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1. Model estimates of water yield and N yield under alternative N management practices across the Mississippi-Atchafalaya River basin during 1980-2017

   https://doi.org/10.25380/iastate.12779954.v1  

   Lu, Chaoqun; Zhang, Jien (August 2020, Iowa State University)

   This dataset contains maps of water yield and nitrogen (N) yield each year, covering the Mississippi/Atchafalaya River Basin (MARB) spanning from 1980 to 2017. These maps were reconstructed by aggregating from a daily model (Dynamic Land Ecosystem Model, DLEM) estimates and are at 5-min×5-min (0.08333° Lat × 0.08333° Lon) resolution. There are two subfolders, "TT" and "DT", within this folder. "TT" and "DT" respectively indicate "traditional timing" and "dynamic timing" of nitrogen fertilizer applications in regards to the model experiments in the main text. The "TT" folder contains the gridded model estimates of water yield (named by "Runoff") and nitrogen yield (named by "Nleach") at annual bases. TT reflects our best estimate of water and N fluxes within the context of multi-factor environmental changes including climate, atmospheric CO2 concentration, N deposition, land use, and human management practices (such as fertilizer use, tillage, tile drainage, etc.). The "DT" folder only contains the model estimates of nitrogen yield (“Nleach”) under an alternative N management practice. More details can be found in the linked publication. 

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2. SES Grants, 2000-2015

   https://doi.org/10.7910/DVN/QCA35O  

   Kaiser, Kendra; Braswell, Anna; Fork, Megan; Fork, Megan (January 2022, Harvard Dataverse)

   Information about grants funded by NSF to support SES research from 2000-2015. The grants included in this dataset are a subset that we identified as having an SES research focus from a set of grants accessed using the Dimensions platform (https://dimensions.ai). CSV file with 35 columns and names in header row: "Grant Searched" lists the granting NSF program (text); "Grant Searched 2" lists a secondary granting NSF program, if applicable (text); "Grant ID" is the ID from the Dimensions platform (string); "Grant Number" is the NSF Award number (integer); "Number of Papers (NSF)" is the count of publications listed under "PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH" in the NSF Award Search page for the grant (integer); "Number of Pubs Tracked" is the count of publications from "Number of Papers (NSF)" included in our analysis (integer); "Publication notes" are our notes about the publication information. We used "subset" to denote when a grant was associated with >10 publications and we used a random sample of 10 publications in our analysis (text); "Unique ID" is our unique identifier for each grant in the dataset (integer); "Collaborative/Cross Program" denotes whether the grant was submitted as part of a set of collaborative or cross-program proposals. In this case, all linked proposals are given the same unique identifier and treated together in the analysis. (text); "Title" is the title of the grant (text); "Title translated" is the title of the grant translated to English, where applicable (text); "Abstract" is the abstract of the grant (text); "Abstract translated" is the abstract of the grant translated to English, where applicable (text); "Funding Amount" is the numeric value of funding awarded to the grant (integer); "Currency" is the currency associated with the field "Funding Amount" (text); "Funding Amount in USD" is the numeric value of funding awarded to the grant expressed in US Dollars (integer); "Start Date" is the start date of the grant (mm/dd/yyyy); "Start Year" is the year in which grant funding began (year); "End Date" is the end date of the grant (mm/dd/yyyy); "End Year" is the year in which the term of the grant expired (year); "Researchers" lists the Principal Investigators on the grant in First Name Last Name format, separated by semi-colons (text); "Research Organization - original" gives the affiliation of the lead PI as listed in the grant (text); "Research Organization - standardized" gives the affiliation of each PI in the list, separated by semi-colons (text); "GRID ID" is a list of the unique identifier for each the Research Organization in the Global Research Identifier Database [https://grid.ac/?_ga=2.26738100.847204331.1643218575-1999717347.1643218575], separated by semi-colons (string); "Country of Research organization" is a list of the countries in which each Research Organization is located, separated by semi-colons (text); "Funder" gives the NSF Directorate that funded the grant (text); "Source Linkout" is a link to the NSF Award Search page with information about the grant (URL); "Dimensions URL" is a link to information about the grant in Dimensions (URL); "FOR (ANZSRC) Categories" is a list of Field of Research categories [from the Australian and New Zealand Standard Research Classification (ANZSRC) system] associated with each grant, separated by semi-colons (string); "FOR [1-5]" give the FOR categories separated. "NOTES" provide any other notes added by the authors of this dataset during our processing of these data. 

