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\articletype{RESEARCH ARTICLE}
\jname{Data \& Policy}
%\artid{20}
\jyear{2020}
%\jvol{4}
%\jissue{1}
\jdoi{10.1017/dap.2020.14}
\raggedbottom

\begin{document}

\begin{Frontmatter}

\title[Emerging health data platforms]{Emerging health data platforms: From individual control
to collective data governance}

\author*[1]{Timothy Kariotis}\email{timothy.kariotis@unimelb.edu.au}\orcid{0000-0002-8514-4315}
\author[2]{Mad Price Ball}
\author[3]{Bastian Greshake Tzovaras}
\author[1]{Simon Dennis}
\author[2]{Tony Sahama}
\author[3]{Carolyn Johnston}
\author[1]{Ann Borda}

\authormark{Timothy Kariotis \etal}

\address*[1]{\orgdiv{Department}, \orgname{Organization}, \orgaddress{\street{Street}, \postcode{Postcode}, \state{State}, \country{Country}}}
\address[2]{\orgdiv{Department}, \orgname{Organization}, \orgaddress{\city{City}, \postcode{Postcode}, \state{State}, \country{Country}}}
\address[3]{\orgdiv{Department}, \orgname{Organization}, \orgaddress{\street{Street}, \city{City}, \state{State}, \postcode{Postcode}, \country{Country}}}

\received{19 December 2019}
\revised{23 June 2020}
\accepted{12 July 2020}

\keywords{data governance; data stewardship; emerging data platforms; health data; person generated health data}

\keywords[Abbreviations]{EHR, Electronic Health Records; EU, European Union; GDPR, General Data Protection Regulation; HRB, Health Record Bank; ODI, Open Data Institute; PLM, Patients Like Me}

\abstract{Abstracts should be 250 words. It must be able to stand alone and so cannot contain citations to the paper's references, equations, etc. An abstract must consist of a single paragraph and be concise. Because of online formatting, abstracts must appear as plain as possible.}


\begin{policy}
Provide a 120 word statement here that summarises the significance of the work for policymakers, written at a level understandable for a broad audience.
\end{policy}

\end{Frontmatter}


\section{Introduction}

The introduction introduces the context and summarizes the manuscript. It is importantly to clearly state the contributions of this piece of work. The potential offered to policy makers by data-driven research is greater than it ever has been. Increasing volumes of data, faster technology, and evolving techniques and methodology enable us to process ever more data faster, providing the opportunity to focus and evaluate policies to deliver more effectively and economically at a time when efficient use of public money is paramount.

More data can now be analyzed faster enabling researchers to explore more and more complex
interactions. Data across a wide spectrum from open data\footnote{\url{https://data.gov.uk/}} that can be downloaded, through licensed data,\footnote{\url{https://www.ukdataservice.ac.uk/}}
to sensitive personal data can now contribute to our understanding of health, social, and economic outcomes.

The United Kingdom has a long tradition of safe data use by researchers. In the 50 years that the UK
Data Archive has been making data available for social and economic research, there have been no
damaging disclosures of personal information by academic researchers\footnote{That is not to say that there have been no breaches---researchers using data for a project they are not licensed for or passing data
to unlicensed colleagues---however, this, as far as we are aware of, has never caused harm to individuals or entities in the data.}. While increasing use of detailed and sometimes sensitive
data can contribute valuable insights for targeting policies, we cannot be
complacent. In order to support our policy needs and continue to use data safely and effectively, we
need a research infrastructure that protects data confidentiality while enabling researchers to undertake
innovative research.

A first step toward protecting sensitive data is to keep control of it, to disseminate access, not data. Data
providers internationally are increasing, moving toward systems in which researchers remotely access and
analyses data that are stored within a controlled environment, with any data that are removed from the
system being checked in some way to minimize chances of disclosure.

This paper will look at remote data access as one method of protecting sensitive data while enabling its
effective use for research. It will look at how the Digital Economy Act can strengthen the UK data research
infrastructure and make some recommendations for other improvements

The United Kingdom has a long tradition of safe data use by researchers. In the 50 years that the UK
Data Archive has been making data available for social and economic research, there have been no
damaging disclosures of personal information by academic researchers. While increasing use of detailed and sometimes sensitive
data can contribute valuable insights for targeting policies, we cannot be
complacent. In order to support our policy needs and continue to use data safely and effectively, we
need a research infrastructure that protects data confidentiality while enabling researchers to undertake
innovative research.

