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Nossos projetos open source: tudo o que você precisa saber para participar.

- January 16, 2019 in código aberto, Conhecimento Livre, css, d3, Destaque, Gastos Abertos, Gastos Públicos, governo aberto, html, jekyll, Open Knowledge Brasil, Open Source, Python, transparência, visualização de dados

Por Pedro Vilanova Se você acompanha projetos de tecnologia – ou trabalha no mercado, independente da área de atuação, provavelmente já ouviu falar sobre projetos open source. Mesmo em crescimento, porém, o universo de projetos de código aberto ainda gera muitas dúvidas até mesmo em profissionais. Esse texto é para facilitar um pouco o entendimento e ajudar as pessoas a colocarem em prática um dos principais conceitos do open source: participação.

O que é Open Source?

Na prática, um projeto open source nada mais é do que um trabalho cujo código é aberto. Isto é, tem seu licenciamento livre, com o conteúdo do software disponível para quem quiser modificar, copiar, estudar e fazer os mais diversos tipo de experiência, inclusive trabalhando e ganhando dinheiro com isso. Apesar do caráter de troca de conhecimento e experiência, se engana quem pensa que a comunidade de código aberto é composta apenas por estudantes e acadêmicos. As maiores empresas do mundo, como IBM e Microsoft, mantém participação ativa e olhos bem abertos sobre iniciativas de código aberto. Isso acaba por trazer mais profissionais experientes para a comunidade e movimentar um maior investimento.

A importância do open source para a ciência.

Projetos em código aberto proporcionam o que chamamos de trabalhos derivados. Qualquer pessoa pode ter acesso e aprimorar o que já foi feito. Qualquer pessoa. Com isso, as possibilidades de melhoria são infinitas. Além disso, em geral, quem contribui com projetos open source também tem muito a ganhar em termos de conhecimento e oportunidades profissionais.

A importância do open source para a democracia.

Dentro da Open Knowledge Brasil, trabalhamos muito com código aberto voltado para iniciativas políticas. Isso porque acreditamos que essa é uma das principais vocações de se trabalhar com software livre. Um projeto que busca democracia precisa ser, acima de tudo, democrático.

Nossas iniciativas open source facilitam o acompanhamento, a transparência e auditoria do nosso trabalho, pilares do que acreditamos ser o conhecimento livre. Isso sem falar no engajamento. Em um país grande e diverso como o Brasil, trabalhar de forma aberta é dar a oportunidade do código passar por todo o país, sendo agregado, adaptado a diferentes realidades e servindo a democracia em seu potencial máximo.

Ok. E como vocês ganham dinheiro com isso?

Se engana quem acha que trabalhar com open source é sinônimo de trabalho voluntário. É bem verdade que muito do universo de código aberto é voluntário, mas o mercado só cresce globalmente, reunindo cada vez mais profissionais experientes e chamando a atenção de grandes empresas. A diferença é que, por não gerar custos em torno da licença, o mercado de código aberto gera maior valor no conhecimento, com investimentos em serviço e formação. Existem algumas formas clássicas de capitalização de trabalhos open source, como por exemplo:
  • Doações: alguns projetos open source servem a um propósito muito forte, o que faz com que pessoas – técnicas ou não – se mobilizem em torno da causa, contribuindo com doações em dinheiro. As plataformas de financiamento coletivo estão repletas de projetos incríveis que alcançaram seus objetivos financeiros para serem desenvolvidos.

  • Desenvolvendo para grandes empresas: é bastante comum que grandes empresas adaptem softwares open source para suas necessidades ou até mesmo internalizem algumas iniciativas. Com a entrada de companhias maiores nesse mercado, a tendência é que tenhamos cada vez mais código aberto dentro de grandes organizações, o que movimenta muito a comunidade financeiramente.

  • Conhecimento: o mercado de open source movimenta muito investimento em conhecimento. Linguagens e softwares open source abrem espaço para aulas, consultorias e demais serviços. Não se paga licença, mas se vê alto valor na aplicação da tecnologia dentro do conhecimento.

