This paper explores a unique dataset of all the SET ratings provided by students of one university in Poland at the end of the winter semester of the 2020/2021 academic year. The SET questionnaire used by this university is presented in Appendix 1. The dataset is unique for several reasons. It covers all SET surveys filled by students in all fields and levels of study offered by the university. In the period analysed, the university was entirely in the online regime amid the Covid-19 pandemic. While the expected learning outcomes formally have not been changed, the online mode of study could have affected the grading policy and could have implications for some of the studied SET biases. This Covid-19 effect is captured by econometric models and discussed in the paper. The average SET scores were matched with the characteristics of the teacher for degree, seniority, gender, and SET scores in the past six semesters; the course characteristics for time of day, day of the week, course type, course breadth, class duration, and class size; the attributes of the SET survey responses as the percentage of students providing SET feedback; and the grades of the course for the mean, standard deviation, and percentage failed. Data on course grades are also available for the previous six semesters. This rich dataset allows many of the biases reported in the literature to be tested for and new hypotheses to be formulated, as presented in the introduction section. The unit of observation or the single row in the data set is identified by three parameters: teacher unique id (j), course unique id (k) and the question number in the SET questionnaire (n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9} ). It means that for each pair (j,k), we have nine rows, one for each SET survey question, or sometimes less when students did not answer one of the SET questions at all. For example, the dependent variable SET_score_avg(j,k,n) for the triplet (j=Calculus, k=John Smith, n=2) is calculated as the average of all Likert-scale answers to question nr 2 in the SET survey distributed to all students that took the Calculus course taught by John Smith. The data set has 8,015 such observations or rows. The full list of variables or columns in the data set included in the analysis is presented in the attached filesection. Their description refers to the triplet (teacher id = j, course id = k, question number = n). When the last value of the triplet (n) is dropped, it means that the variable takes the same values for all n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9}.Two attachments:- word file with variables description- Rdata file with the data set (for R language).Appendix 1. Appendix 1. The SET questionnaire was used for this paper. Evaluation survey of the teaching staff of [university name] Please, complete the following evaluation form, which aims to assess the lecturer’s performance. Only one answer should be indicated for each question. The answers are coded in the following way: 5- I strongly agree; 4- I agree; 3- Neutral; 2- I don’t agree; 1- I strongly don’t agree. Questions 1 2 3 4 5 I learnt a lot during the course. ○ ○ ○ ○ ○ I think that the knowledge acquired during the course is very useful. ○ ○ ○ ○ ○ The professor used activities to make the class more engaging. ○ ○ ○ ○ ○ If it was possible, I would enroll for the course conducted by this lecturer again. ○ ○ ○ ○ ○ The classes started on time. ○ ○ ○ ○ ○ The lecturer always used time efficiently. ○ ○ ○ ○ ○ The lecturer delivered the class content in an understandable and efficient way. ○ ○ ○ ○ ○ The lecturer was available when we had doubts. ○ ○ ○ ○ ○ The lecturer treated all students equally regardless of their race, background and ethnicity. ○ ○

This file contains the data set used to develop a random forest model predict background specific conductivity for stream segments in the contiguous United States. This Excel readable file contains 56 columns of parameters evaluated during development. The data dictionary provides the definition of the abbreviations and the measurement units. Each row is a unique sample described as R** which indicates the NHD Hydrologic Unit (underscore), up to a 7-digit COMID, (underscore) sequential sample month. To develop models that make stream-specific predictions across the contiguous United States, we used StreamCat data set and process (Hill et al. 2016; https://github.com/USEPA/StreamCat). The StreamCat data set is based on a network of stream segments from NHD+ (McKay et al. 2012). These stream segments drain an average area of 3.1 km2 and thus define the spatial grain size of this data set. The data set consists of minimally disturbed sites representing the natural variation in environmental conditions that occur in the contiguous 48 United States. More than 2.4 million SC observations were obtained from STORET (USEPA 2016b), state natural resource agencies, the U.S. Geological Survey (USGS) National Water Information System (NWIS) system (USGS 2016), and data used in Olson and Hawkins (2012) (Table S1). Data include observations made between 1 January 2001 and 31 December 2015 thus coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data (https://modis.gsfc.nasa.gov/data/). Each observation was related to the nearest stream segment in the NHD+. Data were limited to one observation per stream segment per month. SC observations with ambiguous locations and repeat measurements along a stream segment in the same month were discarded. Using estimates of anthropogenic stress derived from the StreamCat database (Hill et al. 2016), segments were selected with minimal amounts of human activity (Stoddard et al. 2006) using criteria developed for each Level II Ecoregion (Omernik and Griffith 2014). Segments were considered as potentially minimally stressed where watersheds had 0 - 0.5% impervious surface, 0 – 5% urban, 0 – 10% agriculture, and population densities from 0.8 – 30 people/km2 (Table S3). Watersheds with observations with large residuals in initial models were identified and inspected for evidence of other human activities not represented in StreamCat (e.g., mining, logging, grazing, or oil/gas extraction). Observations were removed from disturbed watersheds, with a tidal influence or unusual geologic conditions such as hot springs. About 5% of SC observations in each National Rivers and Stream Assessment (NRSA) region were then randomly selected as independent validation data. The remaining observations became the large training data set for model calibration. This dataset is associated with the following publication: Olson, J., and S. Cormier. Modeling spatial and temporal variation in natural background specific conductivity. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(8): 4316-4325, (2019).

