Effect sizes calculated using mean difference for burnt-unburnt study designs and mean change for before-after desings. Outliers, as defined in the methods section of the paper, were excluded prior to calculating effect sizes.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in United States Is Outlier in Tax Trends in Advanced and Large Emerging Economies, PIIE Policy Brief 17-29. If you use the data, please cite as: Djankov, Simeon. (2017). United States Is Outlier in Tax Trends in Advanced and Large Emerging Economies. PIIE Policy Brief 17-29. Peterson Institute for International Economics.

Total DVF statistics: statistics by geographical scale, over the 10 semesters available. Monthly DVF statistics: statistics by geographical scale and by month. Description of treatment The code allows statistics to be generated from the data of land value requests, aggregated at different scales, and their evolution over time (monthly). The following indicators have been calculated on a monthly basis and over the entire period available (10 semesters): * number of mutations * average prices per m2 * median of prices per m2 * Breakdown of sales prices by tranches for each type of property from: * houses * apartments * houses + apartments * commercial premises and for each scale from: * nation * Department * EPCI * municipality * Cadastral section The source data contain the following types of mutations: sale, sale in the future state of completion, sale of building land, tendering, expropriation and exchange. We have chosen to keep only sales, sales in the future state of completion and auctions for statistics*. In addition, for the sake of simplicity, we have chosen to keep only mutations that concern a single asset (excluding dependency)*. Our path is as follows: 1. for a transfer that would include assets of several types (e.g. a house + a commercial premises), it is not possible to reconstitute the share of the land value allocated to each of the assets included. 2. for a transfer that would include several assets of the same type (e.g. X apartments), the total value of the transfer is not necessarily equal to X times the value of an apartment, especially in the case where the assets are very different (area, work to be carried out, floor, etc.). We had initially kept these goods by calculating the price per m2 of the mutation by considering the goods of the mutation as a single good of an area to the sum of the surfaces of the goods, but this method, which ultimately concerned only a marginal quantity of goods, did not convince us for the final version. The price per m2 is then calculated by dividing the land value of the change by the surface area of the building of the property concerned. We finally exclude mutations for which we could not calculate the price per m2, as well as those whose price per m2 is more than € 100k (arbitrary choice)*. We have not incorporated any other outlier restrictions in order to maintain fidelity to the original data and to report potential anomalies. Displaying the median on the site reduces the impact of outliers on color scales. _*: The mentioned filters are applied for the calculation of statistics, but all mutations of the source files are well displayed on the application at the plot level.

This dataset contains a list of outlier sample concentrations identified for 17 water quality constituents from streamwater sample collected at 15 study watersheds in Gwinnett County, Georgia for water years 2003 to 2020. The 17 water quality constituents are: biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), suspended sediment concentration (SSC), total nitrogen (TN), total nitrate plus nitrite (NO3NO2), total ammonia plus organic nitrogen (TKN), dissolved ammonia (NH3), total phosphorus (TP), dissolved phosphorus (DP), total organic carbon (TOC), total calcium (Ca), total magnesium (Mg), total copper (TCu), total lead (TPb), total zinc (TZn), and total dissolved solids (TDS). 885 outlier concentrations were identified. Outliers were excluded from model calibration datasets used to estimate streamwater constituent loads for 12 of these constituents. Outlier concentrations were removed because they had a high influence on the model fits of the concentration relations, which could substantially affect model predictions. Identified outliers were also excluded from loads that were calculated using the Beale ratio estimator. Notes on reason(s) for considering a concentration as an outlier are included.

Kurtosis of error distributions of MARS, GBR, KNN, and RFR for both datasets and both cases.

Machine Learning pipeline used to provide toxicity prediction in FunTox-Networks

01_DATA # preprocessing and filtering of raw activity data from ChEMBL - Chembl_v25 # latest activity assay data set from ChEMBL (retrieved Nov 2019) - filt_stats.R # Filtering and preparation of raw data - Filtered # output data sets from filt_stats.R - toxicity_direction.csv # table of toxicity measurements and their proportionality to toxicity

02_MolDesc # Calculation of molecular descriptors for all compounds within the filtered ChEMBL data set - datastore # files with all compounds and their calculated molecular descriptors based on SMILES - scripts - calc_molDesc.py # calculates for all compounds based on their smiles the molecular descriptors - chemopy-1.1 # used python package for descriptor calculation as decsribed in: https://doi.org/10.1093/bioinformatics/btt105

03_Averages # Calculation of moving averages for levels and organisms as required for calculation of Z-scores - datastore # output files with statistics calculated by make_Z.R - scripts -make_Z.R # script to calculate statistics to calculate Z-scores as used by the regression models

04_ZScores # Calculation of Z-scores and preparation of table to fit regression models - datastore # Z-normalized activity data and molecular descriptors in the form as used for fitting regression models - scripts -calc_Ztable.py # based on activity data, molecular descriptors and Z-statistics, the learning data is calculated

05_Regression # Performing regression. Preparation of data by removing of outliers based on a linear regression model. Learning of random forest regression models. Validation of learning process by cross validation and tuning of hyperparameters.

