International Research Data Analysis Excellence Awards: September 2024

30 September, 2024

Benchmarking !

Benchmarking is the process of comparing a company's performance metrics to industry standards or best practices from other organizations. It helps identify areas for improvement, set performance goals, and implement effective strategies.

There are several types of benchmarking:

Internal Benchmarking: Comparing performance within different departments or units of the same organization.
Competitive Benchmarking: Comparing with direct competitors to understand market position.
Functional Benchmarking: Comparing with organizations from different industries that have similar functions.
Generic Benchmarking: Looking at processes or practices that are considered best in class, regardless of the industry.

The benchmarking process typically involves:

Identifying Objectives: Defining what you want to achieve through benchmarking.
Selecting Metrics: Choosing the specific performance indicators to compare.
Collecting Data: Gathering information from internal sources and external benchmarks.
Analyzing Results: Evaluating performance gaps and identifying areas for improvement.
Implementing Changes: Applying insights to enhance performance.
Monitoring Progress: Continuously tracking performance to ensure improvements are sustained.

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28 September, 2024

Big Data !

Regulatory Changes: Many countries are tightening regulations around data privacy and protection, impacting how organizations manage and govern their data. The EU's GDPR and California's CCPA continue to influence global standards.
AI and Data Governance: As organizations increasingly adopt AI, the need for robust data governance frameworks to ensure ethical and responsible use of data has become critical. Companies are focusing on transparency, bias mitigation, and compliance.
Data Fabric Architecture: Organizations are adopting data fabric architectures to streamline data governance processes. This approach integrates various data sources and provides a unified view, making it easier to enforce governance policies.
Cloud Data Governance: With more data moving to the cloud, organizations are prioritizing cloud-specific governance strategies to manage data security, compliance, and access control effectively.
Collaboration Tools: Companies are investing in collaboration tools that enhance communication between data governance teams and other stakeholders, ensuring better compliance and data quality.
Data Stewardship Programs: Organizations are establishing data stewardship roles to oversee data governance initiatives, focusing on data quality, usage, and compliance across departments.
Training and Awareness: As data governance becomes more critical, there’s a growing emphasis on training employees about data policies and best practices to foster a culture of accountability.

Website: International Research Data Analysis Excellence Awards

Visit Our Website : researchdataanalysis.com
Nomination Link : researchdataanalysis.com/award-nomination
Registration Link : researchdataanalysis.com/award-registration
member ling : researchdataanalysis.com/conference-abstract-submission
Awards-Winners : researchdataanalysis.com/awards-winners
Contact us : contact@researchdataanalysis.com

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27 September, 2024

technology innovation !

Technology innovation refers to the process of developing new technologies or improving existing ones to create better products, services, or processes. It plays a crucial role in driving economic growth, enhancing efficiency, and addressing societal challenges. Key areas of innovation include:

Artificial Intelligence (AI): Advancements in machine learning and natural language processing are transforming industries from healthcare to finance.
Blockchain: This technology is revolutionizing secure transactions and data management, particularly in finance and supply chain.
Renewable Energy: Innovations in solar, wind, and energy storage technologies are making sustainable energy more accessible and efficient.
Biotechnology: Advances in genetic engineering and pharmaceuticals are improving health outcomes and agricultural productivity.
Internet of Things (IoT): Connecting devices to the internet is enabling smarter homes, cities, and industries, enhancing efficiency and data collection.
5G and Connectivity: The rollout of 5G networks is enabling faster communication, which supports innovations in IoT, autonomous vehicles, and more.
Quantum Computing: Although still in its early stages, quantum computing promises to solve complex problems much faster than traditional computers.

26 September, 2024

international awards !

Nobel Prizes 2023

The Nobel Prizes will soon announce their laureates for 2023. Expectations are high for groundbreaking contributions in categories such as Peace, Literature, and Medicine. Keep an eye on the announcements in October!

Academy Awards (Oscars) 2024

As the Oscars approach, discussions around potential nominees are heating up. Films from diverse genres and countries are vying for recognition, with a particular spotlight on international films making waves in mainstream cinema.

Cannes Film Festival Highlights

The 2023 Cannes Film Festival showcased an impressive array of international films. Notably, the Palme d'Or was awarded to a groundbreaking film from a first-time director, generating buzz for future releases.

Grammy Awards 2024

The Grammy nominations are set to be revealed soon, with speculation about artists who have made significant impacts in various genres, including emerging global talents.

UNESCO World Heritage Sites

Recently, several new sites were added to the UNESCO World Heritage list, recognizing their cultural significance and the need for preservation. This year's additions include remarkable historical sites from Africa, Europe, and Asia.

Golden Globe Awards Updates

As the Golden Globes approach, there are discussions about the increasing diversity among nominees, reflecting a broader range of voices in film and television.

International Booker Prize 2023

The International Booker Prize highlighted exceptional translated works, promoting global literature. The winner this year has been praised for its unique narrative style and cultural insights.

Tech Awards

The tech industry continues to celebrate innovation through various awards, including the Webby Awards and the Crunchies. These events honor achievements in technology, media, and the internet.

25 September, 2024

Marketers Are Excited About GenAI, But Foggy On Business Impact !

Generative AI is being heavily used in marketing efforts, with 75% of marketers currently using it in their day-to-day work. However, 90% of CMOs admit they don’t fully understand how the technology will impact their business processes, according to a new global study from SAS and Coleman Parkes Research.

Marketers are leaders in Gen AI adoption, with 63% using the technology for tasks such as copywriting, editing, and content creation. The study found that 90% of marketers are investing in Gen AI in 2025, with the majority hoping to use the technology to save time and costs, improve risk management and compliance, or more efficiently process large data sets.

While these companies are embracing popular Gen AI functions like generating copy and interacting with customers, few are delving into its more sophisticated capabilities. Less than 20% of respondents have used the technology for audience targeting or building, mapping customer journeys, or price optimization.

It isn't surprising that marketers lead the way in Gen AI adoption, as it lends itself to experimentation and creativity — both hallmarks of the marketing profession," SAS chief marketing officer and executive vice president Jenn Chase said in a statement. "However, it is disappointing that a lack of understanding of Gen AI at the CMO and senior management level is holding organizations back from experiencing the full potential of this exciting new technology.

24 September, 2024

AnyChart Named Best in Data Analytics and Visualization !

AnyChart, the world’s leader in cross-platform data visualization, has won a 2023 DEVIES Award for its versatile JavaScript charting library named best in the Data Analytics & Visualization category. (https://www.anychart.com)

The DEVIES Awards (https://www.developerweek.com/awards), hosted by DeveloperWeek, are the definitive annual awards for the software industry, recognizing outstanding design, engineering, and innovation in developer technology.

AnyChart JS Charts lie in the foundation of numerous data analytics and BI systems, operating data visualization for over 75% of Fortune 500 companies across all industries and over half of the top 1000 software vendors globally. As a most recent public example of successful integration, Qlik’s flagship Qlik Sense now has multiple new AnyChart-based capabilities, including a decomposition tree (previously available in Power BI only) and advanced combo charting techniques released last month in dedicated Qlik extensions (https://qlik.anychart.com).

“Celebrating 20 years in the market this year, we are proud of this recognition. We’ve been working hard to build the best-in-class data visualization solutions and remain committed to innovating to help everyone around the world bring their data to life,” said Anton Baranchuk, CEO and Co-founder of AnyChart.

“Developer tools and technology product solutions are leading the way for software developers and engineers to build upon the foundation of the ever-expanding technology sector. AnyChart’s win is evidence of their leading role in the growth and innovation in the software industry,” said Jonathan Pasky, Executive Producer of DevNetwork (producer of DeveloperWeek and the DEVIES Awards).

The 2023 (11th) DEVIES Awards winners were selected from hundreds of nominations by an expert-led panel of the DevNetwork Advisory Board, including representatives of Accenture, DigitalOcean, EA, Google, IBM, Salesforce, and others. The criteria for deciding the best technologies/products were:Attracting notable attention and awareness in the software industry
General regard and use by the developer, engineering, and information technology community
Being a technical leader in its sector for innovation

The awards ceremony will take place on February 15, 2023, at the Oakland Convention Center in Oakland, CA — during DeveloperWeek 2023, the world’s largest developer and engineering conference & expo expecting over 8,000 participants from more than 150 countries.

23 September, 2024

Medical Genetics Research !

Medical genetics research focuses on understanding the genetic basis of diseases and developing new approaches for diagnosis, treatment, and prevention. This field encompasses several key areas:

Genetic Disorders: Investigating hereditary conditions like cystic fibrosis, sickle cell anemia, and muscular dystrophy to understand their genetic causes and develop targeted therapies.
Genomic Medicine: Using whole-genome sequencing and other genomic technologies to identify mutations associated with diseases. This includes personalized medicine, where treatment is tailored based on an individual's genetic profile.
Cancer Genetics: Studying the genetic mutations that lead to various cancers, including inherited syndromes like BRCA mutations. This research aids in developing targeted therapies and screening strategies.
Pharmacogenomics: Exploring how genetic variations affect individual responses to medications, aiming to optimize drug therapies for better efficacy and reduced side effects.
Genetic Epidemiology: Analyzing the role of genetics in the incidence and distribution of diseases within populations, often combining genetic data with environmental factors.
CRISPR and Gene Editing: Researching techniques like CRISPR for potential gene therapies, which could correct genetic mutations at the DNA level.
Ethical, Legal, and Social Implications (ELSI): Addressing the ethical issues surrounding genetic testing, privacy concerns, and potential discrimination based on genetic information.

21 September, 2024

Time Series Forecasting: A Practical Guide to Exploratory Data Analysis !

Introduction

Time series analysis certainly represents one of the most widespread topics in the field of data science and machine learning: whether predicting financial events, energy consumption, product sales or stock market trends, this field has always been of great interest to businesses.

Obviously, the great increase in data availability, combined with the constant progress in machine learning models, has made this topic even more interesting today. Alongside traditional forecasting methods derived from statistics (e.g. regressive models, ARIMA models, exponential smoothing), techniques relating to machine learning (e.g. tree-based models) and deep learning (e.g. LSTM Networks, CNNs, Transformer-based Models) have emerged for some time now.

Despite the huge differences between these techniques, there is a preliminary step that must be done, no matter what the model is: Exploratory Data Analysis.

In statistics, Exploratory Data Analysis (EDA) is a discipline consisting in analyzing and visualizing data in order to summarize their main characteristics and gain relevant information from them. This is of considerable importance in the data science field because it allows to lay the foundations to another important step: feature engineering. That is, the practice that consists on creating, transforming and extracting features from the dataset so that the model can work to the best of its possibilities.

The objective of this article is therefore to define a clear exploratory data analysis template, focused on time series, which can summarize and highlight the most important characteristics of the dataset. To do this, we will use some common Python libraries such as Pandas, Seaborn and Statsmodel.

Data

Let’s first define the dataset: for the purposes of this article, we will take Kaggle’s Hourly Energy Consumption data. This dataset relates to PJM Hourly Energy Consumption data, a regional transmission organization in the United States, that serves electricity to Delaware, Illinois, Indiana, Kentucky, Maryland, Michigan, New Jersey, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia, and the District of Columbia.

The hourly power consumption data comes from PJM’s website and are in megawatts (MW).

Exploratory Data Analysis

Let’s now define which are the most significant analyses to be performed when dealing with time series.

For sure, one of the most important thing is to plot the data: graphs can highlight many features, such as patterns, unusual observations, changes over time, and relationships between variables. As already said, the insight that emerge from these plots must then be taken into consideration, as much as possible, into the forecasting model. Moreover, some mathematical tools such as descriptive statistics and time series decomposition, will also be very useful.

Said that, the EDA I’m proposing in this article consists on six steps: Descriptive Statistics, Time Plot, Seasonal Plots, Box Plots, Time Series Decomposition, Lag Analysis.
1. Descriptive Statistics

Descriptive statistic is a summary statistic that quantitatively describes or summarizes features from a collection of structured data.

Some metrics that are commonly used to describe a dataset are: measures of central tendency (e.g. mean, median), measures of dispersion (e.g. range, standard deviation), and measure of position (e.g. percentiles, quartile). All of them can be summarized by the so called five number summary, which include: minimum, first quartile (Q1), median or second quartile (Q2), third quartile (Q3) and maximum of a distribution.

20 September, 2024

The naturalist perspective of the human being: A critique from the phenomenological perspective !

The remarkable advances in the fields of science and technology during the last few decades are strongly influencing the way we human beings perceive ourselves. A more integrated, humanist perspective is giving way to a mechanist, naturalist perspective.

With the tendency to reduce everything that exists to data or information in the present digital age, the human being too is considered by some as nothing more than a complex biological machine, having reached this stage through merely accidental evolutionary pathways.

A related viewpoint is that the human mind is seen as a highly sophisticated computer whose programming software will be fully known in the near future through research in the cognitive sciences. Though, on the one hand, many intellectuals, including scientists and philosophers, do not agree with the naturalist perspective, on the other, there are a sizable number who are convinced about the truth of this perspective.

