The Biggest Challenges Facing Science

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new STEP ONE: The first step for organizations to solve this problem is to focus on the effective extraction of knowledge from all available sources (i.e., Harvard Business Review, European Business Review, Employee Think-Tanks, Scholars, etc.). In doing this, organizations learn important methods of observation, extraction, and application. Observation is one of the important methods of acquiring knowledge. Recent research shows that observation alone, as a means of acquiring knowledge, can only lead to the illusion of learning among learners. Thus, without extraction and application, organizations can falsely insinuate that things be done in the same way. This causes inertia. False self-confidence, limits current employees who play an important role in gathering, storing, and disseminating future knowledge. This step breaks down the silos and opens up communication to build a knowledge management database. — The European Business Review, 1d ago

new Overview: Collecting human emotion data reliably is notoriously challenging, especially at a large scale. Our latest work proposes a new quality assurance tool that uses straightforward audio and visual tests to assess annotators’ reliability before data collection. This tool filters out unreliable annotators with 80% accuracy, allowing for more efficient and clean data collection. — Montreal AI Ethics Institute, 1d ago

new Because medical AI tools retrieve information via the data that clinicians input, a significant factor influencing just how trustworthy and accurate these machines are depends on how high quality the data is. It should be emphasized that it is not just data accuracy and representativeness but data annotation that is crucial. The paper highlights that it is the most frequently inconsistent data in the annotation process that poses the most problems. For example, differences between doctors’ biases and formats for labeling data and discrepancies between hospitals’ equipment and software have resulted in confusion and conflicting data being fed to medical AI systems. — Montreal AI Ethics Institute, 1d ago

new Data privacy and security remain critical, particularly with sensitive research data. Maintaining robust security measures and responsible data handling is essential for the integrity of scientific research involving AI. — unite.ai, 1d ago

new The challenge is to make sure the right resources are in the right place. For that to happen we need good quality, up-to-date information at the right time, in the hands of people doing the planning. While there is promising linkage of data on local health and wider determinants, previous nationally-driven attempts to gather data to inform these difficult decisions has not been smooth. Data protection and patient safety remain paramount. — The Health Foundation, 1d ago

Jinender Jain, Senior VP and Sales Head UK and Ireland at Tech Mahindra, highlights this complexity. “Data governance and AI model management present complex challenges for businesses. Compliance with data privacy regulations, such as GDPR and CCPA, is non-negotiable to avoid legal consequences and safeguard consumer trust.” Jain also emphasises the pivotal role of data quality and privacy: “Maintaining data quality is equally critical, as AI relies on accurate, unbiased data for dependable outcomes. Data cleansing and validation processes must be continuously upheld to ensure data integrity.”... — technologymagazine.com, 3d ago

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new Having spent several decades now digitizing and collecting health data, there is no excuse for decision making in medicine that is not data-informed. This is particularly imperative when talking about something as life-or-death as organ transplantation. The OPTN should collect and analyze longitudinal data from all aspects of the system, from end-stage organ disease diagnosis to post-transplant care. This data can be used to identify opportunities for quality improvement and ensure that the system is operating as effectively and efficiently as possible. — MedCity News, 2d ago

new Data product platforms are the ideal solution to these bottlenecks. They act as cohesive ecosystems, harmonizing fragmented data landscapes into unified, accessible, and actionable data repositories. By setting universal standards for data quality and validation, these platforms open-up opportunities for deeper insights, strong decision-making, and increased profitability and innovation. These data product platforms provide an in-depth look at the challenges of using data effectively while offering practical solutions and highlighting the high stakes involved in each barrier. — insideBIGDATA, 1d ago

...“Scientists undertaking complex multiplexing tasks in translational research, cancer biology, cell biology and academia often struggle with visualizing and analyzing vast quantities of data,” said Won Yung Choi, Product Manager, Data & Analysis at Leica Microsystems. “We have developed Aivia 13 with these needs in mind, allowing researchers to rapidly identify patterns in the data and gain new levels of insights about their research fueled by easy-to-use AI tools.”... — www.labbulletin.com, 3d ago

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With so much data being pumped out at breakneck rates, it can seem like an insurmountable challenge to ensure data accuracy, completeness, and consistency. And despite technological, governance and team efforts, poor data can still endure. As such, maintaining data quality can feel like a perennial challenge. But quality data is fundamental to a company’s digital success. — The Engineer, 19d ago

Access to quality data is fundamental for the success of AI initiatives. However, data accessibility remains unequal across various domains and regions. Furthermore, data quality is paramount. Inaccurate or biased data can lead to flawed AI outcomes, perpetuating existing biases and generating unreliable predictions. Addressing these issues requires collaborative efforts to improve data collection, sharing, and verification practices. — globaltechcouncil.org, 10w ago

Accurate measurements of light intensity provide vital data for scientists and everyday applications. For example, precise values help optimize microscopy signals, trigger physiological processes in the brain, and drive light-absorbing reactions while enabling different research teams to share and reproduce experimental results. “Nowadays, a vast community of biologists, chemists, engineers, and physicists are concerned with delivering precise numbers of photons,” the team explains. On a larger scale, precision is also essential for critical tasks such as water purification and phototherapeutics. — Lab Manager, 7d ago

Are data management plans essential for FAIR data? This question is fundamental to accepting and using DMPs as a useful and necessary tool. By reformulating the question, one could ask, “Do scientists need a plan to manage ever-increasing data volumes to share them?”. The obvious answer is YES if we want open science to be a reality. (4)... — Open Access Government, 5w ago

While Generative AI holds great promise in healthcare, it also faces several challenges and limitations. One of the foremost concerns is data privacy. Protecting patient privacy is paramount, as healthcare data is exceptionally sensitive. Ensuring that data remains secure and anonymized throughout the AI analysis process is essential to maintain trust and compliance with data protection regulations. Another significant challenge is addressing bias and ensuring fairness in AI models. These models can inherit biases present in training data, potentially perpetuating healthcare disparities. The ongoing challenge is to identify and rectify these biases, striving for impartial and equitable treatment recommendations. Furthermore, healthcare systems often employ diverse electronic health record systems that may not easily share data. Integrating and standardizing data from various sources for AI analysis can be technically challenging, but it’s crucial for comprehensive and accurate insights. — RTInsights, 12w ago

Computers are indispensable tools in the fields of climate science and meteorology. They enable scientists and meteorologists to analyse vast amounts of data, run complex simulations, and provide accurate weather forecasts and climate predictions. While computers have revolutionised climate science and meteorology, several challenges persist. One such challenge is the need for even more powerful computing resources to improve model resolution and accuracy. Additionally data quality and availability, as well as uncertainties associated with model predictions, remain important areas of focus for further research and development. — bcs.org, 12w ago

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new The generation of accurate genomic pathogen data has also been held back to date by limitations in sequencing technology. For example, while many countries recognise the value of wastewater analysis in early detection, the DNA in this sample type is often degraded and exists against a backdrop of other noise. As such, very high sequencing specificity and sensitivity are needed to get an accurate picture of pathogens – and many countries lack access to such sequencing capabilities. Scale has also been a challenge, with whole genome sequencing historically coming at a high price and with low throughput of samples. This has meant it hasn’t been feasible for many countries to maintain a surveillance programme that examines pathogens using highly accurate whole genome sequencing approaches. — www.labbulletin.com, 2d ago

