cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4231/b90h-vh37
title: Period n-tupling in Uniaxial Random Field XY Magnet with rotating driving field

description: &lt;p&gt;&lt;iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/videoseries?list=PLhxhhdi9rQWPrrQ7VWtg6a2xdtZYQMCjO" width="560"&gt;&lt;/iframe&gt;&lt;/p&gt;

&lt;p&gt;Videos/animations description:&lt;/p&gt;

&lt;p&gt;The left panel is divided into 4-panels(2x2):&lt;/p&gt;

&lt;p&gt;Panel Top-Left: The plot of X Magnetization along the horizontal axis and the driving field angle [ϕ(H)] along the vertical axis.&lt;/p&gt;

&lt;p&gt;Panel Top-Right: The direction of the driving field [H(cos(ϕ),sin(ϕ)] given by the green arrow. The direction of spin [cos(&theta;),sin(&theta;)] at each lattice site is given by the color corresponding to the angle (&theta;) on the color wheel.&lt;/p&gt;

&lt;p&gt;Panel Bottom-Left: The plot of X Magnetization along the horizontal axis and Y Magnetization along the vertical axis.&lt;/p&gt;

&lt;p&gt;Panel Bottom-Right: The plot of Y Magnetization along the vertical axis and the driving field angle [ϕ(H)] along the horizontal axis.&lt;/p&gt;

&lt;p&gt;The right panel is the response spin configuration of a LxL lattice at zero temperature to the corresponding driving field for a particular disorder configuration of strength R&lt;sub&gt;x &lt;/sub&gt;(along X-axis of spin).&lt;/p&gt;

&lt;p&gt;Figures description:&lt;/p&gt;

&lt;p&gt;The rainbow coloured part is the magnetization response with a period &lt;em&gt;n&lt;/em&gt;-times the period of the driving field, which is termed as the &lt;em&gt;limit cycle&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Before reaching the limit cycle there is some &lt;em&gt;transient&lt;/em&gt; response for almost all the different simulations which does not repeat, and they are plotted in black.&lt;/p&gt;
description: These videos show the evolution of spin configurations in multiperiod limit cycle, generated from zero temperature simulations of 2D XY model with uniaxial random fields when subjected to a driving field which is rotated very slowly.
DOI: 10.4231/60v2-pj72
title: Building Information Modelling (BIM) data repository with labels

description: &lt;p&gt;The dataset contains five models with extracted elements for each model.&lt;/p&gt;

&lt;p&gt;The elements are manually labeled by researchers with construction/civil engineering backgrounds based on discussion and majority vote. Dataset files were created using Autodesk Revit Architecture 2011.&lt;/p&gt;

&lt;p&gt;Object signatures have been widely used in object detection and classification. Following a similar idea, the authors developed geometric signatures for architecture, engineering, and construction (AEC) objects such as footings, slabs, walls, beams, and columns. The signatures were developed both scientifically and empirically, by following a data-driven approach based on analysis of collected building information modeling (BIM) data using geometric theories. Rigorous geometric properties and statistical information were included in the developed geometric signatures. To enable an open access to BIM data using these signatures, the authors also initiated a BIM data repository with a preliminary collection of AEC objects and their geometric signatures. The developed geometric signatures were preliminarily tested by a small object classification experiment where 389 object instances from an architectural model were used. A rule-based algorithm developed using all parameter values of 14 features from the geometric signatures of the objects successfully classified 336 object instances into the correct categories of beams, columns, slabs, and walls. This higher than 85% accuracy showed the developed geometric signatures are promising. The collected and processed data were deposited into the Purdue University Research Repository (PURR) for sharing.&lt;/p&gt;

&lt;p&gt;Corresponding papers:&lt;/p&gt;

&lt;p&gt;Wu, J., and Zhang, J. (2019). &ldquo;&lt;a href="https://ascelibrary.org/doi/10.1061/9780784482421.034"&gt;Introducing geometric signatures of architecture, engineering, and construction objects and a new BIM dataset&lt;/a&gt;.&rdquo; Proc., 2019 ASCE International Conference on Computing in Civil Engineering, ASCE, Reston, VA, 264-271.&lt;/p&gt;

&lt;p&gt;Wu, J., and Zhang, J. (2019). &ldquo;&lt;a href="https://ascelibrary.org/doi/10.1061/%28ASCE%29CP.1943-5487.0000858?_ga=2.29583295.1663657785.1567019920-167394469.1534795788"&gt;New automated BIM object classification method to support BIM interoperability.&lt;/a&gt;&rdquo; J. Comput. Civ. Eng., 33(5), 04019033.&lt;/p&gt;

