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Reading Group

We are an open reading group that meets every first Tuesday of the month at 15:00 c.t. at the office to read a selected paper. Papers can be voted on up to 14 days before the reading group meeting, then the one with the most votes gets selected.

Maintenance Phase

The reading group is currently on hold, applications for co-leading a new group are welcome! Just write me a mail with your CV attached.

Topics should include, but are not limited to:

  • Trusted Research Environments
  • FAIR Principles
  • Data Management
  • Sensitive Data Infrastructures/Processes/etc.

To ensure a meaningful discussion within the timeframe (~60 mins), papers nominated must fulfill the following requirements:

  • Research paper (in exceptional cases a technical review paper)
  • About 8 pages (if longer, only the main part will be discussed)
  • Must provide at least one question for the group

Every 4 weeks the reading group meets, the procedure follows a weekly schedule:

  • The topic for the next reading group meeting is set, papers can be suggested for subsequent voting
  • The voting starts until the end of the week, the voting ends and the results are sent out via e-mail and mirrored here
  • Paper reading on your own
  • Paper reading on your own (=2 weeks time)

Meetings

2023
2023-11-07
  • Bahareh Alami Milani, Nima Jafari Navimipour. (2016). A Comprehensive Review of the Data Replication Techniques in the Cloud Environments: Major Trends and Future Directions. Journal of Network and Computer Applications, 64 p.229-238, DOI: 10.1016/j.jnca.2016.02.005
    [4 votes — 50%]
2023-09-05
  • Veale, M., & Binns, R. (2017). Fairer Machine Learning in the Real World: Mitigating Discrimination Without Collecting Sensitive Data. Big Data & Society, 4(2). DOI: 10.1177/2053951717743530
    [4 votes — 100%]
2023-08-01
  • Sun, C., Ocaña, M. G., van Soest, J. & Dumontier, M. (2023). ciTIzen-centric DatA pLatform (TIDAL): Sharing Distributed Personal Data in a Privacy-Preserving Manner for Health Research. Semantic Web, 14(5), pp.977-996. DOI: 10.3233/SW-223220
    [4 votes — 75%]
2023-07-04
2023-06-06
  • Saadoon, M., Ab. Hamid, S. H., Sofian, H., Altarturi, H. H. M., Azizul, Z. H., & Nasuha, N. (2022). Fault Tolerance in Big Data Storage and Processing Systems: A Review on Challenges and Solutions. Ain Shams Engineering Journal, 13(2), 101538. DOI: 10.1016/j.asej.2021.06.024
    [5 votes — 60%]
2023-05-02
  • Andrei Vlad Sambra, Essam Mansour, Sandro Hawke, Maged Zereba, Nicola Greco, Abdurrahman Ghanem, Dmitri Zagidulin (2016). Solid: A Platform for Decentralized Social Applications Based on Linked Data. URL: http://emansour.com/research/lusail/solid_protocols.pdf
    [4 votes — 75%]
2023-04-04
  • S. Pröell, R. Mayer and A. Rauber, 2015. Data Access and Reproducibility in Privacy Sensitive eScience Domains. Proceedings of the 11th International Conference on e-Science, pp. 255-258. DOI: 10.1109/eScience.2015.20
    [3 votes — 66%]
2023-03-07
  • Gonzalez-Cebrian, A., McGuinness, L. A., Bradford, M., Chis, A. E., & Gonzalez-Velez, H. (2022). Automatic Versioning of Time Series Datasets: a FAIR Algorithmic Approach. Proceedings of the 18th International Conference on E-Science. DOI: 10.1109/escience55777.2022.00034
    [3 votes — 100%]
2023-02-07
  • Simmhan, Y. L., Plale, B., & Gannon, D. (2005). A survey of data provenance in e-science. ACM SIGMOD Record, 34(3), p.31–36. DOI: 10.1145/1084805.1084812
    [4 votes — 25%]
2023-01-03
  • Bronselaer, A., Billiet, C., De Mol, R., Nielandt, J., & De Tré, G. (2018). Compact Representations of Temporal Databases. The VLDB Journal, 28(4), p.473–496. DOI: 10.1007/s00778-018-0535-4
    [3 votes — 66%]
2022
2022-12-07
  • Bade, F. M., Vollenberg, C., Koch, J., Koch, J., & Coners, A. (2022). The Dark Side of Process Mining. How Identifiable Are Users Despite Technologically Anonymized Data? A Case Study from the Health Sector. Business Process Management, p.219–233. DOI: 10.1007/978-3-031-16103-2_16
    [4 votes — 75%]
2022-11-03
  • Candela, L., Castelli, D. and Pagano, P., 2013. Virtual Research Environments: An Overview and a Research Agenda. Data Science Journal, 12(1), p.1-81. DOI: 10.2481/dsj.GRDI-013
    [3 votes — 66%]
2022-10-04
  • Jones, K. H., Ford, D. V., Jones, C., Dsilva, R., Thompson, S., et al (2014). A Case Study of the Secure Anonymous Information Linkage (SAIL) Gateway: A Privacy-Protecting Remote Access System for Health-related Research and Rvaluation. Journal of Biomedical Informatics, 50, p.196–204. DOI: 10.1016/j.jbi.2014.01.003
    [3 votes — 66%]
2022-09-06
  • Boeckhout, M., Zielhuis, G., Bredenoord, A. (2018). The FAIR Guiding Principles for Data Stewardship: Fair Enough? European Journal of Human Genetics, 26(1), p.931–936. DOI: 10.1038/s41431-018-0160-0
    [4 votes — 50%]
2022-08-02
  • Wilkinson, M., Dumontier, M., Aalbersberg, I. et al (2016) The FAIR Guiding Principles for Scientific Data Management and Stewardship. Scientific Data, 3(160018). DOI: 10.1038/sdata.2016.18
    [0 votes — 100%]

