UNI-CEN Standardized Census Data Tables contain Census data that have been reformatted into a common table format with standardized variable names and codes. The data are provided in two tabular formats for different use cases. "Long" tables are suitable for use in statistical environments, while "wide" tables are commonly used in GIS environments. The long tables are provided in Stata Binary (dta) format, which is readable by all statistics software. The wide tables are provided in comma-separated values (csv) and dBase 3 (dbf) formats with codebooks. The wide tables are easily joined to the UNI-CEN Digital Boundary Files. For the csv files, a .csvt file is provided to ensure that column data formats are correctly formatted when importing into QGIS. A schema.ini file does the same when importing into ArcGIS environments. As the DBF file format supports a maximum of 250 columns, tables with a larger number of variables are divided into multiple DBF files. For more information about file sources, the methods used to create them, and how to use them, consult the documentation at https://borealisdata.ca/dataverse/unicen_docs. For more information about the project, visit https://observatory.uwo.ca/unicen.

In this folder there is the data to replicate the paper "The ties that bind and transform: knowledge remittances, relatedness, and the direction of technical change". In the folder there is a .dta file that contains the data in Stata format, and an .xls file that provides a description of the variables.

This database contains water monitoring data collected by NETN in the CVDT format used by data visualization and other systems. This version of the data is not intended to be an archive.

Working with different File Formats

I have created supporting notebook to explain all these file formats. You can find the notebook here:- https://www.kaggle.com/raj401/working-with-different-file-formats

The Datasets

1. datafile.csv

2. datafile.json

3. datafile.ods

4. datafile.xls

All the above Datasets contain same data in different file formats.

The data shows pattern of land utilisation under categories such as orest, permanent pastures and another grazing lands, land not available for cultivation etc.

The data contains following features:-

'Year (Col.1)' 'Geographical Area (Col.2)' 'Reporting area for Land utilisation statistics (Col.3 = Col.4+Col.7+ Col.11+Col.14+Col.15)' 'Forests (Col.4)' 'Not available for cultivation - Area under non-agricultural uses (Col.5)' 'Not available for cultivation - Barren and unculturable Land (Col.6)' 'Not available for cultivation - Total (Col.7 = Col.5+Col.6)' 'Other uncultivated Land excluding Fallow Land - Permanent pastures & other Grazing Lands (Col.8)' 'Other uncultivated Land excluding Fallow Land - Land under Misc. tree crops & groves (not incl. in net area sown) (Col.9)' 'Other uncultivated Land excluding Fallow Land - Culturable waste Land (Col.10)' 'Other uncultivated Land excluding Fallow Land - Total (Col.11 = Col.8 to Col.10)' 'Fallow Lands - Fallow Lands other than current fallows (Col.12)' 'Fallow Lands - Current fallows (Col.13)' 'Fallow Lands - Total Col.14 = (Col.12+Col.13)' 'Net area Sown (Col.15)' 'Total cropped area (Col.16)' 'Area sown more than once (Col.17 = Col.16-Col.15)' 'Agricultural Land/Cultivable Land/Culturable Land/Arable Land (Col.18 = Col.9+Col.10+Col.14+Col.15)' 'Cultivated Land (Col.19 = Col.13+Col.15)' 'Cropping Intensity (Col.20 = % of Col.16 over Col.15)'

Acknowledgements

I am really thankful to Indian government for storing these valuable data. Source:- https://data.gov.in/

Inspiration

I am inspired by everyone here on Kaggle for the level of their dedication and hard work.

This includes the dataset and replication do file for the tables and figures included in the manuscript "In Capable Hands: An Experimental Study of the Effects of Competence and Consistency on Leadership Approval." The dataset is in a Stata 13 .dta format and the models are in a .do file format.

The .dta file includes the data used in the regression analyses in the article (in Stata format); the .do files contain code that can be run on Stata to replicate the findings in the article.

This data set consists of Conductivity, Temperature, Depth (CTD) data in MATLAB Format from the 2002 Polar Star Mooring Cruise (AWS-02-I). These data are provided in a single mat-file (MATLAB) for the entire cruise.

This dataset contains C-band radar data collected by the NCAR CP3 radar during the Hawaiian Rainband Project (HaRP) from 11 July 1990 to 23 August 1990. The data are in DORADE format and are available as tar files (~500 MB per file).

This data set contains cfRadial format files (version 1) of SMART-R1 radar data taken during the DYNAMO (Dynamics of the Madden-Julian Oscillation) project. The data are available in daily tar files (about 2-3 GB/file). Each order can contain a maximum of 16 GB of data (~6.5 days). For very large orders, it may be preferable for you to access these data via a loaner external hard drive. Please contact Steve Williams (sfw@ucar.edu) for these very large orders.

This dataset contains model-based census tract level estimates in a GIS-friendly format.Estimates were created using a unique PLACES methodology. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the Census Bureau's 2022 census tract GIS file in a GIS system to produce maps for 40 measures at the census tract level.

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-3-78-EG in Eastern Gulf of Alaska from 06/22/1978 to 07/04/1978, http://walrus.wr.usgs.gov/infobank/l/l378eg/html/l-3-78-eg.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l378eg/html/l-3-78-eg.bath.html, http://walrus.wr.usgs.gov/infobank/l/l378eg/html/l-3-78-eg.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l378eg/html/l-3-78-eg.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

This data set consists of Bottle Data in WHP Format from the Spring 2002 U.S. Coast Guard Cutter (USCGC) Healy Cruise (HLY-02-01). The data are in a single zip file which contains an comma-delimited data file for each observation.

This package contains replication data and code for "With or without him? Experimental evidence on cash grants and gender-sensitive trainings in Tunisia" by Jules Gazeaud, Nausheen Khan, Eric Mvukiyehe, and Olivier Sterck. An earlier version of this package for the related Working Paper was published on the World Bank Reproducible Research Repository under the doi: https://doi.org/10.60572/cgwr-5f85. The package contains 2 datasets with data collected from a baseline survey and an endline survey for a Randomized Controlled Trial which took place in Jendouba, Tunisia between 2016 and 2021. This folder contains all the data and code necessary for replicating the tables and figure in the paper and online appendix. The data files are in Stata (.dta) format, and the replication code was written in Stata and requires Python to run. For more information on the data or code, please see the readme.

BioCTS for ISO/IEC Biometric Data Interchange Format Standards and Selected PIV Profiles is a conformance testing architecture that tests biometric data interchange records for conformance to ISO/IEC based biometric data interchange formats. The software includes a graphical user interface, several different conformance test suites, and provides detailed descriptions of any errors found.

Data represents feedback on learning environment from families. Aids in facilitating the understanding of families perceptions of students, teachers, environment of their school. The survey is aligned to the DOE's framework for great schools. It is designed to collect important information about each schools ability to support success.

PM1EPHND is the Aqua Near Real Time (NRT) daily spacecraft definitive ephemeris data file in native format. This is MODIS Ancillary Data. The data collection consists of PM1 Platform Attitude Data that has been preprocessed by ECS to an internal standard supported by the ECS SDP Toolkit. This data is typically used in determining the geolocation of earth remote sensing observations.The file name format is the following:PM1EPHND_NRT.Ayyyyddd.hhmm.vvv where from left to right:PM1 = PM1 (Aqua);EPH = Spacecraft Ephemeris; N = Native format; D = Definitive; A = Acquisition; yyyy = data year, ddd = Julian data day, hh = data hour, mm = data minute; vvv = Version ID.

No description found

This file is an example data set from the Central Valley of California from a drought study corresponding to “recent non-drought conditions” (Scenario 1 in Petrie et al., in review). In 2014, following an 8-year period with 7 below-normal to critically-dry water years, the bioenergetic model TRUEMET was used to assess the impacts of drought on wintering waterfowl habitat and bioenergetics in the Central Valley of California. The goal of the study was to assess whether available foraging habitats could provide enough food to support waterfowl populations (ducks and geese) under a variety of climate and population level scenarios. This information could then be used by managers to adapt their waterfowl habitat management plans to drought conditions. The study area spanned the Central Valley and included the Sacramento Valley in the north, the San Joaquin Valley in the south, and Suisun Marsh and Sacramento-San Joaquin River Delta (Delta) east of San Francisco Bay. The data set consists of two foraging guilds (ducks and geese/swans) and five forage types: harvested corn, rice (flooded), rice (unflooded), wetland invertebrates and wetland moist soil seeds. For more background on the data set, see Petrie et al. in review.