Quality of life conditions reported by city inspectors.

Abstract

The dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.

The full-population dataset (with about 10 million individuals) is also distributed as open data.

Geographic coverage

The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.

Analysis unit

Household, Individual

Universe

The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.

Kind of data

ssd

Sampling procedure

The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.

Mode of data collection

other

Research instrument

The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.

Cleaning operations

The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.

Response rate

This is a synthetic dataset; the "response rate" is 100%.

The dataset records the Ali Desert Environment Integrated Observation and Research Station, the meteorological dataset for 2017-2018, and the time resolution of the data is days. It includes the following basic meteorological parameters: temperature (1.5 meters from the ground, once every half hour, unit: Celsius), relative humidity (1.5 meters from the ground, half an hour, unit: %), wind speed (1.5 meters from the ground, half an hour) , unit: m / s), wind direction (1.5 meters from the ground, once every half hour, unit: degrees), air pressure (1.5 meters from the ground, once every half hour, unit: hPa), precipitation (24 hours, unit: mm ), water vapor pressure (unit: Kpa), evaporation (unit: mm), downward short-wave radiation (unit: W/m2), upward short-wave radiation (unit: W/m2), downward long-wave radiation (unit: W/m2) ), upward long-wave radiation (unit: W/m2), net radiation (unit: W/m2), surface albedo (unit: %). Data collection location: Observation Field of Ali Desert Environment Comprehensive Observation and Research Station, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Longitude: 79°42'5"; Latitude: 33°23'30"; Altitude: 4264 meters.

Replication Data for: Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities Details: YandW_mat.csv contains individual-level observational data. In the dataset, LONGITUDE and LATITUDE refer to the approximate geo-referenced long/lat of observational units. Experimental outcomes are stored in Yobs. The treatment variable is stored in Wobs. The unique image key for each observational unit is saved in UNIQUE_ID. Geo-referenced satellite images are saved in… See the full description on the dataset page: https://huggingface.co/datasets/cjerzak/ImageConfounding.

How preschool teachers and children spend their time in preschool is important for children's engagement and learning. The study aims to give a broad description of how often children and staff spend time in different types of activities, interactions, and environments. Systematic momentary observations of individual children and teachers/staff were conducted continuously during a full day in 78 preschool units (mainly for children 3-5 years) during the autumn term. The observations resulted in frequency data for different types of activities for children and teachers. Frequency data were summarized at the unit level, and percentage distributions of activities were calculated.

Results showed that free play indoors was the main activity setting, followed by free play outdoors. Children interacted as much with other children as with teachers. The focus was dominated by non-pretend play, construction, art and music, followed by pretend play and academic contents. Child engagement was significantly higher in free play indoors compared to outdoors. Teachers engaged in varied tasks, but their central task was managing. Teachers were typically in proximity to small groups of children, or by themselves, and mostly talked to or listened to a single child.

Data was collected with systematic observations with the help of manual-based instruments Child Observation in Preschool (COP) and Teacher Observation in Preschool (TOP). The observations consists of snapshots of individual children/teachers across a preschool day. Several aspects of the individual's current activity are coded. Individual data was aggregated to preschool unit level, and proportions for different activity aspects were calculated. Aggregated frequency and proportionate data are available in the data set for child and teacher data, respectively. Some preschool unit background information is also available.

The NETCDF encompasses monthly time series spans the years 1995-2020 globally at 0.5-degree resolution with gap-free estimates of nine variables, gap-filled using the CLIMFILL (CLIMate data gapFILL) framework [1,2]

The nine variables are:

• surface layer soil moisture from the Climate Change Initiative (CCI) of the European Space Agency (ESA),

• land surface temperature and

• diurnal temperature range from the Moderate Resolution Imaging Spectroradiometer (MODIS),

• precipitation from the Global Precipitation Measurement (GPM),

• terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE),

• ESA-CCI burned area,

• ESA-CCI snow cover fraction,

• two-meter temperature and precipitation from the Climate Research Unit (CRU).