&lt;p&gt;Wu, J., and Zhang, J. (2018). &quot;&lt;a href="https://ascelibrary.org/doi/10.1061/9780784481301.070"&gt;Automated BIM object classification to support BIM interoperability&lt;/a&gt;.&quot; Proc., ASCE Construction Research Congress, ASCE, Reston, VA, 706-715.&lt;/p&gt;
description: The dataset contains five models with extracted elements for each model.
The elements are manually labeled by researchers with construction/civil engineering backgrounds based on discussion and majority vote.
DOI: 10.4231/10es-ah67
title: Potential Suitability for Subirrigation in the U.S. Midwest

description: &lt;p&gt;Subirrigation is the application of irrigation water below the ground surface to raise the water table to within or near the root zone. In areas with subsurface tile drainage, the same drains that remove excess water during wet periods can be used to provide supplemental irrigation to the crops during dry periods of the year. Potential subirrigation suitability maps are developed using numerical suitability criteria and the Gridded Soil Survey Geographic (gSSURGO) database. Numeric criteria were developed for land use, natural drainage class, saturated hydraulic conductivity, and representative slope to determine the potential suitability for subirrigation, and identify limiting factors for locations across the Midwest. Suitable land uses included cropland and pasture/hay from the National Land Cover Database. Maps are viewable through an online tool at &lt;a href="https://transformingdrainage.org/tools/subirrigation-suitability-tool/"&gt;https://transformingdrainage.org/tools/subirrigation-suitability-tool/&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;Individual state downloads are available in the Supporting Docs tab.&lt;/p&gt;
description: This series provides shapefiles showing the potential suitability, and limiting factors, for subirrigation in the U.S. Midwest based on gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
DOI: 10.4231/xtzt-p683
title: Population-specific brain templates for adolescent collision-sport athletes in Purdue Neurotrauma Group longitudinal database

description: &lt;p&gt;We developed a population-specific anatomical (T1-weighted) and a DTI template that target adolescent collision-sport (American football, soccer) athletes, based on the MRI scans from the longitudinal database of Purdue Neurotrauma Group. The templates were developed in an effort to minimize bias introduced in spatial normalization, improve sensitivity of voxel-wise statistical analysis, and therefore better clarify the mechanisms that lead to traumatic brain injury in adolescent athletes, which is one of our long-term commitments to this vulnerable population. Image processing software, including &lt;em&gt;FSL&lt;/em&gt; and &lt;em&gt;ANTS&lt;/em&gt;, were used to create the templates. To scale and speed up the atlas creation, we established a high-throughput high-performance computing workflow which integrated Open Science Grid and Purdue Community Clusters.&lt;/p&gt;
description: Unbiased population-specific brain templates for local adolescent collision-sport athletes in the longitudinal database of Purdue Neurotrauma Group, including a T1-weighted template and a DTI template.
DOI: 10.4231/6d95-rh34
title: Busia Soil Class Map

description: &lt;p&gt;The soil landscape rule-based model was used to disaggregate the soil map units by exploiting information in the Busia soil map legend and the map unit descriptions. These descriptions were used to generate rules that were applied to a fuzzy soil class map generated from a parent material map and a K-means cluster map generated from six terrain attributes, namely, multiresolution ridgetop flatness (MRRTF), multiresolution valley bottom flatness (MRVBF), SAGA Wetness Index (TWI), topographic position index (TPI), planform curvature, and profile curvature. The result was a soil class map with a spatial resolution of 30 m with an overall accuracy of 58% and a Kappa coefficient of 0.54.&lt;/p&gt;
description: This is a disaggregated soil map of the Busia area.
DOI: 10.4231/va77-jb85
title: Busia Crop Suitability Maps

description: &lt;p&gt;To determine specific crop suitability ratings for Busia area, decision matrices developed by Rachilo and Michieka (1991) were used to generate crop suitability classes for each soil map unit. Land suitability is the fitness of a given tract of land for a defined use. Differences in the degree of the suitability were determined by relationship, actual or anticipated, between benefits and required inputs associated with the use of the tract land in question (FAO, 1976). The suitability of the individual tracts of land for rainfed crops growing and other activities is expressed by the following classes (see appendix 3 in Rachilo and Michieka, 1991). These suitability classes are broadly grouped into the following groups;&lt;/p&gt;

