Linkitup is a Web-based dashboard for enrichment of research output published via industry grade data repository services. It takes metadata entered through Figshare.com and tries to find equivalent terms, categories, persons or entities on the Linked Data cloud and several Web 2.0 services. It extracts references from publications, and tries to find the corresponding Digital Object Identifier (DOI). Linkitup feeds the enriched metadata back as links to the original article in the repository, but also builds a RDF representation of the metadata that can be downloaded separately, or published as research output in its own right. In this paper, we compare Linkitup to the standard workflow of publishing linked data, and show that it significantly lowers the threshold for publishing linked research data.

1. 2 Semantics
Datato
From Data
Semantics for Scientific Data Publishers
linkitup
Link Discovery for Research Data
Rinke Hoekstra and Paul Groth
Network Insitute, VU University Amsterdam
Law Faculty, University of Amsterdam
★
★
Linkitup - Link Discovery for Research Data by Rinke Hoekstra
Licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

2. 2 Semantics
Datato
From Data
Semantics for Scientific Data Publishers
linkitup
Link Discovery for Research Data
Rinke Hoekstra and Paul Groth
Network Insitute, VU University Amsterdam
Law Faculty, University of Amsterdam
★
★
How to share, publish, access, analyse, interpret and reuse data?
Linkitup - Link Discovery for Research Data by Rinke Hoekstra
Licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

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DATA

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DATA
.. the fallacies (Kayur Patel)

5. DATA
Silver Bullet?

6. DATA
Silver Bullet?
http://on.wsj.com/XCajtB

7. DATA
Silver Bullet?
http://on.wsj.com/XCajtB

8. www.nature.com/nature
Data’s shameful neglect
Vol 461 | Issue no. 7261 | 10 September 2009
Research cannot flourish if data are not preserved and made accessible. All concerned must act accordingly.
M
ore and more often these days, a research project’s success is
measured not just by the publications it produces, but also by
the data it makes available to the wider community. Pioneering archives such as GenBank have demonstrated just how powerful
such legacy data sets can be for generating new discoveries — especially when data are combined from many laboratories and analysed
in ways that the original researchers could not have anticipated.
All but a handful of disciplines still lack the technical, institutional
and cultural frameworks required to support such open data access
(see pages 168 and 171) — leading to a scandalous shortfall in the
sharing of data by researchers (see page 160). This deficiency urgently
needs to be addressed by funders, universities and the researchers
themselves.
Research funding agencies need to recognize that preservation of
and access to digital data are central to their mission, and need to
be supported accordingly. Organizations in the United Kingdom,
for instance, have made a good start. The Joint Information Systems
Committee, established by the seven UK research councils in 1993,
has made data-sharing a priority, and has helped to establish a Digital
Curation Centre, headquartered at the University of Edinburgh, to be
a national focus for research and development into data issues. Other
European agencies have also pursued initiatives.
The United States, by contrast, is playing catch-up. Since 2005, a
29-member Interagency Working Group on Digital Data has been
trying to get US funding agencies to develop plans for how they will
support data archiving — and just as importantly, to develop policies
on what data should and should not be preserved, and what exceptions should be made for reasons such as patient privacy. Some agencies have taken the lead in doing so; many more are hanging back.
They should all being moving forwards vigorously.
What is more, funding agencies and researchers alike must ensure
that they support not only the hardware needed to store the data, but
also the software that will help investigators to do this. One important facet is metadata management software: tools that streamline
the tedious process of annotating data with a description of what the
bits mean, which instrument collected them, which algorithms have
been used to process them and so on — information that is essential
if other scientists are to reuse the data effectively.
Also necessary, especially in an era when data can be mixed and
combined in unanticipated ways, is software that can keep track of
which pieces of data came from whom. Such systems are essential if
tenure and promotion committees are ever to give credit — as they
should — to candidates’ track-record of
“Data management
data contribution.
Who should host these data? Agencies should be woven
and the research community together into every course in
need to create the digital equivalent science.”
of libraries: institutions that can take
responsibility for preserving digital data and making them accessible
over the long term. The university research libraries themselves are
obvious candidates to assume this role. But whoever takes it on, data
preservation will require robust, long-term funding. One potentially
helpful initiative is the US National Science Foundation’s DataNet
programme, in which researchers are exploring financial mechanisms such as subscription services and membership fees.
Finally, universities and individual disciplines need to undertake a
vigorous programme of education and outreach about data. Consider,
for example, that most university science students get a reasonably
good grounding in statistics. But their studies rarely include anything
about information management — a discipline that encompasses the
entire life cycle of data, from how they are acquired and stored to how
they are organized, retrieved and maintained over time. That needs
to change: data management should be woven into every course in
science, as one of the foundations of knowledge.
■
A step too far?
a base on the Moon, then send them to Mars. This idea immediately
set off a debate that is still continuing, in which sceptics ask whether
there is any point in returning to the Moon nearly half a century
after the first landings. Why not go to Mars directly, or visit nearEarth asteroids, or send people to service telescopes in the deep space
beyond Earth?
Yet that debate is both counter-productive — a new set of rockets
could go to all of these places — and moot, because Bush’s vision
never attracted the hoped-for budget increases. Indeed, a blue-riband
commission reporting to US President Barack Obama this week (see
page 153) finds the organizational malaise unchanged: NASA is still
doing too much with too little. Without more money, the agency won’t
be sending people anywhere beyond the International Space Station,
which resides in low Earth orbit only 350 kilometres up. And even the
ability to do that is in question: Ares I, the US rocket that would return
Research cannot flourish if data are not preserved and made
accessible. All concerned must act accordingly.
DATA
The Obama administration must fund human space
flight adequately, or stop speaking of ‘exploration’.
A
fter the space shuttle Columbia burned up during re-entry
into Earth’s atmosphere in 2003, the board that was convened
to investigate the disaster looked beyond its technical causes
to NASA’s organizational malaise. For decades, the board pointed
out, the shuttle programme had been trying to do too much with
too little money. NASA desperately needed a clearer vision and a
better-defined mission for human space flight.
The next year, then-President George W. Bush attempted to supply
that vision with a new long-term goal: first send astronauts to build
145
145-146 Editorials WF IF.indd 145
8/9/09 14:06:40
Silver Bullet?
http://on.wsj.com/XCajtB

