Introduction

1Experiment is mentioned so regularly in the Digital Humanities that one could think everything has been said about it.¹ Yet it is difficult to find serious attempts at giving a useful definition which is not too vague to be applicable.² This not only leaves the field vulnerable to criticism regarding a metaphorical appropriation of scientific terminology for potentially questionable reasons but also for technocratic speculations on how experiment could supposedly rid the Humanities of their allegedly flawed qualitative tradition by undergoing a process of making them ›more scientific‹. What we need is precise terminology and a sensible definition of where and how experimental methods can be integrated into traditional Humanities hermeneutics. This paper is an attempt at retracing the history of laboratories and experimentation as well as discussing criteria for labelling certain practices as experiments in the Computational Humanities. It makes the argument that a stricter terminological distinction between playful experimentation with the purpose of exploring data and experiment according to scientific definitions should be made.

2Quantitative approaches are often discussed in conjunction with a trope of novelty and in connection to computerization. But a number of recent studies have stressed the fact that quantitative methods are neither new nor unusual in literary studies or historical research – they even predate the success story of the Digital Humanities.³ However, it seems to have been overlooked in the discussion of experimental and quantitative methods in the Digital Humanities so far that experimental methods, too, have a long-standing tradition in Humanities research in disciplines such as Experimental Archaeology or the Experimental History of Science. Discussions of experiments in the Digital Humanities rarely take into account that there are Humanities disciplines which have already integrated experimental methods into their traditional framework of hermeneutics. We can look to those disciplines for a sensible framework of experimentation in the Computational Humanities. This paper suggests that we should adopt the distinction between ›experiencing‹ and ›experimenting‹ which has become commonplace in Experimental Archaeology.⁴ It argues further that a stricter definition of experiment should be limited to a narrow subset of problems in the Computational Humanities, more specifically that which Piotrowski and Fafinski have described as Applied Computational Humanities.⁵ A rigorous definition of experiment seems impossible for the Humanities in general which is why formulating one for the Digital Humanities as a broader umbrella field wouldn’t transcend a metaphorical notion of experimentation.⁶

3This paper discusses how the definitions of historical experiment and re-enactment stemming from the History of Science and more specifically, created in the research tradition of the Experimental History of Science, can be applied and related to Computational Humanities.⁷ It draws on the history of laboratory and experiment to lay out a viable approach for defining experiment in the Digital, and especially, the Computational Humanities. In order to do so, it traces back the historical origins of laboratory and experiment, drawing conclusions for the use of the terms ›laboratory‹ and ›experiment‹ with regard to datafication and quantification in the Humanities. Van Zundert asserts that the »exploration of the hermeneutic potential of computation is a challenge for the digital humanities [...].«⁸ Establishing the role and epistemological makeup of experiment in the Computational Humanities can contribute important insights to this endeavour.

4This paper does not wish to discuss the question of how to best define the term Computational Humanities or differentiate Computational Humanities from the umbrella term Digital Humanities.⁹ Nevertheless, it makes sense to make this distinction with regard to the problem at hand. For the time being, we mean by Computational Humanities roughly those parts of Digital Humanities which are primarily concerned with quantitative approaches (seen as a sub-field), distinguishing ›numerical humanities‹ which use new algorithmic methods which cannot fully be grasped using traditional Humanities hermeneutics from ›digitized humanities‹ which are concerned mainly with presentation, publication and access to data – which Michael Piotrowski has defined as ›public humanities‹.¹⁰ This article argues that speaking of experiments only really makes sense in this sub-field of the Digital Humanities which we refer to by the name of Computational Humanities. In most other cases, a rigorous definition of experiment is not possible. One of the main arguments of this paper is that we should distinguish terminologically between metaphorical experiments, such as in the case where ›experiment‹ refers to playful exploration of data, and experiments which can meet the criteria of a stricter definition of experiment which shall be given in this article. Consequently, open questions are discussed such as the hermeneutical implications of experimental methods and the implementation of practical workflows for documenting data experiments.

A history of the laboratory

5The laboratory has become probably the most common metaphor for scientific as well as experimental knowledge-production.¹¹ As the birthplace of laboratories all around the world, and thus, by extension, also for the Digital Humanities Lab, the alchemical laboratory is a good starting point for the exploration of the meanings and potentials of the laboratory and experiment metaphors.¹² Hannaway states that »indications are that the laboratory was at first linked exclusively with alchemy and chemistry; only gradually, it seems, was the term extended to describe all those distinctive places where the manipulative investigation of natural phenomena was carried out.«¹³ With the emergence of Laboratory Studies in the 1980s¹⁴, the study of the laboratory became popularized but also took a decidedly sociological turn, marked by the anthropological studies in the tradition of Latour and Knorr-Cetina.¹⁵

6If we are to negotiate the place and epistemological status of experimentation in the Computational Humanities, we must first reach an understanding of the terms involved, namely laboratory and experiment. While the Digital Humanities discourse is filled with the metaphors of experiments and laboratories, sound definitions about what constitutes those experimental methodologies and their place in the traditional hermeneutics of the Humanities are still lacking.¹⁶ This isn’t all that surprising given the fact that the exact origin of the term ›laboratory‹ as we now use it remains quite obscure: it seems to appear in the modern meaning since the 16th century at the earliest.¹⁷ Furthermore, indications are that laboratorium »referred almost exclusively to a room or house where chemical operations such as distillation, combustion, and dissolution were performed.«¹⁸ It must be emphasized, however, that there is a significant »shared material culture of the pharmaceutical and academic-chemical laboratory«¹⁹ and some argue that we cannot speak of a laboratory in today’s sense before the pre-industrial age, as »patronage of laboratories is strongly linked to industrialization« and »laboratories are very much a product of, and a symbol of, modern industrial society.”²⁰ While even the first institutionalized chymical laboratories still »resembled the workshops of apothecaries, metalworkers, and pigment makers [...] and shared many of its components with the workplaces of metal smelters, glassmakers,«²¹ the term ›laboratory‹ had acquired the meaning we now associate with it today towards the end of the seventeenth century: From this point forward, laboratories were seen as »the hallmarks of the new science – the site where theories and hypotheses were purportedly tested by experiment and from which new discoveries and useful knowledge emerged. When the word was first used, the spaces referred to were workshops rather than ›sites of science.‹«²²

