Full edition for public use. These data come from a telephone survey of Haitian adults conducted April-June 2020. The study considers whether placing questions about a salient topic (COVID-19) decreases breakoff rates. The overall survey is concerned with democratic attitudes, but this dataset includes only those variables relevant to the paper in Survey Methods: Insights from the Field.

Indicators for evaluation of the psychometric properties of the instruments separated for each sex and country.

Geoscience Australia is releasing its 2014 version of the Marine Seismic Surveys Shape and Kml files. These files have been updated to include recent openfile surveys. The spatial files have been created from a cleansed, updated collection of p190 navigation files. This navigation collection has grown from the checking of navigation submitted to the GA Repository under the Offshore Petroleum and Greenhouse Gas Storage Regulations, checking of the 2003 SNIP navigation files and the digitisation of old survey track maps as required. Soon the individual p190 files will be available for download through the new NOPIMS delivery system. The collection is based on P190 navigation files which follows the UKOOA standard. Extensive industry standard metadata associated with a seismic survey is preserved in the attribute tables of these datasets.

The shapefiles have been categorised into 3D exploration, 2D exploration and 2D investigative seismic files. All marine surveys undertaken by Geoscience Australia for exploration or investigative purposes have been included in the collection. Geoscience Australia (email - AusGeodata@ga.gov.au) appreciates being notified of any errors found in the navigation collection.

The data is available in both KML and Shape file formats.

The KML file can be viewed using a range of applications including Google Earth, NASA WorldWind, ESRI ArcGIS Explorer, Adobe PhotoShop, AutoCAD3D or any other earth browser (geobrowser) that accepts KML formatted data.

Alternatively the Shape files can be downloaded and viewed using any application that supports shape files.

Disclaimer: Geoscience Australia gives no warranty regarding the data downloads provided herein nor the data's accuracy, completeness, currency or suitability for any particular purpose. Geoscience Australia disclaims all other liability for all loss, damages, expense and costs incurred by any person as a result of relying on the information in the data downloads.

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

This statistic shows the results of a survey among women conducted in the United States in 2017 on favorite vibrator shapes. Some 67 percent of respondents stated that they prefer a realistic, phallic shape for a vibrator.The Survey Data Table for the Statista survey Sex Toys in the United States 2017 contains the complete tables for the survey including various column headings.

This catalog record includes detailed variable-level descriptions, enabling data discovery and comparison. The data are not archived at ICPSR. Users should consult the data owners (via the Roper Center for Public Opinion Research) directly for details on obtaining the data. This collection includes variable-level metadata of the 2014 poll What Shapes Health, a survey from National Public Radio/Robert Wood Johnson Foundation/Harvard School of Public Health conducted by Social Science Research Solutions (SSRS). Topics covered in this survey include:Concerned about own healthMeaning of healthControl over own healthEffort into maintaining healthFrequency of healthy activities Description of personal healthTypes of healthy habitsOn diet to lose weightWays to improve healthThings that cause health problemsChildhood problems causing future health issuesParticipation in community organizationsVolunteering improving healthBeing told to improve healthFamily/friend behavior influencing healthHealth habits of family/friendsProblems experienced in adulthoodProblems experience in childhoodReceiving health careDifficulty accessing health careParents' healthRecent serious illnessesDiagnosed with health conditionsFrequency of exercisingPersonal weightSmoking habitsHealth insuranceThe data and documentation files for this survey are available through the Roper Center for Public Opinion Research [Roper #31092363]. Frequencies and summary statistics for the 244 variables from this survey are available through the ICPSR social science variable database and can be accessed from the Variables tab.

Geoscience Australia has been updating its collection of navigation for marine seismic surveys in Australia. These include original navigation files, the 2003 SNIP navigation files and digitised survey track maps. The result will be an updated cleansed navigation collection.

The collection is based on the SNIP format P190 navigation file which follows the UKOOA standard. Industry standard metadata associated with a seismic survey is preserved.

To assist industry, Geoscience Australia is making available its updated version of cleansed navigation. Although the process of updating the navigation data is ongoing and there is still legacy data to check, the navigation data is at a point where a significant improvement has been achieved and it is now usable. Users should be aware that this navigation is not final and there may be errors. Geoscience Australia (email - AusGeodata@ga.gov.au) appreciates being notified of any errors found.

The data is available in both KML and Shape file formats.

The KML file can be viewed using a range of applications including Google Earth, NASA WorldWind, ESRI ArcGIS Explorer, Adobe PhotoShop, AutoCAD3D or any other earth browser (geobrowser) that accepts KML formatted data.

