This article describes a class of graphs, embedded plots, that are particularly useful for analyzing large and complex datasets. Embedded plots organize a collection of graphs into a larger graphic, which can display more complex relationships than would otherwise be possible. This arrangement provides additional axes, prevents overplotting, and allows for multiple levels of visual summarization. Embedded plots also preprocess complex data into a form suitable for the human cognitive system, which can facilitate comprehension. We illustrate the usefulness of embedded plots with a case study, discuss the practical and cognitive advantages of embedded plots, and demonstrate how to implement embedded plots as a general class within visualization software, something currently unavailable. This article has supplementary material online.

Two studies were conducted to explore the use of complex data in character description and hybrid identification. In order to determine if complex data allow the production of better characters, eight groups of plant systematists were given two classes of drawings of plant parts, and asked to divide them into character states (clusters) in two separate experiments. The first class of drawings consisted only of cotyledons. The second class consisted of triplets of drawings: a cotyledon, seedling leaf, and inflorescence bract. The triplets were used to simulate complex data such as might be garnered by looking at a plant. Each experiment resulted in four characters (groups of clusters), one for each group of systematists. Visual and statistical analysis of the data showed that the systematists were able to produce smaller, more precisely defined character states using the more complex drawings. The character states created with the complex drawings also were more consistent across systematists, and agreed more closely with an independent assessment of phylogeny. To investigate the utility of complex data in an applied task, four observers rated 250 hybrids of Dubautia ciliolata X arborea based on the overall form (Gestalt) of the plants, and took measurements of a number of features of the same plants. A composite score of the measurements was created using principal components analysis. The correlation between the scores on the first principal component and the Gestalt ratings was computed. The Gestalt ratings and PC scores were significantly correlated, demonstrating that assessments of overall similarity can be as useful as more conventional approaches in determining the hybrid status of plants.

High rate data processed to single-look complex SAR images for each antenna. Gridded tile (approx 64x64 km2); half swath (left or right side of full swath). Available in netCDF-4 file format.

A collection of data serving as documentation of San Luis National Wildlife Refuge Complex priority resources of concern.

A collection of data serving as documentation of Stillwater National Wildlife Refuge Complex priority resources of concern.

High rate data processed to single-look complex SAR images for each antenna. Gridded tile (approx 64x64 km2); half swath (left or right side of full swath). Available in netCDF-4 file format.

In performance maintenance in large, complex systems, sensor information from sub-components tends to be readily available, and can be used to make predictions about the system's health and diagnose possible anomalies. However, existing methods can only use predictions of individual component anomalies to guess at systemic problems, not accurately estimate the magnitude of the problem, nor prescribe good solutions. Since physical complex systems usually have well-defined semantics of operation, we here propose using anomaly detection techniques drawn from data mining in conjunction with an automated theorem prover working on a domain-specific knowledge base to perform systemic anomalydetection on complex systems. For clarity of presentation, the remaining content of this submission is presented compactly in Fig 1.

A database of manually annotated mammalian protein complexes. To obtain a high-quality dataset, information was extracted from individual experiments described in the scientific literature. Data from high-throughput experiments was not included.

This blog post was posted by Kevin Duvall on December 7, 2020. It was written by By: Kristen Honey, Chief Data Scientist and Senior Advisor to Assistant Secretary for Health (ASH), HHS; Amy Gleason, Data Strategy and Execution Workgroup Lead, U.S. Digital Service; and Kevin Duvall, Deputy Chief Data Officer (CDO), Office of the CDO, HHS.

In performance maintenance in large, complex systems, sensor information from sub-components tends to be readily available, and can be used to make predictions about the system's health and diagnose possible anomalies. However, existing methods can only use predictions of individual component anomalies to guess at systemic problems, not accurately estimate the magnitude of the problem, nor prescribe good solutions. Since physical complex systems usually have well-defined semantics of operation, we here propose using anomaly detection techniques drawn from data mining in conjunction with an automated theorem prover working on a domain-specific knowledge base to perform systemic anomalydetection on complex systems. For clarity of presentation, the remaining content of this submission is presented compactly in Fig 1.

