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ReferencesCambon-Thomsen Assessing the impact of biobanks. Nat Genet. 2003; 34(1):25 . DOI: 10.1038/ng0503-25bCambon-Thomsen et al The role of a Bioresource Research Impact Factor as an incentive to share human bioresources. Nature Genetics. 2011, 43(6):503. DOI: 10.1038/ng.831Mabile et al. Quantifying the use of bioresources for promoting their sharing in scientific research. GigaScience 2013, 2:7 DOI: 10.1186/2047-217X-2-7Bravo et al Developing a guideline to standardize the citation of bioresources in journal articles (CoBRA). BMC Med. 2015;13(1):266. DOI: https://doi-org/10.1186/s12916-015-0266-yWilkinson et al.The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016 15;3:160018 DOI: 10.1038/sdata.2016.18.David et al., T.R.D., 2020. FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles. Data Science Journal, 19(1), p.32. DOI: http://doi.org/10.5334/dsj-2020-032RDA COVID-19 Working Group. Recommendations and Guidelines on data sharing. Research Data Alliance, 2020. DOI: https://doi.org/10.15497/rda00052.Open Journal of Bioresources (OJB) https://openbioresources.metajnl.com/; International audience; For many decades, the immunogenetics community has been among the frontrunners in sharing data and samples, ensuring a great scientific progress in this domain. International histocompatibility workshops and scientific meetings in immunogenetics societies as EFI and ASHI have largely contributed to this progress. In collaboration with Ubiquity press, the Open Journal of Bioresources (OJB) was created in 2014. Bioresources include not only data but also biological collections. OJB is a peer-reviewed open access journal with short papers describing bioresources dedicated to human biomedical research, providing the authors with a citable informative reference and a DOI (Digital Object Identifier). This is part of the recognition and rewarding of such important work. Finally, the citation of bioresources In journals should follow the format CoBRA as described by Bravo et al (2015). References

The Research Data Alliance (ambassador poster) With over 9000 members from 137 countries, RDA provides neutral space where its members can come together to work towards Research Data Sharing without barriers. The Research Data Alliance (RDA) was launched as a community-driven initiative in 2013 by the European Commission, the United States Government's National Science Foundation and National Institute of Standards and Technology, and the Australian Government’s Department of Innovation with the goal of building the social and technical infrastructure to enable open sharing and re-use of data. RDA has a grass-roots, inclusive approach covering all data lifecycle stages in all science domains, engaging data producers, users and stewards, addressing data exchange, processing, and storage. It has succeeded in creating the neutral social platform where international research data experts meet to exchange views and to agree on topics including social hurdles on data sharing, education and training challenges, data management plans and certification of data repositories, disciplinary and interdisciplinary interoperability, as well as technological aspects., Text mainly from RDA website. www.rd-alliance.org/about-rda

