Prognostics and health management (PHM) is a maturing system engineering discipline. As with most maturing disciplines, PHM does not yet have a universally accepted research methodology. As a result, most component life estimation efforts are based on ad-hoc experimental methods that lack statistical rigor. In this paper, we provide a critical review of current research methods in PHM and contrast these methods with standard research approaches in a more established discipline (medicine). We summarize the developmental steps required for PHM to reach full maturity and to generate actionable results with true business impact.

Behavioral data associated with the IBL paper: A standardized and reproducible method to measure decision-making in mice.This data set contains contains 3 million choices 101 mice across seven laboratories at six different research institutions in three countries obtained during a perceptual decision making task.When citing this data, please also cite the associated paper: https://doi.org/10.1101/2020.01.17.909838This data can also be accessed using DataJoint and web browser tools at data.internationalbrainlab.orgAdditionally, we provide a Binder hosted interactive Jupyter notebook showing how to access the data via the Open Neurophysiology Environment (ONE) interface in Python : https://mybinder.org/v2/gh/int-brain-lab/paper-behavior-binder/master?filepath=one_example.ipynbFor more information about the International Brain Laboratory please see our website: www.internationalbrainlab.comBeta Disclaimer. Please note that this is a beta version of the IBL dataset, which is still undergoing final quality checks. If you find any issues or inconsistencies in the data, please contact us at info+behavior@internationalbrainlab.org .

Data standardization of BP neural network input layer.

Fisheries management is generally based on age structure models. Thus, fish ageing data are collected by experts who analyze and interpret calcified structures (scales, vertebrae, fin rays, otoliths, etc.) according to a visual process. The otolith, in the inner ear of the fish, is the most commonly used calcified structure because it is metabolically inert and historically one of the first proxies developed. It contains information throughout the whole life of the fish and provides age structure data for stock assessments of all commercial species. The traditional human reading method to determine age is very time-consuming. Automated image analysis can be a low-cost alternative method, however, the first step is the transformation of routinely taken otolith images into standardized images within a database to apply machine learning techniques on the ageing data. Otolith shape, resulting from the synthesis of genetic heritage and environmental effects, is a useful tool to identify stock units, therefore a database of standardized images could be used for this aim. Using the routinely measured otolith data of plaice (Pleuronectes platessa; Linnaeus, 1758) and striped red mullet (Mullus surmuletus; Linnaeus, 1758) in the eastern English Channel and north-east Arctic cod (Gadus morhua; Linnaeus, 1758), a greyscale images matrix was generated from the raw images in different formats. Contour detection was then applied to identify broken otoliths, the orientation of each otolith, and the number of otoliths per image. To finalize this standardization process, all images were resized and binarized. Several mathematical morphology tools were developed from these new images to align and to orient the images, placing the otoliths in the same layout for each image. For this study, we used three databases from two different laboratories using three species (cod, plaice and striped red mullet). This method was approved to these three species and could be applied for others species for age determination and stock identification.

Data show measurements of total diameter, lumen diameter, and relative theoretical hydraulic conductivity, which were taken on vessel elements and wide-dband tracheids of two non-fibrous cacti species.

This dataset includes the raw data (not mass-corrected) used for standardizing oxygen consumption of two oyster species and age classes using sealed chambers filled with water of salinity 15 and 25°C. The log values of oyster mass (in grams) and respiration (µl 02 h-1) are provided in the data files. These data were used to understand the impacts of different mass-standardization techniques as is presented in Lombardi et al. PLoS ONE.

Market Analysis for Normalizing Service The global normalizing service market is anticipated to reach a value of xx million USD by 2033, exhibiting a CAGR of xx% during the forecast period. The market growth is attributed to the rising demand for efficient data management solutions, increased adoption of cloud-based applications, and growing awareness of data normalization techniques. The market size was valued at xx million USD in 2025. North America dominates the market, followed by Europe and Asia Pacific. The market is segmented based on application into banking and financial services, healthcare, retail, manufacturing, and other industries. The banking and financial services segment is expected to hold the largest market share due to the need for data accuracy and compliance with regulatory requirements. In terms of types, the market is divided into data integration and reconciliation, data standardization, and data profiling. Data integration and reconciliation is expected to dominate the market as it helps eliminate inconsistencies and redundancy in data sets. Major players in the market include Infosys, Capgemini, IBM, Accenture, and Wipro. The Normalizing Service Market reached a value of USD 1.16 Billion in 2023 and is poised to grow at a rate of 11.7% during the forecast period, reaching a value of USD 2.23 Billion by 2032.

