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).

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!

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.

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.

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

LLM Similarity Comparison Dataset

This dataset is pased on the original Alpaca dataset and was synthetically genearted for LLM similarity comparison using ConSCompF framework as described in the original paper. The script used for generating data is available on Kaggle. It is divided into 3 subsets:

quantization - contains 156,000 samples (5,200 for each model) generated by the original Tinyllama and its 8-bit, 4-bit, and 2-bit GGUF quantized versions. comparison - contains 28,600… See the full description on the dataset page: https://huggingface.co/datasets/alex-karev/llm-comparison.

Video Comparison Dataset

This dataset contains pairwise comparisons of AI-generated videos with human preference ratings across multiple evaluation dimensions.

  Dataset Description

The dataset consists of paired videos generated from the same prompts by different AI video generation models. Human evaluators rated these pairs on three dimensions:

Preference: Overall preference between videos Coherence: How logically consistent and sensible the video content is Alignment:… See the full description on the dataset page: https://huggingface.co/datasets/Kchanger/video-comparison-dataset.

As there was no large publicly available cross-domain dataset for comparative argument mining, we create one composed of sentences, potentially annotated with BETTER / WORSE markers (the first object is better / worse than the second object) or NONE (the sentence does not contain a comparison of the target objects). The BETTER sentences stand for a pro-argument in favor of the first compared object and WORSE-sentences represent a con-argument and favor the second object. We aim for minimizing dataset domain-specific biases in order to capture the nature of comparison and not the nature of the particular domains, thus decided to control the specificity of domains by the selection of comparison targets. We hypothesized and could confirm in preliminary experiments that comparison targets usually have a common hypernym (i.e., are instances of the same class), which we utilized for selection of the compared objects pairs. The most specific domain we choose, is computer science with comparison targets like programming languages, database products and technology standards such as Bluetooth or Ethernet. Many computer science concepts can be compared objectively (e.g., on transmission speed or suitability for certain applications). The objects for this domain were manually extracted from List of-articles at Wikipedia. In the annotation process, annotators were asked to only label sentences from this domain if they had some basic knowledge in computer science. The second, broader domain is brands. It contains objects of different types (e.g., cars, electronics, and food). As brands are present in everyday life, anyone should be able to label the majority of sentences containing well-known brands such as Coca-Cola or Mercedes. Again, targets for this domain were manually extracted from `List of''-articles at Wikipedia.The third domain is not restricted to any topic: random. For each of 24~randomly selected seed words 10 similar words were collected based on the distributional similarity API of JoBimText (http://www.jobimtext.org). Seed words created using randomlists.com: book, car, carpenter, cellphone, Christmas, coffee, cork, Florida, hamster, hiking, Hoover, Metallica, NBC, Netflix, ninja, pencil, salad, soccer, Starbucks, sword, Tolkien, wine, wood, XBox, Yale.Especially for brands and computer science, the resulting object lists were large (4493 in brands and 1339 in computer science). In a manual inspection, low-frequency and ambiguous objects were removed from all object lists (e.g., RAID (a hardware concept) and Unity (a game engine) are also regularly used nouns). The remaining objects were combined to pairs. For each object type (seed Wikipedia list page or the seed word), all possible combinations were created. These pairs were then used to find sentences containing both objects. The aforementioned approaches to selecting compared objects pairs tend minimize inclusion of the domain specific data, but do not solve the problem fully though. We keep open a question of extending dataset with diverse object pairs including abstract concepts for future work. As for the sentence mining, we used the publicly available index of dependency-parsed sentences from the Common Crawl corpus containing over 14 billion English sentences filtered for duplicates. This index was queried for sentences containing both objects of each pair. For 90% of the pairs, we also added comparative cue words (better, easier, faster, nicer, wiser, cooler, decent, safer, superior, solid, terrific, worse, harder, slower, poorly, uglier, poorer, lousy, nastier, inferior, mediocre) to the query in order to bias the selection towards comparisons but at the same time admit comparisons that do not contain any of the anticipated cues. This was necessary as a random sampling would have resulted in only a very tiny fraction of comparisons. Note that even sentences containing a cue word do not necessarily express a comparison between the desired targets (dog vs. cat: He's the best pet that you can get, better than a dog or cat.). It is thus especially crucial to enable a classifier to learn not to rely on the existence of clue words only (very likely in a random sample of sentences with very few comparisons). For our corpus, we keep pairs with at least 100 retrieved sentences.From all sentences of those pairs, 2500 for each category were randomly sampled as candidates for a crowdsourced annotation that we conducted on figure-eight.com in several small batches. Each sentence was annotated by at least five trusted workers. We ranked annotations by confidence, which is the figure-eight internal measure of combining annotator trust and voting, and discarded annotations with a confidence below 50%. Of all annotated items, 71% received unanimous votes and for over 85% at least 4 out of 5 workers agreed -- rendering the collection procedure aimed at ease of annotation successful.The final dataset contains 7199 sentences with 271 distinct object pairs. The majority of sentences (over 72%) are non-comparative despite biasing the selection with cue words; in 70% of the comparative sentences, the favored target is named first.You can browse though the data here: https://docs.google.com/spreadsheets/d/1U8i6EU9GUKmHdPnfwXEuBxi0h3aiRCLPRC-3c9ROiOE/edit?usp=sharing Full description of the dataset is available in the workshop paper at ACL 2019 conference. Please cite this paper if you use the data: Franzek, Mirco, Alexander Panchenko, and Chris Biemann. ""Categorization of Comparative Sentences for Argument Mining."" arXiv preprint arXiv:1809.06152 (2018).@inproceedings{franzek2018categorization, title={Categorization of Comparative Sentences for Argument Mining}, author={Panchenko, Alexander and Bondarenko, and Franzek, Mirco and Hagen, Matthias and Biemann, Chris}, booktitle={Proceedings of the 6th Workshop on Argument Mining at ACL'2019}, year={2019}, address={Florence, Italy}}

