"Be sustainable": EOSC-Life recommendations for implementation of FAIR principles in life science data handling.

The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.

Sharing sensitive data in life sciences: an overview of centralized and federated approaches.

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

An assessment of the informative value of data sharing statements in clinical trial registries.

BACKGROUND: The provision of data sharing statements (DSS) for clinical trials has been made mandatory by different stakeholders. DSS are a device to clarify whether there is intention to share individual participant data (IPD). What is missing is a detailed assessment of whether DSS are providing clear and understandable information about the conditions for data sharing of IPD for secondary use. METHODS: A random sample of 200 COVID-19 clinical trials with explicit DSS was drawn from the ECRIN clinical research metadata repository. The DSS were assessed and classified, by two experienced experts and one assessor with less experience in data sharing (DS), into different categories (unclear, no sharing, no plans, yes but vague, yes on request, yes with specified storage location, yes but with complex conditions). RESULTS: Between the two experts the agreement was moderate to substantial (kappa=0.62, 95% CI [0.55, 0.70]). Agreement considerably decreased when these experts were compared with a third person who was less experienced and trained in data sharing ("assessor") (kappa=0.33, 95% CI [0.25, 0.41]; 0.35, 95% CI [0.27, 0.43]). Between the two experts and under supervision of an independent moderator, a consensus was achieved for those cases, where both experts had disagreed, and the result was used as "gold standard" for further analysis. At least some degree of willingness of DS (data sharing) was expressed in 63.5% (127/200) cases. Of these cases, around one quarter (31/127) were vague statements of support for data sharing but without useful detail. In around half of the cases (60/127) it was stated that IPD could be obtained by request. Only in in slightly more than 10% of the cases (15/127) it was stated that the IPD would be transferred to a specific data repository. In the remaining cases (21/127), a more complex regime was described or referenced, which could not be allocated to one of the three previous groups. As a result of the consensus meetings, the classification system was updated. CONCLUSION: The study showed that the current DSS that imply possible data sharing are often not easy to interpret, even by relatively experienced staff. Machine based interpretation, which would be necessary for any practical application, is currently not possible. Machine learning and / or natural language processing techniques might improve machine actionability, but would represent a very substantial investment of research effort. The cheaper and easier option would be for data providers, data requestors, funders and platforms to adopt a clearer, more structured and more standardised approach to specifying, providing and collecting DSS. TRIAL REGISTRATION: The protocol for the study was pre-registered on ZENODO ( https://zenodo.org/record/7064624#.Y4DIAHbMJD8 ).

Medical journal requirements for clinical trial data sharing: Ripe for improvement.

Florian Naudet and co-authors discuss strengthening requirements for sharing clinical trial data.

Promoting stigma coping and empowerment in patients with schizophrenia and depression: results of a cluster-RCT.

There is a need for interventions supporting patients with mental health conditions in coping with stigma and discrimination. A psycho-educational group therapy module to promote stigma coping and empowerment (STEM) was developed and tested for efficacy in patients with schizophrenia or depression. 30 clinical centers participated in a cluster-randomized clinical trial, representing a broad spectrum of mental health care settings: in-patient (acute treatment, rehabilitation), out-patient, and day-hospitals. As randomized, patients in the intervention group clusters/centers received an illness-specific eight sessions standard psychoeducational group therapy plus three specific sessions on stigma coping and empowerment ('STEM'). In the control group clusters the same standard psychoeducational group therapy was extended to 11 sessions followed by one booster session in both conditions. In total, N = 462 patients were included in the analysis (N = 117 with schizophrenia spectrum disorders, ICD-10 F2x; N = 345 with depression, ICD-10 F31.3-F31.5, F32-F34, and F43.2). Clinical and stigma-related measures were assessed before and directly after treatment, as well as after 6 weeks, 6 months, and 12 months (M12). Primary outcome was improvement in quality of life (QoL) assessed with the WHO-QOL-BREF between pre-assessment and M12 analyzed by mixed models and adjusted for pre-treatment differences. Overall, QoL and secondary outcome measures (symptoms, functioning, compliance, internalized stigma, self-esteem, empowerment) improved significantly, but there was no significant difference between intervention and control group. The short STEM module has proven its practicability as an add-on in different settings in routine mental health care. The overall increase in empowerment in both, schizophrenia and depression, indicates patients' treatment benefit. However, factors contributing to improvement need to be explored.The study has been registered in the following trial registers. ClinicalTrials.gov: https://register.clinicaltrials.gov/ Registration number: NCT01655368. DRKS: https://www.drks.de/drks_web/ Registration number: DRKS00004217.

