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3,422 results for “united states”
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - United Kingdom (Northern Ireland)
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund</li> </ul> <p>Disclaimer: In accordance with the Agreement on the Withdrawal of the United Kingdom from the EU, and in particular with the Protocol on IE/NI, the EU requirements on data sampling are also applicable to Northern Ireland.</p>
A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications
<p>This database is related to "A CONSOLIDATED DATABASE OF POLICE-REPORTED MOTOR VEHICLE TRAFFIC ACCIDENTS IN THE UNITED STATES FOR ACTUARIAL APPLICATIONS" (Araiza Iturria C.A., Hardy M., Marriott P.).</p> <p>Author Information</p> <p> A. Author<br> Name: Carlos Andrés Araiza Iturria<br> Email: caraizai@uwaterloo.ca<br> <br> B. Co-author<br> Name: Mary Hardy<br> Email: mary.hardy@uwaterloo.ca</p> <p> C. Co-author<br> Name: Paul Marriott<br> Email: pmarriott@uwaterloo.ca<br> <br> Institution: University of Waterloo<br> Address: 200 University Ave W, Waterloo, ON N2L 3G1</p> <p><br> Funding granted by the Natural Sciences and Engineering Research Council of Canada. Hardy: RGPIN-2018-03754, Marriott: RGPIN-2020-04015.</p> <p>The Python scripts to create the database can be directly accessed through related identifiers in this page.</p> <p>Parameter estimates along with their 90% confidence intervals from the 20 multinomial logistic regressions can be seen through related identifiers in this page.</p> <p> </p>
Supporting Online Data for 'Timber trade in the United States of America 1870 to 2017. A socio-metabolic analysis'
<p>This data file (.xlsx) contains all data used to create tables and figures of the study "Timber trade in the United States of America 1870 to 2017. A socio-metabolic analysis". Main article is available under: https://doi.org/10.1080/01615440.2024.2316039</p>
Electric power outages from 900k simulated hurricanes in a changing climate, for the United States and Puerto Rico
<p>This dataset is described and explored in Rice et al. 2025, "<a href="https://doi.org/10.1088/1748-9326/adad85">Projected Increases in Tropical Cyclone-induced U.S. Electric Power Outage Risk</a>", published in Environmental Research Letters.</p> <p>This dataset collects peak outage levels modeled for 900,000 synthetic tropical cyclones (TCs; also commonly known as hurricanes) representative of a modeled historical (1980-2015) and future (2066-2100) period under SSP5-8.5 warming. Synthetic TCs are generated with the Risk Analysis Framework for Tropical Cyclones (RAFT; see Xu et al. 2024 and Balaguru et al. 2023), forced by climate simulation data from the Coupled Model Intercomparison Project phase 6 (CMIP6; see Eyring et al. 2016). Outages are modeled with the newly introduced Electric Power Outages from Cyclone Hazards (EPOCH) model, which was trained on county-level outage data from 23 historical TC events in the EAGLE-I dataset (Brelsford et al. 2024). </p> <p>The EPOCH model predicts outages based on county population and the maximum wind speed and rainfall rate experienced during the TC. Predicted outage levels are provided in the form of peak outage fraction: the maximum fraction of electricity customers expected to experience an outage at any one time during the storm's lifetime. Although we do not model outage duration, other research suggests peak outage level is strongly correlated with duration (Jamal and Hasan, 2023).</p> <p><strong>Data Format</strong></p> <p>The data is provided in NetCDF4 files, one for each CMIP6 model and time period. Each NetCDF4 files has the following:</p> <p>Dimensions:</p> <ul> <li>ncounties = 2715. The counties in the study domain</li> <li>ntracks = 50000. The number of storms</li> </ul> <p>Variables:</p> <ul> <li>int pseudofips(ncounties). The FIPS code for each county. Puerto Rico data is not available at county level, but instead for six utility-defined regions. We assign "pseudo-FIPS" codes to these region starting at 100000</li> <li>double centroid_lons(ncounties). Longitude of approximate center of county, in the range [-180, 0].</li> <li>double centroid_lats(ncounties). Latitude of approximate center of county, in the range [0, 90].</li> <li>float outage_prediction(ntracks, ncounties). The predicted peak outage fraction for each county, for each storm. Due to the particularities of ensemble models, some predictions may be slightly below zero or above one; we clip these values to the range [0,1] before any analysis in our study.</li> <li>ubyte prediction_complete_flag(ntracks). A verification flag used during dataset generation. This flag should equal 1 everywhere for complete data.</li> </ul> <p>Each file also contains the raw predictors at a county level for every storm, inside the 'predictors' group, for feature analysis.</p> <p>Also provided for convenience is 'counties_pseudofips.csv', which maps the pseudo-FIPS codes to the the name and spatial extent (WKT format) of each county. It can be read easily by Python GeoPandas, or other software.</p>
The Effect of Prescription Drug Monitoring Programs (PDMPs) and Overdose Reversal Drug Accessibility Laws on Opioid Analgesic Mortality in the United States
<p><strong>Purpose: </strong>This analysis focuses on the impact of Prescription Drug Monitoring Programs (PDMPs) and increased layperson access to the overdose reversal drug Naloxone on prescription opioid mortality rates. The prescription opioid mortality rate was analyzed against state laws governing PDMPs and Naloxone accessibility to laypersons to evaluate if there was a correlation between mortality reduction and implementation of the laws.</p> <p>Three main analyses were conducted:</p> <ol> <li>Does a state’s opioid mortality reduction correlate to an effective PDMP and/or Naloxone law?</li> <li>Do states with strong PDMP laws have a corresponding opioid overdose mortality rate reduction?</li> <li>Do states with strong Naloxone accessibility laws have a corresponding opioid overdose mortality rate reduction?</li> </ol> <p><strong>Conclusion:</strong> Patterns show that PDMP and Naloxone accessibility can be successful in reducing mortality rates, but implementations and results vary dramatically between states. Further changes to both PDMPs and Naloxone accessibility are needed for states to see reliable and consistent mortality reductions. Changes to regulations need time to implement and take effect, meaning longer term measurements and data will be required to see if positive impacts can be sustained.</p> <ol> </ol> <p><strong>Data and Datasets:</strong></p> <p>There are three main datasets used in the analysis.</p> <ul> <li>Opioid mortality rate by state from 2006 to 2016. <ul> <li>This datasetis from the CDC website (<a href="https://www.cdc.gov/drugoverdose/data/statedeaths.html">https://www.cdc.gov/drugoverdose/data/statedeaths.html</a>). I used web scraping and regular expression search to extract the data and calculated mortality reduction of each state.</li> <li><em>Mortality reduction formula: (Highest mortality rate) – (2016 mortality rate)</em><br> </li> </ul> </li> <li>PDMP law implementation in each state and its timelinefrom January 1, 1998 to July 1, 2016. <ul> <li>Dataset is from the Prescription Drug Abuse Policy System(<a href="http://pdaps.org./datasets/prescription-monitoring-program-laws-1408223332-1502818372">http://pdaps.org./datasets/prescription-monitoring-program-laws-1408223332-1502818372</a>). This dataset encompasses laws regulating which professions have access to the database and for what purpose, whether practitioners can delegate their access, whether patients can see their own information, and the extent to which access to individually-identified records may be granted for law enforcement purposes.</li> <li>This dataset is not quantitative, but a set of questions on the characteristics of the laws governing the PDMP.I performed the following data preparation to allow for analysis and visualization:</li> <li><em>PDMP law effectiveness calculation:Each question with a “Yes” answer adds one point to the law’s cumulative effectiveness score on the year it was implemented.</em></li> </ul> </li> </ul> <ul> <li>Naloxone accessibility to laypersons law implementation in each state and its timeline from January 1, 2001 to December 31, 2016. <ul> <li>Dataset is also from the Prescription Drug Abuse Policy System (<a href="http://pdaps.org./datasets/laws-regulating-administration-of-naloxone-1501695139">http://pdaps.org./datasets/laws-regulating-administration-of-Naloxone-1501695139</a>).This dataset focuses on state laws that provide civil or criminal immunity to licensed healthcare providers or lay responders for opioid antagonist administration.</li> <li>This dataset is not quantitative, but a set of questions on the characteristics of the laws governing Naloxone accessibility to laypersons. I performed the following data preparation to allow for analysis and visualization:</li> <li><em>Naloxone accessibility law effectiveness calculation:Each question with a “Yes” answer adds one point to the law’s cumulative effectiveness score on the year it was implemented.</em></li> </ul> </li> </ul>
Expansion of coccidioidomycosis (Valley fever) endemic regions in the United States in response to climate change: projections of disease incidence
<p>This file contains estimations of coccidioidomycosis (Valley fever) incidence data in cases per 100,000 population per year for the contemporary time period and projections throughout the 21st century in response to RCP4.5 and RCP8.5 climate scenarios, associated with the publication:</p> <p>Gorris, M. E., Treseder, K. K., Zender, C. S., and Randerson, J. T. (2019). Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. <em>GeoHealth</em>. </p> <p>The data is reported for each county in the conterminous US with its associated FIPS code (Column 1), county name (Column 2), state FIPS code (Column 3), and state name (Column 4). Column 5 contains the estimation of mean annual Valley fever incidence averaged from 2000-2015. Column 6-8 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for RCP4.5 climate scenario. Likewise, Columns 9-11 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for the RCP8.5 climate scenario. </p> <p>Details about how the incidence data was calculated may be read in the Methods subsection of the paper under "Modeling of current and future mean annual Valley fever incidence". The data provided here was used to create Figure 7 and Supporting Information Figure S5. Counties that have non-zero incidence are considered endemic by our climate-constrained niche model, so this data may also be used to create portions of Figures 3, 4, and S3. </p>
Terrestrial water storage changes across the contiguous United States from GPS and GRACE, 2007–2017
<p>In this dataset, we provide terrestrial water storage anomalies (TWSA) from 2007-2017 at weekly time scales derived using Global Positioning System (GPS) displacements, further constrained by lower-resolution TWSA observations from the Gravity Recovery and Climate Experiment (GRACE).</p> <p>There are six fields in the HDF5 product provided here:</p> <ol> <li>'/cmwe', which provides terrestrial water storage in units of cm. of water equivalent.</li> <li>'/latitude', latitude at the center of each 0.5 degree grid cell</li> <li>/longitude', longitude at the center of each 0.5 degree grid cell</li> <li>'/time', time in days since January 1st, 2007. The resolution of our time series is weekly, and the first day in our record is January 3rd, 2007.</li> <li>'/signal_to_noise_ratio', the variance of the signal divided by variance of noise for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> <li>'/uncertainty', 95% confidence interval for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> </ol> <p>As a condition of using these data, we request that you acknowledge the authors of this data set by citing the following peer-reviewed publication. </p> <p>Adusumilli, S., Borsa, A. A., Fish, M. A., McMillan, H. K., & Silverii, F. (2019). A decade of water storage changes across the contiguous United States from GPS and Satellite Gravity. <em>Geophysical Research Letters</em>, 46, 13006-13015. <a href="https://doi.org/10.1029/2019GL085370">https://doi.org/10.1029/2019GL085370</a></p>
Displacements on selected rock glaciers in the United States
<p>Lateral displacements between two years on selected rock glaciers in the United States (Rocky Mountains and westwards). Displacements were measured using image correlation on repeat orthoimages from the United States Geological Survey archive. Correlation software used: CIAS (http://mn.uio.no/icemass).</p> <p><em><strong>The data are the raw data behind the following publication. This publication contains more details about the measurements. </strong></em></p> <p><a href="https://www.nature.com/articles/s41467-024-52093-z">Kääb, A., Røste, J. Rock glaciers across the United States predominantly accelerate coincident with rise in air temperatures. Nat Commun 15, 7581 (2024). https://doi.org/10.1038/s41467-024-52093-z</a></p> <h3>Detailed rock glacier positions [lat°, lon°]:</h3> <p>Star Peak [44.253,-120.417]<br>Galena creek [44.645,-109.791]<br>Sulphur creek [44.617,-109.756]<br>Crater Mtn [44.023,-109.627]<br>Old Hyndman [43.743, -114.106]<br>Ferguson ranch [39.270, -107.194]<br>Thomas lake [39.270,-107.155]<br>Arapaho [40.020,-105.641]<br>Mt Mears [38.018,-107.871]<br>Mt Sneffels [38.010,-107.781]<br>Teakettle Mtn [38.011,-107.768]<br>Twin Sisters [37.762,-107.803]<br>Pine creek [37.078,-118.450]<br>Birch creek [37.065,-118.431]<br>Cardinal Mtn N [37.011,-118.414]<br>Cardinal Mtn S [37.008,-118.409].</p> <h3>Names of individual displacement files:</h3> <p>Short-name-of-rock-glacier_year1_year2_correlation-window-size_search-window-size_xxx.txt/.csv<br>xxx is either 'helm' indicating that the two orthoimages have been coregistered using Helmert transformation, or 'filt' indicating Helmert transformation and filtering of grid-based measurements for outliers. </p> <h3>Data columns of each file:</h3> <p>X: UTM coordinate, Easting of displacement measurement point <br>Y: UTM coordinate, Northing of displacement measurement point<br>dx: displacement component in east<br>dy: displacement component in north<br>length: pythagoras of dx and dy (vector length)<br>direction: azimuth of vector (from North, clock-wise)<br>max_corrcoeff: correlation coefficient of displacement match<br>avg_corrcoeff: background correlation coefficent at a matching poistion. <br><em><strong> </strong></em></p> <p> </p>
Estimated individual methane emission rates for oil and gas facilities from the continental United States in 2021
<p>File containing 500 separate estimates of 673,940 individual facility-level methane emission rates for oil and gas facilities for the year 2021 in the continental United States. Each column contains one full estimate of the individual facility-level emissions, presented in units of kilograms per hour of methane per facility. The facility categories included in these estimates are production well sites, gathering and boosting compressor stations, transmission and storage compressor stations, processing plants, and flares. This data can be used to recreate the 500 emission distributions presented in Figure 3 in the following manuscript (link: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1402) which is currently under review. This dataset may be updated as the review stages progress</p>
National Checklists 2017: United States Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from the United States collected using effechecka and geonames polygons
National Checklists 2019: United States Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from the United States collected using effechecka and geonames polygons
Deep Learning Reach-level Estimates of Mean River Depth at the Conterminous United States Spatial Scale
<p>Abstract: Estimates of riverine channel geometry play a vital role in the physical representation of stream networks in models used to predict flood and drought conditions, manage water resources, and increase our knowledge of fluvial conditions under a changing climate. A well established body of literature exists that explains the relationship between channel geometry parameters width, depth, and velocity to instantaneous river discharge using a log-log linear power-law regression. In this study, a state-of-the-art deep learning regression model is presented and compared against the power-law method to evaluate their ability to estimate cross-sectional mean river depth. Results reveal three key findings, the neural network: (1) decreases RMSE by 22% verse a CONUS scale power-law equation, (2) reduces prediction variance across Strahler stream orders, and (3) generally outperforms regional power-law equations with an average decrease in RMSE of 8.7% Lastly, a reach-level CONUS dataset of estimated mean river depth is delivered.</p> <p> </p> <p>The deep learning model was trained using the following features:</p> <ul> <li>AI - Mean aridity index of unit catchment - Trabucco and Zomer, 2019</li> <li>area - Upstream drainage area (km2) - P. Lin et al., 2020</li> <li>CLY - Mean clay content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>DOR - Stream segment degree of dam regulation (Scale 0. – 100.) - Grill et al., 2019</li> <li>Elev - Stream segment mean elevation - P. Lin et al., 2020</li> <li>K - Mean bedrock permeability of unit catchment surrounding stream segment - Huscroft et al., 2018</li> <li>LAI - Mean leaf area index of unit catchment - Zhu et al., 2013</li> <li>order - Strahler-Horton stream order - P. Lin et al., 2020</li> <li>P - Mean bedrock porosity of unit catchment - Huscroft et al., 2018</li> <li>QMEAN - Stream segment mean annual discharge (m3/s) - P. Lin et al., 2019</li> <li>Sin - Stream segment sinuosity - P. Lin et al., 2020</li> <li>Slp - Stream segment mean longitudinal slope - P. Lin et al., 2020</li> <li>SLT - Mean silt content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>SND - Mean sand content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>stream_wdth_va - Measured stream cross-sectional width (m) - Canova et al., 2016</li> <li>Urb - Mean urban fraction of unit catchment - Liu et al., 2018</li> </ul> <p>The deep learning model was trained using the following label:</p> <ul> <li>mean_depth_va - Measured stream mean depth (m) - Canova et al., 2016</li> </ul> <p>Predictions of mean depth were made by replacing stream_wdth_va from Canova et al., (2016) with bankfull width estimates (width_m) from P. Lin et al., (2020). Missing records from the P. Lin et al., (2020) dataset were excluded when making predictions, thus there are missing reaches in the dataset.</p>
LANID-US: Landsat-based Irrigation Dataset for the United States
<p><strong>Annual 30-m resolution irrigation maps, derivative products, and ground reference locations for the United States, 1997-2017</strong>, including: </p> <p>(1) lanidYYYY (binary): Annual irrigation layer for the year YYYY (ranging from 1997 to 2017), e.g., lanid2017 is the irrigation map for the year 2017.</p> <p>(2) irrFreq (1-21): The number of years irrigated during 1997-2017.</p> <p>(3) irrFreqPasture_West: irrFreq of pasture/hay for the western CONUS.</p> <p>(4) maxIrrExt (binary): Croplands that have been irrigated at least once during 1997-2017.</p> <p>(5) irrFreqChange (1-21): The difference of number of years irrigated between 1998-2007 and that of between 2008-2017 (i.e., irrFreq<sub>2008-2017</sub> - irrFreq<sub>1998-2007</sub>).</p> <p>(6) irrAreaGain (-1684-2333): LANID-derived irrigation gain (in hectare) from 1998-2007 to 2008-2017 at the 6km×6km scale.</p> <p>(7) formerIrr (binary): Not irrigated anytime in 2015-2017, but irrigated at least 3 times prior.</p> <p>(8) irrWholePeriod (binary): Irrigated at least once for both 1997-1999 and 2015-2017.</p> <p>(9) intermittentIrr (binary): Irrigated at least once for both 1997-1999 and 2015-2017, and irrigation frequency ≤ 18.</p> <p>(10) irrSamples_eastCONUS: Sample locations of center pivot fields in the eastern CONUS (4976 sample locations).</p> <p>(11) rainfedSamples_eastCONUS: Sample locations of stable rainfed fields in the eastern CONUS (4978 sample locations).</p> <p><strong>For technical details that used to create the dataset, please refer to <em>https://doi.org/10.1016/j.rse.2021.112445</em>; Data description details are available via <em>https://essd.copernicus.org/preprints/essd-2021-207/</em>.</strong></p>
Data and Code from: On-farm land management strategies and production challenges in United States Organic Agricultural Systems.
<p>This repository contains data and code used in:</p> <p>Isaac Mpanga, Russel Trondstad, Jessica Guo, David LeBauer, and John Omololu, 2021. On-farm land management strategies and production challenges in United States Organic Agricultural Systems. Current Research in Environmental Sustainability.</p> <p>It provides USDA Surveys of Agricultural Production from 2008-2019 to investigate state and national trends by state in organic farm area, number, and sales, as well to evaluate national trends in on-farm land-use practices and challenges facing US organic production.</p> <p>It also includes code used to transform, visualize, and analyze the data, and derived data products - notably organic farm area and sales with values imputed to correct for redacted state level measures.</p>
Data set for Water Affordability in the United States (Cardoso and Wichman, 2022)
<p><strong>Author Information</strong><br> - Diego S. Cardoso, Department of Agricultural Economics - Purdue University<br> - Casey J. Wichman, School of Economics - Georgia Institute of Technology and Resources for the Future</p> <p><strong>Corresponding Author:</strong> Casey J. Wichman: wichman@gatech.edu</p> <p><strong>Period of data collection:</strong> 2017--2018</p> <p><strong>License: </strong>This data set can be used under terms of the <a href="https://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International license</a>. </p> <p><strong>Recommended citation for the research paper</strong> <br> Cardoso, D. S., & Wichman, C. J. (2022). Water affordability in the United States. Water Resources Research, 58, e2022WR032206. https://doi.org/10.1029/2022WR032206</p> <p><strong>Recommended citation for the data:</strong> <br> Cardoso, Diego S., & Wichman, Casey J. (2022). Data set for Water Affordability in the United States [Data set]. In Water Resources Research (v0.1, Vol. 58, Numbers e2022WR032206). Zenodo. https://doi.org/10.5281/zenodo.6991563</p> <p> </p> <p> </p> <p>Please see the README.txt file for the data dictionary and additional information.</p>
United States Flood Database
<p>This dataset is a merged and unified one from seven individual datasets, making it the longest records ever and wide coverage in the US for flood studies. All individual databases and a unified database are provided to accommodate different user needs. It is anticipated that this database can support a variety of flood-related research, such as a validation resource for hydrologic or hydraulic simulations, climatic studies concerning spatiotemporal patterns of floods given this long-term and U.S.-wide coverage, and flood susceptibility analysis for vulnerable geophysical locations.</p> <p>Description of filenames:</p> <p>1. cyberFlood_1104.csv – web-based crowdsourced flood database, developed at the University of Oklahoma (Wan et al., 2014). 203 flood events from 1998 to 2008 are retrieved with the latest version. Data accessed on 11/04/2020.</p> <p>Data attributes: ID, Year, Month, Day, Duration, fatality, Severity, Cause, Lat, Long, Country Code, Continent Code</p> <p>2. DFO.xlsx – the Dartmouth Flood Observatory flood database. It is a tabular form of global flood database, collected from news, government agencies, stream gauges, and remote sensing instruments from 1985 to the present. Data accessed on 10/27/2020.</p> <p>Data attributes: ID, GlodeNumber, Country, OtherCountry, long, lat, Area, Began, Ended, Validation, Dead, Displaced, MainCause, Severity</p> <p>3. <a href="https://zenodo.org/api/files/cc1f4627-ccd7-46e2-9d9f-b7d58b456cd2/emdat_public_2020_11_01_query_uid-MSWGVQ.xlsx?versionId=b7c2ba8d-1aad-4268-9f0b-cdadd95b38c3">emdat_public_2020_11_01_query_uid-MSWGVQ.xlsx</a> – Emergency Events Database (EM-DAT). This flood report is managed by the Centre for Research on the Epidemiology of Disasters in Belgium, which contains all types of global natural disasters from 1900 to the present. Data accessed on 11/01/2020.</p> <p>Data attributes: Dis No, Year, Seq, Disaster Group, Disaster Subgroup, Disaster Type, Disaster Subtype, Disaster Subsubtype, Event Nane, Entity Criteria, Country, ISO, Region, Continent, Location, Origin, Associated Disaster, Associated Disaster2, OFDA Response, Appeal, Declaration, Aid Contribution, Disaster Magnitude, Latitude, Longitude, Local Time, River Basin, Start Year, Start Month, Start Day, End Year, End Month, End Day, Total Death, No. Injured, No. Affected, No. Homeless, Total Affected, Reconstruction, Insured Damages, Total Damages, CPI</p> <p>4. <a href="https://zenodo.org/api/files/cc1f4627-ccd7-46e2-9d9f-b7d58b456cd2/extracted_events_NOAA.csv?versionId=3c47db6f-f908-4c04-93c4-62afb5f8a68f">extracted_events_NOAA.csv</a> – The national weather service storm reports. The NOAA NWS team collects weather-related natural hazards from 1950 to the present. Data accessed on 10/27/2020.</p> <p>Data attributes: BEGIN_YEARMONTH, BEGIN_DAY, BEGIN_TIME, END_YEARMONTH, END_DAY, END_TIME, EPISODE_ID, EVENT_ID, STATE, STATE_FIPS, YEAR, MONTH_NAME, EVENT_TYPE, CZ_TYPE, CZ_FIPS, CZ_NAME, WFO, BEGIN_DATETIME, CZ_TIMEZONE, END_DATE_TIME, INJURIES_DIRECT, INJURIES_INDIRECT, DEATHS_DIRECT, DEATHS_INDIRECT, DAMAGE_PROPERTY, DAMAGE_CROPS, SOURCE, MAGNITUDE, MAGNITUDE_TYPE, FLOOD CAUSE, CATEGORY, TOR_F_SCALE< TOR_LENGTH, TOR_WIDTH, TOR_OTHER_WFO, TOR_OTHER_CZ_STATE, TOR_OTHER_CZ_FIPS, BEGIN_RANGE, BEGIN_AZIMUTH, BEGIN_LOCATION, END_RANGE, END_AZIMUTH, END_LOCATION, BEGIN_LAT, BEGIN_LON, END_LAT, END_LON, EPISODE_NARRATIVE, EVENT_NARRATIVE, DATA_SOURCE<strong> </strong></p> <p>5. FEDB_1118.csv – The University of Connecticut Flood Events Database. Floods retrieved from 6,301 stream gauges in the U.S. after flow separation from 2002 to 2013 (Shen et al., 2017). Data accessed on 11/18/2020.</p> <p>Data attributes: STCD, StartTimeP, EndTimeP, StartTimeF, EndTimeF, Perc, Peak, RunoffCoef, IBF, Vp, Vb, Vt, Pmean, ETr, ELs, VarTr, VarLs, EQ, Q2, CovTrLs, Category, Geometry</p> <p>6. GFM_events.csv – Global Flood Monitoring dataset. It is a crowdsourcing flood database derived from Twitter tweets over the globe since 2014. Data accessed on 11/9/2020.</p> <p>Data attributes: event_id, location_ID, location_ID_url, name, type, country_location_ID, country_ISO3, start, end, time of detection</p> <p>7. mPing_1030.csv – meteorological Phenomena Identification Near the Ground (mPing). The mPing app is a crowdsourcing, weather-reporting software jointly developed by NOAA National Severe Storms Laboratory (NSSL) and the University of Oklahoma (Elmore et al., 2014). Data accessed on 10/30/2020.</p> <p>Data attributes: id, obtime, category, description, description_id, lon, lat</p> <p>8. USFD_v1.1.csv – A merged United States Flood Database from 1900 to the present (UPDATED)</p> <p>Data attributes: DATE_BEGIN, DATE_END, DURATION, LON, LAT, COUNTRY, STATE, AREA, FATALITY, DAMAGE, SEVERITY, SOURCE, CAUSE, SOURCE_DB, SOURCE_ID, DESCRIPTION, SLOPE, DEM, LULC, DISTANCE_RIVER, CONT_AREA, DEPTH, YEAR.</p> <p>Details of attributes:</p> <p>DATE_BEGIN: begin datetime of an event. yyyymmddHHMMSS</p> <p>DATE_END: end datetime of an event. yyyymmddHHMMSS</p> <p>DURATION: duration of an event in hours</p> <p>LON: longitude in degrees</p> <p>LAT: latitude in degrees</p> <p>COUNTRY: United States of America</p> <p>STATE: US state name</p> <p>AREA: affected areas in km^2</p> <p>FATALITY: number of fatalities</p> <p>DAMAGE: economic damages in US dollars</p> <p>SEVERITY: event severity, (1/1.5/2) according to DFO.</p> <p>SOURCE: flood information source.</p> <p>CAUSE: flood cause.</p> <p>SOURCE_DB: source database from item 1-7.</p> <p>SOURCE_ID: original ID in the source database.</p> <p>DESCRIPTION: event description</p> <p>SLOPE: calculated slope based on SRTM DEM 90m</p> <p>DEM: Digital Elevation Model</p> <p>LULC: Land Use Land Cover</p> <p>DISTANCE_RIVER: distance to major river network in km,</p> <p>CONT_AREA: contributing area (km^2), from MERIT Hydro</p> <p>DEPTH: 500-yr flood depth</p> <p>YEAR: year of the event.</p> <p>9. attribution_table.xlsx – description of each database, and URLs are provided to retrieve these databases.</p> <p>The script to merge all sources and figure plots can be found in https://github.com/chrimerss/USFD.</p> <p>If you intend to use this dataset, please cite our description paper:</p> <p>Li, Z., Chen, M., Gao, S., Gourley, J. J., Yang, T., Shen, X., Kolar, R., and Hong, Y.: A multi-source 120-year US flood database with a unified common format and public access, Earth Syst. Sci. Data, 13, 3755–3766, https://doi.org/10.5194/essd-13-3755-2021, 2021.</p> <p> </p>
Data for "Climate change is shifting and narrowing prescribed fire windows in the Western United States"
<p><strong>The data archived here represent two types of information used in the publication “Climate change is shifting and narrowing prescribed fire windows in the Western United States” by Swain et al. 2023:</strong></p> <p>1) The meteorological and vegetation dryness/fuel moisture data (as described within the file) extracted from prescribed fire burn plans around the Western United States (drawn from entities such as the U.S. Forest Service, U.S. National Park Service, and The Nature Conservancy) between 2002 and 2022. This data is provided in tabular form, both as an .xlsx file and a .csv file for the convenience of the user. In addition to the specific values from each burn plan, summaries of median values for forested and non-forested landscapes are provided as well.</p> <p>2) The number of days on which environmental conditions (i.e., weather and vegetation fuel moistures) are acceptable for prescribed fire according to the composite metric described in Swain et al. 2023 (known as “RxDays”). Underlying RxDay definitions are different for forest and non-forested landscapes. This data is provided in geospatially explicit (gridded) form as NetCDF files, which are a self-describing file format. Each file is provided as a single 3-dimensional hypercube (i.e., dimensions of time, latitude, and longitude, respectively; units and details described within the file) corresponding to the number of RxDays per calendar month.</p> <p>One file is provided for each climate model iteration (as identified in each filename); these represent projected RxDays at monthly scale between 1981 and 2060 using an RCP 4.5 climate forcing trajectory. An additional file (rx_gridMET.nc) is provided that represents the same values from an atmospheric reanalysis dataset, which represents a best estimate of observed RxDay values (1981-2020).</p>
Systematic revision of the Spotted and Northern Dusky Salamanders (Plethodontidae: Desmognathus conanti and D. fuscus), with six new species from the eastern United States
<p>Appendix S1 - Specimen, locality, and morphometric data for 674 individual salamanders analyzed in this study. We used the ‘geomorph’ package in R (Adams and Otárola-Castillo 2013) to extract 17 measurements at 0.01mm precision: SVL (snout-vent length), TL (tail length), AG (axilla-groin length), CW (chest width), FL (femur length), HL (humerus length), SG (snout-gular length), TW (tail width at rear of vent), TO (length of third toe), FI (length of third finger), HW (head width), ED (eye diameter), IN (internarial distance), ES (eye-snout distance), ON (orbito-narial distance), IO (inter-orbital distance), and IC (inter-canthal distance).</p>
Two Large-Scale Meteorological Patterns Are Associated with Short-Duration Dry Spells in the Northeastern United States
<p><strong>Description</strong></p> <p>This dataset contains processed data from the ERA5 dataset for some of the atmospheric fields considered in this study. Original (pre-processed) ERA5 data (Hersbach et al. 2020) is available at <a href="https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset">https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset</a>. For each processed data file (netCDF format), the time steps correspond with the events and numerical order as listed in Table 1 of the main manuscript text. Processed data files are given for some of the 12-day averaged dry periods. Other processed data files are available from the authors upon reasonable request. </p> <p> </p> <p><strong>Abstract</strong></p> <p>Large-scale meteorological pattern (LSMP) – based analysis is used novelly to understand antecedent conditions and characteristics of short-duration dry spell events over the northeastern United States. Dry spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting twelve days or longer, which correspond to ~10% of dry spell events, are examined. The 500-hPa stream function anomaly fields for the first twelve days of each event are time-averaged and k-means clustering is applied to isolate the dry spell-related LSMPs. The first cluster has a strong, low-pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong, high-pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm track density across the Northeast is dramatically reduced during these dry spell events. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but quite different remote, properties. More (56%) short-duration dry spells occurred during the numerous non-drought months than drought months, however the frequency of dry spells is more than three times greater during drought than non-drought months.</p> <p> </p>
Pond data: physical, chemical, and biological characteristics with scientific and United States of America state definitions from literature and legislative surveys
Ponds are often identified by their small size and shallow depths, but the lack of a universal definition hampers science and weakens legal protection. In order to determine a working definition of ‘pond’, we conducted a literature search for scientific definitions, a U.S. state survey for management definitions, and looked at pond ecosystem function using data from the literature search. Our dataset includes physical, chemical, and biological data for 1327 waterbodies ≤ 20 ha in surface area and ≤ 9 m in maximum or mean depth from our literature review. These data have a global distribution, we include a table of latitudes and longitudes, and span many years (1946-2019). We have also included a table of 54 pond definitions from the literature review and a table of U.S. state definitions of ponds, wetlands, and lakes resulting from our survey.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.