Nossos principais projetos open source.

Como falamos ali em cima, a OKBr atualmente conta com várias iniciativas open source prontinhas para receber participação. Confira algumas delas:

Serenata de Amor

Linguagens utilizadas: Python (e HTML/CSS com Jekyll). O que o projeto entrega: inteligência artificial para auditoria de gastos com a cota parlamentar. Como contribuir: o Serenata possui três grandes repositórios: o principal, o website e o tool box. No primeiro, é possível contribuir com a Rosie, inteligência artificial que analisa os gastos públicos, melhorando sua performance, e em outras diversas tarefas essenciais para o funcionamento do projeto. Nesse repositório está também o Jarbas, que é nosso painel de visualização desses dados todos.

Perfil Político

Linguagens utilizadas: Python (com Django na API) e Javascript (com D3 no frontend). O que o projeto entrega: perfis detalhados de todos os candidatos a cargos eleitorais no Brasil. Como contribuir: o Perfil possui três repositórios: o principal (API) e o de frontend. O primeiro é um prato cheio para jornalistas de dados: ali são coletadas, tratadas e organizados em um banco de dados único informações de candidatos a diversos cargos, vários deles eleitos, prontos para serem analisadas. O frontend, por sua vez, traz a parte visual, oferecendo uma melhor usabilidade e apresentação dos dados a partir da nossa API. O Perfil é mais um projeto aberto para diferentes perfis profissionais que queiram contribuir.

Vítimas da intolerância

Linguagens utilizadas: Python (com Sanic) O que o projeto entrega: mapeamento de casos de violência com fundo político.   Como contribuir: no repositório do Vítimas é possível conferir todo o código e todos os casos levantados até agora, onde os contribuidores podem adicionar, analisar e melhorar a leitura de dados desenvolvida até agora.

Queremos Saber

Linguagens utilizadas: Python (Django) O que o projeto entrega: possibilidade de fazer pedidos de informação dentro da lei sem revelar sua identidade.   Como contribuir: acesse o repositório do Queremos Saber no GitHub e veja a lista de tarefas em aberto. Em especial, as marcadas como “good first issue” são as que consideramos boas para alguém que ainda está se familiarizando com o código-fonte.

 

Querido Diário

Linguagens utilizadas: Python O que o projeto entrega: raspagem e análise de dados de compras emitidas nos diários oficiais municipais. Como contribuir: os diários oficiais mudam bastante e o Brasil é um país com milhares de municípios. Quem entrar no repositório do Querido Diário encontra tarefas e a documentação necessária para atuar com dados no seu município e fazer a sua parte pelo controle social no país. Por que não criamos uma grande rede e contemplamos o Brasil todo com essa tecnologia? Participe de iniciativas open source. Contribua com os nossos projetos e faça parte da comunidade. Se você não é da parte técnica e quer ajudar a manter os nossos projetos, pode contribuir com o apoia.se/serenata.   Flattr this!

Prototype Fund round 5: Letting machines learn

- August 22, 2018 in AI, germany, OK Germany, Open Source, prototype fund

The Prototype Fund is a public program run by Open Knowledge Foundation Germany that focuses on emerging challenges and radically new solutions. Individuals and small teams can apply for funding to test their ideas and develop open source tools and applications in the fields of civic tech, data literacy, data security and more. The 5th round of the Fund is currently open for applications until 30 September: in this blog Katharina Meyer shares more on its contents and on how to apply.

