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diff --git a/Proposal/WorkPackages/Services.tex b/Proposal/WorkPackages/Services.tex
index 7e8eddc0e02e453ee704924acf61a55bb9ecf982..9332327f6623aa3ee4892ca50dd6a7cac63a31e4 100644
--- a/Proposal/WorkPackages/Services.tex
+++ b/Proposal/WorkPackages/Services.tex
@@ -42,7 +42,11 @@ The objectives of this work package are to:
     \ednote{MK: talk about TGView and TGView3D in MathHub.}
   \end{task}
   \begin{task}[title=Computation with EOSC Data,lead=PS,partners={EMS,OM},PM=12]
-    This may even include persistant memoization as developed in ODK (if Nicolas is interested)
+    This may even include persistent memoization as developed in ODK (if Nicolas is interested)
+  \end{task}
+  \begin{task}[title=Integration with EOSC Hub]
+  \ednote{our goal: one prototype server, designed to be integratable, i.e., API, documentation, easy maintenance, abstraction from math-specific design choices}
+  \ednote{supply math-specific standards to EOSC}
   \end{task}
 \end{tasklist}
 
diff --git a/Proposal/ambition.tex b/Proposal/ambition.tex
index a10eaf39eefb2c67923b6184251fa1620ab85daf..6c50051e2fd89cbb11f16289a73327e78dd6f793 100644
--- a/Proposal/ambition.tex
+++ b/Proposal/ambition.tex
@@ -1,11 +1,63 @@
-\eucommentary{1-2 pages}
+%\subsubsection{State of the art}
+%
+%The scientific community increasingly considers data sets as its own kind of resource that should be shared and published individually, both in conjunction with a traditional paper or even as a standalone digital artifact.
+%Moreover, this trend forms a positive feedback loop with the rise of deep learning methods, which requires large data sets as input.
+%The FAIR principles and the sharing, reusing, and computing with research data critically depend on human- and machine-readable descriptions of the meaning of the data.
+%
+%This development is also occurring in mathematics where data sets have become a kind of digital artifact and the building and publication of data sets has become a recognized achievement.
+%But sharing mathematical data poses some very specific difficulties.
+%
+%To understand the difficulties, first note that scientists typically maintain data sets as table or array-like data structures built from low-level objects like numbers, booleans, and strings.
+%These can be shared easily via standardized encodings such as RDF or CSV.
+%But in doing so, the semantics of the original data is not part of the shared resource.
+%This is not sufficient for mathematics and related scientific disciplines:
+%\begin{compactitem}
+%	\item In mathematics, it is essential to describe the mathematical datatype (e.g., multivariate polynomial with integer coefficients) together the encoding of the values in terms of low-level objects (e.g., list of pairs of exponent-tuple and coefficient). Without such schema-encoding specifications, mathematical datasets tend to be unsharable because no other researchers can correctly interpret the data.
+%	\item In physics, even for data sets consisting of real numbers only, it is essential to store the unit and the precision in addition to the actual number.
+%	%\item In health sciences and biology, the involved data types are often simpler but the same problems exist. For example. in 2016, researchers found that about 20\% of papers in genomics journals contain errors in supplementary spreadsheets that sneak in due to misinterpretation of raw data by tools like Microsoft Excel. Such errors could be easily prevented by using machine-readable schema-encoding spefications for a systematic validation of the datasets.
+%\end{compactitem}
+%
+%For human consumption, these specifications can often be achieved with relatively simple meta-data, or free text documentation, e.g., a descriptive column head in a table.
+%But in mathematical sciences these descriptions often must include mathematical formulas to describe the formal relationship between the values.
+%Moreover, such ad hoc specifications require any user of a data set to correctly understand and implement the semantics of the data.
+%This is error-prone and does not allow automated processing such as data validation or machine learning.
+%
+%The systematic sharing of mathematical data sets critically differs both from the sharing of other kinds of digital mathematical resources and from the sharing of data sets in other sciences.
+%Other digital artifacts in mathematics are usually tied to a specific system or standard: logical formalizations are concentrated in a few large reusable libraries of proof assistants; algorithmic results are shared automatically through package managers in computer algebra systems; and narrative documents are shared through \LaTeX and pdf written a certain uniform style.
+%Thus, in all cases, the semantics of the resource can be uniquely inferred from it.
+%
+%This is not true for mathematical data sets, which involve very complex datatypes such as polynomials, graphs, algebraic structures, or symbolic expressions.
+%Because databases and spreadsheet editors do not support such data types, the data has to be encoded in basic types, e.g., a polynomial as a list of its coefficients.
+%But these encoding are not unique (e.g., shallow or sparse polynomials, order of coefficients) and can be very complex (e.g., representing an isomorphism class of groups by a specific set of invariants).
+%This is not surprising.
+%Among the pervasive kind of mathematical objects are sets and functions --- and these are exactly the kind of objects that database tools cannot represent at all, not even via complex encodings.
+%Therefore, mathematicians have to conduct additional research to devise novel representations of mathematical objects that can be encoded more easily in data base languages.
 