Paper Collection

2023

  • Demchenko, Y., Gallenmuller, S., Fdida, S., Andreou, P., Crettaz, C., & Kirkeng, M. (2023). Experimental Research Reproducibility and Experiment Workflow Management. Proceedings of the 15th International Conference on Communication Systems & Networks. DOI: 10.1109/comsnets56262.2023.10041378.

  • Kunis, S., Bernhardt, K., & Hensel, M. (2023). Setting up a Data Management Infrastructure for Bioimaging. Biological Chemistry. DOI: 10.1515/hsz-2022-0304.

2022

  • Bade, F. M., Vollenberg, C., Koch, J., Koch, J., & Coners, A. (2022). The Dark Side of Process Mining. How Identifiable Are Users Despite Technologically Anonymized Data? A Case Study from the Health Sector. International Conference on Business Process Management, p.219–233. DOI: 10.1007/978-3-031-16103-2_16.

  • Berg, A., Dahlbo, H., Eilu, P., Heikkilä, P., Hentunen, A. et al. (2022). Handbook for a Data-Driven Circular Economy in Finland: Data Sources, Tools, and Governance for Circular Design. [Report]. DOI: 10.32040/2242-122X.2022.T401
    [PDF]

  • Benhamed, O. M., Burger, K., Kaliyaperumal, R., da Silva Santos, L. O. B. et al, 2022. The FAIR Data Point: Interfaces and Tooling. Data Intelligence, p.1-18. DOI: 10.1162/dint_a_00161
    [PDF]

  • Chadwick, S., Graham, S., Dean, J., & Dallmeyer, M., 2022. Leveraging Confidential Computing to Enable Secure Information Sharing. IFIP Advances in Information and Communication Technology, p.235–252. DOI: 10.1007/978-3-031-20137-0_9

  • Corujo, L., 2022. Who Is the FAIRest of Them All? Authors, Entities, and Journals Regarding FAIR Data Principles. Publications, 10(3), p.1-38. DOI: 10.3390/publications10030031
    [PDF]