• datastore # storage of all random forest regression models and average level of Z output value per level and organism (zexp_*.tsv)
• scripts
  • data_preperation.R # set up of regression data set, removal of outliers and optional removal of fields and descriptors
  • Rforest_CV.R # analysis of machine learning by cross validation, importance of regression variables and tuning of hyperparameters (number of trees, split of variables)
  • Rforest.R # based on analysis of Rforest_CV.R learning of final models

rregrs_output

early analysis of regression model performance with the package RRegrs as described in: https://doi.org/10.1186/s13321-015-0094-2

# Data for: Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory

This dataset contains the raw data to reproduce the paper:

D. Schwalbe-Koda, S. Hamel, B. Sadigh, F. Zhou, V. Lordi. "Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory". arXiv:2404.12367 (2024). DOI: [10.48550/arXiv.2404.12367](https://doi.org/10.48550/arXiv.2404.12367)

The raw data in `2025-quests-data.tar.gz` contains all the raw data to reproduce the paper.
The tarfile is sorted by section of the paper (01 through 05) and supplementary information (A01 through A11).
Its structure is the following:


```
  data/
  ├── 02-Aluminum
  ├── 02-GAP20
  ├── 02-rMD17
  ├── 04-TM23
  ├── 05-Cu
  ├── 05-Ta
  ├── A08-Denoiser
  ├── A11-Cu
  ├── A11-QTB
  └── A11-Sn
```

The tarfile contains files of the following formats:

- CSV files containing tables with the data for the analysis
- JSON files containing structured data for the analysis
- logfiles from LAMMPS simulations
- Extended XYZ files containing the results of MD trajectories or materials structure data

### Citing

If you use QUESTS or its data/examples in a publication, please cite the following paper:

```bibtex
@article{schwalbekoda2024information,
  title = {Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory},
  author = {Schwalbe-Koda, Daniel and Hamel, Sebastien and Sadigh, Babak and Zhou, Fei and Lordi, Vincenzo},
  year = {2024},
  journal = {arXiv:2404.12367},
  url = {https://arxiv.org/abs/2404.12367},
  doi = {10.48550/arXiv.2404.12367},
}
```

This index enables users to identify the extent of the relationship grids provided on LDS, which are used to convert heights provided in terms of one of 13 historic local vertical datums to NZVD2016. The polygons comprising the index show the extent of the conversion grids. Users can view the following polygon attributes: Shape_VDR: Vertical Datum Relationship grid area LVD: Local Vertical Datum Control: Number of control marks used to compute the relationship grid Mean: Mean vertical datum relationship value at control points Std: Standard deviation of vertical datum relationship value at control points Min: Minimum vertical datum relationship value at control points Max: Maximum vertical datum relationship value at control points Range: Range of vertical datum relationship value at control points Ref: Reference control mark for the local vertical datum Ref_value: Vertical datum relationship value at the reference mark Grid: Formal grid id Users should note that the values represented in this dataset have been calculated with the outliers excluded. These same outliers were excluded during the computation of the relationship grids, but were included when calculating the 95% confidence intervals More information on converting heights between vertical datums can be found on the LINZ website.

As a 90.P background value, that's 90. Percentile of a Data Collective. It is the value at which 90% of the cases observed so far have lower levels. The calculation is made after the data group of outliers has been cleaned up. The 90. The percentile often serves as the upper limit of the background range to delineate unusually high levels. The total content is determined from the aqua regia extract (according to DIN ISO 11466 (1997)). The concentration is given in mg/kg. The salary classes take into account, among other things, the pension values of the BBodSchV (1999). These are 40 mg/kg for sand, 70 mg/kg for clay, silt and very silty sand and 100 mg/kg for clay. According to LABO (2003) a sample count of >=20 is required for the calculation of background values. However, the map also shows groups with a sample count >= 10. This information is then only informal and not representative.

The result of multivariate power curve modeling of different models.