7The term laboratorium can already be found before the 16th century, its ending in - orium reminiscent of room names in monastic contexts. However, it seems that it had not yet acquired its connotation of being a place of science back then.²³ The increasing fragmentation of political power during the Renaissance period provided new opportunities which

encouraged artisans of all kinds (but especially gunners, fortification builders, architectural writers, machine builders, and alchemists) to write about their techniques, in part to advertise their know-how. [...] Craftsmen [...] wrote treatises in which they both advertised their skills and attempted to provide a theoretical elaboration of their craft experience. The publication of replicable processes and techniques would become a hallmark of laboratory activity by the mid-seventeenth century [...].²⁴

8It thus seems that the turning point for the scientification of experiment came from a financial impetus: The imperative for entrepreneurial artisans to transmit their knowledge textually and explain their theories to advertise themselves as competent practitioners. Combined with a vibrant tradition of ›practical exegesis‹ and commentary in the history of alchemy, showing off one’s ability to explain the chymical theory behind one’s experimenta became a standard requirement to gain funding and patronage.²⁵ After this shift, »the appearance of the laboratory is indicative of a new mode of scientific inquiry, one that involves the observation and manipulation of nature by means of specialized instruments, techniques, and apparatuses that require manual skills as well as conceptual knowledge for their construction and deployment.«²⁶

9Fast forward to the 20th century where Science and Technology Studies (STS) began to perform ethnographical studies in the 1970s, conceptualizing the laboratory as »a gateway for understanding how scientific knowledge is produced.«²⁷ This is around the same time when first ›labs‹ for Humanities subjects started appearing during the 1980s which were »introduced as the infrastructure for facilitating and propelling the advancement of DH« as part of »the first wave of the ›infrastructure turn‹ in the humanities.«²⁸ Sociological studies emphasizing the role of the laboratory as a place of knowledge-making might be the reason why the laboratory metaphor started to become fashionable in the Humanities where Rheinberger’s term of the ›experimental system‹ became a common point of reference for linking Humanities scholarship to the notion of experiment.²⁹ The metaphors of laboratory and experiment have been the subject of a plethora of turns and research paradigms in the Humanities: Be that the cultural and spatial turns related to the sociological tradition of laboratory studies³⁰, the material turn which has been said to have been the catalyst for the Experimental History of Science³¹, the practical turn equally associated with the emergence of experimental methods in historical research³² or, last but not least, in the case of the Digital Humanities, the computational turn.³³ However, there has been criticism regarding the use of the metaphors of laboratory and experiment in the Humanities, suggesting it to be a rhetorical device employed to profit from the prestige and status of the sciences by associating oneself with them.³⁴ This criticism is founded in the lack of a proper definition of experiment in the Humanities. While a merely metaphorical usage of the term ›experiment‹ with possible reference to Rheinberger’s ›experimental system‹ certainly exists and makes sense in the Humanities, the datafication and quantification of Humanities subjects in the Digital Humanities and especially the Computational Humanities, offer the possibility of a more stringent application of the term ›experiment.‹³⁵

On the notion of experiment

10The notion of experiment was a central one in the making of what we now understand as Science.³⁶ Commonly, research is required to involve at least some, if not all, of the following characteristics to count as an experiment in the strict scientific sense: Relevant variables are known and the experiment has been reduced to those which are measurable and can be purposefully manipulated and isolated. The experiment is replicable and falsifiable. Before the experiment, hypotheses need to be formed so they can be investigated during the process. The setup and steps taken should be described in detail for documentation purposes. Furthermore, one might argue that the processes at the core of the investigation need to be observable. However, it should be noted that there is no uniform definition of experiments and definitions vary from field to field.³⁷

11In the digital context, we need to add digital documentation as well as long-term accessibility of both the algorithms and data used. And obviously, the phenomenon which is meant to be experimented on needs to be digitally formalizable. An important advantage of the controlled environment of an experimental setup is that variables of the phenomenon to be investigated can be purposefully manipulated which makes it easier to determine cause and effect using experiment than in the case of mere observation. Experiments also came to be a central aspect in demonstration and teaching. This specific case of experimenting would usually be referred to as ›replication‹ which

generally means the repetition of an experiment in order to check or confirm prior results. Additionally, in educational contexts students perform canonical experiments in exercises that are often considered to be replications. In that case, successful repetition of canonical experiments is a means of calibrating the mind of the student, generally with the aim of proving particular scientific claims as stated in textbooks. When historians rework or reproduce a process or an experiment as a historiographical tool they are not replicating in these scientific or pedagogical senses, but are instead seeking fresh historical information.³⁸

12The issue of replication and replicability is one which has come under scrutiny as of late in connection to a methodological ›replication crisis‹ and its relevance for the Computational Humanities will be discussed in more detail later in the article.³⁹

13In the early modern period, experimenta became vehicles for the deliberate production of experientia, i. e. experience of natural processes the observer was partaking in.⁴⁰ In a dictionary of alchemical specialist vocabulary, experiment is described as both foundation and touchstone for hypotheses.⁴¹ This warrants caution for it mixes up two distinct parts of the research process, that is the formation of hypotheses and the rigorous testing thereof by means of experiment. It shall be argued here that it is crucial to carefully distinguish these different steps involved in scientific knowledge-making practices surrounding experimentation. When it comes to terminology, the Digital and Computational Humanities can draw on fields which have already developed it, such as the Experimental History of Science or Experimental Archaeology. Due to it being based mainly on material objects as evidence, Experimental Archaeology is a less useful comparison here than the Experimental History of Science which has material aspects but is still mostly a field based on textual sources.