  • Franken, J., Birukou, A., Eckert, K., Fahl, W. et al, 2022. Persistent Identification for Conferences. Data Science Journal, 21(11), p.1-18. DOI: 10.5334/dsj-2022-011
    [PDF]

  • Graham, M., Milne, R., Fitzsimmons, P. and Sheehan, M., 2022. Trust and the Goldacre Review: why Trusted Research Environments are not about Trust. Journal of Medical Ethics. DOI: 10.1136/jme-2022-108435
    [PDF]

  • Jefferson, E., Cole, C., Mumtaz, S., Cox, S., Giles, T. C., Adejumo, et al., 2022. A Hybrid Architecture (CO-CONNECT) to Facilitate Rapid Discovery and Access to Data Across the United Kingdom in Response to the COVID-19 Pandemic: Development Study. Journal of Medical Internet Research, 24(12), e40035. DOI: 10.2196/40035
    [PDF]

  • Mayernik, M. S., & Liapich, Y., 2022. The Role of Metadata and Vocabulary Standards in Enabling Scientific Data Interoperability: A Study of Earth System Science Data Facilities. Journal of EScience Librarianship, 12(1). DOI: 10.7191/jeslib.619
    [PDF]

  • Martorana, M., Kuhn, T., Siebes, R. and van Ossenbruggen, J., 2022. Aligning Restricted Access Data with FAIR: a Systematic Review. PeerJ Computer Science, 8(e1038). DOI: 10.7717/peerj-cs.1038
    [PDF]

  • Nicholson, N., Giusti, F., Neamtiu, L., Randi, G. et al, 2022. Dotting the "i" of Interoperability in FAIR Cancer-Registry Data Sets. Cancer Bioinformatics. DOI: 10.5772/intechopen.101330
    [HTML]

  • Pineda‐Pampliega, J., Bernhard, A., Hannisdal, R., Ørnsrud, R., Mathisen, G. H., et al., 2022. Developing a framework for open and FAIR data management practices for next generation risk‐and benefit assessment of fish and seafood. EFSA Journal, 20, e200917. DOI: 10.2903/j.efsa.2022.e200917.
    [PDF]

  • Salazar, A., Wentzel, B., Schimmler, S., Gläser, R., Hanf, S., & Schunk, S. A., 2022. How Research Data Management Plans Can Help in Harmonizing Open Science and Approaches in the Digital Economy. Chemistry - A European Journal. p.1-10. DOI: 10.1002/chem.202202720
    [PDF]

  • Scerri, S., Tuikka, T., de Vallejo, I.L. and Curry, E., 2022. Common European Data Spaces: Challenges and Opportunities. Data Spaces. p.337–357. DOI: 10.1007/978-3-030-98636-0_16
    [PDF]

  • Scheffler, M., Aeschlimann, M., Albrecht, M., Bereau, T. et al, 2022. FAIR Data Enabling New Horizons for Materials Research. Nature, 604, p.635–642. DOI: 10.1038/s41586-022-04501-x
    [PDF] [Preprint]

  • Schwagereit, F., Schwagereit, M., Schwagereit, F., Trypuz, R. et al, 2022. FAIR Data APIs in the FAIR in Vivo Data Sharing Platform. Proceedings of the 18th International Conference on Semantic Systems.
    [PDF]

  • Slamkov, D., Stojanov, V., Koteska, B. & Mishev, A., 2022. A Comparison of Data FAIRness Evaluation Tools. Proceedings of the 9th Workshop on Software Quality, Analysis, Monitoring, Improvement, and Applications (September 11-14th, Serbia).
    [PDF]

  • Weise, M., Kovacevic, F., Popper, N. and Rauber, A., 2022. OSSDIP: Open Source Secure Data Infrastructure and Processes Supporting Data Visiting. Data Science Journal, 21(1), p.4. DOI: 10.5334/dsj-2022-004
    [PDF]

