Evaluating citizen science for dialect research on the nightingale song (Luscinia megarhynchos)

Citizen Science (CS) ist eine Methode, die in den letzten Jahren in der Wissenschaft weltweit an Bedeutung gewonnen hat. Obwohl viele Studien diese Daten mit denen von akademischen Forschenden verglichen, gibt es immer noch Bedenken hinsichtlich ihrer Qualität. In meiner Doktorarbeit zielte ich darauf ab die Methode CS für eine Vogelart mit einem großen Repertoire, der Nachtigall (Luscinia megarhynchos), als Anwendungsfall auf der Grundlage der Dialektforschung zu evaluieren. Ich untersuchte, ob die drei vermeintlichen Hauptgründe für schlechte Qualität (Anonymität, Unerfahrenheit und fehlende Standardisierung) zu unvollständigen, zeitlich oder räumlich verzerrten und ungenauen bioakustischen Daten führten. Dazu analysierte ich nicht-standardisierte CS-Aufnahmen, die mit einem Smartphone über die 'Naturblick' App erstellt wurden, welche einen eingebauten Mustererkennungsalgorithmus enthielt. Ich konnte in meiner Doktorarbeit zeigen, dass mit der Methode CS valide Daten für die bioakustische Forschung gewonnen werden können. Meine Ergebnisse zeigten, dass Anonymität, mangelnde Erfahrung und Standardisierung nicht zu geringer Qualität führten, sondern zu einem großen Datensatz, der genauso wertvoll war wie jene von akademischen Forschenden. Die Ergebnisse sind von großer Bedeutung für künftige CS-Projekte zur Verbesserung der Qualität und des Vertrauens in diese Daten.Citizen science (CS) is a method that has been increased in science worldwide in recent years. Although many studies have compared these data with those of academic researchers, there are still concerns about their quality. In my doctoral thesis I aimed to evaluate the method of CS for a bird species with a large repertoire, the nightingale (Luscinia megarhynchos), as a use case based on dialect research. I investigated whether the three main assumed reasons for poor quality (anonymity, inexperience and lack of standardisation) led to incomplete, temporal or spatial biassed and inaccurate bioacoustic data. Therefore, I analysed non-standardised CS recordings, which were generated with a smartphone via the 'Naturblick' app, which contained an in-built pattern recognition algorithm. In summary (Chapter V), my doctoral thesis showed that the method CS could be used to generate valid data for bioacoustic research. My findings showed that anonymity, lack of experience and standardisation did not lead to low quality but in fact to a large dataset, which was as valuable as ones from academic researchers. The results are of great relevance for future CS projects to improve the quality and the trust in these data

Benefits and Challenges: Data Management Plans in Two Collaborative Projects

The data-driven shift in the science research leads to a wider range of research data. To manage this data in a sustainable and adequate way, data management plans (DMPs) were established as a method. However, some researchers still do not create DMPs due to lack of time, resources and understanding of the needs. Furthermore, most of the existing templates and tools are largely unknown. In this article, we investigated the benefits and challenges of DMPs in two joint research projects of several academic institutions. For this, we described the process during the DMP creation, potential challenges and benefits experienced. We showed that a DMP with completely uniform content among the partner institutions was not possible due to individual and subject differences (e.g., in storage and policies). Instead, individual texts had to be formulated in some cases to overcome the diversity. This complexity could not be handled with the existing tools. Therefore, both projects created an own adapted template with some generic contents. Existing guidelines and internal project policies helped during the generation. We experienced that fewer people work more efficiently on a DMP than many and that all researchers within the project can profit from every individual DMP. Although we were not required to produce one, we recognised the associated benefits as a guide during the research process in joint projects

