Publication

A machine learning streamflow (MLFLOW) model was developed in R (model is in the Rscripts folder) for modeling monthly streamflow from 2012 to 2017 in three watersheds on the Wyoming Range in the upper Green River basin. Geospatial information for 125 site features (vector data are in the Sites.shp file) and discrete streamflow observation data and environmental predictor data were used in fitting the MLFLOW model and predicting with the fitted model. Tabular calibration and validation data are in the Model_Fitting_Site_Data.csv file, totaling 971 discrete observations and predictions of monthly streamflow. Geospatial information for 17,518 stream grid cells (raster data are in the Streams.tif file) and environmental predictor data were used for continuous streamflow predictions with the MLFLOW model. Tabular prediction data for all the study area (17,518 stream grid cells) and study period (72 months; 2012–17) are in the Model_Prediction_Stream_Data.csv file, totaling 1,261,296 predictions of spatially and temporally continuous monthly streamflow. Additional information about the datasets is in the metadata included in the four zipped dataset files and about the MLFLOW model is in the readme included in the zipped model archive folder.

This folder contains the input files required for the R code used to analyse data for the Patterns and prevalence of food allergy in adulthood in the UK (PAFA) project. This includes:pafa_data_dictionary_anonymised.csv: The data dictionary describing each column in the anonymised PAFA dataset. "snomed_field_name" lists all column names in the dataset; "field_name_extended" lists the original column name in the REDCap data download, which was then recoded to include SNOMED and FoodEx2 codes for future analyses; "variable_field_name" denotes the corresponding coded field name in the REDCap form; "field_type" denotes the type of REDCap field; "field_label" describes the field name in plain language; "choices_calculations_or_slider_labels" describes the choices provided to the participant for that question.foodex2_codes_with_other.csv: A CSV file with key-value pairs for identifying foods coded in the dataset.

Input dataset for R code (first sheet), and BOLD spreadsheet downloaded on April 11, 2022 (next sheets) for "Facing the Infinity".

Trends in nutrient fluxes and streamflow for selected tributaries in the Lake Erie watershed were calculated using monitoring data at 10 locations. Trends in flow-normalized nutrient fluxes were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). Site information and streamflow and water-quality records are contained in 3 zipped files named as follows: INFO (site information), Daily (daily streamflow records), and Sample (water-quality records). The INFO, Daily (flow), and Sample files contain the input data, by water-quality parameter and by site as .csv files, used to run trend analyses. These files were generated by the R (version 3.1.2) software package called EGRET - Exploration and Graphics for River Trends (version 2.5.1) (Hirsch and DeCicco, 2015), and can be used directly as input to run graphical procedures and WRTDS trend analyses using EGRET R software. The .csv files are identified according to water-quality parameter (TP, SRP, TN, NO23, and TKN) and site reference number (e.g. TPfiles.1.INFO.csv, SRPfiles.1.INFO.csv, TPfiles.2.INFO.csv, etc.). Water-quality parameter abbreviations and site reference numbers are defined in the file "Site-summary_table.csv" on the landing page, where there is also a site-location map ("Site_map.pdf"). Parameter information details, including abbreviation definitions, appear in the abstract on the Landing Page. SRP data records were available at only 6 of the 10 trend sites, which are identified in the file "site-summary_table.csv" (see landing page) as monitored by the organization NCWQR (National Center for Water Quality Research). The SRP sites are: RAIS, MAUW, SAND, HONE, ROCK, and CUYA. The model-input dataset is presented in 3 parts: 1. INFO.zip (site information) 2. Daily.zip (daily streamflow records) 3. Sample.zip (water-quality records) Reference: Hirsch, R.M., and De Cicco, L.A., 2015 (revised). User Guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R Packages for Hydrologic Data, Version 2.0, U.S. Geological Survey Techniques Methods, 4-A10. U.S. Geological Survey, Reston, VA., 93 p. (at: http://dx.doi.org/10.3133/tm4A10).

This data and code archive provides all the data and code for replicating the empirical analysis that is presented in the journal article "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application" authored by Juan José Price and Arne Henningsen and published in the Journal of Productivity Analysis (DOI: 10.1007/s11123-023-00684-1).