9. www.nature.com/nature
Data’s shameful neglect
Vol 461 | Issue no. 7261 | 10 September 2009
Research cannot flourish if data are not preserved and made accessible. All concerned must act accordingly.
M
ore and more often these days, a research project’s success is
measured not just by the publications it produces, but also by
the data it makes available to the wider community. Pioneering archives such as GenBank have demonstrated just how powerful
such legacy data sets can be for generating new discoveries — especially when data are combined from many laboratories and analysed
in ways that the original researchers could not have anticipated.
All but a handful of disciplines still lack the technical, institutional
and cultural frameworks required to support such open data access
(see pages 168 and 171) — leading to a scandalous shortfall in the
sharing of data by researchers (see page 160). This deficiency urgently
needs to be addressed by funders, universities and the researchers
themselves.
Research funding agencies need to recognize that preservation of
and access to digital data are central to their mission, and need to
be supported accordingly. Organizations in the United Kingdom,
for instance, have made a good start. The Joint Information Systems
Committee, established by the seven UK research councils in 1993,
has made data-sharing a priority, and has helped to establish a Digital
Curation Centre, headquartered at the University of Edinburgh, to be
a national focus for research and development into data issues. Other
European agencies have also pursued initiatives.
The United States, by contrast, is playing catch-up. Since 2005, a
29-member Interagency Working Group on Digital Data has been
trying to get US funding agencies to develop plans for how they will
support data archiving — and just as importantly, to develop policies
on what data should and should not be preserved, and what exceptions should be made for reasons such as patient privacy. Some agencies have taken the lead in doing so; many more are hanging back.
They should all being moving forwards vigorously.
What is more, funding agencies and researchers alike must ensure
that they support not only the hardware needed to store the data, but
also the software that will help investigators to do this. One important facet is metadata management software: tools that streamline
the tedious process of annotating data with a description of what the
bits mean, which instrument collected them, which algorithms have
been used to process them and so on — information that is essential
if other scientists are to reuse the data effectively.
Also necessary, especially in an era when data can be mixed and
combined in unanticipated ways, is software that can keep track of
which pieces of data came from whom. Such systems are essential if
tenure and promotion committees are ever to give credit — as they
should — to candidates’ track-record of
“Data management
data contribution.
Who should host these data? Agencies should be woven
and the research community together into every course in
need to create the digital equivalent science.”
of libraries: institutions that can take
responsibility for preserving digital data and making them accessible
over the long term. The university research libraries themselves are
obvious candidates to assume this role. But whoever takes it on, data
preservation will require robust, long-term funding. One potentially
helpful initiative is the US National Science Foundation’s DataNet
programme, in which researchers are exploring financial mechanisms such as subscription services and membership fees.
Finally, universities and individual disciplines need to undertake a
vigorous programme of education and outreach about data. Consider,
for example, that most university science students get a reasonably
good grounding in statistics. But their studies rarely include anything
about information management — a discipline that encompasses the
entire life cycle of data, from how they are acquired and stored to how
they are organized, retrieved and maintained over time. That needs
to change: data management should be woven into every course in
science, as one of the foundations of knowledge.
■
A step too far?
a base on the Moon, then send them to Mars. This idea immediately
set off a debate that is still continuing, in which sceptics ask whether
there is any point in returning to the Moon nearly half a century
after the first landings. Why not go to Mars directly, or visit nearEarth asteroids, or send people to service telescopes in the deep space
beyond Earth?
Yet that debate is both counter-productive — a new set of rockets
could go to all of these places — and moot, because Bush’s vision
never attracted the hoped-for budget increases. Indeed, a blue-riband
commission reporting to US President Barack Obama this week (see
page 153) finds the organizational malaise unchanged: NASA is still
doing too much with too little. Without more money, the agency won’t
be sending people anywhere beyond the International Space Station,
which resides in low Earth orbit only 350 kilometres up. And even the
ability to do that is in question: Ares I, the US rocket that would return
Research cannot flourish if data are not preserved and made
accessible. All concerned must act accordingly.
DATA
The Obama administration must fund human space
flight adequately, or stop speaking of ‘exploration’.
A
fter the space shuttle Columbia burned up during re-entry
into Earth’s atmosphere in 2003, the board that was convened
to investigate the disaster looked beyond its technical causes
to NASA’s organizational malaise. For decades, the board pointed
out, the shuttle programme had been trying to do too much with
too little money. NASA desperately needed a clearer vision and a
better-defined mission for human space flight.
The next year, then-President George W. Bush attempted to supply
that vision with a new long-term goal: first send astronauts to build
145
145-146 Editorials WF IF.indd 145
8/9/09 14:06:40
Silver Bullet?
http://on.wsj.com/XCajtB

10. www.nature.com/nature
Data’s shameful neglect
Vol 461 | Issue no. 7261 | 10 September 2009
Research cannot flourish if data are not preserved and made accessible. All concerned must act accordingly.
M
ore and more often these days, a research project’s success is
measured not just by the publications it produces, but also by
the data it makes available to the wider community. Pioneering archives such as GenBank have demonstrated just how powerful
such legacy data sets can be for generating new discoveries — especially when data are combined from many laboratories and analysed
in ways that the original researchers could not have anticipated.
All but a handful of disciplines still lack the technical, institutional
and cultural frameworks required to support such open data access
(see pages 168 and 171) — leading to a scandalous shortfall in the
sharing of data by researchers (see page 160). This deficiency urgently
needs to be addressed by funders, universities and the researchers
themselves.
Research funding agencies need to recognize that preservation of
and access to digital data are central to their mission, and need to
be supported accordingly. Organizations in the United Kingdom,
for instance, have made a good start. The Joint Information Systems
Committee, established by the seven UK research councils in 1993,
has made data-sharing a priority, and has helped to establish a Digital
Curation Centre, headquartered at the University of Edinburgh, to be
a national focus for research and development into data issues. Other
European agencies have also pursued initiatives.
The United States, by contrast, is playing catch-up. Since 2005, a
29-member Interagency Working Group on Digital Data has been
trying to get US funding agencies to develop plans for how they will
support data archiving — and just as importantly, to develop policies
on what data should and should not be preserved, and what exceptions should be made for reasons such as patient privacy. Some agencies have taken the lead in doing so; many more are hanging back.
They should all being moving forwards vigorously.
What is more, funding agencies and researchers alike must ensure
that they support not only the hardware needed to store the data, but
also the software that will help investigators to do this. One important facet is metadata management software: tools that streamline
the tedious process of annotating data with a description of what the
bits mean, which instrument collected them, which algorithms have
been used to process them and so on — information that is essential
if other scientists are to reuse the data effectively.
Also necessary, especially in an era when data can be mixed and
combined in unanticipated ways, is software that can keep track of
which pieces of data came from whom. Such systems are essential if
tenure and promotion committees are ever to give credit — as they
should — to candidates’ track-record of
“Data management
data contribution.
Who should host these data? Agencies should be woven
and the research community together into every course in
need to create the digital equivalent science.”
of libraries: institutions that can take
responsibility for preserving digital data and making them accessible
over the long term. The university research libraries themselves are
obvious candidates to assume this role. But whoever takes it on, data
preservation will require robust, long-term funding. One potentially
helpful initiative is the US National Science Foundation’s DataNet
programme, in which researchers are exploring financial mechanisms such as subscription services and membership fees.
Finally, universities and individual disciplines need to undertake a
vigorous programme of education and outreach about data. Consider,
for example, that most university science students get a reasonably
good grounding in statistics. But their studies rarely include anything
about information management — a discipline that encompasses the
entire life cycle of data, from how they are acquired and stored to how
they are organized, retrieved and maintained over time. That needs
to change: data management should be woven into every course in
science, as one of the foundations of knowledge.
■
A step too far?
a base on the Moon, then send them to Mars. This idea immediately
set off a debate that is still continuing, in which sceptics ask whether
there is any point in returning to the Moon nearly half a century
after the first landings. Why not go to Mars directly, or visit nearEarth asteroids, or send people to service telescopes in the deep space
beyond Earth?
Yet that debate is both counter-productive — a new set of rockets
could go to all of these places — and moot, because Bush’s vision
never attracted the hoped-for budget increases. Indeed, a blue-riband
commission reporting to US President Barack Obama this week (see
page 153) finds the organizational malaise unchanged: NASA is still
doing too much with too little. Without more money, the agency won’t
be sending people anywhere beyond the International Space Station,
which resides in low Earth orbit only 350 kilometres up. And even the
ability to do that is in question: Ares I, the US rocket that would return
Research cannot flourish if data are not preserved and made
accessible. All concerned must act accordingly.
DATA
The Obama administration must fund human space
flight adequately, or stop speaking of ‘exploration’.
A
fter the space shuttle Columbia burned up during re-entry
into Earth’s atmosphere in 2003, the board that was convened
to investigate the disaster looked beyond its technical causes
to NASA’s organizational malaise. For decades, the board pointed
out, the shuttle programme had been trying to do too much with
too little money. NASA desperately needed a clearer vision and a
better-defined mission for human space flight.
The next year, then-President George W. Bush attempted to supply
that vision with a new long-term goal: first send astronauts to build
145
145-146 Editorials WF IF.indd 145
8/9/09 14:06:40
Silver Bullet?
http://on.wsj.com/XCajtB

