1. Introduction
The use of computational approaches for understanding (and creating) music predates electronic computers by centuries but has grown enormously since the widespread adoption of computers in research and teaching. Interest in what were previously relatively niche areas, such as computational musicology, has grown, both within existing fields of musical inquiry and within newly popularised specialised fields like music information retrieval/research (MIR) and digital humanities (DH). The availability of increasingly sophisticated analysis techniques (e.g. using machine learning) has made it difficult for individuals in just one field to have the necessary expertise in both musicology and data science – for example, making multidisciplinary collaborations increasingly beneficial, with a corresponding rise in interest in interdisciplinary and multidisciplinary research and education. Unfortunately, a variety of problems can arise that limit such projects in practice, including disciplinary differences in cultures, goals, assumptions, communication styles, vocabularies, data‑collection methodologies, analytical approaches and dissemination channels.
This paper seeks to contribute towards improving the state of interdisciplinary and multidisciplinary music research and education by providing insights on both barriers and enablers, with a special focus on collaborations involving musicology, data science and MIR. This is done partly through the creation, distribution and analysis of a novel survey of researchers, students and professionals who are interested in or who have been involved in interdisciplinary or multidisciplinary music work (Sections 4 and 5), a partial review of highlights from the MIR literature concerning musicology (Section 2) and a critical discussion of barriers to and enablers of interdisciplinary computational music research (Section 3). This critical discussion is based on both insights from the literature and the experiences of the authors in their many collaborations, both together and with others.
For the sake of brevity, the term ‘interdisciplinary’ (the integration of different disciplines, where at least some disciplinary elements are synthesised) is typically used alone in the text below, rather than also explicitly specifying ‘multidisciplinary’ (a collaboration of specialists from discrete disciplines, where disciplinary boundaries are more likely to be maintained). Much of the work described below (including the survey) blurs the boundary between the two, so, in practice, we are generally referring to both when we say ‘interdisciplinary’. Crossdisciplinarity (viewing a discipline from the perspective of another) and transdisciplinarity (conceptually unifying disciplinary frameworks) are also certainly relevant but are less the focus of this paper. Choi and Pak (2006) explore these terms in greater depth.
2. Mir and Musicology: A Partial Literature Review (2000–2025)
In the last 25 years, musicology has increasingly engaged with other fields, including quantitatively oriented fields like data science, DH and, of particular interest here, MIR. This convergence, sometimes called digital or computational musicology, was evident in the early years of MIR and, by 2025, reflections on MIR’s evolution (Peeters et al., 2025) underscored the need for deeper integration with cultural and musicological research.
2.1 2000–2009: laying foundations
The first International Society for Music Information Retrieval (ISMIR) conference in 2000 brought together musicologists, engineers, librarians and others and emphasised technical issues like melody extraction while also acknowledging musicological relevance. Futrelle and Downie (2003) called for MIR to incorporate pitch, harmonic and bibliographic facets, stressing music’s multicultural and multidisciplinary nature. Perceptual and cognitive considerations were also noted (Huron, 2000) and remain highly relevant to both MIR and musicology today. Tools facilitating interdisciplinary work, such as Sonic Visualiser (Cannam et al., 2006), began to be developed, making it easier for musicologists, librarians and others to visualise and digitally annotate music.
Simultaneously, musicology reconsidered its own methods (Brétéché and Esclapez, 2018), with the first Conference on (Parncutt et al., 2004) aiming to bring together musicological subfields. Initiatives such as the Computing in Musicology series and the Journal of Interdisciplinary Music Studies (starting in 2007) also reflect this trend. Cook (2005) described the evolving relationship between computer science and musicology as a prolonged moment of opportunity.
2.2 2010–2014: early integration and diversification
By 2011, MIR researchers were examining their engagement with musicology. Neubarth et al. (2011) analysed citations across ISMIR papers, showing frequent references to music theory and psychology but limited attention to ethnomusicology. They advocated mutual enrichment: musicological depth in MIR and technical tools for musicologists. Barthet and Dixon (2011) studied musicologists’ practices at the British Library, identifying dual listening modes (closed and multimodal) and calling for tools that facilitate both. Cuthbert and Ariza (2010) released music21, a modular toolkit allowing sophisticated analysis of symbolic music.
Volk et al. (2012) combined perspectives from MIR, musicology and cognitive science to propose modelling variation as a foundational concept for musical similarity, which has implications for both computational pattern discovery and traditional musicological comparison.
Gómez et al. (2013) called for a deeper and more integrated approach to combining disciplines with respect to traditional and non‑Western musics in a special issue of the Journal of New Music Research devoted to computational ethnomusicology. Wiering and Benetos (2013) reported on collaborative efforts, including studies of ragtime (Volk and de Haas, 2013) and Carnatic music (Ishwar et al., 2013). These efforts were influenced by the European Union’s CompMusic project (Serra, 2011), which also promoted computational ethnomusicology. Both diversity and interculturality were being stressed as important priorities.
