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Long-term climate indices (SPEI and scPDSI) derived from monthly meteorology data collected at USHCN stations in the northern Chihuahuan Desert of the United States, 1911-2021
Drought indices — Standardized Precipitation Evapotranspiration Index (SPEI) and the self-calibrating Palmer Drought Severity Index (scPDSI) —where derived from 9 United States Historical Climate Network (USHCN) stations on the Chihuahuan Desert in North America for this dataset. USHCN is a subset of the NOAA Cooperative Observer Program (COOP) Network, which consists of selected sites based on spatial coverages and completeness of data. Monthly precipitation depths, minimum, maximum and mean temperature were pulled from the dataset. These drought indices were derived using the SPEI package and scPDSI packages in R. Potential evapotranspiration was also calculated in R using the Thornthwaite method. All 9 sites are within the bounds of the Chihuahuan Desert in the state of New Mexico, with a single site (EL PASO) in the state of Texas.
Lake Shoreline in the Contiguous United States
There are millions of lakes, ponds and reservoirs in the United States. Existing datasets are large and unweildy. With this data product, derived from the US NHD (downloaded Jan 2013), we summarize at a high level the distribution of lakes across the US.
Flash Flood Severity Index (Flashiness) dataset for the United States
<p>(Saharia et al., 2017)</p> <p>Flash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical flood fatalities database.</p>
Output files corresponding to "Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States"
<p>This dataset corresponds to the output files that were produced for the study reported in:</p> <p>Knights, Deon, Kevin C. Parks, Audrey H. Sawyer, Cédric H. David, Trevor N. Browning, Kelsey M. Danner, and Corey D. Wallace, (2017), Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States, <em>Journal of Hydrology,</em> 554, 331-341</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Region used is: Great Lakes (04)</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>Flowlines</em>: This folder contains a shapefile (<em>GL_coastcatchment_NHDflowline</em>) with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in the region used. </li> <li><em>Catchment</em>: This folder contains a shapefile (GL_coastcatchment_polygon) with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in the region used. </li> <li><em>Centroid</em>: This folder contains a shapefile (GL_coastcatchment_centroid) with the centroids of the above catchments. </li> <li><em>DischargeVulnerabilities.csv</em>. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID: Unique feature identifier in NHDPlusV2 ().</li> <li>Length_km: Length of coastline feature (km).</li> <li>Area_sqkm: Area of coastal catchment feature (km<sup>2</sup>).</li> <li>Infiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>REACHCODE: Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature ().</li> <li>RLength_km: Total length of coastline accumulated by REACHCODE (km).</li> <li>RArea_sqkm: Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>RInfiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>DGWD: Average annual direct groundwater discharge for REACHCODE (m<sup>2</sup>/y).</li> <li>Vulnerable_percent: Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>Vulnerable: Vulnerability to coastal contamination (0- not vulnerable; 1-vulnerable)</li> </ul> </li> </ul> <p> </p>
Project Tycho Level 2 data: Counts of multiple diseases reported in UNITED STATES OF AMERICA, 1888-2014
Project Tycho data include counts of infectious disease cases or deaths per time interval. A count is equivalent to a data point.<p></p><p>Project Tycho level 2 version 1.1.0 data include data counts that have been filtered from the raw data to render standardized data that can be used immediately for analysis. All level 2 data were originally reported in a consistent format and have not been transformed into a standard format by Project Tycho staff, except for smallpox records that included repeated counts for the same location and week, but sometimes with different numbers. These duplicate smallpox records have been averaged into one count for each location and week. Level 2 data include counts for a wide variety of diseases and locations for varying time periods. Because we removed data in an inconsistent format from level 2 data, counts may be missing for certain diseases, locations, or years. For the most complete collection of standardized data, we encourage users to use Project Tycho version 2.0 datasets.