Introduction
Partnerships between the public and researchers in participatory research projects have evolved towards true co-creational models over the past decade (McDavid 2003; McDavid 2004). In archaeology, traditional projects involving the public typically include processing archaeological finds, pottery reassembly, image identification, and data entry (Smith 2014). More recently, we have seen a progression towards more collaborative approaches, as seen in projects related to different aspects of so-called public archaeology described by, for example, Bollwerk et al. (2015) and Colwell-Chanthaphonh and Ferguson (2008). However, truly co-creational processes involving the newest scientific methods in the laboratory in combination with a provision of case study examples remain underdeveloped. The experiences and insights that would emerge from such co-creational projects could benefit archaeology and numerous other disciplines.
In Denmark, several large-scale construction works have taken place in medieval city centres over the past ten years. Per Danish law (Museumsloven §8), such building activities have to be preceded by archaeological excavations. The excavations for the metro line of Copenhagen and the restructuring of the central thoroughfare Thomas B. Thriges Gade in Odense yielded an enormous amount of organic material, preserved by the wet stratigraphy of the medieval and renaissance cities. These excavations and the materials they yielded provide a rare and unique glimpse into the history of everyday life in some of the oldest Danish cities. Nevertheless, excavations carried out under the Museum Act §8 have a limited budget for natural science, including conservation and analysis. In praxis this means that artefacts are prioritised—biasing analysis towards complete, recognisable, and also often elite objects. However, finds outside this prioritised group have previously been shown to hold important, new information about, for example, the range of species utilised at an archaeological site (Blusiewicz 2017, 2020). In some cases, artefacts are even discarded, and thus, we forever lose their potential to provide new knowledge. As a result of such prioritisation processes, much less attention is given to incomplete or less visually striking finds, and overall, we possess much less knowledge about the average population of our historical cities. Beyond representing a clear and easily communicated knowledge gap, the inclusion of data about average individuals has inherent appeal to the general public. The inclusion of data about the life lived by the majority of the human population has potential to appeal to volunteer participants in citizen science projects and may serve to connect participants to history at a more personal and emotional level than traditional history lessons by using a strong narrative carried by the object under examination, by motivating participants through the creation of new knowledge, and by encouraging identification with people of the past.
Shirk et al. (2012) proposed that negotiating scientific and public interests in project design can lead to outcomes that benefit both research and participant involvement. Following this principle, we have built on a participatory science model originally used in molecular biodiversity studies (Leerhøi et al. 2024; Tøttrup et al. 2021) to co-develop archaeological research programmes that integrate high school education with active contributions to ongoing scientific investigations (Brandt et al. 2021). Our framework involved actively engaging high school students in authentic archaeological research, thereby expanding the scope of investigation. These initiatives served as the foundation for the Next Generation Lab (Brandt et al. 2022).
Herein, we introduce our participatory science initiative, which actively involved high school students and their teachers in research through collaborative teaching and well-defined learning objectives. Our co-creation framework incorporated researchers from local museums to ensure the development of highly relevant research questions and the practical application of results. The laboratory is built to accommodate some of the newest methods in archaeology, ensuring that our co-creation is based on novel setups.
Additionally, we present findings on the participating students’ understanding of the scientific process and their level of engagement in the research project, as evaluated through self-assessment questionnaires.
Methods and materials
Co-creational concept
The initial phase of the project involved establishing partnerships with local museums and high schools, using already established connections within and across other museum and school service-related projects (Leerhøi et al. 2024). The local museums contributed not only with archaeological materials but also valuable local knowledge, including insights into excavations, the history of the cities, and the research questions that might be asked of the material. In return, they obtained the analyses of the archaeological samples, performed by the students, and a link to an audience that may be hard to attract for museums. High school teachers were invited directly in person, receiving no fees or pay for their participation other than a certificate that could assure the school how many hours each teacher would use in the project. Teachers from different subjects and geographical areas were invited. When explaining their motivation to participate, teachers emphasised their own interest in the topics (archaeology, history, and biotechnology), the chance to take part in a developmental project at university level, and/or the opportunity to involve their students in exciting, out-of-school, learning experiences.