The Alchemy of Experience

14Despite some earlier pioneering works, the Experimental History of Science is a relatively young subfield which has only gained traction since the ›experimental turn‹ in the History of Science, starting in the early 2000s.⁴² In the meantime, the use of experimental methods in the historiography of alchemy, pioneered especially by Lawrence Principe, has resulted in tremendous revisions of the current state of the art in the field, so much so that the journal Science spoke of an ›Alchemical Revolution.‹⁴³ A whole range of experimental methods has become established in the Experimental History of Science under the label of ›RRR methods‹ (reconstruction, replication, re-enactment).⁴⁴ In the context of the ›RRR methods‹, the distinction between ›experiencing‹ and ›experimenting‹ is emphasized, with ›experimenting‹ implying a stronger commitment to the controlled and deliberate testing of hypotheses through experiments.⁴⁵ Like mentioned above, early modern laboratories as the sites of experimentation are characterized as spaces used for the controlled manipulation of one’s material of study with the aim of generating questions, forming hypotheses and linking the observed to theoretical ideas. ›Experiencing data‹, so to speak, for the purpose of testing hypotheses as well as the isolation of specific features so that cause and effect can be determined.

15How can Computational Humanities research be framed as experiment? It seems that it is on the basis of quantitative evidence and manipulation thereof that the notion of experiment hinges in the context of the Computational Humanities.⁴⁶ However, the seminal 1966 publication on Quantification in History is quite reserved with regard to the scope of applicability of quantitative methods in the Humanities, stating »that counting, when circumstances permit it, may assist in the explanation of a limited class of historical problems.«⁴⁷ Some like to frame experimentation as infallible and utterly neutral if done right, sometimes transferring this supposed epistemological prowess to science – the field that experiment is symbolic of – as well as to quantification which seems fundamentally related. Yet there is no way of getting around the qualitative aspect inherent in and pivotal to experimentation when interpreting results of experiments to draw conclusions from them: »the hypotheses used to connect observation and theory are, no matter how plausible they at first appear, always open to challenge.«⁴⁸ The idea of introducing qualitative elements or intuition into experiment seems to be viewed with a certain disdain when, for example, McGillivray et al. demand that we should »exclude intuition as inadmissible as evidence«⁴⁹ but acknowledge that it can be part of experimental research in the process of forming hypotheses: »A hypothesis originates from previous research, intuition, or logical arguments, and is »a claim that can be tested empirically, through statistical hypothesis testing on corpus data« (Jenset and McGillivray, 2017, 42).«⁵⁰ Sinclair and Rockwell bring up the notion of experimentation in the context of quantitative text analysis when they state that »the idea that text analysis and visualization are interpretative practices may seem paradoxical at first glance, since the digital is founded on matching and counting, but no amount of counting can produce meaning. On the other hand, digital tools do facilitate experimentation with the representation of digital texts [...].«⁵¹

16But what exactly do they mean by experimentation in this context? Surely it is not the rigorous testing of hypotheses a strict definition of experiment would imply. Jockers, on the other hand, argues that »[c]omputational analysis may be seen as an alternative methodology for the discovery and the gathering of facts. [...] The computer is a tool that assists in the identification and compilation of evidence. We must, in turn, interpret and explain that derivative data.«⁵² This is the pre-experimental phase McGillivray et al. concede to intuition. There can hardly be experimentation without exploration. In our previously introduced framework of experience and experiment, a hypothesis defined as above is a possible result of experience and can later be used in experimentation for verification or falsification purposes. In a Computer Linguistics publication, McGillivray et al.

define evidence in quantitative historical linguistics as the set of »facts or properties that can be observed, independently accessed, or verified by other researchers« (Jenset and McGillivray, 2017, 39).

[...] Quantitative evidence is »based on numerical or probabilistic observation or inference« (Jenset and McGillivray, 2017, 39), and the quantification should be independently verifiable.⁵³

17Earlier, the notion of metaphorical laboratories was brought up with regard to Digital Humanities Labs. While it is quite clear that the term ›laboratory‹ as used in Digital Humanities context is often a metaphorical usage, following the sociological and anthropological tradition of Laboratory Studies, not all experiments in the Digital Humanities are purely metaphorical ones. In fact, it shall be argued here that it is crucial for the Computational Humanities to make a sharp distinction between different terms referring to experimental methods, as both Experimental Archaeology and the Experimental History of Science are adamant about for good reason. I argue that in order to fully grasp the meaning of experiment in the Computational Humanities, we should include the early modern concept of experientia, i. e. experience, back into our terminology. Not all experiments in the Digital and Computational Humanities can satisfy the criteria for experimentation in the scientific sense. They, however, also aren’t experiments in the metaphorical sense only. Making this distinction is more than merely a matter of being politically correct. Due to the rigorous standards it implies, experiment holds a different epistemological status than mere experimentation in the sense of experientia. Oftentimes, we merely try to ›experience‹ in a playful way for purposes of data exploration rather than engaging in rigorous testing. This is valid, yet should not be confused terminologically with experiment in the strict scientific sense. Oftentimes, we experiment »not to get results but to generate questions [...]. Ramsay (2014) calls this the screwmeneutical imperative.«⁵⁴ Confirmative analysis (experiment) serves to test hypotheses or to falsify them, whereas explorative analysis (experientia) tries to generate hitherto unseen ›views‹ on data, on both the micro- and macroscopic scale, pointing to potentially relevant features or patterns.⁵⁵