</p><p>More detailed methods and additional information about the origin of Projec Tycho level 2 version 1.1.0 data can be found in our original publication in the New England Journal of Medicine: <a href="http://www.nejm.org/doi/full/10.1056/NEJMms1215400">http://www.nejm.org/doi/full/10.1056/NEJMms1215400</a></p><p>Level 2 version 1.1.0 data is represented in a CSV file with 11 columns:</p><ul><li>epi_week: a six digit number that represents the year and epidemiological week for which disease cases or deaths were reported (yyyyww)</li><li>country: a two digit country abbreviation, only including "US" in version 1.1.0</li><li>state: the two digit postal code state abbreviation that represents the state for which a count has been reported</li><li>loc: the name of a state or city for which a count has been reported, capitalized</li><li>loc_type: the type of location (STATE or CITY) for which a count has been reported</li><li>disease: the disease for which a count has been reported, in all capitals</li><li>event: an indicator representing the disease outcome reported, including "CASES" or "DEATHS"</li><li>number: the reported number of cases or deaths</li><li>from_date: the start date of the time interval for which a count was reported, as yyyy-mm-dd</li><li>to_date: the end date of the time interval for which a count was reported, as yyyy-mm-dd</li><li>url: the URL of the source document from which the count was obtained</li></ul><p></p>
Project Tycho Level 1 data: Counts of multiple diseases reported in UNITED STATES OF AMERICA, 1916-2011
<p>Project Tycho data include counts of infectious disease cases or deaths per time interval. A count is equivalent to a data point. Project Tycho level 1 data include data counts that have been standardized for a specific, published, analysis. Standardization of level 1 data included representing various types of data counts into a common format and excluding data counts that are not required for the intended analysis. In addition, external data such as population data may have been integrated with disease data to derive rates or for other applications.</p><p>Version 1.0.0 of level 1 data includes counts at the state level for smallpox, polio, measles, mumps, rubella, hepatitis A, and whooping cough and at the city level for diphtheria. The time period of data varies per disease somewhere between 1916 and 2011. This version includes cases as well as incidence rates per 100,000 population based on historical population estimates. These data have been used by investigators at the University of Pittsburgh to estimate the impact of vaccination programs in the United States, published in the New England Journal of Medicine: <a href="http://www.nejm.org/doi/full/10.1056/NEJMms1215400">http://www.nejm.org/doi/full/10.1056/NEJMms1215400</a>. See this paper for additional methods and detail about the origin of level 1 version 1.0.0 data.</p><p>Level 1 version 1.0.0 data is represented in a CSV file with 7 columns:</p><ul><li>epi_week: a six digit number that represents the year and epidemiological week for which disease cases or deaths were reported (yyyyww)</li><li>state: the two digit postal code state abbreviation that represents the state for which a count has been reported</li><li>loc: the name of a state or city for which a count has been reported, capitalized</li><li>loc_type: the type of location (STATE or CITY) for which a count has been reported</li><li>disease: the disease for which a count has been reported: HEPATITIS A, MEASLES, MUMPS, PERTUSSIS, POLIO, RUBELLA, SMALLPOX, or DIPHTHERIA</li><li>cases: the number of cases reported for the specified disease, epidemiological week, and location</li><li>incidence_per_100000: the number of cases per 100,000 people, computed using historical population counts for cities and states as reported by the US Census Bureau</li></ul><p></p>
Members of the Chinese Students' Alliance in the United States (1912)
<p>The dataset is a list of members of the Chinese Students Alliance in the United States for the academic year 1911-1912. It is sourced from <em>The Directory of Chinese Students in the United States, 1911-1912</em>, compiled by the Chinese Students Alliance in 1912. The directory has been digitized by Google and is available in full view on <a href="https://babel.hathitrust.org/cgi/pt?id=nnc2.ark:/13960/t9574zp5q&seq=5">HathiTrust</a> and <a href="https://archive.org/details/ldpd_11381020_000">Internet Archive</a>.</p> <p>The attached table contains information on the students' names (in Chinese, English, and pinyin transliteration), gender, address in the United States, university in the United States (when available), and the alliance section (Eastern, Midwest, Western) to which they belonged (when available). Additionally, the table provides the geographical coordinates of the cities.