H-1B visa sponsorship trends for Database Administrator, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

A dataset of H1B visa salary records from 2025, including employer name, job title, city, wage offered, and visa status.

A detailed analysis of H-1B visa sponsorship trends, featuring data on labor certifications, top sponsoring employers, most common job titles, leading immigration law firms, key industries, and geographic distribution. This dataset provides valuable insights into employment-based immigration patterns, helping professionals, employers, and policymakers make informed decisions.

A comprehensive dataset of top job titles for H-1B Visa sponsorships in 2025, including salary data, petition trends, and employer insights. Updated annually with the latest trends and employer behavior regarding H-1B visa sponsorship.

Investigative case data involving H-1B non-immigrant visas

H-1B visa sponsorship trends for Data Scientist, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

Abstract

The NIS is the largest publicly available all-payer inpatient healthcare database designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from around 7 million hospital stays each year. Weighted, it estimates around 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels.

Its large sample size is ideal for developing national and regional estimates and enables analyses of rare conditions, uncommon treatments, and special populations.

Usage

IMPORTANT NOTE: Some records are missing from the Severity Measures table for 2017 & 2018, but none are missing from any of the other 2012-2020 data. We are in the process of trying to recover the missing records, and will update this note when we have done so.

Also %3Cu%3EDO NOT%3C/u%3E

use this data without referring to the NIS Database Documentation, which includes:

• Description of NIS Database
• Restrictions on Use

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• Data Elements
• Additional Resources for Data Elements
• ICD-10-CM/PCS Data Included in the NIS Starting with 2015 (More details about this transition available here.)
• Known Data Issues
• NIS Supplemental Files
• HCUP Tools: Labels and Formats
• Obtaining HCUP Data

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Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

HCUP Online Tutorials

For additional assistance, AHRQ has created the HCUP Online Tutorial Series, a series of free, interactive courses which provide training on technical methods for conducting research with HCUP data. Topics include an HCUP Overview Course and these tutorials:

• The HCUP Sampling Design tutorial is designed to help users learn how to account for sample design in their work with HCUP national (nationwide) databases. • The Producing National HCUP Estimates tutorial is designed to help users understand how the three national (nationwide) databases – the NIS, Nationwide Emergency Department Sample (NEDS), and Kids' Inpatient Database (KID) – can be used to produce national and regional estimates. HCUP 2020 NIS (8/22/22) 14 Introduction • The Calculating Standard Errors tutorial shows how to accurately determine the precision of the estimates produced from the HCUP nationwide databases. Users will learn two methods for calculating standard errors for estimates produced from the HCUP national (nationwide) databases. • The HCUP Multi-year Analysis tutorial presents solutions that may be necessary when conducting analyses that span multiple years of HCUP data. • The HCUP Software Tools Tutorial provides instructions on how to apply the AHRQ software tools to HCUP or other administrative databases.

New tutorials are added periodically, and existing tutorials are updated when necessary. The Online Tutorial Series is located on the HCUP-US website at www.hcupus.ahrq.gov/tech_assist/tutorials.jsp.

Important notes about the 2015 data

In 2015, AHRQ restructured the data as described here:

https://hcup-us.ahrq.gov/db/nation/nis/2015HCUPNationalInpatientSample.pdf

Some key points:

• For the 2015 data, all diagnosis and procedure data elements, including any data elements derived from diagnoses and procedures, were moved out of the Core File and into the Diagnosis and Procedure Groups Files.
• Prior to 2015, and for Q1-3 of 2015, the DX1-30 and PR1-15 variables (which use ICD-9 codes) variables were used, but starting in Q4 of 2015, the I10_DX1-30 and I10_PR1-I10-15 (which use ICD-10 codes) were used. The best way to identify discharges for quarter 1-3 or quarter 4 is based on the value of the diagnosis version (DXVER); For quarters 1-3, DXVER has a value of 9; while for quarter 4, DXVER has a value of 10.
• Some other variables also transitioned in Q4 of 2015. Please refer to the link above for more details.
• Starting in 2016, the diagnosis and procedure information returned to the Core file. Additional details about the data in 2016 are available here: https://hcup-us.ahrq.gov/db/nation/nis/NISChangesBeginningDataYr2016.pdf

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NIS Areas of Research and HCUP Publications

H-1B visa sponsorship trends for Data Engineer, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

In the first half of 2024, *** data breach incidents in the healthcare sector in the United States were caused by hacking and other IT incidents. A further ** incidents of breaches originated from unauthorized access, while ***** percent were caused by theft.

