The Office of Gun Violence Prevention (OGVP) shares real-time gun violence data to increase government transparency, improve the public's awareness, and support community-based gun violence prevention and reduction partners. All District crime data is available through Crime Cards. The dashboards below focus on gun violence only. The data in these dashboards is updated daily at 7:40AM with the incidents from the day before. View data covering 7-Day Look-back of Gun Violence and Year-to-date Gun Violence.All statistics presented here are based on preliminary DC criminal code offense definitions. The data do not represent official statistics submitted to the FBI under the Uniform Crime Reporting program (UCR) or National Incident Based Reporting System (NIBRS). All preliminary offenses are coded based on DC criminal code and not the FBI offense classifications. Please understand that any comparisons between MPD preliminary data as published on this website and the official crime statistics published by the FBI under the Uniform Crime Reporting Program (UCR) are inaccurate and misleading. The MPD does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information. The MPD will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. Read complete data notes at buildingblocks.dc.gov/data.

Abstract

Within the frame of PCBS' efforts in providing official Palestinian statistics in the different life aspects of Palestinian society and because the wide spread of Computer, Internet and Mobile Phone among the Palestinian people, and the important role they may play in spreading knowledge and culture and contribution in formulating the public opinion, PCBS conducted the Household Survey on Information and Communications Technology, 2014.

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestine in addition to providing data on the following: -

· Prevalence of computers and access to the Internet. · Study the penetration and purpose of Technology use.

Geographic coverage

Palestine (West Bank and Gaza Strip) , type of locality (Urban, Rural, Refugee Camps) and governorate

Analysis unit

Household. Person 10 years and over .

Universe

All Palestinian households and individuals whose usual place of residence in Palestine with focus on persons aged 10 years and over in year 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of a list of enumeration areas adopted in the Population, Housing and Establishments Census of 2007. Each enumeration area has an average size of about 124 households. These were used in the first phase as Preliminary Sampling Units in the process of selecting the survey sample.

Sample Size The total sample size of the survey was 7,268 households, of which 6,000 responded.

Sample Design The sample is a stratified clustered systematic random sample. The design comprised three phases:

Phase I: Random sample of 240 enumeration areas. Phase II: Selection of 25 households from each enumeration area selected in phase one using systematic random selection. Phase III: Selection of an individual (10 years or more) in the field from the selected households; KISH TABLES were used to ensure indiscriminate selection.

Sample Strata Distribution of the sample was stratified by: 1- Governorate (16 governorates, J1). 2- Type of locality (urban, rural and camps).

Sampling deviation

-

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on persons (aged 10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Preparation of Data Entry Program: This stage included preparation of the data entry programs using an ACCESS package and defining data entry control rules to avoid errors, plus validation inquiries to examine the data after it had been captured electronically.

Data Entry: The data entry process started on 8 May 2014 and ended on 23 June 2014. The data entry took place at the main PCBS office and in field offices using 28 data clerks.

Editing and Cleaning procedures: Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

Response rate

Response Rates= 79%

Sampling error estimates

There are many aspects of the concept of data quality; this includes the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are three components to the quality of statistics: accuracy, comparability, and quality control procedures.

Checks on data accuracy cover many aspects of the survey and include statistical errors due to the use of a sample, non-statistical errors resulting from field workers or survey tools, and response rates and their effect on estimations. This section includes:

Statistical Errors Data of this survey may be affected by statistical errors due to the use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators.

Variance calculations revealed that there is no problem in disseminating results nationally or regionally (the West Bank, Gaza Strip), but some indicators show high variance by governorate, as noted in the tables of the main report.

Non-Statistical Errors Non-statistical errors are possible at all stages of the project, during data collection or processing. These are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, and practical and theoretical training took place during the training course. Training manuals were provided for each section of the questionnaire, along with practical exercises in class and instructions on how to approach respondents to reduce refused cases. Data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

The sources of non-statistical errors can be summarized as: 1. Some of the households were not at home and could not be interviewed, and some households refused to be interviewed. 2. In unique cases, errors occurred due to the way the questions were asked by interviewers and respondents misunderstood some of the questions.

Round 11 Train Dataset This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of image classification AIs trained on synthetic image data build from Cityscapes. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 288 AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Spain Exports of wigs, false beards, eyebrow, eyelashes, switches; articles of human hair to Turkey was US$106 during 2024, according to the United Nations COMTRADE database on international trade. Spain Exports of wigs, false beards, eyebrow, eyelashes, switches; articles of human hair to Turkey - data, historical chart and statistics - was last updated on November of 2025.

