Find research datasets worth reusing

opencc-by-3.0-usFeb 2022View details →

Survey Data Results for Project Strategies for Mitigating Congestion in Small Urban and Rural Areas

File contains the Survey Data Results for Project Strategies for Mitigating Congestion in Small Urban and Rural Areas. Row 1 is the question number. Row 2 is the question. Remaining Rows are individual survey responses.

opencc-by-4.0Apr 2019View details →

Figures - MIDA Project, April 2019

MIDA Figures Figures created for the MIDA Project application document.

Project files provided as supporting information to the manuscript "Membrane binding of pore-forming gamma-hemolysin components studied at different lipid compositions"

Project files provided as supporting information to the manuscript "Membrane binding of pore-forming gamma-hemolysin components studied at different lipid compositions" The dataset contains the following folders: - number_of_contacts: files with the number of contacts between the rim domains of LukF and Hlg2 and the membrane, for different bilayer compositions (Fig. 2). - binding_events: files with the duration of the time interavals in which LukF and Hlg2 are bound to the membrane, and the scripts used to compute for each system the number of binding/unbinding events and the average membrane residence time (Fig. 3). - electrostatic_potential: files of the surface electrostatic potential produced with the adaptive Poisson-Boltzmann solver and used for visualization with Chimera (Fig. 4). - angles: files with the angle values computed between the protein axis and the z-axis of the simulation box (Fig. 5). - contacts_per_residue: files with the number of frames in which each protein residue is in contact with the membrane, with respect to the total number of frames in which the rim domain interacts with the bilayer (Fig. 5). - distance_protein_membrane: files with the minimum distance between the protein and the membrane (Fig. 6). - binding_sites: file produced by PyLipid with relevant information on the main DOPC binding sites identified in LukF. - min_distance_per_residue: files with the minimum distance between each protein residue and the membrane, computed at the binding steps (Fig. S5).

Resources for the Future Socioeconomic Projections (RFF-SPs)

RFF-SPs Monte Carlo output data: This dataset includes the socioeconomic and emissions data generated by the Resources for the Future Socioeconomic Projections (RFF-SPs) model as discussed in Rennert et al. (forthcoming) (<a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/</a>). The data take the form of a Monte Carlo simulation with n = 10,000 draws. File structure and column metadata are described here: --- death_rates/ --- <ul> <li>rffsp_death_rates_run_1.feather</li> <li>rffsp_death_rates_run_2.feather</li> <li>rffsp_death_rates_run_2.feather</li> </ul>        ... <ul> <li>rffsp_death_rates_run_999.feather</li> <li>rffsp_death_rates_run_1000.feather</li> </ul> This folder contains 1,000 files in the .feather file format (<a href="https://arrow.apache.org/docs/python/feather.html">https://arrow.apache.org/docs/python/feather.html</a>), which is optimized for I/O speed and compressed to minimize storage requirements. Each file in this folder contains 3 columns: ISO3, Year, and DeathRate. For each row: - ISO3 contains the ISO numeric-3 code (<a href="https://www.iso.org/iso-3166-country-codes.html">https://www.iso.org/iso-3166-country-codes.html</a>) of the country whose GDP and population are projected. - Year contains the calendar year of the predictions. - year contains the calendar year of the prediction. - DeathRate contains a death rate in average deaths per 1000 people. Baseline mortality data found in data/mortality was derived from the death_rates.csv provided to the RFF team on October 7th from Hana Sevcikova.  In that source file, the column DeathRate is the annual deaths per 1000 people. PopAvg is the denominator (average between two time periods) and PopStart is population at the start of the time interval. Values are average deaths per 1000 people. There is one mortality death rate trajectory for each population trajectory, and each RFF-SP is matched to one of these 1000 trajectories. The file sampled_pop_trajectory_numbers.csv described below maps each of the 10,000 RFF SP scenarios to the baseline mortality scenario (out of 1000) matched to its population draw. --- emissions/ --- <ul> <li>rffsp_co2_emissions.csv</li> <li>rffsp_ch4_emissions.csv</li> <li>rffsp_n2o_emissions.csv</li> </ul> Each file in this folder contains 3 columns: sample, year, and value. For each row: - sample contains the number identifying which draw a prediction belongs to (from 1 to 10,000). - year contains the calendar year of the prediction. - value contains the projected annual global emissions of the gas specified in the filename. The units are as follows: <ul> <li>rffsp_co2_emissions.csv is in gigatons C</li> <li>rffsp_ch4_emissions.csv is in megatons CH4</li> <li>rffsp_n2o_emissions.csv is in megatons N2</li> </ul> --- pop_income/ --- <ul> <li>rffsp_pop_income_run_1.feather</li> <li>rffsp_pop_income_run_2.feather</li> <li>rffsp_pop_income_run_3.feather</li> </ul>        ... <ul> <li>rffsp_pop_income_run_9999.feather</li> <li>rffsp_pop_income_run_10000.feather</li> </ul>   This folder contains 10,000 files in the .feather file format (<a href="https://arrow.apache.org/docs/python/feather.html">https://arrow.apache.org/docs/python/feather.html</a>), which is optimized for I/O speed and compressed to minimize storage requirements. Each file corresponds to one draw of our socioeconomic data, and contains 4 columns: Country, Year, Pop, and GDP. The number in each filename corresponds to the "sample" column in the emissions data. For each row: - Country contains the ISO Alpha-3 code (<a href="https://www.iso.org/iso-3166-country-codes.html">https://www.iso.org/iso-3166-country-codes.html</a>) of the country whose GDP and population are projected. - Year contains the calendar year of the predictions. - Pop contains the projected population for a given country and year, in units of thousands of people. - GDP contains the projected GDP for a given country and year, in units of millions of 2011 USD. --- The probabilistic population projections were produced by Adrian E. Raftery and Hana &Scaron;evčíková (University of Washington), using the methods described by Raftery and &Scaron;evčíková (2021). Please cite this reference in any publications using these projections. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under NIH grant number R01 HD-070936. The probabilistic economic and emissions projections are from Rennert et al. (forthcoming), which in turn are based in part on Müller, Stock, and Watson (forthcoming). --- sample_numbers/ --- <ul> <li>sampled_gdp_trajectory_numbers.csv</li> <li>sampled_pop_trajectory_numbers.csv</li> </ul> These two files hold the sample IDs corresponding to the 10,000 draws from the dataset described above in the pop_income/ section. For reproducibility, these sample sets are available as inputs to the scripts which created them and used when the script is run in deterministic mode.  More information on the weighting and specifications of such sampling is available in the code. --- ypc1990/ --- <ul> <li>rffsp_ypc1990.csv</li> </ul> This CSV file is a matrix of 10,000 rows, pertaining to the 10,000 draws, and 184 columns, pertaining to the 184 countries in this analysis. The columns are labeled with the ISO Alpha-3 code (<a href="https://www.iso.org/iso-3166-country-codes.html">https://www.iso.org/iso-3166-country-codes.html</a>). The values are the GDP for a given country and sample, in units of millions of 2011 USD. References Müller, U.K, Stock, J.H., and Watson, M.W. (forthcoming). An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting. The Review of Economics and Statistics, available online 30 October 2020. URL: <a href="https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth">https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth</a> Raftery, A.E. and &Scaron;evčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. International Journal of Forecasting, available online 7 October 2021. URL: <a href="https://www.sciencedirect.com/science/article/pii/S0169207021001394">https://www.sciencedirect.com/science/article/pii/S0169207021001394</a> Rennert, K., Prest, B.C., Pizer, W., Newell, R.G., Anthoff, D., Kingdon, C., Rennels, L., Cooke, R., Raftery, A.E., &Scaron;evčíková, H, and Errickson, F. (forthcoming). The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates. Brookings Papers on Economic Activity. Available online 27 October 2021. URL: <a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/</a>