High school teachers facilitated connections to the existing high school curriculum, provided information about the varying levels of knowledge among different age classes, and offered practical insights regarding timing and resources available to teachers across Denmark. We formed two working groups—one composed of representatives from local museums and the other of high school teachers. The development process consisted of a loop starting with a meeting and followed by testing the suggested approach, which was then reported back to the group with reflections. This was repeated until the final course of study was agreed on.
Lab work: species identification of leather
Research in species identification of archaeological materials, including leather, has a long history in which traditional morphological methods over the past decades have been supplemented by biomolecular ones. In the Next Generation Lab, high school students were introduced to both: the morphological method, consisting of visual inspections of skin surfaces (grain pattern analysis [Haines 1991; Reed 1972]), and the molecular approach, which involved protein identification by Zooarchaeology by mass spectrometry (ZooMS) (Buckley et al. 2009). The co-creation process aimed to adjust the biomolecular identification process and protocol to high school student needs and abilities while as closely as possible resembling traditional research protocols (Ebsen et al. 2019) to ensure the scientific potential of the analyses.
ZooMS identifies the animal species of collagenous tissues such as bone, antler, enamel, and leather based on differences in the amino acid sequence of the protein. These sequence variations result in differences in the mass of trypsin digests, which can be measured using a matrix-assisted laser desorption ionisation time-of-flight mass spectrometer (MALDI-ToF MS). By comparing these mass measurements to a reference database of known animal species, the most likely species of origin can be determined. ZooMS datasets are normally analysed manually using the mMass software (Strohalm et al. 2008), but new semi-automatic bioinformatics setups such as Bacollite (Hickinbotham et al. 2020) and SpecieScan (Végh and Douka 2024) enable the analysis of larger datasets such as those that would be generated through the participatory science setup. The participating students and teachers received a list of identifications of their samples based on the Bacollite software to conclude their visits.
Collagenous materials are the basis of several themes of high school study plans such as physiology, evolution, protein structure, and function. Likewise, the way collagen proteins are treated enzymatically during the protocol are encompassed in the curriculum as well as different approaches and methods of detailed molecular analysis. In the Next Generation Lab, the students also participated in a cross-disciplinary setting with access to university role models with different scientific backgrounds.
Archaeological materials
The participating students worked on archaeological leather from two excavations in the centre of Copenhagen (Krøyers Plads and Gammel Strand) and one situated in the centre of Odense (Thomas B. Thriges Gade), both in Denmark. Materials from all three excavations were discarded 15th- to 19th-century archaeological leather, for instance, archaeological material that local museums lacked the capacity to store and conserve (Brandt et al. 2022).
The participatory science laboratory
High school students participated through a full-day visit to the Natural History Museum Denmark, during which they worked with morphological and molecular methods to describe archaeological leather, identify species, and discuss advantages and disadvantages of the two methods. They described and documented each leather object with photos of the entire leather piece and microscope photos of the leather surface, revealing hair follicles using a mobile phone zoom lens. Photos were uploaded to a secured database together with all observations and records done by students during the morphological and molecular examination. Afterwards, students were involved in the analysis phase of the archaeological material by performing ZooMS on original archaeological material. They worked in a state-of-the-art laboratory with all the equipment required to carry out protein extraction, enzymatic digestion, and purification of proteins. All participating students were introduced to the project aim and were trained both theoretically and practically, going through a training programme of pipetting in order to ensure that they had the same starting point for carrying out the analysis. The students collaborated closely with university professionals who subsequently prepared samples for MS (using a pipetting robot) and conducted the MS analyses (project partner) (Figure 1).