11. Repository Services
•
•
•
•
•
Data is easy to upload
Landing page for data
Citable reference for data
Default licensing options
Guarantees for long term archival

12. Standard Metadata
•
Provenance metadata
•
Content metadata
•
•
Metadata is locked in
authors, title, publication date
free text tags, categories, links
Hard to interpret the data itself

13. Data is the Bottleneck
Common Motifs in Scientific Workflows:
An Empirical Analysis
Daniel Garijo⇤ , Pinar Alper † , Khalid Belhajjame† , Oscar Corcho⇤ , Yolanda Gil‡ , Carole Goble†
⇤ Ontology
Engineering Group, Universidad Polit´ cnica de Madrid. {dgarijo, ocorcho}@fi.upm.es
e
of Computer Science, University of Manchester. {alperp, khalidb, carole.goble}@cs.manchester.ac.uk
‡ Information Sciences Institute, Department of Computer Science, University of Southern California. gil@isi.edu
† School
Abstract—While workflow technology has gained momentum
in the last decade as a means for specifying and enacting computational experiments in modern science, reusing and repurposing
existing workflows to build new scientific experiments is still a
daunting task. This is partly due to the difficulty that scientists
experience when attempting to understand existing workflows,
which contain several data preparation and adaptation steps in
addition to the scientifically significant analysis steps. One way
to tackle the understandability problem is through providing
abstractions that give a high-level view of activities undertaken
within workflows. As a first step towards abstractions, we report
in this paper on the results of a manual analysis performed over
a set of real-world scientific workflows from Taverna and Wings
systems. Our analysis has resulted in a set of scientific workflow
motifs that outline i) the kinds of data intensive activities that are
observed in workflows (data oriented motifs), and ii) the different
manners in which activities are implemented within workflows
(workflow oriented motifs). These motifs can be useful to inform
workflow designers on the good and bad practices for workflow
development, to inform the design of automated tools for the
generation of workflow abstractions, etc.
I. I NTRODUCTION
Scientific workflows have been increasingly used in the last
decade as an instrument for data intensive scientific analysis.
In these settings, workflows serve a dual function: first as
detailed documentation of the method (i. e. the input sources
and processing steps taken for the derivation of a certain
data item) and second as re-usable, executable artifacts for
data-intensive analysis. Workflows stitch together a variety
of data manipulation activities such as data movement, data
transformation or data visualization to serve the goals of the
scientific study. The stitching is realized by the constructs
made available by the workflow system used and is largely
shaped by the environment in which the system operates and
the function undertaken by the workflow.
A variety of workflow systems are in use [10] [3] [7] [2]
serving several scientific disciplines. A workflow is a software
artifact, and as such once developed and tested, it can be
shared and exchanged between scientists. Other scientists can
then reuse existing workflows in their experiments, e.g., as
sub-workflows [17]. Workflow reuse presents several advantages [4]. For example, it enables proper data citation and
improves quality through shared workflow development by
leveraging the expertise of previous users. Users can also
re-purpose existing workflows to adapt them to their needs
[4]. Emerging workflow repositories such as myExperiment
[14] and CrowdLabs [8] have made publishing and finding
workflows easier, but scientists still face the challenges of reuse, which amounts to fully understanding and exploiting the
available workflows/fragments. One difficulty in understanding
workflows is their complex nature. A workflow may contain
several scientifically-significant analysis steps, combined with
various other data preparation activities, and in different
implementation styles depending on the environment and
context in which the workflow is executed. The difficulty in
understanding causes workflow developers to revert to starting
from scratch rather than re-using existing fragments.
Through an analysis of the current practices in scientific
workflow development, we could gain insights on the creation
of understandable and more effectively re-usable workflows.
Specifically, we propose an analysis with the following objectives:
1) To reverse-engineer the set of current practices in workflow development through an analysis of empirical evidence.
2) To identify workflow abstractions that would facilitate
understandability and therefore effective re-use.
3) To detect potential information sources and heuristics
that can be used to inform the development of tools for
creating workflow abstractions.
In this paper we present the result of an empirical analysis
performed over 177 workflow descriptions from Taverna [10]
and Wings [3]. Based on this analysis, we propose a catalogue
of scientific workflow motifs. Motifs are provided through i)
a characterization of the kinds of data-oriented activities that
are carried out within workflows, which we refer to as dataoriented motifs, and ii) a characterization of the different manners in which those activity motifs are realized/implemented
within workflows, which we refer to as workflow-oriented
motifs. It is worth mentioning that, although important, motifs
that have to do with scheduling and mapping of workflows
onto distributed resources [12] are out the scope of this paper.
The paper is structured as follows. We begin by providing
related work in Section II, which is followed in Section III by
brief background information on Scientific Workflows, and the
two systems that were subject to our analysis. Afterwards we
describe the dataset and the general approach of our analysis.
We present the detected scientific workflow motifs in Section
IV and we highlight the main features of their distribution