opencc-by-4.0Dec 2021View details →

Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines

Code and data for Section 2 of the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines Versions: Version 1.1 This one: <ul> <li>updated region names</li> </ul> Version 1.0 <a href="https://doi.org/10.5281/zenodo.5951626">https://doi.org/10.5281/zenodo.5951626</a> This repository contains the code and data needed to produce the trajectories, projections, and observations for the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines. The report can be found on <a href="https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html">https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html</a> An interactive tool to study the observations, trajectories, and scenarios can be accessed from <a href="https://sealevel.nasa.gov/task-force-scenario-tool">https://sealevel.nasa.gov/task-force-scenario-tool</a> Frequently-asked questions: <a href="https://sealevel.nasa.gov/faq/16/">https://sealevel.nasa.gov/faq/16/</a> Authors <ul> <li>William V. Sweet, NOAA National Ocean Service</li> <li>Benjamin D. Hamlington, NASA Jet Propulsion Laboratory</li> <li>Robert E. Kopp, Rutgers University</li> <li>Christopher P. Weaver, U.S. Environmental Protection Agency</li> <li>Patrick L. Barnard, U.S. Geological Survey</li> <li>Michael Craghan, U.S. Environmental Protection Agency</li> <li>Gregory Dusek, NOAA National Ocean Service</li> <li>Thomas Frederikse, NASA Jet Propulsion Laboratory</li> <li>Gregory Garner, Rutgers University</li> <li>Ayesha S. Genz, University of Hawai‘i at Mānoa, Cooperative Institute for Marine and Atmospheric Research</li> <li>John P. Krasting, NOAA Geophysical Fluid Dynamics Laboratory</li> <li>Eric Larour, NASA Jet Propulsion Laboratory</li> <li>Doug Marcy, NOAA National Ocean Service</li> <li>John J. Marra, NOAA National Centers for Environmental Information</li> <li>Jayantha Obeysekera, Florida International University</li> <li>Mark Osler, NOAA National Ocean Service</li> <li>Matthew Pendleton, Lynker</li> <li>Daniel Roman, NOAA National Ocean Service</li> <li>Lauren Schmied, FEMA Risk Management Directorate</li> <li>William C. Veatch, U.S. Army Corps of Engineers</li> <li>Kathleen D. White, U.S. Department of Defense</li> <li>Casey Zuzak, FEMA Risk Management Directorate</li> </ul> Contents This data and code set contains the following directories: Results The <code>Results</code> folder contains the resulting projections, trajectories and observations from the report. <ul> <li><code>TR_global_projections.nc</code>: GMSL projections, trajectory, and observations</li> <li><code>TR_regional_projections.nc</code>: Regional observations, projections and trajectories</li> <li><code>TR_local_projections.nc</code>: Local observations, projections and trajectories</li> <li><code>TR_gridded_projections.nc</code>: Gridded projections</li> </ul> These files are in the NetCDF forrmat. To read the NetCDF files, many free software packages are available, including <a href="http://meteora.ucsd.edu/~pierce/ncview_home_page.html">ncview</a> and <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a>. Free NetCDF packages are available to directly import the data into <a href="https://github.com/Alexander-Barth/NCDatasets.jl">Julia</a> and <a href="https://unidata.github.io/netcdf4-python/">Python</a> code. Code The <code>Code</code> folder contains all the computer code used to read and analyze the observations and the projections, and to generate the trajectories. To run this code, you need <a href="https://julialang.org/">Julia</a>. The code requires the Julia packages <code>CSV</code>, <code>Interpolations</code>, <code>JSON</code>, <code>LoopVectorization</code>, <code>MAT</code>, <code>NCDatasets</code>, <code>NetCDF</code>, <code>Plots</code>, <code>XLSX</code>, <code>LinearAlgebra</code>, and <code>Statistics</code>. They can be installed by pressing <code>]</code> at the Julia REPL and typing: <pre><code>add CSV Interpolations JSON LoopVectorization MAT NCDatasets NetCDF Plots XLSX LinearAlgebra Statistics </code></pre> This program also requires <a href="http://segal.ubi.pt/hector/">Hector</a>. Hector needs to be installed or compiled. In the file <code>Hector.jl</code> update the path to the Hector executable on lines 30 and 104. Run <code>Run_TR.jl</code> in the REPL or run <code>julia Run_TR.jl</code> from the command line to run the projections. The projections are then written to the <code>.\Data</code> directory. The folder contains the following files: <ul> <li><code>Run_TR.jl</code>: This is the main routine that (eventually) calls all the functions to compute the projections.</li> <li><code>ConvertNCA5ToGrid.jl</code>: Converts the original NCA5 projections to a set of netCDF files that's used throughout this code</li> <li><code>ProcessObservations.jl</code>: Reads and processes the tide-gauge and altimetry observations</li> <li><code>GlobalProjections.jl</code>: Reads and processes the GMSL observations and projections, and computes the trajectory</li> <li><code>RegionalProjections.jl</code>: Reads and processes the regional projections and computes the trajectories</li> <li><code>LocalProjections.jl</code>: Reads and processes the local projections at the tide-gauge locations and computes the trajectories</li> <li><code>GriddedProjections.jl</code>: Reads the gridded NCA5 projections and add a GMSL baseline correction for the 2005 vs 2000 baseline</li> <li><code>SaveFigureData.jl</code>: Reads the results and writes text files for GMT</li> <li><code>Hector.jl</code>: Wrapper for <a href="http://segal.ubi.pt/hector/">Hector</a>, used to compute trends and uncertainties.</li> <li><code>Masks.jl</code>: Defines the region masks for each region.</li> </ul> Data The <code>Data</code> directory contains the input data sets used during the computations. Please appropriately cite the input data if you use it. It contains the following: Directories: <ul> <li><code>ClimIdx</code>: Map with climate indices (NAO, PDO, MEI) used to remove internal variability. All the indices come from NOAA <a href="https://psl.noaa.gov/data/climateindices/">Physical Sciences Laboratory (PSL)</a> and <a href="https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml">NOAA Climate Prediction Centre (CPC)</a></li> <li><code>NCA5_projections</code> Contains the NCA5 projections for each scenario (Low, IntLow, Int, IntHigh, and High). For each scenario, the GMSL projections, projections at tide-gauge locations and on a 1-degree grid are provided.</li> </ul> Files: <ul> <li><code>basin_codes.nc</code>: Map with basin codes. from Eric Leuliette/NOAA. Data provided by the NOAA Laboratory for Satellite Altimetry.</li> <li><code>CDS_monthly_1993_2020.nc</code>: Monthly-mean sea level (1993-2020) from gridded altimetry. Obtained from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global">Copernicus Climate Data Store</a>. This dataset contains modified Copernicus Climate Change Service information [2020]</li> <li><code>enso_correction.mat</code>: GMSL correction for ENSO/PDO from Hamlington, B. D., Frederikse, T., Nerem, R. S., Fasullo, J. T., & Adhikari, S. (2020). Investigating the Acceleration of Regional Sea‐level Rise During the Satellite Altimeter Era. Geophysical Research Letters. <a href="https://doi.org/10.1029/2019GL086528">https://doi.org/10.1029/2019GL086528</a></li> <li><code>filelist_psmsl.txt</code>: List with PSMSL file names and PSMSL IDs. Obtained from the Permanent Service for Mean Sea Level (<a href="http://www.psmsl.org/">PSMSL</a>), 2021, Retrieved 29 Nov 2021. Simon J. Holgate, Andrew Matthews, Philip L. Woodworth, Lesley J. Rickards, Mark E. Tamisiea, Elizabeth Bradshaw, Peter R. Foden, Kathleen M. Gordon, Svetlana Jevrejeva, and Jeff Pugh (2013) New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research: Volume 29, Issue 3: pp. 493 – 504. <a href="https://doi.org/:10.2112/JCOASTRES-D-12-00175.1">https://doi.org/:10.2112/JCOASTRES-D-12-00175.1</a>.</li> <li><code>GEBCO_bathymetry_05.nc</code>: Bathymetry map of the global oceans from the General Bathymetric Chart of the Oceans (<a href="https://www.gebco.net/">GEBCO</a>). Source: GEBCO Compilation Group (2021) GEBCO 2021 Grid (<code>doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f</code>) The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>GIA_Caron_stats_05.nc</code>: Glacial Isostatic Adjustment estimates from Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203–2212. <a href="https://doi.org/10.1002/2017GL076644">https://doi.org/10.1002/2017GL076644</a>. The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>global_timeseries_measures.nc</code>: Time series of estimated 20th-century GMSL and its components, based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_ensembles.nc</code>: Ensemble GMSL reconstruction from tide-gauges based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_TPJAOS_5.0_199209_202106.txt</code>: Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1 [Data set]. NASA Physical Oceanography DAAC. <a href="https://doi.org/10.5067/GMSLM-TJ151">https://doi.org/10.5067/GMSLM-TJ151</a>. This altimetry dataset uses the methods as described in Beckley, B. D., Callahan, P. S., Hancock, D. W., Mitchum, G. T., & Ray, R. D. (2017). On the “Cal-Mode” Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series. Journal of Geophysical Research: Oceans, 122(11), 8371–8384. <a href="https://doi.org/10.1002/2017JC013090">https://doi.org/10.1002/2017JC013090</a></li> <li><code>grd_1992_2020.nc</code>: Seafloor deformation due to contemporary GRD effects based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>region_mask.nc</code>: Mask with the definition of all regions.</li> <li><code>US_tg_monthly.xlsx</code>: Tide gauge observations from the NOAA tide gauge network</li> </ul> GMT This directory contains the <a href="https://www.generic-mapping-tools.org/">GMT</a> scripts to make Figures 1.2, 2.1, 2.2, 2.6, and A.1.2 from the report. To generate the figures, make sure GMT is installed and run the Shell script in each directory.

Project files provided as supporting information to the manuscript "Coarse-grained Mori-Zwanzig dynamics in a time-non-local stationary-action framework"

Project files provided as supporting information to the manuscript "Coarse-grained Mori-Zwanzig dynamics in a time-non-local stationary-action framework" GLE Optimization: Optimizator of GLE parameters. Uses Matlab MD_GLE:           CG GLE and LE simulator. Uses Matlab Water_simulation: Folders for atomistic water system simulation with GROMACS. It requires to be run on Linux with GROMACS                    and VOTCA packages installed.

Dataset provided with "Artifacts in Optical Projection Tomography Due to Refractive-Index Mismatch: Model and Correction"

Dataset and scripts used in "Artifacts in Optical Projection Tomography Due to Refractive-Index Mismatch: Model and Correction".

Data from Test Performance Study Euphresco project 2019-A-327

Data of the test performance study organised in the framework of the Euphresco project 2019-A-327 'Validation of molecular tests for the detection of tomato brown rugose fruit virus (ToBRFV) in seed of tomato and pepper'

opencc-by-4.0Sep 1966View details →

Dr. Sarvapalli Radhakrishnan, then President of India, felicitating Dr. P. L. Vaidya, on the occasion of the conclusion of the Mahābhārata project, 22 September 1966

Dr. Sarvapalli Radhakrishnan, then President of India, felicitating Dr. P. L. Vaidya, on the occasion of the conclusion of the critical ediition of the Mahābhārata on September 22, 1966. After 48 years & a critical study of 1259 manuscripts, the project was completed in 1966. After V.S. Sukthankar, S.K. Belvalkar was appointed as the general editor on April 1, 1943. Upon his retirement, P. L. Vaidya was appointed as General Editor of the project in 1961, while R. N. Dandekar became the joint general editor in 1957.

opencc-by-4.0Mar 2022View details →

Rethinking Sea-Level Projections using Families and Timing Differences

Dataset for publication "Rethinking Sea-Level Projections using Families and Timing Differences"

opencc-by-4.0Mar 2022View details →

mFRR market related datasets for HLU9 of the CROSSBOW project

Datasets for HLU9 of the CROSSBOW project. What is included? <ol> <li>Cross-Zonal Capacities - formatted dataset published as a whole <ul> <li>CZC data for the years 2018 & 2019 were gathered in HLU9. The CZC values were for the timeframe after the ID market took place (before the mFRR market). Parameters included: Date & Time, CZC value to and from the delivering LFC area. CZC data was collected for borders of TSOs from the CROSSBOW project consortium.</li> </ul> </li> <li>mFRR market related data - formatted dataset published, sensitive data omitted as per the guidelines from TSOs <ul> <li>mFRR market data include BSP bids (which are comprised of Price, Timestamp, Direction, Quantity, LFC Area…), TSO demands (which are comprised of Quantity, Direction, Elasticity, LFC area, Timestamp…). Not all data was made available by the TSOs.</li> </ul> </li> </ol>

Eel data (Anguilla anguilla) and associated environment variables for eel in the SUDOE area (SUDOANG project)

