new However, we have yet to see widespread or systematic applications of data science or AI techniques to address major health challenges and to harness the power of the UK’s health data assets. Constraints on progress have included the difficulty in attaining coherent long term health data sets, legitimate and ongoing concerns over the implications for patient privacy, and the sheer complexity of health data, which requires strong collaborations across expertise domains. — The Alan Turing Institute, 8h ago

new Consider, for example, paper-based records. Historically, from drug discovery through to product release, a significant amount of time was spent interacting with systems, recording, transferring and sharing data within cumbersome manual, paper-based systems. Digitizing, automating and streamlining those processes makes that information available automatically and immediately to relevant stakeholders, enabling scientists, for example, to focus on actual science! In a market increasingly driven by positive patient outcomes, freeing human imagination, creativity and curiosity to advance innovation and discovery in medicine, science and technology is key to delivering advanced therapeutics that can tangibly improve patients’ lives. — Contract Pharma, 8h ago

new Climate science is a complex and growing field of knowledge. Employees in specialised roles, such as construction engineers or environmental data specialists, might find it difficult to work with the lack of precision in different climate change models. Since existing scientific research on climate change is still evolving, we found that the most useful approach is to simply work as constructively as possible with the best available science from trusted sources. — orsted.com, 9h ago

new For the most accurate and actionable patient results, variant interpretation must be performed with the latest data – including drug regulatory status, scientific reports, professional guidelines, and more. Ideally, a cancer knowledge base should be updated on a daily basis to ensure that clinical laboratory scientists are working from the most up-to-date information possible. It’s also important to know who is populating and maintaining the knowledge base. Scientific evidence and other information should be curated by experts, such as PhD- or MD-level scientists and clinicians, who have a clear understanding of the data they’re reviewing and how it will be incorporated into variant interpretation results. — The Pathologist, 8h ago

new I was particularly honoured to host two annual ESMO Congresses throughout my term that celebrated multiprofessional dialogue and a wealth of studies reporting some of the most important advancements and practice-changing data in oncology. In so doing we continue to help doctors across the globe to translate rigorous science into better patient care. — esmo.org, 9h ago

new The second most received complaint from our partners in the public sector is the increasing administrative burden facing teams and organisations. Whether aligning with ever-changing regulations or meeting the increasing need for financial transparency, valuable time is spent gathering, collating and formatting data unnecessarily when the tools now exist to allow us to set up reporting structures initially and have the data populated live based on regular updates provided by action owners and team leaders. — Open Access Government, 12h ago

new Large language models can swiftly adapt to new tasks utilizing in-context learning by being given a few demos and real language instructions. This avoids hosting the LLM or annotating big datasets, but it has major performance issues with multistep reasoning, math, having the most recent information, and other things. Recent research suggests giving LLMs access to tools to facilitate more sophisticated reasoning stages or challenging them to emulate a chain of reasoning for multistep reasoning to alleviate these constraints. Nevertheless, it is challenging to adapt established approaches for a chained reason with tool usage to new activities and tools; this requires fine-tuning or prompt engineering specialized for a particular activity or tool. — MarkTechPost, 15h ago

new In these examples, ETL is a better choice over ELT as it allows organizations to efficiently transform and combine data from multiple sources before loading it into a target database. ETL enables efficient data processing, aggregation, transformation, and cleansing before loading, ensuring high-quality data and faster analytics processing times. On the other hand, ELT may lead to slower processing times and more complex data integration workflows, especially when dealing with multiple sources and complex data transformations. — dzone.com, 2d ago

new The past few years have been marked by a literal exponential increase in the number of publications with the words “machine learning,” “artificial intelligence,” and “deep learning” in their titles. These tools now pervade materials science workflows and have been integrated with experimental/computational automation to form autonomous research agents, capable of planning, executing, and analyzing entire scientific campaigns. Lurking beneath the surface truly amazing accomplishments are serious questions around trust, bias, reproducibility, and equity which will ultimately determine the overall adoption of AI and autonomy by the broader community. Here, I will speak to recent work done by our group to systematically (1) remove human bias from experimental data analysis, (2) identify and actively remediate bias in large datasets , and (3) foster and promote a community of equity and reproducibility within the materials AI sub-domain. Specific case studies will center around standard electrochemical impedance spectroscopy analysis, building stability model predictions for complex alloys from large theoretical datasets, and maximizing the amount of information extracted from imaging techniques. — Faculty of Applied Science & Engineering, 1d ago

new Cross-temporal research allows one to go beyond static cultural differences, to model cultural processes and how they change over time. Consequently, it captures the dynamic features of culture that have so far been neglected from some of the academic and international relations discourse. The key challenge for cultural change research concerns availability of reliable data with good temporal resolution over time. Without good data, estimates of cultural change in generosity, traditionalism, violence, or attitudes toward climate change remain speculative. The latter may be convenient for political pundits, as their claims about the direction our societies remain unchecked. To ensure greater accountability, reduce misperceptions, and provide clarity to the public, we need much greater investment into systematic measurement of cross-temporal trends about cardinal aspects of human welfare and societal change at scale. — E-International Relations, 2d ago

new The Graph indexes and queries data the way Google indexes and queries data from websites. Indexing blockchain data isn’t always an easy task. But the Graph has a unique approach that simplifies it. The Graph uses AI to organize data into smaller ‘subgraphs’ for efficient querying. — Altcoin Buzz - Altcoin Buzz is an independent digital media outlet that delivers the latest news and opinions in the world of Cryptocurrencies, Blockchain Technology, Regulations, Adoption and Blockchain Gaming., 1d ago

