Principal component analysis (PCA) is a popular dimension-reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior to applying PCA. Such transformation is usually obtained from previous studies, prior knowledge, or trial-and-error. In this work, we develop a model-based method that integrates data transformation in PCA and finds an appropriate data transformation using the maximum profile likelihood. Extensions of the method to handle functional data and missing values are also developed. Several numerical algorithms are provided for efficient computation. The proposed method is illustrated using simulated and real-world data examples. Supplementary materials for this article are available online.

ABSTRACT. There are researchers who do not recommend data transformation arguing it causes problems in inferences and mischaracterises data sets, which can hinder interpretation. There are other researchers who consider data transformation necessary to meet the assumptions of parametric models. Perhaps the largest group of researchers who make use of data transformation are concerned with experimental accuracy, which provokes the misuse of this tool. Considering this, our paper offer a study about the most frequent situations related to data transformation and how this tool can impact ANOVA assumptions and experimental accuracy. Our database was obtained from measurements of seed physiology and seed technology. The coefficient of variation cannot be used as an indicator of data transformation. Data transformation might violate the assumptions of analysis of variance, invalidating the idea that its use will provoke fail the inferences, even if it does not improve the quality of the analysis. The decision about when to use data transformation is dichotomous, but the criteria for this decision are many. The unit (percentage, day or seedlings per day), the experimental design and the possible robustness of F-statistics to ‘small deviations’ to Normal are among the main indicators for the choice of the type of transformation.

The graph shows the IT transformation initiatives in organizations worldwide. During the 2019 survey, ** percent of responding IT professionals stated that they had taken steps to automate data management. The most common IT transformation initiative were virtualizations.

Predicting the execution time of model transformations can help to understand how a transformation reacts to a given input model without creating and transforming the respective model.

In our previous data set (https://doi.org/10.5281/zenodo.8385957), we have documented our experiments in which we predict the performance of ATL transformations using predictive models obtained from training linear regression, random forest and support vector regression. As input for the prediction, our approach uses a characterization of the input model. In these experiments, we only used data from real models.

However, a common problem is that transformation developers do not have enough models available to use such a prediction approach. Therefore, in a new variant of our experiments, we investigated whether the three considered machine learning approaches can predict the performance of transformations if we use data from generated models for training. We also investigated whether it is possible to achieve good predictions with smaller training data. The dataset provided here offers the corresponding raw data, scripts, and results.

A detailed documentation is available in documentaion.pdf.

Brazil Imports: Volume: Daily Average: YoY: Daily Average: Transformation Industry: Household Common Metal Equipment data was reported at 1.139 % in 30 Apr 2025. This records a decrease from the previous number of 10.753 % for 27 Apr 2025. Brazil Imports: Volume: Daily Average: YoY: Daily Average: Transformation Industry: Household Common Metal Equipment data is updated daily, averaging 21.695 % from Mar 2020 (Median) to 30 Apr 2025, with 250 observations. The data reached an all-time high of 226.507 % in 17 Mar 2024 and a record low of -45.337 % in 05 Jul 2020. Brazil Imports: Volume: Daily Average: YoY: Daily Average: Transformation Industry: Household Common Metal Equipment data remains active status in CEIC and is reported by Special Secretariat for Foreign Trade and International Affairs. The data is categorized under Brazil Premium Database’s Foreign Trade – Table BR.JAA012: Imports: Economic Activity: Product: Volume: Year-on-Year.

🎵 Unveiling Spotify Trends: A Deep Dive into Streaming Data:

Introduction:

This Jupyter Notebook explores data manipulation, aggregation, and visualization techniques using Python’s Pandas, Matplotlib, and Seaborn libraries. The key objectives of this analysis include:

📌 Data Cleaning and Preparation ✔ Handling missing values in key columns. ✔ Standardizing and transforming categorical features (e.g., mode, release_day_name). ✔ Creating new derived features, such as decade classification and energy levels.

📌 Feature Engineering & Data Transformation ✔ Extracting release trends from date-based columns. ✔ Categorizing song durations and popularity levels dynamically. ✔ Applying lambda functions, apply(), map(), and filter() for efficient data transformations. ✔ Using groupby() and aggregation functions to analyze trends in song streams. ✔ Ranking artists based on total streams using rank().