In recent years, Geospatial Data Science – the use of geographic knowledge and AI approaches to extract meaningful insights from large-scale geographic data – has achieved remarkable success in spatial knowledge discovery and reasoning, and geographic phenomena modeling. However, two challenges remain in geospatial data science: (1) geographic phenomena are always treated as functions of a set of physical settings, but human experience has received insufficient attention; (2) there are limited strategies to focus on and address geoethical issues. In this talk, Dr. Kang will present a series of works that utilized geospatial data science to understand human experience and sense of place. In particular, using large-scale street view images, social media data, human mobility data, and advanced GeoAI approaches, he measured and analyzed human subjective safety perceptions (e.g., whether a neighborhood is perceived as a safe place), and emotions (e.g., happiness) at places, as well as human-environment relationships. Also, his work paid attention on geoethical issues such as monitoring perception bias and model bias and protecting geoprivacy. — nyu.edu, 3d ago

First, even though everyone uses multiomics, the major pharmaceutical companies have departments tasked with gathering and analyzing data for a single omics layer, and it is precisely in this isolation that multiomics problems arise. — GEN - Genetic Engineering and Biotechnology News, 3d ago

Laboratory-based research dominates the fields of comparative physiology and biomechanics. The power of lab work has long been recognized by experimental biologists. For example, in 1932, Georgy Gause published an influential paper in Journal of Experimental Biology describing a series of clever lab experiments that provided the first empirical test of competitive exclusion theory, laying the foundation for a field that remains active today. At the time, Gause wrestled with the dilemma of conducting experiments in the lab or the field, ultimately deciding that progress could be best achieved by taking advantage of the high level of control offered by lab experiments. However, physiological experiments often yield different, and even contradictory, results when conducted in lab versus field settings. This is especially concerning in the Anthropocene, as standard laboratory techniques are increasingly relied upon to predict how wild animals will respond to environmental disturbances to inform decisions in conservation and management. In this Commentary, we discuss several hypothesized mechanisms that could explain disparities between experimental biology in the lab and in the field. We propose strategies for understanding why these differences occur and how we can use these results to improve our understanding of the physiology of wild animals. Nearly a century beyond Gause's work, we still know remarkably little about what makes captive animals different from wild ones. Discovering these mechanisms should be an important goal for experimental biologists in the future. — The Company of Biologists, 3d ago

...“Data quality is the biggest focus for us,” emphasised Kumar, explaining the foundational nature of the approach. “If a scenario arises where a feature or data quality is in question, data quality takes precedence. This is because businesses cannot function optimally without the right quality of data. Providing accurate data at the right time is crucial for effective decision-making,” he added. — Analytics India Magazine, 4d ago

The return to volatility is highlighting data challenges within trading risk. From a process perspective, institutions need clear data controls and ownership. Having accurate, comprehensive and usable data is also vital. However, the quality and availability of data – particularly for newer and riskier asset classes – pose significant challenges. — Risk.net, 4d ago

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Finally, Data Quality is not just a technical issue; it’s also a cultural and organizational challenge. Currently, most data tools are designed for use by data professionals working with data, not for the average user. Because they are often used by data analysts or data scientists to manipulate data in order to gain insights or create reports, they may require coding knowledge or prior experience using other data tools for data cleansing, data transformation, data visualization, and data analysis. This may have worked well when data professionals were the only ones using data tools, but that is no longer the case. Modern Data Quality requires collaboration across departments and teams – not just IT teams or data scientists. Without a real culture of Data Governance and accountability, Data Quality issues will persist. — DATAVERSITY, 5w ago

In recent years, awareness of the importance of reproducible research and open science has grown in the research community. The importance of conducting robust, transparent, and open research has especially been highlighted by the reproducibility crisis, or credibility revolution (Baker, 2016; Errington et al., 2021; Vazire, 2018). Reproducible and open science practices increase the likelihood that research will yield trustworthy results, and facilitate reuse of methods, data, code, and software (Chan et al., 2014; Diaba-Nuhoho and Amponsah-Offeh, 2021; Downs, 2021; Ioannidis et al., 2014). Across fields, definitions of ‘reproducible’ and ‘open’ may vary. While some fields use the terms interchangeably, in other fields ‘reproducible’ includes elements of scientific rigor and research quality, whereas ‘open’ simply refers to making research outputs publicly accessible. Overall, these practices seek to improve the transparency, trustworthiness, reusability, and accessibility of scientific findings for the research community and society (Barba, 2018; Claerbout and Karrenbach, 1992; Nosek et al., 2022; Parsons et al., 2022; Wolf, 2017). Examples of specific practices include sharing of protocols, data and code, publishing open access, implementing practices such as blinding and randomization to reduce the risk of bias, engaging patients in designing and conducting research, using reporting guidelines to improve reporting, and using CRediT authorship statements to specify author contributions. Despite these developments, reproducible research and open science practices remain uncommon in many fields (Blanco et al., 2019; Grant et al., 2013; Hardwicke et al., 2022; Hardwicke et al., 2020; Page and Moher, 2017). — eLife, 11d ago

GC: One of the greatest challenges for data and analytics leaders today is the balance between exciting new developments like generative AI and the foundational work necessary for their successful implementation, such as data quality and ethics. The appeal of these ‘shiny objects’ can overshadow the importance of foundational data work. Overcoming this challenge is not straightforward, but I believe it hinges on effective communication and education. — coriniumintelligence.com, 6d ago

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Archiving secure messages is a crucial mechanism for safeguarding patient information and maintaining EHR integrity. This becomes especially pertinent when considering instances where vital data may have eluded EHR upload, potentially leading to information gaps. Hospital systems are turning to comprehensive platforms that streamline the process of adopting and deploying permanent record archiving, which proves beneficial in various scenarios:... — TigerConnect, 3d ago

As a wet-lab scientist, generating vast amounts of experimental data is a fundamental part of your work. Yet, the reliance on bioinformaticians and data specialists for crucial analysis often introduces its own set of challenges. While collaboration with these experts can be valuable, it frequently leads to delays and coordination issues. There are times when you might find yourself with ample data but no expert available for prompt analysis. Moreover, a lack of understanding of the analysis process can lead to misinterpretations when deriving conclusions from the results. — FEBS Network, 5d ago

The opaqueness in the decision-making process of LLMs like GPT-3 or BERT can lead to undetected biases and errors. In fields like healthcare or criminal justice, where decisions have far-reaching consequences, the inability to audit LLMs for ethical and logical soundness is a major concern. For example, a medical diagnosis LLM relying on outdated or biased data can make harmful recommendations. Similarly, LLMs in hiring processes may inadvertently perpetuate gender bi ases. The black box nature thus not only conceals flaws but can potentially amplify them, necessitating a proactive approach to enhance transparency. — unite.ai, 3d ago

In the poverty, machine learning, and satellite imagery domain, the status of transparency, interpretability, and domain knowledge in explainable machine learning approaches varies and falls short of scientific requirements. Explainability, crucial for wider dissemination in the development community, surpasses mere interpretability. Transparency in reviewed papers is mixed, with some well-documented and others lacking reproducibility. Weaknesses in interpretability and explainability persist, as few researchers interpret models or explain predictive data. While domain knowledge is common in feature-based models for selection, it is not widely applied in other modeling aspects. Sorting and ranking among impact features is an important future research direction. — MarkTechPost, 4d ago