 \eucommentary{-- Describe the advance your proposal would provide beyond the
 state-of-the-art, and the extent the proposed work is ambitious. \\
 -- Explain how the proposed work is beyond the state of the art, builds on technologies at TRL 6 or above, and demonstrates innovation potential (e.g. ground-breaking objectives, novel concepts and approaches, new products, services or business and organisational models).
 }
 
-\begin{oldpart}{copied from ODK; it may contain some useful language; adapt}
+\subsubsection{Advance over state of the art}
+
+\subsubsection{Ambitiousness}
+
+\subsubsection{Technologies}
+ framework (WP 2+3): omdoc and MMT URIs, schema theories and encodings, databases
+ services (WP 4):
+ datasets (WP 5):
+
+\ednote{add summary table of technologies with current and final TRL}
+
+\subsubsection{Innovation potential}
+ground-breaking: first time FAIR
+
+novel concepts and approaches: trimodal integrated data representation framework
+
+new services: search, reuse, validation
+
+
+\begin{oldpart}{copied from ODK; it may contain some useful language}
 For most pure mathematicians using computational tools in their
 research, the state of the art at the start of 2015 still consists of
 a collection of
diff --git a/Proposal/concept.tex b/Proposal/concept.tex
index a1aeb9101ba3d03082c1fe58102a2aa1f52239a3..f6042f9782deb8c3bd49025fb4c2740904c3dab8 100644
--- a/Proposal/concept.tex
+++ b/Proposal/concept.tex
@@ -1,12 +1,164 @@
-\eucommentary{5-8 pages}
-\eucommentary{
--- Describe and explain the overall concept underpinning the project.
-Describe the main ideas, models or assumptions involved. Identify
-any trans-disciplinary considerations and use of stakeholder knowledge to enrich the existing EOSC service offering;
--- Describe any national or international research and innovation activities which will be linked with the project, especially where the outputs from these will feed into the project;
+%% FR: The subdivision suggested by the template is unworkable.
+%% Instead, I've divided this into multiple subsubsections, each decorated with the pretinent notes from the template.
+
+\subsubsection{Overall concept and main ideas}
+
+\eucommentary{Describe and explain the overall concept underpinning the project. Describe the main ideas, models or assumptions involved.}
+
+\paragraph{Kinds of mathematical data}
+Mathematics has a rich notion of data.
+A main idea of \TheProject is the following novel categorization of mathematical data.
+Each kind presents specific challenges to FAIR data sharing.
+
+\textbf{Symbolic} data consists of formal expressions such as formulas, formal proofs, programs, graphs, diagrams.
+These are written in a variety of highly-structured formal language specifically designed for individual areas of mathematics.
+Symbolic data involves abstraction principles such as underspecification, quantification, and variable binding.
+Thus, contrary to the other two kinds, symbolic data captures the full semantics in fully specified languages.
+This comes at the price of being context-sensitive: expressions must be interpreted relative to their context and cannot be easily moved across environments, which makes Finding, Reuse, and Interoperability difficult.
+
+Symbolic data can be subdivided into \textbf{modeling}, \textbf{deduction}, and \textbf{computation}.
+Each area employs different sophisticated formal languages: modeling languages, logics, resp. programming languages.
+
+\textbf{Encoded} data employs representation theorems in order to express mathematical objects in terms of simple expressions built from numbers or strings.
+Thus, contrary to the other two kinds, concrete data allows optimized storage and processing while still capturing the whole semantics of the objects.
+But the representations are not always possible, may be very difficult to find, and may not be unique.
+Therefore, Access is difficult because users need to know the representation functions.
+Even if this function is documented, Finding, Reuse, and Interoperability are difficult and error-prone.
+
+Concrete data can be subdivided into \textbf{record} data and \textbf{array} data. %% tree and graph data?
+These can be maintained efficiently in relational resp. array databases.
+
+\textbf{Linked} data introduces identifiers for objects, which are then treated as blackboxes.
+The semantics of the objects remains unspecified except for maintaining a set of named relations between and named attributions of concrete values to these identifiers.
+The former allow forming large networks of objects, and the latter provide limited information about each one.
+Thus, contrary to the other two kinds, linked data has very good FAIR-readiness, in particular allowing for URIs-based Access, efficient Finding via query languages, and URI-mediated Reuse and Interoperability.
+However, this comes at the price of not capturing the complete semantics of the objects so that Access and Finding are limited to the exposed part of the semantics, and Interoperability and Reuse are subject to misinterpretation.
+
+Linked data can be subdivided into \textbf{knowledge graphs} and \textbf{metadata}.
+In both cases, the relations and attributions are supplied in ontologies.
+\ednote{mention Wikidata}
+
+\ednote{add triangle graph: symbolic vs. concrete; encoded vs. accessible; linked vs. complete semantics}
+
+\ednote{add summary table of kinds with their subdivisions}
+
+\paragraph{Stakeholders concerning mathematical data}
+\ednote{describe the roles of stakeholders}
+
+\paragraph{A framework for concrete, accessible, and complete representations}
+The distinct advantages of the three kinds of data are very difficult to combine.
+It would be a mistake to try to find a single standard that covers all datasets.
+
+Instead, we design a trimodal framework that integrates all three kinds in a standardized way and addresses the respective FAIR-related shortcomings.
+
+For symbolic data, we use the OMDoc representation language.
+It provides uniform encodings of symbolic data in a single standardized concrete language.
+\ednote{expand}
+
+For encoded data, we use the codec framework developed in the OpenDreamKit project.
+It provides a systematic link between symbolic specification of mathematical objects and their encoded representation.
+Thus it makes the encoded objects accessible via their symbolic representation.
+
+% move these details to work plan
+%
+%In the OpenDreamKit project, the FAU group has developed a systematic formalism for annotating datasets with schema-encoding specifications.
+%These make data sets interoperable, searchable, and checkable.
+%Moreover, they enable computation with these datasets at the EOSC level, e.g., by importing datasets into Jupyter notebooks.
+%
+%Firstly, we developed a library of data types that are commonly used in mathematical databases.
+%These include base types such as string, integers, boolean; collection types such as finite sets, lists, vectors, matrices; aggregation types such as products, unions, and records; algebraic types such as rings and fields; and symbolic types such as rational fields and polynomials.
+%This library is not quite comprehensive yet because we targeted only the databases used in the OpenDreamKit project.
+%But it is easily extensible.
+%
+%Secondly, we designed a standard for codec --- small algorithms that translate back and forth between the data types in our library and the much simpler data types provided by database languages.
+%Then we developed of codecs to accompany our library of data types.
+%
+%Data types and codecs must be independent of each other because different authors may choose different encodings for the same data type.
+%Already, in the LMFDB database, we encountered at least three different encodings of integers (because there is no obvious best way to encode arbitrarily large integers in terms of standard data base types).
+%
+%Thirdly, data set authors specify their data set by giving for each value (i.e., each schema field or each attribute) both its mathematical data type and its encoding.
+%This allows seamlessly using standard database technology for curating and sharing mathematical data.
+\ednote{expand}
+
+For linked data, we use MMT URIs.
+The MMT URI schema provides a systematic link between the identifiers used in the symbolic representations, which specify the complete semantics, and the blackbox identifiers used in linked data representations.
+MOIs are used to link objects to publications and thus to metadata.
+\ednote{expand}
+
+\paragraph{FAIR services}
+\ednote{describe the main ideas behind the services we envision}
+
+%\textbf{validation}
+
+%\textbf{provenance}
+
+%\textbf{search}
+
+\subsubsection{Use of EOSC stakeholder knowledge}
+\eucommentary{Identify any trans-disciplinary considerations and use of stakeholder knowledge to enrich the existing EOSC service offering}
+
+\ednote{add a summary table of sites/PI and how their expertise covers all kinds of data; name the concrete services, databases they maintain or connect us to}
+\ednote{the text describes that table}
+
+\subsubsection{Linked activities: existing datasets and services}
+
+\eucommentary{Describe any national or international research and innovation activities which will be linked with the project, especially where the outputs from these will feed into the project}
+
+\ednote{add a table of existing services that we build on, together with the site maintaining it (give URLs)}
+\ednote{add a table of existing database that we will include, give their maintainers and size, and which PI connects us to them}
+\ednote{the text describes those tables}
+
+\subsubsection{Trans-disciplinary considerations}
+\eucommentary{Identify any trans-disciplinary considerations and use of stakeholder knowledge to enrich the existing EOSC service offering}
+
+\ednote{describe applications to math sciences, other sciences}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\subsubsection{Overall Methodology}
+
+\eucommentary{Describe and explain the overall methodology, distinguishing, as appropriate, activities indicated in the relevant section of the work programme, e.g. for research, demonstration, piloting, first market replication, etc.\\
+FR: the example activities above are EU-speak for the innovation cycle; more relevant for us are the activities in the scope section, which I've used to derive the formulations used below}
+
+\paragraph{User-oriented open mathematical data}
+show how we design the data representation framework to be user-oriented and open (refer back to stakeholder roles and framework ideas above)
+
+Details in WP 2+3, which should be merged
+
+\paragraph{Innovative services and their integration into the EOSC Hub}
+show how we ensure that our services can be integrated into EOSC Hub after project termination
+
+Details in WP 4
+
+\paragraph{Pilot datasets}
+show how we respond to specific needs of particular communities
+
+We use a representative selection of databases as test cases and major deliverables.
+
+Details in WP 5
+
+\paragraph{Adoption in Different User Communities}
+show how we test scalability by providing our services to user communities from different disciplines during project lifetime
+
+Details in WP 6
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\subsubsection{Gender Dimension}
+\eucommentary{Where relevant, describe how the gender dimension, i.e. sex and/or gender analysis is taken into account in the project’s content.\\
+	Note: Please note that this question does not refer to gender balance in the teams in charge of carrying out the project but to the content of the planned research and innovation activities . Sex and gender analysis refers to biological characteristics and social/cultural factors respectively. For guidance on methods of sex / gender analysis and the issues to be taken into account, please refer to \url{http://ec.europa.eu/research/swafs/gendered-innovations/index_en.cfm?pg=home}
 }
 