Download .zipThis data set represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The metadata describes the lineage, sources and production methods for the data content. The definitions and structure of this data is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This coverage was originally created for the accurate location of the oil and gas wells in the state of Ohio. The original data set was developed as an ArcInfo coverage containing the original land subdivision boundaries for Ohio. Ohio has had a long and varied history of its land subdivisions that has led to the use of several subdivision strategies being applied. In general, these different schemes are composed of the Public Land Surveying System (PLSS) subdivisions and the irregular land subdivisions. The PLSS subdivisions contain townships, ranges, and sections. They are found in the following major land subdivisions: Old Seven Ranges, Between the Miamis (parts of which are known as the Symmes Purchase), Congress Lands East of Scioto River, Congress Lands North of Old Seven Ranges, Congress Lands West of Miami River, North and East of the First Principal Meridian, South and East of the First Principal Meridian, and the Michigan Meridian Survey. The irregular subdivisions include the Virginia Military District, the Ohio Company Purchase, the U.S. Military District, the Connecticut Western Reserve, the Twelve-Mile Square Reservation, the Two-Mile Square Reservation, the Refugee Lands, the French Grants, and the Donation Tract. This data set represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is local records and geographic control coordinates from states, counties as well as federal agencies such as the BLM, USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. This data set includes the following: PLSS Fully Intersected (all of the PLSS feature at the atomic or smallest polygon level), PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non rectangular components of the PLSS) Meandered Water, Corners and Conflicted Areas (known areas of gaps or overlaps between Townships or state boundaries). The Entity-Attribute section of this metadata describes these components in greater detail.This data set is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. This data set includes the following: PLSS Fully Intersected (all of the PLSS feature at the atomic or smallest polygon level), PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non rectangular components of the PLSS) Meandered Water, Corners and Conflicted Areas (known areas of gaps or overlaps between Townships or state boundaries). The Entity-Attribute section of this metadata describes these components in greater detail.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesOffice of Information TechnologyGIS Records2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altered variables is skewed, i.e. when a large fraction of the variables are either positively or negatively affected by the treatment. However, several experiments are likely to generate such skewed distributions, including ChIP-chip experiments for the study of chromatin, gene expression experiments for the study of apoptosis, and SNP-studies of copy number variation in normal and tumour tissues. A preliminary study using spike-in array data established that the capacity of an experiment to identify altered variables and generate unbiased estimates of the fold change decreases as the fraction of altered variables and the skewness increases. We propose the following work-flow for analyzing high-dimensional experiments with regions of altered variables: (1) Pre-process raw data using one of the standard normalization techniques. (2) Investigate if the distribution of the altered variables is skewed. (3) If the distribution is not believed to be skewed, no additional normalization is needed. Otherwise, re-normalize the data using a novel HMM-assisted normalization procedure. (4) Perform downstream analysis. Here, ChIP-chip data and simulated data were used to evaluate the performance of the work-flow. It was found that skewed distributions can be detected by using the novel DSE-test (Detection of Skewed Experiments). Furthermore, applying the HMM-assisted normalization to experiments where the distribution of the truly altered variables is skewed results in considerably higher sensitivity and lower bias than can be attained using standard and invariant normalization methods.

