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21,983 results for “UNITE”
NEON Provisional Continuous and Field Discharge - Water Year 2024 (2023-10-01 - 2024-09-30), United States
As of the date of this publication in EDI, NEON publishes one-minute continuous discharge data that have been corrected and gap-filled in the Continuous Discharge (DP4.00130.001, https://data.neonscience.org/data-products/DP4.00130.001) data product. As part of a recent manuscript describing the data quality improvements to NEON's continuous discharge data brought on my implementation of corrections and gap-filling, a mixture of released and provisional NEON data were downloaded. To ensure the reproducibility of the data set used to conduct the analysis presented in the manuscript, the downloaded provisional data, which is subject to change, is saved as a static data set in this EDI publication. The same is done for the Discharge Field Collection (DP1.20048.001, https://data.neonscience.org/data-products/DP1.20048.001) data product, which is used in the same analysis.
Genetic assignments for Spring Evolutionary Significant Unit reanalysis, Central Valley Chinook Salmon populations, CA, 2011-2024
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is difficult to visually distinguish individuals from the different Evolutionarily Significant Units (ESU). As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to genetic lineage; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program compliance monitoring programs. The genetic lineage was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central V
Oyster Paleobiology along the South Atlantic Coast of the United States
This dataset includes morphometric data collected from 37,805 oyster shells (Crassostrea virginica) from 15 Late Archaic (ca. 4500-3500 cal. BP) through Mississippian (ca. 1150 – 370 cal. BP) period archaeological sites situated along the South Atlantic Coast of the United States. Variations in oyster size is an important proxy for paleoenvironment and human population pressures. These data are part of a larger project examining oyster ecosystem stability and Native American oyster harvesting practices over the last 5,000 years. Oyster measurements were collected by many undergraduate and graduate students from multiple universities. Carey Garland added to and cleaned the data sheet between August 2018 and May 2019. The dataset was structured to include the Island, site name, time period, and provenience (e.g., unit, level, etc.) associated with each oyster measured. The original oyster database contains sensitive information, such as the specific location of archaeological sites. If a professional archaeologist needs site location information, they can contact the Georgia Archaeological Site File.
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.
Data for: 3D in vitro modeling of the exocrine pancreatic unit using tomographic volumetric bioprinting
<p><strong>Abstract</strong></p> <div> <div> <p><span><span>Pancreatic ductal adenocarcinoma (PDAC) is the most frequent type of pancreatic cancer, one of the leading causes of cancer-related deaths worldwide. The first lesions associated with PDAC occur within the functional units of exocrine pancreas</span><span>. T</span><span>he crosstalk between PDAC cells and stromal cells plays a key role in tumor progression.</span><span> Thus,</span> <span>i</span></span><span><span>n vitro</span></span><span><span>, fully human models of the pancreatic cancer microenvironment are needed to foster the development of new, more effective therapies</span><span>.</span> <span>However,</span><span> it is challenging to make these models anatomically and functionally relevant. Here, we used tomographic volumetric bioprinting, a novel method to fabricate </span><span>three-dimensional </span><span>cell-laden constructs</span><span>,</span><span> to produce a </span><span>portion</span><span> of the </span><span>complex convoluted </span><span>exocrine pancreas</span> </span><span><span>in vitro</span></span><span><span>.</span><span> Human fibroblast-laden gelatin methacrylate-based pancreatic models were processed to reassemble the </span><span>tubuloacinar</span><span> structures of the exocrine pancreas and, then human pancreatic ductal epithelial (HPDE) cells overexpressing the KRAS oncogene (HPDE-KRAS) were seeded in the acinar lumen to reproduce the pathological exocrine pancreatic tissue. The growth and organization of HPDE cells within the structure was evaluated and the formation of a thin epithelium which covered the acini inner surfaces in a physiological way inside the 3D model was</span> <span>successfully</span> <span>demonstrated</span><span>. Interestingly, immunofluorescence assays revealed a significantly higher expressions of alpha smooth muscle </span><span>actin</span><span> (α-SMA) vs. </span><span>actin</span><span> in the fibroblasts co-cultured with cancerous than with wild-type HPDE cells. Moreover, α-SMA expression increased with time, and it was found to be higher in fibroblasts that laid closer to HPDE cells than in those </span><span>laying </span><span>deeper into the model. Increased levels of interleukin (IL)-6 were also quantified in supernatants