Figure 1
Schematic overview of work flows in our participatory lab (see Figure 2) developed for high school students with the involvement of local museums and university professionals. The figure presents two integrated flow lines. First, the archaeological leather samples from local museums were subsampled into five replicates with a minimum of three being analysed. Next, the samples were prepared for mass spectrometry (MS) by a pipetting robot, and then university professionals (project partners) conducted the MS. Finally, the students received the results of their work for further analysis and interpretation. The second column presents how students prepared before arriving in the lab. Once in the lab, they received training and an introduction before they performed lab work. In the final step, they received their own results and got access to results from the other classes visiting the lab.
Replication of samples and validation
Each archaeological object underwent multiple sampling events. Three to five subsamples of these objects were each analysed by a different student. An initial test with four high school classes in which a fifth sample, in some cases, was analysed by a senior researcher with extensive experience in ZooMS, demonstrated that the peptide markers (explained in detail below) retrieved by the students never conflicted with the results of the senior researcher or with results of other students working on the same sample, suggesting no or limited cross-contamination between samples (Brandt et al. 2022).
In order to further explore the inter-student performance and the chance of retrieving a sufficient taxonomic identification per object, a subsample of 190 samples originating from 68 objects were randomly selected from five different MALDI runs, representing various development phases of the project. The conventional 9 peptide markers were used as a numeric variable for statistical analysis. Peptide markers refer to specific tryptic peptides used in the taxonomic identification (Buckley et al. 2009). A sufficient taxonomic identification may be obtained based on approximately 5–9 observed peptide markers as some bio-markers are more specific to some taxa. Therefore, identification of some taxa may need only a few but specific peptide markers, whereas the identification of others need almost all. Statistical analyses and visualisations were conducted using packages tidyverse v.2.0.0 (Wickham et al. 2019) and ggplot2 v.3.5.1 (Wickham 2016) using software R (R Core Team 2023) and RStudio (Posit team 2023). The Shapiro–Wilk test (Shapiro and Wilk 1965) was used to test for normality and to determine the appropriate significance test to use, while the Kruskal-Wallis rank sum test (Kruskal and Wallis 1952) was used as a significance test because data were nonparametric. Specific MS settings and triplicate merging will be published in future publications and will be available on online repositories.
Student evaluation
The study programme was designed to introduce students not only to practical scientific work, but also to showcase role models in science and to invite students to engage in interdisciplinary scientific discussions. Furthermore, practical activities were designed and scaffolded to allow students to connect to the authentic material and thus to history as such, to people, and to lifestyles of the medieval and renaissance cities. Each session concluded with the distribution of a questionnaire (Supplemental File 1: Questionnaire) aimed at evaluating the students’ comprehension of the scientific process and their self-assessment of their contributions to the scientific project. While not all numeric data derived from the questionnaire are presented in the results chapter, all the questions are included in Supplemental File 1 to provide a better understanding of the context from which the presented data are drawn, and to support a more nuanced interpretation of how students engaged with and made meaning within the learning environment. All questionnaires were anonymous and voluntary for students, who were informed about the purpose of the evaluation prior to it.
Results
Co-creational setup
From May 12 to December 1, 2021, we co-created the study programme with 12 high schools (a total of 300 students), 12 high school teachers from nine different high schools, and six representatives from three local museums. In our co-creational concept, we present two lines of development. First, local museums contributed relevant research questions of general and local interest, drawing on their expertise in archaeological materials and their desire to expand the scope of their scientific endeavours while increasing representation in the sampled materials. Second, high school teachers ensured the relevance of our project to the general educational programme and high school curriculum. The combination of these two lines of development resulted in a participatory programme that ensured a high level of scientific quality in the resulting data while aligning with the learning objectives of the subject curricula.
Figure 2 (a–c) illustrates the two key dimensions of our co-creational concept; local museums and high schools. The figure demonstrates how the interactions between the lines of development added value to both dimensions, culminating in an optimal study programme in which the level of introduction and training met the necessary requirements for conducting lab work. The exchange of ideas between the two lines of development led to a broader co-development base and improved outcome for the current study programme, as well as the shape of its future trajectory. As an example, teachers proposed ideas and concepts, which were then further developed into specific research questions by the local museums. Figure 2c shows how new research questions developed during the project in collaboration with the museum panel, for instance, when new archaeological materials were implemented. These questions were subsequently evaluated with teachers and students, resulting in a robust application that included preliminary test results, ultimately paving the way for expanding the initiative and securing additional funding.
