Nursing Home Compare has detailed information about every Medicare and Medicaid nursing home in the country. A nursing home is a place for people who can’t be cared for at home and need 24-hour nursing care. These are the official datasets used on the Medicare.gov Nursing Home Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at every Medicare and Medicaid-certified nursing home in the country, including over 15,000 nationwide.

Monthly site compare scripts and output used to generate the model/ob plots and statistics in the manuscript. The AQS hourly site compare output files are not included as they were too large to store on ScienceHub. The files contain paired model/ob values for the various air quality networks.

This dataset is associated with the following publication: Appel, W., S. Napelenok, K. Foley, H. Pye, C. Hogrefe, D. Luecken, J. Bash, S. Roselle, J. Pleim, H. Foroutan, B. Hutzell, G. Pouliot, G. Sarwar, K. Fahey, B. Gantt, D. Kang, R. Mathur, D. Schwede, T. Spero, D. Wong, J. Young, and N. Heath. Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 10: 1703-1732, (2017).

This is a dataset created for use by the DQ Atlas website, and is not intended for use outside that application. For more information on the DQ Atlas and the information contained in this dataset see https://www.medicaid.gov/dq-atlas/welcome

Psychological scientists increasingly study web data, such as user ratings or social media postings. However, whether research relying on such web data leads to the same conclusions as research based on traditional data is largely unknown. To test this, we (re)analyzed three datasets, thereby comparing web data with lab and online survey data. We calculated correlations across these different datasets (Study 1) and investigated identical, illustrative research questions in each dataset (Studies 2 to 4). Our results suggest that web and traditional data are not fundamentally different and usually lead to similar conclusions, but also that it is important to consider differences between data types such as populations and research settings. Web data can be a valuable tool for psychologists when accounting for such differences, as it allows for testing established research findings in new contexts, complementing them with insights from novel data sources.

These are the official datasets used on the Medicare.gov Hospital Compare Website provided by the Centers for Medicare and Medicaid Services. These data allow you to compare the quality of care at over 4,000 Medicare-certified hospitals across the country.

description:

These are the official datasets used on the Medicare.gov Nursing Home Compare Website provided by the Centers for Medicare and Medicaid Services. These data allow you to compare the quality of care at every Medicare and Medicaid-certified nursing home in the country, including over 15,000 nationwide.

These are the official datasets used on the Medicare.gov Nursing Home Compare Website provided by the Centers for Medicare and Medicaid Services. These data allow you to compare the quality of care at every Medicare and Medicaid-certified nursing home in the country, including over 15,000 nationwide.

The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge.

This is a dataset created for use by the DQ Atlas website, and is not intended for use outside that application. For more information on the DQ Atlas and the information contained in this dataset see https://www.medicaid.gov/dq-atlas/welcome

These are the official datasets used on the Medicare.gov Dialysis Facility Compare Website provided by the Centers for Medicare and Medicaid Services. These data allow you to compare the quality of care provided in Medicare-certified dialysis facilities nationwide.

Analysis of ‘Hospital ratings’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/center-for-medicare-and-medicaid/hospital-ratings on 28 January 2022.

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

Context

This are the official datasets used on the Medicare.gov Hospital Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at over 4,000 Medicare-certified hospitals across the country.

Content

Dataset fields:

• Provider ID
• Hospital Name
• Address
• City
• State
• ZIP Code
• County Name
• Phone Number
• Hospital Type
• Hospital Ownership
• Emergency Services
• Meets criteria for meaningful use of EHRs
• Hospital overall rating
• Hospital overall rating footnote
• Mortality national comparison
• Mortality national comparison footnote
• Safety of care national comparison
• Safety of care national comparison footnote
• Readmission national comparison
• Readmission national comparison footnote
• Patient experience national comparison
• Patient experience national comparison footnote
• Effectiveness of care national comparison
• Effectiveness of care national comparison footnote
• Timeliness of care national comparison
• Timeliness of care national comparison footnote
• Efficient use of medical imaging national comparison
• Efficient use of medical imaging national comparison

Acknowledgements

Dataset was downloaded from [https://data.medicare.gov/data/hospital-compare]

Inspiration

If you just broke your leg, you might need to use this dataset to find the best Hospital to get that fixed!

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

Best virtual data rooms 2024 dataset is created to provide the data room users and M&A specialists with detailed information on the best virtual data rooms. The dataset contains the descriptions of each dataroom solution and their ratings.

This dataset is a real-world web page collection used for research on the automatic extraction of structured data (e.g., attribute-value pairs of entities) from the Web. We hope it could serve as a useful benchmark for evaluating and comparing different methods for structured web data extraction.

By acquiring LUR data (air quality) from CACES website, we are able to compare those data with the Google street view data that we collected this past summer. I would like to investigate how their similarities and differences by GIS toolset. I also plan to overlap air pollution data with the census data to explore how social-economic status exposed to air pollution.

