OpenBikeSim Tech is a systematic framework for bicycling simulator technology which supports comparable reporting of setups used in research. It provides a links to repositories of open-source technologies developed and documented by the community.
The systematic framework was developed through a literature review analysing 108 published bicycling simulator setups. The full details on the method are provided in a related research paper which is in review process at the moment. Once published, we will link it here and encourage all users of our framework to cite it in their work.

Systematic Framework for Bicycling Simulator Technology

by Mathis Titgemeyer and Heather Kaths
Chair of Bicycle Traffic, University of Wuppertal

Background

The rapid growth and diversification of bicycling simulator technologies, ranging from low-tech static platforms to high-fidelity systems with motion cueing, has significantly advanced research on bicyclists as vulnerable road users, infrastructure design, and bicyclist training. However, this diversity has introduced challenges in cross-simulator comparability, as variations in technical designs complicate the assessment of how simulator technology influences research outcomes. Bicycling simulator studies still lack a structured framework to ensure transparent reporting of the technology used in experiments. Existing reviews either focus on qualitative syntheses or categorise simulators by fidelity without addressing technical aspects systematically. A systematic framework is therefore urgently needed to standardise reporting and foster trust in bicycling simulators as tools for high-quality, comparable research.

Framework

We apply the abstract concept of “human-in-the-loop simulation” as a framework to derive the functional elements that constitute a bicycling simulator: A human is interacting with a system via input and output variables exchanged through interfaces (Folds 2015). In the case of bicycling simulators, this translates to the human as the object of interest; the system comprising the virtual subsystems which compile the immersive virtual environment; the interfaces in the form of the physical subsystems which allow the human to interact with the simulation; the inputs as control actions taken by the human to execute the bicycling task; and the outputs that are the sensory cues received. The review identified eight control options as input and nine sensory cues as output of the bicycling simulator. The identified technical solutions have been synthesised into functional subsystems for twelve physical and three virtual subsystems that form the bicycling simulator system. In this framework, connected simulators are separate instances of the physical subsystem and the bicycling dynamics subsystem for each human in the simulation. These are connected to a common environment and a common traffic and controls subsystem, which facilitate the shared immersive virtual environment. The resulting system architecture is presented in the figure ‘Framework of simulator technology as a system of functional subsystems’ above and reflects the functional principle of all reviewed bicycling simulators. The following section presents the physical subsystems for each human sensory system they target and describe the prevailing inputs and outputs as well as the technical solutions used to realise them. The last section presents the virtual subsystems, defining them by their functions and presenting the technical approaches for their implementation.

Physical subsystems

The physical subsystems enable the human to interact with the virtual subsystems. All control actions and sensory cues are passed through them, requiring specific hardware and software technologies depending on the functional extent of individual simulators. The following section presents the physical subsystems for each human sensory system they target and describes the technical solutions used to realise the prevailing inputs and outputs. They are summarised quantitatively in the figure ‘Control actions as input and sensory cues as output of the bicycling simulator’, while the figure ‘Technologies used for the physical subsystems per publication and year’ shows the trend in technical development over time derived from the type of technology used to implement each of the inputs and outputs in the physical subsystems.

Visual sense

The visualisation subsystem contains the technology to output a visual representation of the simulation. This includes the virtual environment, the avatars of the bicycle, the human riding it and the surrounding road users, additional HMIs, and instructions for the experimental process. A visualisation as an output was recorded in 76 studies. They usually took the form of three-dimensional virtual environments responsive to human interaction and continuously updated based on the simulated scenario and input variables. Three studies used a recording of a streetscape for constant visualisation and controlled the playback speed depending on the pedalling motion. Only one study was identified that did not use a visualisation, instead focussing on tactile cues (Rakhmatov et al. 2018). The predominant visualisation technologies were monitors and projection screens using either single (n=22) or multiple (n=36) devices surrounding the human. Other identified solutions were cave automatic virtual environment (CAVE) systems (n=3) and head-mounted displays (n=43) maximising the field of view. An illusion of spatial depth generated by stereoscopic footage was in place in 50 studies while 34 studies indicated not providing it. The frequent reporting of visualisation underscores its importance, while the lack of documentation in 31 studies suggests unreported use of visual cues.