There has been fast-paced growth in the application of artificial intelligence (AI) in cancer research. Despite this growth, there are challenges in both collecting and generating data in a way that makes it easily accessible and usable for AI/ML applications while maintaining security and data quality. Data which is accessible and usable for AI/ML applications is referred to as AI-ready data. AI-ready data can lead to the development of well-validated AI/ML models that can be deployed for research and improvement of healthcare. AI-readiness encompasses various characteristics, including completeness of the data (e.g., sufficient volume and managed missing values), incorporation of data standards (e.g., utilizing ontologies and terminologies whenever possible), computable formats, documentation (process and intent in generating the data), data annotations, data balance, data privacy, data security, among other features. — nih.gov, 4d ago

Continuous Emission Monitoring System (CEMS) is a tool for accurate and credible pollution monitoring and reporting in industries.However, the accuracy and credibility assurance of CEMS data comes fromits correct installation, proper operation and maintenance, and calibration. CEMS installed in industries generates huge quantities of data everyday but the quality of data poses a major challenge as its reliability needs to be ensured.To this end, thorough knowledge and skills of regulators, industries and relevant stakeholder needs to be developed on role of - certified instrument, correct installation, operation, maintenance and calibration in making CEMS data credible. — cseindia.org, 4d ago

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Raw data collected from different sources is often noisy, inconsistent, and unstructured. Data preprocessing involves activities such as data cleaning, normalization, and transformation to ensure the data is suitable for analysis. This step is crucial for accurate and meaningful results. — CXOToday.com - Technology News, Business Technology News, Information Technology News, Tech News India, 12w ago

Overall, a Marimekko chart is a powerful tool for holistic data visualization, empowering businesses to extract valuable insights from categorical data. The implementation of such data visualization techniques not only supplements decision-making processes but also amplifies audience engagement and understanding. By mastering the science and art behind a Marimekko chart, your data analysis can reach newer heights of coherence and usefulness. — CupertinoTimes, 9w ago

Despite the evident benefits of data-driven decision-making, challenges persist in its adoption. One significant challenge in data-driven decision-making is the quality and availability of data. Projects often face significant challenges, such as inadequate data collection methods and incomplete datasets. These challenges often undermine the reliability and accuracy of insights derived from data analysis, leading to flawed conclusions and ineffective decision-making. — usaidlearninglab.org, 12w ago

..."To build upon published scientific results, it's important that the data and corresponding analyses are scientifically accurate, reproducible, and accessible," Tischer explained. "For microscopy-based research, this ranges from issues like the legibility of image data in publication figures, providing scale information, and a responsible choice of contrast adjustments, to sharing image data on public archives and making accessible the analysis pipeline on cloud computing platforms."... — phys.org, 10w ago

DataOps has evolved as a vital concept in the digital transformation era, ensuring seamless data flow through an organization. It entails orchestrating data processing and data quality checks to guarantee that data is correct, consistent, and easily accessible. It is especially essential in the field of AI and Machine Learning, where the quality and accessibility of data can have a substantial impact on model performance. To understand patterns and generate accurate predictions, machine learning algorithms rely significantly on high-quality data. As a result, including DataOps in AI and Machine Learning initiatives can result in more efficient data processing, better data quality, and, ultimately, more accurate and trustworthy machine learning models. Here are the 5 applications of AI and machine learning for DataOps. — Analytics Insight, 11w ago

For an early career researcher, mastering the nuances of data is essential. Different research scenarios call for different data formats, and choosing the right one can make your work more efficient and impactful. For basic datasets, such as survey results or experimental observations, the comma-separated value (CSV) format is often ideal. It’s straightforward and compatible with many analysis tools, making it a good starting point for most research projects. — THE Campus Learn, Share, Connect, 5w ago

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A key message from tech companies is that large amounts of data are needed for LLMs to be insightful and useful (with corresponding large amounts of compute) so a major focus is getting large data sets from users (customers) and, interestingly, some companies indicated quality of data is a secondary but still important consideration. — InnovationAus.com, 5d ago

What’s changing now is the growing accuracy and accessibility of biomarker technologies in our daily lives. This is largely thanks to advancements in AI, which are enabling more accurate data capture, evaluation, and pattern spotting. Like in a clinical setting, a single biomarker typically doesn’t provide a complete picture of our health, and the merging of different data streams is critical in achieving accurate results, healthcare plans, and even cosmetic treatments. — MedCity News, 6d ago

While companies like Anthropic and OpenAI aim to safeguard training data through techniques like data filtering, encryption, and model alignment, the findings indicate more work may be needed to mitigate what the researchers call privacy risks stemming from foundation models with large parameter counts. Nonetheless, the researchers frame memorization not just as an issue of privacy compliance but also as a model efficiency, suggesting memorization utilizes sizeable model capacity that could otherwise be allocated to utility. — ReadWrite, 4d ago