We conducted the empirical analysis with the "R" statistical software (version 4.3.0) using the add-on packages "combinat" (version 0.0.8), "miscTools" (version 0.6.28), "quadprog" (version 1.5.8), sfaR (version 1.0.0), stargazer (version 5.2.3), and "xtable" (version 1.8.4) that are available at CRAN. We created the R package "micEconDistRay" that provides the functions for empirical analyses with ray-based input distance functions that we developed for the above-mentioned paper. Also this R package is available at CRAN (https://cran.r-project.org/package=micEconDistRay).

This replication package contains the following files and folders:

README This file

MuseumsDk.csv The original data obtained from the Danish Ministry of Culture and from Statistics Denmark. It includes the following variables:

museum: Name of the museum.

type: Type of museum (Kulturhistorisk museum = cultural history museum; Kunstmuseer = arts museum; Naturhistorisk museum = natural history museum; Blandet museum = mixed museum).

munic: Municipality, in which the museum is located.

yr: Year of the observation.

units: Number of visit sites.

resp: Whether or not the museum has special responsibilities (0 = no special responsibilities; 1 = at least one special responsibility).

vis: Number of (physical) visitors.

aarc: Number of articles published (archeology).

ach: Number of articles published (cultural history).

aah: Number of articles published (art history).

anh: Number of articles published (natural history).

exh: Number of temporary exhibitions.

edu: Number of primary school classes on educational visits to the museum.

ev: Number of events other than exhibitions.

ftesc: Scientific labor (full-time equivalents).

ftensc: Non-scientific labor (full-time equivalents).

expProperty: Running and maintenance costs [1,000 DKK].

expCons: Conservation expenditure [1,000 DKK].

ipc: Consumer Price Index in Denmark (the value for year 2014 is set to 1).

prepare_data.R This R script imports the data set MuseumsDk.csv, prepares it for the empirical analysis (e.g., removing unsuitable observations, preparing variables), and saves the resulting data set as DataPrepared.csv.

DataPrepared.csv This data set is prepared and saved by the R script prepare_data.R. It is used for the empirical analysis.

make_table_descriptive.R This R script imports the data set DataPrepared.csv and creates the LaTeX table /tables/table_descriptive.tex, which provides summary statistics of the variables that are used in the empirical analysis.

IO_Ray.R This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions with the 'optimal' ordering of outputs, imposes monotonicity on this distance function, creates the LaTeX table /tables/idfRes.tex that presents the estimated parameters of this function, and creates several figures in the folder /figures/ that illustrate the results.

IO_Ray_ordering_outputs.R This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions, imposes monotonicity for each of the 720 possible orderings of the outputs, and saves all the estimation results as (a huge) R object allOrderings.rds.

allOrderings.rds (not included in the ZIP file, uploaded separately) This is a saved R object created by the R script IO_Ray_ordering_outputs.R that contains the estimated ray-based Translog input distance functions (with and without monotonicity imposed) for each of the 720 possible orderings.

IO_Ray_model_averaging.R This R script loads the R object allOrderings.rds that contains the estimated ray-based Translog input distance functions for each of the 720 possible orderings, does model averaging, and creates several figures in the folder /figures/ that illustrate the results.

/tables/ This folder contains the two LaTeX tables table_descriptive.tex and idfRes.tex (created by R scripts make_table_descriptive.R and IO_Ray.R, respectively) that provide summary statistics of the data set and the estimated parameters (without and with monotonicity imposed) for the 'optimal' ordering of outputs.

/figures/ This folder contains 48 figures (created by the R scripts IO_Ray.R and IO_Ray_model_averaging.R) that illustrate the results obtained with the 'optimal' ordering of outputs and the model-averaged results and that compare these two sets of results.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This data release supports an analysis of changes in dissolved organic carbon (DOC) and nitrate concentrations in Buck Creek watershed near Inlet, New York 2001 to 2021. The Buck Creek watershed is a 310-hectare forested watershed that is recovering from acidic deposition within the Adirondack region. The data release includes pre-processed model inputs and model outputs for the Weighted Regressions on Time, Discharge and Season (WRTDS) model (Hirsch and others, 2010) to estimate daily flow normalized concentrations of DOC and nitrate during a 20-year period of analysis. WRTDS uses daily discharge and concentration observations implemented through the Exploration and Graphics for River Trends R package (EGRET) to predict solute concentration using decimal time and discharge as explanatory variables (Hirsch and De Cicco, 2015; Hirsch and others, 2010). Discharge and concentration data are available from the U.S. Geological Survey National Water Information System (NWIS) database (U.S. Geological Survey, 2016). The time series data were analyzed for the entire period, water years 2001 (WY2001) to WY2021 where WY2001 is the period from October 1, 2000 to September 30, 2001. This data release contains 5 comma-separated values (CSV) files, one R script, and one XML metadata file. There are four input files (“Daily.csv”, “INFO.csv”, “Sample_doc.csv”, and “Sample_nitrate.csv”) that contain site information, daily mean discharge, and mean daily DOC or nitrate concentrations. The R script (“Buck Creek WRTDS R script.R”) uses the four input datasets and functions from the EGRET R package to generate estimations of flow normalized concentrations. The output file (“WRTDS_results.csv”) contains model output at daily time steps for each sub-watershed and for each solute. Files are automatically associated with the R script when opened in RStudio using the provided R project file ("Files.Rproj"). All input, output, and R files are in the "Files.zip" folder.