Figure 2
A concept for co-creation in participatory science presented as a flow model showing the co-creational dimensions (local museums and high schools) and how interactions between parties improve outcomes. The process is facilitated by a core project team (facilitator team) who takes part in all processes and secures implementation of proposed research questions, ideas, and concepts thus ensuring relevance for local museum partners, teachers, and high school students. Figure highlights how the concept provides value (a) during project development, (b) for project expansion during the project, and (c) for further development into future project phases.
From our co-creation concept, we successfully developed three key products for the participatory science programme. First, we established a study and laboratory protocol for high school students to follow during the full-day programme at the museum. This was based on an already published protocol (Ebsen et al. 2019) with important modifications to secure the best possible success for the untrained students performing the analyses (e.g., ensuring easier pipetting as small volumes are more difficult to monitor and transfer, and the acid, TFA, was also diluted to reduce risk of etching). This protocol directly ensured the achievement of the learning objectives and the appropriate level of complexity in the laboratory work. Second, we developed a crucial pipeline for managing the large volume of data generated from the laboratory work, including options for facilitating easy access for all project participants, contributors, and stakeholders. Third, the co-creational concept resulted in a full-day study programme designed to actively involve high school students in hands-on natural science research in an archaeological research setting, and more specifically, by empowering them to contribute to our broader scientific understanding of the archaeological materials and our historical past. The workflow of the samples is presented in Figure 1.
In practice, the core project team (facilitators) initiated and hosted physical meetings with collaborating local museum staff and high school teachers, as well as follow-up meetings to establish and maintain the co-creational concept while ensuring progress on agreed areas of development. Planning follow-up meeting agendas was done in open conversation, and meetings were used to revisit and align expectations from all parties.
Initially, students were introduced to the lab environment, safety protocols, and the importance of their role in the research project, including laboratory techniques, data collection, and analysis methods. Subsequently, students identified and catalogued archaeological leather samples. They then proceeded to conduct various analyses, including microscopic examinations, to discern the composition and structure of the leather and, if possible, the species origin based on the pattern of the hair follicles. Based on their findings, students interpreted the historical and cultural significance of the leather samples. Upon conclusion of the programme, students engaged in reflective exercises to contemplate their experiences and learning outcomes. Finally, students communicated their findings through reports, presentations, or exhibitions. Students’ experience in the classroom afterwards and the role of the teacher as facilitator of this is supported and evaluated only to a very limited extent by the programme described here.
Data quantity and quality
Including the 12 classes involved in the co-creation process in 2021 and until May 2024, we welcomed 154 high school classes and a total of 3,319 high school students. They studied 917 different objects and analysed 3,900 samples (some students analysed several samples). Each high school class typically worked on five to six objects distributed mainly across three to five replicates (Figure 3) in order to ensure quality control and to increase chances of retrieving sufficient peptide markers for taxonomic identifications.
Figure 3
Plot displaying the number of replicates produced per object. Data is based on a subsample of 679 objects spread across 2,279 samples extracted between February 1, 2022 and May 14, 2024. The average number of replicates per object is 3.50 ± 1.05 SD. Objects with one or two replicates were primarily from the initial co-creation phase of the project (tests, etc.), before the sampling and analyses were standardised by university professionals. Objects with 6, 9, and 12 replicates originate from objects of special interest, where extended sampling was deemed necessary.
Indeed, the implementation of three to five replicates per object handled by different students resulted in the sufficient retrieval of peptide markers, hence adequate taxonomic identifications (Figure 4). Although there is a significant difference in the inter-student performance of extracting peptide markers (Kruskal-Wallis test, p = 0.022), at least one student per object retrieved adequate peptide markers (7.47 ± 1.72 SD out of 9). In fact, no significant difference was found between the best-performing student (one who retrieved the most peptide markers) per object (Kruskal-Wallis test, p = 0.477), standing as a testimony to the high quality of the taxonomic identifications.
