TU Wien Informatics

20 Years
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Guest Professors Honoraries Alumni (LinkedIn) #5qw

Katharina Flicker

Projektass.in / B.A. M.A.

Role

Contact

  • Towards A Data Repository for Educational Factories / Ekaputra, F. J., Weise, M., Flicker, K., bin Salleh, Mohd. R., Abd Rahman, Md. N., Muhamad Azwan, A. R., Miksa, T., & Rauber, A. (2022, November 2). Towards A Data Repository for Educational Factories [Conference Presentation]. 8th International Conference on Data and Software Engineering (ICoDSE 2022), Denpasar, Bali, Indonesia. https://doi.org/10.34726/2944
    Download: Presentation, 25 slides (1.23 MB)
  • The Austrian EOSC mandated organisation/The EOSC support office Austria / Blumesberger, S., Brandt, F., Budroni, P., De Mello Castro Giroletti, J., Ferus, A., Flicker, K., Ganguly, R., Guba, B., Hanslik, S., Hasani-Mavriqi, I., Hönegger, L., Kalová, T., Kranewitter, M., Logar, B., Panigl, C., Rainer, H., Rauber, A., Sánchez Solís, B., Saurugger, B., … Zimmermann, K. (2021). The Austrian EOSC mandated organisation/The EOSC support office Austria. Mitteilungen Der Vereinigung Österreichischer Bibliothekarinnen Und Bibliothekare (VÖB), 74(2), 143–162. https://doi.org/10.31263/VOEBM.V74I2.6270
    Download: PDF (941 KB)
  • Precisely and persistently identifying and citing arbitrary subsets of dynamic data / Rauber, A., Gößwein, B., Zwölf, C. M., Schubert, C., Wörister, F., Duncan, J., Flicker, K., Zettsu, K., Meixner, K., McIntosh, L., Jenkyns, R., Pröll, S., Miksa, T., & Parsons, M. A. (2021). Precisely and persistently identifying and citing arbitrary subsets of dynamic data. Harvard Data Science Review, 3(4). https://doi.org/10.1162/99608f92.be565013
  • Precisely and Persistently Identifying and Citing Arbitrary Subsets of Dynamic Data / Rauber, A., Gößwein, B., Zwölf, C. M., Schubert, C., Wörister, F., Duncan, J., Flicker, K., Zettsu, K., Meixner, K., McIntosh, L. D., Jenkyns, R., Pröll, S., Miksa, T., & Parsons, M. A. (2021). Precisely and Persistently Identifying and Citing Arbitrary Subsets of Dynamic Data. Harvard Data Science Review, 3(4). https://doi.org/10.1162/99608f92.be565013
    Project: CDL-SQI (2018–2024)