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).

Basic Info:

The Dataset represents the County Health Ranking of all states taking into account the various factors The County Health Rankings can be used to highlight regional variations in health, increase public understanding of the various factors that affect health, and inspire actions to improve community health. The Rankings capitalizes on our innate desire to compete by enabling comparisons across adjacent or comparable counties within states.

Dataset Information:

The CSV file contains the rankings and data details for the measures used in the 2022/23 County Health Rankings.
1) Outcomes and Factors Rankings --Ranks are all calculated and reported WITHIN states
2)**Outcomes and Factors SubRankings** --Ranks are all calculated and reported WITHIN states
3) Ranked Measure Data --The measures themselves are listed in bold.
4) Ranked Measure Sources & Years
5) Additional Measure Data --These are supplemental measures reported on the Rankings web site but not used in calculating the rankings.
6) Additional Measure Sources & Years

The Data Types of all Columns are automatically set to "Object" To change it just use data.apply(pd.to_numeric)

Blog or not dataset

This dataset includes whether the page is a blog or not from the website urls. Most of the features are taken from this article [1]. You can review for detailed information. Information about features not included in this dataset will be added soon.

GitHub Repo

[1] Vrbančič, G., Fister Jr, I., & Podgorelec, V. (2020). Datasets for phishing websites detection. Data in Brief, 33, 106438.

Data for multi-site, multi-platform comparison of magnetic resonance imaging (MRI) T1 measurement using the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom. Includes data sets for T1 measurement by inversion recovery (IR) and variable flip angle (VFA) methods at 1.5 tesla and 3 tesla. At 1.5 T, data is from 2 different vendor systems, 9 total MRI machines. At 3 T, data is from 3 different vendor systems, 18 total MRI machines.

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 21 November 2021.

--- 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.

Fine tuning progress validation - RedPajama 3B, StableLM Alpha 7B, Open-LLaMA

This repository contains the progress of fine-tuning models: RedPajama 3B, StableLM Alpha 7B, Open-LLaMA. These models have been fine-tuned on a specific text dataset and the results of the fine-tuning process are provided in the text file included in this repository.

  Fine-Tuning Details

Model: RedPajama 3B, size: 3 billion parameters, method: adapter Model: StableLM Alpha 7B, size: 7 billion… See the full description on the dataset page: https://huggingface.co/datasets/kstevica/llm-comparison.

MedBrowseComp Dataset

This repository contains datasets for medical information-seeking-oriented deep research and computer use tasks.

  Datasets

The repository contains three harmonized datasets:

MedBrowseComp_50: A collection of 50 medical entries for browsing and comparison. MedBrowseComp_605: A comprehensive collection of 605 medical entries. MedBrowseComp_CUA: A curated collection of medical data for comparison and analysis.

  Usage

These datasets can be… See the full description on the dataset page: https://huggingface.co/datasets/AIM-Harvard/MedBrowseComp.

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

CMAQv5.1 Base NEIv2 AQS Hourly site compare output containing paired model/ob values that were used for some of the plots in the manuscript.

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 online application gives manufacturers the ability to compare Iowa to other states on a number of different topics including: business climate, education, operating costs, quality of life and workforce.