A first step toward protecting sensitive data is to keep control of it, to disseminate access, not data. Data
providers internationally are increasing, moving toward systems in which researchers remotely access and
analyses data that are stored within a controlled environment, with any data that are removed from the
system being checked in some way to minimize chances of disclosure.

This paper will look at remote data access as one method of protecting sensitive data while enabling its
effective use for research. It will look at how the Digital Economy Act can strengthen the UK data research
infrastructure and make some recommendations for other improvements.


A first step toward protecting sensitive data is to keep control of it, to disseminate access, not data. Data
providers internationally are increasing, moving toward systems in which researchers remotely access and
analyses data that are stored within a controlled environment, with any data that are removed from the
system being checked in some way to minimize chances of disclosure.

This paper will look at remote data access as one method of protecting sensitive data while enabling its
effective use for research. It will look at how the Digital Economy Act can strengthen the UK data research
infrastructure and make some recommendations for other improvements.

The United Kingdom has a long tradition of safe data use by researchers. In the 50 years that the UK
Data Archive has been making data available for social and economic research, there have been no
damaging disclosures of personal information by academic researchers. While increasing use of detailed and sometimes sensitive
data can contribute valuable insights for targeting policies, we cannot be
complacent. In order to support our policy needs and continue to use data safely and effectively, we
need a research infrastructure that protects data confidentiality while enabling researchers to undertake
innovative research.

A first step toward protecting sensitive data is to keep control of it, to disseminate access, not data. Data
providers internationally are increasing, moving toward systems in which researchers remotely access and
analyses data that are stored within a controlled environment, with any data that are removed from the
system being checked in some way to minimize chances of disclosure.

This paper will look at remote data access as one method of protecting sensitive data while enabling its
effective use for research. It will look at how the Digital Economy Act can strengthen the UK data research
infrastructure and make some recommendations for other improvements.

The United Kingdom has a long tradition of safe data use by researchers. In the 50 years that the UK
Data Archive has been making data available for social and economic research, there have been no
damaging disclosures of personal information by academic researchers. While increasing use of detailed and sometimes sensitive
data can contribute valuable insights for targeting policies, we cannot be
complacent. In order to support our policy needs and continue to use data safely and effectively, we
need a research infrastructure that protects data confidentiality while enabling researchers to undertake
innovative research.


A first step toward protecting sensitive data is to keep control of it, to disseminate access, not data. Data
providers internationally are increasing, moving toward systems in which researchers remotely access and
analyses data that are stored within a controlled environment\footnote{Though this facility is used in the Netherlands for secure access to government data, it does not currently exist in the UK for any
government data access as far as the author is aware.}, with any data that are removed from the
system being checked in some way to minimize chances of disclosure.

\section[This is an A Head]{This is an A head this is an A head this is an A head this is an A head  this~is~an~A~head this is an A head}

Once data are disseminated, whatever contractual or other obligations are placed on those receiving the
data, the data are effectively out of a data providers' control. Data providers must be certain that the data
disseminated do not provide a risk of disclosure necessitating a reduction in the detail available, or they are
constrained to using a resource intensive auditing regime, and are likely to discover any data misuse only
after it has happened.

\subsection{This is a B head this is a B head this is a B head this is a B head this is a B head this~is~a~B head this~is~a~B head}

Once data are disseminated, whatever contractual or other obligations are placed on those receiving the
data, the data are effectively out of a data providers' control. Data providers must be certain that the data
disseminated do not provide a risk of disclosure necessitating a reduction in the detail available, or they are
constrained to using a resource intensive auditing regime, and are likely to discover any data misuse only
after it has happened.


\subsubsection{This is a C head this is a C head this is a C head this is a C head is a C head is a C~head~is~a~C head this is a C head}

Once data are disseminated, whatever contractual or other obligations are placed on those receiving the
data, the data are effectively out of a data providers' control. Data providers must be certain that the data
disseminated do not provide a risk of disclosure necessitating a reduction in the detail available, or they are
constrained to using a resource intensive auditing regime, and are likely to discover any data misuse only
after it has happened.

\paragraph{This is a D head this is a D head this is a D head this is a D head is a D head is a D~head~is~a~D head}

Once data are disseminated, whatever contractual or other obligations are placed on those receiving the
data, the data are effectively out of a data providers' control.
The United Kingdom has a long tradition of safe data use by researchers.