14. Data is the Bottleneck
Common Motifs in Scientific Workflows:
An Empirical Analysis
Daniel Garijo⇤ , Pinar Alper † , Khalid Belhajjame† , Oscar Corcho⇤ , Yolanda Gil‡ , Carole Goble†
⇤ Ontology
Engineering Group, Universidad Polit´ cnica de Madrid. {dgarijo, ocorcho}@fi.upm.es
e
of Computer Science, University of Manchester. {alperp, khalidb, carole.goble}@cs.manchester.ac.uk
‡ Information Sciences Institute, Department of Computer Science, University of Southern California. gil@isi.edu
† School
Abstract—While workflow technology has gained momentum
in the last decade as a means for specifying and enacting computational experiments in modern science, reusing and repurposing
existing workflows to build new scientific experiments is still a
daunting task. This is partly due to the difficulty that scientists
experience when attempting to understand existing workflows,
which contain several data preparation and adaptation steps in
addition to the scientifically significant analysis steps. One way
to tackle the understandability problem is through providing
abstractions that give a high-level view of activities undertaken
within workflows. As a first step towards abstractions, we report
in this paper on the results of a manual analysis performed over
a set of real-world scientific workflows from Taverna and Wings
systems. Our analysis has resulted in a set of scientific workflow
motifs that outline i) the kinds of data intensive activities that are
observed in workflows (data oriented motifs), and ii) the different
manners in which activities are implemented within workflows
(workflow oriented motifs). These motifs can be useful to inform
workflow designers on the good and bad practices for workflow
development, to inform the design of automated tools for the
generation of workflow abstractions, etc.
I. I NTRODUCTION
Scientific workflows have been increasingly used in the last
decade as an instrument for data intensive scientific analysis.
In these settings, workflows serve a dual function: first as
detailed documentation of the method (i. e. the input sources
and processing steps taken for the derivation of a certain
data item) and second as re-usable, executable artifacts for
data-intensive analysis. Workflows stitch together a variety
of data manipulation activities such as data movement, data
transformation or data visualization to serve the goals of the
scientific study. The stitching is realized by the constructs
made available by the workflow system used and is largely
shaped by the environment in which the system operates and
the function undertaken by the workflow.
A variety of workflow systems are in use [10] [3] [7] [2]
serving several scientific disciplines. A workflow is a software
artifact, and as such once developed and tested, it can be
shared and exchanged between scientists. Other scientists can
then reuse existing workflows in their experiments, e.g., as
sub-workflows [17]. Workflow reuse presents several advantages [4]. For example, it enables proper data citation and
improves quality through shared workflow development by
leveraging the expertise of previous users. Users can also
re-purpose existing workflows to adapt them to their needs
[4]. Emerging workflow repositories such as myExperiment
[14] and CrowdLabs [8] have made publishing and finding
workflows easier, but scientists still face the challenges of reuse, which amounts to fully understanding and exploiting the
available workflows/fragments. One difficulty in understanding
workflows is their complex nature. A workflow may contain
several scientifically-significant analysis steps, combined with
various other data preparation activities, and in different
implementation styles depending on the environment and
context in which the workflow is executed. The difficulty in
understanding causes workflow developers to revert to starting
from scratch rather than re-using existing fragments.
Through an analysis of the current practices in scientific
workflow development, we could gain insights on the creation
of understandable and more effectively re-usable workflows.
Specifically, we propose an analysis with the following objectives:
1) To reverse-engineer the set of current practices in workflow development through an analysis of empirical evidence.
2) To identify workflow abstractions that would facilitate
understandability and therefore effective re-use.
3) To detect potential information sources and heuristics
that can be used to inform the development of tools for
creating workflow abstractions.
In this paper we present the result of an empirical analysis
performed over 177 workflow descriptions from Taverna [10]
and Wings [3]. Based on this analysis, we propose a catalogue
of scientific workflow motifs. Motifs are provided through i)
a characterization of the kinds of data-oriented activities that
are carried out within workflows, which we refer to as dataoriented motifs, and ii) a characterization of the different manners in which those activity motifs are realized/implemented
within workflows, which we refer to as workflow-oriented
motifs. It is worth mentioning that, although important, motifs
that have to do with scheduling and mapping of workflows
onto distributed resources [12] are out the scope of this paper.
The paper is structured as follows. We begin by providing
related work in Section II, which is followed in Section III by
brief background information on Scientific Workflows, and the
two systems that were subject to our analysis. Afterwards we
describe the dataset and the general approach of our analysis.
We present the detected scientific workflow motifs in Section
IV and we highlight the main features of their distribution
Data-Oriented Motifs per Domain
Fig. 3.
Distribution of Data-Oriented Motifs per domain

15. Data is the Bottleneck
Common Motifs in Scientific Workflows:
An Empirical Analysis
Daniel Garijo⇤ , Pinar Alper † , Khalid Belhajjame† , Oscar Corcho⇤ , Yolanda Gil‡ , Carole Goble†
⇤ Ontology
Engineering Group, Universidad Polit´ cnica de Madrid. {dgarijo, ocorcho}@fi.upm.es
e
of Computer Science, University of Manchester. {alperp, khalidb, carole.goble}@cs.manchester.ac.uk
‡ Information Sciences Institute, Department of Computer Science, University of Southern California. gil@isi.edu
† School
Abstract—While workflow technology has gained momentum
in the last decade as a means for specifying and enacting computational experiments in modern science, reusing and repurposing
existing workflows to build new scientific experiments is still a
daunting task. This is partly due to the difficulty that scientists
experience when attempting to understand existing workflows,
which contain several data preparation and adaptation steps in
addition to the scientifically significant analysis steps. One way
to tackle the understandability problem is through providing
abstractions that give a high-level view of activities undertaken
within workflows. As a first step towards abstractions, we report
in this paper on the results of a manual analysis performed over
a set of real-world scientific workflows from Taverna and Wings
systems. Our analysis has resulted in a set of scientific workflow
motifs that outline i) the kinds of data intensive activities that are
observed in workflows (data oriented motifs), and ii) the different
manners in which activities are implemented within workflows
(workflow oriented motifs). These motifs can be useful to inform
workflow designers on the good and bad practices for workflow
development, to inform the design of automated tools for the
generation of workflow abstractions, etc.
Fig. 3.
[14] and CrowdLabs [8] have made publishing and finding
workflows easier, but scientists still face the challenges of reuse, which amounts to fully understanding and exploiting the
available workflows/fragments. One difficulty in understanding
workflows is their complex nature. A workflow may contain
several scientifically-significant analysis steps, combined with
various other data preparation activities, and in different
implementation styles depending on the environment and
context in which the workflow is executed. The difficulty in
understanding causes workflow developers to revert to starting
from scratch rather than re-using existing fragments.
Through an analysis of the current practices in scientific
workflow development, we could gain insights on the creation
of understandable and more effectively re-usable workflows.
Specifically, we propose an analysis with the following objectives:
Distribution of Data-Orientedpractices in work- domain
1) To reverse-engineer the set of current Motifs per
I. I NTRODUCTION
Scientific workflows have been increasingly used in the last
decade as an instrument for data intensive scientific analysis.
In these settings, workflows serve a dual function: first as
detailed documentation of the method (i. e. the input sources
and processing steps taken for the derivation of a certain
data item) and second as re-usable, executable artifacts for
data-intensive analysis. Workflows stitch together a variety
of data manipulation activities such as data movement, data
transformation or data visualization to serve the goals of the
scientific study. The stitching is realized by the constructs
made available by the workflow system used and is largely
shaped by the environment in which the system operates and
the function undertaken by the workflow.
A variety of workflow systems are in use [10] [3] [7] [2]
serving several scientific disciplines. A workflow is a software
artifact, and as such once developed and tested, it can be
shared and exchanged between scientists. Other scientists can
then reuse existing workflows in their experiments, e.g., as
sub-workflows [17]. Workflow reuse presents several advantages [4]. For example, it enables proper data citation and
improves quality through shared workflow development by
leveraging the expertise of previous users. Users can also
re-purpose existing workflows to adapt them to their needs
[4]. Emerging workflow repositories such as myExperiment
flow development through an analysis of empirical evidence.
2) To identify workflow abstractions that would facilitate
understandability and therefore effective re-use.
3) To detect potential information sources and heuristics
that can be used to inform the development of tools for
creating workflow abstractions.
In this paper we present the result of an empirical analysis
performed over 177 workflow descriptions from Taverna [10]
and Wings [3]. Based on this analysis, we propose a catalogue
of scientific workflow motifs. Motifs are provided through i)
a characterization of the kinds of data-oriented activities that
are carried out within workflows, which we refer to as dataoriented motifs, and ii) a characterization of the different manners in which those activity motifs are realized/implemented
within workflows, which we refer to as workflow-oriented
motifs. It is worth mentioning that, although important, motifs
that have to do with scheduling and mapping of workflows
onto distributed resources [12] are out the scope of this paper.
The paper is structured as follows. We begin by providing
related work in Section II, which is followed in Section III by
brief background information on Scientific Workflows, and the
two systems that were subject to our analysis. Afterwards we
describe the dataset and the general approach of our analysis.
We present the detected scientific workflow motifs in Section
IV and we highlight the main features of their distribution
Fig. 5.
Fig. 3.
Data-Preparation Motifs per Domain
Data-Oriented Motifs per Domain
Data Preparation Motifs in the Genomics Wo
Distribution of Data-Oriented Motifs per domain

