Digital Humanities Methods and Tools https://dhmethods.hypotheses.org An extension of the OpenMethods metablog for scholars who wish to share the methodological advancements of their research. Wed, 07 Apr 2021 08:14:34 +0000 en-US hourly 1 https://wordpress.org?v=6.5.4 “Here be dragons”: Open Access to Research Data in the Humanities https://dhmethods.hypotheses.org/262 https://dhmethods.hypotheses.org/262#comments Mon, 22 Apr 2019 19:39:34 +0000 https://dhmethods.hypotheses.org/?p=262 Continue reading "“Here be dragons”: Open Access to Research Data in the Humanities"]]> Guest post by our blog competition winner, Ulrike Wuttke.

Introduction

#1 It has been estimated by Stefan Winkler Nees (from the Deutsche Forschungsgemeinschaft-DFG) in 2011 that 90% of all digital research data is lost.[1] We don’t know how many of this data belonged to the Humanities and hopefully, these numbers are better today, but we can assume that still a lot of Humanities data (and other data) is lost, because of missing infrastructures, or because no one has taken care of the long-term availability of this data in time.[2]

Are Your Data FAIR?

But, even if data is not lost: Does the available data sparkle joy, to borrow a term from the ubiquitous Marie Kondo? Are these datasets accessible for research, well documented using standards, available in interoperable formats etc., in short: is it FAIR[3] data, too?

#2 The digital transformation of research has dramatically increased the creation, gathering, and use of research data in all disciplines. It also led to the development of the new paradigm of Open Science which not only promotes that published results should be open access, but also that the underlying data should be open (for example in the H2020 Open Research Data Pilot).[4] Together, these developments resulted in a heightened demand for the publication of research data, promoted by national and international funding agencies[5], and research institutions, often directly reflected in (funding) guidelines, which can be more or less binding.[6] Among the many reasons to underpin the demand for Open Research Data, efficiency (reuse), reproducibility (transparency), impact, and public trust are prominent.[7] Slowly, the responsible handling of research data and FAIR publication of research data is becoming an integral part of Good scientific practice.[8]

The vision goes beyond that: we are talking here about the grand vision of the European Open Science Cloud (EOSC)[9] and an even bigger vision for a global Open Science Ecosystem. To realise this vision and for humanities data playing a considerable role in it, our “efforts should be guided by the twin aims of ensuring that data meets the FAIR principles, and that it is effectively preserved in trusted, certified repositories”[10]. The FAIR principles have gained since their publication in 2016[11] great acceptance as guiding principle, because they take not only into account that not all data can be open[12], leading, for example, the European Research Council to formulate the principle “as open as possible, as closed as necessary”[13], but because they also put great emphasis on “the attributes data need to have to enable and enhance reuse, by humans and machines”, in a nutshell metadata.[14]

Vision for Humanities Data

In the following, I will first line out key challenges related to Open Access to Research Data in the humanities and then discuss some perspectives to improve the current situation. I will focus on educational aspects, and tools for research data management as keystones for Open & FAIR Research Data. As the German situation is best known to me, examples are mainly drawn from a German context.

“Here be dragons”

#4 The phrase “Hic sunt dracones” (transl. “Here be Dragons”), is used on some old maps of the world to describe an area that was unknown to the cartographer. I found it quite appropriate to summarize the ambivalence of humanists towards data and all these “fancy” concepts discussed by “infrastructure people” like FAIR Data, the EOSC, or Research Data Management. First of all, it must be said that humanities researchers tend to be ambivalent about the concept of ‘data’[15] and that “[t]here are issues surrounding […] the acceptance of the ‘research data concept’”[16]. In short, they just don’t use the word “data”, but talk about “sources”, “research materials” etc., which leads to the fact that the whole “data talk” doesn’t appeal to them. Additionally, an expeditionary survey conducted by PARTHENOS in 2017 among researchers in the domain of digital humanities, language studies, and cultural heritage showed that the FAIR Principles and the EOSC, concepts and recommendations, thriving among “infrastructure folks”, are relatively little known in the research communities themselves.[17] Often, the publication of research data only comes as an afterthought (if at all).[18] However, at the end of a project, it is often too late to publish the data in a meaningful way because of the lack of documentation and the lack of resources to prepare the data properly for publishing.

#5 Let me discuss some reasons that currently seem to form barriers for establishing a culture of open sharing in the humanities. In general, “issues surrounding incentivisation”[19] can be observed. Given the strong competition and the traditional humanities reputation system based on traditional ‘long story scientific publication formats’ (monographs, book chapters, or articles as significant scientific publications) as opposed to ‘data’, there is low motivation to publish research data.[20] This lack of incentives goes hand in hand with researchers’ (perceived) fear of being scooped or that someone else will be the first to publish something based on their data, e.g. an exciting manuscript or object they have found.[21] A research tradition that has been based on (and rewarded) secretiveness is not easy to change with nothing fundamental opposite as a reward. Other prejudices often brought up are: nobody will understand the data, nobody will need the data, someone will sell the data and last but not least, (perceived) lack of technical skills.[22]

“The inherent controversy in the meaning of “data” and the importance of personal interpretation on data for humanities researchers is not conducive to sharing.”[23]

#6 Even those who are willing to publish data, as it must also be duly acknowledged that there is a long and thriving tradition of humanities corpora collection and publication continued in the digital, e.g. at the academies, are facing obstacles. Especially legal issues or doubts about possible legal issues are often mentioned in this context: The legal regulations concerning research data are complicated and not internationally aligned and there are many actors involved in the production of humanities research data, not only humanists. This leads to the fact that humanities research is often based on data under copyright restriction (from cultural heritage institutions or other actors) which makes it difficult to publish them as ORD.[24]

#7 Additionally, we are dealing with is issues around the availability and sustainability of specialist support structures for humanities research data support as well as the lack of practical guidance.[25] Humanities data centers and other data services for the humanities are often dependent on third-party funding (project based).[26] This leads to issues of trust on the side of the researchers which may result in these services not being in high demand[27] (and may even result in an unwillingness to “Go Digital” at all), it also leads to the problem of sustaining “living systems”[28] (which need to be frequently updated, migrated, and curated). However, current efforts, especially from the Digital Humanities community, also have led to positive developments:

“For example, the emergence of linked open data over the past decade has been supported both by the establishment of effective standards for modeling and disseminating such data, and the growth of practices and social expectations supporting its creation. These developments have meant that expertly modeled data from specific domains can be accessed and combined flexibly, rather than remaining in isolation or striving for self-sufficiency.”[29]

Fellowship of the Data

#8 To sum up my observations so far: Humanities research data, in general, is rather heterogeneous, idiosyncratic, and complex[30] and humanists are ambivalent about the term “data”. Digital practices are already part of the research activities of many humanists, especially in the Digital Humanities, but they are not equally fully developed. This leads to the fact that the potential of digital research data and methods is not fully exploited, because the digital research process is not carefully planned, with other words many research data already exist in digital form, but they are not findable, quality controlled, and reusable.[31] All in all, the land of FAIR Research Data is still unknown territory for many humanists, or at least scary as if dragons would indeed live there. In the next part, therefore, I will argue for increased efforts for awareness raising and skills building and a “fellowship of the data”, a support system to facilitate the quest for FAIR data in the humanities.

Perpectives

#9 Naturally, I cannot offer immediate solutions, but I would like to point out some paths that need to be pursued with increased intensity in the near future to facilitate FAIR data in the humanities (and beyond). These paths can focus on different stakeholders in the FAIR ecosystem such as research institutions, funding bodies, or publishers, or individual researchers and research communities. In my opinion, special attention has to be paid to the researchers and research communities themselves, so that recommendations, policies, services, etc. are aligned and known to disciplinary practices and cultures.[32] If the researchers are not on our side in this quest, we are prone to lose the battle or at least experience a delay in realising the goals.