The body tracking subsystem uses one or multiple sensors to observe movements of parts of the human body which are not direct actions to control the bicycle, but inputs for non-verbal communication through gestures, or visual cues supporting the perception of the own body in VR. Input in form of body movement was implemented in three studies as gestures which were recorded and transferred to the avatar of the bicyclist in the virtual environment. Martinez Garcia (2021) used this method to improve self-perception in the virtual environment while Keler et al. (2021c) and Lindner et al. (2022) implemented virtual gestures to enable communication with another human in a connected simulator. In 14 studies, body movement data was recorded for later analysis, but was not integrated as an input. In eight studies, depth cameras were combined with a skeleton recognition algorithm and provided data on the movement of the head, hands and body and also recognised pre-defined gestures in real-time. Head-mounted displays tracked head movement in five studies. Integrated gyro sensors were found to record hand movements by use of virtual reality gloves (n=1, Martinez Garcia (2021)) or smart watches (n=2). Eye movements were tracked with optical sensors integrated in head-mounted displays (n=6), or transparent eye-tracking glasses (n=3). One study used a non-wearable but stationary camera (Stevenson et al. 2015). Two studies used manual methods to record body movements unsuitable to create real-time simulation input (Brown et al. 2017; Plumert et al. 2011). The maturity of technology to detect and measure body movements suggests that future research may consider implementing them more strongly depending on the research question.

Vestibular sense

The motion subsystem relays motion cues from the simulator to the human vestibular system and senses the human actions to balance the body and the bicycle. Motioncuesin all three translational and all three rotational degrees of freedom (DOF) were implemented responsively in 13 studies using hexapods, also known as Stewart-Gough platforms. Other studies focused on responsive cues in the roll and pitch degree (n=4), only roll degree (n=3), or only pitch degree (n=2). The motion platforms in these studies were either individual designs (n=3), off-the-shelf gaming hardware (n=4), or off-the-shelf bicycle trainer equipment (n=2). Five studies integrated mechanisms constantly reacting to roll motion caused by the human based on off-the-shelf sports products (n=2), or individual designs (n=3). Motion input to enablesteering by balancing the bicycle laterally was implemented in nine studies, while 81 did not offer it. Two studies used sensors attached to the human body using a head-mounted display (Kingsley et al. 2020) or an off-the-shelf motion tracking sensor (Sawitzky et al. 2020). Two studies detected the balancing using sensors integrated in the actuators of the motion platform, one study implemented force sensors at the base of a constant motion platform (de Souza e Almeida et al. 2020), and one used an off-the-shelf microcontroller attached to the bicycle frame (Schramka et al. 2017). Realistic motion cuing remains a challenge for bicycling simulators due to cost and effort required for multi-DOF motion equipment and the complexity of developing performant bicycling dynamics models to control them. Nonetheless, approaches reducing the complexity to fewer DOFs show potential to optimise the cost and may be an acceptable compromise depending on the prevailing research question.

Haptic sense

The pedalling resistance subsystem senses the human’s pedalling action and creates resistance output against it depending on the set gear ratio. A control optionto increase or maintain the longitudinal speed by pedalling was implemented in 102 studies. Two studies compared pedalling to controlling speed via keyboard (Bogacz et al. 2020) or gamepad (Mittelstaedt et al. 2018). One study omitted pedalling, only providing a joystick to control the speed (Song et al. 2003). The corresponding output is pedalling resistance in form of haptic torque feedback against the pedalling motion. It was responsively controlled in 42 studies. Mechanisms constantly inducing torque were found in 28 studies and two studies did not use any resistance. The review revealed various solutions to implement the pedalling. Individual developments for responsive resistance applied either electronic motors (n=24), magneto-rheologic fluid brakes (n=1, Kikuchi et al. (2012)), or a combination of both (n=2). In addition, 14 of these designs integrated a mechanical simulation of the longitudinal mass inertia effect in form of a flywheel design. Off-the-shelf home training products were used to implement constant resistance in 27 studies, and responsive resistance in 15 studies. Solutions to monitor the speed of the bicycle can be categorised as measuring the power input or a spinning frequency. The pedalling power has been measured in all individually designed resistance systems described above. Some variants of off-the-shelf home trainers also measure pedalling power and offer communication interfaces to export the data
(Schweidler 2022). However, most studies featuring home trainers monitored the resulting spinning frequency of a rotating part (e.g. rear wheel) using Hall effect sensors, optical rotary encoders, side-runner dynamos, and inertial measurement units. The overwhelming majority of studies implementing pedalling underlines the importance of this control for bicycling simulators. A trend towards off-the-shelf equipment can be observed, but the limited documentation available for these products poses challenges for efficient integration in bicycling simulators and transparency of their physical validity.