18Earlier it has been argued that the definition of a strict notion of experiment only makes sense in the subfield of questions which scholars have labelled as the Computational Humanities. However, when it comes to a rigorous definition of experimentation, we need to go even further. Not unlike in chemistry, we can only label something as an experiment in the strict sense when it involves the manipulation and observation of the subject matter. This is only the case in the subfield which Piotrowski and Fafinski have summarized under the title of Applied Computational Humanities, i.e. those cases in which computational methods are used to answer Humanities research questions.⁵⁶ Epistemically, these are in stark contrast to research pertaining to Theoretical Computational Humanities which focuses on improving algorithms or benchmarking. Such work cannot be labelled experimental in the strict sense because it does not have a hypothesis which is being investigated systematically. Improving algorithms primarily concerns the experimental setup, not a subject matter.

19However, some questions remain. For instance, can we speak of experiments in the case of algorithms which function as black boxes? The criterion of manipulating a subject matter is fulfilled because the parameters can be tweaked. Still, the process in itself is not observable. An additional issue with many algorithms is the fact that they include elements of randomization which might lessen the degree of replicability depending on what the goal of the experiment is. Here, procedures should be developed for making standardized judgements with regard to the experimental setup’s ability to reproduce similar effects even if the exact experiment cannot be replicated.

Experimental methods, hermeneutics and the need for replicability

Conclusion

28In this paper, the historical origins of the terms laboratory and experiment as well as their metaphorical and non-metaphorical uses in the Humanities and Sciences were discussed and related to the usage of the term ›experiment‹ in the Computational Humanities. It has defined the role of experimental methods in Humanities hermeneutics as standing in between historical sources and their interpretation, in the phase of ›evidence gathering‹ which informs subsequent interpretations. The view sometimes expressed in the context of science that data could somehow render research more quantitative, replacing elements of qualitative interpretation and thus make research more objective is epistemically naive and a fallacy. Experiments merely serve to reduce information entropy by producing more metadata which can be used to fill in documentary gaps in cases where the historical record doesn’t furnish enough information to answer a given research question.

29It is safe to say that most Digital Humanities scholars disapprove of the metaphorical use of technical terms they consider as pertinent to their field, such as the term ›database‹. Even though we might not admit it openly, it is considered somewhat disrespectful. The metaphorical usage of the term ›experiment‹ in Humanities contexts has already come under scrutiny. It has been argued in this paper that the Computational Humanities should distinguish carefully between experimentation in the sense of experience (experientia) and experiment in the traditional scientific sense. As terminology to address digital research practices which we consider experimental, yet aren’t rigorous enough to count as actual experiments, this paper has suggested we should use the notion of experience and distinguish very carefully between experience and experiment as it is being done both in the Experimental History of Science as well as in Experimental Archaeology.

30We can experience data in an intuitive way as a valid part of the pre-experimental phase of the research process by manipulating it just like early modern scientists set out to study nature by experiencing and purposefully manipulating its processes. Be that as it may, workflows for the documentation of and best practices for both those types of experimental practices are still lacking and need to be established. According to the strict definition of experiment given in this paper, not a lot of research qualifies as experimental thus far. This is mostly due to the lack of institutionalized or standardized approaches for ensuring the replicability of results. Accordingly, this definition is not meant as a description of past research but rather an idealized notion of experiment inviting Computational Humanists to optimize their workflows and publication strategies. As long as reproducibility is not a given, experimental methods will not be able to deliver more the objective results they are often promised to produce. If results cannot be independently verified, bad research practice could become rampant such as the cherry-picking of evidence or even tampering with results.

31While the Digital Humanities can’t agree on a more or less uniform usage of the term ‘experiment’, it will continue to be criticized as merely performative language with the goal of earning prestige associated with the Sciences. A well thought out notion of how experimental methods figure into traditional Humanities hermeneutics might also be able to reduce the artificial divide between supposedly qualitative, intuition-based Humanities and allegedly objective Sciences. Further theoretical reflection of experiment’s epistemic role in research is much needed. This article has proposed a conceptual framework of experiments as part of the hermeneutic process of the narrow field of Applied Computational Humanities. It has been argued that having a strict definition of experiment is important to defend the field against the rhetoric of supposedly improving the Humanities by means of making it ›more scientific‹.

32Despite quantitative methods currently being a more dominant research paradigm in the Humanities than Aydelotte (1966) might have predicted, they remain an epistemically narrow sub-field of Humanities research questions. Quantitative methods alone will never be able to answer the predominantly qualitative Humanities research questions. Not all those can be rendered measurable, quantifiable or computational. Thus most of them don’t lend themselves to experiment in the literal sense of the word. Experimental methods are merely an addition to the methodological arsenal of Humanities hermeneutics which can be used fruitfully for the purposes of exploratory data analysis and evidence gathering to back up interpretations. It is important, however, that we realize there is room for experiments outside of the vague and metaphorical usage of the term in Digital Humanities. Yet those more rigorous definitions of experiment only apply to a fairly narrow subfield of research questions, mainly in the realm of Applied Computational Humanities. Just like we shouldn’t fall for the fallacious belief that a stronger focus on experiment could somehow ›improve‹ Humanities research at its core by making it more data-driven or quantitative, we also should avoid an outright dismissal of such methods as they can be important tools for filling in epistemic gaps in the evidence gathering phase before the interpretation of historical sources.

