Commit aa05879f authored by Katja Bercic's avatar Katja Bercic
Browse files
parents 53094fc3 c8a851f7
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[subrepo]
remote = git@github.com:KWARC/LaTeX-proposal.git
branch = master
commit = 55b2e7d08cf1be209ba8512f2ec34e6b66ac3e2a
commit = c1a9781770da82f98d49de9be299fdb592435153
parent = 3ba83828b052b6d5c40498846f1c46775f01e449
method = merge
cmdver = 0.4.0
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......@@ -164,7 +164,7 @@ Therefore, we will provide two reports each at Month 18 resp. 36 that summarize
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......@@ -121,6 +121,13 @@ Because SageMath is written in Python and MMT exposes a Python API, SageMath can
This will allow seamless and immediate computation with \TheProject datasets, e.g., to access a dataset, perform a computation on it, and share the results as a second dataset --- all in a single operation.
The implementation will be based on and sustain the continuity of OpenDreamKit results, where \site{FAU} and \site{PS} have developed the integration of external datasets (specifically those of LMFDB) with SageMath via codecs.
\begin{newpart}{MK@FR: re-read}
This task will also study the trade-offs between data storage and lazy re-computation, and develop joint abstractions of schema theories and virtual theories that allow to leave open (and thus transparently change) between those two modes of data provisioning.
Even flexible mixed models and models that self-adapt under differing loads are possible and will be studied.
We expect that this will give rise to programming abstractions that allow to memoize useful and computationally expensive objects persistently in data stores and thus directly support large-scale and distributed computations.
We will build on very promising experiments in the OpenDreamKit project.
\end{newpart}
In addition, this task will enable several innovative uses of the EOSC-level infrastructure.
Firstly, it yields deep accessibility and deep reuse where only fragments of a dataset are accessed, computed with, or updated.
Secondly, it enables the use of the FAIRMat sharing services as persistent memoization layer for computational systems.
......@@ -251,4 +258,4 @@ Therefore, they are listed a single deliverable here.
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\begin{abstract}
The scientific community increasingly considers datasets as its own kind of resource that should be shared and published individually, either in conjunction with a traditional paper or as a standalone digital artefact.
The scientific community increasingly considers datasets as their own kind of resource that should be shared and published individually, either in conjunction with a traditional paper or as a standalone digital artefact.
Recently this trend has formed a positive feedback loop with the rise of deep learning methods, which require large datasets as input.
Multiple national and international Open Science initiatives have been started to ensure open availability, easy sharing, and reliable reproducibility of datasets.
Multiple national and international Open Science initiatives have been started to ensure the open availability, easy sharing, and reliable reproducibility of datasets in particular and data-driven research in general.
The FAIR principles in particular have been developed as a goalpost for the openness of datasets, including in particular the sharing across disciplines and between research and industry.
As a rule, mathematicians strongly support the Open Science movement and happily make their datasets public for both for practical and ethics reasons.
As a rule, mathematicians strongly support the Open Science movement and happily make their datasets public for both practical and ethical reasons.
This is accompanied by a vibrant and growing community of Open Source software for computational mathematics.
However, today most mathematical data collections are shared in an ad hoc manner that makes FAIR sharing hard to impossible.
Systematic data collection and archival initiatives are limited in scope and suffer from a lack of interlinking of digital artefacts across platforms.
% \ednote{NT: is FAIR implicitly about data only? otherwise the next sentence is too restrictive; Open Science is quite advanced in mathematics for publications and software; should we say FAIR data in mathematics?}
% \ednote{KB, from Turning FAIR into reality: FAIR Digital Objects (in a FAIR ecosystem comprising services and infrastructures): ``data, code, workflows, models, other digital and material research objects'', but heavily focused on data. 2.2 Definition of FAIR only states the principles for data.}
In effect, FAIR mathematics, while widely welcomed, is effectively non-existent today.
A similar argument applies to related sciences to the extent that they make heavy use of mathematical data, e.g. the mathematical modeling of cyber-physical systems.
However, the systematic FAIR sharing of mathematical data is very difficult due to the inherent complexity of the data.
Therefore, today most mathematical data collections are shared in an ad hoc manner that is limited in scope and suffers from a lack of interlinking of digital artefacts across platforms.
Thus, FAIR mathematics, while widely welcomed, is effectively non-existent today.
A similar argument applies to related sciences to the extent that they make heavy use of mathematical data, e.g., the mathematical modeling of cyber-physical systems.
Generally, reusing shared data requires that the reuser be able to understand the semantics of the data.
This is particularly difficult for system interoperability where the semantics must not only be evident but must itself be accessible for automated processing, and it is particularly critical where data is used in safety-critical systems.
While this problem exists for all data, it is particularly challenging for mathematical data and similar data in related disciplines where the semantics is very difficult to specify.
Today there are virtually no mathematical datasets whose semantics is itself accessible.
While this problem exists for all data, it is particularly challenging for mathematical and similar data in related disciplines where the semantics is very difficult to specify.
