Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Dataset of the article "A new and almost perfectly accurate approximation of the eigenvalue effective population size of a dioecious population: comparisons with other estimates and detailed proofs"

Dataset of the article "A new and almost perfectly accurate approximation of the eigenvalue effective population size of a dioecious population: comparisons with other estimates and detailed proofs"  (https://doi.org/10.5281/zenodo.7927968), recommended by PCI Evol Biol (https://evolbiol.peercommunityin.org/articles/rec?id=651)

opencc-by-4.0May 2023View details →

Data and scripts to reproduce the results shown in "Stability of attractor local dimension estimates in non-Axiom A dynamical systems"

Here we make available all the codes and datasets to reproduce the results of the paper "Stability of attractor local dimension estimates in non-Axiom A dynamical systems" by Flavio Pons, Gabriele Messori and Davide Faranda. The pre-print of the article is available at https://hal.science/hal-04051659/document. Any question/comment can be sent to flavio.pons@gmail.com. License for the codes and simulation/analysis results (*.Rda files): the code is shared under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license, see https://creativecommons.org/licenses/by-nc-sa/4.0/ License and terms of use for the ERA5 data (z500_daily_euro.nc): the ERA5 500 hPa geopotential was downloaded from https://climexp.knmi.nl/start.cgi

opencc-by-4.0May 2023View details →

Simulation data for Doubly Robust Estimation of Business Process Intervention

Event logs of simulated execution of two variants of the same process. A case matrix that contain the outcome, the intervention, and the confounders.

opencc-by-4.0Jun 2023View details →

Urban Traffic Simulation Data from Real Fusion Estimates

The datasets contain vehicle data from the simulation of the city center of Guimarães. The simulation was created using data collected from real sensor data, thus, providing an accurate view of the traffic flows. The data contains route information, fuel consumption, emissions, driving distance, and the amount of time each vehicle is stopped.

opencc-by-4.0Jun 2023View details →

SM2RAIN CHINA: 1 km rainfall estimation for CHINA region derived from SMCI Soil Moisture (2000-2020)

SM2RAIN-SMCI provides rainfall estimates for the whole CHINA at 1 km/daily spatial resolution by applying SM2RAIN algorithm (Brocca et al., 2014; 2019) to Soil Moisture data from SMCI Soil moisture (<a href="https://doi.org/10.5194/essd-14-5267-2022">https://doi.org/10.5194/essd-14-5267-2022</a>) for the period from January 2000 to December 2020 (21 years). The SM2RAIN-SMCI rainfall dataset (in mm/day) is provided over a regular grid at 0.01-degree sampling. The product represents the accumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The uncalibrated SM2RAIN version (<a href="https://doi.org/10.5194/hess-26-2481-2022">https://doi.org/10.5194/hess-26-2481-2022</a>) was adopted to obtain rainfall. Precipitation data from HRLT (<a href="https://doi.org/10.5194/essd-14-4793-2022">https://doi.org/10.5194/essd-14-4793-2022</a>) were adopted to obtain mean climatology. The rainfall dataset is provided in NetCDF format. To simplify data access, the CHINA region is divided in 101 subregion containing 540 (longitude) * 360 (latitude) pixels each.

opencc-by-4.0Jan 2023View details →

Open-population models for estimating roadkill rates - Data and R Code

Roadkill carcass capture-recapture data, capture histories for four and eight-occasion designs, and R code (with JAGS code) for roadkill rates estimation.

openapache2.0Jun 2023View details →

Supplementary Material: A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model

This repository contains supplementary data for the journal paper: <blockquote> Mechtenberg M and Schneider A (2023) A method for the estimation of a motor unit innervation zone center position  evaluated with a computational sEMG model. Front. Neurorobot. 17:1179224. doi:  10.3389/fnbot.2023.1179224 </blockquote> It contains the configuration files for the simulator used in that publication [1]. These configuration files are to be found in the archive EMG_model_configs.zip. The files IP_tracking_opt_res.json<a href="https://zenodo.org/api/files/d21f2990-1849-40d8-90de-674fb0965938/IP_tracking_opt_res.json"> </a>and IP_tracking_opt_res.pkl contain the same information but in different file formats. In these files the results of the optimization described in the corresponding paper are stored. For each optimization condition the optimal parameters for the innervation point tracking algorithm are stored, as well as the error score for all calculated parameter combinations.   [1] Mechtenberg, Malte. (2023). UAS-Embedded-Systems-Biomechatronics/EMG-concentrated-current-sources: v0.2.1 (v0.2.1). Zenodo. https://doi.org/10.5281/zenodo.7995152

opencc-by-4.0Nov 2022View details →

Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space

The products are the first regional-scale and high-resolution (1 and 6 km) irrigation water data sets obtained from remote sensing observations. They cover three major river basins: the Ebro river basin (North-eastern Spain), the Po valley (Northern Italy), and the Murray-Darling basin (South-eastern Australia). The data sets are an outcome of the European Space Agency (ESA) Irrigation+ project (<a href="https://esairrigationplus.org/">https://esairrigationplus.org/</a>). The irrigation amounts have been estimated through the SM-based (Soil-Moisture-based) inversion approach. The satellite-derived irrigation products referring to the European sites have a spatial resolution of 1 km, and they are retrieved by exploiting Sentinel-1 soil moisture data obtained through the RT1 (first-order Radiative Transfer) model. A spatial sampling of 6 km is instead used for the Australian pilot area, since in this case the soil moisture information comes from CYGNSS (Cyclone Global Navigation Satellite System) observations. The three irrigation products are delivered with a weekly temporal aggregation. The 1 km data sets over the two European regions cover a period ranging from January 2016 to July 2020, while the irrigation estimates over the Murray-Darling basin are available for the time span April 2017 – July 2020. Details on the data sets development and  on their performance assessment can be found in: Dari, J., Brocca, L., Modanesi, S., Massari, C., Tarpanelli, A., Barbetta, S., Quast, R., Vreugdenhil, M., Freeman, V., Barella-Ortiz, A., Quintana-Seguí, P., Bretreger, D., Volden, E. Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space. Earth System Science Data, 15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, 2023.   Novelties in v1.1 with respect to v1.0: v1.1 of irrigation estimates through the SM-based inversion approach are currently available for the Ebro basin and the Po valley only. The novelties with respect to the previous version are: (i) the use of high-resolution (1 km) potential evapotranspiration rates in the algorithm and (ii) temporal extension as now the data sets cover a 6-year period from January 2016 to December 2021. Acknowledgements: ESA Irrigation+ project, <a href="https://esairrigationplus.org/">https://esairrigationplus.org/</a>, (contract n. 4000129870/20/I-NB). ESA 4DMED-Hydrology project, <a href="https://esairrigationplus.org/">https://www.4dmed-hydrology.org/</a>, (contract n. 4000136272/21/I-EF).

Simulation result from "Simulating Bark Beetle Outbreak Dynamics and their Influence on Carbon Balance Estimates with ORCHIDEE r7791"

