Please check the README file for more information about the dataset.

Abstract The present study contextualizes Ranganathan’s main theoretical contributions to the classification theory and addresses the Five Laws of Library Science. The major milestones in philosophical and bibliographic classifications are presented to show that the classification system has evolved from purely philosophical schemes, which were focused on the systematization of knowledge, into modern bibliographic classification systems. Facet analysis is considered a contribution to the classification process since it allows the use of an approach that encompasses different points of view of the same subject, as opposed to the enumerative systems. This article also discusses Ranganathan’s five fundamental categories, known as Personality, Matter, Energy, Space and Time, and points out to criticism of this form of categorization in the literature. The Spiral of Scientific Method and the Spiral Model of Development of subjects are presented; the latter is the meta-model of the former. The Colon Classification, which was first published in 1933, was also discussed. Finally, the applicability of the faceted classification in today’s world was addressed.

Objective structured clinical examinations (OSCEs) are widely used performance assessments for medical and dental students. A common limitation of OSCEs is that the evaluation results depend on the characteristics of raters and the scoring rubric. To overcome this limitation, item response theory (IRT) models such as the many-facet models have been proposed to estimate examinee abilities while accounting for the characteristics of raters and evaluation items in a rubric. However, conventional IRT models have two impractical assumptions: constant rater severity across all evaluation items in a rubric and an equal interval rating scale among evaluation items, which can decrease model fitting and ability measurement accuracy. To resolve this problem, we propose a new IRT model that relaxes these assumptions. We demonstrate the effectiveness of the proposed model by applying it to actual data collected from a medical interview test conducted at Tokyo Medical and Dental University as part of a post-clinical clerkship (PostCC) OSCE. The experimental results showed that the proposed model fit our OSCE data well and measured ability accurately. Furthermore, it provided abundant information on rater and item characteristics that conventional models cannot, helping us to better understand rater and item properties. This dataset includes the actual score data collected from the above-mentioned medical interview test in a PostCC OSCE, as well as the program for estimating the parameters of the proposed IRT model.

Kutzer et al data filesData files are labelled according to the experiment and model number(s). Full details on the statistical models and model numbers can be found in Appendix 1, which is a supplementary file for the paper.Kutzer_et_al_data_files.zip

β-Nickel oxyhydroxide (β-NiOOH) is a promising electrocatalyst for the oxygen evolution reaction (OER), which is the more difficult half-reaction involved in water splitting. In this study, we revisit the OER activities of the two most abundant crystallographic facets of pristine β-NiOOH, the (0001) and (1010) facets, which expose 6-fold-lattice-oxygen-coordinated and 5-fold-lattice-oxygen-coordinated Ni sites, respectively. To this end, we model various active sites on these two facets using hybrid density functional theory, which includes a fraction of the exact nonlocal Fock exchange in the electronic description of the system. By evaluating thermodynamic OER overpotentials, we show that the two active sites considered on each crystallographic facet demonstrate OER activities remarkably different from each other. However, the lowest OER overpotentials calculated for the two facets were found to be similar to each other and comparable to the overpotential for the 4-fold-lattice-oxygen-coordinated Ni site on the (1211) facet of β-NiOOH previously examined in J. Am. Chem. Soc. 2019, 141, 1, 693–705. This finding shows that all of the low-index facets investigated so far could be responsible for the experimentally observed OER activity of pristine β-NiOOH. However, the lowest overpotential active sites on these three crystallographic facets operate via different mechanisms, underscoring the importance of considering multiple OER pathways and intermediates on each crystallographic facet of a potential electrocatalyst. Specifically, our work demonstrates that consideration of previously overlooked active sites, transition-metal-ion oxidation states, reaction intermediates, and lattice-oxygen-stabilization are critical to reveal the lowest overpotential OER pathways on pristine β-NiOOH.

The most parsimonious model accounts for data through adding distance and vehicular guidability features, and their multiplicative interaction. I-scores were explicative of distance sensitivities, but only in high influence blocks.

We provide the instances used in the paper “Rapid Influence Maximization on Social Networks: The Positive Influence Dominating Set Problem”, by S. Raghavan and Rui Zhang, published in INFORMS Journal on Computing (https://doi.org/10.1287/ijoc.2021.1144). This repository contains the 100 instances used in the paper.

All the instances used in the paper are provided in a compressed archive. The accompanying data is contained in the following file: • PIDS_Instances.zip

Description: There is one main folder, which contains 100 instances based on 10 real-world graphs.

