What is a facet?

A facet is a metadata element, usually from a controlled list, that provides counts of records in a query result with particular values for the metadata element. The DataCite JSON Response includes data on a variety of facets for each query done using the DataCite API.

Repository Facets

DataCite Commons uses facets on repository pages to provide an overview of repositories. For example, the Metadata Game Changers Commons page shows publication year, work types, licenses, creators and contributors, and some other facets as graphics and lists.

The facets provided by DataCite can be used to 1) understand characteristics of DataCite metadata, 2) understand some aspects of repository completeness, and 3) provide overviews of repositories.

Using Facets to Understand DataCite Metadata

DataCite includes facets and facet values in all query results, so they are a useful tool for answering some "big picture" questions about DataCite metadata. Some of these questions were explored during 2022 in DataCite Facets: Understanding DataCite Usage using a tool called DataCite Facets.

DataCite Facets and Repository Overviews

DataCite facets can be used to provide overviews of any DataCite Repository and understand some characteristics of the repositories. They can also be used, in some cases, to provide insights into some aspects of repository completeness.

Repository Facets and Metadata Completeness

Many useful repository measures focus on completeness of the metadata, i.e., the portion of records in the repository that include some metadata element. The DataCite facet data can provide some insight into completeness, but we must keep in mind that the facet data are limited to top ten values for most facets (except for published and resourceTypes, which can be > 10). The blog DataCite Facets and Metadata Completeness describes how some facets can be used to provide insights into metadata completeness.

This dataset provides selected facets downloaded using the DataCite API and associated statistics as a comma-separated-value (CSV) file.

Column definitions:

The dataset includes a number of columns for the selected facets:

Ecophysiographic facets are unique combinations of climate, lithology, landcover, and landform. This layer is designed for use as a geoprocessing input and to support pop-ups in ArcGIS Online. This layer is designed for use as a geoprocessing input and to support pop-ups in ArcGIS Online. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all black or white layer. Include this layer in web maps by making it draw 100% transparent. This 2015 map contains updates to the 2014 Ecophysiographic Facets layer in the form of landforms and land cover data, which have greater variety of classes and better spatial coherence (less arbitrary fragmentation). The result is more than twice as many unique facet combinations. Ecophysiographic Facets are areas of distinct bioclimate, landform, lithology, and land cover that form the basic components of terrestrial ecosystem structure. The Ecophysiographic Facets layer was produced by combining the values in four 250-m cell-sized rasters using the ArcGIS Combine tool (Spatial Analyst). The first three of these inputs (climate, landforms, lithology) represent the primary environmental factors that determine the distribution of living organisms while the fourth (land cover) is vegetation"s response to the physical environment.This layer provides access to a 250-m cell-sized raster of unique combinations of climate, lithology, land cover, and landform known as ecophysiographic facets. The layer was created in 2015 by Esri and the USGS. The following layers were used to create this map: World BioclimatesWorld Landforms Improved Hammond MethodWorld LithologyWorld Land Cover ESA 2010 A simplified classification of the ecological facets is available in the World Ecophysiographic Land Units layer. A layer summarizing the local diversity of ecophysiographic facets is available here. A service is available providing access to the data tables associated with this and other global layers. These data table services can be used by developers to create custom applications. For more information see the World Ecophysiographic Tables. The process used to produce this layer is documented in the publication:Sayre and others. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages. Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 16,000 x 16,000 pixels. What can you do with this layer?This layer is suitable for analysis and can be used in ArcGIS Online to support pop-ups. It can be used in ArcGIS Desktop. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all white layer. To use in pop-ups set the transparency to 100% and configure the pop-up. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. 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.

We build a tool that accelerates Research and Development (R&D) aimed at introducing Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS). In the proposed effort, FACET will form the basis of a collaborative R&D platform, an environment where users can share open source software modules (software and data sets developed to reside outside the FACET Application Programmers Interface (API)) between users at the same or different universities, so that each user can benefit from the open source software and data contributions of others. Thus, when a student who has never used FACET before enters into a collaborative study of UAS integration in the NAS, he/she is able to download open source software and data to get going on rich R&D experiment without having to start from scratch. A student can download weather data sets, Special Use Airspace (SUA) data, air traffic demand data, UAS models (e.g., Base of Aircraft Data (BADA)) and flight plans, metrics, or anything that is posted on the open source library, to get a "running start" with R&D. When completing innovative modules outside the API, the student can post software to the open source repository for others to benefit. This collaborative environment will also allow for FACET-based research to be performed in a distributed manner – where simulations at one university may be run with models and parameters provided by other users at different universities, and the results posted back to the common repository for all users to share. This open-source collaborative platform is demonstrated on R&D problems aimed at introducing

Ecophysiographic facets are unique combinations of climate, lithology, landcover, and landform. This layer is designed for use as a geoprocessing input and to support pop-ups in ArcGIS Online.This layer is designed for use as a geoprocessing input and to support pop-ups in ArcGIS Online. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all black or white layer. Include this layer in web maps by making it draw 100% transparent.This 2015 map contains updates to the 2014 Ecophysiographic Facets layer in the form of landforms and land cover data, which have greater variety of classes and better spatial coherence (less arbitrary fragmentation). The result is more than twice as many unique facet combinations.Ecophysiographic Facets are areas of distinct bioclimate, landform, lithology, and land cover that form the basic components of terrestrial ecosystem structure. The Ecophysiographic Facets layer was produced by combining the values in four 250-m cell-sized rasters using the ArcGIS Combine tool (Spatial Analyst). The first three of these inputs (climate, landforms, lithology) represent the primary environmental factors that determine the distribution of living organisms while the fourth (land cover) is vegetation's response to the physical environment.Dataset SummaryThis layer provides access to a 250-m cell-sized raster of unique combinations of climate, lithology, land cover, and landform known as ecophysiographic facets. The layer was created in 2015 by Esri and the USGS. The following layers were used to create this map:World BioclimatesWorld Landforms Improved Hammond MethodWorld LithologyWorld Land Cover ESA 2010A simplified classification of the ecological facets is available in the World Ecophysiographic Land Units layer. A layer summarizing the local diversity of ecophysiographic facets is available here. A service is available providing access to the data tables associated with this and other global layers. These data table services can be used by developers to create custom applications. For more information see the World Ecophysiographic Tables.The process used to produce this layer is documented in the publication:Sayre and others. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages. What can you do with this layer?This layer is suitable for analysis and can be used in ArcGIS Online to support pop-ups. It can be used in ArcGIS Desktop. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all white layer. To use in pop-ups set the transparency to 100% and configure the pop-up.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. 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.

Values are calibrated within biological ranges to produce both normoxic and hypoxic regions within the microenvironment.

Purpose: Telehealth may be a solution to access barriers in speech-language pathology. Previous investigations of telehealth assessment have alluded to factors affecting children’s engagement, though these factors have not been comprehensively described. Aim: This study aimed to develop a novel clinical tool to describe the factors affecting children’s engagement in paediatric telehealth assessments. Method: The Factors Affecting Child Engagement in Telehealth Sessions (FACETS) tool was developed using a mixed methods approach. Iterative analysis was conducted through a qualitative evidence synthesis, followed by the application of the tool to seven children aged between 4;3 and 5;7 years old who participated in a speech and language assessment via telehealth. Descriptive data were obtained regarding engagement on both a child-by-child and task-by-task basis. Reliability of the FACETS was determined via percent agreement and Cohen’s kappa between two independent raters. Result: Using a mixed methods design, the FACETS framework was developed and refined. Application of the tool to seven case studies revealed variability in engagement with acceptable inter-rater reliability. Conclusion: The FACETS may be a useful resource for describing the factors that influence children’s engagement in telehealth during the assessment. The FACETS requires further testing with clinical populations.

Legends: CD: Cannot be determined, NA: not applicable, NR: not reported, N: no, Y: yes.Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies.