open-source solutions

• Tiptop bicycling simulator speed sensor module

Gear shifting is the action of changing the transmission ratio between the crank and the resistance actuator to adjust the required cadence and torque for powering the bicycle’s forward movement. All 102 bicycling simulators with pedalling resistance were found to contain a mechanical gear to transmit the power, but only 18 of them offered a control option to adjust it. Real-world bicycle components such as gear hubs, or chain derailleur gears were used as mechanical solutions. Despite the technical capability of responsive resistance solutions, no study was found to use a virtual gear by adjusting the pedalling resistance electronically. The identified technical solutions highlight the simplicity of implementing an adjustable gear, which stands in contrast to only 18 studies offering this control. The option to adjust cadence and torque to achieve a desired pedalling power strongly influences the biomechanical efficiency of the pedalling und thus the validity of a study especially when simulating gradient effects of sloping roads. The ease of implementation and benefit for biomechanical efficiency emphasise the need to make greater use of this control in future research.

The brake subsystem senses the human’s action to decelerate longitudinally. Braking input was implemented in 64 studies, while six reported not to have any brakes so that the virtual bicycle would only stop by rolling to a halt depending on simulated friction e.g. rolling resistance. Mechanical approaches to the braking input include hand-operated brake levers or back-pedal brakes to slow down a rotating part of the resistance subsystem whose rotation was measured to derive the speed, as described above. Mechatronic approaches convert the physical braking action into an electronic input signal for the bicycling dynamics subsystem. Technical solutions for this approach were sensors measuring the manual braking force applied or a longitudinal or rotational displacement of the braking hardware integrated as brake levers, back-pedal motion on the crank, or simplified push buttons. In contrast to the many studies implementing pedalling to accelerate the bicycle, few implemented a brake, which shows the need for more careful consideration of brake subsystems in future studies to ensure physical validity of the bicycling simulator by implementing this highly safety-relevant control.

open-source solutions

• TiptoP bicycling simulator hand brake module

The handlebar steering subsystem provides a physical handlebar that the human can turn to steer and receive haptic feedback in form of steering torque. The handlebar also supports balance and enables study participates to reach interfaces mounted on it, such as brake levers. Steering per handlebar allowing the human to control the direction of travel was implemented in 86 studies. In 15 studies, the avatar bicycle followed a predetermined trajectory, meaning the human was not in control of the direction of travel. To track the rotation of the handlebar, sensors were integrated into the handlebar assembly using potentiometers, incremental encoders, and laser measuring. Other studies installed inertial measurement units on the handlebar, which were integrated with smartphones, microcontrollers, and motion trackers. Mittelstaedt et al. (2018) used a physically separated camera for optical motion capturing of the handlebar rotation. Actuators for hapticsteering torque cueswere implemented responsively in 20 studies integrating electronic motors, or a combination of an electronic motor and a magneto-rheologic fluid brake. One study reported the use of pneumatic actuators for constant feedback (Schulzyk et al. 2009). The variety of solutions to implement handlebar steering reflects the low hurdle to implement this control. However, the absence of motion cues when steering a simulator can lead to sensory conflicts, increasing the risk of simulator sickness. Thus, integrating steering requires careful consideration of strategies to mitigate simulator sickness.

open-source solutions

• Tiptop bicycling simulator steering angle sensor module

Tactile sense

The vibration subsystem relays tactile cues which simulate vibration in the bicycle frame dependent on the virtual movement and environment. Vibration output emitted through the contact points between the human and bicycle was responsively generated in nine studies to simulate uneven road surfaces. 50 studies indicated not including vibration feedback. The identified technical solutions were either electronic vibration actuators installed under the saddle, on the handlebar, or near the crank, or multi-DOF motion platforms that induced vibration from the base of the bicycle mock-up. The studies using vibration feedback indicate positive effects on the perceived realism of the bicycling experience, which suggests making greater use of such cues in future research.

The airflow subsystem relays tactile cues through wind. Responsive airflow was generated in nine studies, either using off-the-shelf home trainer fans (n=2) or household devices with individual control technology (n= 7). One study integrated a fan providing constant airflow (Rittenbruch et al. 2020), but most studies reported not providing airflow (n=40). Airflow can be beneficial to reduce simulator sickness (D’Amour et al. 2017; Harrington et al. 2019) which suggests making greater use of wind in future studies.

