%0 Book Section %1 Prike2022Open %A Prike, Toby %B Towards Bayesian Model-Based Demography: Agency, Complexity and Uncertainty in Migration Studies %C Cham %D 2022 %E Bijak, Jakub %I Springer International Publishing %K Diss %P 175--183 %R 10.1007/978-3-030-83039-7_10 %T Open Science, Replicability, and Transparency in Modelling %X Recent years have seen large changes to research practices within psychology and a variety of other empirical fields in response to the discovery (or rediscovery) of the pervasiveness and potential impact of questionable research practices, coupled with well-publicised failures to replicate published findings. In response to this, and as part of a broader open science movement, a variety of changes to research practice have started to be implemented, such as publicly sharing data, analysis code, and study materials, as well as the preregistration of research questions, study designs, and analysis plans. This chapter outlines the relevance and applicability of these issues to computational modelling, highlighting the importance of good research practices for modelling endeavours, as well as the potential of provenance modelling standards, such as PROV, to help discover and minimise the extent to which modelling is impacted by unreliable research findings from other disciplines. %@ 978-3-030-83039-7

@incollection{Prike2022Open, abstract = {Recent years have seen large changes to research practices within psychology and a variety of other empirical fields in response to the discovery (or rediscovery) of the pervasiveness and potential impact of questionable research practices, coupled with well-publicised failures to replicate published findings. In response to this, and as part of a broader open science movement, a variety of changes to research practice have started to be implemented, such as publicly sharing data, analysis code, and study materials, as well as the preregistration of research questions, study designs, and analysis plans. This chapter outlines the relevance and applicability of these issues to computational modelling, highlighting the importance of good research practices for modelling endeavours, as well as the potential of provenance modelling standards, such as PROV, to help discover and minimise the extent to which modelling is impacted by unreliable research findings from other disciplines.}, added-at = {2024-01-29T00:47:34.000+0100}, address = {{Cham}}, author = {Prike, Toby}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2ee0523eadb6505c0523fe99d5fd1e7e4/hermann}, booktitle = {Towards {{Bayesian Model-Based Demography}}: {{Agency}}, {{Complexity}} and {{Uncertainty}} in {{Migration Studies}}}, doi = {10.1007/978-3-030-83039-7_10}, editor = {Bijak, Jakub}, file = {/Users/sibyllehermann/Zotero/storage/39XBTGEJ/Prike - 2022 - Open Science, Replicability, and Transparency in M.pdf;/Users/sibyllehermann/Zotero/storage/VVYJPEP8/Notizen_Prike2022Open.pdf}, interhash = {7e2965359cfa68eec8ae593309e7cce2}, intrahash = {ee0523eadb6505c0523fe99d5fd1e7e4}, isbn = {978-3-030-83039-7}, keywords = {Diss}, langid = {english}, pages = {175--183}, publisher = {{Springer International Publishing}}, series = {Methodos {{Series}}}, timestamp = {2024-01-29T00:47:34.000+0100}, title = {Open {{Science}}, {{Replicability}}, and {{Transparency}} in {{Modelling}}}, urldate = {2023-02-22}, year = 2022 }