</p> <p>The data was extracted using <a href="https://claude.ai/">Claude (AI</a>) and curated using Excel and R. The complete code for extracting and curating the data is available on <a href="https://github.com/carmand03/csa-directories">GitHub</a>. Additionally, the GitHub repository contains various statistics and visualizations, such as the distribution of students by city.</p> <p>The dataset contains 882 students (unique individuals), 802 men and 80 women. </p> <p>Distribution by sections (p.119): </p> <table> <tbody> <tr> <td><strong>Sections</strong></td> <td><strong>Members</strong></td> <td><strong>Non-Members</strong></td> <td><strong>Total</strong></td> </tr> <tr> <td>Eastern</td> <td>201</td> <td>127</td> <td>328</td> </tr> <tr> <td>Midwest</td> <td>121</td> <td>123</td> <td>244</td> </tr> <tr> <td>Western</td> <td>42</td> <td>66</td> <td>108</td> </tr> <tr> <td>Unreturned (Missing)</td> <td>101</td> <td>96</td> <td>197</td> </tr> <tr> <td>Total</td> <td>465</td> <td>412</td> <td>877</td> </tr> </tbody> </table>
A 30-year high resolution simulation of the ~1980-2010 climate over the Interior Western United States
<p>A high-resolution (4 km) regional climate simulation is conducted in the Interior Western United States (IWUS) using the Weather Research and Forecasting (WRF) model. The IWUS simulation is convection permitting and uses the NoahMP land surface model. The model integration is conducted over a 30-year period from 1 October 1981 through 30 September 2011.</p> <p>This repository contains a 30-year gridded dataset of daily precipitation, and daily minimum and maximum surface (2 m) temperature from the IWUS simulation. Anyone interested in the full dataset of the IWUS simulation is encouraged to contact the lead author at yongganga.wang@gmail.com.</p>
MIRCA-BC-USMX: Irrigated and planted fractions over the continental United States and Mexico for years 1992, 2002, and 2012
<p>The MIRCA-BC-USMX project contains a spatially explicit mean annual cycle of monthly planted and irrigated fractions at 0.0625 degree (6 km) spatial resolution over the continental United States and Mexico for years 1992, 2002, and 2012.</p> <p>These fractions were generated by (1) reconciling the MIRCA2000 Global Monthly Irrigated and Rainfed Crop Areas dataset (Portmann et al., 2010) with the cropland and pasture classes of year 2001 of the harmonized NLCD_INEGI land cover dataset (Bohn and Vivoni, 2019b); (2) bias-correcting the irrigated and planted fractions to match state-by-state total irrigated and planted areas from government records in the United States (USDA, 2016) and Mexico (SADER, 2014; SAGARPA, 2016).</p> <p>These fractions have been added to land surface parameter files for the Variable Infiltration Capacity (VIC) model (Liang et al., 1994) version 5.1 (Hamman et al., 2018), extended to include the irrigation module of Haddeland et al. (2006), available on <a href="https://github.com/tbohn/VIC/tree/feature/irrig.imperv.deep_esoil">GitHub</a>. The parameter files were taken from the MOD-LSP project, available on <a href="https://zenodo.org/record/2612560">Zenodo</a> (Bohn and Vivoni, 2019a). The VIC 5 image driver requires a "domain" file to accompany the parameter file. This domain file is also necessary for disaggregating the daily gridded meteorological forcings to hourly for input to VIC via the disaggregating tool <a href="https://github.com/UW-Hydro/MetSim">MetSim</a> (Bennett et al., 2018). We have provided a domain file compatible with the meteorological forcings of Livneh et al (2015) and the MIRCA-BC-USMX parameters, on <a href="https://zenodo.org/record/2564019">Zenodo</a> (Bohn et al., 2019a,b).</p> <p>Contents:</p> <ul> <li>Input Files <ul> <li>county_codes.csv - table mapping the numerical codes for counties with the county names used by the US Census Bureau and USDA. This was created by parsing this information from US Census tables from years 1990, 2000, and 2010 and USDA tables from years 1992, 2002, and 2012 and manually reconciling discrepancies across years. Thus the names may not match county names in the original files exactly from year to year, but rather represent my own naming convention. However these discrepancies were rare.</li> <li>mun_us.0.01_deg.asc.tgz and mun_mx.0.0.01_deg.asc.tgz - gzipped tar archives containing mun_us.0.01_deg.asc and mun_mx.0.01_deg.asc, which are ascii-format ESRI grid files created by rasterizing publicly available shapefiles of US and Mexican counties/municipios. These have 0.01 degree (1 km) spatial resolution and pixels have numerical values equal to the codes in county_codes.csv.</li> </ul> </li> <li>Output Files <ul> <li>fplant_firr_bc.