ARDN (Agricultural Research Data Network) annotations for "Floridan Aquifer Collaborative Engagement for Sustainability (FACETS) - Field trial data from Live Oak, Florida". The ARDN project (https://usda.figshare.com/ARDN) is a network of datasets harmonized and aggregated using a common vocabulary termed ICASA. ICASA is a recommended data dictionary by USDA NAL (https://data.nal.usda.gov/data-dictionary-examples) described in detail here: www.tinyurl.com/icasa-mvl. ARDN provides dataset annotations which facilitate interoperability. For information on how to use ARDN annotations and other data products, see https://agmip.github.io/ARDN/ARDN_how.html. Research was conducted at the North Florida Research and Education Center - Suwannee Valley, located near Live Oak, Florida (30°18’22” N, 82°54’00” W). Corn, carrots, peanuts, and rye (cover crop) were grown on Hurricane, Chipley, and Blanton soil complexes that are all over 90% sand. They contain very little organic material or clays and have very low water holding capacity. The experimental design utilized a randomized complete block design with split plot that incorporated two fields with eight blocks (treatment replicates) and fifteen plots per block. The main plots contained four irrigation treatments, and the sub-plots contained three different nitrogen rates. The SMS irrigation treatment contained three additional nitrogen treatments. The plots were 12 m x 6 m separated by 6 m alleys. Each block was separated by a 12 m alley. The north field in the study (System 2) was a corn-cover crop-peanut-cover crop rotation, while the south field (System 1) was a corn-carrot-peanut-cover crop rotation. During each growing season, soil moisture was monitored using capacitance type soil moisture sensors, soil nitrogen was measured through bi-weekly soil samples at four depths, and biomass was collected four times with the final sample being collected just prior to harvest. Biomass included dry weight of separate plant tissues as well as Total Kjeldahl Nitrogen (TKN) and yield. Resources in this dataset:Resource Title: ARDN SC2 for FACETS Field trial data from Live Oak, Florida. File Name: FACETS-SC2.jsonResource Description: ARDN sidecar 2 file which allows dataset to be automatically interpreted and translated to end user formats.Resource Software Recommended: AgMIP / ARDN Data Factory,url: https://data.agmip.org/ardn/tools/data_factory

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.

In the context of the publication of DSM-5, the Personality Inventory for DSM-5 (PID-5) has been proposed as a new dimensional assessment tool for personality disorders. This instrument includes a pool of 220 items organized around 25 facets included in a five-factor second-order domain structure. The examination of the replicability of the trait structure across methods and populations is of primary importance. In view of this need, the main objective of the current study was to validate the French version of the PID-5 among French-speaking adults from a European community sample (N=2,532). In particular, the assumption of unidimensionality of the 25 facet and the five domain scales was tested, as well as the extent to which the five-factor structure of the PID-5 and the DSM-5 personality trait hierarchical structure are replicated in the current sample. The results support the assumption of unidimensionality of both the facets and the domains. Exploratory factor and hierarchical analyses replicated the five-factor structure as initially proposed in the PID-5.

This resource contains SWAT-MODFLOW model instances for various land use scenarios for the Santa Fe River of North Central Florida. These land use scenarios were co-developed with stakeholders through a participatory modeling process (PMP) within 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 PMP land use scenarios are the following:

1) Current Condition (Scenario 1) The base model. This model's land uses and management practices are representative of regional production systems. The simulation period is from January 1st, 1980 to December 31st, 2018. The details of this model and its development can be found in, Reaver, N. G. F., D. Lee, R. De Rooij, D. Kaplan, W. Graham (2025). The Floridan Aquifer Collaborative Engagement for Sustainability (FACETS) project SWAT-MODFLOW model of the Santa Fe River, Florida, HydroShare, https://doi.org/10.4211/hs.b80dae5c7cc7421b80c40f9ce856dbf5.

2) Restoration Forestry-High (Scenario 2) A restoration bookend scenario. All agriculture (row crop, pasture, hay) and production forestry lands are converted to low-density longleaf pine savanna.

3) Restoration Forestry-Low (Scenario 3) A more limited restoration scenario. 50% of non-irrigated agriculture in areas prioritized for spring restoration are converted to low-density longleaf pine savanna.

4) Agricultural Expansion (Scenario 4) All current forest land suitable for agriculture (i.e., those with soil group A) switches to row crops.

5) Sod-based Rotation (Scenario 5) A scenario with widespread implementation of rotational grazing (45% of row crops switch to a rotational production system)

6) High Tech Precision Agriculture (Scenario 6) A scenario representing widespread adoption of advanced best nutrient management practices (e.g., controlled release N fertilizer)

7) Solar Farm Expansion (Scenario 7) A scenario representing the current maximum possible regional solar farm expansion in the region (maximum solar area is limited by transmission line capacity)

8) Urban Expansion (Scenario 8) Urban expansion scenario using estimates from FL 2070 Report (https://1000fof.org/florida2070/)

9) Mix-n-Match (Scenario 9) A scenario implementing land use and management practices changes from Scenario 3, Scenario 6, and Scenario 7.

The details of these nine scenarios can be found in the document "Model_Development_SFRB.pdf" within the "contents" folder of this resource. Additionally, this resource included six Simple Scenarios (i.e., CPMS1, CPMS2, CPMS3, CCPMS1, CCPMS2, and CCPMS3). In these scenarios, all production lands were managed under a single management system level developed by the PMP.

Uni-dimensional Rasch report computational time (in minutes).

Network of 45 papers and 69 citation links related to "An Outline of Data Encryption Standard (DES) Tools and Its Facets within the Realm of Cloud Computing".

