New report confirms positive momentum for EU open science

Curated on Posted on by Stefaan Verhulst

Press release: “The Commission released the results and datasets of a study monitoring the open access mandate in Horizon 2020. With a steadily increase over the years and an average success rate of 83% open access to scientific publications, the European Commission is at the forefront of research and innovation funders concluded the consortium formed by the analysis company PPMI (Lithuania), research and innovation centre Athena (Greece) and Maastricht University (the Netherlands).

The Commission sought advice on a process and reliable metrics through which to monitor all aspects of the open access requirements in Horizon 2020, and inform how to best do it for Horizon Europe – which has a more stringent and comprehensive set of rights and obligations for Open Science.

The key findings of the study indicate that the early European Commission’s leadership in the Open Science policy has paid off. The Excellent Science pillar in Horizon 2020 has led the success story, with an open access rate of 86%. Of the leaders within this pillar are the European Research Council (ERC) and the Future and Emerging Technologies (FET) programme, with open access rates of over 88%.

Other interesting facts:

  • In terms of article processing charges (APCs), the study estimated the average cost in Horizon 2020 of publishing an open access article to be around EUR 2,200.  APCs for articles published in ‘hybrid’ journals (a cost that will no longer be eligible under Horizon Europe), have a higher average cost of EUR 2,600. 
  • Compliance in terms of depositing open access publications in a repository (even when publishing open access through a journal) is relatively high (81.9%), indicating that the current policy of depositing is well understood and implemented by researchers.
  • Regarding licences, 49% of Horizon 2020 publications were published using Creative Commons (CC) licences, which permit reuse (with various levels of restrictions) while 33% use publisher-specific licences that place restrictions on text and data mining (TDM).
  • Institutional repositories have responded in a satisfactory manner to the challenge of providing FAIR access to their publications, amending internal processes and metadata to incorporate necessary changes: 95% of deposited publications include in their metadata some type of persistent identifier (PID).
  • Datasets in repositories present a low compliance level as only approximately 39% of Horizon 2020 deposited datasets are findable, (i.e., the metadata includes a PID and URL to the data file), and only around 32% of deposited datasets are accessible (i.e., the data file can be fetched using a URL link in the metadata).  Horizon Europe will hopefully allow to achieve better results.
  • The study also identified gaps in the existing Horizon 2020 open access monitoring data, which pose further difficulties in assessing compliance. Self-reporting by beneficiaries also highlighted a number of issues…(More)”

Commission publishes study on the impact of Open Source on the European economy

Curated on Posted on by Stefaan Verhulst

Press Release (European Commission): “It is estimated that companies located in the EU invested around €1 billion in Open Source Software in 2018, which brought about a positive impact on the European economy of between €65 and €95 billion.

The study predicts that an increase of 10% in contributions to Open Source Software code would annually generate an additional 0.4% to 0.6% GDP, as well as more than 600 additional ICT start-ups in the EU. Case studies reveal that by procuring Open Source Software instead of proprietary software, the public sector could reduce the total cost of ownership, avoid vendor lock-in and thus increase its digital autonomy.

The study gives a number of specific public policy recommendations aimed at achieving a digitally autonomous public sector, open research and innovation enabling European growth, and a digitised and internally competitive industry. In the long-term, the findings of the study may be used to reinforce the open source dimension in the development of future software and hardware policies for the EU industry.

Moreover, since October 2020 the Commission has its own new Open Source Software Strategy 2020-2023, which further encourages and leverages the transformative, innovative and collaborative potential of open source,  in view of achieving the goals of the overarching Digital Strategy of the Commission and contributing to the Digital Europe programme. The Commission’s Strategy puts a special emphasis on the sharing and reuse of software solutions, knowledge and expertise as well as on increasing the use of open source in information technologies and other strategic areas….(More)”.

Afyanet

Curated on Posted on by Stefaan Verhulst

About: “Afyanet is a voluntary, non-profit network of National Health Institutes and Research Centers seeking to leverage crowdsourced health data for disease surveillance and forecasting. Participation in AfyaNet for countries is free.

We aim to use technology and digital solutions to radically enhance how traditional disease surveillance systems function and the ways we can model epidemics.

Our vision is to create a common framework to collect standardized real-time data from the general population, allowing countries to leapfrog existing hurdles in disease surveillance and information sharing.

Our solution is an Early Warning System for Health based on participatory data gathering. A common, real-time framework for disease collection will help countries identify and forecast outbreaks faster and more effectively.

Crowdsourced data is gathered directly from citizens, then aggregated, anonymized, and processed in a cloud-based data lake. Our high-performance computing architecture analyzes the data and creates valuable disease spread models, which in turn provide alerts and notifications to participating countries and helps public health authorities make evidence-based decisions….(More)”

The geography of AI

Curated on Posted on by Stefaan Verhulst

Report by Mark Muro and Sifan Liu: “Much of the U.S. artificial intelligence (AI) discussion revolves around futuristic dreams of both utopia and dystopia. From extreme to extreme, the promises range from solutions to global climate change to a “robot apocalypse.”