ABSTRACT The objective of the present study was to Standardize a Polymerase Chain Reaction (PCR) protocol for the authentication of bovine and buffalo milk, and to detect the presence of Salmonella spp. and Listeria monocytogenes. For this, the target DNA was extracted, mixed, and subjected to a PCR assay. Milk samples were defrauded and experimentally contaminated with microorganisms to assess the detection of target DNA at different times of cultivation, bacterial titers, and concentration of genetic material. In addition, the protocol was tested with DNA extracted directly from food, without a pre-enrichment step. The proposed quadruplex PCR showed good accuracy in identifying target DNA sequences. It was possible to simultaneously identify all DNA sequences at the time of inoculation (0h), when the samples were contaminated with 2 CFU/250mL and with 6h of culture when the initial inoculum was 1 CFU/250mL. It was also possible to directly detect DNA sequences from the food when it was inoculated with 3 CFU/mL bacteria. Thus, the proposed methodology showed satisfactory performance, optimization of the analysis time, and a potential for the detection of microorganisms at low titers, which can be used for the detection of fraud and contamination.

1. Microbiome sequencing data often need to be normalized due to differences in read depths, and recommendations for microbiome analyses generally warn against using proportions or rarefying to normalize data and instead advocate alternatives, such as upper quartile, CSS, edgeR-TMM, or DESeq-VS. Those recommendations are, however, based on studies that focused on differential abundance testing and variance standardization, rather than community-level comparisons (i.e., beta diversity), Also, standardizing the within-sample variance across samples may suppress differences in species evenness, potentially distorting community-level patterns. Furthermore, the recommended methods use log transformations, which we expect to exaggerate the importance of differences among rare OTUs, while suppressing the importance of differences among common OTUs. 2. We tested these theoretical predictions via simulations and a real-world data set. 3. Proportions and rarefying produced more accurate comparisons among communities and were the only methods that fully normalized read depths across samples. Additionally, upper quartile, CSS, edgeR-TMM, and DESeq-VS often masked differences among communities when common OTUs differed, and they produced false positives when rare OTUs differed. 4. Based on our simulations, normalizing via proportions may be superior to other commonly used methods for comparing ecological communities.

Context

This dataset is cleaned and ready to deploy for model building.

Content

This dataset is for learning purpose and thus is simplified and is without any null values or major skewness.

Inspiration

I learned much from Kaggle and the data community and this is my contribution so that flow of knowledge never stops.

Data set used in "A standardized method for the construction of tracer specific PET and SPECT rat brain templates: validation and implementation of a toolbox"

Understanding the biogeography of past and present fire events is particularly important in tropical forest ecosystems, where fire rarely occurs in the absence of human ignition. Open science databases have facilitated comprehensive and synthetic analyses of past fire activity, but charcoal datasets must be standardized (scaled) because of variations in measurement strategy, sediment type, and catchment size. Here, we: i) assess how commonly used metrics of charcoal scaling perform on datasets from tropical forests; ii) introduce a new method called proportional relative scaling, which down-weights rare and infrequent fire; and iii) compare the approaches using charcoal data from four lakes in the Peruvian Amazon. We found that Z-score transformation and relative scaling (existing methods) distorted the structure of the charcoal peaks within the record, inflating the variation in small-scale peaks and minimizing the effect of large peaks. Proportional relative scaling maintained the st...

Refining Poetry for Maximum Impact

To create a powerful and engaging poem, poets can use standardization and optimization techniques. These involve refining the poem's form, language, and structure to convey its message effectively.

Key Aspects:

• Consistency: Ensure consistency in form, language, and tone to create a cohesive poem.
• Effective Language: Choose precise and evocative words to engage the reader.
• Structure: Use line breaks and stanza structure to control the poem's flow and tension.
• Sound and Rhythm: Employ sound devices to create a musical quality.
• Emotional Resonance: Craft the poem to evoke a specific emotional response.

Goals:

• Enhance Impact: Make the poem more effective in conveying its message.
• Improve Reader Experience: Create an engaging and enjoyable reading experience.
• Increase Memorability: Make the poem more memorable and quotable.

Techniques:

• Revision and Editing: Refine the poem through review and revision.
• Feedback: Seek feedback to identify areas for improvement.
• Poetic Devices: Use devices like metaphor and simile to add depth and complexity.
• Experimentation: Try different forms and structures to find the most effective approach.

By refining their poetry, poets can create a more impactful and engaging work that resonates with readers.