#10 The most urgent points in my opinion are the following. We need to work on incentives for the FAIR publication of research data, e.g. wider adoption of DORA (Declaration on Research Assessment).[33] We also need to Invest in the development of beneficial environments for aggregation (think EOSC, German NFDI):

“The interdisciplinary bundling of humanities data repositories and the development of adequate research tools and services for linked data represents a great opportunity for humanities research.”[34]

Another highly important task is educating the next (and this) generation of (digital) humanities researchers[35] to deal with the datafication[36] of research and education practices, but also “infrastructure people” in discipline-specific contexts. Humanists need to be aware of limits of publication tools and how they expose (or not) the underlying data model. We need to strive to offer more idiosyncratic options and take time for critical consideration of how early choices have an impact on how data is published and can be used (curatorial perspective). It is our task to prevent research data from becoming trapped (in specific formats or hardware). In an ideal world we “design our data without tool dependencies” = “tool-agnostic approach” vs. “tool-dependent approach”[37]. The progressing digital transformation of humanities research along with the increasing importance of digital research infrastructures calls not only for a certain level of “data literacy”[38] but even for an expansion of this concept to a certain level of “data infrastructure literacy”, a term recently coined by Gray et al. 2018[39].

Much discussed in the context I have just outlined is Research Data Management:

“Research Data Management describes the process to curate (or manage) research data along the research data lifecycle and includes various activities such as planning, producing, selection, analysis, archiving, and preparation for reuse. Because data are very heterogeneous, discipline and data specific solutions can be required.”[40]

Given this, admittedly not very appealing sounding definition, it may not come as a surprise that researchers in general still consider research data management [41]as an extra, tedious, time-consuming task that diverts them from “real research” and humanists especially consider it as almost opposed to the hermeneutic humanistic research practice[42]. However, acceptance by the researchers is the key success factor for establishing standards in Research Data Management.[43] Therefore, we need to show humanists the added value of Research Data Management for the planning process of digital projects. Lemaire (2018) has recently convincingly argued that RDM is a process already inherent in the research process itself (although at the moment rather implicit) and that it can be an instrument for reviewing the research concept.[44]

#12 The digital turn of humanities research and the requirement for FAIR research data, that is sustainable and quality controlled handling of research data, requires thorough planning of the digital research process before the start, a process that is guided and documented in a Research Data Management Plan.[45] Given all the advantages of Research Data Management planning, efforts should be increased regarding awareness raising and skills building[46] already as part of university curricula[47][, and tools for consequent data management planning (and handling)[48] with the end in mind, that is (if possible) the publication of FAIR research data.

RDMO[49]

#13 Given that the management of research data is increasingly regarded as a process of active support and care during the whole research process (and not only of producing a mere static document), tools are needed that support active Research Data Management Planning[50], e.g. by providing different and up-to-date status information to different participants of the research process. I am currently part of a project that is developing such a tool, the Research Data Management Organiser (RDMO).[51]

#14 In a nutshell, with RDMO the research data management process can be organised as a collaborative effort encompassing its different stakeholders, besides researchers, especially infrastructure partners such as libraries or computing centres. One of the use scenarios of RDMO is library staff using RDMO’s question catalogue to work out the data management strategy for projects with researchers and other partners/experts.[52] RDMO can be adapted to the requirements of communities or organisations (e.g. institutional or discipline-specific guidelines) and has multilingual capabilities. At the moment we are working with a range of very different institutions and communities in Germany to further improve this tool.

Conclusion

#15 On the one hand, researchers need to be aware of the issues at hand and take their responsibility (take the issues of digital research practices seriously!).[53] On the other hand, we need to work on institutional availability and sustainability of research data (management) and support (e.g. via data centres and local experts)[54] and clever and efficient connection between initiatives on different levels. We need to provide adequate RDM tools that support researchers to prepare their data for publication from the very beginning. Libraries already have an important role in this ecosystem and I dare to say that they are capable and designated to take a leading role in this field in the future if they invest in it: in heads and infrastructure(!).[55] Especially I would like to refer here to their role in the creation of vast digital corpora of open research data, which cannot be left as a task to researchers alone.[56] Last but not least, we also need to make research data management less scary: it’s not a scientific revolution and doesn’t mean that all skills learned so far in a typical humanities curriculum are to be thrown overboard, quite the opposite is true.[57]

#16 There is a lot at stake for the humanities, maybe the very question what we want the future of the humanities to be. When it comes to Open and FAIR research data in the humanities, I can only say it with Queen: “I want it all, and I want it now!”

“I want it all, I want all, I want it all, and I want it now.” Queen (1989).

To create this broad culture of FAIR data sharing in the humanities we have to roll up our sleeves, team up, and distribute hats:

  1. Embrace Open principles,
  2. bridge the gap between the digital and the humanities and look what we can learn from the Digital Humanities and other more data-savvy disciplines.[58]

What are your thoughts and suggestions on this topic? Do you agree? Do you have additional hints for me that lead to more discipline specific information and insight about the handling of research data, data sharing, and the FAIR principles in the humanities? Please, leave your thought below or contact me via Twitter or e-mail. Looking forward to discuss this topic further with you!

You can find the original post on Ulrike’s blog at: https://ulrikewuttke.wordpress.com/

Notes

[1] See Stefan Winkler-Nees, Vorwort, In: Büttner, Stephan; Hobohm, Hans-Christoph; Müller, Lars (ed.): Handbuch Forschungsdatenmanagement, Bad Honnef 2011, p. 5.

[2] See Geisteswissenschaftliche Datenzentren im deutschsprachigen Raum, Grundsatzpapier zur Sicherung der langfristigen Verfügbarkeit von Forschungsdaten, Version 1.0, DHd AG Datenzentren, 2018, p. 9. Accessed from http://doi.org/10.5281/zenodo.1134760, 25.03.2019.

[3] See Wilkinson, M. D., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. Accessed from https://doi.org/10.1038/sdata.2016.18, 25.03.2019. Webseite FORCE11: https://www.force11.org/group/fairgroup/fairprinciples.

[4] See European Research Council (ERC), Guidelines on Implementation of Open Access to Scientific Publications and Research Data in projects supported by the European Council under Horizon 2020, Version 1.1., 21. April 2017. Accessed from http://ec.europa.eu/research/participants/data/ref/h2020/other/hi/oa-pilot/h2020-hi-erc-oa-guide_en.pdf, 25.03.2019. See also from the UK document Concordat on Open Research Data, 2016. Accessed from https://www.ukri.org/files/legacy/documents/concordatonopenresearchdata-pdf/, 25.03.2019 or the recommendation from the Steuerungsgremium der Schwerpunktinitiative „Digitale Information“ der Allianz der deutschen Wissenschaftsorganisationen (2017), Den digitalen Wandel in der Wissenschaft gestalten: Die Schwerpunktinitiative „Digitale Information“ der Allianz der deutschen Wissenschaftsorganisationen, Leitbild 2018 – 2022, 2017, Accessed from: http://doi.org/10.2312/allianzoa.015.

[5] See for example EC Directorate-General for Research & Innovation, H2020 Programme: Guidelines on FAIR Data Management in Horizon 2020, Version 3.0, 26. July 2016, Accessed from: http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf, 25.03.2019, Deutsche Forschungsgemeinschaft /DFG), Leitlinien zum Umgang mit Forschungsdaten, 30.09.2015. Accessed from: http://www.dfg.de/download/pdf/foerderung/antragstellung/forschungsdaten/richtlinien_forschungsdaten.pdf, 25.03.2019.

[6] While German funders already give recommendations, other funders already issued more binding guidelines that include the demand for a DMP, such as the Schweizerische Nationalfonds (SNF). See Schweizerische Nationalfonds, Open Research Data, (o.J.). Accessed from: http://www.snf.ch/en/theSNSF/research-policies/open_research_data/Pages/default.aspx#FAIR%20Data%20Principles%20for%20Research%20Data%20Management, 25.03.2019.