Note: this dataset is only validated and tested for the use cases in the accompanying study (DOI to come). Please have caution using the data for analysis that might include trends in high latitude, in regions where the variable has high fraction of missing values, or in mountainous regions.

References:

[1] Bessenbacher, V., Seneviratne, S.I. and Gudmundsson, L. (2022): CLIMFILL v0.9: a framework for intelligently gap filling Earth observations. Geosci. Model Dev., 15, 4569–4596, 2022 https://doi.org/10.5194/gmd-15-4569-2022

[2] https://github.com/climachine/climfill/releases/tag/1.0

Little fine-grained, empirical motion and interaction data is available for working hospital environments (refer to our paper for more information)

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

The global 1° land mapping XCO2 dataset (Mapping-XCO2) is derived from satellite XCO2 retrievals of GOSAT and OCO-2 spanning the period of April 2009 to December 2020. The data product is provided in GeoTIFF format and include two temporal resolutions: 3 days and month. The 3-day data files include gridded XCO2 and mapping uncertainty, which are named like “MappingXCO2_Date.nc” and “MappingUncertainty_Date.nc”. The flag “Date” is defined as date ID of 1426 time-units started from 20 April 2009. The monthly data files only include XCO2 data and named like “MappingXCO2_YYYY_MM.tif”. The number of “YYYY” and “MM” represent year and month, respectively. The domain of the dataset covers global land ranging from 56° S to 65° N and 169° W to 180° E. The spatial reference of the dataset is Geographic Lat/Lon. The unit of XCO2 data is ppm while the nodata values were assigned to NaN.

Vertical profiles of signal to noise ratio (SNR) and winds measurements from the NCAS Mobile Radar Wind Profiler unit 1 deployed at the Met Office Meteorological Research Unit, Cardington. These observations were taken as part of the National Centre for Atmospheric Science (NCAS) long term observations between 20120307 and 20130305.

Data products from this deployment include: snr-winds

For further details of this deployment and the associated dataset please see the internal file metadata.

These data conform to the NCAS data standards and are available under the UK Government Open Licence agreement. Acknowledgement of NCAS as the data provider is required whenever and wherever these data are used.

The data set includes vector reports of kinematic observations between the base station used for each survey and the rover points.

The Parties’ Group Appeals Dataset (PGAD) 269 party-year data points on group appeals from parties' names, manifestos, and print campaign ads (a single party in each election is the unit of observation). In total, the data cover all 69 parties that gained seats in 24 Israeli and Dutch general elections between 1977 and 2015. More detail on the data set can be found in the accompanying release note.

Visibility viewsheds incorporate influences of distance from observer, object size and limits of human visual acuity to define the degree of visibility as a probability between 1 - 0. Average visibility viewsheds represent the average visibility value across all visibility viewsheds, thus representing a middle scenario relative to maximum and minimum visibility viewsheds. Average Visibility viewsheds can be used as a potential resource conflict screening tools as it relates to the Great Plains Wind Energy Programmatic Environmental Impact Statement. Data includes binary and composite viewsheds, and average, maximum, minimum, and composite visibility viewsheds for the NPS unit. Viewsheds have been derived using a 30m National Elevation Dataset (NED) digital elevation model. Additonal viewshed parameters: Observer Height (offset A) was set at 2 meters. A vertical development object height (offset B) was set at 110 meters, representing an average wind tower and associated blade height. A binary viewshed (1 visible, 0 not visible) was created for the defined NPS Unit specific Key Observation Points (KOP). A composite viewshed is the visibility of multiple viewsheds combined into one. A visible value in a composite viewshed implies that across all the combined binary viewsheds (one per key observation pointacross the nps unit in this case), at a minimum at least one of the sample points is visible. On a cell by cell basis throughout the study area of interest the numbers of visible sample points are recorded in the composite viewshed. Composite viewsheds are a quick way to synthesize multiple viewsheds into one layer, thus giving an efficient and cursory overview of potential visual resource effects. To summarize visibility viewsheds across numerous viewsheds, (e.g. multiple viewsheds per high priority segment) three visibility scenario summary viewsheds have been derived: 1) A maximum visibility scenario is evaluated using a "Products" visibility viewshed, which represents the probability that all sample points are visible. Maximum visibility viewsheds are derived by multiplying probability values per visibility viewshed. 2) A minimum visibility scenario is assessed using a "Fuzzy sum" visibility viewshed. Minimum visibility viewsheds represent the probability that one sample point is visible, and is derived by calculating the fuzzy sum value across the probability values per visibility viewsheds. 3) Lastly an average visibility scenario is created from an "Average" visibility calculation. Average visibility viewsheds represent the average visibility value across all visibility viewsheds, thus representing a middle scenario relative to the aforementioned maximum and minimum visibility viewsheds. Equations for the maximum, average and minimum visibility viewsheds are defined below: Maximum Visibility: Products Visibility =(p1*p2*pn...), Average Visibility: Average Visibility =((p1*p2*pn)/n), and Minimum Visibility: Fuzzy Sum Visibility =(1-((1-p1 )*(1-p2 )*(1-pn )* ...). Moving beyond a simplistic binary viewshed approach, visibility viewsheds define the degree of visibility as a probability between 1 - 0. Visibility viewsheds incorporate the influences of distance from observer, object size (solar energy towers, troughs, panels, etc.) and limits of human visual acuity to derive a fuzzy membership value. A fuzzy membership value is a probability of visibility ranging between 1 - 0, where a value of one implies that the object would be easily visible under most conditions and for most viewers, while a lower value represents reduced visibility. Visibility viewshed calculation is performed using the modified fuzzy viewshed equations (Ogburn D.E. 2006). Visibility viewsheds have been defined using: a foreground distance (b1) of 1 km, a visual arc threshold value of 1 minute (limit of 20/20 vision) which is used in the object width multiplier calculation, and an object width value of 10 meters.