16. Make Data Flourish
From data to information to knowledge

17. Make Data Flourish
From data to information to knowledge
Global identification of
data sets and data items
Data uses a common syntax
Papers explicitly
link to data
Metadata expressed using
shared vocabularies
Capture the processes by
which data is manipulated
Track and publish explicit
provenance information

18. Make Data Flourish
From data to information to knowledge
Global identification of
data sets and data items
Metadata expressed using
shared vocabularies
Capture the processes by
"Someone who is not the person who collected the data can
which data is
Data uses a common syntax experiment and data" - Shreejoy Tripathy manipulated
understand the
Papers explicitly
link to data
Track and publish explicit
provenance information

19. Linked Data
•
•
•
•
•
Use existing Web infrastructure
Everything gets a URI and usually a category
Express typed relations between things (triples)
Express sameness or difference
Reuse identifiers as much as possible
+
=

20. Salah, Alkim Almila Akdag, Cheng Gao, Krzysztof Suchecki, and Andrea Scharnhorst. 2012. “Need to Categorize: A Comparative Look at the Categories of Universal
Decimal Classification System and Wikipedia.” Leonardo 45 (1) (February): 84-85. doi:10.1162/LEON_a_00344. (Preprint http://arxiv.org/abs/1105.5912v1)

21. Linked Data for Science
Neuroscience Information Framework
(Ontologies, Semantic Wiki, Catalog)
Nanopublications
(small scientific assertions)
Workflow Systems
(WINGS, Taverna, …)
Linked Science
(tools)
BioPortal
(ontologies)
Organic Data Publishing
Rightfield
(Semantic Wiki)
(systems biology)
Bio2RDF
(big linked data)

22. …Claire Monteleoni

23. Hellenic
FBD
Hellenic
PD
Crime
Reports
UK
Ox
Points
NHS
(EnAKTing)
Ren.
Energy
Generators
Open
Election
Data
Project
EU
Institutions
CO2
Emission
(EnAKTing)
Energy
(EnAKTing)
EEA
Mortality
(EnAKTing)
Ordnance
Survey
legislation
data.gov.uk
UK Postcodes
ESD
standards
ISTAT
Immigration
Lichfield
Spending
Scotland
Pupils &
Exams
Traffic
Scotland
Data
Gov.ie
reference
data.gov.
uk
London
Gazette
TWC LOGD
Eurostat
(FUB)
CORDIS
CORDIS
(FUB)
(RKB
Explorer)
Linked
EDGAR
(Ontology
Central)
EURES
(Ontology
Central)
GovTrack
Finnish
Municipalities
New
York
Times
Italian
public
schools
IdRef
Sudoc
Greek
DBpedia
Geo
Names
World
Factbook
Geo
Species
UMBEL
Freebase
DBLP
(FU
Berlin)
dataopenac-uk
TCM
Gene
DIT
Daily
Med
SIDER
Twarql
EUNIS
PDB
SMC
Journals
Ocean
Drilling
Codices
Turismo
de
Zaragoza
Janus
AMP
Climbing
Linked
GeoData
Alpine
Ski
Austria
AEMET
Metoffice
Weather
Forecasts
Yahoo!
Geo
Planet
National
Radioactivity
JP
ChEMBL
Open
Data
Thesaurus
Sears
DBLP
(RKB
Explorer)
STW
GESIS
Budapest
Pisa
RESEX
Scholarometer
IRIT
ACM
NVD
IBM
DEPLOY
Newcastle
RAE2001
LOCAH
Roma
CiteSeer
Courseware
dotAC
ePrints
IEEE
RISKS
PROSITE
Affymetrix
SISVU
GEMET
Airports
lobid
Organisations
ECS
(RKB
Explorer)
HGNC
(Bio2RDF)
PubMed
ProDom
VIVO
Cornell
STITCH
Linked
Open
Colors
SGD
Gene
Ontology
AGROV
OC
Product
DB
Weather
Stations
Swedish
Open
Cultural
Heritage
LAAS
NSF
KISTI
JISC
WordNet
(RKB
Explorer)
EARTh
ECS
Southampton
EPrints
VIVO
Indiana
UniProt
LODE
WordNet
(W3C)
Wiki
ECS
Southampton
Pfam
LinkedCT
Taxono
my
Cornetto
NSZL
Catalog
P20
Eurécom
totl.net
WordNet
(VUA)
lobid
Resources
UN/
LOCODE
Drug
Bank
Enipedia
Lexvo
DBLP
(L3S)
ERA
Diseasome
lingvoj
Europeana
Deutsche
Biographie
OAI
data
dcs
Uberblic
YAGO
Open
Cyc
BibBase
OS
dbpedia
lite
Norwegian
MeSH
VIAF
UB
Mannheim
Ulm
data
bnf.fr
BNB
Project
Gutenberg
Rådata
nå!
GND
ndlna
Calames
DDC
iServe
riese
GeoWord
Net
El
Viajero
Tourism
URI
Burner
LIBRIS
LCSH
MARC
Codes
List
PSH
RDF
Book
Mashup
Open
Calais
ntnusc
Thesaurus W
SW
Dog
Food
Portuguese
DBpedia
LEM
RAMEAU
SH
LinkedL
CCN
Sudoc
UniProt
US Census
(rdfabout)
Piedmont
Accomodations
Linked
MDB
t4gm
info
Open
Library
(Talis)
theses.
fr
my
Experiment
flickr
wrappr
NDL
subjects
Plymouth
Reading
Lists
Revyu
Fishes
of Texas
(rdfabout)
Scotland
Geography
Pokedex
Event
Media
US SEC
Semantic
XBRL
FTS
Goodwin
Family
NTU
Resource
Lists
Open
Library
SSW
Thesaur
us
Didactal
ia
DBpedia
Linked
Sensor Data
(Kno.e.sis)
Eurostat
Chronicling
America
Telegraphis
Geo
Linked
Data
Source Code
Ecosystem
Linked Data
semantic
web.org
BBC
Music
BBC
Wildlife
Finder
NASA
(Data
Incubator)
transport
data.gov.
uk
Eurostat
Classical
(DB
Tune)
Taxon
Concept
LOIUS
Poképédia
St.
Andrews
Resource
Lists
Manchester
Reading
Lists
gnoss
Last.FM
(rdfize)
BBC
Program
mes
Rechtspraak.
nl
Openly
Local
data.gov.uk
intervals
Music
Brainz
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Jamendo
(DBtune)
Ontos
News
Portal
Sussex
Reading
Lists
Bricklink
yovisto
Semantic
Tweet
Linked
Crunchbase
RDF
ohloh
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Incubator)
(DBTune)
OpenEI
statistics
data.gov.
uk
GovWILD
Brazilian
Politicians
educatio
n.data.g
ov.uk
Music
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(zitgist)
Discogs
FanHubz
patents
data.go
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research
data.gov.
uk
Klappstuhlclub
Lotico
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Incubator)
Last.FM
artists
Population (EnAKTing)
reegle
Surge
Radio
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delicious
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Music
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John
Peel
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EUTC
Productions
business
data.gov.
uk
Crime
(EnAKTing)
GTAA
Magnatune
DB
Tropes
Moseley
Folk
Linked
User
Feedback
LOV
Audio
Scrobbler
OMIM
MGI
InterPro
Smart
Link
Product
Types
Ontology
Open
Corporates
Italian
Museums
Amsterdam
Museum
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
UniParc
UniRef
UniSTS
GeneID
Linked
Open
Numbers
Reactome
OGOLOD
KEGG
Pathway
Medi
Care
Google
Art
wrapper
meducator
KEGG
Drug
Pub
Chem
UniPath
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Chem2
Bio2RDF
Homolo
Gene
VIVO UF
ECCOTCP
bible
ontology
KEGG
Enzyme
PBAC
KEGG
Reaction
KEGG
Compound
KEGG
Glycan
Media
Geographic
Publications
User-generated content
Government
Cross-domain
Life sciences
As of September 2011