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3. Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning

   https://doi.org/10.5281/zenodo.8057232  

   wang, zhuonan; Kumar, Jitendra; R., Samantha Weintraub-Leff; Todd-Brown, Katherine; Mishra, Umakant; Sihi, Debjani (January 2023, Zenodo)

   {"Abstract":["Data Description<\/strong>:<\/p>\n\nTo improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively.<\/p>\n\nFiles in collection (32):<\/p>\n\nCollection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts<\/p>\n\ngNATSGO TIF files:<\/p>\n\n├── available_water_storage_30arc_30cm_us.tif                   [30 cm depth soil available water storage]\n├── available_water_storage_30arc_100cm_us.tif                 [100 cm depth soil available water storage]\n├── caco3_30arc_30cm_us.tif                                                 [30 cm depth soil CaCO3 content]\n├── caco3_30arc_100cm_us.tif                                               [100 cm depth soil CaCO3 content]\n├── cec_30arc_30cm_us.tif                                                     [30 cm depth soil cation exchange capacity]\n├── cec_30arc_100cm_us.tif                                                   [100 cm depth soil cation exchange capacity]\n├── clay_30arc_30cm_us.tif                                                     [30 cm depth soil clay content]\n├── clay_30arc_100cm_us.tif                                                   [100 cm depth soil clay content]\n├── depthWT_30arc_us.tif                                                        [depth to water table]\n├── kfactor_30arc_30cm_us.tif                                                 [30 cm depth soil erosion factor]\n├── kfactor_30arc_100cm_us.tif                                               [100 cm depth soil erosion factor]\n├── ph_30arc_100cm_us.tif                                                      [100 cm depth soil pH]\n├── ph_30arc_100cm_us.tif                                                      [30 cm depth soil pH]\n├── pondingFre_30arc_us.tif                                                     [ponding frequency]\n├── sand_30arc_30cm_us.tif                                                    [30 cm depth soil sand content]\n├── sand_30arc_100cm_us.tif                                                  [100 cm depth soil sand content]\n├── silt_30arc_30cm_us.tif                                                        [30 cm depth soil silt content]\n├── silt_30arc_100cm_us.tif                                                      [100 cm depth soil silt content]\n├── water_content_30arc_30cm_us.tif                                      [30 cm depth soil water content]\n└── water_content_30arc_100cm_us.tif                                   [100 cm depth soil water content]<\/p>\n\nSOC TIF files:<\/p>\n\n├──30cm SOC mean.tif                             [30 cm depth soil SOC]\n├──100cm SOC mean.tif                           [100 cm depth soil SOC]\n├──30cm SOC CV.tif                                 [30 cm depth soil SOC coefficient of variation]\n└──100cm SOC CV.tif                              [100 cm depth soil SOC coefficient of variation]<\/p>\n\nsite observations csv files:<\/p>\n\nISCN_rmNRCS_addNCSS_30cm.csv       30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data<\/p>\n\nISCN_rmNRCS_addNCSS_100cm.csv       100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data<\/p>\n\n\nData format<\/strong>:<\/p>\n\nGeospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution.<\/p>\n\nGeospatial projection<\/strong>: <\/p>\n\nGEOGCS["GCS_WGS_1984",\n DATUM["D_WGS_1984",\n SPHEROID["WGS_1984",6378137,298.257223563]],\n PRIMEM["Greenwich",0],\n UNIT["Degree",0.017453292519943295]]\n(base) [jbk@theseus ltar_regionalization]$ g.proj -w\nGEOGCS["wgs84",\n DATUM["WGS_1984",\n SPHEROID["WGS_1984",6378137,298.257223563]],\n PRIMEM["Greenwich",0],\n UNIT["degree",0.0174532925199433]]\n<\/code>\n\n <\/p>"]} 