[7] “Open research data (ORD) have the potential not only to deliver greater efficiencies in research, but to improve its rigour and reproducibility, to enhance its impact, and to increase public trust in its results.” Open Research Data Task Force, Realising the potential: Final Report of the Open Research Data Task Force. N.P., 2018, p. 3. Accessed from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/775006/Realising-the-potential-ORDTF-July-2018.pdf, 22.03.2019.

[8] See recommendation 7 “Sicherung und Aufbewahrung von Primärdaten” of the DFG-Denkschrift zur Sicherung der guten wissenschaftlichen Praxis, in: Deutsche Forschungsgemeinschaft (DFG), Sicherung guter wissenschaftlicher Praxis: Empfehlungen der Kommission „Selbstkontrolle in der Wissenschaft“, Weinheim 2013, p. 21-22. Accessed from: http://doi.org/10.1002/9783527679188.oth1, 25.03.2019.

[9] https://ec.europa.eu/research/openscience/index.cfm?pg=open-science-cloud.

[10] Open Research Data Task Force, Realising the potential: Final Report of the Open Research Data Task Force. N.P., 2018, p. 8. Accessed from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/775006/Realising-the-potential-ORDTF-July-2018.pdf, 25.03.2019.

[11] The term FAIR was launched in 2014. See https://www.force11.org/group/fairgroup/fairprinciples.

[12] Principles for Open Data in Science have been formulated in the Panton Principles, demanding that data should be placed in public domain. See Panton Principles, Principles for open data in science. Murray-Rust, Peter; Neylon, Cameron; Pollock, Rufus; Wilbanks, John; (19 Feb 2010). Retrieved 31.03.2019 from https://pantonprinciples.org/. This demand has been criticized as difficult to realize because of two main reasons 1) the legal system of some countries, including Germany, does not really allow complete renunciation of rights by the right holder (i.e. public domain) 2) it removes all obligations to quote, which remove an important incentive, see Ulrich Herb, Open Science in der Soziologie. Eine interdisziplinäre Bestandsaufnahme zur offenen Wissenschaft und eine Untersuchung ihrer Verbreitung in der Soziologie, Glückstadt 2015, p. 126, accessed from: https://doi.org/10.5281/zenodo.31234, 25.03.2019.

[13] European Research Council (ERC), Guidelines on Implementation of Open Access to Scientific Publications and Research Data in projects supported by the European Council under Horizon 2020, Version 1.1., 21. April 2017, p. 6. Accessed from http://ec.europa.eu/research/participants/data/ref/h2020/other/hi/oa-pilot/h2020-hi-erc-oa-guide_en.pdf, 25.03.2019. Not all data needs to be published. A selection of what data is relevant and interesting for scientific reuse, like archives have always done, is necessary. For guidelines see for example  Angus Whyte & Andrew Wilson, How to appraise and select research data for curation, Digital Curation Centre How-to Guides, Edinburgh 2010. Accessed from: http://www.dcc.ac.uk/resources/how-guides/appraise-select-data, 25.03.2019.

[14] European Commission Expert Group on FAIR Data, Turning FAIR into reality: Final Report and Action Plan from the European Commission Expert Group on FAIR Data, Brussels 2018, p. 18, accessed from: https://ec.europa.eu/info/publications/turning-fair-reality_en, 25.03.2019. The FAIR principles provide guidance on “how to facilitate knowledge discovery by assisting humans and machines in their discovery of, access to, integration and analysis of, task-appropriate scientific data and their associated algorithms and workflows” Quote from: https://www.force11.org/group/fairgroup/fairprinciples, accessed: 25.03.2019.

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[15] See for example Christof Schöch, Big? Smart? Clean? Messy? Data in the Humanities, in: Journal of Digital Humanities, 2 (2013): 3. Accessed from: http://journalofdigitalhumanities.org/2-3/big-smart-clean-messy-data-in-the-humanities/, 25.03.2019, Miriam Posner, Humanities Data: A Necessary Contradiction | Miriam Posner’s Blog, 25.06.2015. Accessed from: http://miriamposner.com/blog/humanities-data-a-necessary-contradiction/, 25.03.2019. Insightful discussions of the complexity of talking about data in the Humanities are also offered for example by Jennifer Edmond, Georgina Nugent-Folan, D2.1 Redefining what data is and the terms we use to speak of it, KPLEX (Knowledge Complexity) Deliverable D2.1, 2018. Accessed from: https://kplexproject.files.wordpress.com/2018/07/d2-1-redefining-what-data-is-and-the-terms-we-use-to-speak-of-it.pdf, 25.03.2019, Fabian Cremer, Lisa Klaffki, Timo Steyer, T., Der Chimäre auf der Spur: Forschungsdaten in den Geisteswissenschaften. o-bib 5 (2018): 2, p. 142–162. Accessed from: https://doi.org/10.5282/o-bib/2018H2S142-162, 25.03.2019.

[16] Open Research Data Task Force, Case Studies: Annex to the final report of the Open Research Data Task Force, 2018, p. 7. Accessed from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/775379/Case-studies-ORDTF-July-2018.pdf, 25.03.2019.

[17] See PARTHENOS, The FAIR principles and the EOSC concept in the research community of Digital Humanities, Language Studies and Cultural Heritage: An expeditionary survey, 2017, esp. p. 7-8, Accessed from: http://www.parthenos-project.eu/Download/PARTHENOS_FAIR_EOSC_survey.pdf, 25.03.2019.

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[18] See Elke Brehm & Janna Neumann, Anforderungen an Open-Access-Publikationen von Forschungsdaten: Empfehlungen für einen offenen Umgang mit Forschungsdaten, in: o-bib 2018: 3, p. 1-16, p. 3. Accessed from: https://doi.org/10.5282/o-bib/2018H3S1-16, 25.03.2019.

[19] See for example Open Research Data Task Force, Case Studies: Annex to the final report of the Open Research Data Task Force, 2018, p. 7. Accessed from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/775379/Case-studies-ORDTF-July-2018.pdf, 25.03.2019.

[20] The humanities are no exception here. See for example Benedikt Fecher, Cornelius Puschmann, Über die Grenzen der Offenheit in der Wissenschaft: Anspruch und Wirklichkeit bei der Bereitstellung und Nachnutzung von Forschungsdaten, in: Information – Wissenschaft & Praxis 66 (2015): 2-3, p. 146-150, p. 147. Accessed from: https://doi.org/10.1515/iwp-2015-0026, 25.03.2019, Fabian Cremer, Lisa Klaffki, Timo Steyer, T., Der Chimäre auf der Spur: Forschungsdaten in den Geisteswissenschaften. o-bib 5 (2018): 2, p. 142–162, p. 148. Accessed from: https://doi.org/10.5282/o-bib/2018H2S142-162, 25.03.2019.

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[21] See for example Christine L. Borgman, Big Data, Little Data, No Data: Scholarship in the networked World. Cambridge, Mass; London, 2015, p. 177-179, where she characterises humanities data as often being considered as “club goods” (p. 177), meaning access is only granted to very specific individuals, such as local researchers. She describes on the example of the Dead Sea Scrolls this practice of local control (“hoarding”, p. 178), which stems from the fact: “Once scholars obtain access to materials, they may wish to mine the in private until they are ready to publish.” (p. 178).

