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dietz3
data.marincounty.org | Last Updated 2024-04-05T13:30:40.000ZEmergency Medical Service ambulance dispatch incidents in Marin County, CA, for the period beginning March 1, 2013 through March 31, 2017. Data is updated quarterly. Data includes time stamps of events for each dispatch, nature of injury, and location of injury. Data also includes geocoding of most incident locations, however, specific street address locations are "obfuscated" and are generally shown within a block and are not, therefore, exact locations. Geocoding results are also based on the quality of the address information provided, and should therefore not be considered 100% accurate. Some of the data may be interpreted incorrectly without adequate knowledge of the clinical context. Please contact EMS@marincounty.org if you have any questions about the interpretation of fields in this dataset.
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Regional Comparison of Violent Crimes
internal.open.piercecountywa.gov | Last Updated 2023-11-07T19:38:38.000ZNumber of burglary, theft, arson, and destruction of property crimes in Pierce County. <br /><br />Crime data derived from the "Crimes in Washington" annual report (by year) compiled from data submitted to the Washington State Uniform Crime Reporting Program of the Washington Association of Sheriffs and Police Chiefs by Washington State law enforcement agencies. <br /><br />Only specific crimes are highlighted in the crime rates presented here. These numbers represent total numbers of reported crimes in each category (not arrests which may occur over a prolonged period). <br /><br />The following categories represent the violent crimes considered in this data: Murder, Manslaughter, Forcible Sex, Assault, Kidnapping/Abduction, Human Trafficking, and Robbery.<br /><br />The following categories represent the property crimes considered in this data: Burglary, Theft, Arson, and Destruction of Property. <br /><br />Each set of crimes is totaled, then the rate per 1,000 people is calculated using the total # of crimes and the current population of each jurisdiction per year as provided in the same report. <br /><br />This is a voluntary program and as such, some law enforcement agencies do not participate or have only recently participated, which is also reflected in this table.
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Procedure Execution and Projection System
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:15:38.000ZThere is a persistent pressure upon NASA crew members to achieve very high productivity during their missions. Significant challenges exist to maintaining manageable workload while the crew is performing their many and varied tasks allotted for each day while ensuring the crew maintain situation awareness. NASA crew members deal with a large amount of very high technology equipment and perform experiments and procedures that can be extremely long and complex. The solution will require the development of automated management technologies that will operate synergistically with the crew, automating tasks of varying complexity in a dynamic, flexible manner with representations of automation state that the crew is familiar and comfortable with. In this proposal, Cybernet proposes to leverage crew members' capabilities with the design of a distributed Procedure Execution and Projection (PEP) system that focuses on supporting automation of complex procedures while ensuring crew situational awareness and anticipating future problems. Our team will leverage the recent work on the Procedure Representation Language (PRL) and the flexible, distributed and hierarchical capabilities of holonic systems. PRL is an XML encoding of the vehicle/habitat procedures in a form that both crew and automation can use, and the PEP systems' intelligent holonic modules will support crew with a range of capabilities, including automation of procedures, projection of procedures to look for problems and determine courses of action to prevent or mitigate the problems, and make sure that the crew maintain situational awareness of the procedural state. The objectives of the Phase I project are to establish critical requirements for NASA vehicle and habitat crew automation and to design and implement a prototype of the PEP system to demonstrate approach viability.
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Approximate Cartesian Control for Robotic Tool Usage with Graceful Degradation Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:31:39.000ZMany of NASA's exploration scenarios include important roles for autonomous or partially autonomous robots. It is desirable for them to utilize human tools when possible, rather than needing to build custom tools for each robot. Control of robotic manipulators for tool usage generally requires a very precise Cartesian-space trajectory of the tool tip (e.g., moving a marker along the surface of a whiteboard or rotating a screwdriver about an axis). Well-known techniques exist for manipulator control in Cartesian space, most of which necessitate solving a series of Inverse Kinematics (IK) problems. Closed-form IK solvers work well for 7-degree-of-freedom (DOF) arms with rigid tool attachments, but cannot handle non-rigid tools that slip in the robot's hands. Numerical IK approaches are more generic and can handle non-rigid links to tools, but can be slow to converge. More importantly, if any joints fail or become limited in their range of motion, the robot arm essentially becomes 6-DOF or lower. IK solvers often fail in these lower DOF spaces because the configuration space becomes non-continuous and full of "holes". As a result, a 7-DOF robotic arm in space might be rendered largely useless if a single joint fails or even loses mobility until it can be serviced. TRACLabs proposes to investigate an alternative approach to traditional Cartesian control approaches, which rely on complex IK solvers that go from Cartesian space backwards to joint space. We propose to leverage cheap memory and modern processing speeds to instead perform simple computations that go from joint space forwards to Cartesian space. Such techniques should overcome common changes to a manipulation chain caused by tool slippage or the grasping of a new tool and to overcome uncommon changes to a chain caused by joint failures, reduced joint mobility, changes in joint geometry or range of motion, or added joints.