Comprehensive analysis of AI site generation, content creation, image generation, and automation tools

CMAQv5.1 with a new dust module IMPROVE sitex files containing 24-hr (every 3rd day) paired model/ob data for the IMPROVE network.

This dataset is associated with the following publication: Foroutan, H., J. Young, S. Napelenok, L. Ran, W. Appel, R. Gilliam, and J. Pleim. Development and evaluation of a physics-based windblown dust emission scheme implemented in the CMAQ modeling system. Journal of Advances in Modeling Earth Systems. John Wiley & Sons, Inc., Hoboken, NJ, USA, 9(1): 585-608, (2017).

Complete analysis of design flexibility, templates, animations, and customization features

The data for this assignment come from the Hospital Compare web site (http://hospitalcompare.hhs.gov)run by the U.S. Department of Health and Human Services. The purpose of the web site is to provide data and information about the quality of care at over 4,000 Medicare-certied hospitals in the U.S. This dataset essentially covers all major U.S. hospitals. This dataset is used for a variety of purposes, including determining whether hospitals should be ned for not providing high quality care to patients (see http://goo.gl/jAXFX for some background on this particular topic).

Complete feature-by-feature comparison data between Dorik and Framer website builders

Dataset creation

The dataset was created by this notebook: https://www.kaggle.com/douglaskgaraujo/sentence-complexity-comparison-dataset

Context

This data is a pairwise comparison of sentences, together with information about their relative complexity. The original dataset is from the CommonLit Readability Prize competition, and interested readers are referred there (especially the competitions' discussion forums) for more information on the data itself.

Important notice! As per that competition's rules, the license is as follows:

1. COMPETITION DATA. "Competition Data" means the data or datasets available from the Competition Website for the purpose of use in the Competition, including any prototype or executable code provided on the Competition Website. The Competition Data will contain private and public test sets. Which data belongs to which set will not be made available to participants.

A. Data Access and Use. Competition Use and Non-Commercial & Academic Research: *You may access and use the Competition Data for non-commercial purposes only, including for participating in the Competition and on Kaggle.com forums, and for academic research and education. *The Competition Sponsor reserves the right to disqualify any participant who uses the Competition Data other than as permitted by the Competition Website and these Rules.

B. Data Security. You agree to use reasonable and suitable measures to prevent persons who have not formally agreed to these Rules from gaining access to the Competition Data. You agree not to transmit, duplicate, publish, redistribute or otherwise provide or make available the Competition Data to any party not participating in the Competition. You agree to notify Kaggle immediately upon learning of any possible unauthorized transmission of or unauthorized access to the Competition Data and agree to work with Kaggle to rectify any unauthorized transmission or access.

C. External Data. You may use data other than the Competition Data (“External Data”) to develop and test your Submissions. However, you will ensure the External Data is publicly available and equally accessible to use by all participants of the Competition for purposes of the competition at no cost to the other participants. The ability to use External Data under this Section 7.C (External Data) does not limit your other obligations under these Competition Rules, including but not limited to Section 11 (Winners Obligations).

Content

This dataset is a pairwise comparison of each sentence in the CommonLit competition with 500 other randomly-matched sentences. Sentences are divided into a training and validation datasets before being matched randomly. The relative complexity of each sentence is measured, and features such as the distance between this score for both sentences, and a column indicating whether or not the first sentence's readability score is greater than or equal to the score of the second sentence.

Acknowledgements

Thank you for the organisers of this competition for providing this dataset.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This is the full dataset of the 2019 Cryo-EM Map-based Model Metrics Challenge sponsored by EMDataResource (www.emdataresource.org, challenges.emdataresource.org, model-compare.emdataresource.org). The goals of this challenge were (1) to assess the quality of models that can be produced using current modeling software, (2) to check the reproducibility of modeling results from different software developers and users, and (3) compare the performance of current metrics used for evaluation of models. The focus was on near-atomic resolution maps with an innovative twist: three of four target maps formed a resolution series (1.8 to 3.1 Å) from the same specimen and imaging experiment. Tools developed in previous challenges were expanded for managing, visualizing and analyzing the 63 submitted coordinate models, and several new metrics were introduced.

File Descriptions:

2019-EMDataResource-Challenge-web.pdf: Archive of News, Goals, Timeline, Targets, Modelling Instructions, Process, FAQ, Submission Instructions, Submission Summary Statistics source from the EMDR Challenges website

correlation-images.tar.gz: Pairwise correlation tables for selected metric scores from the EMDR Model Compare website

maps.tar.gz: The maps used for Fit-to-Map analyses in the Challenge

models.tar.gz: The 63 models submitted by the modelling teams

results.tar.gz: The output logs for all of the analysis methods

Scores.xlsx: Scores for each model and analysis method, compiled into spreadsheet format

targets.tar.gz: The reference models used in the analysis

Post submission correction to the web archive PDF document: The full list of EMDataResource members on the model committee is as follows: Cathy Lawson, Andriy Kryshtafovych, Greg Pintilie, Mike Schmid, Helen Berman, Wah Chiu.