[22] See for general observations about the (not) sharing of research data for example Carol Tenopir et al., Data Sharing by Scientists: Practices and Perceptions, in: PLOS ONE 6 (6), 29.06.2011, p. e21101, https://doi.org/10.1371/journal.pone.0021101,Veerle van den Eynden & Libby Bishop, Incentives and motivations for sharing research data, a researcher’s perspective. A Knowledge Exchange Report, 2014, accessed from: http://repository.jisc.ac.uk/5662/1/KE_report-incentives-for-sharing-researchdata.pdf, 25.03.2019, Benedikt Fecher et al., What Drives Academic Data Sharing? PLOS ONE, 10(2015):2, p. e0118053. Accessed from: https://doi.org/10.1371/journal.pone.0118053, 25.03.2019, Ulrich Herb, Open Science in der Soziologie. Eine interdisziplinäre Bestandsaufnahme zur offenen Wissenschaft und eine Untersuchung ihrer Verbreitung in der Soziologie, Glückstadt 2015, p. 134-143, accessed from: https://doi.org/10.5281/zenodo.31234, 25.03.2019, Ben Kaden, Warum Forschungsdaten nicht publiziert werden, in: LIBREAS.Dokumente, LIBREAS.Projektberichte, 13.03.2018, accessed from https://libreas.wordpress.com/2018/03/13/forschungsdatenpublikationen/, 25.03.2019.

[23] Open Research Data Task Force, Case Studies: Annex to the final report of the Open Research Data Task Force, 2018, p. 6. Accessed from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/775379/Case-studies-ORDTF-July-2018.pdf, 25.03.2019.

[24] Concerning prevalent legal issues see for example Madeleine de Cock Buning et al., The legal status of research data in the  Knowledge Exchange partner countries, 2011. Accessed from: http://repository.jisc.ac.uk/6280/, 25.03.2019, Bastian Drees, Text und Data Mining: Herausforderungen und Möglichkeiten für Bibliotheken. Perspektive Bibliothek, 5(2016:1, p. 49–73, esp. p. 59-61. Accessed from: http://dx.doi.org/10.11588/pb.2016.1.33691, Elke Brehm & Janna Neumann, Anforderungen an Open-Access-Publikationen von Forschungsdaten: Empfehlungen für einen offenen Umgang mit Forschungsdaten, in: o-bib 2018: 3, p. 1-16, p. 8-11. Accessed from: https://doi.org/10.5282/o-bib/2018H3S1-16, 25.03.2019, Anne Lauber-Rönsberg, Philipp Krahn, Paul Baumann, Gutachten zu den rechtlichen Rahmenbedingungen des Forschungsdatenmanagements im Rahmen des DataJus-Projekts (Kurzfassung), 2018. Accessed from: https://tu-dresden.de/gsw/jura/igewem/jfbimd13/ressourcen/dateien/publikationen/DataJus_Kurzfassung_Gutachten_12-07-18.pdf?lang=de&set_language=de, 25.03.2019.

[25] “The time and effort required to make research data open and accessible in accordance with the FAIR principles (Findable, Accessible, Interoperable, Re-usable) can be considerable; and those researchers who are keen to adopt ORD practices may find themselves stymied by a lack of practical guidance and specialist support.” Open Research Data Task Force, Realising the potential: Final Report of the Open Research Data Task Force. N.P., 2018, p. 23. Accessed from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/775006/Realising-the-potential-ORDTF-July-2018.pdf, 25.03.2019. This report acknowledges that some materials have already been developed (p. 24), which are from the author’s perspective often too general,  (UW often too general), and calls to increase efforts for training and education (p. 24).

[26] See for example Rat für Informationsinfrastrukturen (RfII), Leistung aus Vielfalt: Empfehlungen zu Strukturen, Prozessen und Finanzierung des Forschungsdatenmanagements in Deutschland, 2016, p. 37-39, Accessed from http://www.rfii.de/?wpdmdl=1998, 25.03.2019, DHd AG Datenzentren, Geisteswissenschaftliche Datenzentren im deutschsprachigen Raum:  Grundsatzpapier zur Sicherung der langfristigen Verfügbarkeit von Forschungsdaten (Version 1.0). DHd AG Datenzentren, 2018, p. 24. Accessed from http://doi.org/10.5281/zenodo.1134760, 25.03.2019.

[27] See Open Research Data Task Force, Case Studies: Annex to the final report of the Open Research Data Task Force, 2018, p. 7. Accessed from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/775379/Case-studies-ORDTF-July-2018.pdf, 25.03.2019.

[28] “Lebende Forschungsanwendungen spielen in den Geisteswissenschaften eine zunehmend große Rolle in der digitalen Ergebnissicherung und -präsentation. Im Gegensatz zur Buchpublikation ist jedoch die dauerhafte Erhaltung, Betreuung und Bereitstellung dieser lebenden Systeme eine technische und organisatorische Herausforderung. Während es vergleichsweise einfach möglich ist reine Forschungsdaten in Datenarchiven für die Nachwelt zu konservieren, sind lebende Systeme Teil eines digitalen Ökosystems und müssen sich diesem, z.B. in Form von Updates, regelmäßig anpassen.“ Andreas Witt et al., Forschungsdatenmanagement in den Geisteswissenschaften an der Universität zu Köln, in: o-bib 5 (2018): 3, p. 104–117, p. 111. Accessed from https://doi.org/10.5282/o-bib/2018h3s104-117, 25.03.2019. See also the website of the Project SustainLife at Cologne University: https://dch.phil-fak.uni-koeln.de/sustainlife.html, accessed 25.03.2019.

[29] Julia Flanders & Fotis Jannidis, Data Modeling in a digital humanities context: An introduction. In: Julia Flanders & Fotis Jannidis (ed), The shape of data in the digital humanities: Modeling texts and text-based resources, London, New York, 2019, p. 3–25, here p. 20.

[30] See Open Research Data Task Force, Case Studies: Annex to the final report of the Open Research Data Task Force, 2018, p. 6. Accessed from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/775379/Case-studies-ORDTF-July-2018.pdf, 25.03.2019.

[31] See Marina Lemaire, Vereinbarkeit von Forschungsprozess und Datenmanagement in den Geisteswissenschaften: Forschungsdatenmanagement nüchtern betrachtet. o-bib 5 (2018):4, p. 237–247, here p. 237-238. Accessed from: https://doi.org/10.5282/o-bib/2018H4S237-247, 25.03.2019.

[32] See Open Research Data Task Force, Realising the potential: Final Report of the Open Research Data Task Force. N.P., 2018, p. 23. Accessed from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/775006/Realising-the-potential-ORDTF-July-2018.pdf, 22.03.2019.

[33] The DORA declaration recommends to give credit for more than only articles, for example also for data sets and software, see: https://sfdora.org/read/, accessed 25.03.2019.

[34] Original: “In der interdisziplinären Bündelung geisteswissenschaftlicher Datenrepositorien und der Entwicklung adäquater Forschungswerkzeuge und -dienste für verknüpfte Daten liegt eine große Chance für die geisteswissenschaftliche Forschung.“ Torsten Schrade, Im Datenozean, in: F.A.Z., 2.12. 2018, Accessed from: https://www.faz.net/-in2-9h3jj, 25.03.2019.

[35] During the last years increasingly attention is being paid to Digital Humanities pedagogy and the development of specific Digital Humanities curricula. For Digital Humanities pedagogy see for example David B. Hirsch (ed.), Digital Humanities Pedagogy: Practices, Principles and Politics, 2012. Accessed from: http://www.openbookpublishers.com/reader/161, 25.03.2019, or Matthew K. Gold,  Debates in the Digital Humanities. Minneapolis, 2012, Section V. Accessed from: http://dhdebates.gc.cuny.edu/debates/1. For curricula see for example Patrick Sahle, DH studieren! Auf dem Weg zu einem Kern- und Referenzcurriculum der Digital Humanities. Göttingen: GOEDOC, Dokumenten- und Publikationsserver der Georg-August-Universität, 2013. Accessed from http://webdoc.sub.gwdg.de/pub/mon/dariah-de/dwp-2013-1.pdf, or IANUS, Statement zu minimalen IT-Kenntnissen für Studierende der Altertumswissenschaften, 2017. Accessed from https://www.ianus-fdz.de/projects/ausbildung_qualifizierung/wiki/Empfehlungen_zu_minimalen_IT-Kenntnissen, 25.03.2019. The need to not only being able to use tools and modeling systems, but to be able “to intervene in this ecology by designing more expressive modeling systems, more effective tools, and a compelling pedagogy through which colleagues and new scholars can gain an expert purchase on these questions as well” has been underlined recently by Julia Flanders & Fotis Jannidis, Data Modeling in a digital humanities context: An introduction. In: Julia Flanders & Fotis Jannidis (ed), The shape of data in the digital humanities: Modeling texts and text-based resources, London, New York, 2019, p. 3–25, here p. 20. See also ibid. p. 23. Data literacy, including data modelling literacy is indispensable to exercise control “over our data during its entire life cycle”, ibid. p. 12.

