Eleven strategies for making reproducible research and open science training the norm at research institutions

Across disciplines, researchers increasingly recognize that open science and reproducible research practices may accelerate scientific progress by allowing others to reuse research outputs and by promoting rigorous research that is more likely to yield trustworthy results. While initiatives, training programs, and funder policies encourage researchers to adopt reproducible research and open science practices, these practices are uncommon inmanyfields. Researchers need training to integrate these practicesinto their daily work. We organized a virtual brainstorming event, in collaboration with the German Reproducibility Network, to discuss strategies for making reproducible research and open science training the norm at research institutions. Here, weoutline eleven strategies, concentrated in three areas:(1)offering training, (2)adapting research assessment criteria and program requirements, and (3) building communities. We provide a brief overview of each strategy, offer tips for implementation,and provide links to resources. Our goal is toencourage members of the research community to think creatively about the many ways they can contribute and collaborate to build communities,and make reproducible research and open sciencetraining the norm. Researchers may act in their roles as scientists, supervisors, mentors, instructors, and members of curriculum, hiring or evaluation committees. Institutionalleadership and research administration andsupport staff can accelerate progress by implementing change across their institution

Eleven strategies for making reproducible research and open science training the norm at research institutions

Across disciplines, researchers increasingly recognize that open science and reproducible research practices may accelerate scientific progress by allowing others to reuse research outputs and by promoting rigorous research that is more likely to yield trustworthy results. While initiatives, training programs, and funder policies encourage researchers to adopt reproducible research and open science practices, these practices are uncommon inmanyfields. Researchers need training to integrate these practicesinto their daily work. We organized a virtual brainstorming event, in collaboration with the German Reproducibility Network, to discuss strategies for making reproducible research and open science training the norm at research institutions. Here, weoutline eleven strategies, concentrated in three areas:(1)offering training, (2)adapting research assessment criteria and program requirements, and (3) building communities. We provide a brief overview of each strategy, offer tips for implementation,and provide links to resources. Our goal is toencourage members of the research community to think creatively about the many ways they can contribute and collaborate to build communities,and make reproducible research and open sciencetraining the norm. Researchers may act in their roles as scientists, supervisors, mentors, instructors, and members of curriculum, hiring or evaluation committees. Institutionalleadership and research administration andsupport staff can accelerate progress by implementing change across their institution

Learning of experimental groups.

<p>(A) Change in wrist movement over the time course of learning split by experimental group. The horizontal lines at ± 5° indicate the learning goals for each group. Error bars denote SEM across participants. T-tests for comparison each group mean vs. 0 are indicated in group color, between group comparisons in black: * <i>p</i> ≤ .05; ** <i>p</i> ≤ .01. (B) Joint space trajectories for an exemplary learning participant of the extension group. The lines illustrate the mean trajectories for blocks B0, B2, B3, B4, B7 and B9. The initial and desired target configurations are indicated on the target solution manifold. (C) Actual visual noise added over the time course of learning split by experimental group. Error bars denote SEM across participants. (D) Percentage of rewarded trials over the time course of learning split by experimental group. Error bars denote SEM across participants.</p

Basic movement kinematics.

<p><b>(A)</b> Changes in the angle of shoulder, elbow and wrist joints during the baseline block, averaged across experimental groups. Angles are express relative to start configuration. (<b>B</b>) Angular velocities of shoulder, elbow and wrist joints. (<b>C</b>) Tangential velocity of the endpoint during baseline block. All data are averaged across experimental groups. Trajectories were aligned to the onset of movement (time = 0ms). Shaded areas denote the SEM across participants.</p

Task geometry.

<p>(A, B) Schematic illustration of the setup. (A) Top view on a participant in the redundant task environment with an exemplary end position demonstrating a flexed wrist joint configuration (<b><i>θ</i></b>_{<b><i>wrist</i></b>} = 160°). (B) Exemplary end position demonstrating an extended wrist joint configuration (<b><i>θ</i></b>_{<b><i>wrist</i></b>} = 210°). (C) Start and target positions defined a solution manifold in joint space (black lines). For any joint configuration along this line the effector endpoint, i.e. the fingertip, remained at the same position. The black dot on the start line represents the enforced start configuration (<b><i>θ</i></b>_{<b><i>wrist</i></b>} = 175°) and the two connection lines represent two possible joint trajectories to the flexed (bottom) and extended (top) end configurations from panels A and B. (D) Illustration of the mean absolute visual reaching error in cm (distance between visual target and cursor feedback) as a function of wrist and elbow angles for a fixed shoulder angle. The visual reaching error was here simulated for each wrist angle by drawing visual noise 100.000 times from a standard distribution with zero mean and SD as a function of wrist angle (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180803#pone.0180803.e002" target="_blank">Eq 2</a>). The black dot indicates the average elbow and wrist angle of the participant during the baseline block for trials that were on target. In the extension group, increased wrist flexion is penalized by added error; in the flexion group, wrist extension is penalized. The thin black lines illustrate the actual reaching error without the visual noise (absolute distance between visual target and actual hand position). Note that the solution manifold bends beneath the depicted plane for wrist and elbow angles further away from the baseline angles giving the error zones an ellipsoid instead of striped appearance.</p

Behavior of control groups.