&lt;p&gt;S1: Highly Suitable: Land suitable for sustained high yields with minimum costs of developed associated with the land.&lt;/p&gt;

&lt;p&gt;S2: Moderately Suitable: Land of moderate productivity, or requiring moderate costs for development and management because of slight to moderate limitations in land characteristics.&lt;/p&gt;

&lt;p&gt;S3: Marginally Suitable: Land of restricted productivity of land requiring relatively high costs for development and management because of moderate to severe limitations in land characteristics.&lt;/p&gt;

&lt;p&gt;NS: Unsuitable: Land which has qualities that appear to preclude sustained use of crop growing or livestock keeping.   &lt;/p&gt;

&lt;p&gt;The suitability class defining criteria (requirements) for various crops/ land use types are contained within each crop type folder.&lt;/p&gt;

&lt;p&gt;FAO, 1976. A framework for land evaluation. FAO Soils Bulletin 52, F.A.O. Rome, 79p. [Outlines the basic principles of the FAO approach land evaluation and land use planning].&lt;/p&gt;

&lt;p&gt;Rachilo, J.R., &amp; Michieka, D.O. (1991). Reconnaissance Soil Survey of the Busia Area (quarter degree sheet No. 101). Kenya Soil Survey, pp. 269.&lt;/p&gt;

&lt;p&gt; &lt;/p&gt;
description: These are suitability classes defining the requirements for various crops/ land use types.
DOI: 10.4231/7f9r-4w74
title: Environmental Covariate Data for Spatial Prediction of Soil Properties for the Busia Area, Kenya

description: &lt;p&gt;A raster stack of 23 environmental covariates primarily based on remote sensing for predicting soil properties. These covariates were selected to represent the factors of soil formation and included the following. (1) The 1 Arc-Second (30 m) NASA shuttle Radar Topographic Mission (SRTM) global elevation dataset was downloaded from the USGS Earth Explorer (USGS, 2017a). (2) Eight terrain attributes (TAs), namely, Multiresolution Index of Valley Bottom Flatness (MRVBF), Multiresolution Ridge Top Flatness (MRRTF), plan curvature, profile curvature, relief intensity, slope, topographic position index (TPI), and SAGA wetness index (TWI), all based on the 30 m elevation dataset. (3) Annual average precipitation and temperature derived from monthly averages over the years 1970 &ndash; 2000 obtained from WorldClim ver. 2 at &lt;a href="https://www.worldclim.org/"&gt;https://www.worldclim.org&lt;/a&gt;, all at a spatial resolution of ~1 km&lt;sup&gt; &lt;/sup&gt;(Fick and Hijmans, 2017). (4) All eleven bands from Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) level 2 images of identification number LC08_L1TP_170060_20171226_20180103_01_T1 of path 170 and row 60 with an acquisition date of 26 December 2017 were downloaded from the USGS Earth Explorer (USGS, 2017b). These two sensors provide seasonal coverage of the global landmass at a spatial resolution of 30 m for the visible, near infrared, and short wavelength infrared; 100 m for the thermal infrared sensor; and 15 m for the panchromatic band. (5) The normalized difference vegetation index (NDVI) was calculated from the Landsat 8 level two data as a proxy for the vegetation (organisms) soil forming factor. &lt;/p&gt;

&lt;p&gt;These twenty-three environmental covariates were projected to the WGS84 Web Mercator (Auxiliary Sphere) coordinate system. Climate and Landsat data were subjected to bilinear interpolation resampling technique in ArcMap to a resolution of 30 m.&lt;/p&gt;

&lt;p&gt;USGS. (2017a). NASA Shuttle Radar Topography Mission (SRTM) Global 1 arc second dataset (SRTMGL1), Digital Elevation Version 3. URL: &lt;a href="https://earthexplorer.usgs.gov/"&gt;https://earthexplorer.usgs.gov/&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;USGS. (2017b). Landsat 8 OLI aerial imagery Level 1. URL: &lt;a href="https://earthexplorer.usgs.gov/"&gt;https://earthexplorer.usgs.gov/&lt;/a&gt;. &lt;/p&gt;
description: Environmental covariates were carefully selected to represent factors of soil formation: climate, relief, organisms, and time.
DOI: 10.4231/4dbt-2w68
title: Environmental Covariate Data for the Disaggregation of the Reconnaissance Soil Map of the Busia Area, Kenya