[36] The term datafication seems to have been coined in the publication by Kenneth Neil Cukier & Viktor Mayer-Schoenberger, The Rise of Big Data: How It’s Changing the Way We Think About the World. Foreign Affairs (2013). Accessed from https://www.foreignaffairs.com/articles/2013-04-03/rise-big-data, 25.03.2019.

[37] For this aspect see Julia Flanders & Fotis Jannidis, Data Modeling in a digital humanities context: An introduction. In: Julia Flanders & Fotis Jannidis (ed), The shape of data in the digital humanities: Modeling texts and text-based resources, London, New York, 2019, p. 3–25, esp. p. 13-15, quotes p. 14 and p. 15.

[38] “Die zunehmende Digitalität in den Geisteswissenschaften macht dabei den Aufbau einer Data Literacy, also einer grundlegenden Datenkompetenz von Lernenden, Lehrenden und Forschenden, unerlässlich.” Torsten Schrade, Im Datenozean, in: F.A.Z., 2.12. 2018, Accessed from: https://www.faz.net/-in2-9h3jj, 25.03.2019.

[39] See Jonathan Gray, Carolin Gerlitz & Liliana Bonegru, Data infrastructure literacy. Big Data & Society (2018), p. 1–13. Accessed from: https://doi.org/10.1177/2053951718786316, 25.03.2019.

[40] Translated from: AG Forschungsdaten der Schwerpunktinitiative “Digitale Information” der Allianz der deutschen Wissenschaftsorganisationen, Forschungsdatenmanagement: Eine Handreichung, 2018, p. 4. Accessed from: http://doi.org/10.2312/allianzoa.029, 25.03.2019

[41] Sometimes the term data curation seems to be used (wrongly) as a synonym.

[42] See Marina Lemaire, Vereinbarkeit von Forschungsprozess und Datenmanagement in den Geisteswissenschaften: Forschungsdatenmanagement nüchtern betrachtet. o-bib 5 (2018):4, p. 237–247, here p. 238. Accessed from: https://doi.org/10.5282/o-bib/2018H4S237-247, 25.03.2019.

[43] See Andreas Witt et al., Forschungsdatenmanagement in den Geisteswissenschaften an der Universität zu Köln, in: o-bib 5 (2018): 3, p. 104–117, here p. 106. Accessed from https://doi.org/10.5282/o-bib/2018h3s104-117, 25.03.2019.

[44] See Marina Lemaire, Vereinbarkeit von Forschungsprozess und Datenmanagement in den Geisteswissenschaften: Forschungsdatenmanagement nüchtern betrachtet. o-bib 5 (2018):4, p. 237–247, here p. 244-245. Accessed from: https://doi.org/10.5282/o-bib/2018H4S237-247, 25.03.2019. See also: “The point here is not that these costs are prohibitive or unjustified, but rather that good strategic planning involves balancing the costs and benefits, and focusing the effort in areas that offer a clear advantage.” Julia Flanders & Fotis Jannidis, Data Modeling in a digital humanities context: An introduction. In: Julia Flanders & Fotis Jannidis (ed), The shape of data in the digital humanities: Modeling texts and text-based resources, London, New York, 2019, p. 3–25, p. 8.

[45] See Marina Lemaire, Vereinbarkeit von Forschungsprozess und Datenmanagement in den Geisteswissenschaften: Forschungsdatenmanagement nüchtern betrachtet. o-bib 5 (2018):4, p. 237–247, here p. 245 (accessed from: https://doi.org/10.5282/o-bib/2018H4S237-247, 25.03.2019), who describes that the biggest difference of the digitized research process in the humanities is that researchers need to plan the research process more detailed at an earlier stage, describing their methods more explicit in order to come to machine readable data (processes).

[46] See Marina Lemaire, Vereinbarkeit von Forschungsprozess und Datenmanagement in den Geisteswissenschaften: Forschungsdatenmanagement nüchtern betrachtet. o-bib 5 (2018):4, p. 237–247, here p. 245-246, accessed from: https://doi.org/10.5282/o-bib/2018H4S237-247, 25.03.2019.

[47] See for example Andreas Witt et al., Forschungsdatenmanagement in den Geisteswissenschaften an der Universität zu Köln, in: o-bib 5 (2018): 3, p. 104–117, here p. 113. Accessed from https://doi.org/10.5282/o-bib/2018h3s104-117, 25.03.2019. The authors describe that Research Data Management is already established in the curriculum of the humanities faculty of Cologne University.

[48] “There is a need for new guidance and exemplars to ensure that data meets appropriate quality standards; for tools to standardise and automate data management, documentation and curation processes; and for an increased focus on improving research software, and on recruiting and retaining software engineers.” Open Research Data Task Force, Realising the potential: Final Report of the Open Research Data Task Force. N.P., 2018, p. 8. Accessed from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/775006/Realising-the-potential-ORDTF-July-2018.pdf, 22.03.2019.

[49] About RDMO see esp. the detailed article Heike Neuroth et al., Aktives Forschungsdatenmanagement. ABI Technik, 38(2018):1, p. 55–64. Accessed from https://doi.org/10.1515/abitech-2018-0008, 25.03.2019. See also the RDMO project website: https://rdmorganiser.github.io/, accessed 25.03.2019.

[50] There are several tools available for helping creating a Research Data Management Plan (DMP), but not with a focus on active data management.

[51] My home organisation, the University of Applied Sciences Potsdam (FHP), is currently developing together with the project partners AIP (Leibniz-Institut für Astrophysik Potsdam) and KIT (Karlsruhe Institute of Technology) funded by the Deutsche Forschungsgemeinschaft (DFG) such a tool, the Research Data Management Organiser (RDMO).

[52] Research Data Management should not be a sole task for researchers, but they definitely have to be on board.

[53] “As data creators, academics have a different, more knowing relationship to their data: they create data that is going to be a persistent part of the research environment, and they act as both its creators, managers, and consumers. The stakes of the modeling decisions for research data are thus much higher, and to the extent that these decisions are mediated through tools, there is  significant value—even a burden of responsibility—in understanding that mediation. And within the academy, the stakes for digital humanists are highest of all, since their research concerns not only the knowing and critical use of data models, media, and tools, but also their critical creation.” Julia Flanders & Fotis Jannidis, Data Modeling in a digital humanities context: An introduction. In: Julia Flanders & Fotis Jannidis (ed), The shape of data in the digital humanities: Modeling texts and text-based resources, London, New York, 2019, p. 3–25, here p. 11-12.

[54] For the recommendation of local contact persons see for example, Andreas Witt et al., Forschungsdatenmanagement in den Geisteswissenschaften an der Universität zu Köln, in: o-bib 5 (2018): 3, p. 104–117, esp. p. 106, 115.

[55] See Paul Ayris et al., LIBER Open Science Roadmap, 2018, p. 18-19. Accessed from:  http://doi.org/10.5281/zenodo.1303002, 25.03.2019.