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4. Publications associated with SES grants, 2000-2015

   https://doi.org/10.7910/DVN/OPSR5I  

   Kaiser, Kendra; Braswell, Anna; Fork, Megan; Fork, Megan (January 2022, Harvard Dataverse)

   Information about individual publications associated with grants funded by NSF to support SES research from 2000-2015 (see "SES grants, 2000-2015"). For grants with ten or fewer publications, we included information about all available publications in this dataset. For grants with more than ten publications, we randomly selected ten to include in this dataset. CSV file with 13 columns and names in header row: "Grant ID" is the ID from the Dimensions platform (string); "Grant Number" is the NSF Award number (integer); "Publication Title" is the title of the paper (text); "Publication Year" is the year in which the paper was published (year); "Authors" is a list or abbreviated list of the authors of the paper (text); "Journal" is the name of the scientific journal or outlet in which the paper is published (text); "Interdis Rubric 1" is a metric representing the dataset authors' assessment for the level of interdisciplinarity represented by the paper (integer: “1” indicated social and natural science interdisciplinarity where both social and environmental conditions are measured or explored and/or author affiliations included departments across these disciplines; “2” indicated general interdisciplinarity between two or more different fields (that may both be within natural or social science); and “3” indicated single-disciplinarity) "Citations" is the count of citations the paper had received as of the date listed in "date for cite count", as reported in Google Scholar (integer); "date for cite count" is the date on which citation count for the paper was obtained (ddBBByy); "Abstract" is the text of the abstract of the paper, where available (text); "Notes" are any notes added by the authors of the dataset (text). 

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5. A comparison of amphibian (Xenopus laevis) tadpole and adult frog macrophages

   https://doi.org/10.1016/j.dci.2023.104647  

   Hossainey, Muhammad Riadul; Yaparla, Amulya; Uzzaman, Zarafsha; Moore, Tyler; Grayfer, Leon (April 2023, Developmental & Comparative Immunology)

   The amphibian declines are compounded by emerging pathogens that often preferentially target distinct amphibian developmental stages. While amphibian immune responses remain relatively unexplored, macrophage (Mφ)-lineage cells are believed to be important to both amphibian host defenses and to their pathogen infection strategies. As such, a greater understanding of tadpole and adult amphibian Mφ functionality is warranted. Mφ biology is interdependent of interleukin-34 (IL-34) and colony-stimulating factor-1 (CSF-1) cytokines and we previously showed that CSF-1- and IL-34-derived Mφs of the Xenopus laevis frog are morphologically, transcriptionally, and functionally distinct. Presently, we directly compared the cytology and transcriptomes of X. laevis tadpole and frog CSF-1- and IL-34-Mφs. Our results indicate that tadpole and frog CSF-1-Mφs possess greater non-specific esterase activity, typically associated with Mφ-lineage cells. By contrast, both tadpole and frog IL-34-Mφs have greater specific esterase activity, which is typically attributed to granulocyte-lineage cells. Our comparisons of tadpole CSF-1-Mφ transcriptomes with those of tadpole IL-34-Mφs indicate that the two tadpole populations possess significantly different transcriptional profiles of immune and non-immune genes. The frog CSF-1-Mφ gene expression profiles are likewise significantly disparate from those of frog IL-34-Mφs. Compared to their respective tadpole Mφ subtypes, frog CSF-1- and IL-34-Mφs exhibited greater expression of genes associated with antigen presentation. Conversely, compared to their frog Mφ counterparts, tadpole CSF-1- and IL-34-Mφs possessed greater levels of select Fc-like receptor genes. Presumably, these cytological and transcriptional differences manifest in distinct biological roles for these respective tadpole and frog Mφ subtypes. 

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