Abstract

The National (Nationwide) Kids' Inpatient Database (KID) is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). Only years 2003, 2006, 2009, 2012 are available on the PHS Data Portal.

The Kids' Inpatient Database (KID) is the largest publicly available all-payer pediatric inpatient care database in the United States, containing data from two to three million hospital stays. Its large sample size is ideal for developing national and regional estimates and enables analyses of rare conditions, such as congenital anomalies, as well as uncommon treatments, such as organ transplantation. KID releases for data years 1997, 2000, 2003, 2006, 2009, 2012, 2016, and 2019 are available for purchase online through the Online HCUP Central Distributor. The KID was not produced for 2015 because of the transition from ICD-9-CM to ICD-10-CM/PCS coding.

Usage

KID Database Documentation includes:

• Description of KID Database
• Restrictions on Use
• Files Specifications and Load Programs
• Data Elements
• Additional Resources for Data Elements
• ICD-10-CM/PCS Included in 2016 KID
• Information on Change to KID Design in 2000
• Known Data Issues
• KID Supplemental Files
• HCUP Tools: Labels and Formats
• Obtaining HCUP Data

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Documentation

Please visit the HCUP National KID page for more information.

A comprehensive dataset of top work states for H-1B Visa sponsorships in 2025, including salary data, petition trends, and employer insights. Updated annually with the latest trends and employer behavior regarding H-1B visa sponsorship.

H-1B visa sponsorship trends for Data Analyst, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

In the first half of 2024, Google received 7,498 requests for user information from government agencies in France. This figure has fluctuated since the first half of 2019, when the number of user data requests was 6,777. In the measured period, the highest number of such requests was registered in the second half of 2020.

Dataset

This dataset was created by walaa a.elrazik

Released under Database: Open Database, Contents: Database Contents

Contents

In the first half of 2023, almost 10 thousand user account data requests were received by Apple from law enforcement agencies in the United States, making the country first worldwide by the number of user data requests. Brazil ranked second, with 3,200 issued requests. Furthermore, the law enforcement agencies in Germany issued 1,660 requests in the measured period.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Borehole. The data include parameters of borehole with a geographic location of China, Eastern Asia. The time period coverage is from 450 to -31 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Dataset

This dataset was created by jkadhfiuh

Released under Other (specified in description)

Contents

In the first half of 2024, 86 percent of user data requests sent to Google by government agencies in the United States resulted in the disclosure of some data. Overall, the percentage of user data requests in the U.S. with some disclosure has increased in recent years.

Analysis of ‘Coffee Quality database from CQI’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/volpatto/coffee-quality-database-from-cqi on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Coffee Quality database

These datasets are gathered from Coffee Quality Institute (CQI) in January, 2018. I'm not the Owner of the Datasets, nor scrapping was performed by me. It was done in this GitHub's repo (kudos for the author), see there for further details.

What about the data files?

Three CSV files are provided:

• An Arabica coffee pre-cleaned dataset;

• A Robusta coffee pre-cleaned dataset;

• A dataset constructed through a merging of the datasets.

The file names indicates the above datasets clearly.

And what is inside?

As explained in the repo, the datasets have reviews from specialized reviewers for both coffees: arabica and robusta. The below information is provided in each dataset.

Quality Measures

• Aroma
• Flavor
• Aftertaste
• Acidity
• Body
• Balance
• Uniformity
• Cup Cleanliness
• Sweetness
• Moisture
• Defects

Bean Metadata

• Processing Method
• Color
• Species (arabica / robusta)

Farm Metadata

• Owner
• Country of Origin
• Farm Name
• Lot Number
• Mill
• Company
• Altitude
• Region

Related datasets

There is one related dataset here in Kaggle, please check here. It's pretty much similar to the datasets presented here, but without Robusta coffee data.

--- Original source retains full ownership of the source dataset ---

General description

SAPFLUXNET contains a global database of sap flow and environmental data, together with metadata at different levels. SAPFLUXNET is a harmonised database, compiled from contributions from researchers worldwide.

The SAPFLUXNET version 0.1.5 database harbours 202 globally distributed datasets, from 121 geographical locations. SAPFLUXNET contains sap flow data for 2714 individual plants (1584 angiosperms and 1130 gymnosperms), belonging to 174 species (141 angiosperms and 33 gymnosperms), 95 different genera and 45 different families. More information on the database coverage can be found here: http://sapfluxnet.creaf.cat/shiny/sfn_progress_dashboard/.