Therefore, today there are virtually no mathematical datasets whose semantics is itself accessible.
This is evidenced by the wide gap in the service offerings of the EOSC when it comes to semantics-aware services in general and services for mathematical data in particular.
\TheProject (pronounced ``Fermat'') will deliver a framework and prototype service for the FAIR and semantics-aware sharing of mathematical data.
It will meet the needs of mathematics researchers and education as well as other disciplines and industry that work with mathematical or similarly structured data.
It will meet the needs of mathematics research and education and will also bring added value to other disciplines and industry that work with data that exhibits complex structure or semantics.
It will support all phases of the research life-cycle including the generation, publication, updating, extending, curation, search, reuse, and archival of mathematical data.
\TheProject will be based on a sustainable software ecosystem including open source databases and services as well as nationally funded infrastructures.
\TheProject will be based on a sustainable software ecosystem including Open Source databases and services as well as nationally funded infrastructures.
This includes TRL 6 and above technologies like the Modelica modeling language, the LMFDB database, the SageMath computer algebra suite, or the zbMATH publication information system.
The \TheProject service suite will both support the direct sharing of data as well as be integrated with existing services.
We will demonstrate the scalability of the services by integrating it with several large existing databases and services from diverse communities: databases of combinatorial objects --- e.g. graphs, databases of mathematical models used in the engineering industry, and a database of number-theoretical objects like elliptic curves and modular forms.
To maximize long-term impact, it will be designed to allow a smooth integration into the EOSC hub at the end of the project lifetime.
Besides supporting the direct sharing of newly-produced data, \TheProject will make existing datasets available in a uniform way.
In particular, we will demonstrate the scalability of the \TheProject infrastructure by integrating it with several large and important databases and services from diverse communities including databases of pure mathematical objects (graphs, integer sequences, elliptic curves, \ldots), formalized theorems and proofs, mathematical models used in engineering, linked data from Wikidata, and publication metadata.
Finally, to maximize its long-term impact, \TheProject is designed to culminate in the ISO standardization of its data representation format and the smooth integration of its services into the EOSC Hub at the end of the project lifetime.
\TheProject will be carried out by a consortium of 7 sites with huge experience in designing and maintaining mathematical datasets and services.
The majority of partners are long time Open Science promoters, with a strong experience in large open (software) project management.
......@@ -43,4 +41,4 @@ In particular, all produced documents (including this proposal itself), software
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\ednote{NT: key part is to explain to non-mathematicians what is specific about mathematical data}
\ednote{NT: blurry line between data and computation; data is just a cache for computation; allow representing data lazily by function application in the standard}
\ednote{NT: for long-term archival, storing of enough information to recompute necessary, may be data or functions}
\ednote{NT: Are we bringing added value only for pure math or also for applied math, e.g., numerical solvers, calculation with huge matrices, etc.? here? Applied math data will come from a specific domain and has simpler structure than pure math data; are there tools already that can answer the needs we talk about? Maybe this can be related to the Modelica aspect of the proposal.}
\subsubsection{State of the Art}\label{sec:state_of_the_art}
For most mathematicians, and related researchers and users, the 2019 state of the art in data sharing often still consists of posting their dataset online, e.g., as a CSV-encoded text file or uploading it to a database with a web interface.
......@@ -40,6 +34,12 @@ SageMath has become extremely popular among mathematicians, especially those act
Due to its size and integrative nature, it has become somewhat of a cross--programming language packaging and distribution platform, via which researchers can disseminate specialized Open Source computation libraries.
It integrates a number of important mathematical datasets such as copies of some LMFDB datasets or GAP's small groups library.
\begin{newpart}{MK@FR: re-read}
Note that there is a blurry line between computation and data in mathematics -- and any science where data is computed as opposed to measured: instead of storing data we can always recompute it on demand.
Dually, computation can often replaced by tabulation of results, and which one is ``better'' depends on ``secondary virtues'' like computational complexity, storage costs, and input change rates.
The underlying trade-offs have not been explored for symbolic, record, and linked data; getting a good handle on them will be an important consideration for the \pn data/software framework (see \taskref{services}{I}).
\end{newpart}
\paragraph{Semantic Interoperability}\label{sec:mitm}
The SageMath integration layer described above is largely non-semantic in the sense that it relies on custom ``glue code'' in Python that is unverified and can be broken by any update of one of the integrated systems or datasets.
Moreover, it is specific to SageMath and cannot be reused in other systems.
......@@ -123,7 +123,6 @@ Deep FAIR services are much more difficult, and are either non-existent or are s
\paragraph{General Data Sharing Infrastructures}
Several hardware and software infrastructures are available that support researchers in data sharing.
It is particularly instructive to contrast the EOSC and related infrastructures such as EUDat with GitHub.
\ednote{@FIZ: say something about RADAR?}
The EOSC family of services has been developed top-down based on large national and international research grants.