Objective: To develop a clinical informatics pipeline designed to capture large-scale structured EHR data for a national patient registry.

Materials and Methods: The EHR-R-REDCap pipeline is implemented using R-statistical software to remap and import structured EHR data into the REDCap-based multi-institutional Merkel Cell Carcinoma (MCC) Patient Registry using an adaptable data dictionary.

Results: Clinical laboratory data were extracted from EPIC Clarity across several participating institutions. Labs were transformed, remapped and imported into the MCC registry using the EHR labs abstraction (eLAB) pipeline. Forty-nine clinical tests encompassing 482,450 results were imported into the registry for 1,109 enrolled MCC patients. Data-quality assessment revealed highly accurate, valid labs. Univariate modeling was performed for labs at baseline on overall survival (N=176) using this clinical informatics pipeline.

Conclusion: We demonstrate feasibility of the facile eLAB workflow. EHR data is successfully transformed, and bulk-loaded/imported into a REDCap-based national registry to execute real-world data analysis and interoperability.

Methods eLAB Development and Source Code (R statistical software):

eLAB is written in R (version 4.0.3), and utilizes the following packages for processing: DescTools, REDCapR, reshape2, splitstackshape, readxl, survival, survminer, and tidyverse. Source code for eLAB can be downloaded directly (https://github.com/TheMillerLab/eLAB).

eLAB reformats EHR data abstracted for an identified population of patients (e.g. medical record numbers (MRN)/name list) under an Institutional Review Board (IRB)-approved protocol. The MCCPR does not host MRNs/names and eLAB converts these to MCCPR assigned record identification numbers (record_id) before import for de-identification.

Functions were written to remap EHR bulk lab data pulls/queries from several sources including Clarity/Crystal reports or institutional EDW including Research Patient Data Registry (RPDR) at MGB. The input, a csv/delimited file of labs for user-defined patients, may vary. Thus, users may need to adapt the initial data wrangling script based on the data input format. However, the downstream transformation, code-lab lookup tables, outcomes analysis, and LOINC remapping are standard for use with the provided REDCap Data Dictionary, DataDictionary_eLAB.csv. The available R-markdown ((https://github.com/TheMillerLab/eLAB) provides suggestions and instructions on where or when upfront script modifications may be necessary to accommodate input variability.

The eLAB pipeline takes several inputs. For example, the input for use with the ‘ehr_format(dt)’ single-line command is non-tabular data assigned as R object ‘dt’ with 4 columns: 1) Patient Name (MRN), 2) Collection Date, 3) Collection Time, and 4) Lab Results wherein several lab panels are in one data frame cell. A mock dataset in this ‘untidy-format’ is provided for demonstration purposes (https://github.com/TheMillerLab/eLAB).

Bulk lab data pulls often result in subtypes of the same lab. For example, potassium labs are reported as “Potassium,” “Potassium-External,” “Potassium(POC),” “Potassium,whole-bld,” “Potassium-Level-External,” “Potassium,venous,” and “Potassium-whole-bld/plasma.” eLAB utilizes a key-value lookup table with ~300 lab subtypes for remapping labs to the Data Dictionary (DD) code. eLAB reformats/accepts only those lab units pre-defined by the registry DD. The lab lookup table is provided for direct use or may be re-configured/updated to meet end-user specifications. eLAB is designed to remap, transform, and filter/adjust value units of semi-structured/structured bulk laboratory values data pulls from the EHR to align with the pre-defined code of the DD.