The SAPFLUXNET project has been developed by researchers at CREAF and other institutions (http://sapfluxnet.creaf.cat/#team), coordinated by Rafael Poyatos (CREAF, http://www.creaf.cat/staff/rafael-poyatos-lopez), and funded by two Spanish Young Researcher's Grants (SAPFLUXNET, CGL2014-55883-JIN; DATAFORUSE, RTI2018-095297-J-I00 ) and an Alexander von Humboldt Research Fellowship for Experienced Researchers).

Changelog

Compared to version 0.1.4, this version includes some changes in the metadata, but all time series data (sap flow, environmental) remain the same.

For all datasets, climate metadata (temperature and precipitation, ‘si_mat’ and ‘si_map’) have been extracted from CHELSA (https://chelsa-climate.org/), replacing the previous climate data obtained with Wordclim. This change has modified the biome classification of the datasets in ‘si_biome’.

In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) is now assigned a value of 0 if species are in the understorey. This affects two datasets: AUS_MAR_UBD and AUS_MAR_UBW, where, previously, the sum of species basal area percentages could add up to more than 100%.

In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) has been corrected for datasets USA_SIL_OAK_POS, USA_SIL_OAK_1PR, USA_SIL_OAK_2PR.

In ‘site’ metadata, the vegetation type (‘si_igbp’) has been changed to SAV for datasets CHN_ARG_GWD and CHN_ARG_GWS.

Variables and units

SAPFLUXNET contains whole-plant sap flow and environmental variables at sub-daily temporal resolution. Both sap flow and environmental time series have accompanying flags in a data frame, one for sap flow and another for environmental variables. These flags store quality issues detected during the quality control process and can be used to add further quality flags.

Metadata contain relevant variables informing about site conditions, stand characteristics, tree and species attributes, sap flow methodology and details on environmental measurements. The description and units of all data and metadata variables can be found here: Metadata and data units.

To learn more about variables, units and data flags please use the functionalities implemented in the sapfluxnetr package (https://github.com/sapfluxnet/sapfluxnetr). In particular, have a look at the package vignettes using R:

remotes::install_github(

'sapfluxnet/sapfluxnetr',

build_opts = c("--no-resave-data", "--no-manual", "--build-vignettes")

)

library(sapfluxnetr)

to list all vignettes

vignette(package='sapfluxnetr')

variables and units

vignette('metadata-and-data-units', package='sapfluxnetr')

data flags

vignette('data-flags', package='sapfluxnetr')

Data formats

SAPFLUXNET data can be found in two formats: 1) RData files belonging to the custom-built 'sfn_data' class and 2) Text files in .csv format. We recommend using the sfn_data objects together with the sapfluxnetr package, although we also provide the text files for convenience. For each dataset, text files are structured in the same way as the slots of sfn_data objects; if working with text files, we recommend that you check the data structure of 'sfn_data' objects in the corresponding vignette.

Working with sfn_data files

To work with SAPFLUXNET data, first they have to be downloaded from Zenodo, maintaining the folder structure. A first level in the folder hierarchy corresponds to file format, either RData files or csv's. A second level corresponds to how sap flow is expressed: per plant, per sapwood area or per leaf area. Please note that interconversions among the magnitudes have been performed whenever possible. Below this level, data have been organised per dataset. In the case of RData files, each dataset is contained in a sfn_data object, which stores all data and metadata in different slots (see the vignette 'sfn-data-classes'). In the case of csv files, each dataset has 9 individual files, corresponding to metadata (5), sap flow and environmental data (2) and their corresponding data flags (2).

After downloading the entire database, the sapfluxnetr package can be used to: - Work with data from a single site: data access, plotting and time aggregation. - Select the subset datasets to work with. - Work with data from multiple sites: data access, plotting and time aggregation.

Please check the following package vignettes to learn more about how to work with sfn_data files:

Quick guide

Metadata and data units

sfn_data classes

Custom aggregation

Memory and parallelization

Working with text files

We recommend to work with sfn_data objects using R and the sapfluxnetr package and we do not currently provide code to work with text files.

Data issues and reporting

Please report any issue you may find in the database by sending us an email: sapfluxnet@creaf.uab.cat.

Temporary data fixes, detected but not yet included in released versions will be published in SAPFLUXNET main web page ('Known data errors').

Data access, use and citation

This version of the SAPFLUXNET database is open access and corresponds to the data paper submitted to Earth System Science Data in August 2020.

When using SAPFLUXNET data in an academic work, please cite the data paper, when available, or alternatively, the Zenodo dataset (see the ‘Cite as’ section on the right panels of this web page).