<h2>1. DESCRIPTION</h2><h3>1.1. THE SUDOANG PROJECT</h3>The <a href="https://sudoang.eu/en/">SUDOANG</a> project aims at providing common tools to managers to support eel conservation in the SUDOE area (Spain, France and Portugal). Three main datasets have been used to implement EDA.<ul><li>A database of rivers and their attributes along with tools for chaining</li><li>A database of dams.</li><li>A database of electrofishing (current dataset)</li></ul>Electrofishing data include site locations, fishing operations which can be done several times at one site, and fish data collected during each operation. The operations are attached to stretches of river or river segments whose characteristics describe the conditions for presence, density, size structure, or silvering rate of the eels. The electrofishing operation are classified by type:<ul><li>com full two pass fishing</li><li>coa full two pass electrofishing for eel</li><li>iaa eel abundance point sampling</li><li>ber bank sampling</li><li>gm point fishing for large streams</li><li>oth other, or unspecified.</li></ul><h3>1.2. TEMPORAL SCOPE </h3>From 1985 to 2018, beware incomplete dataset after 2015 in France.<h3>1.3. GEOGRAPHICAL RANGE</h3>The SUDOE area including Iberian Peninsula and France.<h2>2. DATASETS DESCRIPTION</h2><h3>2.1. DENSITIES AND PRESENCE ABSENCE</h3><h4>Dataset: <a href="https://zenodo.org/api/files/c576c330-0219-4710-a831-717762b117aa/frsppt_12_2020.Rdata?versionId=ad5867d9-1a51-413d-9d27-d269462df9a2">frsppt_12_2020.Rdata</a></h4>Most variables are built <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/de433830a1a483fff6404e0f668a38e8e88663a6/eda2.3/report/report2.3/EDA_build.Rnw#L1233">here. </a>The script to create cumulated values for dams can be found <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/S4/BaseEdaRiosRiversegmentsDam.R#L624">here.</a>For a technical description see the <a href="https://sudoang.eu/wp-content/uploads/2022/02/E411_Briand_et_al_2022_EDA_report_opt-1.pdf">report.</a>This file contains the following datasets:<ul><li>ddd => dataset used to calibrate the presence absence model, 46147 lines</li><li>ddg => dataset used to calibrate the gamma model (only positive values retained), 19993 lines.</li><li>tdd => dataset corresponding to places where transport operation have been identified, presence absence model, 6582 lines.</li><li>tdg => dataset corresponding to places where transport operation have been identified, gamma model (only positive values) 984 lines.</li></ul>And the following columns (in alphabetical order):<ul><li>altitudem: Altitude in meter truncated to 800 m</li><li>area_sudo: Area for recruitment (Drouineau et al., 2021)</li><li>codesea: Code of the Sea, factor A = Atlantic, M = Mediterranean</li><li>country: A factor (FR, SP, PT) for France, Spain and Portugal</li><li>country2: Country with grouping for the Iberian Peninsula (SPPT). Other level is France (FR)</li><li>cs_height_08_n: Cumulated height from the sea, dam height transformed with power 0.8, no prediction for missing values</li><li>cs_height_08_n: Same variable but truncated to 300</li><li>cs_height_08_p: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values</li><li>cs_height_08_p: Same variable but truncated to 300</li><li>cs_height_08_pp: Cumulated height from the sea,  dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li>cs_height_08_pps: Cumulated height from the sea,  dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li>cs_height_10_FR: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from France are considered when building on a transnational water course</li><li>cs_height_10_n: Cumulated height from the sea, no transformation, no prediction for missing values</li><li>cs_height_10_n: Same variable but truncated to 200</li><li>cs_height_10_p: Cumulated height from the sea, no transformation, missing height are extrapolated from two different models in France and the Iberian Peninsula</li><li>cs_height_10_p: Same variable but truncated to 200</li><li>cs_height_10_pass0: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without pass are used to build the cumulated value</li><li>cs_height_10_pass1: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with pass are used to build the cumulated value</li><li>cs_height_10_pp: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li>cs_height_10_ppass0: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without pass are used to build the cumulated value</li><li>cs_height_10_ppass1: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with pass are used to build the cumulated value</li><li>cs_height_10_pps: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li>cs_height_10_pscore0: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without score are used to build the cumulated value</li><li>cs_height_10_pscore1: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel)  are used to build the cumulated value</li><li>cs_height_10_PT: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Portugal are considered when building on a transnational water course</li><li>cs_height_10_score0: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without score are used to build the cumulated value</li><li>cs_height_10_score1: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel)  are used to build the cumulated value</li><li>cs_height_10_SP: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Spain are considered when building on a transnational water course.</li><li>cs_height_12_n: Cumulated height from the sea,  dam height transformed with power 1.2, no prediction for missing values.</li><li>cs_height_12_n.: Same variable but truncated to 500</li><li>cs_height_12_p: Cumulated height from the sea,  dam height transformed with power 1.2, with prediction for missing values.</li><li>cs_height_12_p.: Same variable but truncated to 500</li><li>cs_height_12_pp: Cumulated height from the sea,  dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li>cs_height_12_pps: Cumulated height from the sea,  dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li>cs_height_15_n: Cumulated height from the sea,  dam height transformed with power 1.5, no prediction for missing values</li><li>cs_height_15_n.: Same variable but truncated to 800</li><li>cs_height_15_p: Cumulated height from the sea,  dam height transformed with power 1.5, with prediction for missing values</li><li>cs_height_15_p.: Same variable but truncated to 800</li><li>cs_height_15_pp: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li>cs_height_15_pps: Cumulated height from the sea,  dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li>cumnbdamp: Cumulated number of dam from the sea</li><li>cumnbdamso: duplicate of cumnbdamp</li><li>cumwettedsurfacebothkm2.: Surface of water downstream from the segment in the river. Corresponds to both riversegment and waterbodies</li><li>cumwettedsurfacekm2.: Surface of water downstream from the segment in the river. Corresponds only to rivers</li><li>cumwettedsurfaceotherkm2.: Surface of water downstream from the segment in the river. Corresponds only waterbodies (water surfaces, associated with the segment).