📌 Data Aggregation and Trend Analysis ✔ Identifying the most common musical keys used in songs. ✔ Tracking song releases over time with rolling averages. ✔ Comparing Major vs. Minor key distributions in song compositions.

📌 Data Visualization ✔ Bar plots for ranking top artists and stream counts. ✔ Box plots to analyze stream distribution per release year. ✔ Heatmaps to examine feature correlations. ✔ Pie charts to understand song popularity distribution.

📌 Dataset Description The dataset consists of Spotify streaming statistics and includes features such as:

🎵 track_name – Song title. 🎤 artist(s)_name – Name(s) of performing artists. 🔢 streams – Number of times the song was streamed. 📅 released_year, released_month, released_day – Date of song release. 🎼 energy_%, danceability_%, valence_% – Audio feature metrics. 📊 in_spotify_playlists – Number of Spotify playlists featuring the song. 🎹 mode – Musical mode (Major or Minor). 🎯 Purpose This analysis is designed for: ✔ Exploring real-world datasets to develop data analyst skills. ✔ Practicing data transformation, aggregation, and visualization techniques. ✔ Preparing for data analyst interviews by working with structured workflows.

📌 Table of Contents 1️⃣ Data Cleaning & Preparation 2️⃣ Feature Engineering & Transformations (apply(), map(), filter(), groupby(), rank()) 3️⃣ Data Aggregation & Trend Analysis 4️⃣ Data Visualization & Insights 5️⃣ Conclusion and Key Takeaways

The following protocol describes the genetic transformation of wheat using the BioRad PDS/1000-He particle delivery system. Immature embryos are isolated 12–16 days post-anthesis, the embryonic axis is removed, and the immature scutella are precultured for 1–2 days prior to particle bombardment. Gold particles are coated with plasmid DNA containing the gene(s) of interest plus a selectable marker gene, in this instance bar (bialaphos resistance), and are fired into the cells to deliver the DNA. Subsequent tissue culture and regeneration steps allow recovery of plantlets, assisted by the inclusion of PPT (phosphinothricin tripeptide), the active ingredient of glufosinate-ammonium containing herbicides, to help select transformants. This updated method introduces selection earlier in the regeneration process which provides a shortened protocol while maintaining high transformation efficiencies.

OMOP Data Network Integration Market Outlook

According to our latest research, the global OMOP Data Network Integration market size reached USD 1.09 billion in 2024, reflecting a robust expansion driven by the surge in healthcare data interoperability initiatives and real-world evidence analytics. The market is projected to grow at a CAGR of 13.7% from 2025 to 2033, with the total market size expected to reach USD 3.13 billion by 2033. This remarkable growth is primarily attributed to increased adoption of data standardization frameworks, such as the OMOP Common Data Model, across pharmaceutical, healthcare provider, and research sectors, facilitating seamless data integration and advanced analytics.

The OMOP Data Network Integration market is experiencing significant momentum due to the escalating demand for standardized healthcare data models that enable cross-institutional research and regulatory compliance. Healthcare organizations are increasingly recognizing the value of harmonized data to support large-scale observational studies, pharmacovigilance, and real-world evidence generation. The global push towards digital health transformation, coupled with regulatory mandates emphasizing data transparency and interoperability, is further amplifying the adoption of OMOP-based network integration solutions. These factors, along with the proliferation of electronic health records (EHRs) and the need for efficient data sharing across disparate systems, are fueling the market’s sustained growth trajectory.

Another critical growth driver is the rapid advancement in artificial intelligence (AI) and machine learning (ML) technologies, which are being leveraged to extract actionable insights from vast, heterogeneous healthcare datasets. The OMOP Data Network Integration market is benefitting from the convergence of AI-powered analytics and standardized data infrastructures, allowing stakeholders to accelerate drug discovery, optimize clinical trial designs, and enhance patient outcomes. Pharmaceutical and biotechnology companies, in particular, are investing heavily in OMOP integration platforms to streamline real-world data (RWD) aggregation and evidence generation for regulatory submissions, market access, and post-market surveillance.

Moreover, the expansion of collaborative research networks and public-private partnerships is catalyzing the adoption of OMOP Data Network Integration solutions globally. Initiatives such as the Observational Health Data Sciences and Informatics (OHDSI) consortium and the increasing participation of academic and research institutes in multi-site studies are fostering a culture of data sharing and transparency. These collaborative efforts are not only accelerating scientific discovery but also driving the need for robust, scalable, and secure OMOP integration frameworks that can support diverse research objectives and regulatory requirements.