Express goals were enabling, encouraging, and potentially requiring the FAIR sharing of research data. These services are complemented by national infrastructures like RADAR in Germany.
......@@ -385,7 +384,7 @@ open-access digital libraries to commercial publishers.
\noindent\WPtref{cases} hosts individual datasets on the \TheProject platform.
The datasets themselves are already listed in Figure~\ref{fig:datasets}.
Here we only list existing dataset hosting technologies maintained by partner sites that will be reused in or linked with the \TheProject platform.\ednote{FR: This is the best story I could tell; it's still a bit weak; I've added MathHub and removed Modelica distribution standard.}
Here we only list existing dataset hosting technologies maintained by partner sites that will be reused in or linked with the \TheProject platform.
\begin{compactenum}
\setcounter{enumi}{10}
\item The RADAR project has developed a sustainable generic infrastructure for research data management and archiving focusing on the long tail of science.
......@@ -404,6 +403,6 @@ In 2017, the project was successfully transformed into a production service.
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......@@ -85,7 +85,6 @@ But in the face of millions of defined concepts in mathematics, this has so far
Moreover, large mathematical datasets are usually shared in highly optimized encodings (or even a hierarchy of consecutive encodings), which knowledge representation languages must capture as well to allow for data interoperability.
The proposers have developed or been involved with multiple leading candidates for such representation languages that will be integrated into a standard language by \TheProject.
%\ednote{FR: I've commented out some text here that I found rather technical without being very helpful.}
%What we need is a deep FAIR mathematical interoperability framework which takes ``semantic referencing'' of mathematical objects seriously and builds differing, but equivalent, representation approaches into the system at the core.
%OpenMath and content MathML go the first step towards this by ``semantically referencing'' a concept via its name and a pointer to the content dictionary that defines it, but lack a system of interpretations (crosswalks) between content dictionaries.
......@@ -123,10 +122,10 @@ There are two encoding formats for directed graphs, both called \texttt{digraph6
The resulting problem has since been resolved but not without causing some misunderstandings first.
Concrete data can be subdivided into \textbf{record} data, where datasets are sets of records conforming to the same schema, and \textbf{array} data, which consists of very large, multidimensional arrays that require optimized management. %% tree and graph data?
Record data and querying is very vell standardized by the relational (SQL) model.
However, if encodings are used, SQL can never answer queries about the semantics of the orignal object.
Record data and querying is very well-standardized by the relational (SQL) model.
However, if encodings are used, SQL can never answer queries about the semantics of the original object.
The younger array data bases, which offer efficient access to contiguous --- possibly lower-dimensional --- sub-arrays of datasets (voxels), are less standardized, but OPenNDAP~\cite{OPenNDAP:on} is becoming increasingly recognized even outside the GeoData community where it originated.
%We will concentrate on record data in the \pn project, since array data mainly comes up in mathematics as the results of simulations.
Array data tends to come up in settings with large but simply-structured datasets such as simulation time series, while record data is often needed to represent complex objects, especially those from pure mathematics.
\textbf{Linked data} introduces identifiers for objects and then treats them as blackboxes, only representing the identifier and not the original object.
The internal structure and the semantics of the object remain unspecified except for maintaining a set of named relations and attributions for these identifiers.
......@@ -418,7 +417,7 @@ These efforts are detailed in \WPref{dissem}.
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.
Linked data can facilitate research and resulting policies on gender issues. Already, zbMATH data play an essential role in the Gender Gap in Science Project~\cite{gender-gap-science:on}, lead by the International Mathematical Union (IMU) and the International Union of Pure and Applied Chemistry (IUPAC) and supported by other major global scientific organizations, to evaluate different career paths in relation to gender, identify obstacles, and derive recommendations. Making this information available as FAIR data could not only spur further research and result in appropriate policies, but also facilitate the creation of much more granular information. E.g., the project has already identified a significant relevance of geographical and subject data on gender-specific career paths. Further relevant information on these issues would be intrinsically generated by interlinking the various data within the project and made available as a unique new FAIR source.
Linked data can facilitate research and impact policies on gender issues. Already, zbMATH data plays an essential role in the Gender Gap in Science Project~\cite{gender-gap-science:on}, led by the International Mathematical Union (IMU) and the International Union of Pure and Applied Chemistry (IUPAC) and supported by other major global scientific organizations, which aims to evaluate different career paths in relation to gender, identify obstacles, and derive recommendations. Making this information available as FAIR data could not only spur further research and result in appropriate policies, but also facilitate the creation of much more granular information. E.g., the project has already identified a significant impact of the geographical and subject data on gender-specific career paths. Further relevant information on these issues would be intrinsically generated by interlinking the various data within the project and made available as a unique new FAIR source.
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.
......@@ -430,7 +429,7 @@ All partners will follow inclusive practices in recruiting staff for this projec
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......@@ -59,9 +59,8 @@ The concrete researcher roles in addition to the PIs are as follows (compare the
\item \site{CAE} will hire one industrial software engineer with expertise in language parsing and compiler construction. Their focus will be on implementation of the Modelica-related services and tools.