For graphs Gnutella, Anybeat, Advogato, Escorts, Hamster, Ning, and Delicious, the setting is as follows: For each instance file, there are m + 2 lines. The first m lines provide the edges in the graphs. Nodes are labeled from 0 to n where n is the largest number in the first m lines. The (m + 1)th line contains the weight (b) for each node. The (m + 2)th line contains the threshold value (g) for each node.

For graphs Flixster, Youtube, and Lastfm, the setting is as follows: Each real-world graph “G” is described by the file named “G_Graph.txt” which contains the edges in the graph. Nodes are labeled from 0 to n, where n is the largest number in the file. Each line provides the two end nodes of an edge. The 10 instances associated with each graph “G” are provided in the 10 files named “G_i.txt” for i in {0, 1, · · · , 9}. In each file, there are two lines. The first line contains the weight (b) for each node. The second line contains the threshold value (g) for each node.

The excel file “PIDS_Results.xlsx” reports, for each instance, the upper and lower bounds obtained in the paper.

This resource contains the SWAT-MODFLOW model for the Santa Fe River of North Central Florida used in the Floridan Aquifer Collaborative Engagement for Sustainability (FACETS) project. The FACETS project was funded by the USDA National Institute of Food and Agriculture (Award Number: 2017-68007-26319) to promote the economic sustainability of agriculture and silviculture in North Florida and South Georgia while protecting water quantity, quality, and habitat in the Upper Floridan Aquifer and the springs and rivers it feeds (https://floridanwater.research.ufl.edu/). SWAT-MODFLOW couples the Soil and Water Assessment Tool (SWAT) to the U.S. Geological Survey modular finite-difference flow model (MODFLOW) to produce an integrated surface-groundwater model (https://swat.tamu.edu/software/swat-modflow/). Within SWAT-MODFLOW, SWAT handles most surface and soil processes, MODFLOW handles groundwater processes, and both models interact to simulate stream flows.

The SWAT portion of this model was developed using USGS digital elevation models, the 2017 Statewide Land Use / Land Cover map of the Florida Department of Environmental Protection (FDEP), Florida Department of Health septic tank data, STATSGO soil maps, the Public Land Survey System, and NLDAS weather data. Agricultural and silvicultural production land uses and management practices implemented within SWAT were co-developed with stakeholders in a participatory modeling process (PMP) and included row crops (corn-peanut and corn-carrot-peanut rotations) forage crops (bermudagrass hay and pasture), and production forestry (slash pine). Additional land uses implemented in SWAT included urban, low-density residential, septic tanks, rapid infiltration basins, fertilized lawns, natural grass, wetlands, and open water. The MODFLOW portion of the model was developed from the larger North Florida Southeast Georgia (NFSEG) MODFLOW model (version 1.0) as developed by the St John’s River and Suwannee River Water Management Districts. A detailed description of the complete model development process can be found in a document within this resource.

Calibration of the model was conducted using a Bayesian Sample-Importance-Resample method. Data used in the model calibration included: 1) USGS discharge data (Stations 02322500, 02322700, 02322800, and 02321500); 2) USGS operational Simplified Surface Energy Balance (SSEBop) actual evapotranspiration; and 3) Upper Floridan Aquifer potentiometric surfaces from FDEP. The calibration period of the model was 2010-2018 and the validation period was 1980-2009.

The Building Component Library (BCL) is the U.S. Department of Energy's comprehensive online searchable library of energy modeling building blocks and descriptive metadata. Novice users and seasoned practitioners can use the freely available and uniquely identifiable components to create energy models and cite the sources of input data, which will increase the credibility and reproducibility of their simulations. The BCL contains components which are the building blocks of an energy model. They can represent physical characteristics of the building such as roofs, walls, and windows, or can refer to related operational information such as occupancy and equipment schedules and weather information. Each component is identified through a set of attributes that are specific to its type, as well as other metadata such as provenance information and associated files. The BCL also contains energy conservation measures (ECM), referred to as measures, which describe a change to a building and its associated model. For the BCL, this description attempts to define a measure for reproducible application, either to compare it to a baseline model, to estimate potential energy savings, or to examine the effects of a particular implementation. The BCL contains more than 30,000 components and measures. A faceted search mechanism has been implemented on the BCL that allows users to filter through the search results using various facets. Facet categories include component and measure types, data source, and energy modeling software type. All attributes of a component or measure can also be used to filter the results.