Auditory and olfactory senses

The auditory subsystem creates sound as an output of the simulation and records verbal input by the human. Sound rendering resembling acoustic effects of the bicycle, ambient traffic, or other surrounding objects was used in 51 studies. The cues were transmitted via headphones or speaker systems for surround sound. In contrast to gestural communication, no study was found to implement communication input via verbalisations.

The olfactory subsystem creates specific smells depending on the surroundings in the virtual environment. Smell rendering targeting the olfactory sense was not reported in any of the reviewed studies suggesting that no experiment generated olfactory cues.

Additional cues and actions

The human-machine interface (HMI) subsystem transfers uni- or bidirectional information via additional inputs and outputs and combines devices for advanced driver assistance systems (ADAS) or intelligent transportation systems (ITS) which exceed the technology of a conventional manually powered bicycle. Additional HMI inputwas reported in two studies in form of an HMI enabling the human to adjust the level of electric support on an e-bike.

Additional HMI output was provided in 18 studies in a variety of visual, vibration, and sound cues for different purposes including supporting the wayfinding, creating awareness for dangers, creating distraction from the bicycling task, enabling communication with other road users, or controlling the process of the experiment. The variety of studies implementing additional cues to transfer information, especially in recent years, reflects an increasing interest in investigating HMIs supporting bicyclists.

Ergonomics

The ergonomic subsystem is different from the other subsystems as it does not directly take inputs or create outputs but assembles those subsystems that physically touch the human. In this way, it creates ergonomic conditions that allow the human to get into a real-world bicycling position. The hardware to replicate the ergonomics spanned ergometer devices (n=4), real-world bicycles (n=86) and individual designs (n=10) with varying degrees of adjustability to the human body dimensions. The seat height could be adjusted in 62 studies, five of them also allowing to adjust the handlebar height. Other dimensions could be adjusted on individually designed hardware (n=3) or by choosing between real bicycles of different size (n=8). Overall, the ergonomic subsystem plays an important role in ensuring that the hardware can be adapted to human body dimensions, thereby facilitating a realistic and comfortable bicycling posture.

Virtual Subsystems

The virtual subsystems comprise of theoretical models which replicate the real-world dynamic behaviour of the bicycle and bicyclist, the surrounding static and dynamic environment and the behaviour of the traffic and controls subsystem. These models are embedded in operable software technologies creating the immersive virtual environment. The responsiveness of the identified technologies for each virtual subsystem is given in figure ‘Functional extent of the virtual subsystems’, while figure ‘Software used for the virtual subsystems per publication and year’ shows the reported software solutions over time for each of the three subsystems resulting in three counts per study.

Bicycling Dynamics

The bicycling dynamics subsystem emulates the physical behaviour of the combined bicycle and bicyclist system. It is connected to the physical subsystems from which it receives information on the human activity to control the bicycle and computes information to initiate sensory cues. It is also connected with the environment subsystem to which it passes information on the movement of the bicycle and receives information on the environmental conditions. A bicycling dynamics model is based on a theoretical concept that divides the moving masses of bicycle and bicyclist into parts that are mechanically interconnected to derive an abstract mechanical concept. This concept is then described with mathematical equations that establish the equilibrium of masses, momentums, and energy in the overall system under dynamic conditions.

In the reviewed studies, researchers reported creating computational bicycling dynamics subsystems by solving these equations by means of numerical methods. For the underlying dynamic equations, researchers applied mechanics concepts by Åström et al. (2005), Franke et al. (1990), and Wong (1993), or developed individual concepts. Another approach aiming to reduce the required computational power is the use of simplified mathematical models like the Whipple (1899) and Carvallo (1901) models. To obtain operable bicycling dynamics subsystems, different software products were used to transfer the mathematical equations into operable code and to update the variables with inputs and outputs. Individual bicycling dynamics subsystems (n=49) were created using MATLAB Simulink (The MathWorks Inc.) and NVIDIA PhysX (NVIDIA Corporation 2025). Other studies reported using available software products for immersive simulation including DYNA4 (Vector Informatik GmbH 2025) (n=2), SILAB (WIVW 2024) (n=4), SimCreator (FAAC Inc. 2025) (n=3), and the game development software Unity (Unity Software Inc. 2024) (n=1, Sun et al. (2018)). The bicycling dynamics subsystem serves the essential function to replicate the characteristic physical behaviour of a bicycle and hence is a decisive factor to the physical validity of a bicycling simulator. Consequently, detailed documentation of this subsystem is vital to enable comparability while more research is required to develop capable and performant bicycling dynamics subsystems to improve the physical validity.