Moreover, this perspective of the human being is also being widely diffused through the communication media as it appeals to the public imagination and endorses the unprecedented progress in science and technology achieved by humanity. As a result, the naturalist perspective of the human being is increasingly gaining prominence.

19 September, 2024

biostatistics !

Biostatistics is a branch of statistics that focuses on the application of statistical methods to the fields of biology, medicine, and public health. It encompasses the design, analysis, and interpretation of data from biological experiments and health-related studies. Here are some key aspects of biostatistics:

Key Aspects

Study Design: Biostatisticians help plan studies, including clinical trials, surveys, and observational studies, ensuring they are statistically sound and capable of yielding reliable results.
Data Analysis: They analyze data using various statistical techniques to draw meaningful conclusions, identify trends, and test hypotheses.
Statistical Modeling: Biostatistics often involves creating models to predict outcomes or understand relationships between variables, such as the effect of a treatment on health outcomes.
Public Health: Biostatistics is crucial in assessing public health interventions, understanding disease outbreaks, and analyzing health trends in populations.
Epidemiology: It plays a vital role in epidemiological research, helping to determine the causes and spread of diseases.
Software Proficiency: Biostatisticians commonly use statistical software like R, SAS, or SPSS for data analysis.

Applications

Clinical trial design and analysis
Assessment of health risks
Health policy evaluation
Genetic research
Environmental health studies

Biostatistics is essential for transforming raw data into actionable insights that can improve health outcomes and inform healthcare practices. If you have specific questions or topics within biostatistics you'd like to explore, feel free to ask!

16 September, 2024

Introducing Google's new Academic Research Awards !

On June 27, 2024, Google will open applications for the new Google Academic Research Awards (GARA) program. GARA aims to support groundbreaking foundational and applied research in computing and technology around the world.

Each funding cycle, Google will identify key research areas and invite proposals from academics who are advising students and conducting research in a variety of technologically-focused domains that have societal implications. Selected projects will receive unrestricted gifts up to $150,000 USD, enabling researchers to pursue their innovative ideas and contribute to challenges and opportunities that will benefit the scientific community and society.

In addition to receiving funding, recipients have the opportunity to join a community of practice, and are paired with a Google research sponsor who provides long-term support, mentorship, and acts as a liaison to Google's broader research community. This collaborative approach fosters deeper connections between academia and industry, accelerating innovation and knowledge exchange.

Each cycle, GARA's research areas will evolve to address global challenges. This year, we are inviting proposals in the following areas:Creating ML benchmarks for climate problems: Developing data-driven solutions for climate action using machine learning (ML) and artificial intelligence (AI), with a focus on robust benchmarks for model evaluation.

Making education equitable, accessible and effective using AI: Improving educational outcomes for all learners through AI, with a focus on equity, inclusivity and research on AI-powered learning systems, teacher empowerment and accessibility tools.

Quantum transduction and networking for scalable computing applications: Advancing distributed quantum computing through research on transduction of superconducting qubits, alternative platforms, and novel applications beyond parallel compute and quantum key distribution.

Society-centered AI: Harnessing AI's transformative potential for societal good through a multi-stakeholder approach, understanding diverse needs, and creating impactful solutions for underserved communities.

Trust & safety: Improving digital safety across the online ecosystem, tackling issues like scams, misinformation, child safety and generative AI through research from various disciplines.
Using Gemini & Google’s open model family to solve systems and infrastructure problems: Leveraging Gemini and Gemma to advance next-generation computing infrastructure, enhancing efficiency, security and sustainability with a focus on machine learning optimization.

#AcademicResearch #Research #Innovation #Science #Methodology #Study #ResearchFindings #Scholarship #Knowledge #Impact #FieldResearch #DataAnalysis #ResearchGoals #ResearchImpact #ScientificInquiry #AcademicWriting

14 September, 2024

Analytics needs data lakes – but they will also disappoint us !

Peod to analytics because they love using powerful tools to find patterns in data

Sadly, when they get on the job, they do not spend much time doing anything like data science.

Instead, they spend the bulk of their time organizing and cleaning data to get it in a form where they can do at least a little analysis.

This is often referred to as “data wrangling.”
Data lakes

One way to avoid spending so much time doing data wrangling is to routinely collect any relevant data in a manner that it is easier to analyze.

Historically, the tool to do this was called a data warehouse – which aimed to get data from various systems cleanly organized in one place.