References

1. William O. Aydelotte: Quantification in History. In: The American Historical Review 71 (1966), no. 3, 803–25.
2. Jörg Barke: Die Sprache der Chymie. Am Beispiel von vier Drucken aus der Zeit zwischen 1574–1761. Tübingen 1991 (= Germanistische Linguistik, 111).
3. Gunhild Berg: Experimentieren. In: Über die Praxis des Kulturwissenschaftlichen Arbeitens. Ein Handwörterbuch. Edited by Ute Frietsch / Jörg Rogge. 1. edition. Bielefeld 2013, 140–44 (= Mainzer Historische Kulturwissenschaften, 15).
4. Gunhild Berg: Zur Konjunktur des Begriffs ›Experiment‹ in den Natur-, Sozial- und Geisteswissenschaften. In: Wissenschaftsgeschichte als Begriffsgeschichte. Edited by Michael Eggers / Matthias Rothe. Bielefeld 2015, 51–82. DOI: https://doi.org/https://doi.org/10.14361/9783839411841-002.
5. Toni Bernhart: Quantitative Literaturwissenschaft: Ein Fach mit langer Tradition? In: Quantitative Ansätze in Literatur- und Geisteswissenschaften: Systematische und historische Perspektiven. Edited by Toni Bernhart / Marcus Willand / Sandra Richter / Andrea Albrecht. Berlin 2018, 207–20.DOI: https://doi.org/https://doi.org/10.1515/9783110523300-009.
6. David M. Berry: The Computational Turn: Thinking About the Digital Humanities. In: Culture Machine 12 (2011), 1–22.
7. Roderick Black: Peter J. T. Morris: The Matter Factory: A History of the Chemistry Laboratory,. In: Foundations of Chemistry 19 (2017), no. 1, 93–94.
8. Experimentelle Wissenschaftsgeschichte. Edited by Olaf Breidbach / Peter Heering / Matthias Müller / Heiko Weber. München 2010.
9. Arndt Brendecke: Information in tabellarischer Disposition. In: Wissensspeicher der Frühen Neuzeit. Formen und Funktionen. Edited by Frank Grunert / Anette Syndikus. Berlin 2015, 43–60.
10. Michael Buchner / Tobias A. Jopp / Mark Spoerer / Lino Wehrheim: Zur Konjunktur des Zählens – Oder wie man Quantifizierung quantifiziert. Eine empirische Analyse der Anwendung quantitativer Methoden in der deutschen Geschichtswissenschaft. Historische Zeitschrift 310 (2020), no. 3, 580–621.
11. Harry Collins: Tacit and Explicit Knowledge. Chicago 2010.
12. Maurice Crosland: Early Laboratories c.1600–c.1800 and the Location of Experimental Science. In: Annals of Science 62 (2005), no. 2, 233–53.
13. Park Doing: Give Me a Laboratory and I Will Raise a Discipline: The Past, Present, and Future Politics of Laboratory Studies in STS. In: The Handbook of Science and Technology Studies. Edited by Edward J. Hackett / Olga Amsterdamska / Michael Lynch / Judy Wajcman. Cambridge, MA 2008, 279–95.
14. Sven Dupré: Doing It Wrong: The Translation of Artisanal Knowledge and the Codification of Error. In: The Structures of Practical Knowledge. Edited by Matteo Valleriani, Cham 2017, 167–88..
15. Sven Dupré / Anna Harris / Julia Kursell / Patricia Lulof / Maartje Stols-Witlox (2020a): Index of RRR Terminology. In: Reconstruction, Replication and Re-Enactment in the Humanities and Social Sciences. Edited by Sven Dupré / Anna Harris / Julia Kursell / Patricia Lulof / Maartje Stols-Witlox. Amsterdam 2020, 295–96.
16. Sven Dupré / Anna Harris / Julia Kursell / Patricia Lulof / Maartje Stols-Witlox (2020b): Introduction. In: Reconstruction, Replication and Re-Enactment in the Humanities and Social Sciences. Edited by Sven Dupré / Anna Harris / Julia Kursell / Patricia Lulof / Maartje Stols-Witlox. Amsterdam 2020, 9–34.
17. Amy E. Earhart: The Digital Humanities as a Laboratory. In: Between Humanities and the Digital. Edited by Patrik Svensson / David Theo Goldberg. Cambridge, MA 2015, 391–400.
18. Paula Findlen: Die Zeit vor dem Laboratorium: Die Museen und der Bereich der Wissenschaft 1550–1750. In: Macrocosmos in Microcosmo. Die Welt in der Stube. Zur Geschichte des Sammelns 1450 bis 1800. Edited by Andreas Grote. Wiesbaden 1994, 191–207 (= Berliner Schriften zur Museumskunde, 10).
19. Hjalmar Fors / Lawrence M. Principe / H. Otto Sibum: From the Library to the Laboratory and Back Again: Experiment as a Tool for Historians of Science. In: Ambix 63 (2016), no. 2, 85–97.
20. Wilhelm Ganzenmüller: Das chemische Laboratorium der Universität Marburg im Jahre 1615. In: Medizinhistorisches Journal 2 (1967), 68–77.
21. Rolf Gelius: Historische Experimente in Chemie und chemischer Technik. In: Chemie in unserer Zeit 31 (1997), no. 4, 162–67.
22. Thijs Hagendijk: Learning a Craft from Books. Historical Re-Enactment of Functional Reading in Gold- and Silversmithing. In: Nuncius 33 (2018), 198–235.