Desiderata on research data management 2013 and 2022

Forschungsdatenmanagement ist seit den ersten Anforderungen der Deutschen Forschungsgemeinschaft 2015 zu einem Bestandteil guter wissenschaftlicher Praxis geworden. Hochschulen sind dadurch aufgefordert, Forschende bestmöglich zu unterstützen. Seit 2013 erfolgten deutschlandweit Umfragen, um Desiderate bei Infrastruktur- und Serviceleistungen zu ermitteln. Eine Evaluation der Bedarfsäußerungen fand bisher jedoch kaum statt. Der Artikel fasst Entwicklungen und Handlungsfelder auf Basis von zwei Bedarfserhebungen der Humboldt-Universität zu Berlin zusammen.Research data management has become a part of good scientific practice since the first requirements of the Deutschen Forschungsgemeinschaft were introduced in 2015. Universities are thus required to provide researchers with the best possible support. Since 2013, surveys have been conducted throughout Germany to identify desiderates in infrastructure and services. However, an evaluation of the needs has hardly taken place so far. The article summarises developments and issues-based on two needs surveys conducted at Humboldt-Universität zu Berlin.Peer Reviewe

Unravelling the Stability of Nightingale Song Over Time and Space Using Open, Citizen Science and Shared Data

Open science approaches enable and facilitate the investigation of many scientific questions in bioacoustics, such as studies on the temporal and spatial evolution of song, as in vocal dialects. In contrast to previous dialect studies, which mostly focused on songbird species with a small repertoire, here we studied the common nightingale (Luscinia megarhynchos), a bird species with a complex and large repertoire. To study dialects on the population level in this species, we used recordings from four datasets: an open museum archive, a citizen science platform, a citizen science project, and shared recordings from academic researchers. We conducted the to date largest temporal and geographic dialect study of birdsong including recordings from 1930 to 2019 and from 13 European countries, with a geographical coverage of 2,652 km of linear distance. To examine temporal stability and spatial dialects, a catalog of 1,868 song types of common nightingales was created. Instead of dialects, we found a high degree of stability over time and space in both, the sub-categories of song and in the occurrence of song types. For example, the second most common song type in our datasets occurred over nine decades and across Europe. In our case study, open and citizen science data proved to be equivalent, and in some cases even better, than data shared by an academic research group. Based on our results, we conclude that the combination of diverse and open datasets was particularly useful to study the evolution of song in a bird species with a large repertoire.Peer Reviewe

Unravelling the Stability of Nightingale Song Over Time and Space Using Open, Citizen Science and Shared Data

Open science approaches enable and facilitate the investigation of many scientific questions in bioacoustics, such as studies on the temporal and spatial evolution of song, as in vocal dialects. In contrast to previous dialect studies, which mostly focused on songbird species with a small repertoire, here we studied the common nightingale (Luscinia megarhynchos), a bird species with a complex and large repertoire. To study dialects on the population level in this species, we used recordings from four datasets: an open museum archive, a citizen science platform, a citizen science project, and shared recordings from academic researchers. We conducted the to date largest temporal and geographic dialect study of birdsong including recordings from 1930 to 2019 and from 13 European countries, with a geographical coverage of 2,652 km of linear distance. To examine temporal stability and spatial dialects, a catalog of 1,868 song types of common nightingales was created. Instead of dialects, we found a high degree of stability over time and space in both, the sub-categories of song and in the occurrence of song types. For example, the second most common song type in our datasets occurred over nine decades and across Europe. In our case study, open and citizen science data proved to be equivalent, and in some cases even better, than data shared by an academic research group. Based on our results, we conclude that the combination of diverse and open datasets was particularly useful to study the evolution of song in a bird species with a large repertoire

Community engagement and data quality: best practices and lessons learned from a citizen science project on birdsong

