[CODATA-international] Message from the CODATA President, Barend Mons

[Prof. Muliaro Wafula]

This is a powerful and timely statement packed that needs to be resited
over and over again until something happens.

It encompasses the vision of our new president and builds a bridge to the
new dispensation of digital revolution

Joseph

On Thu, 7 Nov 2019, 14:47 Asha CODATA, <asha at codata.org> wrote:

> The field of research data and associated services is in a rapid - and
> epoch-making - phase transition from a data sparse to a data-overloaded
> ecosystem. Many national and international efforts are underway to try and
> deal with the enormous challenges posed by instrumentation and automation
> and the associated explosion in the volume and complexity of data. We all
> try and keep pace with this phenomenon by deploying the analytical
> processes and tools needed to enable data-intensive science, supported by
> machines. In order that high throughput data generation instruments and
> computers may effectively support the scientific and innovation process,
> both data and workflow components need to be machine-actionable. Building
> on and refining many earlier efforts, in 2014 the FAIR principles were
> formulated <https://doi.org/10.1038/sdata.2016.18>. These principles
> recommend that data (and services around them) should be Findable,
> Accessible, Interoperable and (thus) Reuseable, *first and foremost by
> machines*.
>
>  In 21st century science, computers need to be fully enabled to do the
> hard work of processing, pattern identification and machine learning in
> relation to enormous amounts of heterogeneous, distributed data. Human
> researchers, and the science system as a whole, will benefit from
> machine-actionable data as less time will be spent data munging. When data
> is stewarded and processed properly, ambiguity and non-reproducibility will
> be less of a problem as well. In addition, many datasets and resources are
> now either too large or too privacy sensitive, or both, to be effectively
> routed around the globe for multidisciplinary and data-intensive science
> projects. Therefore, distributed machine learning is a new paradigm that I
> refer to as ‘data visiting’ rather than the classical model of ‘data
> sharing’.
>
>  These rapid changes have in significant respects ‘taken science by
> surprise’ and many groups and infrastructures have great difficulties to
> adapt to this revolutionary new way of doing science. Rather than
> ‘excellence in silos’, and scholarly communication mainly designed for
> person-to-person information and knowledge transfer, we now need
> ‘excellence across silos’. We need to conceive of the underpinning
> ecosystem as -in essence- one computer with one, universal dataset.
> Workflows dealing with data and the data themselves are being reused over
> and over and need to be fully interoperable, reusable and reproducible. In
> particular when we address the major challenges facing our planet, as laid
> out in the Sustainable Development Goals, the data needed to gain the
> necessary insights come from many different domains and are frequently not
> purposefully generated for research. For an ‘Internet of FAIR Data and
> Services’ to emerge and flourish, all digital resources should be
> intrinsically FAIR and processable outside the environments and systems in
> which they were created. In other words, they need to be universally
> reusable. The good news is that computers can translate FAIR digital
> resources from one format to the other with high speed and minimal error
> rates as long as the machine has enough information about the resource.
> Another way of expressing the objective of FAIR is that when the resource
> is FAIR, ’machines know what it means’. In essence, the machine can answer
> three major questions for each FAIR digital object or resource they
> encounter:
>
>    1. *What is this?*,
>    2. *What operations can be performed on it?* and,
>    3. *What operations are allowed?*
>
> With properly constructed FAIR digital resources, these questions can be
> answered, which enables machines (and thus also ultimately humans) to reuse
> them with full provenance outside their original context. Elusive as this
> may sound, I am very confident that the current international efforts in
> this exciting domain will soon yield the first scalable ecosystems that
> follow these principles, and major industries are already moving into this
> space as well. So be warned: the coming four years will not be ‘*science
> as usual*’!
>
> CODATA has been around for roughly 50 years, and has lived in the data
> sparse times as well as now in the data rich era, which poses entirely
> different and daunting challenges, also for CODATA itself. CODATA, as a
> committee of the International Science Council <https://council.science/> (ISC),
> supporting the mission of ISC as the global voice of science and its role
> in the UN system, has the responsibility to fill a specific and strategic
> niche in the global ecosystem of research data related activities. Many