$LCYEAR.nc, where $LCYEAR is one of ("s1992","2001", or "2011") - NetCDF-format files at 0.0625 degree (6 km) resolution containing 12 monthly maps each of bias-corrected "fplant" (planted area fraction) and "firr" (irrigated area fraction) for a specific historical year.The value of $LCYEAR indicates the snapshot of the NLCD_INEGI harmonized land cover classification with which fplant and firr were reconciled (so that these area fractions would not exceed the total agricultural/pastoral area given by NLCD_INEGI). Values of fplant and firr were bias corrected so that state-wide total areas matched government records from USDA (USDA, 2014) and SAGARPA (SADER, 2014; SAGARPA, 2016). For $LCYEAR = ("s1992", "2001", "2011"), the agricultural census year used in the bias correction was (1992, 2002, 2012).</li> <li>fplant_firr_bc.2011.mun_mx.nc - same as fplant_firr_bc.2011.nc, but bias-corrected at the municipio level in Mexico. County-level bias correction was not possible in the US due to lack of sufficient resolution USDA records. Similarly, municipio-level records were not available in Mexico prior to year 2003.</li> <li>params.USMX.NLCD_INEGI.$LCYEAR.$YEAR1_$YEAR2.with_irrig.nc - VIC 5 image driver-compliant input parameter files into which fplant and firr of the given $LCYEAR have been inserted. $YEAR1 and $YEAR2 indicate the first and last years of MODIS data used to estimate the annual cycle of monthly LAI, fcanopy, and albedo (independent of the values of fplant and firr).</li> <li>params.USMX.NLCD_INEGI.2011.$YEAR1_$YEAR2.with_irrig.mun_mx.nc - same as params.USMX.NLCD_INEGI.2011.$YEAR1_$YEAR2.with_irrig.nc but with fplant and firr bias-corrected at the municipio level in Mexico.</li> </ul> </li> </ul> <p>These parameters were created with scripts archived on <a href="https://github.com/tbohn/MIRCA-BC-USMX/releases/tag/v1.1">GitHub</a> (Bohn, 2019).</p>
Concentration of daily precipitation in the contiguous United States
<p>The contiguous US exhibits a wide variety of precipitation regimes, first, because of the wide range of latitudes and <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/altitude">altitudes</a>. The physiographic units with a basic meridional configuration contribute to the differentiation between east and west in the country while generating some large <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/continental-interior">interior continental</a> spaces. The frequency distribution of daily precipitation amounts almost anywhere conforms to a negative exponential distribution, reflecting the fact that there are many small daily totals and few large ones. Positive exponential curves, which plot the cumulative percentages of days with precipitation against the cumulative percentage of the rainfall amounts that they contribute, can be evaluated through the Concentration Index. The Concentration Index has been applied to the contiguous United States using a gridded climate dataset of daily precipitation data, at a resolution of 0.25°, provided by CPC/NOAA/OAR/Earth System Research Laboratory, for the period between 1956 and 2006. At the same time, other rainfall indices and variables such as the annual coefficient of variation, seasonal rainfall regimes and the probabilities of a day with precipitation have been presented with a view to explaining spatial CI patterns. The spatial distribution of the CI in the contiguous United States is geographically consistent, reflecting the principal physiographic and climatic units of the country. Likewise, linear correlations have been established between the CI and geographical factors such as latitude, <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/longitude">longitude</a> and altitude. In the latter case the Pearson <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/correlation-coefficient">correlation coefficient</a> (r) between this factor and the CI is −0.51 (<em>p</em>-value < 0.001). For annual probability of days with precipitation and the CI there is also a significant and negative correlation, <em>r</em> = −0.25 (<em>p</em>-value < 0.001).</p> <p> </p> <p>Fig. 8. Concentration Index values (1956–2006).</p> <p>File: ci_raster_USA.tif (geoTIFF)</p> <p>NOTE: After the publication of the research article we calculate the Concentration Index with the <a href="http://www.prism.oregonstate.edu/">PRISM</a> climate data set, which has a higher resolution with 4km (PRISM Climate Group, Oregon State University). Nevertheless, the temporal coverage is limited to the period from 1981 to 2017.</p> <p>File: CI_PRISM_USA.tif (geoTIFF)</p> <p> </p> <p>Fig. 4. Seasonal rainfall regimes (1956–2006) (P, spring, S, summer, A, autumn, W, winter)</p> <p>File: 1) pulvio_regimes_raster_USA.tif (geoTIFF); 2) pulvio_regimes_id.csv (clasification for regimes)</p> <pre>Map projection details:</pre> <p>EPSG:2163; proj4: "+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"</p>