A newer approach, however, is to build what is called a “data lake.”

A data lake comprises a central location that holds a large amount of data in its native, raw format. It differs from a hierarchical data warehouse, which stores data in files or folders.

Compared to a data warehouse, a data lake approach is typically regarded as a much more flexible and scalable solution because it is capable of handling both structured and unstructured data.

So far so good.

There is no point in having a lot of data if you cannot analyze it and data lakes are a big part of the solution.

As such, many people analytics departments are devoting a lot of effort creating a data management infrastructure that will eventually make it much easier to do their analytics work.
Why data lakes will not solve all of our analytics problems

But while data lakes may sound like a good idea for your organization, we shouldn’t let our hopes get too high.

One reason is that creating and maintaining a data lake is a lot of work.

However, there is a deeper – much more fundamental – issue that gets closer to an underlying limitation of people analytics: Often the information you need to answer a business question just does not exist anywhere in the organization’s datasets.

It is not a matter that the data is not readily accessed, or is not integrated, nor is not clean, or not analyzed with sufficiently sophisticated tools. It just out and out does not exist.
Data that doesn’t exist

For example, we might be interested in what leads some HR business partners to be successful whereas others struggle.

We can look at their education, career histories, and performance reviews, but the underlying causes may not be found in any of those.

No matter what kind of sophisticated analysis of our data we do, we will not find the causal mechanisms that lead to HR business partner success.

Similarly, we might be desperate to know how to avoid bad managerial hires but simply not have enough data to draw any conclusions.

In both these cases we will need to search for other types of evidence, maybe even just other hints, on what is causing success or failure and act based on that.
What analytics gets wrong

People analytics is often seen as an application of data science where the essence is grappling with quantitative data.

Yet insights involving nuanced issues around people are not always found in data.

To understand what makes for a successful HR business partner, for example, you might be better off doing a series of in-depth interviews, the way an anthropologist would, to understand the dynamics that lead to success.

The bottom line is that people analytics departments should not be obsessed with analytics; they should be obsessed with providing insights into business issues.

Sometimes those insights will come from quantitative data and other times it will come from softer methods, like interviewing stakeholders.

The best analytics departments are comfortable drawing on both hard and soft insights to paint a picture of what is happening and what the organization should do.

It is smart to invest in getting your data in good shape and that will help quantitative analytics play a role in providing insight into issues.

But, we should be aware that often the data will not have the answers we need and be capable of applying different kinds of methods to guide our decision-making.

13 September, 2024

Global Medical Imaging Informatics Market accelerated by Cloud and AI !

Frost & Sullivan’s recent analysis finds that the emergence of new technologies such as artificial intelligence (AI) and the cloud, evolving clinical and administrative/operational needs, and the introduction of new policies and regulations will boost the global medical imaging informatics market. This market—which comprises radiology IT solutions, ancillary IT solutions, other departmental IT solutions such as cardiovascular information systems, and enterprise imaging IT solutions—is estimated to hit $10.4 billion by 2025 from $8.5 billion in 2019, registering a compound annual growth rate (CAGR) of 3.5%. However, with the impact of COVID-19, the market will experience a slowdown in 2021 as hospitals delay investments in capital purchases and divert most funding to COVID-19 care. Still, it will bounce back in 2022 with higher growth rates due to the pent-up demand for IT purchases over the past two years.

“Medical imaging informatics is poised to play a central role in the intervention and management of illnesses. Digitization in imaging offers several advantages, including higher pixel information, efficient storage and retrieval, and ease in sharing images between the care team members,” said Suresh Kuppuswamy, Healthcare & Life Sciences Industry Principal at Frost & Sullivan. “Radiology IT is forecast to maintain its position as the largest revenue contributor, driven by the adoption of radiology PACS in emerging countries, as most of them are projected to still implement the PACS at the modality or departmental level.

Kuppuswamy added: “From a regional market viewpoint, the North American market will largely drive the enterprise imaging market, underscored by the need for clinical decision support systems and image exchange solutions. Europe, the Middle East, and Africa (EMEA) are expected to witness growth in ancillary and enterprise imaging segments. Similarly, China, Australia, Korea, and Japan are forecast to be the major economies spurring Asia-Pacific’s revenue growth. Continuous healthcare infrastructure improvements in Southeast Asia and India also provide additional growth opportunities for vendors.”