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ICI - Accommodation and Food Services Garbage Composition - Proper Disposal Location
data.calgary.ca | Last Updated 2023-02-01T15:39:37.000ZThis chart shows weight per cent composition grouped by proper disposal location. This dataset is for garbage bin waste from the Industrial, Commercial, and Institutional (ICI) sector. The North American Industry Classification System (NAICS) is used to categorize ICI businesses and organizations into sub-sectors for ease of data collection and reporting. The NAICS sub-sectors included in this study are: Accommodation and Food Services (NAICS code 72), Retail Trade (44-45), Manufacturing (31-33), Health Care and Social Assistance (62), and Public Administration (91). All businesses and organizations included in the study were customers of The City’s Commercial Collections service, except for one privately-serviced customer in the Accommodation and Food Services sub-sector that was added to provide better sector representation. A total of 115 samples are included in the dataset: 30 in Accommodation and Food Services, 35 in Retail Trade, 17 in Manufacturing, 25 in Health Care and Social Assistance, and 8 in Public Administration. The weight per cent for each sub-sector is the pooled average of samples collected in the four seasons of 2019. Waste Composition studies are periodically conducted by Waste and Recycling Services to help assess the performance of diversion and education programs and inform improvements and new program design.
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2014 - 2015 Parent School Survey Data
data.cityofnewyork.us | Last Updated 2022-05-09T22:23:22.000Z2015 NYC School Survey parent data for all schools To understand the perceptions of families, students, and teachers regarding their school. School leaders use feedback from the survey to reflect and make improvements to schools and programs. Also, results from the survey used to help measure school quality. Each year, all parents, teachers, and students in grades 6-12 take the NYC School Survey. The survey is aligned to the DOE's Framework for Great Schools. It is designed to collect important information about each school's ability to support student success.
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AtOrAboveProficient
datahub.smcgov.org | Last Updated 2016-08-30T23:34:30.000ZSTAR Third Grade Reading Scores as a Percentage of Economically Disadvantaged Students classified by the State of California Department of Education as being "At or Above Proficient" reading level for 2013 school year. Hillsborough Unified School District reported a third grade enrollment of 183 students, with 1 student tested. Test results cannot be reported due to identity protection requirements. Las Lomitas Elementary School District reported a third grade enrollment of 170 with 3 students tested. Test results cannot be reported due to identity protection requirements. Menlo Park City Elementary District reported a third grade enrollment of 352 with 6 students tested. Test results cannot be reported due to identity protection requirements. Portola Valley Elementary School District reported a third grade enrollment of 80 with 3 students tested. Test results cannot be reported due to identity protection requirements. Woodside School District reported a third grade enrollment of 51 with 2 students tested. Test results cannot be reported due to identity protection requirements.
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ED Total Private Covered Employment
internal.open.piercecountywa.gov | Last Updated 2024-04-17T18:09:50.000ZTotal private-sector employment for jobs covered by Unemployment Insurance as reported by employers through the Quarterly Census of Employment and Wages (QCEW).
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2011-2012 School Closure Discharge Reporting Ethnicity - School
data.cityofnewyork.us | Last Updated 2022-05-09T22:22:32.000ZIn June 2012, 7 New York City public schools closed for poor performance. This report provides data regarding students enrolled in these schools during the 2011-2012 school year, according to the guidelines set by Local Law 2011/043
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ICI - Retail Garbage Composition - Proper Disposal Location
data.calgary.ca | Last Updated 2023-02-01T15:39:39.000ZThis chart shows weight per cent composition grouped by proper disposal location. This dataset is for garbage bin waste from the Industrial, Commercial, and Institutional (ICI) sector. The North American Industry Classification System (NAICS) is used to categorize ICI businesses and organizations into sub-sectors for ease of data collection and reporting. The NAICS sub-sectors included in this study are: Accommodation and Food Services (NAICS code 72), Retail Trade (44-45), Manufacturing (31-33), Health Care and Social Assistance (62), and Public Administration (91). All businesses and organizations included in the study were customers of The City’s Commercial Collections service, except for one privately-serviced customer in the Accommodation and Food Services sub-sector that was added to provide better sector representation. A total of 115 samples are included in the dataset: 30 in Accommodation and Food Services, 35 in Retail Trade, 17 in Manufacturing, 25 in Health Care and Social Assistance, and 8 in Public Administration. The weight per cent for each sub-sector is the pooled average of samples collected in the four seasons of 2019. Waste Composition studies are periodically conducted by Waste and Recycling Services to help assess the performance of diversion and education programs and inform improvements and new program design.