However, it bears remembering that AI is also becoming a real-world economic fact with major implications for national and regional economic development as the U.S. crawls out of the COVID-19 pandemic.

Based on advanced uses of statistics, algorithms, and fast computer processing, AI has become a focal point of U.S. innovation debates. Even more, AI is increasingly viewed as the next great “general purpose technology”—one that has the power to boost the productivity of sector after sector of the economy.

All of which is why state and city leaders are increasingly assessing AI for its potential to spur economic growth. Such leaders are analyzing where their regions stand and what they need to do to ensure their locations are not left behind.

In response to such questions, this analysis examines the extent, location, and concentration of AI technology creation and business activity in U.S. metropolitan areas.

Employing seven basic measures of AI capacity, the report benchmarks regions on the basis of their core AI assets and capabilities as they relate to two basic dimensions: AI research and AI commercialization. In doing so, the assessment categorizes metro areas into five tiers of regional AI involvement and extracts four main findings reflecting that involvement…(More)”.

Empowered Data Societies: A Human-Centric Approach to Data Relationships

Curated on Posted on by Stefaan Verhulst

World Economic Forum: “Despite ever-increasing supply and demand, data often remains siloed and unavailable to those who seek to use it to benefit people, communities and society.

In this whitepaper, the World Economic Forum and the City of Helsinki propose a new, human-centric approach to making data better available. By prioritizing the values, needs and expectations of people, policy-makers can drive meaningful actions with positive outcomes for society while maintaining the utmost respect for the people who are part of it.

This paper provides frameworks, insights, and best practices for public sector employees and elected officials –from mayors and ministers to data scientists and service developers –to adapt and build systems that use data in responsible and innovative ways….(More)”.

Roadmap to social impact: Your step-by-step guide to planning, measuring and communicating social impact

Curated on Posted on by Stefaan Verhulst

Roadmap developed by Ioana Ramia, Abigail Powell, Katrina Stratton, Claire Stokes, Ariella Meltzer, and Kristy Muir: “…is a step-by-step guide to support you and your organisation through the process of outcomes measurement and evaluation.

While it’s not the silver bullet for outcomes measurement or impact assessment, The Roadmap provides you with eight steps to understand the context in which you operate, who you engage with and the social issue you are addressing, how you address this social issue, what the intended changes are, how and when to measure those changes and how to communicate and use your findings to further improve you work and social impact.

It introduces some established techniques for data collection and analysis, but it is not a guide to research methods. A list of resources is also provided at the end of the guide, including tools for stakeholder engagement, developing a survey, interview questionnaire and data analysis.

The Roadmap is for everyone working towards the creation of positive social impact in Australia who wants to measure the change they are making for individuals, organisations and communities….(More)”.

Participatory data stewardship

Curated on Posted on by Stefaan Verhulst

Report by the Ada Lovelace Institute: “Well-managed data can support organisations, researchers, governments and corporations to conduct lifesaving health research, reduce environmental harms and produce societal value for individuals and communities. But these benefits are often overshadowed by harms, as current practices in data collection, storage, sharing and use have led to high-profile misuses of personal data, data breaches and sharing scandals.

These range from the backlash to Care.Data, to the response to Cambridge Analytica and Facebook’s collection and use of data for political advertising. These cumulative scandals have resulted in ‘tenuous’ public trust in data sharing, which entrenches public concern about data and impedes its use in the public interest. To reverse this trend, what is needed is increased legitimacy, and increased trustworthiness, of data and AI use.

This report proposes a ‘framework for participatory data stewardship’, which rejects practices of data collection, storage, sharing and use in ways that are opaque or seek to manipulate people, in favour of practices that empower people to help inform, shape and – in some instances – govern their own data.

As a critical component of good data governance, it proposes data stewardship as the responsible use, collection and management of data in a participatory and rights-preserving way, informed by values and engaging with questions of fairness.

Drawing extensively from Sherry Arnstein’s ‘ladder of citizen participation’ and its more recent adaptation into a spectrum, this new framework is based on an analysis of over 100 case studies of different methods of participatory data stewardship. It demonstrates ways that people can gain increasing levels of control and agency over their data – from being informed about what is happening to data about themselves, through to being empowered to take responsibility for exercising and actively managing decisions about data governance….(More)”.

Artificial intelligence masters’ programs

Curated on Posted on by Stefaan Verhulst

An analysis “of curricula building blocks” by JRC-European Commission: “This report identifies building blocks of master programs on Artificial Intelligence (AI), on the basis of the existing programs available in the European Union. These building blocks provide a first analysis that requires acceptance and sharing by the AI community. The proposal analyses first, the knowledge contents, and second, the educational competences declared as the learning outcomes, of 45 post-graduate academic masters’ programs related with AI from universities in 13 European countries (Belgium, Denmark, Finland, France, Germany, Italy, Ireland, Netherlands, Portugal, Spain, and Sweden in the EU; plus Switzerland and the United Kingdom).