Visibility viewsheds incorporate influences of distance from observer, object size and limits of human visual acuity to define the degree of visibility as a probability between 1 - 0. Average visibility viewsheds represent the average visibility value across all visibility viewsheds, thus representing a middle scenario relative to maximum and minimum visibility viewsheds. Average Visibility viewsheds can be used as a potential resource conflict screening tools as it relates to the Great Plains Wind Energy Programmatic Environmental Impact Statement. Data includes binary and composite viewsheds, and average, maximum, minimum, and composite visibility viewsheds for the NPS unit. Viewsheds have been derived using a 30m National Elevation Dataset (NED) digital elevation model. Additonal viewshed parameters: Observer Height (offset A) was set at 2 meters. A vertical development object height (offset B) was set at 110 meters, representing an average wind tower and associated blade height.

 A binary viewshed (1 visible, 0 not visible) was created for the defined NPS Unit specific Key Observation Points (KOP). A composite viewshed is the visibility of multiple viewsheds combined into one. A visible value in a composite viewshed implies that across all the combined binary viewsheds (one per key observation pointacross the nps unit in this case), at a minimum at least one of the sample points is visible. On a cell by cell basis throughout the study area of interest the numbers of visible sample points are recorded in the composite viewshed. Composite viewsheds are a quick way to synthesize multiple viewsheds into one layer, thus giving an efficient and cursory overview of potential visual resource effects.

 To summarize visibility viewsheds across numerous viewsheds, (e.g. multiple viewsheds per high priority segment) three visibility scenario summary viewsheds have been derived: 1) A maximum visibility scenario is evaluated using a "Products" visibility viewshed, which represents the probability that all sample points are visible. Maximum visibility viewsheds are derived by multiplying probability values per visibility viewshed. 2) A minimum visibility scenario is assessed using a "Fuzzy sum" visibility viewshed. Minimum visibility viewsheds represent the probability that one sample point is visible, and is derived by calculating the fuzzy sum value across the probability values per visibility viewsheds. 3) Lastly an average visibility scenario is created from an "Average" visibility calculation. Average visibility viewsheds represent the average visibility value across all visibility viewsheds, thus representing a middle scenario relative to the aforementioned maximum and minimum visibility viewsheds. Equations for the maximum, average and minimum visibility viewsheds are defined below: Maximum Visibility: Products Visibility =(p1*p2*pn...), Average Visibility: Average Visibility =((p1*p2*pn)/n), and Minimum Visibility: Fuzzy Sum Visibility =(1-((1-p1 )*(1-p2 )*(1-pn )* ...).

