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Linked Data: Een extra ontstluitingslaag op archieven

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Presentations on behalf of the workshop series Digital Humanities & Archieven. Nationaal Archief, The Hague, The Netherlands. March 25, 2019. Language @nl.

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Linked Data: Een extra ontstluitingslaag op archieven

  1. 1. Linked Data Een extra ontsluitingslaag op collecties Richard Zijdeman IISG / University of Stirling @rlzijdeman Digital Humanities & Archieven Nationaal Archief, Den Haag
 Maart 25, 2019
  2. 2. Hoe onderscheiden de digital humanities zich van de “analoge” voorganger?
  3. 3. telescopische astronomie
  4. 4. 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 [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 Fig. 3. Distribution of Data-Oriented Motifs per domain Fig. 3. Distribution of Data-Oriented Motifs per domain Fig. 5. Data Preparation Motifs in the Genomics Workflows
  5. 5. Dit doen we keer op keer voor dezelfde datasets!
  6. 6. “Throwaway Science” Rinke Hoekstra (VU Amsterdam)
  7. 7. Welke soorten data staan centraal? Hoe dragen ze bij aan prangende vragen binnen de geesteswetenschappen?
  8. 8. HHuCAP Bron: https://delpher.nl
  9. 9. Are there any non-elites electorate and if so where did they live?
  10. 10. Are there any non-elites electorate and if so where did they live? 1983 Individual level data Link to HISGIS.nl Link to HISCO Link to HISCAM AmsterdamTimeMachine.nl
  11. 11. ‘Elite-dichtheid’, Amsterdam eind 19e eeuw. Bron: Ivo Zandhuis
  12. 12. Welke potentie heeft het digital humanities domein voor de archieven?
  13. 13. Potentie • Kwaliteitscheck • Verrijking eigen collecties • Inhoud nog meer toegankelijk
  14. 14. https://bit.ly/2CNfpy7 1. Kattenburgs 2. Rapenburgs 3. Jonker- en Ridderstraat 4. Jodenhoeks 5. Nieuwmarkts 6. Zeedijks 13. Haarlemmerdijks 14. Nieuwendijks 15. Kalverstraats 16. Gebed-zonder-ends 17. Vismarkts 18. Botermarkts 19. Duvelhoeks 7. Bierkaais 8. Komkommerbuurts 9. Noorsebossies 10. Leidsebuurt 11. Jordaans 12. Fransepads luimspieze (slaapplaats, logement) loizig (luizig) beis (twee, buurt) leuizig (luizig) wittebrooskeentje (verwend kind) wel me liefe mins (wel mijn lieve mens)
  15. 15. Wat vraagt het digital humanities veld van ons?
  16. 16. The Vision for a Web of Data http://www.ted.com/talks/tim_berners_lee_on_the_next_web Sir Tim Berners-Lee
  17. 17. The Vision for a Web of Data open license and on the web machine readable structured data (excel beats pdf) as two stars, but in a non-propietary format (csv beats excel) use open standards to describe things (RDF and SPARQL) all of the above and link your data to others (to provide context)
  18. 18. Linked Data: een extra ontsluitingslaag Eva van Oene (IISG)

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