Citizen Science (CS) is a research approach that has become popular in recent years and offers innovative potential for dialect research in ornithology. As the scepticism about CS data is still widespread, we analysed the development of a 3-year CS project based on the song of the Common Nightingale (Luscinia megarhynchos) to share best practices and lessons learned. We focused on the data scope, individual engagement, spatial distribution and species misidentifications from recordings generated before (2018, 2019) and during the COVID-19 outbreak (2020) with a smartphone using the ‘Naturblick’ app. The number of nightingale song recordings and individual engagement increased steadily and peaked in the season during the pandemic. 13,991 nightingale song recordings were generated by anonymous (64%) and non-anonymous participants (36%). As the project developed, the spatial distribution of recordings expanded (from Berlin based to nationwide). The rates of species misidentifications were low, decreased in the course of the project (10–1%) and were mainly affected by vocal similarities with other bird species. This study further showed that community engagement and data quality were not directly affected by dissemination activities, but that the former was influenced by external factors and the latter benefited from the app. We conclude that CS projects using smartphone apps with an integrated pattern recognition algorithm are well suited to support bioacoustic research in ornithology. Based on our findings, we recommend setting up CS projects over the long term to build an engaged community which generates high data quality for robust scientific conclusions.Gesellschaftliches Engagement und Datenqualität: Bewährte Praktiken und Erfahrungen aus einem bürgerwissenschaftlichen Projekt zum Vogelgesang Citizen Science (CS) ist eine Forschungsmethode, die in den letzten Jahren an Bedeutung gewonnen hat und innovatives Potenzial für die Dialektforschung in der Ornithologie bietet. Da die Vorbehalte gegenüber CS-Daten immer noch weit verbreitet sind, haben wir die Entwicklung eines dreijährigen CS-Projekts zum Gesang der Nachtigall (Luscinia megarhynchos) analysiert, um bewährte Praktiken und gewonnene Erfahrungen darzustellen. Wir fokussierten uns auf den Datenumfang, das individuelle Engagement von Teilnehmenden, die räumliche Verteilung und die Fehlbestimmungen von Arten aus Aufnahmen, die vor (2018, 2019) und während des COVID-19-Ausbruchs (2020) mit einem Smartphone unter Verwendung der "Naturblick" App erstellt wurden. Die Anzahl der Aufnahmen von Nachtigallgesängen und das individuelle Engagement stiegen stetig an und erreichten ihren Höhepunkt in der Saison während der Pandemie. 13.991 Aufnahmen von Nachtigallgesängen wurden von anonymen (64%) und nicht-anonymen Teilnehmenden (36%) erstellt. Im Laufe des Projekts weitete sich die räumliche Verteilung der Aufnahmen aus (von Berlin auf bundesweit). Die Rate der Fehlbestimmungen war gering, ging im Laufe des Projekts zurück (von 10% auf 1%) und wurde hauptsächlich von gesanglichen Ähnlichkeiten mit anderen Vogelarten beeinflusst. Unsere Studie zeigte außerdem, dass das gesellschaftliche Engagement und die Datenqualität nicht direkt von den durchgeführten Disseminationsaktivitäten beeinflusst wurden, sondern dass erstere von externen Faktoren abhingen und letztere von der App profitierte. Wir schließen daraus, dass CS-Projekte, die Smartphone-Apps mit einem integrierten Mustererkennungsalgorithmus verwenden, gut geeignet sind, um die bioakustische Forschung in der Ornithologie zu unterstützen. Auf der Grundlage unserer Ergebnisse empfehlen wir, CS-Projekte langfristig zu etablieren, um eine aktive Teilnehmergemeinschaft (Community) aufzubauen, die qualitativ hochwertige Daten für fundierte wissenschaftliche Schlussfolgerungen generiert