[56] This point especially relates to the creation of corpora that are digitized and made accessible in meaningful ways for research purposes, e.g. HathiTrust Digital Library (https://www.hathitrust.org/) or the Deutsche Textarchiv (DTA) (http://www.deutschestextarchiv.de/). See for example the recommendations in Lisa Klaffki, Stefan Schmunk, Thomas Stäcker, Stand der Kulturgutdigitalisierung in Deutschland: Eine Analyse und Handlungsvorschläge des DARIAH–DE Stakeholdergremiums „Wissenschaftliche Sammlungen“, Göttingen: GOEDOC, Dokumenten- und Publikationsserver der Georg-August-Universität, 2018 (DARIAH-De Working Papers, 26), Accessed from: http://webdoc.sub.gwdg.de/pub/mon/dariah-de/dwp-2018-26.pdf, 04.04.2019.

[57] See Marina Lemaire, Vereinbarkeit von Forschungsprozess und Datenmanagement in den Geisteswissenschaften: Forschungsdatenmanagement nüchtern betrachtet. o-bib 5 (2018):4, p. 237–247, here p. 245-246, Julia Flanders & Fotis Jannidis, Data Modeling in a digital humanities context: An introduction. In: Julia Flanders & Fotis Jannidis (ed), The shape of data in the digital humanities: Modeling texts and text-based resources, London, New York, 2019, p. 3–25, here p. 3, Torsten Schrade, Im Datenozean, in: F.A.Z., 2.12. 2018, Accessed from: https://www.faz.net/-in2-9h3jj, 25.03.2019.

[58] See for example Julia Flanders & Fotis Jannidis, Data Modeling in a digital humanities context: An introduction. In: Julia Flanders & Fotis Jannidis (ed), The shape of data in the digital humanities: Modeling texts and text-based resources, London, New York, 2019, p. 3–25, here p. 5.

 

 
 
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Tide Prediction Machines, Prosopography and Digital Humanities: what are they and how do they fit together? https://dhmethods.hypotheses.org/229 https://dhmethods.hypotheses.org/229#respond Thu, 18 Apr 2019 07:58:19 +0000 https://dhmethods.hypotheses.org/?p=229 Continue reading "Tide Prediction Machines, Prosopography and Digital Humanities: what are they and how do they fit together?"]]> Guest post by our blog competition winner, Helen Mair Rawsthorne.

Tide prediction machines, prosopography and digital humanities are the three main axes upon which my first-year Master’s degree research project is constructed. I’m a student of the “Cultural History of Science and Technology, Digital Humanities and Mediation” course at the University of Western Brittany’s Centre François Viète research laboratory in Brest. In this blogpost I’m going to explain what these three most important components of my project are, how they link together and how you could integrate certain elements into your own work, with a focus on digital humanities tools.

Tide Prediction Machines

Tide prediction machines are analogue computers that were used to predict the times of high and low tides worldwide from the end of the 1800s up to the digital age. The first tide prediction machine was designed by William Thomson (later Lord Kelvin) and built in London in 1873. It was developed as a response to increasing pressure from commercial shipping lines towards the middle of the 19th century who wanted a greater number of more accurate tidal predictions more quickly than could be calculated by hand. As well as helping shipping lines and navies to safely navigate the seas and the shores, tide prediction machines became crucial to the building of ports and effective flood defences. A total of 25 of the 33 tide prediction machines ever built were constructed in the UK but were then shipped to other countries who wanted to do their own calculations. Although analogue tide prediction machines are no longer in use, nearly all still exist today as museum pieces across the world. The photo below shows the Bidston Kelvin tide prediction machine, which was built by Kelvin Bottomley and Baird Ltd. in Glasgow (Scotland) between 1924 and 1925. It was first used in Bidston (England) by Liverpool Observatory and Tidal Institute before being shipped to Paris for use by the French navy’s hydrographic service (today the Shom) and then to their site in Brest (France), where it can still be found today.

Photograph of the Bidston Kelvin tide prediction machine at the SHOM site in Brest (France), taken by the author on 18/12/2018. Licensed under CC BY 4.0.

Prosopography

Prosopography is a research method usually used by historians to study the lives of groups of people. It involves the creation of a collective biography or the gathering of data relating to the common aspects of the lives of individuals who are part of a particular population. It can take the form of a database of all of the people within the chosen population along with information about the biographical phenomena that transcend their individual lives. Prosopography overcomes the problem of the scarcity of historical data by collecting together all available fragments, which can then be compared, synthesised and analysed, thus compensating for any gaps in the data. Instead of looking at the exceptional and unique, prosopography focuses on the general and average. It is in this way that prosopography makes visible the particular characteristics representative of the chosen population.

Digital Humanities

Digital humanities is an interdisciplinary area of study that lies in the intersection between the humanities and digital technology. Research in digital humanities goes in two directions: it uses digital technologies to ask questions about and to create new knowledge in the humanities, and uses the humanities to ask questions of and to reflect upon digital technology. The digital technologies in digital humanities come in the form of tools, applications and software (purpose-built for digital humanities or not) that can be used for the effective production and dissemination of research in the humanities.

That’s great, but… how do they all feature in one project?

Tide prediction machines

Tide prediction machines have been studied very little, despite having been used to perform life-saving calculations and making a crucial contribution to maritime history. I want to find and analyse the common features in the lives of tide prediction machines, to give an appreciation of their importance, the extent of their use and usefulness, and an impression of the shape of their lives. I therefore decided to study the “life cycle” of analogue tide prediction machines: the methods used to predict tides before the construction of the first machine, the motivation for creating this machine, the manufacturers, users and uses of the machines, maintenance and repair of the machines, the decommissioning of the machines, subsequent methods of predicting tides and finally the state of the machines today in terms of conservation and scientific mediation.

Prosopography

My project supervisor then introduced me to the practice of prosopography, but as you might have realised above it is a research method used almost exclusively to study groups of people. The 33 tide prediction machines that I’m studying form a coherent group in which each artefact can be described by a time evolution cycle. Given the analogies with human groups, my methodological research hypothesis is that it is possible to apply prosopographic methods to successfully analyse the life cycle of tide prediction machines.

Every member of the “population” of tide prediction machines must be subjected to the same questionnaire. I do not expect to find an answer to each question for each machine but that will not pose a problem in this prosopographical study; the aim is to find the common features in the lives of analogue tide prediction machines.

Digital humanities

Given the data-centric nature of prosopography, I see the use of digital humanities tools as essential to my project. Much of my data is temporal and geospatial: dates of “life events” such as construction, displacement, renovation and decommissioning, and locations of construction, use and conservation. Each machine has its own unique life and has its own story to tell, but there are nevertheless many features that group them together and events that they have in common. I’m using diverse digital humanities tools to associate, visualise and interpret this data, thereby distinguishing particular characteristics representative of tide prediction machines.

Which digital humanities tools?

I’m now going to introduce you to some of my favourite digital humanities tools and I’ll explain how I’m using them in my current work, how they’ve helped me and how they could be useful to you.

Palladio

Designed and built by Stanford University’s Humanities + Design Lab, Palladio is a digital humanities tool for the visualisation of complex historical data. This web-based open source application focuses on data-driven historical research. Palladio is ideal for those who want to visualise networks, temporal data or geospatial data all from the same database using an intuitive graphical interface. From a single database, researchers can create various visual elements that can be customised according to the data available. I have used Palladio to create a few visuals of my data, all of which have helped me by highlighting patterns in the data and making me ask new questions of it. I will now show you examples of how I’ve used three different features in Palladio to visualise my data.

Graphs in Palladio

The graph function offered by Palladio can be used to visualise a network or networks and the relationships within and between them. The points can be filtered and sized according to other parameters in the database, which is why the initial database needs to be well thought out and constructed with Palladio in mind. The graph below shows the relationship between the city of construction (dark grey) and the current location (light grey) of tide prediction machines; the points are sized according to the number of machines in each case.

