Jedním z nejvýraznějších počinů v oblasti sdílení informací bylo v poslední době jednoznačně rozšíření sociálních sítí. Tyto webové nástroje slouží především pro rychlé sdílení různých typů informací, ať už textových či multimediálních, širokému publiku v prostředí internetu. Jejich uplatnění nalezneme dnes již v široké škále lidských činností, postupně začínají čím dál tím více zasahovat i do oblasti vědy a výzkumu. Sociální sítě mohou vědcům sloužit mimo jiné jako velmi užitečný informační zdroj.
Expansion of social networks was one of the most important acts in the area of information sharing during last few years. These Web services are used pimarily for quick sharing of various types of information to a wide audience on the Internet. Their application can be found in many different types of human activities. They start to be used also in fields of science and research. Social networks can serve scientists also as a very useful source of information.
Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand.Here we present a community-driven curation effort, supported by ELIXIR-the European infrastructure for biological information-that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners.As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.
Myositis International Health and Research Collaborative Alliance (MIHRA) is a newly formed purpose-built non-profit charitable research organization dedicated to accelerating international clinical trial readiness, global professional and lay education, career development and rare disease advocacy in IIM-related disorders. In its long form, the name expresses the community's scope of engagement and intent. In its abbreviation, MIHRA, conveys linguistic roots across many languages, that reflects the IIM community's spirit with meanings such as kindness, community, goodness, and peace. MIHRA unites the global multi-disciplinary community of adult and pediatric healthcare professionals, researchers, patient advisors and networks focused on conducting research in and providing care for pediatric and adult IIM-related disorders to ultimately find a cure. MIHRA serves as a resourced platform for collaborative efforts in investigator-initiated projects, consensus guidelines for IIM assessment and treatment, and IIM-specific career development through connecting research networks.MIHRA's infrastructure, mission, programming and operations are designed to address challenges unique to rare disease communities and aspires to contribute toward transformative models of rare disease research such as global expansion and inclusivity, utilization of community resources, streamlining ethics and data-sharing policies to facilitate collaborative research. Herein, summarises MIHRA operational cores, missions, vision, programming and provision of community resources to sustain, accelerate and grow global collaborative research in myositis-related disorders.
- MeSH
- Gene Deletion * MeSH
- Phenotype MeSH
- Genetic Association Studies * MeSH
- Genome * MeSH
- Genotype * MeSH
- Internet MeSH
- International Cooperation MeSH
- Mutagenesis MeSH
- Mouse Embryonic Stem Cells cytology metabolism MeSH
- Mice, Knockout MeSH
- Mice MeSH
- Information Dissemination MeSH
- Animals MeSH
- Check Tag
- Mice MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
The Protein Data Bank in Europe (PDBe, pdbe.org) is actively engaged in the deposition, annotation, remediation, enrichment and dissemination of macromolecular structure data. This paper describes new developments and improvements at PDBe addressing three challenging areas: data enrichment, data dissemination and functional reusability. New features of the PDBe Web site are discussed, including a context dependent menu providing links to raw experimental data and improved presentation of structures solved by hybrid methods. The paper also summarizes the features of the LiteMol suite, which is a set of services enabling fast and interactive 3D visualization of structures, with associated experimental maps, annotations and quality assessment information. We introduce a library of Web components which can be easily reused to port data and functionality available at PDBe to other services. We also introduce updates to the SIFTS resource which maps PDB data to other bioinformatics resources, and the PDBe REST API.