\section[This is an A Head]{This is an A head this is an A head this is an A head this is~an~A~head}
\subsection{This is a B head this is a B head this is a B head this is a B head this is a B head}
\subsubsection{This is a C head this is a C head this is a C head this is a C head}
\paragraph{This is a D head this is a D head this is a D head this is a D head}
In the 50 years that the UK
Data Archive has been making data available for social and economic research, there have been no
damaging disclosures of personal information by academic researchers. While increasing use of detailed and sometimes sensitive
data can contribute valuable insights for targeting policies, we cannot be
complacent. In order to support our policy needs and continue to use data safely and effectively, we
need a research infrastructure that protects data confidentiality while enabling researchers to undertake
innovative research.

A first step toward protecting sensitive data is to keep control of it, to disseminate access, not data. Data
providers internationally are increasing, moving toward systems in which researchers remotely access and
analyses data that are stored within a controlled environment, with any data that are removed from the
system being checked in some way to minimize chances of disclosure.

Once data are disseminated, whatever contractual or other obligations are placed on those receiving the
data, the data are effectively out of a data providers' control.


\section{Equations}

Equations in \LaTeX{} can either be inline or on-a-line by itself. For
inline equations use the \verb+$...$+ commands. Eg: The equation
$H\psi = E \psi$ is written via the command $H \psi = E \psi$.

For on-a-line by itself equations (with auto generated equation numbers)
one can use the equation or eqnarray environments \textit{D}.
\begin{equation}
\mathcal{L} = i {\psi} \gamma^\mu D_\mu \psi
    - \frac{1}{4} F_{\mu\nu}^a F^{a\mu\nu} - m {\psi} \psi
\label{eq1}
\end{equation}
where,
\begin{align}
D_\mu &=  \partial_\mu - ig \frac{\lambda^a}{2} A^a_\mu
\nonumber \\
F^a_{\mu\nu} &= \partial_\mu A^a_\nu - \partial_\nu A^a_\mu
    + g f^{abc} A^b_\mu A^a_\nu
\label{eq2}
\end{align}
Notice the use of \verb+\nonumber+ in the align environment at the end
of each line, except the last, so as not to produce equation numbers on
lines where no equation numbers are required. The \verb+\label{}+ command
should only be used at the last line of an align environment where
\verb+\nonumber+ is not used.
\begin{equation}
Y_\infty = \left( \frac{m}{\textrm{GeV}} \right)^{-3}
    \left[ 1 + \frac{3 \ln(m/\textrm{GeV})}{15}
    + \frac{\ln(c_2/5)}{15} \right]
\end{equation}
The class file also supports the use of \verb+\mathbb{}+, \verb+\mathscr{}+ and
\verb+\mathcal{}+ commands. As such \verb+\mathbb{R}+, \verb+\mathscr{R}+
and \verb+\mathcal{R}+ produces $\mathbb{R}$, $\mathscr{R}$ and $\mathcal{R}$
respectively.


\section{Tables}

Tables can be inserted via the normal table and tabular environment. To put
footnotes inside tables one has to use the additional ``fntable" environment
enclosing the tabular environment. The footnote appears just below the table
itself.

%\begin{table}[h]
%\TBL{\caption{Caption text.\label{tab1}}}
%{%
%\begin{tabular}{@{}llll@{}}\toprule
%\TCH{column 1} & \TCH{column 2}  & \TCH{column 3} &
%\TCH{column 4}\\\midrule
%row 1    & data 1   & data 2  & data 3  \\
%row 2    & data 4   & data 5  & data 6  \\
%row 3    & data 7   & data 8  & data 9  \\\botrule
%\end{tabular}}
%\end{table}
%%

\begin{table}[b]
\tabcolsep=0pt%
\TBL{\caption{Tables which are too long to fit,
should be written using the ``table*'' environment as shown here\label{tab2}}}
{\begin{fntable}
\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}lcccccc@{}}\toprule%
 & \multicolumn{3}{@{}c@{}}{\TCH{Element 1}}& \multicolumn{3}{@{}c@{}}{\TCH{Element 2\smash{\footnotemark[1]}}}
 \\\cmidrule{2-4}\cmidrule{5-7}%
\TCH{Projectile} & \TCH{Energy} & \TCH{$\sigma_{\mathit{calc}}$} & \TCH{$\sigma_{\mathit{expt}}$} &
\TCH{Energy} & \TCH{$\sigma_{\mathit{calc}}$} & \TCH{$\sigma_{\mathit{expt}}$} \\\midrule
\TCH{Element 3}&990 A &1168 &$1547\pm12$ &780 A &1166 &$1239\pm100$\\
{\TCH{Element 4}}&500 A &961 &$922\pm10$ &900 A &1268 &$1092\pm40$\\
\botrule
\end{tabular*}%
\footnotetext[]{{Note:} This is an example of table footnote this is an example of table footnote this is an example of table footnote this is an example of~table footnote this is an example of table footnote}
\footnotetext[1]{This is an example of table footnote}%
\end{fntable}}
\end{table}