Concurrent UK projects – Transforming Musicology (https://tm.web.ox.ac.uk) and the Digital Music Lab (Weyde et al., 2014) – addressed data infrastructure and usability. Semantic Web approaches were increasingly proposed (Fazekas et al., 2010) as a way of linking diverse types of information in ways that would, among other things, facilitate interdisciplinary research. Boyd and Crawford (2012) stressed transparency and reproducibility, likening the process to science. Important challenges identified included better optical music recognition (OMR), linking audio with notation and disciplinary communication gaps.
2.3 2015–2019: big data and critical reflection
Projects expanded in scale and scope during this period, albeit not without growing pains. Pugin (2015) emphasised the need for interoperability and high‑quality data infrastructures to support digital musicology, noting both opportunities and challenges posed by the scale and complexity of digital corpora. Marsden (2016) argued that computational methods should not aim to ‘mimic human analysis’ but rather to answer ‘specific music‑analytical questions’. Cottrell (2018) analysed large datasets to track orchestral pitch trends and tempo variations. While MIR’s potential was clear, he noted its limited uptake in musicological circles and urged better contextualisation. Wiering and Inskip (2015) conducted a survey of the experiences of musicologists using computational tools, work indicative of increasingly reflective and critical perspectives on how new technologies could be meaningfully applied to musicological questions.
De Valk et al. (2017) urged collaboration between MIR researchers and archivists, identifying obstacles like a lack of usable tools and documentation. Miller et al. (2019) introduced the concept of ‘visual musicology’, applying visualisation techniques to complex, multimodal datasets. Timelines and network graphs, adapted from other humanities domains, facilitated expanded music analysis.
The application of MIR to symbolic digital music representations (e.g. Musical Instrument Digital Interface (MIDI) and Music Encoding Initiative (MEI)) was advanced through tools like jSymbolic 2 (McKay et al., 2018), which enabled broad statistical feature extraction from scores. Capable of calculating 1497 feature values – including information related to pitch distributions, melodies, chords, rhythm, dynamics and voice texture – jSymbolic empowered a new approach to empirical musicology, particularly in areas such as composer attribution and stylistic analysis. Integration with the SIMSSA DB (Hopkins et al., 2019) enabled a new kind of content‑based search across corpora.
Global engagement grew, with large projects like CompMusic (Plaja‑Roglans et al., 2024) exploring non‑Western musics. Indian initiatives produced a monograph providing a guide for applying MIR to Carnatic and Hindustani music (Srinivasamurthy et al., 2023). This underscored that interdisciplinary musicology was increasingly global. Notable here is the Computational Phonogram Archiving (COMSAR) framework, which proposed an integrative model uniting MIR, physical modelling and ethnomusicological fieldwork into a cohesive toolset (Bader, 2019). It used self‑organising maps and Hidden Markov Models to classify global music while trying to avoid Western bias; a Physical Culture Theory was proposed that views music as a self‑organising, culturally embedded phenomenon. This model enabled cross‑repertoire comparisons – such as of gamelan tunings and Balinese drumming – by analysing interactions among factors like rhythm, pitch glide and timbre.
In related work, the DaCaRyH project exemplified the shift towards participatory design in computational ethnomusicology. Focused on calypso and steel band music from Trinidad and Tobago, it combined field recordings, cultural context and machine learning to investigate tempo and stylistic shifts across five decades of Panorama competitions (Ben‑Tal et al., 2019).
2.4 2020–2025: open science and responsible research
The 2020s brought greater emphasis on openness and responsibility. A special issue of Empirical Musicology Review (Moss and Neuwirth, 2021) promoted open science, findable, accessible, interoperable and reusable (FAIR) data principles and transparency as prerequisites for interdisciplinary collaboration. The aim was to ensure that musicological insights could be verified and reused across domains.
Huang et al. (2023) proposed ‘responsible MIR’, rejecting tokenistic inclusion of diverse datasets. They emphasised that MIR should begin from the premise of musical plurality and argued that MIR must respect cultural differences rather than simplify them for algorithmic ease. Lenchitz (2021) critiqued MIR’s reliance on Western pitch and rhythm quantisation norms, advocating for alternatives accommodating diverse musical systems. He highlighted the risks of limiting inquiry to what’s computationally convenient, calling for support of microtonality and flexible rhythm in MIR tools.
Martin (2024) emphasised the epistemological and interpretive limits of corpus‑based studies, underscoring the enduring value of close listening and theoretical engagement.
A particular reality check came when Borsan et al. (2023) found that few of even those ISMIR papers claiming musicological relevance were cited in the actual musicology literature. They called for stronger bridges and genuine interdisciplinary reciprocity.