description: &lt;p&gt;How can soil classes within a soil map unit be disaggregated? To answer this question, a soil landscape-rule based approach was used to disaggregate the soil map units by exploiting information in the map legend and the map unit descriptions. The descriptions often contain the mental soil landscape models used by the original soil surveyor(s) and were extracted from both the Busia soil survey report and soil class descriptions from the legend of the Soil Map of the World (FAO/UNESCO, 1974) and quantified by a set of rules. Second, a digital model must be developed that represents the landscape and environmental conditions of the original landscape. Environmental conditions of the study area were captured using six terrain attributes derived from a 30 m digital elevation model (DEM) in SAGA-GIS and parent material map extracted from the soil polygon map were used. Third, the rules that describe the locations of the different soil classes in the landscape must be applied to the digital model to place the soil classes in their most likely position(s) in the landscape.&lt;/p&gt;

&lt;p&gt;FAO-UNESCO. (1974). Soil Map of the World, 1:5,000,000. Vol. 1: Legend. UNESCO, Paris, France.&lt;/p&gt;
description: Enviromental covariate data used to develop a digital model that represents the landscape and environmental conditions of the Busia landscape.
DOI: 10.4231/tdn8-pm14
title: Busia Fuzzy Soil Class Map

description: &lt;p&gt;Soil landscape inference model (SoLIM) maps soil type, not soil mapping units, under the assumption that soil properties are fairly homogeneous over a small spatial extent. Thus, typically it takes a raster-based approach which means that it divides the area to be mapped into small pixels and determines the soil type for each pixel. Soil at each pixel is expressed in terms of its similarity to a set of prescribed soil types often referred to as &lsquo;fuzzy soil mapping&rsquo;. To avoid assigning a single soil type to a given location, similarity values (fuzzy membership values) are assigned to each soil type expressing the similarity of the local soil to each of the prescribed soil types. One must therefore know the types of soil existing in the area to be mapped. Soil types at a given location are predicted using the similarity values by assessing the environmental conditions at the location according to knowledge on how these conditions are related to the development of each soil type. Similarity values are generated by calculation the zonal statistics for each cluster in SAGA-GIS to obtain the range of values for each environmental covariate. The two key inputs to SoLIM are: data on the selected environmental covariates related to soil conditions in the area, and knowledge that describes the relationships between soils and the environmental variables.&lt;/p&gt;
description: Map based on the concept that soil classes can be spatially inferred from soil-related environmental conditions.
DOI: 10.4231/4q9t-ft90
title: Busia K-Means Cluster Map

description: &lt;p&gt;Six terrain attributes were subjected to K-means clustering performed in SAGA-GIS using the hill-climbing method (Rubin, 1967) using a terrain attribute combination of multiresolution valley bottom flatness, multiresolution ridgetop flatness, planform curvature, profile curvature, SAGA wetness index, and topographic position index. This process was intended to mimic the geometry of &lsquo;fully developed slopes&rsquo; observed within the landscape. (Wood, 1942; King, 1957). Examination of 5-cluster map by overlaying it on the satellite imagery showed that clustered correspond to different slope positions &lt;em&gt;viz&lt;/em&gt; summit, shoulder, backslope, toeslope, and footslope and was used to predict soil properties and disaggregated for the Busia area.&lt;/p&gt;

&lt;p&gt;King, C. (1957). The uniformitarian nature of hillslopes. Transactions of the Edinburgh Geological Society, 17:81-102.&lt;/p&gt;

&lt;p&gt;Rubin, J. (1967). Optimal classification into groups: an approach for solving the taxonomy problem. Journal of Theoretical Biology, 15:103-144.&lt;/p&gt;

&lt;p&gt;Wood, A. (1942): The development of hillslopes. Geologists Association Proceedings 53:128-138.&lt;/p&gt;
description: Map that mimics the geometry of 'fully developed slopes'.
DOI: 10.4231/kyj5-s732
title: Busia Land Quality Maps

description: &lt;p&gt;Although in many systems of land evaluation, single or land characteristics such as drainage condition or texture are used as a basis for diagnosis and for establishing suitability class-determining specifications, it is most useful and ideal to consider the characteristics. Single characteristics are therefore grouped into a combination called land quality, which can be used as an independent diagnostic criterion reflecting limitations for land use. It can be quantified and rated and then used to specify criteria or suitability class units. The Kenya soil survey prepared proposals for the rating of the land qualities, which are laid in the Internal Communications Nos. 7 and 29 (Kenya Soil Survey, 1977 and 1988 respectively). Land qualities are classified into three to five grades ranging from very high to very low. The current suitability evaluation of &lsquo;each mapping unit&rsquo; for each land use alternative was carried out by comparison of the rating of the land qualities of that particular map unit with the specifications for each land use alternative (decision matrices).&lt;/p&gt;