From waste to value: Recovering critical raw materials from urban mines in the European Union and the United States
<p><strong>Submitted data was used to write an article: </strong>Jędrusiak, R., Bielowicz, B., Drobniak, A., 2023, From waste to value: Recovering critical raw materials from urban mines in the European Union and the United States, Mineral Resource Management 39 (3), 43-63. <a href="https://doi.org/10.24425/gsm.2023.147557">https://doi.org/10.24425/gsm.2023.147557</a></p> <p> </p> <p><strong>Funding acknowledgments: </strong>Agnieszka Drobniak contribution comes from the support of the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), and the National Science Center, Poland (2022/01/1/ST10/00024). This research was funded by the Ministry of Science and Higher Education of Poland (subsidies no. 16.16.140.315).</p> <p> </p> <p><strong>Article Abstract: </strong>Modern human consumption, rapid urbanization and further increases in the world’s population lead to the demand for more goods and materials. However, after utilization, only some of these materials are recovered or recycled, many are discarded due to a lack of implemented recovery technologies and regulations, or due to the content of contaminants. Moreover, many of the potentially recoverable materials are deposited in landfills or shipped to less developed countries for disposal where they can cause environmental contamination. The new approach to waste management follows the hierarchy of waste prevention. First, waste is prepared for reuse and repair without the need for treatment processes, or it is recycled. If this is not possible, the waste is incinerated with energy recovery, or failing that, it is disposed of in landfills. This waste hierarchy has become one of the key factors in the transformation of a linear economy into a circular economy. Particularly noteworthy is waste containing raw materials of significant economic importance, especially those of a high supply risk due to the level of concentration in another country and import dependence. These critical raw materials (CRM) are an inherent part of our modern, technology-driven life. They are essential to national security and the economic development of every country. Their use is drastically increasing, and with it, the need to assure their reliable and unrestricted access along with lowering the environmental impact from their production and extraction. Currently, scientists and industry direct a lot of effort into finding new supplies of these materials, not only from traditional sources in nature but also from new sources like anthropogenic waste. The purpose of this study is to present the raw material potential which remains mostly unused in residues from municipal waste incineration in regions with highly developed economies – the United States and the European Union. These economies have shortages of their own raw material extraction capacity due to high levels of consumption and insufficient amounts of raw-material content in natural resources.</p>
Online Data for 'The role of wildfires in the interplay of forest carbon stocks and wood harvest in the contiguous United States during the 20th century'
<p>This data file (.xlsx) contains all data used to create table 1, figures 1a-d, figure 2, figure S1, S2, and S5 of the study "The role of wildfires in the interplay of forest carbon stocks and wood harvest in the contiguous United States during the 20th century". Main article is available under: https://doi.org/10.1029/2023GB007813</p>
Paired field measurements of suspended-sediment concentration, turbidity, acoustic backscatter, and particle size compiled from various estuaries in the United States and Australia
<p>Field measurements of suspended-sediment concentration, turbidity, acoustic backscatter, and particle size are compiled from various estuaries in the United States and Australia to investigate the utility of combining optical and acoustic backscatter measurements for the estimation of suspended-sediment concentration under changes in floc particle size and density. </p> <p>Theory, analysis, and interpretation of the data is available in Livsey et al (2023). Data collected from the Chesapeake Bay, US were compiled from Fall et al (2022). Data collected on the Brisbane River were collected by Livsey et al (2022). Data collected for all other locations were compiled from Livsey et al (2022). </p> <p>Data collected by Fall et al (2022) utilized a LISST 100x. Data collected by Livsey et al (2022, 2023) utilized a LISST 200x. Data files for each instrument are provided. </p> <p>Funding for this research was provided by an Advance Queensland Industry Research Fellowship, Queensland University of Technology, and Queensland Department of Environment and Science.