Encouragingly, new collaborations continued to emerge. Bainbridge et al. (2023) developed a digital archive that emphasised domain‑specific metadata and user‑centred design, showing that joint work between archivists and technologists can preserve cultural context, not just content. The DIDONE project (Torrente and Llorens, 2021) offered an important contribution to symbolic MIR by employing big data approaches to map emotional expression in eighteenth‑century opera seria. By analysing over 3000 arias, DIDONE combined historical musicology, computational analysis and performance studies.
MIR techniques have increasingly been applied to early music. The Josquin Research Project (Rodin and Sapp, n.d.) has continued to enable symbolic MIR work: by converting Renaissance polyphony into machine‑readable formats, it has enabled large‑scale stylistic comparisons. The Citations: The Renaissance Imitation Mass (CRIM) Project has digitised Renaissance Masses and their sources, linking them through a digital infrastructure that supports detailed contrapuntal analysis. The CRIM Intervals Python library and annotation ontology enable relational mapping of musical borrowings and transformations, bridging corpus‑level pattern analysis with critical hermeneutics (Freedman, 2023; Freedman, n.d.a). Similarly, the Lost Voices Project revives sixteenth‑century French chansons by using MEI and web‑based visualisations to support scholarly reconstructions and performance‑based inquiry (Freedman, n.d.b). Additionally, the PolifonIA tool addresses the problem of mensural notation through OMR trained on Spanish heritage manuscripts. Developed within the Handwritten Spanish Music Heritage Preservation by Automatic Transcription (HISPAMUS) project, it enables the transformation of handwritten scores into machine‑readable formats, thereby opening centuries of music to digital search and analysis (Calvo‑Zaragoza et al., 2021).
At IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2025, Peeters et al. (2025) presented a 25‑year review of MIR. They highlighted successes – technical innovation, standardised evaluations (e.g. Music Information Retrieval Evaluation eXchange (MIREX)) and growing interdisciplinarity – while acknowledging persistent gaps with musicology. They advocated cross‑disciplinary training and ethically grounded research as keys to the next phase. Wiering and Inskip (2025) conducted a survey providing insights on how musicologists’ attitudes towards technology were affected by lockdowns during the coronavirus disease 2019 pandemic indicating a notably more positive attitude to technological approaches than that found in their earlier survey. Morgado Vega et al. (2025) distributed a survey of tools used for computational musicology and found a mismatch between what scholars wanted and what was available; issues with usability were identified, and the need for more direct collaborations between developers and scholars was emphasised.
3. Enablers and Barriers to Interdisciplinary Computational Music Research
Interdisciplinary collaboration operates within a complex ecosystem of barriers and enabling factors, both in general and with respect to music specialists and data scientists in particular. Happily, there is a useful body of literature providing insight on such issues.
At the institutional level, misaligned incentives and structural silos can stifle collaboration. Universities and funding bodies often reward discipline‑specific outputs, making it risky for researchers to venture beyond traditional boundaries (and, sadly, the reverse can happen as well, especially in the humanities, where specialised research can be disincentivised even when appropriate). The pressure to publish in specialised venues and the dominance of discipline‑specific evaluation metrics are known deterrents (Leahey et al., 2016). As Born (2021) argues, although interdisciplinary frameworks have gained rhetorical support, deep‑seated institutional norms, entrenched disciplinary divisions and evaluation protocols grounded in scientific rationalism frequently inhibit meaningful cross‑domain collaboration. These conditions often lead to a rigid reproduction of epistemic frameworks, making alternative or hybrid approaches difficult to sustain.
On the level of individual researchers, communication and cultural differences emerge as dominant challenges. Music scholars and technologists often speak different disciplinary ‘languages’ (Savage et al., 2023), not just in terms of technical jargon or musical terminology but also in what they value as evidence or rigour. Empirical studies of distributed teams have identified key challenges – such as discrepancies in work styles, differing interpretations of shared artefacts and misalignments in motivation – that often lead to misunderstandings and friction, particularly when team members are not collocated (Olson and Olson, 2000).
Cook (2005) highlights how, in early collaborations, MIR researchers often inappropriately worked with scores that were ‘drastically simplified representations’ and how MIR experts can misrepresent computational tools essentially as upgrades to traditional approaches, even when the proposed methodologies are arguably musically naïve. Such factors can alienate music researchers, whose qualitative and context‑rich insights may be undervalued. Conversely, music scholars not versed in technology might be wary of seemingly opaque algorithms. Without deliberate efforts at mutual understanding, each speciality may struggle to appreciate the other’s contributions.