24. Eurostat
Finnish
Municipalities
0
(rdfabout)
Scotland
Geography
US Census
(rdfabout)
GeoWord
Net
Piedmont
Accomodations
Italian
public
schools
El
Viajero
Tourism
Greek
DBpedia
World
Factbook
Geo
Species
UMBEL
Freebase
Project
Gutenberg
dbpedia
lite
DBLP
(FU
Berlin)
dataopenac-uk
TCM
Gene
DIT
Daily
Med
SIDER
SMC
Journals
Ocean
Drilling
Codices
Turismo
de
Zaragoza
Janus
AMP
EUNIS
Climbing
Twarql
Linked
GeoData
WordNet
(W3C)
Alpine
Ski
Austria
AEMET
Metoffice
Weather
Forecasts
WordNet
(RKB
Explorer)
UniProt
(Bio2RDF)
Affymetrix
SISVU
GEMET
ChEMBL
Open
Data
Thesaurus
Product
DB
Airports
National
Radioactivity
JP
LODE
Taxono
my
Sears
Linked
Open
Colors
PDB
PROSITE
Open
Corporates
Italian
Museums
PubMed
MGI
InterPro
Amsterdam
Museum
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
UniRef
HGNC
SGD
Gene
Ontology
OMIM
UniParc
UniSTS
Linked
Open
Numbers
Reactome
OGOLOD
Pub
Chem
GeneID
ECS
Southampton
EPrints
lobid
Organisations
ECS
(RKB
Explorer)
DBLP
(RKB
Explorer)
UniPath
way
Chem2
Bio2RDF
Swedish
Open
Cultural
Heritage
STW
GESIS
Budapest
Pisa
RESEX
Scholarometer
IRIT
ACM
NVD
IBM
DEPLOY
Newcastle
RAE2001
LOCAH
Roma
CiteSeer
Courseware
KEGG
Drug
KEGG
Pathway
Homolo
Gene
dotAC
ePrints
LAAS
NSF
KISTI
JISC
VIVO UF
ECCOTCP
bible
ontology
KEGG
Enzyme
PBAC
KEGG
Reaction
KEGG
Compound
IEEE
RISKS
VIVO
Cornell
STITCH
Medi
Care
Google
Art
wrapper
meducator
Wiki
ECS
Southampton
VIVO
Indiana
ProDom
Smart
Link
Product
Types
Ontology
NSZL
Catalog
Pfam
LinkedCT
AGROV
OC
EARTh
Weather
Stations
Yahoo!
Geo
Planet
Cornetto
lobid
Resources
P20
Eurécom
totl.net
WordNet
(VUA)
Ulm
UN/
LOCODE
Drug
Bank
Enipedia
Lexvo
DBLP
(L3S)
ERA
Diseasome
lingvoj
Europeana
Deutsche
Biographie
OAI
data
dcs
Uberblic
YAGO
Open
Cyc
BibBase
OS
VIAF
UB
Mannheim
Calames
BNB
UniProt
US SEC
Semantic
XBRL
FTS
Geo
Names
riese
8 okt. 2007
Linked
EDGAR
(Ontology
Central)
EURES
(Ontology
Central)
GovTrack
URI
Burner
Norwegian
MeSH
GND
ndlna
data
bnf.fr
iServe
Fishes
of Texas
Linked
Sensor Data
(Kno.e.sis)
Eurostat
1 mei 2007
CORDIS
(FUB)
(RKB
Explorer)
IdRef
Sudoc
DDC
Open
Calais
Rådata
nå!
PSH
RDF
Book
Mashup
DBpedia
Geo
Linked
Data
CORDIS
New
York
Times
LIBRIS
LCSH
MARC
Codes
List
Sudoc
SW
Dog
Food
Portuguese
DBpedia
ntnusc
Thesaurus W
23 feb. 2012
TWC LOGD
Eurostat
(FUB)
Event
Media
LEM
RAMEAU
SH
LinkedL
CCN
14 jul. 2009
Data
Gov.ie
100
London
Gazette
NASA
(Data
Incubator)
transport
data.gov.
uk
Linked
MDB
27 mrt. 2009
Traffic
Scotland
data.gov.uk
intervals
flickr
wrappr
t4gm
info
Open
Library
(Talis)
theses.
fr
my
Experiment
5 mrt. 2009
Scotland
Pupils &
Exams
reference
data.gov.
uk
Pokedex
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subjects
Plymouth
Reading
Lists
Revyu
Taxon
Concept
LOIUS
Chronicling
America
Telegraphis
200
Goodwin
Family
NTU
Resource
Lists
Open
Library
SSW
Thesaur
us
semantic
web.org
BBC
Music
BBC
Wildlife
Finder
Rechtspraak.
nl
Openly
Local
Classical
(DB
Tune)
Source Code
Ecosystem
Linked Data
Didactal
ia
18 sep. 2008
ISTAT
Immigration
Lichfield
Spending
OpenEI
statistics
data.gov.
uk
GovWILD
ESD
standards
educatio
n.data.g
ov.uk
Ordnance
Survey
legislation
data.gov.uk
UK Postcodes
Brazilian
Politicians
300
Poképédia
Last.FM
(rdfize)
BBC
Program
mes
Ontos
News
Portal
Manchester
Reading
Lists
gnoss
31 mrt. 2008
Open
Election
Data
Project
EU
Institutions
CO2
Emission
(EnAKTing)
Energy
(EnAKTing)
EEA
Mortality
(EnAKTing)
Jamendo
(DBtune)
28 feb. 2008
Ren.
Energy
Generators
(DBTune)
patents
data.go
v.uk
research
data.gov.
uk
Music
Brainz
(DBTune)
FanHubz
Last.FM
artists
Population (EnAKTing)
NHS
(EnAKTing)
(Data
Incubator)
yovisto
Semantic
Tweet
Linked
Crunchbase
RDF
ohloh
Discogs
10 nov. 2007
Ox
Points
reegle
business
data.gov.
uk
Crime
(EnAKTing)
Surge
Radio
Music
Brainz
(zitgist)
(Data
Incubator)
7 nov. 2007
Crime
Reports
UK
400
Lotico
St.
Andrews
Resource
Lists
19 sep. 2011
Hellenic
PD
EUTC
Productions
Klappstuhlclub
Sussex
Reading
Lists
Bricklink
(DBTune)
Music
Brainz
John
Peel
(DBTune)
tags2con
delicious
Slideshare
2RDF
22 sep. 2010
Hellenic
FBD
GTAA
Magnatune
DB
Tropes
Moseley
Folk
Linked
User
Feedback
LOV
Audio
Scrobbler
KEGG
Glycan
Media
Geographic
Publications
User-generated content
Government
Cross-domain
Life sciences
As of September 2011

25. 62.224.812.703 Triples!