Data Dictionary (DD)

EHR clinical laboratory data is captured in REDCap using the ‘Labs’ repeating instrument (Supplemental Figures 1-2). The DD is provided for use by researchers at REDCap-participating institutions and is optimized to accommodate the same lab-type captured more than once on the same day for the same patient. The instrument captures 35 clinical lab types. The DD serves several major purposes in the eLAB pipeline. First, it defines every lab type of interest and associated lab unit of interest with a set field/variable name. It also restricts/defines the type of data allowed for entry for each data field, such as a string or numerics. The DD is uploaded into REDCap by every participating site/collaborator and ensures each site collects and codes the data the same way. Automation pipelines, such as eLAB, are designed to remap/clean and reformat data/units utilizing key-value look-up tables that filter and select only the labs/units of interest. eLAB ensures the data pulled from the EHR contains the correct unit and format pre-configured by the DD. The use of the same DD at every participating site ensures that the data field code, format, and relationships in the database are uniform across each site to allow for the simple aggregation of the multi-site data. For example, since every site in the MCCPR uses the same DD, aggregation is efficient and different site csv files are simply combined.

Study Cohort

This study was approved by the MGB IRB. Search of the EHR was performed to identify patients diagnosed with MCC between 1975-2021 (N=1,109) for inclusion in the MCCPR. Subjects diagnosed with primary cutaneous MCC between 2016-2019 (N= 176) were included in the test cohort for exploratory studies of lab result associations with overall survival (OS) using eLAB.

Statistical Analysis

OS is defined as the time from date of MCC diagnosis to date of death. Data was censored at the date of the last follow-up visit if no death event occurred. Univariable Cox proportional hazard modeling was performed among all lab predictors. Due to the hypothesis-generating nature of the work, p-values were exploratory and Bonferroni corrections were not applied.

Data to run the IMACLIM-R France code

https://github.com/CIRED/IMACLIM-R_France

Input data for R script.

These are economic models in Make and Use formats with variations of one and two-region versions where the one region is just a U.S. state of interest (SoI) and the two-region version include both the SoI and Rest of the U.S. (RoUS). Inudstry and Commodity output vectors are also provided. Models are available representing annual totals for each year for each state from 2012 to 2017. Variations for "Domestic" forms of models are available. See the associated publication, also available without fees in PubMed, for details. These models were created with stateior v0.1.0 (https://github.com/USEPA/stateior/releases/tag/0.1.0). and can be used in that R software. See https://github.com/USEPA/stateior/tree/0.1.0 for usage details. The provided data link reveals many R Data Format (.RDS) files that can be read into R, along with metadata files in JSON format that provide information on provenance of the data. File names corresponded with the definitions in the associated data dictionary (for two-region files) and the associated supporting link (for one-region files). Other files are precursors to the one and two-region models with data that are used in the model building process and can be read into R. All model files corresponding to the associated publication have the the text "0.1.0" in the filename, for example "Census_StateExport_2013_0.1.0.rds". Each file contains all states for the year in the file name with a year is included. This dataset is associated with the following publication: Li, M., J. Ferreira, C.D. Court, D. Meyer, M. Li, and W.W. Ingwersen. StateIO - Open Source Economic Input-Output Models for the 50 States of the United States of America. International Regional Science Review. SAGE Publications, THOUSAND OAKS, CA, USA, 46(4): 428-481, (2023).

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The model.zip file contains input data and code supporting the cod_v2 population estimates. The file modelData.RData provides the input data to the JAGS model and the file modelCode.R contains the source code for the model in the JAGS language. The files can be used to run the model for further assessments and as a starting point for further model development. The data and the model were developed using the statistical software R version 4.0.2 (https://cran.r-project.org/bin/windows/base/old/4.0.2) and JAGS 4.3.0 (https://mcmc-jags.sourceforge.io), a program for analysis of Bayesian graphical models using Gibbs sampling, through the R package runjags 2.2.0 (https://cran.r-project.org/web/packages/runjags).