Eight locations were selected which represent the range of climatic conditions within the distribution area of spruce in Europe (Picea Abies Karst L.) as shown in Table 4. Half-hourly weather data from the FLUXNET database <a href="https://www.zotero.org/google-docs/?ibBw15">(Pastorello et al., 2020)</a> for these locations were used to drive ORCHIDEE.  Some of these locations (FON, SOR, HES, COL, WET) are not populated with spruce but all are located within the species distribution. For each location, a pure spruce stand was simulated and the available FLUXNET data was looped to simulate a 100-year period. The study did not investigate the effect of species mixture in the simulation experiments. Other inputs, including soil texture, pH and soil color were obtained from the USDA map derived from <a href="https://www.zotero.org/google-docs/?aaWPI6">Eswaran et al. (2003</a>), for the corresponding pixel. The amount of fresh breeding woody substrate inputs used by the bark beetles to breed was controlled by modifying the maximum wind speed of a windthrow event in ORCHIDEE. Seven wind speeds ranging between 19 m/s and 40 m/s were selected (Table 3). This range is justified by the observation that mean wind speeds below 19 m/s could not trigger a windthrow event in ORCHIDEE <a href="https://www.zotero.org/google-docs/?jEqNDm">(Chen et al., 2018)</a> while for wind speeds exceeding 40 m/s, more than 60% of the trees are uprooted, leaving too few living trees to trigger a bark beetle outbreak within the same pixel.  To investigate the impact of windthrow intensity and background climate on bark beetle outbreaks, the study conducted a total of 56 [8 sites x 7 wind speed intensities] simulations as given in table 3. The same 56 simulations were also used to analyze the sensitivity of the carbon balance of spruce forests to windthrow intensity and background climate. Where most land surface models use a turnover time to simulate continuous mortality <a href="https://www.zotero.org/google-docs/?5jGfXF">(Thurner et al., 2014; Pugh et al., 2019)</a>, ecological reality is better described by abrupt mortality events. An idealized simulation experiment was used to qualify the impact of abrupt mortality on net biome productivity by changing from a framework in which mortality is approximated by a constant background mortality to a framework in which mortality occurs in abrupt, discrete events. To test the impact of a change in mortality framework two versions of ORCHIDEE were compared to create an idealized simulation experiment: (1) a version simulating mortality as a continuous process, labeled ”the continuous version”, and (2) the version capable of simulating abrupt mortality from windthrow and subsequent bark beetle outbreaks, labeled ”the abrupt version”. The effect of simulating abrupt mortality was evaluated over 20-, 50-, and 100-year time horizons. The effect of changing the framework of simulating mortality from continuous to abrupt was qualified on the basis of 112 simulations (8 sites x 7 wind speeds x 2 model versions) of 100 years each. The simulations with abrupt mortality were run first. Subsequently, the number of trees killed was quantified and used as a reference value for the continuous mortality set-up. This approach resulted in the same quantities of dead trees at the end of the simulation for both frameworks, which then differed only in the timing of the simulated mortality.  This precaution is necessary to avoid comparing two different mortality regimes where the result would mainly be explained by the intensity of the mortality rather than by its underlying mechanisms.

Data and code for gmd-2023-113 "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"

Data and code for the paper "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast" includes:  The model is Community Earth System Model (v1.2.1) (provided by www.cesm.ucar.edu) Data assimilation code is initially provided by Data Assimilation Research Testbed (DART) (https://dart.ucar.edu/), some modifications are made to enable parameter estimation function of ocean background vertical diffusivity coefficients. And the programs and scripts for deal with OISST and EN4 profiles are also developed. The parameter sensitivity experiment results are saved as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012.nc">sensitive2008-2012.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012salt.nc">sensitive2008-2012salt.nc</a> for temperature and salinity, respectively. And the python script to draw the results is  The state estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Salt_05-17.nc</a> for temperature and salinity, respectively. The parameter estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Salt_05-17.nc</a> for temperature and salinity, respectively. the estimated paremeter ensemble is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/parameters.nc">parameters.nc</a> the python script for comparing the SE and PE results is <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/plot_analysis.py">plot_analysis.py</a> the nino3.4 indices computed by the forecast experiment is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/fcst_correlation.nc">fcst_correlation.nc</a>

Estimating three-dimensional structures of eddy in the South Indian Ocean from the satellite observations based on the isQG method

Supporting data for Estimating three-dimensional structures of eddy in the South Indian Ocean from the satellite observations based on the isQG method Matlab Codes to reconstruct the subsurface structures (Codes without Figure_*.m) and plot the figures (Figure_*.m) in the manuscript. The file in Netcdf format is our reconstructed 3D density and currents.