Regionally, North America continues to dominate the OMOP Data Network Integration market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The strong presence of leading healthcare IT vendors, advanced healthcare infrastructure, and supportive regulatory landscape in the United States and Canada have positioned North America as a frontrunner in OMOP adoption. Meanwhile, Europe’s focus on cross-border health data exchange and Asia Pacific’s burgeoning investments in healthcare digitization are expected to drive substantial market growth in these regions over the forecast period.

Component Analysis

The OMOP Data Network Integration market, when analyzed by component, is segmented into Software, Services, and Platforms. Software solutions form the backbone of the market, enabling seamless data mapping, transformation, and integration across disparate healthcare systems. These software tools are designed to facilitate the adoption of the OMOP Common Data Model, ensuring that data from various sources such as EHRs, claims, and registries can be harmonized for analytics and research. The growing complexity of healthcare data and the need for real-time data processing have led to the emergence of advanced software platforms that support automated ETL (Extract, Transform, Load) processes, robust data quality checks, and compliance with global data standards.

Services play a pivotal role in the OMOP Data Network Integration ecosyste

This dataset is about book subjects. It has 3 rows and is filtered where the books is The common core mathematics standards : transforming practice through team leadership. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The OMOP Common Data Model allows for the systematic analysis of disparate observational databases. The concept behind this approach is to transform data contained within those databases into a common format (data model) as well as a common representation (terminologies, vocabularies, coding schemes), and then perform systematic analyses using a library of standard analytic routines that have been written based on the common format.

ObjectiveThe German Health Data Lab is going to provide access to German statutory health insurance claims data ranging from 2009 to the present for research purposes. Due to evolving data formats within the German Health Data Lab, there is a need to standardize this data into a Common Data Model to facilitate collaborative health research and minimize the need for researchers to adapt to multiple data formats. For this purpose we selected transforming the data to the Observational Medical Outcomes Partnership Common Data Model.MethodsWe developed an Extract, Transform, and Load (ETL) pipeline for two distinct German Health Data Lab data formats: Format 1 (2009-2016) and Format 3 (2019 onwards). Due to the identical format structure of Format 1 and Format 2 (2017 -2018), the ETL pipeline of Format 1 can be applied on Format 2 as well. Our ETL process, supported by Observational Health Data Sciences and Informatics tools, includes specification development, SQL skeleton creation, and concept mapping. We detail the process characteristics and present a quality assessment that includes field coverage and concept mapping accuracy using example data.ResultsFor Format 1, we achieved a field coverage of 92.7%. The Data Quality Dashboard showed 100.0% conformance and 80.6% completeness, although plausibility checks were disabled. The mapping coverage for the Condition domain was low at 18.3% due to invalid codes and missing mappings in the provided example data. For Format 3, the field coverage was 86.2%, with Data Quality Dashboard reporting 99.3% conformance and 75.9% completeness. The Procedure domain had very low mapping coverage (2.2%) due to the use of mocked data and unmapped local concepts The Condition domain results with 99.8% of unique codes mapped. The absence of real data limits the comprehensive assessment of quality.ConclusionThe ETL process effectively transforms the data with high field coverage and conformance. It simplifies data utilization for German Health Data Lab users and enhances the use of OHDSI analysis tools. This initiative represents a significant step towards facilitating cross-border research in Europe by providing publicly available, standardized ETL processes (https://github.com/FraunhoferMEVIS/ETLfromHDLtoOMOP) and evaluations of their performance.

Abstract: The use of a transformation method for the passage from one geodetic system to another requires the use of some common points as support points, these points are used in the determination of the transformation parameters. Generally, the choice of the support points is effected manually by choosing the best distribution of these points in the transformation area. In the present study, we present a methodology of selection of these points where an algorithm takes into account the computation of the transformation parameters with all combinations between the common points and the best result will be adopted. An application of this methodology are carried out in North-East of Algeria to determine the best set of the 09 transformation parameters between the WGS84 system and the National North Sahara system using 10 common points (05 support and 05 control). This methodology is efficient in the case where the common points are near one another.