Pathogen diversity resulting in quasispecies can enable persistence and adaptation to host defenses and therapies. However, accurate quasispecies characterization can be impeded by errors introduced during sample handling and sequencing which can require extensive optimizations to overcome. We present complete laboratory and bioinformatics workflows to overcome many of these hurdles. The Pacific Biosciences single molecule real-time platform was used to sequence PCR amplicons derived from cDNA templates tagged with universal molecular identifiers (SMRT-UMI). Optimized laboratory protocols were developed through extensive testing of different sample preparation conditions to minimize between-template recombination during PCR and the use of UMI allowed accurate template quantitation as well as removal of point mutations introduced during PCR and sequencing to produce a highly accurate consensus sequence from each template. Handling of the large datasets produced from SMRT-UMI sequencing was facilitated by a novel bioinformatic pipeline, Probabilistic Offspring Resolver for Primer IDs (PORPIDpipeline), that automatically filters and parses reads by sample, identifies and discards reads with UMIs likely created from PCR and sequencing errors, generates consensus sequences, checks for contamination within the dataset, and removes any sequence with evidence of PCR recombination or early cycle PCR errors, resulting in highly accurate sequence datasets. The optimized SMRT-UMI sequencing method presented here represents a highly adaptable and established starting point for accurate sequencing of diverse pathogens. These methods are illustrated through characterization of human immunodeficiency virus (HIV) quasispecies. Methods This serves as an overview of the analysis performed on PacBio sequence data that is summarized in Analysis Flowchart.pdf and was used as primary data for the paper by Westfall et al. "Optimized SMRT-UMI protocol produces highly accurate sequence datasets from diverse populations – application to HIV-1 quasispecies" Five different PacBio sequencing datasets were used for this analysis: M027, M2199, M1567, M004, and M005 For the datasets which were indexed (M027, M2199), CCS reads from PacBio sequencing files and the chunked_demux_config files were used as input for the chunked_demux pipeline. Each config file lists the different Index primers added during PCR to each sample. The pipeline produces one fastq file for each Index primer combination in the config. For example, in dataset M027 there were 3–4 samples using each Index combination. The fastq files from each demultiplexed read set were moved to the sUMI_dUMI_comparison pipeline fastq folder for further demultiplexing by sample and consensus generation with that pipeline. More information about the chunked_demux pipeline can be found in the README.md file on GitHub. The demultiplexed read collections from the chunked_demux pipeline or CCS read files from datasets which were not indexed (M1567, M004, M005) were each used as input for the sUMI_dUMI_comparison pipeline along with each dataset's config file. Each config file contains the primer sequences for each sample (including the sample ID block in the cDNA primer) and further demultiplexes the reads to prepare data tables summarizing all of the UMI sequences and counts for each family (tagged.tar.gz) as well as consensus sequences from each sUMI and rank 1 dUMI family (consensus.tar.gz). More information about the sUMI_dUMI_comparison pipeline can be found in the paper and the README.md file on GitHub. The consensus.tar.gz and tagged.tar.gz files were moved from sUMI_dUMI_comparison pipeline directory on the server to the Pipeline_Outputs folder in this analysis directory for each dataset and appended with the dataset name (e.g. consensus_M027.tar.gz). Also in this analysis directory is a Sample_Info_Table.csv containing information about how each of the samples was prepared, such as purification methods and number of PCRs. There are also three other folders: Sequence_Analysis, Indentifying_Recombinant_Reads, and Figures. Each has an .Rmd file with the same name inside which is used to collect, summarize, and analyze the data. All of these collections of code were written and executed in RStudio to track notes and summarize results. Sequence_Analysis.Rmd has instructions to decompress all of the consensus.tar.gz files, combine them, and create two fasta files, one with all sUMI and one with all dUMI sequences. Using these as input, two data tables were created, that summarize all sequences and read counts for each sample that pass various criteria. These are used to help create Table 2 and as input for Indentifying_Recombinant_Reads.Rmd and Figures.Rmd. Next, 2 fasta files containing all of the rank 1 dUMI sequences and the matching sUMI sequences were created. These were used as input for the python script compare_seqs.py which identifies any matched sequences that are different between sUMI and dUMI read collections. This information was also used to help create Table 2. Finally, to populate the table with the number of sequences and bases in each sequence subset of interest, different sequence collections were saved and viewed in the Geneious program. To investigate the cause of sequences where the sUMI and dUMI sequences do not match, tagged.tar.gz was decompressed and for each family with discordant sUMI and dUMI sequences the reads from the UMI1_keeping directory were aligned using geneious. Reads from dUMI families failing the 0.7 filter were also aligned in Genious. The uncompressed tagged folder was then removed to save space. These read collections contain all of the reads in a UMI1 family and still include the UMI2 sequence. By examining the alignment and specifically the UMI2 sequences, the site of the discordance and its case were identified for each family as described in the paper. These alignments were saved as "Sequence Alignments.geneious". The counts of how many families were the result of PCR recombination were used in the body of the paper. Using Identifying_Recombinant_Reads.Rmd, the dUMI_ranked.csv file from each sample was extracted from all of the tagged.tar.gz files, combined and used as input to create a single dataset containing all UMI information from all samples. This file dUMI_df.csv was used as input for Figures.Rmd. Figures.Rmd used dUMI_df.csv, sequence_counts.csv, and read_counts.csv as input to create draft figures and then individual datasets for eachFigure. These were copied into Prism software to create the final figures for the paper.