Letting machines learn: technologies for the future

New technologies such as artificial intelligence and machine learning are on everyone’s lips – but mostly not in our hands. With the focus of the 5th call for applications we want to encourage more people to participate in shaping these new technologies and have a stake in our future. We want to find out what opportunities new technological developments offer to society. How much of this hype is righteous, what are the risks, how can we gain a better insight into the emergence of technologies and influence this process? Artificial intelligence is an example of the challenges we face in the verge of tomorrow’s technologies. The development of intelligent systems is only accessible to a few people and companies because the technologies are highly specialized. The amount of data needed to train the machines is often owned by large corporations and platforms. The development of new technologies and intelligent systems is often directed towards industry or embedded in the theoretical framework of universities. To ensure that emerging technologies reflect social reality and do not discriminate against people, we need to incorporate a wide range of experience and expertise into their development. We also aim to better understand what exactly we are talking about when we say AI and demystify technology. We therefore especially encourage software projects to apply, which deal with the following questions as part of their conceptual and practical work:
  • Which social topics can be better explored and addressed with the help of machine learning, and how?
  • How can new technologies help us address (and reduce) existing injustice instead of reinforcing it?
  • Explaining and understanding new technologies: How do Machine Learning or Artificial Intelligence work? What are the challenges, myths and opportunities?
We are not seeking to apply new technologies to random problems, but instead to examine developments and fields of application in detail and placing people and their needs at the centre of technical development. In a blog post (in German) we have collected projects and ideas that illustrate our main topic. Projects outside this focus can also be supported if they are in the areas of digital infrastructure, data security, data literacy or civic tech.

How to apply

Applications are open to individuals and small teams who live in Germany. You can read more about the fifth round and apply at https://prototypefund.de/en/. The projects we currently support can be found here.  

How to innovate? Prototype everything!

- December 19, 2017 in civic tech, funding, germany, OK Germany, Open Data, Open Source

We recognized a problem. There are so many individuals and small teams with good ideas out there, but there is little to no financial support. We wanted to change that. This is how the idea for the Prototype Fund came to life. Usually, in order to receive funding, teams need to have a clear-cut business model, be an established company, or pursue a long-term research project. But innovation requires a different environment. Innovation needs room for trial and error, changing plans, and short-term sprints. Innovation is not just planning business models, but identifying problems and needs within your community and addressing these. The Prototype Fund aims to suit the needs for innovation. The Prototype Fund is a public program run by Open Knowledge Foundation Germany that focuses on emerging challenges and radically new solutions. Individuals and small teams can apply for funding to test their ideas and develop open source tools and applications in the fields of civic tech, data literacy, data security and more. Our early-stage funding encourages people to follow unusual approaches. The application process aims to be as unbureaucratic as possible and is adjusted to the needs of software developers, civic hackers, and creatives. The Prototype Fund brings iterative software development and government funding together. The German Federal Ministry of Education and Research funds eight rounds from 2016 through 2020. Each round, we can thus support up to 25 innovative open source projects. Each project is funded with up to 47,500€. Our goal is to support code for all and strengthen the open source community in Germany. In true open source spirit, we want to pave the way for innovation for everyone. During the first two rounds we received more than 500 applications. There was an enormous amount of feedback and the need for an open source funding program became apparent. While the first round was an open call, the second round focused on ‘Tools for a strong Civil Society’. Projects included Pretix, a tool that facilitates the ticket sale and registration for events, while allowing more privacy for the user and self-hosted applications, or Pluragraph, that offers social media benchmarking and analysis in the non-commercial sector. In the third round, we focused on ‘Diversity: more open source for everyone!’, which led to 19 percent of applications that were submitted by women and a wide range of thrilling projects of which our jury selected 23 projects for funding. A menstrual tracking app, for example, allows the privacy-friendly and customized pursuit of the cycle beyond commercial interests. Another example is Briar, a messenger app that allows encrypted communication without a central server, but directly from device to device. Many of our projects address questions such as: How can we reduce bureaucracy, build strong communities, establish skill-sharing and foster lifelong learning? As much as we are happy with how things are turning out so far, the Prototype Fund itself is that: a prototype. We are constantly trying to improve and to come up with new ideas. Do you want to get in touch or find out more about our projects? Here is a list with all the projects we funded in Round 1 to 3, subscribe to our newsletter (in German), or get in touch under info@prototypefund.de. Or simply come to our next Demo Day on 28 February 2018 in Berlin and get some live Prototype-Fund spirit!  