The mean is the median (synonym: 50. percentile, central value). It is the value above or below which 50% of all cases of a data group are located. The calculation is carried out on outlier-adjusted data collectives. The total content is determined from the aqua regia extract (according to DIN ISO 11466 (1997)). The concentration is given in mg/kg. The salary classes take into account, among other things, the pension values of the BBodSchV (1999). These are 30 mg/kg for sand, 60 mg/kg for clay, silt and very silty sand and 100 mg/kg for clay. According to LABO (2003) a sample count of >=20 is required for the calculation of background values. However, the map also shows groups with a sample count >= 10. This information is then only informal and not representative.

Metric multidimensional scaling (MDS) is a widely used multivariate method with applications in almost all scientific disciplines. Eigenvalues obtained in the analysis are usually reported in order to calculate the overall goodness-of-fit of the distance matrix. In this paper, we refine MDS goodness-of-fit calculations, proposing additional point and pairwise goodness-of-fit statistics that can be used to filter poorly represented observations in MDS maps. The proposed statistics are especially relevant for large data sets that contain outliers, with typically many poorly fitted observations, and are helpful for improving MDS output and emphasizing the most important features of the dataset. Several goodness-of-fit statistics are considered, and both Euclidean and non-Euclidean distance matrices are considered. Some examples with data from demographic, genetic and geographic studies are shown.

As a 90.P background value, that's 90. Percentile of a Data Collective. It is the value at which 90% of the cases observed so far have lower levels. The calculation is made after the data group of outliers has been cleaned up. The 90. The percentile often serves as the upper limit of the background range to delineate unusually high levels. The total content is determined from the aqua regia extract (according to DIN ISO 11466 (1997)). The concentration is given in mg/kg. The salary classes take into account, among other things, the pension values of the BBodSchV (1999). These are 0.4 mg/kg for sand, 1.0 mg/kg for clay, silt and very silty sand and 1.5 mg/kg for clay. According to LABO (2003) a sample count of >=20 is required for the calculation of background values. However, the map also shows groups with a sample count >= 10. This information is then only informal and not representative.

This study underscores the significance of assessing the capabilities of rehabilitation officers in navigating challenges, devising innovative work methods, and successfully executing the rehabilitation process. This is particularly crucial amid the dual challenges of overcapacity and the repercussions of the Covid-19 pandemic, making it an essential area for research. To be specific, it aims to obtain empirical evidence about the influence of proactive personality and supportive supervision on proactive work behavior, as well as the mediating role of Role Breadth Self-efficacy and Change Orientation. This research was conducted on all rehabilitation officers at the Narcotics Penitentiary in Sumatra, totaling 272 respondents. This study employs a quantitative method via a questionnaire using a purposive sampling technique. The data was subsequently examined using the Lisrel 8.70 software and Structural Equation Modeling (SEM). It can be concluded from the results that the rehabilitation officers for narcotics addicts at the Narcotics Penitentiary can create and improve proactive work behavior properly through the influence of proactive personality, supportive supervision, role breadth self-efficacy, and change orientation. The study may suggest new ways of working and generate new ideas to increase initiative, encourage feedback, and voice employee concerns. Furthermore, this research has the potential to pinpoint deficiencies in proactive work behavior, serving as a foundation for designing interventions or training programs. These initiatives aim to enhance the innovative and creative contributions of rehabilitation officers in the rehabilitation process.

As a 90.P background value, that's 90. Percentile of a Data Collective. It is the value at which 90% of the cases observed so far have lower levels. The calculation is made after the data group of outliers has been cleaned up. The 90. The percentile often serves as the upper limit of the background range to delineate unusually high levels. The total content is determined from the aqua regia extract (according to DIN ISO 11466 (1997)). The concentration is given in mg/kg. The salary classes take into account, among other things, the pension values of the BBodSchV (1999). These are 30 mg/kg for sand, 60 mg/kg for clay, silt and very silty sand and 100 mg/kg for clay. According to LABO (2003) a sample count of >=20 is required for the calculation of background values. However, the map also shows groups with a sample count >= 10. This information is then only informal and not representative.

Descriptive statistics of the variables of interest (Study 1).

aStatistics for the highest resolution bin are shown in parentheses. bRsym were calculated by , where h is the index for unique reflections and j is the index for symmetry redundant reflections. Ih is the mean weighted intensity after rejection of outliers. cRwork and Rfree were calculated by Σ||Fobserved|−k|Fcalculated||/Σ|Fobserved|. Rfree was calculated using 5% random data omitted from refinement. dPercentage of Ramachandran outliers and favored.

*Represents thrombin (T) and antithrombin (AT).†1JMO is not included in the calculation of radius of gyration, an outlier that is not bound to GAG.‡1T1F is not included in the calculation of radius of gyration (Rg), an outlier that has incompletely built important amino acids including R47, K114 and K125 and is not an activated form of antithrombin.