Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.

• MeSH
• big data * MeSH
• databáze genetické * MeSH
• fenotyp MeSH
• genotyp MeSH
• šlechtění rostlin MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH

The scarcity of long-term observational data has limited the use of statistical or machine-learning techniques for predicting intraannual ecological variation. However, time-stamped citizen-science observation records, supported by media data such as photographs, are increasingly available. In the present article, we present a novel framework based on the concept of relative phenological niche, using machine-learning algorithms to model observation records as a temporal sample of environmental conditions in which the represented ecological phenomenon occurs. Our approach accurately predicts the temporal dynamics of ecological events across large geographical scales and is robust to temporal bias in recording effort. These results highlight the vast potential of citizen-science observation data to predict ecological phenomena across space, including in near real time. The framework is also easily applicable for ecologists and practitioners already using machine-learning and statistics-based predictive approaches.

• Klíčová slova
• citizen science, digital data, ecological monitoring, phenological niche, seasonality prediction,
• Publikační typ
• časopisecké články MeSH

OBJECTIVES: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. METHOD: A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. RESULTS: The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. CONCLUSION: Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

• Klíčová slova
• big data, bipolar disorder, data mining, deep learning, machine learning, personalized psychiatry, predictive psychiatry, risk prediction,
• MeSH
• big data * MeSH
• bipolární porucha epidemiologie   terapie   MeSH
• datové vědy MeSH
• fenotyp MeSH
• hodnocení rizik MeSH
• klinické rozhodování * MeSH
• lidé MeSH
• poradní výbory MeSH
• prognóza MeSH
• sebevražedné myšlenky * MeSH
• strojové učení * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH

Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation.

• Klíčová slova
• Education, FAIR Principles, Open Access, Open Data, Open Science, Quality Management, Standardisation,
• MeSH
• biologické vědy * MeSH
• důvěra * MeSH
• kvalita života MeSH
• metadata MeSH
• mezinárodní spolupráce MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Understanding animal movement is at the core of ecology, evolution and conservation science. Big data approaches for animal tracking have facilitated impactful synthesis research on spatial biology and behavior in ecologically important and human-impacted regions. Similarly, databases of animal traits (e.g. body size, limb length, locomotion method, lifespan) have been used for a wide range of comparative questions, with emerging data being shared at the level of individuals and populations. Here, we argue that the proliferation of both types of publicly available data creates exciting opportunities to unlock new avenues of research, such as spatial planning and ecological forecasting. We assessed the feasibility of combining animal tracking and trait databases to develop and test hypotheses across geographic, temporal and biological allometric scales. We identified multiple research questions addressing performance and distribution constraints that could be answered by integrating trait and tracking data. For example, how do physiological (e.g. metabolic rates) and biomechanical traits (e.g. limb length, locomotion form) influence migration distances? We illustrate the potential of our framework with three case studies that effectively integrate trait and tracking data for comparative research. An important challenge ahead is the lack of taxonomic and spatial overlap in trait and tracking databases. We identify critical next steps for future integration of tracking and trait databases, with the most impactful being open and interlinked individual-level data. Coordinated efforts to combine trait and tracking databases will accelerate global ecological and evolutionary insights and inform conservation and management decisions in our changing world.

• Klíčová slova
• Biologging, Integration, Macroecology, Repository, Tracking data, Trait data,
• MeSH
• big data MeSH
• databáze faktografické MeSH
• ekologie * metody   MeSH
• migrace zvířat MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH

Bioinformaticians and biologists rely increasingly upon workflows for the flexible utilization of the many life science tools that are needed to optimally convert data into knowledge. We outline a pan-European enterprise to provide a catalogue ( https://bio.tools ) of tools and databases that can be used in these workflows. bio.tools not only lists where to find resources, but also provides a wide variety of practical information.

• MeSH
• biologické vědy * MeSH
• databáze faktografické * MeSH
• internet MeSH
• software * MeSH
• Publikační typ
• dopisy MeSH
• práce podpořená grantem MeSH

We provide a post-mission assessment of the science and data from the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) investigation on the NASA Van Allen Probes mission. An overview of important scientific results is presented, covering all of the key wave modes and DC magnetic fields measured by EMFISIS. Discussion of the data products, which are publicly available, follows to provide users with guidance on characteristics and known issues of the measurements. We present guidance on the correct use of derived products, in particular, the wave-normal analysis (WNA) which yields fundamental wave properties such as polarization, ellipticity, and Poynting flux. We also give information about the plasma density derived from measuring the upper hybrid line in the inner magnetosphere.