One of the key challenges in interdisciplinary collaboration stems from divergent disciplinary expectations and priorities. For instance, while a music analyst may emphasise interpretative nuance, a data scientist might focus on scalability or generalisability. As Miller et al. (2019) argue, fostering successful collaboration between domains such as musicology and information visualisation requires the construction of a shared problem‑design space, in which the goals and needs of both fields are acknowledged and integrated. This can mean data scientists investing time to grasp music theory concepts and music researchers becoming conversant with data‑analysis techniques, even if only at relatively basic levels. Effective teams often include boundary spanners – individuals with dual expertise who can translate and mediate between domains (Zhang et al., 2023). These individuals exemplify the ‘agonistic interdisciplinarity’ proposed by Born (2010) and facilitate non‑hierarchical dialogue. They also represent a move towards the ideal of transdisciplinarity.
Integrating more sophisticated and context‑aware concepts from music theory and analysis into the computational pipeline is another enabling factor that strengthens collaboration. Rather than treating musicological input as an afterthought, projects should actively incorporate such expertise at the design stage, such as in CRIM (Freedman, n.d.a) or jSymbolic (McKay et al., 2018). Musicologists can guide which musical features or structures are meaningful to study.
However, the process of computational modelling can demand that qualitative concepts from music theory be formalised for algorithmic implementation. This is not always easy to do and can present both challenges and opportunities (De Valk et al., 2017). Ultimately, collaboration enables a process where musicologists articulate implicit knowledge and domain‑specific questions in precise terms and data scientists collaboratively adapt and computationally implement them.
As Volk et al. (2011) emphasise, an essential benefit of computational musicology lies in its ability to bridge disciplinary divides by connecting the formalised, testable models of data science with nuanced, context‑sensitive insights from musicology. This requires not only collaborative implementation but also an epistemological negotiation where computational musicology becomes a catalyst for interdisciplinary understanding, enabling previously discrete domains to together generate insights that neither could achieve alone.
A useful framework for approaching interdisciplinary collaboration is to adopt a dual perspective. From an organisational standpoint, creating environments that encourage and reward cross‑disciplinary work is essential (Leahey et al., 2016). From an individual perspective, developing communication competencies, shared goals and mutual intellectual empathy is key (Miller et al., 2019).
4. Survey Methodology
4.1 Design
A core part of this project was the design and deployment of a survey that could further empirically investigate issues like those discussed in the sections above. The objective was to understand the views and experiences of researchers, students and professionals (e.g. librarians) relating to interdisciplinary collaborations involving music and data science. This survey employed a descriptive, observational and cross‑sectional design.
We began by designing a draft version of the survey, based on insights from both the literature and our own expertise. Following the guidelines of Elangovan and Sundaravel (2021), this survey was then reviewed by a multidisciplinary committee of six external experts. Their valuable feedback led to substantial improvements.
The final version of the survey consisted of 36 core questions, including nine on participant demographics and backgrounds, 23 on experiences with interdisciplinary collaborations and four on software tools/ platforms. Seventeen of these questions were optional. Thirteen were longer form open‑ended questions, seven were potentially multi‑answer multiple‑choice questions and four were based on Likert scales. Twenty‑three questions permitted the option of free‑text answers outside of options provided, with the goal of soliciting answers beyond our own potentially limited or biased expectations.
In addition to the 36 core questions, participants were asked to agree to the terms of the survey and to specify an e‑mail address (to ensure uniqueness and permit future follow‑ups for longitudinal analysis). Careful steps were taken to maintain privacy, in terms of both internal analysis and data storage and distribution.
The complete set of survey questions, answer constraints and accompanying information is available at https://zenodo.org/records/15635262.
4.2 Distribution
Participation was solicited via institutional e‑mails to a range of mailing lists: JISCMa@il’s musicology lists, International Musicological Society (IMS), American Musicological Society (AMS), Medieval and Renaissance Music Conference (Med‑Ren), Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT), Sociedad de Análisis y Teoría Musical (SATMUS). Numerous professional contacts of the authors were also contacted directly, and all recipients were encouraged to forward the participation request to others.
One initial e‑mail and one reminder were sent. The e‑mails explained the study’s purpose and provided a link to the survey, administered via Google Forms. Responses were collected between 5 and 27 May 2025.
4.3 Data analysis
The analysis of responses was broken into two parts. The first (quantitative) exploration focused on the multiple‑choice questions and involved a basic statistical exploration of responses. This included both questions considered individually and comparisons made across multiple questions (using Pearson correlation coefficients and comparisons of average Likert‑scale values). Reported percentages for the optional multiple‑choice questions (unlike the free‑text questions) indicate percentages among those who responded meaningfully to individual questions, not across all participants.
