This survey was sponsored by the Wisconsin Public Television and conducted by the Princeton Survey Research Associates. A national adult sample were interviewed from July 31 to August 17, 1997. Questions dealt with perception of America, immigration, racism, and how America compared to other International countries in these areas.

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at the Roper Center for Public Opinion Research at https://doi.org/10.25940/ROPER-31096566. We highly recommend using the Roper Center version as they may make this dataset available in multiple data formats in the future.

Though we dream of the day when humans will first walk on Mars, these dreams remain in the distance. For now, we explore vicariously by sending robotic agents like the Curiosity rover in our stead. Though our current robotic systems are extremely capable, they lack perceptual common sense. This characteristic will be increasingly needed as we create robotic extensions of humanity to reach across the stars, for several reasons. First, robots can go places that humans cannot. If we manage to get a human on Mars by 2035, as predicted by the current NASA timeline, this will still represent a 60 year lag from the time of the first robotic lander. Second, while it is possible to replace common sense in robots with human teleoperated control to some extent, this becomes infeasible as the distance to the base planet and the associated radio signal delay increase. Finally, as we pack more and more sensors onboard, the fraction of data that can be sent back to earth decreases. Data triage (finding the few frames containing a curious object on a planet's surface out of terabytes of data) becomes more important.

In the last few years, research into a class of scalable unsupervised algorithms, also called deep learning algorithms, has blossomed, in part due to state of the art performance in a number of areas. A common thread among many recent deep learning algorithms is that they tend to represent the world in ways similar to how our brains represent the world. For example, thanks to decades of work by neuroscientists, we now know that in the V1 area of the visual cortex, the first region that visual information passes through after the retina, neurons tune themselves to respond to oriented edges and do so in a way that groups them together based on similarity. With this behavior as a goal, researchers set out to devise simple algorithms that reproduce this effect. It turns out that there are several. One, known as Topographic Independent Component Analysis, has each neuron start with random connections and then look for patterns that are statistically out of the ordinary. When it finds one, it locks onto this pattern, discouraging other neurons from duplicating its findings but simultaneously trying to group itself with other neurons that have learned patterns which are similar, but not identical.

My proposed research plan is to develop existing and new unsupervised learning algorithms of this type and apply them to a robotic system. Specifically, I will demonstrate a prototype system capable of (1) learning about itself and its environment and of (2) actively carrying out experiments to learn more about itself and its environment. Research will be kept focused by developing a system aimed at eventual deployment on an unmanned space mission. Key components of the project will include synthetic data experiments, experiments on data recorded from a real robot, and finally experiments with learning in the loop as the robot explores its environment and learns actively.

The unsupervised algorithms in question are applicable not only to a single domain, but to creating models for a wide range of applications. Thus, advances are likely to have far-reaching implications for many areas of autonomous space exploration. Tantalizing though this is, it is equally exciting that unsupervised learning is already finding application with surprisingly impressive performance right now, indicating great promise for near-term application to unmanned space exploration.

Standard labor force activity data for the week prior to the survey are provided in this data collection. Comprehensive data are supplied on the employment status, occupation, and industry of persons 15 years old and over. Also presented are personal characteristics such as age, sex, race, marital status, veteran status, household relationship, educational background, and Spanish origin. Supplemental data pertaining to work schedules include items on the usual number of hours worked daily and weekly, usual number of days and specific days worked weekly, starting and ending times of an individual's work day, and whether these starting and ending times could be varied. For deviations from regular work schedules, the main reason a particular schedule or shift was worked is elicited. Questions dealing with overtime include number of extra hours worked and rate of pay. For dual jobholders, data are provided on starting and ending times of the work day, number of weekly hours worked, earnings, occupation, industry, and main reason for working more than one job. Questions are included about primary job-related activities completed at home and about temporary work. Data on volunteer work are also provided. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09472.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Led by the Massachusetts Executive Office of Energy and Environmental Affairs (EEA), in partnership with Cornell University, U.S. Geological Survey and Tufts University, the Massachusetts Climate and Hydrologic Risk Project (Phase 1) has developed new climate change projections for the Commonwealth. These new temperature and precipitation projections are downscaled for Massachusetts at the HUC8 watershed scale using Global Climate Models (GCMs) and a Stochastic Weather Generator (SWG) developed by Cornell University.

Stochastic weather generators provide a computationally efficient and complementary alternative to direct use of GCMs for investigating water system performance under climate stress. These models are configured based on existing meteorological records (i.e., historical weather) and are then used to generate large ensembles of simulated daily weather records that are similar to but not bound by variability in past observations. Once fit to historical data, model parameters can be systematically altered to produce new traces of weather that exhibit a wide range of change in their distributional characteristics, including the intensity and frequency of average and extreme precipitation, heatwaves, and cold spells.

