Connecting the data landscape of long‐term ecological studies: The SPI‐Birds data hub
Culina, Antica
Adriaensen, Frank
Bailey, Liam D.
Burgess, Malcolm D.
Charmantier, Anne
Cole, Ella F.
Eeva, Tapio
Matthysen, Erik
Nater, Chloé R.
Sheldon, Ben C.
Sæther, Bernt-Erik
Vriend, Stefan J. G.
Zajkova, Zuzana
Adamík, Peter
Aplin, Lucy M.
Angulo, Elena
Artemyev, Alexandr
Barba, Emilio
Barišić, Sanja
Belda, Eduardo
Bilgin, Cemal Can
Bleu, Josefa
Both, Christiaan
Bouwhuis, Sandra
Branston, Claire J.
Broggi, Juli
Burke, Terry
Bushuev, Andrey
Camacho, Carlos
Campobello, Daniela
Canal, David
Cantarero, Alejandro
Caro, Samuel P.
Cauchoix, Maxime
Chaine, Alexis
Cichoń, Mariusz
Ćiković, Davor
Cusimano, Camillo A.
Deimel, Caroline
Dhondt, André A.
Dingemanse, Niels J.
Doligez, Blandine
Dominoni, Davide M.
Doutrelant, Claire
Drobniak, Szymon M.
Dubiec, Anna
Eens, Marcel
Einar Erikstad, Kjell
Espín, Silvia
Farine, Damien R.
Figuerola, Jordi
Kavak Gülbeyaz, Pınar
Grégoire, Arnaud
Hartley, Ian R.
Hau, Michaela
Hegyi, Gergely
Hille, Sabine
Hinde, Camilla A.
Holtmann, Benedikt
Ilyina, Tatyana
Isaksson, Caroline
Iserbyt, Arne
Ivankina, Elena
Kania, Wojciech
Kempenaers, Bart
Kerimov, Anvar
Komdeur, Jan
Korsten, Peter
Král, Miroslav
Krist, Miloš
Lambrechts, Marcel
Lara, Carlos E.
Leivits, Agu
Liker, András
Lodjak, Jaanis
Mägi, Marko
Mainwaring, Mark C.
Mänd, Raivo
Massa, Bruno
Massemin, Sylvie
Martínez-Padilla, Jesús
Mazgajski, Tomasz D.
Mennerat, Adèle
Moreno, Juan
Mouchet, Alexia
Nakagawa, Shinichi
Nilsson, Jan-Åke
Nilsson, Johan F.
Cláudia Norte, Ana
van Oers, Kees
Orell, Markku
Potti, Jaime
Quinn, John L.
Réale, Denis
Kristin Reiertsen, Tone
Rosivall, Balázs
Russell, Andrew F
Rytkönen, Seppo
Sánchez-Virosta, Pablo
Santos, Eduardo S. A.
Schroeder, Julia
Senar, Juan Carlos
Seress, Gábor
Slagsvold, Tore
Szulkin, Marta
Teplitsky, Céline
Tilgar, Vallo
Tolstoguzov, Andrey
Török, János
Valcu, Mihai
Vatka, Emma
Verhulst, Simon
Watson, Hannah
Yuta, Teru
Zamora-Marín, José M.
Visser, Marcel E.
1.The integration and synthesis of the data in different areas of science is drastically slowed and hindered by a lack of standards and networking programmes. Long‐term studies of individually marked animals are not an exception. These studies are especially important as instrumental for understanding evolutionary and ecological processes in the wild. Furthermore, their number and global distribution provides a unique opportunity to assess the generality of patterns and to address broad‐scale global issues (e.g. climate change).
2. To solve data integration issues and enable a new scale of ecological and evolutionary research based on long‐term studies of birds, we have created the SPI‐Birds Network and Database (www.spibirds.org)—a large‐scale initiative that connects data from, and researchers working on, studies of wild populations of individually recognizable (usually ringed) birds. Within year and a half since the establishment, SPI‐Birds has recruited over 120 members, and currently hosts data on almost 1.5 million individual birds collected in 80 populations over 2,000 cumulative years, and counting.
3. SPI‐Birds acts as a data hub and a catalogue of studied populations. It prevents data loss, secures easy data finding, use and integration and thus facilitates collaboration and synthesis. We provide community‐derived data and meta‐data standards and improve data integrity guided by the principles of Findable, Accessible, Interoperable and Reusable (FAIR), and aligned with the existing metadata languages (e.g. ecological meta‐data language).
4. The encouraging community involvement stems from SPI‐Bird's decentralized approach: research groups retain full control over data use and their way of data management, while SPI‐Birds creates tailored pipelines to convert each unique data format into a standard format. We outline the lessons learned, so that other communities (e.g. those working on other taxa) can adapt our successful model. Creating community‐specific hubs (such as ours, COMADRE for animal demography, etc.) will aid much‐needed large‐scale ecological data integration.
1.The integration and synthesis of the data in different areas of science is drastically slowed and hindered by a lack of standards and networking programmes. Long‐term studies of individually marked animals are not an exception. These studies are especially important as instrumental for understanding evolutionary and ecological processes in the wild. Furthermore, their number and global distribution provides a unique opportunity to assess the generality of patterns and to address broad‐scale global issues (e.g. climate change).
2. To solve data integration issues and enable a new scale of ecological and evolutionary research based on long‐term studies of birds, we have created the SPI‐Birds Network and Database (www.spibirds.org)—a large‐scale initiative that connects data from, and researchers working on, studies of wild populations of individually recognizable (usually ringed) birds. Within year and a half since the establishment, SPI‐Birds has recruited over 120 members, and currently hosts data on almost 1.5 million individual birds collected in 80 populations over 2,000 cumulative years, and counting.
3. SPI‐Birds acts as a data hub and a catalogue of studied populations. It prevents data loss, secures easy data finding, use and integration and thus facilitates collaboration and synthesis. We provide community‐derived data and meta‐data standards and improve data integrity guided by the principles of Findable, Accessible, Interoperable and Reusable (FAIR), and aligned with the existing metadata languages (e.g. ecological meta‐data language).
4. The encouraging community involvement stems from SPI‐Bird's decentralized approach: research groups retain full control over data use and their way of data management, while SPI‐Birds creates tailored pipelines to convert each unique data format into a standard format. We outline the lessons learned, so that other communities (e.g. those working on other taxa) can adapt our successful model. Creating community‐specific hubs (such as ours, COMADRE for animal demography, etc.) will aid much‐needed large‐scale ecological data integration.