\item \site{PS} will hire one research software engineer, who will focus on SageMath-related implementation work.
\end{compactitem}
\ednote{@EMS,@CHA,@FIZ: This is a description of who you will hire, which doubles as a plausibility check that the tasks assigned to you add up to an attractive and feasible job descriptions; check it and revise as needed}
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\subsubsection{Previous Collaborations}
\input{coherence}
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% 2000 characters; objectives, methodology, relevance to work programme; will be used in the evaluation process (this line has 143 characters)
The scientific community increasingly considers datasets as its own kind of scientific resource akin to publications.
The scientific community increasingly considers datasets as their own kind of scientific resource akin to publications.
This also creates a positive feedback loop with deep learning methods, which require large datasets as input.
Multiple national and international Open Science initiatives have been started to ensure open availability, easy sharing, and reliable reproducibility of results.
The FAIR principles especially have been developed as a goalpost for the openness of datasets, including the sharing across disciplines and between research and industry.
......
\noindent\textbf{Mathematics as a Motor for Innovation}: \emph{Innovations based on mathematical knowledge and algorithms} yield many improvements in economy, ecology, health care, security, and in society overall.
Our global positioning system (GPS) needs relativistic mathematics, our mobile phones use allocated frequencies through combinatorial optimisation, the combinatorics of our genome yields clues to curing rare diseases, the privacy of our communications depends on cryptographic protocols steeped in number theory, and our national security is relying on the mathematical analysis of increasingly complex networks.
Fundamental mathematical research and its direct application in practical situations make
many engineering, science, and business innovations that enrich society and mankind possible.
\noindent\textbf{Mathematics as a Motor for Innovation}: Innovations based on mathematical knowledge and algorithms yield many improvements in economy, ecology, health care, security, and in society overall.
Our global positioning system (GPS) needs the mathematics of relativistic physics, our mobile phones use frequencies allocated through combinatorial optimization, the combinatorics of our genome yields clues to curing rare diseases, the privacy of our communications depends on cryptographic protocols steeped in number theory, and our national security is relying on the mathematical analysis of increasingly complex networks.
Fundamental mathematical research and its direct application in practical situations enable many engineering, science, and business innovations that enrich society and mankind.
%
Such applications increasingly drive modern mathematical research, which
depends critically on collaborative tools, computational environments, and online databases.
Many of these digital tools have
revolutionised the way mathematical research is conducted and how it is taken into applications.
For example, engineers use mathematical tools to build and simulate physical models with millions of variables, combining building blocks from databases and algorithms from subroutine libraries.
Another example are automated representations for high-dimensional datasets via structured tensors.\smallskip
Such applications more and more drive modern mathematical research, which depends critically and increasingly on collaborative tools, computational environments, and online databases.
Many of these digital tools have revolutionized the way mathematical research is conducted and how it is turned into applications.
For example, engineers now use mathematical tools to build and simulate physical models based on systems of differential equations and using millions of variables, combining building blocks and algorithms taken from libraries from all over the internet.
%Another example are automated representations for high-dimensional datasets via structured tensors.
\smallskip
\noindent\textbf{Problem: Oligopolization of Mathematics} There is very high commercial interest in the development of mathematical representations as proprietary services and datasets, which leads to the danger of monopolizing their availability.
Indeed we are seeing that the large engineering and internet companies are strategically buying (all) the relevant, innovative startups and hiring top researchers, essentially privatizing and oligopolizing public data, knowledge and technological know-how.
Even in the field of mathematics -- which could be assumed to be ``pure'' and thus immune -- this is the case for, e.g., machine learning algorithms and datasets, e.g. in the case of Wolfram Inc., which has started integrating mathematical data into the ``Wolfram Language'' almost a decade ago. \smallskip
Indeed we are seeing that the large engineering and internet companies are strategically buying (all) the relevant, innovative startups and hiring top researchers, essentially privatizing and oligopolizing public data, knowledge, and technological know-how.
Even in the field of mathematics --- which could be assumed to be ``pure'' and thus immune --- this is the case for, e.g., machine learning algorithms or the data curation of Wolfram Inc., which has started integrating mathematical data into the Wolfram Language almost a decade ago. \smallskip
\noindent\textbf{The Cure: Open Data/Software}: We are strongly convinced that mathematical data and algorithms should be openly available for the research community and industry according to the FAIR principles~\cite{FAIR} and their should be open access to all resources.
The members of this consortium have demonstrated this commitment and its benefits with the open access services and datasets they have developed in the past, such as Modelica~\cite{Modelica:on}, SLICOT~\cite{BenMeh:sslsct99}, SageMath~\cite{SageMath:on}, EuDML~\cite{EuDML:on}, swMATH~\cite{swMATH:on}, or LMFDB~\cite{lmfdb:github}. \smallskip
\noindent\textbf{The Cure: Open Data/Software}: We are strongly convinced that mathematical data and algorithms should be openly available for the research community and industry according to the FAIR principles~\cite{FAIR} and that there should be open access to all resources.