• Klíčová slova
• Data usage, Inner magnetosphere, Wave measurements,
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

It is rare to meet protistologists who are not passionate about their study subject. The vast majority of people, however, never get the chance to hear about the work of these researchers. Although every researcher working on protists is likely to be aware of this situation, efforts made and tools employed for dissemination of knowledge are rarely documented. Following a proposal by the Italian Society of Protistology, a workshop at the 2019 VIII European Congress of Protistology in Rome, Italy, was dedicated to protistological knowledge dissemination. Through the many interventions, we discovered the diversity of efforts to reveal the protistan world to the general public, including museum exhibitions and activities, public understanding of science events, citizen science projects, specific book publications, the use of protists in teaching at all levels from primary school children to university undergraduate students, and to a global audience via social media. The participation of the workshop delegates in the discussions indicated that presentations on the wonderful world of protists to the public not only increase the visibility and accessibility of protistology research but are also very important for the scientific community. Here we report on some of the key aspects of the presentations given in the dissemination workshop.

• Klíčová slova
• Citizen science, Protistology education, Public understanding of science, Science communication,
• MeSH
• Eukaryota * MeSH
• šíření informací * MeSH
• výchova a vzdělávání * MeSH
• výzkum * trendy   MeSH
• Publikační typ
• časopisecké články MeSH

Among medical specialties, laboratory medicine is the largest producer of structured data and must play a crucial role for the efficient and safe implementation of big data and artificial intelligence in healthcare. The area of personalized therapies and precision medicine has now arrived, with huge data sets not only used for experimental and research approaches, but also in the "real world". Analysis of real world data requires development of legal, procedural and technical infrastructure. The integration of all clinical data sets for any given patient is important and necessary in order to develop a patient-centered treatment approach. Data-driven research comes with its own challenges and solutions. The Findability, Accessibility, Interoperability, and Reusability (FAIR) Guiding Principles provide guidelines to make data findable, accessible, interoperable and reusable to the research community. Federated learning, standards and ontologies are useful to improve robustness of artificial intelligence algorithms working on big data and to increase trust in these algorithms. When dealing with big data, the univariate statistical approach changes to multivariate statistical methods significantly shifting the potential of big data. Combining multiple omics gives previously unsuspected information and provides understanding of scientific questions, an approach which is also called the systems biology approach. Big data and artificial intelligence also offer opportunities for laboratories and the In Vitro Diagnostic industry to optimize the productivity of the laboratory, the quality of laboratory results and ultimately patient outcomes, through tools such as predictive maintenance and "moving average" based on the aggregate of patient results.

• Klíčová slova
• artificial intelligence, big data, data science, patient outcomes, personalized healthcare, precision medicine,
• MeSH
• algoritmy MeSH
• big data * MeSH
• individualizovaná medicína metody   MeSH
• lidé MeSH
• poskytování zdravotní péče MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Polar regions are critically implicated in our understanding of global climate change. This is particularly the case for the Arctic, where positive feedback loops and climate tipping points enhance complexity and urgency. Half of the Arctic and much of the world's permafrost zone lie within Russian territory. Heightened geopolitical tensions, however, have severely damaged scientific collaboration between Russia and previously well established academic partners in western countries. Isolation is now causing increasingly large data gaps in arctic research that affect our ability to make accurate predictions of the impact of climate change on natural and societal systems at all scales from local to global. Here, we argue that options to resume both practical knowledge of collaborative working and flows of research data from Russia for global arctic science must continue to be asserted, despite an increasing tendency for the Arctic to become disconnected. Time is short, as preparations for the fifth International Polar Year begin to gather momentum. While sanctions remain in place, efforts to foster peer to peer connections and re-activate effective institutional cooperation are vital to address the grand challenges of global climate change.

• Klíčová slova
• Arctic science, Crisis, Russia, Scientific collaboration,
• MeSH
• klimatické změny * MeSH
• mezinárodní spolupráce MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Arktida MeSH
• Rusko MeSH