-\begin{oldpart}{partially related text from ODK, this may contain some useful language; adapt}
+Mathematical data is inherently sex and gender-neutral, and this extends to all innovations and services of \TheProject.
+Examples and use cases in the generated documentations will be chosen in sex/gender neutral ways.
+
+All partners will follow inclusive practices in recruiting staff for this project, in inviting the community to our workshops and outreach events and in choosing users to evaluate prototypes.
+
+\ednote{ODK additionally said this, which seems overkill: ``We will consult with the Head of Equality and
+	Diversity at St Andrews, about any known gender differences in collaborative working and
+	ensure that our collaborative tools properly support open, equitable and inclusive
+	patterns of cooperation. This will be reported in deliverable ???''}
+
+
+\begin{oldpart}{ODK did not follow the template and mostly used the following free text}
 
 \begin{center}
 \begin{boxedminipage}{.95\textwidth}\em
@@ -25,7 +177,7 @@ remaining tasks needed to realise our goal, and to maximise impact.
 
 \subsubsection{Background: Mathematics and Innovation in the Digital World}\label{sec:innovation}
 
-\paragraph{Mathematics is at the heart of innovation}\
+\paragraph{Mathematics is at the heart of innovation}
 
 We live in an innovation-driven society and mathematics is a key enabling tool for
 many of those innovations.
@@ -228,8 +380,6 @@ that a more flexible approach can be  fruitful:
   $j$-function, and its conclusion eventually led to Borcherds' Fields medal;
 \end{compactitem}
 