Yearly citation counts for the publication titled "Mixed effects models for complex data Lang Wu, Chapman & Hall/CRC, Boca Raton, 2010. No. of pages: xx +419. Price: $89.95. ISBN: 978‐1‐4200‐7402‐4".

EDDEN stands for *E*valuation of *D*MRI *DEN*oising approaches. The data correspond to the publication: Manzano Patron, J.P., Moeller, S., Andersson, J.L.R., Yacoub, E., Sotiropoulos, S.N.. Denoising Diffusion MRI: Considerations and implications for analysis. doi: https://doi.org/10.1101/2023.07.24.550348. Please, cite it if you use this dataset.

• Description of the dataset RAW Complex data (magnitude and phase) is acquired for a single subject at different SNR/resolution regimes, under ~/EDDEN/sub-01/ses-XXX/dwi/:

  • Dataset A (2mm)

    • This dataset represents a relatively medium-to-high SNR regime.
    • 6 repeats of a 2mm isotropic multi-shell dataset each implementing the UK Biobank protocol (Miller et al., 2016)
    • TR=3s, TE=92ms, MB=3, no in-plane acceleration, scan time ∼6 minutes per repeat.
    • For each repeat, 116 volumes were acquired: 105 volumes with AP phase encoding direction, such as 5 b = 0 s/mm2 volumes, and 100 diffusion encoding orientations, with 50 b = 1000 s/mm2 and 50 b = 2000 s/mm2 volumes; and 4 b = 0 s/mm2 volumes with reversed phase encoding direction (PA) for susceptibility induced distortion correction (Andersson and Skare, 2002).
    • NOTES: Only 1 PA set of volumes was acquired for all the runs.

  • Dataset B (1p5mm):

    • This is a low-to-medium SNR dataset, with relatively high resolution.
    • 5 repeats of a 1.5 mm isotropic multi-shell dataset, each implementing an HCP-like protocol in terms of q-space sampling (Sotiropoulos et al., 2013a).
    • TR=3.23 s, TE=89.2 ms, MB=4 no in-plane acceleration, scan time ∼16 minutes per repeat.
    • For each repeat, 300 volumes were acquired: 297 volumes with AP phase encoding direction, such as 27 b = 0 s/mm2 volumes, and 270 diffusion encoding orientations, with 90 b = 1000 s/mm2, 90 b = 2000 s/mm2, and 90 b = 3000 s/mm2 volumes; and 3 b = 0 s/mm2 volumes with PA phase encoding for susceptibility-induced distortion correction.

  • Dataset C (0p9mm):

    • This is a very low SNR dataset to represent extremely noisy data that without denoising are expected to be borderline unusable (particularly for the higher b).
    • 4 repeats of an ultra-high-resolution multi-shell dataset with 0.9mm isotropic resolution.
    • TR=6.569 s, TE=91 ms, MB=3, in-plane GRAPPA=2, scan time ∼22 minutes per repeat.
    • For each repeat, 202 volumes were acquired with orientations as in (Harms et al., 2018): 199 volumes with AP phase encoding direction, such as 14 b = 0 s/mm2 volumes, and 185 diffusion encoding orientations, with 93 b = 1000 s/mm2, and 92 b = 2000 s/mm2 volumes; and 3 b = 0 s/mm2 volumes with PA phase encoding for susceptibility-induced distortion correction.
    • NOTES: The phase of the PAs is not available, and the same PA is used for runs 3 and 4.

Each dataset contains their own T1w-MPRAGE under ~/EDDEN/sub-01/ses-XXX/anat/. Each data set was acquired on a different day, to minimise fatigue, but all repeats within a dataset were acquired in the same session. All acquisitions were obtained parallel to the anterior and posterior commissure line, covering the entire cerebrum.

DERIVATIVES Here are the different denoised version of the raw data for the different datasets, the pre-processed data for the raw, denoised and averages, and the FA, MD and V1 outputs from the DTI model fitting (see *Data pre-processin section below). - Denoised data: - NLM (NLM), for Non-Local Means denoising applied to magnitude raw data. - MPPCA (|MPPCA|), for Marchenko-Pastur PCA denoising applied to magnitude raw data. - MPPCA_complex (MPPCA*), for Marchenko-Pastur PCA denoising applied to complex raw data. - NORDIC (NORDIC), for NORDIC applied to complex raw data. - AVG_mag (|AVG|), for the average of the multiple repeats in magnitude. - AVG_complex (AVG*), for the average in the complex space of the multiple repeats. - Masks: Under ~/EDDEN/derivatives/ses-XXX/masks we can find different masks for each dataset: - GM_mask: Gray Matter mask. - WM_mask: White Matter mask. - CC_mask: Corpus Callosum Matter mask. - CS_mask: Centrum Semiovale mask. - ventricles_mask: CSF ventricles mask. - nodif_brain_mask: Eroded brain mask.