</li><li>densCS: Density from Carle and Strub, number in second pass extrapolated from efficiency if only one pass</li><li>dist_from_gibraltar_km: Distance to Gibraltar calculated using an enveloppe along the coastline (e.g. the estuaries ingress inland are not counted for this distance).</li><li>distanceseakm: Distance to the sea</li><li>distanceseakm.: Distance to the sea, truncated at 500</li><li>distancesourcem: distance to the source in meters</li><li>downstdrainagewettedsurfaceboth.: Percentage of wetted surface downstream for both rivers and associated waterbodies (cumwettedsurfacebothkm2) divided by land surface for all segments within the basin located at a same or lesser distance to the sea.</li><li>downstreamwettedsurface: Percentage of wetted surface downstream (cumwettedsurfacekm2) divided by land surface for all segments within the basin located at a same or lesser distance to the sea</li><li>drainage_density_perm.: River length / surface of basin in the basin downstream (m-1)  Numeric, multiplied by $10^4$, truncated to 20</li><li>ef_fishingmethod: Fishing method. See details in text and Briand et al., (2022)</li><li>ef_wetted_area: Surface of the electrofishing station</li><li>emu: Eel management unit. See <a href="https://github.com/ices-eg/wg_WGEEL/wiki">Git WGEEL</a></li><li>hydraulicdensityperm2.: Number of riversegments per surface of basin (m-2). Numeric Multiplied by 10^6, truncated to 1.5</li><li>idsegment: Unique identifier of the segment TEXT</li><li>laltitudem.: Log transformed value of altitude (truncated)</li><li>lcs_height_10_n.: Log transformed value of cumulated height</li><li>lddws: Log transformed value of downstdrainagewettedsurfaceboth</li><li>ldownstreamwettedsurface: Log of previous column</li><li>lriverwidthm.: Log transformed value of river width</li><li>month: Month</li><li>NCS: Number of eels estimated by Carle and Strub</li><li>ob_id: Operation (observation) identifier</li><li>op_id: Station (observation place) identifier</li><li>riverwidthm: Width of the river in m (comes from various sources: see <a href="https://doi.org/10.5281/zenodo.7546419">Atlas</a> and Briand et al., 2022).</li><li>seaidsegment: Identifier of the sea idsegment</li><li>temperature: Average temperature from 1960-2000 from the CCM (Vogt, 2007)</li><li>temperature.1: duplicate of temperature</li><li>transport: code of transport operation</li><li>year: year of electrofishing</li></ul><h3>2.2. SIZE STRUCTURE OF EELS </h3><h4>Dataset <a href="https://zenodo.org/api/files/c576c330-0219-4710-a831-717762b117aa/table_ind.Rdata?versionId=a81c7bb4-0f36-44e3-82ea-86d9a2caac8e">table_ind.Rdata</a> </h4>A dataset of 494163 lines. In this dataset, one line correspond to one eel.<ul><li>altitudem: Altitude in meter</li><li>altitudem.: Altitude in meter truncated to 800 m</li><li>area_sudo: Area for recruitment (Drouineau et al., 2021)</li><li>basin: Name (or code from bd_carthage France) of the basin</li><li>codesea: Code of the Sea, factor A = Atlantic, M = Mediterranean</li><li>country: A factor (FR, SP, PT) for France, Spain, and Portugal</li><li>cs_height_08_n: Cumulated height from the sea, dam height transformed with power 0.8, no prediction for missing values.</li><li>cs_height_08_n.: Same variable but truncated to 300</li><li>cs_height_08_p: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values.</li><li>cs_height_08_p.: Same variable but truncated to 300</li><li>cs_height_08_pp: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li>cs_height_08_pps: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li>cs_height_10_FR: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from France are considered when building on a transnational water course.</li><li>cs_height_10_n: Cumulated height from the sea, no transformation, no prediction for missing values.</li><li>cs_height_10_n.: Same variable but truncated to 200</li><li>cs_height_10_p: Cumulated height from the sea, no transformation, missing height are extrapolated from two different models in France and the Iberian Peninsula</li><li>cs_height_10_p.: Same variable but truncated to 200</li><li>cs_height_10_pass0: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with pass are used to build the cumulated value</li><li>cs_height_10_pass1: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without pass are used to build the cumulated value</li><li>cs_height_10_pp: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li>cs_height_10_ppass0: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with pass are used to build the cumulated value</li><li>cs_height_10_ppass1: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without pass are used to build the cumulated value</li><li>cs_height_10_pps: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li>cs_height_10_pscore0: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li>cs_height_10_pscore1: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without score are used to build the cumulated value</li><li>cs_height_10_PT: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Portugal are considered when building on a transnational water course</li><li>cs_height_10_score0: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li>cs_height_10_score1: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without score are used to build the cumulated value</li><li>cs_height_10_SP: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Spain are considered when building on a transnational water course</li><li>cs_height_12_n: Cumulated height from the sea, dam height transformed with power 1.2, no prediction for missing values</li><li>cs_height_12_n.: Same variable but truncated to 500</li><li>cs_height_12_p: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values</li><li>cs_height_12_p.: Same variable but truncated to 500</li><li>cs_height_12_pp: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li>cs_height_12_pps: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li>cs_height_15_n: Cumulated height from the sea, dam height transformed with power 1.5, no prediction for missing values</li><li>cs_height_15_n.: Same variable but truncated to 800</li><li>cs_height_15_p: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values</li><li>cs_height_15_p.: Same variable but truncated to 800</li><li>cs_height_15_pp: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li>cs_height_15_pps: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li>cumheightdam: Cumulated height of dam (ignore)</li><li>cumnbdamp: Cumulated number of dam from the sea</li><li>cumnbdamso: Cumulated number of dam from the sea only for dams whose height is larger than zero</li><li>densCS: Density from Carle and Strub, number in second pass extrapolated from efficiency if only one pass.</li><li>dist_from_gibraltar_km: Distance to Gibraltar calculated using an enveloppe along the coastline (e.g. the estuaries ingress inland are not counted for this distance).