The members of this consortium have demonstrated this commitment and its benefits with the open access services and datasets they have developed or contributed to in the past, such as Modelica~\cite{Modelica:on}, SLICOT~\cite{BenMeh:sslsct99}, SageMath~\cite{SageMath:on}, EuDML~\cite{EuDML:on}, swMATH~\cite{swMATH:on}, or LMFDB~\cite{lmfdb:github}. \smallskip
% what is this proposal about - aim
\noindent\textbf{Project Aim}: We will provide mathematicians and scientists with
......@@ -28,49 +26,57 @@ The members of this consortium have demonstrated this commitment and its benefit
\smallskip
% How will we achieve this?
\noindent\textbf{Prerequisite: Deep FAIRness}: To achieve this, we will need to build services that understand the semantics~\cite[Rec. 7]{FAIR} of the mathematical data they operate on --- only if the mathematical meaning of the data is accessible in all its depth can computer applications provide mathematically sound, interoperable services. We call this \emph{deep} FAIRness.\smallskip
\noindent\textbf{Prerequisite: Deep FAIRness}: To achieve this, we will need to build services that understand the semantics~\cite[Rec. 7]{FAIR} of the mathematical data they operate on --- only if the mathematical meaning of the data is accessible in all its depth can computer applications provide mathematically sound, interoperable services. We call this \emph{deep} FAIRness.
\smallskip
Moreover, because the mathematical standard of rigor and the inherent complexity of mathematical data make Deep FAIRness more essential than in other scientific disciplines, \emph{mathematics is an ideal test case for developing the semantic aspects of the FAIR principles}.
\highlight{Due to the mathematical standard of rigor and the inherent complexity of mathematical data, deep FAIRness is both more difficult and more important for mathematics than for other scientific disciplines.
That also means that mathematics is an ideal test case for developing the semantic aspects of the FAIR principles in general.}
A lot of knowledge sharing motivated by FAIRness has already been done; to name just a few examples from different walks of mathematics:
A lot of FAIR-motivated knowledge sharing has already been done.
A few examples from different walks of mathematics are:
\begin{compactenum}[\em i\rm.]
\item The Modelica language uses symbolic representations of differential equations and control algorithms to model cyber-physical systems and bases simulation services on that. Hundreds of reusable libraries are available on GitHub alone.
\item Highly standardised subroutine libraries like LAPACK~\cite{LAPACK:on}, SLICOT, or MUMPS~\cite{MUMPS:on} form the backbone of almost all engineering software packages.
\item Mathematical information services like zbMATH~\cite{zbMATH:on}, EuDML, and swMATH extend bibliographic metadata of mathematical publications with math subject classifications (essentially taxonomic semantic information) and use automatic extraction to give users enhanced, semantic search capabilities.
\item Libraries of formalized mathematics directly specify the meaning of mathematical definitions, theorems, and proofs in a machine-verifiable way. Tens of thousands of such formal proofs are available in open libraries.
\item Mathematical databases like the ``L-functions and modular forms database'' (LMFDB), the ``GAP Small Groups Library''~\cite{GapSmallGroups:on}, or the ``Open Encyclopedia of Integer Sequences'' (OEIS~\cite{OEIS:on}) store millions of mathematical objects together with their semantic properties, both human-curated or machine-generated.
\item Mathematical databases like the L-functions and Modular Forms DataBase (LMFDB~\cite{lmfdb:on}), the GAP Small Groups Library~\cite{GapSmallGroups:on}, or the Open Encyclopedia of Integer Sequences (OEIS~\cite{OEIS:on}) store millions of mathematical objects together with their semantic properties, both human-curated or machine-generated.
\end{compactenum}
These are used industrially (\emph{i}.-\emph{iv}.) and academically (\emph{i}.-\emph{v}.), inner-mathematically (\emph{ii}., \emph{iv}., \emph{v}.) and transdisciplinarily (e.g. \emph{i}.-\emph{ii}. in engineering and \emph{iii}. in program verification).
These are used industrially (\emph{i}.-\emph{iv}.) and academically (\emph{i}.-\emph{v}.), and inner-mathematically (\emph{ii}., \emph{iv}., \emph{v}.) and transdisciplinarily (e.g. \emph{i}.-\emph{ii}. in engineering and \emph{iii}. in program verification).
But the various representations are non-interoperable, and the datasets therefore are not reusable across systems and communities. This leads to large gaps in the FAIRness of mathematical data and results in missed opportunities for innovative services that could revolutionize mathematical research and applications.\smallskip
\noindent\textbf{Open Source/Data Ethos}: The mathematical community predominantly shares the ethos of open access to publications, software (including source code), and datasets. In fact, all of the examples above are either fully open, partly open, or are currently in the process of opening up the data/software further.