Anwendung des FDM-Referenzmodells RISE-DE im Verbund

  Der vorliegende Bericht liefert anwendungsbezogene Einblicke in die institutionelle Selbstevaluation von Forschungsdatenmanagement-Services und -Infrastrukturen mit RISE-DE, einem Referenzmodell für Strategieprozesse im institutionellen Forschungsdatenmanagement (FDM), im Verbundkontext der Berlin University Alliance (BUA). Die Selbstevaluation wurde an allen vier Einrichtungen der BUA (Freie Universität Berlin, Humboldt-Universität zu Berlin, Technische Universität Berlin und Charité – Universitätsmedizin Berlin) durchgeführt. Im Ergebnis konnte ein strukturierter und systematischer Überblick über die Gemeinsamkeiten und Unterschiede im Bereich FDM an den vier Einrichtungen gewonnen werden. Der Bericht beschreibt die Erfahrungen bei der Anwendung des RISE-DE-Instruments und gibt Empfehlungen für dessen Einsatz in Verbundstrukturen. Insgesamt hat sich RISE-DE im Kontext der BUA als hilfreiches Werkzeug für die gemeinsame Standortbestimmung im FDM erwiesen, die als Ausgangspunkt zur Konzeptentwicklung für nachhaltige, verbundweit genutzte FDM-Services und -Infrastrukturen dienen soll

A workshop report on the FDNext project funded by the German Research Foundation

Nach zwei Jahren Projektlaufzeit lud der DFG-geförderte Projektverbund FDNext zu einem zweiten Community-Workshop ein. Unter dem Motto „Nachhaltiges Forschungsdatenmanagement gemeinsam umsetzen“ wurde eine projektweite Ergebnisbilanz gezogen und im Rahmen einer Online-Veranstaltung vorgestellt. Einzelne Formate ermöglichten den Austausch und die Diskussion zur Vision des Kulturwandels und eines ganzheitlichen FDMs durch Initiativen wie die Nationale Forschungsdateninfrastruktur (NFDI) sowie die Möglichkeiten der Zusammenarbeit zwischen einzelnen Konsortien und Hochschulen. Dabei wurden Aufgaben identifiziert, welche nur gemeinsam mit der FDM- bzw. Wissenschafts-Community bearbeitet werden können.Two years into the project duration, the collaborative project FDNext convened its second community workshop titled “Implementing Sustainable Research Data Management in a Joint Project”. Focusing on a review of achievements, the online event presented findings from all participating parties. Various formats fostered exchange and debates about perspectives of cultural change and a holistic research data management through initiatives such as the Nationale Forschungsdateninfrastruktur NFDI (national research data infrastructure), as well as collaboration opportunities between individual consortia and universities. Tasks and challenges that can only be dealt with in cooperation with RDM and scientific communities have been identified.Peer Reviewe

Corrigendum zu: Desiderate zum Forschungsdatenmanagement 2013 und 2022

Dieser Beitrag ist mit Zustimmung des Rechteinhabers (De Gruyter) frei zugänglich.Peer Reviewe

Opportunities and limitations: A comparative analysis of citizen science and expert recordings for bioacoustic research

Citizen science is an approach that has become increasingly popular in recent years. Despite this growing popularity, there still is widespread scepticism in the academic world about the validity and quality of data from citizen science projects. And although there might be great potential, citizen science is a rarely used approach in the field of bioacoustics. To better understand the possibilities, but also the limitations, we here evaluated data generated in a citizen science project on nightingale song as a case study. We analysed the quantity and quality of song recordings made in a non-standardized way with a smartphone app by citizen scientists and the standardized recordings made with professional equipment by academic researchers. We made comparisons between the recordings of the two approaches and among the user types of the app to gain insights into the temporal recording patterns, the quantity and quality of the data. To compare the deviation of the acoustic parameters in the recordings with smartphones and professional devices from the original song recordings, we conducted a playback test. Our results showed that depending on the user group, citizen scientists produced many to a lot of recordings of valid quality for further bioacoustic research. Differences between the recordings provided by the citizen and the expert group were mainly caused by the technical quality of the devices used—and to a lesser extent by the citizen scientists themselves. Especially when differences in spectral parameters are to be investigated, our results demonstrate that the use of the same high-quality recording devices and calibrated external microphones would most likely improve data quality. We conclude that many bioacoustic research questions may be carried out with the recordings of citizen scientists. We want to encourage academic researchers to get more involved in participatory projects to harness the potential of citizen science—and to share scientific curiosity and discoveries more directly with society