 Moving beyond a simplistic binary viewshed approach, visibility viewsheds define the degree of visibility as a probability between 1 - 0. Visibility viewsheds incorporate the influences of distance from observer, object size (solar energy towers, troughs, panels, etc.) and limits of human visual acuity to derive a fuzzy membership value. A fuzzy membership value is a probability of visibility ranging between 1 - 0, where a value of one implies that the object would be easily visible under most conditions and for most viewers, while a lower value represents reduced visibility. Visibility viewshed calculation is performed using the modified fuzzy viewshed equations (Ogburn D.E. 2006). Visibility viewsheds have been defined using: a foreground distance (b1) of 1 km, a visual arc threshold value of 1 minute (limit of 20/20 vision) which is used in the object width multiplier calculation, and an object width value of 10 meters.

NorWeST is an interagency stream temperature database and model for the western United States containing data from over 20,000 unique stream locations. Temperature observations were solicited from state, federal, tribal, private, and municipal resource organizations and processed using a custom cleaning script developed by Gwynne Chandler. Summaries of daily, weekly, and monthly means, minima, and maxima are provided for observation years. The data summaries and location information are available in user-friendly file formats that include: 1) a map (PDF) depicting the locations of in-stream thermographs (temperature sensors) for each processing unit, 2) a GIS shapefile (SHP) containing the location of these sensors for each processing unit, and 3) a tabular file (XLSX) containing observed temperature database summaries for data generally ranging from 1993 to 2015, dependent on the processing unit. Each point shapefile extent corresponds to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs). The tabular data can be joined to the observation point shapefile using the ID field OBSPRED_ID. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.

The Religious Characteristics of States Dataset (RCS) was created to fulfill the unmet need for a dataset on the religious dimensions of countries of the world, with the state-year as the unit of observation. The third phase, Chief Executives' Religions, provides data on religious affiliations of countries' 'chief executives,' i.e., their presidents, prime ministers, or other heads of state/government exercising largely real, not ceremonial, political power. The dataset, like others in the RCS data project, is designed expressly for easy merger with datasets of the Correlates of War and Polity projects, datasets by the United Nations, the Religion And State datasets by Jonathan Fox, and the ARDA national profiles.

This report documents the acquisition of source data, and calculation of land cover summary statistics datasets for six National Park Service Klamath Network park units and seven custom areas of analysis: Crater Lake National Park, Lassen Volcanic National Park, Lava Beds National Monument, Oregon Caves National Monument, Redwood National and State Parks, Whiskeytown National Recreation Area, and the seven custom areas of analysis. The source data and land cover calculations are available for use within the National Park Service (NPS) Inventory and Monitoring Program. Land cover summary statistics datasets can be calculated for all geographic regions within the extent of the NPS; this report includes statistics calculated for the conterminous United States. The land cover summary statistics datasets are calculated from multiple sources, including Multi-Resolution Land Characteristics Consortium products in the National Land Cover Database (NLCD) and the United States Geological Survey’s (USGS) Earth Resources Observation and Science (EROS) Center products in the Land Change Monitoring, Assessment, and Projection (LCMAP) raster dataset. These summary statistics calculate land cover at up to three classification scales: Level 1, modified Anderson Level 2, and Natural versus Converted land cover. The output land cover summary statistics datasets produced here for the six Klamath Network park units and seven custom areas of analysis utilize the most recent versions of the source datasets (NLCD and LCMAP). These land cover summary statistics datasets are used in the NPS Inventory and Monitoring Program, including the NPS Environmental Settings Monitoring Protocol and may be used by networks and parks for additional efforts.

This report documents the acquisition of source data, and calculation of land cover summary statistics datasets for four National Park Service Greater Yellowstone Network park units and six custom areas of analysis: Bighorn Canyon National Recreation Area, Grand Teton National Park, John D. Rockefeller Jr. Memorial Parkway, Yellowstone National Park, and the six custom areas of analysis. The source data and land cover calculations are available for use within the National Park Service (NPS) Inventory and Monitoring Program. Land cover summary statistics datasets can be calculated for all geographic regions within the extent of the NPS; this report includes statistics calculated for the conterminous United States. The land cover summary statistics datasets are calculated from multiple sources, including Multi-Resolution Land Characteristics Consortium products in the National Land Cover Database (NLCD) and the United States Geological Survey’s (USGS) Earth Resources Observation and Science (EROS) Center products in the Land Change Monitoring, Assessment, and Projection (LCMAP) raster dataset. These summary statistics calculate land cover at up to three classification scales: Level 1, modified Anderson Level 2, and Natural versus Converted land cover. The output land cover summary statistics datasets produced here for the four Greater Yellowstone Network park units and six custom areas of analysis utilize the most recent versions of the source datasets (NLCD and LCMAP). These land cover summary statistics datasets are used in the NPS Inventory and Monitoring Program, including the NPS Environmental Settings Monitoring Protocol and may be used by networks and parks for additional efforts.