  • Wilde, K., Anderson, L., Boyle, M., Pinder, A. and Weir, A., 2022. Introducing a new Trusted Research Environment: the Safe Haven Artificial Platform (SHAIP). International Journal of Population Data Science, 7(3). DOI: 10.23889/ijpds.v7i3.2056
    [PDF]

2021

  • Cox, N., Bernard-Salas, J., 2021. Scientific Data Applications for Science Analysis Platforms. Astronomical Data Analysis Software and Systems XXX, 532. p.1-23.
    [PDF]

  • Rauber, A., Gößwein, B., Zwölf, C.M., Schubert, F. et al, 2021. Precisely and Persistently Identifying and Citing Arbitrary Subsets of Dynamic Data. Harvard Data Science Review, 3(4). DOI: 10.1162/99608f92.be565013
    [PDF]

  • UK Health Data Research Alliance and NHSX, 2021. Building Trusted Research Environments. Principles and Best Practices. Towards TRE Ecosystems. DOI: 10.5281/zenodo.5767586 : wikimedia-open-access:
    [PDF]

2020

  • Lamprecht, A.-L., Garcia, L., Kuzak, M., Martinez, C., Arcila, R. et al. (2020). Towards FAIR Principles for Research Software. Data Science, 3(1), 37–59. DOI: 10.3233/ds-190026.

  • Lee, D., Kohlbrenner, D., Shinde, S., Asanovic, K. et al, 2020. Keystone: an Open Framework for Architecting Trusted Execution Environments. Proceedings of the 15th European Conference on Computer Systems, p.1-16. DOI: 10.1145/3342195.3387532
    [PDF]

  • Williamson, E., Walker, A., Bhaskaran, K., Bacon, S. et al, 2020. Factors associated with COVID-19-related death using OpenSAFELY. Nature, 584(7821), p.1-436. DOI: 10.1038/s41586-020-2521-4
    [PDF]

2019

  • Cuggia, M. and Combes, S., 2019. The French Health Data Hub and the German Medical Informatics Initiatives: Two National Projects to Promote Data Sharing in Healthcare. Yearbook of Medical Informatics, 28(1), p.1-202. DOI: 10.1055/s-0039-1677917
    [PDF]

  • Remy, L., Ivanovic, D., Theodoridou, M., Kritsotaki, A. et al, 2019. Building an Integrated Enhanced Virtual Research Environment Metadata Catalogue. The Electronic Library, 37(6), p.1-951. DOI: 10.1108/EL-09-2018-0183
    [PDF]

2018

  • Boeckhout, M., Zielhuis, G., Bredenoord, A., 2018. The FAIR Guiding Principles for Data Stewardship: Fair Enough? * European Journal of Human Genetics*, 26(1), p.931–936. DOI: 10.1038/s41431-018-0160-0
    [PDF]

  • Elliot, M., O'Hara, K., Raab, C., O'Keefe C.M. et al, 2018. Functional Anonymisation: Personal Data and the Data Environment. Computer Law & Security Review, 34(2), p.1-221. DOI: 10.1016/j.clsr.2018.02.001
    [PDF]

  • Wilkinson, M., Sansone, S-A., Schultes, E., Doorn, P. et al, 2018. A Design Framework and Exemplar Metrics for FAIRness. Scientific Data, 5(180118). DOI: 10.1038/sdata.2018.118
    [PDF]

2017

  • Mons, B., Neylon, C., Velterop, J., Dumontier, M. et al, 2017. Cloudy, Increasingly FAIR; Revisiting the FAIR Data Guiding Principles for the European Open Science Cloud. Information Services & Use, 37(1), p.1-56. DOI: 10.3233/ISU-170824
    [PDF]

