This document discusses using linked data technology to solve the problem of disconnected and isolated economic and social science datasets. It proposes hosting updated versions of important historical databases as linked data in a single location. This will allow users to easily query across datasets, upload and link their own datasets, and build a large graph of interconnected public datasets. The document demonstrates some triplestore and linked data browsing tools, including a SPARQL query editor and lightweight linked data browser. It also introduces the team working on the structured datahub and linked data solutions for economic and social historians.

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WP4: the structured datahub: linked data, big and
small
Auke Rijpma, a.rijpma@uu.nl
May 20, 2016

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Today
▶ Data problem in ES(D)H
▶ Linked-data solution
▶ Demos interaction triplestore

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Problems to solve
▶ Economic, social, and demographic historians have a long tradition
of data-intensive research.
▶ Two issues:
1. As databases grow bigger and more complex, working with them
becomes more difficult. Examples: HSN, CamPop, NAPP.
2. There are many small-to-medium size datasets that are isolated (exist
on one computer, one repository, or are not harmonised with other
datasets): the “long tail” of research data.
▶ Difficult to describe, share, and replicate research results.
▶ Equally important: difficult to answer questions that span more than
one dataset (comparative or multilevel research).

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Disconnected data
!

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Disconnected data
Data Preparation
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´ecnica de Madrid. {dgarijo, ocorcho}@fi.upm.es
†School 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
Abstract—While workflow technology has gained momentum
in the last decade as a means for specifying and enacting compu-
tational 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. INTRODUCTION
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 advan-
tages [4]. For example, it enables proper data citation and
[14] and CrowdLabs [8] have made publishing and finding
workflows easier, but scientists still face the challenges of re-
use, 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 objec-
tives:
1) To reverse-engineer the set of current practices in work-
flow development through an analysis of empirical evi-
dence.
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 data-
oriented motifs, and ii) a characterization of the different man-
ners 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
Fig. 3. Distribution of Data-Oriented Motifs per domain Fig. 5. Data
Fig. 3. Distribution of Data-Oriented Motifs per domain Fig. 5. Data Preparation Motifs in the Genomics Workflows

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The team
Rinke Hoekstra, Kathrin Dentler, Albert Meroño Peñuela (VU), Laurens
Rietveld (Triply), Richard Zijdeman, Ashkan Ashkpour (IISH).

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Linked data as a solution
▶ To solve this, we use web-based linked data technology.
▶ Method of publishing data on the web so that it can be interlinked
and given semantic meaning.
+ : Very flexible, sidesteps harmonisations, very expressive query
language (SPARQL), cross-database queries (even on different
servers), live querying, browseable database, ability to combine
metadata and “codebook” with actual data.
– : not optimised for very big databases (10m+ observations → 100m
triples), unfamiliar technology.

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Plan
▶ Offer updated versions of important databases for economic and
social historians (HSN, Clio-Infra, Campop, Mosaic, Henry-Fleury,
CMGPD, Opgaafrollen, etc.) in one place, as linked data, in an
accessible way:.
▶ Allow users to upload and share their datasets.
▶ Allow and encourage users to link datasets to other datasets or
important standards to grow a graph of connected datasets.
▶ Provide direct, browseable, queryable access and tooling for
visualisation and analysis.
Empower Individual Researchers
• Augment and link individual datasets according to best
practices of the community or against colleagues
• Share machine-interpretable code books with fellow
researchers
• Align codes and identifiers across datasets
• Publish standards-compliant, reusable datasets
Grow a giant graph of interconnected
datasets

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Demos
Demonstrate triplestore and tools to interact with it.
QBer: http://qber.clariah-sdh.eculture.labs.vu.nl
Brwsr: http://data.clariah-sdh.eculture.labs.vu.nl/doc/
resource/napp/observation/canada1891/62489
YASGUI: http://virtuoso.clariah-sdh.eculture.labs.vu.nl/
yasgui-auth/
Grlc: http://grlc.clariah-sdh.eculture.labs.vu.nl/
CLARIAH/wp4-queries/api-docs

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Demos
▶ QBer: “Connect your data to the cloud” (Rinke)
1. Augment and link datasets according to best practice.
2. Align codes and identifiers across datasets.
3. Share machine-readable codebooks.
4. Publish standardised datasets.
▶ http://qber.clariah-sdh.eculture.labs.vu.nl
▶ http://inspector.clariah-sdh.eculture.labs.vu.nl

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Demos
▶ Brwsr: Lightweight linked data browser (Rinke).
1. Browse linked data as if it is a web page.
▶ http://data.clariah-sdh.eculture.labs.vu.nl/doc/
resource/napp/observation/canada1891/62489

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Demos
▶ Querying: Virtuoso, SPARQL, and YASGUI (Kathrin, Laurens)
1. Virtuoso triplestore, currently with 1b+ triples (1 188 852 440)
2. SPARQL as expressive query language
3. YASGUI as feature-rich editor.
▶ http://virtuoso.clariah-sdh.eculture.labs.vu.nl/
yasgui-auth/

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Demos
▶ Grlc: git repository linked data API constructor (Albert).
1. Builds Web APIs using SPARQL queries stored in git repositories.
2. Store and share queries.
3. Parametrise queries to overcome unfamiliarity with Grlc.
▶ http://grlc.clariah-sdh.eculture.labs.vu.nl/
CLARIAH/wp4-queries/api-docs

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Demos
entify locally, extrapolate
obally…?
canada sweden
(Intercept) 3.616*** 4.430***
(0.134) (0.033)
log(gdppc) 0.036** -0.070***
(0.018) (0.004)
I(age^2) -0.000*** -0.000***
0.000 0.000
age 0.007*** 0.001***
0.000 0.000
R2 0.013 0.021
Adj. R2 0.012 0.021
Num. obs. 36201 275127
RMSE 0.142 0.102
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Future work
▶ Continue working with researchers (Ivo Zandhuis, Ruben Schalk)
for use cases and spread the gospel.
▶ Increase data volume.
▶ Make sure triplestore can handle data volume.
▶ Make all components work together.
▶ Make a good (appealing and accessible) interface.
More info:
▶ http://datalegend.net
▶ https://github.com/clariah
▶ See(n) us at conferences: ESSHC, Posthumus, WHiSe, …