from co-cultures of stromal and HPDE-KRAS cells. These findings correlate with inflamed tumor-associated fibroblast behavior, thus being relevant biomarkers to </span><span>monitor</span><span> the early progression of the disease and to target drug efficacy. </span></span><span> </span></p> </div> <div> <p><span><span>To our knowledge, this is the first</span> <span>demonstration of a </span><span>3D </span><span>bioprinted</span> <span>portion</span><span> of </span><span>pancreas that</span> <span>rec</span><span>apit</span><span>ulates</span> <span>its</span> <span>true 3-dimensional </span><span>microanatomy</span><span>,</span><span> and which shows </span><span>tumor triggered </span><span>inflammation</span><span>. </span></span><span> </span></p> </div> </div> <p> </p> <p><strong>Contents</strong></p> <p>This repository contains the raw data, materials list, protocols, and code necessary to reproduce the work in the namesake preprint.</p> <p> </p>
DATA SET: Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units
<p>This repository contains the data sets of the article:</p> <p>Mesquida, J., Caballer, A., Cortese, L. <em>et al.</em> Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units. <em>Crit Care</em> <strong>25, </strong>381 (2021). https://doi.org/10.1186/s13054-021-03803-2</p>
Predicted and experimental chemical and ecotoxicological properties for the toxic unit based hazard assessment
<p><strong>Description</strong></p> <p>This dataset contains ecotoxicity data of 1585 chemicals of environmental concern (CECs) and chemical identifiers. The ecotoxicity data was retrieved from <a href="https://cfpub.epa.gov/ecotox">US EPA ECOTOX Knowlegdebase</a> in ASCII file format and was aggregated for the ecotoxicity groups algae, crustaceans, and fish based on the ideas of <a href="https://dx.doi.org/10.1002/etc.3460">Busch et al. 2016</a>. The dataset includes the 5-percentile, the mean and the geomean of all retrieved ecotoxicity for each compound. Missing ecotoxicity data was estimated with ECOSAR 1.0 algorithms for green algae, daphnids, and fish using <a href="https://www.ufz.de/index.php?en=34593">ChemProp 6.8</a>. The main purpose of this dataset is the <a href="http://doi.org/10.1016/0043-1354(70)90018-7">toxic unit</a> (TU) based hazard assessment of environmental water samples. Chemical properties were estimated using <a href="https://github.com/kmansouri/OPERA">OPERA 2.7</a>, <a href="https://chemaxon.com/products/instant-jchem">Instant JChem</a>, and ACD Percepta 2015 based on QSAR-ready SMILES derived from OPERA 2.7. All data aggregated from EcoTox Knowledgebase (e.g., raw values, species, etc.) is available in the dataset in the detailed sheets. REcoTox, the processing script written in R is available on <a href="https://github.com/tsufz/REcoTox/releases/latest">GitHub</a>.</p> <p><strong>CAUTION</strong></p> <p>It needs to be emphasized that quantitative-structure activity relationship data is just an estimate, which does not necessarily reflect the real property and behaviour of a modelled compound. The calculated data needs to be reviewed in deep. Especially for non-polar or very polar compounds, the QSAR predictions might fail. If a compound ranks high in the TU ranking, it is required to search for literature or regulative data evidences to underpin the finding to avoid false positive prioritizations.</p> <p><strong>RELEASE NOTE</strong></p> <p>Version 210714_v1 was created with <a href="https://github.com/tsufz/REcoTox/releases/tag/v0.1.0">REcoTox version v0.1.0</a>.</p>
Unit Map and Dips/Strikes of Sakarya Vallis, Gale Crater, Mars
<p>This dataset comprises the unit map derived from 3D analysis of Sakarya Vallis in Gale crater, Mars. The units are named Package 1–7. These packages have been extrapolated from morpho-stratigraphic analysis of a HiRISE scene in PRo3D. They have been further extrapolated using underlying image data. Included here is a shapefile representing the marker bed (Milliken et al. 2010) in Gale crater. The "CDD" refers to the Central Debris Deposit, identified by Hughes (2021).</p> <p>The structural data represents dip measurements along the boundaries of these packages within the feature; the "sub-package" data represent layering within the packages. For more on how dip is calculated in PRo3D, see https://pro3d.space/.</p> <p>Finally, the profiles mark the locations where topographic profiles were extracted for constructing cross-sections, as discussed in the thesis Persaud (2022).</p> <p>These data are intended to be displayed with the HiRISE ORI (https://doi.org/10.5281/zenodo.5808371) and CTX ORI mosaic (https://doi.org/10.5281/zenodo.5808357) over Sakarya Vallis, and over the basemap over the northwest of Aeolis Mons (https://doi.org/10.5281/zenodo.5808381).</p> <p>Format: SHP, SHX, DBF, PRJ, QPJ<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)</p> <p>N.B. the PROJ4 format of the project is "+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +a=3396190 +b=3396190 +units=m +no_defs"</p>
The soil province geodatabase of Italy, storing information of soil typological units and broad soil regions at the 1:1,000,000 and 1:10,000,000 scales
<p>The Soil Map of Italy at 1:1,000,000 scale, was the result of the work of Edoardo AC Costantini, Giovanni L'Abate, Roberto Barbetti, Maria Fantappié, Romina Lorenzetti, and Simona Magini affiliated to Research Centre for agrobiology and soil science (CREA-ABP), in collaboration with several regional institutions, universities and other research centers of the CREA - Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria. The map, was printed by S.EL.CA. of Florence. The map is an informative and educational work of general scientific interest, which updates the previous one edited by prof. Fiorenzo Mancini and collaborators in 1966 both in terms of knowledge and of the adopted methods. It was produced processing of all data within a geographical and soil geodatabase, collected by the CREA-ABP and other institutions collaborating in over ten years of work and using the latest international methods. The soil map shows the distribution of major soils in the country and constitutes a milestone in the process launched in 1999 as part of the project the Soil Map of Italy at a scale of 1: 250,000, funded by MIPAAF and implemented in collaboration with the regional institutions. Both broad soil regions and soil provinces (reference scale 1:10,000,000 and 1:1,000,000) are reported.</p> <p>Most small-scale soil maps report dominant typological units and allow only a partial appraisal of pedodiversity since territories with similar dominant soils can actually possess different pedodiversity. This is particularly true at the national scale, where a great wealth of soil information collected at more detailed scales is generalized.</p> <p>A methodology was set up, which aimed at preserving pedodiversity in upscaling soil maps by using geomatic techniques and the World Reference Base for soil resources (WRB). The main source of information was the soil system geodatabase of Italy, storing information of soil typological units and soilscapes at the 1:500,000 reference scale. Qualitative aggregation of soil taxa followed upscaling rules aimed at (i) maintaining the information about pedogenetic processes and (ii) grouping soilscapes showing recurrent patterns of soil forming processes. The upscaling methodology can be summarized in seven steps as follows: (1) soil forming processes selection, retrieved from soil typological units stored in the national database; (2) upscaling soil systems and creation of broad soil regions at 1:10,000,000 reference scale; (3) semantic upscaling of typological units to form taxa showing different soil forming processes; (4) ranking and associating soil forming processes; (5) geography upscaling of soil systems geometry to form polygons at 1:1,000,000 reference scale, called subregions; (6) ranking subregions according to their extension; (7) naming subregions by ranking the taxa according to the number of soil typological units.</p> <p>The soil subregion map reported 47 map unit and 148 taxa, belonging to 22 reference soil group of WRB and showing from one to four qualifiers. Each map unit had from 2 to 18 taxa, for a total of 317 occurrences. Thirty taxa had 3 or more occurrences, while the remaining took place in one or two subregions only.</p>
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>
Circumpolar Landcover Units
<p><strong>This record is obsolete.</strong></p> <p><strong>A new version has been published under <a href="https://zenodo.org/records/14235736">https://zenodo.org/records/14235736</a></strong></p> <p>Landcover units have been derived from Copernicus Sentinel-1 und Sentinel-2 data acquired between 2016 and 2022 for the Arctic tundra biome. 23 units of which 20 represent different vegetation characteristics and soil conditions are provided. The units have been identified with K-means over a representative transect and in a second step retrieved across the entire Arctic north of the treeline. The description of the units is based on several thousand samples from vegetation surveys and soil probes.</p> <p>The units are supplied at 10m resolution in nine irregular tiles. Auxiliary data for quality information and input acquisition dates are supplied in a separate shape file. The majority within 1kmx1km areas is supplied as csv (centre points). Units are documented in detail in Bartsch, A., Efimova, A., Widhalm, B., Muri, X., von Baeckmann, C., Bergstedt, H., Ermokhina, K., Hugelius, G., Heim, B., and Leibman, M.: Circumarctic land cover diversity considering wetness gradients, Hydrol. Earth Syst. Sci., 28, 2421–2481, https://doi.org/10.5194/hess-28-2421-2024, 2024.</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>
Public, military and industrial units
<p>Urban Atlas based data subset for Comune di Napoli, where every element with CODE 12100 was extracted as Public, military and industrial units elements with the next information:</p> <p>gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real run_off_coefficient real context real fua_tunnel real</p> <p>This data is an input for local effects calculation.</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>
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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)
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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.