-
-
 \paragraph{The diversity of needs in the mathematical community}
 
 Certain scientific areas, for example in genomics, have large
@@ -288,55 +438,8 @@ resources. Here are some typical scenarios to illustrate this:
   computations, machine-checked proofs and other computational elements to the
   discussion and the collaboratively assembled proof.
 \end{compactitem}
-
-These are just a few of the many forms of computational or
-collaborative mathematical project that we aim to support.
-
-\subsubsection{Specific requests of the call}
-
-\paragraph{Use of Existing Basic Services}
-This will be addressed primarily in work packages \WPref{component-architecture},
-\WPref{dksbases} and \WPref{hpc}. Our architecture will include interfaces to standard
-APIs for cloud provisioning, authentication, cloud storage, HPC scheduling and so
-forth. Actual VREs will be deployed by users integrating those with specialist tools
-according to the needs of their projects.
-
-\paragraph{Gender analysis}
-
-All partners will follow inclusive practices in recruiting staff for this project, in
-inviting the community to our workshops and outreach events and in choosing users to
-evaluate our demonstrator applications. We will consult with the Head of Equality and
-Diversity at St Andrews, about any known gender differences in collaborative working and
-ensure that our collaborative tools properly support open, equitable and inclusive
-patterns of cooperation. This will be reported in deliverable \delivref{management}{ipr}.
-
-\paragraph{Service discovery}
-
-The \TheProject web pages (\taskref{dissem}{dissemination-communication}) and the
-dedicated training portal (\taskref{dissem}{training-portal}) will
-provide a central point of service discovery, providing a directory to
-all components, demonstrators, online services, protocol and interface
-specifications, software and other project activities and outputs.
-
-\subsubsection{Linked research and innovation activities}\label{linked-projects}
-
-\eucommentary{Describe any national or international research and
-  innovation activities which will be linked with the project,
-  especially where the outputs from these will feed into the project;}
-
-\paragraph{OpenDreamKit}
-\url{OpenDreamKit.org}
-\ednote{Give a short ODK description}
 \end{oldpart}
 
-\subsection{Methodology}
-
-\eucommentary{
--- Describe and explain the overall methodology, distinguishing, as appropriate, activities indicated in the relevant section of the work programme, e.g. for research, demonstration, piloting, first market replication, etc.\\
--- Where relevant, describe how the gender dimension, i.e. sex and/or gender analysis is taken into account in the project’s content.\\
-Note: Please note that this question does not refer to gender balance in the teams in charge of carrying out the project but to the content of the planned research and innovation activities . Sex and gender analysis refers to biological characteristics and social/cultural factors respectively. For guidance on methods of sex / gender analysis and the issues to be taken into account, please refer to \url{http://ec.europa.eu/research/swafs/gendered-innovations/index_en.cfm?pg=home}
-}
-
 
 %%% Local Variables:
 %%% mode: latex
diff --git a/Proposal/excellence.tex b/Proposal/excellence.tex
index 4c94a4b6421729f32cef2c6c7b3e09168073fdf4..d3fd343f2f5a65c0d863292918350910b52e17d2 100644
--- a/Proposal/excellence.tex
+++ b/Proposal/excellence.tex
@@ -1,4 +1,4 @@
-\begin{oldpart}{copied from ODK; adapt}
+\begin{oldpart}{ODK put a project summary here that is not required by the template; check if we need it or can use it elsewhere}
   Improvements of the economy, ecology, health care, security and society overall are
   driven by innovation. Key tools for innovation are mathematical knowledge and
   algorithms. Our global positioning system (GPS) needs relativistic mathematics, our
diff --git a/Proposal/impact.tex b/Proposal/impact.tex
index e94a55b23b526debd4f67ba6e7e45c591a9fd978..45a5069b1c8e3e306a77b064ad18d0ba808f7483 100644
--- a/Proposal/impact.tex
+++ b/Proposal/impact.tex
@@ -7,9 +7,9 @@
 
 \subsection{Expected Impacts}
 
-\eucommentary{Please be specific, and provide only information that applies to the proposal and its objectives. Wherever possible, use quantified indicators and targets. }
+\eucommentary{Please be specific, and provide only information that applies to the proposal and its objectives. Wherever possible, use quantified indicators and targets.}
 
-\begin{oldpart}{2.1 as copied from ODK; it may contain some useful language; adapt}
+\begin{oldpart}{copied from ODK; it may contain some useful language; adapt}
 	The project, with its ambitious vision, general and broad approach, and 
 	challenging work plan, will offer the opportunity to all partners and beyond 
 	to complement their research expertise with methodologies and tools not 
@@ -21,10 +21,27 @@
 	dissemination and exploitation strategy.
 \end{oldpart}	
 
-\subsubsection{Impacts as Listed in the Work Programme}
+\subsubsection{Expected Impacts Listed in the Work Programme}
 
 \eucommentary{Describe how your project will contribute to: each of the expected impacts mentioned in the work programme, under the relevant topic}
 
+We describe how \TheProject contributes to each expected impact listed in the work programme. 
+
+\paragraph{Expected Impact: Integrating research and service development}
+\eucommentary{Integrating co-design into research and development of new services to better support	scientific, industrial and societal applications benefiting from a strong user orientation}
+
+\ednote{describe how mathematicians are already codesigning research an services but we provide the infrastrucutre to make that easier; we enable scientific and maybe industrial applications by making it as easy as possible for users to access/search/reuse/interoperate math data}
+
+\paragraph{Expected Impact: Supporting the objectives of Open Science}
+\eucommentary{Supporting the objectives of Open Science by improving access to content and resources, and facilitating interdisciplinary collaborations}
+
+\ednote{describe how we introduce FAIR to mathematics and support data-sharing-based collaborations within and across disciplines}
+
+\paragraph{Expected Impact: Opening up the EOSC ecosystem to new innovative actors}
+\eucommentary{Fostering the innovation potential by opening up the EOSC ecosystem of e-infrastructure service providers to new innovative actors.}
+
+\ednote{describe how we open up the EOSC to mathematics}
+
 \begin{oldpart}{copied from ODK}
 The following Key Performance Indicators (KPI) show how \TheProject  addresses the specific impacts
 listed in the work programme. KPIs were thought through by the members
@@ -211,8 +228,20 @@ to this generation.
 