Romania Exports of wigs, false beards, eyebrow, eyelashes, switches; articles of human hair to Norway was US$55 during 2024, according to the United Nations COMTRADE database on international trade. Romania Exports of wigs, false beards, eyebrow, eyelashes, switches; articles of human hair to Norway - data, historical chart and statistics - was last updated on November of 2025.

The U.S Fish and Wildlife Services (USFWS) Lower Great Lakes Fish and Wildlife Conservation Office (LGLFWCO) Aquatic Invasive Species (AIS) program conducts Eurasian Ruffe (Gymnocephalus cernua) bottom trawl surveys annually. Dedicated Ruffe surveillance occurs across harbors and ports of the Great Lakes with the intent of detecting Ruffe range expansions. The LGLFWCO began conducting Ruffe surveys in 1994 and continue this effort to present day, excluding 2020 due to COVID-19 restrictions which prevented sampling for that year. LGLFWCO Ruffe sampling focuses on harbors and ports of the lower Great Lakes (Erie and Ontario) and has included the following locations: Toledo (Ohio), Sandusky (Ohio), Cleveland (Ohio), Ashtabula (Ohio), Conneaut (Ohio), Erie (Pennsylvania), Buffalo (New York), and Rochester (New York).All LGLFWCO Ruffe sampling is conducted using bottom trawls (4.9 m headrope) and intends to sample the benthic fish community. Sampling locations are visited biannually (spring and fall) and trawls occur at historical trawling transects denoted for each port or harbor. Although each location is sampled biannually, there is some variability in the seasonality of surveys - this can be made apparent in the water temperatures collected during sampling events. Although the primary objective of these surveys is to search for Ruffe, many native and non-native (i.e. Round Goby) species are collected. Similar to our Early Detection and Monitoring (EDM) program, all fish collected during the trawls are counted and identified to species in the field using taxonomic keys. If an identification cannot be made in the field, the specimen or some of its tissue is preserved using 95% ethanol (EtOH) and identifications are made in the laboratory either taxonomically or genetically (recent survey years; Northeast Fishery Center). A subset of individuals from each species are measured (total length; mm) in the field. Any significant AIS detections are reported to partners following an internal communications protocol. The information within this dataset is geospatial in nature and documents Ruffe trawling sampling events. Both abiotic and biotic data is collected for each individual trawling event. It is possible that over time, variations in the trawling protocol (duration, speed, warp length, etc.) have been modified which could influence the effectiveness of the trawl. Also, although infrequent, some fish identifications within this data set may be inaccurate and without photographs or preservation of the individual to confirm identification, those records will remain within this data set unless otherwise detected and removed.

The EV-GHG Mobile Source Data asset contains measured mobile source GHG emissions summary compliance information on light-duty vehicles, by model, for certification as required by the 1990 Amendments to the Clean Air Act, and as driven by the 2010 Presidential Memorandum Regarding Fuel Efficiency and the 2005 Supreme Court ruling in Massachusetts v. EPA that supported the regulation of CO2 as a pollutant. Manufacturers submit data on an annual basis, or as needed to document vehicle model changes. This asset will be expanded to include medium and heavy duty vehicles in the future.The EPA performs targeted GHG emissions tests on approximately 15% of vehicles submitted for certification. Confirmatory data on vehicles is associated with its corresponding submission data to verify the accuracy of manufacturer submissions beyond standard business rules.Submitted data comes in XML format or as documents, with the majority of submissions sent in XML, and includes descriptive information on the vehicle itself, emissions information, and the manufacturer's testing approach. This data may contain proprietary information (CBI) such as information on estimated sales or other data elements indicated by the submitter as confidential. CBI data is not publically available; however, CBI data can accessed within EPA under the restrictions of the Office of Transportation and Air Quality (OTAQ) CBI policy [RCS Link]. Pollutants data includes CO2, CH4, N2O. Datasets are divided by vehicle/engine model and/or year with corresponding emission, test, and certification data. Data assets are stored in EPA's Verify system.Coverage began in 2011, with summary light duty data available to the public on request. Raw data is only available to select EPA employees.EV-GHG Mobile Source Data submission documents with metadata, certificate and summary decision information is stored in Verify after it has been quality assured. Where summary data appears inaccurate, OTAQ returns the entries for review to their originator.

India Imports from Turkey of Wigs, False Beards, Eyebrow, Eyelashes, Switches; Articles of Human Hair was US$286 during 2010, according to the United Nations COMTRADE database on international trade. India Imports from Turkey of Wigs, False Beards, Eyebrow, Eyelashes, Switches; Articles of Human Hair - data, historical chart and statistics - was last updated on November of 2025.