- MeSH
- Molecular Sequence Annotation MeSH
- Databases as Topic MeSH
- Databases, Protein * MeSH
- Internet MeSH
- Protein Conformation, alpha-Helical MeSH
- Protein Conformation, beta-Strand MeSH
- Humans MeSH
- Models, Molecular MeSH
- Computer Graphics MeSH
- Proteins chemistry genetics metabolism MeSH
- Amino Acid Sequence MeSH
- Sequence Analysis, Protein methods MeSH
- Information Dissemination MeSH
- User-Computer Interface * MeSH
- Computational Biology methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Europe MeSH
Background: In the range of Semantic Web, the idea of linking and sharing the resources generated by different authors, like ontologies, knowledge bases, or datasets, is referred to “Linked data”. Then, an ambitious project within the “Linked Data” paradigm is the “Linking Open Data” community project. It aims at publishing open data sets on the Web and semantically connecting data items belonging to different data sources. Objectives: The purpose of this paper is to present a literature review on the subject of Linked Open Data in Health and Clinical Care. In fact, the availability of open data would increase evidence of the results of biomedical research, and consequently, of clinical practice. Methods: Selection criteria have been defined and searching in PubMed/Medline and Scopus citation databases - for all years the database were available - journals papers have been retrieved. Finally, an evaluation grid has been defined for analysing the retrieved papers, to answer some defined research questions. Results: Nine journal articles have been analysed according to the defined evaluation grid. In five out of nine papers, the main contributions are strategies and methodologies for the integration of systems, including bridging the information gap among forms for clinical research and the one for patient care. Then, in three papers the main contributions are the development of consistent triple stores according to the “Linked Data” paradigm. Finally, the last paper aims at building an open dataset for public health purposes. Conclusions: The review was able to answer the research questions, despite the limited number of included papers.
AIMS: To estimate the direct and indirect costs associated with disability due to multiple sclerosis (MS) in Poland. METHODS: Recently a cost-of-illness study was conducted in the Czech Republic, involving 909 patients with different levels of disability (the COMS study). Data on resource use from this trial was extrapolated to Polish patients and combined with Polish unit costs in 2012. The mean annual costs from societal and payers perspective were calculated for patients according to EDSS. RESULTS: The estimated mean annual cost per patient with MS from a societal perspective ranges from 6970 EUR to 26,791 EUR. Indirect costs (production loss due to early retirement, sick-leave and informal care) cover up to 70% of total costs. CONCLUSIONS: With an estimated 40-60,000 patients with MS in Poland, the disease poses a high economic burden. Indirect costs have a substantial share in these costs. A high-quality prospective study on costs is needed.
- MeSH
- Humans MeSH
- Cost of Illness * MeSH
- Multiple Sclerosis economics MeSH
- Health Resources statistics & numerical data MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Czech Republic MeSH
- Poland MeSH
The known challenge of underutilization of data and biological material from biorepositories as potential resources for medical research has been the focus of discussion for over a decade. Recently developed guidelines for improved data availability and reusability-entitled FAIR Principles (Findability, Accessibility, Interoperability, and Reusability)-are likely to address only parts of the problem. In this article, we argue that biological material and data should be viewed as a unified resource. This approach would facilitate access to complete provenance information, which is a prerequisite for reproducibility and meaningful integration of the data. A unified view also allows for optimization of long-term storage strategies, as demonstrated in the case of biobanks. We propose an extension of the FAIR Principles to include the following additional components: (1) quality aspects related to research reproducibility and meaningful reuse of the data, (2) incentives to stimulate effective enrichment of data sets and biological material collections and its reuse on all levels, and (3) privacy-respecting approaches for working with the human material and data. These FAIR-Health principles should then be applied to both the biological material and data. We also propose the development of common guidelines for cloud architectures, due to the unprecedented growth of volume and breadth of medical data generation, as well as the associated need to process the data efficiently.
The biomedical research community addresses reproducibility challenges in animal studies through standardized nomenclature, improved experimental design, transparent reporting, data sharing, and centralized repositories. The ARRIVE guidelines outline documentation standards for laboratory animals in experiments, but genetic information is often incomplete. To remedy this, we propose the Laboratory Animal Genetic Reporting (LAG-R) framework. LAG-R aims to document animals' genetic makeup in scientific publications, providing essential details for replication and appropriate model use. While verifying complete genetic compositions may be impractical, better reporting and validation efforts enhance reliability of research. LAG-R standardization will bolster reproducibility, peer review, and overall scientific rigor.
Today, applications for Grids emerge in various scientific fields, each with specific requirements. We present concept and architecture which enables biomedical experts to collaborate and share resources by encapsulating their knowledge and expertise as grid services, with (semi-)formally described semantics. Grid Services allow machine processing of the encapsulated knowledge, while their semantic description provides means for their automated discovery and interaction. This brings new possibilities of building biomedical systems offering machine-driven assistance to the biomedical experts.