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This USGS data release represents the input data, R script, and output data for WRTDS analyses used to identify trends in suspended sediment loads of Coastal Plain streams and rivers in the eastern United States.

The core data in this publication are empirical data on two coupled network layers inferred from cross-industrial citation links (patent citation network) and input-output flows among industries (input-output (IO) network). These data are available in quinquennial time steps for the years 1977, 1982, 1987, 1992, 1997, 2002, 2006. The data is available at the 6-digit level. The analyses in the paper mainly refer to 4-digit level results of a balanced panel of industries, i.e. industries for which both patent and IO data are available for the full time horizon. This publication also contains a sample of panel data on industry characteristics (mainly industry size by patent stock and output and network indicators). The data are available ein RData format and supplemented by the R-scripts used to compile and analyze the data.

This data publication contains all data and R-code used for the paper:

Hötte, Kerstin (2021): "Demand-pull Demand-pull and technology-push: What drives the direction of technological change? An empirical network-based approach".

[Forthcoming. If you use the data, please cite the most recent version of the paper.]

The paper also offers a description of the data and its compilation.

Abstract:

Demand-pull and technology-push are linked to an empirical two-layer network based on coupled cross-industrial input-output (IO) and patent-citation links among 155 4-digit (NAICS) US-industries in 1976-2006. I study the evolution of industry hierarchies and link formation. Both layers co-evolve, but differently: The patent network became denser and increasingly skewed, while market hierarchies are balanced and sluggish in change. Industries became more similar by patent-citations, but less by input use. Similar R&D capabilities as other big industries is beneficial for innovation providing access to knowledge but relying on the same market inputs is unfavorable if it intensifies competition. This may incite industries to explore other technological pathways. Growth in the market is constrained by scarcity and competition, but knowledge as innovation input is non-rival leading to increasing returns and a skewed distribution. This may strengthen existing R&D trajectories while market pressure may trigger a re-direction in both layers. This work is limited by its reliance on endogenously evolving classifications.

To reproduce the results and the data from the raw data, you must run the code provided in the

following order:

(1) CREATING THE DATA: (a) The patent data can not be fully reconstructed from the data that are available in this data publication because one of the intermediate steps relies on proprietary data that can not be provided here. For the remainder: You can compile parts of the patent and the IO data from the raw data. To do so, please use the code and raw data provided in the folders io_data_R_files and patent_data_R_files. Further detail is provided below.
(b) The folder R_scripts_both provides all code needed to create the merged panel data that is used in the analysis.

(2) REPRODUCING THE ANALYSES: The folder R_script_both provides all code needed to reproduce the figures, tables, descriptive statistics and regression analyses. Further detail is provided below.

This data publication also provides additional results on the analyses at different levels of data aggregation. You will find it in the folder statistical_output but you can also produce additional results running the code provided.

This data publication consists of 6 folders:

(1) patent_data_R_files (2) io_data_R_files (3) R_scripts_both (4) data_combined (5) statistical_output

Details:

(1) patent_data_R_files

This folder contains 2 subfolders: code, data

• code: This subfolder contains the R-scripts of all single steps executed to process the patent raw data. These steps are explained in detail in the Supplementary Material of the paper Hötte, K (2021): "Demand-pull and technology-push [forthcoming]"

• data: This subfolder contains the processed data at different levels of aggregation and a folder where to put the source files, i.e. the original NBER patent data used in this analysis that need to be downloaded from https://sites.google.com/site/patentdataproject/Home [accessed on Mar 17, 2021]. To us

This dataset served as the input for the METS-R simulator. Data include the historical and predicted demand, cache of transit scheduling results, cache of candidate paths for routing, and link-level average speed and corresponding standard deviations.

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