To analyse the qualitative data, we employed a thematic content analysis approach, guided by the principles of inductive coding (Braun and Clarke, 2006). First, all responses were reviewed line by line to identify preliminary codes that captured recurring ideas or needs. Responses that were left blank, contained filler (e.g. ‘n/a’ or ‘no response’) or were otherwise not meaningful were grouped under the category ‘No response’. All other responses were included in the coding process. Next, similar codes were grouped into broader response categories or themes, developed iteratively to reflect the most frequent and salient patterns. Once finalised, all responses were re‑reviewed and assigned to one category each. For each theme, we calculated the frequency and corresponding percentage of the total sample. Representative quotations were selected to illustrate major themes, prioritising the clarity and typicality of the ideas expressed.
This process was carried out both manually and using ChatGPT‑4o (https://openai.com/index/hello-gpt-4o/) to discover additional patterns. All results were manually cross‑checked over several iterations to ensure correctness in both theme assignment and quote accuracy, a step that was essential given that, on the initial pass, ChatGPT hallucinated several non‑existent responses. The final categories reflect both the frequency and conceptual richness of participant input, and all final analyses were manually verified in detail.
5. Survey Results and Discussion
5.1 Participants and biases
A total of 101 unique external respondents submitted valid responses (two additional invalid submissions were rejected). These individuals were relatively active, with a mean 67% response rate to the 17 optional questions, including many lengthy and informative answers.
A strong majority of respondents were academics or students, with 61% of respondents identifying themselves as professors or lecturers, 15% as graduate students and 11% as postdoctoral researchers or research assistants. Meanwhile, 4% were retired and the remaining 9% were industry professionals or independent researchers.
Respondents were mostly quite experienced, with only 9% indicating they had 10 or fewer years of experience in their primary discipline, while 48% had more than 21 years. In addition, 97% had at least a master’s degree and 79% had a doctorate. Only one participant was under 25 years of age, and 89% were older than 34 years of age.
Specialists in music made up the majority of respondents in terms of primary discipline, with musicologists accounting for 40%, theorists 11%, performers 9% and composers 6%. MIR specialists represented 13% and other music technologists represented 4%. Only 11% of respondents were primarily experts in fields like computer science and software engineering, while the remaining 6% were a mix of librarians, DH specialists, psychologists and others.
Respondents were quite interdisciplinary: when asked to specify additional disciplines, all but one did, and the median number of disciplines per person was 3. Based on the provided options, 87% had at least one musical speciality, 58% had at least one data‑ or computation‑related speciality and 48% had at least one of each. Ninety‑one per cent had participated in at least one interdisciplinary project involving both humanities and data science, and 53% had participated in three or more. Participants also played a median of two roles in such collaborations, with by far the most common role being musical domain expert (70%). Other roles were also not uncommon, however, including data science (25%), software development (21%) and management/coordination (40%).
Participants were relatively gender‑balanced, with 44% identifying as female, 50% identifying as male, 1% identifying as non‑binary and 5% preferring not to say.
The countries participants were living in were quite concentrated: 54% European and 40% North American, with the United States (21%) and Canada (18%) the most represented single countries and the United Kingdom (12%), Germany (8%) and Spain (7%) coming next. Notably, 52% were from countries where English is the majority language. These results unfortunately neglect much of the world, although they indicate only the current place of residence.
There is no guarantee that this sample was in fact representative of the overall population of those interested in this kind of work. Participants self‑selected and, as outlined in Section 4.2, the initial seeding of the survey emphasised those involved in musicology, music theory and MIR. It was also biased towards researchers currently in Europe and North America, especially those who are English‑ or Spanish‑speaking. That being said, participation from diverse other fields was also strongly encouraged and attained. However, future work should include efforts to geographically diversify respondents.
Overall, our sample size of 101, while certainly not enormous statistically speaking, was encouraging given the highly specialised domain. Also, as indicated above, the participants reported an impressively high degree of existing interdisciplinary expertise and experience, which bodes well for the quality of the insights they offered. Nonetheless, results should be seen more as useful insights rather than definitively representative information.
5.2 Quantitative analysis of multiple‑choice questions
When participants were asked why they engaged in interdisciplinary collaborations, professional interest was important (88%), as was curiosity or personal interest (78%); 95% answered at least one of these. Reasons relating more directly to career development were present but individually less prevalent: publishing opportunities (48%), funding opportunities (47%), networking opportunities (38%) and institutional requirements (14%). 79% answered at least one of these. Most participants had multiple motivations (a median of three), and 75% indicated motivations based on both interest and career.
With respect to meeting and building relationships with collaborators from other disciplines, shared contacts (70%) and conferences (61%) were especially important. Other approaches included research group meetings (39%), guest lectures or workshops (35%), reading publications (28%) and classes (27%). Participants listed a median of three approaches.
Overall, 72% of participants reported having been involved in classes involving interdisciplinary approaches to music, including 33% as instructors, 14% as students and 26% as both. This underlines the potential importance of pedagogy in developing and transmitting interdisciplinary experience.