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I hope everyone enjoyed the long weekend and short break! It is hard to believe that the semester is coming to a close. Each year, I rent a house on the Outer Banks of North Carolina to spend the break with my two sons. Sometimes, it’s just us. Other times, friends come along and fill the house. This year, it was just us. On one of the days, it rained – not in the way it rains in Colorado, but a 14-hour soaking rain. The day opened space to contemplate the future of public health how we make strides toward improving the health of our society.The COVID-19 pandemic was, and may continue as, one of the most substantial infectious disease threats in modern times that required an immediate public health response. However, the United States alongside other nations, was slow to provide widespread and convenient testing, distribute masks, and effectively communicate about safe practices and the changing scientific landscape. Nonetheless, the United States invested in new technology and developed an efficacious vaccine in record time. While its distribution, deployment, and uptake could have been improved, the scientific community achieved remarkable breakthroughs by sharing data and tissue samples at a pace not previously seen. Researchers openly collaborated at an international level. Meanwhile, the COVID-19 pandemic laid bare an inadequate public health infrastructure especially around inconsistent communication between federal, state, and local policies that prevented a cohesive response to the pandemic.What can we learn from the public-private partnerships that brought us exciting new treatments but also highlighted some of the shortcomings of public health? How can we use these lessons to reimagine the public health infrastructure? As the new dean of the Colorado School of Public Health, I’ve reflected at length as to why and how our nation rapidly responded to developing a new treatment, but large scale, transformational public health investments such as access to health care, new models of care delivery, and data integration across systems for policy development have been slower to come. In my first State of the School address, I suggested that public health, as a field and practice, is plagued by three myths that must be overcome. These myths are: public health isn’t sexy; public health isn’t a science; and public health is invisible until it fails.Myth #1: Public health isn’t sexy.As a society, we are drawn to new treatments and promises for a cure. The technology is exciting; the breakthroughs are breathtaking. What government or individual donor does not want to invest in an early-stage treatment that may cure or slow the progress of a disease that affects millions of people? The motivation for financial support is higher if this disease affects them or their loved ones. This enthusiasm remains high, almost without regard to a treatment’s chances of success, costs, and possible risk. How do we make the case for public health to be as equally exciting and breathtaking? Public health breakthroughs (e.g., clean water, sanitation practices, food inspection) have changed the course of history for civilization and have prevented countless deaths. Yet, the achievements of public health are not widely promoted as life-saving interventions. Public health interventions have a high chance of success, often come at low costs relative to the development of pharmaceutical interventions, and are generally associated with few downside risks. Tobacco companies made smoking sexy, a habit that is deadly, stinky, costly, and turns its users’ teeth yellow. Surely the case for public health’s ‘sex appeal’ is easier to make than the case made for tobacco products. We must be creative in how we change the narrative for public health.Myth #2: Public health isn’t a science.A quick google search defines science as “the pursuit and application of knowledge and understanding of the natural and social world following a systematic methodology based on evidence.” Public health professionals produce research that is grounded in theory, data driven, and evidence-based. Our papers are subject to rigorous review and our researchers compete for incredibly scarce resources—it is public health after all. Yet, the message of “science” often gets lost in the work we do and has even come under attack in recent years. “Science” is sometimes lost when we disseminate our evidence to colleagues in basic, translational, and clinical science who may not appreciate the complexity of our work. The average person understands that microbiology is a science but is unaware that public health research and practice is also a science and is guided by economic, social, and behavioral theories, among others.As a public health community, we must take responsibility for this perception and communicate more effectively about the thought and rigor that goes into what we do. Public health science uses data from complex tracking systems assembled for public health purposes, and often enhances those data with additional data that were assembled for other purposes but can inform our models and subsequent decisions. These data are stress tested with varying assumptions and sensitivity analyses and then frequently updated with new data. Furthermore, our scientists develop new methods to handle the ensuing complex analyses. Public health science exists at the intersections of human behavior, environmental forces, policy, society at large, and health. Therefore, our landscape is continually changing, and our scientists have to be nimble in response. A good example is how well our faculty worked together to produce evidence for Colorado’s governor to make data-driven and evidence-based decisions. We must do much more to educate everyone within and outside of our field about the science of public health and that our process is no different than basic, translational, and clinical science.Myth #3: Public health is invisible until it fails.Despite public health’s struggles with sex appeal and perceptions about its science, much of public health is “invisible” because it works so well. We take for granted that our food and drinking water are safe and that smoking is prohibited on airplanes. Most of us instinctively reach for the seatbelt when we settle into a car – all because of public health. However, when these measures were first introduced, they were met with resistance. We owe it to our field to point out the areas where public health continues to save lives. It is in these examples where we regain trust and convince the population, including policymakers, to adopt new measures that make our world a safer place where we can all thrive.How is public health not sexy when it saves so many lives? How is it not science when public health is theory grounded, data driven, and evidence-based? And how is public health invisible when there are so many examples of public health in action all around us, every day? Public health is visible, but it needs to be clearly understood.There are not enough resources in our society to treat each individual who has a health need. Because of this, societal level interventions are needed to make us safer, saner, and stronger. It is public health where such interventions are developed – and it is worthy of repeating that they are grounded in theory, data driven, and evidence-based, or simply put, science. — cuanschutz.edu, 4d ago

What’s hindering progress? Lack of data quality and data infrastructure are the two most often cited reasons in the survey. And “many CFOs complain about a prevailing silo mentality that hinders cross-divisional collaboration — a basic prerequisite for making large amounts of data usable,” said Horváth. “Resistance to change” was the fourth most-cited reason. — CFO, 5d ago

Standardized Frameworks: The lack of a standardized approach to collecting, analyzing and utilizing SDOH data is a significant hurdle. Without a coherent framework, the efforts of healthcare providers may remain siloed, thereby diminishing the potential impact of the collected data on patient care and outcomes. Developing standardized frameworks for SDOH data collection, analysis and utilization can drive consistency, collective utility, and quality in SDOH data practices. — hitconsultant.net, 6d ago

The role of a data quality assistant is critical in ensuring the accuracy and reliability of data. This position offers a practical understanding of the importance of data integrity in data analyst careers. It’s also a stepping stone to senior data analyst jobs, where professionals apply these skills to larger data sets and more complex projects. — Emeritus Online Courses, 5d ago

The cornerstone of modernising a laboratory is digitising data management. The era of relying on manual logs and paper-based records is fading, giving way to advanced digital solutions. Laboratory Information Management Systems (LIMS) are at the forefront of this transformation. By integrating a LIMS into your laboratory’s workflow, you’re not only streamlining the way data is recorded and accessed but also minimising the potential for human error, which is crucial in any scientific research. — ValiantCEO, 5d ago

Does the success of FMT in combating C. difficile infection mean researchers can manipulate the microbiome to preventatively remove (decolonize) resistant microbes, making antimicrobials more effective in clinical settings? What microbes are the most important for preventing and combating resistant infections? Answering these, and other similar questions, is the goal of many research teams worldwide; however, these teams do not answer such questions in isolation. Policymakers play a critical role in determining the direction of research by setting broad scientific goals, allocating and administering funding and creating infrastructure for research and implementation. While researchers' primary focus should be the pursuit of science, there is a need for engagement in the policymaking process. Moreover, there is an explicit role for researchers in addressing the broader policy questions surrounding microbiome-based therapies, such as scalability, equitable access, regulation and the ethics of such therapies. — ASM.org, 6d ago

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At this time, a time of extraordinary advances in computer science, robotics, artificial intelligence, and general technical capabilities, the point has been reached when new and emergent technologies can be used to explore large data banks in a faster and more efficient way. Increased data storage capability and data acquisition and transmission speed, more than ever before justify and facilitate the use of archived materials to decrease the number of animals needed to be sacrificed for biomedical research and give investigators additional tools to advance human and animal health-related research. — Open Access Government, 6d ago

The study shows that in order to truly inform disease management, RWD needs to be collected for all patients and not restricted to specific populations. In contrast to clinical trial data, which can be considered to represent ‘perfect data from the imperfect, highly selected patient’, RWD represents ‘imperfect data from the perfect patient’, that is, the patient we see in the clinic. The beauty of RWD is that imperfect data can be made better by improving data sources and by using advances in technology to enhance data extraction from electronic medical records (EMRs) and to facilitate data curation and qualification. — Daily Reporter, 5d ago

This well-illustrated technical data story by GIJN Turkish editor Pınar Dağ emerged as one of GIJN’s most-read stories of 2023. And it is perhaps no coincidence that one of the more popular stories in 2022 — “Free, Game-Changing Data Extraction Tools that Require No Coding Skills” — was also a practical story on data scraping that required no technical coding. This speaks to the reality that only a small fraction of watchdog journalists have coding or even intermediate computer programming skills, but that a majority recognize the importance of getting available data into spreadsheets they can work with. Dağ’s story was also significant for editors because it lists the many reasons why data scraping is essential for many projects. Most importantly: this piece explains, point by point, how any reporter can use the free Data Miner browser extension to extract relevant information from websites, so reporters in under-resourced newsrooms can focus on the culprits and human impacts of the issue they’re probing. — gijn.org, 5d ago

Nuclear power has long been a crucial energy source, but as plants age, decommissioning them becomes a pressing concern. The process of decommissioning nuclear facilities and cleaning up radioactive materials is complex, hazardous, and requires meticulous attention to safety protocols. In recent years, advancements in robotics and automation have emerged as indispensable tools in this critical task. — InnovateEnergy, 6d ago

Data manipulation and analysis form the core of data science. Software engineers can leverage their coding skills to become proficient in libraries like Pandas (Python) or data.table (R) for efficient data manipulation. Additionally, gaining expertise in SQL is crucial, as it remains a fundamental tool for extracting, transforming, and loading (ETL) data. — Analytics Insight, 6d ago

