FAIRifying Clinical Studies Metadata: A Registry for the Biomedical Research.

The data produced during a research project are too often collected for the sole purpose of the study, therefore hindering profitable reuse in similar contexts. The growing need to counteract this trend has recently led to the formalization of the FAIR principles that aim to make (meta)data Findable, Accessible, Interoperable and Reusable, for humans and machines. Since their introduction, efforts are ongoing to encourage FAIR principles adoption and to implement solutions based on them. This paper reports on the FAIR-compliant registry we developed to collect and serve metadata describing clinical trials. The design of the registry is based on the FAIR Data Point (FDP) specifications, the state-of-the-art reference for FAIRified metadata sharing. To map the metadata relevant to our use case, we have extended the DCAT-based semantic model of the FDP adopting well-established ontologies in the biomedical and clinical domain, like the Semanticscience Integrated Ontology (SIO). Current implementation is based on the Molgenis software and provides both a user interface and a REST API for metadata discovering. At present the registry is being loaded with the metadata of the 18 clinical studies included in the 'I FAIR Program', a project finalised to the dissemination of FAIR best practices among the clinical researchers in Sardinia (Italy). After a testing phase, the registry will be publicly available, while the new model and the source code will be released open source

Linking provenance and its metadata in multi-organizational environments

Reproducibility issues are widely reported in life sciences. As a response, scientific communities have called for enhanced provenance information documenting the complete research life cycle, starting from biological or environmental material acquisition and ending with translating research results into practice. The integrity and trustworthiness of such provenance can be achieved by applying versioning mechanisms and cryptographic techniques, such as hashes or digital signatures, which are provenance metadata. However, the available provenance literature lacks an analysis of mechanisms for the exchange of provenance and its metadata between organizations as well as a grounded proposal of linking provenance and its metadata. In this work, we provide an in-depth analysis of the approaches for coupling provenance information and its metadata with documented research objects in the context of multi-organizational processes, leading to the categorization of possible approaches, description of their key properties, and derivation of requirements for underlying provenance models. We address the requirements by proposing a mechanism for linking provenance and its metadata by extending the Common Provenance Model, the open conceptual foundation for the ISO 23494 provenance standard series, currently under development. The concepts are demonstrated and validated on two complex use cases. This work is intended as a harmonized source of information on provenance coupling in the context of exchange of provenance between organizations, which can be used when designing or choosing a provenance solution. This type of usage is exemplified in the extension of the Common Provenance Model as another step toward a provenance standard for life sciences

Attitudes toward relevant aspects of medical practice: a cross-sectional study with a random sample of second- and sixth-year students

This was a cross-sectional study with two random samples: 50 second-year and 50 sixth-year undergraduate medical students. An open-ended questionnaire was applied, in addition to a scale known as the Instrument for evaluating medical students' attitudes towards key aspects of medical practice (Colares, 2002). The scale contains 52 questions on: 1. Psychological and emotional aspects of physical and mental illness; 2. managing situations related to death; 3. primary healthcare; 4. aspects related to mental illness; 5. the physician's contribution to scientific progress in medicine; and 6. other aspects of medical work and health policies. The attitudes are categorized as positive, negative, or conflictive. According to the findings, students had positive attitudes towards at least three of the six aspects. Second-year and sixth-year students differed significantly (chi² = 6.901, d.f. = 1, p < 0.05) in their attitudes toward factor 2 (managing situations related to death).Estudo transversal com duas amostras randomizadas de 50 alunos do segundo e 50 do sexto ano de graduação em Medicina. Foi aplicado um questionário com questões abertas e a escala Instrumento de avaliação de atitudes de estudantes de medicina frente a aspectos relevantes da prática médica (Colares, 2002). A escala contém 52 questões referentes à: 1. Aspectos psicológicos e emocionais nas doenças orgânicas e mentais; 2. Manejo de situações relacionadas à morte; 3. Atenção primária à saúde; 4. Aspectos relacionados à doença mental; 5. Contribuição do médico ao avanço científico da medicina; 6. Outros aspectos relacionados à atuação médica e às políticas de saúde. As atitudes são categorizadas em positivas, negativas e conflitantes. Observou-se que os estudantes apresentaram atitudes positivas frente a pelo menos três dos seis aspectos abordados; os alunos do segundo ano e do sexto ano apresentaram diferença estatisticamente significativa (chi² = 6,901, g.l. = 1, p < 0,05) nas atitudes relacionadas ao fator 2 (manejo de situações relacionadas à morte).Universidade Federal de São Paulo (UNIFESP)Secretaria de Estado da Saúde de São Paulo Instituto de SaúdeUNIFESP, Psiquiatra-EPMSciEL

ISO 23494: Biotechnology - Provenance Information Model for Biological Specimen and Data

Exchange of research data and samples in biomedical research has become a common phenomenon demanding for their effective quality assessment. At the same time, several reports address reproducibility of research, where history of biological samples (acquisition, processing, transportation, storage, and retrieval) and data history (data generation and processing) defines their fitness for purpose, and hence their quality. The project aims at developing a comprehensive W3C PROV based provenance information standard intended for the biomedical research domain. The standard is being developed by the working group 5 ("data processing and integration") of the ISO (International Standardisation Organisation) technical committee 276 "biotechnology". The outcome of the project will be published in parts as international standards or technical specifications. The poster informs about the goals of the standardisation activity, presents the proposed structure of the standards, briefly describes its current state and outlines its future development and open issues

Toward a common standard for data and specimen provenance in life sciences

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.</p

Towards a Common Standard for Data and Specimen Provenance in Life Sciences

The exchange of biological material and data has become an issue of major importance for research in biotechnology. At the same time, many reports indicate problems with quality, trustworthiness and reproducibility of research results, mainly due to poor documentation of data generation or collection of samples. Consequently, there is an urgent need for improved and standardized documentation of data and specimen used in research studies. In response to these issues, we are developing a provenance information standard for the biotechnology domain within the ISO Technical Committee 276 “Biotechnology”. The major objectives of the standard, now registered as ISO/WD 23494, are improved reproducibility of research results, enabling the assessment of the quality of biological samples and data, traceability and higher reliability of observations. We are convinced that the standardization project is of substantial interest to a broader audience, who we would also invite to comment and contribute to this comprehensive effort.Manuscript under consideration

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost