Find research datasets worth reusing

SuperDARN data in netCDF format (1994-Feb)

1994-Feb SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN data in netCDF format (1994-Dec)

1994-Dec SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN data in netCDF format (1994-Jan)

1994-Jan SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN data in netCDF format (1994-Jul)

1994-Jul SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN data in netCDF format (1994-Mar)

1994-Mar SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN data in netCDF format (1993-Oct)

1993-Oct SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN data in netCDF format (1993-Dec)

1993-Dec SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN data in netCDF format (1994-Jun)

1994-Jun SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

Shoreline data at 30-m spatial resolution for regions of the USA, in geoJSON format. Region 7: Alaska

Data file: USA_Alaska_ref_shoreline.geojson Region: Alaska Data fields: <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk The data are described in the following publication Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a>

Dataset for A Revision of the Formation Conditions of the Vredefort Crater.

Dataset associated with Allen et al. 2022 - A Revision of the Formation Conditions of the Vredefort Crater. Contained are all of our input asteroid.inp files, along with their associated material.inp file (the same of which was used for all of the simulations), the full jdata.dat file for our Model A, and a movie of our three discussed models over the full time of the simulation. All simulations were created using iSALE (https://isale-code.github.io/), and some python code showing potential visualization techniques for the data can be found at https://github.com/natalieallen/iSALE_scripts.

Supporting material for "Impact of gender on the formation and outcome of formal mentoring relationships in the life sciences"

This repository contains data and analysis code associated with the manuscript: L.P. Schwartz, J. Liénard, S. V. David. (2022) "Impact of gender on formation and outcome of formal mentoring relationships in the life sciences." Figures and tables in the manuscript can be produced by running the make_figures.ipynb notebook. Figures have been marked with headings indicating their position in the manuscript (Figure 1, Figure S1, etc.). In addition, the notebook contains code to reproduce regression analyses that are cited in the text but not directly associated with a figure. Data on mentoring relationships derives from Academic Family Tree (AFT, www.academictree.org) and public data sources on funding, publications, and awards. Inclusion criteria, public data sources, and procedures for linking across sources are described in the manuscript.  Personal identifiers for researchers have been anonymized, but remain consistent across all data in the repository. In other words, the personal identifier "1" refers to the same person in all dataframes in the repository. But, that person is *not* the same researcher identified as "1" on the public AFT website. Installation Requires Python 3.x. and Pandas. To load required libraries using Anaconda, run: `conda create --name aft -c conda-forge pandas numpy scipy ipython jupyterlab scipy scikit-learn pandas matplotlib numpy statsmodels seaborn pytables` Dataframes Data is stored as a series of Pandas dataframes within HDF5 or CSV files: * cng_tc: The primary dataset used in the analysis. The name is an acronym for "connections" (i.e. training relationships, "cn"), "gender" ("g"), and "trainee count" ("tc"). Each row contains data on the mentor and trainee in one training relationship. See manuscript for inclusion criteria. * mentors: Data on mentors. Each row contains data on one mentor. See manunscript for inclusion criteria. * mentors_grants, mentors_hindex, mentors_locs_ranked: Subset of mentors with data available for funding (mentors_grants), citation (mentors_hindex), and institution rank (mentors_locs_ranked). * mentors_nobel, mentors_hhmi, mentors_nas: Subsets of mentors that received a Nobel (mentors_nobel), Howard Hughes Medical Institute grants (mentors_hhmi), or membership in the National Academy of Sciences (mentors_nas). See manuscript for details of data sources and linking procedures. * cn, cng, first_names, gn, gn_all, locs: Partial data (connections only, inferred gender only, connections and gender only, location only, first names and inferred gender only) for more inclusive sets of researchers in AFT. They are generally not used used for analysis, but have been included here to calculate statistics on the total amount of data included and to screen for data from U.S. locations. * nsf_gender_phds, nsf_gender_pds: National Science Foundation survey data on gender and fraction PhDs conferred per year (nsf_gender_phds) or fraction postdocs employed per year (nsf_gender_pds). See manuscript for details of data source. * photo: Data for validation of gender inference method. Dataframe columns * amount: Mentor's total funding * amount_adj: Mentor's total funding (adjusted to 2020 dollars) * broad_field: Mentor's general research area (e.g., life sciences, engineering, based on National Science Foundation classifications) * continue: Whether trainee went on to become a mentor (i.e., has trainees listed in AFT) * country: Country in which mentor's current institution is located * firstname: First name of researcher (table of first names is not aligned with tables containing anonymized personal identifiers) * first_grant_year: Year of mentor's first grant * funding_rate: Mentor's annual funding rate (since first grant) * funding_rate_adj: Mentor's annual funding rate (since first grant) adjusted to 2020 dollars * hhmi: Whether mentor was granted HHMI funding * hindex: Mentor's hindex * location: Name of mentor's current institution * locid: Identifier for mentor's institution * locid_rank: Postion of mentor's institution in 2015 Quacquarelli-Symonds rankings (lower numbers are better) * locid_rank_rev: Reversed version of "locid_rank" (i.e., higher numbers are better) * majorarea: Mentor's specific research area (e.g, neuroscience) * male_mentor, male trainee: Whether the probability that a researcher's first name is used by a person identifying as a man meets threshold (see manuscript for details on gender inference using first names) * match_score: Score for string match between institution or name of awardee and researcher * mentor_career_start: The date at which the mentor's academic career began * mentor_continue_rate: Fraction of mentor's trainees that become mentors * mentor_continue_rate_ft: Fraction of mentor's woman trainees that become mentors * mentor_continue_rate_mt: Fraction of mentor's man trainees that become mentors * mentor_t_p_male0: Fraction of mentor's trainees that are men * mentor_t_p_male0_gs: Fraction of mentor's trainees that are men (graduate students only) * mentor_t_p_male0_pd: Fraction of mentor's trainees that are men (postdocs only) * mentor_tcount0: Mentor's total number of trainees * nas: Whether mentor is a member of the National Academy of Sciences * nobel: Whether mentor is a Nobel laureate * p_male_mentor, p_male_trainee: Probability that a researcher's first name is used by a person identifying as a man * pid: Anonymized identifier of researcher * pid_mentor: Anonymized identifier of mentor in training relationship * pid_trainee: Anonymized identifier of trainee in training relationship * pq: "1" if data on training relationship is drawn from ProQuest database and has not been manually edited a human AFT user * relation: Type of training relationship (1: graduate student, 2: postdoc) * scorer1, scorer2, scorer3: Results of photo validation of gender inference for each scorer * start: Training start year * stop: Training end year * trainee_tcount: Total people that the trainee has trained * triad: Whether trainee has participated in both a graduate-level and postdoctoral training relationship The cn dataframe follows slightly different naming conventions, but is not generally used in the analysis (pid1 = pid_trainee, pid2 = pid_mentor, startdate = start, stopdate = stop).

opencc-by-4.0Jun 2022View details →

Dataset for Effect of matrix solidification on the structure formation in electromagnetic suspensions

Introduction: Dataset from numerical simulations describing the structure formation in suspensions with electromagnetically active particles in an external field, as described in detail in the paper <ul> <li>Konstantinos Manikas, Markus Hütter, Patrick D. Anderson: Effect of matrix solidification on the structure formation in electromagnetic suspensions. Appl. Phys. A, 128: 709 (11 pages), 2022. DOI: 10.1007/s00339-022-05844-y  WWW: https://doi.org/10.1007/s00339-022-05844-y</li> </ul> which should be cited whenever this dataset is used. The data compiled here is the basis for figures 4, 5, and 6 in that paper.   Format: The files are provided in plain-text format (ascii).   Filenames: The nomenclature for the filenames follows the following scheme: <ul> <li>data_fig{number}_{param}_{value}.txt</li> </ul> where {number} specifies the number of the figure in the paper, {param} is the parameter that is studied and {value} denotes its numerical value (see the paper for details).   File content: Each datafile contains 1 header line, in which the meaning of the data-columns are explained (time, S2, N*, lambda*  --  see paper for details), and the studied parameter and its value are listed. After this header line, the data is presented in tab-delimited columns.   For further details, the reader is referred to the paper mentioned above.

Geoelectrical and hydro-chemical monitoring of karst formation at the laboratory scale

We propose a dataset of two Estaillades limestone core samples subjected to acid percolation at two different rates. We monitored: <ul> <li>Permeability from high-frequency pressure gradient acquisition. Facing noise, we filtered this permeability with a Savitsky-Golay filter. Then, we resampled permeability values to compare them at specific times when we performed other measurements.</li> <li>pH at the inlet and outlet.</li> <li>Calcium concentration at the outlet.</li> <li>Porosity was initially measured with petrophysical measurements and then calculated from calcium concentration during percolation experiments. We resampled the values to compare them at specific times when we performed other measurements.</li> <li>Water electrical conductivity at the inlet and outlet.</li> <li>Rock sample electrical conductivity, from which we calculated the formation factor. Rock sample electrical conductivity was high-frequency acquired. We thus resampled the values to compare them at specific times when we performed other measurements.</li> </ul> We also present the pore size distribution of both samples before and after the percolation experiments. The distribution comes from the mean chord lengths distribution generated from tomography imaging. In addition to this dataset, we display a picture of the mesh of the sample and the electrodes generated with EIDORS software and with which we computed the geometric factor.

Shoreline data at 30-m spatial resolution for 2001 coastal provinces or regions of the world, in geoJSON format.

Region: 2001 coastal provinces or regions of the world Data fields: <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk The data are described in the following publication Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a> Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): province_files_bounds.json

Replication package for "Teams and Individuals in Standard Auction Formats: Decision and Emotions"

This is the replication package for the paper "Teams and Individuals in Standard Auction Formats: Decision and Emotions" by Maria Karmeliuk, Martin Kocher and Georg Schmidt, forthcoming in Experimental Economics. The package contains instructions and zTree code for running the laboratory experiment, as well as raw data from the experiment including the data from face recognition software FaceReader. It also contains Stata and R code for data cleaning, cleaned datasets and Stata code for replicating data analysis and statistics reported in the paper.

opencc-by-4.0Aug 2022View details →

Structure of MAPbI3 surface models in VASP format

Structure of MAPbI3 surface models in VASP format, with the slab thickness of 6 stoichiometric units, along (001) planes. These models were generated for the study published in reference 1. (1) Lodeiro, L.; Barría-Cáceres, F.; Jiménez, K.; Contreras, R.; Montero-Alejo, A. L.; Menéndez-Proupin, E. Methodological Issues in First-Principle Calculations of CH3NH3PbI3 Perovskite Surfaces: Quantum Confinement and Thermal Motion. ACS Omega 2020, 5 (45), 29477–29491. https://doi.org/10.1021/acsomega.0c04420.

opencc-by-4.0Jun 2022View details →

Dataset for "A comprehensive model for pickup ion formation at the Moon"

Dataset for "A comprehensive model for pickup ion formation at the Moon", submitted to J. Geophys. Res.: Planets for publication. The dataset consists of in-situ measurements of ion fluxes and densities around the Moon as observed by NASA's ARTEMIS mission, as described in the manuscript.

opencc-by-4.0Aug 2022View details →

4 image cytochrome dataset with per-shot spectra recorded on the Jungfrau 16M detector at SwissFEL in NeXus format

Cytochrome data recorded in 2019 on the Jungfrau 16M detector recorded at SwissFEL including per-shot spectra measure each SASE pulse. First a NeXus master file was created using scripts in cctbx.xfel: libtbx.python `libtbx.find_in_repositories xfel`/swissfel/jf16m_cxigeom2nexus.py unassembled_file=run_000795.JF07T32V01.h5 geom_file=16M_bernina_backview_optimized_adu_quads.geom wavelength=1.3038 detector_distance=163.9 output_file=./run_000795.JF07T32V01_master_spectrum.h5 beam_file=run_000795.BSREAD.h5 include_spectra=True Then it was sliced down to 4 images that successfully index using the attached script, sliceit.py The file can be processed by DIALS using this command: dials.stills_process run_000795.JF07T32V01_master_spectrum_4img.h5 indexing.phil  Where the indexing phil refers to the attached mask and refined geometry file, both produced by DIALS.

Shoreline data at 30-m spatial resolution for 298 coastal counties of the conterminous USA, in geoJSON format.

Region: 298 coastal counties of the conterminous USA Data fields: <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk The data are described in the following publication Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a> Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): files_bounds.json Shoreline data files: us_county0_ref_shoreline.geojson us_county1036_ref_shoreline.geojson us_county111_ref_shoreline.geojson us_county112_ref_shoreline.geojson us_county113_ref_shoreline.geojson us_county1148_ref_shoreline.geojson us_county1149_ref_shoreline.geojson us_county114_ref_shoreline.geojson us_county115_ref_shoreline.geojson us_county116_ref_shoreline.geojson us_county117_ref_shoreline.geojson us_county118_ref_shoreline.geojson us_county1195_ref_shoreline.geojson us_county119_ref_shoreline.geojson us_county1200_ref_shoreline.geojson us_county120_ref_shoreline.geojson us_county121_ref_shoreline.geojson us_county122_ref_shoreline.geojson us_county123_ref_shoreline.geojson us_county1246_ref_shoreline.geojson us_county124_ref_shoreline.geojson us_county125_ref_shoreline.geojson us_county126_ref_shoreline.geojson us_county1272_ref_shoreline.geojson us_county1273_ref_shoreline.geojson us_county1277_ref_shoreline.geojson us_county127_ref_shoreline.geojson us_county128_ref_shoreline.geojson us_county129_ref_shoreline.geojson us_county1302_ref_shoreline.geojson us_county1304_ref_shoreline.geojson us_county1305_ref_shoreline.geojson us_county1306_ref_shoreline.geojson us_county130_ref_shoreline.geojson us_county1317_ref_shoreline.geojson us_county131_ref_shoreline.geojson us_county1324_ref_shoreline.geojson us_county132_ref_shoreline.geojson us_county133_ref_shoreline.geojson us_county134_ref_shoreline.geojson us_county135_ref_shoreline.geojson us_county136_ref_shoreline.geojson us_county137_ref_shoreline.geojson us_county138_ref_shoreline.geojson us_county139_ref_shoreline.geojson us_county140_ref_shoreline.geojson us_county141_ref_shoreline.geojson us_county142_ref_shoreline.geojson us_county143_ref_shoreline.geojson us_county144_ref_shoreline.geojson us_county145_ref_shoreline.geojson us_county146_ref_shoreline.geojson us_county147_ref_shoreline.geojson us_county148_ref_shoreline.geojson us_county149_ref_shoreline.geojson us_county150_ref_shoreline.geojson us_county151_ref_shoreline.geojson us_county152_ref_shoreline.geojson us_county153_ref_shoreline.geojson us_county154_ref_shoreline.geojson us_county155_ref_shoreline.geojson us_county156_ref_shoreline.geojson us_county157_ref_shoreline.geojson us_county1585_ref_shoreline.geojson us_county158_ref_shoreline.geojson us_county1596_ref_shoreline.geojson us_county159_ref_shoreline.geojson us_county1605_ref_shoreline.geojson us_county160_ref_shoreline.geojson us_county161_ref_shoreline.geojson us_county162_ref_shoreline.geojson us_county163_ref_shoreline.geojson us_county1649_ref_shoreline.geojson us_county164_ref_shoreline.geojson us_county1650_ref_shoreline.geojson us_county1651_ref_shoreline.geojson us_county1653_ref_shoreline.geojson us_county1655_ref_shoreline.geojson us_county1658_ref_shoreline.geojson us_county165_ref_shoreline.geojson us_county1667_ref_shoreline.geojson us_county166_ref_shoreline.geojson us_county167_ref_shoreline.geojson us_county168_ref_shoreline.geojson us_county169_ref_shoreline.geojson us_county170_ref_shoreline.geojson us_county171_ref_shoreline.geojson us_county172_ref_shoreline.geojson us_county173_ref_shoreline.geojson us_county174_ref_shoreline.geojson us_county175_ref_shoreline.geojson us_county176_ref_shoreline.geojson us_county177_ref_shoreline.geojson us_county178_ref_shoreline.geojson us_county179_ref_shoreline.geojson us_county180_ref_shoreline.geojson us_county181_ref_shoreline.geojson us_county182_ref_shoreline.geojson us_county183_ref_shoreline.geojson us_county184_ref_shoreline.geojson us_county185_ref_shoreline.geojson us_county186_ref_shoreline.geojson us_county187_ref_shoreline.geojson us_county188_ref_shoreline.geojson us_county189_ref_shoreline.geojson us_county190_ref_shoreline.geojson us_county191_ref_shoreline.geojson us_county192_ref_shoreline.geojson us_county193_ref_shoreline.geojson us_county194_ref_shoreline.geojson us_county195_ref_shoreline.geojson us_county196_ref_shoreline.geojson us_county197_ref_shoreline.geojson us_county198_ref_shoreline.geojson us_county199_ref_shoreline.geojson us_county200_ref_shoreline.geojson us_county201_ref_shoreline.geojson us_county202_ref_shoreline.geojson us_county203_ref_shoreline.geojson us_county204_ref_shoreline.geojson us_county205_ref_shoreline.geojson us_county206_ref_shoreline.geojson us_county207_ref_shoreline.geojson us_county208_ref_shoreline.geojson us_county209_ref_shoreline.geojson us_county210_ref_shoreline.geojson us_county2118_ref_shoreline.geojson us_county211_ref_shoreline.geojson us_county2125_ref_shoreline.geojson us_county2127_ref_shoreline.geojson us_county2128_ref_shoreline.geojson us_county212_ref_shoreline.geojson us_county2130_ref_shoreline.geojson us_county2133_ref_shoreline.geojson us_county213_ref_shoreline.geojson us_county2148_ref_shoreline.geojson us_county2149_ref_shoreline.geojson us_county214_ref_shoreline.geojson us_county215_ref_shoreline.geojson us_county216_ref_shoreline.geojson us_county217_ref_shoreline.geojson us_county218_ref_shoreline.geojson us_county219_ref_shoreline.geojson us_county220_ref_shoreline.geojson us_county221_ref_shoreline.geojson us_county222_ref_shoreline.geojson us_county223_ref_shoreline.geojson us_county224_ref_shoreline.geojson us_county225_ref_shoreline.geojson us_county226_ref_shoreline.geojson us_county227_ref_shoreline.geojson us_county228_ref_shoreline.geojson us_county229_ref_shoreline.geojson us_county230_ref_shoreline.geojson us_county231_ref_shoreline.geojson us_county232_ref_shoreline.geojson us_county233_ref_shoreline.geojson us_county234_ref_shoreline.geojson us_county235_ref_shoreline.geojson us_county236_ref_shoreline.geojson us_county237_ref_shoreline.geojson us_county238_ref_shoreline.geojson us_county239_ref_shoreline.geojson us_county240_ref_shoreline.geojson us_county241_ref_shoreline.geojson us_county242_ref_shoreline.geojson us_county243_ref_shoreline.geojson us_county244_ref_shoreline.geojson us_county245_ref_shoreline.geojson us_county246_ref_shoreline.geojson us_county2478_ref_shoreline.geojson us_county247_ref_shoreline.geojson us_county248_ref_shoreline.geojson us_county2493_ref_shoreline.geojson us_county249_ref_shoreline.geojson us_county250_ref_shoreline.geojson us_county2517_ref_shoreline.geojson us_county2518_ref_shoreline.geojson us_county251_ref_shoreline.geojson us_county252_ref_shoreline.geojson us_county2532_ref_shoreline.geojson us_county253_ref_shoreline.geojson us_county254_ref_shoreline.geojson us_county255_ref_shoreline.geojson us_county256_ref_shoreline.geojson us_county257_ref_shoreline.geojson us_county258_ref_shoreline.geojson us_county259_ref_shoreline.geojson us_county260_ref_shoreline.geojson us_county261_ref_shoreline.geojson us_county262_ref_shoreline.geojson us_county263_ref_shoreline.geojson us_county264_ref_shoreline.geojson us_county265_ref_shoreline.geojson us_county266_ref_shoreline.geojson us_county267_ref_shoreline.geojson us_county2688_ref_shoreline.geojson us_county268_ref_shoreline.geojson us_county269_ref_shoreline.geojson us_county272_ref_shoreline.geojson us_county2749_ref_shoreline.geojson us_county275_ref_shoreline.geojson us_county276_ref_shoreline.geojson us_county277_ref_shoreline.geojson us_county278_ref_shoreline.geojson us_county279_ref_shoreline.geojson us_county280_ref_shoreline.geojson us_county281_ref_shoreline.geojson us_county282_ref_shoreline.geojson us_county283_ref_shoreline.geojson us_county284_ref_shoreline.geojson us_county285_ref_shoreline.geojson us_county286_ref_shoreline.geojson us_county287_ref_shoreline.geojson us_county288_ref_shoreline.geojson us_county289_ref_shoreline.geojson us_county290_ref_shoreline.geojson us_county291_ref_shoreline.geojson us_county292_ref_shoreline.geojson us_county293_ref_shoreline.geojson us_county294_ref_shoreline.geojson us_county295_ref_shoreline.geojson us_county296_ref_shoreline.geojson us_county297_ref_shoreline.geojson us_county298_ref_shoreline.geojson us_county299_ref_shoreline.geojson us_county300_ref_shoreline.geojson us_county301_ref_shoreline.geojson us_county302_ref_shoreline.geojson us_county303_ref_shoreline.geojson us_county304_ref_shoreline.geojson us_county305_ref_shoreline.geojson us_county3069_ref_shoreline.geojson us_county306_ref_shoreline.geojson us_county307_ref_shoreline.geojson us_county308_ref_shoreline.geojson us_county309_ref_shoreline.geojson us_county310_ref_shoreline.geojson us_county311_ref_shoreline.geojson us_county312_ref_shoreline.geojson us_county313_ref_shoreline.geojson us_county314_ref_shoreline.geojson us_county315_ref_shoreline.geojson us_county316_ref_shoreline.geojson us_county317_ref_shoreline.geojson us_county318_ref_shoreline.geojson us_county319_ref_shoreline.geojson us_county320_ref_shoreline.geojson us_county321_ref_shoreline.geojson us_county322_ref_shoreline.geojson us_county323_ref_shoreline.geojson us_county324_ref_shoreline.geojson us_county325_ref_shoreline.geojson us_county326_ref_shoreline.geojson us_county327_ref_shoreline.geojson us_county328_ref_shoreline.geojson us_county329_ref_shoreline.geojson us_county330_ref_shoreline.geojson us_county331_ref_shoreline.geojson us_county332_ref_shoreline.geojson us_county333_ref_shoreline.geojson us_county334_ref_shoreline.geojson us_county335_ref_shoreline.geojson us_county336_ref_shoreline.geojson us_county337_ref_shoreline.geojson us_county338_ref_shoreline.geojson us_county339_ref_shoreline.geojson us_county340_ref_shoreline.geojson us_county341_ref_shoreline.geojson us_county342_ref_shoreline.geojson us_county343_ref_shoreline.geojson us_county344_ref_shoreline.geojson us_county345_ref_shoreline.geojson us_county346_ref_shoreline.geojson us_county347_ref_shoreline.geojson us_county348_ref_shoreline.geojson us_county349_ref_shoreline.geojson us_county350_ref_shoreline.geojson us_county351_ref_shoreline.geojson us_county352_ref_shoreline.geojson us_county353_ref_shoreline.geojson us_county354_ref_shoreline.geojson us_county416_ref_shoreline.geojson us_county417_ref_shoreline.geojson us_county418_ref_shoreline.geojson us_county419_ref_shoreline.geojson us_county420_ref_shoreline.geojson us_county421_ref_shoreline.geojson us_county422_ref_shoreline.geojson us_county423_ref_shoreline.geojson us_county685_ref_shoreline.geojson us_county686_ref_shoreline.geojson us_county700_ref_shoreline.geojson us_county73_ref_shoreline.geojson us_county74_ref_shoreline.geojson us_county754_ref_shoreline.geojson us_county79_ref_shoreline.geojson us_county97_ref_shoreline.geojson