For mathematical software, the open-source ethos has been established already for more than 50 years in subroutine libraries such as LAPACK, % SCALAPACK,
SLICOT, or MUMPS which are produced according to a widely accepted documentation and implementation standard, and are at the core of almost all successful commercial or non-commercial software packages including MATLAB~\cite{MATLAB:on} or SageMath.
For mathematical software, the Open Source ethos has been established already for more than 50 years in subroutine libraries such as LAPACK, % SCALAPACK, SLICOT, or MUMPS
which are produced according to a widely accepted documentation and implementation standard and are at the core of almost all successful commercial and non-commercial software packages including MATLAB~\cite{MATLAB:on} and SageMath \cite{sagemath:on}.
Throughout this project we will reuse and extend open source code, and \TheProject will benefit from future open source contributions during and beyond the lifetime of the project. Moreover, the \pn project will follow the example of the H2020 OpenDreamKit project and conduct all of its development openly in public repositories. In fact like the OpenDreamKit project, which \pn follows up on, the \pn proposal was developed publicly (on \url{https://gl.kwarc.info/mathhub/data-proposal/}).
Thanks to this ``by-users-for-users'' model, \TheProject will be steered by the actual needs of the community.
Thanks to this by-users-for-users model, \TheProject will be steered by the actual needs of the community.
\noindent\textbf{The \TheProject team} is a Europe-wide collaboration that brings together a leading body of
mathematicians and transdisciplinary computational researchers, with an extensive track
record of delivering innovative open source software solutions.\smallskip
\smallskip
\noindent\textbf{Impact}: Standardizing a data framework, unifying services, and hosting all on a public, high-profile infrastructure like the EOSC will enable huge progress in effective research, research communication, and reproducibility in computational mathematics and science.
By focusing on public, open standards and service interoperability \TheProject will simultaneously maximise sustainability and impact. Even though the primary target users are \emph{researchers in mathematics}, the set of beneficiaries extends to researchers, teachers, and industry practitioners in scientific computing, physics, chemistry, biology, engineering, medicine, earth sciences and geography, as well as social sciences and finance.
\noindent\textbf{Impact}: Standardizing a data framework, unifying services, and hosting all on a public, high-profile infrastructure like the EOSC will enable huge progress in effective research, research communication, and reproducibility in computational mathematics and related sciences.
By focusing on public, open standards and service interoperability \TheProject will simultaneously maximize sustainability and impact. Even though the primary target users are researchers in mathematics, the set of beneficiaries extends to researchers, teachers, and industry practitioners in, e.g., scientific computing, physics, chemistry, biology, engineering, medicine, earth sciences and geography, as well as social sciences and finance.
\TheProject will foster the development of models that are mutually beneficial to academia and highly innovative SMEs and enable tool chains that bridge the gap between fundamental mathematical research and domain-specific computational technology, thus supporting the faster application, exploitation, and commercialization of basic research.
Finally, by preparing an ISO standard for mathematical data, \TheProject will scale up these impacts and make them sustainable.\smallskip
\smallskip
\noindent\textbf{Sustainable \pn Infrastructure} It is important to note that while the result of the \pn project is a software infrastructure consisting of
\noindent\textbf{Sustainability}
The result of the \pn project will be a software infrastructure consisting of
\begin{inparaenum}[\em i\rm)]
\item multiple mathematical data that jointly comprise multi-terabyte data bases,
\item various specialized indexes over them, and
\item mathematical services that serve, compute with, visualize, and validate this data.
\item a uniform data representation standard that is ready for semantics-aware FAIR data sharing,
\item innovative user-oriented services that validate, serve, compute with, and visualize this data and leverage it in existing widely used applications, and
\item the uniform integration of multiple community-driven mathematical datasets that jointly comprise multi-terabyte databases.
\end{inparaenum}
The complete \pn software infrastructure will run on a single, well-equipped, modern commodity-grade server that can be maintained sustainably by a part-time (1/4 FTE) experienced system administrator. In particular, the \pn project is not applying for or incurring the future need of dedicated hardware infrastructure or large maintenance teams.
To make these results sustainable beyond the project duration, we will submit the above standard for ISO certification, develop all services in a way that allows for easy deployment on the EOSC Hub, and exploit the datasets made available uniformly through \TheProject in extensive community outreach efforts to publicize the EOSC Hub and FAIR data sharing.
Moreover, the complete \pn software infrastructure will run on a single, well-equipped, modern commodity-grade server that can be maintained sustainably by a part-time (1/4 FTE) experienced system administrator.
In particular, the \pn project is not applying for or incurring the future need of dedicated hardware infrastructure or large maintenance teams.
% why do we need money at all
\noindent\textbf{Funding Need}: One might think that many of the solutions described above will eventually be organized by the mathematical community anyway. But a coordinated effort is needed to create a single, semantic data representation standard and corresponding FAIR service framework. Without such a concerted effort --- which requires the funding and institutional support as provided by the EOSC --- we will likely see the continued development of a multitude of non-interoperable system-specific ``standards'' and competing commercial offerings, which are already becoming more and more entrenched.