Estimating the silica content and loss-on-ignition in the North American Soil Geochemical Landscapes datasets: a recursive inversion approach

Abstract: A novel method of estimating the silica (SiO2) and loss-on-ignition (LOI) concentrations for the North American Soil Geochemical Landscapes (NASGL) project datasets is proposed. Combining the precision of the geochemical determinations with the completeness of the mineralogical NASGL data, we suggest a ‘reverse normative’ or inversion approach to calculate first the minimum SiO2, water (H2O) and carbon dioxide (CO2) concentrations in weight percent (wt%) in these samples. These can be used in a first step to compute minimum and maximum estimates for SiO2. In a recursive step, a ‘consensus’ SiO2 is then established as the average between the two aforementioned estimates, trimmed as necessary to yield a total composition (major oxides converted from reported Al, Ca, Fe, K, Mg, Mn, Na, P, S, and Ti elemental concentrations + ‘consensus’ SiO2 + reported trace element concentrations converted to wt% + ‘normative’ H2O + ‘normative’ CO2) of no more than 100 wt%. Any remaining compositional gap between 100 wt% and this sum is considered ‘other’ LOI and likely includes H2O and CO2 from the reported ‘amorphous’ phase (of unknown geochemical or mineralogical composition) as well as other volatile components present in soil. We validate the technique against a separate dataset from Australia where geochemical (including all major oxides) and mineralogical data exist on the same samples. The correlation between predicted and observed SiO2 is linear, strong (R2 = 0.91) and homoscedastic. We also compare the estimated NASGL SiO2 concentrations with another publicly available continental-scale survey over the conterminous USA, the ‘Shacklette and Boerngen’ dataset. This comparison shows the new data to be a reasonable representation of SiO2 values measured on the ground over the same study area. We recommend the approach of combining geochemical and mineralogical information to estimate missing SiO2 and LOI by the recursive inversion approach in datasets elsewhere, with the caveat to validate results. Datasets: The original geochemical and mineralogical data for soils of the conterminous United States (A and C horizon datasets) were downloaded from <a href="https://mrdata.usgs.gov/ds-801/">https://mrdata.usgs.gov/ds-801/</a>. The ‘Shacklette and Boerngen’ dataset was downloaded from <a href="https://mrdata.usgs.gov/ussoils/">https://mrdata.usgs.gov/ussoils/</a>. A worked example for the five selected samples of Figure 5 is available as a Microsoft Excel spreadsheet (NALG_Ch_oxides_with_estimated_SiO2_LOI_worked example.xlsx) on Zenodo.org. The new datasets including sample identification, coordinates, converted major oxide concentrations, and the concentration estimates for SiO2 and LOI in wt% for the A and C horizon datasets from the North American Soil Geochemical Landscapes (NASGL) project are available as comma separated value files (NALG_Ah_oxides_with_estimated_SiO2_LOI.csv and NALG_Ch_oxides_with_estimated_SiO2_LOI.csv) on Zenodo.org.

opencc-by-4.0Feb 2023View details →

An exploratory approach to estimate point emission sources

The main purpose of this dataset is to improve the spatial and temporal distribution of the public power, refineries, manufacturing, and construction emissions in the European emission inventories using Portugal as a case study. More information about it can be found in the published article "An exploratory approach to estimate point emission sources" (<a href="https://doi.org/10.1016/j.atmosenv.2023.120026">https://doi.org/10.1016/j.atmosenv.2023.120026</a>). This dataset includes the following folders and files: 1. Source_Info   1.1 Sources_Info_NFR_1_A_1_a.xlsx: this excel file includes the public plant names, situation, region, location, start, fuel used, power (MW), technology, treatment of gas effluents, stack height (m), and facility ID.   1.2 Sources_Info_NFR_1_A_1_b.xlsx: this excel file includes the refineries' names, situation, region, location, start, fuel power (MW), stack height (m), and facility ID.   1.3 Sources_Info_NFR_1_A_2.xlsx: this excel file includes the manufacturer's names, situation, region, NFR code, energy balance (PT_code), capital (€), stack height (m), production index code (PT_code), and facility ID. 2. Spatial_Location   2.1. NFR_1_A_1_a.gdb: geodatabase with the public power locations and their facility ID (polygons and points shapefiles).   2.2. NFR_1_A_1_b.gdb: geodatabase with the refineries' locations and their facility ID (polygons and points shapefiles).   2.3. NFR_1_A_2.gdb: geodatabase with the manufacturer's locations and their facility ID (polygons and points shapefiles).   2.4. UrbanAreas.gdb: geodatabase with the Portuguese urban areas (polygons shapefiles). 3. Temporal_Profiles   3.1. Hourly     3.1.1. Hourly_NFR_1_A_1_a.xlsx: excel file with the hourly profiles of the public power sites in Portugal's mainland (using wood waste, natural gas, hard coal, another thermal, “Central de Ciclo Combinado do Pego”, and “Central Termoeléctrica de Lares”) and Madeira island (using natural gas, fuel, and urban solid waste).     3.1.2. Hourly_NFR_1_A_2_construction.xlsx: excel file with the hourly profiles for construction activities by Portuguese municipalities (305 municipalities).   3.2. Monthly     3.2.1. Monthly_NFR_1_A_1_a.xlsx: excel file with the monthly profiles of the public power sites in the Azores (using fuel oil and diesel).     3.2.2. Monthly_ProductionIndex.xlsx: excel file with the monthly profiles of the Portuguese industries (including information for twenty-five types of activities). 4. Emission factors 4.1. EF_NFR1_A_1_a.xlsx: excel file with the emission factors for the public power sector. 4.2. EF_NFR1_A_2.xlsx: excel file with the emission factors for the refinery activities. 4.3. EF_NFR1_A_b.xlsx: excel file with the emission factors for the manufacturing and construction combustion activities. 4.4 Fuels_to_EF.xlsx: excel file linking the fuels with available emission factors. 5. Data_Sources.docx: this file provides the sources of the dataset.

BeBOD estimates of mortality and years of life lost for 131 causes of death, 2004-2020

Belgian National Burden of Disease Study Estimates of the fatal burden of disease Causes of death Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person. Years of Life Lost In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live. More information For additional background on BeBOD, please visit <a href="https://www.sciensano.be/en/projects/belgian-national-burden-disease-study">https://www.sciensano.be/en/projects/belgian-national-burden-disease-study</a>. Explore the estimates via <a href="https://burden.sciensano.be/shiny/mortality">https://burden.sciensano.be/shiny/mortality</a>.

opencc-by-4.0Dec 2020View details →

Field Line Resonances estimated using Machine Learning methods

This data set contains the machine learning input matrix (composed by 1D Fourier cross-spectra) + additional information, for the Classification algorithm implemented in Foldes et al. (Automatic Detection of Field Line Resonance Frequencies in the Earth’s Plasmasphere, 2023) for the pair of station Tartu-Birzai (TAR-BRZ). Each file contains the following header at line 1. Columns are: - P(f0)-P(f211): Cross-phase value per frequency bin - YEAR - DOY (Day Of Year) - HOUR - ToD_flag: "Umbra", "Penumbra", 'Light' - L: McIllwain parameter - stat_tag: "tarbrz" - Kp - Kp_w_05d: Kp index weighted on a 12hrs time window - Kp_w_10d: Kp index weighted on a 24hrs time window - Kp_w_15d: Kp index weighted on a 36hrs time window - Kp_w_20d: Kp index weighted on a 2-day time window - Kp_w_25d: Kp index weighted on a 2.5-day time window - Kp_w_30d: Kp index weighted on a 3-day time window - Kp_m_05d: Kp index max on a 12hrs time window - Kp_m_10d: Kp index max on a 24hrs time window - Kp_m_15d: Kp index max on a 36hrs time window - Kp_m_20d: Kp index max on a 2-day time window - Kp_m_25d: Kp index max on a 2.5-day time window - Kp_m_30d: Kp index max on a 3-day time window - DST - DST_m_05d: DST index min on a 12hrs time window - DST_m_10d: DST index min on a 24hrs time window - DST_m_15d: DST index min on a 36hrs time window - DST_m_20d: DST index min on a 2-day time window - DST_m_25d: DST index min on a 2.5-day time window - DST_m_30d: DST index min on a 3-day time window - F107: F10.7 solar activity proxy - EField: Earth Electric co-rotation field - f(mHz): FLR frequency in mHz - df(mHz): Uncertainty on the validated frequency - class: 0 for "NoFreq", 1 for "Freq" and 2 for "PBL"