Alternatively the Shape files can be downloaded and viewed using any application that supports shape files.

Disclaimer: Geoscience Australia gives no warranty regarding the data downloads provided herein nor the data's accuracy, completeness, currency or suitability for any particular purpose. Geoscience Australia disclaims all other liability for all loss, damages, expense and costs incurred by any person as a result of relying on the information in the data downloads.

The dataset collection in question is comprised of a series of related tables, which are organized in a systematic manner with rows and columns for the ease of data interpretation. These tables are part of a larger dataset collection that is primarily sourced from the website of Lantmäteriet (The Land Survey of Sweden), located in Sweden. Each table within this collection contains a variety of information and data points, providing a comprehensive overview of the subject matter at hand. The dataset collection as a whole serves as a valuable resource for comprehensive data analysis and interpretation.

Abstract: Amazon’s Mechanical Turk (MTurk) is widely used for data collection, however, data quality may be declining due to the use of Virtual Private Servers (VPSs) to fraudulently gain access to studies. Unfortunately, we know little about the scale and consequence of this fraud, and tools for social scientists to detect and prevent this fraud are underdeveloped. We first analyze 38 studies and show that this fraud is not new, but has increased recently. We then show that these fraudulent respondents provide particularly low-quality data and can weaken treatment effects. Finally, we provide two solutions: an easy-to-use application for identifying fraud in existing datasets and a method for blocking fraudulent respondents in Qualtrics surveys.

Most indicators throughout Vital Signs are created by acquiring and analyzing data collected from governmental agencies for some public administration purpose, such as 311 calls or housing inspections. However, data from the United States Bureau of the Census remains the best source for demographic and socioeconomic indicators for neighborhoods. The Census Bureau collects a wide variety of information through administration of both the decennial Census and the annual American Community Survey (ACS).

The presentation of Task force meeting of ALiSEA in Laos€ , 27th November 2018, Lao PDR

Record of Survey Index is a Polygon FeatureClass representing approximate boundaries of Records of Survey recorded at Santa Clara County Clerk Recorders Office. Records are indexed by City assigned ROS number. It is primarily used index layer. Users query this layer and are able to find appropriate docuements on laserfische or hardcopy. The layer is updated as-needed by the Public Works Department. Record of Survey Index has the following fields:

OBJECTID: Unique identifier automatically generated by Esri type: OID, length: 4, domain: none

ROS: The Record of Survey number used to find the appropriate documentation type: String, length: 50, domain: none

Shape: Field that stores geographic coordinates associated with feature type: Geometry, length: 4, domain: none

BookPage: Refers to specific location within a book and a page of the document type: String, length: 50, domain: none

Shape.STArea():

The area of the shape - in square feet type: Double, length: 0, domain: none

Shape.STLength():

The length of the shape - in feettype: Double, length: 0, domain: none

last_edited_date:

The date the database row was last updated type: Date, length: 8, domain: none

created_date:

The date the database row was initially created type: Date, length: 8, domain: none

Characterization of the study sample.

Most indicators throughout Vital Signs are created by acquiring and analyzing data collected from governmental agencies for some public administration purpose, such as 311 calls or housing inspections. However, data from the United States Bureau of the Census remains the best source for demographic and socioeconomic indicators for neighborhoods. The Census Bureau collects a wide variety of information through administration of both the decennial Census and the annual American Community Survey (ACS).

This dataset is associated with the University of Michigan Dept. of Physics dissertation titled "Shedding Light on the Dark: Exploring the Relation Between Galaxy Cluster Mass and Temperature Through Weak Gravitational Lensing" by Rutuparna Das. It is also associated with a paper, currently in preparation, by Das et al (details to be added once paper is submitted/accepted).;This work contains information about shapes of galaxies observed by the Dark Energy Survey (DES) during its Science Verification (SV) run. The official DES SV shape catalog has already been released to the public (see details in Jarvis et al. (2016), henceforth called "J16"). This work follows the methods presented in J16, and contains shapes from areas of the sky that were not processed as part of the official DES-SV catalog but were necessary for the work presented in the aforementioned dissertation. Each catalog contains information for galaxies in a 80′ × 80′ cutout centered at a given galaxy cluster.;Note that these catalogs are not entirely analogous to the official DES-SV catalog. For one, we only measure shapes for galaxies, as stars and other objects were not needed for the dissertation. Our catalogs also only extend to a magnitude of 24 in r-band, whereas a small fraction of the objects in the official Im3shape catalog are dimmer (see Figure 29 of J16).;We also include other information necessary for weak lensing studies. Aside from all fields from Im3shape and noise bias calibration (listed and described in J16), these catalogs contain columns for object positions (“ra_gold”, “dec_gold”) and magnitudes in various filters (“mag_detmodel_g”, “mag_detmodel_r”, “mag_detmodel_i”, “mag_detmodel_z”) from the SVA1-Gold catalog (https://des.ncsa.illinois.edu/releases/sva1/docs/docs-gold). Additionally, we include mean redshift measurements from two DES photo-z measurement pipelines, TPZ and DESDM Neural Network (“z_TPZ”, “z_DESDMnn”) (more details in Sanchez et al. (2014)).;References: Jarvis, M., Sheldon, E., Zuntz, J., et al. 2016, Monthly Notices of the Royal Astronomical Society, 460, 2245. Sanchez, C., Carrasco Kind, M., Lin, H., et al. 2014, Monthly Notices of the Royal Astronomical Society, 445, 1482.