</li><li>distanceseakm: Distance to the sea in kilometers</li><li>distanceseakm.: Distance to the sea, truncated at 500</li><li>distanceseam: Distance to the sea in meters</li><li>distancesourcem: Distance to the source in meters</li><li>ef_electrofishing_mean: Percentage of wetted surface downstream (cumwettedsurfacekm2) divided by land surface for all segments within the basin located at a same or lesser distance to the sea</li><li>ef_fished_length: Length of the fishing operation</li><li>ef_fished_width: Width of the fishing operation</li><li>ef_fishingmethod: Method of electrofishing</li><li>ef_nbpas: Number of pass in the electrofishing operation</li><li>ef_wetted_area: Surface of the electrofishing station</li><li>emu: Eel Management Unit</li><li>id: Comes from uncout R function, which transforms counts into lines</li><li>idsegment: Unique identifier of the segment [data type: UUID]. Use the <a href="https://doi.org/10.5281/zenodo.7546419">Atlas</a> to link with spatial table in PostgreSQL</li><li>isendoreic: Is the riversegment coming from an endoreic river?</li><li>issea: Is the riversegment a sea outlet?</li><li>laltitudem.: Log transformed value of altitude (truncated)</li><li>lcs_height_10_n.: Log transformed value of cumulated height</li><li>lengthm: Length of the electroshing station in meters</li><li>lengthriverm: length of the riversegment in meters</li><li>lriverwidthm.: Log transformed value of river width</li><li>medianflowm3ps: Median flow of the river in cubic meter per second</li><li>month: Month</li><li>name: Name of the river (Spain and Portugal)</li><li>nb_size_measured: Number of size measured in the electrofishing operation</li><li>nbp1: Number of eel collected in the first pass</li><li>nbp2: Number of eel collected in the second pass</li><li>nbp3: Number of eel collected in the third pass</li><li>NCS: Number estimated during the electrofishing operation by Carle and Strubb and interpolation of first pass efficiency if only one pass</li><li>nextdownidsegment: Code of the next downstream idsegment</li><li>Npred: Number of eel predicted on the riversegment pdeltagamma * watersurface</li><li>ob_dp_name: Data provider for the operation</li><li>ob_id: Operation (observation) identifier</li><li>ob_starting_date: Date of the operation</li><li>op_id: Station (observation place) identifier</li><li>pdelta: prediction of the delta (presence absence model)</li><li>pdeltagamma: pdelta*pgamma</li><li>pgamma: prediction of the gamma (positive densities model)</li><li>rdelta: residuals of the delta (presence absence model)</li><li>rdeltagamma: rdelta*rgamma</li><li>rgamma: residuals of the gamma (positive densities model)</li><li>riverwidthm: River width in meters</li><li>riverwidthm.: same variable as riverwidthm (no truncation for riverwidth)</li><li>rN: residuals in number (difference in number of eels between observed and predicted there are some NA where we had to get the data from "density" as there was no water surface available</li><li>seaidsegment: Identifier of the sea idsegment</li><li>sector: please ignore</li><li>shreeve: Shreve rank of the segment</li><li>size: Size class, "1 - <150", "2 - [150-300[", "3 - [300-450[", "4 - [450-600[", "5 - [600-750[", "6 - >=750"</li><li>strahler: Strahler rank of the segment</li><li>surfacebvkm2: surface of the watershed in m2</li><li>surfacebvm2: surface of the watershed in km3</li><li>surfaceunitbvm2: surface of the unit basin surrounding the segment in m2</li><li>temperature: Average temperature from 1960-2000 from the CCM (Vogt 2007)</li><li>temperaturejan: January temperature (France)</li><li>temperaturejul: July temperature (France)</li><li>totalnumber: Total number of eel caught during the operation</li><li>transport: transport area</li><li>wettedsurfacem2: Surface of water downstream from the segment in the river. Corresponds only to rivers</li><li>wettedsurfaceotherm2: Surface of water downstream from the segment in the river. Corresponds only waterbodies (water surfaces, associated with the segment).</li><li>year: Year of electrofishing operation</li></ul><h3>2.3. SILVER EEL DATA</h3><h4>Dataset <a href="https://zenodo.org/api/files/c576c330-0219-4710-a831-717762b117aa/silver_eel.Rdata?versionId=9df184ef-241d-46af-989e-2bddc0179df0">silver_eel_2020.Rdata</a></h4>A dataset of  20101 lines corresponding to yellow and silver eel along with their measurements for silvering for eel > 150 mm.This dataset has been built <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/eda2.3/report/report2.3/EDA_build.Rnw#L6454">here</a>, and validated <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/eda2.3/report/report2.3/EDA_build.Rnw#L6812">here</a> and <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/eda2.3/report/report2.3/EDA_build.Rnw#L8409">here</a>, For more information look at the <a href="https://sudoang.eu/wp-content/uploads/2022/02/E411_Briand_et_al_2022_EDA_report_opt-1.pdf">report</a> especially the annexes.The columns are:<ul><li>BL: Body length</li><li>Dv: Vertical eye diameter</li><li>FL: Pectoral fin length</li><li>MD: Mean eye diameter</li><li>W: Eel weight</li><li>contrast: Body contrast for the eel</li><li>Dh: Horizontal eye diameter</li><li>diam_max: Max of horizontal and vertical eye  diameter</li><li>diam_min: Min of horizontal and vertical eye  diameter</li><li>distance_foyer_c: sqrt(diam_max^2 - diam_min^2)</li><li>excentricite: Eye excentricity = distance_foyer_c / diam_max</li><li>IO: Occular index Pankhurst  = 100*((Dh+Dv/2)^2*pi/BL</li><li>K_ful: Fulton coefficient= 100*W/(BL/10)^3</li><li>maturite_durif: Durif (2009) maturity class</li><li>oc_surface: Occular surface</li><li>sexe_durif: Sex according to Durif (2009)</li><li>silver: Is it a silver eel, corresponds to one of "MII", "FIV", "FV" in Durif stage [data type: Boolean]</li><li>stade_pankhurst: Stage according to Pankhurst</li><li>stage_Durif: I, MII, FII, FIII, FIV, FV see Durif (2009)</li><li>pdeltagamma: Density predicted from the delta gamma model in EDA (Briand et al., 2022)</li><li>dist_from_gibraltar_km: Distance from Gibraltar along the coastline, negative in the Mediterranean</li><li>surfacebvm2: Surface of the watershed in m2</li><li>altitudem.: Altitude in m truncated at 400</li><li>temperature: Average temperature from 1960-2000 from the CCM (Vogt, 2007)</li><li>distanceseakm.: Distance to the sea in km truncated at 500</li><li>strahler: Strahler order of the stream</li><li>month2: Month of electrofishing with values grouped for <=7 or >=10</li><li>month: Month of electrofishing</li><li>year: Year of electrofishing</li></ul><h2>3. VERSIONS</h2><ul><li><a href="https://doi.org/10.5281/zenodo.6023561">10.5281/zenodo.6023561 </a>1.0.0 - 2022-02-09 - Initial upload (open access)</li><li><a href="https://doi.org/10.5281/zenodo.6397009">10.5281/zenodo.6397009 </a>1.0.1 - 2023-08-02 - Fixed description (open access)</li></ul><h2>4. READ MORE</h2><ul><li>Atlas of European Eel Distribution (Anguilla anguilla) in Portugal, Spain and France (<a href="https://doi.org/10.5281/zenodo.7546419">10.5281/zenodo.7546419</a>)</li><li>Electrofishing data for eel in the Iberian Peninsula (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.8348353">10.5281/zenodo.8348353</a>)</li><li>Cumulated dam impact in France and the Iberian Peninsula (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.8348374">10.5281/zenodo.8348374</a>)</li></ul><h2>5. FUNDING</h2>Project co-financed by the INTERREG SUDOE Programme through the European Regional Development Fund (ERDF).