<p>Mean change in relative wrist angle over time with signed values (flexion negative, extension positive) for the low-noise control group and high-noise control group. Error bars denote SEM across participants. Horizontal lines indicate the learning goals for the two experimental groups depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180803#pone.0180803.g003" target="_blank">Fig 3</a>.</p

Characteristics and opinions of MD-PhD students and graduates from different European countries: a study from the European MD-PhD Association.

BACKGROUND MD-PhD programmes throughout the world provide a platform for medical trainees to commit to a physician-scientist career, qualifying with both a medical degree (MD or equivalent) and Doctor of Philosophy (PhD). However, there are limited studies assessing the characteristics of MD-PhD programmes in Europe and the outcomes of MD-PhD students and graduates. PURPOSE This study aims at a first country-wise exploration of characteristics, opinions, and academic outcomes of MD-PhD students and graduates in Europe. METHODS Two questionnaires were developed to assess the demographics, MD-PhD programme characteristics, opinions, future career paths and academic outcomes of European MD-PhD students and graduates. An online survey of 278 MD-PhD students and 121 MD-PhD graduates from nine and six European countries, respectively, was completed between April 2016 and December 2017. The country-wise categorical responses were then compared through chi-square analysis followed by multiple logistic regression. RESULTS Responses from 266 MD-PhD students and 117 MD-PhD graduates were considered valid. Significant country-wise differences (p &lt;0.05) were observed for age group, resident status, clinical time allocation, duration of studies, sources of funding, publications, average impact factor of the journals in which the research was published, satisfaction with the duration of MD-PhD studies and future career choices of MD-PhD students. Responses related to self-perception about clinical and research competence and challenges faced during MD-PhD training did not show a significant country-wise difference. CONCLUSION The MD-PhD workforce in Europe is highly diverse in their demographics, programme characteristics and career paths but does not differ in opinions related to the challenges faced. The results of this study may be helpful for implementation and improvement of MD-PhD programmes

Comorbidities, biomarkers and cause specific mortality in patients with irritable bowel syndrome: A phenome-wide association study

BackgroundIrritable bowel syndrome (IBS) is one of the most common functional digestive disorders. Our understanding about its comorbidities, biomarkers, or long-term risks is still incomplete. ObjectiveTo characterize comorbidities and biomarkers for IBS and establish the effect of IBS on overall- and cause specific mortality. MethodsWe analyzed data from the population-based cohort of the UK Biobank (UKB) with 493,974 participants, including self-reported physician-diagnosed (n = 20,603) and ICD-10 diagnosed (n = 7656) IBS patients, with a mean follow-up of 11 years. We performed a phenome-wide association study (PheWAS) and competing risk analysis to characterize common clinical features in IBS patients. ResultsIn PheWAS analyses, 260 PheCodes were significantly overrepresented in self-reported physician-diagnosed IBS patients, 633 in patients with ICD-10 diagnosed IBS (ICD-10-IBS), with 221 (40%) overlapping. In addition to gastrointestinal diseases, psychiatric, musculoskeletal, and endocrine/metabolic disorders represented the most strongly associated PheCodes in IBS patients. Self-reported physician-diagnosed IBS was not associated with increased overall mortality and the risk of death from cancer was decreased (hazard ratio [HR] = 0.78 [95% CI = 0.7-0.9]). Lastly, we evaluated changes in serum metabolites in IBS patients and identified glycoprotein acetyls (GlycA) as a potential biomarker in IBS. One standard deviation increase in GlycA raised the risk of self-reported IBS/ICD-10 coded by 9%-20% (odds ratio [OR] = 1.09 [95% CI = 1.1-1.1]/OR = 1.20 [95% CI = 1.1-1.3]) and the risk of overall mortality in ICD-10-IBS patients by 28% (HR = 1.28 [95% CI = 1.1-1.5]). ConclusionOur large-scale association study determined IBS patients having an increased risk of several different comorbidities and that GlycA was increased in IBS patients

Minimizing endpoint variability through reinforcement learning during reaching movements involving shoulder, elbow and wrist

Reaching movements are comprised of the coordinated action across multiple joints. The human skeleton is redundant for this task because different joint configurations can lead to the same endpoint in space. How do people learn to use combinations of joints that maximize success in goal-directed motor tasks? To answer this question, we used a 3-degree-of-freedom manipulandum to measure shoulder, elbow and wrist joint movements during reaching in a plane. We tested whether a shift in the relative contribution of the wrist and elbow joints to a reaching movement could be learned by an implicit reinforcement regime. Unknown to the participants, we decreased the task success for certain joint configurations (wrist flexion or extension, respectively) by adding random variability to the endpoint feedback. In return, the opposite wrist postures were rewarded in the two experimental groups (flexion and extension group). We found that the joint configuration slowly shifted towards movements that provided more control over the endpoint and hence higher task success. While the overall learning was significant, only the group that was guided to extend the wrist joint more during the movement showed substantial learning. Importantly, all changes in movement pattern occurred independent of conscious awareness of the experimental manipulation. These findings suggest that the motor system is generally sensitive to its output variability and can optimize joint-space solutions that minimize task-relevant output variability. We discuss biomechanical biases (e.g. joint’s range of movement) that could impose hurdles to the learning process