23. Thijs Hagendijk / Peter Heering / Lawrence M. Principe / Sven Dupré: Reworking Recipes and Experiments in the Classroom. In: Reconstruction, Replication and Re-Enactment in the Humanities and Social Sciences. Edited by Sven Dupré / Anna Harris / Julia Kursell / Patricia Lulof / Maartje Stols-Witlox. Amsterdam 2020, 199–224.
24. Gary Hall: Toward a Postdigital Humanities: Cultural Analytics and the Computational Turn to Data-Driven Scholarship. In: American Literature 85 (2013), no. 4, 781–809. DOI: https://doi.org/10.1215/00029831-2367337
25. Owen Hannaway: Laboratory Design and the Aim of Science: Andreas Libavius versus Tycho Brahe. In: Isis 77 (1986), no. 4, 584–610.
26. Gerald Hartung: Das ›Chymische Laboratorium‹. Zur Funktion des Experiments im Naturwissenschaftsdiskurs des 17. Jahrhunderts. In: Instrumente in Kunst und Wissenschaft. Zur Architektonik kultureller Grenzen im 17. Jahrhundert. Edited by Helmar Schramm / Ludger Schwarte/ Jan Lazardzig. Berlin 2006, 220–4 (= Theatrum Scientiarum, 2).
27. Marieke M. A. Hendriksen: Rethinking Performative Methods in the History of Science. In: Berichte zur Wissenschaftsgeschichte 43 (2020), 313–22.
28. Marieke M. A. Hendriksen / Ruben E. Verwaal: Boerhaave’s Furnace. Exploring Early Modern Chemistry Through Working Models. In: Berichte zur Wissenschaftsgeschichte 43 (2020), 385–411.
29. Frank James: Introduction. In: The Development of the Laboratory. Essays on the Place of Experiments in Industrial Civilization. Edited by Frank James. London 1989, 1–7.
30. Matthew L. Jockers: Macroanalysis. Digital Methods and Literary History. Chicago 2013.
31. Bernard Joly: Qu’est-ce qu’un Laboratoire Alchimique? In: Cahiers d’histoire et de Philosophie des Sciences 40 (1992), 86-102.
32. Randa El Khatib / Alyssa Arbuckle / Lynne Siemens / Ray Siemens / Caroline Winter: An ›Open Lab?‹ The Electronic Textual Cultures Lab in the Evolving Digital Humanities Landscape. In: DHQ: Digital Humanities Quarterly 14 (2020), no. 3.
33. Ursula Klein: The Laboratory Challenge: Some Revisions of the Standard View of Early Modern Experimentation. In: Isis 99 (2008), no. 4, 769–82.
34. Karin Knorr Cetina: Chapter 5. The Scientist as a Literary Reasoner, or the Transformation of Laboratory Reason. In: The Manufacture of Knowledge. An Essay on the Constructivist and Contextual Nature of Science. Pergamon 1981, 94–135.
35. Karin Knorr Cetina: Chapter 7: Laboratory Studies: The Cultural Approach to the Study of Science. In: Handbook of Science and Technology Studies. Edited by Sheila Jasanoff / Gerald E. Markle / James C. Peterson / Trevor Pinch. London 1995, 140. DOI: 10.4135/9781412990127.n7
36. Karin Knorr Cetina: Epistemic Cultures: How the Sciences Make Knowledge. Cambridge, MA 1999.
37. Karin Knorr Cetina: Laboratory Studies: Historical Perspectives. In: International Encyclopedia of the Social and Behavioral Sciences. Edited by Neil J. Smelser/ Paul B. Baltes. Oxford 2001, 8232–8.
38. Karin Knorr Cetina: The Couch, the Cathedral, and the Laboratory: On the Relationship Between Experiment and Laboratory in Science. In: Science as Practice and Culture. Edited by Andrew Pickering. Chicago 1992, 113–38.
39. Robert E. Kohler: Lab History: Reflections. In: Isis 99 (2008), no. 4, 761–68.
40. Mareike König: Jenseits der Metaphorik: Experimente in den Digital Humanities #dhiha6. In: Digitale:Geschichte (Digital Humanities Universität Wien) . Blog post from June 7, 2015. https://dguw.hypotheses.org/257
41. Richard J. Lane: The Big Humanities: Digital Humanities/Digital Laboratories. London 2017.
42. Sarah Lang: The Computational Humanities and Toxic Masculinity? A (long) reflection. In: LaTeX Ninja’ing and the Digital Humanities. Blog post from April 19, 2020. https://latex-ninja.com/2020/04/19/the-computational-humanities-and-toxic-masculinity-a-long-reflection/
43. Sarah Lang: Alchemical Laboratories: Texts, Practices, Material Relics. An Introduction. In: Alchemische Labore. Praktiken, Texte und materielle Hinterlassenschaften / Alchemical Laboratories. Practices, texts, material relics. Edited by Sarah Lang / Michael Fröstl / Patrick Fiska. Graz 2022.
44. Gerhard Lauer: Über den Wert der exakten Geisteswissenschaften. In: Geisteswissenschaft – Was bleibt? Zwischen Theorie, Tradition und Transformation. Edited by Hans Joas / Jörg Noller. Freiburg 2020, 152–73 (= Geist und Geisteswissenschaft, 5).
45. Verena Lehmbrock: Laboratorium. In: Über die Praxis des kulturwissenschaftlichen Arbeitens. Ein Handwörterbuch. Edited by Ute Frietsch / Jörg Rogge. 1. edition. Bielefeld 2013, 245–48 (= Mainzer Historische Kulturwissenschaften, 15).