Uncertainty quantification, Bayesian statistics, the reported experimental literature, and density functional theory are synthesized to identify the active sites for the non-oxidative propane dehydrogenation on platinum catalysts. This study tests three different platinum surface models as active sites, Pt(100), Pt(111), and Pt(211), and two different methodologies for generating uncertainty, using data from four density functional theory functionals and data from the BEEF–vdW ensembles. By comparing these three surface facets using two uncertainty sources, a total of six different computational models were evaluated. Three experimental data sets, with varying numbers of reported observables, such as turnover frequencies, selectivity to propylene, apparent activation energy, and reaction orders, are calibrated and validated for these six models. This study finds no evidence for Pt(100) as the dominant active facet and finds that Pt(211) has some evidence for being the most relevant active site on the catalyst. In addition, all four functional models were excluded from final data analysis due to poor “goodness-of-fit”. In contrast, the BEEF–vdW model with ensembles (BMwEs) was found to pass “goodness-of-fit” for most of the models tested. Finally, for both Pt(111) and Pt(211), this study finds that the majority of simulations found the kinetically rate-controlling step the first dehydrogenation step from propane to C3H7*.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Adaptation of communities to environmental fluctuations can emerge from different facets of biodiversity, which may impact ecosystem functioning differently. Previous work examined how ecosystem functions can be influenced by two sources of adaptive potential: sorting (i.e., changes in community composition due to fitness differences) can occur when multiple species or groups are present (richness), and trait adaptability (i.e., trait adjustments within species or functional groups) can emerge from genetic or phenotypic diversity. However, their effect is typically studied separately, and often in the context of only one trophic level. Therefore, we used a bitrophic trait-based model varying in richness and in the presence of trait adaptability at each trophic level, to investigate how sorting and trait adaptability, at one or two trophic levels, separately or jointly shape ecosystem functions. We found that the adaptive potential emerging from any facet of diversity-induced changes in trophic interactions, in turn, affects biomass distributions within and across trophic levels, dynamical behaviour, and synchrony of biomass dynamics within a trophic level. Particularly, sorting and trait adaptability could contribute to a similar degree and at a similar time to temporal changes in ecosystem functions, but their respective contribution depended on the speed of trait adaptation, the trait range between similar functional groups, and trophic interactions. We thus suggest to consider multiple facets of diversity and their corresponding sources of adaptive potential to deepen our mechanistic understanding of ecosystem functioning, especially in a context of rapid biodiversity change. Methods The datasets were generated and not collected in the field and the laboratory. We briefly summarise the methods used, which are extensively explained in the associated Oikos article. We solved numerically the ordinary differential equations of an an extended Rosenzweig-MacArthur predator-prey model in C using the SUNDIALS CVODE solver 5.7.0}. Then, we used several packages in Python 3.10 among which NumPy, Pandas, and Matplotlib to analyse the biomass and trait dynamics, and to quantify ecosystem functions. We notably compared the temporal means and variation (coefficient of variation) of ecosystem functions and properties (e.g. total biomass, production, biomass-weighted mean trait, synchrony of prey and predators, and the ratio between prey losses due to predation and the sum of prey losses due to competition and predation) of food webs with different sources of adaptive potential.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes:

Percent of neighborhood over 8% of slope

Slope Classes

0 - 20%

400

21% -50%

300

51% - 80%

200

81%

100

Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:

Change in elevation

Relief Class ID

0 – 30 meters

10

31 meter – 90 meters

20

91 meter – 150 meters

30

151 meter – 300 meters

40

301 meter – 900 meters

50

900 meters

60

The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:

Percent of neighborhood over 8% slope in upland or lowland areas

Profile Class

Less than 50% gentle slope is in upland or lowland

0

More than 75% of gentle slope is in lowland

1

50%-75% of gentle slope is in lowland

2

50-75% of gentle slope is in upland

3

More than 75% of gentle slope is in upland

4

Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

To approximate the distribution of shrubland species based on their postfire reproductive strategy (resprouter, seeder, and facultative seeder) across Southern California, we created a raster layer subdividing the landscape into a number of different facet classes. This raster dataset is at 30 meters pixel resolution and contains 12 different landscape facet classes based on vegetation and physiography. Specifically, the facets included several different vegetation types based on the California Wildlife Habitat Relations (WHR) classification (three shrubland categories, annual grasslands, valley-foothill riparian woodland, and ‘other’ vegetation types) which were intersected with aspect (two classes: north or south facing) and topography (summit, ridges, slopes, valleys, flats, and depressions). The combination of factors is intended to capture warmer, more exposed vegetation types dominated by seeder species (occurring on south-facing slopes, summits and ridges) versus cooler, less exposed vegetation types associated with resprouter species (occurring on north-facing slopes, valleys, depressions, and flats).