• Data pre-processing Both magnitude and phase data were retained for each acquisition to allow evaluations of denoising in both magnitude and complex domains. In order to allow distortion correction and processing for complex data and avoid phase incoherence artifacts, the raw complex-valued diffusion data were rotated to the real axis using the phase information. A spatially varying phase-field was estimated and complex vectors were multiplied with the conjugate of the phase. The phase-field was estimated uniquely for each slice and volume by firstly removing the phase variations from k-space sampling and coil sensitivity combination, and secondly by removing an estimate of a smooth residual phase-field. The smooth residual phase-field was estimated using a low-pass filter with a narrowed tapered cosine filter (a Tukey filter with an FWHM of 58%). Hence, the final signal was rotated approximately along the real axis, subject to the smoothness constraints.

Having the magnitude and complex data for each dataset, denoising was applied using different approaches prior to any pre-processing to minimise potential changes in statistical properties of the raw data due to interpolations (Veraart et al., 2016b). For denoising, we used the following four algorithms:

- **Denoising in the magnitude domain**: i) The Non-Local Means (**NLM**) (Buades et al., 2005) was applied as an exemplar of a simple non-linear filtering method adapted from traditional signal pre-processing. We used the default implementation in DIPY (Garyfallidis et al., 2014), where each dMRI volume is denoised independently. ii) The Marchenko-Pastur PCA (MPPCA) (denoted as **|MPPCA|** throughout the text) (Cordero-Grande et al., 2019; Veraart et al., 2016b), reflecting a commonly used approach that performs PCA over image patches and uses the MP theorem to identify noise components from the eigenspectrum. We used the default MrTrix3 implementation (Tournier et al., 2019).

- **Denoising in the complex domain**: i) MPPCA applied to complex data (rotated along the real axis), denoted as **MPPCA***. We applied the MrTrix3 implementation of the magnitude MPPCA to the complex data rotated to the real axis (we found that this approach was more stable in terms of handling phase images and achieved better denoising, compared to the MrTrix3 complex MPPCA implementation). ii) The **NORDIC** algorithm (Moeller et al., 2021a), which also relies on the MP theorem, but performs variance spatial normalisation prior to noise component identification and filtering, to ensure noise stationarity assumptions are fulfilled.

All data, both raw and their four denoised versions, underwent the same pre-processing steps for distortion and motion correction (Sotiropoulos et al., 2013b) using an in-house pipeline (Mohammadi-Nejad et al., 2019). To avoid confounds from potential misalignment in the distortion-corrected diffusion native space obtained from each approach, we chose to compute a single susceptibility-induced off-resonance fieldmap using the raw data for each of the Datasets A, B and C; and then use the corresponding fieldmap for all denoising approaches in each dataset so that the reference native space stays the same for each of A, B and C. Note that differences between fieldmaps before and after denoising are small anyway, as the relatively high SNR b = 0 s/mm2 images are used to estimate them. But these small differences can cause noticeable misalignments between methods and confounds when attempting quantitative comparisons, which we avoid here using our approach. Hence, for each of the Datasets A, B and C, the raw blip-reversed b = 0 s/mm2 were used in FSL’s topup to generate a fieldmap (Andersson and Skare, 2002). This was then used into individual runs of FSL’s eddy for each approach (Andersson and Sotiropoulos, 2016) that applied the common fieldmap and performed corrections for eddy current and subject motion in a single interpolation step. FSL’s eddyqc (Bastiani et al.,2019) was used to generate quality control (QC) metrics, including SNR and angular CNR for each b value. The same T1w image was used within each dataset. A linear transformation estimated using with boundary-based registration (Greve and Fischl, 2009) was obtained from the corrected native diffusion space to the T1w space. The T1w image was skull-stripped and non-linearly registered to the MNI standard space allowing further analysis. Masks of white and grey matter were obtained from the T1w image using FSL’s FAST (Jenkinson et al., 2012) and they were aligned to diffusion space.