Brazil Exports: FOB: Daily Average: Transformation Industry: Household Common Metal Equipment data was reported at 979.523 USD th in 30 Apr 2025. This records a decrease from the previous number of 1,010.917 USD th for 27 Apr 2025. Brazil Exports: FOB: Daily Average: Transformation Industry: Household Common Metal Equipment data is updated daily, averaging 852.899 USD th from Mar 2019 (Median) to 30 Apr 2025, with 262 observations. The data reached an all-time high of 1,616.916 USD th in 09 Oct 2022 and a record low of 338.644 USD th in 06 Aug 2023. Brazil Exports: FOB: Daily Average: Transformation Industry: Household Common Metal Equipment data remains active status in CEIC and is reported by Special Secretariat for Foreign Trade and International Affairs. The data is categorized under Brazil Premium Database’s Foreign Trade – Table BR.JAA002: Exports: Economic Activity: Product: Value.

The text file "Wind speed.txt" contains hourly data and associated data-source flag from January 1, 1948, to September 30, 2016. The primary source of the data is the Argonne National Laboratory, Illinois (ANL). The data-source flag consist of a three-digit sequence in the form "xyz" that describe the origin and transformations of the data values. They indicate if the data are original or missing, the method that was used to fill the missing periods, and any other transformations of the data. Missing and apparently erroneous data values were replaced with adjusted values from nearby stations used as “backup”. As stated in Over and others (2010), temporal variations in the statistical properties of the data resulting from changes in measurement and data storage methodologies were adjusted to match the statistical properties resulting from the data collection procedures that have been in place since January 1, 1989. The adjustments were computed based on the regressions between the primary data series from ANL and the backup series using data obtained during common periods; the statistical properties of the regressions were used to assign estimated standard errors to values that were adjusted or filled from other series. Each hourly value is assigned a corresponding data source flag that indicates the source of the value and its transformations. As described in Over and others (2010), each flag is of the form "xyz" that allows the user to determine its source and the methods used to process the data. During the period 01/09/2016 hour 21 to 01/10/2016 hour 24 both ANL and the primary backup station at St. Charles, Illinois had missing wind speed data. The O'Hare International Airport (ORD) is used as an alternate backup station and the new regression equation and the corresponding new flag for wind speed are established using daily wind data from ORD for the period 10/01/2007 through 09/30/2016 following the guideline described in Over and others (2010). Reference Cited: Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/.

Brazil Imports: FOB: Daily Average: YoY: Daily Average: Transformation Industry: Household Common Metal Equipment data was reported at -4.340 % in 30 Apr 2025. This records a decrease from the previous number of 7.903 % for 27 Apr 2025. Brazil Imports: FOB: Daily Average: YoY: Daily Average: Transformation Industry: Household Common Metal Equipment data is updated daily, averaging 16.596 % from Mar 2020 (Median) to 30 Apr 2025, with 250 observations. The data reached an all-time high of 170.322 % in 17 Mar 2024 and a record low of -47.617 % in 05 Jul 2020. Brazil Imports: FOB: Daily Average: YoY: Daily Average: Transformation Industry: Household Common Metal Equipment data remains active status in CEIC and is reported by Special Secretariat for Foreign Trade and International Affairs. The data is categorized under Brazil Premium Database’s Foreign Trade – Table BR.JAA011: Imports: Economic Activity: Product: Value: Year-on-Year.

Brazil Exports: FOB: YoY: Daily Average: Transformation Industry: Household Common Metal Equipment data was reported at 28.107 % in 30 Apr 2025. This records a decrease from the previous number of 45.434 % for 27 Apr 2025. Brazil Exports: FOB: YoY: Daily Average: Transformation Industry: Household Common Metal Equipment data is updated daily, averaging 13.333 % from Mar 2020 (Median) to 30 Apr 2025, with 250 observations. The data reached an all-time high of 151.182 % in 09 May 2021 and a record low of -55.026 % in 07 Aug 2022. Brazil Exports: FOB: YoY: Daily Average: Transformation Industry: Household Common Metal Equipment data remains active status in CEIC and is reported by Special Secretariat for Foreign Trade and International Affairs. The data is categorized under Brazil Premium Database’s Foreign Trade – Table BR.JAA005: Exports: Economic Activity: Product: Value: Year-on-Year.