Figure 4
Graph illustrating the inter-student performance per object in terms of peptide markers count (maximum of nine) based on a subsample ranked by average peptide marker count. The x-axis displays the object ID (n = 68), while the y-axis represents the number of peptide markers observed. Smaller dark dots (n = 190) represent each individual student’s retrieval of peptide markers. Larger dark dots and their respective lines illustrate the mean peptide marker per specimen and the standard deviation. The light green line highlights the maximum peptide marker observed per object.
Additionally, individual students may retrieve various peptide markers within the same objects, making it possible to piece together a more complete peptide mass fingerprint, and sometimes resulting in a more specific taxonomic identification. This approach is not necessary because students already demonstrated high retrieval of peptide markers per object. Only 4 out of 68 (5.88%) objects had fewer than four peptide markers observed, resulting in insufficient taxonomic identifications. The number of peptide markers needed for sufficient taxonomic identification varies between taxa, where certain species have several distinct markers, and other species have fewer. Meanwhile, the poor proteomic output in the four objects may well be due to poor preservation and not student performance. Sufficient taxonomic identifications were thus reached in approximately 95% of the objects, reaching mostly genus or species level after adjusting for the geographical and temporal context, which is equally important for taxonomic specificity when working with ZooMS. For example, a combination of peptide markers may give the proteomic taxonomic identification of ten different Equus species with the current reference database. A subsequent exclusion of Equus species that seem unlikely in a southern Scandinavian Medieval period context (for example, Equus quagga, or common zebra) will thus narrow the taxonomic identification down to Equus caballus (horse) or possibly Equus asinus (donkey).
Student evaluation
A total of 1,168 questionnaires were gathered from the participating students. The responses provide evidence that the respondents possessed a clear understanding of their involvement in their research endeavor. The students indicated that they were able to comprehend scientific problems, indicative of a robust understanding of the scientific process fostered by the project (see Figure 5). Conversely, a minimal proportion of responses (<10%) suggested either no or limited understanding of the project (see Figure 5).
Figure 5
After a full day in the lab, students were asked to self-evaluate their own performance and understanding of the research project (n = 1,368). Overall, respondents indicated that they provide results of scientific value, demonstrating a high understanding of the scientific project, and think of their work as of great value. (a) Students were asked to rate the scientific value of their work on a scale from 1 to 5, with 1 indicating the least usefulness and 5 indicating the greatest usefulness (n = 1,245, excluding 123 No reply). 94.78% (n = 1,180) students gave a score of 3 or more, indicating that they are providing outputs of scientific value. Only 5.22% (n = 65) gave a score of 2 or less, indicating they see no scientific value in their work. (b) Students were asked to rate their understanding of the scientific issues related to their work on a scale from 1 to 5, with 1 indicating the little to no understanding and 5 indicating a complete understanding (n = 1,243, excluding 124 No reply). 91.07% (n = 1,132) gave a score of 3 or more, regarding themselves as being fast and having a good or complete understanding of the scientific issues. Only 8.93% (n = 111) of students gave a score of 2 or less, demonstrating a more negative view of their own skills after participating in the project. (c) Students were asked to rate their skills in conducting scientific work on a scale from 1 to 5, with 1 indicating not good and 5 indicating very good (n = 1,244, excluding 123 No reply). 90.52% (n = 1,126) of students gave a score of 3 or more, or scored themselves as doing a sound scientific job, while only 9.49% (n = 118) gave a score of 2 or less, indicating that they were not performing well.
Discussion
The generation of one of the world’s largest ZooMS datasets of high quality is made possible by the co-creational concept presented in this paper. Through the interaction with partners and through tests with target group participants, the facilitator team has adjusted all observed parameters and bottlenecks that could affect data quality and the generation of a large dataset in a negative manner. Thus, the study programme, including pipette training and explanations, and the protocol with adjustments for the target group and the pipeline, which secures the flow from sampling to result, are major products of this project. They are also key explanations of the high quality of data and the success of the project.