Orchestrating differential data access for translational research: a pilot implementation.

BACKGROUND: Translational researchers need robust IT solutions to access a range of data types, varying from public data sets to pseudonymised patient information with restricted access, provided on a case by case basis. The reason for this complication is that managing access policies to sensitive human data must consider issues of data confidentiality, identifiability, extent of consent, and data usage agreements. All these ethical, social and legal aspects must be incorporated into a differential management of restricted access to sensitive data. METHODS: In this paper we present a pilot system that uses several common open source software components in a novel combination to coordinate access to heterogeneous biomedical data repositories containing open data (open access) as well as sensitive data (restricted access) in the domain of biobanking and biosample research. Our approach is based on a digital identity federation and software to manage resource access entitlements. RESULTS: Open source software components were assembled and configured in such a way that they allow for different ways of restricted access according to the protection needs of the data. We have tested the resulting pilot infrastructure and assessed its performance, feasibility and reproducibility. CONCLUSIONS: Common open source software components are sufficient to allow for the creation of a secure system for differential access to sensitive data. The implementation of this system is exemplary for researchers facing similar requirements for restricted access data. Here we report experience and lessons learnt of our pilot implementation, which may be useful for similar use cases. Furthermore, we discuss possible extensions for more complex scenarios.

Bayesian evidence synthesis for exploring generalizability of treatment effects: a case study of combining randomized and non-randomized results in diabetes.

In this paper, we present a unified modeling framework to combine aggregated data from randomized controlled trials (RCTs) with individual participant data (IPD) from observational studies. Rather than simply pooling the available evidence into an overall treatment effect, adjusted for potential confounding, the intention of this work is to explore treatment effects in specific patient populations reflected by the IPD. In this way, by collecting IPD, we can potentially gain new insights from RCTs' results, which cannot be seen using only a meta-analysis of RCTs. We present a new Bayesian hierarchical meta-regression model, which combines submodels, representing different types of data into a coherent analysis. Predictors of baseline risk are estimated from the individual data. Simultaneously, a bivariate random effects distribution of baseline risk and treatment effects is estimated from the combined individual and aggregate data. Therefore, given a subgroup of interest, the estimated treatment effect can be calculated through its correlation with baseline risk. We highlight different types of model parameters: those that are the focus of inference (e.g., treatment effect in a subgroup of patients) and those that are used to adjust for biases introduced by data collection processes (e.g., internal or external validity). The model is applied to a case study where RCTs' results, investigating efficacy in the treatment of diabetic foot problems, are extrapolated to groups of patients treated in medical routine and who were enrolled in a prospective cohort study.

Clinical Trial Information Mediator.

For research in biomedical sciences, cross-domain searches through several different databases are an increasingly necessary task that often becomes a time consuming and labour-intense process. This is especially the case when different domain databases have to be combined, for example combined searches in clinical trials registries, publication databases and research databases. The Clinical Trial Information Mediator (CTIM) addresses this problem and offers a novel way for the combined search in ClinicalTrials.gov, PubMed and BioSamples. CTIM was developed based on a requirements analysis and implemented using open source technology. A search engine with a graphical user interface was developed in order to search linked data in the three databases ClinicalTrials.gov, PubMed and BioSamples; thereby enabling CTIM to bridge the gap between different knowledge domains of clinical trials, publications of research results and biosamples/genetic information. CTIM was applied in three use cases demonstrating that information retrieval could be considerably improved in sense for complex queries. These use cases show that more relevant results were obtained and more associated publications and biosamples could be retrieved in comparison to a separate single search. Main advantages of CTIM are identifying related information between clinical trials and publications employing a clinical trial centred kind of search, simplified access to its databases and thus reduced search time. In addition it can be used by researchers without prior training because of the intuitive usage.

Legal assessment tool (LAT): an interactive tool to address privacy and data protection issues for data sharing.