Most database search tools for proteomics have their own scoring parameter sets depending on experimental conditions such as fragmentation methods, instruments, digestion enzymes, and so on. These scoring parameter sets are usually predefined by tool developers and cannot be modified by users. The number of different experimental conditions grows as the technology develops, and the given set of scoring parameters could be suboptimal for tandem mass spectrometry data acquired using new sample preparation or fragmentation methods. Here we introduce a new approach to optimize scoring parameters in a data-dependent manner using a spectrum quality filter. The new approach conducts a preliminary search for the spectra selected by the spectrum quality filter. Search results from the preliminary search are used to generate data-dependent scoring parameters; then, the full search over the entire input spectra is conducted using the learned scoring parameters. We show that the new approach yields more and better peptide-spectrum matches than the conventional search using built-in scoring parameters when compared at the same 1% false discovery rate.

Spain Exports of wigs, false beards, eyebrow, eyelashes, switches; articles of human hair to Bosnia And Herzegovina was US$1.88 Thousand during 2020, according to the United Nations COMTRADE database on international trade.

Dataset Generated by Stream Engine from Ocean Observatories Initiative AssetManagementRecordLastModified=2020-08-28T17:25:04.418000 AssetUniqueID=CGINS-CTDBPP-50126 cdm_data_type=Other collection_method=recovered_inst Conventions=CF-1.6, NCCSV-1.0 defaultDataQuery=practical_salinity,ctdbp_seawater_conductivity,ctdbp_seawater_pressure,ctdbp_seawater_conductivity_qartod_executed,ctd_time,ctdbp_seawater_temperature,conductivity,ctdbp_seawater_temperature_qartod_executed,temperature,density,ctdbp_seawater_pressure_qartod_results,pressure_temp,ctdbp_seawater_pressure_qartod_executed,pressure,ctdbp_seawater_temperature_qartod_results,ctdbp_seawater_conductivity_qartod_results,time&time>=max(time)-1days Description=CTD Pumped: CTDBP Series P feature_Type=point FirmwareVersion=Not specified. geospatial_lat_resolution=0.1 geospatial_lat_units=degrees_north geospatial_lon_resolution=0.1 geospatial_lon_units=degrees_east geospatial_vertical_positive=down geospatial_vertical_resolution=0.1 geospatial_vertical_units=meters history=2020-09-01T02:11:09.353650 generated from Stream Engine id=GI01SUMO-RII11-02-CTDBPP031-recovered_inst-ctdbp_cdef_instrument_recovered infoUrl=http://oceanobservatories.org/ institution=Ocean Observatories Initiative lat=59.9341 lon=-39.4673 Manufacturer=Sea-Bird Electronics Metadata_Conventions=Unidata Dataset Discovery v1.0 Mobile=False ModelNumber=SBE 16plus-IM V2 naming_authority=org.oceanobservatories nodc_template_version=NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1 node=RII11 Notes=Not specified. Owner=Not specified. processing_level=L2 project=Ocean Observatories Initiative references=More information can be found at http://oceanobservatories.org/ RemoteResources=[] requestUUID=e16def65-db8a-4044-a89c-46787bb6ab12 sensor=02-CTDBPP031 SerialNumber=16-50126 ShelfLifeExpirationDate=Not specified. SoftwareVersion=Not specified. source=GI01SUMO-RII11-02-CTDBPP031-recovered_inst-ctdbp_cdef_instrument_recovered sourceUrl=http://oceanobservatories.org/ standard_name_vocabulary=NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29 stream=ctdbp_cdef_instrument_recovered subsite=GI01SUMO time_coverage_end=2017-03-06T00:00:00Z time_coverage_resolution=P3620.26S time_coverage_start=2016-07-10T18:01:40Z uuid=e16def65-db8a-4044-a89c-46787bb6ab12

European Union Imports of wigs, false beards, eyebrow, eyelashes, switches; articles of human hair from Kyrgyzstan was US$290 during 2024, according to the United Nations COMTRADE database on international trade.

Papua New Guinea Imports from Hong Kong of Wigs, False Beards, Eyebrow, Eyelashes, Switches; Articles of Human Hair was US$5.74 Thousand during 2023, according to the United Nations COMTRADE database on international trade.

