Creative Commons awarded $800,000 from Arcadia to support discovery and collaboration in the global commons

Creative Commons is pleased to announce an award of new funding in the amount of $800,000 over two years from Arcadia, a charitable fund of Lisbet Rausing and Peter Baldwin, in support of CC Search, a Creative Commons technology project designed to maximize discovery and use of openly licensed content in the Commons.

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Get the research

“Our plan: provide access to both content and context, for free, in one place. To do that, we’re going to bring together an open a database of OA papers with a suite AI-powered support tools we’re calling an Explanation Engine. We’ve already finished the database of OA papers. So that’s good. With the free Unpaywall database, we’ve now got 20 million OA articles from 50k sources, built on open source, available as open data, and with a working nonprofit sustainability model….”

OATP introduction – Harvard Open Access Project

“The Open Access Tracking Project (OATP) is a crowd-sourced project running on free and open-source software to capture news and comment on open access (OA) to research. It has two missions: (1) create real-time alerts for OA-related developments, and (2) organize knowledge of the field, by tag or subtopic, for easy searching and sharing….”

An interview with the co-founder of Iris.ai – the world’s first Artificial Intelligence science assistant | The Saint

“Have you ever spent hours sifting through journal papers? Ever got frustrated at your inability to find relevant research? Ever wished that there was an easier way to filter the seemingly endless stream of information on the web? The team at Iris.ai certainly did, which is why they have created an AI-powered science assistant to help anyone that wants to find related papers for an original research question. The software – Iris.ai – can be used to build a precise reading list of research documents, and the company claims that it can solve your research problems 78% faster (without compromising quality) than if you were carrying out the tasks manually. The concept for Iris.ai was first established three years ago at NASA Ames Research Centre. The team was taking part in a summer programme run by Singularity University (SU) when they were set the task of creating a concept that would positively affect the lives of a billion people. This exercise got the team thinking about the current state of scientific research, and more specifically about the restrictions created by paywalls, and the inability of human intelligence alone to process the three thousand or so research papers that are published around the world every single day….

When asked about challenges that the team have experienced so far, Ms Ritola was quick to point out the issue of paywalls. She explained that the Iris.ai system is connected to about 130 million open access papers – almost all those available to the public – but that many useful documents are still hidden behind systems that require users to pay for access.

However, rather than just accepting this situation as it is, the Iris.ai team have devised a scheme to solve the problem– Project Aiur – an initiative that aims to revolutionise the current workings of the research world.

“What we’re trying to do is to build a community, which is not owned by us, but by a community of researchers, a community of coders, anyone who wants to contribute to building a new economic model for science that works around a community governed AI-based Knowledge Validation Engine and an open, validated repository of science. Over time, the goal is to give access to all the research articles that are in this world”, Ms Ritola told The Saint.

This is not a straightforward task, as the Iris.ai team are faced with the challenge of encouraging researchers to publish and carry out their investigations using Aiur rather than the current systems- something that will take a fair amount of research and incentivisation. The team have started a pledge, offering students and researchers the chance to be an “advocate for validated, reproducible, open-access scientific research.” At the time of the interview,Ms Ritola informed The Saint that more than 5,000 people had signed the pledge….”

ScienceFair

“ScienceFair uses blazing-fast search and a clean user interface to help you find and filter the literature you need. No hidden menus or complex settings….Instead of static PDFs, ScienceFair uses the eLife Lens reader for a rich reading experience that helps you navigate and interpret scientific papers better….Search your own library and any number of distributed literature collections simultaneously – the results are seamlessly merged as they stream in from the peer-to-peer network….Results are automatically data-mined in real-time, giving you a live updating dashboard you can use to analyse the literature and refine your discovery process.”

Project AIUR by Iris.ai: Democratize Science through blockchain-enabled disintermediation

“There are a number of problems in the world of science today hampering global progress. In an almost monopolized industry with terrible incentive misalignments, a radical change is needed. The only way to change this is with a grassroots movement – of researchers and scientists, librarians, scientific societies, R&D departments, universities, students, and innovators – coming together. We need to remove the powerful intermediaries, create new incentive structures, build commonly owned tools to validate all research and build a common Validated Repository of human knowledge. A combination of blockchain and artificial intelligence provides the technology framework, but as with all research, the scientist herself needs to be in the center. That is what we are proposing with Project Aiur, and we hope you will join us….

The outlined core software tool of the community will be the Knowledge Validation Engine (KVE). It will be a fully-fledged technical platform able to pinpoint: ? the building blocks of a scientific text;

? what the reader needs to know to be able to understand the text;

? what are the text’s factual sources; and,

? what is the reproducibility level of the different building blocks.

The platform will take a scientific document in the form of a scientific paper or technical report as an input, and it will provide an analytical report presenting:

? the knowledge architecture of the document;

? the hypotheses tree supporting the presented document’s hypothesis;

? the support level found for each of the hypotheses on the hypotheses tree; and,

? their respective reproducibility. All of this will be based on the knowledge database of scientific documents accessible to the system at any given point in time (knowledge in an Open Access environment). …”