The overall findings of the evaluation questionnaires provide evidence that the students perceive their contribution as valuable and scientifically rigorous, aligning seamlessly with our assessment of the high data quality and impact.
Based on previous studies, collagen preservation in leather can be challenged by the archaeological environment and probably also the tanning process. Only a few studies of a larger number of archaeological leather objects exist (Viñas-Caron and Brandt 2025). In one previous study, 115 medieval leather shoe elements from three different sites in Denmark (one of them included in this project, as well) analysed by an experienced researcher resulted in respectively 22%, 70%, and 89% successful taxonomic identifications. In another study of 45 archaeological leather objects, a success rate of 73% was obtained on Ukrainian leather objects (Brandt et al. 2023). The success rate of sufficient taxonomic identifications demonstrated in this project is therefore comparably very impressive. Furthermore, adjusting the number of replicates from a maximum of two (Brandt et al. 2022), as were commonly sampled in the very early phases of the project, to three to five per object seems to have increased the taxonomic identification success rate significantly. Though inter-student variation exists, sampling more than three samples per object appears to be sufficient in obtaining an adequate taxonomic identification. The replicate adjustment meant that in 95% of all instances, at least one student retrieved enough peptide markers for a sufficient taxonomic identification. The remaining insufficiently identified objects may, however, not be related to inadequate student performance, but poor collagen preservation in the archaeological environments.
A key takeaway from the project is the importance of aligning expectations among all parties involved at the outset of the co-creation process. We addressed this as a key agenda item at both the start and final stages of meetings. A number of practical challenges emerged during the development and implementation of the project, requiring careful negotiation between scientific goals and educational feasibility. For instance, one initial aim was for students to work on the exact same sample throughout the entire analytical process. However, this proved logistically unfeasible due to the extensive time required for sample preparation. In another case, the selection of materials was shaped by the scientific research questions, which sometimes meant prioritising specific materials over a broader diversity that might have had greater educational appeal. Teachers expressed frustration over the slow turnaround time for analytical results, which initially created delays in delivering meaningful feedback to students. Addressing this issue became a major focus, and after nearly two years of development, we succeeded in streamlining the process. Overall, the facilitator team has continuously balanced competing priorities, such as scientific integrity, educational value, and practical feasibility, informed by ongoing dialogue with project partners. This balancing act reflects a dual commitment to advancing both student learning and high-quality research, in alignment with the project’s co-creational ethos. In conclusion, while not all ideas, suggestions, and requests can be accommodated owing to resource constraints or the need to maintain project focus, we also found that some initially accepted ideas later proved to be impractical or inappropriate, or required deprioritisation. Making these difficult decisions was facilitated by maintaining a clear and honest dialogue about expectations from the very beginning.
Because the co-creational approach requires extensive collaboration, regular meetings, and continuous adaptation to feedback, all of which demand considerable time and effort, the resources needed to support the involvement of diverse stakeholders is costly. This highlights the need to secure sufficient resources (e.g., institutional support, external funding) for large participatory science projects. Moreover, the facilitator team needs to acknowledge the need for and allocate adequate time for co-creation. Both securing resources and setting aside time are central to the project’s success.
Lastly, local museums showed an interest in having access to the data and thereby gaining additional knowledge and understanding of the location, the period, resource utilisation, production methods, etc. For example, our results revealed that leather used in medieval and renaissance Copenhagen came mainly from domesticated animals, and that leather used in shoe production in the period came mainly from cattle. Furthermore, exceptional data proved the presence of animal species never previously seen in an archaeological context in Copenhagen (Brandt et al. 2022).
