Björn Brembs

With about 2 years delay, US President Donald Trump hired [Kelvin
Droegemeier](https://en.wikipedia.org/wiki/Kelvin_Droegemeier) as
director for the White House Office of Science and Technology Policy
(OSTP) in February 2019. In a recent
[interview](https://www.aip.org/fyi/2019/interview-ostp-director-kelvin-droegemeier)
Dr. Droegemeier made the following broad, categorical statement in
response to a PLan S question:

> One of the things this government will not do is to tell researchers
> where they have to publish their papers. That is absolutely up to the
> scholar who's doing the publication. There's just no question about
> that.  
>   
> \[...\]  
>   
> This goes back to the president's fundamental philosophy of let's not
> shackle people, let's let them be free to choose where they publish.
> Let's not put any administrative constraints on them.

As [Hanlon's razor](https://en.wikipedia.org/wiki/Hanlon%27s_razor)
requires to first rule out incompetence before attributing malice to any
action, one needs to assess the competence of Dr. Droegemeier with
regard to such sweeping policy statements. On his Wikipedia entry one
can find numerous political appointments for various policy posts since
2004. It is thus fair to assume that Dr. Droegemeier has a fair bit of
experience and expertise with regard to the long-standing policies of
the US government. Moreover, previous director Holdren and several other
scientists have spoken out in favor of Dr. Droegemeier's appointment.
Attributing any false statement to incompetence would thus seem
unlikely, even if he was appointed by Trump.

It is thus straightforward to conclude that the above statement was a
lie with the purpose of scoring political points, as his statements are
very easy to verify. For instance, one can have a look at job
advertisements of a federal agency, such as the NIH, top see if they
contain any language that implies that the government may "tell
researchers where they have to publish their papers". Such statements
are, of course, easy enough to find. Here are two examples:

In [this ad for a postdoctoral
position](https://jobs.sciencecareers.org/job/497983/postdoctoral-position-in-the-translational-allergic-immunopathology-unit-niaid-nih/)
in the NIAID/NIH Translational Allergic Immunopathy Unit it lists under
expected qualifications: "a track record of publication in peer-reviewed
journals". Clearly, you are not welcome at the NIH ("this government")
if you haven't published in these particular venues.

[This ad for a postdoctoral
position](https://jobs.sciencecareers.org/job/498501/post-doctoral-research-fellow-/)
at the NCI/NIH cancer center goes even further. It makes the explicit
expectation that the candidate, once hired, continues to publish in such
journals, as the candidate "is anticipated to complete this study with
one or more high-impact publications in peer review journals."

Clearly, one of the things this US government is doing is precisely
telling researchers in no uncertain terms where they have to publish
their papers if they want to be/stay hired by this government -- in a
clear contradiction to what Dr. Droegemeier has stated in the interview.
Surely a man with such ample experience in science policy is aware of
the policies of the US's largest scientific agency?  


With about 2 years delay, US President Donald Trump hired Kelvin Droegemeier as director for the White House Office of Science and Technology Policy (OSTP) in February 2019.

New OSTP director caught lying in interview

Cameron Neylon

Response to Request for Information -- FR Doc. 2011-28621

Dr Cameron Neylon â€" U.K. based research scientist writing in a
personal capacity

**Introduction**

Thankyou for the opportunity to respond to this request for information
and to the parallel RFI on access to scientific publications. Many of
the higher level policy issues relating to data are covered in my
response to the other RFI and I refer to that response where appropriate
here. Specifically I re-iterate my point that a focus on IP in the
publication is a non-productive approach. Rather it is more productive
to identify the *outcomes* that are desired as a result of the federal
investment in generating data and from those outcomes to identify the
services that are required to convert the raw material of the research
process into accessible outputs that can be used to support those
outcomes.

**Response**

*(1) What specific Federal policies would encourage public access to and
the preservation of broadly valuable digital data resulting from
federally funded scientific research, to grow the U.S. economy and
improve the productivity of the American scientific enterprise?*

Where the Federal government has funded the generation of digital data,
either through generic research funding or through focussed programs
that directly target data generation, the purpose of this investment is
to generate outcomes. Some data has clearly defined applications, and
much data is obtained to further very specific research goals. However
while it is possible to identify likely applications it is not possible,
indeed is foolhardy, to attempt to define and limit the full range of
uses which data may find.

Thus to ensure that data created through federal investment is optimally
exploited it is crucial that data be a) accessible, b) discoverable,
c)interpretable and d) legally re-usable by any person for any purpose.
To achieve this requires investment in infrastructure, markup,Â  and
curation. This investment is not currently seen as either a core
activity for researchers themselves, or a desirable service for them to
purchase. It is rare therefore for such services or resource need to be
thoughtfully costed in grant applications.

The policy challenge is therefore to create incentives, both symbolic
and contractual, but also directly meaningful to researchers with an
impact on their career and progression, that encourage researchers to
either undertake these necessary activities directly themselves or to
purchase and appropriately cost third party services to have them
carried out.

