First Line Research Data Management for Life Sciences: a Case Study


 Modern life sciences studies depend on the collection, management and analysis of comprehensive datasets in what has become data-intensive research. Life science research is also characterised by having relatively small groups researchers. This combination performed a few people led to an increasing bottleneck data (RDM). Parallel this, there been urgent call initiatives like FAIR Open Science openly publish which put additional pressure improving quality RDM. Here, we reflect lessons learnt DataHub Maastricht, RDM support group Maastricht University Medical Centre (MUMC+) Netherlands, providing first-line for sciences. operates with core team, complemented disciplinary stewards, many whom have joint positions group. organisational model helps creating shared knowledge between including insights how focus most reusable datasets. shown be very beneficial given limited time personnel. We found that co-hosting tailored platforms specific domains, reducing storage costs implementing tiered promoting cross-institutional collaboration through federated authentication were all effective features stimulate researchers initiate Overall, utilising expertise communication channel embedded stewards was instrumental our success. Looking into future, foresee need further embed role lifeblood organisation, along policies finance long-term data. The latter, remain feasible, needs combined formalising appraisal reappraisal archived data.