Future work
Making the data generated by the project easily accessible to teachers, students, and local museums was a primary goal at the project’s inception. Teachers and students expressed a strong interest in engaging with and utilising the data beyond the initial visit. As extensive data analysis could not be accommodated during the single-day visit to the museum, providing access to the data for subsequent analysis at their schools enhanced the students’ experience of the scientific method. This approach also contributed to integrating data literacy into their educational outcomes. The current programme had very limited insights into what actual follow-up activities were being facilitated by teachers and how the students responded. Unsystematic, self-reported feedback from participating teachers did reveal major challenges in facilitating thorough data analysis of primary MS data with their students when returning from the out-of-school study programme. This highlights the need to develop supporting materials and a follow-up programme to maximise the impact of student participation and to evaluate the project’s effect on students’ data literacy outcomes.
A limiting factor for establishing local partnerships between museums and high schools is the uneven preservation of large materials of archaeological leather, which requires very specific environments such as waterlogged stratigraphies in the archaeological contexts. In the future, we aim at expanding the range of collagenous materials we analyse to also encompass tissues such as bone, antler, and teeth. Such materials would not only allow a broader range of museums to participate in the project—and thus potentially increase local museum and high school partnerships—but would also facilitate student work with themes such as animal evolution, adaptation to niches, etc., which could be implemented in the day programme.
Conclusion
This Next Generation Lab project was developed by co-creation involving high school teachers and archaeological researchers from museums, as well as facilitators with expertise within participatory science. The co-creational concept provided essential value for the development and continued progress of the current project as well as new ideas and approaches for future development. The project outcome provides high school students with a unique opportunity to actively participate in scientific research, fostering their curiosity, scientific identity, cross-disciplinarity, and scientific skills.
Additionally, the participatory science project made it possible to establish a very large archaeological dataset, one of the world’s largest, because of the large quantities of archaeological materials provided in combination with the data pipeline. The inclusion of the newest methods and technologies as well as incorporating solid validation of outcomes through replications secures the highest data quality, resulting in data that will be of high value for archaeologists now and in the future. These results clearly demonstrate the huge potential of the co-creation framework and the success that resulted from working with local museums and high school teachers from the outset and throughout.
Data Accessibility Statement
The proteomic data are not currently available in this publication or in an online repository, as they will be included in a forthcoming article that presents not only the sub-sample used in this study but the complete dataset generated by the project. To ensure consistent and accessible data sharing, the sub-sample analysed in this article, along with the remaining data, will be deposited in an online repository, either zenodo.org or proteomeXchange.org. The repository entry will clearly specify which subset of data was utilised in this study.
Supplementary File
The supplementary file for this article can be found as follows:
Acknowledgements
We are immensely grateful to our wonderful collaborators in the boards of museums and high school teachers. We would like to thank Morten Søvsø from Museum of Southwest Jutland, Jannie Amsgaard Ebsen, Kirstine Haase, and Mads Runge from Museum Odense, Vivi Lena Andersen (formerly Museum of Copenhagen), and Lotte Sparrevohn from Kroppedal Museum. From the high schools, we would like to thank the following teachers for sparring and collaboration: Liane Damsø, Rødovre Gymnasium, Bo Kildeager, Gl. Hellerup Gymnasium, Gabriel Bjerre, Hillerød Tekniske Gymnasium – U/Nord, Lea Sloth, Ellen Bjerrum-Bohr and Mattias Lange Nielsen, Niels Steensens Gymnasium, Jens Folke Harrits, Odder Gymnasium, Stine Beenfeldt Weber, Helsingør Gymnasium, Christian Rix, Rødkilde Gymnasium, Hans Marker and Ulrik Brasch, Skt. Annæ Gymnasium. From Natural History Museum Denmark, Maria Rytter and Frederik Leerhøi provided valuable assistance with laboratory-, data- and didactic pipeline.
Competing Interests
The authors have no competing interests to declare.
Author Contributions
APT, MRL, and LØB were responsible for the majority of conception and design of the work. All authors made crucial contributions to the acquisition, analysis, and interpretation of the data as well as drafting and reviewing the content. APT, MRL, JH, SHS, and LØB managed and approved the final version to be published.