For a detailed subsystem architecture and a review of approaches please refer to our page on bicycling dynamics in bicycling simulators.

Environment

The environment subsystem contains the digital representation of the environment through which the bicycle and bicyclist avatars move. It maps information into a virtual three-dimensional space to create representations of external factors, such as road infrastructure, surrounding objects, environment conditions, and avatars. It updates the location of the avatar in the virtual environment based on the input of the bicycling dynamics subsystem and feeds back information as described above. The momentary location is sent to the traffic and controls subsystem to allow it to compute the trajectories of surrounding virtual road users. Information on the surroundings is directly sent to the visual, auditory, olfactory, and HMI subsystems.

The reported practices to create the virtual environment subsystems vary from manual methods defining each object individually to semi-automated tools for allocating pre-defined objects from content libraries. To operationalise the virtual environment subsystem, individually developed software was used in 16 studies, but more commonly available software products were used. The products reported included the graphics rendering engines OGRE (OGRE Team 2025) (n=1, Herpers et al. (2012)) and 3ds Max (Autodesk Inc. 2025) (n=1, Chen et al. (2007)), the above mentioned immersive simulation products SimCreator (FAAC Inc. 2025) (n=6), DYNA4 (Vector Informatik GmbH 2025) (n=8), and SILAB (WIVW 2024) (n=4), and the game development software Unreal Engine (Epic Games Inc. 2025) (n=1, Martinez Garcia (2021)) and Unity (Unity Software Inc. 2024) (n=36). 32 studies did not report which software was used and details on the definition of static and moving elements in the environment were limited to basic descriptions of road infrastructure and surrounding objects in all studies. To enable independent reproducibility of the environment subsystem researchers should apply standardised reporting formats such as OpenSCENARIO (ASAM e.V. 2022) and consider providing their environment models via repositories.

open-source solutions

• step by step guides for setting up CARLA as an environment subsystem
• for a registry of open virtual environments please visit our OpenBikeSim Library

Traffic and Controls

The traffic and controls subsystem replicates the behaviour of surrounding road users in virtual reality. It contains information on the traffic flows through the virtual network, and the behavioural characteristics of individual road users. It also contains the logic of the implemented traffic controls. It updates a traffic model based on input from the environment subsystem and returns information on the surrounding road users and traffic controls. Individually developed traffic and controls subsystems were found in 25 studies. Open-source and commercially available software products used for this purpose are the microscopic traffic simulation software products PTV Vissim (PTV 2025) (n=3) and SUMO (Alvarez Lopez et al. 2024) (n=9), the immersive simulation product SILAB (WIVW 2024) (n=4), and the game development software Unreal Engine (Epic Games Inc. 2025) (n=1,Martinez Garcia (2021)) and Unity (Unity Software Inc. 2024) (n=3). Five studies reported not having implemented a traffic and controls subsystem, and 57 studies made no report. This shows that previous studies tended to neglect interactions with other road users which poses a limitation to many research questions and indicates that there is a need for the development of easy to integrate yet capable traffic and controls subsystems.

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Is Your Technology Missing? Join the Community!

Have you developed an open-source subsystem for bicycling simulators? If so, we invite you to add it to our open-source reference lists above and share your work with the community!

To do this, please send an email to bicycletraffic[at]uni-wuppertal.de with the following details:

• Title of the subsystem
• Short description
• Link to the repository
• License

Together, we can create an open, collaborative resource for bicycling simulator research!