To that aim, a FAIR criteria assessment survey has been launched to seek feedback from the scientific community on intelligible, realistic and human-readable assessment criteria that could help guiding the scientist to follow FAIR practices as much as possible. It should also help the evaluator to objectively achieve his/her task. The criteria to be assessed by the respondents to the survey are organised in 5 groups. In addition to ‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’ criteria we have included ‘Motivations for Sharing’. For each criterion, 4 choices are proposed (‘Never / Not Assessable’; ‘If mandatory only’; ‘Sometimes’; ‘Always’). One choice and only one, must be ticked for each criterion. The final score consists of the sum of the number of each ticked degree compare to the total number of criteria in each group; the ‘sharing motivations’ are appreciated qualitatively in the final interpretation. This survey is aimed at gathering comments to build realistic and usable tools for scientists and evaluators. It will help to construct an evaluation tool for FAIR practices that will enable to consider data sharing activities as a valuable research output and will foster this activity by promoting fairness literacy and dedicated support for data management. The poster will present the first results of the survey. Special emphasis will be put on highlighting the differences in understanding the human readable FAIR compliance criteria between different users (researchers and evaluators, young and senior researchers, IT people and others…). To that aim, a FAIR criteria assessment survey has been launched to seek feedback from the scientific community on intelligible, realistic and human-readable assessment criteria that could help guiding the scientist to follow FAIR practices as much as possible. It should also help the evaluator to objectively achieve his/her task. The criteria to be assessed by the respondents to the survey are organised in 5 groups. In addition to ‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’ criteria we have included ‘Motivations for Sharing’. For each criterion, 4 choices are proposed (‘Never / Not Assessable’; ‘If mandatory only’; ‘Sometimes’; ‘Always’). One choice and only one, must be ticked for each criterion. The final score consists of the sum of the number of each ticked degree compare to the total number of criteria in each group; the ‘sharing motivations’ are appreciated qualitatively in the final interpretation. This survey is aimed at gathering comments to build realistic and usable tools for scientists and evaluators. It will help to construct an evaluation tool for FAIR practices that will enable to consider data sharing activities as a valuable research output and will foster this activity by promoting fairness literacy and dedicated support for data management. The poster will present the first results of the survey. Special emphasis will be put on highlighting the differences in understanding the human readable FAIR compliance criteria between different users (researchers and evaluators, young and senior researchers, IT people and others…).To that aim, a FAIR criteria assessment survey has been launched to seek feedback from the scientific community on intelligible, realistic and human-readable assessment criteria that could help guiding the scientist to follow FAIR practices as much as possible. It should also help the evaluator to objectively achieve his/her task.The criteria to be assessed by the respondents to the survey are organised in 5 groups. In addition to ‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’ criteria we have included ‘Motivations for Sharing’. For each criterion, 4 choices are proposed (‘Never / Not Assessable’; ‘If mandatory only’; ‘Sometimes’; ‘Always’). One choice and only one, must be ticked for each criterion. The final score consists of the sum of the number of each ticked degree compare to the total number of criteria in each group; the ‘sharing motivations’ are appreciated qualitatively in the final interpretation.This survey is aimed at gathering comments to build realistic and usable tools for scientists and evaluators. It will help to construct an evaluation tool for FAIR practices that will enable to consider data sharing activities as a valuable research output and will foster this activity by promoting fairness literacy and dedicated support for data management.The poster will present the first results of the survey. Special emphasis will be put on highlighting the differences in understanding the human readable FAIR compliance criteria between different users (researchers and evaluators, young and senior researchers, IT people and others…).; National audience; The RDA-SHARC (SHAring Reward & Credit) interest group is an interdisciplinary group endorsed by Research Data Alliance. It intends to improve crediting and rewarding mechanisms for scientists who work towards sharing their data for potential reuse. In this perspective, assessing the compliance with FAIR practices and increasing the understanding of FAIRness criteria are critical steps.