opencc-by-4.0Apr 2022View details →

Raw Sensor Data for STRIDE Project J "Improving Work Zone Mobility through Planning, Design and Operations"

This dataset contains the raw traffic data from 9 sensors located on I-59 southbound near Tuscaloosa, Alabama, from October 3 to October 16, 2016.  These data were used in the research for STRIDE Project J "Improving Work Zone Mobility through Planning, Design and Operations" and described in the STRIDE Final Project for this project.

opencc-by-4.0Aug 2021View details →

Deep Reinforcement Learning-based Project Prioritization for Rapid Post-Disaster Recovery of Transportation Infrastructure Systems

Among various natural hazards that threaten transportation infrastructure, flooding represents a major hazard in Region 6's states to roadways as it challenges their design, operation, efficiency, and safety. The catastrophic flooding disaster event generally leads to massive obstruction of traffic, direct damage to highway/bridge structures/pavement, and indirect damages to economic activities and regional communities that may cause loss of many lives. After disasters strike, reconstruction and maintenance of an enormous number of damaged transportation infrastructure systems require each DOT to take extremely expensive and long-term processes. In addition, planning and organizing post-disaster reconstruction and maintenance projects of transportation infrastructures are extremely challenging for each DOT because they entail a massive number and the broad areas of the projects with various considerable factors and multi-objective issues including social, economic, political, and technical factors. Yet, amazingly, a comprehensive, integrated, data-driven approach for organizing and prioritizing post-disaster transportation reconstruction projects remains elusive. In addition, DOTs in Region 6 still need to improve the current practice and systems to robustly identify and accurately predict the detailed factors and their impacts affecting post-disaster transportation recovery. The main objective of this proposed research is to develop a deep reinforcement learning-based project prioritization system for rapid post-disaster reconstruction and recovery of damaged transportation infrastructure systems. This project also aims to provide a means to facilitate the systematic optimization and prioritization of the post-disaster reconstruction and maintenance plan of transportation infrastructure by focusing on social, economic, and technical aspects. The outcomes from this project would help engineers and decision-makers in Region 6's State DOTs optimize and sequence transportation recovery processes at a regional network level with necessary recovery factors and evaluating its long-term impacts after disasters.

opencc-by-4.0Mar 2021View details →

SOON Project presentation at CHIST-ERA Projects Seminar 2021 (April 12-14 2021)