Alaska Survey Boundary contains miscellaneous state, federal, and private surveys. This shape file characterizes the geographic representation of land parcels within the State of Alaska contained by the Base - Survey Boundary category. It has been extracted from data sets used to produce the State status plats. This data set includes cases noted on the digital status plats up to one day prior to data extraction. Each state survey feature has an associated attribute record, including a Land Administration System (LAS) file-type and file-number which serves as an index to related LAS case-file information. Additional LAS case-file and customer information may be obtained at: http://www.dnr.state.ak.us/las/LASMenu.cfm Those requiring more information regarding State land records should contact the Alaska Department of Natural Resources Public Information Center directly.

DOI We study the supercluster shape properties of the recently compiled Sloan Digital Sky Survey (SDSS) cluster catalogue using an approach based on differential geometry. We detect superclusters by applying the percolation algorithm to observed cluster populations, extended out to z_max_=8 cluster members and find that filamentary morphology is the dominant supercluster shape feature, in agreement with previous studies. Cone search capability for table J/MNRAS/344/602/table1 (List of the SDSS superclusters using R_pr_=26h^-1^Mpc.)

Alaska Survey Boundary contains miscellaneous state, federal, and private surveys. This shape file characterizes the geographic representation of land parcels within the State of Alaska contained by the Base - Survey Boundary category. It has been extracted from data sets used to produce the State status plats. This data set includes cases noted on the digital status plats up to one day prior to data extraction. Each state survey feature has an associated attribute record, including a Land Administration System (LAS) file-type and file-number which serves as an index to related LAS case-file information. Additional LAS case-file and customer information may be obtained at: https://dnr.alaska.gov/projects/las/ Those requiring more information regarding State land records should contact the Alaska Department of Natural Resources Public Information Center directly.

We engaged 315 participants from Credamo platform, a well-established and reliable source for large-scale data collection. We gathered survey data across three distinct time points, each separated by a two-week interval. During the first time point, we invited 600 participants to provide information on their gender, age, education level, and the frequency of using Gen AI. Additionally, we asked them to provide ratings on their individual regulatory focus, AI awareness and perceived CSR. We obtained 590 responses, which corresponds to a high response rate of 98%. During the second time point, we invited the 590 participants who responded at Time 1 to rate on their work passion. We received 540 responses, resulting in a response rate of 90%. At Time 3, we invited the 540 respondents to complete the final assessment, in which they rated their job crafting behaviors. In all, 316 participants returned their responses, yielding a response rate of 88%.

ShapeCalc is an Excel-based tool that uses 2D crystal intersection widths and lengths to estimate 3D crystal shape. ShapeCalc provides 3D (S:I:L) shape estimates for 2D width-length (w-l) input data. Best estimates are found by comparing the sample w/l distribution with 2618 model w/l distributions covering shapes from 1:1:1 to 1:20:20. Model w/l distributions are obtained by 20000 random sections of a given model shape using the CSDCorrections algorithm (Higgins, 2000). For each model shape, the 20000 w/l datapoints are binned into 25 bins with w/l increments of 0.04. Binned distributions for all 2618 models are stored in the database tab. Compared to existing 2D-to-3D projection tools, ShapeCalc offers more robust constraints in 3D crystal shape (including uncertainty estimates) for a wider range of naturally occurring crystal shapes. ShapeCalc is described and published, with open access; Mangler, M.F., Humphreys, M.C.S., Wadsworth, F.B. et al. Variation of plagioclase shape with size in intermediate magmas: a window into incipient plagioclase crystallisation. Contributions to Mineralogy and Petrology 177, 64 (2022). https://doi.org/10.1007/s00410-022-01922-9

Associations of gains in BMI and waist circumference during different age intervals with spine modes at age 60–64.