The RDA-SHARC (SHAring Reward & Credit) interest group is an interdisciplinary group endorsed by Research Data Alliance. It intends to improve crediting and rewarding mechanisms for scientists who work towards sharing their data for potential reuse. In this perspective, assessing the compliance with FAIR practices and increasing the understanding of FAIRness criteria are critical steps. To that aim, a FAIR criteria assessment survey has been launched to seek feedback from the scientific community on intelligible, realistic and human-readable assessment criteria that could help guiding the scientist to follow FAIR practices as much as possible. It should also help the evaluator to objectively achieve his/her task. The criteria to be assessed by the respondents to the survey are organised in 5 groups. In addition to ‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’ criteria, ‘Motivations for Sharing’ has been included. For each criterion, 4 choices are proposed (‘Never / Not Assessable’; ‘If mandatory only’; ‘Sometimes’; ‘Always’). One choice and only one, must be ticked for each criterion. The final score consists of the sum of the number of each ticked degree compare to the total number of criteria in each group; the ‘sharing motivations’ are appreciated qualitatively in the final interpretation. This survey is aimed at gathering comments to build realistic and usable tools for scientists and evaluators. It will help to construct an evaluation tool for FAIR practices that will enable to consider data sharing activities as a valuable research output and will foster this activity by promoting fairness literacy and dedicated support for data management. The poster will present the first results of the survey. Special emphasis will be put on highlighting the differences in understanding the human readable FAIR compliance criteria between different users (researchers and evaluators, young and senior researchers, IT people and others…)., References Reymonet N et al. Réaliser un plan de gestion de données « FAIR » : modèle, 2018. 〈sic_01690547v2 Wilkinson MD et al. (2018). A design framework and exemplar metrics for FAIRness. Scientific data, 5, 180118. doi:10.1038/sdata.2018.118 Wilkinson MD, The FAIR Guiding Principles for scientific data management and stewardship.Sci Data. 2016 Mar 15;3:160018. doi: 10.1038/sdata.2016.18. E.U. European Commission Directorate-General for Research and Innovation report: Evaluation of Research Careers fully acknowledging Open Science Practices; Rewards, incentives and/or recognition for researchers practicing Open Science. 2017 E.U. European Commission Directorate-General for Research and Innovation report: H2020 Programme Guidelines on FAIR Data Management in Horizon 2020, Version 3.0, 26 July 2016

International audience; The immunogenetics community is characterised by a widespread international collaboration between laboratories with exchange of biological samples and data. However, the researchers who are involved in building and curating bioresources do not always get the credit they deserve and this is an obstacle for sharing practices. Several international initiatives have been taken in order to promote sharing of samples and data. BRIF (Bioresource Research Impact Factor) was initiated in 2003 and intended to develop a framework to enable crediting bioresources that were uniquely and persistently identified. The way of citing bioresources was standardised according to a guideline called CoBRA (Citation of BioResources in journal Articles), published in 2015. This guideline was recommended by the Equator network as well as by other organisations such as GA4GH (Global Alliance for Genomics and Health) and BBMRIERIC, the European infrastructure consortium of biobanking and biomolecular resources. In collaboration with Ubiquity press, the Open Journal of Bioresources (OJB) was created. It is a peer-reviewed open access journal with short papers describing bioresources dedicated to human biomedical research, providing them with a citable reference. However, as the problem of sharing data and resources and the recognition of such activities in research goes beyond biomedical research, we joined the Research Data Alliance (RDA), creating an interest group called ShaRC (Sharing Rewards and Credits) in 2017. This interest group works with use cases from different research areas. The ongoing work concerns operationalisation and evaluation of the sharing activity complying with the so-called FAIR principles, which mean Findable, Accessible, Interoperable and Reusable. The amount of data published in research in all areas is becoming bigger and bigger and it is important that all sorts of data can be reanalysed and possibly be co-analysed with other data by researchers that are not necessarily those who produced the original data. A grid of evaluation is being built by the ShaRC IG and will benefit from inputs at the next RDA meeting in Philadelphia (April 2019). Some of the results could be of particular interest for the Immunogenetics community and the grid will be updated to be presented at EFI.