Policy intervention in this area will be complex and will need to be
thoughtful. Three simple policy moves however are highly tractable and
productive, without requiring significant process adjustments in the
short term:

a\) Require researchers to provide a data management or data
accessibility plan within grant requests. The focus of these plans
should be showing how the project will enable third party groups to
discover and re-use data outputs from the project.

b\) As part of the project reporting, require measures of how data
outputs have been used. These might include download counts, citations,
comments, or new collaborations generated through the data. In the short
term this assessment need to be directly used but it sends a message
that agencies consider this important.

c\) Explicitly measure performance on data re-use. Require as part of
bio sketches and provide data on previous performance to grant panels.
In the longer term it may be appropriate to provide guidance to panels
on the assessment of previous performance on data re-use but in the
first instance simply providing the information will affect behaviour
and the general awareness of issues of data accessibility,
discoverability, and usability.

*(2) What specific steps can be taken to protect the intellectual
property interests of publishers, scientists, Federal agencies, and
other stakeholders, with respect to any existing or proposed policies
for encouraging public access to and preservation of digital data
resulting from federally funded scientific research?*

As noted in my response to the other RFI, the focus on intellectual
property is note helpful. Private contributors of data such as
commercial collaborators should be free to exploit their own
contribution of IP to projects as they see fit. Federally funded
research should seek to maximise the exploitation and re-use of data
generated through public investment.

It has been consistently and repeatedly demonstrated in a wide range of
domains that the most effective way of exploiting the outputs of
research innovation, be they physical samples, or digital data, to
support further research, to drive innovation, or to support economic
activity globally is to make those outputs freely available with no
restrictive terms. That is, the most effective way to use research data
to drive economic activity and innovation at a national level is to give
the data away.

The current IP environment means that in specific cases, such as where
there is very strong evidence of a patentable result with demonstrated
potential, that the optimisation of outcomes does require protection of
the IP. There are also situations where privacy and other legal
considerations mean that data cannot be released or not be fully
released. These should however be seen as the exception rather than the
rule.

*(3) How could Federal agencies take into account inherent differences
between scientific disciplines and different types of digital data when
developing policies on the management of data?*

At the Federal level only very high-level policy decisions should be
taken. These should provide direction and strategy but enable tactics
and the details of implementation to be handled at agency or community
levels. What both the Federal Agencies and coordination bodies such as
OSTP can provide is an oversight and, where appropriate, funding support
to maintain, develop, and expand interoperability between developing
standards in different communities. Federal agencies can also
effectively provide an oversight function that supports activities that
enhance interoperability.

Local custom, dialects, and community practice will always differ and it
is generally unproductive to enforce standardisation on implementation
details. The policy objectives should be to set the expectations and the
frameworks within local implementation can be developed and approaches
to developing criteria against which those local implementations can be
assessed.

*(4) How could agency policies consider differences in the relative
costs and benefits of long-term stewardship and dissemination of
different types of data resulting from federally funded research?*

Prior to assessing differences in performance and return on investment
it will be necessary to provide data gathering frameworks and to develop
significant expertise in the detailed assessment of the data gathered. A
general principle that should be considered is that the *administrative
and performance data* related to accessibility and re-use of *research*
data should provide an outstanding exemplar of best practice in terms of
accessibility, curation, discoverability, and re-usability.

The first step in cost benefit analysis must be to develop an
information and data base that supports that analysis. This will mean
tracking and aggregating forms of data use that are available today
(download counts, citations) as well as developing mechanisms for
tracking the use and impact of data in ways that are either challenging
or impossible today (data use in policy development, impact of data in
clinical practice guidelines).

Only once this assessment data framework is in place can detailed
process of cost benefit analysis be seriously considered. Differences
will exist in the measurable and imponderable return on investment in
data availability, and also in the timeframes over which these returns
are realised. We have only a very limited understanding of these issues
today.

*(5) How can stakeholders (e.g., research communities, universities,
research institutions, libraries, scientific publishers) best contribute
to the implementation of data management plans?*

If stakeholders have serious incentives to optimise the use and re-use
of data then all players will seek to gain competitive advantage through
making the highest quality contributions. An appropriate incentives
framework obviates the need to attempt to design in or pre-suppose how
different stakeholders can, will, or should best contribute going
forward.

*(6) How could funding mechanisms be improved to better address the real
costs of preserving and making digital data accessible?*

As with all research outputs there should be a clear obligation on
researchers to plan on a best efforts basis to publish these (as in make
public) in a form that most effectively support access and re-use
tensioned against the resources available. Funding agencies should make
clear that they expect communication of research outputs to be a core
activity for their funded research, that researchers and their
institutions will be judged based on their performance in optimising the
choices they make in selecting the appropriate modes of communication.