BACKGROUND: In an unprecedented rate data in the life sciences is generated and stored in many different databases. An ever increasing part of this data is human health data and therefore falls under data protected by legal regulations. As part of the BioMedBridges project, which created infrastructures that connect more than 10 ESFRI research infrastructures (RI), the legal and ethical prerequisites of data sharing were examined employing a novel and pragmatic approach. METHODS: We employed concepts from computer science to create legal requirement clusters that enable legal interoperability between databases for the areas of data protection, data security, Intellectual Property (IP) and security of biosample data. We analysed and extracted access rules and constraints from all data providers (databases) involved in the building of data bridges covering many of Europe's most important databases. These requirement clusters were applied to five usage scenarios representing the data flow in different data bridges: Image bridge, Phenotype data bridge, Personalised medicine data bridge, Structural data bridge, and Biosample data bridge. A matrix was built to relate the important concepts from data protection regulations (e.g. pseudonymisation, identifyability, access control, consent management) with the results of the requirement clusters. An interactive user interface for querying the matrix for requirements necessary for compliant data sharing was created. RESULTS: To guide researchers without the need for legal expert knowledge through legal requirements, an interactive tool, the Legal Assessment Tool (LAT), was developed. LAT provides researchers interactively with a selection process to characterise the involved types of data and databases and provides suitable requirements and recommendations for concrete data access and sharing situations. The results provided by LAT are based on an analysis of the data access and sharing conditions for different kinds of data of major databases in Europe. CONCLUSIONS: Data sharing for research purposes must be opened for human health data and LAT is one of the means to achieve this aim. In summary, LAT provides requirements in an interactive way for compliant data access and sharing with appropriate safeguards, restrictions and responsibilities by introducing a culture of responsibility and data governance when dealing with human data.

Second-Line Treatment of Advanced Urothelial Cancer with Paclitaxel and Everolimus in a German Phase II Trial (AUO Trial AB 35/09).

OBJECTIVE: The efficacy of second-line treatment after failure of platinum-based chemotherapy in patients with advanced urothelial cancer is limited. Based on encouraging preclinical and clinical phase I data, we evaluated the safety and efficacy of the combination of paclitaxel and everolimus in these patients. METHODS: In this trial, patients having failed to respond to prior platinum-based combination treatment of urothelial cancer were treated with paclitaxel (175 mg/m(2) i.v., 3-weekly) and the mTOR-inhibitor everolimus (10 mg p.o., once daily). The patients were treated until tumor progression or until a maximum of 6 cycles was completed. A one-stage design was used to evaluate the objective response rate (ORR) as the primary endpoint. RESULTS: A total of 27 patients (67% male; median age 63 years) were enrolled. The most frequent grade III/IV toxicities were anemia (28%), peripheral neuropathy (28%), and fatigue (24%). No treatment-related deaths were reported. Complete and partial remissions were observed in 0/24 and 3/24 patients eligible for efficacy analysis, respectively (ORR 13%). Progression-free survival was 2.9 months [95% confidence interval (95% CI) 1.9-4.2], and the median overall survival was 5.6 months (95% CI 4.8-10.2). CONCLUSION: The combination of paclitaxel and everolimus has not achieved the expected efficacy in second-line treatment of urothelial cancer and should not be further explored.

Using electronic health records for clinical research: the case of the EHR4CR project.

OBJECTIVES: To describe the IMI EHR4CR project which is designing and developing, and aims to demonstrate, a scalable, widely acceptable and efficient approach to interoperability between EHR systems and clinical research systems. METHODS: The IMI EHR4CR project is combining and extending several previously isolated state-of-the-art technical components through a new approach to develop a platform for reusing EHR data to support medical research. This will be achieved through multiple but unified initiatives across different major disease areas (e.g. cardiovascular, cancer) and clinical research use cases (protocol feasibility, patient identification and recruitment, clinical trial execution and serious adverse event reporting), with various local and national stakeholders across several countries and therefore under various legal frameworks. RESULTS: An initial instance of the platform has been built, providing communication, security and terminology services to the eleven participating hospitals and ten pharmaceutical companies located in seven European countries. Proof-of-concept demonstrators have been built and evaluated for the protocol feasibility and patient recruitment scenarios. The specifications of the clinical trial execution and the adverse event reporting scenarios have been documented and reviewed. CONCLUSIONS: Through a combination of a consortium that brings collectively many years of experience from previous relevant EU projects and of the global conduct of clinical trials, of an approach to ethics that engages many important stakeholders across Europe to ensure acceptability, of a robust iterative design methodology for the platform services that is anchored on requirements of an underlying Service Oriented Architecture that has been designed to be scalable and adaptable, EHR4CR could be well placed to deliver a sound, useful and well accepted pan-European solution for the reuse of hospital EHR data to support clinical research studies.