The SHARC (SHAring Reward & Credit) interest group (IG) is an interdisciplinary group set up in the framework of RDA (Research Data Alliance) to improve crediting and rewarding mechanisms in the sharing process throughout the data life cycle. Notably, one of the objectives is to promote data sharing activities in research assessment schemes at national and European levels. To this aim,the RDA-SHARC IG is developing assessment grids using criteria to establish if data are compliant to the FAIR principles (findable /accessible / interoperable / reusable). The grid is aiming to be extensive, generic and trans-disciplinary. It is meant to be used by evaluators to assess the quality of the sharing practice of the researcher/scientist over a given period, taking into account the means & support available over that period. The grid displays a mind-mapped tree-graph structure based on previous works on FAIR data management (Reymonet et al., 2018; Wilkinson et al., 2016; Wilkinson et al., 2018; and E.U.Guidelines about FAIRness Data Management Plans). The criteria used are based on the work from FORCE 11*, and the Open Science Career Assessment Matrix designed by the EC Working group on Rewards under Open science. The criteria are organised in 5 clusters: ‘Motivations for sharing’; ‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’. For each criterion, 4 graduations are proposed (‘Never / Not Assessable’; ‘If mandatory’; ‘Sometimes’; ‘Always’). Only one value must be selected per criterion. Evaluation should be done by cluster; the final overall assessment will be based on the sum of the number of each ticked value / total number of criteria in each cluster; the ‘motivations for sharing’ should be appreciated qualitatively in the final interpretation. The final goals are to develop a graduated assessment of the researcher FAIRness literacy and help identifying needs to build FAIRness guidelines to improve the sharing capacity of researchers.

Le poster a été déposé dans Zenodo; International audience; The SHARC (SHAring Reward & Credit) interest group (IG) is an interdisciplinary group set up in the framework of RDA (Research Data Alliance) to improve crediting and rewarding mechanisms in the sharing process throughout the data life cycle. Notably, one of the objectives is to promote data sharing activities in research assessment schemes at national and European levels. To this aim, the RDA-SHARC IG is developing assessment grids using criteria to establish if data are compliant to the FAIR principles (findable /accessible / interoperable / reusable).The grid is aiming to be extensive, generic and trans-disciplinary. It is meant to be used by evaluators to assess the quality of the sharing practice of the researcher/scientist over a given period, taking into account the means & support available over that period. The grid displays a mind-mapped tree-graph structure based on previous works on FAIR data management (Reymonet et al., 2018; Wilkinson et al., 2016; Wilkinson et al., 2018; and E.U.Guidelines about FAIRness Data Management Plans). The criteria used are based on the work from FORCE 11*, and the Open Science Career Assessment Matrix designed by the EC Working group on Rewards under Open science. The criteria are organised in 5 clusters: ‘Motivations for sharing’; ‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’. For each criterion, 4 graduations are proposed (‘Never / Not Assessable’; ‘If mandatory’; ‘Sometimes’; ‘Always’). Only one value must be selected per criterion. Evaluation should be done by cluster; the final overall assessment will be based on the sum of the number of each ticked value / total number of criteria in each cluster; the ‘motivations for sharing’ should be appreciated qualitatively in the final interpretation. The final goals are to develop a graduated assessment of the researcher FAIRness literacy and help identifying needs to build FAIRness guidelines to improve the sharing capacity of researchers.

SHARC (SHAring Reward & Credit) est un groupe d'intérêt scientifique interdisciplinaire créé dans le cadre de RDA (Research Data Alliance) dans le but de faciliter le partage des données de recherche (et des ressources) par la valorisation de l'ensemble des activités pré-requises à ce partage, tout au long du cycle de vie des données. Dans ce cadre, un sous-groupe de travail SHARC élabore des grilles d'évaluation des chercheurs afin de mesurer leur niveau de prise en compte des principes FAIR dans la gestion de leurs données. La grille d'évaluation présentée dans ce poster est destinée à être complétée par tout scientifique produisant et / ou utilisant des données. Il s'agit d'un résumé d'une grille d'évaluation plus étendue conçue pour un partage optimal des données (non encore mise en œuvre pour le moment par la plupart des scientifiques). L'évaluation est basée sur les critères de conformité FAIR. Pour remplir cet objectif, la grille affiche le minimum de critères qui doivent absolument être appliqués par les chercheurs pour attester de leur pratique FAIR. Ces critères sont organisés en 5 groupes: «Motivations de partage»; "Trouvable", "Accessible", "Interopérable" et "Réutilisable". Pour chaque critère, 4 degrés d'évaluation sont proposés ("Jamais / Non évaluable"; "Si obligatoire"; "Parfois"; "Toujours"). Au moins un degré mais un seul doit être sélectionné par critère. L'évaluation doit être effectuée pour chaque catégorie F / A / I / R; L'évaluation finale est la somme de chaque degré coché rapportée au nombre total de critères dans chaque catégorie F / A / I / R. Des règles d'interprétation prenant en compte les «motivations du partage» sont proposées.