The Phase 1 SWG was developed, calibrated, and validated across all HUC8 watersheds that intersect with the state of Massachusetts. A set of climate change scenarios for those watersheds were generated that only reflect mechanisms of thermodynamic climate change deemed to be most credible. These thermodynamic climate changes are based on the range of temperature projections produced by a set of downscaled GCMs for the region. The temperature and precipitation projections presented in this dashboard reflect a warming scenario linked to the Representation Concentration Pathway (RCP) 8.5, a comparatively high greenhouse gas emissions scenario.

The statistics presented in this series of map layers are expressed as either a percent change or absolute change (see list of layers with units and definitions below). These changes are referenced to baseline values that are calculated based on the median value across the 50 model ensemble members associated with the 0°C temperature change scenario derived from observational data (1950-2013) from Livneh et al. (2015). The temperature projections derived from the downscaled GCMs for the region, which are used to drive the SGW, are averaged across 30 years and centered on a target decade (i.e., 2030, 2050, 2070). Projections for 2090 are averaged across 20 years.Definitions of climate projection metrics (with units of change):Total Precipitation (% change): The average total precipitation within a calendar year. Maximum Precipitation (% change): The maximum daily precipitation in the entire record. Precipitation Depth – 90th Percentile Storm (% change): The 90th percentile of non-zero precipitation. Precipitation Depth –99th Percentile Storm (% change): The 99th percentile of non-zero precipitation. Consecutive Wet Days (# days): The average number of days that exist within a run of 2 or more wet days. Consecutive Dry Days (# days): The average number of days that exist within a model run of 2 or more dry days. Days above 1 inch (# days): The number of days with precipitation greater than 1 inch. Days above 2 inches (# days): The number of days with precipitation greater than 2 inches.Days above 4 inches (# days): The number of days with precipitation greater than 4 inches.Maximum Temperature (°F): The maximum daily average temperature value in the entire recordAverage Temperature (°F): Daily average temperature.Days below 0 °F (# days): The number of days with temperature below 0 °F.Days below 32 °F (# days): The number of days with temperature below 32 °F.Maximum Duration of Coldwaves (# days): Longest duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Average Duration of Coldwaves (# days): Average duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Number of Coldwave Events (# events): Number of instances with ten or more consecutive days with temperature below 20 °F.Number of Coldstress Events (# events): Number of instances when a 3-day moving average of temperature is less than 32 °F. Days above 100 °F (# days): The number of days with temperature above 100 °F.Days above 95 °F (# days): The number of days with temperature above 95 °F.Days above 90 °F (# days): The number of days with temperature above 90 °F.Maximum Duration of Heatwaves (# days): Longest duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Average Duration of Heatwaves (# days): Average duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Number of Heatwave Events (# events): Number of instances with three or more consecutive days with temperature over 90 °F.Number of Heatstress Events (# events): Number of instances when a 3-day moving average of temperature is above 86 °F.Cooling Degree Days (# degree-day): Cooling degree days assume that when the outside temperature is below 65°F, we don't need cooling (air-conditioning) to be comfortable. Cooling degree-days are the difference between the daily temperature mean and 65°F. For example, if the temperature mean is 85°F, we subtract 65 from the mean and the result is 20 cooling degree-days for that day. (Definition adapted from National Weather Service).Heating Degree Days (# degree-day): Heating degree-days assume that when the outside temperature is above 65°F, we don't need heating to be comfortable. Heating degree days are the difference between the daily temperature mean and 65°F. For example, if the mean temperature mean is 25°F, we subtract the mean from 65 and the result is 40 heating degree-days for that day. (Definition adapted from National Weather Service).Growing Degree Days (# degree-day): A growing degree day (GDD) is an index used to express crop maturity. The index is computed by subtracting a base temperature of 50°F from the average of the maximum and minimum temperatures for the day. Minimum temperatures less than 50°F are set to 50, and maximum temperatures greater than 86°F are set to 86. These substitutions indicate that no appreciable growth is detected with temperatures lower than 50° or greater than 86°. (Adapted from National Weather Service).Please see additional information related to this project and dataset in the Climate Change Projection Dashboard on the Resilient MA Maps and Data Center webpage.

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer with a ground resolution of 56 meters. The CDL is produced using satellite imagery from the Indian Remote Sensing RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS) collected during the current growing season. Some Cropland Data Layer states used Landsat 5 TM and/or Landsat 7 ETM+ satellite imagery to supplement the classification. Ancillary classification inputs include: the United States Geological Survey (USGS) National Elevation Dataset (NED), the USGS National Land Cover Dataset 2001 (NLCD 2001), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2001 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2010 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 5 TM sensor, Landsat 7 ETM+ sensor, and the Indian Remote Sensing RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS) collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the USGS National Land Cover Dataset 2001 (NLCD 2001), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2001 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.

This data collection supplies standard monthly labor force data for the week prior to the survey. Comprehensive information is given on the employment status, occupation, and industry of persons 14 years old and older. Additional data are available concerning weeks worked and hours per week worked, reason not working full-time, total income and income components, and residence. Supplemental information on respondents with more than one job includes weekly income, reason for additional job, hourly wage amount, days and hours worked per week, labor union membership, and time of day work began and ended. Also included are data on pension plan coverage, employee contributions, and pension provisions made by the self-employed. Information on demographic characteristics, such as age, sex, race, marital status, veteran status, household relationship, educational attainment, and Hispanic origin, is available for each person in the household enumerated. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR07974.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Standard labor force activity data for the week prior to the survey are provided in this data collection. Comprehensive data are supplied on the employment status, occupation, and industry of persons 15 years old and over. Also presented are personal characteristics such as age, sex, race, marital status, veteran status, household relationship, educational background, and Spanish origin. Supplemental data pertaining to work schedules include items on the usual number of hours worked daily and weekly, usual number of days and specific days worked weekly, starting and ending times of an individual's work day, and whether these starting and ending times could be varied. For deviations from regular work schedules, the main reason and length of time a particular schedule or shift was worked is elicited. Questions dealing with overtime include number of extra hours worked and rate of pay. For dual jobholders, data are provided on starting and ending times of the work day, number of weekly hours worked, earnings, occupation, industry, and main reason for working more than one job. Questions are included about primary job-related activities completed at home and about temporary work. Data on volunteer work are also provided. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09809.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

These data contain daily and sub-daily coded data on historical civil wars. The data are interval. The date, day, action type, location, each sides' action, captures, injuries and deaths are shown, and there is a description of each event with the identification of the original source, which in these data is typically a history book.

These data contain daily and sub-daily coded data on protest and coercion. The data are interval. The date, day, action type, location, protest group and targets are shown, the organizational strength of the protesters is estimated and there is a description of each event with the identification of the original source.

This data collection presents information about the causes of all deaths occurring in the United States during 1992. Data are provided concerning underlying causes of death, multiple conditions that caused the death, place of death and residence of the deceased (e.g., region, division, state, county), whether an autopsy was performed, and the month and day of the week of the death. In addition, data are supplied on the sex, race, age, marital status, education, usual occupation, and origin or descent of the deceased. The multiple cause of death fields were coded from the MANUAL OF THE INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES, INJURIES, AND CAUSE-OF-DEATH, NINTH REVISION (ICD-9), VOLUMES 1 AND 2. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR06546.v1. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

This data collection presents information about the causes of all deaths occurring in the United States during 1988. Data are provided concerning underlying causes of death, multiple conditions that caused the death, place of death and residence of the deceased (e.g., region, division, state, county), whether an autopsy was performed, and the month and day of the week of the death. In addition, data are supplied on the sex, race, age, marital status, education, usual occupation, and origin or descent of the deceased. The multiple cause of death fields were coded from the MANUAL OF THE INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES, INJURIES, AND CAUSE-OF-DEATH, NINTH REVISION (ICD-9), VOLUMES 1 AND 2. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR06299.v1. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

This data collection presents information about the causes of all deaths occurring in the United States during 1989. Data are provided concerning underlying causes of death, multiple conditions that caused the death, place of death and residence of the deceased (e.g., region, division, state, county), whether an autopsy was performed, and the month and day of the week of the death. In addition, data are supplied on the sex, race, age, marital status, education, usual occupation, and origin or descent of the deceased. The multiple cause of death fields were coded from the MANUAL OF THE INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES, INJURIES, AND CAUSE-OF-DEATH, NINTH REVISION (ICD-9), VOLUMES 1 AND 2. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR06257.v1. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

This data collection presents information about the causes of all recorded deaths occurring in the United States, Puerto Rico, the Virgin Islands, and Guam during 1995. Data are provided concerning underlying causes of death, multiple conditions that caused the death, place of death, residence of the deceased (e.g., region, division, state, county), whether an autopsy was performed, and the month and day of the week of the death. In addition, data are supplied on the sex, race, age, marital status, education, usual occupation, and origin or descent of the deceased. Along with the Combined Territories Public Use file, a subset based on state of occurrence has been created for Puerto Rico, Virgin Islands and Guam. Mortality detail data for 1995 also can be extracted from this file. The mortality detail records are contained in the first 159 positions of these multiple cause records. The multiple cause of death fields were coded from the MANUAL OF THE INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES, INJURIES, AND CAUSE-OF-DEATH, NINTH REVISION (ICD-9), VOLUMES 1 AND 2. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR02392.v2. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

Nationwide Personal Transportation Survey (NPTS) data are collected by the Federal Highway Administration (FHWA). It is the only national source of information on personal travel for all modes of transportation and all trip purposes. NPTS also tracks the economic, social, demographic, and geographic characteristics of the traveler. Data are collected from a nationally representative sample of households. In 1995, approximately 42,000 households were surveyed. Surveys have been conducted in 1969, 1977, 1983, 1990 and 1995. Information is collected on all trips taken by each household member during a designated 24-hour period, known as the travel day, and on trips 75 miles or more one-way in the two-week period ending on and including the travel day, known as the travel period. In addition to the characteristics of the trip and traveler, the NPTS collects information on, among other things, vehicles, satisfaction with the transportation system, and seat-belt use.