46. Michael Lynch: Art and Artifact in Laboratory Science. London 1985.
47. Matteo Martelli: Greek Alchemists at Work: ›Alchemical Laboratory‹ in the Greco-Roman Egypt. In: Nuncius 26 (2011), no. 2, 271–311.
48. Matteo Martelli: Translating Ancient Alchemy: Fragments of Graeco-Egyptian Alchemy in Arabic Compendia. In: Ambix 64 (2017), no. 4, 326–42.
49. Barbara McGillivray / Jon Wilson / Tobias Blanke: Towards a Quantitative Research Framework for Historical Disciplines. In: Proceedings of the Workshop on Computational Methods in the Humanities 2018. Lausanne, Switzerland, June 4–5 , 2018. Edited by Michael Piotrowski. Lausanne 2018, 53–58.
50. Franco Moretti: Distant Reading. London 2013.
51. Franco Moretti: Graphs, Maps, Trees. Abstract Models for a Literary History. London 2005.
52. Peter J. T. Morris: The Matter Factory: A History of the Chemistry Laboratory. London 2015.
53. Sébastien Moureau / Nicolas Thomas: Understanding Texts with the Help of Experimentation: The Example of Cupellation in Arabic Scientific Literature. In: Ambix 63 (2016), no. 2, 98–117.
54. Sylvie Neven: Transmission of Alchemical and Artistic Knowledge in German Mediaeval and Premodern Recipe Books. In: Laboratories of Art. Alchemy and Art Technology from Antiquity to the 18th Century. Edited by Sven Dupré. Cham 2014, 23–52 (= Archimedes. New Studies in the History and Philosophy of Science and Technology, 37).
55. Sylvie Neven: Recording and Reading Alchemy and Art-Technology in Medieval and Premodern German Recipe Collections. In: Nuncius 31 (2016), 32–49.
56. William R. Newman: Alchemical Symbolism and Concealment: The Chemical House of Libavius. In: The Architecture of Science. Edited by Peter Galison / Emily Thompson. Cambridge, MA 1999, 59–77.
57. William R. Newman / Lawrence M. Principe (1998), Alchemy vs. Chemistry: The Etymological Origins of a Historiographic Mistake. In: Early Science and Medicine 3 (1998), no. 1, 32–65.
58. William R. Newman: Decknamen or pseudochemical language? Eirenaeus Philalethes and Carl Jung. Revue d’histoire des sciences 49 (1996), 159–188.
59. William R. Newman / Lawrence M. Principe: The Chymical Laboratory Notebooks of George Starkey. In: Reworking the Bench. Research Notebooks in the History of Science. Edited by Frederic L. Holmes / Jürgen Renn / Hans-Jörg Rheinberger. Dordrecht 2003, 25–42 (= Archimedes. New Studies in the History and Philosophy of Science and Technology, 7).
60. Tara E. Nummedal: Words and Works in the History of Alchemy. In: Isis 102 (2011), no. 2, 330–37.
61. Adi Ophir / Steven Shapin: The Place of Knowledge: A Methodological Survey. In: Science in Context 4 (1991), no. 1, 3–21.
62. Urszula Pawlicka-Deger: A Laboratory as the Infrastructure of Engagement: Epistemological Reflections. In: Open Library of Humanities 6(2): 24 (2020). DOI: http://doi.org/10.16995/olh.569
63. Rik Peels: Replicability and Replication in the Humanities. Research Integrity and Peer Review 4 (2019). DOI: https://doi.org/https://doi.org/10.1186/s41073-018-0060-4
64. Rik Peels / Lex Bouter: The Possibility and Desirability of Replication in the Humanities. In: Palgrave Communications 4 (2018). DOI: https://doi.org/10.1057/s41599-018-0149-x.
65. Michael Piotrowski (2019a): Ain’t No Way Around It: Why We Need to Be Clear About What We Mean by ›Digital Humanities‹. In: Wozu digitale Geisteswissenschaften? Innovationen, Revisionen, Binnenkonflikte (November 20–22, 2019). Lüneburg 2019.
66. Michael Piotrowski (2019b): Historical Models and Serial Sources. In: Journal of European Periodical Studies 4 (2019), no. 1, 8–18.
67. Michael Piotrowski / Mateusz Fafinski: Nothing New Under the Sun? Computational Humanities and the Methodology of History. In: CHR 2020: Workshop on Computational Humanities Research, November 18–20, 2020. Amsterdam 2020, 171–80.
68. Michael Piotrowski / Markus Neuwirth: Prospects for Computational Hermeneutics. In: Atti Del Ix Convegno Annuale Aiucd. La Svolta Inevitabile: Sfide E Prospettive Per L’informatica Umanistica (Milan, Jan. 15–17, 2020). Edited by Cristina Marras / Marco Passarotti / Greta Franzini / Eleonora Litta. Milan 2020, 204–9. DOI: https://doi.org/10.6092/UNIBO/AMSACTA/6316.
69. Andrew Piper: Can We Be Wrong? The Problem of Textual Evidence in a Time of Data. Cambridge, UK 2020 (Elements in Digital Literary Studies).
70. Michael Polanyi: The Tacit Dimension (1966). With a New Foreword by Amartya Sen. Chicago, 2009.