\section{Figures}

As per the \LaTeX\ standards eps images in \verb!latex! and pdf/jpg/png images in
\verb!pdflatex! should be used. This is one of the major differences between \verb!latex!
and \verb!pdflatex!. The images should be single page documents. The command for inserting images
for latex and pdflatex can be generalized. The package that should be used
is the graphicx package.

\begin{figure}[t]%
\FIG{\includegraphics[width=0.9\textwidth]{Fig}}
{\caption{This is a widefig. This is an example of long caption this is an example of long caption  this~is~an example of long caption this is an example of long caption}
\label{fig1}}
\end{figure}



\section{Cross referencing}

Environments such as figure, table, equation, align can have a label
declared via the \verb+\label{#label}+ command. For figures and table
environments one should use the \verb+\label{}+ command inside or just
below the \verb+\caption{}+ command.  One can then use the
\verb+\ref{#label}+ command to cross-reference them. As an example, consider
the label declared for Figure \ref{fig1} which is
\verb+\label{fig1}+. To cross-reference it, use the command
\verb+ Figure \ref{fig1}+, for which it comes up as
``Figure \ref{fig1}".
The reference citations should used as per the "natbib" packages. Some sample citations:  \cite{bib1,bib2,bib3,bib4,bib5,bib6,bib7}.

\section{Lists}

List in \LaTeX{} can be of three types: enumerate, itemize and description.
In each environments, new entry is added via the \verb+\item+ command.
Enumerate creates numbered lists, itemize creates bulleted lists and
description creates description lists.
List in \LaTeX{} can be of three types: enumerate, itemize and description.
In each environments, new entry is added via the \verb+\item+ command.
Enumerate creates numbered lists, itemize creates bulleted lists and
description creates description lists.
\begin{enumerate}[1.]
\item This is the 1st item
\item Enumerate creates numbered lists, itemize creates bulleted lists and
description creates description lists.
\item Numbered lists continue.
\end{enumerate}
List in \LaTeX{} can be of three types: enumerate, itemize and description.
In each environments, new entry is added via the \verb+\item+ command.
\begin{itemize}
\item This is the 1st item
\item Itemize creates bulleted lists and
description creates description lists.
\item Bullet lists continue.
\end{itemize}



\section{Conclusion}

Some Conclusions here.


\begin{Backmatter}

\paragraph{Acknowledgments}
We are grateful for the technical assistance of A. Author.

\paragraph{Funding Statement}
This research was supported by grants from the <funder-name><doi>(<award ID>); <funder-name><doi>(<award ID>).

\paragraph{Competing Interests}
A statement about any financial, professional, contractual or personal relationships or situations that could be perceived to impact the presentation of the work --- or `None' if none exist

\paragraph{Data Availability Statement}
A statement about how to access data, code and other materials allowing users to understand, verify and replicate findings --- e.g. Replication data and code can be found in Harvard Dataverse: \url{https://doi.org/link}.

\paragraph{Ethical Standards}
The research meets all ethical guidelines, including adherence to the legal requirements of the study country.

\paragraph{Author Contributions}
A.A. and A.B.C. designed the study, abstracted the data wrote the first draft, and approved the final
version of the manuscript. A.R.E.J., M.R.L., K.L.S., and A.D.P. revised the manuscript and approved the final version.

\paragraph{Supplementary Material}
State whether any supplementary material intended for publication has been provided with the submission.

%\bibliographystyle{apalike}
%\bibliography{Sample-refs}

\begin{thebibliography}{}

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\bibitem[Herbst-Damm and Kulik, 2005]{bib1}
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\bibitem[Schiraldi, 2001]{bib5}
\textbf{Schiraldi GR} (2001) \textit{The Post-traumatic Stress Disorder Sourcebook}. Springer.

\bibitem[Shotton, 1989]{bib4}
\textbf{Shotton MA} (1989) \textit{Computer Addiction? {A} Study of Computer Dependency}.  Taylor \& Francis.

\bibitem[VandenBos, 2007]{bib6}
\textbf{VandenBos GR}, editor (2007) \textit{APA Dictionary of Psychology}. American Psychological Association.
\end{thebibliography}

\end{Backmatter}

\end{document}