How to innovate? Prototype everything!

- December 19, 2017 in civic tech, funding, germany, OK Germany, Open Data, Open Source

We recognized a problem. There are so many individuals and small teams with good ideas out there, but there is little to no financial support. We wanted to change that. This is how the idea for the Prototype Fund came to life. Usually, in order to receive funding, teams need to have a clear-cut business model, be an established company, or pursue a long-term research project. But innovation requires a different environment. Innovation needs room for trial and error, changing plans, and short-term sprints. Innovation is not just planning business models, but identifying problems and needs within your community and addressing these. The Prototype Fund aims to suit the needs for innovation. The Prototype Fund is a public program run by Open Knowledge Foundation Germany that focuses on emerging challenges and radically new solutions. Individuals and small teams can apply for funding to test their ideas and develop open source tools and applications in the fields of civic tech, data literacy, data security and more. Our early-stage funding encourages people to follow unusual approaches. The application process aims to be as unbureaucratic as possible and is adjusted to the needs of software developers, civic hackers, and creatives. The Prototype Fund brings iterative software development and government funding together. The German Federal Ministry of Education and Research funds eight rounds from 2016 through 2020. Each round, we can thus support up to 25 innovative open source projects. Each project is funded with up to 47,500€. Our goal is to support code for all and strengthen the open source community in Germany. In true open source spirit, we want to pave the way for innovation for everyone. During the first two rounds we received more than 500 applications. There was an enormous amount of feedback and the need for an open source funding program became apparent. While the first round was an open call, the second round focused on ‘Tools for a strong Civil Society’. Projects included Pretix, a tool that facilitates the ticket sale and registration for events, while allowing more privacy for the user and self-hosted applications, or Pluragraph, that offers social media benchmarking and analysis in the non-commercial sector. In the third round, we focused on ‘Diversity: more open source for everyone!’, which led to 19 percent of applications that were submitted by women and a wide range of thrilling projects of which our jury selected 23 projects for funding. A menstrual tracking app, for example, allows the privacy-friendly and customized pursuit of the cycle beyond commercial interests. Another example is Briar, a messenger app that allows encrypted communication without a central server, but directly from device to device. Many of our projects address questions such as: How can we reduce bureaucracy, build strong communities, establish skill-sharing and foster lifelong learning? As much as we are happy with how things are turning out so far, the Prototype Fund itself is that: a prototype. We are constantly trying to improve and to come up with new ideas. Do you want to get in touch or find out more about our projects? Here is a list with all the projects we funded in Round 1 to 3, subscribe to our newsletter (in German), or get in touch under info@prototypefund.de. Or simply come to our next Demo Day on 28 February 2018 in Berlin and get some live Prototype-Fund spirit!  

Frictionless Data Case Study: OpenML

- December 6, 2017 in case study, Data Package, Frictionless Data, Open Source

The Frictionless Data project is about making it effortless to transport high quality data among different tools and platforms for further analysis. We are doing this by developing a set of software, specifications, and best practices for publishing data. The heart of Frictionless Data is the Data Package specification, a containerization format for any kind of data based on existing practices for publishing open-source software. The Frictionless Data  case study series highlights projects and organisations who are working with Frictionless Data specifications and software in interesting and innovative ways. OpenML is one such organization. This case study has been made possible by OpenML’s Heidi Seibold and Joaquin Vanschoren, the authors of this blog.   OpenML is an online platform and service for machine learning, whose goal is to make machine learning and data analysis simple, accessible, collaborative and open with an optimal division of labour between computers and humans. People can upload and share data sets and questions (prediction tasks) on OpenML that they then collaboratively solve using machine learning algorithms. We first heard about the Frictionless Data project through School of Data. One of the OpenML core members is also involved in School of Data and used Frictionless Data’s data packages in one of the open data workshops from School of Data Switzerland. We offer open source tools to download data into your favourite machine learning environments and work with it. You can then upload your results back onto the platform so that others can learn from you. If you have data, you can use OpenML to get insights on what machine learning method works well to answer your question. Machine Learners can use OpenML to find interesting data sets and questions that are relevant for others and also for machine learning research (e.g. learning how algorithms behave on different types of data sets).