> other organisations have complementary roles that are either domain
> specific, national or regional or they are grass roots and community based.
> CODATA is actively engaging with these other international players in
> defining complementary and synergistic roles.
>
> The data-intensive science and innovation challenge is obviously a global
> one, it should equitably involve all regions of the world and it cannot be
> solved sustainably within disciplinary or national silos. That is the niche
> in which CODATA should operate. CODATA also has a key role to play in the
> involvement of regions of the world that have been traditionally data and
> science-deprived. With the Internet of FAIR Data and Services emerging 'as
> we click’, we should not widen the digital divide but leap-frog to close
> it, such that the new research ecosystem is also fair in the traditional
> sense. Open Science, must also mean that no-one is left behind. The second
> bit of good news is that activities in the Global South are emerging at an
> early stage and some are ambitious enough to lead future developments.
>
> As the CODATA President I work with the Executive Director
> <http://www.codata.org/about-codata/secretariat>, with the officers and Executive
> Committee <http://www.codata.org/about-codata/executive-committee>, and
> with CODATA’s core staff to serve this multi-organisational ecosystem in
> service of the global science community. We also work with regional
> organisations such as the European Commission and the EU Member states with
> their major leading initiative for the European Open Science Cloud, which
> has an increasing number of partner initiatives in other regions. We build
> on the excellent work of our predecessors in CODATA, including the
> intellectual leadership of the past President Geoffrey Boulton and in close
> collaboration our parent organisation, the International Science Council.
>
> As of 2017, and extending for the duration of my CODATA presidency, I also
> serve on the US National Academy of Sciences Board for Research Data and
> Information. With my election as president of CODATA, I will gradually hand
> over operational leadership in GO FAIR to others, and I will seek to play
> an ambassadorial role for both, to help drive a joint, converging and
> balanced ecosystem for international policies supporting open, data driven
> science. We also work to consolidate and make explicit the key role for
> each of the internationally operating data organisations and in particular
> to bring RDA, GO FAIR, WDS and CODATA even closer together, with clear and
> complementary mandates. When we lock arms at all levels from institutional
> to international, I am optimistic that by the end of my term as President,
> the first phase of the Internet of FAIR data and services will be up and
> running.
>
> For all this to happen, it will be of critical importance that each of the
> data supporting organisations is mandated and properly funded (although at
> the leanest necessary level) to serve the science and innovation
> communities, without competing for the same funds as the community they
> should serve. They should focus on those supra-level tasks that never make
> it to the top of the priority list of individual countries, regions,
> funders, researchers and innovators. In this set of partnerships, it is the
> CODATA mission to act strategically and globally to advance equitable Open
> Science, the FAIR ecosystem and to make data work for interdisciplinary
> global challenge research
> <https://council.science/publications/science-as-a-global-public-good-isc-action-plan-2019-2021>
> .
>
> Research infrastructures have traditionally been almost an ‘afterthought’
> or considered ‘other peoples’ problem’, which has resulted in a very
> dangerous situation where core resources, massively used by researchers,
> such as curated data bases and collections, mapping and standard services
> are ‘operating on a shoe string’ and go through a near-death experience
> each time funded projects run out. We, as the research community, should
> collectively speak with one voice, on these infrastructural and
> interoperability issues as trusted representatives of the real needs of the
> research community itself and society as a whole, towards policy makers,
> funders and unions dealing with the enormous data and analytics challenges
> we will face in the decades to come. It is an honour to be elected as the
> new president of CODATA and I hope to serve the community as expected.
> Thanks,
> Asha
> --
> ___________________________
>
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>
> *October 2019 Publications*
> <http://codata.org/blog/2019/11/01/october-2019-publications-in-the-data-science-journal/>* in
> the CODATA Data Science Journal <https://datascience.codata.org/> *
>
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> ___________________________
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>
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