As Mathematics is a small -- albeit foundational -- discipline, the \pn proposal stays well below the maximal funding level provided in the INFRAEOSC-02-2019 call to make the proposed project cost-effective.\smallskip
\noindent\textbf{Funding Need}: One might think that many of the solutions described above will eventually be organized by the mathematical community anyway. But a coordinated effort is needed to create a single, coherent data representation standard and the corresponding FAIR service framework. Without such a concerted effort --- which requires the funding and institutional support as provided by the EOSC --- we will likely see the continued development of a multitude of non-interoperable system-specific ``standards'' and competing commercial offerings, which are already becoming more and more entrenched.
As mathematics is a small --- albeit foundational --- discipline, the \pn proposal stays well below the recommended funding level provided in the INFRAEOSC-02-2019 call in order to make the proposed project cost-effective.
\smallskip
The time for the \pn project is ideal as it is a follow-up to the successful OpenDreamKit project (2015--2019), which has built A Virtual Research Environment Toolkit for Mathematics.
OpenDreamKit has identified many of the problems and designed many of the solutions described in this proposal.
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......@@ -90,7 +90,7 @@ Generally speaking, it will improve the capacities of multiple groups of ESOC ec
\item \emph{Funding agencies and Hiring Committees} have a central framework to judge direct contributions and re-use patterns in mathematical data. This will ultimately better integrate the recognition of data contributions into the academic reputation economy and make data production a more attractive proposition for junior researchers.
\item \emph{Industry} --- in particular engineering companies --- can use mathematical model databases and related services.
Industrial stakeholders will be directly involved in the development of the data standards and framework, so that the services will be exactly tailored to their specific needs as well as to the needs of the scientific community.
Moreover, this will allow short time-to-market and will facilitate the technology uptake.
Moreover, this will allow short time-to-market and will facilitate the technology uptake (see \taskref{services}{I})
\end{compactenum}
More concretely, in the next table we describe different market needs and how we can leverage \TheProject results to address them:
......@@ -371,7 +371,7 @@ The following strategic access points will be used to maximize visibility:
%%% TeX-master: "proposal"
%%% End:
% LocalWords: eucommentary programme subsubsection tablehead longtable hline sur est oldpart ednote kpis newenvironment myaim noindent begingroup endgroup popcon organized centering synchronization table:dissem-plan organizations smallskip Vulgarization Organizing taskref dissem outreachul tasktref delivref modelica modelicaservice eosc newpart standardized organization WPref idealized oeis,Sloane:OEIS LuzKoh:fsarfo16 Enxhell Luzhnica sec:expimp inparahighlight findability cocalc:on realization reuser sec:eog summarize Standardization maximization compactitem impact-maximizing minimizing
% LocalWords: eucommentary programme subsubsection tablehead longtable hline sur est oldpart ednote kpis newenvironment myaim noindent begingroup endgroup popcon organized centering synchronization table:dissem-plan organizations smallskip Vulgarization Organizing taskref dissem outreachul tasktref delivref modelica modelicaservice eosc newpart standardized organization WPref idealized oeis,Sloane:OEIS LuzKoh:fsarfo16 Enxhell Luzhnica sec:expimp inparahighlight findability cocalc:on realization reuser sec:eog summarize Standardization maximization compactitem impact-maximizing minimizing Luzhnica:bsc16
% LocalWords: e-infrastracture sémantique données amont j'ai mal si répond vraiment ce
% LocalWords: critère Systeme flushleft arraybslash Ergonomie il faut réflechir façon
% LocalWords: rendre l'outil attractif jeune génération génération des chercheurs va je
......
\begin{newpart}{KPIs draft -- MK: these general KPIs should probably go into implementation section.}
\begin{itemize}
\item Coverage of types of mathematical data.
\item Services, datasets standardized?
\item Standards developed?
\item ISO standard success story
\item Number of services.
\item Number of users signed up.
\item Number of datasets included (coverage).
\item Number of citations of datasets/services
\item Poll/interviews with researchers.
\item Press releases, Carpentry lessons
\item Interviews with authors of papers using datasets.
\item Blog posts.
\item Social media followers.
\item Website visits.
\item Participation in conferences and workshops.
\item Workshop organization.
\item Diversity?
\item Use cases, papers, blog posts.
\end{itemize}
\end{newpart}
\ednote{KB: include side benefits as aims}
\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
of \TheProject so that they are meaningful, reusable, realistic and easily measurable. The following
qualitative and quantitative indicators are divided into the four aims of \TheProject.
If quantitative indicators are more useful for reporting and internal evaluation, qualitative
indicators will give content for further dissemination and communication purposes,
for example through the project website
\footnote{We will survey mathematical departments
(and relevant members of other departments)
at the end of each Reporting Period (M18, and M36) to gauge the awareness of the
existence and capabilities of \TheProject and its components, and to collect
statistical data for estimating Key Performance Indicators listed
in the table. The success factor is a positive change between the three surveys.}.