Image of data set list on OpenML

OpenML currently works with tabular data in Attribute Relation File Format (ARFF) accompanied by metadata in an xml or json file. It is actually very similar to Frictionless Data’s tabular data package specification, but with ARFF instead of csv. 

Image of a data set overview on openML

In the coming months, we are looking to adopt Frictionless Data specifications to improve user friendliness on OpenML. We hope to make it possible for users to upload and connect datasets in data packages format. This will be a great shift because it would enable people to easily build and share machine learning models trained on any dataset in the frictionless data ecosystem. We firmly believe that if data packages become the go-to specification for sharing data in scientific communities, accessibility to data that’s currently ‘hidden’ in data platforms and university libraries will improve vastly, and are keen to adopt and use the specification on OpenML in the coming months. Interested in contributing to OpenML’s quest to adopt the data package specification as an import and export option for data on the OpenML platform? Start here.

Visual gateways into science: Why it’s time to change the way we discover research

- November 14, 2017 in open knowledge maps, Open Science, Open Source, tools

Have you ever noticed that it is really hard to get an overview of a research field that you know nothing about? Let’s assume for a minute that a family member or a loved one of yours has fallen ill and unfortunately, the standard treatment isn’t working. Like many other people, you now want to get into the research on the illness to better understand what’s going on. You proceed to type the name of the disease into PubMed or Google Scholar – and you are confronted with thousands of results, more than you could ever read. It’s hard to determine where to start, because you don’t understand the terminology in the field, you don’t know what the main areas are, and it’s hard to identify important papers, journals, and authors just by looking at the results list. With time and patience you could probably get there. However, this is time that you do not have, because decisions need to be made. Decisions that may have grave implications for the patient. If you have ever had a similar experience, you are not alone. We are all swamped with the literature, and even experts struggle with this problem. In the Zika epidemic in 2015 for example, many people scrambled to get an overview of what was until then an obscure research topic. This included researchers, but also practitioners and public health officials. And it’s not just medicine; almost all areas of research have become so specialized that they’re almost impenetrable from the outside. But the thing is, there are many people on the outside that could benefit from scientific knowledge. Think about journalists, fact checkers, policy makers or students. They all have the same problem – they don’t have a way in. Reuse of scientific knowledge within academia is already limited, but when we’re looking at transfer to practice, the gap is even wider. Even in application-oriented disciplines, only a small percentage of research findings ever influence practice – and even if they do so, often with a considerable delay. At Open Knowledge Maps, a non-profit organization dedicated to improving the visibility of scientific knowledge for science and society, it is our mission to change that. We want to provide visual gateways into research – because we think that it is important that we do not only provide access to research findings, but also to enable discoverability of scientific knowledge. At the moment, there is a missing link between accessibility and discoverability – and we want to provide that link. Imagine a world, where you can get an overview of any research field at a glance, meaning you can easily determine the main areas and relevant concepts in the field. In addition, you can instantly identify a set of papers that are relevant for your information need. We call such overviews knowledge maps. You can find an example for the field of heart diseases below. The bubbles represent the main areas and relevant papers are already attached to each of the areas. Now imagine that each of these maps is adapted to the needs of different types of users, researchers, students, journalists or patients. And not only that: they are all structured and connected and they contain annotated pathways through the literature as to what to read first, and how to proceed afterwards. This is the vision that we’ve have been working on for the past 1.5 years as a growing community of designers, developers, communicators, advisors, partners, and users. On our website, we are offering an openly accessible service, which allows you to create a knowledge map for any discipline. Users can choose between two databases: Bielefeld Academic Search Engine (BASE) with more than 110 million scientific documents from all disciplines, and PubMed, the large biomedical database with 26 million references. We use the 100 most relevant results for a search term as reported by the respective database as a starting point for our knowledge maps. We use text similarity to create the knowledge maps. The algorithm groups those papers together that have many words in common. See below for an example map of digital education. We have received a lot of positive feedback on this service from the community. We are honored and humbled by hundreds of enthusiastic posts in blogs, and on Facebook and Twitter. The service has also been featured on the front pages of reddit and HackerNews, and recently, we won the Open Minds Award, the Austrian Open Source Award. Since the first launch of the service in May 2016, we have had more than 200,000 visits on Open Knowledge Maps. Currently, more than 20,000 users leverage Open Knowledge Maps for their research, work, and studies per month. The “Open” in Open Knowledge Maps does not only stand for open access articles – we want to go the whole way of open science and create a public good. This means that all of our software is developed open source. You can also find our development roadmap on Github and leave comments by opening an issue. The knowledge maps themselves are licensed under a Creative Commons Attribution license and can be freely shared and modified. We will also openly share the underlying data, for example as Linked Open Data. This way, we want to contribute to the open science ecosystem that our partners, including Open Knowledge Austria, rOpenSci, ContentMine, the Internet Archive Labs and Wikimedia are creating. Open Knowledge International has played a crucial role in incubating the idea of an open discovery platform, by way of a Panton Fellowship where the first prototype of the search service was created. Since then, the Open Knowledge Network has enthusiastically supported the project, in particular the Austrian chapter as well as Open Knowledge International, Open Knowledge Germany and other regional organisations. Members of the international Open Knowledge community have become indispensable for Open Knowledge Maps, be it as team members, advisors or active supporters. A big shout-out and thank you to you! As a next step, we want to work on structuring and connecting these maps – and we want to turn discovery into a collaborative process. Because someone has already gone that way before and they have all the overview and the insights. We want to enable people to communicate this knowledge so that we can start laying pathways through science for each other. We have created a short video to illustrate this idea:

Visual gateways into science: Why it’s time to change the way we discover research

- November 14, 2017 in open knowledge maps, Open Science, Open Source, tools

Have you ever noticed that it is really hard to get an overview of a research field that you know nothing about? Let’s assume for a minute that a family member or a loved one of yours has fallen ill and unfortunately, the standard treatment isn’t working. Like many other people, you now want to get into the research on the illness to better understand what’s going on. You proceed to type the name of the disease into PubMed or Google Scholar – and you are confronted with thousands of results, more than you could ever read. It’s hard to determine where to start, because you don’t understand the terminology in the field, you don’t know what the main areas are, and it’s hard to identify important papers, journals, and authors just by looking at the results list. With time and patience you could probably get there. However, this is time that you do not have, because decisions need to be made. Decisions that may have grave implications for the patient. If you have ever had a similar experience, you are not alone. We are all swamped with the literature, and even experts struggle with this problem. In the Zika epidemic in 2015 for example, many people scrambled to get an overview of what was until then an obscure research topic. This included researchers, but also practitioners and public health officials. And it’s not just medicine; almost all areas of research have become so specialized that they’re almost impenetrable from the outside. But the thing is, there are many people on the outside that could benefit from scientific knowledge. Think about journalists, fact checkers, policy makers or students. They all have the same problem – they don’t have a way in. Reuse of scientific knowledge within academia is already limited, but when we’re looking at transfer to practice, the gap is even wider. Even in application-oriented disciplines, only a small percentage of research findings ever influence practice – and even if they do so, often with a considerable delay. At Open Knowledge Maps, a non-profit organization dedicated to improving the visibility of scientific knowledge for science and society, it is our mission to change that. We want to provide visual gateways into research – because we think that it is important that we do not only provide access to research findings, but also to enable discoverability of scientific knowledge. At the moment, there is a missing link between accessibility and discoverability – and we want to provide that link. Imagine a world, where you can get an overview of any research field at a glance, meaning you can easily determine the main areas and relevant concepts in the field. In addition, you can instantly identify a set of papers that are relevant for your information need. We call such overviews knowledge maps. You can find an example for the field of heart diseases below. The bubbles represent the main areas and relevant papers are already attached to each of the areas. Now imagine that each of these maps is adapted to the needs of different types of users, researchers, students, journalists or patients. And not only that: they are all structured and connected and they contain annotated pathways through the literature as to what to read first, and how to proceed afterwards. This is the vision that we’ve have been working on for the past 1.5 years as a growing community of designers, developers, communicators, advisors, partners, and users. On our website, we are offering an openly accessible service, which allows you to create a knowledge map for any discipline. Users can choose between two databases: Bielefeld Academic Search Engine (BASE) with more than 110 million scientific documents from all disciplines, and PubMed, the large biomedical database with 26 million references. We use the 100 most relevant results for a search term as reported by the respective database as a starting point for our knowledge