Screenshot of a graph created by the author on the Palladio platform. Licensed under CC BY 4.0.

Maps in Palladio

To make use of the map function, Palladio requires data in the “latitude, longitude” format. These points can be plotted on a map of the world and can be coloured, sized, linked and filtered. The base map can also be changed to show different geographical features, or a custom map can be uploaded. I created the map below to show the number of tide prediction machines used in any given place (red), and how many can be found in each different place today (green).

Screenshot of a map created by the author on the Palladio platform. Licensed under CC BY 4.0.

The points displayed on a maps or in a graph can be filtered by time, if the database contains data in a date format.

Timespans and timelines in Palladio

Any date data can also be visualised with a timespan, which can have various different layouts, or with a timeline, which takes the form of a bar graph. The timespan below shows the lifetime of tide prediction machines, and the timeline shows the number of machines that were built each year.

Screenshot of a timespan and a timeline created by the author on the Palladio platform. Licensed under CC BY 4.0.

Palladio is an excellent example of a digital humanities tool that can be used by historians to visualise data. Data visualisations can help researchers to make the most of the available data at any point during the research process. They also give meaning to data and facilitate its comprehension by the audience.

Time.Graphics

Time.Graphics is a web-based tool for the creation of timelines. It offers only this one function, but it does it well. Single events or timespans can be added to the timeline with a title, description and colour. I made the timeline below to show all the significant dates of tide prediction machines built in the UK: date of manufacture, displacements, renovations, prizes awarded and decommissioning. I assigned each individual machine with a different colour to help identify them on the timeline. Interpreting and comparing data from a colourful and visual representation can be much easier than reading a database, for me and for those not familiar with my research.

Screenshot of a timeline created by the author with the Time.Graphics tool; this timeline can be viewed at https://time.graphics/line/180223. Licensed under CC BY 4.0.

Hypotheses

As part of the OpenEdition infrastructure, Hypotheses is a platform for blogs in humanities and social sciences research. My classmates and I have a joint class blog hosted by Hypotheses for our Master’s course, and we regularly write about our classes, texts we’re reading and how our research is advancing. Blogging is new to me but I’m finding it to be an excellent way of organising my thoughts by forcing me to write about what I’m doing in a way that’s easily understood by others. It’s also proving to be an excellent way for us to learn more about how our classmates are conducting their research and what they’re discovering.

Twitter

Where would I be without Twitter? Not writing this blogpost, that’s for sure. I started using Twitter when I discovered that researchers and academics use it a lot, and I wanted to be part of it! Without Twitter I wouldn’t have found out about this competition, nor about a conference I’ll be talking at later this month, nor about many other events, interesting people or research. There’s a real sense of community amongst us #twitterstorians, who I’ve found to be encouraging, supportive and sympathetic (because things don’t always go well!).

Other tools

There are a few other digital humanities tools that I like to use such as CmapTools for making concept maps with which I can keep track of my thoughts in a visual way, Zotero for creating a bibliography and storing information about what I’m reading, and Overleaf: the best LaTeX editor ever (in my opinion).

It may seem that the use of digital humanities tools is labour-intensive, but the right ones for your project used in the right way can be immensely useful for your audience and, more importantly, for yourself and your work. They can help you to see your research in a new light, give you alternative ways of doing things, provide you with more opportunities for sharing your work and collaborating with others.

Why should you care about …?

Tide prediction machines

The accuracy of the calculations made by tide prediction machines were of global importance, as the lives of people at sea and the efficient running of worldwide maritime transport depended upon it. These marvellous machines deserve to be better known; I’m trying to better understand their lives so that their stories can be told and shared.

Prosopography

The prosopographical method can be very useful for researchers whose data is incomplete; it avoids generalising from select examples and favours studying collectively all available cases, regardless of completeness, thereby ensuring the data is correctly represented.

Prosopography is a well-established research method in history that can be used for a wide range of studies in various historical periods. These studies have, until now, all been based upon populations of people, but what I’m doing by using prosopography to study artefacts is relatively new. By studying historical data in this way we can analyse relationships across time and space and attempt to better understand the past.

Digital humanities

Digital humanities tools can be useful in the three main stages of a project: they can help you to advance in your research, to disseminate your results more effectively, and they can help your audience to understand what you’re trying telling them.

Different tools can facilitate the advancement of your research by giving you an environment in which your data and research can be stored, managed and analysed. Data visualisation tools can highlight relationships within and between networks, help you to spot patterns, uncover and draw your attention to trends previously hidden in the data, encourage you to view information in other ways and cause you to make new enquiries. Many digital humanities tools also offer a collaboration function, meaning that you can work together with your colleagues on the same file.

As for diffusing your findings, digital humanities can offer you a wide range of platforms for presenting and sharing your data, in many different ways.

Data presented in innovative formats will make your research more accessible and understandable by different types of learners. Visual representations could be better for younger audiences or those unfamiliar with your area of study because reading and interpreting visualisations of data can be easier and quicker than databases or text. Data in visual forms are also more attractive and more likely to be remembered, thereby facilitating their retelling.

What’s next?

I’ve already gathered a lot of data for my prosopography of tide prediction machines, but I’m still searching for answers to certain questions. I need to continue analysing the data using various digital humanities tools and then I will be able to write up my results about the life cycles of tide prediction machines and evaluate the success of this prosopographical study of artefacts.

You can read more about the story of tide prediction machines, prosopography, Digital Humanities and other interesting topics on Helen’s blog.

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We are pleased to announce the winners of the Digital Humanities tools and methods blog competition https://dhmethods.hypotheses.org/222 https://dhmethods.hypotheses.org/222#comments Mon, 15 Apr 2019 15:07:04 +0000 https://dhmethods.hypotheses.org/?p=222 Continue reading "We are pleased to announce the winners of the Digital Humanities tools and methods blog competition"]]> To spread the word on emerging good practices around data management in Digital Humanities and to give visibility to young scholars’ critical reflections on how they adopt digital methods and tools to their research questions, DARIAH-EU offered two travel scholarships, open to early career researchers, to attend the Annual Event and showcase their topic as a poster presentation at the conference.

The two winning posts are:

  • Tide Prediction Machines, Prosopography and Digital Humanities: what are they and how do they fit together? – Helen Mair Rawsthorne
  • “Here be dragons”: Open Access to Research Data in the Humanities – Ulrike Wuttke

A few words about the authors:

Helen Mair Rawsthorne is studying for a Master’s degree in Epistemology and the History of Science and Technology: the Cultural History of Science and Technology, Digital Humanities and Mediation as an online student at the University of Western Brittany in Brest, France. Her Master’s thesis will be a prosopographical study of analogue tide prediction machines. She graduated with a BSc in Physics from the University of Bristol in 2016 and wrote her Bachelor’s dissertation on the evolution of the relationship between science and technology throughout history.

Ulrike Wuttke is a medievalist and textual scholar by training with a specialisation in Medieval Dutch Literature (Doctor of Literature, Universiteit Gent). Since her PhD, she contributes to digital humanities projects and networks such as the Working Group Data Centres of the Verband Digital Humanities im deutschsprachigen Raum, the metablog OpenMethods or the DARIAH Working Group DiMPO (Digital Methods and Practices Observatory). Since 2017, she works and teaches at the University of Applied Sciences Potsdam, Department of Information Science, where she has recently joined the team of the DFG-Projekt RDMO (Research Data Management Organizer). Her special interests are Open Science, Research Data Management and Digital Methods and Tools.

The winning entries will be published on the blog in the coming days – stay tuned!

Congratulations to both our winners and thanks again to everyone who participated in the blog competition!