71. Lawrence Principe: Apparatus and Reproducibility in Alchemy. In: Instruments and Experimentation in the History of Chemistry. Edited by Frederic Lawrence Holmes / Trevor Harvey Levere. Cambridge, MA 2000, 55–74.
72. Lawrence Principe: ›Chemical Translation‹ and the Role of Impurities in Alchemy: Examples from Basil Valentine’s Triumph-Wagen. In: Ambix 34 (1987), no. 1, 21–30.
73. Lawrence Principe: Goldsmiths and Chymists: The Activity of Artisans Within Alchemical Circles. In: Laboratories of Art. Alchemy and Art Technology from Antiquity to the 18th Century. Edited by Sven Dupré. Cham 2014, 157–80 (= Archimedes. New Studies in the History and Philosophy of Science and Technology, 37).
74. Lawrence M. Principe / William R. Newman: Some Problems with the Historiography of Alchemy. In: Secrets of Nature: Astrology and Alchemy in Early Modern Europe . Edited by William R. Newman / Anthony Grafton. Cambridge, MA 2001, 385–432.
75. Jennifer M. Rampling: The Experimental Fire: Inventing English Alchemy, 1300–1700. Chicago 2020.
76. Sara Reardon: The Alchemical Revolution. In: Science 332 (2011), 914–15.
77. Malte Rehbein: Informationsvisualisierung. In: Digital Humanities. Eine Einführung. Edited by Fotis Jannidis / Hubertus Kohle / Malte Rehbein. Stuttgart 2017, 328–42.
78. Hans-Jörg Rheinberger: History of Science and the Practices of Experiment. In: History and Philosophy of the Life Sciences 23 (2001), no. 1, 51–63.
79. Camille Roth: Digital, Digitized, and Numerical Humanities. In: Digital Scholarship in the Humanities 34 (2019), no. 3, 616–32. DOI: https://doi.org/10.1093/llc/fqy057.
80. Jole Shackelford: Tycho Brahe, Laboratory Design, and the Aim of Science: Reading Plans in Context. In: Isis 84(1993), no. 2, 211–30.
81. Steven Shapin: The House of Experiment in Seventeenth-Century England. In: Isis. A Special Issue on Artifact and Experiment 79 (1988), no. 3, 373–404.
82. Steven Shapin: The Invisible Technician. In: American Scientist 77 (1989), no. 6, 554–63.
83. H. Otto Sibum: Science and the Knowing Body: Making Sense of Embodied Knowledge in Scientific Experiment. In: Reconstruction, Replication and Re-Enactment in the Humanities and Social Sciences. Edited by Sven Dupré / Anna Harris / Julia Kursell / Patricia Lulof / Maartje Stols-Witlox. Amsterdam 2020, 275–94.
84. Lynne Siemens / Raymond Siemens. Notes from the Collaboratory: An Informal Study of an Academic DH Lab in Transition. In: Book of Abstracts of DH 2012 in Hamburg. Hamburg 2012. dh2012.uni-hamburg.de/conference/programme/abstracts/notes-from-the-collaboratory-an-informal-study-of-an-academic-dh-lab-in-transition/
85. Stéfan Sinclair / Geoffrey Rockwell (2016a): Text Analysis and Visualization: Making Meaning Count. In: A New Companion to Digital Humanities. Edited by Susan Schreibmann / Ray Siemens / John Unsworth. Oxford 2016, 274–90.
86. Stéfan Sinclair / Geoffrey Rockwell (2016b): Hermeneutica. Computer-Assisted Interpretation in the Humanities. Cambridge, MA 2016.
87. Pamela H. Smith: Laboratories. In: The Cambridge History of Science 3 / Early Modern Science. Edited by Katharine Park / Lorraine Daston. Cambridge 2006, 290–305.
88. Pamela H. Smith: The Codification of Vernacular Theories of Metallic Generation in Sixteenth-Century European Mining and Metalworking. In: The Structures of Practical Knowledge. Edited by Matteo Valleriani. Cham 2017, 371–92.
89. Pamela H. Smith: The Making and Knowing Project (2015–2020). 2020. makingandknowing.org
90. Herbert Stachowiak: Allgemeine Modelltheorie. Wien 1973.
91. Tillmann Taape / Pamela H. Smith / Tianna Helena Uchacz: Schooling the Eye and Hand: Performative Methods of Research and Pedagogy in the Making and Knowing Project. In: Berichte zur Wissenschaftsgeschichte 43 (2020), 323–40.
92. Matteo Valleriani: The Epistemology of Practical Knowledge. In: The Structures of Practical Knowledge. Edited by Matteo Valleriani. Cham 2017, 1–21.
93. Dominique Vinck: Back to the Laboratory as a Knowledge Production Space. In: Revue d’anthropologie des Connaissances 1 (2007), no. 2, 160–66.
94. Georg Vogeler: Warum werden mittelalterliche und frühneuzeitliche Rechnungsbücher eigentlich nicht digital ediert? In: Grenzen und Möglichkeiten der Digital Humanities. Edited by Constanze Baum / Thomas Stäcker. 2015 (= Sonderband der Zeitschrift für digitale Geisteswissenschaften, 1). DOI: https://doi.org/10.17175/sb001_007
95. Joris van Zundert: Screwmeneutics and Hermeneuticals: The Computationality of Hermeneutics. In: A New Companion to Digital Humanities. Edited by Susan Schreibmann / Ray Siemens / John Unsworth. Oxford 2016, 331–47.1