opencc-by-3.0-usSep 2023View details →

Data for 2D Signal Estimation for Sparse Distributed Target Photon Counting Data

Data used in publication of 2D Signal Estimation for Sparse Distributed Target Photon Counting Data Data provided consists of: raw and PTV processed MicroPulse DIAL (MPD) data (10.26023/MX0D-Z722-M406) simulated photon counting data and the processed results

Estimation of biomass combustion carbon emissions data for 2018 in Africa based on GABAM burned area products.

Estimated biomass combustion carbon emissions data for the African region in 2018, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.

Estimation of biomass combustion carbon emissions data for 2020 in Africa based on GABAM burned area products.

Estimated biomass combustion carbon emissions data for the African region in 2020, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.

Estimation of biomass combustion carbon emissions data for 2019 in Africa based on GABAM burned area products.

Estimated biomass combustion carbon emissions data for the African region in 2019, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.

Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression

Supplemental Data 1 is single-cell response to rapamycin count data first sequenced in this work and deposited in GEO with accession GSE242556. It is a 173348 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Gene', 'Replicate', 'Pool', and 'Experiment') are cell-specific metadata. Supplemental Data 2 is bulk response to rapamycin count data first sequenced in this work. It is a 33 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Oligo', 'Time', 'Replicate', and 'Sample_barcode') are sample-specific metadata. Supplemental Data 3 is single-cell count data published as GSE125162 and re-analyzed with the pipeline used for single-cell quantification in this work. It is a 65068 rows × 5850 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 7 columns ('Condition', 'Sample', 'Genotype_Group', 'Genotype_Individual', 'Genotype', 'Replicate', 'Cell_Barcode') are cell-specific metadata. Supplemental Data 4 is the four deep learning models trained in this work. It is a TAR.GZ file containing the final biophysical transcription/decay model, the pre-trained decay model, the velocity prediction model, and the count prediction model. Each model file is an h5 file containing a pytorch model that can be loaded with supirfactor\_dynamical.read(). Supplemental Data 5 is the prior knowledge network used to constrain the models for TF interpretability. It is a 1574 rows × 204 columns [Genes x TFs] TSV.GZ file where the first row is a header with TF names, the first column is an index of gene names, and TF-gene interactions are indicated by non-zero values in the matrix. There are 2799 TF-gene interactions. Supplemental Table 6 is the oligonucleotide sequences used in this work. It is a TSV file with a header row. Supplemental Table 7 is the yeast strains used in this work. It is a TSV file with a header row. Supplemental Table 8 is gene metadata used in this work (e.g. Ribosomal Protein gene labels, etc). It is a TSV file with a header row. Supplemental Table 9 is FY4/5 growth curve data generated in this work. It is a 20 rows × 7 columns TSV file where the first row is a header with replicate IDs, the first column is an index of times in minutes, and values are cell densities in YPD culture, in units of 10$^6$ cells / mL. Supplemental Data 10 is a TAR.GZ file containing the yeast SacCer3 genome, modified to add UTR sequences, that was used to generate transcripts for kallisto pseudoalignment in this work.