The complexity of the molecular recognition and assembly of biotic–abiotic interfaces on a scale of 1 to 1000 nm can be understood more effectively using simulation tools along with laboratory instrumentation. We discuss the current capabilities and limitations of atomistic force fields and explain a strategy to obtain dependable parameters for inorganic compounds that has been developed and tested over the past decade. Parameter developments include several silicates, aluminates, metals, oxides, sulfates, and apatites that are summarized in what we call the INTERFACE force field. The INTERFACE force field operates as an extension of common harmonic force fields (PCFF, COMPASS, CHARMM, AMBER, GROMACS, and OPLS-AA) by employing the same functional form and combination rules to enable simulations of inorganic–organic and inorganic–biomolecular interfaces. The parametrization builds on an in-depth understanding of physical–chemical properties on the atomic scale to assign each parameter, especially atomic charges and van der Waals constants, as well as on the validation of macroscale physical–chemical properties for each compound in comparison to measurements. The approach eliminates large discrepancies between computed and measured bulk and surface properties of up to 2 orders of magnitude using other parametrization protocols and increases the transferability of the parameters by introducing thermodynamic consistency. As a result, a wide range of properties can be computed in quantitative agreement with experiment, including densities, surface energies, solid–water interface tensions, anisotropies of interfacial energies of different crystal facets, adsorption energies of biomolecules, and thermal and mechanical properties. Applications include insight into the assembly of inorganic–organic multiphase materials, the recognition of inorganic facets by biomolecules, growth and shape preferences of nanocrystals and nanoparticles, as well as thermal transitions and nanomechanics. Limitations and opportunities for further development are also described.

Filter holder: Number of vertices, edges and facets of the meshes and time to generate them.

Idler tensioner: Number of vertices, edges and facets of the meshes and time to generate them.

Motor bracket: Number of vertices, edges and facets of the meshes and time to generate them.

Background: A wider subjective time horizon is assumed to be positively associated with longevity and vitality. In particular, a lifestyle with exposure to novel and varied information is considered beneficial for healthy cognitive aging. At present, measures that specifically assess individuals' perceived temporal extension to engage in active lifestyles in the future are not available. Objectives: We introduce and validate a new self-report measure, the Subjective Health Horizon Questionnaire (SHH-Q). The SHH-Q assesses individuals' future time perspectives in relation to four interrelated but distinct lifestyle dimensions: (1) novelty-oriented exploration (Novelty), (2) bodily fitness (Body), (3) work goals (Work), and (4) goals in life (Life Goals). The present study aims at: (a) validating the hypothesized factor structure of the SHH-Q, according to which the SHH-Q consists of four interrelated but distinct subscales, and (b) testing the hypothesis that the Novelty and Body subscales of the SHH-Q show positive and selective associations with markers of cognition and somatic health, respectively. Methods: Using structural equation modeling, we analyzed data from 1,371 healthy individuals (51% women) with a mean age of 70.1 years (SD = 3.6) who participated in the Berlin Aging Study II (BASE-II) and completed the SHH-Q. Results: As predicted, the SHH-Q formed four correlated but distinct subscales: (1) Novelty, (2) Body, (3) Work, and (4) Life Goals. Greater self-reported future novelty orientation was associated with higher current memory performance, and greater future expectations regarding bodily fitness with better current metabolic status. Conclusion: The SHH-Q reliably assesses individual differences in four distinct dimensions of future time perspective. Two of these dimensions, Novelty and Body, show differential associations with cognitive status and somatic health. The SHH-Q may serve as a tool to assess how different facets of future time perspective relate to somatic health, cognition, motivation, and affect, and may help to identify the socioeconomic and individual antecedents, correlates, and consequences of an active lifestyle.

Despite over 14,000 known species of ants on earth, a massive biomass, and their intrinsic social evolution, very little is known about how ants perceive their environment. In the face of such vast biodiversity, only about 50 species have been reported to use tools, which suggests unknown facets in myrmecological research. Herein, we report on a field observation where multiple Tapinoma workers restrained a large Camponotus worker for several hours without apparently inflicting injury. The Tapinoma workers used tools (stones) that were placed under the Camponotus worker, seemingly employing them as anchors against which they affixed themselves to restrain the seized ant. In addition, Tapinoma workers attached themselves to the head of the Camponotus which seemed to blind it temporarily and restrict use of its mandibles. Such behaviour in ants demonstrates possible cognitive understanding of their environment and colony socio-adaptation.

Structure of the World Health Organization (WHO) QOL domains and facets.