 
 \subsubsection{Obstacles and Framework Conditions}
+
 \eucommentary{Describe any barriers/obstacles, and any framework conditions (such as regulation, standards, public acceptance, workforce considerations, financing of follow-up steps, cooperation of other links in the value chain), that may determine whether and to what extent the expected impacts will be achieved. (This should not include any risk factors concerning implementation, as covered in section 3.2.)}
 
+\paragraph{Adoption of EOSC}
+
+\paragraph{Adoption of EOSC-based Mathematical Services}
+
+\ednote{We need a good integration of our services with existing non-EOSC services that may be more flexible or lightweight}
+\ednote{math datasets may be living objects, continued synchronization must be supported by our infrastructure}
+
+\paragraph{Competing frameworks}
+\ednote{extensions of existing systems like Mathematica, Sage, ZBMath, Wikidata}
+
+
 \begin{oldpart}{partially related text from ODK}
 The following barriers to impact will be addressed and overcome using the mitigation
 strategies provided. These are distinct from the risks to project delivery
diff --git a/Proposal/objectives.tex b/Proposal/objectives.tex
index 1dc835641b10d85ef24a81e872c12c3b35ec704c..ccfbd54566baf2d65d6b9fe6881fd949dc92ba67 100644
--- a/Proposal/objectives.tex
+++ b/Proposal/objectives.tex
@@ -1,139 +1,73 @@
-\begin{oldpart}{copied from ODK; adapt}
-  \eucommentary{1-2 pages}
 \eucommentary{\emph{Describe the specific objectives for the project,
-which should be clear, measurable, realistic and achievable within the
-duration of the project. Objectives should be consistent with the expected
-exploitation and impact of the project (see section 2).}}
-
-The specific aims of \TheProject are:
-\begin{compactenum}[\textbf{Aim} 1:]
-\item \label{aim:collaboration} Improve the productivity of researchers in pure
-  mathematics and applications by promoting collaborations based on mathematical
-  \textbf{data} and \textbf{knowledge}.
-\item \label{aim:impact} Maximise sustainability and impact in mathematics, neighbouring
-  fields, and scientific computing.
-\end{compactenum}
-
-We will achieve our aims through nine\ednote{adapt} objectives, as listed below.
-
-\begin{compactenum}[\textbf{Objective} 1:]
-\item \label{objective:community} To bring together research communities (e.g. users of
-  \Jupyter, \Sage, \Singular, and \GAP) to symbiotically exploit overlaps in tool creation
-  building efforts, avoid duplication of effort in different disciplines, and share best
-  practice. This supports Aims~\ref{aim:collaboration}, \ref{aim:sharing}
-  and~\ref{aim:impact}.
-
-\item \label{objective:data} Identify and extend ontologies and
-  standards to facilitate safe and efficient storage, reuse,
-  interoperation and sharing of rich mathematical data whilst taking
-  account of provenance and citability. This fulfills parts of
-  Aims~\ref{aim:vre} and~\ref{aim:sharing}.
-
-\item \label{objective:demo} Demonstrate the effectiveness of Virtual Research
-  Environments built on top of \TheProject components for a number of real-world use cases
-  that traverse domains. This addresses part of Aim~\ref{aim:vre} and through documenting
-  best practice in reproducible demonstrator documents Aim~\ref{aim:sharing}.
-
-\item \label{objective:disseminate} Promote and disseminate
-  \TheProject to the scientific community by active communication,
-  workshop organisation, and training in the spirit of open-source
-  software. This addresses Aim~\ref{aim:impact}.
-\end{compactenum}
-
-\subsection*{Detailed Descriptions of Objectives}
-
-\paragraph{Objective~\ref{objective:community}: Community Building across Disciplines}\
-
-Open source development is most efficient when the load is shared as
-widely as possible. However, across different communities a lack of
-communication can mean that good ideas are re-invented or
-re-implemented, when a shared resource would be more efficient. By
-fostering a more \emph{cross-disciplinary} community, sharing tools
-where possible and by creating generic tools for wide distribution we
-will reduce duplication of effort. This will lead to high
-\emph{quality} software that is more \emph{sustainable}. The
-maintenance and development effort can be focused on one tool rather
-than a disparate spread of codebases. This will ensure innovative
-ideas and best-practice are shared more effectively, increasing
-research productivity.
-
-While each of the communities such as the developers of \Sage,
-\Singular, and \GAP need somewhat special features for their research,
-they are united through being (i)~focussed on mathematical challenges,
-and (ii)~needing a computational workflow. \IPython and the \Jupyter
-Notebook are used widely in science and engineering. These communities
-are based on (iii)~applications of mathematics that also require
-computational workflows for collaborative research and
-dissemination. These three common attributes distill the requirements
-for core features of the \VREs described in this proposal. Community
-building will also help to sustain ongoing and community driven
-maintenance of a such a tool.
-
-\paragraph{Objective~\ref{objective:data}: Next Generation Mathematical Databases}\
-
-Mathematics has a rich notion of data: it can be either
-numeric or symbolic data; knowledge about mathematical objects given as
-statements (definitions, theorems or proofs); or software that computes
-with these mathematical objects.
-%
-All this data is really a common resource, and should be maintained as
-such. Much of this proposal, and the prior work of many of the experts
-involved, is concerned with open source mathematical software, through
-permissive licensing of their work.
-
-The objective described here is to \emph{build infrastructure},
-enabling mathematicians to collaboratively build this common resource,
-while fostering a virtuous circle of interoperability between these
-different types of data: a mathematician might implement an
-algorithm, to be run later on numerical data collected by a
-scientist.
-
-\paragraph{Objective~\ref{objective:demo}: Collaborative Research Environments that Transcend Domains}\
-
-Wide dissemination of our \VREs is critical to ensure sustainability
-and reduce duplication of effort between communities. This
-dissemination is not restricted to the traditional arena of
-conferences, journal papers and workshops, but should exploit the high
-bandwidth communication provided by the internet. To ensure
-applicability of our framework, we will create a number of
-\emph{demonstrators} to highlight the power of \TheProject{}
-(\taskref{UI}{structdocs}, \taskref{dissem}{ibook}) across mathematics,
-engineering and science. They will act so as to provide recipes for
-state-of-the-art computational infrastructure tools, and provide
-avenues for ensuring the repeatability of mathematical analysis.
-
-\paragraph{Objective~\ref{objective:disseminate}: Training and Dissemination}\
-
-The success of any research software or service is strongly linked to
-its ability to attract and retain a large number of users. The
-different communities (Sage, Gap, \PariGP, Singular, \Jupyter, ...)
-have each developed sustainable networks. For example, Sage has
-accumulated thousands of users in under 10 years. This has been
-achieved thanks to a very strong community building philosophy,
-especially through the organisation of ``Sage-Days'' all over the
-world. The first Sage-Days was held in 2006; to date there has been
-at least 63 of them, including 10 during 2014, as well as Sage
-Education days, Sage Bug days, Sage Doc days, Sage Days aimed
-specifically at women, and more. Many of the \TheProject{} project
-members have been involved in these events either as organisers or
-participants, and are convinced that they are a most efficient way to
-promote our software. More precisely, our objective is to create a
-constant dialogue between the different communities, through frequent
-workshops, conferences, user groups, and mailing lists. By building on
-existing tools, we intend to involve the communities in the
-development process itself in the spirit of open-source software.
-
-We also intend to reach a larger crowd of researchers by minimising
-technical (non-research) obstacles to access existing tools: building
-better documentation and tutorials, developing easy-to-install
-distributions, enabling easy web and cloud access, better user
-interfaces, better interactions between different software.  We will
-run a series of workshops to inject additional momentum into the
-process. By doing this, our objective will be to \emph{help the
-  communities to grow} themselves and interact together using our
-work.
-\end{oldpart}
+		which should be clear, measurable, realistic and achievable within the
+		duration of the project. Objectives should be consistent with the expected
+		exploitation and impact of the project (see section 2).}}
 