Extension of the primary care research object model (PCROM) as clinical research information model (CRIM) for the "learning healthcare system".

BACKGROUND: Patient data from general practices is already used for many types of epidemiological research and increasingly, primary care systems to facilitate randomized clinical trials. The EU funded project TRANSFoRm aims to create a "Learning Healthcare System" at a European level that is able to support all types of research using primary care data, to recruit patients and follow patients in clinical studies and to improve diagnosis and therapy. The implementation of such a Learning Healthcare System needs an information model for clinical research (CRIM), as an informational backbone to integrate aspects of primary care with clinical trials and database searches. METHODS: Workflow descriptions and corresponding data objects of two clinical use cases (Gastro-Oesophageal Reflux Disease and Type 2 Diabetes) were described in UML activity diagrams. The components of activity diagrams were mapped to information objects of PCROM (Primary Care Research Object Model) and BRIDG (Biomedical Research Integrated Domain Group) and evaluated. The class diagram of PCROM was adapted to comply with workflow descriptions. RESULTS: The suitability of PCROM, a primary care information model already used for clinical trials, to act as an information model for TRANSFoRm was evaluated and resulted in its extension with 14 new information object types, two extensions of existing objects and the introduction of two new high-ranking concepts (CARE area and ENTRY area). No PCROM component was redundant. Our result illustrates that in primary care based research an important but underestimated portion of research activity takes place in the area of care (e.g. patient consultation, screening, recruitment and response to adverse events). The newly introduced CARE area for care-related research activities accounts for this shift and includes Episode of Care and Encounter as two new basic elements. In the ENTRY area different aspects of data collection were combined, including data semantics for observations, assessment activities, intervention activities and patient reporting to enable case report form (CRF) based data collection combined with decision support. CONCLUSIONS: Research with primary care data needs an extended information model that covers research activities at the care site which are characteristic for primary care based research and the requirements of the complicated data collection processes.

Assessing the impact of sex on high-frequency repetitive transcranial magnetic stimulation´s clinical response in schizophrenia - results from a secondary analysis.

BACKGROUND: The evidence for repetitive transcranial magnetic stimulation (rTMS) to treat negative symptoms in schizophrenia (SCZ) is increasing, although variable response rates remain a challenge. Subject´s sex critically influences rTMS´ treatment outcomes. Females with major depressive disorder are more likely to respond to rTMS, while SCZ data is scarce. METHODS: Using data from the 'rTMS for the Treatment of Negative Symptoms in Schizophrenia' (RESIS) trial we assessed the impact of sex on rTMS´ clinical response rate from screening up to 105 days after intervention among SCZ patients. The impact of resting motor threshold (RMT) on response rates was also assessed. RESULTS: 157 patients received either active or sham rTMS treatment. No significant group differences were observed. Linear mixed model showed no effects on response rates (all p > 0.519). Apart from a significant sex*time interaction for the positive subscale of the positive and negative syndrome scale (PANSS) scores (p = 0.032), no other significant effects of sex on continuous PANSS scores were observed. RMT had no effect on response rate. CONCLUSION: In the largest rTMS trial on the treatment of SCZ negative symptoms we did not observe any significant effect of sex on treatment outcomes. Better assessments of sex-related differences could improve treatment individualisation.

Effects of nicotine on social cognition, social competence and self-reported stress in schizophrenia patients and healthy controls.

More than 80 % of patients diagnosed with schizophrenia are nicotine-dependent. Self-medication of cognitive deficits and an increased vulnerability to stress are discussed as promoting factors for the development of nicotine dependence. However, the effects of nicotine on social cognition and subjective stress responses in schizophrenia are largely unexplored. A 2 × 2-factorial design (drug × group) was used to investigate the effects of nicotine versus placebo in smoking schizophrenia patients and healthy controls after 24 h of abstinence from smoking. Participants performed a facial affect recognition task and a semi-standardized role-play task, after which social competence and self-reported stress during social interaction were assessed. Data analysis revealed no significant group differences in the facial affect recognition task. During social interaction, healthy controls showed more non-verbal expressions and a lower subjective stress level than schizophrenia patients. There were no significant effects of nicotine in terms of an enhanced recognition of facial affect, more expressive behaviour or reduced subjective stress during social interaction. While schizophrenia patients unexpectedly recognized facial affect not significantly worse than healthy controls, the observed group differences in subjective stress and non-verbal expression during social interaction in the role-play situation are in line with previous findings. Contrary to expectations derived from the self-medication hypothesis, nicotine showed no significant effects on the dependent variables, perhaps because of the dosage used and the delay between the administration of nicotine and the performance of the role-play.

Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence.

BACKGROUND: Computerized clinical trial recruitment support is one promising field for the application of routine care data for clinical research. The primary task here is to compare the eligibility criteria defined in trial protocols with patient data contained in the electronic health record (EHR). To avoid the implementation of different patient definitions in multi-site trials, all participating research sites should use similar patient data from the EHR. Knowledge of the EHR data elements which are commonly available from most EHRs is required to be able to define a common set of criteria. The objective of this research is to determine for five tertiary care providers the extent of available data compared with the eligibility criteria of randomly selected clinical trials. METHODS: Each participating study site selected three clinical trials at random. All eligibility criteria sentences were broken up into independent patient characteristics, which were then assigned to one of the 27 semantic categories for eligibility criteria developed by Luo et al. We report on the fraction of patient characteristics with corresponding structured data elements in the EHR and on the fraction of patients with available data for these elements. The completeness of EHR data for the purpose of patient recruitment is calculated for each semantic group. RESULTS: 351 eligibility criteria from 15 clinical trials contained 706 patient characteristics. In average, 55% of these characteristics could be documented in the EHR. Clinical data was available for 64% of all patients, if corresponding data elements were available. The total completeness of EHR data for recruitment purposes is 35%. The best performing semantic groups were 'age' (89%), 'gender' (89%), 'addictive behaviour' (74%), 'disease, symptom and sign' (64%) and 'organ or tissue status' (61%). No data was available for 6 semantic groups. CONCLUSIONS: There exists a significant gap in structure and content between data documented during patient care and data required for patient eligibility assessment. Nevertheless, EHR data on age and gender of the patient, as well as selected information on his disease can be complete enough to allow for an effective support of the manual screening process with an intelligent preselection of patients and patient data.

ECRIN - CESSDA strategies for cross metadata mappings in selected areas between life sciences and social sciences and humanities.

Background: The recent COVID-19 (Corona Virus Disease 2019) pandemic dramatically underlined the multi-faceted nature of health research, requiring input from basic biological sciences, pharmaceutical technologies, clinical research), social sciences and public health and social engineering. Systems that could work across different disciplines would therefore seem to be a useful idea to explore. In this study we investigated whether metadata schemas and vocabularies used for discovering scientific studies and resources in the social sciences and in clinical research are similar enough to allow information from different source disciplines to be easily retrieved and presented together. Methods: As a first step a literature search was performed, exemplarily identifying studies and resources, in which data from social sciences have been usefully employed or integrated with that from clinical research and clinical trials. In a second step a comparison of metadata schemas and related resource catalogues in ECRIN (European Clinical Research Infrastructure Network) and CESSDA (Consortium of European Social Science Data Archives) was performed. The focus was on discovery metadata, here defined as the metadata elements used to identify and locate scientific resources. Results: A close view at the metadata schemas of CESSDA and ECRIN and the basic discovery metadata as well as a crosswalk between ECRIN and CESSDA metadata schemas have shown that there is considerable resemblance between them. Conclusions: The resemblance could serve as a promising starting point to implement a common search mechanism for ECRIN and CESSDA metadata. In the paper four different options for how to proceed with implementation issues are presented.

Akathisia and suicidal ideation in first-episode schizophrenia.

Patients with first-episode schizophrenia (FES) are known to be notably sensitive for developing extrapyramidal adverse effects, but the relation of akathisia and suicidal ideation has rarely been studied. The current report is an ongoing analysis of an 8-week double-blind randomized controlled multicenter trial in 289 FES, comparing risperidone and haloperidol. Assessments were conducted weekly and included the Hillside Akathisia Scale and 21-item Hamilton Depression Rating Scale ratings. Suicidal ideation was significantly associated with clinician observed akathisia, depressed mood, younger age, and use of propranolol. The allocated treatment, anxiety, and nervousness had no influence. The present findings suggest a promoting effect of akathisia on suicidal ideation can not be ruled out in patients with FES.