 

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Digital Humanities methods and tools blog competition https://dhmethods.hypotheses.org/215 https://dhmethods.hypotheses.org/215#respond Tue, 12 Mar 2019 08:13:35 +0000 https://dhmethods.hypotheses.org/?p=215 Continue reading "Digital Humanities methods and tools blog competition"]]> Have you ever considered blogging about your research tools and methods? Come and share the methodological advancements or challenges of your research and win a scholarship to attend the DARIAH Annual Event 2019 on “Humanities Data”, to be held in Warsaw, on May 15-17.

DARIAH-EU is offering two travel scholarships, open to early career researchers, to attend our Annual Event. On top of the travel bursary, successful applicants will automatically win the opportunity to showcase their topic as a poster presentation at the conference.

To participate, all you need to do is to send us a blog post focusing on one or more of these topics below:

  • Descriptions of or critical reflections on methods and tools from your own research projects
  • Case studies about the optimal reuse of data or tools
  • Practical or theoretical reflections about how and why humanities research is conducted digitally
  • How the increasing influence of digital methods and tools changes scholarly attitudes and scientific practices of humanities research

How to apply

You don’t need to have your own blog to apply. Please send us your blog post together with a little introduction of yourself to the following e-mail addresses: erzsebet.toth-czifra@dariah.eu and eliza.papaki@dariah.eu 

Scholarship details:

DARIAH-EU will reimburse your basic travel costs to Warsaw (flight/train/bus ticket, accommodation and public transport transfer) up to 500 EUR.

Ready, set, go!

The deadline for submissions is the 9th of April. We are looking forward to receiving your posts and will announce the winner on the 12th April 2019.

For any questions, please contact us via email.

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NERD and Isidore services bring more visibility to content on OpenMethods https://dhmethods.hypotheses.org/182 https://dhmethods.hypotheses.org/182#respond Wed, 20 Feb 2019 14:08:05 +0000 https://dhmethods.hypotheses.org/?p=182 Continue reading "NERD and Isidore services bring more visibility to content on OpenMethods"]]> Helping Digital Humanities scholars to find the tools and methods that are the most relevant to their work is a core mission of OpenMethods. By shifting perspectives from research outputs to the underlying workflow, procedures and tools, we aim to strengthen the culture of reuse of already existing resources in DH. But we do not only propagate the culture of reuse. Putting our money where our mouth is, we are also seeking ways to put DH tools in service of more effective content discovery and enrichment.

As a reusability exercise, recently we have created plugins to achieve interoperability with the entity recognition NERD service and the research discovery platform ISIDORE to increase the visibility and discoverability of our content

The NERD plugin

NERD is a service that recognizes and disambiguates named entities.

This plugin allows integration of the NERD service with WordPress. As a form of content enrichment, the plugin automatically creates tags from the named entities offered by NERD when provided the full text of the original article that has been republished on OpenMethods. The tags, in return, are used to propose extra information coming from Wikipedia and Wikidata. These tags contribute to the better discoverability and searchability of content on OpenMethods and add extra context layers to our content.

Automatically generated tags on OpenMethods content with links to Wikidata and Wikipedia

The plugin is free to download and is available for reuse form here:  https://wordpress.org/plugins/nerd-wp/

It is reusable and only needs a NERD server to work with, one instance of this server is freely available at Huma-Num, please contact them for more information.

The Rich meta in RDFa plugin

The second plugin is for increasing the findability of our content on other discovery platforms via metadata enrichment. We wanted Isidore, an indexing and search service for the humanities and social sciences, to harvest our content and make it searchable and findable on their platform that serves as a single point entry for a wide range of open SSH resources, such as data, publications and other materials. To enable indexing by Isidore, OpenMethods has to be able to present information (i.e. a set of dublin core metadata elements such as title, description, date, etc.) in a format understood by Isidore. For this purpose we created this WordPress plugin that allows users to add Dublin Core metadata enrichments in RDFa within the HTML header of each post.

Dublin Core enrichment in OpenMethods’ metadata

Currently it is only used for harvesting by ISIDORE, but it can easily be changed to accommodate other applications. In the future, it will be possible to change the namespace and element names of the RDFa data for a given input such as title or excerpt.

As a result, content from OpenMethods is now connected with and embedded in a large corpus of electronic publications, corpora, databases and scientific news, and thus Isidore users can find OpenMethods posts when looking for information about Digital Humanities. Another benefit of the plugin is that it indirectly, through OpenMethods, enables the integration of content that could not be indexed otherwise on research discovery platforms because of their language (other than English) or content type properties (e.g. blogs, videos or podcasts scattered across the web with insufficient metadata).

The plugin is free to download and is available for reuse from here:  https://wordpress.org/plugins/rich-meta-in-rdfa/

The figure below allows for a sneak peek behind the scenes and gives an at-a-glance summary on how the plugins build on DH resources.



  1. As OpenMethods only offers users a very short introduction to and a quotation from the republished source content, first we have to go and look for the full text of the original content (the source of the entire article) from the article’s source site through an HTTP(S) request and keep the relevant textual elements (and exclude other, irrelevant elements of the page e.g. advertisements, menu etc.).
  2. Once the data is retrieved, it is followed by a call to NERD (runned on Huma-Num servers) so that we return the Named Entities that could be found in the text.
  3. These Named Entities are then ingested by OpenMethods to create tags and to be able to send the user back to entities on Wikipedia / Wikidata for more information.

To add an extra discovery layer to the OpenMethods platform, we also integrated a third plugin developed by the Isidore team: it enables listing of similar content recommendations on OpenMethods posts.

Similar content recommendations from Isidore

You are more than welcome to explore how the two plugins jointly contribute to the enrichment and greater visibility of the OpenMethods posts. Besides, as you would expect, both of them are free to download and are available for reuse. Take a look into their documentation and let us know if it inspired you to think about ways to put them to good (re)use in your own projects.

We owe special thanks to Pierre Mounier and Nicolas Larrousse for their idea to develop the two plugins, to Yoann Moranville who created them and to the Huma-Num team, especially to Laurent Capelli who helped us with the design and the Isidore indexing.

 

This post has been published under a Creative Commons CC-BY 4.0. license.

 

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Digital Humanities Methods and Tools https://dhmethods.hypotheses.org/6 https://dhmethods.hypotheses.org/6#comments Thu, 15 Feb 2018 10:15:35 +0000 http://dhmethods.hypotheses.org/?p=1 Continue reading "Digital Humanities Methods and Tools"]]> Have you ever considered blogging about your research tools? Is it the hassles around maintaining your own blog or finding your audience that holds you back? Would you write about what helps you to do effective research only occasionally? Then this blog is for you. It collects guest posts about Digital Humanities tools and methods. Digital Humanities scholars as well as their collaborators (data scientists, computer scientists, developers, librarians, archivists etc.) are writing for each other about know-hows, best practices, limitations & benefits or reuse potentials of research tools and methods.

This blog is an extension of the OpenMethods metablog. OpenMethods is a platform aimed at republishing and bringing together all formats of Open Access publications (e.g. re-search articles, preprints, blog posts, videos, or podcasts) in different languages about Digital Humanities methods and tools to spread the knowledge and raise peer recognition for them. The platform has been developed in close partnership with and supervision of the DARIAH community since it is an offspring of the DARIAH “Humanities at Scale” project. Relevant content is selected and curated by an international group of Digital Humanities experts to be republished on OpenMethods. The Digital Humanities methods and tools blog will serve as a pool for selection: the most interesting posts will be republished on OpenMethods.

Our goal is to reach and engage the widest possible array of Digital Humanities communities ranging from scholars taking the first steps towards going digital in their research to Digital Humanities experts who are shaping specific research areas as representatives for particular methods.

On the OpenMethods platform we do not publish original contributions but if you have an interesting blog post or video to share with the broader community around Digital Humanities, you are very welcome to share it with us as a post on this blog.

This post has been published under a Creative Commons CC-BY 4.0. license.

Image credit: Pixabay (CC0).

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