 10 years with people, URIs,...
Crowdsourcing can be used to achieve goals for semantic applications and the Semantic Web. There are different considerations for the design of a crowdsourcing project, including what tasks will be completed, who will complete them, how the process will work, and what incentives will motivate participants. Effective crowdsourcing relies on using the right crowd for the right task, and diversity within the crowd can improve outcomes. Various workflows and incentive structures should be tested to maximize quality, efficiency and participation when crowdsourcing semantic data.
More from Elena Simperl (20)
When stars align: studies in data quality, knowledge graphs, and machine lear...
When stars align: studies in data quality, knowledge graphs, and machine lear...
Knowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and back
This talk was not generated with ChatGPT: how AI is changing science
This talk was not generated with ChatGPT: how AI is changing science
Knowledge graph use cases in natural language generation
Knowledge graph use cases in natural language generation
Knowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and back
What Wikidata teaches us about knowledge engineering
What Wikidata teaches us about knowledge engineering
Open government data portals: from publishing to use and impact
Open government data portals: from publishing to use and impact
What Wikidata teaches us about knowledge engineering
What Wikidata teaches us about knowledge engineering
Data commons and their role in fighting misinformation.pdf
Data commons and their role in fighting misinformation.pdf
Crowdsourcing and citizen engagement for people-centric smart cities
Crowdsourcing and citizen engagement for people-centric smart cities
Pie chart or pizza: identifying chart types and their virality on Twitter
Pie chart or pizza: identifying chart types and their virality on Twitter
The human face of AI: how collective and augmented intelligence can help sol...
The human face of AI: how collective and augmented intelligence can help sol...
Qrowd and the city: designing people-centric smart cities
Qrowd and the city: designing people-centric smart cities
One does not simply crowdsource the Semantic Web: 10 years with people, URIs,...
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快速办理(Vic毕业证书)惠灵顿维多利亚大学毕业证完成信一模一样
学校原件一模一样【微信：741003700 】《(Vic毕业证书)惠灵顿维多利亚大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Bengaluru Dreamin' 24 - Personal Branding
Session on Personal Branding presented at Bengaluru Dreamin
HijackLoader Evolution: Interactive Process Hollowing
CrowdStrike researchers have identified a HijackLoader (aka IDAT Loader) sample that employs sophisticated evasion techniques to enhance the complexity of the threat. HijackLoader, an increasingly popular tool among adversaries for deploying additional payloads and tooling, continues to evolve as its developers experiment and enhance its capabilities.
In their analysis of a recent HijackLoader sample, CrowdStrike researchers discovered new techniques designed to increase the defense evasion capabilities of the loader. The malware developer used a standard process hollowing technique coupled with an additional trigger that was activated by the parent process writing to a pipe. This new approach, called "Interactive Process Hollowing", has the potential to make defense evasion stealthier.
快速办理(新加坡SMU毕业证书)新加坡管理大学毕业证文凭证书一模一样
学校原件一模一样【微信：741003700 】《(新加坡SMU毕业证书)新加坡管理大学毕业证文凭证书》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Securing BGP: Operational Strategies and Best Practices for Network Defenders...
Md. Zobair Khan,
Network Analyst and Technical Trainer at APNIC, presented 'Securing BGP: Operational Strategies and Best Practices for Network Defenders' at the Phoenix Summit held in Dhaka, Bangladesh from 23 to 24 May 2024.
一比一原版新西兰林肯大学毕业证(Lincoln毕业证书)学历如何办理
原版办【微信号:95270640】【新西兰林肯大学毕业证(Lincoln毕业证书)】【微信号:95270640】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(uc毕业证书)加拿大卡尔加里大学毕业证如何办理
原版一模一样【微信：741003700 】【(uc毕业证书)加拿大卡尔加里大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(uc毕业证书)加拿大卡尔加里大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uc毕业证书)加拿大卡尔加里大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uc毕业证书)加拿大卡尔加里大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uc毕业证书)加拿大卡尔加里大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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怎么办理(umiami毕业证书)美国迈阿密大学毕业证文凭证书实拍图原版一模一样
原版定制【微信:bwp0011】《(umiami毕业证书)美国迈阿密大学毕业证文凭证书》【微信:bwp0011】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信bwp0011】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信bwp0011】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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The business of open data
- 1. Thebusiness of open data Lessons learnt from ODINE Elena Simperl – University of Southampton, UK CeBIT 2017, Hannover 23rd March 2017
- 2. ODINEbuilds a business ecosystem of open data startups and SMEs in Europe
- 3. What weoffer 3 Up to €100K equity-free funding Expert advice from business mentors Peer-networking and support High-quality exposure in The Guardian &other media outlets Introductions to investors
- 4. Competition for EUstartups & SMEs • Call open from May 2015 to August 2016 (Call is closed ) • Submissions reviewed by external experts every two months • 6 months projects for – THINGS THAT MATTER – Business ideas enabled by open data € 5.5M
- 7. Lesson 1: High demand, open data offers USP visualization.opendataincubator.eu/public/odine.html
- 8. Lesson 2: National open data ecosystems matter
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