</p> <p>References</p> <p>Fall, Kelsey A., Massey, Grace M., and Friedrichs, Carl T., (2020). The importance of organic content to fractal floc properties in estuarine surface waters, insights from video, LISST, and pump sampling: Supporting data. Data. William & Mary. https://doi.org/10.25773/7gbc-794 6739</p> <p>Livsey, D., Turner, R., Grace, P., and Crosswell, & Andy Steven. (2022). Field and laboratory measurements of suspended-sediment particle size and concentration from nine rivers draining to the Great Barrier Reef (1.0). Data. Zenodo. https://doi.org/10.5281/zenodo.6788303</p> <p>Livsey, D., Turner, R., and Grace, P. (2023). Combining optical and acoustic backscatter measurements for monitoring of fine suspended-sediment concentration under changes in particle size and density. Water Resources Research. <a href="https://doi.org/10.1029/2022WR033982">https://doi.org/10.1029/2022WR033982</a></p> <p> </p>
Regional and local variation in chemical, structural, and physical leaf traits for tree species in the northeastern United States, 2016-2023.
This dataset is a compilation of leaf trait measurements for 25 different Northern American tree species in the northeastern United States collected between 2016 and 2023 by the Terrestrial Ecosystems Analysis Lab at the University of New Hampshire. Currently, this dataset contains measurements for 2,006 samples across 18 chemical, physical, and structural traits. Measured traits include stable isotopes for carbon (C) and nitrogen (N), chlorophyll estimates, leaf and petiole dimensions, and leaf and petiole water content. Traits have been measured at plots spanning a wide range of latitude, longitude, elevation, and forest types. A simple table containing these plot descriptions has been included. Additional leaf physiological and optical traits have been measured concurrently on many of these samples and have been or will be published separately. This is a continuous dataset that will be updated on an as needed basis.
Atmospheric Wet Deposition in Urban and Suburban Sites Across the United States
These data are for the publication: Conrad-Rooney, E., J. Gewirtzman, Y. Pappas, V.J. Pasquarella, L.R. Hutyra, and P.H. Templer. 2023. Atmospheric Wet Deposition in Urban and Suburban Sites Across the United States. Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2023.119783 This study investigated long-term trends in atmospheric wet deposition of nitrogen (ammonium and nitrate), sulfate, cations, and chloride for urban and suburban sites in the U.S. using data from the National Atmospheric Deposition Program and assessed whether urban NADP sites are hotspots for atmospheric wet deposition. To examine potential impacts of urbanization on atmospheric wet deposition, percent impervious surface area and population density data were extracted from Google Earth Engine. The results of this study highlight that urban areas have greater rates of many forms of atmospheric deposition and that there should be more long-term monitoring of atmospheric deposition in cities and suburban sites throughout the U.S. Data sources: - Dewitz, J., and U.S. Geological Survey, 2021, National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021): U.S. Geological Survey data release, doi:10.5066/P9KZCM54 - National Atmospheric Deposition Program (NRSP-3). 2022. NADP Program Office, Wisconsin State Laboratory of Hygiene, 465 Henry Mall, Madison, WI 53706. https://nadp.slh.wisc.edu/networks/national-trends-network/ - United States Census Bureau, TIGER: US Census Blocks, 2010 United States Census. https://developers.google.com/earth-engine/datasets/catalog/TIGER_2010_Blocks#description
Local water years for 4-digit hydrologic unit areas across the conterminous United States
Quantifying and predicting precipitation and water flow, and their influence on ecosystems is challenged by the dynamic relationships between and timing of precipitation and water fluxes. To help with these challenges, scientists use “water year” to examine and predict the impacts of precipitation and relevant extreme climatic and hydrological events on ecosystems. However, traditional water year definitions used in the U.S. have limited considerations of areal variations in climate and hydrology, which need to be considered when studying ecosystems at regional or national scales. We developed local water year (LWY) values that consider spatial variation using existing definitions whereby the water year begins in the month with the lowest or highest average monthly streamflow. We employed a spatial interpolation technique to assign the start and end months of two LWY timeframes to 202 subregions across the conterminous U.S. that range from 4,384 to 134,755 km2. This dataset can be linked with diverse climate, terrestrial, and aquatic data for broad-scale studies.