The dataset is a key input into a tool developed for resource managers to aid in the prioritization of restoration activities in shrublands postfire. The tool is available at https://github.com/adhollander/postfire and described in the following technical guide:

Underwood, Emma C., and Allan D. Hollander. 2019. “Post-Fire Restoration Prioritization for Chaparral Shrublands Technical Guide.” https://github.com/adhollander/postfire/blob/master/Postfire_Restoration_Priorization_Tool_Technical_Guide.pdf

Methods The following are the GIS processing workflow steps used to create this dataset. A diagram illustrating this workflow is in the attached file collection (SoCal_Veg_Topo_Facets_Workflow.png).

1) Compile GIS layers. There were two input layers to the GIS workflow, a 30 meter digital elevation model for California (dem30) and a vegetation raster layer of the state from the California Department of Forestry and Fire Protection (fveg15). The 30 meter DEM was downloaded from the USGS National Map (https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map). The vegetation data is the FVEG dataset published in 2015 by the California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (https://frap.fire.ca.gov/media/10894/fveg15_1.zip). This is a 30 meter raster representation of statewide vegetation using the California Wildlife Habitat Relationships vegetation classification system (https://wildlife.ca.gov/Data/CWHR).

2) Import data into GIS. Both data layers were imported into GRASS 7 for further processing, using a mask of the Southern California study region (encompassing the Angeles, Cleveland, Los Padres, and San Bernardino National Forests) to filter processing to the study footprint.

3) Calculate aspect for elevation model. Using the command r.slope.aspect, we generated a raster layer (aspect) giving the topographic aspect (0-360 degrees) of slopes across the study region.

4) Generate north-south aspect layer. Using the command r.mapcalc, we subdivided the aspect layer into north and south-facing slopes through creating a raster layer (nsaspect) with two categories for north and south.

5) Generate geomorphons for study region. The geomorphon raster layer derives from the dem30m surface and classifies the landscape into 10 discrete landform types, examples being ridges, slopes, hollows, and valleys. The algorithm for geomorphon classification uses a pattern recognition approach based on line of sight analysis (Jasiewisc and Stepinski 2013) and was generated using the r.geomorphons extension for GRASS 7.

6) Merge geomorphons with north-south aspect layer. In this step we combined the north-south aspect layer with the geomorphons layer to create a layer entitled nsgeomorphon2a. In so doing we grouped the geomorphon types spurs, slopes, and hollows into a single “slope” category and assigned these to north-facing slopes and south-facing slopes depending upon the value of the north-south aspect layer.

7) Regroup merged layer into three groupings. In this step we took the merged nsgeomorphon2a layer and assigned the classes in it to three different physiographic groups, namely 1) flats 2) valleys, depressions, and north-facing slopes/spurs/hollows/footslopes/shoulders and 3) summits and ridges and south-facing slopes/spurs/hollows/footslopes/shoulders. This grouped layer was named nsgeomorphon2d.

8) Reclass vegetation layer to main habitat types. The vegetation layer fveg15 contains information about many details of the vegetation, including canopy size, canopy cover, and main habitat type. This reclass step extracts the main habitat type into a separate raster named fveg15whr.

9) Combine vegetation layer with physiography layer. Using the command r.cross, we combined the layers fveg15whr and nsgeomorphon2d into a new layer nsgeoxfvegwhr with a separate category for each combination of the raster values from the two input layers.

10) Reclass combined layer into small set of groupings. Taking the nsgeoxfvegwhr layer, we recategorized the 196 combinations of raster values into a set of 12 different combinations using the command r.reclass. This layer is named nsgeoxfvegnbclasses. The 12 different classes generated as an output are the following, with their raster values paired with their classes:

0 Annual grassland: south-facing slopes; summits; ridges

1 Annual grassland: north-facing slopes; valleys; depressions; flats

2 Chamise-redshanks chaparral: south-facing slopes; summits; ridges

3 Chamise-redshanks chaparral: north-facing slopes; valleys; depressions; flats

4 Mixed or montane chaparral: south-facing slopes; summits; ridges

5 Mixed or montane chaparral: north-facing slopes; valleys; depressions; flats

6 Valley-foothill riparian: south-facing slopes; summits; ridges

7 Valley-foothill riparian: north-facing slopes; valleys; depressions; flats

8 Coastal scrub: south-facing slopes; summits; ridges

9 Coastal scrub: north-facing slopes; valleys; depressions; flats

10 Other: south-facing slopes; summits; ridges

11 Other: north-facing slopes; valleys; depressions; flats

11) Export dataset. Using the command r.out.gdal, we exported the nsgeoxfvegnbclasses layer as the raster geotiff file SoCal_Veg_Topo_Facets.tif.