To tap growth prospects in the medical imaging and informatics market, vendors need to focus on the following:New opex business models to encourage small and medium hospitals to adopt modern informatics technology: Although opex models are mostly utilized in developed countries, the future potential is large in developing countries because the high demand for modern informatics solutions in these markets is constrained by the associated capital costs.
Teleradiology to enable radiologists to work from home and increase their productivity: Create the necessary infrastructure for radiologists to work remotely without compromising quality and speed of work.
AI integration into the imaging workflow to increase efficiency and quality of care: A large segment of the work is currently focused on modalities such as CT, MRI, and mammography. Algorithms can increase the accuracy and efficiency of radiologists in these areas.
Unsustainable costs in healthcare to shift the focus on precision medicine and precision imaging: Ample opportunities exist for the imaging industry to further refine the imaging process by integrating innovative technologies along the imaging path, e.g., 3D camera at the point of acquisition to ensure centering is accurate and to avoid a repeat exam.

Digital Transformation in Imaging Powering the Next Wave of Growth in Informatics is the latest addition to Frost & Sullivan’s Healthcare & Life Sciences research and analyses available through the Frost & Sullivan Leadership Council, which helps organizations identify a continuous flow of growth opportunities to succeed in an unpredictable future.

12 September, 2024

data exploration !

Data exploration is a crucial step in data analysis and involves examining and understanding datasets to uncover patterns, trends, and insights. Here’s a structured approach to data exploration:

Data Collection and Preparation:
- Data Acquisition: Gather data from various sources (databases, files, APIs, etc.).
- Data Cleaning: Handle missing values, outliers, and inconsistencies. Standardize formats and correct errors.
- Data Integration: Combine data from different sources if needed.
Descriptive Statistics:
- Summary Statistics: Compute measures such as mean, median, mode, standard deviation, and range to get a sense of the central tendency and dispersion.
- Frequency Distributions: Examine how often each value or range of values occurs.
Data Visualization:
- Histograms: Visualize the distribution of numerical data.
- Box Plots: Identify outliers and visualize the spread of data.
- Scatter Plots: Explore relationships between two numerical variables.
- Bar Charts and Pie Charts: Compare categorical data.
Exploratory Data Analysis (EDA):
- Correlation Analysis: Investigate relationships between variables using correlation coefficients and heatmaps.
- Feature Engineering: Create new features from existing data to enhance the analysis.
- Dimensionality Reduction: Use techniques like PCA (Principal Component Analysis) to reduce the number of features while retaining significant information.
Pattern Recognition:
- Trend Analysis: Look for patterns or trends over time.
- Clustering: Group similar data points together using methods like K-means or hierarchical clustering.
- Anomaly Detection: Identify unusual data points or outliers.
Data Profiling:
- Data Types and Structures: Understand the types of data (numeric, categorical, date, etc.) and their structures.
- Data Relationships: Explore how different variables relate to each other.
Statistical Testing:
- Hypothesis Testing: Conduct tests (e.g., t-tests, chi-square tests) to make inferences about the data.
- Regression Analysis: Assess relationships between dependent and independent variables.
Documentation and Reporting:
- Document Findings: Keep detailed notes on insights, visualizations, and any anomalies discovered.
- Create Reports: Summarize your findings and insights in a clear and structured format for stakeholders.
Iteration:
- Refine Analysis: Based on initial findings, iteratively refine your approach, explore new questions, and adjust the analysis as needed.

Effective data exploration helps in forming hypotheses, guiding further analysis, and making data-driven decisions.

11 September, 2024

Aspirnaut scientific symposium set for Friday, July 14 !

The Vanderbilt University Medical Center community is invited to attend a scientific symposium on Friday, July 14, presented by high school participants in the Aspirnaut K-20 STEM pipeline for diversity and wellness.

Now in its 16th year, the pipeline offers a summer research program at Vanderbilt primarily for rural and diverse high school and undergraduate students.

Since 2009, 334 students from 34 states have participated in the program, funded largely by donations and grants.

More than 200 program alumni have college degrees (83 are still in college), 51 have graduated from medical school, 28 have earned a PhD and seven have dual MD/PhD degrees. Another 75 students have completed master’s degrees or directly entered the STEM (science, technology, engineering, or mathematics) workforce.

“Aspirnaut provides students with the experience and skills to access and persist in STEM studies,” said Aspirnaut executive director Julie Hudson, MD, VUMC Vice President for Medical Center Relations. “Persistence in STEM studies is a gateway step in accessing competitive STEM careers.”