Participants largely indicated positive experiences during previous interdisciplinary collaborations: on a 5‑point Likert scale, 97% specified values of 3 points or greater, 79% specified 4 points or greater and 38% specified the highest possible 5 points. Most participants also intend to continue such collaborations in the future: 98% indicated Likert values of 3 points or higher, 90% indicated 4 points or higher and 63% indicated the maximum 5 points.
However, participants still reported difficulties with interdisciplinary collaborations. Sixty‑nine per cent of eligible participants had encountered problems arising from differences in research culture or disciplinary approaches, and other common issues included problems with terminology or fundamental concepts (55%), methodological disagreements (48%), conflicting research priorities or difficulty aligning project goals (47%) and differences in publication or dissemination practices (39%). A median of two types of difficulties was reported.
Data quality and preparation, which were interrogated separately, were also common sources of difficulty, with 54% reporting problems with one or both.
Communication across disciplines actually seems to have often functioned well but still created some difficulties. For example, 51% said project goals between disciplines had been clearly and comprehensively discussed from the beginning, but 48% reported that they had only been implicitly discussed (only 1% said they had not been discussed at all ahead of time). In terms of communication methods found most effective by participants, 82% identified regular meetings, 62% informal interactions (free‑text ‘other’ entries also particularly stressed basic conversation), 61% shared documents or platforms, 35% structured workshops and only 18% mediators or interpreters between fields. Perhaps unsurprisingly, meeting and speaking regularly seem to be important. Variety may be useful too: a median of three approaches per person were highlighted as effective.
Language (e.g. technical terminology or collaborators with different first languages) was also found to pose some difficulties, with 53% reporting values of 3 points or more on a 5‑point Likert scale (with higher values indicating greater difficulty), and 26% reporting values of 4 or 5 points. Calculating the Pearson correlation coefficients with these Likert values and the Likert values for likelihood to participate in future interdisciplinary research and the perceived success of past such research led to values of −0.074 and −0.008, respectively, suggesting perhaps only a small overall impact of language. Interestingly, however, the mean Likert score for perceived success of past projects for those living in primarily Anglophone countries was 4.2 points, compared to 3.7 points for those in other countries. There are many possible explanations for this, of course, and the mean Likert score for likelihood to participate in future projects was the same for both groups (4.5 points), but Anglocentrism may be a problem.
On an encouraging note, respondents identifying as female had similar mean Likert scores for likelihood to participate in future interdisciplinary research (4.5 points) and perceived success of past projects (4.0 points) as those identifying as male (4.6 and 4.0 points, respectively).
5.3 Qualitative analysis of open‑ended questions
‘What have you found to be the most effective ways of sharing perspectives, priorities, knowledge, methodologies, etc. when collaborating with, teaching, or learning from people from other disciplines?’: Participants highlighted a range of practices. The single largest share (28%) clustered in an ‘Other’ bucket that captured reflections about issues such as mindset, openness to different vocabularies or the importance of shared goals. One entry encapsulating recurring themes stated: ‘Knowing how they think and speak, so that it is possible to engage. Vocabulary in one discipline means a different thing in another, and a lot of translation of notions and concepts is needed, as well as keeping an open mind to understand how the same words might be used differently’. Among concrete practices, collaborative project work dominated (20%), followed by regular face‑to‑face meetings or hands‑on demonstrations (6%). Others emphasised formal lectures, presentations, conferences or workshops (12%); informal conversations and networking occasions (5%); and active listening or empathy‑building techniques (5%). Mentions of teaching or student‑centred activities (3%), plus scattered references to fieldwork, publications or writing (each about 1%), rounded out the list. Twenty per cent provided the equivalent of ‘No response’.
‘What factors most make an interdisciplinary or multidisciplinary collaboration successful?’: A recurring theme was the importance of openness and curiosity – of individuals approaching collaborations with humility and a willingness to learn from others (21%). One respondent emphasised ‘openness to adapting the methods to the research questions and specific context’. Sixteen per cent highlighted the need for clear, shared goals and expectations set early in the partnership. Communication and active listening were mentioned by 11%, while mutual respect and trust appeared in 9% of answers. Practical issues such as complementary expertise and well‑defined roles were mentioned in 5% of responses, and only a handful noted institutional resources or structural support (2%). Seventeen per cent provided no substantive answer.
Overall, the data suggest that interpersonal attitudes – openness, goal alignment, communication and trust – matter far more to the respondents than formal structures or resources.
‘Where have you found the greatest amount of agreement or ease when working with collaborators from other disciplines?’: Among substantive answers, the most common bucket was a heterogeneous ‘Other’ catch‑all. Some recurring themes included collaboration during the early ideation or brainstorming stages (7%), possessing shared research goals or interests (4%) and alignment around methodology or data practices (5%). A similar share emphasised interpersonal factors such as mutual respect, enthusiasm or good communication (5%). Over a third declined to offer substantive responses. A representative response indicating both areas of agreement and disagreement stated: ‘The overall goals were never at issue, but the best way to get there was a matter of debate, or a lack of clarity due to differing approaches [to] how to put things into practice’.