Ensuring the security and privacy of data has always been a challenge for organizations. Handling requests for data access is a significant operational challenge, as well. Organizations need to establish processes for verifying the identity of data subjects and responding to their requests within regulatory timeframes. — DATAQUEST, 7d ago

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..."Nowadays, a vast community of biologists, chemists, engineers, and physicists are concerned with delivering precise numbers of photons," explains a research team whose work has just been published in Nature Methods. On a larger scale, precision is also essential for critical tasks such as water purification and phototherapeutics. — phys.org, 6d ago

However, current studies are limited, often examining only one or two genotypes, and are constrained by time-intensive manual measurements. Recent advances in high-throughput phenotyping, using technologies like RGB cameras and LiDAR, have facilitated more efficient data collection. Despite these advancements, a significant gap persists in the development of automatic, field-based methods for tracking maize leaf orientation—an essential aspect for comprehending genotype-to-environment interactions and optimizing yield under high-density conditions. — phys.org, 6d ago

What sets ARNA Genomics apart is not only its technological innovation but also the integration of blockchain technology for data management of clinical trials. This integration ensures the highest levels of security and reliability in handling sensitive medical data, crucial in the field of healthcare. The use of blockchain technology in this context is a testament to ARNA Genomics' commitment to leveraging the latest advancements in technology to enhance healthcare services. Trust in clinical trials is indeed a cornerstone of modern medicine. It underpins the validity and acceptability of research outcomes in the medical community and among the public. Trust is built through rigorous scientific methodologies, transparent reporting, adherence to ethical standards, and regulatory oversight. Ensuring participant safety, data integrity, and unbiased results are essential to maintaining this trust. Internal software development is the key instrument to have reliable and trustable data from clinical trials of ARNA Genomics technology. This software system with blockchain and deep-learning technologies on board is under development by Vladimir Savanovich and his partner George Nikitin. — Science Times, 6d ago

Bullfrag, 27d ago

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medicalxpress.com, 18d ago

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Problem-solving: Problem-solving remains a constant need, particularly as AI and LLMs introduce new challenges. The stable importance of this skill indicates that while AI can provide significant data analysis, humans are still needed for ultimate decision-making, especially when ethical or complex considerations are involved. — ISACA, 8w ago

Genomic data generated by next generation sequencing (NGS) plays an increasingly important role in scientific innovation and research. The NGS process encompasses a series of detailed steps, including sample collection and processing, nucleic acid extraction, library preparation, quality control, sequencing, data analysis and integration into laboratory information management systems. This process can be daunting, especially for labs that are new to NGS and are considering routine use of genomic sequencing data. This data is crucial to driving research into human health improvements as reflected in research publications and ongoing studies in many disease identification efforts, including neonatal research applications. — www.labbulletin.com, 5w ago

...“To build upon published scientific results, it’s important that the data and corresponding analyses are scientifically accurate, reproducible, and accessible,” Tischer explained. “For microscopy-based research, this ranges from issues like the legibility of image data in publication figures, providing scale information, and a responsible choice of contrast adjustments, to sharing image data on public archives and making accessible the analysis pipeline on cloud computing platforms.”... — EMBL, 11w ago

The integration of AI in mental health care is not without its challenges. Notably, ethical concerns arise, encompassing issues of privacy, data security and the potential for bias in algorithms. Furthermore, the lack of human touch in AI interventions may hinder the vital human connection and empathy necessary for effective mental health care, potentially impacting patient trust and engagement. Limited data availability poses another obstacle, as AI models require extensive, high-quality data to be trained accurately, yet mental health data is often limited, fragmented and sensitive. This limitation can make it challenging to develop precise AI algorithms for mental health care. — Telecom Review, 5w ago

Data pipelines play a fundamental role in maintaining data quality. By enforcing data cleansing, validation, and transformation processes, pipelines ensure that data is accurate, consistent, and reliable. High data quality is a prerequisite for meaningful analysis and reporting. Well-designed data pipelines ensure that data is processed efficiently, reducing latency and enabling faster data-driven decisions. — Datamation, 8w ago

...mapMECFS is the largest data repository for ME/CFS data and serves as a primary location for researchers to share the results they generate in their laboratories. Research that involves human subjects requires careful planning and ethical reviews, as well as participant briefings, enrolment and follow-up. As a result, these studies can be costly and take many years to complete. — rti.org, 4w ago

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Employing automated methods for sample preparation can improve data consistency and reproducibility, as well as increase throughput and reduce processing time. This is particularly essential with multiplex assays, which are extremely time-consuming and open to excessive variability when performed manually. The foundational vision of Parhelia Biosciences was to provide a cost-effective solution allowing labs of all sizes to eliminate variation in multiplex assays and optimize data quality and reproducibility. — GEN - Genetic Engineering and Biotechnology News, 7d ago

As science progresses from molecular to organism levels, integrating AI models across various stages of drug discovery requires high data compatibility and substantial contextual data—both strong drivers for lab automation. Scientists are actively seeking innovative systems to intelligently complete missing values in multidimensional data matrices. In addition, the advancement of personalized medicine and better diagnostics has produced numerous compounds in smaller quantities. Ensuring data reproducibility from the early stages of research, even in medicinal chemistry labs, is integral to this process. — insideBIGDATA, 11d ago

While the topic of women’s contributions to boardroom decision-making is important, Professor Edmans believes this particular paper falls short in providing robust evidence. The issues with the study design, sample selection, and data analysis methods mean that the conclusions drawn cannot become reliably supported by the evidence presented. It is crucial to explore boardroom diversity further, but with more rigorous research methods that can yield unbiased and substantiated findings. — Rebellion Research, 8d ago

The most important takeaways are these: In today’s world, statistical and data literacy are as important as any other literacy. Nor are data interpretation or risk analysis or statistical reasoning just for researchers. — Inside Higher Ed | Higher Education News, Events and Jobs, 7d ago

Elevated data quality – These tools incorporate data cleansing and validation mechanisms to identify and correct errors, inconsistencies, and missing values. This process ensures that the data used for analysis and decision-making is accurate and reliable. — Insightssuccess Media and Technology Pvt. Ltd., 7d ago

Automating the data visualization process becomes a designer's invaluable ally when tackling data sets containing millions of data points. With such vast amounts of data to work with, automation streamlines and simplifies the designer's tasks, ensuring efficiency and accuracy in the visualization process. As a result, designers can save substantial time and effort by automating data visualization with map drawing software. But the question remains: which tool is the best for mapping?... — SME Business Daily Media, 8d ago

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In recent years, data science has emerged as a crucial tool in the realm of environmental protection. Its integration into conservation efforts has proven to be highly effective. Data science is revolutionizing environmental protection by leveraging data to solve complex challenges. It aids in monitoring pollution levels, analyzing climate trends, and predicting natural disasters. By analyzing air and water quality data, data science helps track pollution sources and their impact. Real-time data aids authorities in taking swift actions to mitigate pollution and protect ecosystems. Data science facilitates the creation of intricate models simulating complex environmental systems. These models help researchers simulate various scenarios, predicting the outcomes of different conservation strategies. — globaltechcouncil.org, 9w ago

Data is only valuable when it is accurate, reliable, and consistent. Data quality assurance processes are crucial for identifying and rectifying data errors, inconsistencies, and duplications. But data quality assurance isn’t a one-and-done exercise—it’s an ongoing commitment that requires continuous monitoring and improvement to maintain data accuracy and reliability over time. By regularly and systematically cleansing and validating your data, you can mitigate the risk of faulty information leading to costly errors, misguided decisions, and a loss of trust in the information being used. — Datamation, 10w ago