+\subsubsection{Objective 1: FAIR Mathematics}
+
+\paragraph{Overview}
+Traditionally, mathematics has not paid particular attention to the creation and sharing of data.
+This has changed with the advent of computer-supported mathematics, and modern mathematics is increasingly data-driven.
+Today it is routine to use mathematical datasets in the Gigabyte range, including both human-curated and machine-produced data.
+In some areas, mathematics has even become similar to experimental sciences in that mathematical reality is ``measured'' at large scale by running computations.
+
+There is wide agreement in mathematics that these datasets should be a common resource and be open and freely available.
+Moreover, the software used to produce them is usually open source and free as well.
+
+However, the datasets are produced, published, and maintained with virtually no systematic attention to the FAIR principles.
+In fact, often the sharing of data is an afterthought.
+Moreover, the inherent complexity of mathematical data makes it very difficult to share mathematical data in practice: even freely available datasets are often very hard or impossible to reuse, let alone make machine-interoperable because there is no systematic way of specifying the relation between the raw data and its mathematical meaning.
+Therefore, FAIR mathematics essentially does not exist today.
+
+Our objective is to systematize the way how mathematical data is represented and shared in a way that enables FAIR mathematics.
+More concretely, we will develop a data framework that allows both the representation of the various forms of mathematical datasets and the formal specification of their semantics.
+
+\paragraph{The FAIR Principles}
+We discuss the resulting challenges for the FAIR principles in increasing order of difficulty.
+\ednote{fill in details}
+
+\textbf{accessible}
+
+\textbf{reusable}
+
+\textbf{findable}
+
+\textbf{interoperable}
+
+\paragraph{Secondary Benefits}
+This will lead to several incidental benefits:
+\begin{compactitem}
+	\item It creates incentives for mathematicians to share datasets, e.g., by datasets citable.
+	\item It allows making the provenance of datasets more explicit.
+	\item It enables collaborations via shared datasets that are currently prohibitively expensive due to the difficulty of understanding other researchers' data.
+	\item It increases the productivity of mathematicians by allowing them to focus on the mathematical datasets themselves while leaving issues of encoding, management, and search to dedicated systems.
+	\item It makes research more sustainable by guaranteeing that datasets can be archived and their meaning understood in perpetuity.
+\end{compactitem}
+
+\subsubsection{Objective 2: Semantics-Aware Open Science Cloud}
+We leverage the semantics-aware framework for mathematical data developed in Objective 1 in three ways.
+
+Firstly, we build an EOSC-level infrastructure for sharing mathematical datasets.
+\ednote{fill in details}
+
+Secondly, on top of this infrastructure we develop a suite of universal, scalable, and freely available services for interacting with these datasets.
+\ednote{fill in details}
+These services are currently non-existent, limited, or expensive.
+
+Thirdly, we make a representative set of major existing datasets available through our infrastrucutre.
+\ednote{fill in details}
+
+Our infrastructure will be realized as a self-contained prototype server with both a machine-oriented API and a human-oriented web interface.
+All services will be open to the public and free.
+We will also take all necessary steps to make the eventual integration into the EOSC hub easy.
+
+\paragraph{Secondary Benefits}
+The above-mentioned challenge of specifying the semantics of datasets is not unique to mathematics --- in all sciences, it is vital to share not only the raw data but also the description of its meaning.
+Mathematics is just an area in which the latter in which the latter is particularly difficult.
+Moreover, mathematical data is so rich in structure that our framework that can represent all mathematical data automatically can represent virtually all scientific data.
+Therefore, our solution for mathematical datasets will also benefit other sciences.
+
+\ednote{fill in details}
 