  • Popper, N., Florian, E., Mayer, R., Bicher, M. et al, 2017. Planning Future Health: Developing Big Data and System Modelling Pipelines for Health System Research. Simulation Notes Europe, 27(4), p.1-208. DOI: 10.11128/sne.27.tn.10396
    [PDF]

2016

  • Desai, T., Ritchie, F. and Welpton, R., 2016. Five Safes: Designing Data Access for Research. Economics Working Paper Series.
    [PDF]

  • Gkelis, S. and Panou, M., 2016. Capturing Biodiversity: Linking a Cyanobacteria Culture Collection to the "Scratchpads" Virtual Research Environment Enhances Biodiversity Knowledge. Biodiversity Data Journal, 4(1). DOI: 10.3897/BDJ.4.e7965
    [PDF]

  • Wilkinson, M., Dumontier, M., Aalbersberg, I. et al, 2016. The FAIR Guiding Principles for Scientific Data Management and Stewardship. Scientific Data, 3(160018). DOI: 10.1038/sdata.2016.18
    [PDF]

2015
  • S. Pröell, R. Mayer and A. Rauber, 2015. Data Access and Reproducibility in Privacy Sensitive eScience Domains. Proceedings of the 11th International Conference on e-Science, Munich, Germany, pp. 255-258. DOI: 10.1109/eScience.2015.20
    [PDF]
2014
  • Jones, K., Ford, D., Jones, C. and Dsilva, R., 2016. A Case Study of the Secure Anonymous Information Linkage (SAIL) Gateway: A Privacy-protecting Remote Access System for Health-related Research and Evaluation. Journal of Biomedical Informatics, 50(1), p.1-204. DOI: 10.1016/j.jbi.2014.01.003
    [PDF]
2013

  • Candela, L., Castelli, D. and Pagano, P., 2013. Virtual Research Environments: An Overview and a Research Agenda. Data Science Journal, 12(1), p.1-81. DOI: 10.2481/dsj.GRDI-013
    [PDF] [PDF2]

  • Sarwara, M., Doherty, T., Watt, J. and Sinnott, R., 2013. Towards a Virtual Research Environment for Language and Literature Researchers. Future Generation Computer Systems, 29(2), p.1-559. DOI: 10.1016/j.future.2012.03.015
    [PDF]

2012
  • Scholz, M. and Goerz, G., 2012. WissKI: A Virtual Research Environment for Cultural Heritage. Frontiers in Artificial Intelligence and Applications, 242(1), p.1-1018. DOI: 10.3233/978-1-61499-098-7-1017
    [PDF]
2011
  • Lauter, K., Naehrig, M. and Vaikuntanathan, 2011. Can Homomorphic Encryption be Practical? Proceedings of the 3rd ACM workshop on Cloud computing security workshop, p.1-124, DOI: 10.1145/2046660.2046682
    [PDF]
2010
  • Ahmed, S.F., Rodie, M., Jiang, J. and Sinnott, R.O., 2010. The European Disorder of Sex Development Registry: A Virtual Research Environment. Sexual Development, 4(1), p.1-198, DOI: 10.1159/000313434
    [PDF]
2009

  • Ford, D. V., Jones, K. H., Verplancke, J.-P., Lyons, R. A. et al, 2009. The SAIL Databank: Building a National Architecture for E-Health Research and Evaluation. BMC Health Services Research, 9(1) DOI: 10.1186/1472-6963-9-157
    [PDF]

  • Lyons, R. A., Jones, K. H., John, G., Brooks, C. J. et al, 2009. The SAIL Databank: Linking Multiple Health and Social Care Datasets. BMC Medical Informatics and Decision Making, 9(1) DOI: 10.1186/1472-6947-9-3
    [PDF]

2005
  • Candela, L., Castelli, D., Pagano, P., & Simi, M. (2005). From Heterogeneous Information Spaces to Virtual Documents. Proceedings of the 8th International Conference on Asian Digital Libraries, p.11–22. DOI: 10.1007/11599517_2
    > [PDF]