- API opendata.ramseycounty.us | Last Updated 2017-08-03T17:47:36.000Z
Dataset showing commute to work by transportation type.
- API data.baltimorecity.gov | Last Updated 2019-02-15T18:53:09.000Z
This data represents the top arrest charge of those processed at Baltimore's Central Booking & Intake Facility. This data does not contain those who have been processed through Juvenile Booking.
- API data.oaklandnet.com | Last Updated 2015-06-05T21:34:05.000Z
- API data.hartford.gov | Last Updated 2019-08-01T09:53:04.000Z
This dataset reflects reported incidents of crime (with the exception of sexual assaults, which are excluded by statute) that occurred in the City of Hartford from 2005 to the present, minus the most recent ten days. Data is extracted from the City of Hartford Police Department's CrimeView database on a daily basis. Should you have questions about this dataset, you may contact the Crime Analysis Division of the Hartford Police Department at 860.757.4020 or policechief@Hartford.gov. Disclaimer: These incidents are based on crimes verified by the Hartford Police Department's Crime Analysis Division. The crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Hartford Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Hartford Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate. The Hartford Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Hartford or Hartford Police Department web page. The user specifically acknowledges that the Hartford Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Hartford Police Department", "Hartford Police", "HPD" or any colorable imitation of these words or the unauthorized use of the Hartford Police Department logo is unlawful. This web page does not, in any way, authorize such use. The dataset contains more than 400,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Hartford Police Department - Uniform Crime Reporting (UCR) codes, select the about tab on the right side of this page and scroll down to the attachments and open the PDF document.
- API data.opendatanetwork.com | Last Updated 2014-05-12T03:20:31.000Z
Cases created since 7/1/2008 with location information.
- API data.nasa.gov | Last Updated 2018-07-19T07:41:48.000Z
Missions to Solar System bodies must meet increasingly ambitious objectives requiring highly reliable capabilities in ranging and mapping for soft and precision landing to avoid hazardous sites. A compact and light weight LiDAR instrument is needed for topography mapping, position sensing, laser altimetry, and autonomous rendezvous of satellites. Missions to small bodies such as asteroids, comets, and moons require precision rendezvous and accurate identification of landing or sampling sites. Precision range data significantly improves spacecraft control in close-approach and landing scenarios. Range data is most critical in the final descent phase where the spacecraft is within a few kilometers of the target surface. These missions require improved precision from previously flown lidar technologies as well as significant reductions in size, weight, and power (SWaP) given the resource-constrained class of missions likely to utilize this capability. Q-Peak, in partnership with Sigma Space Corp., is proposing a low-SWaP laser integrated into a compact laser LiDAR instrument that can achieve the desired ranging accuracy and precision with minimum resource from spacecraft bus. In Phase I, Q-Peak proposes the development of an ultra-compact, passively Q-switched laser, < 4 cm3 in volume that will produce > 0.1 mJ pulse energies and < 2 ns-duration pulses at 523 nm at pulse repetition rates of 10-30 kHz. This laser will be specifically designed for integration and testing in the newly developed LiDAR instrument at Sigma Space. In Phase II, Q-Peak will bond the passive Q-switch to the laser gain medium to make it monolithic and essentially alignment free. We will harden the laser and integrate it into the LiDAR instrument to advance the TRL level by subjecting them to a space-like environment.
- API data.nj.gov | Last Updated 2019-05-14T18:27:25.000Z
This is a report for all the relevant columns of EDA - The Amount Awarded and Recipient broken down by federal agency, county, and municipality.
- API data.nj.gov | Last Updated 2019-05-14T18:27:25.000Z
This is a report for all the relevant columns of EDA - The Amount Allocated, Obligated and Paid broken down by federal agency, program, vendor, project, county, and municipality.
- API data.nasa.gov | Last Updated 2018-07-19T08:42:09.000Z
<p>To provide economical, reliable and safe access to space, design weaknesses should be identified earlier in the engineering life cycle, using model-based systems engineering. The slow manual approach to performing Failure Modes and Effects Analysis (FMEA) is a barrier to early identification of weaknesses. To semi-automate the identification of failure modes and causes use a prototype FMEA Assistant, including a library with standard terminology, to classify components associated with failure modes and automatically identify candidate functions, infrastructure and failure modes. This automation will reduce cost and increase coverage, standardization and reuse. Early identification of design weaknesses can substantially reduce rework costs later in the life cycle, which are all too common in the testing phase. Use of SysML will closely link safety analysis with the overall engineering process, resulting in smoother collaboration and safer vehicles and missions. The resulting reusable model would become part of the model-based system engineering process.<p/><p>This project was a small proof-of-concept case study, generating SysML model information as a side effect of safety analysis. A prototype FMEA Assistant was used to semi-automate safety analysis that identifies failure modes and causes, using a library with standard SysML-compatible terminology to classify components associated with failure modes and to automatically identify candidate functions, infrastructure and failure modes. FMEA analysts select from standard functions and failures to systematically narrow down failure mode selection (presented in automatically created pick lists). Standard terminology from an existing Aerospace Ontology is used to classify components and automatically identify candidate functions and failure modes. With automatically created pick lists, analysts can easily and correctly select standard functions and failures for a SysML architecture model as a side effect of using FMEA Assistant. A white paper reports on a concept for using SysML profiles for safety analysis, to standardize FMEA-related terminology for reuse in several types of safety analysis (hazard analyses, fault trees, reliability block diagrams). See related project: Failure Modes and Effects Analysis (FMEA) Simulation Tool</p>
- API finances.worldbank.org | Last Updated 2016-09-08T22:55:07.000Z
This dataset contains documents related to this project funded by the Afghanistan Reconstruction Trust Fund (ARTF). For questions, go to www.artf.af or contact: firstname.lastname@example.org.