new Unfortunately, this valuable analysis is cost-prohibitive, due to challenges with the most common methods. These methods produce reliable results, but are also slow and difficult to reproduce at scale. Until now, this has acted as a barrier to the wider use of stable isotope analysis. — scientistlive.com, 1d ago

new Helfstein: I echo Jon’s sentiment and have several concerns with relying on AI. First, AI is still prone to error, a byproduct of combining disparate data sources and trying to reduce inconsistency. Of course, humans err as well, but in research, humans source the material they use to draw their conclusions. AI doesn’t yet. Sourcing is an annoying but critical part of research and required by regulators for a reason. Sourcing helps to identify the origin of errors when they arise. — IBKR Campus, 1d ago

new Concentriq for Research now enables whole slide image scoring and peer review with the introduction of a Studies module. Scientists can manage study frameworks, simultaneously collect pathologists’ assessments, and instantly evaluate results, easily capturing structured data to help improve reproducibility and drive more efficient analysis. — Research & Development World, 1d ago

new Looking into the future, the research team expect instrumentation advances will continue to increase the data throughput on temporal, spatial and spectral dimensions. They should provide more features on data structures, such as sparsity and correlation. Meanwhile, new computational methods can be harnessed to break the design space trade-offs and provide enriched chemical compositions for biomedical research. With rapid advances in computational optical microscopy, we expect more ideas to infiltrate CRS. — phys.org, 1d ago

new Alternative data sources, such as social media activity, satellite imagery, and geolocation data, can give significant insights into customer behavior, market trends, and industry dynamics that traditional data sources cannot easily capture. Yet, interpreting this data can be difficult since it is frequently unstructured and requires specialized techniques to extract valuable insights. This is where machine learning can play a crucial role. — IoT Worlds, 1d ago

new Advanced analytics can be a powerful tool for businesses, but only when the data is of good enough quality to provide accurate insights. Companies must prioritize collecting and maintaining clean data if they want their advanced analysis models to work effectively—all efforts will otherwise prove fruitless!... — martechexec.com, 1d ago

new Researchers solving today’s most important and complex energy challenges can’t always conduct real-world experiments. This is especially true for nuclear energy research. Considerations such as cost, safety and limited resources can often make laboratory tests impractical. In some cases, the facility or capability necessary to conduct a proper experiment doesn’t…... — INL, 2d ago

new ...have analyzed the citation patterns of researchers from Latin America who don’t copublish with peers from developed countries in recognized journals. The general trend reflects a phenomenon of under-citation when authors publish without global visibility. The researchers readily acknowledged that scientific research visibility benefits from collaboration. However, they wrote, it remains a concern whether under-citation is due to a “psycho-social bias or real differences in scientific relevance of these articles.”... — Eos, 1d ago

new ...at UCalgary with Dr. John Yackel, MSc’95, PhD, works in the field to collect data so researchers can learn how to better account for these errors and correct satellite algorithms to produce accurate measurements critical for climate-change projections. He is also a research associate at the University of Manitoba with Dr. Julienne Stroeve, PhD. — News, 1d ago

new ...– some maritime technology vendors show data as-is, while others do basic clean-up. Windward heavily invests in cleaning the data, because if data is not clean, everything built on top of that flawed foundation will be wobbly. Maritime domain expertise is key for understanding and constantly evaluating data points. — Windward, 2d ago

new Many CIOs believe that starting with clearly defined business outcomes is essential. To attain business outcomes from digital transformation and AI, a modern data stack is foundational. It's the central nervous system for an organization. Undoubtedly, attempting to digitally transform with legacy data stacks will impede the transformation processand increase technical debt within the organization. Put simply, a modern data stack should enable a business to be data driven, to gain insights faster and to unlock the value of digital assets and enable innovation. It is without question the starting point for digital transformation. Milroy says, therefore, “a modern data stack should result in less data silos, less tech debt, more data exchange (internal/external), self-service data access, and... — 7wData, 2d ago

new A great example of high-value digitalization is converting paper-based methods including inventory, production tracking and quality information. When digitalized, this information, which has typically been siloed and locked in disparate systems, is easily shared and used to broadly communicate, empowering every part of the organization to share and leverage data. The inefficiency of using paper forms contributes to low worker productivity and a significant probability of inaccurate information. Paper-based methods can now be digitized cost-effectively to increase productivity using no-code platforms without programmers. The new platforms using high performance and low cost of tablet computers and smartphones coupled with no-code application development software puts digital systems in frontline workers hands. This is analogous to how spreadsheets became a huge enabler providing nonprogrammers with the ability to leverage the power of computing. — automation.com, 2d ago

new HPLC instruments continue to provide high value to labs, especially QC labs, through the consistency of the methods they can run and the data they produce. The Alliance iS instrument continues that trend of easy method transfers and connection to legacy data, experiments, and samples. — Lab Manager, 2d ago

new Most efforts to date have focused on improving the content, accessibility, and delivery of scientific communications. This communication skills approach relies on a "knowledge deficit model," the idea that the lack of support for science and "good policies" merely reflects a lack of scientific information. — Psychology Today, 2d ago

new The fungal awakening is also part of a bigger ecological turn, sparked perhaps by concern about climate change, the demise of species and the need to avert our precarious trajectory. From hi-tech labs to kitchen tables, innovations and applications of myco-technology are emerging at a rapid rate. Many hold promise for developing fungal alternatives to current technologies for remediating damaged environments. The great challenge is to scale them up to a useful or meaningful level. But solutions to the environmental issues created by humans are unlikely to be found in a technological fungal fix. That requires a change in thinking. Will fungi save the world? Probably not, but they could offer insights into more sensitive ways of being in the world. Remediating our relationship with the natural world could be a first step toward using fungi to remediate environments. — the Guardian, 2d ago