 %%% Local Variables:
 %%% mode: latex
diff --git a/Proposal/proposal.pdf b/Proposal/proposal.pdf
index 7ee651d911b8ebcc23ea3cac18aa6d0b9f856c70..906805ca91207a8797e54c82166d4dd8ae5760c2 100644
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diff --git a/Proposal/proposal.tex b/Proposal/proposal.tex
index dd0d545dd9b7f2d10357367bfca2ba08cce9ed84..4f1723c835303004ca3a36ac8245bc30072da454 100644
--- a/Proposal/proposal.tex
+++ b/Proposal/proposal.tex
@@ -63,10 +63,104 @@
 \ifsubmit\else\setcounter{tocdepth}{4}\fi
 \tableofcontents
 
+\eucommentary{Summary of the work programme for research infrastrucutres:\\
+	Research infrastructures are facilities, resources and services that are used by the research
+	communities to conduct research and foster innovation in their fields. Where relevant, they
+	may be used beyond research, e.g. for education or public services. They include: major
+	scientific equipment (or sets of instruments); knowledge-based resources such as collections,
+	archives or scientific data; e-infrastructures, such as data and computing systems and
+	communication networks; and any other infrastructure of a unique nature essential to achieve
+	excellence in research and innovation. Such infrastructures may be 'single-sited', ‘virtual’ or
+	'distributed'.\\
+	Research infrastructures play an increasing role in the advancement of knowledge and
+	technology and their exploitation. By offering high quality research services to users from
+	different countries, by attracting young people to science and by networking facilities,
+	research infrastructures help to structure the scientific community and play a key role in the
+	construction of an efficient research and innovation environment. Because of their ability to
+	assemble a ‘critical mass’ of people, knowledge and investment, they contribute to national,
+	regional and European economic development. Research infrastructures are also key in
+	helping Europe to lead a global movement towards open, interconnected, data-driven and
+	computer-intensive science and engineering. e-Infrastructures will make every European
+	researcher digital, increasing creativity and efficiency of research and bridging the divide
+	between developed and less developed regions.\\
+	While Member States remain central in the development and financing of most research
+	infrastructures, the Union play a catalysing and leveraging role in this field. A European
+	approach helps pooling resources across Europe in order to properly address the cost and
+	complexity of new world-class research infrastructures. It also ensures wider and more
+	efficient access to and use of the infrastructures existing in the different Member States.
+	Research infrastructures provide and open research opportunities and services to researchers
+	in many areas also addressed by other Parts of Horizon 2020 within "Societal Challenges",
+	"Leadership in Enabling and Industrial Technologies" (LEIT), and “Excellent Science”. This
+	is also reflected in the close links between several of the topics in Research Infrastructures
+	and certain Focus Areas. Research Infrastructures also contributes to other cross-cutting
+	objectives of Horizon 2020, such as climate action and sustainable development, biodiversity,
+	and social sciences and humanities. Furthermore, production-level e-infrastructures are able to
+	serve the computing and data needs of any project in the framework programme fostering
+	economies of scale in the use of ICT systems by projects supported by Horizon 2020.
+	The Research Infrastructures Work Programme 2018-2020 contributes to the implementation
+	of the ESFRI (European Strategy Forum on Research Infrastructures) Roadmap, through the
+	support to the preparatory phase of the ESFRI projects identified in the Roadmap as well as
+	targeted support to their implementation and operation. It will put wide emphasis on fostering
+	the long-term sustainability of research infrastructures and on expanding the role and impact
+	of research infrastructures in the innovation chain.\\
+	E-infrastructures developments for the establishment by 2020 of a single and open European
+	space for online research, including ubiquitous and reliable services for networking and
+	computing, and seamless and open access to e-Science environments and global data
+	resources, will help to free the potential of Big Data for the benefit of researchers, innovators
+	and business, and to advance research and innovation, therefore contributing to the objectives
+	of the Priority 2 of the Juncker Commission: A Connected Digital Single Market.
+	The Research Infrastructures Work Programme 2018-2020 will provide support to actions
+	included in the 2016 Communication on the European Cloud Initiative, in particular to further
+	integrate and consolidate e-infrastructure platforms, to connect the ESFRI infrastructures to
+	the European Open Science Cloud, and to develop a European Data Infrastructures (EDI).
+	Research Infrastructure activities contribute also to widening participation in the programme
+	by supporting the development of Regional Partner Facilities 1 . The use of European Structural
+	and Investment Funds to build capacities and infrastructures at national and regional level in
+	line with the relevant smart specialisation strategy is encouraged (further information can be
+	found in section “Specific features for Research Infrastructures”).\\
+	This Research Infrastructures Work Programme implements several overall recommendations
+	expressed in the Horizon 2020 interim evaluation. It also addresses areas for improvement
+	identified by the Research Infrastructures specific assessment of the interim evaluation of
+	Horizon 2020, such as the need to reassess the balance between the support given to starting
+	and advanced communities and to further develop actions to promote innovation. Specific
+	measures to tackle overall and specific issues in the last Work Programme for 2018-2020
+	include an increase of the budget for starting communities, a better integration between
+	research infrastructures and e-infrastructures actions, wider actions to promote innovation as
+	well as large initiatives of international cooperation.}
+
+\eucommentary{Summary of the EOSC call within that work programme:\\
+	This call will achieve the vision put forward by the European Cloud Initiative 7 and it will
+	make the European Open Science Cloud (EOSC) a reality. In order to realise an EOSC that
+	truly supports interdisciplinary research and Open Science, a new pan-European model for
+	research data and related services that is both scalable and flexible needs to be put in place,
+	so that it can be adapted to the emerging needs of the scientific community and support the
+	whole research data lifecycle. The new model will build on a pan-European service access
+	mechanism – the EOSC hub 8 – providing access primarily to public e-infrastructure services
+	supplied at national, regional and institutional levels. The Call will support the setup of an
+	appropriate governance for such a relevant endeavour, which takes into account the
+	outcomes of previous efforts and the active contribution of all scientific stakeholders. The
+	Call will ensure strong positioning of EOSC in the context of similar initiatives in other world
+	regions to enhance and ‘open to the world’ international collaboration.\\
+	The Call will develop the EOSC ecosystem providing all European researchers with
+	seamless 9 , non-discriminatory and secure access to public and commercial services and
+	appropriate access modalities to a wider user community like industry, public sector, citizen
+	scientists, etc. Capacity building for this ecosystem, in particular concerning storage,
+	computing, software and other resources and services, could in the future allow piloting of
+	innovative financial schemes and/or consider pan-European joint procurement facilitated by
+	the EOSC governance - and implemented in close conjunction with the funders - for
+	aggregation of demand. The coordination between national initiatives aiming at making data
+	FAIR as well as the connection to the EOSC of priority European Research
+	Infrastructures, in particular the ESFRI ones, will provide access from the EOSC entry point,
+	to a wealth of services and curated resources in a wide range of scientific domains.}
+
+\eucommentary{The summary of our topic within that call is distributed into the relevant sections of the proposal}
+
+
+\ednote{@MK: formatting rules are ``The reference font for the body text of H2020 proposals is Times New Roman (Windows platforms), Times/Times New Roman (Apple platforms) or Nimbus Roman No. 9 L (Linux distributions). The use of a different font for the body text is not advised [...].	The minimum font size allowed is 11 points.''}
+
 % ---------------------------------------------------------------------------
 %  Section 1: Excellence
 % ---------------------------------------------------------------------------
-
 \section{Excellence}
 \input{excellence}
 