Tashi Shopping Complex Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Data Wrangling Market size was valued at USD 1.99 Billion in 2024 and is projected to reach USD 4.07 Billion by 2032, growing at a CAGR of 9.4% during the forecast period 2026-2032.• Big Data Analytics Growth: Organizations are generating massive volumes of unstructured and semi-structured data from diverse sources including social media, IoT devices, and digital transactions. Data wrangling tools become essential for cleaning, transforming, and preparing this complex data for meaningful analytics and business intelligence applications.• Machine Learning and AI Adoption: The rapid expansion of artificial intelligence and machine learning initiatives requires high-quality, properly formatted training datasets. Data wrangling solutions enable data scientists to efficiently prepare, clean, and structure raw data for model training, driving sustained market demand across AI-focused organizations.

Data Lineage for LLM Training Market Outlook

According to our latest research, the global Data Lineage for LLM Training market size reached USD 1.29 billion in 2024, with an impressive compound annual growth rate (CAGR) of 21.8% expected through the forecast period. By 2033, the market is projected to grow to USD 8.93 billion, as organizations worldwide recognize the critical importance of robust data lineage solutions in ensuring transparency, compliance, and efficiency in large language model (LLM) training. The primary growth driver stems from the surging adoption of generative AI and LLMs across diverse industries, necessitating advanced data lineage capabilities for responsible and auditable AI development.

The exponential growth of the Data Lineage for LLM Training market is fundamentally driven by the increasing complexity and scale of data used in training modern AI models. As organizations deploy LLMs for a wide array of applications—from customer service automation to advanced analytics—the need for precise tracking of data provenance, transformation, and usage has become paramount. This trend is further amplified by the proliferation of multi-source and multi-format data, which significantly complicates the process of tracing data origins and transformations. Enterprises are investing heavily in data lineage solutions to ensure that their AI models are trained on high-quality, compliant, and auditable datasets, thereby reducing risks associated with data bias, inconsistency, and regulatory violations.

Another significant growth factor is the evolving regulatory landscape surrounding AI and data governance. Governments and regulatory bodies worldwide are introducing stringent guidelines for data usage, privacy, and accountability in AI systems. Regulations such as the European Union’s AI Act and the U.S. AI Bill of Rights are compelling organizations to implement comprehensive data lineage practices to demonstrate compliance and mitigate legal risks. This regulatory pressure is particularly pronounced in highly regulated industries such as banking, healthcare, and government, where the consequences of non-compliance can be financially and reputationally devastating. As a result, the demand for advanced data lineage software and services is surging, driving market expansion.

Technological advancements in data management platforms and the integration of AI-driven automation are further catalyzing the growth of the Data Lineage for LLM Training market. Modern data lineage tools now leverage machine learning and natural language processing to automatically map data flows, detect anomalies, and generate real-time lineage reports. These innovations drastically reduce the manual effort required for lineage documentation and enhance the scalability of lineage solutions across large and complex data environments. The continuous evolution of such technologies is enabling organizations to achieve higher levels of transparency, trust, and operational efficiency in their AI workflows, thereby fueling market growth.

Regionally, North America dominates the Data Lineage for LLM Training market, accounting for over 42% of the global market share in 2024. This dominance is attributed to the early adoption of AI technologies, the presence of leading technology vendors, and a mature regulatory environment. Europe follows closely, driven by strict data governance regulations and a rapidly growing AI ecosystem. The Asia Pacific region is witnessing the fastest growth, with a projected CAGR of 24.6% through 2033, fueled by digital transformation initiatives, increased AI investments, and a burgeoning startup landscape. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a relatively nascent stage.

