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.

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

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.

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

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

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This dataset collects a raw dataset and a processed dataset derived from the raw dataset. There is a document containing the analytical code for statistical analysis of the processed dataset in .Rmd format and .html format.

The study examined some aspects of mechanical performance of solid wood composites. We were interested in certain properties of solid wood composites made using different adhesives with different grain orientations at the bondline, then treated at different temperatures prior to testing.

Performance was tested by assessing fracture energy and critical fracture energy, lap shear strength, and compression strength of the composites. This document concerns only the fracture properties, which are the focus of the related paper.

Notes:

* the raw data is provided in this upload, but the processing is not addressed here.
* the authors of this document are a subset of the authors of the related paper.
* this document and the related data files were uploaded at the time of submission for review. An update providing the doi of the related paper will be provided when it is available.

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates with nonlinear relationships, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records that are spatially autocorrelated. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. A primary application is mapping MMI predictions and prediction errors at 1.1 million perennial stream reaches across the conterminous United States. For the spatial regression model, we develop a novel transformation procedure that estimates Box-Cox transformations to linearize covariate relationships and handles possibly zero-inflated covariates. We find that the spatial regression model with transformations, and a subsequent selection of significant covariates, has cross-validation performance comparable to random forests. We also find that prediction interval coverage is close to nominal for each method, but that spatial regression prediction intervals tend to be narrower and have less variability than quantile regression forest prediction intervals. A simulation study is used to generalize results and clarify advantages of each modeling approach.

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

In this project we have reviewed existing methods used to homogenize data and developed several new methods for dealing with this diversity in survey questions on the same subject. The project is a spin-off from the World Database of Happiness, the main aim of which is to collate and make available research findings on the subjective enjoyment of life and to prepare these data for research synthesis. The first methods we discuss were proposed in the book ‘Happiness in Nations’ and which were used at the inception of the World Database of Happiness. Some 10 years later a new method was introduced: the International Happiness Scale Interval Study (HSIS). Taking the HSIS as a basis the Continuum Approach was developed. Then, building on this approach, we developed the Reference Distribution Method.

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

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.

This data set was used in my master thesis "Reproducibility of Machine Learning results". On one hand, this dataset was used as an input in creating Support Vector Machine , Naive Bayes and Random Forest models in R, Python, RapidMiner and Weka in order to experiment the influence that different development environments have on reproducibility of machine learning results. Furthermore all the models implemented in all of previously mentioned environments were tested in different operating systems such as Windows, Linux and Mac OS.

Differential Coexpression ScriptThis script contains the use of previously normalized data to execute the DiffCoEx computational pipeline on an experiment with four treatment groups.differentialCoexpression.rNormalized Transformed Expression Count DataNormalized, transformed expression count data of Medicago truncatula and mycorrhizal fungi is given as an R data frame where the columns denote different genes and rows denote different samples. This data is used for downstream differential coexpression analyses.Expression_Data.zipNormalization and Transformation of Raw Count Data ScriptRaw count data is transformed and normalized with available R packages and RNA-Seq best practices.dataPrep.rRaw_Count_Data_Mycorrhizal_FungiRaw count data from HtSeq for mycorrhizal fungi reads are later transformed and normalized for use in differential coexpression analysis. 'R+' indicates that the sample was obtained from a plant grown in the presence of both mycorrhizal fungi and rhizobia. 'R-' indicate...

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.

Sustainable land system transformations are necessary to avert biodiversity and climate collapse. However, it remains unclear where entry points for transformations exist in complex land systems. Here, we conceptualize land systems along land-use trajectories, which allows us to identify and evaluate leverage points; i.e., entry points on the trajectory where targeted interventions have particular leverage to influence land-use decisions. We apply this framework in the biodiversity hotspot Madagascar. In the Northeast, smallholder agriculture results in a land-use trajectory originating in old-growth forests, spanning forest fragments, and reaching shifting hill rice cultivation and vanilla agroforests. Integrating interdisciplinary empirical data on seven taxa, five ecosystem services, and three measures of agricultural productivity, we assess trade-offs and co-benefits of land-use decisions at three leverage points along the trajectory. These trade-offs and co-benefits differ between leverage points: two leverage points are situated at the conversion of old-growth forests and forest fragments to shifting cultivation and agroforestry, resulting in considerable trade-offs, especially between endemic biodiversity and agricultural productivity. Here, interventions enabling smallholders to conserve forests are necessary. This is urgent since ongoing forest loss threatens to eliminate these leverage points due to path-dependency. The third leverage point allows for the restoration of land under shifting cultivation through vanilla agroforests and offers co-benefits between restoration goals and agricultural productivity. The co-occurring leverage points highlight that conservation and restoration are simultaneously necessary. Methodologically, the framework shows how leverage points can be identified, evaluated, and harnessed for land system transformations under the consideration of path-dependency along trajectories.

■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 proprietors conducting business ("Small and Medium-Sized Enterprises persons" falling under Article 2, Paragraph 1 of Small and Medium-Sized Enterprises Business Enhancement Act) 2: not delinquent in payment of municipal taxes Small and Medium-Sized Enterprises, Kurume City, 3: using Digital Transformation Facilitated Diagnosis Project 1: not falling under any of the following (a) to (o) (a) Religious corporations prescribed in Article 4, Paragraph 2 of the Religious Corporations Act (Act No. 126 of 1951) (a) Political organizations prescribed in Article 3, Paragraph 1 of the Political Funds Control Act (Act No. 194 of 1948) (c) Engage in "sex-related special business" prescribed in Article 2 of the Act on Regulation and Improvement of Amusement Business, etc. (Act No. 122 of 1948) and "hospitality business consigned" related to the business Person d. Does not fall under the category of an organized crime group, an organized crime group member, or a person closely related to an organized crime group or an organized crime group member. (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: 1 Projects that utilize digital technology to improve operational efficiency and resolve management issues Small and Medium-Sized Enterprises, Kurume, 2 Projects proposed by advisors under Digital Transformation Facilitation Diagnosis Project Projects that are not eligible for subsidies under the IT Introduction grant 2024 Project and Small and Medium-Sized Enterprises Labor Saving Investment Subsidy Project implemented by 3.

■ Subsidised Expenses Subsidised expenses meet all of the following requirements. ・ The following expenses necessary to implement the subsidized projects: software usage fees, outsourcing expenses *, equipment purchase expenses *, and Others expenses * The outsourcing expenses do not include the cost of building your own website. * Equipment purchase expenses are limited to the case where it is installed in conjunction with software, which includes any of the functions of "accounting, ordering and settlement." (Similar to the invoice correspondence type of IT introduction grant 2024) • Expenses incurred after the grant decision date and paid and executed by the business operator within the business period specified in this project (maximum by January 31, 2025) • Expenses for which the fact and amount of payment can be confirmed by payment evidence (Receipts, account transfer records, etc.)

■ Contact: Kurume City Commerce, Industry, Tourism and Labor Department Commerce and Industry 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

Dataset description

This dataset was created during the research carrried out for the PhD of Negin Afsharzadeh and the subsequent manuscript arising from this research. The main purpose of this dataset is to create a record of the raw data that was used in the analyses in the manuscript.

This dataset includes:

• raw data generated from experiments stored in an Excel spreadsheet with each sheet corresponding to a specific experiment or part of an experiment (Afsharzadeh_et_al_2024.xlsx)
• R script used to analyse the raw data in the software, R (Afsharzadeh_et_al.R)
• datasets that were used to analyse the data in the statistical software, R (germindata.txt, light.txt)

Context and methodology

Brief description of experiments:

In this study, we aimed to optimize approaches to improve the biotechnological production of important metabolites in G. glabra. The study is made up of four experiments that correspond to particular figures/tables in the manuscript and data, as described below.

Experiment 1:

We tested approaches for the cultivation of G. glabra, specifically the breaking of seed dormancy, to ensure timely and efficient seed germination. To do this, we tested the effect of different pretreatments, sterilization treatments and growth media on the germination success of G. glabra.

This experiment corresponds to:

• Manuscript: Table 1 and Figure 1
• Data: Afsharzadeh_et_al_2024.xlsx (Sheet 'Table_1'); Afsharzadeh_et_al.R; germindata.txt

Experiment 2 (Table 2):

We aimed to optimize the induction of hairy roots in G. glabra. Four strains of R. rhizogenes were tested to identify the most effective strain for inducing hairy root formation and we tested different tissue explants (cotyledons/hypocotyls) and methods of R. rhizogenes infection (injection or soaking for different durations) in these tissues.

This experiment corresponds to:

• Manuscript: Table 2
• Data: Afsharzadeh_et_al_2024.xlsx (Sheet 'Table_2')

Experiment 3 (Figure 2):

Eight distinct hairy root lines were established and the growth rate of these lines was measured over 40 days.

This experiment corresponds to:

• Manuscript: Figure 2, Table S2
• Data: Afsharzadeh_et_al_2024.xlsx (Sheet 'Figure_2')

Experiment 4 (Figure 3):

We aimed to test different qualities of light on hairy root cultures in order to induce higher growth and possible enhanced metabolite production. A line with a high growth rate from experiment 3, line S, was selected for growth under different light treatments: red light, blue light, and a combination of blue and red light. To assess the overall impact of these treatments, the growth of line S, as well as the increase in antioxidant capacity and total phenolic content, were tracked over this induction period.

This experiment corresponds to:

• Manuscript: Figure 3, Figure S4
• Data: Afsharzadeh_et_al_2024.xlsx (Sheets 'Figure_3_FW', 'Figure_3_FRAP', 'Figure_3_Phenol'); Afsharzadeh_et_al.R; light.txt

Technical details

To work with the .R file and the R datasets, it is necessary to use R: A Language and Environment for Statistical Computing and a package within R, aDHARMA. The versions used for the analyses are R version 4.4.1 and aDHARMA version 0.4.6.

The references for these are:

R Core Team, R: A Language and Environment for Statistical Computing 2024. https://www.R-project.org/

Hartig F, DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models 2022. https://CRAN.R-project.org/package=DHARMa

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.

Riemerella anatipestifer is a gram-negative bacterium that is the first naturally competent bacterium identified in the family Flavobacteriaceae. However, the determinants that influence the natural transformation and the underlying mechanism remain unknown. In this study, we evaluated the effects of various nutritional factors of the GCB medium [glucose, L-glutamine, vitamin B1, Fe (NO3)3, NaCl, phosphate, and peptone], on the natural transformation of R. anatipestifer ATCC 11845. Among the assayed nutrients, peptone and phosphate affected the natural transformation of R. anatipestifer ATCC 11845, and the transformation frequency was significantly decreased when phosphate or peptone was removed from the GCB medium. When the iron chelator 2,2′-dipyridyl (Dip) was added, the transformation frequency was decreased by approximately 100-fold and restored gradually when Fe (NO3)3 was added, suggesting that the natural transformation of R. anatipestifer ATCC 11845 requires iron. Given the importance of TonB in nutrient transportation, we further identified whether TonB is involved in the natural transformation of R. anatipestifer ATCC 11845. Mutation of tonBA or tonBB, but not tbfA, was shown to inhibit the natural transformation of R. anatipestifer ATCC 11845 in the GCB medium. In parallel, it was shown that the tonBB mutant, but not the tonBA mutant, decreased iron acquisition in the GCB medium. This result suggested that the tonBB mutant affects the natural transformation frequency due to the deficiency of iron utilization.

This package contains the anonymized dataset, R notebook results, and R code for processing the meaning preserving transformations and human subject study. Note that the title has been changed from the earlier version on arvix which was "Do People Prefer 'Natural' Code?".

See the README file for more details.