\newenvironment{myaim}[1]
{\noindent{\textbf{#1:}} \begingroup\it}
{\endgroup}
\begin{myaim}{Aim 1}
Improve the productivity of researchers in pure mathematics and
applications by promoting collaborations based on mathematical
software, data, and knowledge.
\end{myaim}
\begin{itemize}
\item Success stories reported as blogposts (Qualitative).
\end{itemize}
\begin{myaim}{Aim 2}
Make it easy for teams of researchers of any size to set up custom,
collaborative Virtual Research Environments tailored to their
specific needs, resources and workflows. The \VREs should support
the entire life-cycle of computational work in mathematical
research, from initial exploration to publication, teaching and
outreach.
\end{myaim}
\begin{itemize}
\item Success stories about \ODK based VRE deployments and
generally speaking adoption of \ODK's components (Qualitative);
\item List of known \ODK based VRE deployments (Quantitative);
\item Number of installs of \ODK's components via platform-specific
distribution channels: Debian popcon, Arch statistics, installer
downloads, etc. (Quantitative).
\end{itemize}
\begin{myaim}{Aim 3}
Identify and promote best practices in computational mathematical
research including: making results easily reproducible; producing
reusable and easily accessible software; sharing data in a
semantically sound way; exploiting and supporting the growing
ecosystem of computational tools.
\end{myaim}
\begin{itemize}
\item Success stories (Qualitative);
\item Number of PyPI hosted packages for \Sage, and similarly for
other components (Quantitative);
\item Number of additional systems made interoperable with the
Math-in-the-Middle architecture, on top of the three for the Month
36 prototype (Quantitative);
\item Metrics on the scale of the Math-in-the-Middle architecture;
e.g. number of API CDs generated and number of alignments
(Quantitative).
\end{itemize}
\begin{myaim}{Aim 4}
Maximise sustainability and impact in mathematics, neighbouring
fields, and scientific computing.
\end{myaim}
\begin{itemize}
\item Success stories resulting from dissemination activities such as
workshops (Qualitative);
\item Statistics on workshops organized and conference presentations
delivered as part of our dissemination activities, including
estimates of number of attendees (Quantitative);
\item Number of courses and departments \ODK worked with directly and
an estimate of how many students this subsequently affected
(Quantitative).
\end{itemize}
\end{oldpart}
%%% Local Variables:
%%% mode: latex
%%% TeX-master: "proposal"
%%% End:
......@@ -57,8 +57,8 @@ We will also take all necessary steps to make the eventual integration into the
The above-mentioned \inparahighlight{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 is particularly difficult; on the other hand, mathematics is also the domain, where --- due to the inherent complexity of the domain --- methods and practices have been developed to deal explicitly with the semantics of objects and their relations.
Moreover, mathematical data is so rich in structure that a framework like ours that can represent all mathematical data automatically can represent virtually all scientific data.
After all --- according to none less than Galileo --- ``mathematics is the language in which God has written the universe''.
Because mathematical data is so rich in structure, a framework like ours that can elegantly represent all mathematical data can also represent virtually all scientific data.
After all --- according to none less than Galileo --- ``mathematics is the language in which God has written the universe''.
Therefore, \inparahighlight{the solutions for mathematical datasets developed in the \pn project will also carry over and thus benefit other sciences}.
%%% Local Variables:
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......@@ -6,26 +6,9 @@
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.}
% \ednote{Agile properties (http://agilemanifesto.org/principles.html)
% 1. Early and continuous delivery of valuable software.\ednote{Does it make sense to start by advertising EUDAT to the mathematics community?}
% 2. Welcome changing requirements.
% 3. Deliver working software frequently.
% 4. Continuous attention to technical excellence, simplicity (the art of maximising the amount of work not done) and reflection at regular intervals.
% For services:
% 1. Individuals and interactions over processes and tools.
% 2. Working services over comprehensive documentation.
% 3. Customer collaboration over contract negotiation.
% 4. Responding to change over following a plan.
% }
% \ednote{Fit-for-purpose is a common term to describe the ideal level of quality for products, services, processes or information. The term implies that quality is a subjective or situational term that can only be defined in terms of the goals of an organization, individual or set of individuals such as the customers of a business.
% appropriate, and of a necessary standard, for its intended use
% no more and no less than what the client needs}
% \ednote{sustainable: economically, environmentally, ...?}
The objectives of \TheProject fits the scope of the call perfectly.
There are currently a variety of highly innovative and widely used services for digital data in the mathematical sciences.
......
......@@ -48,7 +48,7 @@ A Pert chart showing the \textbf{interrelation of the tasks} is given in Figure~
\input{WorkPackages/WorkPackages}
\gantttaskchart[draft,xscale=.33,yscale=.33,milestones]
\ednote{MK: This chart only contains preliminary information, we will redo/tweak this when the tasks have stabilized.}
\ifgrantagreement\else
\newpage
\subsubsection{List of Deliverables}\label{sec:deliverables}
......
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