Overall, the greatest areas of agreement often centred on early conceptual collaboration, aligned research aims and compatible methodological or interpersonal working styles.
‘Have you found it necessary to modify project goals as interdisciplinary or multidisciplinary collaborations progressed? If so, what necessitated the changes?’: Several participants described methodological or disciplinary mismatches (15%), where differing research logics, publication styles or analytic lenses forced revision. A representative response stated that ‘tensions may arise between very ambitious goals and the realities and complexities of doing the actual research’ so that, as another participant noted, ‘it was necessary to adjust goals as collaborators begin to understand one another’s strengths and limitations’. This underscores the pragmatic nature of adjustments made to ensure feasibility.
Other issues mentioned included: technical or resource constraints (5%), such as equipment failures or funding gaps; emerging findings or new knowledge (8%), where evolving data or insights prompted goal‑refinement; external circumstances or opportunities (2%), for example, shifts in institutional context; and collaboration dynamics (2%), referring to negotiation, communication or team‑building issues. Variants of ‘No modification needed’ were reported by 12%. Twenty‑five per cent did not reply to this question.
Taken together, the findings highlight the dynamic character of interdisciplinary collaboration, where flexibility in project aims appears to not only be common but often essential to accommodating evolving contexts, emerging insights and diverse disciplinary logics.
A common theme for the question on ‘difficulties relating to language’ centred on terminology differences (issues with discipline‑specific jargon, ambiguity or the need to agree on shared definitions), representing 15% of responses. One comment advised: ‘I think the main thing is being clear and consistent with communication and being open‑minded to what someone might actually be saying; it’s okay to double check and ask lots of questions, in person communications help a lot more than online ‑ online it can be hard to interpret and easy to misunderstand’. Another 17% described nuanced linguistic challenges (e.g. negotiating conceptual metaphors).
Language‑proficiency or translation hurdles – working with non‑native English speakers, translating between languages or needing an interpreter – were mentioned by 7%. Finally, isolated references pointed to issues such as cultural communication differences (1%).
Interestingly, most participants either left this question unanswered (59%) or reported no problems (1%), indicating that language barriers may not be as prevalent as some other problems. However, four in 10 respondents did highlight genuine linguistic obstacles, with mismatched terminology standing out as the principal concern.
‘What conditions might lead you to become more interested in taking part in or expanding your involvement in interdisciplinary or multidisciplinary collaborations in the future?’: The most frequently mentioned conditions were the availability of adequate funding or tangible resources (20%). Access to like‑minded or complementary collaborators was cited by 16% of respondents. Smaller groups pointed to: topic or project relevance (3%), opportunities to publish results (3%), a commitment to public sharing of outcomes (2%), improved communication and mutual understanding across disciplines (1%), and increased time or reduced workload (1%). Forty‑three per cent left the question unanswered and 4% stated that no additional conditions were necessary, reporting, for example, that ‘I feel I am already completely committed to this approach’.
Overall, the responses revealed an emphasis on structural support (funding, collaborators, etc.) and intrinsic motivation, alongside more modest attention to dissemination, transparency and working conditions.
‘What would contribute most to making future interdisciplinary or multidisciplinary collaborations successful?’: The most frequently mentioned factor was the need for financial support (15%). Closely following were responses emphasizing the importance of sufficient time and practical resources for collaboration (12%), as illustrated by the comment: ‘Give enough time and occasion to learn the other discipline’s basics’. Another 12% highlighted the necessity of clear communication and developing shared understanding across disciplines. Six per cent underscored the value of fostering a respectful, open attitude and a shared vision among collaborators. Less frequently mentioned but still notable were the need for networking opportunities such as joint workshops (4%), recognition within academic publishing and peer‑review systems (3%), help in finding suitable collaborators (3%) and institutional support that formally encourages interdisciplinary work (2%). Thirty‑one per cent did not provide an answer.
Overall, the analysis reveals that, while financial support is a central concern, respondents also care about other practical, communicative and cultural conditions necessary for meaningful interdisciplinary collaboration.
‘List any publications, conferences or other platforms that you have found to be especially helpful for disseminating or learning about music research that crosses disciplinary boundaries’: The most commonly cited platforms were academic conferences (17%), followed by both conferences and publications (3%) or publications alone (3%). Three per cent also mentioned professional societies without naming particular journals or events, and 2% referenced online platforms or research networks (ResearchGate and the Open Science Framework). Together with isolated references that do not fit the above groups (referring to miscellaneous or highly specific venues) (16%), 44% of the sample identified at least one concrete outlet for sharing interdisciplinary music research. One response demonstrated the diversity of relevant platforms: ‘AAWM, FMA, systematic musicology‑ related ones (e.g. a conference in Vienna this year), ISMIR, ICMPC/ESCOM, AIMC, [and] SoMoS (ICTMD)’.