The RDA-SHARC (SHAring Reward & Credit) interest group is an interdisciplinary volunteer member-based group set up as part of RDA (Research Data Alliance) to unpack and improve crediting and rewarding mechanisms in the sharing process throughout the data life cycle. Background and objectives of this group are reported here. Notably, one of the objectives is to promote the inclusion of data sharing activities in the research (& researchers) assessment scheme at national and European levels. To this aim, the RDA-SHARC-IG is developing two assessment grids using criteria to establish if data are compliant to the F.A.I.R principles (findable /accessible / interoperable / reusable) based on previous works on FAIR data management (Reymonet et al., 2018; Wilkinson et al., 2018; and E.U.Guidelines*): 1/ The self-assessment grid to be used by a scientist as a ‘checklist’ to identify her/his own activities and to pinpoint the hurdles that hinder efficient sharing and reuse of his/her data by all potential users. 2/ The two-level grid (quick/extensive) to be used by the evaluator to assess the quality of the researcher/scientist sharing practice, over a given period, taking into account the means & support available over that period. Assessment criteria are classified according their importance with regards to FAIRness (essential / recommended / desirable) meanwhile good practices are recommended for critical steps. To implement a highly fair assessment of the sharing process, appropriate criteria must be selected in order to design optimal generic assessment grids. This process requires participation, time and input from volunteer scientists data producers/users from various fields.

International audience; Vascular smooth muscle cells (SMC) play an important role in the pathophysiology of transplant vasculopathy (TV), a major cause of late death in patients receiving an organ transplant. In this review we describe the proliferative effect in vitro and in vivo of HLA class I antibodies on human SMC. We have developed an experimental model using segments of human mesenteric arteries transplanted in the position of the infrarenal aorta in immunodeficient mice (SCID/beige). Weekly injections of transplanted mice with a monoclonal antibody towards HLA class I provoked typical lesions of TV after 6 weeks in the human graft while transplanted mice receiving an irrelevant antibody did not develop any significant lesion. In vitro, the anti-HLA antibodies were mitogenic to SMC and we showed that they activate a stress-induced signaling pathway implicating matrix metalloproteinases (MMP) and neutral sphingomyelinase 2 (nSMase-2). The proliferative effect of anti-HLA antibodies could be blocked by pharmacological inhibitors or by siRNA. Administration of pharmacological inhibitors diminished the development of TV in grafted mice injected with anti-HLA antibodies demonstrating an important role of the MMP/nSMase-2 pathway in antibody-induced TV. This observation opens new perspectives for the management of TV in clinical settings.

International audience; Rheumatoid arthritis (RA) is a common autoimmune disease with a strong genetic component. Numerous aberrant immune responses have been described during the evolution of the disease. In later years, the appearance of anti-citrullinated protein antibodies (ACPAs) has become a hallmark for the diagnosis and prognosis of RA. The post-translational transformation of arginine residues of proteins and peptides into citrulline (citrullination) is a natural process in the body, but for unknown reasons autoreactivity towards citrullinated residues may develop in disposed individuals. ACPAs are often found years before clinical manifestations. ACPAs are present in about 70% of RA patients and constitute an important disease marker, distinguishing patient groups with different prognoses and different responses to various treatments. Inside the human leukocyte antigen (HLA) region, some HLA-DRB1 alleles are strongly associated with their production. Genome-wide association studies in large patient cohorts have defined a great number of single nucleotide polymorphisms (SNPs) outside of the HLA region that are associated with ACPA positive (ACPA+) RA. The SNPs are generally located close to or within genes involved in the immune response or signal transduction in immune cells. Some environmental factors such as tobacco smoking are also positively correlated with ACPA production. In this review, we will describe the genes and loci associated with ACPA+ RA or ACPA- RA and attempt to clarify their potential role in the development of the disease.