In addition to being designated as a DOE Office of Science PuRe Data resource, the ARM Data Center has been recognized as a CoreTrustSeal repository. It is also a member of the World Data System. The center uses the FAIR data principles of Findability, Accessibility, Interoperability and Reusability in its data management practice. Following the FAIR principles helps ensure that data are findable and useful for repeatable research. These principles are especially important as scientists increasingly rely on data digitization and artificial intelligence. — ornl.gov, 10w ago

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...3. Pandas: Pandas is a foundational library for data manipulation and analysis. In 2024, Pandas continues to be an essential tool for cleaning, transforming, and analyzing data. With its intuitive DataFrame structure and extensive functionality, Pandas is the backbone of many data science projects, facilitating efficient data exploration and preparation. — Analytics Insight, 10d ago

Despite great advances in data collection, the detailed picture of global country-country migrant flows remains incomplete. The lack of a comprehensive, high-quality data base covering not only stocks, but also flows is and remains the pivotal obstacle for the analysis of international migration at a global level. In the current situation it occurs, that methodological progress enabling the handling of large migration data sets is much more advanced than (raw) data collection and the harmonization of that information. Therefore, some results of the novel methods converting information on stocks into flow estimates might be instructive, but cannot be fully used for research and sound policy advice, as long, as the raw-data problem is not solved or significantly tempered. — knomad.org, 11d ago

The process of data collection has never been straightforward. Back in the 1990s, researchers had to manually capture photographs to assemble datasets for objects and faces. The 2000s saw individuals scouring the internet for data. However, this raw, uncurated data often contained discrepancies when compared to real-world scenarios and reflected societal biases, presenting a distorted view of reality. The task of cleansing datasets through human intervention is not only expensive, but also exceedingly challenging. Imagine, though, if this arduous data collection could be distilled down to something as simple as issuing a command in natural language. — SciTechDaily, 10d ago

We are delighted that Prof. Xiaoxiang Zhu is one of the world's most frequently cited scientists. The Chairholder of Data Science in Earth Observation conducts research on globally available geoinformation derived from data from Earth observation satellites, which is indispensable for tackling major societal challenges. Whether energy, urbanization, climate change or food security - her innovative technological approaches and analysis methods for processing large amounts of data are crucial for a sustainable future. Zhu develops innovative methods of signal processing and machine learning as well as solutions for Big Data analysis in order to obtain precise geoinformation from a large amount of earth observation data. — tum.de, 14d ago

The core focus of the lab's work is making AI/ML datasets and models both leaner and more robust. This involves increasing information density and signal-to- noise ratio; preserving privacy of sensitive information and PII; and implementing novel data compression techniques. These measures optimize training data storage and processing, elevating model performance and scalability. The research lab is central to shaping a more privacy-conscious future for AI, exemplified by its groundbreaking products Granica Crunch, Granica Screen and Granica Chronicle. — siliconindia.com, 9d ago

Abstract:Large language models (LLMs), deep neural networks with billions of parameters, are revolutionizing scientific research, particularly in chemistry, where traditional methods often involve lengthy and resource-intensive processes. Traditionally, scientific research follows a trial-and-error strategy, often requiring extensive time and resources. For instance, the drug development process requires an investment of ~15 years and $1 billion. However, the impracticality of exploring the vast space of possible chemical compounds through such methods reveals the need for a more efficient strategy. Data-driven methods offer an alternative way of addressing this paradigm to accelerate discovery in chemistry. More specifically, LLMs provide an even further paradigm shift by requiring only natural language as input. These models allow the use of natural language to interact with complex data, simplifying and speeding up research processes. Their applications extend to property prediction, molecule optimization, and efficient knowledge retrieval, demonstrating that language can effectively represent chemical data. Our research shows that language models can be applied to predict blood-brain barrier permeation and solubility with uncertainty. We deployed this model in an open web application to improve usability and reproducibility. Another result of our research shows how we can use natural language descriptions of chemical procedures to optimize their outcome. These are examples of how LLMs are not just reshaping the approach to chemical research but also significantly reducing the time and resources required for scientific breakthroughs. For this reason, our vision is that LLMs represent a significant step forward in how we do science nowadays and that language is the future for chemical representation. — rit.edu, 13d ago

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Navigating the complexities of scientific research often involves juggling large data sets, multiple tools, and specialized software–especially in the realm of high performance computing (HPC). In an environment where the lack of reproducibility in scientific studies is a growing concern, tools that enhance accuracy and consistency are invaluable. In the Ebbert Lab, a part of the University of Kentucky’s Sanders-Brown Center on Aging, we’ve found that software containers are essential, not just for streamlining our workflows, but also for addressing the critical issue of reproducibility in our Alzheimer’s disease research. In this article, we will explore the challenges of achieving reliable and repeatable scientific results, our focus on Alzheimer’s disease research, and how software containers have improved both the efficiency and reproducibility of our work. — HPCwire, 12w ago

To improve advances in scientific research, the National Institutes of Health has emphasized rigor and reproducibility, where rigor ensures “robust and unbiased experimental design, methodology, analysis, interpretation, and reporting of results,” while reproducibility is evident when data can be “reproduced by multiple scientists” (1). However, even in rigorous and reproducible research, there is increasing evidence that results using genetically homogeneous preclinical models for disease can fail to translate to a genetically diverse human patient population. The relative ease with which results can be gathered using a single model often leads researchers to discount the possibility that the results may not be representative of more diverse genetic backgrounds, reducing the translational potential for humans. To improve translation, we propose as one solution that a robustness test should be considered to confirm that results are “robust across heterogeneous genetic contexts,” thereby improving prediction of likely responses in heterogeneous patient populations. Furthermore, robustness approaches could be leveraged to identify biomarkers that prognosticate likely responders, heightening public health outcomes and alleviating financial burden. This general concept pertains to all genetically homogeneous preclinical models as well as large, genetically ill-defined outbred animals used in small numbers for safety testing, but mice will be used as the exemplar given their extensive use in modeling therapeutic efficacy in human diseases. — jci.org, 11w ago

With advanced data collection, ethical considerations take center stage. The challenge lies in finding a balance between comprehensive data collection and preserving privacy. Data relevance and use are also important. Data should not be collected unless the need for it is clear, and collected data must be analyzed, the statistics produced made widely available, and effectively used. This calls for strong national systems that follow the ten fundamental principles of official statistics, as well as government officials, civil society and development practitioners who are data literate and equipped to use data effectively. — ESCAP, 28d ago

While data catalogs are often seen as boring and arduous, their importance and potential shouldn’t be underestimated. Although cataloging data can be a tedious and time-consuming process, it serves as a vital component for long-term data quality initiatives. Rather than treating data catalogs as disconnected tools, organizations can integrate them into their systems, allowing for ongoing updates and leveraging the captured information for future benefits. — Datanami, 11w ago

Another vital aspect is Data Quality. Poor-quality data can lead to incorrect analysis and flawed decision-making. Data Management solutions provide mechanisms for data cleansing, validation, and enrichment, ensuring that only high-quality information is used throughout the implementation process. — DATAVERSITY, 12w ago