Hudson co-founded the program in 2007 with her husband, Billy Hudson, PhD, the Elliott V. Newman Professor of Medicine at VUMC, his brother, Johnny Hudson, and his sister, Ann Kincl.

The symposium will be held from 10:30 a.m. to 5 p.m. in room 1220 MRBIII. For more information, visit researchdataanalysis.com.

10 September, 2024

GenAI could illuminate decades worth of dark data !

GenAI is transforming the way organizations manage and utilize unstructured data
Unstructured data, including documents, photos and videos, is plentiful but difficult to harness
While GenAI offers powerful tools for extracting and utilizing this data, experts emphasize the need for strong data governance

Generative AI (GenAI) is revolutionizing how organizations manage and use their unstructured data, a resource that has long been abundant yet difficult to harness. But without a clear strategy, they could be opening Pandora's box.

Structured data includes your standard tables, Excel sheets and databases, while unstructured data spans everything from old emails, PDFs, purchase orders and invoices to training manuals and repair guides, noted Steward Bond, VP of data intelligence and integration software at IDC. This data has often become invisible thanks to a lack of proper management, sitting unused on hard drives or in cloud storage.

“Where I think there is tremendous opportunity is in the use of GenAI to shed light on dark data,” Bond told Fierce Network. “GenAI could be used to read through content that is not labelled or tagged, identify what is in the content including any sensitive information, add the appropriate metadata so that it becomes known and available for use.”

Organizations generate truly massive amounts of unstructured data, with the IDC Global DataSphere estimating that 132 ZettaBytes of data were created in 2023 alone, 64% of which came from enterprises. Much of this data is "dark data," Bond said, meaning it is not properly captured, tagged or managed, making it difficult to access and use.

Large language models (LLMs), though, are uniquely well-suited to understanding and processing unstructured data because they are trained on vast amounts of such content. Bond explained that LLMs can answer a wide range of questions and generate content based on unstructured data inputs

Related
To overcome AI grounding challenges, we must dig deeper

Indeed, techniques like retrieval-augmented generation (RAG) provide a way to incorporate additional data into the model's prompts, improving the accuracy and relevance of the generated content.
What can unstructured tell us?

Enterprises and service providers alike are recognizing the potential of GenAI to extract and structure data from unstructured sources like long-form documents, research papers and emails.

This capability is crucial for organizations that need to connect unstructured data to downstream processes, such as integrating purchase orders into an ERP system, said Amy Machado, research manager for enterprise content and knowledge management strategies at IDC. Beyond simple extraction, GenAI can also help organizations search, discover, summarize and even generate new content based on existing unstructured data.

This turns previously inaccessible knowledge into actionable insights that can drive business processes and decision-making, Machado told Fierce. "A lot of unstructured data has knowledge stored in it," she added.

In one such instance, AWS is now using GenAI to enhance its sales teams by combining structured data from CRM systems with unstructured data like sales collateral. This allows the models to generate comprehensive customer account summaries that provide sales teams with more contextually relevant insights.

Structured data provides the quantitative foundation (e.g., consumption, pipeline), as well as historical trends, while unstructured data adds qualitative depth. Unstructured content such sales collateral and external web data provides context and nuance that structured data alone might miss.

“Generative AI and LLMs have revolutionized what we can do with unstructured content which historically has been challenging to analyze at scale,” Rupa Boddu, principal tech product manager, Generative AI, AWS, told Fierce Network.
Weeding out the bad data

While the benefits of leveraging unstructured data with GenAI are clear, it is equally important to ensure that the data feeding these models is accurate, unbiased and free of sensitive information.

Unstructured data is much like a digital landfill, where information has been tossed and forgotten for years. Now, many companies have little understanding of what’s buried within, and opening it up could have unforeseen consequences.

Bart Willemsen, VP analyst at Gartner, said using unstructured data for GenAI applications introduces critical questions around privacy and data governance, “something most organizations seem to not have solved yet.”

Most companies don’t actually know the data they’ve accumulated—in some cases “decades and decades of history,” Willemsen said—why they had that data to begin with, or what purposes it served.

Without proper data governance, there is a risk that GenAI could propagate inaccuracies or biases, leading to flawed outputs and potentially harmful decisions. Organizations must, therefore, implement robust data governance frameworks to manage the quality and security of the unstructured data used in training and deploying GenAI models.

General data governance is something organizations should have with “absolute, granular control before they can be successful at using any AI,” Willemsen concluded. “I don't care how good the AI technology itself is, if you have crappy data, you will have crappy AI.”

