Centering inclusivity in the design of online conferences: An OHBM-Open Science perspective

As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data

Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavor is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants' privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU general data protection regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.Development of the BIDS Standard has been supported by the International Neuroinformatics Coordinating Facility, Laura and John Arnold Foundation, National Institutes of Health (R24MH114705, R24MH117179, R01MH126699, R24MH117295, P41EB019936, ZIAMH002977, R01MH109682, RF1MH126700, R01EB020740), National Science Foundation (OAC-1760950, BCS-1734853, CRCNS-1429999, CRCNS-1912266), Novo Nordisk Fonden (NNF20OC0063277), French National Research Agency (ANR-19-DATA-0023, ANR 19-DATA-0021), Digital Europe TEF-Health (101100700), EU H2020 Virtual Brain Cloud (826421), Human Brain Project (SGA2 785907, SGA3 945539), European Research Council (Consolidator 683049), German Research Foundation (SFB 1436/425899996), SFB 1315/327654276, SFB 936/178316478, SFB-TRR 295/424778381), SPP Computational Connectomics (RI 2073/6-1, RI 2073/10-2, RI 2073/9-1), European Innovation Council PHRASE Horizon (101058240), Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative, ERAPerMed Pattern-Cog, and the Virtual Research Environment at the Charité Berlin – a node of EBRAINS Health Data Cloud.N

The past, present, and future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS

Facteurs associés à la prévalence des troubles musculo-squelettiques en milieu hospitalier: Recherche des éléments à prendre en compte pour l’évaluation des risques

National audienceAim of the studyTo use a questionnaire to assess the prevalence of Musculo-Skeletal Disorders (MSDs) among hospital staff, and to investigate relevant underlying factors, in particular psychosocial and organizational considerations (POCs). The ultimate aim is to establish a methodological basis for risk evaluation.MethodHorizontal survey of 403 staff members attending the Occupational Health Department of Grenoble University Hospital for a routine medical consultation. Items. Socioprofessional characteristics; localization of any pain experienced over the previous twelve months; degree of physical effort (three items); and POCs (decision latitude, psychological demands, social support and recognition). The risk of MSDs was analyzed by logistical regression adjusted for gender, position and - when possible - on length of employment in the hospital.ResultsThe prevalence of reported MSDs was between 52% and 20.6% depending on the anatomical location. MSDs are significantly more common among nurses than in other occupations (p < 0.05). For staff members who had been working in the hospital for less than fifteen years, factors that were significantly associated with MSPs (p < 0.05) were being a nurse (OR = 3.16 [1.46; 6.84]), and high demands, both physical (OR = 4.03 [1.89; 8.62]) and psychological (OR = 3.11 [1.45; 6.70]). For staff members who had been working for fifteen or more years in the hospital, the factors that were significantly associated with MSDs (p < 0.05) were inadequate social support at work (OR = 2.61 [1.00; 6.80]). and the absence of professional recognition of their contribution (OR = 2.67 [1.12; 6. 37]).ConclusionThis study detected a high prevalence of MSDs among hospital staff and shows that, for the evaluation of the risk of MSDs, a simple questionnaire can be used to assess not only physical effort but also POCs.ObjectifsEstimer, avec un autoquestionnaire simple, la prévalence des troubles musculo-squelettiques (TMS) dans le personnel hospitalier et étudier les facteurs liés aux TMS, plus particulièrement les contraintes psycho-sociales et organisationnelles (CPO), afin de proposer les bases d’une méthodologie d’évaluation des risques.MéthodeEtude transversale, menée sur 403 agents venus en visite médicale systématique de médecine du travail au CHU de Grenoble. Recueil : caractéristiques socioprofessionnelles, localisation des épisodes douloureux au cours des 12 derniers mois, contraintes physiques (3 items) et CPO (latitude de décision, demande psychologique, soutien social et reconnaissance). Le risque de TMS est étudié par régression logistique ajustée sur le sexe, la fonction et, quand cela était possible, sur l’ancienneté au CHU.RésultatsLa prévalence des TMS déclarés était entre 52 % et 20,6 % selon la localisation anatomique. La prévalence des TMS, est significativement plus élevée chez les soignants que dans les autres groupes professionnels (p < 0,05). Pour les agents ayant moins de 15 ans d’ancienneté au sein de l’établissement, les facteurs significativement liés aux TMS (p < 0,05) étaient la fonction soignant (OR = 3,16 [1,46 ; 6,84]), le niveau élevé de contrainte physique (OR = 4,03 [1,89 ; 8,62]) et de demande psychologique (OR = 3,11 [1,45 ; 6,70]). Pour les agents ayant 15 ans et plus d’ancienneté au sein de l’établissement, les facteurs significativement liés aux TMS (p < 0,05) étaient le faible niveau de soutien social au travail (OR = 2,61 [1,00 ; 6,80]). et l’absence de reconnaissance professionnelle du travail effectué (OR = 2,67 [1, 12 ; 6, 37]).ConclusionCette étude met en évidence la forte prévalence des TMS au sein de la population des professionnels hospitaliers. Cette étude montre que, pour évaluer les risques de TMS, il est possible de prendre en compte avec un questionnaire simple les contraintes physiques mais aussi les CPO

Facteurs associés à la prévalence des TMS en milieu de soins. Recherche des éléments à prendre en compte pour l’évaluation des risques

National audienc

Sharing brain mapping statistical results with the neuroimaging data model.

Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress