This is an archive of the data contained in the "Transformations" section in PubChem for integration into patRoon and other workflows.

For further details see the ECI GitLab site: README and main "tps" folder.

Credits:

Concepts: E Schymanski, E Bolton, J Zhang, T Cheng;

Code (in R): E Schymanski, R Helmus, P Thiessen

Transformations: E Schymanski, J Zhang, T Cheng and many contributors to various lists!

PubChem infrastructure: PubChem team

Reaction InChI (RInChI) calculations (v1.0): Gerd Blanke (previous versions of these files)

Acknowledgements: ECI team who contributed to related efforts, especially: J. Krier, A. Lai, M. Narayanan, T. Kondic, P. Chirsir, E. Palm. All contributors to the NORMAN-SLE transformations!

March 2025 released as v0.2.0 since the dataset grew by >3000 entries! The stats are:

14 March 2025

Unique Transformation Entries: 10904# Unique Reactions by CID: 9152# Unique Reactions by IK: 9139# Unique Reactions by IKFB: 8574# Unique NORMAN-SLE Compounds by CID: 8207# Unique ChEMBL Compounds by CID: 1419# Unique Compounds (all) by CID: 9267# Unique Predecessors (all) by CID: 3724# Unique Successors (all) by CID: 7331# Range of XlogP Differences: -9.9,10# Range of Mass Differences: -957.97490813,820.227106427

File List glmmeg.R: R code demonstrating how to fit a logistic regression model, with a random intercept term, to randomly generated overdispersed binomial data. boot.glmm.R: R code for estimating P-values by applying the bootstrap to a GLMM likelihood ratio statistic. Description glmm.R is some example R code which show how to fit a logistic regression model (with or without a random effects term) and use diagnostic plots to check the fit. The code is run on some randomly generated data, which are generated in such a way that overdispersion is evident. This code could be directly applied for your own analyses if you read into R a data.frame called “dataset”, which has columns labelled “success” and “failure” (for number of binomial successes and failures), “species” (a label for the different rows in the dataset), and where we want to test for the effect of some predictor variable called “location”. In other cases, just change the labels and formula as appropriate. boot.glmm.R extends glmm.R by using bootstrapping to calculate P-values in a way that provides better control of Type I error in small samples. It accepts data in the same form as that generated in glmm.R.

Abstract: This repository provides different artifacts developed in and used for the evaluation of the dissertation "Building Transformation Networks for Consistent Evolution of Interrelated Models". It serves as a reproduction package for the contributions and evaluations of that thesis. The artifacts comprise an approach to evaluate compatibility of QVT-R transformations, evaluations of interoperability issues in transformation networks and approaches to avoid them, a language to define consistency between multiple models, and an evaluation of this language. The package contains a prepared execution environment for the different artifacts. In addition, it provides a script to run the environment for some of the artifacts and automatically resolve all dependencies based on Docker. TechnicalRemarks: Instructions on how to use the data can be found within the repository.

In our previous study, it was shown that Riemerella anatipestifer, a Gram-negative bacterium, is naturally competent, but the genes involved in the process of natural transformation remain largely unknown. In this study, a random transposon mutant library was constructed using the R. anatipestifer ATCC11845 strain to screen for the genes involved in natural transformation. Among the 3000 insertion mutants, nine mutants had completely lost the ability of natural transformation, and 14 mutants showed a significant decrease in natural transformation frequency. We found that the genes RA0C_RS04920, RA0C_RS04915, RA0C_RS02645, RA0C_RS04895, RA0C_RS05130, RA0C_RS05105, RA0C_RS09020, and RA0C_RS04870 are essential for the occurrence of natural transformation in R. anatipestifer ATCC11845. In particular, RA0C_RS04895, RA0C_RS05130, RA0C_RS05105, and RA0C_RS04870 were putatively annotated as ComEC, DprA, ComF, and RecA proteins, respectively, in the NCBI database. However, RA0C_RS02645, RA0C_RS04920, RA0C_RS04915, and RA0C_RS09020 were annotated as proteins with unknown function, with no homology to any well-characterized natural transformation machinery proteins. The homologs of these proteins are mainly distributed in the members of Flavobacteriaceae. Taken together, our results suggest that R. anatipestifer encodes a unique natural transformation machinery.

R Code for transformation SVS coordinates to the orthomosaic coordinate system

Functional diversity (FD) is an important component of biodiversity that quantifies the difference in functional traits between organisms. However, FD studies are often limited by the availability of trait data and FD indices are sensitive to data gaps. The distribution of species abundance and trait data, and its transformation, may further affect the accuracy of indices when data is incomplete. Using an existing approach, we simulated the effects of missing trait data by gradually removing data from a plant, an ant and a bird community dataset (12, 59, and 8 plots containing 62, 297 and 238 species respectively). We ranked plots by FD values calculated from full datasets and then from our increasingly incomplete datasets and compared the ranking between the original and virtually reduced datasets to assess the accuracy of FD indices when used on datasets with increasingly missing data. Finally, we tested the accuracy of FD indices with and without data transformation, and the effect of missing trait data per plot or per the whole pool of species. FD indices became less accurate as the amount of missing data increased, with the loss of accuracy depending on the index. But, where transformation improved the normality of the trait data, FD values from incomplete datasets were more accurate than before transformation. The distribution of data and its transformation are therefore as important as data completeness and can even mitigate the effect of missing data. Since the effect of missing trait values pool-wise or plot-wise depends on the data distribution, the method should be decided case by case. Data distribution and data transformation should be given more careful consideration when designing, analysing and interpreting FD studies, especially where trait data are missing. To this end, we provide the R package “traitor” to facilitate assessments of missing trait data.

The Economic Transformation Database of Transition Economies [ETD-TE] provides a balanced panel of internationally comparable sectoral data on output and employment in fourteen former-Soviet Republics. The ETD-TE is designed to easily combine with the GGDC/UN-WIDER Economic Transformation Database [ETD]. It enables a comparative analysis of growth and structural transformation between the former-Soviet Union countries, and other advanced and developing countries. More information about this data set can be found on the associated page on the website of the Groningen Growth and Development Centre. When using these data (for whatever purpose), please make the following reference: Hamilton, C. and G. J. de Vries (2025). The Structural Transformation of Transition Economies. World Development, 191, Article 106977. User information The ETD-TE includes the following data: Countries Armenia Azerbaijan Belarus Estonia Georgia Kazakhstan Kyrgyzstan Latvia Lithuania Moldova Russian Federation Tajikistan Ukraine Uzbekistan Variables Persons Employed (thousands) Constant price value added in local currency (millions) Nominal price value added in local currency (millions) *Where countries changed or revalued currency during the sample period, all VA data is provided in units of the most recent/current currency. Sectors Agriculture, ISIC Rev. 4 code: A Mining, ISIC Rev. 4 code: B Manufacturing, ISIC Rev. 4 code: C Utilities, ISIC Rev. 4 code: D+E Construction, ISIC Rev. 4 code: F Trade Services, ISIC Rev. 4 code: G+I Transport, ISIC Rev. 4 code: H Business Services, ISIC Rev. 4 code: J+M+N Financial Services, ISIC Rev. 4 code: K Real Estate, ISIC Rev. 4 code: L Government Services, ISIC Rev. 4 code: O+P+Q Other Services, ISIC Rev. 4 code: R+S+T+U Time period Persons employed and constant price value added is provided annually for the period 1990-2019, and nominal price value added for the period 1995-2019.

Abstract This dataset was created within the Bioregional Assessment Programme. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. This dataset contains a set of generic R scripts that are used in the propagation of uncertainty through numerical models. ## Dataset History The dataset contains a set of R scripts that are loaded as a library. The R scripts are used to carry out the propagation of uncertainty through numerical models. The scripts contain the functions to create the statistical emulators and do the necessary data transformations and backtransformations. The scripts are self-documenting and created by Dan Pagendam (CSIRO) and Warren Jin (CSIRO). ## Dataset Citation Bioregional Assessment Programme (2016) R-scripts for uncertainty analysis v01. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/322c38ef-272f-4e77-964c-a14259abe9cf.

The C2Metadata (“Continuous Capture of Metadata”) Project automates one of the most burdensome aspects of documenting the provenance of research data: describing data transformations performed by statistical software. Researchers in many fields use statistical software (SPSS, Stata, SAS, R, Python) for data transformation and data management as well as analysis. Scripts used with statistical software are translated into an independent Structured Data Transformation Language (SDTL), which serves as an intermediate language for describing data transformations. SDTL can be used to add variable-level provenance to data catalogs and codebooks and to create “variable lineages” for auditing software operations. This repository provides examples of scripts and metadata for use in testing C2Metadata tools.

This package contains the anonymized dataset, R notebook results, and R code for processing the meaning preserving transformations and human subject study.

See the README file for more details.

Data sets used to prepare Figures 1-14 in the Journal of Non-Crystalline Solids X article entitled "Pressure induced structural transformations in amorphous MgSiO_3 and CaSiO_3." The files are labelled according to the figure numbers. The data sets were created using the methodology described in the manuscript. Each of the plots was drawn using QtGrace (https://sourceforge.net/projects/qtgrace/). The data set corresponding to a plotted curve within an QtGrace file can be identified by clicking on that curve. The units for each axis are identified on the plots.

Figure 1 shows the pressure-volume EOS at room temperature for amorphous and crystalline (a) MgSiO_3 and (b) CaSiO_3.

Figure 2 shows the pressure dependence of the neutron total structure factor S_{N}(k) for amorphous (a) MgSiO_3 and (b) CaSiO_3.

Figure 3 shows the pressure dependence of the neutron total pair-distribution function G_{N}(r) for amorphous (a) MgSiO_3 and (b) CaSiO_3.

Figure 4 shows the pressure dependence of several D′_{N}(r) functions for amorphous MgSiO_3 measured using the D4c diffractometer.

Figure 5 shows the pressure dependence of the Si-O coordination number in amorphous (a) MgSiO_3 and (b) CaSiO_3, the Si-O bond length in amorphous (c) MgSiO_3 and (d) CaSiO_3, and (e) the fraction of n-fold (n = 4, 5, or 6) coordinated Si atoms in these materials.

Figure 6 shows the pressure dependence of the M-O (a) coordination number and (b) bond length for amorphous MgSiO_3 and CaSiO_3.

Figure 7 shows the S_{N}(k) or S_{X}(k) functions for (a) MgSiO_3 and (b) CaSiO_3 after recovery from a pressure of 8.2 or 17.5 GPa.

Figure 8 shows the G_{N}(r) or G_{X}(r) functions for (a) MgSiO_3 and (b) CaSiO_3 after recovery from a pressure of 8.2 or 17.5 GPa.

Figure 9 shows the pressure dependence of the Q^n speciation for fourfold coordinated Si atoms in amorphous (a) MgSiO_3 and (b) CaSiO_3.

Figure 10 shows the pressure dependence in amorphous MgSiO_3 and CaSiO_3 of (a) the overall M-O coordination number and its contributions from M-BO and M-NBO connections, (b) the fractions of M-BO and M-NBO bonds, and (c) the associated M-BO and M-NBO bond distances.

Figure 11 shows the pressure dependence of the fraction of n-fold (n = 4, 5, 6, 7, 8, or 9) coordinated M atoms in amorphous (a) MgSiO_3 and (b) CaSiO_3.

Figure 12 shows the pressure dependence of the O-Si-O, Si-O-Si, Si-O-M, O-M-O and M-O-M bond angle distributions (M = Mg or Ca) for amorphous MgSiO_3 (left hand column) and CaSiO_3 (right hand column).

Figure 13 shows the pressure dependence of the q-parameter distributions for n-fold (n = 4, 5, or 6) coordinated Si atoms in amorphous (a) MgSiO_3 and (b) CaSiO_3.

Figure 14 shows the pressure dependence of the q-parameter distributions for the M atoms in amorphous MgSiO_3 (left hand column) and CaSiO_3 (right hand column).

The dataset contains results obtained during simulations and measurements of a novel concept of an impedance-transforming transdirectional directional couplers with maximum achievable transformation ratio. It is shown that such couplers can be designed as an appropriate connection of two uncoupled two-wire transmission lines, having electrical lengths equal 90° and 270°. The major advantage of the proposed circuits is their reciprocity which allows for application in multiport amplifiers. The concept has been verified by the design and measurement of a 3-dB transdirectional coupler operating at a center frequency of 1 GHz that allows for achieving impedance transformation ratio R=2 proving the correctness of the presented analysis.

Transformations towards sustainable land systems require leverage points where land-use policies can benefit people and nature. Here, we present a novel approach that identifies and evaluates leverage points along land-use trajectories, which explicitly incorporate path dependency. We apply the approach in the biodiversity hotspot Madagascar, where smallholder agriculture results in a land-use trajectory reaching from old-growth forests via forest fragments and vanilla agroforests to shifting cultivation. Integrating interdisciplinary empirical data on biodiversity, ecosystem functions and agricultural productivity, we assess trade-offs and co-benefits at three leverage points along the trajectory. We find that leverage points are path-dependent: two leverage points target the transformation of old-growth forests and forest fragments to other land uses and result in considerable trade-offs. In contrast, one leverage point allows for the transformation of land under shifting cultivation into agroforests and offers clear co-benefits. Incorporating path-dependency is essential to identify leverage points for sustainable land-use transformations.

To get the consumption model from Section 3.1, one needs load execute the file consumption_data.R. Load the data for the 3 Phases ./data/CONSUMPTION/PL1.csv, PL2.csv, PL3.csv, transform the data and build the model (starting line 225). The final consumption data can be found in one file for each year in ./data/CONSUMPTION/MEGA_CONS_list.Rdata To get the results for the optimization problem, one needs to execute the file analyze_data.R. It provides the functions to compare production and consumption data, and to optimize for the different values (PV, MBC,). To reproduce the figures one needs to execute the file visualize_results.R. It provides the functions to reproduce the figures. To calculate the solar radiation that is needed in the Section Production Data, follow file calculate_total_radiation.R. To reproduce the radiation data from from ERA5, that can be found in data.zip, do the following steps: 1. ERA5 - download the reanalysis datasets as GRIB file. For FDIR select "Total sky direct solar radiation at surface", for GHI select "Surface solar radiation downwards", and for ALBEDO select "Forecast albedo". 2. convert GRIB to csv with the file era5toGRID.sh 3. convert the csv file to the data that is used in this paper with the file convert_year_to_grid.R

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

We recently published a study in Biological Reviews that examined the evolution of reproductive modes in chondrichthyan fishes using ancestral state reconstruction (Blackburn and Hughes 2024). While our paper was in the review process, the study by Katona et al. (2023) appeared in the Journal of Evolutionary Biology with comparable goals and methods as our own. Although these two published analyses agreed that the common ancestor of sharks and rays was oviparous, they reached dramatically different conclusions about the evolution of reproductive patterns. For example, although our study found that transformations from oviparity to viviparity were unidirectional, Katona et al. (2023) claimed multiple reversals from viviparity back to oviparity. Likewise, while our analysis concluded that lecithotrophic (“yolk-only”) viviparity probably evolved irreversibly into matrotrophy (maternal provision of nutrients), their study inferred multiple reversions from matrotrophy back to the ancestral mode. Further, we concluded that placentotrophy originated in a basal viviparous carcharhiniform shark, but their analysis supported numerous independent origins and losses of placentotrophy in the group. Overall, our analysis concluded that reproductive evolution in chondrichthyan fish involved as few as 19 reproductive mode transformations, while theirs supported ~57 such transformations. Because our two studies drew upon similar phylogenetic sources to reconstruct traits in the same taxonomic group, such major discrepancies between our results could lead readers to infer that little that is definitive can be concluded about chondrichthyan reproductive evolution. We believe that such an inference would be unjustified. Given long-standing (e.g., Compagno 1990; Musick and Ellis 2005) and recent (Marion et al. 2024; Mull et al. 2024) interest in this topic, we undertook a detailed comparison between the two studies to bring clarity to reproductive mode evolution in sharks and rays. We have identified two factors that cast doubt on the findings of Katona et al. (2023): 1) problematic assignments of reproductive patterns to species in their analysis; and 2) ambiguous methodological procedures. We find that these aspects explain discrepancies between our analyses and that their resolution yields an evolutionary reconstruction that is more empirically justified and methodologically sound. Methods Our paper is a critique of another study, in which we used quantitative metrics to draw inferences between studies. We took this approach to point out possible reasons for discrepancies between the paper we were critiquing (Katona et al. 2023) and our original study on the same subject (Blackburn and Hughes, 2024). The primary dataset we used in our critique was provided by Katona et al. (2023) and originally collected through a literature review. We processed this primary dataset to be used in four separate analyses based on specific criteria as a means to compare results among those choices and to those of Blackburn & Hughes (2024), our original paper on the subject. Briefly, the first dataset is the original data provided by Katona et al. (2023) with their coding of species traits (for results, see D in Table 2 of Hughes & Blackburn [2025]). The second dataset is a corrected version of the original data provided by Katona et al. (2023), where we fixed definite errors in their assignments of reproductive modes (for results, see E in Table 2 of Hughes & Blackburn [2025]). The third and fourth datasets include the corrected version of the original data provided by Katona et al. (2023), but we also recoded the species traits into fewer categories for analysis, which is why there are two datasets here (for results, see F in Table 2 of Hughes & Blackburn [2025]). All datasets were analyzed via the Stein et al. (2018) consensus phylogeny. See our critique paper (Hughes & Blackburn [2025]) for more details. The datasets from our original study and that which we critiqued are both available as supplemental material in their respective publications: Original dataset 1: Katona et al. (2023) Evolution of reproductive modes in sharks and rays. Journal of Evolutionary Biology, 36(11): 1630–1640. https://doi.org/10.1111/jeb.14231 Original dataset 2: Blackburn & Hughes (2024) Phylogenetic analysis of viviparity, matrotrophy, and other reproductive patterns in chondrichthyan fishes. Biological Reviews, 99(4): 1314–1356. https://doi.org/10.1111/brv.13070

In-situ TEM observation of both single-stage B2−B19′ transformation and two-stage B2−R−B19′ transformation in different regions

In this lesson, students will explore the relationship between reef cover and human disturbance. Students will manipulate a large dataset and perform normality tests, data transformations, correlations, and a simple linear regression in R Studio.

This is for the Beginners, Who are just starting Machine Learning. 1) Delivery_time -> Predict delivery time using sorting time.

Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python.

■Purpose and Overview In order to improve the productivity of local Small and Medium-Sized Enterprises residents, this grant will partially cover the costs of projects to help local Small and Medium-Sized Enterprises residents improve operational efficiency and solve management issues using digital technology.

■ Grounded Law Digital Transformation Promotion grant Grant Guidelines \Small and Medium-Sized Enterprises r grant Grant Regulations

■ Eligibility grant is open to applicants who meet all of the following requirements: Have offices, stores, etc. in Kurume City, 1, Small and Medium-Sized Enterprises/sole proprietor conducting business ("Small and Medium-Sized Enterprises Kurume-shi" falling under Article 2, Paragraph 1 of Small and Medium-Sized Enterprises Management Strengthening Act) Not delinquent in payment of 2 City tax 3 Using Digital Transformation Facilitation Diagnosis Project of Small and Medium-Sized Enterprises, Kurume City in fiscal year 2023 1 Not falling under any of the following (a) ~ (o) a Religious corporation prescribed in Article 4, Paragraph 2 of the Religious Corporations Act (Act No. 126 of 1951) a Political organization prescribed in Article 3, Paragraph 1 of the Political Funds Control Act (Act No. 194 of 1948) c "Special sex-related businesses" prescribed in Article 2 of the Act on Regulation and Improvement of Amusement Business, etc. (Act No. 122 of 1948) and "Hospitality Business Consigned" pertaining to such businesses d A person who does not fall under the category of an organized crime group, a member of an organized crime group, or a person who is closely related to an organized crime group or a member of an organized crime group. (In the case of a corporation, the representative and officers, etc. shall not fall under any of the above.) grant \Others r

■ Subsidised Projects Projects that meet all of the following requirements are eligible for subsidy: Projects that utilize 1 digital technology to improve operational efficiency and resolve management issues Projects to be implemented based on expert proposals under Digital Transformation Accelerated Diagnosis Project in Small and Medium-Sized Enterprises, 2 2023 Projects not eligible for subsidies under grant 2023 for introducing IT in 3

■ Subsidised Expenses Subsidised Expenses must meet all of the following requirements. The following expenses necessary to implement ・ subsidized projects software usage fees, outsourcing expenses, equipment purchase expenses * and Others expenses * Equipment purchase expenses are limited to the introduction of accounting, ordering, settlement and e-commerce software. Expenses incurred on or after the date of the decision to issue ・, and for which payment and project execution were completed by the business operator within the project period specified in this project (maximum by January 31, 2024). Expenses for which the fact and amount of payment can be confirmed by ・ Evidence of Payment (Receipts, account transfer records, etc.).

■ Contact: Kurume City Commerce, Industry, Tourism and Labor Department Commerce Policy Division Phone: 0942-30-9133 Fax: 0942-30-9707 E-mail: syoko@city.kurume.lg.jp

■ Reference URL: Kurume City: Kurume City Small and Medium-Sized Enterprises Digital Transformation Promotion grant