This dataset contains gridded monthly Standardized Precipitation Index (SPI) at 10 timescales: 1-, 3-, 6-, 9-, 12-, 18-, 24-, 36-, 48-, and 60-month intervals from 1920 to 2012 at 250 m resolution for seven of the eight main Hawaiian Islands (18.849°N, 154.668°W to 22.269°N, 159.816°W; the island of Ni‘ihau is excluded due to lack of data). The gridded data use a World Geographic Coordinate System 1984 (WGS84) and are stored as individual GeoTIFF files for each month-year, organized by SPI interval, as indicated by the GeoTIFF file name. Thus, for example, the file “spi3_1999_11.tif” would contain the gridded 3-month SPI values calculated for the month of November in the year 1999. Currently, the data are available from 1920 to 2012, but the datasets will be updated as new gridded monthly rainfall data become available.SPI is a normalized drought index that converts monthly rainfall totals into the number of standard deviations (z-score) by which the observed, cumulative rainfall diverges from the long-term mean. The conversion of raw rainfall to a z-score is done by fitting a designated probability distribution function to the observed precipitation data for a site. In doing so, anomalous rainfall quantities take the form of positive and negative SPI z-scores. Additionally, because distribution fitting is based on long-term (>30 years) precipitation data at that location, SPI score is relative, making comparisons across different climates possible.The creation of a statewide Hawai‘i SPI dataset relied on a 93-year (1920-2012) high resolution (250 m) spatially interpolated monthly gridded rainfall dataset [1]. This dataset is recognized as the highest quality precipitation data available [2] for the main Hawaiian Islands. After performing extensive quality control on the monthly rainfall station data (including homogeneity testing of over 1,100 stations [1,3]) and a geostatistical method comparison, ordinary kriging was using to generate a time series of gridded monthly rainfall from January 1920 to December 2012 at 250 m resolution [3]. This dataset was then used to calculate monthly SPI for 10 timescales (1-, 3-, 6-, 9-, 12-, 18-, 24-, 36-, 48-, and 60-month) at each grid cell. A 3-month SPI in May 2001, for example, represents the March-April-May (MAM) total rainfall in 2001 compared to the MAM rainfall in the entire time series. The resolution of the gridded rainfall dataset provides a more precise representation of drought (and pluvial) events compared to the other available drought products.Frazier, A.G.; Giambelluca, T.W.; Diaz, H.F.; Needham, H.L. Comparison of geostatistical approaches to spatially interpolate month-year rainfall for the Hawaiian Islands. Int. J. Climatol. 2016, 36, 1459–1470, doi:10.1002/joc.4437.Giambelluca, T.W.; Chen, Q.; Frazier, A.G.; Price, J.P.; Chen, Y.-L.; Chu, P.-S.; Eischeid, J.K.; Delparte, D.M. Online Rainfall Atlas of Hawai‘i. B. Am. Meteorol. Soc. 2013, 94, 313–316, doi:10.1175/BAMS-D-11-00228.1.Frazier, A.G.; Giambelluca, T.W. Spatial trend analysis of Hawaiian rainfall from 1920 to 2012. Int. J. Climatol. 2017, 37, 2522–2531, doi:10.1002/joc.4862.

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Online Data Science Training Programs Market Size 2025-2029

The online data science training programs market size is forecast to increase by USD 8.67 billion, at a CAGR of 35.8% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for data science professionals in various industries. The job market offers lucrative opportunities for individuals with data science skills, making online training programs an attractive option for those seeking to upskill or reskill. Another key driver in the market is the adoption of microlearning and gamification techniques in data science training. These approaches make learning more engaging and accessible, allowing individuals to acquire new skills at their own pace. Furthermore, the availability of open-source learning materials has democratized access to data science education, enabling a larger pool of learners to enter the field. However, the market also faces challenges, including the need for continuous updates to keep up with the rapidly evolving data science landscape and the lack of standardization in online training programs, which can make it difficult for employers to assess the quality of graduates. Companies seeking to capitalize on market opportunities should focus on offering up-to-date, high-quality training programs that incorporate microlearning and gamification techniques, while also addressing the challenges of continuous updates and standardization. By doing so, they can differentiate themselves in a competitive market and meet the evolving needs of learners and employers alike.