26. 62.224.812.703 Triples!
(1.75 Billion)

27. LODStats Analysis
http://stats.lod2.eu
140
134
2%
4%
4%
105
7%
84
35
HTTP
Other
12
Unknown response
28%
30
No URL provided
11
XML
6
Connection reset
0
Not RDF
22
10%
45%
70
Not RDF
Connection reset
Unknown response
XML
No URL provided
Other
HTTP
Hoekstra, Rinke; Groth, Paul (2013): Distribution of Errors Reported by LOD2 LODStats Project. figshare.
http://dx.doi.org/10.6084/m9.figshare.695949
299 out of 639 datasets have errors

28. An Ambient Agent Model for Monitoring and
Analysing Dynamics of Complex Human
Behaviour
Tibor Bossea*, Mark Hoogendoorna, Michel C.A. Kleina, and Jan Treura
a
Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam,
The Netherlands
Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying
whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dynamic interactions between human and environment. In order to enable such more complex types of monitoring, this paper
presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1)
the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents,
and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex monitoring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving
behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations,
respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers
within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have
shown that the framework is easy to use and applicable in a wide variety of domains.
Keywords: ambient agent model, human behaviour, dynamics
Journal of Ambient Intelligence and Smart Environments

29. “Whoah! Cool, you should publish that stuff as Linked Data”
An Ambient Agent Model for Monitoring and
Analysing Dynamics of Complex Human
Behaviour
Tibor Bossea*, Mark Hoogendoorna, Michel C.A. Kleina, and Jan Treura
a
Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam,
The Netherlands
Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying
whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dynamic interactions between human and environment. In order to enable such more complex types of monitoring, this paper
presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1)
the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents,
and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex monitoring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving
behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations,
respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers
within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have
shown that the framework is easy to use and applicable in a wide variety of domains.
Keywords: ambient agent model, human behaviour, dynamics
Journal of Ambient Intelligence and Smart Environments

30. “Whoah! Cool, you should publish that stuff as Linked Data”
An Ambient Agent Model
“Um, but doesn’t TTL have incompatible semantics?” for Monitoring and
Analysing Dynamics of Complex Human
Behaviour
Tibor Bossea*, Mark Hoogendoorna, Michel C.A. Kleina, and Jan Treura
a
Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam,
The Netherlands
Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying
whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dynamic interactions between human and environment. In order to enable such more complex types of monitoring, this paper
presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1)
the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents,
and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex monitoring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving
behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations,
respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers
within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have
shown that the framework is easy to use and applicable in a wide variety of domains.
Keywords: ambient agent model, human behaviour, dynamics
Journal of Ambient Intelligence and Smart Environments

31. “Whoah! Cool, you should publish that stuff as Linked Data”
An Ambient Agent Model
“Um, but doesn’t TTL have incompatible semantics?” for Monitoring and
Analysing Dynamics of Complex Human
“Nah, silly, who cares? We’ll just start a new W3C WG!”
Behaviour
Tibor Bossea*, Mark Hoogendoorna, Michel C.A. Kleina, and Jan Treura
a
Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam,
The Netherlands
Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying
whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dynamic interactions between human and environment. In order to enable such more complex types of monitoring, this paper
presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1)
the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents,
and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex monitoring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving
behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations,
respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers
within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have
shown that the framework is easy to use and applicable in a wide variety of domains.
Keywords: ambient agent model, human behaviour, dynamics
Journal of Ambient Intelligence and Smart Environments

32. “Whoah! Cool, you should publish that stuff as Linked Data”
An Ambient Agent Model
“Um, but doesn’t TTL have incompatible semantics?” for Monitoring and
Analysing Dynamics of Complex Human
“Nah, silly, who cares? We’ll just start a new W3C WG!”
Behaviour
“Uh, ok, if we must. But, Mark Hoogendoorn , Michel C.A. Klein , and Jan Treur
even then, we can’t just publish the model as is!”
Tibor Bosse
a*
a
a
a
a
Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam,
The Netherlands
Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying
whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dynamic interactions between human and environment. In order to enable such more complex types of monitoring, this paper
presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1)
the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents,
and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex monitoring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving
behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations,
respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers
within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have
shown that the framework is easy to use and applicable in a wide variety of domains.
Keywords: ambient agent model, human behaviour, dynamics
Journal of Ambient Intelligence and Smart Environments

33. “Whoah! Cool, you should publish that stuff as Linked Data”
An Ambient Agent Model
“Um, but doesn’t TTL have incompatible semantics?” for Monitoring and
Analysing Dynamics of Complex Human
“Nah, silly, who cares? We’ll just start a new W3C WG!”
Behaviour
“Uh, ok, if we must. But, Mark Hoogendoorn , Michel C.A. Klein , and Jan Treur
even then, we can’t just publish the model as is!”
Tibor Bosse
a*
a
a
a
a
Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam,
The Netherlands
“”No worries, just add the provenance using PROV-O, annotate the PDF
Abstract. In ambient intelligent systems, monitoring of a human could consist of more link to other research using CITO.”
with OA, tasks may involvetasks than merelycomplex dyand complex monitoring of identifying
whether a certain value of a sensor is above a certain threshold. Instead, such
namic interactions between human and environment. In order to enable such more complex types of monitoring, this paper
presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1)
the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents,
and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex monitoring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving
behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations,
respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers
within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have
shown that the framework is easy to use and applicable in a wide variety of domains.
Keywords: ambient agent model, human behaviour, dynamics
Journal of Ambient Intelligence and Smart Environments

34. “Whoah! Cool, you should publish that stuff as Linked Data”
An Ambient Agent Model
“Um, but doesn’t TTL have incompatible semantics?” for Monitoring and
Analysing Dynamics of Complex Human
“Nah, silly, who cares? We’ll just start a new W3C WG!”
Behaviour
“Uh, ok, if we must. But, Mark Hoogendoorn , Michel C.A. Klein , and Jan Treur
even then, we can’t just publish the model as is!”
Tibor Bosse
a*
a
a
a
a
Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam,
The Netherlands
“”No worries, just add the provenance using PROV-O, annotate the PDF
Abstract. In ambient intelligent systems, monitoring of a human could consist of more link to other research using CITO.”
with OA, tasks may involvetasks than merelycomplex dyand complex monitoring of identifying
whether a certain value of a sensor is above a certain threshold. Instead, such
namic interactions between human and environment. In order to enable such more complex types of monitoring, this paper
“And that’s it?” a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1)
presents
the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents,
and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex monitoring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving
behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations,
respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers
within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have
shown that the framework is easy to use and applicable in a wide variety of domains.
Keywords: ambient agent model, human behaviour, dynamics
Journal of Ambient Intelligence and Smart Environments