As a closely related and relevant part of Informatics and Computer Science, major AI-related curricula on data science have been also taken into consideration for the analysis. The definition of a specific AI curriculum besides data science curricula is motivated by the necessity of a deeper understanding of topics and skills of the former that build up the foundations of strong AI versus narrow AI, which is the general focus of the latter. The body of knowledge with the proposed building blocks for AI consists of a number of knowledge areas, which are classified as Essential, Core, General and Applied.

First, the AI Essentials cover topics and competences from foundational disciplines that are fundamental to AI. Second, topics and competences showing a close interrelationship and specific of AI are classified in a set of AI Core domain-specific areas, plus one AI General area for non-domain-specific knowledge. Third, AI Applied areas are built on top of topics and competences required to develop AI applications and services under a more philosophical and ethical perspective. All the knowledge areas are refined into knowledge units and topics for the analysis. As the result of studying core AI knowledge topics from the master programs sample, machine learning is observed to prevail, followed in order by: computer vision; human-computer interaction; knowledge representation and reasoning; natural language processing; planning, search and optimisation; and robotics and intelligent automation. A significant number of master programs analysed are significantly focused on machine learning topics, despite being initially classified in another domain. It is noteworthy that machine learning topics, along with selected topics on knowledge representation, depict a high degree of commonality in AI and data science programs. Finally, the competence-based analysis of the sample master programs’ learning outcomes, based on Bloom’s cognitive levels, outputs that understanding and creating cognitive levels are dominant.

Besides, analysing and evaluating are the most scarce cognitive levels. Another relevant outcome is that master programs on AI under the disciplinary lenses of engineering studies show a notable scarcity of competences related with informatics or computing, which are fundamental to AI….(More)”.

Public engagement and net zero

Curated on Posted on by Stefaan Verhulst

Report by Tom Sasse, Jill Rutter, and Sarah Allan: “The government must do more to involve the public in designing policies to help the UK transition to a zero-carbon economy.

This report, published in partnership with Involve, sets out recommendations for when and how policy makers should engage with citizens and residents – such as on designing taxes and subsidies to support the replacement of gas boilers or encouraging changes in diet – to deliver net zero.

But it warns there is limited government capability and expertise on public engagement and little co-ordination of activities across government. In many departments, engaging the public is not prioritised as a part of policy making.

Climate Assembly UK, organised in 2020 by parliament (not government), involved over a hundred members of the public, informed by experts, deliberating over the choices involved in the UK meeting its net zero target. But the government has not built on its success. It has yet to commit to making public engagement part of its net zero strategy, nor set out a clear plan for how it might go about it.

The report recommends that:

  • departments invest in strengthening the public engagement expertise needed to plan and commission exercises effectively
  • either the Cabinet Office or the Department for Business, Energy and Industrial Strategy (BEIS) take increased responsibility for co-ordinating net zero public engagement across government
  • the government use its net zero strategy, due in the autumn of this year, to set out how it intends to use public engagement to inform the design of net zero policies
  • the independent Climate Change Committee should play a greater role in advising government on what public engagement to commission….(More)”.

The Innovation Project: Can advanced data science methods be a game-change for data sharing?

Curated on Posted on by Stefaan Verhulst

Report by JIPS (Joint Internal Displacement Profiling Service): “Much has changed in the humanitarian data landscape in the last decade and not primarily with the arrival of big data and artificial intelligence. Mostly, the changes are due to increased capacity and resources to collect more data quicker, leading to the professionalisation of information management as a domain of work. Larger amounts of data are becoming available in a more predictable way. We believe that as the field has progressed in filling critical data gaps, the problem is not the availability of data, but the curation and sharing of that data between actors as well as the use of that data to its full potential.

In 2018, JIPS embarked on an innovation journey to explore the potential of state-of-the-art technologies to incentivise data sharing and collaboration. This report covers the first phase of the innovation project and launches a series of articles in which we will share more about the innovation journey itself, discuss safe data sharing and collaboration, and look at the prototype we developed – made possible by the UNHCR Innovation Fund.

We argue that by making data and insights safe and secure to share between stakeholders, it will allow for a more efficient use of available data, reduce the resources needed to collect new data, strengthen collaboration and foster a culture of trust in the evidence-informed protection of people in displacement and crises.

The paper first defines the problem and outlines the processes through which data is currently shared among the humanitarian community. It explores questions such as: what are the existing data sharing methods and technologies? Which ones constitute a feasible option for humanitarian and development organisations? How can different actors share and collaborate on datasets without impairing confidentiality and exposing them to disclosure threats?…(More)”.