What will be the Size of the Online Data Science Training Programs Market during the forecast period?

Request Free SampleThe online data science training market continues to evolve, driven by the increasing demand for data-driven insights and innovations across various sectors. Data science applications, from computer vision and deep learning to natural language processing and predictive analytics, are revolutionizing industries and transforming business operations. Industry case studies showcase the impact of data science in action, with big data and machine learning driving advancements in healthcare, finance, and retail. Virtual labs enable learners to gain hands-on experience, while data scientist salaries remain competitive and attractive. Cloud computing and data science platforms facilitate interactive learning and collaborative research, fostering a vibrant data science community. Data privacy and security concerns are addressed through advanced data governance and ethical frameworks. Data science libraries, such as TensorFlow and Scikit-Learn, streamline the development process, while data storytelling tools help communicate complex insights effectively. Data mining and predictive analytics enable organizations to uncover hidden trends and patterns, driving innovation and growth. The future of data science is bright, with ongoing research and development in areas like data ethics, data governance, and artificial intelligence. Data science conferences and education programs provide opportunities for professionals to expand their knowledge and expertise, ensuring they remain at the forefront of this dynamic field.

How is this Online Data Science Training Programs Industry segmented?

The online data science training programs industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeProfessional degree coursesCertification coursesApplicationStudentsWorking professionalsLanguageR programmingPythonBig MLSASOthersMethodLive streamingRecordedProgram TypeBootcampsCertificatesDegree ProgramsGeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Type Insights

The professional degree courses segment is estimated to witness significant growth during the forecast period.The market encompasses various segments catering to diverse learning needs. The professional degree course segment holds a significant position, offering comprehensive and in-depth training in data science. This segment's curriculum covers essential aspects such as statistical analysis, machine learning, data visualization, and data engineering. Delivered by industry professionals and academic experts, these courses ensure a high-quality education experience. Interactive learning environments, including live lectures, webinars, and group discussions, foster a collaborative and engaging experience. Data science applications, including deep learning, computer vision, and natural language processing, are integral to the market's growth. Data analysis, a crucial application, is gaining traction due to the increasing demand

Dataset for the publication: "Lock, stock, and barrel: a comprehensive and standardized analysis method for electrochemical CO₂ reduction" divided by paper Figure. The dataset contains data that are both raw and processed using the open-source software available at http://dgbowl.github.io

The label-free quantitative mass spectrometry methods, in particular the SWATH-MS approach, have gained popularity and became a powerful technique for comparison of large datasets. In the present work, it is introduced the use of recombinant proteins as internal standards for untargeted label-free methods. The proposed internal standard strategy reveals a similar intragroup normalization capacity when compared with the most common normalization methods, with the additional advantage of maintaining the overall proteome changes between groups (which are lost using the methods referred above). Thus, proving to be able to maintain a good performance even when large qualitative and quantitative differences in sample composition are observed, such as the ones induced by biological regulation (as observed in secretome and other biofluids’ analyses) or by enrichment approaches (such as immunopurifications). Moreover, it corresponds to a cost-effective alternative, easier to implement than the current stable-isotope labeling internal standards, therefore being an appealing strategy for large quantitative screening, as clinical cohorts for biomarker discovery.

Reference standardization was developed to address quantification and harmonization challenges for high-resolution metabolomics (HRM) data collected across different studies or analytical methods. Reference standardization relies on the concurrent analysis of calibrated pooled reference samples at predefined intervals and enables a single-step batch correction and quantification for high-throughput metabolomics. Here, we provide quantitative measures of approximately 200 metabolites for each of three pooled reference materials (220 metabolites for Qstd3, 211 metabolites for CHEAR, 204 metabolites for NIST1950) and show application of this approach for quantification supports harmonization of metabolomics data collected from 3677 human samples in 17 separate studies analyzed by two complementary HRM methods over a 17-month period. The results establish reference standardization as a method suitable for harmonizing large-scale metabolomics data and extending capabilities to quantify large numbers of known and unidentified metabolites detected by high-resolution mass spectrometry methods.