RustMapper: White Pine Blister Rust Risk in the Western United States, 1980-2023
White pine blister rust (WPBR) is a highly destructive disease threatening high-elevation five-needle white pines across North America. To better understand risk patterns, we analyzed data from independent studies conducted across the western U.S. between 1995 and 2020. Using this data, we assessed WPBR risk for high-elevation five-needle pine species (High-5) from 1980 to 2023, integrating the results into the adaptive management tool "RustMapper." These projections estimate the annual probability of WPBR occurrence, providing valuable insights for monitoring and management. Risk ranges from 0 to 1, and values closer to 1 indicate a higher likelihood of disease occurrence based on conditions.
RustMapper: White Pine Blister Rust Risk in the Western United States, 2030-2099
White pine blister rust (WPBR) is a highly destructive disease threatening high-elevation five-needle white pines across North America. To better understand risk patterns, we analyzed data from independent studies conducted across the western U.S. between 1995 and 2020. Using this data, we assessed WPBR risk for high-elevation five-needle pine species (High-5) from 2030 to 2099, integrating the results into the adaptive management tool "RustMapper." These projections estimate the annual probability of WPBR occurrence, providing valuable insights for monitoring and management. Risk ranges from 0 to 1, and values closer to 1 indicate a higher likelihood of disease occurrence based on conditions.
Land use and management effects on soil carbon in the Lake States, Northern United States - Nave, et al 2022
There is growing need to quantify and communicate how land use and management activities influence soil organic carbon (SOC) at scales relevant to, and in the tangible control of landowners and forest managers. The continued proliferation of publications and growth of datasets, data synthesis and meta-analysis approaches allows the application of powerful tools to such questions at ever finer scales. In this analysis, we combined a literature review and effect-size meta-analysis with two large, independent, observational databases to assess how land use and management impact SOC stocks, primarily with regards to forest land uses. We performed this work for the (Great Lakes) Lake States, which comprise 6% of the land area, but 7% of the forest and 9% of the forest SOC in the U.S., as the second in a series of ecoregional SOC assessments. Most importantly, our analysis indicates that natural factors, such as soil texture and parent material, exert more control over SOC stocks than land use or management. With that for context, our analysis also indicates which natural factors most influence management impacts on SOC storage. We report an overall trend of significantly diminished topsoil SOC stocks with harvesting, consistent across all three datasets, while also demonstrating how certain sites and soils diverge from this pattern, including some that show opposite trends. Impacts of fire grossly mirror those of harvesting, with declines near the top of the profile, but potential gains at depth and no net change when considering the whole profile. Land use changes showing significant SOC impacts are limited to reforestation on barren mining substrates (large and variable gains) and conversion of native forest to cultivation (losses). We describe patterns within the observational data that reveal the physical basis for preferential land use, e.g., cultivation of soils with the most favorable physical properties, and forest plantation establishment on the most marginal so
American Residential Macrosystems - Bird community data within parks and residential yards in six major metropolitan areas in the United States, 2017-2018
"This dataset includes abundance of breeding bird species recorded in residential yards and nearby natural and interstitial areas (i.e.unmanaged vegetation areas in the residential/wildland interface) in six cities across the U.S. Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, Minneapolis-St. Paul, MN, and Phoenix, AZ. Yards were grouped in 4 categories based on fertilizer input frequency, landscaping style and their impact on hydrology: high-input lawns, low-input lawns, wildlife-certified yards and yards with low impact on hydrology (or rain gardens). Bird data was collected via standardized 10-min point counts during the breeding season in 2017 or 2018. "
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.