Due to the lack of uniformity in documentation standards, varying accessibility, and outdated systems making data abstraction difficult, reporting data across multiple EHRs can be a daunting task. The central challenge for ACOs is to aggregate data from diverse EHRs, normalize it, and identify and address care gaps. This preparation is crucial for successful quality reporting via eCQMs. However, it offers new opportunities for cost and quality improvements across providers. — Health IT Answers, 4w ago

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Why, you might ask, is Data Governance so critical in today’s data-driven world? Imagine data as the lifeblood of an organization, coursing through its veins to make informed decisions, power operations, and drive innovation. Without proper governance, this lifeblood can become polluted, leak away, or stagnate, leading to dire consequences. Data Governance instills trust. A robust governance framework ensures compliance with regulatory requirements, safeguarding sensitive information and preserving the organization’s reputation. By defining data ownership, establishing data standards, and enforcing data quality controls, organizations can rely on their data as a trusted asset. When data is well-managed, employees spend less time searching for information and more time using it productively. This optimization of data processes directly impacts the bottom line, reducing costs and increasing revenue. — Comparitech, 12d ago

Data privacy is a critical concern in eDiscovery, as organizations handle vast amounts of sensitive data during the discovery process. It is essential to protect individuals’ personal information from unauthorized access, disclosure, and misuse. To ensure data privacy, organizations must implement strict access controls, encryption, and anonymization techniques. Additionally, data minimization principles should be followed to only collect and process the necessary data for legal purposes. By prioritizing data privacy, organizations can maintain compliance with regulations and build trust with stakeholders. — Techiexpert.com, 9d ago

My work concerns large-scale interoperation of observation and experimentation systems coupled with information systems (before on terrestrial and marine (agro)ecosystems, now in health sciences with a focus on pathogenic agents). This includes the dissemination of life science knowledge and data sharing practices for meta-analyses. I am particularly interested in new methods of data mining and data representation in the form of graphs to analyze heterogeneous data, and in metadata schemas, data annotation, data quality and semantic interoperability topics. I am also a FAIR data management and open science addict and "Data Management Plan with FAIR Compliance" evangelizer. — RDA, 14d ago

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In conclusion, SoT emerges as a promising solution to the persistent challenge of slow LLMs. The research team’s innovative approach of treating LLMs as black boxes and focusing on data-level efficiency optimization provides a fresh perspective on accelerating content generation. By prompting LLMs to construct a skeleton of the answer and then executing parallel expansion, SoT introduces an effective means of improving response times. The results from the evaluation demonstrate not only considerable speed-ups but also the ability to maintain or enhance answer quality, addressing the dual challenges of efficiency and effectiveness. This work opens up avenues for future exploration in dynamic thinking processes for artificial intelligence, encouraging a shift towards more efficient and versatile language models. — MarkTechPost, 10d ago

In biopharma manufacturing, there is a fine line between being data rich and information poor. Sites are filled with disparate systems, increasing the potential for siloed data. But since data is so key, biopharma companies must find better ways to mine that data and use it to improve manufacturing processes and achieve data infrastructure excellence. Which side of the line a company lands on largely comes down to data infrastructure excellence – mastering how data is collected, stored and leveraged to drive meaningful change. Many are looking at data historian technology to deliver multiple-system and cross-site data collection, storage and analysis to drive business innovation and efficiencies. — BioPharma Dive, 14d ago

An AI system is only as good as the data it’s trained on. Ensuring this data is diverse, comprehensive, and high-quality is essential. This means sourcing data from varied points, vetting it for accuracy, and ensuring it represents a broad spectrum. Diverse data leads to more robust AI models, minimizing biases and maximizing applicability. — Datanami, 10d ago

Application – High-quality data is vital in any data-related operation, being a priority in scenarios where you deal with analytics, visualization, reporting, and decision-making. Data with a high degree of integrity is important in business sectors where the reliability of data is key. For example, data integrity is essential in the healthcare system and financial records. — Robotics & Automation News, 10d ago

Beyond legal obligations, data anonymization fosters a culture of data sharing and collaboration among researchers. By alleviating concerns about privacy breaches, institutions are more likely to share datasets, accelerating scientific progress. Collaborative efforts become more viable, as researchers can pool resources without compromising patient confidentiality. — DATAVERSITY, 13d ago

Additionally, variability in sample handling, storage, and processing can also affect biomarker measurements. Standardization of pre-analytical, analytical, and post-analytical processes is crucial to minimize these variations and ensure reliable biomarker results. Efforts are underway to develop standardized guidelines and protocols for biomarker measurement and interpretation. However, the lack of uniformity in biomarker assays and methodologies remains a challenge for the market. — Medgadget, 12d ago

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Firstly, ensuring data encryption for critical information remains a significant challenge, especially in large, distributed organizations where data might be spread across various locations. Encrypting all this data requires meticulous attention and resources. Additionally, obtaining consent for data usage from individuals becomes crucial. This consent aspect adds complexity, especially concerning the storage and usage of sensitive data. Organizations need to navigate through these consent-related procedures while ensuring compliance with regulatory frameworks. — DATAQUEST, 11d ago

However, this study is not without its limitations. The reliance on GPT-4 as an evaluator for benchmarks introduces a bias towards models that are distilled from it, potentially favoring over accurate responses. Additionally, the scalability of this method to larger models, such as LLAMA2-70B, and its impact on performance gains remain areas for further research. These limitations highlight the need for continuous innovation and the development of unbiased evaluation methods in the AI community. — unite.ai, 11d ago

But the fact that explainability is important doesn’t make it easy. Even before ChatGPT, when less spectacular forms of AI roamed the Earth, explainability was a challenge. Machine-learning applications vary significantly. For the most part, developers and deployers rely on after-the-fact analyses, which can be horribly misleading. With big data, many methods do not lend themselves to both accuracy and true interpretability. This is an even bigger problem with foundational models and generative AI. — Centre for International Governance Innovation, 11d ago

While batch processing has been a reliable workhorse in the data world, it struggles to fulfill real-time requirements for freshness, especially when results need to be delivered within seconds or sub-seconds. To achieve faster computation results with batch processing, users may consider using orchestration tools to schedule computations at regular intervals. Pairing orchestration tools with batch processing jobs at regular intervals could suffice for large-scale datasets, but it falls short for ultra-fast real-time needs. — Datanami, 13d ago

...– Patient data is dispersed across various sources, necessitating meticulous planning for aggregation. Standardizing data types and formats, ensuring quality and accuracy, and establishing data governance processes are crucial. Collaboration among organizations is essential for successful data pooling and analysis. — WriteUpCafe.com, 14d ago

When working with data, a picture may be worth a thousand words, but an actionable dashboard is worth much more. An agile data science team should implement back-end improvements in the data architecture, improve data quality, and evaluate data sets every sprint, but the goal should be to present a working tool to end-users as early as possible. Agile data science teams need early feedback, even if all the capabilities and data improvements are works in progress. — infoworld.com, 13d ago

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The existing literature reveals four key objectives in data processing. Data cleansing is used to improve data quality, consistency, and reliability; feature selection to identify the most relevant variables; sampling to balance data distributions; and feature engineering to refine raw data for modeling. Our study reviews methods for each objective and analyzes the association between them with data modalities, rarity groups, and downstream tasks. — aihub.org, 12d ago