The overall results suggest a strong orientation towards academic gatherings as key forums for interdisciplinary exchange, while publication venues such as journals or books remained secondary for many respondents.
‘Please share any additional insights or comments you may have regarding interdisciplinary or multidisciplinary research or teaching’: Thirteen per cent of responses emphasised the value of interdisciplinary work, highlighting its importance to the future of musicology research, as reflected in the comment: ‘It is the only way forward for musicology! Otherwise, the discipline will either become at the very least increasingly isolated, at the worst it will atrophy and die’. Eleven per cent mentioned challenges or barriers, for example: ‘If you’re TOO interdisciplinary, you fall between the cracks. You don’t fit into the existing funding bodies, academic departments, etc. after graduation’.
Other frequent themes included collaboration and teamwork (10%), often referencing the value of openness and mutual respect. Teaching issues (7%) and institutional or cultural barriers (7%) were also mentioned.
6. Discussion and Conclusion
The findings of our survey agree with many but not all of the ideas encountered in our literature survey. For example, it was found that interdisciplinary collaborations are primarily driven by intrinsic motivations like curiosity and interest, although career‑related incentives like publishing and funding also play a role. This aligns with the broader literature suggesting that scholars engage across disciplines when intellectual and professional gains are evident (Leahey et al., 2016). Educational experiences, particularly interdisciplinary teaching and coursework, are key to fostering boundary spanners capable of navigating multiple knowledge domains (Zhang et al., 2023).
Participants reported strongly positive experiences and high intent to continue such work, suggesting a cultural shift in the field (Born, 2021) perhaps leading to a non‑hierarchical form of interdisciplinarity (Huang et al., 2023). Nevertheless, common challenges persist, including disciplinary clashes, terminology issues and misaligned methodologies. These tensions echo Born’s (2010) thoughts on the transdisciplinary concept of ‘agonistic interdisciplinarity’, where productive collaboration depends on navigating epistemic differences. Regular meetings and informal dialogue were stressed as effective tools for bridging gaps (Olson and Olson, 2000), while reliance on external mediators seemed minimal. Although language barriers (aside from jargon) seemed to only weakly impact perceived success, lower ratings among residents of non‑anglophone countries suggested possible persistent inequities (Savage et al., 2023).
Project goals frequently evolved mid‑course due to feasibility, disciplinary differences or emergent insights, highlighting the importance of adaptable project frameworks (Savage et al., 2023). Three needs were emphasised: funding, time and institutional recognition. This reflects continued gaps between policy rhetoric and practice in supporting interdisciplinary work (Born, 2021).
Despite ongoing limitations, the survey signals a maturing interdisciplinarity in the fields discussed. Nonetheless, realizing the full potential of interdisciplinary music research will require coordinated structural reforms and investment in collaborative culture.
We believe that educational programs can play an essential role and should explicitly incorporate substantial project‑based interdisciplinary and transdisciplinary learning. Such education should be sure to clarify terminology to enhance communication and be based on diverse assessment criteria that reflect all the fields involved.
Future research will report on the remaining survey questions (focusing on software and platforms) and expand the scope of this work in general, with a special focus on DH collaborations. Additional surveys and ethnographic inquiries will be carried out, with the specific goals of seeking an internationally wider base of respondents, including more students, and adopting designs permitting more sophisticated statistical analyses. If possible, a follow‑up longitudinal survey of the same participants will also be carried out.
Acknowledgements
We gratefully thank all those who participated in our survey. Special recognition is due to Julie Cumming, Richard Freedman, Andrew Hankinson, Ana Llorens Martín, Alexander Morgan and Andrea Puentes, whose comments on the first draft of the survey helped greatly in improving it.
Data Accessibility
The complete survey questions and the response data (scrubbed for anonymity) are available at https://zenodo.org/records/15635262.
Ethics and Consent
The survey was conducted in accordance with the research ethics policies of the Autonomous University of Madrid and EU Regulation 2016/679. The identities of participants were anonymised for both internal analysis and the publication of results. All participants provided explicit prior informed consent.
Funding Information
We wish to thank the Fonds de recherche du Québec (FRQ) and the Social Sciences and Humanities Research Council (SSHRC) of Canada for their generous funding (grants 282456 and 895‑2022‑1004, respectively).
Competing Interests
The authors have no competing interests to declare. Neither author is currently a member of the journal’s editorial team or board, nor has held such a position within the last three years.
Authors’ Contributions
Both authors contributed to all parts of this work, although Cory McKay played a primary role in the quantitative analysis and María Elena Cuenca Rodríguez played a primary role in the qualitative analysis.