While welcoming the comment of Ho et al. (2015), we find little that undermines the strength of our criticism, and it would appear they have misunderstood our central argument. Here we respond with the purpose of reiterating that we are (i) generally critical of much of the evidence presented in support of the time-dependent molecular rate (TDMR) hypothesis and (ii) specifically critical of estimates of μ derived from tip-dated sequences that exaggerate the importance of purifying selection as an explanation for TDMR over extended timescales. In response to assertions put forward by Ho et al. (2015), we use panmictic coalescent simulations of temporal data to explore a fundamental assumption for tip-dated tree shape and associated mutation rate estimates, and the appropriateness and utility of the date randomization test. The results reveal problems for the joint estimation of tree topology, effective population size and μ with tip-dated sequences using beast. Given the simulations, beast consistently obtains incorrect topological tree structures that are consistent with the substantial overestimation of μ and underestimation of effective population size. Data generated from lower effective population sizes were less likely to fail the date randomization test yet still resulted in substantially upwardly biased estimates of rates, bringing previous estimates of μ from temporally sampled DNA sequences into question. We find that our general criticisms of both the hypothesis of time-dependent molecular evolution and Bayesian methods to estimate μ from temporally sampled DNA sequences are further reinforced.

Hong Kong Exports of wigs, false beards, eyebrow, eyelashes, switches; articles of human hair to Tonga was US$59 during 2019, according to the United Nations COMTRADE database on international trade. Hong Kong Exports of wigs, false beards, eyebrow, eyelashes, switches; articles of human hair to Tonga - data, historical chart and statistics - was last updated on November of 2025.

Singapore Exports of wigs, false beards, eyebrow, eyelashes, switches; articles of human hair to Turkey was US$9.07 Thousand during 2023, according to the United Nations COMTRADE database on international trade.

Canada Imports from Uganda of Wigs, False Beards, Eyebrow, Eyelashes, Switches; Articles of Human Hair was US$2 during 2024, according to the United Nations COMTRADE database on international trade. Canada Imports from Uganda of Wigs, False Beards, Eyebrow, Eyelashes, Switches; Articles of Human Hair - data, historical chart and statistics - was last updated on November of 2025.

sy1998/EarthMind-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Thailand Exports of wigs, false beards, eyebrow, eyelashes, switches; articles of human hair to Kyrgyzstan was US$79 during 2017, according to the United Nations COMTRADE database on international trade.

Dataset Generated by Stream Engine from Ocean Observatories Initiative AssetManagementRecordLastModified=2022-09-02T13:10:19.568000 AssetUniqueID=CGINS-METLGR-00011 cdm_data_type=Other collection_method=telemetered Conventions=CF-1.6, NCCSV-1.0 defaultDataQuery=met_latnflx,met_rainflx,barometric_pressure,met_stablty,air_temperature,met_rainrte,met_tempa2m,met_wind10m,met_mommflx,met_sensflx,met_timeflx,met_buoyfls,precipitation,met_netlirr,met_netsirr_hourly,met_buoyflx,met_tempskn,northward_velocity,met_sphum2m,eastward_velocity,longwave_irradiance,shortwave_irradiance,relative_humidity,sea_surface_temperature,time,met_heatflx,met_relwind_speed,met_frshflx&time>=max(time)-1days Description=Bulk Meteorology Instrument Package: METBK LGR Module feature_Type=point FirmwareVersion=Not specified. geospatial_lat_resolution=0.1 geospatial_lat_units=degrees_north geospatial_lon_resolution=0.1 geospatial_lon_units=degrees_east geospatial_vertical_positive=down geospatial_vertical_resolution=0.1 geospatial_vertical_units=meters history=2022-09-13T07:10:37.979433 generated from Stream Engine id=CE02SHSM-SBD11-06-METBKA000-telemetered-metbk_a_dcl_instrument infoUrl=http://oceanobservatories.org/ institution=Ocean Observatories Initiative lat=44.63833 lon=-124.30388 Manufacturer=Star Engineering Metadata_Conventions=Unidata Dataset Discovery v1.0 Mobile=False ModelNumber=ASIMET naming_authority=org.oceanobservatories nodc_template_version=NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1 node=SBD11 Notes=Not specified. Owner=Oregon State University processing_level=L2 project=Ocean Observatories Initiative references=More information can be found at http://oceanobservatories.org/ RemoteResources=[] requestUUID=0b28ea12-655f-44c8-87c0-8cd5e1eee0a7 sensor=06-METBKA000 SerialNumber=LGR011 ShelfLifeExpirationDate=Not specified. SoftwareVersion=Not specified. source=CE02SHSM-SBD11-06-METBKA000-telemetered-metbk_a_dcl_instrument sourceUrl=http://oceanobservatories.org/ standard_name_vocabulary=NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29 stream=metbk_a_dcl_instrument subsite=CE02SHSM time_coverage_end=2015-08-25T18:01:40Z time_coverage_resolution=P61.46S time_coverage_start=2015-04-02T20:45:50Z uuid=0b28ea12-655f-44c8-87c0-8cd5e1eee0a7