The quality of data from each geological case is essential, but quality monitoring is a huge issue when working on a continental scale using hundreds of millions of data points. So, Earth AI built a half-automated expert-driven data review system that dramatically speeds up data quality review. For example, domain-specific software focuses on finding and remembering data errors and inconsistencies and fixing them at scale. Teslyuk says:... — diginomica, 14d ago

Data Classification: First and foremost, AI’s ability to facilitate precise data classification lays the foundation for enhanced Data Management. This capability empowers leaders to meticulously organize and categorize their data assets, resulting in improved Data Quality and accessibility. Such streamlined Data Management processes not only fuel more efficient decision-making but also fuel innovation by providing a solid data foundation upon which creative solutions can be built. — DATAVERSITY, 12d ago

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Another significant challenge in developing a data strategy is insufficient data quality and governance. Poor data quality can lead to inaccurate insights, flawed analysis, and misguided decision-making. Without proper data governance practices in place, organizations may struggle to ensure data consistency, integrity, and compliance with relevant regulations. — 7wData, 5w ago

In the vast landscape of analytics, data stands as both the compass and the map. Ensuring its accuracy and clarity is paramount for analysts seeking true insights. Through proactive data cleaning strategies and continuous maintenance, not only are immediate errors rectified, but a culture of data quality is nurtured. As the digital age surges forward, with data at its core, mastering the art of data cleaning becomes even more vital. It’s a commitment to precision, to clarity, and ultimately, to the truth that lies within the numbers. For the modern analyst, clean data isn’t just a goal; it’s the gold standard. — Startup Info, 24d ago

The key challenge for scientific code is balancing the need for flexibility and stability. This is especially true of science because results should be reproducible (between labs, between the past and the future, and between different experimental setups) while keeping up with rapidly changing requirements (e.g., due to new kinds of data, theories, and analysis methods). To meet these needs, we designed Pynapple, a general toolbox for data analysis in systems neuroscience with a few principles in mind. — eLife, 6w ago

Researchers, data scientists, and AI developers using big, pervasive data about people face a significant challenge: navigating norms and practices for ethical and trustworthy data use. In response, the six‐campus PERVADE project has conducted research with data scientists, data subjects, and regulators, and has discovered two entwined trust problems: participant unawareness of much research, and the relationship of social data (re)use to corporate datafication and surveillance. In response, we have developed a decision support tool for researchers, inspired by research practices in a related but perhaps surprising research discipline: ethnography. This talk will introduce PERVADE's research findings and the resulting decision support tool and discuss ways that researchers and developers working with pervasive data can incorporate reflection on awareness and power into their research. — UCF Events, 11w ago

Due to the critical nature of SAP master data and the complexity of master data records, automating maintenance processes can often be challenging. Many companies focus on initial data quality; however, data errors are easily introduced during later updates—particularly when the organization lacks a robust strategy for ensuring data integrity. — Agility PR Solutions, 8w ago

OpenRefine (formerly Google Refine) is a powerful tool for cleaning and transforming messy data. While not as prominent as other tools, its data preparation capabilities are invaluable for ensuring data quality and usability. — Analytics Insight, 12w ago

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...“In the ACWA lab, I enjoy developing water topologies (line, star, and bus) and mounting sensors on them. It was a unique and required experience for me as a postdoctoral associate. This lab provides a platform for conducting different water and soil experiments, which are otherwise very expensive and complex. The development of this lab was needed, especially considering the lack of data availability for AI advancement in water and agriculture domains. Using this lab, researchers can simulate different water distribution and soil experiments and understand the chemicals' effects while collecting real-time data. This data can be further used to develop AI techniques for essential applications such as soft sensors, anomaly detection, and data poisoning.”... — newswise.com, 13d ago

Existing at-home collection methods are either not-suitable or not-tested for untargeted metabolomics. Typical devices often use salts and detergents that are incompatible with liquid chromatography mass spectrometry (LC/MS). Some may contain reagents that create unwanted reactivity. Any devices must maintain the metabolomic profile of the sample without a loss of sensitivity and accuracy. Metabolon validates at-home collection devices for sensitivity, precision, fidelity, stability and field testing. Validation ensures that the data generated from samples collected using these devices are reliable and accurate. Validation is essential for making informed decisions in research, clinical diagnostics, and patient care. Validation facilitates integration with our Global Discovery Panel workflow. — Metabolon, 14d ago

Just as customer data is critical in personalization, it is also important in improving AI systems. Customer feedback, in the form of surveys and reviews, is a catalyst for refining applications and making improvements if AI expectations change. Developers can refer to this feedback loop to inform ongoing adjustments. Teams must remember to regularly fine tune the AI model to incorporate new data, learn from unanswered or ambiguous queries, and understand changing language nuances. Partnering with an AI provider that prioritizes continual monitoring and improvement can ease the burden on internal IT teams. — RTInsights, 11d ago

By reviewing multiple data points from various sources and angles, we can capture richer insights and patient behavioral trends to inform smarter decisions in drug development, but this happens with intent, not by accident. Given the massive amounts of data acquired in clinical tests, it is simply not efficient to manually collect, monitor, clean and analyze large data volumes without a meaningful data strategy. To ensure data insights are appropriately leveraged to inform decisions with patient safety and data quality in mind, sponsors and clinical research organization partners must define the data strategy before protocol design. This strategy will map out optimal data collection from a growing mix of traditional and digital data sources during trial planning, including the notable considerations for managing data flow detailed below. — hitconsultant.net, 13d ago

One major challenge clinicians face is justifying their coding choices, particularly in time-sensitive situations, such as when coding for mental health issues like depression or conducting alcohol screenings. Many clinicians grasp the importance of precise coding, not only for billing purposes but also for analyzing behavior, such as prescribing practices, clinical patterns, and the measurement of quality metrics. For instance, coding analytics may highlight instances where antibiotic prescribing patterns appear inappropriate, prompting clinicians to question whether their decision-making in patient charting truly matters. Appropriate documentation is how clinicians communicate with patients and each other and relays pertinent information regarding our thought process when making clinical decisions.Coding is not one of them but a necessary evil a clinician must embrace-and embrace well. — Physicians Practice, 13d ago

At the same time, “there are diminishing returns for training large models on big datasets,” Lake says. Eventually, it becomes a challenge to find high-quality data, the energy costs rack up and model performance improves less quickly. Instead, as his own past research has demonstrated, big strides in machine learning can come from focusing on slimmer neural networks and testing out alternate training strategies. — Scientific American, 13d ago

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He continues: “Many analytical programs haven’t delivered the ROI businesses expected because data scientists often haven’t received the correct data. Additionally, the data might not be updated frequently enough, or it might not meet the quality and conformity standards the business needs. The depth and history of data are also essential for analytical models.”... — coriniumintelligence.com, 4w ago

Furthermore, big data management involves dealing with data veracity. With the multitude of data sources, it becomes essential to ensure the accuracy and reliability of the data. Data quality issues, such as inconsistencies and errors, can impact decision-making processes and lead to inaccurate insights. — Techiexpert.com, 12w ago

Not only does standardization become paramount when ensuring accurate measurement, attribution and optimized campaign results, but without good data in place, AI tools would be ineffective. — Digiday, 6w ago

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Data Science Vs Software Engineer: Which Is A Better Career? - Plato Data Intelligence

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AI-driven data analysis could exacerbate misaligned incentives in biomedical research

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How Should We View Biased Clinical Data in Medical Machine Learning? A Call for an Archaeological Perspective