SCENIHR (Scientific Committee on Emerging and Newly Identified Health Risks): et al., The Scientific Committee on Emerging and Newly Identified Health Risks (SCENIHR) has been asked to evaluate the role of tobacco additives in the addictiveness and attractiveness of tobacco products. The criteria for dependence established in humans indicate that tobacco has a high addictive potential, but it remains difficult to assess the addictiveness of individualadditives. In animal studies the addictive potency of the final tobacco product cannot be assessed. The reinforcing potency of drugs is measured after intravenous injections and suggests that the abuse liability of pure nicotine is weaker than the addictive potential of tobacco products in humans. The currently used methods to define addictiveness of nicotine and additives are thus not considered adequate. In humans, the positive correlation between tobacco consumption and dependence suggests that individuals with high nicotine levels in their blood are more dependent. In animal studies using self-administration, an inverted U-shaped dose-response curve has generally been revealed suggesting that the addictiveness of nicotine is not directly linearwith the dose. There is however substantial variation in the response to nicotine in both animals and humans, and genetic factors probably play an important role. No tobacco additives which are addictive by themselves have so far been identified. However, sugars, polysaccharides and cellulose fibres which are naturally present in tobacco, or sugars added in high quantities to most tobacco products, give rise to numerous aldehydes, such as acetaldehyde, in tobacco smoke. Acetaldehyde given intravenously is self-administered and enhances the addictiveness of nicotine in experimental animals. Additives that facilitate deeper inhalation (e.g. menthol) or inhibit the metabolism of nicotine may enhance the addictiveness of nicotine indirectly. Substances such as ammonia that increase the pH of the tobacco and the smoke, result in higher amounts of uncharged nicotine. However, it is uncertain if more nicotine isabsorbed with higher smoke pH. For smokeless tobacco it seems that an increased pH enhances nicotine absorption in the mouth., The methods used to quantify the addictive potency of additives have limitations because of technical challenges in experimentally manipulating the presence or absence of anadditive in a tobacco product. Such experiments require large technical and financial resources. In addition, there are ethical issues if testing in humans is considered. Due to these limitations, the available methodologies are not considered adequate. A number of technical characteristics of cigarettes (paper, filter, packing, geometry) influence the content of different substances in the smoke and the size of smoke particles. Many smokers compensate for a lower dose of nicotine by increasing puff volume and frequency, and by deeper inhalation. The particle size of the smoke aerosol does not seem to substantially influence the exposure to nicotine. The technical characteristics of cigarettes may thus modulate smoking behaviour but it is uncertain if this leads to a higher risk of addiction. Attractiveness is defined as the stimulation to use a product. The attractiveness of tobacco products may be increased by a number of additives but is also influenced by external factors such as marketing, price etc. Animal models do not currently exist for the assessment of attractiveness. In humans, the attractiveness of individual tobaccoproducts may be compared in panel studies, surveys, and by experimental measures. Another method is to experimentally adjust tobacco products to exclude or include individual additives and test responses to them. However, this type of research is difficult due to ethical considerations that will usually preclude human testing of tobacco products, particularly among non-users or children. The use of fruit and candy flavours seems to favour smoking initiation in young people. Menthol also attracts a number of smokers, in particular African Americans. Some additives decrease the harshness and increase the smoothness of the smoke. Certain additives yield a full and white smoke and other additives reduce the lingering odour of the smoke in order to favour the acceptability of smoking to people around. Additives considered attractive may in principle lead to brand preference or a higher consumption of tobacco products. However, it remains difficult to distinguish the direct effects of these additives from indirect effects such as the marketing towards specific groups.