35. “Whoah! Cool, you should publish that stuff as Linked Data”
An Ambient Agent Model
“Um, but doesn’t TTL have incompatible semantics?” for Monitoring and
Analysing Dynamics of Complex Human
“Nah, silly, who cares? We’ll just start a new W3C WG!”
Behaviour
“Uh, ok, if we must. But, Mark Hoogendoorn , Michel C.A. Klein , and Jan Treur
even then, we can’t just publish the model as is!”
Tibor Bosse
a*
a
a
a
a
Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam,
The Netherlands
“”No worries, just add the provenance using PROV-O, annotate the PDF
Abstract. In ambient intelligent systems, monitoring of a human could consist of more link to other research using CITO.”
with OA, tasks may involvetasks than merelycomplex dyand complex monitoring of identifying
whether a certain value of a sensor is above a certain threshold. Instead, such
namic interactions between human and environment. In order to enable such more complex types of monitoring, this paper
“And that’s it?” a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1)
presents
the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents,
and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex moni“Noo! You’ll need persistent Cool URI’s and publish your endpoint
toring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving
behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations,
respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers
for eternity of course. Duh.”
within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have
shown that the framework is easy to use and applicable in a wide variety of domains.
Keywords: ambient agent model, human behaviour, dynamics
Journal of Ambient Intelligence and Smart Environments

36. “Whoah! Cool, you should publish that stuff as Linked Data”
An Ambient Agent Model
“Um, but doesn’t TTL have incompatible semantics?” for Monitoring and
Analysing Dynamics of Complex Human
“Nah, silly, who cares? We’ll just start a new W3C WG!”
Behaviour
“Uh, ok, if we must. But, Mark Hoogendoorn , Michel C.A. Klein , and Jan Treur
even then, we can’t just publish the model as is!”
Tibor Bosse
a*
a
a
a
a
Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam,
The Netherlands
“”No worries, just add the provenance using PROV-O, annotate the PDF
Abstract. In ambient intelligent systems, monitoring of a human could consist of more link to other research using CITO.”
with OA, tasks may involvetasks than merelycomplex dyand complex monitoring of identifying
whether a certain value of a sensor is above a certain threshold. Instead, such
namic interactions between human and environment. In order to enable such more complex types of monitoring, this paper
“And that’s it?” a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1)
presents
the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents,
and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex moni“Noo! You’ll need persistent Cool URI’s and publish your endpoint
toring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving
behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations,
respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers
for eternity of course. Duh.”
within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have
shown that the framework is easy to use and applicable in a wide variety of domains.
“Eh?”
Keywords: ambient agent model, human behaviour, dynamics
“Oh... and don’t forget all data collected by the agents, in all runs,
including the first experiments. Now THAT would be ultra cool.
“Ngh!?”
Journal of Ambient Intelligence and Smart Environments

37. Creating Linked Data
http://linkeddatabook.com
•
•
•
•
•
•
•
•
Decide on resources to describe
Mint cool URIs
Decide on triples to include
Describe the dataset
Choose vocabularies
Define terms
Make links
Publish to triple store/annotations/dump

38. If this already is tedious...
... can you expect researchers to publish Linked Research Data?

39. If this already is tedious...
... can you expect researchers to publish Linked Research Data?

40. Conclusion?

41. We need to make publishing Linked Research Data...
...a lot easier...
... more persistent ...
... and more rewarding.
Linked Data is sóóóóó 2005

42. We need to make publishing Linked Research Data...
...a lot easier...
... more persistent ...
... and more rewarding.
“People as frontier in computing” - Haym Hirsch, Pietro Michelucci

43. We need to make publishing Linked Research Data...
...a lot easier...
... more persistent ...
... and more rewarding.
http://linkitup.data2semantics.org

44. We need to make publishing Linked Research Data...
...a lot easier...
... more persistent ...
•
•
•
•
•
•
... and more rewarding.
Lightweight web application
Interface to API of existing data repositories
Enrich metadata by linking to (linked) data resources
Human in the Loop
Track provenance
Publish rich metadata as new data publication
Nanopublication + OA
+ PROV-O + DCTerms + FOAF
http://linkitup.data2semantics.org

45. We need to make publishing Linked Research Data...
...a lot easier...
... more persistent ...
•
•
•
•
•
•
... and more rewarding.
Lightweight web application
Interface to API of existing data repositories
Enrich metadata by linking to (linked) data resources
Human in the Loop
Track provenance
Publish rich metadata as new data publication
Nanopublication + OA
+ PROV-O + DCTerms + FOAF
http://linkitup.data2semantics.org

49. Use tags & categories to query the DBpedia endpoint

50. Use authors to query the DBLP endpoint

51. Use tags & categories to query the NeuroLex endpoint

52. Use author names to query the ORCID API

53. Extract references to resolve to CrossRef DOIs

54. Every operation is tracked automatically

55. http://semweb.cs.vu.nl/provoviz
Connection to PROV-O-Viz service

56. Review selected links, and publish to Figshare

58. Plugins
Name
DBLP
ORCID
LinkedLifeData
Crossref
Elsevier LDR
DANS EASY
SameAs
DBPedia Spotlight
DBPedia/Wikipedia
NeuroLex
NIF Registry
your
Service
SPARQL
REST
REST
Custom
REST
Custom
REST
REST
SPARQL
SPARQL
REST
data
Source
Authors
Authors
Tags & Categories
Citations
Tags & Categories
Tags & Categories
Links
Description, Tags &
Categories
Tags & Categories
Tags & Categories
Tags & Categories
set
Links to
Author Identifiers
Author Identifiers
Biomedical Entities
DOIs
Funding agencies
General Datasets
General Entities
General Entities
General Entities
Neuroscience Concepts
Neuroscience Datasets
here

59. What does this solve?
http://linkeddatabook.com
•
•
•
•
•
•
•
•
Decide on resources to describe
Mint cool URIs
Decide on triples to include
Describe the dataset
Choose vocabularies
Define terms
Make links
Publish to triple store/annotations/dump

60. What does this solve?
http://linkeddatabook.com
•
•
•
•
•
•
•
•
You decide on resources to describe
We mint cool URIs
We decide on triples to include
We describe the dataset
We choose vocabularies
We define terms
Together we make links
We publish the dataset to a reliable repository

61. Coming up…
•
•
•
•
•
•
Publish directly from Dropbox, Github, …
Reconstruct provenance information (http://git2prov.org)
Analyze, convert and enrich on the fly
Generate a data report for advertisement purposes
Measure for information content of datasets (“D-Index”)
Integrate a data dashboard

62. 84
70
12
22
30
HTTP
11
Other
6
No URL provided
0
XML
35
Unknown response
… enhancing the data publication…
105
Connection reset
http://linkitup.data2semantics.org
134
Not RDF
linkitup
140
… increasing findability …
… boosting reusability …
… result is stored persistently
http://git2prov.org
http://semweb.cs.vu.nl/provoviz
http://yasgui.data2semantics.org
http://www.data2semantics.org