diff --git a/Proposal/relation_to_the_work_programme.tex b/Proposal/relation_to_the_work_programme.tex
index 115be79f204ce01dfeaca2a62617b56bcff91c69..08f45bf544268982dc54a3adb4057b81a0c58ccf 100644
--- a/Proposal/relation_to_the_work_programme.tex
+++ b/Proposal/relation_to_the_work_programme.tex
@@ -1,32 +1,46 @@
-\begin{oldpart}{copied from ODK, adapt}
  \eucommentary{
- Indicate the work programme topic to which your proposal relates, and
- explain how your proposal addresses the specific challenge and scope
- of that topic, as set out in the work programme.}
-
-\enlargethispage{4cm}
-
-Below we explain how the project addresses the specific challenge and
-the scope of the topic ``E-infrastructures for Virtual Research
-Environments (\VREs)'' under E-Infrastructures-2015 call, as set out in the work program.
-\begin{center}
-\begin{tabular}{|m{.37\textwidth}|m{.60\textwidth}|}
-  \hline
-  \textbf{Specific challenge} &
-  \textbf{\TheProject contribution} \\\hline
-Where data are concerned, projects will define the semantics,
-ontologies, the \emph{what} metadata, as
-well as the best computing models and levels of abstraction (e.g. by
-means of open web services) to process the rich semantics at machine
-level, as to ensure interoperability. &
-We will investigate patterns to share data, ontologies, and semantics
-across computational systems, possibly
-connected remotely. We will leverage the well established semantics used
-in mathematics (categories, type systems) to give powerful
-abstractions on computational objects (Objectives~\ref{objectives:core} and \ref{objective:data}, \WPref{component-architecture} and \WPref{dksbases}).\\\hline
-\end{tabular}
-\end{center}
-\end{oldpart}
+ 	Indicate the work programme topic to which your proposal relates, and
+ 	explain how your proposal addresses the specific challenge and scope
+ 	of that topic, as set out in the work programme.}
+
+This proposal relates to the topic ``Prototyping new innovative services (INFRAEOSC-02-2019)'' of the call ``Implementing the European Open Science Cloud
+(H2020-INFRAEOSC-2018-2020)''.
+
+\ednote{The ODK proposal said very little here. It may be wise to be more specific.}
+
+\subsubsection{Specific Challenge}
+
+\eucommentary{Develop an agile, fit-for-purpose and sustainable service offering
+accessible through the EOSC hub that can satisfy the evolving needs of the scientific
+community by stimulating the design and prototyping of novel innovative digital services.
+Innovative models of collaboration that genuinely include incentive mechanisms for a user
+oriented open science approach should be considered.}
+
+\subsubsection{Scope}
+
+\eucommentary{Research and Innovation Actions that target gaps in the service offering of the EOSC
+	hub and develop innovative services that address relevant aspects of the research data cycle
+	(from inception to publication, curation, preservation and reuse), for example allowing
+	implementation of new scientific data-related developments and intelligent linking and
+	discovering of all research artefacts.\\
+	Whereas initially the new services would have to respond to specific needs of particular
+	scientific communities by the end of the project they should be leveraged to foster
+	interdisciplinary research, serving a wider remit of research needs, as well as new users like
+	industry and the public sector. Scalability of the new solution should be tested by user
+	communities from different disciplines during the project lifetime.\\
+	These services should be based on systems and technologies that have reached TRL 6 before the start of the project and
+	will be brought to at least TRL 8 by the end of the project. Proposals should demonstrate how
+	the resulting services complement, enrich and could potentially be integrated into the EOSC
+	hub. \\
+	Proposals retained for funding under this topic should take due consideration of any
+	accessibility requirements set under the projects funded under EINFRA-12-2017 topic that
+	may be available at the time the call will be open, in view of their integration into the
+	mainstream services of the EOSC hub.\\
+	Consortia are encouraged to include SMEs that are willing to develop or contribute to the
+	development of new innovative interdisciplinary services with a view of future integration in
+	the EOSC hub.}
+
+
 
 \clearpage