Common-Stock-Shares-Outstanding Time Series for Change Inc. CHANGE Holdings,Inc., together with its subsidiaries, engages in the digital transformation (DX) business in Japan. It operates through the NEW-IT Transformation Business and Publitech Business segments. The NEW-IT Transformation Business segment engages in the development of digital human resources, business processes, digitalization, as well as mergers and acquisitions brokerage and cybersecurity. Its Publitech Business segment is involved in regional creation and public sector digital transformation. The company was formerly known as Change Inc. and changed its name to CHANGE Holdings,Inc. in April 2023. CHANGE Holdings,Inc. was incorporated in 2003 and is headquartered in Tokyo, Japan.

These data are a derived product of the NOAA VDatum tool and they extend the tool's Mean Higher High Water (MHHW) tidal datum conversion inland beyond its original extent. VDatum was designed to vertically transform geospatial data among a variety of tidal, orthometric and ellipsoidal vertical datums - allowing users to convert their data from different horizontal/vertical references into a common system and enabling the fusion of diverse geospatial data in desired reference levels (http://vdatum.noaa.gov/). However, VDatum's conversion extent does not completely cover tidally-influenced areas along the coast. For more information on why VDatum does not provide tidal datums inland, see http://vdatum.noaa.gov/docs/faqs.html. Because of the extent limitation and since most inundation mapping activities use a tidal datum as the reference zero (i.e., 1 meter of sea level rise on top of Mean Higher High Water), the NOAA Office for Coastal Management created this dataset for the purpose of extending the MHHW tidal datum beyond the areas covered by VDatum. The data do not replace VDatum, nor do they supersede the valid datum transformations VDatum provides. However, the data are based on VDatum's underlying transformation data and do provide an approximation of MHHW where VDatum does not provide one. In addition, the data are in a GIS-friendly format and represent MHHW in NAVD88, which is the vertical datum by which most topographic data are referenced. Data are in the UTM NAD83 projection. Horizontal resolution varies by VDatum region, but is either 50m or 100m. Data are vertically referenced to NAVD88 meters.

Protein-Protein, Genetic, and Chemical Interactions for Lee PS (2003):Sumoylation of Smad4, the common Smad mediator of transforming growth factor-beta family signaling. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Transforming growth factor-beta (TGF-beta) and TGF-beta-related factors regulate cell growth, differentiation, and apoptosis, and play key roles in normal development and tumorigenesis. TGF-beta family-induced changes in gene expression are mediated by serine/threonine kinase receptors at the cell surface and Smads as intracellular effectors. Receptor-activated Smads combine with a common Smad4 to translocate into the nucleus where they cooperate with other transcription factors to activate or repress transcription. The activities of the receptor-activated Smads are controlled by post-translational modifications such as phosphorylation and ubiquitylation. Here we show that Smad4 is modified by sumoylation. Sumoylation of Smad4 was enhanced by the conjugating enzyme Ubc9 and members of the PIAS family of SUMO ligases. A major sumoylation site in Smad4 was localized to Lys-159 in its linker segment with an additional site at Lys-113 in the MH-1 domain. Increased sumoylation in the presence of the PIASy E3 ligase correlated with targeting of Smad4 to subnuclear speckles that contain SUMO-1 and PIASy. Replacement of lysines 159 and 113 by arginines or increased sumoylation enhanced the stability of Smad4, and transcription in mammalian cells and Xenopus embryos. These observations suggest a role for Smad4 sumoylation in the regulation of TGF-beta signaling through Smads.

Hippocampal circuits form cognitive maps representing spatial position and integrating contextual information, including affective cues, within episodic memory representations. We investigated how spatial and affective information combine in the population activity of CA3 axons by imaging intermediate-to-dorsal and dorsal-to-dorsal projections in mice navigating a linear track before, during, and after exposure to an aversive air puff stimulus. Our analyses reveal that both axonal populations maintain a robust, time-invariant spatial coding manifold across recordings, independent of affective context. Alterations to this common manifold encode the presence of the aversive stimulus without disrupting the spatial representation. Both axonal pathways encoded affective information with similar efficacy. This population-level encoding was distributed similarly across place and non-place cells. Our findings demonstrate that hippocampal CA3 axons integrate spatial and affective information within a common representational geometry while preserving the ability to extract each information type separately.