Further funding agencies should explicitly set guidance levels on the
proportion of a research grant that is expected under normal
circumstances to be used to support the communication of outputs. Based
on calculations from the Wellcome Trust where projected expenditure on
the publication of traditional research papers was around 1-1.5% of
total grant costs, it would be reasonable to project total communication
costs once data and other research communications are considered of 2-4%
of total costs. This guidance and the details of best practice should
clearly be adjusted as data is collected on both costs and performance.

*(7) What approaches could agencies take to measure, verify, and improve
compliance with Federal data stewardship and access policies for
scientific research? How can the burden of compliance and verification
be minimized?*

Ideally compliance and performance will be trackable through automated
systems that are triggered as a side effect of activities required for
enabling data access. Thus references for new data should be registered
with appropriate services to enable discovery by third parties â€" these
services can also be used to support the tracking of these outputs
automatically. Frameworks and infrastructure for sharing should be built
with tracking mechanisms built in. Much of the aggregation of data at
scale can build on the existing work in the STARMETRICS program and draw
inspiration from that experience.

Overall it should be possible to reduce the burden of compliance from
its *current* level while gathering vastly more data and information of
much higher quality than is currently collected.

*(8) What additional steps could agencies take to stimulate innovative
use of publicly accessible research data in new and existing markets and
industries to create jobs and grow the economy?*

There are a variety of proven methods for stimulating innovative use of
data at both large and small scale. The first is *to make it available*.
If data is made available at scale then it is highly likely that some of
it will be used somewhere. The more direct encouragement of specific
uses can be achieved through directed â€œhack eventsâ€? that bring
together data handling and data production expertise from specific
domains. There is significant US expertise in successfully managing
these events and generating exciting outcomes. These in turn lead to new
startups and new innovation.

There is also a significant growth in the number of data-focussed
entrepreneurs who are now veterans of the early development of the
consumer web. Many of these have a significant interest in research as
well as significant resources and there is great potential for
leveraging their experience to stimulate further growth. However this
interface does need to be carefully managed as the cultures involved in
research data curation and web-scale data mining and exploitation are
very different.

*(9) What mechanisms could be developed to assure that those who
produced the data are given appropriate attribution and credit when
secondary results are reported?*

The existing norms of the research community that recognise and
attribute contributions to further work should be strengthened and
supported. While it is tempting to use legal instruments to enforce a
need for attribution there is growing evidence that this can lead to
inflexible systems that cannot adapt to changing needs. Thus it is
better to utilise social enforcement than legal enforcement.

The current good work on data citation and mechanisms for tracking the
re-use of data should be supported and expanded. Funders should
explicitly require that service providers add capacity for tracking data
citation to the products that are purchased for assessment purposes.
Where possible the culture of citation should be expanded into the wider
world in the form of clinical guidelines, government reports, and policy
development papers.

*(10) What digital data standards would enable interoperability, reuse,
and repurposing of digital scientific data? For example, MIAME (minimum
information about a microarray experiment; see Brazma et al., 2001,
Nature Genetics 29, 371) is an example of a community-driven data
standards effort.*

At the highest level there are a growing range of interoperable
information transfer formats that can provide machine readable and
integratable data transfer including RDF, XML, OWL, JSON and others. My
own experience is that attempting to impose global interchange standards
is an enterprise doomed to failure and it is more productive to support
these standards within existing communities of practice.

Thus the appropriate policy action is to recommend that communities
adopt and utilise the most widely used possible set of standards and to
support the transitions of practice and infrastructure required to
support this adoption. Selecting standards at the highest level is
likely to counterproductive. Identifying and disseminating best practice
in the development and adoption of standards is however something that
is the appropriate remit of federal agencies.

*(11) What are other examples of standards development processes that
were successful in producing effective standards and what
characteristics of the process made these efforts successful?*

There is now a significant literature on community development and
practice and this should be referred to. Many lessons can also be drawn
from the development of effective and successful open source software
projects.

*(12) How could Federal agencies promote effective coordination on
digital data standards with other nations and international
communities?*

There are a range of global initiatives that communities should engage
with. The most effective means of practical engagement will be to
identify communities that have a desire to standardise or integrate
systems and to support the technical and practical transitions to enable
this. For instance there is a widespread desire to support interoperable
data formats from analytical instrumentation but few examples of
bringing this to transition. Funding could be directed to supporting a
specific analytical community and the vendors that support them to apply
an existing standard to their work.

*(13) What policies, practices, and standards are needed to support
linking between publications and associated data?*

Development in this area is at an early stage. There is a need to
reconsider the form of publication in its widest sense and this will
have a significant impact on the forms and mechanisms of linking. This
is a time for experimentation and exploration rather than standards
development.

*Â *

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Response to Request for Information – FR Doc. 2011-28621  Dr Cameron Neylon â€“ U.K. based research scientist writing in a personal capacity
<strong>
 Introduction
</strong>
Thankyou for the opportunity to respond to this request for information and to the parallel RFI on access to scientific publications.

Messaggi di Rogue Scholar

New OSTP director caught lying in interview

Response to the OSTP Request for Information on Public Access to Research Data