&lt;p&gt;FAO. (1988). FAO/UNESCO Soil Map of the World, Revised Legend. World Soil Resources Report, 60, pp.110.&lt;/p&gt;

&lt;p&gt;FAO. (1977). Guidelines for Soil Profile Description. 2nd edition. FAO, Rome.&lt;/p&gt;
description: Independent diagnostic criteria reflecting limitations for land use.
DOI: 10.4231/0fgr-z715
title: Principal Components for Stepwise Multiple Linear Regression

description: &lt;p&gt;To ensure that multivariate covariates are independent of each other, Gobin (2000) used principal components instead of the original environmental covariates as predictors to improve on the prediction for soil-landscape modelling. Therefore, all the original environment covariates for digital soil mapping were subjected to a standardized principal component analysis (PCA) to generate a smaller number of linear combinations that capture most of the variation within the raster stack as a whole (Crawley, 2012). RStudio version 3.5.1 was used to conduct a standardized PCA using the &lt;em&gt;RStoolbox&lt;/em&gt; package (Leutner and Horning, 2017). These principal components were uses as the predictors for stepwise multiple linear regression.&lt;/p&gt;

&lt;p&gt;Crawley, M.J. (2012). The R book. John Wiley &amp; Sons, pp. 1051.&lt;/p&gt;

&lt;p&gt;Gobin, A. (2000). Participatory and spatial-modelling methods for land resources analysis. Doctoral dissertation, Katholieke Universiteit Leuven, pp. 344.&lt;/p&gt;

&lt;p&gt;Leutner, B., &amp; Horning, N. (2017). RStoolbox: tools for remote sensing data analysis. R package version 0.1,7.&lt;/p&gt;
description: Independent predictor variables for stepwise multiple linear regression.
DOI: 10.4231/00r1-hm25
title: Soil Property Data for Spatial Prediction of Soil Properties for the Busia Area, Kenya

description: &lt;p&gt;The use of legacy soil data for digital soil mapping is determined by the type of data available. When soil profile data is available, it can be used as inputs to predict soil properties at unsampled locations (McBratney et al., 2003). This dataset contains two types of data (i) Data Set A which consists of 76 georeferenced locations for which A and B horizons were sampled. Composite samples of the A horizon were collected between depths of 0 &ndash; 30 cm for fertility analysis, while the B horizon was sampled to an unspecified depth. (ii) Data Set B consists of detailed soil profile pit descriptions and analytical data for 48 georeferenced profiles presumably from pits. To make Data Set B comparable with Data Set A, an equal area quadratic smoothing spline function (Ponce-Hernandez et al., 1986; Bishop et al., 1999; Malone et al., 2009) in the &lt;em&gt;ithir &lt;/em&gt;package in R statistical software (Malone, 2017) was used to predict soil attributes for Data Set B over the interval of 0 &ndash; 30 cm. This data set was used to determine whether existing legacy soil data for a selected potion of Kenya could be used to quantitatively predict soil properties at a higher spatial resolution than current maps by using DSM techniques without conducting additional field work. Data Set A was used as the calibration dataset whereas Data Set B was used as the evaluation dataset.&lt;/p&gt;

&lt;p&gt;Bishop, T. F. A., McBratney, A. B., &amp; Laslett, G. M. (1999). Modelling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma, 91:27-45.&lt;/p&gt;

&lt;p&gt;Malone, B. P., McBratney, A. B., Minasny, B., &amp; Laslett, G. M. (2009). Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma, 154:138-152.&lt;/p&gt;

&lt;p&gt;McBratney, A. B. (1998). Some considerations on methods for spatially aggregating and disaggregating soil information. Nutrient Cycling in Agroecosystems, 50:51-62.&lt;/p&gt;

&lt;p&gt;McBratney, A. B., Santos, M. M., &amp; Minasny, B. (2003). On digital soil mapping. Geoderma, 117:3-52.&lt;/p&gt;

&lt;p&gt;Ponce‐Hernandez, R., Marriott, F. H. C., &amp; Beckett, P. H. T. (1986). An improved method for reconstructing a soil profile from analyses of a small number of samples. European Journal of Soil Science, 37:455-467.&lt;/p&gt;
description: Soil property data mined from the Reconnaissance Soil Survey of the Busia Area (quarter degree sheet No. 101) for digital soil mapping.