Short video presentation proposed for CHIST-ERA Projects Seminar 2021 Video contest event - April 12-14 2021.

dryad36/100

Evolution of thermal physiology alters the projected range of threespine stickleback under climate change

Species distribution models (SDMs) are widely used to predict range shifts but could be unreliable under climate change scenarios because they do not account for evolution. The thermal physiology of a species is a key determinant of range and thus incorporating thermal trait evolution into SDMs might be expected to alter projected ranges. We identified a genetic basis for physiological and behavioural traits that evolve in response to temperature change in natural populations of threespine stickleback (Gasterosteus aculeatus). Using these data, we created geographic range projections using a mechanistic niche envelope approach under two climate change scenarios. Under both scenarios, trait data was either static ('no evolution' model), allowed to evolve at observed evolutionary rates for the trait ('evolution' model), or allowed to evolve at a rate of evolution scaled in association with the variance that is explained by quantitative trait loci (QTL; 'scaled evolution' models). We show that incorporating these traits and their evolution substantially altered the projected ranges for a widespread panmictic marine population, with over 7-fold increases in area under climate change projections when traits are allowed to evolve. Evolution-informed SDMs should improve the precision of forecasting range dynamics under climate change, and aid in their application to management and the protection of biodiversity.

opencc-zeroApr 2022View details →

LibOCS project promotional video

LibOCS project promotional video.  Project entitled: “University libraries strengthening the academia-society connection through citizen science in the Baltics” under the Erasmus+ Programme, Key Action 2: Partnerships for Cooperation. PROJECT NUMBER 2021-1-EE01-KA220-HED-000031125 AGREEMENT NUMBER – 2021-KA220-HED-01

Project files provided as supporting information to the manuscript "Information-theoretical measures identify accurate low-resolution representations of protein configurational space"

The dataset contains the following compressed folder: -Notebooks.zip: This folder contains:   -python_script:         -RESREL.py: script performing the clusterization and computing the relevance resolution curves         -random_curves.py: script generating the random value and computing the corresponding RES-REV curves_s         -Cluster_distance_matrix.py: script returning the distance among clusters for a given partition.   -python_notebook:         -Exploratory_analysis.ipynb:  Analysis performed on the 12-protein_dataset         -DMAPS_ANTI.ipynb: Diffusion Map for the Antibody         -DMAPS_COV_1ake.ipynb: Diffusion Map + Inter-Intra state decomposition of covariance for 1ake Packages required for the usage of these python scripts/notebooks:   -numpy   -pandas   -matplotlib   -seaborn   -multiprocessing   -scipy   ======== RAW DATA ======== The raw data produced and employed in this study are available on a Google Drive folder at the following address: https://drive.google.com/drive/folders/1PasAUCgpR5-gdzUVEdyusgZIayQN0Le9 In this folder, together with the compressed Notebooks.zip folder, one can fin the compressed folder Data.zip, within which the following data are present: -12-protein_dataset:     -md.mdp: the .mdp file used in the MD simulations     -PROTEIN_PDB_CODE:         -Hk_{sel}.npy & Hs_{sel}.npy: the Rel & Res curves, sel=[all, CA, CB]         -RMSD_{sel}.npy: the RMSD matrix, sel=[all, CA, CB]         -npt.gro:protein+water+ions structure @TEO the equilibration (NVT+NPT)         -MSR_df.csv: a dataset containing the following columns                   'area' : area behind the Relevance-Resolution curve;                   'selection': the atomic selection (['all', 'CA', 'CB']) used to compute the RMSD matrix used for the clusterization (and consequently the Relevance-Resolution curves)                   'method': the linkage measure used in the clustering procedure, an integer in [0,6];                   'method_name': the linkage measure used in the clustering procedure, a string in ['average','ward','complete','single','centroid','median','weighted'];                   'rmsd_mean': the mean value of the rmsd vector along the trajectory computed wrt the first frame;                   'rmsd_var': the variance of the rmsd vector along the trajectory computed wrt the first frame;                   'rgy_mean': the mean value of the radius of gyration  along the trajectory;                   'rgy_var': the variance of the radius of gyration  along the trajectory;                   'rmsf_mean': the mean value of the rmsf;                   'rmsf_var': the variance of the rmsf;                   'RMSD_M_mean': the mean value of the RMSD matrix.                   'RMSD_M_var': the variance of the RMSD matrix.   -Random:     -curves.npy= 100K Relevance-Resolution Random curves for M=40001     -curves_s.npy= 100K Relevance-Resolution Random curves for M=15000   -validation_dataset:     -antibody:         -Hk_CB.npy & Hs_CB.npy: the Rel & Res curves         -RMSD_CB.npy: the RMSD matrix         -DIFF_{M}.npy: the eigenvalue/vector of the 10-D diffusion space         -Label_{method}.npy: the label vector for n_clusters     -1ake:         -Hk_{sel}.npy & Hs_{sel}.npy: the Rel & Res curves         -RMSD_{sel}.npy: the RMSD matrix         -DIFF_{M}.npy: the eigenvalue/vector of the 10-D diffusion space         -Label_{method}.npy: the label vector for n_clusters         -intra_{m}.npy: the intra-cluster covariance matrix         -inter_cov_{m}.npy: the inter-cluster correlation matrix   NOTE ===== The matrices of the cluster distances for adenylate kinase and antibody have been computed through the script Cluster_distance_matrix.py. These matrices have not been included in the dataset because of their large size; the raw data are however available upon request.

DATA OF THE TELEPSY-COVID19 PROJECT

The data were collected and processed during the COVID pandemic in the framework of a free psychological teleconsulting called TELEPSY-COVID19 made available by SIMP Italian Society of Psychosomatic Medicine.

South-East US historical and projected population per county for FUTURES urban growth modeling in GRASS GIS

South East US historical (2001-2019) and projected (2020-2100) population per county for 6 states (NC, SC, TN, GA, AL, FL). Historical data come from The National Vital Statistics System (https://seer.cancer.gov/popdata/download.html) and future data are projected by Hauer 2019 (https://doi.org/10.1038/sdata.2019.5) for SSP2 scenario. Data are formatted for r.futures.demand module, which is a GRASS GIS addon for computing future land demand for FUTURES urban growth model.