09 September, 2024

Researchers design a national testing facility to simulate tornadoes, downbursts and gusts; Experiments will help them engineer buildings that can stand up to extreme winds !

AMES, Iowa – The foundation of a house remains, the basement ripped open and exposed, with the rest of the house blown away. A brick-veneered bank building partially caved in. A collapsed high school gym. Gravestones knocked over. Debris piercing a building.

Partha Sarkar kept hitting next, scrolling through the photo evidence of the destruction he gathered and assessed the day after an EF5 tornado ripped through Parkersburg on May 25, 2008.

Parkersburg tornado damage, May 2008.

Then Sarkar, professor and interim chair of aerospace engineering at Iowa State University, opened a photo showing a house located on the edge of the tornado’s path. It was damaged, but still standing, shingles gone, but the roof structure mostly intact. Sarkar’s engineering studies, based on small-scale models of structures tested in wind tunnels, are designed to produce more of that – structures that stand up to extreme windstorms, protecting lives and property.

Now those studies, spanning his entire academic career, are getting a major upgrade – and the potential for acquiring a better and more powerful wind simulator.

Sarkar and a research team he’s leading have just won a four-year, $14 million grant from the U.S. National Science Foundation (NSF) to design and plan a National Testing Facility for Enhancing Wind Resiliency of Infrastructure in Tornado-Downburst-Gust Front Events, or NEWRITE.

If it’s built, the facility would allow testing at large-scales (a full-scale house or 1:10 scale models of buildings with large footprints such as retail buildings, shopping malls or hospitals) and high wind speeds (86-225 mph for EF1 to EF5 tornadoes, 100-125 mph for downbursts, 80-100 mph for gust fronts) in simulated windstorms.

“Being able to test structures at much larger scales, in extreme winds produced in these windstorms, will bring us closer to understanding reality and help engineers to improve the wind resilience of structures,” Sarkar said.

The grant will also support replacing Iowa State’s Tornado/Microburst Simulator that was completed in 2005. The existing simulator, housed in Howe Hall, is capable of 80 mph winds and a tornado-like vortex that’s 3.7 feet in diameter. The new simulator will be about a 1/20th-scale model of the full-scale NEWRITE and about the same size as the existing facility (18 feet in diameter) but will have the capacity to generate 225 mph tornado-like winds.

Researchers will also model and produce a “digital twin” of the full-scale and 1/20th-scale NEWRITE simulators to help them design the proposed facility.

Sarkar said the project is all about simulating tornadoes and other types of localized, non-synoptic windstorms, measuring the wind loads they exert on homes and other structures and engineering improvements that reduce structural damage.

“NEWRITE will be designed to be a state-of-the-art research and testing platform to mitigate the impacts of (localized windstorm) hazards on the built environment and significantly reduce fatalities and economic losses,” the researchers wrote.

Sarkar will lead the project team. Other project co-leaders are Alice Alipour, an associate professor of civil, construction and environmental engineering at Iowa State; Anupam Sharma, an associate professor of aerospace engineering at Iowa State; Guirong (Grace) Yan, an associate professor of structural engineering at Missouri University of Science and Technology in Rolla; and Delong Zuo, a professor of civil, environmental and construction engineering at Texas Tech University in Lubbock.

Other senior researchers (see sidebar) are from Iowa State, Clemson University in South Carolina; Northeastern University in Boston; the University of Arkansas in Fayetteville; the University of Florida in Gainesville; the University of Washington in Seattle; and the University of Wisconsin in Madison.

This design grant does not commit the NSF to supporting construction of the full-scale NEWRITE facility. If there is future support, Sarkar said it is likely to be built at Iowa State. He estimates the national testing facility would require a five-story building that could cover the square footage of four football fields. The facility would also require 5 to 10 megawatts of electricity.

In addition to NEWRITE, the NSF also awarded grants to three other research infrastructure projects: design of a laser laboratory at the University of Rochester in New York; an ocean observatory for earthquake activity at the University of Washington; and a cybersecurity institute at the University of Southern California in Los Angeles.

The four projects “exemplify the most novel, innovative infrastructure being designed and built in our country to advance the best ideas and train the highly skilled talent in science and engineering for our future,” said Sethuraman Panchanathan, director of the NSF. “By investing in the most innovative infrastructure, NSF aims to strengthen opportunities for all Americans and advance the frontiers of science and technology.”