Component Analysis

The Data Lineage for LLM Training market is segmented by component into software and services, each playing a pivotal role in supporting organizations’ lineage initiatives. The software segment holds the largest market share, accounting for nearly 68% of the total market revenue in 2024. This dominance is primarily due to the widespread adoption of advanced data lineage platforms that offer features such as automated lineage mapping, visualization, impact analysis, and integration with existing data management and AI training workflows. These platforms are essential for organ

NASA has some of the largest and most complex data sources in the world, with data sources ranging from the earth sciences, space sciences, and massive distributed engineering data sets from commercial aircraft and spacecraft. This talk will discuss some of the issues and algorithms developed to analyze and discover patterns in these data sets. We will also provide an overview of a large research program in Integrated Vehicle Health Management. The goal of this program is to develop advanced technologies to automatically detect, diagnose, predict, and mitigate adverse events during the flight of an aircraft. A case study will be presented on a recent data mining analysis performed to support the Flight Readiness Review of the Space Shuttle Mission STS-119.

The Exploratory Data Analysis (EDA) tools market is experiencing robust growth, driven by the increasing need for businesses to derive actionable insights from their ever-expanding datasets. The market, currently estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $45 billion by 2033. This growth is fueled by several factors, including the rising adoption of big data analytics, the proliferation of cloud-based solutions offering enhanced accessibility and scalability, and the growing demand for data-driven decision-making across diverse industries like finance, healthcare, and retail. The market is segmented by application (large enterprises and SMEs) and type (graphical and non-graphical tools), with graphical tools currently holding a larger market share due to their user-friendly interfaces and ability to effectively communicate complex data patterns. Large enterprises are currently the dominant segment, but the SME segment is anticipated to experience faster growth due to increasing affordability and accessibility of EDA solutions. Geographic expansion is another key driver, with North America currently holding the largest market share due to early adoption and a strong technological ecosystem. However, regions like Asia-Pacific are exhibiting high growth potential, fueled by rapid digitalization and a burgeoning data science talent pool. Despite these opportunities, the market faces certain restraints, including the complexity of some EDA tools requiring specialized skills and the challenge of integrating EDA tools with existing business intelligence platforms. Nonetheless, the overall market outlook for EDA tools remains highly positive, driven by ongoing technological advancements and the increasing importance of data analytics across all sectors. The competition among established players like IBM Cognos Analytics and Altair RapidMiner, and emerging innovative companies like Polymer Search and KNIME, further fuels market dynamism and innovation.

Success.ai’s LinkedIn Data Solutions offer unparalleled access to a vast dataset of 700 million public LinkedIn profiles and 70 million LinkedIn company records, making it one of the most comprehensive and reliable LinkedIn datasets available on the market today. Our employee data and LinkedIn data are ideal for businesses looking to streamline recruitment efforts, build highly targeted lead lists, or develop personalized B2B marketing campaigns.

Whether you’re looking for recruiting data, conducting investment research, or seeking to enrich your CRM systems with accurate and up-to-date LinkedIn profile data, Success.ai provides everything you need with pinpoint precision. By tapping into LinkedIn company data, you’ll have access to over 40 critical data points per profile, including education, professional history, and skills.

Key Benefits of Success.ai’s LinkedIn Data: Our LinkedIn data solution offers more than just a dataset. With GDPR-compliant data, AI-enhanced accuracy, and a price match guarantee, Success.ai ensures you receive the highest-quality data at the best price in the market. Our datasets are delivered in Parquet format for easy integration into your systems, and with millions of profiles updated daily, you can trust that you’re always working with fresh, relevant data.

Global Reach and Industry Coverage: Our LinkedIn data covers professionals across all industries and sectors, providing you with detailed insights into businesses around the world. Our geographic coverage spans 259M profiles in the United States, 22M in the United Kingdom, 27M in India, and thousands of profiles in regions such as Europe, Latin America, and Asia Pacific. With LinkedIn company data, you can access profiles of top companies from the United States (6M+), United Kingdom (2M+), and beyond, helping you scale your outreach globally.

Why Choose Success.ai’s LinkedIn Data: Success.ai stands out for its tailored approach and white-glove service, making it easy for businesses to receive exactly the data they need without managing complex data platforms. Our dedicated Success Managers will curate and deliver your dataset based on your specific requirements, so you can focus on what matters most—reaching the right audience. Whether you’re sourcing employee data, LinkedIn profile data, or recruiting data, our service ensures a seamless experience with 99% data accuracy.