maps. We use text similarity to create the knowledge maps. The algorithm groups those papers together that have many words in common. See below for an example map of digital education. We have received a lot of positive feedback on this service from the community. We are honored and humbled by hundreds of enthusiastic posts in blogs, and on Facebook and Twitter. The service has also been featured on the front pages of reddit and HackerNews, and recently, we won the Open Minds Award, the Austrian Open Source Award. Since the first launch of the service in May 2016, we have had more than 200,000 visits on Open Knowledge Maps. Currently, more than 20,000 users leverage Open Knowledge Maps for their research, work, and studies per month. The “Open” in Open Knowledge Maps does not only stand for open access articles – we want to go the whole way of open science and create a public good. This means that all of our software is developed open source. You can also find our development roadmap on Github and leave comments by opening an issue. The knowledge maps themselves are licensed under a Creative Commons Attribution license and can be freely shared and modified. We will also openly share the underlying data, for example as Linked Open Data. This way, we want to contribute to the open science ecosystem that our partners, including Open Knowledge Austria, rOpenSci, ContentMine, the Internet Archive Labs and Wikimedia are creating. Open Knowledge International has played a crucial role in incubating the idea of an open discovery platform, by way of a Panton Fellowship where the first prototype of the search service was created. Since then, the Open Knowledge Network has enthusiastically supported the project, in particular the Austrian chapter as well as Open Knowledge International, Open Knowledge Germany and other regional organisations. Members of the international Open Knowledge community have become indispensable for Open Knowledge Maps, be it as team members, advisors or active supporters. A big shout-out and thank you to you! As a next step, we want to work on structuring and connecting these maps – and we want to turn discovery into a collaborative process. Because someone has already gone that way before and they have all the overview and the insights. We want to enable people to communicate this knowledge so that we can start laying pathways through science for each other. We have created a short video to illustrate this idea:

Introducing W4P, a crowdsourcing for open, social and local projects.

- June 24, 2016 in crowdfunding, crowdsourcing, Featured, General, Open Innovation, Open Source

After 10 months of figuring what we need to build, building it and then testing it in real life situation we can now say: W4P is alive! Or at least in a solid bèta. You can find our presentation in English here:
Interested in hearing this talk again and do you have a location and or crowd? Or are you ready to start up a W4P crowdsourcing platform?
Contact us!

Introducing W4P, a crowdsourcing for open, social and local projects.

- June 24, 2016 in crowdfunding, crowdsourcing, Featured, General, Open Innovation, Open Source

After 10 months of figuring what we need to build, building it and then testing it in real life situation we can now say: W4P is alive! Or at least in a solid bèta. You can find our presentation in English here: Interested in hearing this talk again and do you have a location and or crowd? Or are you ready to start up a W4P crowdsourcing platform? Contact us!

Introducing W4P, a crowdsourcing for open, social and local projects.

- June 24, 2016 in crowdfunding, crowdsourcing, Featured, General, Open Innovation, Open Source

After 10 months of figuring what we need to build, building it and then testing it in real life situation we can now say: W4P is alive! Or at least in a solid bèta. You can find our presentation in English here:
Interested in hearing this talk again and do you have a location and or crowd? Or are you ready to start up a W4P crowdsourcing platform?
Contact us!