The GRASS commands used for these 11 steps are below:

r.in.gdal input="/home/adh/CARangelands/Vegetation/fveg15_11.tif" output="fveg15" memory=300 offset=0

r.proj input="dem1sec_calif" location="CAllnad83" mapset="statewide" output="dem30m" method="bilinear" memory=300 resolution=30

r.slope.aspect elevation=dem30m@statewide slope=slope aspect=aspect

r.mapcalc 'nsaspect = if(aspect <= 180, 1, 2)'

r.geomorphon --overwrite dem=dem30m@statewide forms=SoCalgeomorphons search=11 skip=4 flat=1 dist=0

r.mapcalc --overwrite 'nsgeomorphon = if((SoCalgeomorphons@socalNF == 5 ||| SoCalgeomorphons@socalNF == 6 ||| SoCalgeomorphons@socalNF == 7) &&& nsaspect == 1, 11, if(((SoCalgeomorphons@socalNF == 5 ||| SoCalgeomorphons@socalNF == 6 ||| SoCalgeomorphons@socalNF == 7) &&& nsaspect == 2), 12, SoCalgeomorphons@socalNF))'

r.reclass input=nsgeomorphon2a@socalNF output=nsgeomorphon2d rules=/home/adh/SantaClaraRiver/PostfireRestoration/jupyter/datasets/nsgeomorphon-reclass2d.lut

r.reclass input="fveg15@statewide" output="fveg15whr" rules="/home/adh/CARangelands/Vegetation/fveg15whr.lut"

r.cross --overwrite input=fveg15whr@statewide,nsgeomorphon2d@socalNF output=nsgeoxfvegwhr

r.reclass --overwrite input=nsgeoxfvegwhr@socalNF output=nsgeoxfvegnbclasses rules=/home/adh/SantaClaraRiver/PostfireRestoration/datasets/fvegwhrtonbclasses.lut

r.out.gdal --overwrite input=nsgeoxfvegnbclasses@socalNF output=SoCal_Veg_Topo_Facets.tif format=GTiff type=Byte createopt=COMPRESS=DEFLATE

Loadings of items from the MSMS questionnaire on the three main factors.

Demographic characteristics of medical students.

Raw data of validation for the lumbar spine model. (XLSX 16 kb)

Cronbach’s alpha coefficient, convergent and discriminant validity for three subscales.

The COVID-19 pandemic has prompted an unprecedented global effort to understand and mitigate the spread of the SARS-CoV-2 virus. In this study, we present a comprehensive analysis of COVID-19 in Western New York (WNY), integrating individual patient-level genomic sequencing data with a spatially informed agent-based disease Susceptible-Exposed-Infectious-Recovered (SEIR) computational model. The integration of genomic and spatial data enables a multi-faceted exploration of the factors influencing the transmission patterns of COVID-19, including genetic variations in the viral genomes, population density, and movement dynamics in New York State (NYS). Our genomic analyses provide insights into the genetic heterogeneity of SARS-CoV-2 within a single lineage, at region-specific resolutions, while our population analyses provide models for SARS-CoV-2 lineage transmission. Together, our findings shed light on localized dynamics of the pandemic, revealing potential cross-county transmission networks. This interdisciplinary approach, bridging genomics and spatial modeling, contributes to a more comprehensive understanding of COVID-19 dynamics. The results of this study have implications for future public health strategies, including guiding targeted interventions and resource allocations to control the spread of similar viruses.

To investigate the ability of coarse-grained molecular dynamics simulations to predict the relative growth rates of crystal facets of pharmaceutical molecules, we apply two coarse-graining strategies to two drug molecules, phenytoin and carbamazepine. In the first method, we map an atomistic model to a MARTINI-level coarse-grained (CG) force field that uses 2 or 3 heavy atoms per bead. This is followed by applying Particle Swarm Optimization (PSO), a global optimum searching algorithm, to the CG Lennard-Jones intermolecular potentials to fit the radial distribution functions of both the crystalline and melt structures. In the second, a coarser-grained method, we map 5 or more heavy atoms into one bead with the help of the Iterative Boltzmann Inversion (IBI) method to derive a tabulated longer-range force field (FF). Simulations using the FF’s derived from both strategies were able to stabilize the crystal in the correct structure and to predict crystal growth from the melt with modest computational resources. We evaluate the advantages and limitations of both methods and compare the relative growth rates of various facets of both drug crystals with those predicted by the Bravais–Friedel–Donnay–Harker (BFDH) and attachment energy (AE) theories. While all methods, except for the simulations conducted with the coarser-grained IBI-generated model, produced similarly good results for phenytoin, the finer-grained PSO-generated FF using MARTINI mapping rules outperformed the other methods in its prediction of the facet growth rates and resulting crystalline morphology for carbamazepine.