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Appendix A1 – Table of reviewed publications

Reference	Publication type
Abadi et al. (2019)	journal article
Adedokun et al. (2019)	project report
Ahmed et al. (2021)	project report
Bialkova and Ettema (2019)	conference paper
Bialkova et al. (2022)	journal article
Bialkova et al. (2018)	conference paper
Birenboim et al. (2019)	journal article
Bogacz et al. (2020)	journal article
Brown et al. (2017)	journal article
Bruzelius and Augusto (2018)	project report
Roche Cerasi et al. (2021)	conference paper
Chen et al. (2007)	journal article
Chihak et al. (2014)	journal article
Chihak et al. (2010)	journal article
Cobb (2020)	doctoral dissertation
Cobb et al. (2021)	journal article
de Leeuw and de Kruijf (2015)	conference paper
de Souza e Almeida et al. (2020)	journal article
Dialynas (2020)	doctoral dissertation
Dialynas et al. (2019)	journal article
Engbers et al. (2016)	journal article
Englund et al. (2016)	journal article
Grechkin et al. (2013)	journal article
Grigoropoulos et al. (2019)	conference paper
Guo et al. (2021)	conference paper
Guo et al. (2022)	self-published report
Handa and Mitobe (2020)	journal article
He et al. (2005)	journal article
Heinovski et al. (2019)	conference paper
Hernández-Melgarejo et al. (2020)	journal article
Herpers et al. (2009)	book chapter
Herpers et al. (2012)	book chapter
Horne et al. (2018)	journal article
Huemer et al. (2022)	journal article
Hurwitz et al. (2019)	project report
Jashami et al. (2022)	conference paper
Kaß et al. (2020)	conference paper
Kaths et al. (2021)	journal article
Kaths et al. (2019)	conference paper
Keler et al. (2021a)	conference paper
Keler et al. (2020)	conference paper
Keler et al. (2019)	conference paper
Keler et al. (2018)	conference paper
Keler et al. (2021b)	journal article
Keler et al. (2021c)	conference paper
Kikuchi et al. (2012)	conference paper
Kingsley et al. (2020)	conference paper
Kuroda et al. (2022)	journal article
Kwigizile et al. (2017)	project report
Kwon et al. (2001)	conference paper
Lee et al. (2017)	conference paper
Lindner et al. (2022)	conference paper
Lindström et al. (2019)	conference paper
Liu et al. (2012)	journal article
Malcolm et al. (2021)	poster presentation
Martinez Garcia (2021)	master thesis
Matviienko et al. (2018) [study 2]	conference paper
Matviienko et al. (2018) [study 2]	conference paper
Michahelles and Wintersberger (2021)	conference paper
Mittelstaedt et al. (2018)	journal article
Nazemi et al. (2021)	journal article
Nazemi et al. (2019b)	conference paper
Nazemi (2020)	doctoral dissertation
Nazemi et al. (2019a)	conference paper
Nikolas et al. (2016)	journal article
Norén (2022)	bachelor thesis
O’Hern et al. (2018)	journal article
O’Hern et al. (2017)	journal article
Plumert et al. (2004)	journal article
Plumert and Kearney (2018)	book chapter
Plumert et al. (2011)	journal article
Al-Kefagy and Pokrajac (2019)	master thesis
Powell (2017)	master thesis
Powell et al. (2018)	journal article
Rakhmatov et al. (2018)	conference paper
Rittenbruch et al. (2020)	conference paper
Schenkel et al. (2020)	conference paper
Schramka et al. (2017)	journal article
Schulzyk et al. (2007)	conference paper
Schulzyk et al. (2009)	conference paper
Schwab and Recuero (2013)	conference paper
Schweidler (2022)	diploma thesis
Scott-Deeter (2021)	master thesis
Shin and Lee (2004)	journal article
Shin and Lee (2002)	conference paper
Shoman and Imine (2021)	journal article
Shoman and Imine (2020a)	conference paper
Shoman and Imine (2020b)	conference paper
Song et al. (2003)	journal article
Stange et al. (2021)	conference paper
Stevens et al. (2013)	journal article
Stevenson et al. (2015)	journal article
Stroh (2016)	bachelor thesis
Sun et al. (2018)	journal article
Thorslund and Lindström (2020)	journal article
Thorslund et al. (2020)	conference paper
Tsuboi et al. (2018)	conference paper
Ullmann et al. (2020)	conference paper
Ullmann et al. (2022)	journal article
van Veen et al. (1998)	journal article
Sawitzky et al. (2020)	conference paper
Sawitzky et al. (2022b)	conference paper
Sawitzky et al. (2022a)	journal article
Wintersberger et al. (2022)	conference paper
Wintersberger et al. (2021)	conference paper
Yamaguchi et al. (2018)	conference paper
Yin and Yin (2007b)	journal article
Yin and Yin (2007a)	journal article