An iterative and interdisciplinary categorisation process towards FAIRer digital resources for sensitive life-sciences data.

For life science infrastructures, sensitive data generate an additional layer of complexity. Cross-domain categorisation and discovery of digital resources related to sensitive data presents major interoperability challenges. To support this FAIRification process, a toolbox demonstrator aiming at support for discovery of digital objects related to sensitive data (e.g., regulations, guidelines, best practice, tools) has been developed. The toolbox is based upon a categorisation system developed and harmonised across a cluster of 6 life science research infrastructures. Three different versions were built, tested by subsequent pilot studies, finally leading to a system with 7 main categories (sensitive data type, resource type, research field, data type, stage in data sharing life cycle, geographical scope, specific topics). 109 resources attached with the tags in pilot study 3 were used as the initial content for the toolbox demonstrator, a software tool allowing searching of digital objects linked to sensitive data with filtering based upon the categorisation system. Important next steps are a broad evaluation of the usability and user-friendliness of the toolbox, extension to more resources, broader adoption by different life-science communities, and a long-term vision for maintenance and sustainability.

Status, use and impact of sharing individual participant data from clinical trials: a scoping review.

OBJECTIVES: To explore the impact of data-sharing initiatives on the intent to share data, on actual data sharing, on the use of shared data and on research output and impact of shared data. ELIGIBILITY CRITERIA: All studies investigating data-sharing practices for individual participant data (IPD) from clinical trials. SOURCES OF EVIDENCE: We searched the Medline database, the Cochrane Library, the Science Citation Index Expanded and the Social Sciences Citation Index via Web of Science, and preprints and proceedings of the International Congress on Peer Review and Scientific Publication. In addition, we inspected major clinical trial data-sharing platforms, contacted major journals/publishers, editorial groups and some funders. CHARTING METHODS: Two reviewers independently extracted information on methods and results from resources identified using a standardised questionnaire. A map of the extracted data was constructed and accompanied by a narrative summary for each outcome domain. RESULTS: 93 studies identified in the literature search (published between 2001 and 2020, median: 2018) and 5 from additional information sources were included in the scoping review. Most studies were descriptive and focused on early phases of the data-sharing process. While the willingness to share IPD from clinical trials is extremely high, actual data-sharing rates are suboptimal. A survey of journal data suggests poor to moderate enforcement of the policies by publishers. Metrics provided by platforms suggest that a large majority of data remains unrequested. When requested, the purpose of the reuse is more often secondary analyses and meta-analyses, rarely re-analyses. Finally, studies focused on the real impact of data-sharing were rare and used surrogates such as citation metrics. CONCLUSIONS: There is currently a gap in the evidence base for the impact of IPD sharing, which entails uncertainties in the implementation of current data-sharing policies. High level evidence is needed to assess whether the value of medical research increases with data-sharing practices.

European survey on national harmonization in clinical research.

BACKGROUND: Clinical trials remain key to the development of evidence-based medical practice. However, they are becoming increasingly complex, mainly in a multinational setting. To address these challenges, the European Union (EU) adopted the Clinical Trial Regulation EU No. 536/2014 (CTR). Once in force, the CTR will lead to more consistent rules and simplification of procedures for conducting clinical trials throughout the EU. Existing harmonization initiatives and "research infrastructures" for clinical trials may facilitate this process. This publication offers a snapshot of the current level of harmonization activities in academic clinical research in Europe. METHODS: A survey was performed among the member and observer countries of the European Clinical Research Infrastructure Network (ECRIN), using a standardized questionnaire. Three rounds of data collection were performed to maximize completeness and comparability of the received answers. The survey aimed to describe the harmonization of academic clinical research processes at national level, to facilitate the exchange of expertise and experience among countries, and to identify new fields of action. RESULTS: Most scientific partners already have in place various working groups and harmonization activities at national level. Furthermore, they are involved in and open to sharing their know-how and documents. Since harmonization was mainly a bottom-up approach up until now, the extent and topics dealt with are diverse and there is only little cross-networking and cross-country exchange so far. CONCLUSIONS: Currently, the ECRIN member countries offer a very solid base and collaborative spirit for further aligning processes and exchanging best practices for clinical research in Europe. They can support a smooth implementation of the EU CTR and may act as single contact with consolidated expertise in a country.