• Best Price Guarantee: We offer unbeatable pricing on LinkedIn data, and we’ll match any competitor.
• Global Scale: Access 700 million LinkedIn profiles and 70 million company records globally.
• AI-Verified Accuracy: Enjoy 99% data accuracy through our advanced AI and manual validation processes.
• Real-Time Data: Profiles are updated daily, ensuring you always have the most relevant insights.
• Tailored Solutions: Get custom-curated LinkedIn data delivered directly, without managing platforms.
• Ethically Sourced Data: Compliant with global privacy laws, ensuring responsible data usage.
• Comprehensive Profiles: Over 40 data points per profile, including job titles, skills, and company details.
• Wide Industry Coverage: Covering sectors from tech to finance across regions like the US, UK, Europe, and Asia.

Key Use Cases:

• Sales Prospecting and Lead Generation: Build targeted lead lists using LinkedIn company data and professional profiles, helping sales teams engage decision-makers at high-value accounts.
• Recruitment and Talent Sourcing: Use LinkedIn profile data to identify and reach top candidates globally. Our employee data includes work history, skills, and education, providing all the details you need for successful recruitment.
• Account-Based Marketing (ABM): Use our LinkedIn company data to tailor marketing campaigns to key accounts, making your outreach efforts more personalized and effective.
• Investment Research & Due Diligence: Identify companies with strong growth potential using LinkedIn company data. Access key data points such as funding history, employee count, and company trends to fuel investment decisions.
• Competitor Analysis: Stay ahead of your competition by tracking hiring trends, employee movement, and company growth through LinkedIn data. Use these insights to adjust your market strategy and improve your competitive positioning.
• CRM Data Enrichment: Enhance your CRM systems with real-time updates from Success.ai’s LinkedIn data, ensuring that your sales and marketing teams are always working with accurate and up-to-date information.
• Comprehensive Data Points for LinkedIn Profiles: Our LinkedIn profile data includes over 40 key data points for every individual and company, ensuring a complete understanding of each contact:

LinkedIn URL: Access direct links to LinkedIn profiles for immediate insights. Full Name: Verified first and last names. Job Title: Current job titles, and prior experience. Company Information: Company name, LinkedIn URL, domain, and location. Work and Per...

In many phenomena, data are collected on a large scale and at different frequencies. In this context, functional data analysis (FDA) has become an important statistical methodology for analyzing and modeling such data. The approach of FDA is to assume that data are continuous functions and that each continuous function is considered as a single observation. Thus, FDA deals with large-scale and complex data. However, visualization and exploratory data analysis, which are very important in practice, can be challenging due to the complexity of the continuous functions. Here we introduce a type of record concept for functional data, and we propose some nonparametric tools based on the record concept for functional data observed over time (functional time series). We study the properties of the trajectory of the number of record curves under different scenarios. Also, we propose a unit root test based on the number of records. The trajectory of the number of records over time and the unit root test can be used for visualization and exploratory data analysis. We illustrate the advantages of our proposal through a Monte Carlo simulation study. We also illustrate our method on two different datasets: Daily wind speed curves at Yanbu, Saudi Arabia and annual mortality rates in France. Overall, we can identify the type of functional time series being studied based on the number of record curves observed. Supplementary materials for this article are available online.

The size of the Data Preparation Analytics Industry market was valued at USD 6.74 Million in 2024 and is projected to reach USD 22.43 Million by 2033, with an expected CAGR of 18.74% during the forecast period. Recent developments include: December 2022: Alteryx, Inc., the Analytics Automation company, announced a strategic investment in MANTA, the data lineage company. MANTA enables businesses to achieve complete visibility into the most complex data environments. With this investment from Alteryx Ventures, the company can bolster product innovation, expand its partner ecosystem, and grow in key markets., November 2022: Amazon Web Services (AWS) announced a series of new features for Amazon QuickSight, the cloud computing giant's analytics platform. The update includes new query, forecasting, and data preparation features, adding functionality to QuickSight Q, a natural language query (NLQ) tool.. Key drivers for this market are: Demand for Self-service Data Preparation Tools, Increasing Demand for Data Analytics. Potential restraints include: Limited Budgets and Low Investments owing to Complexities and Associated Risks.. Notable trends are: IT and Telecom Segment is Expected to Hold a Significant Market Share.